cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.24764457.v1
title: participant_7

description: Human-to-human communication via the computer is mainly done using a keyboard or microphone. In the field of Virtual Reality (VR), where the most immersive experience possible is desired, the use of a keyboard contradicts this goal, while the use of a microphone is not always desirable (e.g. silent commands during task force training) or simply not possible (e.g. if the user has a hearing loss). Data gloves help to increase immersion within the VR as they correspond to our natural interaction. At the same time, they offer the possibility to accurately capture hand shapes, such as those used in non-verbal communication (e.g. thumbs up, okay gesture, ...) and in sign language. In this paper, we present a hand shape recognition system using Manus Prime X data gloves, including data acquisition, data preprocessing, and data classification to enable nonverbal communication within VR. We investigate the impact on accuracy and classification time of using an Outlier Detection and a Feature Selection approach in our data preprocessing. To obtain a more generalized approach, we also studied the impact of artificial Data Augmentation, i.e., we create new artificial data from the recorded and filtered data to augment the training dataset. With our approach, 56 different hand shapes could be distinguished with an accuracy of up to 93.28%. With a reduced number of 27 hand shapes, an accuracy of up to 95.55% could be achieved. Voting Meta-Classifier (VL2) has proven to be the most accurate, albeit slowest, classifier. A good alternative is Random Forest (RF), which was even able to achieve better accuracy values in a few cases and was generally somewhat faster. Outlier Detection has proven to be a effective approach, especially in improving classification time. Overall, we have shown that our hand shape recognition system using data gloves is suitable for communication within VR.
64 different hand shapes were recorded from 20 participants, each with 3 repetitions. The files are structured as follows:

 1st line Name of the hand shape
 2nd line Left or right hand
 3rd line empty
 4th-23rd line Features Repetition 1
 24th line empty
 25th - 44th line Features repetition 2
 45th line blank
 46th - 65th line Features repetition 3

The features are ordered as follows:

 Thumb spread
 Index Finger spread
 Middle Finger spread
 Ring Finger spread
 Pinky spread
 Thumb stretch CMC
 Thumb stretch MCP
 Thumb stretch IP
 Index Finger stretch MCP
 Index Finger stretch PIP
 Index Finger stretch DIP
 Middle Finger stretch MCP
 Middle Finger stretch PIP
 Middle Finger stretch DIP
 Ring Finger stretch MCP
 Ring Finger stretch PIP
 Ring Finger stretch DIP
 Pinky stretch MCP
 Pinky stretch PIP
 Pinky stretch DIP
DOI: 10.6084/m9.figshare.24764445
title: participant_4

description: Human-to-human communication via the computer is mainly done using a keyboard or microphone. In the field of Virtual Reality (VR), where the most immersive experience possible is desired, the use of a keyboard contradicts this goal, while the use of a microphone is not always desirable (e.g. silent commands during task force training) or simply not possible (e.g. if the user has a hearing loss). Data gloves help to increase immersion within the VR as they correspond to our natural interaction. At the same time, they offer the possibility to accurately capture hand shapes, such as those used in non-verbal communication (e.g. thumbs up, okay gesture, ...) and in sign language. In this paper, we present a hand shape recognition system using Manus Prime X data gloves, including data acquisition, data preprocessing, and data classification to enable nonverbal communication within VR. We investigate the impact on accuracy and classification time of using an Outlier Detection and a Feature Selection approach in our data preprocessing. To obtain a more generalized approach, we also studied the impact of artificial Data Augmentation, i.e., we create new artificial data from the recorded and filtered data to augment the training dataset. With our approach, 56 different hand shapes could be distinguished with an accuracy of up to 93.28%. With a reduced number of 27 hand shapes, an accuracy of up to 95.55% could be achieved. Voting Meta-Classifier (VL2) has proven to be the most accurate, albeit slowest, classifier. A good alternative is Random Forest (RF), which was even able to achieve better accuracy values in a few cases and was generally somewhat faster. Outlier Detection has proven to be a effective approach, especially in improving classification time. Overall, we have shown that our hand shape recognition system using data gloves is suitable for communication within VR.
64 different hand shapes were recorded from 20 participants, each with 3 repetitions. The files are structured as follows:

 1st line Name of the hand shape
 2nd line Left or right hand
 3rd line empty
 4th-23rd line Features Repetition 1
 24th line empty
 25th - 44th line Features repetition 2
 45th line blank
 46th - 65th line Features repetition 3

The features are ordered as follows:

 Thumb spread
 Index Finger spread
 Middle Finger spread
 Ring Finger spread
 Pinky spread
 Thumb stretch CMC
 Thumb stretch MCP
 Thumb stretch IP
 Index Finger stretch MCP
 Index Finger stretch PIP
 Index Finger stretch DIP
 Middle Finger stretch MCP
 Middle Finger stretch PIP
 Middle Finger stretch DIP
 Ring Finger stretch MCP
 Ring Finger stretch PIP
 Ring Finger stretch DIP
 Pinky stretch MCP
 Pinky stretch PIP
 Pinky stretch DIP
DOI: 10.6084/m9.figshare.24764445.v1
title: participant_4

description: Human-to-human communication via the computer is mainly done using a keyboard or microphone. In the field of Virtual Reality (VR), where the most immersive experience possible is desired, the use of a keyboard contradicts this goal, while the use of a microphone is not always desirable (e.g. silent commands during task force training) or simply not possible (e.g. if the user has a hearing loss). Data gloves help to increase immersion within the VR as they correspond to our natural interaction. At the same time, they offer the possibility to accurately capture hand shapes, such as those used in non-verbal communication (e.g. thumbs up, okay gesture, ...) and in sign language. In this paper, we present a hand shape recognition system using Manus Prime X data gloves, including data acquisition, data preprocessing, and data classification to enable nonverbal communication within VR. We investigate the impact on accuracy and classification time of using an Outlier Detection and a Feature Selection approach in our data preprocessing. To obtain a more generalized approach, we also studied the impact of artificial Data Augmentation, i.e., we create new artificial data from the recorded and filtered data to augment the training dataset. With our approach, 56 different hand shapes could be distinguished with an accuracy of up to 93.28%. With a reduced number of 27 hand shapes, an accuracy of up to 95.55% could be achieved. Voting Meta-Classifier (VL2) has proven to be the most accurate, albeit slowest, classifier. A good alternative is Random Forest (RF), which was even able to achieve better accuracy values in a few cases and was generally somewhat faster. Outlier Detection has proven to be a effective approach, especially in improving classification time. Overall, we have shown that our hand shape recognition system using data gloves is suitable for communication within VR.
64 different hand shapes were recorded from 20 participants, each with 3 repetitions. The files are structured as follows:

 1st line Name of the hand shape
 2nd line Left or right hand
 3rd line empty
 4th-23rd line Features Repetition 1
 24th line empty
 25th - 44th line Features repetition 2
 45th line blank
 46th - 65th line Features repetition 3

The features are ordered as follows:

 Thumb spread
 Index Finger spread
 Middle Finger spread
 Ring Finger spread
 Pinky spread
 Thumb stretch CMC
 Thumb stretch MCP
 Thumb stretch IP
 Index Finger stretch MCP
 Index Finger stretch PIP
 Index Finger stretch DIP
 Middle Finger stretch MCP
 Middle Finger stretch PIP
 Middle Finger stretch DIP
 Ring Finger stretch MCP
 Ring Finger stretch PIP
 Ring Finger stretch DIP
 Pinky stretch MCP
 Pinky stretch PIP
 Pinky stretch DIP
DOI: 10.6084/m9.figshare.24764448.v1
title: participant_5

description: Human-to-human communication via the computer is mainly done using a keyboard or microphone. In the field of Virtual Reality (VR), where the most immersive experience possible is desired, the use of a keyboard contradicts this goal, while the use of a microphone is not always desirable (e.g. silent commands during task force training) or simply not possible (e.g. if the user has a hearing loss). Data gloves help to increase immersion within the VR as they correspond to our natural interaction. At the same time, they offer the possibility to accurately capture hand shapes, such as those used in non-verbal communication (e.g. thumbs up, okay gesture, ...) and in sign language. In this paper, we present a hand shape recognition system using Manus Prime X data gloves, including data acquisition, data preprocessing, and data classification to enable nonverbal communication within VR. We investigate the impact on accuracy and classification time of using an Outlier Detection and a Feature Selection approach in our data preprocessing. To obtain a more generalized approach, we also studied the impact of artificial Data Augmentation, i.e., we create new artificial data from the recorded and filtered data to augment the training dataset. With our approach, 56 different hand shapes could be distinguished with an accuracy of up to 93.28%. With a reduced number of 27 hand shapes, an accuracy of up to 95.55% could be achieved. Voting Meta-Classifier (VL2) has proven to be the most accurate, albeit slowest, classifier. A good alternative is Random Forest (RF), which was even able to achieve better accuracy values in a few cases and was generally somewhat faster. Outlier Detection has proven to be a effective approach, especially in improving classification time. Overall, we have shown that our hand shape recognition system using data gloves is suitable for communication within VR.
64 different hand shapes were recorded from 20 participants, each with 3 repetitions. The files are structured as follows:

 1st line Name of the hand shape
 2nd line Left or right hand
 3rd line empty
 4th-23rd line Features Repetition 1
 24th line empty
 25th - 44th line Features repetition 2
 45th line blank
 46th - 65th line Features repetition 3

The features are ordered as follows:

 Thumb spread
 Index Finger spread
 Middle Finger spread
 Ring Finger spread
 Pinky spread
 Thumb stretch CMC
 Thumb stretch MCP
 Thumb stretch IP
 Index Finger stretch MCP
 Index Finger stretch PIP
 Index Finger stretch DIP
 Middle Finger stretch MCP
 Middle Finger stretch PIP
 Middle Finger stretch DIP
 Ring Finger stretch MCP
 Ring Finger stretch PIP
 Ring Finger stretch DIP
 Pinky stretch MCP
 Pinky stretch PIP
 Pinky stretch DIP
DOI: 10.6084/m9.figshare.24764451.v1
title: participant_6

description: Human-to-human communication via the computer is mainly done using a keyboard or microphone. In the field of Virtual Reality (VR), where the most immersive experience possible is desired, the use of a keyboard contradicts this goal, while the use of a microphone is not always desirable (e.g. silent commands during task force training) or simply not possible (e.g. if the user has a hearing loss). Data gloves help to increase immersion within the VR as they correspond to our natural interaction. At the same time, they offer the possibility to accurately capture hand shapes, such as those used in non-verbal communication (e.g. thumbs up, okay gesture, ...) and in sign language. In this paper, we present a hand shape recognition system using Manus Prime X data gloves, including data acquisition, data preprocessing, and data classification to enable nonverbal communication within VR. We investigate the impact on accuracy and classification time of using an Outlier Detection and a Feature Selection approach in our data preprocessing. To obtain a more generalized approach, we also studied the impact of artificial Data Augmentation, i.e., we create new artificial data from the recorded and filtered data to augment the training dataset. With our approach, 56 different hand shapes could be distinguished with an accuracy of up to 93.28%. With a reduced number of 27 hand shapes, an accuracy of up to 95.55% could be achieved. Voting Meta-Classifier (VL2) has proven to be the most accurate, albeit slowest, classifier. A good alternative is Random Forest (RF), which was even able to achieve better accuracy values in a few cases and was generally somewhat faster. Outlier Detection has proven to be a effective approach, especially in improving classification time. Overall, we have shown that our hand shape recognition system using data gloves is suitable for communication within VR.
64 different hand shapes were recorded from 20 participants, each with 3 repetitions. The files are structured as follows:

 1st line Name of the hand shape
 2nd line Left or right hand
 3rd line empty
 4th-23rd line Features Repetition 1
 24th line empty
 25th - 44th line Features repetition 2
 45th line blank
 46th - 65th line Features repetition 3

The features are ordered as follows:

 Thumb spread
 Index Finger spread
 Middle Finger spread
 Ring Finger spread
 Pinky spread
 Thumb stretch CMC
 Thumb stretch MCP
 Thumb stretch IP
 Index Finger stretch MCP
 Index Finger stretch PIP
 Index Finger stretch DIP
 Middle Finger stretch MCP
 Middle Finger stretch PIP
 Middle Finger stretch DIP
 Ring Finger stretch MCP
 Ring Finger stretch PIP
 Ring Finger stretch DIP
 Pinky stretch MCP
 Pinky stretch PIP
 Pinky stretch DIP
DOI: 10.5281/zenodo.10287704
title: Brecha de género en la Investigación en el Cauca, Colombia
DOI: 10.5281/zenodo.10287705
title: Brecha de género en la Investigación en el Cauca, Colombia
DOI: 10.26138/sxs:nsns:0001v1.1
title: Binary neutron-star simulation SXS:NSNS:0001
DOI: 10.26138/sxs:nsns:0001v1.2
title: Binary neutron-star simulation SXS:NSNS:0001
DOI: 10.26138/sxs:nsns:0001v1.3
title: Binary neutron-star simulation SXS:NSNS:0001
DOI: 10.26138/sxs:nsns:0001v1.4
title: Binary neutron-star simulation SXS:NSNS:0001
DOI: 10.26138/sxs:nsns:0001v1.5
title: Binary neutron-star simulation SXS:NSNS:0001
DOI: 10.26138/sxs:nsns:0002v1.1
title: Binary neutron-star simulation SXS:NSNS:0002
DOI: 10.26138/sxs:nsns:0002v1.2
title: Binary neutron-star simulation SXS:NSNS:0002
DOI: 10.26138/sxs:nsns:0002v1.3
title: Binary neutron-star simulation SXS:NSNS:0002
DOI: 10.26138/sxs:nsns:0002v1.4
title: Binary neutron-star simulation SXS:NSNS:0002
DOI: 10.26138/sxs:nsns:0002v1.5
title: Binary neutron-star simulation SXS:NSNS:0002
DOI: 10.26138/sxs:bhns:0001v1.1
title: Black-hole neutron-star binary simulation SXS:BHNS:0001
DOI: 10.26138/sxs:bhns:0001v1.2
title: Black-hole neutron-star binary simulation SXS:BHNS:0001
DOI: 10.26138/sxs:bhns:0001v1.3
title: Black-hole neutron-star binary simulation SXS:BHNS:0001
DOI: 10.26138/sxs:bhns:0001v1.4
title: Black-hole neutron-star binary simulation SXS:BHNS:0001
DOI: 10.26138/sxs:bhns:0002v1.1
title: Black-hole neutron-star binary simulation SXS:BHNS:0002
DOI: 10.26138/sxs:bhns:0002v1.2
title: Black-hole neutron-star binary simulation SXS:BHNS:0002
DOI: 10.26138/sxs:bhns:0002v1.3
title: Black-hole neutron-star binary simulation SXS:BHNS:0002
DOI: 10.26138/sxs:bhns:0002v1.4
title: Black-hole neutron-star binary simulation SXS:BHNS:0002
DOI: 10.26138/sxs:bhns:0003v1.1
title: Black-hole neutron-star binary simulation SXS:BHNS:0003
DOI: 10.26138/sxs:bhns:0003v1.2
title: Black-hole neutron-star binary simulation SXS:BHNS:0003
DOI: 10.26138/sxs:bhns:0003v1.3
title: Black-hole neutron-star binary simulation SXS:BHNS:0003
DOI: 10.26138/sxs:bhns:0003v1.4
title: Black-hole neutron-star binary simulation SXS:BHNS:0003
DOI: 10.26138/sxs:bhns:0004v1.1
title: Black-hole neutron-star binary simulation SXS:BHNS:0004
DOI: 10.26138/sxs:bhns:0004v1.2
title: Black-hole neutron-star binary simulation SXS:BHNS:0004
DOI: 10.26138/sxs:bhns:0004v1.3
title: Black-hole neutron-star binary simulation SXS:BHNS:0004
DOI: 10.26138/sxs:bhns:0004v1.4
title: Black-hole neutron-star binary simulation SXS:BHNS:0004
DOI: 10.26138/sxs:bhns:0004v1.5
title: Black-hole neutron-star binary simulation SXS:BHNS:0004
DOI: 10.26138/sxs:bhns:0005v1.1
title: Black-hole neutron-star binary simulation SXS:BHNS:0005
DOI: 10.26138/sxs:bhns:0005v1.2
title: Black-hole neutron-star binary simulation SXS:BHNS:0005
DOI: 10.26138/sxs:bhns:0005v1.3
title: Black-hole neutron-star binary simulation SXS:BHNS:0005
DOI: 10.26138/sxs:bhns:0005v1.4
title: Black-hole neutron-star binary simulation SXS:BHNS:0005
DOI: 10.26138/sxs:bhns:0005v1.5
title: Black-hole neutron-star binary simulation SXS:BHNS:0005
DOI: 10.26138/sxs:bhns:0006v1.1
title: Black-hole neutron-star binary simulation SXS:BHNS:0006
DOI: 10.26138/sxs:bhns:0006v1.2
title: Black-hole neutron-star binary simulation SXS:BHNS:0006
DOI: 10.26138/sxs:bhns:0006v1.3
title: Black-hole neutron-star binary simulation SXS:BHNS:0006
DOI: 10.26138/sxs:bhns:0006v1.4
title: Black-hole neutron-star binary simulation SXS:BHNS:0006
DOI: 10.26138/sxs:bhns:0006v1.5
title: Black-hole neutron-star binary simulation SXS:BHNS:0006
DOI: 10.26138/sxs:bhns:0007v1.1
title: Black-hole neutron-star binary simulation SXS:BHNS:0007
DOI: 10.26138/sxs:bhns:0007v1.2
title: Black-hole neutron-star binary simulation SXS:BHNS:0007
DOI: 10.26138/sxs:bhns:0007v1.3
title: Black-hole neutron-star binary simulation SXS:BHNS:0007
DOI: 10.5066/p94g4w8u
title: Data on separated and dead foals from Conger and Frisco Herd Management Areas, Utah, USA, between 2017 and 2020

description: We studied factors affecting separation of feral horse foals from their mothers and foal mortality, including variables such as group changes by the mother and behavior of the foal. We include data on feral horse foals that died or were separated, and their siblings. We conducted this study between 2017 and 2020 at two Herd Management Areas (HMAs) in western Utah, USA: Conger and Frisco. We conducted demographic observations year round at both HMAs to record group composition and foaling rate. We additionally recorded behavioral observations for a subset of foals at Conger HMA annually between March and September. This dataset includes 2 .csv files:
Data_foals_dead_separated_siblings.csv - These data contain 52 unique foals across Conger and Frisco HMAs with associated data on mother and group parameters.
Behavior_foals_dead_separated_sibs.csv - These data contain behavioral observations recorded on 15 foals just in Conger HMA.
DOI: 10.26138/sxs:bhns:0007v1.4
title: Black-hole neutron-star binary simulation SXS:BHNS:0007
DOI: 10.26138/sxs:bhns:0007v1.5
title: Black-hole neutron-star binary simulation SXS:BHNS:0007
DOI: 10.5281/zenodo.10265716
title: Organic Reactivity Made Easy and Accurate with Automated Multireference Calculations

description: All data needed to reproduce the work in "Organic Reactivity Made Easy and Accurate with Automated Multireference Calculations." This includes converged orbitals, CI vectors, multireference diagnostics, and energies.
DOI: 10.5281/zenodo.10265717
title: Organic Reactivity Made Easy and Accurate with Automated Multireference Calculations

description: All data needed to reproduce the work in "Organic Reactivity Made Easy and Accurate with Automated Multireference Calculations." This includes converged orbitals, CI vectors, multireference diagnostics, and energies.
DOI: 10.5281/zenodo.8140717
title: A deep learning framework combining molecular image and protein structural representation identifies candidate drugs for chronic pain

description: Official dataset for A deep learning framework combining molecular image and protein structural representation identifies candidate drugs for chronic pain. 
The related code can be found at here and here. 
The dataset is now public.
DOI: 10.5281/zenodo.8140718
title: A ligand image-based and structure-based representation dataset for identifying candidate therapeutics for treating chronic pain by targeting GPCRs

description: Official dataset for <em>A ligand image-based and structure-based representation deep learning framework identifies candidate therapeutics for treating chronic pain by targeting GPCRs</em>. The dataset will be open to all access once the journal paper is online.
DOI: 10.5281/zenodo.8145406
title: A ligand image-based and structure-based representation dataset for identifying candidate therapeutics for treating chronic pain by targeting GPCRs

description: Official dataset for <em>A ligand image-based and structure-based representation deep learning framework identifies candidate therapeutics for treating chronic pain by targeting GPCRs</em>. The dataset will be open to all access once the journal paper is online. Version 0.2: Pre-publish version. Update some data.
DOI: 10.5281/zenodo.8150913
title: A deep learning framework combining molecular image and protein structural representation identifies candidate drugs for chronic pain

description: Official dataset for A deep learning framework combining molecular image and protein structural representation identifies candidate drugs for chronic pain. 
The related code can be found at here and here. 
The dataset is now public.
DOI: 10.34740/kaggle/dsv/7149566
title: Monthly Bilateral USD Exchange Rates 2013-2023
DOI: 10.6073/pasta/b4705e463640fa1fb0fa27cc3a2363de
title: Canopy Trimming Experiment (CTE) Canopy invertebrate responses to disturbance

description: Seven tree species were selected to represent early (Cecropia, Prestoea) and late (Dacryodes, Manilkara, Sloanea) successional, and overstory (Cecropia, Dacryodes, Manilkara, Sloanea) and understory (Prestoea, Miconia, Psychotria), species in forests at El Verde. These trees were sampled in all CTE plots.
 
 Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
DOI: 10.6084/m9.figshare.24768660
title: Compressed folder with all dataset content

description: LLM4TDD in an incremental development process where we guide Large Language Models to generate code iteratively using a test-driven development methodology. Specifically, our framework has the user write test code, but uses Large Language Models to generate the actual implementation. This dataset is the content used to empirically evaluated LLM4TDD.
DOI: 10.6084/m9.figshare.24768660.v1
title: Compressed folder with all dataset content

description: LLM4TDD in an incremental development process where we guide Large Language Models to generate code iteratively using a test-driven development methodology. Specifically, our framework has the user write test code, but uses Large Language Models to generate the actual implementation. This dataset is the content used to empirically evaluated LLM4TDD.
DOI: 10.34740/kaggle/dsv/7149587
title: Monthly Bilateral USD Exchange Rates 2013-2023