We continue with our series on Trust Markers. The previous blog looked into Data Availability Statements or DAS, and now we look into Data Sharing Locations. Let’s dive in!

What are Data Sharing Locations?

Data location is the place where either raw or processed data can be accessed. Ideally, this can be done without permissions and without the threat of broken links.

Even when articles have data availability statements (DAS), the data itself is not always easily accessible. Where and how authors make their data available strongly influences how easily others can access it. Therefore, it is important to track where authors make their data available, not just whether authors have included data availability statements.  

Why/When to share data?

Trust in science is in part related to the transparency of data collection — both the process and the results. Data sharing also promotes accountability and collaboration among scientists, which, in turn, can yield a stronger study overall.

The FAIR guiding principle for data sharing is that data should be findable, accessible, interoperable, and reusable. For more information on these principles, visit the FAIR website.

When Not to share data

Data is a powerful resource, and there are cases where sharing data would be inappropriate and potentially dangerous to individuals and communities. Data that should not be shared includes sensitive, commercial, and proprietary information and datasets that include personal identifiers.

For example, one crucial mechanism to protect data is the CARE Principles for Indigenous Data Governance created by the Global Indigenous Data Alliance. The Global Indigenous Data Alliance developed these principles to control data about their peoples, lands, and resources. Many of the current open data movements do not consider the effect that data sharing can have on indigenous sovereignty and ignore the potential for carelessness and misuse of sensitive data. 

When Dimensions Research Integrity was being developed (as Ripeta), the legitimate reasons for authors to restrict access to certain data, particularly protected health information, were considered.

How & where to share data

There are options for choosing how and where to share data. While some basic guidelines exist, each dataset is different, and each location type should be considered.

The Qualitative Data Repository and the Social Science Research Council have compiled a set of use questions for researchers to consider when determining where to share their data. For additional information, please see https://managing-qualitative-data.org/modules/3/d/.

Types of data location

External repository

A repository is a location that stores, organizes, allows access to, and preserves data. Common repositories include Dryad Digital Repository, Figshare, Harvard Dataverse, Zenodo, and institutional data repositories.

What does the DAS look like?

For cases in which data is shared through one or more repositories, the data location will include keywords of deposited and database. Generally, the accession number and/or link for said repository should also be stated.

All raw sequencing data and ancillary analyses are deposited in the GEO database under the accession number GSE94518.

Repositories are the most common and reliable way to store data. It is recommended to use whatever repository fits the data best if possible. Tip: re3data.org is a great resource for figuring out which repository fits the research best.

 Article and supporting files

This is when the paper states that data is accessible within the article and/or within its supporting files.

What does the DAS look like?

Contains a statement along the lines of “all relevant data can be found…”

  • “…in a published article (and its Supplementary Information files)”
  • “…within the paper and its Supporting Information files”

Within paper

The data location type in which data is stated to be accessible within the paper itself. This would not include situations in which a paper states that data is accessible within the paper and supporting files.

What does the DAS look like?

Similar to “In article & supplementary files,” but qualifies as a separate data location type as it does not include supplementary files and is thus less robust. 

Not publicly available

This is when the authors claim that data is not publicly available due to circumstances such as patient confidentiality or may simply state data is not publicly available with no further explanation.

What does the DAS look like?

This is consistently stated in the two examples above following the pattern of “datasets (generated and/or) analyzed during/in the current study are not publicly available due to…”. 

Upon request

This location type is the ‘data is available upon request to the authors.’ Occasionally, a paper may state the data is available upon request to an organization, such as the hospital from which data was acquired; either way, the data is available “upon request.”

What does the DAS look like?

This data location type will always contain the phrase “Upon (reasonable) request.” 

Not applicable 

This is when the authors claim that data sharing does not apply to their study.

What does the DAS look like?

N/A is the least favorable of buckets but is easy to identify. Often, a paper may have a DAS heading, but just put N/A in this section because the journal required a DAS. 

State of data location

Funding agencies, institutions, publishers, and researchers can gain valuable insights by understanding where and how researchers share their data. Ethical, broad sharing of research data increases reuse, upholds scientific integrity, and accelerates the impact of scientific results. 

Author credit: This article originally appeared in the Ripeta blog and was authored by August DeVore . August was the Communications Specialist on the team. Ripeta is now part of the Dimensions family known as Dimensions Research Integrity.

Interested in learning more about responsible reporting of research within your organization, across your portfolio, or within a corpus of manuscripts? Visit the Dimensions Research Integrity product page or contact the Dimensions team.