The center for expanded data annotation and retrieval

Musen, Mark A.; Bean, Carol A.; Cheung, Kei‐Hoi; Dumontier, Michel; Durante, Kim; Gevaert, Olivier; González-Beltrán, Alejandra N.; Khatri, Purvesh; Kleinstein, Steven H.; O’Connor, Martin J.; Pouliot, Yannick; Rocca-Serra, Philippe; Sansone, Susanna-Assunta; Wiser, Jeffrey

doi:10.1093/jamia/ocv048

Cited by 74 publications

(57 citation statements)

References 17 publications

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“…As it is very burdensome to peer review the quality of data at the time they are first published, the ongoing and extended review and annotation of data sources during the period of their existence and reuse is a crucial process in Open Science, an approach addressed for instance in the CEDAR project [14]. We argue that both intrinsic and user-defined provenance (e.g.…”

Section: Partly Fair May Be Fair Enoughmentioning

confidence: 99%

Cloudy, increasingly FAIR; revisiting the FAIR Data guiding principles for the European Open Science Cloud

Mons

Neylon

Velterop³

et al. 2017

ISU

Self Cite

274

224

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Abstract. The FAIR Data Principles propose that all scholarly output should be Findable, Accessible, Interoperable, and Reusable. As a set of guiding principles, expressing only the kinds of behaviours that researchers should expect from contemporary data resources, how the FAIR principles should manifest in reality was largely open to interpretation. As support for the Principles has spread, so has the breadth of these interpretations. In observing this creeping spread of interpretation, several of the original authors felt it was now appropriate to revisit the Principles, to clarify both what FAIRness is, and is not.

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Section: Partly Fair May Be Fair Enoughmentioning

confidence: 99%

Cloudy, increasingly FAIR; revisiting the FAIR Data guiding principles for the European Open Science Cloud

Mons

Neylon

Velterop³

et al. 2017

ISU

Self Cite

274

224

View full text Add to dashboard Cite

show abstract

“…Community participation was coordinated via the bioCADDIE's Descriptive Metadata WG3 and the Accessibility Metadata WG7; participants are listed in the Acknowledgement section. The Metadata WG is a joint activity with a wider Metadata WG -encompassing other BD2K centers of excellence -and closely connected to the NIH BD2K Center for Expanded Data Annotation and Retrieval 6 and ELIXIR activities in Europe (https://www.elixir-europe.org).…”

Section: Resultsmentioning

confidence: 99%

DATS: the data tag suite to enable discoverability of datasets

Sansone

González-Beltrán

Rocca-Serra

et al. 2017

Preprint

Self Cite

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Today's science increasingly requires effective ways to find and access existing datasets that are distributed across a range of repositories. For researchers in the life sciences, discoverability of datasets may soon become as essential as identifying the latest publications via PubMed. Through an international collaborative effort funded by the National Institutes of Health (NIH)'s Big Data to Knowledge (BD2K) initiative, we have designed and implemented the DAta Tag Suite (DATS) model to support the DataMed data discovery index. DataMed's goal is to be for data what PubMed has been for the scientific literature. Akin to the Journal Article Tag Suite (JATS) used in PubMed, the DATS model enables submission of metadata on datasets to DataMed. DATS has a core set of elements, which are generic and applicable to any type of datasets, and an extended set that can accommodate more specialized data types. DATS is a platform-independent model also available as a Schema.org annotated serialization to be used beyond DataMed, for example, in projects like DataCite.

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“…For example, the Stanford University School of Medicine established the Center for Expanded Data Annotation and Retrieval (CEDAR) project in 2015 to facilitate researchers’ standard use of metadata by developing an authoring-friendly computational ecosystem for metadata development, evaluation, use, and refinement [4]. By 2017, they had developed a CEDAR Workbench, which was an ontology-assisted tool to help scientific experiment metadata authoring [5].…”

Section: Introductionmentioning

confidence: 99%

Developing a healthcare dataset information resource (DIR) based on Semantic Web

et al. 2018

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BackgroundThe right dataset is essential to obtain the right insights in data science; therefore, it is important for data scientists to have a good understanding of the availability of relevant datasets as well as the content, structure, and existing analyses of these datasets. While a number of efforts are underway to integrate the large amount and variety of datasets, the lack of an information resource that focuses on specific needs of target users of datasets has existed as a problem for years. To address this gap, we have developed a Dataset Information Resource (DIR), using a user-oriented approach, which gathers relevant dataset knowledge for specific user types. In the present version, we specifically address the challenges of entry-level data scientists in learning to identify, understand, and analyze major datasets in healthcare. We emphasize that the DIR does not contain actual data from the datasets but aims to provide comprehensive knowledge about the datasets and their analyses.MethodsThe DIR leverages Semantic Web technologies and the W3C Dataset Description Profile as the standard for knowledge integration and representation. To extract tailored knowledge for target users, we have developed methods for manual extractions from dataset documentations as well as semi-automatic extractions from related publications, using natural language processing (NLP)-based approaches. A semantic query component is available for knowledge retrieval, and a parameterized question-answering functionality is provided to facilitate the ease of search.ResultsThe DIR prototype is composed of four major components—dataset metadata and related knowledge, search modules, question answering for frequently-asked questions, and blogs. The current implementation includes information on 12 commonly used large and complex healthcare datasets. The initial usage evaluation based on health informatics novices indicates that the DIR is helpful and beginner-friendly.ConclusionsWe have developed a novel user-oriented DIR that provides dataset knowledge specialized for target user groups. Knowledge about datasets is effectively represented in the Semantic Web. At this initial stage, the DIR has already been able to provide sophisticated and relevant knowledge of 12 datasets to help entry health informacians learn healthcare data analysis using suitable datasets. Further development of both content and function levels is underway.

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The center for expanded data annotation and retrieval

Cited by 74 publications

References 17 publications

Cloudy, increasingly FAIR; revisiting the FAIR Data guiding principles for the European Open Science Cloud

Cloudy, increasingly FAIR; revisiting the FAIR Data guiding principles for the European Open Science Cloud

DATS: the data tag suite to enable discoverability of datasets

Developing a healthcare dataset information resource (DIR) based on Semantic Web

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