Metadata for the Longitudinal Data Life Cycle: The Role and Benefit of Metadata Management and Reuse

Hoyle, Larry; Castillo, Fortunato; Clark, Benjamin F.; Kashyap, Neeraj; Perpich, Denise; Wackerow, Joachim; Wenzig, Knut

doi:10.3886/ddilongitudinal03

Cited by 3 publications

(4 citation statements)

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“…Additional research demonstrating how ODM can be effectively used and extended in support of secondary use and big data would be of keen interest to the academic and practitioner communities. Improved ODM data citation support, to include an extension to better support the Dublin Core, would make attribution easier and encourage data sharing [108]. …”

Section: 0 Discussionmentioning

confidence: 99%

Current applications and future directions for the CDISC Operational Data Model standard: A methodological review

Hume

Aerts

Sarnikar

et al. 2016

Journal of Biomedical Informatics

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Introduction In order to further advance research and development on the Clinical Data Interchange Standards Consortium (CDISC) Operational Data Model (ODM) standard, the existing research must be well understood. This paper presents a methodological review of the ODM literature. Specifically, it develops a classification schema to categorize the ODM literature according to how the standard has been applied within the clinical research data lifecycle. This paper suggests areas for future research and development that address ODM’s limitations and capitalize on its strengths to support new trends in clinical research informatics. Methods A systematic scan of the following databases was performed: (1) ABI/Inform, (2) ACM Digital, (3) AIS eLibrary, (4) Europe Central PubMed, (5) Google Scholar, (5) IEEE Xplore, (7) PubMed, and (8) ScienceDirect. A Web of Science citation analysis was also performed. The search term used on all databases was “CDISC ODM.” The two primary inclusion criteria were: (1) the research must examine the use of ODM as an information system solution component, or (2) the research must critically evaluate ODM against a stated solution usage scenario. Out of 2,686 articles identified, 266 were included in a title level review, resulting in 183 articles. An abstract review followed, resulting in 121 remaining articles; and after a full text scan 69 articles met the inclusion criteria. Results As the demand for interoperability has increased, ODM has shown remarkable flexibility and has been extended to cover a broad range of data and metadata requirements that reach well beyond ODM’s original use cases. This flexibility has yielded research literature that covers a diverse array of topic areas. A classification schema reflecting the use of ODM within the clinical research data lifecycle was created to provide a categorized and consolidated view of the ODM literature. The elements of the framework include: (1) EDC (Electronic Data Capture) and EHR (Electronic Health Record) infrastructure; (2) planning; (3) data collection; (4) data tabulations and analysis; and (5) study archival. The analysis reviews the strengths and limitations of ODM as a solution component within each section of the classification schema. This paper also identifies opportunities for future ODM research and development, including improved mechanisms for semantic alignment with external terminologies, better representation of the CDISC standards used end-to-end across the clinical research data lifecycle, improved support for real-time data exchange, the use of EHRs for research, and the inclusion of a complete study design. Conclusions ODM is being used in ways not originally anticipated, and covers a diverse array of use cases across the clinical research data lifecycle. ODM has been used as much as a study metadata standard as it has for data exchange. A significant portion of the literature addresses integrating EHR and clinical research data. The simplicity and readability of ODM has likely contributed to its succe...

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Section: 0 Discussionmentioning

confidence: 99%

Current applications and future directions for the CDISC Operational Data Model standard: A methodological review

Hume

Aerts

Sarnikar

et al. 2016

Journal of Biomedical Informatics

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“…DDI Lifecycle enables the extensive and thorough description of social science research data and their origin. DDI, in general, is the most elaborate and most commonly used metadata standard for social science survey data (Hoyle et al 2011;Rasmussen 2014;Jensen, Zenk-Möltgen, and Wasner 2019). It is an international metadata standard that supports the documentation of data from the social, behavioral, economic, and health sciences, and it was initiated in 1995 as a project of the US-American archive ICPSR (Inter-University Consortium for Political Social Research).…”

Section: Choice Of Ddimentioning

confidence: 99%

“…Reproduced by permission of DDI Alliance. Permission to reuse must be obtained from the rightsholder.considerations, we determined DDI to be the most natural choice for documenting georeferenced survey data(Green and Humphrey 2013;Jensen, Zenk-Möltgen, and Wasner 2019;Hoyle et al 2011;Rasmussen 2014;Zenk-Möltgen 2012;Vardigan, Heus, and Thomas 2008):…”

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confidence: 99%

Documenting Georeferenced Social Science Survey Data: Limits of Metadata Standards and Possible Solutions

Jünger

Borschewski

Zenk-Möltgen

2019

Journal of Map & Geography Libraries

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In this article, we present documentation of the georeferenced social science survey data that are spatially linked to geospatial data attributes. We introduce the challenges of documentation, as different metadata standards are used for both data sources: social science survey data and geospatial data. In particular, we analyze the extent to which the social sciences metadata standard DDI Lifecycle is capable of incorporating the geosciences metadata standard ISO 19115. We find that the most challenging attributes to describe are those concerning the geographic structure of the geospatial data, especially if they stem from different sources. To navigate these issues, we developed and evaluated four workaround approaches which we demonstrate in a case study on the georeferenced German General Social Survey. Because not all of the approaches apply equally to every research project and institution, we provide a scheme to assist in making informed and weighted decisions.

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“…DDI Lifecycle is the more elaborate of both specifications. It covers all aspects of the research data lifecycle (defined by the DDI Alliance), starting with the planning and data collection right through to archiving (Green and Humphrey 2014;Hoyle et al 2011;Rasmussen 2014;Vardigan et al 2008;Zenk-Möltgen 2012).…”

Section: Introductionmentioning

confidence: 99%

The matter of meta in research data management: Introducing the CESSDA Metadata Office Project

Förster

Borschewski

Bolton

et al. 2020

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Accompanying the growing importance of research data management, the provision and maintenance of metadata – understood as data about (research) data – have obtained a key role in contextualizing, understanding, and preserving research data. Acknowledging the importance of metadata in the social sciences, the Consortium of European Social Science Data Archives started the Metadata Office project in 2019. This project report presents the various activities of the Metadata Office (MDO). Metadata models, schema, controlled vocabularies and thesauri are covered, including the MDO’s collaboration with the DDI Alliance on multilingual translations of DDI vocabularies for CESSDA Service Providers. The report also summarizes the communication, training and advice provided by MDO, including DDI use across CESSDA, illustrates the impact of the project for the social science and research data management community, and offers an outline regarding future plans of the project.

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Metadata for the Longitudinal Data Life Cycle: The Role and Benefit of Metadata Management and Reuse

Cited by 3 publications

References 1 publication

Current applications and future directions for the CDISC Operational Data Model standard: A methodological review

Current applications and future directions for the CDISC Operational Data Model standard: A methodological review

Documenting Georeferenced Social Science Survey Data: Limits of Metadata Standards and Possible Solutions

The matter of meta in research data management: Introducing the CESSDA Metadata Office Project

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