Data Repositories

Selecting a Data Repository

A data repository is a place to archive and make publicly available research datasets. To select an appropriate repository take the following steps

Step Note
1. Are you required to deposit in a certain repository? Some funders and journals require or recommend datasets be deposited in their repositories. Check the specific requirements or contact us for assistance in making this determination
2. Is there a discipline-specific repository? If you have a choice of where to deposit, look for commonly used repositories in your discipline. Some repositories are geared towards groups of disciplines while others are specific to a specific kind of research.
3. If there is no discipline-specific repository, select a general repository There are several general-purpose repositories that can fulfill funder and journal sharing requirements. The choice often comes down to personal preferences.

For a one-stop shop that addresses all funder, journal, and University data archiving and sharing requirements, consider ReDATA, the University of Arizona's Research Data Repository.

For archiving open access manuscripts, theses/dissertations, monographs, etc., please visit the Campus Repository.  Contact Kimberly Chapman, Director.

Desirable Characteristics of Data Repositories 

 The list below is adapted from the NIH's guidance for selecting a data repository, which itself is based on the Office of Science and Technology Policy's Desirable Characteristics of Data Repositories for Federally Funded Research

Characteristic Description ReDATA
Unique Persistent Identifiers Assigns datasets a citable, unique persistent identifier, such as a digital object identifier (DOI) or accession number, to support data discovery, reporting, and research assessment. The identifier points to a persistent landing page that remains accessible even if the dataset is de-accessioned or no longer available Yes
Long-Term Sustainability Has a plan for long-term management of data, including maintaining integrity, authenticity, and availability of datasets; building on a stable technical infrastructure and funding plans; and having contingency plans to ensure data are available and maintained during and after unforeseen events. Yes
Metadata Ensures datasets are accompanied by metadata to enable discovery, reuse, and citation of datasets, using schema that are appropriate to, and ideally widely used across, the community(ies) the repository serves.  Yes
Curation and Quality Assurance Provides, or has a mechanism for others to provide, expert curation and quality assurance to improve the accuracy and integrity of datasets and metadata. Yes
Free and Easy Access Provides broad, equitable, and maximally open access to datasets and their metadata free of charge in a timely manner after submission, consistent with legal and ethical limits required to maintain privacy and confidentiality, Tribal sovereignty, and protection of other sensitive data. Yes*
Broad and Measured Reuse Makes datasets and their metadata available with broadest possible terms of reuse; and provides the ability to measure attribution, citation, and reuse of data Yes
Clear Use Guidance Provides accompanying documentation describing terms of dataset access and use. Yes
Security and Integrity Has documented measures in place to meet generally accepted criteria for preventing unauthorized access to, modification of, or release of data, with levels of security that are appropriate to the sensitivity of data. Yes*
Confidentiality Has documented capabilities for ensuring that administrative, technical, and physical safeguards are employed to comply with applicable confidentiality, risk management, and continuous monitoring requirements for sensitive data. Yes*
Common Format Allows datasets and metadata downloaded, accessed, or exported from the repository to be in widely used, preferably non-proprietary, formats consistent with those used in the community(ies) the repository serves. Yes
Provenance Has mechanisms in place to record the origin, chain of custody, and any modifications to submitted datasets and metadata. Yes**
Retention Policy Provides documentation on policies for data retention within the repository. Yes

*Technical and administrative measures (e.g., NetIDs login, curatorial review prior to publication) help ensure data is not modified without authorization. Furthermore, administrative mechanisms help ensure sensitive data is not made public and, where applicable, that requirements for ethical data sharing are met.

**This information is retained internally and generally is not made publicly available.

Tools for Finding Repositories

Data Indexers
Re3Data Registry of Research Data Repositories. A worldwide index of data repositories.
Fairsharing A database of data repositories and related metadata standards and policies. Also useful for identifying metadata standards for writing a DMP.
Google Dataset Search Search for data across many data repositories and government websites Collection of community contributed datasets
Awesome Datasets Collection of community contributed datasets
Other Resources
Examples of Well-known Data Repositories

We created this table to help with writing DMPs but it's also useful for finding repositories at the publication stage.