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The Anatomy of Data Sharing Locations

July 7, 2021

Objective

Learn the benefits of sharing data and when, how, and where to share your data.

Introduction

Data location is the place where either raw or processed data can be accessed. Ideally, your data location is one that can be accessed without permissions and without the threat of broken links (e.g., using a PID instead of a URL leading to GitHub).

Even when articles have data availability statements (DAS), their data are not always easily accessible. Where and how authors make their data available strongly influences how easy they are to access. Furthermore, research shows that data location can also serve as a proxy for the completeness of the data; for instance, full data sets are more likely to be available when they are shared in external repositories or upon request rather than when they are made available in the article or supplemental files. Therefore, Ripeta believes it is very important to track how authors make their data available, not just whether or not authors have included data availability statements. Particularly, we see if authors have stored their data in external repositories, as data tend to be both easier to access and more complete when they are stored in this way. Currently, we have ranked locations in the following order: 1) external repository, 2) in article and supporting files, 3) within paper, 4) not publicly available, 5) upon request, and 6) not applicable.

We have gone in-depth about data availability statements (DAS) and data citations — both of which require publicly accessible data, but what does sharing your data mean?

Why/When share your data?

At ripeta, our mission is to build trust in science by making better science easier. 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 your data should be: findable, accessible, interoperable, and reusable. For more information on these principles, visit the FAIR website.

When Not to Share Your 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, as well as datasets which include personal identifiers.

One crucial mechanism to protect data is that of the CARE Principles for Indigenous Data Governance created by the Global Indigenous Data Alliance. The Global Indigenous Data Alliance developed these principles as a way to control data about their peoples, lands, and resources. Many of the current open data movements do not take into account the effect that data sharing can have on indigenous sovereignty, and ignore the potential for carelessness and misuse of sensitive data. Read more about the CARE Principles, as well as their alliance with FAIR to create “Be FAIR and CARE,” here:

Ripeta recognizes that there are legitimate reasons for authors to restrict access to certain data, particularly protected health information. We are therefore training our software to identify when authors have stated reasons for their data restrictions. This improvement will allow us to differentiate between legitimate and arbitrary data restrictions, and we will be able to rank papers more highly when they have provided a reason for their restrictions.

How & Where to Share Your Data?

There are options for choosing how and where to share your data. While there are some basic questions you can ask yourself each dataset is different and each location type should be taken into consideration.

The Qualitative Data Repository and the Social Science Research Council has 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/.

How much does it cost to deposit your data in the different venue choices?
Are you going to want direction and assistance with depositing data – is it important to you to be able to interact face-to-face with the people who are handling your data? Or would you rather be able to post your data quickly and efficiently without interacting much with repository personnel, answering questions about curation, etc.?
How findable and accessible do you want your data to be for other scholars?
How much do you want and need your data to be carefully curated?
How sensitive are your data?
Will you need to place some kinds of access controls on your data?
How well-described and automated do you want the methods for accessing your data to be?
To what types of scholars do the different venue choices seem to cater, and does that match the audience with whom you would like to share your data?
Will other scholars who wish to access your data be charged?
How easily and effectively do you want other scholars to be able to reuse your data?
What kinds of reputations do the different venue choices have?
How much do you trust the different venue choices to keep your data safe?
Does the venue have Core Trust Seal certification?
Can the venue issue DOIs? (Social Science Research Council, 2021)

Types of Data Location

External Repository

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

What does a DAS look like when it is referencing a repository or online location?
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 be stated as well.

Note: Though a paper may state that further information/data is located in supplementary files online, it is important to check what information is shared (unless it specifically states which datasets are available).

Repository
All raw sequencing data and ancillary analyses are deposited in the GEO database under the accession number GSE94518.
Online
All statistical data are deposited in Supplementary Table 1, which is available at the journal’s website.

Repositories are the most common and reliable way to store your data. If you are able, it is recommended you use whatever repository fits your data best. Reference the chart below for the different types of publicly accessible repositories:

Repository types for public access data sharing

Repository	Example	Pro	Con

Domain/discipline-specific data repository, data center or ‘scientific database’ (DR)	BioFresh, ArcGIS Online	Most likely to offer both the specialist domain knowledge and data management expertise needed to ensure your data collection is properly kept and used (Whyte 2015).	Most likely to be selective, requiring advance planning of the effort needed to meet high standards for metadata and documentation (ibid).

General-purpose or Open Research (OR) data repository	Dryad, Figshare, Zenodo	Most likely to offer useful search, navigation, and visualization functionality (ibid).	Requires scrutiny of terms and conditions to ensure consistency with your funder, journal, or institution’s policies on cost recovery, copyright/IP, long-term preservation (ibid).

Institutional data repository (IR)	Texas Data Repository	Most likely to accept any data of value, especially if no suitable home can be found for it elsewhere, and to ensure that policy requirements for long-term access are met (ibid).	Unlikely to be as well-resourced as either general-purpose or domain repositories (ibid).

Sources: Digital Curation Center, Stanford Libraries

Tip: re3data.org is a great resource for figuring out which repository fits your research best!

Article and Supporting Files

This is when the paper states that data is accessible in the article itself and its supporting files.