cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.8134430
title: Tracking behavior in naturalistic situations using artificial intelligence

description: Folder "Data for analysis" : entire data preprocessed used for analysis Folder "Examples" :<br> - Folder "Example Data To Preprocess" : raw data after extraction for one subject (test data) with OpenPose folder containing a json file for each frame and OpenFace csv containing the extracted metrics of a whole video<br> - Example_Data_For_Analysis : test data for analysis with reduce amount of data in all subjects <em><strong>Abstract</strong> : Environmental context, e.g. social interactions, can shape our behavior, due to the range of stimuli it entails. Current research on this topic often relies on controlled laboratory settings, which may not fully capture the complexity and richness of real-life social situations.</em> <em>To overcome this limitation, we propose a new approach that leverages artificial intelligence (AI) tools, specifically computer vision, machine learning algorithms and explainability methods to extract, classify and highlight behaviors in naturalistic situations.<br> To validate our methodology, we conducted a study with sixteen participants in various naturalistic situations that varied socially and emotionally, including periods of silence, listening to music or neutral sounds, and listening to someone speaking about autobiographical or insignificant content in front of them.</em> <em>We adapted two open-source computer vision models, OpenFace and OpenPose, to extract objective whole-body metrics, such as skeletal and facial points, gaze, and facial action unit activations.<br> Applying two different machine learning models on the extracted behavioral metrics, extreme gradient boosting (XGBoost) and a long short-term memory neural network (LSTM), we accurately classified the situations up to around 90% accuracy for the social vs non-social situations.<br> Using advanced state-of-the-art explainability methods in conjunction with hypothesis-driven analysis, we identified distinct clusters or specific behaviors that were influenced by the social and emotional aspects of the situations. These behaviors included movements related to gaze, limbs, facial features, and facial expressions activations.<br> Our study highlights the potential of AI methodology to track behavior precisely and objectively in naturalistic contexts, paving the way for further research.</em>
DOI: 10.5281/zenodo.8123860
title: Tracking behavior in naturalistic situations using artificial intelligence

description: Folder "Data for analysis" :<br> - Folder "Extracted_Data_For_Preprocessing" : raw data after extraction for all subjects (1 folder by subs) that contains OpenPose folder (json file for each frame) and OpenFace csv with the extracted metrics of a whole video. It also contains 1 file .tps / 1 file .syncIN / 1 file .txt (SliderRes) for the script 01_loader (see on the associated github repository) and 1 file .tps (logs of OpenFace).<br> - File "Data_For_Analysis_Michelot_2023.csv" : entire data after preprocessing used for analysis Folder "Examples" :<br> - Folder "Example Data To Preprocess" : raw data after extraction for one subject (test data) with OpenPose folder containing a json file for each frame and OpenFace csv containing the extracted metrics of a whole video. It also contains 1 file .tps / 1 file .syncIN / 1 file .txt (SliderRes) for the script 01_loader (see on the associated github repository) and 1 file .tps (logs of OpenFace).<br> - Example_Data_For_Analysis.csv : test data for analysis with reduce amount of data in all subjects. <em><strong>Abstract</strong> : Environmental context, e.g. social interactions, can shape our behavior, due to the range of stimuli it entails. Current research on this topic often relies on controlled laboratory settings, which may not fully capture the complexity and richness of real-life social situations.</em> <em>To overcome this limitation, we propose a new approach that leverages artificial intelligence (AI) tools, specifically computer vision, machine learning algorithms and explainability methods to extract, classify and highlight behaviors in naturalistic situations.<br> To validate our methodology, we conducted a study with sixteen participants in various naturalistic situations that varied socially and emotionally, including periods of silence, listening to music or neutral sounds, and listening to someone speaking about autobiographical or insignificant content in front of them.</em> <em>We adapted two open-source computer vision models, OpenFace and OpenPose, to extract objective whole-body metrics, such as skeletal and facial points, gaze, and facial action unit activations.<br> Applying two different machine learning models on the extracted behavioral metrics, extreme gradient boosting (XGBoost) and a long short-term memory neural network (LSTM), we accurately classified the situations up to around 90% accuracy for the social vs non-social situations.<br> Using advanced state-of-the-art explainability methods in conjunction with hypothesis-driven analysis, we identified distinct clusters or specific behaviors that were influenced by the social and emotional aspects of the situations. These behaviors included movements related to gaze, limbs, facial features, and facial expressions activations.<br> Our study highlights the potential of AI methodology to track behavior precisely and objectively in naturalistic contexts, paving the way for further research.</em>
DOI: 10.5281/zenodo.8138258
title: Tracking behavior in naturalistic situations using artificial intelligence

description: Folder "Data for analysis" : entire data preprocessed used for analysis Folder "Examples" :<br> - Folder "Example Data To Preprocess" : raw data after extraction for one subject (test data) with OpenPose folder containing a json file for each frame and OpenFace csv containing the extracted metrics of a whole video<br> - Example_Data_For_Analysis : test data for analysis with reduce amount of data in all subjects <em><strong>Abstract</strong> : Environmental context, e.g. social interactions, can shape our behavior, due to the range of stimuli it entails. Current research on this topic often relies on controlled laboratory settings, which may not fully capture the complexity and richness of real-life social situations.</em> <em>To overcome this limitation, we propose a new approach that leverages artificial intelligence (AI) tools, specifically computer vision, machine learning algorithms and explainability methods to extract, classify and highlight behaviors in naturalistic situations.<br> To validate our methodology, we conducted a study with sixteen participants in various naturalistic situations that varied socially and emotionally, including periods of silence, listening to music or neutral sounds, and listening to someone speaking about autobiographical or insignificant content in front of them.</em> <em>We adapted two open-source computer vision models, OpenFace and OpenPose, to extract objective whole-body metrics, such as skeletal and facial points, gaze, and facial action unit activations.<br> Applying two different machine learning models on the extracted behavioral metrics, extreme gradient boosting (XGBoost) and a long short-term memory neural network (LSTM), we accurately classified the situations up to around 90% accuracy for the social vs non-social situations.<br> Using advanced state-of-the-art explainability methods in conjunction with hypothesis-driven analysis, we identified distinct clusters or specific behaviors that were influenced by the social and emotional aspects of the situations. These behaviors included movements related to gaze, limbs, facial features, and facial expressions activations.<br> Our study highlights the potential of AI methodology to track behavior precisely and objectively in naturalistic contexts, paving the way for further research.</em>
DOI: 10.5281/zenodo.8139206
title: Tracking behavior in naturalistic situations using artificial intelligence

description: Folder "Data for analysis" :<br> - Folder "Extracted_Data_For_Preprocessing" : raw data after extraction for all subjects (1 folder by subs) that contains OpenPose folder (json file for each frame) and OpenFace csv with the extracted metrics of a whole video. It also contains 1 file .tps / 1 file .syncIN / 1 file .txt (SliderRes) for the script 01_loader (see on the associated github repository) and 1 file .tps (logs of OpenFace).<br> - File "Data_For_Analysis_Michelot_2023.csv" : entire data after preprocessing used for analysis Folder "Examples" :<br> - Folder "Example Data To Preprocess" : raw data after extraction for one subject (test data) with OpenPose folder containing a json file for each frame and OpenFace csv containing the extracted metrics of a whole video. It also contains 1 file .tps / 1 file .syncIN / 1 file .txt (SliderRes) for the script 01_loader (see on the associated github repository) and 1 file .tps (logs of OpenFace).<br> - Example_Data_For_Analysis.csv : test data for analysis with reduce amount of data in all subjects. <em><strong>Abstract</strong> : Environmental context, e.g. social interactions, can shape our behavior, due to the range of stimuli it entails. Current research on this topic often relies on controlled laboratory settings, which may not fully capture the complexity and richness of real-life social situations.</em> <em>To overcome this limitation, we propose a new approach that leverages artificial intelligence (AI) tools, specifically computer vision, machine learning algorithms and explainability methods to extract, classify and highlight behaviors in naturalistic situations.<br> To validate our methodology, we conducted a study with sixteen participants in various naturalistic situations that varied socially and emotionally, including periods of silence, listening to music or neutral sounds, and listening to someone speaking about autobiographical or insignificant content in front of them.</em> <em>We adapted two open-source computer vision models, OpenFace and OpenPose, to extract objective whole-body metrics, such as skeletal and facial points, gaze, and facial action unit activations.<br> Applying two different machine learning models on the extracted behavioral metrics, extreme gradient boosting (XGBoost) and a long short-term memory neural network (LSTM), we accurately classified the situations up to around 90% accuracy for the social vs non-social situations.<br> Using advanced state-of-the-art explainability methods in conjunction with hypothesis-driven analysis, we identified distinct clusters or specific behaviors that were influenced by the social and emotional aspects of the situations. These behaviors included movements related to gaze, limbs, facial features, and facial expressions activations.<br> Our study highlights the potential of AI methodology to track behavior precisely and objectively in naturalistic contexts, paving the way for further research.</em>
DOI: 10.6084/m9.figshare.23677407
title: The data for the NEE paper: Structural complexity biases vegetation greenness measures

description: The data for the NEE paper: Structural complexity biases vegetation greenness measures
DOI: 10.5281/zenodo.8144304
title: GenB_Dataset

description: This dataset describes the management of all the data generated during GenB activities, including how data are collected, managed, stored and made available during the project and how they will be shared upon project completion.
DOI: 10.5281/zenodo.8144303
title: GenB_Dataset

description: This dataset describes the management of all the data generated during GenB activities, including how data are collected, managed, stored and made available during the project and how they will be shared upon project completion.
DOI: 10.6084/m9.figshare.23677263
title: <strong>“Back to the Fundamentals”: Global Trophic Levels and Functional Groups respond differently to Land Use Impacts</strong>

description: A global understanding of biodiversity responses to land use impacts can improve our future prediction to aid conservation planning and management. However, two problems hinder this: 1.) Land use impact studies are taxonomically biased. 2.) Land use history is understudied; To tackle these challenges, the function of biodiversity (trophic levels and functional groups) is focused on equally exploring responses to the effect of land type and intensity (LUTI), and history (LUH). I used PREDICTS to calculate LUTI species richness and assigned trophic levels and functional groups using Newbold et al.'s (2020) dataset, cGloNAF, and FunGuild. LUH was calculated from LUH-v2h. The species richness, LUH, and LUTI were modelled with a generalised linear mixed-effect model. Land use responses showed that producers’ richness benefited from intermediate disturbances, while consumers and decomposers suffered. Producers’ and decomposers’ species richness is better modelled by LUH, suggesting a soil legacy effect. Consumers were more impacted by LUTI. Responses to LUH revealed a co-interaction effect of: R-strategy dominating early recovery, carnivores and omnivores targeting sensitive herbivores, and decomposers richness depending on plant hosts. This study revealed the hidden responses of biodiversity function to LUTI and LUH. Future studies can aim to build on my work with finer functional groups, better fungal records, or incorporate distance to the closest forest.
DOI: 10.5683/sp2/90qhuu
title: Canadian Culture, 2015

description: Canadian culture has long been exposed to outside influences, but most consider Canadian culture to be unique and worthy of protection. This survey looks at two major Canadian cultural institutions – the Canadian Broadcasting Corporation (CBC) and the Canadian Radio-television and Telecommunications Commission (CRTC).
DOI: 10.25345/c5wd3qb8w
title: MassIVE MSV000092433 - Molecular Basis for Maternal Inheritance of Human Mitochondrial DNA
title: Uniparental inheritance of mitochondrial DNA (mtDNA) is an evolutionary trait found in nearly all eukaryotes. In many species, including humans, the sperm mitochondria are introduced to the oocyte during fertilization. The mechanisms hypothesized to prevent paternal mtDNA transmission include ubiquitination of the sperm mitochondria and mitophagy. However, the causative mechanisms of paternal mtDNA elimination have not been defined. We found that mitochondria in human spermatozoa are devoid of mtDNA and lack mitochondrial transcription factor A (TFAM), the major nucleoid protein required to protect, maintain, and transcribe mtDNA. During spermatogenesis, sperm cells express an isoform of TFAM, which retains the mitochondrial pre-sequence, ordinarily removed upon mitochondrial import. Phosphorylation of this pre-sequence prevents mitochondrial import and directs TFAM to the spermatozoon nucleus. TFAM re-localization from the mitochondria of spermatogonia to the spermatozoa nucleus directly correlates with the elimination of mitochondrial DNA, thereby explaining maternal inheritance in this species.
DOI: 10.5281/zenodo.8144290
title: Terrestrial and biological activities shaped the fate of dissolved organic nitrogen in a subtropical river-dominated estuary and adjacent coastal area

description: Here are two files including the nutrient and environmental parameters and other data in the Pearl River Estuary.
DOI: 10.5281/zenodo.8144289
title: Terrestrial and biological activities shaped the fate of dissolved organic nitrogen in a subtropical river-dominated estuary and adjacent coastal area

description: Here are two files including the nutrient and environmental parameters and other data in the Pearl River Estuary.
DOI: 10.2908/tin00072
title: Individuals who have carried out 5 or 6 of the computer related activities
title: Particuliers qui ont effectué 5 ou 6 des activités liées à l'informatique
title: Personen, die 5 oder 6 der computerbezogenen Aktivitäten ausgeführt haben

description: Computerkenntnisse werden durch Eigeneinschätzung bewertet, je nachdem wieviele Computer bezogenen Aufgaben durchgeführt wurden, ohne diese Kenntnisse zu beurteilenden, zu überprüfen oder tatsächlich zu beobachten. 5 oder 6 der folgenden Aufgaben wurden bereits ausgeführt: Kopieren / Verschieben von Dateien / Ordnern, Kopieren / Ausschneiden / Einfügen von Informationen auf dem Bildschirm, Verwenden grundlegender arithmetischer Formeln, Komprimieren von Dateien, Anschliessen und Installieren von neuen Geräten oder Schreiben eines Computer-Programmes.
description: Computer skills are measured using a self-assessment approach, depending on how many computer related tasks have been carried out, without these skills being assessed, tested or actually observed. 5 or 6 of the following activities have been done: copied/moved a file/folder, copy/cut/paste to move information on the screen, used basic arithmetic formulae, compressed/zipped a file, connecting and installing new devices or written a computer program.
description: Les compétences informatiques sont mesurées en utilisant une approche d'auto-évaluation, dépendant du nombre des tâches informatiques effectuées, sans que ces compétences aient été évaluées, testées ou effectivement observées. Avoir effectué 5 ou 6 des tâches suivantes: copié / déplacé un fichier / dossier, copié / coupé / collé pour déplacer des informations sur l'écran, utilisé des formules arithmétiques de base, comprimé / zippé un fichier, connecté et installé de nouveaux appareils ou écrit un programme informatique.
DOI: 10.7281/t1/7vky8k/mcsmyu
title: Data_for_Ireland_etal_2018_ChargedPolymersNOVEC.zip

description: Data tables and graphs in Igor Pro format  PDF supplementing Fig.5-6 (Demo version available to view at https://www.wavemetrics.com)
DOI: 10.5281/zenodo.8144238
title: Synthetic OSCD

description: A synthetic dataset for change detection, generated using a diffusion model trained on OSCD
description: {"references": ["Rodrigo Caye Daudt et al. Urban change detection for multispectral Earth observation using convolutional neural networks. In IGARSS 2018-2018 IEEE Inter- national Geoscience and Remote Sensing Symposium. Ieee, 2018"]}
DOI: 10.5281/zenodo.8144237
title: Synthetic OSCD

description: A synthetic dataset for change detection, generated using a diffusion model trained on OSCD
description: {"references": ["Rodrigo Caye Daudt et al. Urban change detection for multispectral Earth observation using convolutional neural networks. In IGARSS 2018-2018 IEEE Inter- national Geoscience and Remote Sensing Symposium. Ieee, 2018"]}
DOI: 10.13012/b2idb-0477888_v1
title: Integrating multiple data sources improves prediction and inference for upland game occupancy models

description: Data and documentation for Ornithological Applications manuscript “Integrating multiple data sources improves prediction and inference for upland game bird occupancy models” by Robert L. Emmet, Thomas J. Benson, Maximilian L. Allen, and Kirk W. Stodola We combined data from the North American Breeding Bird Survey and eBird with a targeted survey (IDNR upland game) to estimate habitat use of northern bobwhite and ring-necked pheasant in Illinois and to document the efficiency and overlap among the various data sources. Data include, eBird, USGS Breeding Bird Survey, National Land Cover Database, Upland game bird surveys, stream data)
DOI: 10.13012/b2idb-5748609_v1
title: Data from: DISCO+QR: Rooting Species Trees in the Presence of GDL and ILS

description: Data sets from "DISCO+QR: Rooting Species Trees in the Presence of GDL and ILS." It contains trees and sequences simulated with gene duplication and loss under a variety of different conditions. Note: - trees.tar.gz contains the simulated gene-family trees used in our experiments (both true trees from SimPhy as well as trees estimated from alignments). - alignments.tar.gz contains simulated sequence data used for estimating the gene-family trees
DOI: 10.13012/b2idb-1613645_v1
title: Primary Occurrence Data for "Clem, Hart, & McElrath. 2023. A century of Illinois hover flies (Diptera: Syrphidae): Museum and citizen science data reveal recent range expansions, contractions, and species of potential conservation significance"

description: Primary occurrence data for Clem, Hart, & McElrath. 2023. A century of Illinois hover flies (Diptera: Syrphidae): Museum and citizen science data reveal recent range expansions, contractions, and species of potential conservation significance. Included are a license.txt file, the cleaned occurrences from each of the six merged datasets, and a cleaned, merged dataset containing all occurrence records in one spreadsheet, formatted according to Darwin Core standards, with a few extra fields such as GBIF identifiers that were included in some of the original downloads.
DOI: 10.6084/m9.figshare.23677522
title: <i>In-silico</i> structural studies on anti-inflammatory activity of phytocompounds from the genus Andrographis

description: Plant species from the genus <i>Andrographis</i> were used in Ayurveda and in other folklore medicines for treating ailments for centuries. In this study, we have hypothesized to evaluate the secondary metabolites as potent anti-inflammatory agents from the genus <i>Andrographis.</i> A library of secondary metabolites of the said genus was curated from the PubChem database and was subjected to energy minimization using UCSF Chimera software employing the AMBER force field. Initially, molecular docking was performed to evaluate the binding affinity of the curated library against the enzymes cyclooxygenase-1 (COX-1), cyclooxygenase-2 (COX-2), and 5-Lipoxygenase (5-LOX) using AutoDock Vina. This resulted in shortlisting of two metabolites Echioidinin 5-O-glucoside was bound and 5,2',6'-Trihydroxy-6,7,8-trimethoxy flavone 2'-O-glucoside with high binding affinity than standard drugs Ibuprofen and Zileuton. In addition, molecular dynamic simulation studies confirm that these compounds form relatively more stable complexes than standard drugs with the above-said enzymes. The free binding energy values using MMGBSA of the above said ligands with COX-1, COX-2, and 5-LOX were found to be −49.18 kcal/mol, −38.60 kcal/mol, and −54.27 kcal/mol respectively, Whereas the standards have −21.77 kcal/mol, −9.96 kcal/mol, and −10.29 kcal/mol. Moreover, the <i>in-silico</i> ADMET analysis confirms the druggability of the shortlisted compounds. Later, this work will act as a base for <i>in-vitro</i> and <i>in-vivo</i> experimental studies to validate the anti-inflammatory potential of the same. Communicated by Ramaswamy H. Sarma