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Research Data Sharing at WashU

This guide is designed to support WashU researchers to effectively share data and materials generated as a result of their research.

Curating Data for Sharing & Archiving

image illustrating the value of curation

  • Make data more findable
  • Verification, reproducibility
  • Meet the Desirable Characteristic for digital objects, Data Curation and QA in repositories
  • Meet funder requirements
  • Increase data citation
  • Meet journal expectations
  • Meet community expectations
  • Extend the life of the data

What is curation:

The active and ongoing management of data through its lifecycle of interest and usefulness to scholarship, science, and education. Data curation enables data discovery and retrieval, maintains data quality, adds value, and provides for re-use over time through activities including authentication, archiving, management, preservation, and representation. 

Johnston, Lisa R. 2017. Curating Research Data, Volume One: Practical Strategies for Your Digital Repository. 1 edition. Chicago, Illinois: Amer Library Assn.

What WashU curators do:

  1. Advise on repository selection​

  2. Review data and documentation​

  3. Provide suggestions to improve the FAIRness​

  4. Help you prepare a readme file to accompany your data​

The Office of Science and Technology Policy charged a subgroup to explore and write a report on the Desirable Characteristic of Data Repositories for Federally Funded Research. Below is an overview of the recommendations. For WashU, we can address these characteristics directly, but because there are many options to choose from in the other categories, specificity is more difficult. Here is a desirable characteristics checklist, which you can copy and to help you evaluate whether a data repository meets these characteristics. Check out https://www.re3data.org/ to explore data repositories.

 

Overview of the Desirable Characteristics by Repository Type

Guidance

 Institutional (WashU) General Domain
Free and Easy Access yes varies varies
Clear Use Guidance yes varies varies
Risk Management Yes and.... varies varies
Retention Policy yes varies varies
Long-term Organizational Sustainability yes varies varies
Authentication yes yes yes
Long-term Technical Sustainability yes varies varies
Security and Integrity yes varies varies
Unique Persistent Identifiers yes varies varies
Metadata yes yes yes
Curation/ Quality Assurance yes usually not varies
Broad and Measured Reuse yes varies varies
Common Format yes varies varies
Provenance yes varies varies
Organization Infrastructure
Technology
Digital Object Management

Tools and Resources Created and Collected by the Data Curation Network  (DCN)

The Data Curation Network (DCN) is a membership organization of institutional and non-profit data repositories whose vision is to advance open research by making data more ethical, reusable, and understandable.

Tools from Others

How FAIR is your data?

What is FAIR

In 2016, the ‘FAIR Guiding Principles for scientific data management and stewardship’ were published in Scientific Data. The authors intended to provide guidelines to improve the Findability, Accessibility, Interoperability, and Reuse of digital assets. The principles emphasise machine-actionability (i.e., the capacity of computational systems to find, access, interoperate, and reuse data with none or minimal human intervention) because humans increasingly rely on computational support to deal with data as a result of the increase in volume, complexity, and creation speed of data. (Go-FAIR)

Findable​

  • In an indexed repository, with a unique, persistent ID, and rich metadata​

Accessible​

  •  Repository uses open, standard protocols so the metadata and data can be accessed​

Interoperable​

  •  data are in formal, standard, open application languages​

Reusable​

  • well documented, explicit provenance, open licenses, follows community standards​