Dataset Information

Human performance data collected in a virtual environment.

ABSTRACT: This data article describes the experimental data used in the research article "Incorporating individual differences in human reliability analysis: an extension to the virtual experimental technique" (Musharraf et al., 2017) [1]. The article provides human performance data for 36 individuals collected using a virtual environment. Each participant was assigned to one of two groups for training: 1) G1: high level training and 2) G2: low level training. Participants' performance was tested in 4 different virtual scenarios with different levels of visibility and complexity. Several performance metrics of the participants were recorded during each scenario. The metrics include: time to muster, time spent running, interaction with fire doors and watertight doors, interaction with hazards, and reporting at different muster locations.

SUBMITTER: Musharraf M

PROVIDER: S-EPMC5633156 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature

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Human performance data collected in a virtual environment.

Musharraf Mashrura M Smith Jennifer J Khan Faisal F Veitch Brian B MacKinnon Scott S

Data in brief 20170922

This data article describes the experimental data used in the research article "Incorporating individual differences in human reliability analysis: an extension to the virtual experimental technique" (Musharraf et al., 2017) [1]. The article provides human performance data for 36 individuals collected using a virtual environment. Each participant was assigned to one of two groups for training: 1) G1: high level training and 2) G2: low level training. Participants' performance was tested in 4 dif ...[more]

PMID: 29021999

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Project description:Years of sequence feature curation by UniProtKB/Swiss-Prot, PIR-PSD, NCBI-CDD, RefSeq and other database biocurators has led to a rich repository of information on functional sites of genes and proteins. This information along with variation-related annotation can be used to scan human short sequence reads from next-generation sequencing (NGS) pipelines for presence of non-synonymous single-nucleotide variations (nsSNVs) that affect functional sites. This and similar workflows are becoming more important because thousands of NGS data sets are being made available through projects such as The Cancer Genome Atlas (TCGA), and researchers want to evaluate their biomarkers in genomic data. BioMuta, an integrated sequence feature database, provides a framework for automated and manual curation and integration of cancer-related sequence features so that they can be used in NGS analysis pipelines. Sequence feature information in BioMuta is collected from the Catalogue of Somatic Mutations in Cancer (COSMIC), ClinVar, UniProtKB and through biocuration of information available from publications. Additionally, nsSNVs identified through automated analysis of NGS data from TCGA are also included in the database. Because of the petabytes of data and information present in NGS primary repositories, a platform HIVE (High-performance Integrated Virtual Environment) for storing, analyzing, computing and curating NGS data and associated metadata has been developed. Using HIVE, 31 979 nsSNVs were identified in TCGA-derived NGS data from breast cancer patients. All variations identified through this process are stored in a Curated Short Read archive, and the nsSNVs from the tumor samples are included in BioMuta. Currently, BioMuta has 26 cancer types with 13 896 small-scale and 308 986 large-scale study-derived variations. Integration of variation data allows identifications of novel or common nsSNVs that can be prioritized in validation studies. Database URL: BioMuta: http://hive.biochemistry.gwu.edu/tools/biomuta/index.php; CSR: http://hive.biochemistry.gwu.edu/dna.cgi?cmd=csr; HIVE: http://hive.biochemistry.gwu.edu.

Project description:The dataset presented in the article consists of finger forces of participants during a finger pressing task. The finger pressing task involves the production of fingertip forces using Index, Middle, Ring, and Little (I, M, R&L) fingers of the right hand. The participant performed two types of task, namely MVC task and visual occlusion task. The participants completed the Maximum Voluntary Contraction (MVC) task first, where they were instructed to produce maximum possible force from each finger individually and all fingers together. The visually occluded finger pressing task followed the MVC task. In this task, the participant's visual feedback was removed after 8s. There were two conditions in this task, one with post-trial performance feedback (referred to as "epilogue" condition in this manuscript) and another that does not have this post-trial performance feedback (referred to as "no epilogue" condition in this manuscript). The epilogue condition is a particular case of post-trial visual feedback where, at the end of each trial, the performance in that trial is shown to the participant. This was followed by the next trial. Normalization of force levels for visual occlusion tasks was performed for the forces with the participants produced in the MVC task. Fourteen healthy participants were recruited for performing the experiments. For the experiments, they were instructed to produce fingertip forces using four fingers of the right hand with the target line at 15% MVC (15% of the force that they produced in the MVC task). The two visual occlusion conditions had 30 trials each. In both conditions, a single trial lasted 16 s. For the initial 8 s, there is visual feedback, which follows an eight-second visual occlusion period where there is no visual feedback. The dataset consists of three files; the first file has the data of Maximum Voluntary Contraction (MVC) data, the second file has the data for the "without epilogue" condition, and the third file has the data of "epilogue" case.

Dataset Information

Human performance data collected in a virtual environment.

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Human performance data collected in a virtual environment.

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