Research Data Management Overview

Sharing research data is essential for transparency, reproducibility, and advancing scientific discovery. Many funding agencies and publishers now require compliance with data-sharing policies, and doing so can enhance your professional reputation and open opportunities for collaboration and career growth. This page will provide guidance and resources on best practices in publishing and sharing data. Make sure to also visit the sub-pages which take a deeper dive into sharing federally funded data, preserving data, long-term data storage, and data access and reuse. Some considerations for data sharing practices include:

• Deposit your data in a data repository
  • Review our guidance on Data Repositories for more information on deciding which is the best fit for your data
• Choose a data repository that has integration for persistent identifiers (PIDs)
  • Your dataset should be assigned an a digital object identifier (DOI) and be indexed by search engines.    
  • Include your ORCiD ID to ensure the dataset is discoverable and connected to you
• Include any documentation with the dataset
  • This should at minimum include a README file and resources such as a codebook or a data dictionary
• Include metadata to facilitate discovery and understanding
  • Use the metadata standard for your discipline when creating metadata about the dataset
• Provide copyright information or apply an open license
  • Review our guidance on Data Access and Reuse to learn more about terms you can set on the access and reuse of your dataset
• Specify how the dataset is related to other research products
  • This could include peer-reviewed articles referencing the data or any software or codes
  • Be sure to also include any applicable persistent identifiers (PIDs) so the works are easily found

Recommended practices of research data sharing are based on the FAIR Data Principles. The FAIR Guiding Principles for scientific data management and stewardship were published in 2016 with the intention of providing guidelines to improve the Findability, Accessibility, Interoperability, and Reuse of digital assets. Please refer to the FAIR Principles website to discover more about the framework and each of the FAIR components.

• Findable - The first step in (re)using data is to find them. Metadata and data should be easy to find for both humans and computers. Machine-readable metadata are essential for automatic discovery of datasets and services.
• Accessible - Once the user finds the required data, she/he/they need to know how they can be accessed, possibly including authentication and authorization.
• Interoperable - The data usually need to be integrated with other data. In addition, the data need to interoperate with applications or workflows for analysis, storage, and processing.
• Reusable - The ultimate goal of FAIR is to optimize the reuse of data. To achieve this, metadata and data should be well-described so that they can be replicated and/or combined in different settings.

FAIR guiding principles for data resources is licensed under the Creative Commons Attribution-Share Alike 4.0 International license.

In addition to FAIR, there are certain situations in which the CARE Data Principles may also apply. The CARE Principles were established in the wake of the open science movement which primarily focus on characteristics of data that will facilitate increased data sharing among researchers and the broader public. The emphasis on greater data sharing alone creates a tension for Indigenous Peoples and ignores power differentials and historical contexts. Please refer to the CARE Principles for Indigenous Data Governance to discover more about the framework and each of the CARE components.

• Collective Benefit - Data ecosystems shall be designed and function in ways that enable Indigenous Peoples to derive benefit from the data.
• Authority to Control - Indigenous data governance enables Indigenous Peoples and governing bodies to determine how Indigenous Peoples, as well as Indigenous lands, territories, resources, knowledges and geographical indicators, are represented and identified within data.
• Responsibility - Those working with Indigenous data have a responsibility to share how those data are used to support Indigenous Peoples’ self-determination and collective benefit.
• Ethics - Indigenous Peoples’ rights and well-being should be the primary concern at all stages of the data life cycle and across the data ecosystem.

CARE Principles for Indigenous Data Governance is licensed under the Creative Commons Attribution-Share Alike 4.0 International license.