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This page is intended to capture information related to NDS-211 - Getting issue details... STATUS .


Background 

Registries of Research Data Repositories

There are (at least) two major registries of research data repositories.  Publishers and funding agencies often direct researchers to search for repositories using these tools:

Publishers refer to both in their lists of recommended repositories, but both services appear to be intended for librarians, curators, publishers and funding agencies instead of the average researcher. The re3data is easily available for download and could be incorporated into our system. It's not clear whether the Bioshare data is available (technically, it could be crawled).

Question: How is our recommender different than these systems? What need are we meeting that these systems don't meet? 

Approved and Recommended Repositories 

Publishers, funding agencies, research/domain organizations(e.g., AGU, ACM), and libraries often provide lists of recommended or supported repositories for depositing research data.  The motivations and requirements are often different, but the lists themselves might serve as the basis for our analysis. We can review these (and other) lists to determine the factors in recommending data repositories to researchers. Note that the Biosharing database already includes information about whether a repository is recommended by a funding agency.

(This list is not exhaustive – it's likely that many publishers, agencies, and organizations will provide similar lists):

NIH:

Elsevier: 

Nature

PLOS

Libraries

Other

SEAD C3PR/Matchmaker

The SEAD Matchmaker is used to pair datasets to repositories. It was originally part of the SEAD Virtual Archive, which enabled automatic depositing of data into institutional repositories via OAIS Submission Information Packages (SIP).  The matchmaker is now part of the SEAD 2.0 C3PR service (https://github.com/Data-to-Insight-Center/sead2/tree/master/sead-matchmaker).  Repositories can register with C3PR, providing information including accepted data types, maximum collection depth, maximum dataset size, minimum metadata fields, affiliations, and global identifier requirements. These are to some extent technical requirements.

See also:

Other sources of information:

What other sources of information might we include in a recommender service?

  • Researcher identifiers, such as ORCID Persistent digital identifier for researchers: these might be helpful in collecting researcher profile information that can be used for recommendation.
  • Journal/publication information: We can relate specific journals to data repositories. If the user is publishing in a specific journal, we can recommend where to put the data.  
  • Abstract: Use text matching techniques to match an abstract to a repository.
  • https://www.datacite.org/
  • BrownDog: Can we use information from extractors to identify criteria for recommendation?

Harvesting information

  • Many of the data repositories are crawl-able or implement standard APIs (OAI-PMH) for harvesting metadata.  It might be interesting to consider whether we can harvest descriptive metadata – particularly citation information – and use journal or other publication metadata as part of the recommendation process. 

Analysis

Reviewing the above publisher lists and registries, we can identify factors in the recommendation of repositories to researchers:

FactorDescription
Funding agency approvalFunding agencies (e.g. NIH) have lists of approved repositories
Researcher communitiesSome repositories restrict to researchers in certain communities
Publisher integrationPublishers (e.g., Elsevier) have arrangements with repositories (e.g., bi-directional linking)
DomainRepositories are often restricted by domain, with some generalist services
Technical restrictionsRepositories have technical restrictions (e.g., maximum file size, supported formats)
Community mandatesSome research communities have mandated repositories (see Nature list)
Data type

Some repositories are restricted to specific types of data. These criteria vary, for example:

    • Protein structures
    • Human or non-human derived
    • Phenotypes

Data types are often directly related to domain/field of study.

Metadata formatSome repositories are restricted to specific types of metadata (e.g., MIAME)

 

Publishers, funding agencies, and libraries construct these lists of approved repositories to meet the needs of researchers, Many of these sites now link to centralized services, such as re3data.org. However, re3data.org does not capture all of the information needed to make a recommendation (e.g., technical restrictions).

Use cases

Q. Who are the users? While the re3data and biosharing sites seem more targeted at experts, perhaps our service is targeted at the novice researcher?

For example:

  • A researcher in the area of information retrieval has code and data to deposit related to a recent publication. How do they determine where to publish the data?
    • What does the publisher require? JASIST, TOIS/ACM
    • What does the funding agency require? NSF
    • What does the community generally do?
    • Where have I or my collaborators previously published data?



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