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Yet, the high dimensionality and strong spectral redundancy in HSIs often reduce the efficiency and reliability of machine learning models. These challenges are especially important in wildfire science and prescribed-fire monitoring, where spectral responses vary due to burn severity, char deposition, canopy structure, and early vegetation recovery. Benchmark datasets such as Indian Pines and Pavia University and others provide controlled environments for algorithms’ evaluation, but real-world post-fire forest conditions pose additional complexity. This study presents a unified and comprehensive evaluation of five dimensionality reduction strategies: Principal Component Analysis (PCA), Spatial–Spectral Edge Preservation (SSEP), Spectral-Redundancy Penalized Attention (SRPA), and a Deep Reinforcement Learning (DRL)-based selector together with a clustering based baseline, K-Means Clustering-Based Band Selection (KMCBS). These strategies are combined with classical machine learning and deep learning classifiers: Random Forest (RF), Support Vector Machines (SVMs), K-Nearest Neighbors (KNNs), and 3D Convolutional Neural Networks (3D-CNN). The full pipeline includes exploratory data analysis, preprocessing, patch-based spatial–spectral modeling, consistent train–validation protocols, and multi-dataset evaluation across Indian Pines, Pavia University, and a new custom VNIR hyperspectral dataset collected after prescribed burns at the Lubrecht Experimental Forest in Montana, USA. By systematically comparing statistical, edge-aware, attention-guided, and reinforcement learning-based band-selection strategies, this work identifies compact yet informative spectral subsets that enhance classification performance while reducing computational cost. 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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" } ], "@type": [ "http://purl.org/wf4ever/ro#ResearchObject", "http://purl.org/wf4ever/roevo#LiveRO", "http://schema.org/Dataset", "http://w3id.org/ro/earth-science#DataResearchObject", "https://w3id.org/ro/terms/earth-science#DataResearchObject" ], "https://www.w3.org/ns/iana/link-relations/relation#cite-as": [ { "@value": "Jørgen Berge, Stephen Grant, Rune Bjørgum, Jonathan H. 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}, { "@id": "http://eurovoc.europa.eu/3946" }, { "@id": "http://eurovoc.europa.eu/5966" } ], "http://schema.org/author": [ { "@id": "mailto:nathalie.reuter@rohub.com" } ], "http://schema.org/contentSize": [ { "@value": "9478", "@type": "http://www.w3.org/2001/XMLSchema#integer" } ], "http://schema.org/contentUrl": [ { "@value": "https://api.rohub.org/api/ros/b7df46c2-d54e-4cf0-8600-36516706cfbe/crate/download/" } ], "http://schema.org/creator": [ { "@id": "mailto:georgehadib@gmail.com" } ], "http://schema.org/dateCreated": [ { "@value": "2022-03-22 02:18:37.698329+00:00" } ], "http://schema.org/dateModified": [ { "@value": "2025-03-05 00:45:29.658387+00:00" } ], "http://schema.org/datePublished": [ { "@value": "2022-03-22 02:18:37.698329+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: 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. 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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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"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: 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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Mar 22 ,2022. https://w3id.org/ro-id/6d7f7856-f2c8-4172-9a35-ef22b6cc4561." } ] }, { "@id": "mailto:nathalie.reuter@rohub.com", "http://schema.org/email": [ { "@value": "nathalie.reuter@rohub.com" } ], "http://schema.org/name": [ { "@value": "Nathalie Reuter" } ], "@type": [ "http://xmlns.com/foaf/0.1/Agent" ] }, { "@id": "mailto:georgehadib@gmail.com", "http://schema.org/name": [ { "@value": "Geo H." } ], "@type": [ "http://xmlns.com/foaf/0.1/Agent" ] }, { "@id": "https://w3id.org/ro-id/6d7f7856-f2c8-4172-9a35-ef22b6cc4561/folders/1bf7ee26-d3bb-4cc3-a7e4-830ccc3308db", "http://schema.org/name": [ { "@value": "metadata" } ], "@type": [ "http://purl.org/wf4ever/wf4ever#Folder", "http://schema.org/Dataset" ] }, { "@id": "https://w3id.org/ro-id/6d7f7856-f2c8-4172-9a35-ef22b6cc4561/folders/24bfb8db-86a7-4643-a7f7-fd82ac93af38", "http://schema.org/name": [ { "@value": "raw data" } ], "@type": [ "http://purl.org/wf4ever/wf4ever#Folder", "http://schema.org/Dataset" ] }, { "@id": "https://w3id.org/ro-id/6d7f7856-f2c8-4172-9a35-ef22b6cc4561/folders/9697f106-4d50-471c-acb9-de5359dfa56e", "http://schema.org/hasPart": [ { "@id": "https://w3id.org/ro-id/6d7f7856-f2c8-4172-9a35-ef22b6cc4561/resources/d429730d-0d92-45e0-8dec-7729b90a2ab9" } ], "http://schema.org/name": [ { "@value": "data" } ], "@type": [ "http://purl.org/wf4ever/wf4ever#Folder", "http://schema.org/Dataset" ] }, { "@id": "https://w3id.org/ro-id/6d7f7856-f2c8-4172-9a35-ef22b6cc4561/folders/9d0344fb-3af9-4551-8b59-c6ae2e47250e", "http://schema.org/hasPart": [ { "@id": "https://w3id.org/ro-id/6d7f7856-f2c8-4172-9a35-ef22b6cc4561/resources/9faa8baa-e43c-459d-a5bf-d4dcf265d187" } ], "http://schema.org/name": [ { "@value": "biblio" } ], "@type": [ "http://purl.org/wf4ever/wf4ever#Folder", "http://schema.org/Dataset" ] }, { "@id": "https://w3id.org/ro-id/6d7f7856-f2c8-4172-9a35-ef22b6cc4561/resources/d429730d-0d92-45e0-8dec-7729b90a2ab9", "http://purl.org/dc/terms/bibliographicCitation": [ { "@value": "Reuter, N. (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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If you have doubt, please send email to LINK." } ], "http://schema.org/license": [ { "@id": "https://choosealicense.com/no-permission/" } ], "http://schema.org/name": [ { "@value": "2019_Khan_etal_JCTC" } ], "http://schema.org/sdDatePublished": [ { "@value": "2021-11-22 00:00:00" } ], "@type": [ "http://purl.org/wf4ever/wf4ever#Dataset", "http://purl.org/wf4ever/wf4ever#Resource", "http://schema.org/MediaObject" ], "https://schema.org/maintainer": [ { "@value": "Nathalie Reuter" } ] }, { "@id": "https://w3id.org/ro-id/6d7f7856-f2c8-4172-9a35-ef22b6cc4561/resources/9faa8baa-e43c-459d-a5bf-d4dcf265d187", "http://schema.org/author": [ { "@id": "mailto:georgehadib@gmail.com" } ], "http://schema.org/contentUrl": [ { "@value": "https://doi.org/10.1021/acs.jctc.8b00839" } ], "http://schema.org/creator": [ { "@id": "mailto:georgehadib@gmail.com" } ], "http://schema.org/dateCreated": [ { "@value": "2022-03-22 02:19:11.787124+00:00" } ], "http://schema.org/dateModified": [ { "@value": 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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" } ], "@type": [ "http://purl.org/wf4ever/roterms#Hypothesis", "http://purl.org/wf4ever/wf4ever#Resource", "http://schema.org/MediaObject" ] }, { "@id": "https://w3id.org/ro-id/a21484de-74d2-4fd0-9518-d3faa42130ad/resources/51d0e2aa-38ce-462d-ae38-148baa2ddd52", "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" } ], "@type": [ "http://purl.org/wf4ever/roterms#Result", "http://purl.org/wf4ever/wf4ever#Resource", "http://schema.org/MediaObject" ] }, { "@id": "https://w3id.org/ro-id/a21484de-74d2-4fd0-9518-d3faa42130ad/resources/d6f030ec-71ee-424e-a7f9-f306b32945d0", "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" } ], "@type": [ "http://purl.org/wf4ever/roterms#Sketch", "http://purl.org/wf4ever/wf4ever#Resource", "http://schema.org/MediaObject" ] }, { "@id": "https://w3id.org/ro-id/a21484de-74d2-4fd0-9518-d3faa42130ad/resources/ed3bd8c8-2411-4818-ab51-92c37623f439", "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. Benthic Habitat Map of the Southern Adriatic Sea (Mediterranean Sea) from Object-Based Image Analysis of Multi-Source Acoustic Backscatter Data. 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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. 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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. 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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. 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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. 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