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Over the last decade, significant changes have affected the work that data repositories of all kinds do. First, the emergence of globally unique and persistent identifiers (PIDs) has created new opportunities for repositories to engage with the global research community by connecting existing repository resources to the global research infrastructure. Second, repository use cases have evolved from data discovery to data discovery and reuse, significantly increasing metadata requirements.To respond to these evolving requirements, we need retrospective and on-going curation, i.e. re-curation, processes that 1) find identifiers and add them to existing metadata to connect datasets to a wider range of communities, and 2) add elements that support reuse to globally connected metadata.The goal of this work is to introduce the concept of re-curation with representative examples that are generally applicable to many repositories: 1) increasing completeness of affiliations and identifiers for organizations and funders in the Dryad Repository and 2) measuring and increasing FAIRness of DataCite metadata beyond required fields for institutional repositories.These re-curation efforts are a critical part of reshaping existing metadata and repository processes so they can take advantage of new connections, engage with global research communities, and facilitate data reuse.
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Categorical misalignment: Making autism(s) in big data biobanking
The opaque relationship between biology and behavior is an intractable problem for psychiatry, and it increasingly challenges longstanding diagnostic categorizations. While various big data sciences have been repeatedly deployed as potential solutions, they have so far complicated more than they have managed to disentangle. Attending to categorical misalignment, this article proposes one reason why this is the case: Datasets have to instantiate clinical categories in order to make biological sense of them, and they do so in different ways. Here, I use mixed methods to examine the role of the reuse of big data in recent genomic research on autism spectrum disorder (ASD). I show how divergent regimes of psychiatric categorization are innately encoded within commonly used datasets from MSSNG and 23andMe, contributing to a rippling disjuncture in the accounts of autism that this body of research has produced. Beyond the specific complications this dynamic introduces for the category of autism, this paper argues for the necessity of critical attention to the role of dataset reuse and recombination across human genomics and beyond.
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- Award ID(s):
- 2341622
- PAR ID:
- 10563265
- Publisher / Repository:
- SAGE
- Date Published:
- Journal Name:
- Social Studies of Science
- ISSN:
- 0306-3127
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1177/03063127241288223
