@prefix rdf: . @prefix rdfs: . @prefix xsd: . @prefix owl: . @prefix dct: . @prefix pav: . @prefix prov: . @prefix orcid: . @prefix fdof: . @prefix ns1: . @prefix ns3: . @prefix skos: . @prefix iop: . @prefix rfr-set: . @prefix rfr-shw: . @prefix ero-core: . @prefix rfp: . @prefix rfr-sep: . @prefix rfr-per: . @prefix ero-alias: . @prefix ero-util: . @prefix ssn: . @prefix sosa: . @prefix cora: . @prefix iso8373: . @prefix qudt: . @prefix unit: . @prefix sh: . @prefix fdoc: . a , fdof:FAIRDigitalObject; dct:creator orcid:0000-0001-8652-5227, orcid:0000-0003-1056-1269, orcid:0009-0000-5139-5100, orcid:0009-0004-0831-4562; dct:publisher ; dct:subject ; rdfs:label "Dual Accuracy-Quality-Driven Neural Network for Prediction Interval Generation"; ; fdof:hasMetadata ; "joseph.shaw@montana.edu"; "April 24, 2025"; "2024" . a , fdof:FAIRDigitalObject; dct:creator orcid:0000-0003-1056-1269, orcid:0009-0001-0271-9794; dct:publisher ; dct:subject ; rdfs:comment "We describe the design and implementation of a dichroic Mueller pulsed lidar (DMPL) testbed. Operating at 532- and 1064-nm wavelengths simultaneously, the polarimetric lidar returns a full set of range-resolved Mueller matrix coefficients after a measurement period of just under 3 min. The instrument is designed as a source of reference measurements to produce proof-of-concept and high-fidelity scene models for the validation of a wide variety of other, more complex polarimetric lidar systems. Using polarimetric optimization theory and assessing the limits of our alignment and calibration, we bounded the theoretical Mueller coefficient errors to no more than 2.04%. Initial measurements show expected polarimetric features for samples characterized in the existing literature and a vast majority of measured coefficients well within the theoretical maximum error; the mean observed Mueller coefficient error across multiple scenarios was 0.86%."; rdfs:label "Dichroic Mueller pulsed lidar testbed"; ; fdof:hasMetadata ; "joseph.shaw@montana.edu"; "7 July 2025"; "2024" . a , fdof:FAIRDigitalObject; dct:creator orcid:0000-0002-5036-2152, orcid:0000-0003-1056-1269, orcid:0000-0003-1245-1653; dct:publisher ; dct:subject ; rdfs:comment "This paper summarizes the design and calibration of a radiometer system designed for validating Landsat Band 7 and Sentinel-2 Band 12 (2110 to 2290nm) measurements from an unoccupied aerial vehicle (UAV). This system and these satellite bands are often used to measure snow reflectance, which is critical to the Earth’s energy and water balance. The system uses two radiometers, one for measuring upwelling radiance [W/(m2 sr)] and one for measuring downwelling irradiance [W/m2]. The upwelling radiometer was constructed with a single-pixel photodiode and a lens assembly to maximize the field of view for measuring snow-reflected radiance. The downwelling radiometer uses the same detector with a ground-glass diffuser to measure all-sky downwelling irradiance. The upwelling and downwelling radiometers use custom interference filters to achieve the closest match to the satellite spectral bands. The upwelling system has a half-angle field of view of 3.43 degrees. When flown at an above grove level of 120m, this corresponds to a ground swath of diameter 14.39m, a spatial resolution over twice the 30m spatial resolution of a Landsat Band 7 pixel. The instrument is intended to be deployed in colder climates; therefore, a temperature calibration was conducted to reduce uncertainties in the radiometric calibration due to large deviations in the operating temperature."; rdfs:label "Development of a SWIR UAV-based radiometer for validating Landsat 8/9 snow products"; ; fdof:hasMetadata ; "joseph.shaw@montana.edu"; "March 19 2025"; "2024" . a , fdof:FAIRDigitalObject; dct:creator orcid:0000-0002-4892-454X, orcid:0000-0002-8415-6808, orcid:0000-0003-1346-5352, orcid:0009-0004-6009-6304; dct:publisher ; dct:subject ; rdfs:comment "We report hourly surface observations of PM2.5, CO, NOx, O3, and 75 speciated VOCs in Missoula, Montana, during a strong smoke event in 2020. This study tests our current understanding of wildfire emissions, chemistry, and health effects as implemented in the GEOS-Chem chemical transport model. Three-or-more-day-old smoke transported from California and the Pacific Northwest increased CO, PM2.5, and total measured VOCs by factors of 2–8, with hourly maxima of 800 ppb, 120 µg m-3, and 85 ppb, respectively. In contrast, NOx levels were not elevated compared to the urban background. O3 showed a non-monotonic response to wildfire smoke: MDA8 O3 increased under light smoke but flattened or declined when PM2.5 exceeded ~30–40 µg m-3, a feature that GEOS-Chem failed to reproduce. A 2020-style wildfire season recurring annually would yield an excess lifetime cancer risk of 100-in-1 million or approximately 7 times the non-smoke baseline. The noncancer hazard index (HI) would reach 3.0, meaning substantially elevated acute risks during high-smoke periods. About 90 % of cancer risks are from PM2.5, whereas non-cancer risks are dominated by formaldehyde, benzene, acrolein, and acetaldehyde. GEOS-Chem captured major smoke intrusions but underestimated CO, PM2.5, and VOCs by 30–90 %. These model biases propagate to health metrics, with GEOS-Chem underestimating smoke-attributable cancer risk by ~40 % and chronic HI by ~10 times. We attribute the model errors to underpredicted fire emissions and unrepresented VOC chemistry, which together led to an overestimation of OH and insufficient secondary production."; rdfs:label "Characterizing emissions, chemistry, and health impacts of aged wildfire smoke in a western US city"; ; fdof:hasMetadata ; "lixu.jin@umontana.edu"; "January 21 2026"; "2025" . a , fdof:FAIRDigitalObject; dct:contributor orcid:0000-0002-4135-7634; dct:creator orcid:0009-0004-4939-2970, orcid:0009-0009-0781-3438; dct:publisher ; dct:subject ; rdfs:comment """This article presents a pseudomultitask (PMT) segmentation neural network (PMTNet) for cropland mapping in mountainous regions using high-resolution remote sensing images. PMTNet extends BsiNet by introducing two key innovations: 1) a pixel-level mask and edge features fusing module using distance features (MEF_D), and 2) a PMT module that replaces the conventional multibranch-task predictions. The MEF_D module leverages spatial attention guided by distance features as weighting indicators to effectively fuse mask and edge features at the pixel level, leading to improved boundary representation. The PMT module, serving as the core prediction component, consists of a single branch dedicated to mask prediction. The two auxiliary tasks—edge detection and distance mapping—are derived directly from the mask output using the Canny edge detecting algorithm and Euclidean distance transformation, respectively. The model was trained and evaluated using cropland samples from Chongqing and Fenghuang, China, based on high-resolution remote sensing images. Comparative experiments were conducted against two representative multitask neural networks (BsiNet and SEANet) and two transformer-based semantic segmentation models (HRFormer + OCR and LRFormer). The results demonstrated that PMTNet consistently outperformed these baselines, achieving the highest scores across multiple metrics, including precision, recall, F1- score, intersection over union, overall accuracy, and the Kappa coefficient—all within a compact model size. Applicability analysis confirmed that PMTNet can effectively identify croplands of diverse types, shapes, and cultivation stages, as long as their boundaries in the images are visually distinguishable"""; rdfs:label "A Pseudomultitask Neural Network Classification Model for Cropland Mapping in Mountainous Areas Using High-Resolution Remote Sensing Images"; ; fdof:hasMetadata ; "xzhou@mtech.edu"; "January 2026"; "2025" . a , fdof:FAIRDigitalObject; dct:contributor orcid:0000-0001-6917-8729; dct:creator orcid:0009-0005-1443-3928; dct:publisher ; dct:subject ; rdfs:comment """Prescribed fire is essential for reducing wildfire severity and fostering ecological resilience, yet implementation remains limited in the western United States. While prior research often isolates barriers through surveys or conflates wildfire risk with prescribed burning risks, this study examines how decision-makers navigate these complexities in real-world contexts. Focusing on land management in Western Montana, the research utilizes qualitative, semi-structured interviews with agency personnel to explore the mental, social, institutional, and biophysical dimensions of decision-making. Findings indicate that implementation is driven by interconnected processes, including collective risk management, strategic resource leveraging, and the management of public perceptions. Additionally, a cultural shift toward normalized fire use is emerging, supported by peer networks and evolving professional norms. This study introduces a practitioner-centered conceptual framework to map factors influencing fire-related decisions. By centering these perspectives, the research informs agency training, cross-boundary collaboration, and communication strategies intended to expand safe and effective prescribed fire use."""; rdfs:label "A Practitioner-Centered Framework for Prescribed Fire Decision-Making: Implications for Managers"; ; fdof:hasMetadata ; "elizabeth.metcalf@umontana.edu"; "March 6 2026"; "2025" . a , fdof:FAIRDigitalObject; dct:creator orcid:0000-0002-5492-8758, orcid:0000-0002-7839-0702, orcid:0009-0003-3585-8023; dct:publisher ; dct:subject ; rdfs:comment """We present the demonstration of a high spectral resolution lidar (HSRL) capable of directly retrieving vertical profiling of the aerosol-to-molecular backscatter ratio up to 8 km during daytime operation and 14 km at night. Unlike conventional elastic lidars, the HSRL provides calibrated aerosol optical property profiles without assuming a lidar ratio or requiring auxiliary instruments such as solar radiometers or radiative transfer models, which are commonly needed by micro pulse lidars or ceilometers. This capability is particularly critical for wildfire studies, where smoke plumes exhibit rapidly changing aerosol properties that make such assumptions unreliable. Wildfires generate complex smoke plumes with significant impacts on air quality, visibility, and climate forcing. Vertically resolved aerosol measurements from the HSRL can be used to verify and refine smoke and radiation transport models. The HSRL was developed under the NSF EPSCoR SMART FIRES project as part of the Smart Optical Sensors initiative to advance the study of smoke-borne aerosols. The lidar transmits eye-safe 780 nm light and utilizes a rubidium-87 atomic filter in the molecular channel to isolate the molecular backscatter from the total lidar return. This spectral separation enables direct comparison of molecular and total returns to calculate the aerosol-to-molecular backscatter ratio, a key indicator of aerosol loading in the lower atmosphere. The system is housed in an environmentally controlled enclosure for future field deployments near active wildfires. This poster presents the HSRL architecture and initial aerosol-to-molecular backscatter measurements collected in Bozeman, Montana, demonstrating the instrument’s performance in a region frequently impacted by wildfire smoke originating in the western United States and Canada."""; rdfs:label "Demonstration of a high spectral resolution lidar for vertical profiling of aerosols"; ; fdof:hasMetadata ; "owen.cruikshank@uwyo.edu"; "January 29 2026"; "2025" . a , fdof:FAIRDigitalObject; dct:creator orcid:0000-0001-9487-5622; dct:publisher ; dct:subject ; rdfs:comment "Ant Colony Optimization (ACO) has served as a widely-utilized metaheuristic algorithm for decades for solving combinatorial optimization problems. Since its initial construction, ACO has seen a wide variety of modifications and connections to Reinforcement Learning (RL). Substantial parallels can be seen as early as 1995 with Ant-Q's relationship with Q-learning, through 2022 with ADACO's connection with Policy Gradient. In this work, we describe ACO, more specifically the Stochastic Gradient Descent ACO algorithm (ACOSGD), explicitly as an off-policy Policy Gradient (PG) method. We also incorporate experience replay into several ACO algorithm variants, including AS, MaxMin-ACO, ACOSGD, ADACO, and our two policy gradient-based versions: PGACO and PPOACO, drawing the connection to elitist ACO strategies. We show that our implementation of PG in ACO with experience replay and a baselined reward update strategy applied to eight TSP problems of varying sizes performs competitively with both fundamental ACO and SGD-based ACO versions. We also show that the replay buffer seems to unilaterally improve the performance of ACO algorithms through an ablation study"; rdfs:label "Ant Colony Optimization with Policy Gradients and Replay"; fdof:hasMetadata ; "john.sheppard@montana.edu"; "July 13 2025"; "2024" . a fdof:FAIRDigitalObject, fdof:FAIRDigitalObject, fdof:FAIRDigitalObject, fdof:FAIRDigitalObject; dct:conformsTo , , , ; ns3:loc "", "", "", ""; ; ; "Annotation"; "2024-12-18T15:52:41.871Z"; "Distributed System of Scientific Collections"; ; "ACTIVE"; ; "1"; ""; ; ; "CC0 1.0 Universal"; "DigitalSpecimen"; rdfs:label "20.5000.1025/J7E-C1H-1X2", "20.5000.1025/J7E-C1H-1X2", "20.5000.1025/J7E-C1H-1X2"; "", "", ""; "Annotation", "Annotation", "Annotation"; , "https://doi.org/21.T11148/cf458ca9ee1d44a5608f", "https://doi.org/21.T11148/cf458ca9ee1d44a5608f"; , "https://spdx.org/licenses/CC0-1.0.json", "https://spdx.org/licenses/CC0-1.0.json"; "CC0 1.0 Universal", "CC0 1.0 Universal", "CC0 1.0 Universal"; , "https://hdl.handle.net/20.5000.1025/J7E-C1H-1X2", "https://hdl.handle.net/20.5000.1025/J7E-C1H-1X2"; 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rdfs:label "soil", "soil", "soil" . a fdof:FAIRDigitalObject, iop:Variable; dct:conformsTo ; dct:created "2026-04-20T12:49:31Z"^^xsd:dateTime; dct:creator orcid:0009-0006-1978-4302; dct:identifier "iadopt-variable-20260420T124931-07"; pav:createdWith "LLM-assisted I-ADOPT variable generation"; rdfs:comment "soil temperature at 40 cm depth"; rdfs:label "soil temperature at 40 cm depth"; skos:altLabel "temperature of soil layer: 40 cm depth"; skos:definition "soil temperature at 40 cm depth"; skos:prefLabel "soil temperature at 40 cm depth"; prov:wasAttributedTo orcid:0009-0006-1978-4302; iop:hasConstraint ; iop:hasObjectOfInterest ; iop:hasProperty . a iop:Constraint; rdfs:label "depth 40 cm"; iop:constrains . a fdof:FAIRDigitalObject, iop:Variable; dct:conformsTo ; dct:created "2026-04-17T09:01:49Z"^^xsd:dateTime; dct:creator orcid:0009-0006-1978-4302; dct:identifier "iadopt-variable-20260417T090149-76"; pav:createdWith "LLM-assisted I-ADOPT variable generation"; rdfs:comment "my system is measuring air temp at height 1.7 meter."; rdfs:label "air temperature at height 1.7 meter"; skos:altLabel "temperature of air height: 1.7 meter in system"; skos:definition "my system is measuring air temp at height 1.7 meter."; skos:prefLabel "air temperature at height 1.7 meter"; prov:wasAttributedTo orcid:0009-0006-1978-4302; iop:hasConstraint , ; iop:hasObjectOfInterest ; iop:hasProperty . a iop:Constraint; rdfs:label "height: 1.7 meter"; iop:constrains . a iop:Constraint; rdfs:label "maximum"; iop:constrains . a iop:Entity, iop:Entity, iop:Entity, iop:Entity, iop:Entity, iop:Entity, iop:Entity, iop:Entity; rdfs:label "air", "air", "air", "air", "air", "air", "air", "air" . orcid:0000-0003-2195-3997 rdfs:label "Barbara Magagna", "Barbara Magagna", "Barbara Magagna", "Barbara Magagna", "Barbara Magagna", "Barbara Magagna", "Barbara Magagna", "Barbara Magagna" . a fdof:FAIRDigitalObject, iop:Variable; dct:conformsTo ; dct:created "2026-04-13T13:47:57Z"^^xsd:dateTime; dct:creator orcid:0000-0003-2195-3997; dct:identifier "iadopt-variable-20260413T134757-31"; pav:createdWith "LLM-assisted I-ADOPT variable generation"; rdfs:comment "air temp at height of 1.7 meter from the ground"; rdfs:label "Air temp at height of 1.7 meter from the ground"; skos:altLabel "temperature of air height: 1.7 meter from the ground"; skos:definition "air temp at height of 1.7 meter from the ground"; skos:prefLabel "air temp at height of 1.7 meter from the ground"; prov:wasAttributedTo orcid:0000-0003-2195-3997; iop:hasConstraint ; iop:hasObjectOfInterest ; iop:hasProperty . a iop:Constraint; rdfs:label "height: 1.7 meter from the ground"; iop:constrains . a fdof:FAIRDigitalObject, iop:Variable; dct:conformsTo ; dct:created "2026-04-13T13:34:30Z"^^xsd:dateTime; dct:creator orcid:0000-0003-2195-3997; dct:identifier "iadopt-variable-20260413T133430-55"; pav:createdWith "LLM-assisted I-ADOPT variable generation"; rdfs:comment "air temp at height of 1.7 meter from the ground"; rdfs:label "Air temp at height of 1.7 meter from the ground"; skos:altLabel "temperature of air at 1.7 meter from the ground"; skos:definition "air temp at height of 1.7 meter from the ground"; skos:prefLabel "air temp at height of 1.7 meter from the ground"; prov:wasAttributedTo orcid:0000-0003-2195-3997; iop:hasConstraint ; iop:hasObjectOfInterest ; iop:hasProperty . a iop:Constraint; rdfs:label "location: 1.7 meter from the ground"; iop:constrains . a fdof:FAIRDigitalObject, iop:Variable; dct:conformsTo ; dct:created "2026-04-08T12:45:17Z"^^xsd:dateTime; dct:creator orcid:0009-0006-1978-4302; dct:identifier "iadopt-variable-20260408T124517-65"; pav:createdWith "LLM-assisted I-ADOPT variable generation"; rdfs:comment "tree width stem growth"; rdfs:label "Tree width stem growth"; skos:altLabel "measurement: width growth of tree"; skos:definition "measuring tree width stem growth"; skos:prefLabel "tree width stem growth"; prov:wasAttributedTo orcid:0009-0006-1978-4302; iop:hasConstraint ; iop:hasObjectOfInterest ; iop:hasProperty . a iop:Constraint; rdfs:label "measurement: width"; iop:constrains . a iop:Entity; rdfs:label "tree" . a iop:Property; rdfs:label "growth" . a fdof:FAIRDigitalObject, iop:Variable; dct:conformsTo ; dct:created "2026-04-08T08:17:18Z"^^xsd:dateTime; dct:creator orcid:0009-0006-1978-4302; dct:identifier "iadopt-variable-20260408T081718-31"; pav:createdWith "LLM-assisted I-ADOPT variable generation"; rdfs:comment "air temperature at 1.7 meter height"; rdfs:label "Air temperature at 1.7 meter height"; skos:altLabel "temperature of air at 1.7 meter height"; skos:definition "my system is measuring air temp at 1.7 meter height."; skos:prefLabel "air temperature at 1.7 meter height"; prov:wasAttributedTo orcid:0009-0006-1978-4302; iop:hasConstraint ; iop:hasObjectOfInterest ; iop:hasProperty . a iop:Constraint; rdfs:label "location: 1.7 meter height"; iop:constrains . a fdof:FAIRDigitalObject, iop:Variable; dct:conformsTo ; dct:created "2026-04-01T11:27:28Z"^^xsd:dateTime; dct:creator orcid:0009-0006-1978-4302; dct:identifier "iadopt-variable-20260401T112728-32"; pav:createdWith "LLM-assisted I-ADOPT variable generation"; rdfs:comment "Air temperature at 1.7 meter height"; rdfs:label "Air temperature at 1.7 meter height"; skos:altLabel "temperature of air at 1.7 meter height"; skos:definition "I am measuring air temp at 1.7 meter height."; skos:prefLabel "Air temperature at 1.7 meter height"; prov:wasAttributedTo orcid:0009-0006-1978-4302; iop:hasConstraint ; iop:hasObjectOfInterest ; iop:hasProperty . a iop:Constraint; rdfs:label "location: 1.7 meter height"; iop:constrains . a fdof:FAIRDigitalObject, iop:Variable; dct:conformsTo ; dct:created "2026-04-01T11:26:21Z"^^xsd:dateTime; dct:creator orcid:0009-0006-1978-4302; dct:identifier "iadopt-variable-20260401T112621-33"; pav:createdWith "LLM-assisted I-ADOPT variable generation"; rdfs:comment "Air temperature at 1.7 meter height"; rdfs:label "Air temperature at 1.7 meter height"; skos:altLabel "temperature of air height: 1.7 meter"; skos:definition "I am measuring air temp at 1.7 meter height."; skos:prefLabel "Air temperature at 1.7 meter height"; prov:wasAttributedTo orcid:0009-0006-1978-4302; iop:hasConstraint ; iop:hasObjectOfInterest ; iop:hasProperty . a iop:Constraint; rdfs:label "height: 1.7 meter"; iop:constrains . a fdof:FAIRDigitalObject, iop:Variable; dct:conformsTo ; dct:identifier "iadopt-variable-20260401T111443-05"; rdfs:comment "Air temperature at 1.7 meter height"; rdfs:label "Air temperature at 1.7 meter height"; skos:altLabel "temperature of air height: 1.7 meter"; skos:definition "I am measuring air temp at 1.7 meter height."; skos:prefLabel "Air temperature at 1.7 meter height"; iop:hasConstraint ; iop:hasObjectOfInterest ; iop:hasProperty . a iop:Constraint; rdfs:label "height: 1.7 meter"; iop:constrains . a fdof:FAIRDigitalObject, iop:Variable; dct:conformsTo ; dct:identifier "iadopt-variable-20260401T095251-27"; rdfs:comment "I am measuring air temp at 1.7 meter height"; rdfs:label "Air temperature at 1.7 meter height"; skos:altLabel "temperature of air at 1.7 meter height"; skos:definition "I am measuring air temp at 1.7 meter height"; skos:prefLabel "Air temperature at 1.7 meter height"; iop:hasConstraint ; iop:hasObjectOfInterest ; iop:hasProperty . a iop:Constraint; rdfs:label "location: 1.7 meter height"; iop:constrains . a iop:Entity; rdfs:label "water" . a fdof:FAIRDigitalObject, iop:Variable; dct:conformsTo ; dct:identifier "iadopt-variable-20260327T125003-02"; rdfs:comment "LLM-proposed preferred label is stored in skos:prefLabel. The alternative label is generated from the simple-entity formula."; rdfs:label "Water turbidity of stagnant surface water"; skos:altLabel "turbidity of water condition: stagnant in surface water"; skos:definition "Water turbidity of stagnant surface water"; skos:prefLabel "Water turbidity of stagnant surface water"; iop:hasConstraint ; iop:hasMatrix ; iop:hasObjectOfInterest ; iop:hasProperty . a iop:Constraint; rdfs:label "condition: stagnant"; iop:constrains . a iop:Entity; rdfs:label "surface water" . a iop:Property; rdfs:label "turbidity" . a fdof:FAIRDigitalObject; dct:conformsTo ; rdfs:label "Slides for Nanopubs4FDO talk at FDO conference."; fdof:isMaterializedBy . a fdof:FAIRDigitalObject, iop:Variable; dct:conformsTo ; dct:identifier "iadopt-variable-20260323T163937-70"; rdfs:comment "LLM-proposed preferred label is stored in skos:prefLabel. The alternative label is generated from the simple-entity formula."; skos:altLabel "temperature of soil location: surface depth: 10 cm"; skos:definition "surface soil temperature at 10 cm dept"; skos:prefLabel "surface soil temperature at 10 cm dept"; iop:hasConstraint , ; iop:hasObjectOfInterest ; iop:hasProperty . a iop:Constraint; rdfs:label "location: surface"; iop:constrains . a iop:Constraint; rdfs:label "depth: 10 cm"; iop:constrains . a iop:Entity; rdfs:label "polystyrene PS042" . a fdof:FAIRDigitalObject, iop:Variable; dct:conformsTo ; dct:identifier "iadopt-variable-20260323T131734-85"; rdfs:comment "LLM-proposed preferred label is stored in skos:prefLabel. The alternative label is generated from the simple-entity formula."; skos:altLabel "type: dynamic shear viscosity of polystyrene PS042 condition: DIN 51810-1"; skos:definition "Dynamic shear viscosity of polystyrene PS042 under the testing conditions of DIN 51810-1."; skos:prefLabel "Dynamic shear viscosity of polystyrene PS042"; iop:hasConstraint , ; iop:hasObjectOfInterest ; iop:hasProperty . a iop:Constraint; rdfs:label "type: dynamic shear"; iop:constrains . a iop:Constraint; rdfs:label "condition: DIN 51810-1"; iop:constrains . a iop:Property; rdfs:label "viscosity" . a , , , , fdof:FAIRDigitalObject; dct:conformsTo ; dct:creator ; ; rdfs:comment "The I-ADOPT Framework acts as a semantic broker and is based on an ontology in order to facilitate interoperability between existing variable description models. It offers core atomic components and relations between them that can be applied to define machine-interpretable variable descriptions that reuse FAIR terminology concepts."; rdfs:label "I-ADOPT Framework"; rdfs:seeAlso ; skos:exactMatch ; ; ; . sh:hasValue ; sh:maxCount "1"; sh:minCount "1"; sh:path dct:conformsTo . sh:hasValue ; sh:maxCount "1"; sh:minCount "1"; sh:path rdf:type . sh:maxCount "1"; sh:minCount "1"; sh:path rdfs:label . sh:maxCount "1"; sh:minCount "1"; sh:path rdfs:comment . sh:maxCount "1"; sh:path . sh:maxCount "1"; sh:minCount "1"; sh:path rdf:type . sh:maxCount "1"; sh:minCount "1"; sh:path rdf:type . sh:path . sh:path rdfs:seeAlso . sh:maxCount "1"; sh:path . sh:path . sh:path skos:exactMatch . sh:maxCount "1"; sh:path skos:broadMatch . sh:minCount "1"; sh:path rdf:type . sh:path dct:creator . sh:maxCount "1"; sh:path . sh:path . sh:maxCount "1"; sh:path . a sh:NodeShape; sh:property , , , , , , , , , , , , , , , , , ; sh:targetClass fdof:FAIRDigitalObject . a fdoc:FdoProfile, fdof:FAIRDigitalObject; dct:conformsTo ; rdfs:label "FAIR Supporting Resource Specification Profile"; fdoc:hasShape . a owl:DatatypeProperty, fdoc:FdoAttribute, fdof:FAIRDigitalObject; dct:conformsTo ; dct:description "The version of the CreativeWork embodied by a specified resource."; rdfs:domain fdof:FAIRDigitalObject; rdfs:label "Version" . rdfs:comment a owl:DatatypeProperty, fdoc:FdoAttribute, fdof:FAIRDigitalObject; dct:conformsTo ; dct:description "A description of the subject resource."; rdfs:domain fdof:FAIRDigitalObject; rdfs:label "has the description" . sh:hasValue iop:Variable; sh:minCount "1"; sh:path rdf:type . sh:hasValue ; sh:maxCount "1"; sh:minCount "1"; sh:path dct:conformsTo . sh:maxCount "1"; sh:minCount "1"; sh:path skos:prefLabel . sh:maxCount "1"; sh:path skos:altLabel . sh:maxCount "1"; sh:minCount "1"; sh:path skos:definition . sh:maxCount "1"; sh:path iop:hasStatisticalModifier . sh:maxCount "1"; sh:minCount "1"; sh:path iop:hasProperty . sh:maxCount "1"; sh:minCount "1"; sh:path iop:hasObjectOfInterest . sh:maxCount "1"; sh:path iop:hasMatrix . sh:path iop:hasContextObject . sh:path iop:hasConstraint . sh:maxCount "1"; sh:path rdfs:seeAlso . a sh:NodeShape; sh:property , , , , , , , , , , , ; sh:targetClass fdof:FAIRDigitalObject . a fdoc:FdoProfile, fdof:FAIRDigitalObject; dct:conformsTo ; rdfs:label "I-ADOPT Variable Profile"; fdoc:hasShape . iop:hasConstraint a owl:ObjectProperty, fdoc:FdoAttribute, fdof:FAIRDigitalObject; dct:conformsTo ; dct:description "A Variable has a Constraint, that confines an Entity involved in the observation."; rdfs:domain iop:Variable; rdfs:label "has constraint"; rdfs:range iop:Constraint; fdoc:hasObjectShape . a fdof:FAIRDigitalObject, iop:Variable; dct:conformsTo ; dct:created "2026-03-16T13:44:28Z"^^xsd:dateTime; dct:creator orcid:0000-0003-2195-3997; dct:identifier "iadopt-variable-20260316T134428-06"; pav:createdWith "LLM-assisted I-ADOPT variable generation"; rdfs:comment "LLM-proposed preferred label is stored in skos:prefLabel. The alternative label is generated from the simple-entity formula."; rdfs:label "Air temperature at height 1.7 meter"; skos:altLabel "temperature of air location: height 1.7 meter"; skos:definition "measuring air temp at height 1.7 meter"; skos:prefLabel "Air temperature at height 1.7 meter"; prov:wasAttributedTo orcid:0000-0003-2195-3997; iop:hasConstraint ; iop:hasObjectOfInterest ; iop:hasProperty . a iop:Constraint; rdfs:label "location: height 1.7 meter"; iop:constrains . a iop:Property; rdfs:label "temperature" . a iop:Entity; rdfs:label "air" . skos:prefLabel a owl:DatatypeProperty, fdoc:FdoAttribute, fdof:FAIRDigitalObject; dct:conformsTo ; dct:description "A lexical label is a string of UNICODE characters, such as \"romantic love\" or \"れんあい\", in a given natural language, such as English or Japanese (written here in hiragana)."; rdfs:domain fdof:FAIRDigitalObject; rdfs:label "preferred label" . fdoc:hasObjectShape a owl:ObjectProperty, fdoc:FdoAttribute, fdof:FAIRDigitalObject; dct:conformsTo ; dct:description "Attaches a SHACL shape for the object of an FDO attribute definition."; rdfs:domain fdof:FAIRDigitalObject; rdfs:label "has object shape"; rdfs:range sh:NodeShape . a fdoc:FdoProfile, fdof:FAIRDigitalObject; dct:conformsTo ; rdfs:label "Profile for FDO Attributes"; fdoc:hasShape . sh:hasValue fdoc:FdoAttribute; sh:maxCount "1"; sh:minCount "1"; sh:path rdf:type . sh:hasValue ; sh:maxCount "1"; sh:minCount "1"; sh:path dct:conformsTo . sh:maxCount "1"; sh:minCount "1"; sh:path rdfs:label . sh:maxCount "1"; sh:path dct:description . sh:maxCount "1"; sh:path rdfs:domain . sh:maxCount "1"; sh:path rdfs:range . sh:maxCount "1"; sh:path fdoc:hasObjectShape . a sh:NodeShape; sh:property , , , , , , ; sh:targetClass fdof:FAIRDigitalObject . a , fdof:FAIRDigitalObject; dct:creator orcid:0000-0002-2554-3816, orcid:0000-0002-5831-1237, orcid:0000-0003-2175-7072, orcid:0000-0003-3453-2729, orcid:0000-0003-3690-3052; dct:hasVersion "2.0.0"; dct:language ; dct:subject ; rdfs:comment """Large-scale multi-label benchmark dataset for remote sensing image classification based on Sentinel-1 and Sentinel-2 imagery. Contains 549,488 pairs of image patches across 10 European countries with 19 land use/land cover classes. Each patch is 1.2 x 1.2 km with multi-spectral bands and pixel-level reference maps from CORINE Land Cover 2018. Enables deep learning research for Earth observation applications including multi-label classification, content-based image retrieval, and multi-modal learning. LICENSING: Base Sentinel-1/2 imagery is provided under EU Regulations 377/2014 and 1159/2013 (free, full and open access for any lawful use including commercial). Dataset annotations are provided under Community Data License Agreement – Permissive, Version 1.0 (CDLA-Permissive-1.0). Attribution required: 'Contains modified Copernicus Sentinel data [2017-2018]' for imagery; citation of the BigEarthNet papers for the dataset."""; rdfs:label "BigEarthNet v2.0: Large-Scale Sentinel Benchmark Archive"; fdof:hasMetadata ; "k.clasen@tu-berlin.de" . a , fdof:FAIRDigitalObject; dct:creator orcid:0000-0001-8454-4301, orcid:0000-0002-4660-2627, orcid:0000-0002-6100-8255, orcid:0000-0002-6473-3348; dct:hasVersion "v2"; dct:language ; dct:subject ; rdfs:comment """Benchmark dataset for satellite image classification based on Sentinel-2 imagery. Contains 27,000 labeled and geo-referenced images across 10 land use/land cover classes with 13 spectral bands. Enables federated learning research for Earth observation applications. LICENSING: Base Sentinel-2 imagery is provided under EU Regulations 377/2014 and 1159/2013 (free, full and open access for any lawful use including commercial). Dataset labels and annotations are provided under MIT License. Attribution required: 'Contains modified Copernicus Sentinel data [2017-2018]' for imagery; citation of Helber et al. (2019) for the dataset."""; rdfs:label "EuroSAT: Land Use and Land Cover Classification Dataset"; fdof:hasMetadata ; "Patrick.Helber@dfki.de" . a , fdof:FAIRDigitalObject; dct:creator orcid:0000-0003-4124-6472; dct:language ; rdfs:comment "Food web network data for the Serengeti ecosystem including 161 species (129 plants, 23 herbivores, 9 carnivores) and 592 feeding relationships. Food web data compiled from multiple sources spanning 50 years of Serengeti ecological research. Published as supplementary tables S1 (species) and S2 (interactions) in Baskerville et al. 2011."; rdfs:label "Serengeti Spatial Food Web Data"; fdof:hasMetadata ; "ebaskerv@umich.edu" . a , fdof:FAIRDigitalObject; dct:creator orcid:0000-0001-8726-8226, orcid:0000-0001-9487-5622, orcid:0000-0002-3588-6257; dct:publisher ; dct:subject ; rdfs:comment "Community detection is a valuable tool for analyzing social networks given its potential for identifying groups with common characteristics and common interests. In this work, we focused on detecting scholarly communities based on researchers’ publication data to discover interdisciplinary collaboration recommendations (researchers working on different domains). Specifically, instead of using any physical or direct relationship between researchers, we utilized a topic model to obtain the topic-based similarity between researchers to construct the social network graph. Next, we employed an edge-weakening procedure to alter the initially constructed network to uncover a more refined community structure. Two community detection algorithms, Louvain and Spectral clustering, were utilized to find the community structures in the modified network. The results of our experiments revealed the ability to discover possible research communities for both algorithms that were comparable, which suggests that our method has the potential for identifying hidden interdisciplinary research collaboration recommendations using topical relationships as the basis for building and analyzing the social network graph."; rdfs:label "Finding Potential Research Collaborations from Social Networks Derived from Topic Models"; ; fdof:hasMetadata ; "john.sheppard@montana.edu"; "2024-01-17"; "2022" . a , fdof:FAIRDigitalObject; dct:creator orcid:0000-0001-8726-8226, orcid:0000-0001-9487-5622, orcid:0000-0002-3588-6257; dct:publisher ; dct:subject ; rdfs:comment """Community detection plays a pivotal role in social network analysis by partitioning networks into cohesive groups of vertices with dense intra-group connections and sparse intergroup connections. In this paper, we utilized a scholarly social network based on researchers’ topic similarity derived from their publication metadata to identify interdisciplinary research communities. As topics often form a hierarchy, we hypothesize that the constructed scholarly network will exhibit hierarchical community structures. Therefore, we explore the efficacy of two prominent community detection algorithms, Louvain and Spectral clustering, known for their capacity to detect hierarchical community structures within networks. While both algorithms demonstrate this capability, the original Louvain algorithm is susceptible to the resolution limit problem due to its reliance on the modularity measure. To address this limitation, we propose the nested hierarchical Louvain algorithm, which iteratively partitions the network based on previously identified subgraphs, and we find that the bias towards large communities is mitigated. To evaluate the hierarchy produced by each of the algorithms, we employ the Cophenetic Correlation Coefficient (CPCC), a metric commonly used in hierarchical clustering evaluations but less frequently utilized in hierarchical community analysis. We argue that CPCC can be a useful measure to identify the presence of implicit hierarchical community structure in social networks when it is not explicitly available from domain knowledge while also further mitigating the inherent bias present in using modularity as a metric. Experimental results, conducted on both synthetic networks and the scholarly social network, demonstrate that the nested hierarchical Louvain algorithm, as well as Spectral Clustering, successfully identifies more finely structured hierarchical communities, offering greater depth in the dendrogram compared to the basic Louvain algorithm. Index Terms—Social Networks, Hierarchical Community Detection, Clustering, Topic Models"""; rdfs:label "Identifying Hierarchical Community Structures in Content-Based Scholarly Social Networks"; ; fdof:hasMetadata ; "john.sheppard@montana.edu"; "2025-03-04"; "2023" . a , fdof:FAIRDigitalObject; dct:creator orcid:0000-0002-0785-2025, orcid:0000-0002-4892-454X, orcid:0000-0002-9282-0502, orcid:0000-0003-1514-0279; dct:publisher ; dct:subject ; rdfs:comment "The magnitude and evolution of Black Carbon (BC) and Brown Carbon (BrC) absorption with time remain unclear, causing uncertainty in climate models. Using data from the WE-CAN airborne measurement campaign, we show that absorption of BC from wildfire is relatively constant over time. BrC tends to be darker in more oxidated smoke plumes, challenging the idea that oxidation causes bleaching. We show that water-soluble BrC contributes 23 % of the total absorption at 660 nm."; rdfs:label "Understanding Absorption by Black Versus Brown Carbon in Biomass Burning Plumes from the WE-CAN Campaign"; ; fdof:hasMetadata ; "shane.murphy@uwyo.edu"; "2024-01-18"; "2022" . a , fdof:FAIRDigitalObject, , fdof:FAIRDigitalObject; dct:creator orcid:0000-0001-6039-3158, orcid:0000-0001-6917-8729, orcid:0000-0001-6039-3158, orcid:0000-0001-6917-8729; dct:publisher , ; rdfs:comment "Rangeland social-ecological systems (SESs), which make up vast tracts of Earth’s terrestrial surface, are facing unprecedented change—from climate change and vegetation transitions to large-scale shifts in human land use and changing social and economic conditions. Understanding how people who manage and depend on rangeland resources are adapting to change has been the focus of a rapidly growing body of research, which has the potential to provide important insights for climate change adaptation policy and practice. Here, we use quantitative, qualitative, and bibliometric analyses to systematically review the scope, methods, and findings of 56 studies that examine the social dimensions of adaptation in rangeland SESs. Our review focuses on studies within the climate adaptation, adaptive capacity, and adaptive decision-making sub-fields, finding that this body of research is highly diverse in its disciplinary roots and theoretical origins, and therefore uses a wide range of frameworks and indicators to evaluate adaptation processes. Bibliometric analyses revealed that the field is fragmented into distinct scholarly communities that use either adaptive capacity or adaptive decision-making frameworks, with a lack of cross-field citation. Given the strengths (and weaknesses) inherent in each sub-field, this review suggests that greater cross-pollination across the scholarship could lead to new insights, particularly for capturing cross-scale interactions related to adaptation on rangelands. Results also showed that a majority of studies that examine adaptation in either “ranching” or “rangeland” systems are geographically concentrated in few, high-income countries (i.e., USA, Australia, China), demonstrating a need to extend future research efforts to understudied regions of the globe with rangeland-based livelihoods. Finally, our review highlights the need for more translational rangeland science, where policy- and practice-relevant frameworks evaluating adaptation in rangeland SESs might be developed by co-producing research working with rangeland communities. Major findings: The findings indicate that nitrogen dioxide typically represents over half of the total reactive nitrogen in fresh plumes, and the ratio of reactive nitrogen to organic gases increases exponentially with combustion efficiency. These parameterizations provide more accurate boundary conditions for predicting the formation of secondary pollutants like ozone downwind of fire events.", "Rangeland social-ecological systems (SESs), which make up vast tracts of Earth’s terrestrial surface, are facing unprecedented change—from climate change and vegetation transitions to large-scale shifts in human land use and changing social and economic conditions. Understanding how people who manage and depend on rangeland resources are adapting to change has been the focus of a rapidly growing body of research, which has the potential to provide important insights for climate change adaptation policy and practice. Here, we use quantitative, qualitative, and bibliometric analyses to systematically review the scope, methods, and findings of 56 studies that examine the social dimensions of adaptation in rangeland SESs. Our review focuses on studies within the climate adaptation, adaptive capacity, and adaptive decision-making sub-fields, finding that this body of research is highly diverse in its disciplinary roots and theoretical origins, and therefore uses a wide range of frameworks and indicators to evaluate adaptation processes. Bibliometric analyses revealed that the field is fragmented into distinct scholarly communities that use either adaptive capacity or adaptive decision-making frameworks, with a lack of cross-field citation. Given the strengths (and weaknesses) inherent in each sub-field, this review suggests that greater cross-pollination across the scholarship could lead to new insights, particularly for capturing cross-scale interactions related to adaptation on rangelands. Results also showed that a majority of studies that examine adaptation in either “ranching” or “rangeland” systems are geographically concentrated in few, high-income countries (i.e., USA, Australia, China), demonstrating a need to extend future research efforts to understudied regions of the globe with rangeland-based livelihoods. Finally, our review highlights the need for more translational rangeland science, where policy- and practice-relevant frameworks evaluating adaptation in rangeland SESs might be developed by co-producing research working with rangeland communities. Major findings: The findings indicate that nitrogen dioxide typically represents over half of the total reactive nitrogen in fresh plumes, and the ratio of reactive nitrogen to organic gases increases exponentially with combustion efficiency. These parameterizations provide more accurate boundary conditions for predicting the formation of secondary pollutants like ozone downwind of fire events."; rdfs:label "Social dimensions of adaptation to climate change in rangelands: a systematic literature review", "Social dimensions of adaptation to climate change in rangelands: a systematic literature review"; , ; fdof:hasMetadata , ; "ada.smith@oregonstate.edu", "ada.smith@oregonstate.edu"; "2023-12-14", "2023-12-14"; "2022", "2022" . a , fdof:FAIRDigitalObject; dct:creator orcid:0000-0003-0531-7659, orcid:0000-0003-0906-9269, orcid:0000-0003-3896-6516; dct:publisher ; dct:subject ; rdfs:comment "There is growing recognition of the connection between ecosystem conservation and human health. For example, protection of tropical forests can affect the spread of infectious diseases, water quality, and dietary diversity, while forest loss can have important consequences for respiratory health due to the use of fire for converting land to alternative uses in many countries. Studies demonstrating links between ecosystems and health often conclude with recommendations to expand policies that protect natural ecosystems. However, there is little empirical evidence on the extent to which conservation policies actually deliver health benefits when they are implemented in real contexts. We estimate the effects of protected areas (PAs), the dominant type of conservation policy, on hospitalizations for respiratory illness in the Brazilian Amazon biome. We find that doubling upwind PAs reduces PM2.5 by 10% and respiratory hospitalizations by 7% in the months of most active biomass burning. Brazil has an extensive network of PAs, but investments in management and enforcement have declined in recent years. Forest fires have increased dramatically over the same period. We estimate that the value of the health benefits exceed current average expenditures on PA management for the 1/3 of PAs with the largest local populations, although not for PAs in more remote locations. Our findings highlight how quantifying the contributions to the wellbeing of local populations can support conservation objectives, even if global environmental benefits are not a high priority for decision makers. Major findings: Doubling the area of upwind PAs results in a 10% reduction in $PM_{2.5}$ and a 7% decrease in hospital admissions during active biomass burning months. These health improvements are primarily driven by reduced cases of pneumonia and acute upper respiratory infections among children under 15 years old. For the most populous regions, the economic value of avoided hospitalizations exceeds the direct costs of managing and enforcing protected area boundaries."; rdfs:label "Protecting Life and Lung: Protected Areas Affect Fine Particulate Matter and Respiratory Hospitalizations in the Brazilian Amazon Biome"; ; fdof:hasMetadata ; "katrina.mullan@umontana.edu"; "2023-11-06"; "2022" . a , fdof:FAIRDigitalObject; dct:creator orcid:0000-0002-4608-3695, orcid:0000-0002-5763-1925, orcid:0000-0003-3462-2126; dct:language ; dct:publisher ; dct:subject ; rdfs:comment "Extensive airborne measurements from the FIREX-AQ campaign established new parameterizations for US wildfire emissions by correlating primary pollutant levels with modified combustion efficiency. Researchers demonstrated that the sum of primary non-methane organic gas (NMOG) mixing ratios maintains a near-perfect correlation with carbon monoxide ($R^2$ = 0.98), identifying CO as a robust proxy for initializing organic gas emissions in models. Nitrogen-containing species correlate more effectively with nitrogen dioxide and black carbon than with carbon monoxide, reflecting their origin in high-temperature flaming combustion. The findings indicate that nitrogen dioxide typically represents over half of the total reactive nitrogen in fresh plumes, and the ratio of reactive nitrogen to organic gases increases exponentially with combustion efficiency. These parameterizations provide more accurate boundary conditions for predicting the formation of secondary pollutants like ozone downwind of fire events."; rdfs:label "Parameterizations of US wildfire and prescribed fire emission ratios and emission factors based on FIREX-AQ aircraft measurements"; ; fdof:hasMetadata ; "g.gkatzelis@juelich.de"; "2024-01-23"; "2023" . a , fdof:FAIRDigitalObject; dct:creator orcid:0000-0002-0441-2614, orcid:0000-0003-3930-010X; dct:publisher ; dct:subject ; rdfs:comment """Biomass burning particulate matter (BBPM) affects regional air quality and global climate, with impacts expected to continue to grow over the coming years. We show that studies of North American fires have a systematic altitude dependence in measured BBPM normalized excess mixing ratio (NEMR; ΔPM/ΔCO), with airborne and high-altitude studies showing a factor of 2 higher NEMR than ground-based measurements. We report direct airborne measurements of BBPM volatility that partially explain the difference in the BBPM NEMR observed across platforms. We find that when heated to 40− 45 °C in an airborne thermal denuder, 19% of lofted smoke PM1 evaporates. Thermal denuder measurements are consistent with evaporation observed when a single smoke plume was sampled across a range of temperatures as the plume descended from 4 to 2 km altitude. We also demonstrate that chemical aging of smoke and differences in PM emission factors can not fully explain the platformdependent differences. When the measured PM volatility is applied to output from the High Resolution Rapid Refresh Smoke regional model, we predict a lower PM NEMR at the surface compared to the lofted smoke measured by aircraft. These results emphasize the significant role that gas-particle partitioning plays in determining the air quality impacts of wildfire smoke. KEYWORDS: Biomass burning organic aerosol volatility, volatility basis set. Major findings: Research regarding biomass burning organic aerosol volatility reveals a systematic altitude dependence in particulate matter concentrations, with airborne studies recording values twice as high as ground-based measurements. Direct airborne quantification demonstrates that approximately 19% of lofted smoke particulate matter evaporates when subjected to surface-level temperatures. These findings indicate that gas-particle partitioning, rather than chemical aging or initial emission factors, is the primary driver of platform-dependent differences in smoke density. Applying these volatility constraints to regional models reduces predicted surface smoke concentrations by 31%, aligning model outputs more closely with observed ground-level impacts."""; rdfs:label "Impact of Biomass Burning Organic Aerosol Volatility on Smoke Concentrations Downwind of Fires"; ; fdof:hasMetadata ; "demetriospagonis@weber.edu"; "2023"; "2022" . a , fdof:FAIRDigitalObject; dct:creator orcid:0000-0002-6982-0934, orcid:0000-0003-1628-0353; dct:publisher ; rdfs:comment "Crop residue and prescribed fires emit pollution that impacts air quality. FIREX-AQ provided observations of these emissions to better characterize their variability with a detailed set of chemical observations. These observations showed significant differences in the emissions from burning different crops (corn, rice, soybean, wheat) compared to other prescribed fires or grasslands that may be due to differences in the fuel composition, the use of agricultural chemicals, and moisture levels. Overall, FIREX-AQ observations for crop residue fires compared better with previous results in the region than with globally averaged information. The campaign observed even greater variability across EFs than previous studies, suggesting that new methods must be developed to take this into account to improve predictions of the air quality impacts of burning these fuels. Major findings:The FIREX-AQ campaign provided a comprehensive chemical characterization of 53 crop residue and 22 prescribed fires in the Eastern United States to establish regionally specific emission factors. Research revealed that corn residue burns at a significantly higher modified combustion efficiency than rice or soybean residues, leading to distinct emission profiles. The study identified twenty-three chemical species where crop residue emissions differed by over fifty percent from prescribed fires at similar combustion efficiencies, noting higher levels of nitrogen, halogens, and markers related to agricultural chemical use. Conversely, prescribed fires released ten times more monoterpenes than agricultural residues due to the presence of stored plant resins in woody biomass. These findings indicate that fuel-specific and regionally specific data are essential for reducing uncertainty in air quality models that previously relied on global averages."; rdfs:label "Emission Factors for Crop Residue and Prescribed Fires in the Eastern US During FIREX-AQ"; ; fdof:hasMetadata ; "katherine.travis@nasa.gov"; "2023"; "2022" . a , fdof:FAIRDigitalObject; dct:creator orcid:0000-0001-9487-5622, orcid:0000-0003-2911-8558; dct:publisher ; dct:subject ; rdfs:comment "Accurate uncertainty quantification is necessary to enhance the reliability of deep learning (DL) models in real-world applications. In the case of regression tasks, prediction intervals (PIs) should be provided along with the deterministic predictions of DL models. Such PIs are useful or “high-quality (HQ)” as long as they are sufficiently narrow and capture most of the probability density. In this article, we present a method to learn PIs for regression-based neural networks (NNs) automatically in addition to the conventional target predictions. In particular, we train two companion NNs: one that uses one output, the target estimate, and another that uses two outputs, the upper and lower bounds of the corresponding PI. Our main contribution is the design of a novel loss function for the PI-generation network that takes into account the output of the target-estimation network and has two optimization objectives: minimizing the mean PI width and ensuring the PI integrity using constraints that maximize the PI probability coverage implicitly. Furthermore, we introduce a self-adaptive coefficient that balances both objectives within the loss function, which alleviates the task of fine-tuning. Experiments using a synthetic dataset, eight benchmark datasets, and a real-world crop yield prediction dataset showed that our method was able to maintain a nominal probability coverage and produce significantly narrower PIs without detriment to its target estimation accuracy when compared to those PIs generated by three state-of-the-art neural-network-based methods. In other words, our method was shown to produce higher quality PIs. Major findings:The DualAQD framework produces \"prediction intervals\" that inform users of the confidence level associated with an AI model's specific estimate. This method generates narrower and more accurate confidence ranges than existing methods while maintaining high overall target accuracy. The system successfully identifies regions of high uncertainty in crop yield predictions, increasing the reliability of deep learning models for high-stakes decision-making."; rdfs:label "Dual Accuracy-Quality-Driven Neural Network for Prediction Interval Generation"; ; fdof:hasMetadata ; "john.sheppard@montana.edu"; "2023"; "2022" . a , fdof:FAIRDigitalObject; dct:creator orcid:0000-0001-9487-5622, orcid:0000-0003-2911-8558; dct:publisher ; rdfs:comment "In Precision Agriculture, the utilization of management zones (MZs) that take into account within-field variability facilitates effective fertilizer management. This approach enables the optimization of nitrogen (N) rates to maximize crop yield production and enhance agronomic use efficiency. However, existing works often neglect the consideration of responsivity to fertilizer as a factor influencing MZ determination. In response to this gap, we present a MZ clustering method based on fertilizer responsivity. We build upon the statement that the responsivity of a given site to the fertilizer rate is described by the shape of its corresponding N fertilizer-yield response (N-response) curve. Thus, we generate N-response curves for all sites within the field using a convolutional neural network (CNN). The shape of the approximated N-response curves is then characterized using functional principal component analysis. Subsequently, a counterfactual explanation (CFE) method is applied to discern the impact of various variables on MZ membership. The genetic algorithm-based CFE solves a multi-objective optimization problem and aims to identify the minimum combination of features needed to alter a site's cluster assignment. Results from two yield prediction datasets indicate that the features with the greatest influence on MZ membership are associated with terrain characteristics that either facilitate or impede fertilizer runoff, such as terrain slope or topographic aspect. Major findings:Researchers at Montana State University developed a new method for creating \"management zones\" in farm fields by using artificial intelligence to predict how crops will respond to nitrogen fertilizer. Unlike older methods that only look at historical yields, this approach uses a neural network to generate \"N-response curves\"—graphs showing how yield changes as fertilizer increases—for every spot in a field. To make the AI's decisions easier to understand, the researchers used \"counterfactual explanations,\" which essentially ask: \"What would have to change for this spot to behave differently?\" The study found that terrain features like slope and soil moisture are the most important factors; for example, steep slopes often lead to fertilizer runoff, which makes those areas less responsive to treatment. This helps farmers apply fertilizer more accurately, saving money and reducing environmental impact."; rdfs:label "Counterfactual Analysis of Neural Networks Used to Create Fertilizer Management Zones"; ; fdof:hasMetadata ; "john.sheppard@montana.edu"; "2024"; "2023" . a , fdof:FAIRDigitalObject; dct:creator orcid:0000-0001-6917-8729, orcid:0000-0001-9532-585X; dct:publisher ; rdfs:comment """Post-industrial communities across the world are transitioning from industrial economies and identities to an uncertain future. Their successful transitions depend on communities’ abilities to navigate change and maintain a quality of life, or their community’s resilience. Previous scholarship offers resources and capabilities that facilitate or inhibit community resilience such as leadership, social capital, and information. However, collective memory is not well integrated within the community resilience literature. Drawing on data from interviews with 33 community leaders in the town of Anaconda, Montana, we illuminate the impact of collective memory on community resilience. The AnacondaSmelter Stack stands out as a specific landmark and prominent feature of the built environment that perpetuates particular collective memories in Anaconda. We find that collective memory is an integral part of community resilience, where memories can aid in a community’s recovery and rebuilding or constrain thinking and divide viewpoints. We argue that ignoring collective memory’s connections to resilience can undermine efforts to face changes in these communities. Keywords: Community resilience, collective memory, post-industrial towns, mining A 2023 study of Anaconda, Montana, explores how a community's \"collective memory\" of its industrial past—symbolized by the iconic 585-foot Smelter Stack—impacts its ability to transition to a new future. Researchers found that while these shared memories can be a source of pride and a \"galvanizing force\" for recovery, they can also act as a constraint that divides viewpoints and makes it harder for the town to embrace new economic paths like tourism. The study concludes that ignoring these deep emotional ties to history can undermine efforts to build community resilience, suggesting that successful transitions in post-industrial towns require cleanups and development plans that are \"historically informed\" and respect the community's lived experiences. Major Findings: A 2023 study of Anaconda, Montana, explores how a community's \"collective memory\" of its industrial past—symbolized by the iconic 585-foot Smelter Stack—impacts its ability to transition to a new future. Researchers found that while these shared memories can be a source of pride and a \"galvanizing force\" for recovery, they can also act as a constraint that divides viewpoints and makes it harder for the town to embrace new economic paths like tourism. The study concludes that ignoring these deep emotional ties to history can undermine efforts to build community resilience, suggesting that successful transitions in post-industrial towns require cleanups and development plans that are \"historically informed\" and respect the community's lived experiences."""; rdfs:label "Connecting Collective Memory and Community Resilience: A Case Study of Anaconda, Montana"; ; fdof:hasMetadata ; "http://www.cfc.umt.edu/research/humandimensions/"; "2023"; "2022" . a , fdof:FAIRDigitalObject; dct:creator orcid:0000-0002-4892-454X, orcid:0000-0002-9282-0502; dct:publisher ; dct:subject ; rdfs:comment "Formic acid (FA) and acetic acid (AA), two of the most abundant organic acids in the atmosphere, are typically underestimated by atmospheric models. Here we investigate their emissions, chemistry, and measurement uncertainties in biomass burning smoke sampled during the WE-CAN and FIREX-AQ aircraft campaigns. Our observed FA emission ratios (ERs) and emission factors (EFs) were generally higher than the 75th percentile of literature values, with little dependence on fuel type or combustion efficiency. Rapid in-plume FA production was observed (2.7 ppb ppmCO−1 h−1), representing up to ∼20% of the total emitted reactive organic carbon being converted to FA within half a day. AA ERs and EFs showed good agreement with the literature, with little or no secondary production observed within <8 hours of plume aging. Observed FA and AA trends in the near-field were not captured by a box model using the explicit Master Chemical Mechanism nor simplified GEOS-Chem chemistry, even after tripling the model's initial VOC concentrations. Consequently, the GEOS-Chem chemical transport model underestimates both acids in the western U.S. by a factor of >4. This is likely due to missing secondary chemistry in biomass burning smoke and/or coniferous forest biogenic emissions. This work highlights uncertainties in measurements (up to 100%) and even large unknowns in the chemical formation of organic acids in polluted environments, both of which need to be addressed to better understand their global budget. Major findings:Using data from the WE-CAN and FIREX-AQ aircraft campaigns, researchers discovered that formic acid emissions and secondary production in wildfire smoke are 3.5 times higher than previously reported in scientific literature. Despite these significantly higher observed levels, current global atmospheric models (such as GEOS-Chem) still underestimate formic and acetic acids by more than a factor of four. This study suggests that models are missing key chemical pathways—including secondary production from unknown precursors in biomass burning and biogenic emissions from coniferous forests—which are critical for accurately predicting air quality and cloud chemistry during wildfire seasons."; rdfs:label "Assessing formic and acetic acid emissions and chemistry in western U.S. wildfire smoke: implications for atmospheric modeling"; ; fdof:hasMetadata ; "wade.permar@umontana.edu"; "2023"; "2022" . a , fdof:FAIRDigitalObject; dct:creator orcid:0000-0001-6917-8729, orcid:0000-0001-9532-585X; dct:publisher ; rdfs:comment "Rangelands across the world are facing rapid and unprecedented social and ecological change. In the US West, sustaining the ecological and economic integrity of rangelands across both public and private lands depends largely on ranchers who make adaptive decisions in the face of variability and uncertainty. In this study, we build on previous conceptualizations of adaptive decision making that situate individual-level decisions within complex rangeland social-ecological systems. We surveyed 450 (36% response rate) Montana ranchers to gain insight into how key factors influenced adaptive decision making, specifically in the context of ongoing drought and climate-related change affecting rangeland ecology and productivity. We predicted that ranchers’ management goals, their use of information sources, and their use of monitoring would significantly influence the use of adaptive practices, with monitoring mediating the relationship between the explanatory and response variables. We tested these predictions using a path model analysis and found that management goals related to both stewardship and profit/production, the number of information sources used, and monitoring were all significantly and positively related to ranchers’ use of adaptive management practices. Interestingly, we found that these factors were hierarchical with monitoring and the use of information was the strongest predictor while management goals were secondary. The significant, mediating effect of monitoring on the use of adaptive practices suggests that monitoring may be an important means for providing ranchers with useful and timely information about rangeland condition that is needed to adjust their actions, meet their management goals, and adapt to drought and climate-related change. We argue there is a need to better understand the efficacy of monitoring designs—of what, by whom, and how—for adaptive decision making, and we discuss other considerations related to the provision of useful drought and climate information for adaptive decision making based on our findings. Major findings: A 2023 study of 450 Montana ranchers explored how they make management decisions during periods of drought and climate change. The researchers found that while a rancher's goals (like taking care of the land or making a profit) and their sources of information are important, the single strongest predictor of whether they use \"adaptive\" management practices is whether they have a formal monitoring program. Monitoring acts as a \"feedback loop,\" giving ranchers the data they need to adjust their grazing plans or water usage in real-time. Even though monitoring is incredibly helpful for keeping land healthy, fewer than half of the ranchers surveyed (42.9%) currently use a formal system, suggesting that helping ranchers with the cost and time of monitoring could greatly improve how rangelands are managed in the future."; rdfs:label "A Revised Adaptive Decision-Making Framework for Rangeland Management"; ; fdof:hasMetadata ; "ada.smith@oregonstate.edu"; "2023"; "2022" . a , fdof:FAIRDigitalObject; dct:creator orcid:0000-0001-6753-8620, orcid:0000-0002-0385-1826, orcid:0000-0002-3890-4916, orcid:0000-0002-8415-6808; dct:publisher ; dct:subject ; rdfs:comment "Climate change has led to an increase in the frequency and size of wildfires in the Western United States. The gases and particles released from wildfires impact air quality and climate, so it is important to understand the chemical composition of these emissions. In current air quality forecasts and climate models, the composition of wildfire emissions is based on the dominant vegetation burned and is assumed to be constant over time. In contrast, measurements from laboratory burns indicate that the composition of emissions from fires changes over time, as fires progress from more flaming combustion to flameless burning dominated by smoke (smoldering). It is challenging to have daily field measurements of the emissions from long-lived wildfires, but there are instruments in space that can make daily observations of wildfires globally. In this study, we show how the composition of emissions from wildfires in California, Oregon, and Washington changed over time, as they progressed from more flaming to more smoldering combustion, using observations from a satellite instrument called TROPOMI. The analysis of the composition of wildfire emissions and their evolution over time using TROPOMI could improve air quality forecasting and climate modeling globally. Major findings: A 2023 study used the TROPOMI satellite instrument to track how the chemical composition of wildfire smoke changes as fires evolve. By analyzing 15 large wildfires in the Western U.S., researchers discovered that the ratio of nitrogen dioxide (NO2) to carbon monoxide (CO) drops significantly as a fire moves from its \"flaming\" stage to its \"smoldering\" stage. This is a vital finding because current air quality forecasts often assume smoke composition stays the same throughout a fire's duration. Using daily satellite observations allows scientists to see these shifts in real-time, even when ground-based measurements are unavailable. This information helps improve models that predict how wildfire smoke will impact public health and the global climate over several weeks."; rdfs:label "Analyzing the Impact of Evolving Combustion Conditions on the Composition of Wildfire Emissions Using Satellite Data"; ; fdof:hasMetadata ; "joost.degouw@colorado.edu"; "2023"; "2022" . a , fdof:FAIRDigitalObject; dct:creator orcid:0009-0001-1115-9741; dct:publisher ; rdfs:comment "This research investigates the impact of missing data on the performance of machine learning algorithms, with a particular focus on the MIMIC-IV dataset. This project aims to investigate the extent to which missing data negatively impacts the training of machine learning algorithms, and whether demographic groups with a higher proportion of missing data (i.e.,ethnicity) have lower predictive accuracy. Using advanced machine learning and data analysis techniques, our results highlight important considerations related to missing data in medical datasets and provide useful insights for improving predictive modeling and decision support systems in clinical practice offers. Major findings:This investigation leveraged the MIMIC-IV v2.2 dataset—containing de-identified data from 73,141 ICU admissions at Beth Israel Deaconess Medical Center—to study the impact of missing data on machine learning. The research found that while electronic health records (EHRs) offer massive clinical datasets, they are often non-standardized and riddled with missing values. By predicting hospital Length of Stay (LOS), the study showed that as data is missing \"not at random,\" algorithm performance (measured by RMSE) degrades. Specifically, when datasets were intentionally biased to have more missing entries for certain racial groups (Asian, Black, Hispanic, etc.), the predictive error for those specific groups increased in 83% of \"aggressive\" data removal tests. This highlights that simply imputing or completing missing data can entrench existing healthcare inequities."; rdfs:label "Addressing the Challenge of Missing Medical Data in Healthcare Analytics: A Focus on Machine Learning Predictions for ICU Length of Stay"; ; fdof:hasMetadata ; "mahmad.isaq@outlook.com"; "2024"; "2023" . a , fdof:FAIRDigitalObject; dct:creator orcid:0000-0001-6917-8729, orcid:0000-0001-9532-585X; dct:publisher ; rdfs:comment "Beef production systems are at the center of ongoing discussion and debate on food systems sustainability. There is a growing interest among beef producers, consumers, and other beef supply chain stakeholders in achieving greater sustainability within the industry, but the relationship of this interest to general sustainability issues such as climate change, biodiversity loss, food security, livelihood risks, and animal welfare concerns is unclear. Specifically, there is very little research documenting how beef producers define and view the concept of sustainability and how to achieve it. Producer perspectives are critical to identifying constraints to sustainability transitions or to help build agreement with other producers about the shared values such transitions may support. Through a secondary analysis of survey data of U.S. beef producers (n = 911) conducted in 2021 by the Trust in Food division of Farm Journal, a corporation that provides content, data, and business insights to the agricultural community (e.g., producers, processors/distributors, and retailers), we investigated what “sustainable beef” means to U.S. beef producers, highlighting the key components and constraints they perceive to achieving desirable sustainability outcomes. Leveraging the three-pillar model of sustainability as a framework for analysis, we identified key themes producers use to define “sustainable beef.” We found that producers collectively viewed sustainability as: (1) multidimensional and interconnected; (2) semi-closed and regenerative; (3) long-lasting; and (4) producer-centered, although an integrated perspective uniting these aspects was rare. We discuss how these perspectives may be the basis for sustainability efforts supported by producers and raise future research considerations toward a shared understanding of what sustainability is and what is needed for enduring sustainability solutions in the U.S. beef industry. Major findings: A 2021 survey of 911 U.S. beef producers investigated how those in the industry define \"sustainable beef.\" The study found that 71% (n = 649) of respondents came from families with three or more generations in beef production. Producers collectively identified sustainability through four themes: it is multidimensional (covering environmental, social, and economic needs), semi-closed (minimizing outside inputs), long-lasting (focused on the future), and producer-centered (focused on fair markets). While 49% of responses mentioned environmental topics and 43% discussed economic viability, only 5% of producers expressed a fully integrated view including all three \"pillars\" of sustainability. The research highlights that while the industry accounts for 17% of total U.S. agricultural cash receipts ($72.9 billion in 2021), producers face significant external constraints, such as market consolidation where the four largest packing firms control roughly 75% of the market."; rdfs:label "U.S. beef producer perspectives on “sustainable beef” and implications for sustainability transitions"; ; fdof:hasMetadata ; "ada.smith@umontana.edu"; "2025"; "2024" . a , fdof:FAIRDigitalObject; dct:creator orcid:0000-0001-6917-8729, orcid:0000-0001-9532-585X, orcid:0000-0002-4455-7607; dct:publisher ; rdfs:comment "Despite years of research, concepts such as human tolerance andacceptability of wildlife remain inconsistently defined and measured,creating confusion, undermining comparative and longitudinalresearch, and limiting utility to practitioners. To address these short-comings, the wildlife attitude-acceptability framework proposed inter-secting attitudes toward wildlife species with acceptability of impactsfrom that species to reveal four archetypes of human cognitionstoward wildlife. Here, we use data from western US household surveysto populate the conceptual space of the wildlife attitude-acceptabilityframework with human cognitions toward three carnivore species:gray wolf (Canis lupis), cougar (Puma concolor), and grizzly bear(Ursus arctos horribilis). This empirical application of the wildlife atti-tude-acceptability framework demonstrates its potential to informmanagement and conservation efforts, promote consistent measure-ment across species and studies, and extend theoretical understand-ing of concepts like tolerance, which are necessary for human–wildlifecoexistence. We discuss these opportunities and remaining needs forimprovement before wider adoption.KEYWORDSCarnivores; coexistence;cognitions; conservation;methods; quantitativesurvey; toleranceIntroduction and Literature ReviewHuman dimensions of wildlife researchers have increasingly sought to define and oper-ationalize concepts relating to human–wildlife interactions, including people’s cognitionstoward species and their evaluations of wildlife-related costs and benefits (Carlson et al.,2023; König et al., 2020). Despite this literature, or perhaps because of it (Bruskotter et al.,2015), wildlife scientists and practitioners continue to hold shared, contested, and some-times confused perspectives toward concepts such as tolerance, acceptability, coexistence,and other cognitions such as beliefs, attitudes, and behavioral intentions when used withregard to wildlife (Glikman et al., 2021; Hill, 2021). Universally shared definitions of theseCONTACT Alexander L. Metcalf alex.metcalf@umontana.edu Wildlife Biology Degree Program, Department ofSociety & Conservation, W.A. Franke College of Forestry & Conservation, University of Montana, 440 CHCB, 32 Campus Drive,Missoula, MT 59812, USAThis article has been republished with a minor change. This change does not impact on the academic content of the article.HUMAN DIMENSIONS OF WILDLIFE2025, VOL. 30, NO. 4, 415–429https://doi.org/10.1080/10871209.2024.2318330© 2024 The Author(s). Published with license by Taylor & Francis Group, LLC.This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided theoriginal work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. Major findings: Researchers developed a new tool called the \"wildlife attitude-acceptability framework\" to better understand how people think about large predators like wolves, cougars, and grizzly bears. By looking at whether people like the animal (their attitude) and whether they are okay with the impacts the animal has on their lives (their acceptability), the researchers identified four main groups: advocates, conditional supporters, opponents, and those who tolerate the species. The study found that while many people like these animals, they are often \"conditional supporters\" who only want them around if they don't cause too much trouble. This framework helps wildlife managers set clearer goals and create better plans for helping humans and wildlife coexist."; rdfs:label "The wildlife attitude-acceptability framework’s potential toinform human dimensions of wildlife science and practice"; ; fdof:hasMetadata ; "elizabeth.metcalf@umontana.edu"; "2025"; "2024" . a , fdof:FAIRDigitalObject; dct:creator orcid:0000-0001-6917-8729, orcid:0000-0001-6920-3402, orcid:0000-0001-9532-585X; dct:publisher ; dct:subject ; rdfs:comment "Wildlife conservation depends on supportive social as well as biophysical conditions. Social identities such as hunter and nonhunter are often associated with different attitudes toward wildlife. However, it is unknown whether dynamics within and among these identity groups explain how attitudes form and why they differ. To investigate how social identities help shape wildlife-related attitudes and the implications for wildlife policy and conservation, we built a structural equation model with survey data from Montana (USA) residents (n = 1758) that tested how social identities affect the relationship between experiences with grizzly bears (Ursus arctos horribilis) and attitudes toward the species. Model results (r2 = 0.51) demonstrated that the hunter identity magnified the negative effect of vicarious property damage on attitudes toward grizzly bears (β = −0.381, 95% confidence interval [CI]: −0.584 to −0.178, p < 0.001), which in turn strongly influenced acceptance (β = −0.571, 95% CI: −0.611 to −0.531, p < 0.001). Our findings suggested that hunters’ attitudes toward grizzly bears likely become more negative primarily because of in-group social interactions about negative experiences, and similar group dynamics may lead nonhunters to disregard the negative experiences that out-group members have with grizzly bears. Given the profound influence of social identity on human cognitions and behaviors in myriad contexts, the patterns we observed are likely important in a variety of wildlife conservation situations. To foster positive conservation outcomes and minimize polarization, management strategies should account for these identity-driven perceptions while prioritizing conflict prevention and promoting positive wildlife narratives within and among identity groups. This study illustrates the utility of social identity theory for explaining and influencing human–wildlife interactions. Major findings: This study examined how belonging to a social group—specifically identifying as a \"hunter\" or \"nonhunter\"—affects how people in Montana feel about grizzly bears. The researchers found that hunters' attitudes become much more negative when they hear stories of property damage from other hunters (their \"in-group\"), because people naturally trust and empathize more with members of their own group. Conversely, nonhunters are less influenced by these negative stories but tend to develop more positive feelings after having neutral or peaceful experiences with bears. The findings suggest that to protect wildlife effectively, managers must understand these social identities and work to prevent groups from becoming divided or angry with each other."; rdfs:label "The influence of social identity on attitudes toward wildlife"; ; fdof:hasMetadata ; "alex.metcalf@umontana.edu"; "2024-03-03"; "2023-08-01" . a , fdof:FAIRDigitalObject; dct:creator orcid:0000-0002-1539-6656, orcid:0000-0002-2149-9133, orcid:0000-0002-4892-454X, orcid:0009-0004-6009-6304; dct:publisher ; dct:subject ; rdfs:comment "Salt Lake City, Utah has higher concentrations of ozone, a pollutant harmful to human and plant life, in the atmosphere than the standard set by the United States Environmental Protection Agency (US EPA). The reasons for the high levels of ozone remain uncertain. Volatile organic compounds (VOCs) are a class of air pollutants that undergo reactions that produce ozone. Understanding their sources and reactions is important to be able to reduce air pollution. In this study, we measured 35 VOCs in SLC in August and September 2022 and used a model to identify their major sources. Concentrations of hazardous VOCs identified by the US EPA increased by 45%–217% when wildfire smoke was present in the air. Methanol and ethanol were the most important VOCs in terms of total concentration in the air, while isoprene and monoterpenes were the most important in terms of reactions that could create ozone. According to the model results, VOCs are emitted from five major sources including traffic and solvent use. Further measurements are needed to confirm the model results and reduce uncertainty of the important sources of VOCS. Major findings:The Salt Lake regional Smoke, Ozone and Aerosol Study (SAMOZA) conducted in late 2022 measured 35 different volatile organic compounds (VOCs) using advanced mass spectrometry and liquid chromatography. The researchers found that total VOC levels in the city averaged 32 parts per billion (ppb), but spiked as high as 141 ppb during certain hours. This data is used to calculate \"OH reactivity,\" which helps scientists understand how quickly these chemicals react in the air to form pollutants like smog and ozone."; rdfs:label "Sources of Atmospheric Volatile Organic Compounds During the Salt Lake Regional Smoke, Ozone and Aerosol Study (SAMOZA) 2022"; ; fdof:hasMetadata ; "emily.cope@umontana.edu"; "2024-09-06"; "2023-08-01" . a , fdof:FAIRDigitalObject; dct:creator orcid:0000-0001-6917-8729; dct:publisher ; dct:subject ; rdfs:comment """Social connections among individuals are essential components of social-ecological systems (SESs), enabling people to take actions to more effectively adapt or transform in response to widespread social-ecological change. Although scholars have associated social connections and cognitions with adaptive capacity, measuring actors' social networks may further clarify pathways for bolstering resilience-enhancing actions. We asked how social networks and socio-cognitions, as components of adaptive capacity, and SES regime shift severity affect individual landscape management behaviours using a quantitative analysis of ego network survey data from livestock producers and landcover data on regime shift severity (i.e. juniper encroachment) in the North American Great Plains. Producers who experienced severe regime shifts or perceived high risks from such shifts were not more likely to engage in transformative behaviour like prescribed burning. Instead, we found that social network characteristics explained significant variance in transformative behaviours. Policy implications: Our results indicate that social networks enable behaviours that have the potential to transform SESs, suggesting possible leverage points for enabling capacity and coordination toward sustainability. Particularly where private lands dominate and cultural practices condition regime shifts, clarifying how social connections promote resilience may provide much needed insight to bolster adaptive capacities in the face of global change. Major findings: This research explores how social networks and individual beliefs influence land management behaviors among livestock producers in the North American Great Plains. The study found that social networks are significantly more effective at predicting \"transformative\" behaviors—such as prescribed burning—than the actual ecological condition of the land. While \"adaptive\" behaviors like mechanical tree removal are often triggered by observing physical changes in the environment (regime shifts), major transformative actions depend heavily on the social support, information access, and trust provided by an individual's community and professional connections. These findings highlight that social constraints often limit environmental management more than a lack of information about ecological risks."""; rdfs:label "Social networks and transformative behaviours in a grassland social-ecological system"; ; fdof:hasMetadata ; "elizabeth.metcalf@umontana.edu"; "2024-08-12"; "2023-08-01" . a , fdof:FAIRDigitalObject; dct:creator orcid:0000-0001-8726-8226, orcid:0000-0001-9487-5622, orcid:0000-0002-3588-6257; dct:language ; dct:publisher ; dct:subject ; rdfs:comment "Major findings: ScholarNodes, a web interface that successfully integrates partition-based (Louvain) and similarity-based (Spectral) community detection algorithms with the BM25 ranking algorithm to recommend interdisciplinary collaborations within academic social networks. By analyzing publication metadata from the OpenAlex dataset, the authors demonstrated that a topic-similarity network can effectively identify latent researcher communities and provide meaningful mentorship and grant-teaming recommendations that transcend traditional departmental boundaries."; rdfs:label "ScholarNodes: Applying Content-based Filtering to RecommendInterdisciplinary Communities within Scholarly Social Networks"; ; fdof:hasMetadata ; "mdasaduzzamannoor@montana.edu"; "2024-07-11"; "2023-08-01" . a , fdof:FAIRDigitalObject; dct:creator orcid:0000-0002-5258-5472, orcid:0000-0003-1056-1269; dct:language ; dct:publisher ; dct:subject ; rdfs:comment "The increasing prevalence of nuisance benthic algal blooms in freshwater systems has led to water quality monitoring programs based on the presence and abundance of algae. Large blooms of the nuisance filamentous algae, Cladophora glomerata, have become common in the waters of the Upper Clark Fork River in western Montana. To aid in the understanding of algal growth dynamics, unoccupied aerial vehicle (UAV)-based hyperspectral images were gathered at three field sites along the length of the river throughout the growing season of 2021. Select regions within images covering the spectral range of 400 to 850 nm were labeled based on a combination of professional judgment and spectral profiles and used to train a random forest classifier to identify benthic algal growth across several classes, including benthic growth dominated by Cladophora (Clado), benthic growth dominated by growth forms other than Cladophora (non-Clado), and areas below a visually detectable threshold of benthic growth (bare substrate). After classification, images were stitched together to produce spatial distribution maps of each river reach while also calculating the average percent cover for each reach, achieving an accuracy of approximately 99% relative to manually labeled images. Results of this analysis showed strong variability across each reach, both temporally (up to 40%) and spatially (up to 46%), indicating that UAV-based imaging with high-spatial resolution could augment and therefore improve traditional measurement techniques that are spatially limited, such as spot sampling. Major findings: The study successfully utilized UAV-based hyperspectral imaging and a random forest classification model to map benthic algae in the Upper Clark Fork River with 99% accuracy, effectively distinguishing Cladophora from other growth forms and bare substrate. The researchers discovered significant spatial and temporal variability in algal coverage, with levels fluctuating by over 40% within short river reaches, demonstrating that high-resolution remote sensing provides a more comprehensive and accurate assessment of ecosystem health than traditional spot-sampling."; rdfs:label "Benthic river algae mapping using hyperspectral imagery from unoccupied aerial vehicles"; ; fdof:hasMetadata ; "joseph.shaw@montana.edu"; "2024-06-13"; "2023-07-31" . a , fdof:FAIRDigitalObject; dct:creator orcid:0000-0002-4892-454X, orcid:0000-0002-5886-3314, orcid:0000-0002-9282-0502, orcid:0000-0003-3852-3491; dct:language ; dct:publisher ; rdfs:comment "Wildfires have torn across western North America over the last decade. Smoke from wildland fires in Canada can travel thousands of kilometers to US cities and reacts with urban pollution to create harmful ozone, a criteria pollutant regulated by the US Environmental Protection Agency. Accurately quantifying this impact is needed to inform US air quality policy, but is challenging due to complex physical and chemical processes. In this study, we analyze surface and airborne measurements, alongside a new variable-resolution global chemistry-climate model, to better understand these processes. We show that the near-field conversion of nitrogen oxide (NOx) emissions from wildfires to peroxyacetyl nitrate (PAN) and other more oxidized forms reduces their localized impacts on ozone. PAN is the principal tropospheric reservoir for NOx radicals. When aged smoke plumes descend southward from Canada toward US cities, higher temperatures cause PAN to decompose and thus help production of ozone during smoke transport. On days when the observed ozone levels exceed the air quality limit (70 ppbv for 8-hr average), wildfire smoke can contribute 5–25 ppbv. Major Findings: Research using the AM4VR model demonstrates that wildfire nitrogen emissions are rapidly sequestered as peroxyacyl nitrates ($PAN$), which remain stable during long-range transport before decomposing to release NO and fuel ozone O3 production in distant urban areas. These findings reveal that such chemical evolution can increase surface O3 levels by 5 to 25 ppbv in cities thousands of kilometers downwind, particularly when pyrogenic organic compounds interact with existing urban pollution."; rdfs:label "Reactive Nitrogen Partitioning Enhances the Contribution of Canadian Wildfire Plumes to US Ozone Air Quality"; ; fdof:hasMetadata ; "Meiyun.Lin@noaa.gov"; "2024-08-06"; "2023-08-01" . a , fdof:FAIRDigitalObject; dct:creator orcid:0000-0003-0906-9269, orcid:0000-0003-1469-6486; dct:language ; dct:publisher ; dct:subject ; rdfs:comment "A significant cost of wildfires is the exposure of local and regional populations to air pollution from smoke, which can travel hundreds of miles from the source fire and is associated with significant negative health consequences. Wildfires are increasing in frequency and intensity in the United States, driven by historic fire management approaches and global climate change. These influences will take many decades or longer to reverse, so the main opportunities for mitigating health effects involve minimizing human exposure through changes in behavior or infrastructure. One key recommendation for reducing pollution exposures during wildfire smoke events is to limit time and physical activity outdoors, but there is limited evidence on the extent to which people make this change. We estimate how use of parks and playgrounds changes with air quality during wildfire season in the northwest United States. We find small reductions in park and playground visits on moderately polluted days, and large reductions, to 50–60% of baseline visits, when pollution levels are high. Disaggregating results by neighborhood characteristics, we find a significantly greater behavioral response to moderate levels of air pollution in neighborhoods with higher socio-economic status, although responses to high levels of pollution are similar across neighborhood types. Major findings: Park and playground visitation in the Northwest United States decreases significantly as wildfire-driven air quality worsens, with visits dropping by up to 50% during hazardous conditions. A key disparity emerged showing that residents of higher-income and more educated neighborhoods begin taking protective action at much lower pollution levels than those in less advantaged areas, who typically only reduce activity when air quality reaches severe levels. These findings suggest that socioeconomic differences in health outcomes from wildfire smoke are driven partly by the varying ability of different groups to bear the costs of forgoing outdoor recreation."; rdfs:label "Impacts of wildfire-season air quality on park and playground visitation in the Northwest United States"; ; fdof:hasMetadata ; "katrina.mullan@umontana.edu"; "2024-10-01"; "2023-08-01" . a , fdof:FAIRDigitalObject; dct:creator orcid:0000-0001-6917-8729, orcid:0000-0003-1862-3428; dct:publisher ; dct:subject ; rdfs:comment "Climate change is expected to increase the frequency and intensity of drought in many parts of the world, including Montana. In the face of worsening drought conditions, agricultural producers need to adapt their operations to mitigate risk. This study examined the role of local knowledge and climate information in drought-related decisions through five focus groups with Montana farmers and ranchers. We found that trust and risk perceptions mediated how producers utilized both local knowledge and climate information. More specifically, producers relied on local knowledge in drought-related decisions, regarding their own observation and past experience as trustworthy and not particularly risky. In contrast, climate information and seasonal climate forecasts in particular were regarded as risky and untrustworthy, largely due to a perceived lack of accuracy. Since producers tended to be risk averse, especially given market and climate uncertainties, they rarely relied on “risky” climate information. At the same time, producers actively managed risk and tested out new technologies and practices through processes of trial and error, what they called “experimenting,” which enabled them to build firsthand knowledge of potential adaptations. In the context of uncertainty and risk aversion, programs that reduce the financial risk of experimenting with new technologies and adaptive practices are needed to enable producers to develop direct experience with innovations designed to mitigate drought risk. Further, scientists developing climate information need to work directly with farmers and ranchers to better integrate local knowledge into climate information. Major findings: Montana agricultural producers prioritize local experiential knowledge over seasonal climate forecasts because they frequently view science-driven data as inaccurate, untrustworthy, or poorly scaled to their specific lands. Due to extreme financial vulnerability, these producers are highly risk-averse and often perceive unproven climate information as an added danger to their livelihoods rather than a tool for mitigation. To manage this uncertainty, they utilize small-scale on-farm \"experiments\" to build trusted local knowledge before adopting new practices or technologies on a larger scale. The study concludes that successful drought adaptation requires a \"farmer first\" approach where scientists co-produce climate services that integrate local observations and provide financial support for producer-led experimentation."; rdfs:label "How agricultural producers use local knowledge, climate information, and on-farm “experiments” to address drought risk"; ; fdof:hasMetadata ; "adam.snitker@colostate.edu"; "2024-06-28"; "2023-06-28" . a , fdof:FAIRDigitalObject; dct:creator orcid:0000-0001-7477-9078, orcid:0000-0002-6065-8643, orcid:0000-0002-8415-6808; dct:language ; dct:publisher ; dct:subject ; rdfs:comment "Large-scale atmospheric field campaigns conducted over the last half-century have fundamentally transformed the understanding of how vegetation fires influence global air quality and climate. From early studies of fire behavior to recent, sophisticated missions like WE-CAN and FIREX-AQ, research has successfully identified key chemical transformations in smoke plumes, including the evolution of nitrogen and the mechanisms of daytime ozone formation. Despite these advances, significant gaps remain regarding the effects of weather and fuel characteristics on emissions. Future research must prioritize understudied regions such as tropical peatlands and sub-Saharan Africa, integrate emerging technologies like unmanned aerial vehicles (UAVs) and machine learning, and address the specific health risks associated with burning non-vegetative fuels in the wildland-urban interface. Major findings:Research has evolved to show how smoke chemically changes ozone and climate, but better ground-based data is still needed to supplement satellite coverage gaps. Future efforts must focus on under-studied regions like Africa and the unique health risks created when non-vegetative materials burn in residential areas."; rdfs:label "Findings from Biomass Burning Field Campaigns Set Directions for Future Research on Atmospheric Impacts"; ; fdof:hasMetadata ; "bob.yokelson@mso.umt.edu"; "2024-11-06"; "2023-08-23" . a , fdof:FAIRDigitalObject; dct:creator orcid:0000-0002-0754-6298, orcid:0000-0002-6542-5013; dct:language ; dct:publisher ; dct:subject ; rdfs:comment """settingsOrder Article Reprints Open AccessReview Prescribed Fire Smoke: A Review of Composition, Measurement Methods, and Analysis by Kayode I. Fesomade 1,2ORCID andRobert A. Walker 1,2,*ORCID 1 Montana Materials Science Program, Montana State University, Bozeman, MT 59717, USA 2 Department of Chemistry and Biochemistry, Montana State University, Bozeman, MT 59717, USA * Author to whom correspondence should be addressed. Fire 2025, 8(7), 241; https://doi.org/10.3390/fire8070241 Submission received: 30 April 2025 / Revised: 16 June 2025 / Accepted: 17 June 2025 / Published: 20 June 2025 (This article belongs to the Section Fire Science Models, Remote Sensing, and Data) Downloadkeyboard_arrow_down Browse Figures Versions Notes Abstract Prescribed fire has become an increasingly important strategy for removing biomass from forests and mitigating the risk of severe wildfire. When considering where and to what extent prescribed fire should be applied, land resource managers must consider a host of concerns including biomass density, moisture content, and meteorological conditions. These variables will not only affect how effective the burn will be, but also what sort of smoke is produced by the prescribed fire and how that smoke impacts individuals and local communities. After briefly summarizing how prescribed fire practices have evolved, this review describes how the properties of prescribed fire smoke depend on prescribed fire conditions and the methods used to measure molecular and particulate species in prescribed fire smoke. The closing section of this review identifies areas where advances in smoke monitoring and characterization can continue to improve our understanding of prescribed fire behavior. Keywords: prescribed fire; wildfire; smoke; particulate matter; emission factor; measurement; modified combustion efficiency; carbon budget. Major findings: Prescribed fire is a vital strategy for managing forests and reducing wildfire danger by clearing away excess plants through low-intensity, controlled burns that produce significantly less harmful smoke than major wildfires. This review highlights how using high-tech monitoring tools to track specific chemicals like CO2 and particulate matter helps experts protect community air quality while making local ecosystems more resilient to future disasters."""; rdfs:label "Prescribed Fire Smoke: A Review of Composition, Measurement Methods, and Analysis"; ; fdof:hasMetadata ; "rawalker@montana.edu"; "2025-06-20"; "2023-11-13" . a , fdof:FAIRDigitalObject; dct:creator orcid:0000-0003-0906-9269, orcid:0009-0002-2822-8317, orcid:0009-0005-2832-9144; dct:publisher ; dct:subject ; rdfs:comment "Although climate change is increasing wildfire and smoke events globally, public health messaging and individual access to resources for protection are limited. Individual interventions can be highly effective at reducing wildfire smoke exposure. However, studies related to individual responses to wildfire smoke are limited and demonstrate mixed protective behaviors and risk perception. Our research helps fill this gap by assessing the self-reported behavior of 20 participants during wildfire season in Western Montana from 28 June through 1 November, 2022. We also measured continuous outdoor and indoor fine particulate matter (PM2.5) concentrations at participant residencies during this time period using PurpleAir sensors (PAII-SD, PurpleAir, Inc, USA) while participants took up to 16 self-reported online weekly activity surveys. Mixed-effect Poisson regression models were used to assess associations between exposure variables and participant reported behaviors. These results were compared with end-of-study interview findings. Wildfire smoke impacted days and increased concentrations of PM2.5 were associated with decreased outdoor exercise and opening of windows for ventilation. Interview themes were congruent with the regression analysis, with the additional finding of high portable air cleaner (PAC) use among participants. Additionally, these interviews gave context to both the tradeoffs participants face when making protective decisions and the importance of personal air quality data in increasing awareness about wildfire smoke risks. Future wildfire smoke studies can build off this research by providing personally relevant air quality data and PACs to participants and by improving public health messaging to address the compounding risks of wildfire smoke exposure and heat. Findings of this article are that residents in Western Montana respond to wildfire smoke by reducing outdoor exercise by 22% to 23% and window ventilation by 22% to 29%. Data from a group of participants who were 100% White and 85% female shows that 70% utilized portable air cleaners, a rate significantly higher than the 29% observed in broader national studies. While high education levels and access to personal air quality data drive these protective behaviors, residents without air conditioning face critical trade-offs between limiting smoke infiltration and managing extreme heat."; rdfs:label "Behavioral Responses to Wildfire Smoke: A Case Study in Western Montana"; ; fdof:hasMetadata ; "ethan.walker@umontana.edu"; "2024-08-25"; "2023-08-01" . a , fdof:FAIRDigitalObject; dct:creator orcid:0000-0001-5740-8179, orcid:0000-0002-4135-7634; dct:language ; dct:publisher ; dct:subject ; rdfs:comment """Abstract: In remote sensing image processing for Earth and environmental applications, super-resolution (SR) is a crucial technique for enhancing the resolution of low-resolution (LR) images. In this study, we proposed a novel algorithm of frequency-domain super-resolution with reconstruction from compressed representation. The algorithm follows a multistep procedure: first, an LR image in the space domain is transformed to the frequency domain using a Fourier transform. The frequency-domain representation is then expanded to the desired size (number of pixels) of a high-resolution (HR) image. This expanded frequency-domain image is subsequently inverse Fourier transformed back to the spatial domain, yielding an initial HR image. A final HR image is then reconstructed from the initial HR image using a low-rank regularization model that incorporates a nonlocal smoothed rank function (SRF). We evaluated the performance of the new algorithm by comparing the reconstructed HR images with those generated by several commonly used SR algorithms, including: 1) bicubic interpolation; 2) sparse representation; 3) adaptive sparse domain selection and adaptive regularization; 4) fuzzy-rule-based (FRB) algorithm; 5) SR convolutional neural networks (SRCNNs); 6) fast SR convolutional neural networks (FSRCNNs); 7) practical degradation model for deep blind image SR; 8) the frequency separation for real-world SR (FSSR); and 9) the enhanced SR generative adversarial networks (ESRGANs). The algorithms were tested on Landsat-8 and Moderate Resolution Imaging Spectroradiometer (MODIS) multiresolution images over various locations, as well as on images with artificially added noise to assess the robustness of each algorithm. Results show that: 1) the proposed new algorithm outperforms the others in terms of the peak signal-to-noise ratio, structure similarity, and root-mean-square error and 2) it effectively suppresses noise during HR reconstruction from noisy low-resolution (LR) images, overcoming a key limitation of existing SR methods."""; ; fdof:hasMetadata ; "xzhou@mtech.edu"; "2024-04-01"; "2023-08-01" . a , fdof:FAIRDigitalObject; dct:creator orcid:0000-0001-6917-8729, orcid:0000-0003-4938-8440; dct:publisher ; dct:subject ; rdfs:comment "River restoration is one of the most common, expensive, and environmentally influential forms of restoration, but has little post-restoration assessment of social success. In this study, we use social network theory and analysis (SNA), an emerging approach for understanding social dynamics in restoration projects, to examine the social connections, perceptions of project success, and attitudes of stakeholders involved in a river restoration project. We find that positive and negative social network ties have asymmetrical effects on stakeholders’ attitudes and satisfaction with project outcomes. Trust ties positively influence perceptions of public engagement, while avoidance ties negatively influence satisfaction. Trust in leaders positively influences satisfaction and both public engagement and perceived conflict influence the development of that trust. We contribute to the growing body of research using SNA in natural resource contexts through quantitative tests of social networks’ effects on stakeholder satisfaction with project outcomes. Major findings: Trust in project leaders is the most significant factor determining stakeholder satisfaction with river restoration outcomes. While positive social ties increase engagement, negative \"avoidance\" networks and perceived conflict have a disproportionately large impact on lowering project satisfaction. These results indicate that managing negative interpersonal dynamics is more critical to the social success of restoration efforts than fostering positive collaborations."; rdfs:label "Effects of Trust, Public Engagement, Conflict, and Social Networks on Satisfaction with Ecological Restoration"; ; fdof:hasMetadata ; "theresa.floyd@umontana.edu"; "2024-04-20"; "2023-08-01" . a , fdof:FAIRDigitalObject; dct:creator orcid:0000-0001-5419-8735, orcid:0000-0002-0611-1364, orcid:0000-0002-2480-9774, orcid:0000-0002-4135-7634, orcid:0000-0002-9506-5311, orcid:0000-0003-0438-9124; dct:language ; dct:publisher ; dct:subject ; rdfs:comment "The freeze-thaw cycle of near-surface soils significantly affects energy and water exchanges between the atmosphere and land surface. Passive microwave remote sensing is commonly used to observe the freeze-thaw state. However, existing algorithms face challenges in accurately monitoring near-surface soil freeze/thaw in alpine zones. This article proposes a framework for enhancing freeze/thaw detection capability in alpine zones, focusing on band combination selection and parameterization. The proposed framework was tested in the three river source region (TRSR) of the Qinghai-Tibetan Plateau. Results indicate that the framework effectively monitors the freeze/thaw state, identifying horizontal polarization brightness temperature at 18.7 GHz (TB18.7H) and 23.8 GHz (TB23.8H) as the optimal band combinations for freeze/thaw discrimination in the TRSR. The framework enhances the accuracy of the freeze/thaw discrimination for both 0 and 5-cm soil depths. In particular, the monitoring accuracy for 0-cm soil shows a more significant improvement, with an overall discrimination accuracy of 90.02%, and discrimination accuracies of 93.52% for frozen soil and 84.68% for thawed soil, respectively. Furthermore, the framework outperformed traditional methods in monitoring the freeze-thaw cycle, reducing root mean square errors for the number of freezing days, initial freezing date, and thawing date by 16.75, 6.35, and 12.56 days, respectively. The estimated frozen days correlate well with both the permafrost distribution map and the annual mean ground temperature distribution map. This study offers a practical solution for monitoring the freeze/thaw cycle in alpine zones, providing crucial technical support for studies on regional climate change and land surface processes. The findings show that the use of satellite microwave signals at 18.7 GHz and 23.8 GHz provides highly accurate tracking of ground freezing and thawing in mountain regions. This method reaches 90% accuracy at the soil surface and identifies seasonal changes much more reliably than older tools. These precise measurements help track how environmental changes affect water and land in cold, high-altitude climates."; fdof:hasMetadata ; "xzhou@mtech.edu"; "2024-11-08"; "2023-08-01" . a , fdof:FAIRDigitalObject; dct:contributor orcid:0009-0004-1020-475X; dct:creator orcid:0000-0002-9162-987X; dct:language ; dct:publisher ; rdfs:comment "Cohorts collected during 20 years long Netherlands Study on Depression and Anxiety-NESDA, containing more than 670000patients data with possibility of access for further replication"; rdfs:label "NESDA COHORT"; ; fdof:hasMetadata ; "nesda@amsterdamumc.nl" . dct:issued "2025-12-08"; dct:license ; fdof:hasEncodingFormat ; fdof:materializes ; . a , fdof:FAIRDigitalObject; dct:contributor orcid:0000-0001-6551-6030, orcid:0000-0001-7871-2073, orcid:0000-0001-8888-635X; dct:creator orcid:0000-0001-5862-4375; dct:hasVersion "Final_20220421"; dct:language ; dct:publisher ; dct:subject ; rdfs:comment "Final proposal submitted for Hypermarker"; rdfs:label "Proposal for Hypermarker"; fdof:hasMetadata ; "support@fair.lmac.nl"; "2026-12-31"; "2023-01-01" . dct:issued "2025-11-17"; dct:license ; fdof:hasEncodingFormat ; fdof:materializes ; . a , fdof:FAIRDigitalObject; dct:contributor orcid:0000-0001-5862-4375, orcid:0000-0001-6551-6030, orcid:0000-0001-7871-2073; dct:creator orcid:0000-0001-5862-4375; dct:hasVersion "v.02.00"; dct:language ; dct:publisher ; dct:subject ; rdfs:comment "The measurement method used for the Hypermarker sample analysis"; rdfs:label "Methods for project Hypermarker"; ; fdof:hasMetadata ; "support@fair.lmac.nl" . dct:issued "2025-11-17"; dct:license ; fdof:hasEncodingFormat ; fdof:materializes ; . a , fdof:FAIRDigitalObject; dct:contributor orcid:0000-0001-6551-6030, orcid:0000-0001-7871-2073, orcid:0000-0001-8888-635X; dct:creator orcid:0000-0001-5862-4375; dct:hasVersion "v.01.00"; dct:language ; dct:publisher ; dct:subject ; rdfs:comment "The sample_prep methods used for the Hypermarker sample analysis"; rdfs:label "Methods for project Hypermarker"; ; fdof:hasMetadata ; "support@fair.lmac.nl" . dct:issued "2025-11-17"; dct:license ; fdof:hasEncodingFormat ; fdof:materializes ; . a , fdof:FAIRDigitalObject; dct:contributor orcid:0000-0001-6551-6030, orcid:0000-0001-7871-2073, orcid:0000-0001-8888-635X; dct:creator orcid:0000-0001-5862-4375; dct:hasVersion "v.01.00"; dct:language ; dct:publisher ; dct:subject ; rdfs:comment "The data-preprocessing methods used for the Hypermarker sample analysis"; rdfs:label "Methods for project Hypermarker"; ; fdof:hasMetadata ; "support@fair.lmac.nl" . rfr-per:RunObstacleAvoidanceDemo a ero-core:ExperimentRun, fdof:FAIRDigitalObject; dct:conformsTo rfp:TrivialProfile; ero-core:hasExperimentType rfr-set:ObstacleAvoidanceDemo; rfr-sep:observedRobot rfr-shw:Artemis; rfr-sep:travelTime 38.0; rdfs:comment "A specific execution of an obstacle avoidance experiment."; rdfs:label "Run of an obstacle avoidance experiment" . rfr-set:ObstacleAvoidanceDemo a ero-core:ExperimentType, fdof:FAIRDigitalObject; dct:conformsTo rfp:TrivialProfile; ero-core:hasMethodologySpec ; ero-core:hasParameterUsage , ; rdfs:comment "An experiment type focused on testing robot navigation around obstacles."; rdfs:label "Obstacle Avoidance Experiment"; fdof:isMaterializedBy rfr-set:ObstacleAvoidanceDemo.ttl . a ero-core:ParameterUsage; ero-core:usesParameter rfr-sep:observedRobot; ero-util:hasVariableRole ero-util:IndependentVariable; ero-util:isRequired true; rdfs:label "Observed Robot Usage for Obstacle Avoidance" . a ero-core:ParameterUsage; ero-core:usesParameter rfr-sep:travelTime; ero-util:hasVariableRole ero-util:DependentVariable; ero-util:isRequired true; rdfs:label "Travel Time Usage for Obstacle Avoidance" . a ero-core:MethodologySpec; ero-alias:BFO_hasPart , , , . a ero-util:ResearchObjectiveSpec; dct:description "The objective of the obstacle avoidance experiment is to demonstrate SherpaTT's ability to step over an obstacle without contact, thanks to its actively controlled suspension. For constrast, a variation of the experiment is conducted where the robot drives around the obstacle. This variation is also performed by ARTEMIS, which, due to its passive suspension, cannot step over the obstacle. Both variants are conducted only on moist and compacted soil." . a ero-util:ExperimentSetupSpec; dct:description "The obstacle is a cuboid with variable dimensions (height, width, depth). It is placed half way between the start and finish line of the track, which has a total length of 5m. The sides of the cuboid are parallel to the ground, the start line, and the direction of the track." . a ero-util:ProcedureSpec; dct:description "In an experiment run, the robot first drives at a constant commanded speed straight up to the obstacle. The operator then issues any commands necessary to either step over or drive around the obstacle, depending on the experiment variant. After a successful avoidance maneuver, the robot continues in a straight line towards the finish, at the intially commanded speed." . a ero-util:SuccessCriteriaSpec; dct:description "The obstacle must be surpassed without making contact." . rfr-sep:observedRobot a ero-core:ExperimentParameter, owl:ObjectProperty, fdof:FAIRDigitalObject; dct:conformsTo rfp:TrivialProfile; rdfs:comment "The specific robot being observed or tested in an experiment."; rdfs:label "Observed Robot"; rdfs:range rfr-sep:AvailableRobot . rfr-sep:AvailableRobot a owl:Class, fdof:FAIRDigitalObject; dct:conformsTo rfp:TrivialProfile; rdfs:comment "The class of mobile robots available for experiments in the RoBivaL project."; rdfs:label "Available Robot"; rdfs:subClassOf sosa:FeatureOfInterest, sosa:Platform, ssn:System; owl:equivalentClass . a owl:Class; owl:oneOf rfr-shw:Artemis, rfr-shw:Bonirob, rfr-shw:NaioOz, rfr-shw:Sherpatt . rfr-shw:Sherpatt a cora:MobileRobot, cora:Robot, iso8373:AutonomousRobot, iso8373:MobileRobot, owl:NamedIndividual, sosa:Platform, fdof:FAIRDigitalObject; dct:conformsTo rfp:TrivialProfile; rdfs:label "SherpaTT" . rfr-shw:NaioOz a cora:MobileRobot, cora:Robot, iso8373:AutonomousRobot, iso8373:MobileRobot, owl:NamedIndividual, sosa:Platform, fdof:FAIRDigitalObject; dct:conformsTo rfp:TrivialProfile; rdfs:label "Naio Oz" . rfr-shw:Bonirob a cora:MobileRobot, cora:Robot, iso8373:AutonomousRobot, iso8373:MobileRobot, owl:NamedIndividual, sosa:Platform, fdof:FAIRDigitalObject; dct:conformsTo rfp:TrivialProfile; rdfs:label "BoniRob" . rfr-shw:Artemis a cora:MobileRobot, cora:Robot, iso8373:AutonomousRobot, iso8373:MobileRobot, owl:NamedIndividual, sosa:Platform, fdof:FAIRDigitalObject; dct:conformsTo rfp:TrivialProfile; rdfs:label "ARTEMIS" . rfr-sep:travelTime a ero-core:ExperimentParameter, owl:DatatypeProperty, fdof:FAIRDigitalObject; dct:conformsTo rfp:TrivialProfile, rfp:travelTimeProfile; qudt:unit unit:SEC; rdfs:comment "The time required for a robot to travel from start to end of the track, measured in seconds."; rdfs:label "Travel time"; rdfs:range ; fdof:isMaterializedBy rfr-sep:travelTime.ttl . a rdfs:Datatype; owl:onDatatype xsd:float; owl:withRestrictions . xsd:minInclusive 0.0 . rfp:travelTimeProfile a sh:NodeShape, fdoc:FdoProfile, fdof:FAIRDigitalObject; dct:conformsTo rfp:FundamentalProfile; rdfs:comment "travelTimeProfile is a maximally strict FDO profile. The only FDO record that conforms to travelTimeProfile is http://w3id.org/RoBivaL/FDORecord/Specification/ExperimentParameter/travelTime."@en; rdfs:label "travelTimeProfile"; sh:closed true; sh:property , , , , , , , , , ; sh:targetNode rfr-sep:travelTime; fdof:isMaterializedBy rfp:travelTimeProfile.ttl . sh:path rdf:type; sh:qualifiedMaxCount 1; sh:qualifiedMinCount 1; sh:qualifiedValueShape . sh:hasValue rfp:travelTimeProfile . sh:hasValue rfr-sep:travelTime.ttl; sh:maxCount 1; sh:minCount 1; sh:path fdof:isMaterializedBy . sh:hasValue "Travel time"; sh:maxCount 1; sh:minCount 1; sh:path rdfs:label . sh:hasValue "The time required for a robot to travel from start to end of the track, measured in seconds."; sh:maxCount 1; sh:minCount 1; sh:path rdfs:comment . sh:path rdfs:range; sh:qualifiedMaxCount 1; sh:qualifiedMinCount 1; sh:qualifiedValueShape . a sh:NodeShape; sh:closed true; sh:property , , . sh:hasValue rdfs:Datatype; sh:maxCount 1; sh:minCount 1; sh:path rdf:type . sh:hasValue xsd:float; sh:maxCount 1; sh:minCount 1; sh:path owl:onDatatype . sh:maxCount 1; sh:minCount 1; sh:node ; sh:path owl:withRestrictions . a sh:NodeShape; sh:property . sh:hasValue fdof:FAIRDigitalObject . sh:hasValue 0.0; sh:maxCount 1; sh:minCount 1; sh:path xsd:minInclusive . sh:hasValue unit:SEC; sh:maxCount 1; sh:minCount 1; sh:path qudt:unit . sh:path rdf:type; sh:qualifiedMaxCount 1; sh:qualifiedMinCount 1; sh:qualifiedValueShape . sh:hasValue ero-core:ExperimentParameter . sh:path rdf:type; sh:qualifiedMaxCount 1; sh:qualifiedMinCount 1; sh:qualifiedValueShape . sh:hasValue owl:DatatypeProperty . sh:path dct:conformsTo; sh:qualifiedMaxCount 1; sh:qualifiedMinCount 1; sh:qualifiedValueShape . sh:hasValue rfp:TrivialProfile . sh:path dct:conformsTo; sh:qualifiedMaxCount 1; sh:qualifiedMinCount 1; sh:qualifiedValueShape .