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[ { "@value": "2021-03-11 12:48:21.251000+00:00" } ], "http://schema.org/dateModified": [ { "@value": "2025-03-05 01:14:11.720011+00:00" } ], "http://schema.org/datePublished": [ { "@value": "2021-03-11 12:48:21.251000+00:00" } ], "http://schema.org/description": [ { "@value": "Subjective experience along with physiological activity are fundamental components of emotional responding. We present a publicly available dataset of psychophysiological responses to positive and negative emotions of 1157 healthy participants, collected across seven studies. In our studies were continuously recorded affect and physiological activity during resting baseline and emotional responding. We recorded physiological responses using electrocardiography (ECG), impedance cardiography (ICG), electrodermal activity (EDA), photoplethysmography (PPG, the blood pressure measures), respiratory, and temperature sensors. In our studies, we elicited emotions with films, pictures, speech preparation, and expressive writing. We studied a wide range of positive and negative emotions, including amusement, anger, disgust, excitement, fear, gratitude, sadness, tenderness, and threat. To the best of our knowledge, psychophysiology of positive and negative emotions (POPANE) database is the largest, consistent psychophysiological dataset on emotions ever collected and publicly shared. We hope that POPANE will provide individuals, companies, and laboratories with the data they need to perform their analyses, corroborate their results, and create robust psychophysiological models of emotions. The individuals data are openly available in POPANE dataset at https://data.psychosensing.psnc.pl/popane/index.html. " }, { "@value": "Subjective experience along with physiological activity are fundamental components of emotional responding. We present a publicly available dataset of psychophysiological responses to positive and negative emotions of 1157 healthy participants, collected across seven studies. In our studies were continuously recorded affect and physiological activity during resting baseline and emotional responding. We recorded physiological responses using electrocardiography (ECG), impedance cardiography (ICG), electrodermal activity (EDA), photoplethysmography (PPG, the blood pressure measures), respiratory, and temperature sensors. In our studies, we elicited emotions with films, pictures, speech preparation, and expressive writing. We studied a wide range of positive and negative emotions, including amusement, anger, disgust, excitement, fear, gratitude, sadness, tenderness, and threat. To the best of our knowledge, psychophysiology of positive and negative emotions (POPANE) database is the largest, consistent psychophysiological dataset on emotions ever collected and publicly shared. We hope that POPANE will provide individuals, companies, and laboratories with the data they need to perform their analyses, corroborate their results, and create robust psychophysiological models of emotions. The individuals data are openly available in POPANE dataset at https://data.psychosensing.psnc.pl/popane/index.html." } ], "http://schema.org/encodingFormat": [ { "@value": "application/ld+json" } ], "http://schema.org/hasPart": [ { "@id": "https://w3id.org/ro-id/9bf840e3-7a39-41fc-be39-7eed9dc294db/folders/22d9141e-0466-41c5-92c4-1429745c0fb5" }, { "@id": "https://w3id.org/ro-id/9bf840e3-7a39-41fc-be39-7eed9dc294db/folders/b18404e5-d43f-4941-b48f-d34824fb4d1a" }, { "@id": "https://w3id.org/ro-id/9bf840e3-7a39-41fc-be39-7eed9dc294db/folders/c22bad10-a38c-46ca-9d00-f5067b9c1008" }, { "@id": "https://w3id.org/ro-id/9bf840e3-7a39-41fc-be39-7eed9dc294db/folders/dd8e31ae-c297-4c34-9e4f-f55ec1bf1659" }, { "@id": "https://w3id.org/ro-id/9bf840e3-7a39-41fc-be39-7eed9dc294db/resources/3fcd7e4d-86fc-400a-80ab-756e677a5559" } ], "http://schema.org/identifier": [ { "@value": "https://w3id.org/ro-id/9bf840e3-7a39-41fc-be39-7eed9dc294db" } ], "http://schema.org/license": [ { "@id": "https://choosealicense.com/no-permission/" } ], "http://schema.org/name": [ { "@value": "POPANE DATASET - Psychophysiology Of Positive And Negative Emotions" } ], "https://www.w3.org/ns/iana/link-relations/relation#cite-as": [ { "@value": "Maciej Behnke. \"POPANE DATASET - Psychophysiology Of Positive And Negative Emotions.\" ROHub. 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These data have been used for geological, biological and habitat mapping purposes, but a single and consistent interpretation of all the acquired backscatter data has never been carried out. Here, we aimed at coherently interpreting the seafloor reflectivity datasets in order to produce aseabed and benthic habitat maps of the southern Adriatic Sea showing the spatial distribution of substrate and sediment type and grain size within the basin. The methodology here applied consists of a semi-automated classification of backscatter images through object-based image analysis (OBIA) performed through the ArcGIS tool RSOBIA (Remote Sensing OBIA). This unsupervised image segmentation was carried out on each backscatter dataset separately and then validated through comparison with bottom samples and images acquired during the different oceanographic cruises. The substrate was then classified following the a classification scheme proposed within the CoCoNet specifically elaborated in-house project. The results were described and discussed as well as the methodology applied and the significance of the backscatter data in general." } ], "http://schema.org/encodingFormat": [ { "@value": "text/plain" } ], "http://schema.org/license": [ { "@id": "https://choosealicense.com/no-permission/" } ], "http://schema.org/name": [ { "@value": "Abstract of the scientific paper" } ], "http://schema.org/sdDatePublished": [ { "@value": "2021-05-04 13:01:48.522000+00:00" } ] }, { "@id": "https://w3id.org/ro-id/a21484de-74d2-4fd0-9518-d3faa42130ad/resources/51d0e2aa-38ce-462d-ae38-148baa2ddd52", "@type": [ "http://purl.org/wf4ever/roterms#Result", "http://purl.org/wf4ever/wf4ever#Resource", "http://schema.org/MediaObject" ], "http://schema.org/author": [ { "@id": "https://orcid.org/0000-0002-3489-268X" } ], "http://schema.org/contentSize": [ { "@value": "57493288", "@type": "http://www.w3.org/2001/XMLSchema#integer" } ], "http://schema.org/contentUrl": [ { "@value": "https://api.rohub.org/api/resources/51d0e2aa-38ce-462d-ae38-148baa2ddd52/download/" } ], "http://schema.org/creator": [ { "@id": "https://orcid.org/0000-0002-3489-268X" } ], "http://schema.org/dateCreated": [ { "@value": "2021-05-04 12:58:45.722000+00:00" } ], "http://schema.org/dateModified": [ { "@value": "2022-03-24 13:17:01.157003+00:00" } ], "http://schema.org/description": [ { "@value": "http://libeccio.bo.ismar.cnr.it:8080/geonetwork/srv/ita/catalog.search#/metadata/a99af2f9-a4d5-4c57-9477-b01935bed0e8" } ], "http://schema.org/encodingFormat": [ { "@value": "application/pdf" } ], "http://schema.org/license": [ { "@id": "https://choosealicense.com/no-permission/" } ], "http://schema.org/name": [ { "@value": "RSOBIA_map_30.04.2021.pdf" } ], "http://schema.org/sdDatePublished": [ { "@value": "2021-05-04 12:58:45.722000+00:00" } ] }, { "@id": "https://w3id.org/ro-id/a21484de-74d2-4fd0-9518-d3faa42130ad/resources/d6f030ec-71ee-424e-a7f9-f306b32945d0", "@type": [ "http://purl.org/wf4ever/roterms#Sketch", "http://purl.org/wf4ever/wf4ever#Resource", "http://schema.org/MediaObject" ], "http://schema.org/author": [ { "@id": "https://orcid.org/0000-0002-3489-268X" } ], "http://schema.org/contentSize": [ { "@value": "545334", "@type": "http://www.w3.org/2001/XMLSchema#integer" } ], "http://schema.org/contentUrl": [ { "@value": "https://api.rohub.org/api/resources/d6f030ec-71ee-424e-a7f9-f306b32945d0/download/" } ], "http://schema.org/creator": [ { "@id": "https://orcid.org/0000-0002-3489-268X" } ], "http://schema.org/dateCreated": [ { "@value": "2021-08-27 09:12:01.708000+00:00" } ], "http://schema.org/dateModified": [ { "@value": "2022-03-24 13:17:07.765085+00:00" } ], "http://schema.org/description": [ { "@value": "Workflow applied in the present work visualized in Model Builder (ArcGIS 10.5) and showing the tools of RSOBIA toolset used in the present work (yellow), the input data - rasters to be seg-mented and ground-truthing data – (purple), the segmentation parameters to be chosen by the operator (light blue), the results (green) and the ArcGIS workspace (dark blue)" } ], "http://schema.org/encodingFormat": [ { "@value": "image/jpeg" } ], "http://schema.org/license": [ { "@id": "https://choosealicense.com/no-permission/" } ], "http://schema.org/name": [ { "@value": "Image of the workflow in model builder" } ], "http://schema.org/sdDatePublished": [ { "@value": "2021-08-27 09:12:01.708000+00:00" } ] }, { "@id": "https://w3id.org/ro-id/a21484de-74d2-4fd0-9518-d3faa42130ad/resources/ed3bd8c8-2411-4818-ab51-92c37623f439", "@type": [ "http://purl.org/dc/terms/BibliographicResource", "http://purl.org/wf4ever/wf4ever#Resource", "http://schema.org/MediaObject" ], "http://schema.org/author": [ { "@id": "https://orcid.org/0000-0002-0408-4411" } ], "http://schema.org/contentUrl": [ { "@value": "https://www.mdpi.com/2072-4292/13/15/2913" } ], "http://schema.org/creator": [ { "@id": "https://orcid.org/0000-0002-0408-4411" } ], "http://schema.org/dateCreated": [ { "@value": "2021-04-29 14:17:24.578000+00:00" } ], "http://schema.org/dateModified": [ { "@value": "2022-03-24 13:17:07.985558+00:00" } ], "http://schema.org/description": [ { "@value": "Prampolini, M.; Angeletti, L.; Castellan, G.; Grande, V.; Le Bas, T.; Taviani, M.; Foglini, F. 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The observatory consists of an array of light sensors including an all sky camera. It is located outside the settlement of Ny-Ålesund, approximately 1 km N-NW of the airport towards Brandalspynten. The array of sensors is mounted on a tripod under a transparent dome. This dataset contains the data of the hyperspectral radiometer USSIMO (In-situ Marine Optics, Perth, WA, Australia). It is equipped with a Zeiss MMS1 UV-VIS NIR detector with National Institute of Standards and Technology, USA traceable radiometric calibration between 380 and 900 nm. This instrument is used for time-series measurement of down-welling spectral irradiance in energy Wm-2 nm-1. Spectral resolution is 10 nm (3.3 nm pixel spacing) and a cosine-corrected polytetrafluoroethylene (PTFE) light diffusor with cosine error: <3% (0 - 60°), <10% (60 - 87.5°), is fitted. The device acquired measurements with a 16 bit analogue to digital converter. It samples continuously internally. Integration time is controlled by the sensor depending on the light intensity, with a maximum of 6 seconds. Actual integration time is stored with the data in each sample. The sensor output is saved on a PC with custom software which records 30 seconds of output data every 29:30 min. Number of samples collected in that period depends on the USSIMO integration time. The sensor is equipped with a pitch and roll sensor which is used to ensure that the spectroradiometer remains in the fixed position throughout the time-series acquisition. This dataset contains the 2019 data." } ], "http://schema.org/encodingFormat": [ { "@value": "application/ld+json" } ], "http://schema.org/hasPart": [ { "@id": "https://w3id.org/ro-id/2eed7eb1-bb6d-4336-b4ec-b832d41270af/folders/181c6825-0463-4f40-a4ee-98895d733b13" }, { "@id": "https://w3id.org/ro-id/2eed7eb1-bb6d-4336-b4ec-b832d41270af/folders/25c651d8-dec4-41fa-b30e-4026d92db36b" }, { "@id": "https://w3id.org/ro-id/2eed7eb1-bb6d-4336-b4ec-b832d41270af/folders/3b0854c9-dfa1-4ff6-9056-954761e9266f" }, { "@id": "https://w3id.org/ro-id/2eed7eb1-bb6d-4336-b4ec-b832d41270af/folders/87eb382c-130c-49ad-b0fb-d6ae5239f3a6" } ], "http://schema.org/identifier": [ { "@value": "https://w3id.org/ro-id/2eed7eb1-bb6d-4336-b4ec-b832d41270af" } ], "http://schema.org/license": [ { "@id": "https://choosealicense.com/no-permission/" } ], "http://schema.org/name": [ { "@value": "USSIMO spectroradiometer raw data time series (2019) measured under the dome of a light observatory in the Arctic (Ny-Ålesund, Svalbard, Norway)" } ], "http://w3id.org/ro-id/rohub/model#creation_mode": [ { "@value": "MANUAL" } ], "http://www.opengis.net/ont/geosparql#hasGeometry": [ { "@value": "https://w3id.org/ro-id/2eed7eb1-bb6d-4336-b4ec-b832d41270af/50972f9a-041f-46bd-9d1e-f5c5a55a40db" } ], "https://www.w3.org/ns/iana/link-relations/relation#cite-as": [ { "@value": "Jørgen Berge, Stephen Grant, Rune Bjørgum, Jonathan H. Cohen, David McKee, Geir Johnsen, Artur Zolich, Tomasz Piotr Kopec, Daniel Ludwig Vogedes, and UiT The Arctic University of Norway (UiT). \"USSIMO spectroradiometer raw data time series (2019) measured under the dome of a light observatory in the Arctic (Ny-Ålesund, Svalbard, Norway).\" ROHub. 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H., McKee, D., Johnsen, G., Zolich, A., Kopec, T. P., Vogedes, D. L., UiT The Arctic University of Norway (2021).USSIMO spectroradiometer raw data time series (2019) measured under the dome of a light observatory in the Arctic (Ny-Ålesund, Svalbard, Norway) [Data set]. Norstore. https://doi.org/10.11582/2021.00045" } ], "http://purl.org/dc/terms/rightsHolder": [ { "@value": "UiT The Arctic University of Norway (UiT)" } ], "http://purl.org/dc/terms/type": [ { "@value": "Observation" } ], "http://schema.org/author": [ { "@id": "mailto:georgehadib@gmail.com" } ], "http://schema.org/contentUrl": [ { "@value": "https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00045" } ], "http://schema.org/creator": [ { "@id": "mailto:georgehadib@gmail.com" } ], "http://schema.org/dateCreated": [ { "@value": "2021-05-19 00:00:00" } ], "http://schema.org/dateModified": [ { "@value": "2022-03-22 01:32:34.318806+00:00" } ], "http://schema.org/description": [ { "@value": "UiT The Arctic University of Norway (UiT) and the Norwegian University of Science and Technology (NTNU) established a light observatory at Kings Bay, Ny-Ålesund (Svalbard, Norway) in January 2017. The observatory consists of an array of light sensors including an all sky camera. It is located outside the settlement of Ny-Ålesund, approximately 1 km N-NW of the airport towards Brandalspynten. The array of sensors is mounted on a tripod under a transparent dome. This dataset contains the data of the hyperspectral radiometer USSIMO (In-situ Marine Optics, Perth, WA, Australia). It is equipped with a Zeiss MMS1 UV-VIS NIR detector with National Institute of Standards and Technology, USA traceable radiometric calibration between 380 and 900 nm. This instrument is used for time-series measurement of down-welling spectral irradiance in energy Wm-2 nm-1. Spectral resolution is 10 nm (3.3 nm pixel spacing) and a cosine-corrected polytetrafluoroethylene (PTFE) light diffusor with cosine error: <3% (0 - 60°), <10% (60 - 87.5°), is fitted. The device acquired measurements with a 16 bit analogue to digital converter. It samples continuously internally. Integration time is controlled by the sensor depending on the light intensity, with a maximum of 6 seconds. Actual integration time is stored with the data in each sample. The sensor output is saved on a PC with custom software which records 30 seconds of output data every 29:30 min. Number of samples collected in that period depends on the USSIMO integration time. The sensor is equipped with a pitch and roll sensor which is used to ensure that the spectroradiometer remains in the fixed position throughout the time-series acquisition. This dataset contains the 2019 data." } ], "http://schema.org/license": [ { "@id": "https://choosealicense.com/no-permission/" } ], "http://schema.org/name": [ { "@value": "USSIMO spectroradiometer raw data time series (2019) measured under the dome of a light observatory in the Arctic (Ny-Ålesund, Svalbard, Norway)" } ], "http://schema.org/sdDatePublished": [ { "@value": "2021-05-19 00:00:00" } ], "https://schema.org/maintainer": [ { "@value": "Daniel Ludwig Vogedes" } ] }, { "@id": "https://w3id.org/ro-id/2eed7eb1-bb6d-4336-b4ec-b832d41270af/ro-crate-metadata.json", "@type": [ "http://schema.org/CreativeWork" ], "http://purl.org/dc/terms/conformsTo": [ { "@id": "https://w3id.org/ro/crate/1.1" } ], "http://schema.org/about": [ { 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For ease of processing, results from both works are kept together. Results are divided in QM calculations+force field comparison and improvements, and force field parameter validations.\n\nIf you have trouble navigating, please send email to LINK: mailto:reza.611@gmail.com.\n\n\n### Please see the file \"file_organisations\" for the directories and subdirectories.\n\n# Results files are organised based on QM calculations and force field comparisons (also improvements), and Parameters validations where you have the test cases. For the QM calculations, they are based on the softwares and methods used. PSI4 directories for energy decompositions and SAPT methods, further divided in subdiectories. NWCHEM is for all other QM methods, and those can be differentiated by the dircetoty names which contains the method (DFT, MP2, CCSD(T)) and basis sets. CHARMM* directories contains the force field comparison vs the improvements test.\n\nThe Khan_etal_JCTC_2016 paper contains phenol-TMA and benzene-TMA cases. The Khan_etal_JCTC_2019 contains the indole-TMA case.\n\nFor the QM part, benzene-tma and indole-tma directories have naming explicitly (i.e. NWCHEM_cat_pi_benzene_tma/, PSI4_calc_benzene_tma/, CHARMM_pes_cat_pi_benzene_tma/). For phenol-TMA, look at the *_RESTART directories; these are the production ones for CHARMM and NWCHEM cases. For PSI4, it is PSI4_calc/). Other directories are old tests with different PES conditions (relaxation etc.). I did not remove them for my personal reference.\n\n# For the Parameters validations, reboot_piplc-dmpc/ is the test case for Khan_etal_JCTC_2016; all others are for Khan_etal_JCTC_2019.\n\n### Manuscripts files are seperated for both these works in the main directories with submission files and figures. The raw data are also there. You will also find the raw data in the different results subdirectories.\n\n*** I am writing this very quickly, so some explanations might not be obvious. 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(2021).2019_Khan_etal_JCTC [Data set]. Norstore. https://doi.org/10.11582/2021.00104" } ], "http://purl.org/dc/terms/rightsHolder": [ { "@value": "Nathalie Reuter" } ], "http://purl.org/dc/terms/type": [ { "@value": "Simulation" } ], "http://schema.org/author": [ { "@id": "mailto:georgehadib@gmail.com" } ], "http://schema.org/contentUrl": [ { "@value": "https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00104" } ], "http://schema.org/creator": [ { "@id": "mailto:georgehadib@gmail.com" } ], "http://schema.org/dateCreated": [ { "@value": "2021-11-22 00:00:00" } ], "http://schema.org/dateModified": [ { "@value": "2022-03-22 02:19:16.071315+00:00" } ], "http://schema.org/description": [ { "@value": "Results for Tyrosine-choline, Phenylalanine-choline and Tryptophan-choline force field improvements. For ease of processing, results from both works are kept together. Results are divided in QM calculations+force field comparison and improvements, and force field parameter validations.\n\nIf you have trouble navigating, please send email to LINK.\n\n\n### Please see the file \"file_organisations\" for the directories and subdirectories.\n\n# Results files are organised based on QM calculations and force field comparisons (also improvements), and Parameters validations where you have the test cases. For the QM calculations, they are based on the softwares and methods used. PSI4 directories for energy decompositions and SAPT methods, further divided in subdiectories. NWCHEM is for all other QM methods, and those can be differentiated by the dircetoty names which contains the method (DFT, MP2, CCSD(T)) and basis sets. CHARMM* directories contains the force field comparison vs the improvements test.\n\nThe Khan_etal_JCTC_2016 paper contains phenol-TMA and benzene-TMA cases. 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"@value": "Subjective experience along with physiological activity are fundamental components of emotional responding. We present a publicly available dataset of psychophysiological responses to positive and negative emotions of 1157 healthy participants, collected across seven studies. In our studies were continuously recorded affect and physiological activity during resting baseline and emotional responding. We recorded physiological responses using electrocardiography (ECG), impedance cardiography (ICG), electrodermal activity (EDA), photoplethysmography (PPG, the blood pressure measures), respiratory, and temperature sensors. In our studies, we elicited emotions with films, pictures, speech preparation, and expressive writing. We studied a wide range of positive and negative emotions, including amusement, anger, disgust, excitement, fear, gratitude, sadness, tenderness, and threat. To the best of our knowledge, psychophysiology of positive and negative emotions (POPANE) database is the largest, consistent psychophysiological dataset on emotions ever collected and publicly shared. We hope that POPANE will provide individuals, companies, and laboratories with the data they need to perform their analyses, corroborate their results, and create robust psychophysiological models of emotions. The individuals data are openly available in POPANE dataset at https://data.psychosensing.psnc.pl/popane/index.html. " }, { "@value": "Subjective experience along with physiological activity are fundamental components of emotional responding. We present a publicly available dataset of psychophysiological responses to positive and negative emotions of 1157 healthy participants, collected across seven studies. In our studies were continuously recorded affect and physiological activity during resting baseline and emotional responding. We recorded physiological responses using electrocardiography (ECG), impedance cardiography (ICG), electrodermal activity (EDA), photoplethysmography (PPG, the blood pressure measures), respiratory, and temperature sensors. In our studies, we elicited emotions with films, pictures, speech preparation, and expressive writing. We studied a wide range of positive and negative emotions, including amusement, anger, disgust, excitement, fear, gratitude, sadness, tenderness, and threat. To the best of our knowledge, psychophysiology of positive and negative emotions (POPANE) database is the largest, consistent psychophysiological dataset on emotions ever collected and publicly shared. We hope that POPANE will provide individuals, companies, and laboratories with the data they need to perform their analyses, corroborate their results, and create robust psychophysiological models of emotions. The individuals data are openly available in POPANE dataset at https://data.psychosensing.psnc.pl/popane/index.html." } ], "http://schema.org/encodingFormat": [ { "@value": "application/ld+json" } ], "http://schema.org/hasPart": [ { "@id": "https://w3id.org/ro-id/a3a81739-a5b7-4897-b732-7aba23d6fa5a/folders/1acbaa0f-a556-4db4-9f34-facff9c7e3eb" }, { "@id": "https://w3id.org/ro-id/a3a81739-a5b7-4897-b732-7aba23d6fa5a/folders/784698d7-52ef-4848-9ba5-fde7307a0175" }, { "@id": "https://w3id.org/ro-id/a3a81739-a5b7-4897-b732-7aba23d6fa5a/folders/d0b652a8-c923-4338-9329-2748e88ab632" }, { "@id": "https://w3id.org/ro-id/a3a81739-a5b7-4897-b732-7aba23d6fa5a/folders/fa341e1e-81a3-429b-bd12-6a303b3cb9c4" }, { "@id": "https://w3id.org/ro-id/a3a81739-a5b7-4897-b732-7aba23d6fa5a/resources/8d3bb9fc-ec66-4067-a4fd-e95eb17152fb" } ], "http://schema.org/identifier": [ { "@value": "https://w3id.org/ro-id/a3a81739-a5b7-4897-b732-7aba23d6fa5a" } ], "http://schema.org/license": [ { "@id": "https://choosealicense.com/no-permission/" } ], "http://schema.org/name": [ { "@value": "POPANE DATASET - Psychophysiology Of Positive And Negative Emotions" } ], "http://www.w3.org/ns/prov#wasRevisionOf": [ { "@value": "http://sandbox.rohub.org/rodl/ROs/POPANE-1-snapshot-1/" }, { "@value": "https://w3id.org/ro-id/a3a81739-a5b7-4897-b732-7aba23d6fa5a" } ], "https://www.w3.org/ns/iana/link-relations/relation#cite-as": [ { "@value": "Maciej Behnke. \"POPANE DATASET - Psychophysiology Of Positive And Negative Emotions.\" ROHub. 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We present a publicly available dataset of psychophysiological responses to positive and negative emotions of 1157 healthy participants, collected across seven studies. In our studies were continuously recorded affect and physiological activity during resting baseline and emotional responding. We recorded physiological responses using electrocardiography (ECG), impedance cardiography (ICG), electrodermal activity (EDA), photoplethysmography (PPG, the blood pressure measures), respiratory, and temperature sensors. In our studies, we elicited emotions with films, pictures, speech preparation, and expressive writing. We studied a wide range of positive and negative emotions, including amusement, anger, disgust, excitement, fear, gratitude, sadness, tenderness, and threat. To the best of our knowledge, psychophysiology of positive and negative emotions (POPANE) database is the largest, consistent psychophysiological dataset on emotions ever collected and publicly shared. We hope that POPANE will provide individuals, companies, and laboratories with the data they need to perform their analyses, corroborate their results, and create robust psychophysiological models of emotions. The individuals data are openly available in POPANE dataset at https://data.psychosensing.psnc.pl/popane/index.html. " }, { "@value": "Subjective experience along with physiological activity are fundamental components of emotional responding. We present a publicly available dataset of psychophysiological responses to positive and negative emotions of 1157 healthy participants, collected across seven studies. In our studies were continuously recorded affect and physiological activity during resting baseline and emotional responding. We recorded physiological responses using electrocardiography (ECG), impedance cardiography (ICG), electrodermal activity (EDA), photoplethysmography (PPG, the blood pressure measures), respiratory, and temperature sensors. In our studies, we elicited emotions with films, pictures, speech preparation, and expressive writing. We studied a wide range of positive and negative emotions, including amusement, anger, disgust, excitement, fear, gratitude, sadness, tenderness, and threat. To the best of our knowledge, psychophysiology of positive and negative emotions (POPANE) database is the largest, consistent psychophysiological dataset on emotions ever collected and publicly shared. We hope that POPANE will provide individuals, companies, and laboratories with the data they need to perform their analyses, corroborate their results, and create robust psychophysiological models of emotions. The individuals data are openly available in POPANE dataset at https://data.psychosensing.psnc.pl/popane/index.html." } ], "http://schema.org/encodingFormat": [ { "@value": "application/ld+json" } ], "http://schema.org/hasPart": [ { "@id": "https://w3id.org/ro-id/78c3584e-592e-4af6-bf23-605e8a1b84c0/folders/45b2430b-eca8-40fa-b9b6-0bc93d775282" }, { "@id": "https://w3id.org/ro-id/78c3584e-592e-4af6-bf23-605e8a1b84c0/folders/bc86183a-7370-47fe-b3a8-4ce30af0a95c" }, { "@id": "https://w3id.org/ro-id/78c3584e-592e-4af6-bf23-605e8a1b84c0/folders/c9095baa-7f91-441f-899b-9bcd12b0164a" }, { "@id": "https://w3id.org/ro-id/78c3584e-592e-4af6-bf23-605e8a1b84c0/folders/f8336560-acdf-405d-9ba1-669d1ded4539" }, { "@id": "https://w3id.org/ro-id/78c3584e-592e-4af6-bf23-605e8a1b84c0/resources/4b052295-b892-492e-8137-612fbc977f9a" } ], "http://schema.org/identifier": [ { "@value": "https://w3id.org/ro-id/78c3584e-592e-4af6-bf23-605e8a1b84c0" } ], "http://schema.org/license": [ { "@id": "https://choosealicense.com/no-permission/" } ], "http://schema.org/name": [ { "@value": "POPANE DATASET - Psychophysiology Of Positive And Negative Emotions" } ], "http://www.w3.org/ns/prov#wasRevisionOf": [ { "@value": "http://sandbox.rohub.org/rodl/ROs/POPANE-1-snapshot/" } ], "https://www.w3.org/ns/iana/link-relations/relation#cite-as": [ { "@value": "Maciej Behnke. \"POPANE DATASET - Psychophysiology Of Positive And Negative Emotions.\" ROHub. 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The RO consists of three different workflows created through the Taverna Workbench Enterprise application and they are supposed to be run sequentially (“SinkTrackRead”, \"sinkTrackGraph\", \"SinkTrack\")." } ], "http://schema.org/author": [ { "@id": "https://w3id.org/ro-id/aaed8d9a-b995-4de7-a84a-35534103581b/" } ], "http://schema.org/contentSize": [ { "@value": "16352", "@type": "http://www.w3.org/2001/XMLSchema#integer" } ], "http://schema.org/contentUrl": [ { "@value": "https://api.rohub.org/api/ros/aaed8d9a-b995-4de7-a84a-35534103581b/crate/download/" } ], "http://schema.org/creator": [ { "@id": "http://everest.psnc.pl/users/antonio.petrizzo" } ], "http://schema.org/dateCreated": [ { "@value": "2019-12-05 15:31:13.771000+00:00" } ], "http://schema.org/dateModified": [ { "@value": "2025-03-05 01:19:05.544145+00:00" } ], "http://schema.org/datePublished": [ { "@value": "2019-12-05 15:31:13.771000+00:00" } ], "http://schema.org/description": [ { "@value": "It reads and elaborates ASCII file produced with FM Midwater in order to calculate some statistics, mean, standard deviation of backscatter, depth, angle of net for each ping, and to produce a graph with the track of the net" } ], "http://schema.org/encodingFormat": [ { "@value": "application/ld+json" } ], "http://schema.org/funder": [ { "@value": "EASME/EMFF/2017/1.2.1.12/S2/05/SI2.789314" } ], "http://schema.org/hasPart": [ { "@id": "https://w3id.org/ro-id/aaed8d9a-b995-4de7-a84a-35534103581b/folders/07f86f7c-cf27-4647-95f3-4e77be577654" }, { "@id": "https://w3id.org/ro-id/aaed8d9a-b995-4de7-a84a-35534103581b/folders/51dab0dc-d891-4b00-a058-643f86c613b0" }, { "@id": "https://w3id.org/ro-id/aaed8d9a-b995-4de7-a84a-35534103581b/folders/904fe1cb-bd56-4cd5-98dc-b82766822dea" }, { "@id": "https://w3id.org/ro-id/aaed8d9a-b995-4de7-a84a-35534103581b/folders/b36459ea-4af5-4324-a7bd-2bc67433454c" } ], "http://schema.org/identifier": [ { "@value": "https://w3id.org/ro-id/aaed8d9a-b995-4de7-a84a-35534103581b" } ], "http://schema.org/license": [ { "@id": "https://choosealicense.com/no-permission/" } ], "http://schema.org/name": [ { "@value": "SinkTrack" } ], "http://www.acsys.it/everest#vrc": [ { "@value": "Sea Monitoring" } ], "http://www.w3.org/ns/prov#wasRevisionOf": [ { "@value": "http://sandbox.rohub.org/rodl/ROs/sinktrack_SNAPSHOT/" }, { "@value": "https://w3id.org/ro-id/aaed8d9a-b995-4de7-a84a-35534103581b" } ], "https://www.w3.org/ns/iana/link-relations/relation#cite-as": [ { "@value": "Aleksandra Kruss (ISMAR-CNR Venice), Antonio Petrizzo (ISMAR-CNR Venice), and Fantina Madricardo (ISMAR-CNR Venice). \"SinkTrack.\" ROHub. 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