Dataset Information

The health care and life sciences community profile for dataset descriptions.

ABSTRACT: Access to consistent, high-quality metadata is critical to finding, understanding, and reusing scientific data. However, while there are many relevant vocabularies for the annotation of a dataset, none sufficiently captures all the necessary metadata. This prevents uniform indexing and querying of dataset repositories. Towards providing a practical guide for producing a high quality description of biomedical datasets, the W3C Semantic Web for Health Care and the Life Sciences Interest Group (HCLSIG) identified Resource Description Framework (RDF) vocabularies that could be used to specify common metadata elements and their value sets. The resulting guideline covers elements of description, identification, attribution, versioning, provenance, and content summarization. This guideline reuses existing vocabularies, and is intended to meet key functional requirements including indexing, discovery, exchange, query, and retrieval of datasets, thereby enabling the publication of FAIR data. The resulting metadata profile is generic and could be used by other domains with an interest in providing machine readable descriptions of versioned datasets.

SUBMITTER: Dumontier M

PROVIDER: S-EPMC4991880 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Publications

The health care and life sciences community profile for dataset descriptions.

Dumontier Michel M Gray Alasdair J G AJG Marshall M Scott MS Alexiev Vladimir V Ansell Peter P Bader Gary G Baran Joachim J Bolleman Jerven T JT Callahan Alison A Cruz-Toledo José J Gaudet Pascale P Gombocz Erich A EA Gonzalez-Beltran Alejandra N AN Groth Paul P Haendel Melissa M Ito Maori M Jupp Simon S Juty Nick N Katayama Toshiaki T Kobayashi Norio N Krishnaswami Kalpana K Laibe Camille C Le Novère Nicolas N Lin Simon S Malone James J Miller Michael M Mungall Christopher J CJ Rietveld Laurens L Wimalaratne Sarala M SM Yamaguchi Atsuko A

PeerJ 20160816

Access to consistent, high-quality metadata is critical to finding, understanding, and reusing scientific data. However, while there are many relevant vocabularies for the annotation of a dataset, none sufficiently captures all the necessary metadata. This prevents uniform indexing and querying of dataset repositories. Towards providing a practical guide for producing a high quality description of biomedical datasets, the W3C Semantic Web for Health Care and the Life Sciences Interest Group (HCL ...[more]

PMID: 27602295

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Project description:BackgroundThe web has seen an explosion of chemistry and biology related resources in the last 15 years: thousands of scientific journals, databases, wikis, blogs and resources are available with a wide variety of types of information. There is a huge need to aggregate and organise this information. However, the sheer number of resources makes it unrealistic to link them all in a centralised manner. Instead, search engines to find information in those resources flourish, and formal languages like Resource Description Framework and Web Ontology Language are increasingly used to allow linking of resources. A recent development is the use of userscripts to change the appearance of web pages, by on-the-fly modification of the web content. This opens possibilities to aggregate information and computational results from different web resources into the web page of one of those resources.ResultsSeveral userscripts are presented that enrich biology and chemistry related web resources by incorporating or linking to other computational or data sources on the web. The scripts make use of Greasemonkey-like plugins for web browsers and are written in JavaScript. Information from third-party resources are extracted using open Application Programming Interfaces, while common Universal Resource Locator schemes are used to make deep links to related information in that external resource. The userscripts presented here use a variety of techniques and resources, and show the potential of such scripts.ConclusionThis paper discusses a number of userscripts that aggregate information from two or more web resources. Examples are shown that enrich web pages with information from other resources, and show how information from web pages can be used to link to, search, and process information in other resources. Due to the nature of userscripts, scientists are able to select those scripts they find useful on a daily basis, as the scripts run directly in their own web browser rather than on the web server. This flexibility allows the scientists to tune the features of web resources to optimise their productivity.

Project description:BackgroundDespite increasing availability of environmental health science (EHS) data, development, and implementation of relevant semantic standards, such as ontologies or hierarchical vocabularies, has lagged. Consequently, integration and analysis of information needed to better model environmental influences on human health remains a significant challenge.ObjectivesWe aimed to identify a committed community and mechanisms needed to develop EHS semantic standards that will advance understanding about the impacts of environmental exposures on human disease.MethodsThe National Institute of Environmental Health Sciences sponsored the "Workshop for the Development of a Framework for Environmental Health Science Language" hosted at North Carolina State University on 15-16 September 2014. Through the assembly of data generators, users, publishers, and funders, we aimed to develop a foundation for enabling the development of community-based and data-driven standards that will ultimately improve standardization, sharing, and interoperability of EHS information.DiscussionCreating and maintaining an EHS common language is a continuous and iterative process, requiring community building around research interests and needs, enabling integration and reuse of existing data, and providing a low barrier of access for researchers needing to use or extend such a resource.ConclusionsRecommendations included developing a community-supported web-based toolkit that would enable a) collaborative development of EHS research questions and use cases, b) construction of user-friendly tools for searching and extending existing semantic resources, c) education and guidance about standards and their implementation, and d) creation of a plan for governance and sustainability.CitationMattingly CJ, Boyles R, Lawler CP, Haugen AC, Dearry A, Haendel M. 2016. Laying a community-based foundation for data-driven semantic standards in environmental health sciences. Environ Health Perspect 124:1136-1140;?http://dx.doi.org/10.1289/ehp.1510438.

Dataset Information

The health care and life sciences community profile for dataset descriptions.

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The health care and life sciences community profile for dataset descriptions.

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