Dataset Information

Data from a fingerprint matching task with experts, trained students and untrained novices.

ABSTRACT: The data described here provide standard performance measures following administration of a fingerprint matching task to expert analysts, trained students and novice control participants. Measures include accuracy on 'same' and 'different' trials, and the associated measures of sensitivity of discrimination (d') and response bias (C). In addition, speed of correct response is provided. The provision of these data will enable the interested reader to conduct meta-analyses relating to questions of fingerprint expertise and fingerprint training (see "Fact or friction: examination of the transparency, reliability and sufficiency of the ACE-V method of fingerprint analysis" (Stevenage and Pitfield, in press) [1]).

SUBMITTER: Stevenage SV

PROVIDER: S-EPMC5066182 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature

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Data from a fingerprint matching task with experts, trained students and untrained novices.

Stevenage Sarah V SV Pitfield Christy C

Data in brief 20160923

The data described here provide standard performance measures following administration of a fingerprint matching task to expert analysts, trained students and novice control participants. Measures include accuracy on 'same' and 'different' trials, and the associated measures of sensitivity of discrimination (d') and response bias (C). In addition, speed of correct response is provided. The provision of these data will enable the interested reader to conduct meta-analyses relating to questions of ...[more]

PMID: 27766291

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Project description:BACKGROUND:Increasingly, eHealth involves health data visualizations to enable users to better understand their health situation. Selecting efficient and ergonomic visualizations requires knowledge about the task that the user wants to carry out and the type of data to be displayed. Taxonomies of abstract tasks and data types bundle this knowledge in a general manner. Task-data taxonomies exist for visualization tasks and data. They also exist for eHealth tasks. However, there is currently no joint task taxonomy available for health data visualizations incorporating the perspective of the prospective users. One of the most prominent prospective user groups of eHealth are older adults, but their perspective is rarely considered when constructing tasks lists. OBJECTIVE:The aim of this study was to construct a task-data taxonomy for health data visualizations based on the opinion of older adults as prospective users of eHealth systems. eHealth experts served as a control group against the bias of lacking background knowledge. The resulting taxonomy would then be used as an orientation in system requirement analysis and empirical evaluation and to facilitate a common understanding and language in eHealth data visualization. METHODS:Answers from 98 participants (51 older adults and 47 eHealth experts) given in an online survey were quantitatively analyzed, compared between groups, and synthesized into a task-data taxonomy for health data visualizations. RESULTS:Consultation, diagnosis, mentoring, and monitoring were confirmed as relevant abstract tasks in eHealth. Experts and older adults disagreed on the importance of mentoring (?24=14.1, P=.002) and monitoring (?24=22.1, P<.001). The answers to the open questions validated the findings from the closed questions and added therapy, communication, cooperation, and quality management to the aforementioned tasks. Here, group differences in normalized code counts were identified for "monitoring" between the expert group (mean 0.18, SD 0.23) and the group of older adults (mean 0.08, SD 0.15; t96=2431, P=.02). Time-dependent data was most relevant across all eHealth tasks. Finally, visualization tasks and data types were assigned to eHealth tasks by both experimental groups. CONCLUSIONS:We empirically developed a task-data taxonomy for health data visualizations with prospective users. This provides a general framework for theoretical concession and for the prioritization of user-centered system design and evaluation. At the same time, the functionality dimension of the taxonomy for telemedicine-chosen as the basis for the construction of present taxonomy-was confirmed.

Dataset Information

Data from a fingerprint matching task with experts, trained students and untrained novices.

Publications

Data from a fingerprint matching task with experts, trained students and untrained novices.

Similar Datasets

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