Hyperspectral image (HSI) analysis plays a central role in remote sensing tasks requiring fine-grained material discrimination, vegetation health assessment, and post-disturbance monitoring. Yet, the high dimensionality and strong spectral redundancy in HSIs often reduce the efficiency and reliability of machine learning models. These challenges are especially important in wildfire science and prescribed-fire monitoring, where spectral responses vary due to burn severity, char deposition, canopy structure, and early vegetation recovery. Benchmark datasets such as Indian Pines and Pavia University and others provide controlled environments for algorithms’ evaluation, but real-world post-fire forest conditions pose additional complexity. This study presents a unified and comprehensive evaluation of five dimensionality reduction strategies: Principal Component Analysis (PCA), Spatial–Spectral Edge Preservation (SSEP), Spectral-Redundancy Penalized Attention (SRPA), and a Deep Reinforcement Learning (DRL)-based selector together with a clustering based baseline, K-Means Clustering-Based Band Selection (KMCBS). These strategies are combined with classical machine learning and deep learning classifiers: Random Forest (RF), Support Vector Machines (SVMs), K-Nearest Neighbors (KNNs), and 3D Convolutional Neural Networks (3D-CNN). The full pipeline includes exploratory data analysis, preprocessing, patch-based spatial–spectral modeling, consistent train–validation protocols, and multi-dataset evaluation across Indian Pines, Pavia University, and a new custom VNIR hyperspectral dataset collected after prescribed burns at the Lubrecht Experimental Forest in Montana, USA. By systematically comparing statistical, edge-aware, attention-guided, and reinforcement learning-based band-selection strategies, this work identifies compact yet informative spectral subsets that enhance classification performance while reducing computational cost. Importantly, the inclusion of the Montana prescribed-burn dataset provides a unique real-world testbed for understanding band selection behavior in fire-affected forest environments. Overall, this study contributes a generalizable and extensible framework for HSI dimensionality reduction and classification, laying the groundwork for future applications in wildfire assessment, vegetation recovery monitoring, and remote sensing.
Keywords: hyperspectral imaging; band selection; machine learning; prescribed fire
Hyperspectral Band Selection for Ground Fuel Classification for Prescribed Fires
mahmadisaq.karankot@student.montana.edu
6 May 2026
2024
Finding potential research collaborators is a challenging task, especially in today’s fast-growing, interdisciplinary research landscape. While traditional methods rely on observable ties like co-authorships and citations, we focus solely on publication content to build a topic-based research network using BERTopic with a fine-tuned SciBERT model that connects and recommends researchers across disciplines based on shared topical interests. A key challenge we address is publication imbalance, where some researchers publish much more than others, often across several topics. Without careful handling, their less frequent interests are hidden under dominant topics, limiting the network’s ability to capture their full research scope. To tackle this, we introduce a cloning strategy that clusters a researcher’s publications and treats each cluster as a separate node. This allows researchers to belong to multiple communities, improving the detection of interdisciplinary links. Evaluation shows that the cloned network leads to more meaningful communities and uncovers broader collaboration opportunities.
Handling Publication Imbalance for Effective Community Detection in Scholarly Networks
mdasaduzzamannoor@montana.edu
Feburary 3 2026
2024
Many resources and professionals reference the 3-2-1 backup rule as an effective strategy to prevent active research data loss. However, the changes in storage technology and the pace of research data growth have outgrown the 3-2-1 rule. Objectives: The authors want to contribute background information and invite community input to evolve the 3-2-1 rule to fit modern research data and storage better. This evolution would provide better information to research data management professionals and researchers for more resilient research data. Methods: The authors facilitated a workshop at the Research Data Access and Preservation (RDAP) Summit in 2025 to present the necessary information for understanding the current storage and backup landscape. Backups were reframed as failure modes for data loss and corresponding preventative data protection measures. Results: The workshop resulted in an overview and summary of data protection methods and the ways in which they mitigate different “failures,” which allows for a more nuanced discussion of data protection that is not enabled by use of the 3-2-1 rule nor the term “backup” alone. Workshop participants brainstormed ways that the information presented in the workshop could be synthesized and incorporated into various learning materials, including materials for data professionals and researchers.
Evolving the 3-2-1 backup rule for more resilient data
dmccaffrey@starfishstorage.com
January 2026
2025
This Project generated data in the proces of replication of Weber et al 2025, paper validated on UK Biobank subset of 15000people, by reuse of MIMIC-IV-ECG Physionet clinical dataset, on close to 300000 records, in creation of ECG-extracted enriched cluster biomarkers acting as autonomic profiles for estimation of depression severity and potential suicide risk.
FAIR Mind
micu@3ega.nl
his paper describes the use of dynamic focus control in a coherent lidar and compares the theoretical and experimental results. The system uses a MEMS variable-focus mirror, which has an electrostatically driven deformable membrane with a diameter of 4 mm and a manufacturer’s published 10%-to-90% rise time of less than 200 µs. The device permits fast, intra-scan adjustment of the downrange beam focus from 2.2 m to infinity for a lidar with a 25 mm diameter exit pupil. Experimentally, the variable-focus mirror improved the signal strength by more than 20 dB for objects within the first Rayleigh distance, in good agreement with theory. The fast response time allows the system to dynamically adjust the focus to different distances within a single sweep of a raster scan, in order to match the system focus on-the-fly to targets at different distances. Three-dimensional point clouds show that this technique allows some targets that are below the noise level when using a fixed focus to become visible when the focus is changed dynamically.
Dynamic focusing using a MEMS mirror in a coherent lidar
drandrewoliver@ieee.org
April 24 2025
2024
Computational prediction of AgMata aggregation propensity score (0.7004717948717948, unitless) for SARS-CoV-2 Spike RBD variant Alpha (N169Y) using the Bio2Byte AgMata predictor. Sequence-derived: one FDO per variant.
Alpha AgMata = 0.7004717948717948 — Bio2Byte
0.7004717948717948
e.a.schultes@lacdr.leidenuniv.nl
Deep mutational scanning measurements (Bloom Lab) for SARS-CoV-2 Spike RBD variant Alpha (N169Y). Six values: bind=9.81374, delta_bind=1.04213, expr=10.05266, delta_expr=-0.13322, confidence_bind=0.0732354593527125, confidence_expr=0.0713853600674372. All unitless.
Alpha DMS observation — Bloom Lab (bind=9.81374, expr=10.05266)
9.81374
0.0732354593527125
0.0713853600674372
1.04213
-0.13322
10.05266
e.a.schultes@lacdr.leidenuniv.nl
Computational prediction of RMSD (13.378861773140596 Angstroms) for SARS-CoV-2 Spike RBD variant Alpha (N169Y) using ESM2.
Alpha RMSD = 13.378861773140596 Å — ESM2
13.378861773140596
e.a.schultes@lacdr.leidenuniv.nl
Computational prediction of RMSD (2.277628683231952 Angstroms) for SARS-CoV-2 Spike RBD variant Alpha (N169Y) using AlphaFold 2.
Alpha RMSD = 2.277628683231952 Å — AlphaFold 2
2.277628683231952
e.a.schultes@lacdr.leidenuniv.nl
Computational prediction of SASA (10141.236988525969 square Angstroms) for SARS-CoV-2 Spike RBD variant Alpha (N169Y) using AlphaFold 2.
Alpha SASA = 10141.236988525969 Ų — AlphaFold 2
10141.236988525969
e.a.schultes@lacdr.leidenuniv.nl
Computational prediction of SASA (16494.878394358453 square Angstroms) for SARS-CoV-2 Spike RBD variant Alpha (N169Y) using ESM2.
Alpha SASA = 16494.878394358453 Ų — ESM2
16494.878394358453
e.a.schultes@lacdr.leidenuniv.nl
Computational prediction of pLDDT (23.673561732385306 unitless, 0-100) for SARS-CoV-2 Spike RBD variant Alpha (N169Y) using ESM2.
Alpha pLDDT = 23.673561732385306 — ESM2
23.673561732385306
e.a.schultes@lacdr.leidenuniv.nl
Computational prediction of pLDDT (93.74392833444035 unitless, 0-100) for SARS-CoV-2 Spike RBD variant Alpha (N169Y) using AlphaFold 2.
Alpha pLDDT = 93.74392833444035 — AlphaFold 2
93.74392833444035
e.a.schultes@lacdr.leidenuniv.nl
Real-world detection of SARS-CoV-2 Spike variant Alpha (B.1.1.7) in England during late 2020-2021, as reported in GISAID (accession EPI_ISL_601443).
Alpha real-world occurrence — England (late 2020-2021)
EPI_ISL_601443
late 2020-2021
e.a.schultes@lacdr.leidenuniv.nl
WHO classification of SARS-CoV-2 Spike variant Alpha (B.1.1.7) as a Variant of Concern (VOC) on 2020-12-18.
Alpha — WHO Variant of Concern (2020-12-18)
2020-12-18
e.a.schultes@lacdr.leidenuniv.nl
Computational prediction of AgMata aggregation propensity score (0.7194974358974362, unitless) for SARS-CoV-2 Spike RBD variant Epsilon (L120R) using the Bio2Byte AgMata predictor. Sequence-derived: one FDO per variant.
Epsilon AgMata = 0.7194974358974362 — Bio2Byte
0.7194974358974362
e.a.schultes@lacdr.leidenuniv.nl
Deep mutational scanning measurements (Bloom Lab) for SARS-CoV-2 Spike RBD variant Epsilon (L120R). Six values: bind=8.93862, delta_bind=0.16701, expr=10.25356, delta_expr=0.06768, confidence_bind=0.0729917177063099, confidence_expr=0.0705230077448188. All unitless.
Epsilon DMS observation — Bloom Lab (bind=8.93862, expr=10.25356)
8.93862
0.0729917177063099
0.0705230077448188
0.16701
0.06768
10.25356
e.a.schultes@lacdr.leidenuniv.nl
Computational prediction of RMSD (12.801648143493145 Angstroms) for SARS-CoV-2 Spike RBD variant Epsilon (L120R) using ESM2.
Epsilon RMSD = 12.801648143493145 Å — ESM2
12.801648143493145
e.a.schultes@lacdr.leidenuniv.nl
Computational prediction of RMSD (2.975776098053212 Angstroms) for SARS-CoV-2 Spike RBD variant Epsilon (L120R) using AlphaFold 2.
Epsilon RMSD = 2.975776098053212 Å — AlphaFold 2
2.975776098053212
e.a.schultes@lacdr.leidenuniv.nl
Computational prediction of SASA (10181.968717666516 square Angstroms) for SARS-CoV-2 Spike RBD variant Epsilon (L120R) using AlphaFold 2.
Epsilon SASA = 10181.968717666516 Ų — AlphaFold 2
10181.968717666516
e.a.schultes@lacdr.leidenuniv.nl
Computational prediction of SASA (17620.862527029858 square Angstroms) for SARS-CoV-2 Spike RBD variant Epsilon (L120R) using ESM2.
Epsilon SASA = 17620.862527029858 Ų — ESM2
17620.862527029858
e.a.schultes@lacdr.leidenuniv.nl
Computational prediction of pLDDT (23.73609314359643 unitless, 0-100) for SARS-CoV-2 Spike RBD variant Epsilon (L120R) using ESM2.
Epsilon pLDDT = 23.73609314359643 — ESM2
23.73609314359643
e.a.schultes@lacdr.leidenuniv.nl
Computational prediction of pLDDT (94.19297808764844 unitless, 0-100) for SARS-CoV-2 Spike RBD variant Epsilon (L120R) using AlphaFold 2.
Epsilon pLDDT = 94.19297808764844 — AlphaFold 2
94.19297808764844
e.a.schultes@lacdr.leidenuniv.nl
Real-world detection of SARS-CoV-2 Spike variant Epsilon (B.1.427 + B.1.429) in the United States on 2021-01-13, as reported in GISAID (accession EPI_ISL_2295356).
Epsilon real-world occurrence — United States (2021-01-13)
EPI_ISL_2295356
2021-01-13
e.a.schultes@lacdr.leidenuniv.nl
WHO classification of SARS-CoV-2 Spike variant Epsilon (B.1.427 + B.1.429) as a Variant of Interest (VOI) on 2021-03-05.
Epsilon — WHO Variant of Interest (2021-03-05)
2021-03-05
e.a.schultes@lacdr.leidenuniv.nl
Computational prediction of AgMata aggregation propensity score (0.680579487179487, unitless) for SARS-CoV-2 Spike RBD variant Eta (E152K) using the Bio2Byte AgMata predictor. Sequence-derived: one FDO per variant.
Eta AgMata = 0.680579487179487 — Bio2Byte
0.680579487179487
e.a.schultes@lacdr.leidenuniv.nl
Deep mutational scanning measurements (Bloom Lab) for SARS-CoV-2 Spike RBD variant Eta (E152K). Six values: bind=9.01256, delta_bind=0.24095, expr=10.21898, delta_expr=0.03311, confidence_bind=0.0722604927671021, confidence_expr=0.0697838486111459. All unitless.
Eta DMS observation — Bloom Lab (bind=9.01256, expr=10.21898)
9.01256
0.0722604927671021
0.0697838486111459
0.24095
0.03311
10.21898
e.a.schultes@lacdr.leidenuniv.nl
Computational prediction of RMSD (2.476768477258109 Angstroms) for SARS-CoV-2 Spike RBD variant Eta (E152K) using ESM2.
Eta RMSD = 2.476768477258109 Å — ESM2
2.476768477258109
e.a.schultes@lacdr.leidenuniv.nl
Computational prediction of RMSD (2.7195180252543003 Angstroms) for SARS-CoV-2 Spike RBD variant Eta (E152K) using AlphaFold 2.
Eta RMSD = 2.7195180252543003 Å — AlphaFold 2
2.7195180252543003
e.a.schultes@lacdr.leidenuniv.nl
Computational prediction of SASA (10226.641288430395 square Angstroms) for SARS-CoV-2 Spike RBD variant Eta (E152K) using AlphaFold 2.
Eta SASA = 10226.641288430395 Ų — AlphaFold 2
10226.641288430395
e.a.schultes@lacdr.leidenuniv.nl
Computational prediction of SASA (18026.596842240684 square Angstroms) for SARS-CoV-2 Spike RBD variant Eta (E152K) using ESM2.
Eta SASA = 18026.596842240684 Ų — ESM2
18026.596842240684
e.a.schultes@lacdr.leidenuniv.nl
Computational prediction of pLDDT (23.15035644847704 unitless, 0-100) for SARS-CoV-2 Spike RBD variant Eta (E152K) using ESM2.
Eta pLDDT = 23.15035644847704 — ESM2
23.15035644847704
e.a.schultes@lacdr.leidenuniv.nl
Computational prediction of pLDDT (94.05421698739244 unitless, 0-100) for SARS-CoV-2 Spike RBD variant Eta (E152K) using AlphaFold 2.
Eta pLDDT = 94.05421698739244 — AlphaFold 2
94.05421698739244
e.a.schultes@lacdr.leidenuniv.nl
Real-world detection of SARS-CoV-2 Spike variant Eta (B.1.525) in Nigeria on 2020-12-15, as reported in GISAID (accession EPI_ISL_941290).
Eta real-world occurrence — Nigeria (2020-12-15)
EPI_ISL_941290
2020-12-15
e.a.schultes@lacdr.leidenuniv.nl
WHO classification of SARS-CoV-2 Spike variant Eta (B.1.525) as a Variant of Interest (VOI) on 2021-03-05.
Eta — WHO Variant of Interest (2021-03-05)
2021-03-05
e.a.schultes@lacdr.leidenuniv.nl
AlphaFold 2 (DeepMind) protein structure prediction system.
AlphaFold 2 — DeepMind structure predictor
e.a.schultes@lacdr.leidenuniv.nl
AgMata aggregation propensity predictor, Bio2Byte toolkit.
Bio2Byte AgMata — aggregation propensity predictor
e.a.schultes@lacdr.leidenuniv.nl
Deep mutational scanning of SARS-CoV-2 RBD binding and expression, Bloom Lab.
Deep mutational scanning — Bloom Lab SARS-CoV-2 RBD
e.a.schultes@lacdr.leidenuniv.nl
ESM2 protein language model (Meta AI) for structure and property prediction from sequence.
ESM2 — Meta AI protein language model
e.a.schultes@lacdr.leidenuniv.nl
GISAID SARS-CoV-2 genomic surveillance platform.
GISAID — Global Initiative on Sharing All Influenza Data
e.a.schultes@lacdr.leidenuniv.nl
SARS-CoV-2 Spike RBD variant Alpha, carrying the N169Y substitution (corresponding to N501Y in the full Spike numbering). Pango lineage B.1.1.7. Designated a Variant of Concern (VOC) by the WHO. RBD anchor FDO for the MAC FAIR Digital Twin observation cluster — all Type 2-8 observation FDOs of this variant link back here via mac:isObservationOf.
Spike RBD Variant Alpha (N169Y)
EPI_ISL_601443
N501Y
N169Y
B.1.1.7
TNLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTYGVGYQPYRVVVLSFELLHAPATVCGP
Alpha
e.a.schultes@lacdr.leidenuniv.nl
SARS-CoV-2 Spike RBD variant Epsilon, carrying the L120R substitution (corresponding to L452R in the full Spike numbering — Epsilon's canonical defining mutation). Pango lineage B.1.427 + B.1.429 (composite). RBD anchor FDO for the MAC FAIR Digital Twin observation cluster — all Type 2-8 observation FDOs of this variant link back here via mac:isObservationOf.
Spike RBD Variant Epsilon (L120R)
EPI_ISL_2295356
L452R
L120R
B.1.427 + B.1.429
TNLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYRYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFELLHAPATVCGP
Epsilon
e.a.schultes@lacdr.leidenuniv.nl
SARS-CoV-2 Spike RBD variant Eta, carrying the E152K substitution (corresponding to E484K in the full Spike numbering). Pango lineage B.1.525. Designated a Variant of Interest (VOI) by the WHO. RBD anchor FDO for the MAC FAIR Digital Twin observation cluster — all Type 2-8 observation FDOs of this variant link back here via mac:isObservationOf.
Spike RBD Variant Eta (E152K)
EPI_ISL_941290
E484K
E152K
B.1.525
TNLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVKGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFELLHAPATVCGP
Eta
e.a.schultes@lacdr.leidenuniv.nl
2025-07-01
v1.0.0
SARS-COV-2 Spike protein RBD variant structural features calculated with AlphaFold2
AlphaFold2 Dataset STAYAHEAD
e.a.schultes@lacdr.leidenuniv.nl
2025-07-01
v1.0.0
SARS-COV-2 Spike protein RBD variant structural features calculated with ESM
ESM Dataset STAYAHEAD
e.a.schultes@lacdr.leidenuniv.nl
2023-03-01
This project will develop rapid SARS-CoV-2 detection and variant characterization using mass spectrometry, FAIR Digital Twins of variants, and coupled to AI predictions of variants of high risk.
STAYAHEAD
LSHM22038-H026
e.a.schultes@lacdr.leidenuniv.nl
2026-03-31
2023-03-01
Chemistry
Małgorzata Wolniewicz
3749
https://api.rohub.org/api/ros/0c470650-84d9-40e1-bc80-4591a27f6c4d/crate/download/
2022-01-12 16:34:39.917729+00:00
2026-05-19 11:46:00.057896+00:00
2022-01-12 16:34:39.917729+00:00
Aromatic compounds are those chemical compounds (most commonly organic) that contain one or more rings with pi electrons delocalized all the way around them. In contrast to compounds that exhibit aromaticity, aliphatic compounds lack this delocalization. The term "aromatic" was assigned before the physical mechanism determining aromaticity was discovered, and referred simply to the fact that many such compounds have a sweet or pleasant odour; however, not all aromatic compounds have a sweet odour, and not all compounds with a sweet odour are aromatic compounds. Aromatic hydrocarbons, or arenes, are aromatic organic compounds containing solely carbon and hydrogen atoms. The configuration of six carbon atoms in aromatic compounds is called a "benzene ring", after the simple aromatic compound benzene, or a phenyl group when part of a larger compound.
Not all aromatic compounds are benzene-based; aromaticity can also manifest in heteroarenes, which follow Hückel's rule (for monocyclic rings: when the number of its π electrons equals 4n + 2, where n = 0, 1, 2, 3, ...). In these compounds, at least one carbon atom is replaced by one of the heteroatoms oxygen, nitrogen, or sulfur. Examples of non-benzene compounds with aromatic properties are furan, a heterocyclic compound with a five-membered ring that includes a single oxygen atom, and pyridine, a heterocyclic compound with a six-membered ring containing one nitrogen atom.
application/ld+json
https://w3id.org/ro-id/0c470650-84d9-40e1-bc80-4591a27f6c4d
chemistry
Aromatic compounds
MANUAL
Wolniewicz, Małgorzata. "Aromatic compounds." ROHub. Jan 12 ,2022. https://w3id.org/ro-id/0c470650-84d9-40e1-bc80-4591a27f6c4d.
False
2022-01-14 22:19:57.392548+00:00
False
2025-07-05 19:04:55.078129+00:00
False
2025-07-05 18:47:59.392957+00:00
False
2025-07-04 09:08:44.261623+00:00
none
Geographical Scope
arene
4.658385093167703
4.5
User Needs (RAST)
Other Chemical Sciences
none
none
none
none
Funding
none
aromatic compound benzene
13.168724279835391
6.4
chemical compound
12.525879917184268
12.1
Key Type Measures
scent
5.279503105590063
5.1
aromatic compounds aromatic compounds
46.70781893004115
22.7
IPCC
carbon atom
10.973084886128367
10.6
benzene
12.224108658743631
7.2
aliphatic compound
8.488964346349743
5.0
nitrogen
4.244306418219462
4.1
electron
4.244306418219462
4.1
Chemistry and materials (general)
organic chemistry
65.86151368760065
40.9
compound
17.48726655348047
10.3
chemistry
34.13848631239936
21.2
aromatic hydrocarbon
7.979626485568759
4.7
Climate-ADAPT Adaptation Sectors
ring
3.209109730848862
3.1
none
Aromatic compounds Aromatic compounds are those chemical compounds (most commonly organic) that contain one or more rings with pi electrons delocalized all the way around them.
47.727272727272734
27.3
Chemicals
heterocyclic compound
6.521739130434784
6.3
carbon atom
15.789473684210526
9.3
Chemical Sciences
Climate Hazard
nitrogen atom
18.51851851851852
9.0
larger compound
10.493827160493826
5.1
Organic Chemistry
none
none
aliphatic compound
5.900621118012424
5.7
Organic chemical
Economy, business and finance/Economic sector/Chemicals/Organic chemical
The term "aromatic" was assigned before the physical mechanism determining aromaticity was discovered, and referred simply to the fact that many such compounds have a sweet or pleasant odour; however, not all aromatic compounds have a sweet odour, and not all compounds with a sweet odour are aromatic compounds.
19.230769230769234
11.0
Chemistry and materials
aromatic hydrocarbon
5.693581780538303
5.5
Inorganic, organic and physical chemistry
Methodology
Jewellery
Arts, culture and entertainment/Arts and entertainment/Fashion/Jewellery
oxygen atom
11.11111111111111
5.4
aromatic
19.772256728778473
19.1
benzene
9.316770186335406
9.0
heterocyclic compound
9.507640067911712
5.6
The configuration of six carbon atoms in aromatic compounds is called a "benzene ring", after the simple aromatic compound benzene, or a phenyl group when part of a larger compound.
33.04195804195804
18.9
benzene ring
3.4161490683229823
3.3
aromatic compound
28.522920203735136
16.8
oxygen atom
4.244306418219462
4.1
Knowledge Sector (EEA)
Stakeholders
none
Policy Scale
service-account-enrichment
Earth sciences
ISIDe Working Group
data span
Dipartimento della Protezione Civile
monitoring network
Dipartimento della Protezione Civile
Creative Commons Attribution
Mt. Etna
website
data
Italy
seismicity
Etna
Istituto Nazionale di Geofisica
Ro
monitoring
INGV website
Internet
Italy
POINT (14.9063 37.6002)
POINT (15.1897 37.793)
POINT (15.1075 37.711)
POINT (14.9805 37.8083)
10f9db58-8991-4b1f-b552-35fbc6cb71df
POINT (15.1897 37.793)
14.9063
37.6002
POINT (14.9063 37.6002)
53655fd9-abb3-4276-b2ff-d53bca6d3fad
POINT (14.9805 37.8083)
15.1075
37.711
POINT (15.1075 37.711)
15.1897
37.793
POINT (15.1897 37.793)
14.9805
37.8083
POINT (14.9805 37.8083)
c1fb3e30-00ac-40ca-82ed-285f663b7fdf
POINT (14.9063 37.6002)
service-account-enrichment
fa2c8fea-8cc9-44b7-9adc-b18ebfdd1b43
POINT (15.1075 37.711)
54109
https://api.rohub.org/api/ros/7dbe7eb1-c0c9-4722-ad47-885dd01eaee8/crate/download/
2022-02-02 13:22:22.374580+00:00
2025-03-05 01:04:55.096466+00:00
2022-02-02 13:22:22.374580+00:00
This RO contains the seismicity occurring at Mt. Etna (Italy) from local and national monitoring networks managed by INGV. Data span of 12 days, 2016-07-06 - 2016-07-17 and it is taken from the INGV website. Data and results published on this website by Istituto Nazionale di Geofisica e Vulcanologia are licensed under a Creative Commons Attribution 4.0 International License. ISIDe Working Group at National Earthquake Observatory benefited from funding provided by the Italian Presidenza del Consiglio dei Ministri, Dipartimento della Protezione Civile.
application/ld+json
https://w3id.org/ro-id/7dbe7eb1-c0c9-4722-ad47-885dd01eaee8
Mt. Etna (Italy) seismic activity from 2016-07-06 - 2016-07-17
AUTOMATIC
https://w3id.org/ro-id/7dbe7eb1-c0c9-4722-ad47-885dd01eaee8/3d0e7c5a-bedd-4802-aa5d-ae94adb6e1da
https://w3id.org/ro-id/7dbe7eb1-c0c9-4722-ad47-885dd01eaee8/86640593-6e89-4c59-9335-e875ce2e7e94
https://w3id.org/ro-id/7dbe7eb1-c0c9-4722-ad47-885dd01eaee8/c742e8e6-d19f-4d43-9ca8-db1807596aa7
https://w3id.org/ro-id/7dbe7eb1-c0c9-4722-ad47-885dd01eaee8/cff0fc1f-eaae-4657-978b-cadf35dbcf76
Degassing
Etna Seismic Actitvity
Monitoring Networks
Seismic Activity
service-account-generation-service. "Mt. Etna (Italy) seismic activity from 2016-07-06 - 2016-07-17." ROHub. Feb 02 ,2022. https://w3id.org/ro-id/7dbe7eb1-c0c9-4722-ad47-885dd01eaee8.
Data
biblio
49499
https://api.rohub.org/api/resources/6fa21b20-d907-425d-9f50-88c998128b1e/download/
2022-02-02 13:22:34.686024+00:00
2022-02-02 13:22:34.686925+00:00
image/png
Sketch
2022-02-02 13:22:34.686024+00:00
14649
https://api.rohub.org/api/resources/711ccdaa-7da2-435c-94b0-f8ddf68d395a/download/
2022-02-02 13:22:27.784863+00:00
2022-02-02 13:22:27.785737+00:00
application/vnd.google-earth.kml+xml
Seismic locations - kml
2022-02-02 13:22:27.784863+00:00
606
https://api.rohub.org/api/resources/9b066a45-103f-46b9-bdd3-996acc663d93/download/
2022-02-02 13:22:31.494399+00:00
2022-02-02 13:22:31.495365+00:00
text/plain
Seismic locations - txt
2022-02-02 13:22:31.494399+00:00
service-account-generation-service
Earth sciences
ISIDe Working Group
data span
Dipartimento della Protezione Civile
monitoring network
Dipartimento della Protezione Civile
Creative Commons Attribution
https://w3id.org/ro-id/8f442e85-5fef-4ac9-ba51-510ecb12b6e5/Mt.%20Etna
website
data
Italy
seismicity
Etna
Istituto Nazionale di Geofisica
Ro
monitoring
INGV website
Internet
Italy
POINT (14.9557 37.6837)
POINT (14.9435 37.6752)
14.9435
37.6752
POINT (14.9435 37.6752)
14.9557
37.6837
POINT (14.9557 37.6837)
d35ebce5-c992-4bcb-9b59-9aceec571bd2
POINT (14.9557 37.6837)
e2a11086-31fb-46e1-9d46-9b5dcd2368e9
POINT (14.9435 37.6752)
service-account-enrichment
50261
https://api.rohub.org/api/ros/8f442e85-5fef-4ac9-ba51-510ecb12b6e5/crate/download/
2022-02-02 13:23:00.765925+00:00
2025-03-05 01:04:55.534739+00:00
2022-02-02 13:23:00.765925+00:00
This RO contains the seismicity occurring at Mt. Etna (Italy) from local and national monitoring networks managed by INGV. Data span of 12 days, 2016-07-30 - 2016-08-10 and it is taken from the INGV website. Data and results published on this website by Istituto Nazionale di Geofisica e Vulcanologia are licensed under a Creative Commons Attribution 4.0 International License. ISIDe Working Group at National Earthquake Observatory benefited from funding provided by the Italian Presidenza del Consiglio dei Ministri, Dipartimento della Protezione Civile.
application/ld+json
https://w3id.org/ro-id/8f442e85-5fef-4ac9-ba51-510ecb12b6e5
Mt. Etna (Italy) seismic activity from 2016-07-30 - 2016-08-10
AUTOMATIC
https://w3id.org/ro-id/8f442e85-5fef-4ac9-ba51-510ecb12b6e5/6335b778-a46a-452d-8b06-280671309547
https://w3id.org/ro-id/8f442e85-5fef-4ac9-ba51-510ecb12b6e5/9dc5ab51-8961-4f72-b28b-96f00a7cbb29
Degassing
Etna Seismic Actitvity
Monitoring Networks
Seismic Activity
service-account-generation-service. "Mt. Etna (Italy) seismic activity from 2016-07-30 - 2016-08-10." ROHub. Feb 02 ,2022. https://w3id.org/ro-id/8f442e85-5fef-4ac9-ba51-510ecb12b6e5.
Data
biblio
13565
https://api.rohub.org/api/resources/268bca24-da87-4ee1-9914-ee87a39a5e0c/download/
2022-02-02 13:23:04.933416+00:00
2022-02-02 13:23:04.934205+00:00
application/vnd.google-earth.kml+xml
Seismic locations - kml
2022-02-02 13:23:04.933416+00:00
45584
https://api.rohub.org/api/resources/8d4174be-b324-4ee4-80fa-d46e6d8fbce3/download/
2022-02-02 13:23:12.296870+00:00
2022-02-02 13:23:12.297677+00:00
image/png
Sketch
2022-02-02 13:23:12.296870+00:00
367
https://api.rohub.org/api/resources/e4ffa5d4-891f-4864-81f0-61ab6ff7016a/download/
2022-02-02 13:23:08.432203+00:00
2022-02-02 13:23:08.432976+00:00
text/plain
Seismic locations - txt
2022-02-02 13:23:08.432203+00:00
service-account-generation-service
Earth sciences
ISIDe Working Group
data span
Dipartimento della Protezione Civile
monitoring network
Dipartimento della Protezione Civile
Creative Commons Attribution
Mt. Etna
website
data
Italy
seismicity
Etna
Istituto Nazionale di Geofisica
Ro
monitoring
INGV website
Internet
Italy
POINT (14.9513 37.8298)
14.9513
37.8298
POINT (14.9513 37.8298)
9303ad21-e095-4a8b-944e-76347e5c27c6
POINT (14.9513 37.8298)
service-account-enrichment
46670
https://api.rohub.org/api/ros/618c682d-22bf-4f4d-9fdb-3ca9196897a2/crate/download/
2022-02-02 13:23:19.199340+00:00
2025-03-05 01:04:55.749971+00:00
2022-02-02 13:23:19.199340+00:00
This RO contains the seismicity occurring at Mt. Etna (Italy) from local and national monitoring networks managed by INGV. Data span of 12 days, 2016-08-11 - 2016-08-22 and it is taken from the INGV website. Data and results published on this website by Istituto Nazionale di Geofisica e Vulcanologia are licensed under a Creative Commons Attribution 4.0 International License. ISIDe Working Group at National Earthquake Observatory benefited from funding provided by the Italian Presidenza del Consiglio dei Ministri, Dipartimento della Protezione Civile.
application/ld+json
https://w3id.org/ro-id/618c682d-22bf-4f4d-9fdb-3ca9196897a2
Mt. Etna (Italy) seismic activity from 2016-08-11 - 2016-08-22
AUTOMATIC
https://w3id.org/ro-id/618c682d-22bf-4f4d-9fdb-3ca9196897a2/0f6d8100-fa61-4557-99bb-82924ebc9a7d
Degassing
Etna Seismic Actitvity
Monitoring Networks
Seismic Activity
service-account-generation-service. "Mt. Etna (Italy) seismic activity from 2016-08-11 - 2016-08-22." ROHub. Feb 02 ,2022. https://w3id.org/ro-id/618c682d-22bf-4f4d-9fdb-3ca9196897a2.
Data
biblio
13019
https://api.rohub.org/api/resources/529ba3b6-ab55-4a46-9d86-51a7e8ef8c88/download/
2022-02-02 13:23:23.316237+00:00
2022-02-02 13:23:23.317209+00:00
application/vnd.google-earth.kml+xml
Seismic locations - kml
2022-02-02 13:23:23.316237+00:00
254
https://api.rohub.org/api/resources/7298b9d7-0696-423f-a4b4-28bf98d2ce53/download/
2022-02-02 13:23:26.542196+00:00
2022-02-02 13:23:26.543388+00:00
text/plain
Seismic locations - txt
2022-02-02 13:23:26.542196+00:00
43187
https://api.rohub.org/api/resources/e70a2924-4465-403b-a450-51c0783fc5cd/download/
2022-02-02 13:23:31.102326+00:00
2022-02-02 13:23:31.104047+00:00
image/png
Sketch
2022-02-02 13:23:31.102326+00:00
service-account-generation-service
Mathematics
cohomology of the mod
usage of the dataset
mathematics
minimal resolution
algebra
map
mountain range
document
cohomology
dataset
range
usage
document CohomA2.pdf
Steenrod algebra
squaring
resolution
subject
service-account-enrichment
7227
https://api.rohub.org/api/ros/ea70fc98-a80f-4758-8ae5-067ae64905ed/crate/download/
2022-03-22 01:20:46.276139+00:00
2025-03-05 01:26:34.044209+00:00
2022-03-22 01:20:46.276139+00:00
The dataset contains a minimal resolution of the mod 2 Steenrod algebra in the range 0 <= s <= 128, 0 <= t <= 184, together with chain maps for each cocycle in that range and for the squaring operation Sq^0 in the cohomology of the Steenrod algebra. The included document CohomA2.pdf explains the contents and usage of the dataset in detail.
application/ld+json
https://w3id.org/ro-id/ea70fc98-a80f-4758-8ae5-067ae64905ed
The cohomology of the mod 2 Steenrod algebra
MANUAL
Robert Bruner, and John Rognes. "The cohomology of the mod 2 Steenrod algebra." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/ea70fc98-a80f-4758-8ae5-067ae64905ed.
biblio
raw data
data
metadata
https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00078
2022-03-22 01:21:00.644689+00:00
2022-03-22 01:21:05.591782+00:00
The dataset contains a minimal resolution of the mod 2 Steenrod algebra in the range 0 <= s <= 128, 0 <= t <= 184, together with chain maps for each cocycle in that range and for the squaring operation Sq^0 in the cohomology of the Steenrod algebra. The included document CohomA2.pdf explains the contents and usage of the dataset in detail.
The cohomology of the mod 2 Steenrod algebra
2022-03-22 01:21:00.644689+00:00
Geo H.
john.rognes@rohub.com
John Rognes
robert.bruner@rohub.com
Robert Bruner
Mathematics
cohomology of the mod
usage of the dataset
mathematics
minimal resolution
algebra
map
mountain range
document
cohomology
dataset
range
usage
document CohomA2.pdf
Steenrod algebra
squaring
resolution
subject
service-account-enrichment
8675
https://api.rohub.org/api/ros/32eba436-e9ef-4ed2-8911-fec14c5a3779/crate/download/
2022-03-22 01:21:07.680973+00:00
2025-03-05 01:26:33.828703+00:00
2022-03-22 01:21:07.680973+00:00
The dataset contains a minimal resolution of the mod 2 Steenrod algebra in the range 0 <= s <= 128, 0 <= t <= 184, together with chain maps for each cocycle in that range and for the squaring operation Sq^0 in the cohomology of the Steenrod algebra. The included document CohomA2.pdf explains the contents and usage of the dataset in detail.
application/ld+json
https://w3id.org/ro-id/32eba436-e9ef-4ed2-8911-fec14c5a3779
The cohomology of the mod 2 Steenrod algebra
MANUAL
Robert Bruner, and John Rognes. "The cohomology of the mod 2 Steenrod algebra." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/32eba436-e9ef-4ed2-8911-fec14c5a3779.
raw data
biblio
data
metadata
Bruner, R., Rognes, J. (2022).The cohomology of the mod 2 Steenrod algebra [Data set]. Norstore. https://doi.org/10.11582/2022.00015
Robert Ray Bruner
Observation
https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00077
None
2022-03-22 01:21:29.913760+00:00
The dataset contains a minimal resolution of the mod 2 Steenrod algebra in the range 0 <= s <= 128, 0 <= t <= 184, together with chain maps for each cocycle in that range and for the squaring operation Sq^0 in the cohomology of the Steenrod algebra. The included document CohomA2.pdf explains the contents and usage of the dataset in detail.
The cohomology of the mod 2 Steenrod algebra
None
Robert Ray Bruner
Geo H.
john.rognes@rohub.com
John Rognes
robert.bruner@rohub.com
Robert Bruner
Environmental research
Life sciences
Physical sciences
Biology
Svalbard
time series station
West Spitsbergen Current
West Spitsbergen Current
ecology
Atlantic Ocean
mouth of Adventfjorden
Atlantic water
hydrography
ecosystem
time series
dataset
stream mouth
broadcasting station
Spitsbergen
inflow
Atlantic Ocean
ecosystem effects of climate change
effects of climate change
Spitsbergen
station
variability
15.52992
78.26105
POINT (15.52992 78.26105)
c9a6edac-883c-4415-aad5-5270dff5e8fb
POINT (15.52992 78.26105)
service-account-enrichment
6795
https://api.rohub.org/api/ros/1b7874e4-6c2f-4b84-87cb-74db13d49196/crate/download/
2022-03-22 01:21:31.715074+00:00
2025-03-05 01:01:11.704641+00:00
2022-03-22 01:21:31.715074+00:00
The Isfjorden-Adventfjorden (IsA) time series station is a marine station operated by the University Centre in Svalbard (UNIS). It is located in the mouth of Adventfjorden within Isfjorden on the west coast of Spitsbergen, and is frequently influenced by inflow of warm Atlantic Water from the West Spitsbergen Current. The station is therefore well suited for monitoring seasonal variability and ecosystem effects of climate change. IsA has been sampled on a monthly basis since December 2011. This dataset represents the acid-corrected Chl a values from several depths.
application/ld+json
https://w3id.org/ro-id/1b7874e4-6c2f-4b84-87cb-74db13d49196
ISA_Svalbard_Chlorophyll_A_2011_2019
MANUAL
https://w3id.org/ro-id/1b7874e4-6c2f-4b84-87cb-74db13d49196/870010a7-224a-4b80-8ee6-bcdb220e619e
University Centre in Svalbard (UNIS). "ISA_Svalbard_Chlorophyll_A_2011_2019." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/1b7874e4-6c2f-4b84-87cb-74db13d49196.
POINT (15.52992 78.26105)
raw data
data
metadata
biblio
https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00069
2022-03-22 01:21:41.637352+00:00
2022-03-22 01:21:46.049432+00:00
The Isfjorden-Adventfjorden (IsA) time series station is a marine station operated by the University Centre in Svalbard (UNIS). It is located in the mouth of Adventfjorden within Isfjorden on the west coast of Spitsbergen, and is frequently influenced by inflow of warm Atlantic Water from the West Spitsbergen Current. The station is therefore well suited for monitoring seasonal variability and ecosystem effects of climate change. IsA has been sampled on a monthly basis since December 2011. This dataset represents the acid-corrected Chl a values from several depths.
ISA_Svalbard_Chlorophyll_A_2011_2019
2022-03-22 01:21:41.637352+00:00
UNIS@rohub.com
University Centre in Svalbard (UNIS)
Geo H.
Environmental research
Life sciences
Physical sciences
Earth sciences
Svalbard
temperature logger
physics
medicine
temperature
processing
observatory
fluorescence
diagram
dataset
recovery
moor
Svalbard
mooring diagram
fluorescence data
observatory layout
marine biology
logger
layout
information
watercraft and nautical navigation
UiT The Arctic University of Norway and The
Scottish Association
observatories consist
occupational overuse syndrome
11.8239
78.9589
POINT (11.8239 78.9589)
bece0544-4961-412d-91ad-ecf25c63d637
POINT (11.8239 78.9589)
service-account-enrichment
11468
https://api.rohub.org/api/ros/b4960d2f-d2a6-462c-83b1-9b85f4c046ac/crate/download/
2022-03-22 01:21:47.502650+00:00
2025-03-05 01:24:10.636796+00:00
2022-03-22 01:21:47.502650+00:00
As part of the "KROP - Kongsfjorden Rijpfjorden Observatory Programme" UiT The Arctic University of Norway and The Scottish Association for Marine Science maintain marine observatories (moorings) in two high-Arctic fjords in Svalbard: Kongsfjorden and Rijpfjorden. The observatories consists of an array of CTDs, temperature loggers, ADCPs and a sediment trap, in addition to various other instruments or installations that change from year to year.
This dataset contains the CTD, PAR and fluorescence data from Kongsfjorden 2017-2018.
Fluorescence data is given as raw voltage only, due to calibration and fouling issues. It is meant as an indication of the timing of the phytoplankton bloom, not as absolute chlorophyll a concentration.
No post-recovery processing of light data (to correct for fouling) has been performed.
The observatory layout is available in the mooring diagram provided.
application/ld+json
https://w3id.org/ro-id/b4960d2f-d2a6-462c-83b1-9b85f4c046ac
Temperature, salinity, light and fluorescence (CTD) measurements from the Kongsfjorden (Svalbard) marine observatory (mooring) August 2017-August 2018
MANUAL
https://w3id.org/ro-id/b4960d2f-d2a6-462c-83b1-9b85f4c046ac/1815b2b8-8a48-4223-a69b-52dcee7b0fca
Finlo Cottier, Jørgen Berge, Estelle Dumont, Tomasz Piotr Kopec, Emily Joanne Venables, Daniel Ludwig Vogedes, UiT The Arctic University of Norway (UiT), and Scottish Association for Marine Science (SAMS). "Temperature, salinity, light and fluorescence (CTD) measurements from the Kongsfjorden (Svalbard) marine observatory (mooring) August 2017-August 2018." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/b4960d2f-d2a6-462c-83b1-9b85f4c046ac.
POINT (11.8239 78.9589)
data
biblio
metadata
raw data
Cottier, F., Berge, J., Dumont, E., Kopec, T. P., Venables, E. J., Vogedes, D. L., UiT The Arctic University of Norway, Scottish Association for Marine Science (2021).Temperature, salinity, light and fluorescence (CTD) measurements from the Kongsfjorden (Svalbard) marine observatory (mooring) August 2017-August 2018 [Data set]. Norstore. https://doi.org/10.11582/2021.00065
UiT The Arctic University of Norway (UiT)
Observation
https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00065
2021-07-16 00:00:00
2022-03-22 01:22:40.368999+00:00
As part of the "KROP - Kongsfjorden Rijpfjorden Observatory Programme" UiT The Arctic University of Norway and The Scottish Association for Marine Science maintain marine observatories (moorings) in two high-Arctic fjords in Svalbard: Kongsfjorden and Rijpfjorden. The observatories consists of an array of CTDs, temperature loggers, ADCPs and a sediment trap, in addition to various other instruments or installations that change from year to year.
This dataset contains the CTD, PAR and fluorescence data from Kongsfjorden 2017-2018.
Fluorescence data is given as raw voltage only, due to calibration and fouling issues. It is meant as an indication of the timing of the phytoplankton bloom, not as absolute chlorophyll a concentration.
No post-recovery processing of light data (to correct for fouling) has been performed.
The observatory layout is available in the mooring diagram provided.
Temperature, salinity, light and fluorescence (CTD) measurements from the Kongsfjorden (Svalbard) marine observatory (mooring) August 2017-August 2018
2021-07-16 00:00:00
Daniel Ludwig Vogedes
SAMS@rohub.com
Scottish Association for Marine Science (SAMS)
UiT@rohub.com
UiT The Arctic University of Norway (UiT)
daniel.ludwig.vogedes@rohub.com
Daniel Ludwig Vogedes
emily.joanne.venables@rohub.com
Emily Joanne Venables
estelle.dumont@rohub.com
Estelle Dumont
finlo.cottier@rohub.com
Finlo Cottier
Geo H.
jorgen.berge@rohub.com
Jørgen Berge
tomasz.piotr.kopec@rohub.com
Tomasz Piotr Kopec
Environmental research
Life sciences
Physical sciences
Earth sciences
11.8237
78.9592
POINT (11.8237 78.9592)
6dbca1e0-2173-4f1d-b61e-f4e28bec383e
POINT (11.8237 78.9592)
service-account-enrichment
11480
https://api.rohub.org/api/ros/04c3f1b2-96ec-4065-98a1-9499762d2405/crate/download/
2022-03-22 01:22:41.914748+00:00
2025-03-05 01:24:10.453697+00:00
2022-03-22 01:22:41.914748+00:00
As part of the "KROP - Kongsfjorden Rijpfjorden Observatory Programme" UiT The Arctic University of Norway and The Scottish Association for Marine Science maintain marine observatories (moorings) in two high-Arctic fjords in Svalbard: Kongsfjorden and Rijpfjorden. The observatories consists of an array of CTDs, temperature loggers, ADCPs and a sediment trap, in addition to various other instruments or installations that change from year to year.
This dataset contains the CTD, PAR and fluorescence data from Kongsfjorden 2016-2017.
Fluorescence data is given as raw voltage only, due to calibration and fouling issues. It is meant as an indication of the timing of the phytoplankton bloom, not as absolute chlorophyll a concentration.
No post-recovery processing of light data (to correct for fouling) has been performed.
The observatory layout is available in the mooring diagram provided.
At this deployment, two settlement plates were deployed (25m and 208m).
application/ld+json
https://w3id.org/ro-id/04c3f1b2-96ec-4065-98a1-9499762d2405
Temperature, salinity, light and fluorescence (CTD) measurements from the Kongsfjorden (Svalbard) marine observatory (mooring) August 2016-August 2017
MANUAL
Svalbard
dataset
diagram
fluorescence
information
layout
mooring
observatory
occupational overuse syndrome
processing
recovery
temperature
earth sciences
CTD
Kongsfjorden Rijpfjorden Observatory Programme
data
diagram
fluorescence
mooring
observatory
space sciences
fluorescence data
mooring diagram
observatories consist
observatory layout
recovery processing
As part of the "KROP - Kongsfjorden Rijpfjorden Observatory Programme" UiT The Arctic University of Norway and The Scottish Association for Marine Science maintain marine observatories (moorings) in two high-Arctic fjords in Svalbard: Kongsfjorden and Rijpfjorden.
The observatory layout is available in the mooring diagram provided.
This dataset contains the CTD, PAR and fluorescence data from Kongsfjorden 2016-2017.
2016-2017
Aug-2016-Aug-2017
https://w3id.org/ro-id/04c3f1b2-96ec-4065-98a1-9499762d2405/43a67b20-7c3d-4cd5-b299-35283289cf7f
armed forces
medicine
physics
Svalbard
Finlo Cottier, Jørgen Berge, Estelle Dumont, Colin Griffith, John Beaton, Daniel Ludwig Vogedes, UiT The Arctic University of Norway (UiT), and Scottish Association for Marine Science (SAMS). "Temperature, salinity, light and fluorescence (CTD) measurements from the Kongsfjorden (Svalbard) marine observatory (mooring) August 2016-August 2017." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/04c3f1b2-96ec-4065-98a1-9499762d2405.
POINT (11.8237 78.9592)
metadata
raw data
biblio
data
Cottier, F., Berge, J., Dumont, E., Griffith, C., Beaton, J., Vogedes, D. L., UiT The Arctic University of Norway, Scottish Association for Marine Science (2021).Temperature, salinity, light and fluorescence (CTD) measurements from the Kongsfjorden (Svalbard) marine observatory (mooring) August 2016-August 2017 [Data set]. Norstore. https://doi.org/10.11582/2021.00062
UiT The Arctic University of Norway (UiT)
Observation
https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00062
2021-07-12 00:00:00
2022-03-22 01:23:32.132406+00:00
As part of the "KROP - Kongsfjorden Rijpfjorden Observatory Programme" UiT The Arctic University of Norway and The Scottish Association for Marine Science maintain marine observatories (moorings) in two high-Arctic fjords in Svalbard: Kongsfjorden and Rijpfjorden. The observatories consists of an array of CTDs, temperature loggers, ADCPs and a sediment trap, in addition to various other instruments or installations that change from year to year.
This dataset contains the CTD, PAR and fluorescence data from Kongsfjorden 2016-2017.
Fluorescence data is given as raw voltage only, due to calibration and fouling issues. It is meant as an indication of the timing of the phytoplankton bloom, not as absolute chlorophyll a concentration.
No post-recovery processing of light data (to correct for fouling) has been performed.
The observatory layout is available in the mooring diagram provided.
At this deployment, two settlement plates were deployed (25m and 208m).
Temperature, salinity, light and fluorescence (CTD) measurements from the Kongsfjorden (Svalbard) marine observatory (mooring) August 2016-August 2017
2021-07-12 00:00:00
Daniel Ludwig Vogedes
SAMS@rohub.com
Scottish Association for Marine Science (SAMS)
UiT@rohub.com
UiT The Arctic University of Norway (UiT)
colin.griffith@rohub.com
Colin Griffith
daniel.ludwig.vogedes@rohub.com
Daniel Ludwig Vogedes
estelle.dumont@rohub.com
Estelle Dumont
finlo.cottier@rohub.com
Finlo Cottier
Geo H.
john.beaton@rohub.com
John Beaton
jorgen.berge@rohub.com
Jørgen Berge
Environmental research
Life sciences
Physical sciences
Earth sciences
40e24f2d-a1b1-42dd-ba2e-eedfdfeea4ec
POINT (11.8238 78.9589)
11.8238
78.9589
POINT (11.8238 78.9589)
service-account-enrichment
11633
https://api.rohub.org/api/ros/868972b2-e340-4007-8521-8f03b58cb7b9/crate/download/
2022-03-22 01:23:33.372212+00:00
2025-03-05 01:24:10.793750+00:00
2022-03-22 01:23:33.372212+00:00
As part of the "KROP - Kongsfjorden Rijpfjorden Observatory Programme" UiT The Arctic University of Norway and The Scottish Association for Marine Science maintain marine observatories (moorings) in two high-Arctic fjords in Svalbard: Kongsfjorden and Rijpfjorden. The observatories consists of an array of CTDs, temperature loggers, ADCPs and a sediment trap, in addition to various other instruments or installations that change from year to year.
This dataset contains the CTD, PAR and fluorescence data from Kongsfjorden 2015-2016.
Fluorescence data is given as raw voltage only, due to calibration and fouling issues. It is meant as an indication of the timing of the phytoplankton bloom, not as absolute chlorophyll a concentration.
No post-recovery processing of light data (to correct for fouling) has been performed.
The observatory layout is available in the mooring diagram provided.
For this deployment a RAS500 water sampler and a SUNA nitrate sensor were deployed for a specific project, data are not part of the long-term monitoring efforts and are available upon request.
application/ld+json
https://w3id.org/ro-id/868972b2-e340-4007-8521-8f03b58cb7b9
Temperature, salinity, light and fluorescence (CTD) measurements from the Kongsfjorden (Svalbard) marine observatory (mooring) September 2015-August 2016
MANUAL
Svalbard
data
dataset
diagram
fluorescence
information
mooring
nitrate
observatory
occupational overuse syndrome
sampler
sensor
temperature
earth sciences
CTD
Kongsfjorden Rijpfjorden Observatory Programme
data
diagram
fluorescence
mooring
observatory
space sciences
SUNA nitrate sensor
fluorescence data
mooring diagram
observatories consist
observatory layout
As part of the "KROP - Kongsfjorden Rijpfjorden Observatory Programme" UiT The Arctic University of Norway and The Scottish Association for Marine Science maintain marine observatories (moorings) in two high-Arctic fjords in Svalbard: Kongsfjorden and Rijpfjorden.
The observatory layout is available in the mooring diagram provided.
This dataset contains the CTD, PAR and fluorescence data from Kongsfjorden 2015-2016.
2015-2016
Sep-2015-Aug-2016
https://w3id.org/ro-id/868972b2-e340-4007-8521-8f03b58cb7b9/f68b0cde-9eb9-4e5e-a13e-f980ee3ccbf5
armed forces
marine biology
medicine
physics
Svalbard
Finlo Cottier, Jørgen Berge, Colin Griffith, Estelle Dumont, John Beaton, Daniel Ludwig Vogedes, UiT The Arctic University of Norway (UiT), and Scottish Association for Marine Science (SAMS). "Temperature, salinity, light and fluorescence (CTD) measurements from the Kongsfjorden (Svalbard) marine observatory (mooring) September 2015-August 2016." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/868972b2-e340-4007-8521-8f03b58cb7b9.
POINT (11.8238 78.9589)
metadata
biblio
raw data
data
Cottier, F., Berge, J., Griffith, C., Dumont, E., Beaton, J., Vogedes, D. L., UiT The Arctic University of Norway, Scottish Association for Marine Science (2021).Temperature, salinity, light and fluorescence (CTD) measurements from the Kongsfjorden (Svalbard) marine observatory (mooring) September 2015-August 2016 [Data set]. Norstore. https://doi.org/10.11582/2021.00061
Daniel Ludwig Vogedes
Observation
https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00061
2021-07-12 00:00:00
2022-03-22 01:24:23.830029+00:00
As part of the "KROP - Kongsfjorden Rijpfjorden Observatory Programme" UiT The Arctic University of Norway and The Scottish Association for Marine Science maintain marine observatories (moorings) in two high-Arctic fjords in Svalbard: Kongsfjorden and Rijpfjorden. The observatories consists of an array of CTDs, temperature loggers, ADCPs and a sediment trap, in addition to various other instruments or installations that change from year to year.
This dataset contains the CTD, PAR and fluorescence data from Kongsfjorden 2015-2016.
Fluorescence data is given as raw voltage only, due to calibration and fouling issues. It is meant as an indication of the timing of the phytoplankton bloom, not as absolute chlorophyll a concentration.
No post-recovery processing of light data (to correct for fouling) has been performed.
The observatory layout is available in the mooring diagram provided.
For this deployment a RAS500 water sampler and a SUNA nitrate sensor were deployed for a specific project, data are not part of the long-term monitoring efforts and are available upon request.
Temperature, salinity, light and fluorescence (CTD) measurements from the Kongsfjorden (Svalbard) marine observatory (mooring) September 2015-August 2016
2021-07-12 00:00:00
Daniel Ludwig Vogedes
SAMS@rohub.com
Scottish Association for Marine Science (SAMS)
UiT@rohub.com
UiT The Arctic University of Norway (UiT)
colin.griffith@rohub.com
Colin Griffith
daniel.ludwig.vogedes@rohub.com
Daniel Ludwig Vogedes
estelle.dumont@rohub.com
Estelle Dumont
finlo.cottier@rohub.com
Finlo Cottier
Geo H.
john.beaton@rohub.com
John Beaton
jorgen.berge@rohub.com
Jørgen Berge
Environmental research
Life sciences
Physical sciences
Svalbard
National Institute of Standards and Technology
Ny-Ålesund Svalbard
Australia
time-series measurement
USSIMO
sensor output
Norway
USSIMO spectroradiometer raw data time series
physics
United States of America
light observatory
data
observatory
Teflon
sensor
time series
spectroradiometer
dataset
raw data
Norway
Arctic Zone
UiT The Arctic University of Norway
Norwegian University of Science and Technology
Kings Bay
N-NW
NTNU
UiT The Arctic University
Perth
UiT
56f9c025-4431-44aa-8c4a-c90ceb56df10
POINT (11.84213 78.94116)
11.84213
78.94116
POINT (11.84213 78.94116)
service-account-enrichment
13192
https://api.rohub.org/api/ros/2eed7eb1-bb6d-4336-b4ec-b832d41270af/crate/download/
2022-03-22 01:31:31.343064+00:00
2025-03-05 02:47:05.411745+00:00
2022-03-22 01:31:31.343064+00:00
UiT The Arctic University of Norway (UiT) and the Norwegian University of Science and Technology (NTNU) established a light observatory at Kings Bay, Ny-Ålesund (Svalbard, Norway) in January 2017. The observatory consists of an array of light sensors including an all sky camera. It is located outside the settlement of Ny-Ålesund, approximately 1 km N-NW of the airport towards Brandalspynten. The array of sensors is mounted on a tripod under a transparent dome. This dataset contains the data of the hyperspectral radiometer USSIMO (In-situ Marine Optics, Perth, WA, Australia). It is equipped with a Zeiss MMS1 UV-VIS NIR detector with National Institute of Standards and Technology, USA traceable radiometric calibration between 380 and 900 nm. This instrument is used for time-series measurement of down-welling spectral irradiance in energy Wm-2 nm-1. Spectral resolution is 10 nm (3.3 nm pixel spacing) and a cosine-corrected polytetrafluoroethylene (PTFE) light diffusor with cosine error: <3% (0 - 60°), <10% (60 - 87.5°), is fitted. The device acquired measurements with a 16 bit analogue to digital converter. It samples continuously internally. Integration time is controlled by the sensor depending on the light intensity, with a maximum of 6 seconds. Actual integration time is stored with the data in each sample. The sensor output is saved on a PC with custom software which records 30 seconds of output data every 29:30 min. Number of samples collected in that period depends on the USSIMO integration time. The sensor is equipped with a pitch and roll sensor which is used to ensure that the spectroradiometer remains in the fixed position throughout the time-series acquisition. This dataset contains the 2019 data.
application/ld+json
https://w3id.org/ro-id/2eed7eb1-bb6d-4336-b4ec-b832d41270af
USSIMO spectroradiometer raw data time series (2019) measured under the dome of a light observatory in the Arctic (Ny-Ålesund, Svalbard, Norway)
MANUAL
https://w3id.org/ro-id/2eed7eb1-bb6d-4336-b4ec-b832d41270af/50972f9a-041f-46bd-9d1e-f5c5a55a40db
Jørgen Berge, Stephen Grant, Rune Bjørgum, Jonathan H. Cohen, David McKee, Geir Johnsen, Artur Zolich, Tomasz Piotr Kopec, Daniel Ludwig Vogedes, and UiT The Arctic University of Norway (UiT). "USSIMO spectroradiometer raw data time series (2019) measured under the dome of a light observatory in the Arctic (Ny-Ålesund, Svalbard, Norway)." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/2eed7eb1-bb6d-4336-b4ec-b832d41270af.
POINT (11.84213 78.94116)
metadata
biblio
raw data
data
Berge, J., Grant, S., Bjørgum, R., Cohen, J. H., McKee, D., Johnsen, G., Zolich, A., Kopec, T. P., Vogedes, D. L., UiT The Arctic University of Norway (2021).USSIMO spectroradiometer raw data time series (2019) measured under the dome of a light observatory in the Arctic (Ny-Ålesund, Svalbard, Norway) [Data set]. Norstore. https://doi.org/10.11582/2021.00045
UiT The Arctic University of Norway (UiT)
Observation
https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00045
2021-05-19 00:00:00
2022-03-22 01:32:34.318806+00:00
UiT The Arctic University of Norway (UiT) and the Norwegian University of Science and Technology (NTNU) established a light observatory at Kings Bay, Ny-Ålesund (Svalbard, Norway) in January 2017. The observatory consists of an array of light sensors including an all sky camera. It is located outside the settlement of Ny-Ålesund, approximately 1 km N-NW of the airport towards Brandalspynten. The array of sensors is mounted on a tripod under a transparent dome. This dataset contains the data of the hyperspectral radiometer USSIMO (In-situ Marine Optics, Perth, WA, Australia). It is equipped with a Zeiss MMS1 UV-VIS NIR detector with National Institute of Standards and Technology, USA traceable radiometric calibration between 380 and 900 nm. This instrument is used for time-series measurement of down-welling spectral irradiance in energy Wm-2 nm-1. Spectral resolution is 10 nm (3.3 nm pixel spacing) and a cosine-corrected polytetrafluoroethylene (PTFE) light diffusor with cosine error: <3% (0 - 60°), <10% (60 - 87.5°), is fitted. The device acquired measurements with a 16 bit analogue to digital converter. It samples continuously internally. Integration time is controlled by the sensor depending on the light intensity, with a maximum of 6 seconds. Actual integration time is stored with the data in each sample. The sensor output is saved on a PC with custom software which records 30 seconds of output data every 29:30 min. Number of samples collected in that period depends on the USSIMO integration time. The sensor is equipped with a pitch and roll sensor which is used to ensure that the spectroradiometer remains in the fixed position throughout the time-series acquisition. This dataset contains the 2019 data.
USSIMO spectroradiometer raw data time series (2019) measured under the dome of a light observatory in the Arctic (Ny-Ålesund, Svalbard, Norway)
2021-05-19 00:00:00
Daniel Ludwig Vogedes
UiT@rohub.com
UiT The Arctic University of Norway (UiT)
artur.zolich@rohub.com
Artur Zolich
daniel.ludwig.vogedes@rohub.com
Daniel Ludwig Vogedes
david.mckee@rohub.com
David McKee
geir.johnsen@rohub.com
Geir Johnsen
Geo H.
jonathan.h.cohen@rohub.com
Jonathan H. Cohen
jorgen.berge@rohub.com
Jørgen Berge
rune.bjorgum@rohub.com
Rune Bjørgum
stephen.grant@rohub.com
Stephen Grant
tomasz.piotr.kopec@rohub.com
Tomasz Piotr Kopec
Environmental research
Life sciences
Physical sciences
11.84213
78.94116
POINT (11.84213 78.94116)
2b40daef-2152-44ad-9a28-a0d37f288bec
POINT (11.84213 78.94116)
service-account-enrichment
13206
https://api.rohub.org/api/ros/e4ed6bdc-327b-4a57-86c0-394bf4a37b3f/crate/download/
2022-03-22 01:32:35.810584+00:00
2025-03-05 02:47:05.631433+00:00
2022-03-22 01:32:35.810584+00:00
UiT The Arctic University of Norway (UiT) and the Norwegian University of Science and Technology (NTNU) established a light observatory at Kings Bay, Ny-Ålesund (Svalbard, Norway) in January 2017. The observatory consists of an array of light sensors including an all sky camera. It is located outside the settlement of Ny-Ålesund, approximately 1 km N-NW of the airport towards Brandalspynten. The array of sensors is mounted on a tripod under a transparent dome. This dataset contains the data of the hyperspectral radiometer USSIMO (In-situ Marine Optics, Perth, WA, Australia). It is equipped with a Zeiss MMS1 UV-VIS NIR detector with National Institute of Standards and Technology, USA traceable radiometric calibration between 380 and 900 nm. This instrument is used for time-series measurement of down-welling spectral irradiance in energy Wm-2 nm-1. Spectral resolution is 10 nm (3.3 nm pixel spacing) and a cosine-corrected polytetrafluoroethylene (PTFE) light diffusor with cosine error: <3% (0 - 60°), <10% (60 - 87.5°), is fitted. The device acquired measurements with a 16 bit analogue to digital converter. It samples continuously internally. Integration time is controlled by the sensor depending on the light intensity, with a maximum of 6 seconds. Actual integration time is stored with the data in each sample. The sensor output is saved on a PC with custom software which records 30 seconds of output data every 29:30 min. Number of samples collected in that period depends on the USSIMO integration time. The sensor is equipped with a pitch and roll sensor which is used to ensure that the spectroradiometer remains in the fixed position throughout the time-series acquisition. This dataset contains the 2020 data.
application/ld+json
https://w3id.org/ro-id/e4ed6bdc-327b-4a57-86c0-394bf4a37b3f
USSIMO spectroradiometer raw data time series (2020) measured under the dome of a light observatory in the Arctic (Ny-Ålesund, Svalbard, Norway)
MANUAL
Norway
Teflon
data
dataset
observatory
raw data
sensor
spectroradiometer
time series
earth sciences
IT-computer sciences
Nanotechnology
Software
Synthetic and plastic chemicals
University
Norway
data
dataset
observatory
sensor
spectroradiometer
time series
engineering
USSIMO spectroradiometer raw data time series
UiT The Arctic University
light observatory
sensor output
time-series measurement
The observatory consists of an array of light sensors including an all sky camera.
This dataset contains the data of the hyperspectral radiometer USSIMO In-situ Marine Optics, Perth, WA, Australia) It is equipped with a Zeiss MMS1 UV-VIS NIR detector with National Institute of Standards and Technology, USA traceable radiometric calibration between 380 and 900 nm.
USSIMO spectroradiometer raw data time series (2020) measured under the dome of a light observatory in the Arctic (Ny-Ålesund, Svalbard, Norway) UiT The Arctic University of Norway (UiT) and the Norwegian University of Science and Technology (NTNU) established a light observatory at Kings Bay, Ny-Ålesund Svalbard, Norway) in January 2017.
2020
30 seconds
in Jan-2017
of 6 seconds
https://w3id.org/ro-id/e4ed6bdc-327b-4a57-86c0-394bf4a37b3f/83569c44-db68-4953-bb10-2a5bb71c2635
computer science
database
physics
software
National Institute of Standards and Technology
Arctic Zone
Australia
Norway
Perth
Svalbard
United States of America
Jørgen Berge, Stephen Grant, Rune Bjørgum, Jonathan H. Cohen, David McKee, Geir Johnsen, Artur Zolich, Tomasz Piotr Kopec, Daniel Ludwig Vogedes, and UiT The Arctic University of Norway (UiT). "USSIMO spectroradiometer raw data time series (2020) measured under the dome of a light observatory in the Arctic (Ny-Ålesund, Svalbard, Norway)." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/e4ed6bdc-327b-4a57-86c0-394bf4a37b3f.
POINT (11.84213 78.94116)
metadata
raw data
data
biblio
Berge, J., Grant, S., Bjørgum, R., Cohen, J. H., McKee, D., Johnsen, G., Zolich, A., Kopec, T. P., Vogedes, D. L., UiT The Arctic University of Tromsø (2021).USSIMO spectroradiometer raw data time series (2020) measured under the dome of a light observatory in the Arctic (Ny-Ålesund, Svalbard, Norway) [Data set]. Norstore. https://doi.org/10.11582/2021.00046
UiT The Arctic University of Norway (UiT)
Observation
https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00046
2021-05-19 00:00:00
2022-03-22 01:33:42.099209+00:00
UiT The Arctic University of Norway (UiT) and the Norwegian University of Science and Technology (NTNU) established a light observatory at Kings Bay, Ny-Ålesund (Svalbard, Norway) in January 2017. The observatory consists of an array of light sensors including an all sky camera. It is located outside the settlement of Ny-Ålesund, approximately 1 km N-NW of the airport towards Brandalspynten. The array of sensors is mounted on a tripod under a transparent dome. This dataset contains the data of the hyperspectral radiometer USSIMO (In-situ Marine Optics, Perth, WA, Australia). It is equipped with a Zeiss MMS1 UV-VIS NIR detector with National Institute of Standards and Technology, USA traceable radiometric calibration between 380 and 900 nm. This instrument is used for time-series measurement of down-welling spectral irradiance in energy Wm-2 nm-1. Spectral resolution is 10 nm (3.3 nm pixel spacing) and a cosine-corrected polytetrafluoroethylene (PTFE) light diffusor with cosine error: <3% (0 - 60°), <10% (60 - 87.5°), is fitted. The device acquired measurements with a 16 bit analogue to digital converter. It samples continuously internally. Integration time is controlled by the sensor depending on the light intensity, with a maximum of 6 seconds. Actual integration time is stored with the data in each sample. The sensor output is saved on a PC with custom software which records 30 seconds of output data every 29:30 min. Number of samples collected in that period depends on the USSIMO integration time. The sensor is equipped with a pitch and roll sensor which is used to ensure that the spectroradiometer remains in the fixed position throughout the time-series acquisition. This dataset contains the 2020 data.
USSIMO spectroradiometer raw data time series (2020) measured under the dome of a light observatory in the Arctic (Ny-Ålesund, Svalbard, Norway)
2021-05-19 00:00:00
Daniel Ludwig Vogedes
UiT@rohub.com
UiT The Arctic University of Norway (UiT)
artur.zolich@rohub.com
Artur Zolich
daniel.ludwig.vogedes@rohub.com
Daniel Ludwig Vogedes
david.mckee@rohub.com
David McKee
geir.johnsen@rohub.com
Geir Johnsen
Geo H.
jonathan.h.cohen@rohub.com
Jonathan H. Cohen
jorgen.berge@rohub.com
Jørgen Berge
rune.bjorgum@rohub.com
Rune Bjørgum
stephen.grant@rohub.com
Stephen Grant
tomasz.piotr.kopec@rohub.com
Tomasz Piotr Kopec
Environmental research
Life sciences
Physical sciences
Earth sciences
22.2991
80.2951
POINT (22.2991 80.2951)
8a93a745-c942-415f-a9d8-def5bede99b1
POINT (22.2991 80.2951)
service-account-enrichment
11405
https://api.rohub.org/api/ros/4ff3f101-109b-4e35-babc-46862cd4d330/crate/download/
2022-03-22 01:39:08.957789+00:00
2025-03-05 01:24:11.330191+00:00
2022-03-22 01:39:08.957789+00:00
As part of the "KROP - Kongsfjorden Rijpfjorden Observatory Programme" UiT The Arctic University of Norway and The Scottish Association for Marine Science maintain marine observatories (moorings) in two high-Arctic fjords in Svalbard: Kongsfjorden and Rijpfjorden. The observatories consists of an array of CTDs, temperature loggers, ADCPs and a sediment trap, in addition to various other instruments or installations that change from year to year.
This dataset contains the CTD, PAR and fluorescence data from Rijpfjorden 2018-2020.
Fluorescence data is given as raw voltage only, due to calibration and fouling issues. It is meant as an indication of the timing of the phytoplankton bloom, not as absolute chlorophyll a concentration.
No post-recovery processing of light data (to correct for fouling) has been performed.
The observatory layout is available in the mooring diagram provided.
The mooring was deployed for 2 years due to heavy ice cover on Rijpfjorden in 2019 which made recovery impossible. It was equipped with 6 SBE37 to get a good picture of the water mass exchange throughout the water column. All sensor still logging after 2 years, the sediment trap only collected the 2018-19 samples.
application/ld+json
https://w3id.org/ro-id/4ff3f101-109b-4e35-babc-46862cd4d330
Temperature, salinity, light and fluorescence (CTD) measurements from the Rijpfjorden (Svalbard) marine observatory (mooring) August 2018-September 2020
MANUAL
Svalbard
dataset
diagram
fluorescence
information
mooring
observatory
occupational overuse syndrome
recovery
sediment
temperature
trap
earth sciences
CTD
Kongsfjorden Rijpfjorden Observatory Programme
Rijpfjorden
data
fluorescence
mooring
observatory
space sciences
fluorescence data
mooring diagram
observatories consist
observatory layout
sediment trap
As part of the "KROP - Kongsfjorden Rijpfjorden Observatory Programme" UiT The Arctic University of Norway and The Scottish Association for Marine Science maintain marine observatories (moorings) in two high-Arctic fjords in Svalbard: Kongsfjorden and Rijpfjorden.
The observatory layout is available in the mooring diagram provided.
This dataset contains the CTD, PAR and fluorescence data from Rijpfjorden 2018-2020.
2018-2020
Aug-2018-Sep-2020
after 2 years
for 2 years
in 2019 which
the 2018-2019
https://w3id.org/ro-id/4ff3f101-109b-4e35-babc-46862cd4d330/0bf6a835-85bd-4000-bb1d-2c6077e214bb
armed forces
medicine
physics
Svalbard
Jørgen Berge, Finlo Cottier, Tomasz Piotr Kopec, Estelle Dumont, Emily Joanne Venables, Daniel Ludwig Vogedes, and UiT The Arctic University of Norway (UiT). "Temperature, salinity, light and fluorescence (CTD) measurements from the Rijpfjorden (Svalbard) marine observatory (mooring) August 2018-September 2020." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/4ff3f101-109b-4e35-babc-46862cd4d330.
POINT (22.2991 80.2951)
biblio
data
raw data
metadata
Berge, J., Cottier, F., Kopec, T. P., Dumont, E., Venables, E. J., Vogedes, D. L., UiT The Arctic University of Norway (2021).Temperature, salinity, light and fluorescence (CTD) measurements from the Rijpfjorden (Svalbard) marine observatory (mooring) August 2018-September 2020 [Data set]. Norstore. https://doi.org/10.11582/2021.00031
UiT The Arctic University of Norway (UiT)
Observation
https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00031
2021-04-26 00:00:00
2022-03-22 01:40:00.650124+00:00
As part of the "KROP - Kongsfjorden Rijpfjorden Observatory Programme" UiT The Arctic University of Norway and The Scottish Association for Marine Science maintain marine observatories (moorings) in two high-Arctic fjords in Svalbard: Kongsfjorden and Rijpfjorden. The observatories consists of an array of CTDs, temperature loggers, ADCPs and a sediment trap, in addition to various other instruments or installations that change from year to year.
This dataset contains the CTD, PAR and fluorescence data from Rijpfjorden 2018-2020.
Fluorescence data is given as raw voltage only, due to calibration and fouling issues. It is meant as an indication of the timing of the phytoplankton bloom, not as absolute chlorophyll a concentration.
No post-recovery processing of light data (to correct for fouling) has been performed.
The observatory layout is available in the mooring diagram provided.
The mooring was deployed for 2 years due to heavy ice cover on Rijpfjorden in 2019 which made recovery impossible. It was equipped with 6 SBE37 to get a good picture of the water mass exchange throughout the water column. All sensor still logging after 2 years, the sediment trap only collected the 2018-19 samples.
Temperature, salinity, light and fluorescence (CTD) measurements from the Rijpfjorden (Svalbard) marine observatory (mooring) August 2018-September 2020
2021-04-26 00:00:00
Daniel Ludwig Vogedes
UiT@rohub.com
UiT The Arctic University of Norway (UiT)
daniel.ludwig.vogedes@rohub.com
Daniel Ludwig Vogedes
emily.joanne.venables@rohub.com
Emily Joanne Venables
estelle.dumont@rohub.com
Estelle Dumont
finlo.cottier@rohub.com
Finlo Cottier
Geo H.
jorgen.berge@rohub.com
Jørgen Berge
tomasz.piotr.kopec@rohub.com
Tomasz Piotr Kopec
Environmental research
Life sciences
Physical sciences
Earth sciences
5fd6c729-070e-411d-b60c-08a8daa9cce2
POINT (22.3038 80.2943)
22.3038
80.2943
POINT (22.3038 80.2943)
service-account-enrichment
11334
https://api.rohub.org/api/ros/65a52384-b1db-4b77-ab42-d61b495ba937/crate/download/
2022-03-22 01:49:03.345354+00:00
2025-03-05 01:24:12.299726+00:00
2022-03-22 01:49:03.345354+00:00
As part of the "KROP - Kongsfjorden Rijpfjorden Observatory Programme" UiT The Arctic University of Norway and The Scottish Association for Marine Science maintain marine observatories (moorings) in two high-Arctic fjords in Svalbard: Kongsfjorden and Rijpfjorden. The observatories consists of an array of CTDs, temperature loggers, ADCPs and a sediment trap, in addition to various other instruments or installations that change from year to year.
This dataset contains the CTD, PAR and fluorescence data from Rijpfjorden 2014-2015.
Fluorescence data is given as raw voltage only, due to calibration and fouling issues. It is meant as an indication of the timing of the phytoplankton bloom, not as absolute chlorophyll a concentration.
No post-recovery processing of light data (to correct for fouling) has been performed.
Together with the top and bottom SBE37 two plastic settlement plates had been deployed for a settlement experiment for the recruitment of benthic invertebrates. The sediment trap was mounted at 58m instead the usual depth of 100 m because of specific requirements for an experiment.
The observatory layout is available in the mooring diagram provided.
application/ld+json
https://w3id.org/ro-id/65a52384-b1db-4b77-ab42-d61b495ba937
Temperature, salinity, light and fluorescence (CTD) measurements from the Rijpfjorden (Svalbard) marine observatory (mooring) September 2014-September 2015
MANUAL
Svalbard
dataset
diagram
fluorescence
information
layout
mooring
observatory
occupational overuse syndrome
sediment
temperature
trap
earth sciences
Synthetic and plastic chemicals
CTD
Kongsfjorden Rijpfjorden Observatory Programme
Rijpfjorden
data
fluorescence
mooring
observatory
space sciences
fluorescence data
mooring diagram
observatories consist
observatory layout
sediment trap
As part of the "KROP - Kongsfjorden Rijpfjorden Observatory Programme" UiT The Arctic University of Norway and The Scottish Association for Marine Science maintain marine observatories (moorings) in two high-Arctic fjords in Svalbard: Kongsfjorden and Rijpfjorden.
The observatory layout is available in the mooring diagram provided.
This dataset contains the CTD, PAR and fluorescence data from Rijpfjorden 2014-2015.
2014-2015
Sep-2014-Sep-2015
https://w3id.org/ro-id/65a52384-b1db-4b77-ab42-d61b495ba937/1280bc3c-1c8e-41f1-be9b-3e605e49280a
armed forces
marine biology
medicine
physics
Svalbard
Jørgen Berge, Finlo Cottier, Estelle Dumont, John Beaton, Colin Griffith, Daniel Ludwig Vogedes, and UiT The Arctic University of Norway (UiT). "Temperature, salinity, light and fluorescence (CTD) measurements from the Rijpfjorden (Svalbard) marine observatory (mooring) September 2014-September 2015." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/65a52384-b1db-4b77-ab42-d61b495ba937.
POINT (22.3038 80.2943)
raw data
biblio
metadata
data
Berge, J., Cottier, F., Dumont, E., Beaton, J., Griffith, C., Vogedes, D. L., UiT The Arctic University of Norway (2021).Temperature, salinity, light and fluorescence (CTD) measurements from the Rijpfjorden (Svalbard) marine observatory (mooring) September 2014-September 2015 [Data set]. Norstore. https://doi.org/10.11582/2021.00018
Daniel Ludwig Vogedes
Observation
https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00018
2021-03-22 00:00:00
2022-03-22 01:49:53.563806+00:00
As part of the "KROP - Kongsfjorden Rijpfjorden Observatory Programme" UiT The Arctic University of Norway and The Scottish Association for Marine Science maintain marine observatories (moorings) in two high-Arctic fjords in Svalbard: Kongsfjorden and Rijpfjorden. The observatories consists of an array of CTDs, temperature loggers, ADCPs and a sediment trap, in addition to various other instruments or installations that change from year to year.
This dataset contains the CTD, PAR and fluorescence data from Rijpfjorden 2014-2015.
Fluorescence data is given as raw voltage only, due to calibration and fouling issues. It is meant as an indication of the timing of the phytoplankton bloom, not as absolute chlorophyll a concentration.
No post-recovery processing of light data (to correct for fouling) has been performed.
Together with the top and bottom SBE37 two plastic settlement plates had been deployed for a settlement experiment for the recruitment of benthic invertebrates. The sediment trap was mounted at 58m instead the usual depth of 100 m because of specific requirements for an experiment.
The observatory layout is available in the mooring diagram provided.
Temperature, salinity, light and fluorescence (CTD) measurements from the Rijpfjorden (Svalbard) marine observatory (mooring) September 2014-September 2015
2021-03-22 00:00:00
Daniel Ludwig Vogedes
UiT@rohub.com
UiT The Arctic University of Norway (UiT)
colin.griffith@rohub.com
Colin Griffith
daniel.ludwig.vogedes@rohub.com
Daniel Ludwig Vogedes
estelle.dumont@rohub.com
Estelle Dumont
finlo.cottier@rohub.com
Finlo Cottier
Geo H.
john.beaton@rohub.com
John Beaton
jorgen.berge@rohub.com
Jørgen Berge
Environmental research
Life sciences
Physical sciences
Earth sciences
22.29918
80.29443
POINT (22.29918 80.29443)
d52a1692-016e-478f-95fc-cc06435a0ee2
POINT (22.29918 80.29443)
service-account-enrichment
11088
https://api.rohub.org/api/ros/2939a6bb-c42b-4c16-af6a-a56cc065079b/crate/download/
2022-03-22 01:51:43.001070+00:00
2025-03-05 01:24:11.147171+00:00
2022-03-22 01:51:43.001070+00:00
As part of the "KROP - Kongsfjorden Rijpfjorden Observatory Programme" UiT The Arctic University of Norway and The Scottish Association for Marine Science maintain marine observatories (moorings) in two high-Arctic fjords in Svalbard: Kongsfjorden and Rijpfjorden. The observatories consists of an array of CTDs, temperature loggers, ADCPs and a sediment trap, in addition to various other instruments or installations that change from year to year.
This dataset contains the CTD, PAR and fluorescence data from Rijpfjorden 2017-2018.
Fluorescence data is given as raw voltage only, due to calibration and fouling issues. It is meant as an indication of the timing of the phytoplankton bloom, not as absolute chlorophyll a concentration.
No post-recovery processing of light data (to correct for fouling) has been performed.
The observatory layout is available in the mooring diagram provided.
application/ld+json
https://w3id.org/ro-id/2939a6bb-c42b-4c16-af6a-a56cc065079b
Temperature, salinity, light and fluorescence (CTD) measurements from the Rijpfjorden (Svalbard) marine observatory (mooring) August 2017-August 2018
MANUAL
Svalbard
dataset
diagram
fluorescence
information
layout
mooring
observatory
occupational overuse syndrome
processing
recovery
temperature
earth sciences
CTD
Kongsfjorden Rijpfjorden Observatory Programme
Rijpfjorden
data
fluorescence
mooring
observatory
space sciences
fluorescence data
mooring diagram
observatories consist
observatory layout
recovery processing
As part of the "KROP - Kongsfjorden Rijpfjorden Observatory Programme" UiT The Arctic University of Norway and The Scottish Association for Marine Science maintain marine observatories (moorings) in two high-Arctic fjords in Svalbard: Kongsfjorden and Rijpfjorden.
The observatory layout is available in the mooring diagram provided.
This dataset contains the CTD, PAR and fluorescence data from Rijpfjorden 2017-2018.
2017-2018
Aug-2017-Aug-2018
https://w3id.org/ro-id/2939a6bb-c42b-4c16-af6a-a56cc065079b/e1bb7d0c-c504-422b-a45e-3dbd5b72448d
medicine
physics
Svalbard
Jørgen Berge, Finlo Cottier, Tomasz Piotr Kopec, Estelle Dumont, Emily Joanne Venables, Daniel Ludwig Vogedes, and UiT The Arctic University of Norway (UiT). "Temperature, salinity, light and fluorescence (CTD) measurements from the Rijpfjorden (Svalbard) marine observatory (mooring) August 2017-August 2018." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/2939a6bb-c42b-4c16-af6a-a56cc065079b.
POINT (22.29918 80.29443)
biblio
metadata
raw data
data
Berge, J., Cottier, F., Kopec, T. P., Dumont, E., Venables, E. J., Vogedes, D. L., UiT The Arctic University of Norway (2021).Temperature, salinity, light and fluorescence (CTD) measurements from the Rijpfjorden (Svalbard) marine observatory (mooring) August 2017-August 2018 [Data set]. Norstore. https://doi.org/10.11582/2021.00017
UiT The Arctic University of Norway (UiT)
Observation
https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00017
2021-03-12 00:00:00
2022-03-22 01:52:37.735565+00:00
As part of the "KROP - Kongsfjorden Rijpfjorden Observatory Programme" UiT The Arctic University of Norway and The Scottish Association for Marine Science maintain marine observatories (moorings) in two high-Arctic fjords in Svalbard: Kongsfjorden and Rijpfjorden. The observatories consists of an array of CTDs, temperature loggers, ADCPs and a sediment trap, in addition to various other instruments or installations that change from year to year.
This dataset contains the CTD, PAR and fluorescence data from Rijpfjorden 2017-2018.
Fluorescence data is given as raw voltage only, due to calibration and fouling issues. It is meant as an indication of the timing of the phytoplankton bloom, not as absolute chlorophyll a concentration.
No post-recovery processing of light data (to correct for fouling) has been performed.
The observatory layout is available in the mooring diagram provided.
Temperature, salinity, light and fluorescence (CTD) measurements from the Rijpfjorden (Svalbard) marine observatory (mooring) August 2017-August 2018
2021-03-12 00:00:00
Daniel Ludwig Vogedes
UiT@rohub.com
UiT The Arctic University of Norway (UiT)
daniel.ludwig.vogedes@rohub.com
Daniel Ludwig Vogedes
emily.joanne.venables@rohub.com
Emily Joanne Venables
estelle.dumont@rohub.com
Estelle Dumont
finlo.cottier@rohub.com
Finlo Cottier
Geo H.
jorgen.berge@rohub.com
Jørgen Berge
tomasz.piotr.kopec@rohub.com
Tomasz Piotr Kopec
Environmental research
Life sciences
Physical sciences
Earth sciences
data from Norwegian Meteorological Institute
meteorology
Norway
trends in cold spell
observational data
frequency
analysis
cold weather
dataset
reanalysis data
data from ERA5
Norway
spell
re-analysis
trend
information
Norwegian Meteorological Institute
POLYGON ((-20.0 80.0, 40.0 80.0, 40.0 40.0, -20.0 40.0, -20.0 80.0))
-20.0 80.0, 40.0 80.0, 40.0 40.0, -20.0 40.0, -20.0 80.0
b5d7b903-059c-4f00-9347-ffa7082d698d
POLYGON ((-20.0 80.0, 40.0 80.0, 40.0 40.0, -20.0 40.0, -20.0 80.0))
service-account-enrichment
7693
https://api.rohub.org/api/ros/cb7986b3-a55f-4281-af69-7269917b8a02/crate/download/
2022-03-22 01:52:57.468777+00:00
2025-03-05 00:50:49.038103+00:00
2022-03-22 01:52:57.468777+00:00
This data set contains an analysis of observational data from Norwegian Meteorological Institute and reanalysis data from ERA5.
application/ld+json
https://w3id.org/ro-id/cb7986b3-a55f-4281-af69-7269917b8a02
Frequency and trends in cold and warm spells in Norway in relation to large-scale atmospheric circulation
MANUAL
https://w3id.org/ro-id/cb7986b3-a55f-4281-af69-7269917b8a02/f7002b1d-65c6-4458-9cf7-c54a1f7585b9
Marek Ratajczak. "Frequency and trends in cold and warm spells in Norway in relation to large-scale atmospheric circulation." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/cb7986b3-a55f-4281-af69-7269917b8a02.
POLYGON ((-20.0 80.0, 40.0 80.0, 40.0 40.0, -20.0 40.0, -20.0 80.0))
biblio
metadata
data
raw data
Ratajczak, M. (2021).Frequency and trends in cold and warm spells in Norway in relation to large-scale atmospheric circulation [Data set]. Norstore. https://doi.org/10.11582/2021.00016
Marek Grzegorz Ratajczak
Observation
https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00016
2021-03-11 00:00:00
2022-03-22 01:53:15.007448+00:00
This data set contains an analysis of observational data from Norwegian Meteorological Institute and reanalysis data from ERA5.
Frequency and trends in cold and warm spells in Norway in relation to large-scale atmospheric circulation
2021-03-11 00:00:00
Marek Grzegorz Ratajczak
Geo H.
marek.ratajczak@rohub.com
Marek Ratajczak
Environmental research
Life sciences
Physical sciences
Biology
service-account-enrichment
7126
https://api.rohub.org/api/ros/d6328caa-df3b-4b17-9595-d2e361dfccf1/crate/download/
2022-03-22 01:55:05.007973+00:00
2025-03-05 01:19:15.480150+00:00
2022-03-22 01:55:05.007973+00:00
Custom sequence database from assembled NCBI SRA reads. Supplementary Data to Undheim and Jenner, Nat. Commun., 2021
application/ld+json
https://w3id.org/ro-id/d6328caa-df3b-4b17-9595-d2e361dfccf1
SRA transcriptome assemblies
MANUAL
custom
data
database
national
sequence
transcriptome
earth sciences
Genetics
IT-computer sciences
Newspaper
Periodical
Commun
NCBI SRA
Undheim
custom
data
database
transcriptome
mathematical and computer sciences
Nat. Commun
SRA transcriptome assembly
custom sequence database
data to Undheim
supplementary data
Custom sequence database from assembled NCBI SRA reads.
SRA transcriptome assemblies.
Supplementary Data to Undheim and Jenner, Nat. Commun.
database
Eivind Undheim. "SRA transcriptome assemblies." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/d6328caa-df3b-4b17-9595-d2e361dfccf1.
data
metadata
biblio
raw data
Undheim, E. (2020).SRA transcriptome assemblies [Data set]. Norstore. https://doi.org/10.11582/2020.00067
Eivind Andreas Baste Undheim
Observation
https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2020.00067
2020-12-29 00:00:00
2022-03-22 01:55:21.374031+00:00
Custom sequence database from assembled NCBI SRA reads. Supplementary Data to Undheim and Jenner, Nat. Commun., 2021
SRA transcriptome assemblies
2020-12-29 00:00:00
Eivind Andreas Baste Undheim
eivind.undheim@rohub.com
Eivind Undheim
Geo H.
Environmental research
Life sciences
Physical sciences
Earth sciences
ground station
report on the gap
fill process
physics
machine learning
soil
short wave
dataset
composition
system
mailto
forest
radiation
stations from GEBA archive
total
information
station
aperture
mailing
machine learning technique
UiO
trude.storelvmo@geo.uio.no
Trude Storelvmo
0000-0002-0068-2430
service-account-enrichment
7618
https://api.rohub.org/api/ros/46f90d7b-1e36-4701-9acd-2681dfe565a6/crate/download/
2022-03-22 01:55:23.106126+00:00
2025-03-05 00:59:11.026646+00:00
2022-03-22 01:55:23.106126+00:00
Global (diffuse and direct) shortwave downwelling radiation at the surface between year 1961 and 2014. A total of 1847 ground stations from GEBA archive has been selected and been through the machine learning technique "random forests" (Breiman, 2001) to fill gaps in from original GEBA dataset. A report on the gap filling process can be attained by e-mailing Trude Storelvmo (LINK: mailto:truds@uio.no).
application/ld+json
https://w3id.org/ro-id/46f90d7b-1e36-4701-9acd-2681dfe565a6
Gap filled GEBA data
MANUAL
Trude Storelvmo. "Gap filled GEBA data." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/46f90d7b-1e36-4701-9acd-2681dfe565a6.
biblio
raw data
metadata
data
Storelvmo, T. (2020).Gap filled GEBA data [Data set]. Norstore. https://doi.org/10.11582/2020.00066
Trude Storelvmo
Observation
https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2020.00066
2020-12-21 00:00:00
2022-03-22 01:55:40.621748+00:00
Global (diffuse and direct) shortwave downwelling radiation at the surface between year 1961 and 2014. A total of 1847 ground stations from GEBA archive has been selected and been through the machine learning technique "random forests" (Breiman, 2001) to fill gaps in from original GEBA dataset. A report on the gap filling process can be attained by e-mailing Trude Storelvmo (<a href="mailto:truds@uio.no" class="linkified" target="_blank">LINK</a>).
Gap filled GEBA data
2020-12-21 00:00:00
Trude Storelvmo
Geo H.
Environmental research
Life sciences
Physical sciences
Biology
15.52992
78.26105
POINT (15.52992 78.26105)
dc5618a8-8b14-4ece-a082-c6a3b691c89b
POINT (15.52992 78.26105)
service-account-enrichment
8670
https://api.rohub.org/api/ros/271b95da-7537-43a1-b65b-a5d84f2227ab/crate/download/
2022-03-22 01:55:42.873140+00:00
2025-03-05 01:01:11.472723+00:00
2022-03-22 01:55:42.873140+00:00
The Isfjorden-Adventfjorden (IsA) time series station is a marine station operated by the University Centre in Svalbard (UNIS). It is located in the mouth of Adventfjorden within Isfjorden on the west coast of Spitsbergen, and is frequently influenced by inflow of warm Atlantic Water from the West Spitsbergen Current. The station is therefore well suited for monitoring seasonal variability and ecosystem effects of climate change. IsA has been sampled on a monthly basis since December 2011. This dataset represents the acid-corrected Chl a values from several depths.
application/ld+json
https://w3id.org/ro-id/271b95da-7537-43a1-b65b-a5d84f2227ab
ISA_Svalbard_Chlorophyll_A_2011_2019
MANUAL
Atlantic Ocean
Spitsbergen
broadcasting station
dataset
ecosystem
effects of climate change
inflow
station
stream mouth
time series
variability
earth sciences
Climate change
Ecosystem
IT-computer sciences
Weather
Atlantic Ocean
ISA_Svalbard_Chlorophyll_A_2011_2019
Isfjorden-Adventfjorden
University Centre
dataset
inflow
time series
geosciences
Atlantic water
West Spitsbergen Current
ecosystem effects of climate change
mouth of Adventfjorden
time series station
IsA has been sampled on a monthly basis since December 2011.
The Isfjorden-Adventfjorden (IsA) time series station is a marine station operated by the University Centre in Svalbard (UNIS) It is located in the mouth of Adventfjorden within Isfjorden on the west coast of Spitsbergen, and is frequently influenced by inflow of warm Atlantic Water from the West Spitsbergen Current.
The station is therefore well suited for monitoring seasonal variability and ecosystem effects of climate change.
since Dec-2011
https://w3id.org/ro-id/271b95da-7537-43a1-b65b-a5d84f2227ab/388c2df4-da51-4c59-8696-918f504891a6
ecology
hydrography
Atlantic Ocean
Spitsbergen
Svalbard
University Centre in Svalbard (UNIS). "ISA_Svalbard_Chlorophyll_A_2011_2019." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/271b95da-7537-43a1-b65b-a5d84f2227ab.
POINT (15.52992 78.26105)
data
raw data
metadata
biblio
University Centre in Svalbard (2020).ISA_Svalbard_Chlorophyll_A_2011_2019 [Data set]. Norstore. https://doi.org/10.11582/2020.00063
University Centre in Svalbard (UNIS)
Observation
https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2020.00063
2020-12-09 00:00:00
2022-03-22 01:56:01.214326+00:00
The Isfjorden-Adventfjorden (IsA) time series station is a marine station operated by the University Centre in Svalbard (UNIS). It is located in the mouth of Adventfjorden within Isfjorden on the west coast of Spitsbergen, and is frequently influenced by inflow of warm Atlantic Water from the West Spitsbergen Current. The station is therefore well suited for monitoring seasonal variability and ecosystem effects of climate change. IsA has been sampled on a monthly basis since December 2011. This dataset represents the acid-corrected Chl a values from several depths.
ISA_Svalbard_Chlorophyll_A_2011_2019
2020-12-09 00:00:00
Luke Marsden
UNIS@rohub.com
University Centre in Svalbard (UNIS)
Geo H.
Environmental research
Life sciences
Physical sciences
Chemistry
force field
23.076923076923077
9.9
biochemistry
58.82352941176471
6.0
file
13.51981351981352
5.8
results file
13.58428805237316
8.3
Organic chemical
Economy, business and finance/Economic sector/Chemicals/Organic chemical
betterment
11.616766467065867
9.7
Physics
Science and technology/Natural science/Physics
raw data
7.18562874251497
6.0
chemistry and materials (general)
100.0
0.38146594166755676
validation
10.955710955710956
4.7
force field
16.047904191616766
13.4
Newspaper
Arts, culture and entertainment/Mass media/Newspaper
Results are divided in QM calculations+force field comparison and improvements, and force field parameter validations.
37.755102040816325
14.8
Results for Tyrosine-choline, Phenylalanine-choline and Tryptophan-choline force field improvements.
28.061224489795915
11.0
improvement
16.083916083916083
6.9
earth sciences
100.0
0.620610237121582
result
6.107784431137724
5.1
manuscripts file
8.67430441898527
5.3
comparison
4.6706586826347305
3.9
quartermaster
8.622754491017965
7.2
chemistry and materials
100.0
0.38146594166755676
force field comparison
20.29459901800327
12.4
QM calculation
12.76595744680851
7.8
computer science
18.627450980392158
1.9
service-account-enrichment
9478
https://api.rohub.org/api/ros/b7df46c2-d54e-4cf0-8600-36516706cfbe/crate/download/
2022-03-22 02:18:37.698329+00:00
2025-03-05 00:45:29.658387+00:00
2022-03-22 02:18:37.698329+00:00
Results for Tyrosine-choline, Phenylalanine-choline and Tryptophan-choline force field improvements. For ease of processing, results from both works are kept together. Results are divided in QM calculations+force field comparison and improvements, and force field parameter validations.
If you have trouble navigating, please send email to LINK: mailto:reza.611@gmail.com.
### Please see the file "file_organisations" for the directories and subdirectories.
# Results files are organised based on QM calculations and force field comparisons (also improvements), and Parameters validations where you have the test cases. For the QM calculations, they are based on the softwares and methods used. PSI4 directories for energy decompositions and SAPT methods, further divided in subdiectories. NWCHEM is for all other QM methods, and those can be differentiated by the dircetoty names which contains the method (DFT, MP2, CCSD(T)) and basis sets. CHARMM* directories contains the force field comparison vs the improvements test.
The Khan_etal_JCTC_2016 paper contains phenol-TMA and benzene-TMA cases. The Khan_etal_JCTC_2019 contains the indole-TMA case.
For the QM part, benzene-tma and indole-tma directories have naming explicitly (i.e. NWCHEM_cat_pi_benzene_tma/, PSI4_calc_benzene_tma/, CHARMM_pes_cat_pi_benzene_tma/). For phenol-TMA, look at the *_RESTART directories; these are the production ones for CHARMM and NWCHEM cases. For PSI4, it is PSI4_calc/). Other directories are old tests with different PES conditions (relaxation etc.). I did not remove them for my personal reference.
# For the Parameters validations, reboot_piplc-dmpc/ is the test case for Khan_etal_JCTC_2016; all others are for Khan_etal_JCTC_2019.
### Manuscripts files are seperated for both these works in the main directories with submission files and figures. The raw data are also there. You will also find the raw data in the different results subdirectories.
*** I am writing this very quickly, so some explanations might not be obvious. If you have doubt, please send email to LINK: mailto:reza.611@gmail.com.
application/ld+json
https://w3id.org/ro-id/b7df46c2-d54e-4cf0-8600-36516706cfbe
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https://w3id.org/ro-id/f43a3122-c71d-487c-bf34-0aea1c6a60d1
Nathalie Reuter. "2016_Khan_etal_JCTC." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/b7df46c2-d54e-4cf0-8600-36516706cfbe.
biblio
data
metadata
raw data
Reuter, N. (2021).2016_Khan_etal_JCTC [Data set]. Norstore. https://doi.org/10.11582/2021.00103
Nathalie Reuter
Simulation
https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00103
2021-11-22 00:00:00
2022-03-22 02:18:56.113155+00:00
Results for Tyrosine-choline, Phenylalanine-choline and Tryptophan-choline force field improvements. For ease of processing, results from both works are kept together. Results are divided in QM calculations+force field comparison and improvements, and force field parameter validations.
If you have trouble navigating, please send email to <a href="mailto:reza.611@gmail.com" class="linkified" target="_blank">LINK</a>.
### Please see the file "file_organisations" for the directories and subdirectories.
# Results files are organised based on QM calculations and force field comparisons (also improvements), and Parameters validations where you have the test cases. For the QM calculations, they are based on the softwares and methods used. PSI4 directories for energy decompositions and SAPT methods, further divided in subdiectories. NWCHEM is for all other QM methods, and those can be differentiated by the dircetoty names which contains the method (DFT, MP2, CCSD(T)) and basis sets. CHARMM* directories contains the force field comparison vs the improvements test.
The Khan_etal_JCTC_2016 paper contains phenol-TMA and benzene-TMA cases. The Khan_etal_JCTC_2019 contains the indole-TMA case.
For the QM part, benzene-tma and indole-tma directories have naming explicitly (i.e. NWCHEM_cat_pi_benzene_tma/, PSI4_calc_benzene_tma/, CHARMM_pes_cat_pi_benzene_tma/). For phenol-TMA, look at the *_RESTART directories; these are the production ones for CHARMM and NWCHEM cases. For PSI4, it is PSI4_calc/). Other directories are old tests with different PES conditions (relaxation etc.). I did not remove them for my personal reference.
# For the Parameters validations, reboot_piplc-dmpc/ is the test case for Khan_etal_JCTC_2016; all others are for Khan_etal_JCTC_2019.
### Manuscripts files are seperated for both these works in the main directories with submission files and figures. The raw data are also there. You will also find the raw data in the different results subdirectories.
*** I am writing this very quickly, so some explanations might not be obvious. If you have doubt, please send email to <a href="mailto:reza.611@gmail.com" class="linkified" target="_blank">LINK</a>.
2016_Khan_etal_JCTC
2021-11-22 00:00:00
Nathalie Reuter
https://doi.org/10.1021/acs.jctc.6b00654
2022-03-22 02:18:51.634853+00:00
2022-03-22 02:18:51.891040+00:00
https://doi.org/10.1021/acs.jctc.6b00654
2022-03-22 02:18:51.634853+00:00
force field parameter validation
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8.7
geology
100.0
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subdirectory
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choline
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file
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directory
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calculation
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armed forces
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# Results files are organised based on QM calculations and force field comparisons (also improvements), and Parameters validations where you have the test cases.
34.183673469387756
13.4
choline
12.121212121212121
5.2
QM
12.121212121212121
5.2
tryptophan-choline force field improvement
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18.6
IT-computer sciences
Science and technology/Technology and engineering/IT-computer sciences
Diseases and conditions
Health/Diseases and conditions
Geo H.
nathalie.reuter@rohub.com
Nathalie Reuter
Environmental research
Life sciences
Physical sciences
Chemistry
Physics
Science and technology/Natural science/Physics
tryptophan-choline force field improvement
30.441898527004913
18.6
manuscripts file
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subdirectory
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quartermaster
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betterment
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Economy, business and finance/Economic sector/Chemicals/Organic chemical
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100.0
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force field
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Health/Diseases and conditions
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100.0
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force field parameter validation
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calculation
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5.0
service-account-enrichment
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https://api.rohub.org/api/ros/6d7f7856-f2c8-4172-9a35-ef22b6cc4561/crate/download/
2022-03-22 02:18:57.862656+00:00
2025-03-05 00:45:31.255755+00:00
2022-03-22 02:18:57.862656+00:00
Results for Tyrosine-choline, Phenylalanine-choline and Tryptophan-choline force field improvements. For ease of processing, results from both works are kept together. Results are divided in QM calculations+force field comparison and improvements, and force field parameter validations.
If you have trouble navigating, please send email to LINK: mailto:reza.611@gmail.com.
### Please see the file "file_organisations" for the directories and subdirectories.
# Results files are organised based on QM calculations and force field comparisons (also improvements), and Parameters validations where you have the test cases. For the QM calculations, they are based on the softwares and methods used. PSI4 directories for energy decompositions and SAPT methods, further divided in subdiectories. NWCHEM is for all other QM methods, and those can be differentiated by the dircetoty names which contains the method (DFT, MP2, CCSD(T)) and basis sets. CHARMM* directories contains the force field comparison vs the improvements test.
The Khan_etal_JCTC_2016 paper contains phenol-TMA and benzene-TMA cases. The Khan_etal_JCTC_2019 contains the indole-TMA case.
For the QM part, benzene-tma and indole-tma directories have naming explicitly (i.e. NWCHEM_cat_pi_benzene_tma/, PSI4_calc_benzene_tma/, CHARMM_pes_cat_pi_benzene_tma/). For phenol-TMA, look at the *_RESTART directories; these are the production ones for CHARMM and NWCHEM cases. For PSI4, it is PSI4_calc/). Other directories are old tests with different PES conditions (relaxation etc.). I did not remove them for my personal reference.
# For the Parameters validations, reboot_piplc-dmpc/ is the test case for Khan_etal_JCTC_2016; all others are for Khan_etal_JCTC_2019.
### Manuscripts files are seperated for both these works in the main directories with submission files and figures. The raw data are also there. You will also find the raw data in the different results subdirectories.
*** I am writing this very quickly, so some explanations might not be obvious. If you have doubt, please send email to LINK: mailto:reza.611@gmail.com.
application/ld+json
https://w3id.org/ro-id/6d7f7856-f2c8-4172-9a35-ef22b6cc4561
2019_Khan_etal_JCTC
MANUAL
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Nathalie Reuter. "2019_Khan_etal_JCTC." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/6d7f7856-f2c8-4172-9a35-ef22b6cc4561.
metadata
raw data
data
biblio
https://doi.org/10.1021/acs.jctc.8b00839
2022-03-22 02:19:11.787124+00:00
2022-03-22 02:19:12.026713+00:00
https://doi.org/10.1021/acs.jctc.8b00839
2022-03-22 02:19:11.787124+00:00
Reuter, N. (2021).2019_Khan_etal_JCTC [Data set]. Norstore. https://doi.org/10.11582/2021.00104
Nathalie Reuter
Simulation
https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00104
2021-11-22 00:00:00
2022-03-22 02:19:16.071315+00:00
Results for Tyrosine-choline, Phenylalanine-choline and Tryptophan-choline force field improvements. For ease of processing, results from both works are kept together. Results are divided in QM calculations+force field comparison and improvements, and force field parameter validations.
If you have trouble navigating, please send email to <a href="mailto:reza.611@gmail.com" class="linkified" target="_blank">LINK</a>.
### Please see the file "file_organisations" for the directories and subdirectories.
# Results files are organised based on QM calculations and force field comparisons (also improvements), and Parameters validations where you have the test cases. For the QM calculations, they are based on the softwares and methods used. PSI4 directories for energy decompositions and SAPT methods, further divided in subdiectories. NWCHEM is for all other QM methods, and those can be differentiated by the dircetoty names which contains the method (DFT, MP2, CCSD(T)) and basis sets. CHARMM* directories contains the force field comparison vs the improvements test.
The Khan_etal_JCTC_2016 paper contains phenol-TMA and benzene-TMA cases. The Khan_etal_JCTC_2019 contains the indole-TMA case.
For the QM part, benzene-tma and indole-tma directories have naming explicitly (i.e. NWCHEM_cat_pi_benzene_tma/, PSI4_calc_benzene_tma/, CHARMM_pes_cat_pi_benzene_tma/). For phenol-TMA, look at the *_RESTART directories; these are the production ones for CHARMM and NWCHEM cases. For PSI4, it is PSI4_calc/). Other directories are old tests with different PES conditions (relaxation etc.). I did not remove them for my personal reference.
# For the Parameters validations, reboot_piplc-dmpc/ is the test case for Khan_etal_JCTC_2016; all others are for Khan_etal_JCTC_2019.
### Manuscripts files are seperated for both these works in the main directories with submission files and figures. The raw data are also there. You will also find the raw data in the different results subdirectories.
*** I am writing this very quickly, so some explanations might not be obvious. If you have doubt, please send email to <a href="mailto:reza.611@gmail.com" class="linkified" target="_blank">LINK</a>.
2019_Khan_etal_JCTC
2021-11-22 00:00:00
Nathalie Reuter
directory
12.121212121212121
5.2
QM
12.121212121212121
5.2
force field comparison
20.29459901800327
12.4
IT-computer sciences
Science and technology/Technology and engineering/IT-computer sciences
choline
12.121212121212121
5.2
chemistry and materials (general)
100.0
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result
6.107784431137724
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raw data
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Results are divided in QM calculations+force field comparison and improvements, and force field parameter validations.
37.755102040816325
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choline
8.502994011976048
7.1
directory
8.74251497005988
7.3
# Results files are organised based on QM calculations and force field comparisons (also improvements), and Parameters validations where you have the test cases.
34.183673469387756
13.4
QM calculation
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chemistry and materials
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validation
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Arts, culture and entertainment/Mass media/Newspaper
file
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computer science
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Results for Tyrosine-choline, Phenylalanine-choline and Tryptophan-choline force field improvements.
28.061224489795915
11.0
Geo H.
nathalie.reuter@rohub.com
Nathalie Reuter
Environmental research
Life sciences
Physical sciences
service-account-enrichment
7833
https://api.rohub.org/api/ros/d5486b94-4c58-4cb4-8b1e-6945adf5eba9/crate/download/
2022-03-22 02:19:17.388294+00:00
2025-03-05 01:19:14.776191+00:00
2022-03-22 02:19:17.388294+00:00
This dataset contains molecular dynamic simulation data of Loxosceles phospholipase D enzymes, of both clades, in presence of different bilayer compositions. The enzymes simulated are Li_alphaIA1, St_beta1B1, Ll_alphaIII and R44Y/S60Y St_beta1B1. The bilayers are used are a pure POPC, PC:SM:CHOL (70:20:10) and POPC:POPE (50:50) bilayer. The trajectories are in DCD format and the topology file in PSF format.The following simulations are available:
1. Li_alphaIA1 on a pure POPC bilayer;
2. Li_alphaIA1 on a PC:SM:CHOL (70:20:10) bilayer;
3. Li_alphaIA1 on a POPC:POPE (50:50) bilayer;
4. Ll_alphaIII on a pure POPC bilayer;
5. Ll_alphaIII on a PC:SM:CHOL (70:20:10) bilayer;
6. R44Y/S60Y St_beta1B1 on a pure POPC bilayer;
7. St_beta1B1 on a pure POPC bilayer;
8. St_beta1B1 on a PC:SM:CHOL (70:20:10) bilayer;
9. St_beta1B1 on a POPC:POPE (50:50) bilayer;
application/ld+json
https://w3id.org/ro-id/d5486b94-4c58-4cb4-8b1e-6945adf5eba9
Specificity of Loxosceles alpha clade phospholipase D enzymes for choline-containing lipids: role of a conserved aromatic cage
MANUAL
bilayer
choline
clade
dataset
enzyme
information
lipid
phospholipase
simulation
earth sciences
Hardware
IT-computer sciences
Pope
Religious leader
POPC
S60Y St_beta1B1
bilayer
clade
enzyme
lipid
phospholipase
chemistry and materials
Loxosceles phospholipase d enzyme
POPC bilayer
bilayer composition
choline-containing lipid
pure POPC
Specificity of Loxosceles alpha clade phospholipase D enzymes for choline-containing lipids: role of a conserved aromatic cage.
The bilayers are used are a pure POPC, PC:SM:CHOL (70:20:10) and POPC:POPE (50:50) bilayer.
This dataset contains molecular dynamic simulation data of Loxosceles phospholipase D enzymes, of both clades, in presence of different bilayer compositions.
biochemistry
Emmanuel Moutoussamy. "Specificity of Loxosceles alpha clade phospholipase D enzymes for choline-containing lipids: role of a conserved aromatic cage." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/d5486b94-4c58-4cb4-8b1e-6945adf5eba9.
metadata
biblio
data
raw data
Moutoussamy, E. (2021).Specificity of Loxosceles alpha clade phospholipase D enzymes for choline-containing lipids: role of a conserved aromatic cage [Data set]. Norstore. https://doi.org/10.11582/2021.00099
Nathalie Reuter
Simulation
https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00099
2021-11-19 00:00:00
2022-03-22 02:19:33.483910+00:00
This dataset contains molecular dynamic simulation data of Loxosceles phospholipase D enzymes, of both clades, in presence of different bilayer compositions. The enzymes simulated are Li_alphaIA1, St_beta1B1, Ll_alphaIII and R44Y/S60Y St_beta1B1. The bilayers are used are a pure POPC, PC:SM:CHOL (70:20:10) and POPC:POPE (50:50) bilayer. The trajectories are in DCD format and the topology file in PSF format.The following simulations are available:
1. Li_alphaIA1 on a pure POPC bilayer;
2. Li_alphaIA1 on a PC:SM:CHOL (70:20:10) bilayer;
3. Li_alphaIA1 on a POPC:POPE (50:50) bilayer;
4. Ll_alphaIII on a pure POPC bilayer;
5. Ll_alphaIII on a PC:SM:CHOL (70:20:10) bilayer;
6. R44Y/S60Y St_beta1B1 on a pure POPC bilayer;
7. St_beta1B1 on a pure POPC bilayer;
8. St_beta1B1 on a PC:SM:CHOL (70:20:10) bilayer;
9. St_beta1B1 on a POPC:POPE (50:50) bilayer;
Specificity of Loxosceles alpha clade phospholipase D enzymes for choline-containing lipids: role of a conserved aromatic cage
2021-11-19 00:00:00
Emmanuel Edouard Moutoussamy
emmanuel.moutoussamy@rohub.com
Emmanuel Moutoussamy
Geo H.
Environmental research
Life sciences
Physical sciences
Earth sciences
Norway
debris flow field observation
calculations data
geology
West Norway
RAMMS simulation
slide deposit
measure
perimeter
data
mudslide
sediment
data type
treatise
angle of repose
domain
dataset
GNSS measurement
profile
Department of Geosciences
Oslo
calculation
University of Oslo
service-account-enrichment
8150
https://api.rohub.org/api/ros/fc0d05e2-f5b4-4b47-b520-ea9d05d9f707/crate/download/
2022-03-22 02:21:05.280489+00:00
2025-03-05 00:50:09.588296+00:00
2022-03-22 02:21:05.280489+00:00
Data created for Marius Julian Grønli’s Master thesis at the Department of Geosciences at the University of Oslo fall 2021.
Title: Quantitative back calculation of three debris flows in western Norway
Dataset includes:
GNSS measurements of three debris flows on the west coast of Norway. Logged perimeter with 15 m increments and several profiles across the flow paths.
Grain size distribution, angle of repose for several soil samples at each study site. Including samples of slide deposits and Origin material.
RAMMS simulations of the three events with varying input parameters.
Each data type has a documentation file explaining the workflow of each data set.
Event dates:
Stamnes: 16th of February 2020
Jordalen: 5th of August 2019
Osdalsvatenet: 21st of January 2020
application/ld+json
https://w3id.org/ro-id/fc0d05e2-f5b4-4b47-b520-ea9d05d9f707
Debris flow field observations and RAMMS back calculations
MANUAL
Marius Julian Grønli. "Debris flow field observations and RAMMS back calculations." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/fc0d05e2-f5b4-4b47-b520-ea9d05d9f707.
raw data
biblio
metadata
data
Grønli, M. J. (2021).Debris flow field observations and RAMMS back calculations [Data set]. Norstore. https://doi.org/10.11582/2021.00092
Marius Julian Grønli
Simulation
https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00092
2021-10-14 00:00:00
2022-03-22 02:21:24.160454+00:00
Data created for Marius Julian Grønli’s Master thesis at the Department of Geosciences at the University of Oslo fall 2021.
Title: Quantitative back calculation of three debris flows in western Norway
Dataset includes:
GNSS measurements of three debris flows on the west coast of Norway. Logged perimeter with 15 m increments and several profiles across the flow paths.
Grain size distribution, angle of repose for several soil samples at each study site. Including samples of slide deposits and Origin material.
RAMMS simulations of the three events with varying input parameters.
Each data type has a documentation file explaining the workflow of each data set.
Event dates:
Stamnes: 16th of February 2020
Jordalen: 5th of August 2019
Osdalsvatenet: 21st of January 2020
Debris flow field observations and RAMMS back calculations
2021-10-14 00:00:00
Marius Julian Grønli
Geo H.
marius.julian.gronli@rohub.com
Marius Julian Grønli
Environmental research
Life sciences
Physical sciences
publication
12.651821862348179
12.5
data for the publication
11.322645290581162
11.3
computer modelling
24.898785425101217
24.6
experiment
29.322548028311424
29.0
data
33.46814964610718
33.1
service-account-enrichment
7174
https://api.rohub.org/api/ros/5e8cbd3f-7b47-4d75-976a-b3d2c1d207e9/crate/download/
2022-03-22 02:21:25.532255+00:00
2025-03-05 00:45:26.451604+00:00
2022-03-22 02:21:25.532255+00:00
raw experiment/simulation data for the publication
application/ld+json
https://w3id.org/ro-id/5e8cbd3f-7b47-4d75-976a-b3d2c1d207e9
2012_Grauffel_etal_PLoSONE
MANUAL
https://w3id.org/ro-id/df41cd03-d30a-42df-9c90-70a3a34bfe9f
https://w3id.org/ro-id/118fb288-3b7c-4e01-8a2d-604ee92fdec6
https://w3id.org/ro-id/24d62ac8-ce28-468c-b126-d51d011d186f
https://w3id.org/ro-id/bd9594b0-a34c-4a13-9e05-1f53a8a7cf77
https://w3id.org/ro-id/c3dbab5f-3057-475d-8d95-e81050e0af2c
https://w3id.org/ro-id/8e20766c-e0f4-4c8d-9ed1-2be1852aaaa6
https://w3id.org/ro-id/a2bd4f65-14ed-4820-ad2d-3d7c5eb57515
https://w3id.org/ro-id/4179efb5-d3c5-4ec7-90c1-29d0a08fa7c3
https://w3id.org/ro-id/5285a352-03c0-4dac-97f9-7c8a1176b0af
https://w3id.org/ro-id/6c5ad61d-9ed9-4975-96f0-72bf193f5d10
https://w3id.org/ro-id/9c06af6c-1ff6-4fd5-acc6-ec57b549da79
https://w3id.org/ro-id/d5d9a091-9e58-4a01-a8ed-728f8ec8dd8d
https://w3id.org/ro-id/e6948f96-6520-4b3f-9646-ddb3925dde31
https://w3id.org/ro-id/12fdff6e-f2ec-490d-b64b-7fa11480934b
https://w3id.org/ro-id/685b3d26-2957-47d4-bff7-4a355bebf2bb
https://w3id.org/ro-id/7d1d290d-bc88-48b2-8895-9ba409d88240
https://w3id.org/ro-id/d8911953-eb97-44fe-8dcd-16b624f19d22
Nathalie Reuter. "2012_Grauffel_etal_PLoSONE." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/5e8cbd3f-7b47-4d75-976a-b3d2c1d207e9.
raw data
metadata
biblio
data
Reuter, N. (2021).2012_Grauffel_etal_PLoSONE [Data set]. Norstore. https://doi.org/10.11582/2021.00090
Nathalie Reuter
Simulation
https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00090
2021-10-13 00:00:00
2022-03-22 02:21:45.800191+00:00
raw experiment/simulation data for the publication
2012_Grauffel_etal_PLoSONE
2021-10-13 00:00:00
Nathalie Reuter
https://doi.org/10.1371/journal.pone.0052642
2022-03-22 02:21:40.999932+00:00
2022-03-22 02:21:41.269967+00:00
https://doi.org/10.1371/journal.pone.0052642
2022-03-22 02:21:40.999932+00:00
simulation data
88.27655310621243
88.1
simulation
25.176946410515672
24.9
simulation data for the publication
0.40080160320641284
0.4
earth sciences
100.0
0.9631021022796631
publication
12.032355915065722
11.9
atmospheric sciences
100.0
0.9631021022796631
data
31.781376518218625
31.4
experiment
30.668016194331983
30.3
life sciences (general)
100.0
0.756223201751709
2012_Grauffel_etal_PLoSONE. raw experiment/simulation data for the publication
100.0
100.0
computer science
100.0
16.0
life sciences
100.0
0.756223201751709
Geo H.
nathalie.reuter@rohub.com
Nathalie Reuter
Environmental research
Life sciences
Physical sciences
Earth sciences
service-account-enrichment
8544
https://api.rohub.org/api/ros/c35062bf-df50-4667-b876-e5c69570125c/crate/download/
2022-03-22 02:21:47.331157+00:00
2025-03-05 00:56:57.081384+00:00
2022-03-22 02:21:47.331157+00:00
This dataset contains the model output (atmospheric component only) used in
Blichner, S. M., Sporre, M. K., and Berntsen, T. K.: Reduced effective radiative forcing from cloud-aerosol interactions (ERFaci) with improved treatment of early aerosol growth in an Earth System Model, Atmos. Chem. Phys., LINK: http://doi.org/10.5194/acp-2021-151, accepted, 2021.
See LINK: http://github.com/sarambl/OAS-ERF for analysis code.
application/ld+json
https://w3id.org/ro-id/c35062bf-df50-4667-b876-e5c69570125c
Model output for "Reduced effective radiative forcing from cloud-aerosol interactions (ERFaci) with improved treatment of early aerosol growth in an Earth System Model"
MANUAL
aerosol
cloud
interaction
output
radiative forcing
therapy
tumor
earth sciences
Therapy
T. K. Reduced
aerosol
cloud
growth
interaction
radiative forcing
treatment
mathematical and computer sciences
Chem. Phys
aerosol growth
cloud-aerosol interaction
improved treatment
model output
Blichner, S. M. Sporre, M. K. and Berntsen, T. K. Reduced effective radiative forcing from cloud-aerosol interactions (ERFaci) with improved treatment of early aerosol growth in an Earth System Model, Atmos.
Model output for "Reduced effective radiative forcing from cloud-aerosol interactions (ERFaci) with improved treatment of early aerosol growth in an Earth System Model" This dataset contains the model output (atmospheric component only) used in
See LINK: http: github.com/sarambl/OAS-ERF for analysis code.
medicine
physics
Sara Blichner, and University of Oslo (UiO). "Model output for "Reduced effective radiative forcing from cloud-aerosol interactions (ERFaci) with improved treatment of early aerosol growth in an Earth System Model"." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/c35062bf-df50-4667-b876-e5c69570125c.
raw data
metadata
data
biblio
https://acp.copernicus.org/preprints/acp-2021-151/
2022-03-22 02:22:07.384799+00:00
2022-03-22 02:22:07.648299+00:00
https://acp.copernicus.org/preprints/acp-2021-151/
2022-03-22 02:22:07.384799+00:00
Blichner, S., University of Oslo (2021).Model output for "Reduced effective radiative forcing from cloud-aerosol interactions (ERFaci) with improved treatment of early aerosol growth in an Earth System Model" [Data set]. Norstore. https://doi.org/10.11582/2021.00087
Sara Marie Blichner
Simulation
https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00087
2021-10-11 00:00:00
2022-03-22 02:22:11.450221+00:00
This dataset contains the model output (atmospheric component only) used in
Blichner, S. M., Sporre, M. K., and Berntsen, T. K.: Reduced effective radiative forcing from cloud-aerosol interactions (ERFaci) with improved treatment of early aerosol growth in an Earth System Model, Atmos. Chem. Phys., <a href="http://doi.org/10.5194/acp-2021-151" class="linkified" target="_blank">LINK</a>, accepted, 2021.
See <a href="http://github.com/sarambl/OAS-ERF" class="linkified" target="_blank">LINK</a> for analysis code.
Model output for "Reduced effective radiative forcing from cloud-aerosol interactions (ERFaci) with improved treatment of early aerosol growth in an Earth System Model"
2021-10-11 00:00:00
Sara Marie Blichner
UiO@rohub.com
University of Oslo (UiO)
Geo H.
sara.blichner@rohub.com
Sara Blichner
Environmental research
Life sciences
Physical sciences
Chemistry
computer modelling
16.75025075225677
16.7
computer science
29.648241206030153
11.8
mathematical and computer sciences
100.0
0.29949691891670227
manuscript
7.321965897693079
7.3
source code
38.581856100104275
37.0
atmospheric sciences
100.0
0.7998380064964294
analysis data
35.03503503503503
35.0
manuscript
7.7163712200208545
7.4
source code
36.91073219658976
36.8
computer programming and software
100.0
0.29949691891670227
source simulation
64.96496496496498
64.9
publication
6.673618352450469
6.4
simulation
18.039624608967674
17.3
data
28.988529718456725
27.8
IT-computer sciences
Science and technology/Technology and engineering/IT-computer sciences
service-account-enrichment
7302
https://api.rohub.org/api/ros/bc9a215f-a0b0-4a5b-85e9-668f59874669/crate/download/
2022-03-22 02:22:13.239874+00:00
2025-03-05 00:45:27.011181+00:00
2022-03-22 02:22:13.239874+00:00
source simulation files, analysis data, source code, manuscript etc. for the publication
application/ld+json
https://w3id.org/ro-id/bc9a215f-a0b0-4a5b-85e9-668f59874669
2013_Fuglebakk_Reuter_Hinsen_JCTC
MANUAL
https://w3id.org/ro-id/21c7efbc-5b32-4252-80de-54332db3c22e
https://w3id.org/ro-id/be1f0560-2efb-451a-a480-fbb59273aa12
https://w3id.org/ro-id/0e60f9a3-f509-473d-9a01-daa3b9af0c71
https://w3id.org/ro-id/2f981695-fcdb-4a4d-a62a-a416209dff5f
https://w3id.org/ro-id/91ff8e38-d049-4480-8c58-a03a85c6d88d
https://w3id.org/ro-id/d44514a5-4fa2-4f20-ba70-9f65dc27e197
https://w3id.org/ro-id/d6888ca3-5ceb-4084-b28b-78afa529f035
https://w3id.org/ro-id/d708cc8d-b7c1-4844-874e-386804236c5d
https://w3id.org/ro-id/81b43073-1f12-4ec3-bda0-31871849c11b
https://w3id.org/ro-id/e80c508a-eca3-422c-beda-5efab23b0e1f
https://w3id.org/ro-id/b1205b18-910f-49c5-b884-3f07f974e3e2
https://w3id.org/ro-id/702af2a6-4a74-491c-98c7-6ff822826ed6
https://w3id.org/ro-id/8b4f92fc-157c-4f04-90b9-1374279a7827
https://w3id.org/ro-id/9ff7e001-1a91-49bc-a62a-64545b49363a
https://w3id.org/ro-id/aca95257-9b1b-4273-b9e4-bc9e3cb28849
https://w3id.org/ro-id/b01a20c5-99b9-4550-8b4c-2eed1451cc07
https://w3id.org/ro-id/2a3e1c91-9dd8-4008-a413-921498379217
https://w3id.org/ro-id/9ab04305-2c0f-4079-87dd-2c08edbf499d
https://w3id.org/ro-id/844ae3d8-ebe8-46c9-904d-06f0515adb07
https://w3id.org/ro-id/9da1ffd8-f477-445b-88b3-138e0db8ff94
https://w3id.org/ro-id/fb3dea67-85bc-40ab-b9b2-75efecaf7471
Nathalie Reuter. "2013_Fuglebakk_Reuter_Hinsen_JCTC." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/bc9a215f-a0b0-4a5b-85e9-668f59874669.
data
metadata
biblio
raw data
Reuter, N. (2021).2013_Fuglebakk_Reuter_Hinsen_JCTC [Data set]. Norstore. https://doi.org/10.11582/2021.00085
Nathalie Reuter
Simulation
https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00085
None
2022-03-22 02:22:36.027755+00:00
source simulation files, analysis data, source code, manuscript etc. for the publication
2013_Fuglebakk_Reuter_Hinsen_JCTC
None
Nathalie Reuter
https://doi.org/10.1021/ct400399x
2022-03-22 02:22:31.146218+00:00
2022-03-22 02:22:31.420467+00:00
https://doi.org/10.1021/ct400399x
2022-03-22 02:22:31.146218+00:00
computer programming
70.35175879396985
28.0
publication
6.41925777331996
6.4
data
28.68605817452357
28.6
analysis
3.9117352056168504
3.9
earth sciences
100.0
0.7998380064964294
2013_Fuglebakk_Reuter_Hinsen_JCTC. source simulation files, analysis data, source code, manuscript etc. for the publication
100.0
100.0
Geo H.
nathalie.reuter@rohub.com
Nathalie Reuter
mailto:nathalie.reuter@rohub.com
7203
https://api.rohub.org/api/ros/359fc143-420a-45a9-8cdb-fc03c7188df5/crate/download/
mailto:georgehadib@gmail.com
2022-03-22 02:22:37.897281+00:00
2025-03-05 00:45:25.879983+00:00
2022-03-22 02:22:37.897281+00:00
source simulation/analysis data for the publication
application/ld+json
https://w3id.org/ro-id/359fc143-420a-45a9-8cdb-fc03c7188df5
2009_Hajjar_Dejaegere_Reuter_JPCA
http://eurovoc.europa.eu/2919
http://eurovoc.europa.eu/3941
http://eurovoc.europa.eu/3946
http://eurovoc.europa.eu/5966
MANUAL
https://w3id.org/ro-id/b598ff14-9f40-4dd4-9a51-c6c876d6c4f9
https://w3id.org/ro-id/2d2279a8-e28b-41b5-8d9b-c84d8c56b8cd
https://w3id.org/ro-id/8c471337-2d21-4603-af51-8c51424cbc15
https://w3id.org/ro-id/93d0b3c7-5dea-4858-9818-e71de222b644
https://w3id.org/ro-id/b71244db-b6a4-4983-ac77-8aff82f87827
https://w3id.org/ro-id/e87b3e9f-cd16-4868-8c58-cd48d7be649c
https://w3id.org/ro-id/4e238fdb-4e45-4543-85f9-56abf3a1c08b
https://w3id.org/ro-id/cc72f552-8791-47f5-b30a-26a4a0932ba5
https://w3id.org/ro-id/6829dc3e-b758-42a8-8f96-1accf4a2bca5
https://w3id.org/ro-id/87d5a9cb-bf7e-4817-a12c-95bc4f2a2e67
https://w3id.org/ro-id/8b5e5476-a79e-4512-9427-614a57ffc7e5
https://w3id.org/ro-id/dcb3f7ba-85af-42fb-9f23-bfbc19e7037c
https://w3id.org/ro-id/f342e568-22c4-4f61-b78c-511f0e6655c4
https://w3id.org/ro-id/a01cccbe-ac89-4399-b258-c4a5822d7d46
https://w3id.org/ro-id/ebcad90a-8c01-4dee-9547-bb3e6c1f2dcb
https://w3id.org/ro-id/d03cfd7d-7cad-411f-9af0-9621c4a2497f
https://w3id.org/ro-id/f9191935-e027-403b-9935-032cc2a49be6
https://w3id.org/ro-id/36bd90da-c1d0-4adc-82c4-14adf1a98f01
Nathalie Reuter. "2009_Hajjar_Dejaegere_Reuter_JPCA." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/359fc143-420a-45a9-8cdb-fc03c7188df5.
raw data
data
metadata
biblio
Reuter, N. (2021).2009_Hajjar_Dejaegere_Reuter_JPCA [Data set]. Norstore. https://doi.org/10.11582/2021.00086
Published
Nathalie Reuter
Simulation
mailto:nathalie.reuter@rohub.com
https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00086
mailto:georgehadib@gmail.com
None
2022-03-22 02:22:57.660517+00:00
source simulation/analysis data for the publication
CC-BY-4.0
https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00086
None
Nathalie Reuter
Dandan Xue
mailto:nathalie.reuter@rohub.com
https://doi.org/10.1021/jp902930u
mailto:georgehadib@gmail.com
2022-03-22 02:22:52.717818+00:00
2022-03-22 02:22:52.997316+00:00
https://doi.org/10.1021/jp902930u
2022-03-22 02:22:52.717818+00:00
Geo H.
nathalie.reuter@rohub.com
Nathalie Reuter
mailto:service-account-enrichment
Environmental research
Life sciences
Physical sciences
Earth sciences
service-account-enrichment
8000
https://api.rohub.org/api/ros/e7f5d844-fa99-4407-af00-82fc4ba618f1/crate/download/
2022-03-22 02:22:59.259226+00:00
2025-03-05 01:23:33.595114+00:00
2022-03-22 02:22:59.259226+00:00
Sea-level pressure, SST, latent and sensible heat fluxes, large-scale and convective precipitation, specific humidity and temperatures at 850 hPa, and winds at both 925 and 300 hPa from the AFES simulations. In addition to a control simulation with realistic SST, there is one simulation each with strongly smoothed SSTs in either the Gulf Stream or Kuroshio Extension region. All simulations cover the time period 1982-2000.
application/ld+json
https://w3id.org/ro-id/e7f5d844-fa99-4407-af00-82fc4ba618f1
Subset of the AFES simulations used for doi 10.5194/wcd-2020-50
MANUAL
Gulf stream
latent heat
precipitation
pressure
sea surface temperature
simulation
subset
supersonic transport aircraft
temperature
earth sciences
Weather
AFES
Gulf stream
SST
doi 10.5194
hPa
simulation
subset
geosciences
AFES simulation
Kuroshio Extension region
convective precipitation
sensible heat fluxes
smoothed SST
In addition to a control simulation with realistic SST, there is one simulation each with strongly smoothed SSTs in either the Gulf Stream or Kuroshio Extension region.
Sea-level pressure, SST, latent and sensible heat fluxes, large-scale and convective precipitation, specific humidity and temperatures at 850 hPa, and winds at both 925 and 300 hPa from the AFES simulations.
Subset of the AFES simulations used for doi 10.5194/wcd-2020-50.
the time period 1982-2000
meteorology
physics
Akira Kuwano-Yoshida. "Subset of the AFES simulations used for doi 10.5194/wcd-2020-50." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/e7f5d844-fa99-4407-af00-82fc4ba618f1.
metadata
biblio
raw data
data
Kuwano-Yoshida, A. (2021).Subset of the AFES simulations used for doi 10.5194/wcd-2020-50 [Data set]. Norstore. https://doi.org/10.11582/2021.00075
Akira Kuwano-Yoshida
Simulation
https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00075
2021-09-22 00:00:00
2022-03-22 02:23:18.675518+00:00
Sea-level pressure, SST, latent and sensible heat fluxes, large-scale and convective precipitation, specific humidity and temperatures at 850 hPa, and winds at both 925 and 300 hPa from the AFES simulations. In addition to a control simulation with realistic SST, there is one simulation each with strongly smoothed SSTs in either the Gulf Stream or Kuroshio Extension region. All simulations cover the time period 1982-2000.
Subset of the AFES simulations used for doi 10.5194/wcd-2020-50
2021-09-22 00:00:00
Clemens Spensberger
https://wcd.copernicus.org/preprints/wcd-2020-50/
2022-03-22 02:23:14.397390+00:00
2022-03-22 02:23:14.649349+00:00
https://wcd.copernicus.org/preprints/wcd-2020-50/
2022-03-22 02:23:14.397390+00:00
akira.kuwano-yoshida@rohub.com
Akira Kuwano-Yoshida
Geo H.
Environmental research
Life sciences
Physical sciences
Earth sciences
service-account-enrichment
8136
https://api.rohub.org/api/ros/c0f7a882-6123-477c-aac5-25e8f9e49dcd/crate/download/
2022-03-22 02:23:20.356464+00:00
2025-03-05 12:49:07.078365+00:00
2022-03-22 02:23:20.356464+00:00
The data set contains wind power related variables for the North Sea, the Norwegian Sea, and parts of the Barents Sea. The user is encourage to read the README file contained within the data set.
application/ld+json
https://w3id.org/ro-id/c0f7a882-6123-477c-aac5-25e8f9e49dcd
Norwegian hindcast archive's wind power data set (NORA3-WP)
MANUAL
Barents Sea
North Sea
Norwegian Sea
archive file
dataset
hindcast
user
variable
wind power
earth sciences
Alternative energy
Hardware
IT-computer sciences
Renewable energy
Barents Sea
North Sea
Norwegian Sea
archive
data set
variable
wind power
mathematical and computer sciences
archive's wind power data set
contain wind power
hindcast archive's wind power data set
parts of the Barents Sea
related variable
Norwegian hindcast archive's wind power data set (NORA3-WP) The data set contains wind power related variables for the North Sea, the Norwegian Sea, and parts of the Barents Sea.
The user is encourage to read the README file contained within the data set.
database
hydrography
software
Barents Sea
North Sea
Norwegian Sea
Ida Marie Solbrekke, Asgeir Sorteberg, and University of Bergen, Institute of biomedicine (UiB). "Norwegian hindcast archive's wind power data set (NORA3-WP)." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/c0f7a882-6123-477c-aac5-25e8f9e49dcd.
raw data
metadata
biblio
data
Solbrekke, I. M., Sorteberg, A., University of Bergen (2021).Norwegian hindcast archive's wind power data set (NORA3-WP) [Data set]. Norstore. https://doi.org/10.11582/2021.00068
University of Bergen (UiB)
Simulation
https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00068
2021-08-25 00:00:00
2022-03-22 02:23:42.107526+00:00
The data set contains wind power related variables for the North Sea, the Norwegian Sea, and parts of the Barents Sea. The user is encourage to read the README file contained within the data set.
Norwegian hindcast archive's wind power data set (NORA3-WP)
2021-08-25 00:00:00
Ida Marie Solbrekke
UiB@rohub.com
University of Bergen, Institute of biomedicine (UiB)
asgeir.sorteberg@rohub.com
Asgeir Sorteberg
Geo H.
ida.marie.solbrekke@rohub.com
Ida Marie Solbrekke
Environmental research
Life sciences
Physical sciences
Earth sciences
WRF model datum
South America
newspaper
understanding
fact
Future Precipitation Projections for South America
South America
Understanding Model Diversity in Future Precipitation Projections
further detail
South America WRF model datum
detail
newspaper publisher
service-account-enrichment
7320
https://api.rohub.org/api/ros/6b0cdc2d-2857-44ef-bc5a-c2051ad20ba9/crate/download/
2022-03-22 02:23:45.142410+00:00
2025-03-05 02:47:01.794301+00:00
2022-03-22 02:23:45.142410+00:00
WRF model data used in the paper "Understanding Model Diversity in Future Precipitation Projections for South America", in review. Further details are given in the paper and in the README file.
application/ld+json
https://w3id.org/ro-id/6b0cdc2d-2857-44ef-bc5a-c2051ad20ba9
Understanding Model Diversity in Future Precipitation Projections for South America
MANUAL
Center for International Climate Research (CICERO). "Understanding Model Diversity in Future Precipitation Projections for South America." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/6b0cdc2d-2857-44ef-bc5a-c2051ad20ba9.
metadata
raw data
biblio
data
Center for International Climate Research (2021).Understanding Model Diversity in Future Precipitation Projections for South America [Data set]. Norstore. https://doi.org/10.11582/2021.00067
Øivind Hodnebrog
Simulation
https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00067
2021-08-12 00:00:00
2022-03-22 02:24:01.479217+00:00
WRF model data used in the paper "Understanding Model Diversity in Future Precipitation Projections for South America", in review. Further details are given in the paper and in the README file.
Understanding Model Diversity in Future Precipitation Projections for South America
2021-08-12 00:00:00
Øivind Hodnebrog
CICERO@rohub.com
Center for International Climate Research (CICERO)
Geo H.
Environmental research
Life sciences
Physical sciences
Earth sciences
service-account-enrichment
7111
https://api.rohub.org/api/ros/6a656f34-de1f-4d96-9ce2-a93ebd1f1a4c/crate/download/
2022-03-22 02:24:02.954436+00:00
2025-03-05 01:19:16.225993+00:00
2022-03-22 02:24:02.954436+00:00
Bakgrunnsdata for masteroppgaven "Statistisk prediksjonsmodellering av steinbreer i Norge" av Harald Wathne Hestad ved Institutt for geofag, Universitetet i Oslo, våren 2021.
application/ld+json
https://w3id.org/ro-id/6a656f34-de1f-4d96-9ce2-a93ebd1f1a4c
Statistisk prediksjonsmodellering av steinbreer i Norge
MANUAL
Norway
Oslo
earth sciences
Harald Wathne Hestad
Norway
Oslo
geosciences
Bakgrunnsdata for masteroppgaven
Institutt for geofag
ved Institutt for geofag
ved Institutt
Bakgrunnsdata for masteroppgaven "Statistisk prediksjonsmodellering av steinbreer i Norge" av Harald Wathne Hestad ved Institutt for geofag, Universitetet i Oslo, våren 2021.
Statistisk prediksjonsmodellering av steinbreer i Norge.
Norway
Oslo
Harald Wathne Hestad. "Statistisk prediksjonsmodellering av steinbreer i Norge." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/6a656f34-de1f-4d96-9ce2-a93ebd1f1a4c.
metadata
biblio
data
raw data
Hestad, H. W. (2021).Statistisk prediksjonsmodellering av steinbreer i Norge [Data set]. Norstore. https://doi.org/10.11582/2021.00066
Harald Wathne Hestad
Simulation
https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00066
2021-08-05 00:00:00
2022-03-22 02:24:19.675060+00:00
Bakgrunnsdata for masteroppgaven "Statistisk prediksjonsmodellering av steinbreer i Norge" av Harald Wathne Hestad ved Institutt for geofag, Universitetet i Oslo, våren 2021.
Statistisk prediksjonsmodellering av steinbreer i Norge
2021-08-05 00:00:00
Harald Wathne Hestad
Geo H.
harald.wathne.hestad@rohub.com
Harald Wathne Hestad
Environmental research
Life sciences
Physical sciences
Biology
in silico antibody-antigen binding database.
25.165562913907287
22.8
The reference publication is available on biorxiv, ID BIORXIV/2021/451258: Robert et al., A billion synthetic 3D-antibody-antigen complexes enable unconstrained machine-learning formalized investigation of antibody specificity prediction
41.16997792494481
37.3
publication
7.323943661971831
5.2
antigen
27.605633802816904
19.6
life sciences
100.0
0.6574541926383972
antibody
23.253012048192772
19.3
geochemistry
100.0
0.840290367603302
software
9.014084507042254
6.4
51258:
antigen
24.698795180722893
20.5
forecast
4.457831325301205
3.7
IT-computer sciences
Science and technology/Technology and engineering/IT-computer sciences
publication
6.506024096385542
5.4
service-account-enrichment
8927
https://api.rohub.org/api/ros/71c90c65-34e6-441e-bb44-d1b31dd1007b/crate/download/
2022-03-22 02:24:21.217126+00:00
2025-03-05 00:45:34.790548+00:00
2022-03-22 02:24:21.217126+00:00
This is a database of in silico generated antibody-antigen bindings (159 antigens times 6.9 million CDRH3 murine sequences), as resource for benchmarking machine learning methods.
The content of the files is explained in Readme.pdf
The reference publication is available on biorxiv, ID BIORXIV/2021/451258: Robert et al., A billion synthetic 3D-antibody-antigen complexes enable unconstrained machine-learning formalized investigation of antibody specificity prediction
The software used to generate the database is available at LINK: http://github.com/csi-greifflab/Absolut for all explanations.
application/ld+json
https://w3id.org/ro-id/71c90c65-34e6-441e-bb44-d1b31dd1007b
Absolut! in silico antibody-antigen binding database
MANUAL
https://w3id.org/ro-id/b3fb3d46-5fd8-41e7-a090-58181fbb01f1
https://w3id.org/ro-id/e0ff8858-262a-485b-a8dd-728c838e8a36
https://w3id.org/ro-id/15a39d24-4daa-417e-99ed-4b635b56cae9
https://w3id.org/ro-id/4ad66687-f699-45ff-a561-8aea7c629d2a
https://w3id.org/ro-id/5195bb19-e10e-4d1d-90e7-b7a591e2af67
https://w3id.org/ro-id/6aa13e0d-8332-4955-9f68-5e08e7b97b22
https://w3id.org/ro-id/7b70fcab-fd6b-461b-a79b-43cb859348f1
https://w3id.org/ro-id/85946983-f823-4c28-88ad-638fd7745f37
https://w3id.org/ro-id/89a060f4-22d1-43df-8cca-c29ee9213d21
https://w3id.org/ro-id/9d840ee9-e861-4815-ad57-62fc3d3d062b
https://w3id.org/ro-id/c1a70a12-6e53-4c98-9b4d-481c69d5a481
https://w3id.org/ro-id/c4ebfa3e-5087-4695-abbd-28cb65a0d1e6
https://w3id.org/ro-id/49e70145-19cf-4ac3-9da9-fb2b49f5471c
https://w3id.org/ro-id/286e5fd8-7d69-4aaf-854e-a0921db660a8
https://w3id.org/ro-id/8063038f-6dbd-4148-85a2-afb472b141cd
https://w3id.org/ro-id/5782c378-2b05-41eb-b48b-de733932007a
https://w3id.org/ro-id/c008e2b7-f43b-4e10-ae2b-e9b7c99d2c24
https://w3id.org/ro-id/0ce573ff-277b-4be8-9a5b-6a9c675e96f5
https://w3id.org/ro-id/0f6898fa-86e3-42c4-8cff-48968d07dbdf
https://w3id.org/ro-id/45689fb7-0f9f-4c04-b03e-13e29ca1b3b7
https://w3id.org/ro-id/9021ea62-7039-4cc7-b810-4c89cf540ee5
https://w3id.org/ro-id/a164d3a9-ac2e-4c63-b573-2cc2e497ee5e
https://w3id.org/ro-id/c6ab0333-3816-48d6-a0c5-f430a5569eff
https://w3id.org/ro-id/e912021a-590a-404b-a2e0-3f2335f06f0a
https://w3id.org/ro-id/14804972-fa34-4e28-b8d1-7c1a8c800f2e
https://w3id.org/ro-id/fec8824a-34e3-4f6c-be08-b0863b0c242a
https://w3id.org/ro-id/7247b893-bbcd-47aa-86dd-b27e9f592c25
https://w3id.org/ro-id/a1c41d03-c971-45df-bfc6-b023608097a5
https://w3id.org/ro-id/c2b8dfdb-c1fc-42e1-841c-7b0e25ca1af3
https://w3id.org/ro-id/d1dc98f7-1b59-477f-b20e-5ce10570a46b
https://w3id.org/ro-id/fff4fa92-d823-45bd-8cbb-df9573cbb862
https://w3id.org/ro-id/011545de-72ea-4477-b87e-e4a0e9c8df4b
https://w3id.org/ro-id/092bc252-092d-439d-8551-fada7f4d509e
https://w3id.org/ro-id/89a51f68-a057-4f1a-9f39-051fe69eefb6
Philippe ROBERT, Victor Greiff, and Rahmad Akbar. "Absolut! in silico antibody-antigen binding database." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/71c90c65-34e6-441e-bb44-d1b31dd1007b.
data
raw data
metadata
biblio
https://www.biorxiv.org/content/10.1101/2021.07.06.451258v2
2022-03-22 02:24:46.046298+00:00
2022-03-22 02:24:46.241115+00:00
https://www.biorxiv.org/content/10.1101/2021.07.06.451258v2
2022-03-22 02:24:46.046298+00:00
ROBERT, P., Greiff, V., Akbar, R. (2021).Absolut! in silico antibody-antigen binding database [Data set]. Norstore. https://doi.org/10.11582/2021.00063
University of Oslo (UiO)
Simulation
https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00063
2021-07-12 00:00:00
2022-03-22 02:24:50.074536+00:00
This is a database of in silico generated antibody-antigen bindings (159 antigens times 6.9 million CDRH3 murine sequences), as resource for benchmarking machine learning methods.
The content of the files is explained in Readme.pdf
The reference publication is available on biorxiv, ID BIORXIV/2021/451258: Robert et al., A billion synthetic 3D-antibody-antigen complexes enable unconstrained machine-learning formalized investigation of antibody specificity prediction
The software used to generate the database is available at <a href="http://github.com/csi-greifflab/Absolut" class="linkified" target="_blank">LINK</a> for all explanations.
Absolut! in silico antibody-antigen binding database
2021-07-12 00:00:00
Philippe Paul Auguste Robert
unconstrained machine-learning
7.515923566878981
5.9
database
6.144578313253011
5.1
earth sciences
100.0
0.840290367603302
binding
7.590361445783133
6.3
machine learning
12.891566265060241
10.7
This is a database of in silico generated antibody-antigen bindings (159 antigens times 6.9 million CDRH3 murine sequences), as resource for benchmarking machine learning methods.
33.6644591611479
30.5
antibody
25.774647887323944
18.3
complex
5.783132530120482
4.8
machine learning
14.647887323943662
10.4
silico antibody-antigen
24.331210191082807
19.1
biochemistry
50.0
12.7
Software
Economy, business and finance/Economic sector/Computing and information technology/Software
specificity
3.6144578313253013
3.0
specificity prediction
22.802547770700635
17.9
investigation
5.0602409638554215
4.2
binding
8.591549295774648
6.1
antigens times
7.388535031847134
5.8
immunology
50.0
12.7
Robert
7.042253521126761
5.0
life sciences (general)
100.0
0.6574541926383972
antibody-antigen binding
37.961783439490446
29.8
Geo H.
philippe.robert@rohub.com
Philippe ROBERT
rahmad.akbar@rohub.com
Rahmad Akbar
victor.greiff@rohub.com
Victor Greiff
Environmental research
Life sciences
Physical sciences
Earth sciences
service-account-enrichment
7096
https://api.rohub.org/api/ros/063f3c98-2306-4388-86b2-4b09976fc16d/crate/download/
2022-03-22 02:24:51.576414+00:00
2025-03-05 00:52:20.015916+00:00
2022-03-22 02:24:51.576414+00:00
Accumulated preciptiation, 15 min temporal resolution, 3km spatial resolution. Year 1985 to 2005. Covering the inner most part of Oslofjorden in Norway.
application/ld+json
https://w3id.org/ro-id/063f3c98-2306-4388-86b2-4b09976fc16d
HARMONIE-AROME precipitation
MANUAL
Norway
precipitation
earth sciences
Executive (government)
Medicine
Ministers (government)
Weather
HARMONIE-AROME
Norway
Oslofjorden
precipitation
general
HARMONIE-AROME precipitation
part of Oslofjorden
Covering the inner most part of Oslofjorden in Norway.
HARMONIE-AROME precipitation.
Year 1985 to 2005.
15 min
Year 1985 to 2005
Norway
Eirik Nordgård. "HARMONIE-AROME precipitation." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/063f3c98-2306-4388-86b2-4b09976fc16d.
biblio
metadata
data
raw data
Nordgård, E., Nordgård, E. (2021).HARMONIE-AROME precipitation [Data set]. Norstore. https://doi.org/10.11582/2021.00058
Eirik Nordgård
Simulation
https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00058
2021-06-21 00:00:00
2022-03-22 02:25:09.852200+00:00
Accumulated preciptiation, 15 min temporal resolution, 3km spatial resolution. Year 1985 to 2005. Covering the inner most part of Oslofjorden in Norway.
HARMONIE-AROME precipitation
2021-06-21 00:00:00
Eirik Nordgård
eirik.nordgard@rohub.com
Eirik Nordgård
Geo H.
Elisa Trasatti; Stefano Sal
http://w3id.org/ro-id/rohub/model#change_specifications/a41f86bc-d941-490c-bd77-0d346847de48/changes/07defd9e-e852-4b3b-bd53-d8804eabf75c
http://w3id.org/ro-id/rohub/model#change_specifications/a41f86bc-d941-490c-bd77-0d346847de48/changes/51cc4efc-08f0-4f07-9e5f-adf0d894c4af
http://indico.ictp.it/event/a08176/session/82/contribution/62/material/0/0.pdf
http://indico.ictp.it/event/a08176/session/82/contribution/62/material/0/0.pdf
deformation model
United Kingdom
University of Leeds
Fortran code
seismic hazard
University of LeedsU.K.
Fortran
Mogi model
Tim Wright
computer programming
computer code
volcanology
models
volcano deformation model
system
Elisa Trasatti
service-account-enrichment
http://sandbox.rohub.org/rodl/ROs/Mogi_model/
2021-05-11T11:11:04.115+02:00
https://plus.google.com/103457129851450819242
http://w3id.org/ro-id/rohub/model#change_specifications/a41f86bc-d941-490c-bd77-0d346847de48
6343
https://api.rohub.org/api/ros/4cc2cb8d-4ace-4fd2-8ea4-674ac0514f76/crate/download/
2021-05-11 09:11:04.115000+00:00
2025-03-05 00:56:57.842643+00:00
2021-05-11 09:11:04.115000+00:00
This is the Mogi (1958) model. Fortran code.
application/ld+json
https://w3id.org/ro-id/4cc2cb8d-4ace-4fd2-8ea4-674ac0514f76
Mogi model (1958)
https://w3id.org/ro-id/4cc2cb8d-4ace-4fd2-8ea4-674ac0514f76
Elisa Trasatti; Stefano Sal. "Mogi model (1958)." ROHub. May 11 ,2021. https://w3id.org/ro-id/4cc2cb8d-4ace-4fd2-8ea4-674ac0514f76.
Biblio
tool
http://indico.ictp.it/event/a08176/session/82/contribution/62/material/0/0.pdf
2022-03-24 13:14:05.984220+00:00
2022-03-24 13:14:09.157706+00:00
application/pdf
presentation of the model
2022-03-24 13:14:05.984220+00:00
1104
https://api.rohub.org/api/resources/a6fe3471-3b69-4508-a607-0851897d612a/download/
2021-05-11 09:05:12.945000+00:00
2022-03-24 13:14:08.904850+00:00
mogi.f
2021-05-11 09:05:12.945000+00:00
service-account-generation-service
Elisa Trasatti; Stefano Sal
https://w3id.org/ro-id/1a03b374-b9cd-467b-a8b8-39c5a31a2624
2021-05-11T11:11:04.115+02:00
https://plus.google.com/103457129851450819242
http://sandbox.rohub.org/rodl/ROs/Mogi_model-fork/
deformation model
United Kingdom
University of Leeds
Fortran code
seismic hazard
University of LeedsU.K.
Fortran
Mogi model
Tim Wright
computer programming
computer code
volcanology
models
volcano deformation model
system
Elisa Trasatti
Elisa Trasatti
service-account-enrichment
http://sandbox.rohub.org/rodl/ROs/Mogi_model-snapshot/
202373
https://api.rohub.org/api/ros/1a03b374-b9cd-467b-a8b8-39c5a31a2624/crate/download/
https://orcid.org/0000-0002-2983-045X
2021-05-11 08:47:47.797000+00:00
2025-03-05 00:56:58.032094+00:00
2021-05-11 08:47:47.797000+00:00
This is the Mogi (1958) model. Fortran code.
application/ld+json
https://w3id.org/ro-id/1a03b374-b9cd-467b-a8b8-39c5a31a2624
volcanoes, model, isotropic source, mogi
Mogi model (1958)
Trasatti, Elisa, and Elisa Trasatti. "Mogi model (1958)." ROHub. May 11 ,2021. https://w3id.org/ro-id/1a03b374-b9cd-467b-a8b8-39c5a31a2624.
Biblio
tool
1104
https://api.rohub.org/api/resources/033b8c11-3a5f-4f84-8c67-1868dddd65ab/download/
2021-05-11 09:05:12.945000+00:00
2022-03-24 13:14:30.663106+00:00
mogi.f
2021-05-11 09:05:12.945000+00:00
http://indico.ictp.it/event/a08176/session/82/contribution/62/material/0/0.pdf
2021-05-11 08:47:47.797000+00:00
2022-03-24 13:14:29.572171+00:00
application/pdf
presentation of the model
2021-05-11 08:47:47.797000+00:00
279102
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service-account-generation-service
material data
benthic habitat map
hydrography
workflow
material
results
Italy
Mariacristina PRAMPOLINI
Adriatic Sea
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Substrate and benthic habitat map of the southern Adriatic Sea (Italy) using RSOBIA - Supplementary material.
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Data, workflow and results of the RSOBIA analysis carried out on the Southern Adriatic Sea CNR-ISMAR datasets
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classification
cnr-ismar
rsobia
seafloor map
south adriatic sea
Substrate and benthic habitat map of the southern Adriatic Sea (Italy) using RSOBIA - Supplementary material
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Mariacristina PRAMPOLINI, Mariacristina PRAMPOLINI, and Valentina Grande. "Substrate and benthic habitat map of the southern Adriatic Sea (Italy) using RSOBIA - Supplementary material." ROHub. Apr 29 ,2021. https://w3id.org/ro-id/a21484de-74d2-4fd0-9518-d3faa42130ad.
Backscatter
Bathymetry
Input_data
Workflow
Results
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Bathymetry ARCADIA
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Bathymetry MAGIC0709-ADRIA
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A huge amount of multibeam backscatter data has been acquired from the east to the west side of thein the southern Adriatic Sea in the last 15 years and coveringby CNR âISMAR. from the continental shelf down to the basin floor, from the west to east side of the Adriatic Basin. These data have been used for geological, biological and habitat mapping purposes, but a single and consistent interpretation of all the acquired backscatter data has never been carried out. Here, we aimed at coherently interpreting the seafloor reflectivity datasets in order to produce aseabed and benthic habitat maps of the southern Adriatic Sea showing the spatial distribution of substrate and sediment type and grain size within the basin. The methodology here applied consists of a semi-automated classification of backscatter images through object-based image analysis (OBIA) performed through the ArcGIS tool RSOBIA (Remote Sensing OBIA). This unsupervised image segmentation was carried out on each backscatter dataset separately and then validated through comparison with bottom samples and images acquired during the different oceanographic cruises. The substrate was then classified following the a classification scheme proposed within the CoCoNet specifically elaborated in-house project. The results were described and discussed as well as the methodology applied and the significance of the backscatter data in general.
text/plain
Abstract of the scientific paper
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RSOBIA_map_30.04.2021.pdf
2021-05-04 12:58:45.722000+00:00
https://www.mdpi.com/2072-4292/13/15/2913
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The workflow consits of three steps: 1. RSOBIA segmentation Segmentation is a method to aggregate pixels together to create a thematic map. The segmentation process is a licensed tool taking a multi-layered image and creates a set of polygons defined by the statistics associated with the layered image. Clusters of the imagery pixels are created in n-dimensional space and created into classes. Aggregation into geographic regions (polygons) is done according to a minimum polygon size rule, and clustering rules. Input is a single multi-layered file â not a geodatabase. Output is a single polygon vector shapefile and its default filename is the same as the input basename but with a different filetype. 2.Add Segment Attributes Following segmentation, the attributes for each polygon only relate to the polygon shape and class. It is often required to view and use the values of initial raster layers. These can be aggregated for each polygon as an attribute value of the mean and standard deviation of the pixel values within the polygon for each layer. 3. GroundTruth Samples The attributes for each polygon can be extended if groundtruth point data is available. This is a very simple join of two datasets and effectively adds the attributes of a groundtruth data point to the relevant polygon. In this way the samples may be utilised to characterise the class type. Some polygons may not have groundtruth samples and will therefore be left blank. Hopefully enough of the polygons will have groundtruth points to inform the class descriptions.
Arc Toolbox - Prampolini et al. 2021
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Bathymetry COCOMAP13
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Backscatter ALTRO
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Backscatter MAGIC0211
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Bathymetry COCOMAP14
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Bathymetry SAGA-03
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Bathymetry OBAMA
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Backscatter COCOMAP14
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TOBI side scan sonar image
Side scan sonar SAGA03
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Result of the paper
image/jpeg
Substrate and benthic hanitat map of the sourthern Adriatic Sea
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Backscatter OBAMA
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Backscatter MAGIC0709
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Workflow applied in the present work visualized in Model Builder (ArcGIS 10.5) and showing the tools of RSOBIA toolset used in the present work (yellow), the input data - rasters to be seg-mented and ground-truthing data â (purple), the segmentation parameters to be chosen by the operator (light blue), the results (green) and the ArcGIS workspace (dark blue)
image/jpeg
Image of the workflow in model builder
2021-08-27 09:12:01.708000+00:00
1306366
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image/jpeg
Samples used to classify substrates and benthic habitats
2021-05-31 13:53:02.389000+00:00
https://www.mdpi.com/2072-4292/13/15/2913
2021-04-29 14:17:24.578000+00:00
2022-03-24 13:17:07.985558+00:00
Prampolini, M.; Angeletti, L.; Castellan, G.; Grande, V.; Le Bas, T.; Taviani, M.; Foglini, F. Benthic Habitat Map of the Southern Adriatic Sea (Mediterranean Sea) from Object-Based Image Analysis of Multi-Source Acoustic Backscatter Data. Remote Sens. 2021, 13, 2913. https://doi.org/10.3390/rs13152913
Scientific paper
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Backscatter MAGIC040910
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Bathymetry MAGIC0211
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Bathymetry MS15
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Backscatter acquired with Reson Seabat 7160
Backscatter MSFD15
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Italy
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testing
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Giorgio Castellan
Kd Kw A nLw nLw B
Estimation
Mediterranean Sea
University of Hawai
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seabed Estimation
service-account-enrichment
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Estimation of PAR light reaching the seabed in the Mediterranean Sea
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Estimation of light at seabed
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Runcieetal2008.pdf
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1753877
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Subjective experience along with physiological activity are fundamental components of emotional responding. We present a publicly available dataset of psychophysiological responses to positive and negative emotions of 1157 healthy participants, collected across seven studies. In our studies were continuously recorded affect and physiological activity during resting baseline and emotional responding. We recorded physiological responses using electrocardiography (ECG), impedance cardiography (ICG), electrodermal activity (EDA), photoplethysmography (PPG, the blood pressure measures), respiratory, and temperature sensors. In our studies, we elicited emotions with films, pictures, speech preparation, and expressive writing. We studied a wide range of positive and negative emotions, including amusement, anger, disgust, excitement, fear, gratitude, sadness, tenderness, and threat. To the best of our knowledge, psychophysiology of positive and negative emotions (POPANE) database is the largest, consistent psychophysiological dataset on emotions ever collected and publicly shared. We hope that POPANE will provide individuals, companies, and laboratories with the data they need to perform their analyses, corroborate their results, and create robust psychophysiological models of emotions. The individuals data are openly available in POPANE dataset at https://data.psychosensing.psnc.pl/popane/index.html.
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Subjective experience along with physiological activity are fundamental components of emotional responding. We present a publicly available dataset of psychophysiological responses to positive and negative emotions of 1157 healthy participants, collected across seven studies. In our studies were continuously recorded affect and physiological activity during resting baseline and emotional responding. We recorded physiological responses using electrocardiography (ECG), impedance cardiography (ICG), electrodermal activity (EDA), photoplethysmography (PPG, the blood pressure measures), respiratory, and temperature sensors. In our studies, we elicited emotions with films, pictures, speech preparation, and expressive writing. We studied a wide range of positive and negative emotions, including amusement, anger, disgust, excitement, fear, gratitude, sadness, tenderness, and threat. To the best of our knowledge, psychophysiology of positive and negative emotions (POPANE) database is the largest, consistent psychophysiological dataset on emotions ever collected and publicly shared. We hope that POPANE will provide individuals, companies, and laboratories with the data they need to perform their analyses, corroborate their results, and create robust psychophysiological models of emotions. The individuals data are openly available in POPANE dataset at https://data.psychosensing.psnc.pl/popane/index.html.
Subjective experience along with physiological activity are fundamental components of emotional responding. We present a publicly available dataset of psychophysiological responses to positive and negative emotions of 1157 healthy participants, collected across seven studies. In our studies were continuously recorded affect and physiological activity during resting baseline and emotional responding. We recorded physiological responses using electrocardiography (ECG), impedance cardiography (ICG), electrodermal activity (EDA), photoplethysmography (PPG, the blood pressure measures), respiratory, and temperature sensors. In our studies, we elicited emotions with films, pictures, speech preparation, and expressive writing. We studied a wide range of positive and negative emotions, including amusement, anger, disgust, excitement, fear, gratitude, sadness, tenderness, and threat. To the best of our knowledge, psychophysiology of positive and negative emotions (POPANE) database is the largest, consistent psychophysiological dataset on emotions ever collected and publicly shared. We hope that POPANE will provide individuals, companies, and laboratories with the data they need to perform their analyses, corroborate their results, and create robust psychophysiological models of emotions. The individuals data are openly available in POPANE dataset at https://data.psychosensing.psnc.pl/popane/index.html.
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https://w3id.org/ro-id/9554adec-5201-4db9-9d2a-b519cd9ee0cc
https://w3id.org/ro-id/a6e3b2dd-1bcc-4025-b262-12d4156be3c3
https://w3id.org/ro-id/e87ad228-21df-4b77-acfe-c5ba67b102f1
https://w3id.org/ro-id/1c3ab0e6-20ee-4524-a616-d48e94cf7610
https://w3id.org/ro-id/8001189e-9a48-4e1c-8f57-a41d9e63e638
https://w3id.org/ro-id/29a12137-373b-4d46-a648-de00ea10519e
https://w3id.org/ro-id/2e933347-2e99-4257-a22b-af679de384c5
https://w3id.org/ro-id/3431e1a2-094f-41d0-a8e8-a83b19726915
https://w3id.org/ro-id/5733ed6b-ac53-4e4f-ba7d-e8d2e78ebfe7
https://w3id.org/ro-id/da620f27-9164-4279-bd9e-814de56eb258
https://w3id.org/ro-id/2bfc63ba-becf-4e5e-9659-beb0b2d97440
https://w3id.org/ro-id/4d1be4c6-ae8e-4d49-9f90-592f277faa6e
https://w3id.org/ro-id/b25440f9-f459-4080-acc4-7ee27ae80e0c
https://w3id.org/ro-id/87095d85-791a-427d-854a-873bbebfdd35
https://w3id.org/ro-id/af5b88b8-b48a-47c2-8534-6eeedc5a276a
https://w3id.org/ro-id/ee99313a-437c-4544-ad83-0be3ce0dbc72
Fouilloux, Anne. "5 years CLM-FATES simulation for Nordic site ALP1 ." ROHub. Jan 12 ,2021. https://w3id.org/ro-id/4086e5b7-284e-4551-a156-e3453ddcee58.
20149
https://api.rohub.org/api/resources/38f14396-c1fd-478e-b5b2-135655b83783/download/
2021-01-12 07:48:52+00:00
2022-03-24 13:26:32.864730+00:00
Galaxy Workflow
CLM-FATES_ALP1_simulation_5years.ga
2021-01-12 07:48:52+00:00
3407
https://api.rohub.org/api/resources/496190bd-ec8e-4d8f-a76b-77f61b82258d/download/
2021-01-12 07:48:51.919000+00:00
2022-03-24 13:26:31.837948+00:00
Workflow in Common Workflow Language
CLM-FATES_ALP1_simulation_5years.cwl
2021-01-12 07:48:51.919000+00:00
42158
https://api.rohub.org/api/resources/b7cf277d-f2e7-44e2-9e61-f6d67f8bb8aa/download/
2021-04-29 14:26:54.977000+00:00
2022-03-24 13:26:29.832570+00:00
image/png
Overview of FATES workflow for a single location in Norway
2021-04-29 14:26:54.977000+00:00
distribution
10.538922155688624
8.8
5 years CLM-FATES simulation for Nordic site ALP1 .
13.426853707414828
13.4
Weather
Weather
Galaxy climate
14.320388349514563
11.8
computer science
67.79661016949152
4.0
climate
8.982035928143713
7.5
fate
6.347305389221557
5.3
Wireless technology
Economy, business and finance/Economic sector/Computing and information technology/Wireless technology
Ecosystem
Environment/Nature/Ecosystem
ALP1
13.874788494077832
8.2
space sciences
100.0
0.3373451828956604
5 years
botany
32.20338983050847
1.9
model
14.890016920473775
8.8
regeneration
14.213197969543147
8.4
simulation
19.161676646706585
16.0
from a 300 year
atmospheric sciences
100.0
0.9884166121482849
The simulation has been done with Galaxy climate (https://climate.usegalaxy.eu/
12.5250501002004
12.5
rebirth
9.580838323353293
8.0
http
4.910179640718562
4.1
Death and dying
Society/Values/Death and dying
regeneration of plants
12.742718446601941
10.5
Functionally Assembled Terrestrial Ecosystem Simulator
13.367174280879864
7.9
of 5 years
Industrial accident and incident
Disaster, accident and emergency incident/Accident and emergency incident/Explosion accident and incident/Industrial accident and incident
service-account-generation-service
agriculture
home farming
practices in the context
use
fertilizers
Esteban Gonz��lez
service-account-enrichment
67072
https://api.rohub.org/api/ros/a3f460ba-5e28-465f-9043-575ccc3692c1/crate/download/
2021-01-11 02:30:21.779000+00:00
2025-03-05 00:55:14.657661+00:00
2021-01-11 02:30:21.779000+00:00
The project aims to understand and map the use of pesticides and fertilizers and sustainable alternative practices in the context of home farming and gardening. Simultaneously, it aims to disseminate information on the topic with the final aim of reducing the use of pesticides and fertilizers.
application/ld+json
https://w3id.org/ro-id/a3f460ba-5e28-465f-9043-575ccc3692c1
In My Backyard
backyard
context
farming
fertiliser
horticulture
information
pesticide
practice
project
purpose
subject
use
earth sciences
Agriculture
Fertiliser
farming
fertilizer
gardening
pesticide
practice
project
topic
life sciences
alternative practice
final aim
home farming
information on the topic
use of pesticide
In My Backyard.
Simultaneously, it aims to disseminate information on the topic with the final aim of reducing the use of pesticides and fertilizers.
The project aims to understand and map the use of pesticides and fertilizers and sustainable alternative practices in the context of home farming and gardening.
agriculture
Esteban Gonz��lez. "In My Backyard." ROHub. Jan 11 ,2021. https://w3id.org/ro-id/a3f460ba-5e28-465f-9043-575ccc3692c1.
Dataset
Raw Data
Documentation
Biblio
Used
Produced
Metadata
Data
https://doi.org/10.5281/zenodo.4081597
https://doi.org/10.5281/zenodo.4081585
2021-01-11 02:30:21.779000+00:00
2022-03-24 13:26:50.237880+00:00
In My Backyard: project reflections and take aways
2021-01-11 02:30:21.779000+00:00
https://doi.org/10.5281/zenodo.4081597
2021-01-11 02:30:21.779000+00:00
2022-03-24 13:26:50.367381+00:00
In My Backyard: project final report
2021-01-11 02:30:21.779000+00:00
61755
https://api.rohub.org/api/resources/66bbddcb-e258-434f-a684-5361e6688032/download/
2021-01-11 02:30:51.468000+00:00
2022-03-24 13:26:49.761269+00:00
image/png
diagram-InMyBackyard.png
2021-01-11 02:30:51.468000+00:00
https://doi.org/10.5281/zenodo.4081606
https://doi.org/10.5281/zenodo.4081778
2021-01-11 02:30:21.779000+00:00
2022-03-24 13:26:49.904734+00:00
In My Backyard: On-Site Survey Responses Raw Dataset
2021-01-11 02:30:21.779000+00:00
https://doi.org/10.5281/zenodo.4081597
https://doi.org/10.5281/zenodo.4081606
2021-01-11 02:30:21.779000+00:00
2022-03-24 13:26:50.093236+00:00
In My Backyard: key insights
2021-01-11 02:30:21.779000+00:00
service-account-generation-service
gain real time feedback of the data
multimedia
time and date
hydrophone
speaker
hardware
recording
cost
electronics
Esteban Gonz��lez
service-account-enrichment
662879
https://api.rohub.org/api/ros/e3d872de-a4bd-4803-bf31-5395d4748905/crate/download/
2021-01-11 02:09:08.766000+00:00
2025-03-05 01:19:10.897816+00:00
2021-01-11 02:09:08.766000+00:00
The Sonic Kayak system is a low cost open hardware for gathering and mapping fine-scale marine environmental data, which has not been previously possible to obtain. Data is sonified through an onboard speaker allowing paddlers to seek out areas of interest and gain real time feedback of the data. The system currently includes underwater temperature sensors and a hydrophone for measuring underwater sound, each recording data every second with GPS, time and date.
application/ld+json
https://w3id.org/ro-id/e3d872de-a4bd-4803-bf31-5395d4748905
Sonic Kayacs
amplifier
canoeist
cost
data
equipment
feedback
global positioning system
hydrophone
information
real time
sensor
temperature
time and date
earth sciences
Canoeing
Sonic Kayak
data
feedback
hydrophone
real time
sensor
speaker
engineering
Sonic Kayak system
real time feedback
recording data
temperature sensor
time and date
Data is sonified through an onboard speaker allowing paddlers to seek out areas of interest and gain real time feedback of the data.
The Sonic Kayak system is a low cost open hardware for gathering and mapping fine-scale marine environmental data, which has not been previously possible to obtain.
The system currently includes underwater temperature sensors and a hydrophone for measuring underwater sound, each recording data every second with GPS, time and date.
computer science
electronics
Esteban Gonz��lez. "Sonic Kayacs." ROHub. Jan 11 ,2021. https://w3id.org/ro-id/e3d872de-a4bd-4803-bf31-5395d4748905.
Biblio
Metadata
Raw Data
Used
Device
Data
Dataset
Produced
Dissemination
Documentation
594964
https://api.rohub.org/api/resources/1a799453-ca28-41fa-963f-eb323f6c722d/download/
2021-01-11 02:14:55.977000+00:00
2022-03-24 13:27:07.750442+00:00
image/png
full-wiring-action.png
2021-01-11 02:14:55.977000+00:00
https://doi.org/10.5281/zenodo.3923743
2021-01-11 02:09:08.766000+00:00
2022-03-24 13:27:08.698797+00:00
Sonic Kayak survey
2021-01-11 02:09:08.766000+00:00
https://github.com/fo-am/sonic-kayaks/wiki
https://doi.org/10.5281/zenodo.4041588
2021-01-11 02:09:08.766000+00:00
2022-03-24 13:27:08.225461+00:00
Dataset generated for the device
Measurements
2021-01-11 02:09:08.766000+00:00
95539
https://api.rohub.org/api/resources/424544a5-6157-4d09-ad73-5e3ac1f9d2cf/download/
2021-01-11 02:09:44.849000+00:00
2022-03-24 13:27:06.391281+00:00
image/png
diagram-sonickayacs.png
2021-01-11 02:09:44.849000+00:00
https://magpi.raspberrypi.org/issues/97/pdf
2021-01-11 02:09:08.766000+00:00
2022-03-24 13:27:08.042715+00:00
Magazine with an article describing the device
The MagPi â Issue 97
2021-01-11 02:09:08.766000+00:00
https://www.flickr.com/photos/foam/albums/72157715979200366
2021-01-11 02:09:08.766000+00:00
2022-03-24 13:27:08.589186+00:00
Sonic Kayak data visualisations
2021-01-11 02:09:08.766000+00:00
https://doi.org/10.5281/zenodo.3923743
https://fo.am/blog/2020/08/17/sonic-kayak-update-new-sensors-sonifications-and-visualisations/
2021-01-11 02:09:08.766000+00:00
2022-03-24 13:27:08.480939+00:00
Sonic Kayak update - new sensors, sonifications, and visualisations
2021-01-11 02:09:08.766000+00:00
https://github.com/fo-am/sonic-kayaks/raw/master/hardware/full-wiring-action.png
2021-01-11 02:09:08.766000+00:00
2022-03-24 13:27:06.600078+00:00
image/png
https://github.com/fo-am/sonic-kayaks/raw/master/hardware/full-wiring-action.png
2021-01-11 02:09:08.766000+00:00
https://www.youtube.com/watch?v=puLXKj1AVAk
2021-01-11 02:09:08.766000+00:00
2022-03-24 13:27:08.915023+00:00
Sonic Kayaks - citizen science in the marine environment for the ACTION project
2021-01-11 02:09:08.766000+00:00
https://fo.am/blog/2020/06/30/sonic-kayak-environmental-data-sonification/
2021-01-11 02:09:08.766000+00:00
2022-03-24 13:27:08.374657+00:00
Sonic Kayaks are rigged with sensors, both underwater (temperature, sound, and turbidity) and above water (air pollution). As the kayaker paddles around, the sensors pick up changes in the environment, and these are played to the kayaker in real time through an on-board speaker. Originally this approach came from a sound art project (Kaffe Matthewsâ Sonic Bikes), but we realised it could also be useful for researchers to be able to follow data or seek out particular environments through sound, and along the way we gathered interest from the visually impaired community for using the sounds to augment their kayaking experience or guide navigation on the water.
Sonic Kayak environmental data sonification
2021-01-11 02:09:08.766000+00:00
https://doi.org/10.5281/zenodo.3923743
https://fo.am/blog/2020/05/05/sonic-kayak-progress-new-pollution-sensors-for-citizen-science/
2021-01-11 02:09:08.766000+00:00
2022-03-24 13:27:08.806456+00:00
Sonic Kayak progress â new pollution sensors for citizen science
2021-01-11 02:09:08.766000+00:00
https://github.com/fo-am/sonic-kayaks/wiki
2021-01-11 02:09:08.766000+00:00
2022-03-24 13:27:06.743586+00:00
This wiki documents the hardware and software used in the current Sonic Kayaks setup.
Sonic Kayacs Wiring Schematic: Water and Air Pollution Edition
2021-01-11 02:09:08.766000+00:00
https://w3id.org/ro-id/e3d872de-a4bd-4803-bf31-5395d4748905/full-wiring-action.png
service-account-generation-service
maps noise map
empower community
Maps
Noise
heritage
folklore
Esteban Gonz��lez
service-account-enrichment
135152
https://api.rohub.org/api/ros/54e02cd6-ee8c-4d6d-af46-c7421de99997/crate/download/
2020-11-25 09:14:49.401000+00:00
2025-03-05 02:45:35.165781+00:00
2020-11-25 09:14:49.401000+00:00
NOISE MAPS allows citizens to generate and analyse urban sound data, empowering communities to take action to reduce unwanted noise and protect the local sonic heritage. The pilot builds on existing cultural practices of collective documentation of the sound heritage of neighbourhoods (Mapa Sonor). Thanks to project activities citizens will be able to filter unwanted noise out from authentic, locally unique sounds, thus allowing communities to take action to preserve their sonic heritage. NOISE MAPS deploys a combination of tested tech and methods with a novel approach, to empower communities to leverage the power of citizen science to tackle local challenges of global relevance.
application/ld+json
https://w3id.org/ro-id/54e02cd6-ee8c-4d6d-af46-c7421de99997
Noise Maps
activity
citizen
combination
community
documentation
engineering
heritage
information
noise
pilot
practice
quarter
environmental sciences
Customs and tradition
MAPS
citizen
community
heritage
noise
pilot
practice
life sciences
combination of tested tech
noise MAPS
project activities citizen
sound heritage
unwanted noise
NOISE MAPS allows citizens to generate and analyse urban sound data, empowering communities to take action to reduce unwanted noise and protect the local sonic heritage.
NOISE MAPS deploys a combination of tested tech and methods with a novel approach, to empower communities to leverage the power of citizen science to tackle local challenges of global relevance.
The pilot builds on existing cultural practices of collective documentation of the sound heritage of neighbourhoods (Mapa Sonor) Thanks to project activities citizens will be able to filter unwanted noise out from authentic, locally unique sounds, thus allowing communities to take action to preserve their sonic heritage.
sociology
Esteban Gonz��lez. "Noise Maps." ROHub. Nov 25 ,2020. https://w3id.org/ro-id/54e02cd6-ee8c-4d6d-af46-c7421de99997.
Software
Raw_Data
Datasets
Dissemination
https://freesound.org/people/bitlab_coop/packs/
https://github.com/pzinemanas/AudioMoth-Firmware-SPL
2020-11-25 09:14:49.401000+00:00
2022-03-24 13:27:28.032478+00:00
Sounds recorded
2020-11-25 09:14:49.401000+00:00
https://ars.electronica.art/keplersgardens/en/sonic-heritage/
2020-11-25 09:14:49.401000+00:00
2022-03-24 13:27:28.365817+00:00
Ars Electronica - Sonic Heritage
2020-11-25 09:14:49.401000+00:00
https://github.com/pzinemanas/AudioMoth-Firmware-SPL
2020-11-25 09:14:49.401000+00:00
2022-03-24 13:27:28.439176+00:00
https://github.com/pzinemanas/AudioMoth-Firmware-SPL
2020-11-25 09:14:49.401000+00:00
138785
https://api.rohub.org/api/resources/d4898e2f-704f-407d-b143-d8a16f981950/download/
2020-11-25 09:15:52.369000+00:00
2022-03-24 13:27:27.751199+00:00
image/png
diagram-noisemaps.png
2020-11-25 09:15:52.369000+00:00
https://ars.electronica.art/keplersgardens/en/what-is-noise/
2020-11-25 09:14:49.401000+00:00
2022-03-24 13:27:28.214158+00:00
Ars Electronica - What is noise
2020-11-25 09:14:49.401000+00:00
https://doi.org/10.5281/zenodo.4059533
2020-11-25 09:14:49.401000+00:00
2022-03-24 13:27:27.896482+00:00
Noise Maps ACTION pilot data 2020
2020-11-25 09:14:49.401000+00:00
service-account-generation-service
Nordic country
ecology
air pollution in schools Norway
Oslo
electric cars
Oslo
air quality
pilot
ACTION
Esteban González+Guardia
High schools
Education/School/High schools
sensor platform
49.447077409162716
31.3
high school
6.68859649122807
6.1
student
8.333333333333334
7.6
Oslo
11.764705882352942
6.0
electric car
13.92156862745098
7.1
project
3.6184210526315788
3.3
Air pollution
Environment/Environmental pollution/Air pollution
Oslo
https://www.wikidata.org/wiki/Q585
Oslo
8.552631578947368
7.8
atmospheric sciences
100.0
0.6394369006156921
earth sciences
100.0
0.6394369006156921
information
4.166666666666667
3.8
pilot of the action project
7.266982622432858
4.6
air quality project
24.170616113744074
15.3
PM10
7.2368421052631575
6.6
electric car
10.087719298245613
9.2
ecology
100.0
13.0
air pollution
14.117647058823529
7.2
We use the Nova SDS011 sensor for measuring PM2.5 and PM10 that is transmitting data to an Arduino board.
24.727272727272727
13.6
engineering
100.0
0.7565824389457703
PM10
10.392156862745098
5.3
air pollution
10.197368421052632
9.3
By actively promoting the purchase of electric cars, the Norwegian government is aiming at protecting the environment and not least improving air quality, especially in urban areas.
30.90909090909091
17.0
air quality
26.274509803921568
13.4
service-account-enrichment
202135
https://api.rohub.org/api/ros/b04c829e-997d-44e6-8ef2-5893622403d2/crate/download/
2020-11-03 17:22:37.815000+00:00
2025-03-05 00:45:35.558116+00:00
2020-11-03 17:22:37.815000+00:00
Norway is the land of fjords, trolls and – electric cars. By actively promoting the purchase of electric cars, the Norwegian government is aiming at protecting the environment and not least improving air quality, especially in urban areas. Air quality is still a reason for concern in many European countries, including the Nordic countries. Not many people are aware of this fact, and this is where the Norwegian pilot of the ACTION project comes in.The pilot gives high school students in Oslo and the larger Oslo area the opportunity to design and carry out their own air quality projects, using an off-the-shelf air quality sensor platform. The aim is to create awareness about the sources of air pollution, make the students think of ways to reduce both emission and exposure and teach them scientific working methods. We use the Nova SDS011 sensor for measuring PM2.5 and PM10 that is transmitting data to an Arduino board. The data can be obtained through an SD card.
application/ld+json
https://w3id.org/ro-id/b04c829e-997d-44e6-8ef2-5893622403d2
ACTION - Air Pollution in Schools
https://w3id.org/ro-id/628c2aeb-5036-4192-95c0-11fead761441
https://w3id.org/ro-id/37394f65-575e-41f1-9c3b-8886317b9379
https://w3id.org/ro-id/bdb8e64c-d4c8-41ba-95a3-d43005c062b7
https://w3id.org/ro-id/10de924a-94ca-4126-bf32-847de386f7a4
https://w3id.org/ro-id/128b085e-0ab8-404d-9a66-c90d6ae05e90
https://w3id.org/ro-id/2e2e97a5-493c-49cd-b0b0-26763a4dbd8c
https://w3id.org/ro-id/3fc4bb19-f881-48b8-a8ff-c400e2f78171
https://w3id.org/ro-id/49e46db1-78f3-40bc-a454-8027f4e7168a
https://w3id.org/ro-id/583ae044-ba1c-4302-b34d-8451107e6de4
https://w3id.org/ro-id/58c9fa1d-994b-42e5-a662-32d05cda9be1
https://w3id.org/ro-id/85077b7a-4107-406f-a23e-8b6775c00af5
https://w3id.org/ro-id/b2e63529-710c-47ff-8fd7-8ce2718e0088
https://w3id.org/ro-id/d133904e-b935-4a36-ad0f-a60d3baa1451
https://w3id.org/ro-id/ec4cd90d-be2b-457e-a49a-bc77d729aa5f
https://w3id.org/ro-id/ee74f830-35df-41e4-b9ae-0f731ab41038
https://w3id.org/ro-id/ef8a303d-1d19-4b73-b0f6-66c1dcbb9977
https://w3id.org/ro-id/4128512f-df8d-465e-9eef-4f92af23a5a7
https://w3id.org/ro-id/4219f70b-3a6f-4cd1-b0db-7f9ddbec5f15
https://w3id.org/ro-id/07fe7d14-d4bf-4540-b8f9-c5691f488d62
https://w3id.org/ro-id/34bbfc83-4b45-4f51-8011-d46bcb83c955
https://w3id.org/ro-id/ef8b75da-19d0-43d7-a2ff-bb06c52d9cae
https://w3id.org/ro-id/1b45fcec-bd5c-4d73-8f91-a5ff895651d5
https://w3id.org/ro-id/2ae05b14-ac5f-4e55-9446-0b3fcbec1912
https://w3id.org/ro-id/6564fc22-9a8b-4a3e-925c-bc4ab6c59c1d
https://w3id.org/ro-id/827ca094-21bf-4063-93b7-b31122508431
https://w3id.org/ro-id/9d58dbb8-102c-4217-8d03-e34cba02385a
https://w3id.org/ro-id/c48d970f-ec65-4bda-be91-cc2872842314
https://w3id.org/ro-id/e8eb655a-bc33-4705-9392-fb1a92c20095
https://w3id.org/ro-id/778c2726-c900-459c-a44b-2161059c95ae
https://w3id.org/ro-id/e61b5bb9-7e0f-45f3-95e4-12a9a150b1de
https://w3id.org/ro-id/0df21445-75d3-44dc-b745-8892f3b14578
https://w3id.org/ro-id/4f025fd9-55b7-4534-833f-7eb416e8c2ac
https://w3id.org/ro-id/54bcf67c-d704-4844-93c8-445166d183af
https://w3id.org/ro-id/e996a0c6-3624-4e6e-9214-be26a38e613b
https://w3id.org/ro-id/ffe8406d-7011-4880-9a1f-d85467d6e39f
https://w3id.org/ro-id/72402c2b-9c74-4ad5-8cde-067dd154cf3f
https://w3id.org/ro-id/87648c4f-b7dc-4884-a6f2-93be7336f0a0
https://w3id.org/ro-id/ef99ba9f-8142-4cc1-a9af-ccf44ac6996e
Esteban González+Guardia. "ACTION - Air Pollution in Schools." ROHub. Nov 03 ,2020. https://w3id.org/ro-id/b04c829e-997d-44e6-8ef2-5893622403d2.
Documentation
Biblio
Teaching_Material
Produced
Dataset
Deliverables
Metadata
Raw Data
Hardware
Presentations
Used
Data
https://zenodo.org/record/3737595#.X6GVelNKi8o
2020-11-03 17:22:37.815000+00:00
2022-03-24 13:28:31.216813+00:00
Systematiske målinger: Bilfritt sentrum og virkningen av denne
2020-11-03 17:22:37.815000+00:00
195381
https://api.rohub.org/api/resources/2fdf70df-0dad-4b06-a941-710579f0bfd0/download/
2020-11-03 17:23:51.299000+00:00
2022-03-24 13:28:29.878147+00:00
image/png
research-object-nilu.png
2020-11-03 17:23:51.299000+00:00
https://zenodo.org/record/3737799#.X6GSnVNKi8o
https://doi.org/10.5281/zenodo.3956481
2020-11-03 17:22:37.815000+00:00
2022-03-24 13:28:30.158519+00:00
DATALOG_Lambertseter_VGS_20190329_Sensor_2
2020-11-03 17:22:37.815000+00:00
https://zenodo.org/record/3730478#.X6GUGFNKi8o
2020-11-03 17:22:37.815000+00:00
2022-03-24 13:28:30.725184+00:00
Forskningsprosjekt luftforurensning
2020-11-03 17:22:37.815000+00:00
https://zenodo.org/record/3737595#.X6GVqlNKi8o
2020-11-03 17:22:37.815000+00:00
2022-03-24 13:28:31.359856+00:00
Systematiske målinger: Bilfritt sentrum og virkningen av denne
2020-11-03 17:22:37.815000+00:00
NOVA SDS011 Sensor
https://zenodo.org/record/3956481#.X6GWeVNKi8o
2020-11-03 17:22:37.815000+00:00
2022-03-24 13:28:32.083544+00:00
RDUINO_UNO_WITH_NOVASDS011_Firmware
2020-11-03 17:22:37.815000+00:00
https://zenodo.org/record/3737565#.X6GWPVNKi8o
2020-11-03 17:22:37.815000+00:00
2022-03-24 13:28:31.940094+00:00
Er luftkvalitaten ved Ullern VGS skadelig for elevene?
2020-11-03 17:22:37.815000+00:00
https://zenodo.org/record/3737759#.X6GSQFNKi8o
https://zenodo.org/record/3956481#.X6GXUFNKi8o
2020-11-03 17:22:37.815000+00:00
2022-03-24 13:28:30.218641+00:00
DATALOG_Lambertseter_VGS_20190329
2020-11-03 17:22:37.815000+00:00
https://zenodo.org/record/3737799
https://zenodo.org/record/3737759
2020-11-03 17:22:37.815000+00:00
2022-03-24 13:28:32.467392+00:00
DATALOG
2020-11-03 17:22:37.815000+00:00
https://zenodo.org/record/3730465#.X6GTTFNKi8o
2020-11-03 17:22:37.815000+00:00
2022-03-24 13:28:30.595340+00:00
Air Pollution Perception in Public Participation
2020-11-03 17:22:37.815000+00:00
https://zenodo.org/record/3737635#.X6GUj1NKi8o
2020-11-03 17:22:37.815000+00:00
2022-03-24 13:28:30.880503+00:00
Trafikkforurensing: Trafikkerte områder er mer utsatt for forurensing
2020-11-03 17:22:37.815000+00:00
https://zenodo.org/record/3737577#.X6GV-VNKi8o
2020-11-03 17:22:37.815000+00:00
2022-03-24 13:28:31.639184+00:00
Luftkvalitet: Andel svevestøv i lufta øker ved rushtiden
2020-11-03 17:22:37.815000+00:00
https://zenodo.org/record/3730457#.X6GTGVNKi8o
2020-11-03 17:22:37.815000+00:00
2022-03-24 13:28:30.458800+00:00
Tutorial for air quality projects in high schools
2020-11-03 17:22:37.815000+00:00
https://zenodo.org/record/3737569#.X6GWGVNKi8o
2020-11-03 17:22:37.815000+00:00
2022-03-24 13:28:31.787207+00:00
Hvordan utvikler luftkvaliteten i et klasserom seg?
2020-11-03 17:22:37.815000+00:00
https://zenodo.org/record/3737608#.X6GVUFNKi8o
2020-11-03 17:22:37.815000+00:00
2022-03-24 13:28:31.020395+00:00
Systematiske målinger: Vi ønsker å finne ut om det akustiske miljøet har effekt på luftkvalitetens endringer
2020-11-03 17:22:37.815000+00:00
https://zenodo.org/record/3737589#.X6GV0FNKi8o
2020-11-03 17:22:37.815000+00:00
2022-03-24 13:28:31.504178+00:00
Optimalt arbeidsmiljø: systematiske målinger av støy og temperatur i klasserom, og sammenhengem mellom dem
2020-11-03 17:22:37.815000+00:00
air quality
18.750000000000004
17.1
Norway
https://www.wikidata.org/wiki/Q20
sensor
12.156862745098039
6.2
data
4.934210526315789
4.5
instrumentation and photography
100.0
0.7565824389457703
student
11.372549019607844
5.8
in.the pilot
10.584518167456554
6.7
pilot
4.714912280701754
4.3
opportunity
3.837719298245614
3.5
sensor
8.881578947368421
8.1
Students
Education/Teaching and learning/Students
Not many people are aware of this fact, and this is where the Norwegian pilot of the ACTION project comes in.The pilot gives high school students in Oslo and the larger Oslo area the opportunity to design and carry out their own air quality projects, using an off-the-shelf air quality sensor platform.
44.36363636363637
24.4
air pollution in school
8.530805687203792
5.4
service-account-generation-service
Antonio Petrizzo
http://sandbox.rohub.org/rodl/ROs/sinktrack_FORK/
Matlab, Taverna, Linux environment
http://w3id.org/ro-id/rohub/model#change_specifications/5ef61e03-1c33-483b-9abc-e527bc6ab6be/changes/23e62dba-df50-4485-a4f5-2e508a712418
http://w3id.org/ro-id/rohub/model#change_specifications/5ef61e03-1c33-483b-9abc-e527bc6ab6be/changes/332cb24b-df2a-4235-b600-51b78ea934f5
http://w3id.org/ro-id/rohub/model#change_specifications/5ef61e03-1c33-483b-9abc-e527bc6ab6be/changes/37692d3f-0fea-4dd1-ad7b-ec8da1aca48a
http://w3id.org/ro-id/rohub/model#change_specifications/5ef61e03-1c33-483b-9abc-e527bc6ab6be/changes/3a10914c-23cb-4be9-b7cc-880d8c97dd25
http://w3id.org/ro-id/rohub/model#change_specifications/5ef61e03-1c33-483b-9abc-e527bc6ab6be/changes/408afda8-85ab-4b04-9705-0fcaa16cb98d
http://w3id.org/ro-id/rohub/model#change_specifications/5ef61e03-1c33-483b-9abc-e527bc6ab6be/changes/ca895d77-7f18-46a5-aac8-0b0580021094
https://box.everest.psnc.pl/f/2a521bb3de8345b1ae9b/
https://box.everest.psnc.pl/f/2a521bb3de8345b1ae9b/
https://box.everest.psnc.pl/f/67b5ddc8a3ac4047b3ef/
https://box.everest.psnc.pl/f/421adc4b01b24d2a906c/
https://box.everest.psnc.pl/f/421adc4b01b24d2a906c/
https://box.everest.psnc.pl/f/67b5ddc8a3ac4047b3ef/
standard deviation of backscatter
transports
elaborate ASCII file
produce a graph
SinkTrack It
statistics
angle
standard deviation
graph
ASCII file
net
depth
angle of net
ping
net
graph with the track
statistics
backscattering
angle of net
FM Midwater
service-account-enrichment
http://ever-est.eu/value#/sinktrack
http://ever-est.eu/value#0226e45d-59ef-45dd-992f-824a0dbff431
http://ever-est.eu/value#Work
10.24424/ro-id.EHMJMDN68Q
2019-12-09T08:16:15.705+01:00
http://everest.psnc.pl/users/antonio.petrizzo
http://sandbox.rohub.org/rodl/ROs/sinktrack/
http://w3id.org/ro-id/rohub/model#change_specifications/5ef61e03-1c33-483b-9abc-e527bc6ab6be
An ASCII file with beam time series extracted from the water column data, output of FM Midwater, is imported in Matlab where it is read and adpated to be further processed. The noise is filtered setting a threshold for the intensity values. Then the pings are stacked with the all values of the water column backscatter intensity for all beams over time. A graph with the track of the net, pings vs depth, and some statistics (mean and standard deviation of backscatter, depth, angle of net for each ping) are provided as output. The RO consists of three different workflows created through the Taverna Workbench Enterprise application and they are supposed to be run sequentially (“SinkTrackRead”, "sinkTrackGraph", "SinkTrack").
18252
https://api.rohub.org/api/ros/7bc38514-796e-47e6-81cb-b8f91247a854/crate/download/
2019-12-09 07:16:15.705000+00:00
2025-03-05 01:19:05.791030+00:00
2019-12-09 07:16:15.705000+00:00
It reads and elaborates ASCII file produced with FM Midwater in order to calculate some statistics, mean, standard deviation of backscatter, depth, angle of net for each ping, and to produce a graph with the track of the net
application/ld+json
EASME/EMFF/2017/1.2.1.12/S2/05/SI2.789314
https://w3id.org/ro-id/7bc38514-796e-47e6-81cb-b8f91247a854
MarGnet, net, sinking velocity, water column, backscatter
SinkTrack
Sea Monitoring
http://sandbox.rohub.org/rodl/ROs/sinktrack-release/
https://w3id.org/ro-id/7bc38514-796e-47e6-81cb-b8f91247a854
Aleksandra Kruss (ISMAR-CNR Venice), Antonio Petrizzo (ISMAR-CNR Venice), and Fantina Madricardo (ISMAR-CNR Venice). "SinkTrack." ROHub. Dec 09 ,2019. https://doi.org/10.24424/ro-id.EHMJMDN68Q.
produced
web services
main
components
software
datasets
inputs
config
setup
workflows
scripts
biblio
results
nested
used
105094 KB
https://box.everest.psnc.pl/f/2a521bb3de8345b1ae9b/
2022-03-24 13:29:20.753711+00:00
2022-03-24 13:29:22.977211+00:00
text/plain
sinkTrackGraph1.t2flow
2022-03-24 13:29:20.753711+00:00
105100 KB
https://box.everest.psnc.pl/f/421adc4b01b24d2a906c/
2022-03-24 13:29:20.753264+00:00
2022-03-24 13:29:22.867495+00:00
text/plain
sinkTrackRead.t2flow
2022-03-24 13:29:20.753264+00:00
111274 KB
https://box.everest.psnc.pl/f/67b5ddc8a3ac4047b3ef/
2022-03-24 13:29:20.754047+00:00
2022-03-24 13:29:22.725044+00:00
text/plain
sinkTrack.t2flow
2022-03-24 13:29:20.754047+00:00
service-account-generation-service
Antonio Petrizzo
http://sandbox.rohub.org/rodl/ROs/sinktrack_FORK/
Matlab, Taverna, Linux environment
http://w3id.org/ro-id/rohub/model#change_specifications/bb9cdc2c-73eb-4d80-800e-664c1275dc70/changes/0153bea4-5f13-4ee7-b702-3df1e9e6af6c
http://w3id.org/ro-id/rohub/model#change_specifications/bb9cdc2c-73eb-4d80-800e-664c1275dc70/changes/056b9f5e-da5c-4242-b388-84a45f9d08f3
http://w3id.org/ro-id/rohub/model#change_specifications/bb9cdc2c-73eb-4d80-800e-664c1275dc70/changes/414dbcbc-a673-41eb-882b-c9f804c97458
http://w3id.org/ro-id/rohub/model#change_specifications/bb9cdc2c-73eb-4d80-800e-664c1275dc70/changes/8ef9d8c4-ff4b-44ea-8270-d4d3534f3331
http://w3id.org/ro-id/rohub/model#change_specifications/bb9cdc2c-73eb-4d80-800e-664c1275dc70/changes/9e6e3487-2529-449d-a7f4-3865e8f95705
http://w3id.org/ro-id/rohub/model#change_specifications/bb9cdc2c-73eb-4d80-800e-664c1275dc70/changes/f19400a3-8818-4a77-9170-9ddda31bee7a
https://box.everest.psnc.pl/f/67b5ddc8a3ac4047b3ef/
https://box.everest.psnc.pl/f/2a521bb3de8345b1ae9b/
https://box.everest.psnc.pl/f/2a521bb3de8345b1ae9b/
https://box.everest.psnc.pl/f/421adc4b01b24d2a906c/
https://box.everest.psnc.pl/f/421adc4b01b24d2a906c/
https://box.everest.psnc.pl/f/67b5ddc8a3ac4047b3ef/
standard deviation of backscatter
transports
elaborate ASCII file
produce a graph
SinkTrack It
statistics
angle
standard deviation
graph
ASCII file
net
depth
angle of net
ping
net
graph with the track
statistics
backscattering
angle of net
FM Midwater
service-account-enrichment
http://ever-est.eu/value#/sinktrack
http://ever-est.eu/value#0226e45d-59ef-45dd-992f-824a0dbff431
http://ever-est.eu/value#Work
10.24424/ro-id.CGRSLQGW8A
2019-12-09T08:14:43.199+01:00
http://everest.psnc.pl/users/antonio.petrizzo
http://sandbox.rohub.org/rodl/ROs/sinktrack/
http://w3id.org/ro-id/rohub/model#change_specifications/bb9cdc2c-73eb-4d80-800e-664c1275dc70
An ASCII file with beam time series extracted from the water column data, output of FM Midwater, is imported in Matlab where it is read and adpated to be further processed. The noise is filtered setting a threshold for the intensity values. Then the pings are stacked with the all values of the water column backscatter intensity for all beams over time. A graph with the track of the net, pings vs depth, and some statistics (mean and standard deviation of backscatter, depth, angle of net for each ping) are provided as output. The RO consists of three different workflows created through the Taverna Workbench Enterprise application and they are supposed to be run sequentially (“SinkTrackRead”, "sinkTrackGraph", "SinkTrack").
18326
https://api.rohub.org/api/ros/84eea92b-94f8-43ea-a9ee-ad57b832298f/crate/download/
2019-12-09 07:14:43.199000+00:00
2025-03-05 01:19:06.321946+00:00
2019-12-09 07:14:43.199000+00:00
It reads and elaborates ASCII file produced with FM Midwater in order to calculate some statistics, mean, standard deviation of backscatter, depth, angle of net for each ping, and to produce a graph with the track of the net
application/ld+json
EASME/EMFF/2017/1.2.1.12/S2/05/SI2.789314
https://w3id.org/ro-id/84eea92b-94f8-43ea-a9ee-ad57b832298f
MarGnet, net, sinking velocity, water column, backscatter
SinkTrack
Sea Monitoring
http://sandbox.rohub.org/rodl/ROs/sinktrack_ARCHIVE/
https://w3id.org/ro-id/84eea92b-94f8-43ea-a9ee-ad57b832298f
Aleksandra Kruss (ISMAR-CNR Venice), Antonio Petrizzo (ISMAR-CNR Venice), and Fantina Madricardo (ISMAR-CNR Venice). "SinkTrack." ROHub. Dec 09 ,2019. https://doi.org/10.24424/ro-id.CGRSLQGW8A.
components
results
biblio
main
scripts
setup
software
inputs
nested
produced
web services
workflows
config
datasets
used
105100 KB
https://box.everest.psnc.pl/f/421adc4b01b24d2a906c/
2022-03-24 13:29:33.236065+00:00
2022-03-24 13:29:36.949488+00:00
text/plain
sinkTrackRead.t2flow
2022-03-24 13:29:33.236065+00:00
111274 KB
https://box.everest.psnc.pl/f/67b5ddc8a3ac4047b3ef/
2022-03-24 13:29:33.236597+00:00
2022-03-24 13:29:36.849067+00:00
text/plain
sinkTrack.t2flow
2022-03-24 13:29:33.236597+00:00
105094 KB
https://box.everest.psnc.pl/f/2a521bb3de8345b1ae9b/
2022-03-24 13:29:33.237122+00:00
2022-03-24 13:29:37.076804+00:00
text/plain
sinkTrackGraph1.t2flow
2022-03-24 13:29:33.237122+00:00
service-account-generation-service
Antonio Petrizzo
Antonio Petrizzo (ISMAR-CNR)
Matlab, Taverna, Linux environment
http://w3id.org/ro-id/rohub/model#change_specifications/bded69c5-cf1d-4133-881c-9a6b0910abeb/changes/119ac5c2-463b-4ede-be70-cc579609a602
http://w3id.org/ro-id/rohub/model#change_specifications/bded69c5-cf1d-4133-881c-9a6b0910abeb/changes/6c63d6b6-05a9-4384-b76f-bbf4317339d8
http://w3id.org/ro-id/rohub/model#change_specifications/bded69c5-cf1d-4133-881c-9a6b0910abeb/changes/74ce7a8a-fc42-4ccf-b6f9-5a8475997af9
http://w3id.org/ro-id/rohub/model#change_specifications/bded69c5-cf1d-4133-881c-9a6b0910abeb/changes/7b28d672-9760-4db0-bff1-acff17898381
http://w3id.org/ro-id/rohub/model#change_specifications/bded69c5-cf1d-4133-881c-9a6b0910abeb/changes/815809d7-4c1a-4970-a4d9-6c6c0f056f9c
http://w3id.org/ro-id/rohub/model#change_specifications/bded69c5-cf1d-4133-881c-9a6b0910abeb/changes/a8984f97-b3e3-45fb-9868-ec2aade61d47
http://w3id.org/ro-id/rohub/model#change_specifications/bded69c5-cf1d-4133-881c-9a6b0910abeb/changes/a98bc604-d7ed-4d40-bb4e-6f75f9c604fc
http://w3id.org/ro-id/rohub/model#change_specifications/bded69c5-cf1d-4133-881c-9a6b0910abeb/changes/e340790a-bc79-422b-a5f5-a86a43f9b1c1
https://box.everest.psnc.pl/f/da8ec060b9d84acbb927/
https://box.everest.psnc.pl/f/26ebe24c79a341dab257/
https://box.everest.psnc.pl/f/5c10551086ec469fab48/
https://box.everest.psnc.pl/f/ffc720db2af44b84bbe3/
https://box.everest.psnc.pl/f/5c10551086ec469fab48/
https://box.everest.psnc.pl/f/ffc720db2af44b84bbe3/
https://box.everest.psnc.pl/f/26ebe24c79a341dab257/
https://box.everest.psnc.pl/f/da8ec060b9d84acbb927/
start from water column data
sink velocity of a net
thermohydraulics
velocity
net
service-account-enrichment
http://ever-est.eu/value#/sinkvel
http://ever-est.eu/value#0226e45d-59ef-45dd-992f-824a0dbff431
http://ever-est.eu/value#Work
10.24424/ro-id.IKMY8URJ9Q
2019-12-06T12:11:06.653+01:00
http://everest.psnc.pl/users/antonio.petrizzo
http://sandbox.rohub.org/rodl/ROs/sinkvel/
http://w3id.org/ro-id/rohub/model#change_specifications/bded69c5-cf1d-4133-881c-9a6b0910abeb
An ASCII file with beam time series extracted from the water column data, output of FM Midwater, is imported in Matlab where it is read and adpated to be further processed. The signal of the seafloor is identified and removed and the noise is filtered setting a threshold for the intensity values. Then the pings are stacked with the values of the water column backscatter intensity for each beam over time. Observing the point with higher intensity it is possible to reconstruct the path of the ML type within the specified beam. To estimate the sinking velocity a portion of the stacked pings is selected on the basis of the minimum and maximum number of pings and depths extracted with FM Midwater when the ASCII files where created. The selected window corresponds to the time interval when the ML is freely falling in the water column. The first input asks for a link to a .zip file containing the ASCII .txt data of the water column backscatter intensity values extracted with FM Midwater and a .inp file containing the values of the window used to calculate the sinking velocity. The second input (“working_dir”) is need only to specify the work directory when workin in a local machine.
20786
https://api.rohub.org/api/ros/f8a252e5-47da-410b-9096-526bc50d19a3/crate/download/
2019-12-06 11:11:06.653000+00:00
2025-03-05 01:19:06.823521+00:00
2019-12-06 11:11:06.653000+00:00
It calculates the sink velocity of a net floating in water starting from water column data.
application/ld+json
EASME/EMFF/2017/1.2.1.12/S2/05/SI2.789314
https://w3id.org/ro-id/f8a252e5-47da-410b-9096-526bc50d19a3
MarGnet, net, sink velocity, water column, backscatter
SinkVel
information
net
sink
velocity
water column
water
earth sciences
data
net
sink
velocity
water column
water
engineering
float in water
sink velocity of a net
sink velocity
velocity of a net
water column data
It calculates the sink velocity of a net floating in water starting from water column data.
SinkVel.
Sea Monitoring
https://w3id.org/ro-id/f8a252e5-47da-410b-9096-526bc50d19a3
thermohydraulics
Aleksandra Kruss (ISMAR-CNR Venice), Antonio Petrizzo (ISMAR-CNR Venice), Fantina Madricardo (ISMAR-CNR Venice), and Lukasz Janowski (University of Gdansk). "SinkVel." ROHub. Dec 06 ,2019. https://doi.org/10.24424/ro-id.IKMY8URJ9Q.
main
config
results
web services
workflows
datasets
software
used
inputs
nested
biblio
scripts
components
produced
setup
45619 B
https://box.everest.psnc.pl/f/ffc720db2af44b84bbe3/
2022-03-24 13:29:52.552114+00:00
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image/png
sinkVel_a.png
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48521 KB
https://box.everest.psnc.pl/f/5c10551086ec469fab48/
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Exemple of output
application/zip
results.zip
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102628 KB
https://box.everest.psnc.pl/f/26ebe24c79a341dab257/
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text/plain
sinkVel.t2flow
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7338044 KB
https://box.everest.psnc.pl/f/da8ec060b9d84acbb927/
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Input file for the workflow
application/zip
myInput.zip
2022-03-24 13:29:52.552700+00:00
service-account-generation-service
Antonio Petrizzo
Matlab, Taverna, Linux environment
http://sandbox.rohub.org/rodl/ROs/sinktrack/
standard deviation of backscatter
transports
elaborate ASCII file
produce a graph
SinkTrack It
statistics
angle
standard deviation
graph
ASCII file
net
depth
angle of net
ping
net
graph with the track
statistics
backscattering
angle of net
FM Midwater
service-account-enrichment
http://ever-est.eu/value#/sinktrack
http://ever-est.eu/value#0226e45d-59ef-45dd-992f-824a0dbff431
http://ever-est.eu/value#Work
An ASCII file with beam time series extracted from the water column data, output of FM Midwater, is imported in Matlab where it is read and adpated to be further processed. The noise is filtered setting a threshold for the intensity values. Then the pings are stacked with the all values of the water column backscatter intensity for all beams over time. A graph with the track of the net, pings vs depth, and some statistics (mean and standard deviation of backscatter, depth, angle of net for each ping) are provided as output. The RO consists of three different workflows created through the Taverna Workbench Enterprise application and they are supposed to be run sequentially (“SinkTrackRead”, "sinkTrackGraph", "SinkTrack").
16462
https://api.rohub.org/api/ros/ac013b9e-5f40-4fd1-98b8-b6159150c2bd/crate/download/
2019-12-05 15:43:07.194000+00:00
2025-03-05 01:19:06.548263+00:00
2019-12-05 15:43:07.194000+00:00
It reads and elaborates ASCII file produced with FM Midwater in order to calculate some statistics, mean, standard deviation of backscatter, depth, angle of net for each ping, and to produce a graph with the track of the net
application/ld+json
EASME/EMFF/2017/1.2.1.12/S2/05/SI2.789314
https://w3id.org/ro-id/ac013b9e-5f40-4fd1-98b8-b6159150c2bd
MarGnet, net, sink velocity, water column, backscatter
SinkTrack
Sea Monitoring
http://sandbox.rohub.org/rodl/ROs/sinktrack/
Aleksandra Kruss (ISMAR-CNR Venice), Antonio Petrizzo (ISMAR-CNR Venice), and Fantina Madricardo (ISMAR-CNR Venice). "SinkTrack." ROHub. Dec 05 ,2019. https://w3id.org/ro-id/ac013b9e-5f40-4fd1-98b8-b6159150c2bd.
biblio
nested
inputs
web services
scripts
setup
config
datasets
results
components
software
used
produced
workflows
main
111274 KB
https://box.everest.psnc.pl/f/67b5ddc8a3ac4047b3ef/
2022-03-24 13:30:11.012688+00:00
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text/plain
sinkTrack.t2flow
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105100 KB
https://box.everest.psnc.pl/f/421adc4b01b24d2a906c/
2022-03-24 13:30:11.013158+00:00
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text/plain
sinkTrackRead.t2flow
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105094 KB
https://box.everest.psnc.pl/f/2a521bb3de8345b1ae9b/
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text/plain
sinkTrackGraph1.t2flow
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service-account-generation-service