cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.26138/sxs:bbh:2159v1.3
title: Binary black-hole simulation SXS:BBH:2159
DOI: 10.26138/sxs:bbh:2159v1.4
title: Binary black-hole simulation SXS:BBH:2159
DOI: 10.26138/sxs:bbh:2160v1.1
title: Binary black-hole simulation SXS:BBH:2160
DOI: 10.26138/sxs:bbh:2160v1.2
title: Binary black-hole simulation SXS:BBH:2160
DOI: 10.26138/sxs:bbh:2160v1.3
title: Binary black-hole simulation SXS:BBH:2160
DOI: 10.26138/sxs:bbh:2160v1.4
title: Binary black-hole simulation SXS:BBH:2160
DOI: 10.26138/sxs:bbh:2161v1.1
title: Binary black-hole simulation SXS:BBH:2161
DOI: 10.26138/sxs:bbh:2161v1.2
title: Binary black-hole simulation SXS:BBH:2161
DOI: 10.26138/sxs:bbh:2161v1.3
title: Binary black-hole simulation SXS:BBH:2161
DOI: 10.26138/sxs:bbh:2161v1.4
title: Binary black-hole simulation SXS:BBH:2161
DOI: 10.26138/sxs:bbh:2162v1.1
title: Binary black-hole simulation SXS:BBH:2162
DOI: 10.26138/sxs:bbh:2162v1.2
title: Binary black-hole simulation SXS:BBH:2162
DOI: 10.26138/sxs:bbh:2162v1.3
title: Binary black-hole simulation SXS:BBH:2162
DOI: 10.26138/sxs:bbh:2162v1.4
title: Binary black-hole simulation SXS:BBH:2162
DOI: 10.26138/sxs:bbh:2163v1.1
title: Binary black-hole simulation SXS:BBH:2163
DOI: 10.26138/sxs:bbh:2163v1.2
title: Binary black-hole simulation SXS:BBH:2163
DOI: 10.26138/sxs:bbh:2163v1.3
title: Binary black-hole simulation SXS:BBH:2163
DOI: 10.26138/sxs:bbh:2163v1.4
title: Binary black-hole simulation SXS:BBH:2163
DOI: 10.26138/sxs:bbh:2265v1.1
title: Binary black-hole simulation SXS:BBH:2265
DOI: 10.6073/pasta/bb78351041555558f4575ad1e4aefc19
title: Canopy Trimming Experiment (CTE) Snail data

description: The objective of these data is to determine how green litter deposition and canopy opening associated with a hurricane independently and jointly affect population dynamics and community composition of terrestrial gastropods. Because canopy openness can be expected to increase abiotic stress on gastropods, whereas litter deposition should provide increased resources and refugia, tradeoffs can be expected.
 
 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.24765034.v1
title: participant_3

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.24768582
title: userdependency

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.24768582.v1
title: userdependency

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.24765031
title: participant_2

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.24765031.v1
title: participant_2

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.24765034
title: participant_3

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.24768579
title: evaluation

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