USGS Data Management

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Publish/Share > Why Share Your Data?
U.S. Geological Survey Data Lifecycle Diagram Plan Acquire Process Analyze Preserve Publish/Share Manage Quality Describe (Metadata, Documentation) Backup & Secure The USGS Science Data Lifecycle

Why Share Your Data?

Data sharing benefits the researcher, research sponsors, data repositories, the scientific community, and the public. It encourages more connection and collaboration between scientists, and better science leads to better decisionmaking.


Key Points

  • Data sharing benefits
    • Incentivizes researchers to produce and ensure higher quality data for sharing with peers, the scientific community, and the public
    • Enables research sponsors to promote and inspire research within a field
    • Encourages collaboration among researchers to share resources, acquire more data, and produce new findings
    • Assists the production of more meta-analyses, which address big picture topics
    • Reduces redundancy of data production in scientific research, which saves investment dollars and time
    • Helps to better inform planning and policy
  • Data sharing concerns
    • Inappropriate use of the shared data
    • Security concerns over the handling of sensitive or confidential data
    • Lack of acknowledgement or citation for the shared data
    • Loss of data results from others, giving competitive advantage over research dollars
  • To alleviate data sharing concerns, practice good data sharing techniques, and provide detailed metadata

Data sharing is typically encouraged within the scientific community but it requires a great deal of effort, resources, and collaboration. Preparing data to be shared takes time and careful documentation of the research process and the data results. Nevertheless, data sharing has important long- and short-term benefits for the researcher, the research sponsor, the data repository, the scientific community, and the public.


There are inherent benefits of data sharing for the researcher and research sponsor. Making the data available to their peers and the public incentivizes researchers to better manage their data and ensure their data are of high quality. Research sponsors can benefit from shared data by stimulating interest and mobilizing continued research within their scientific field. Thus, data sharing can help raise recognition and prominence for both the researcher and the research sponsor.


Data sharing encourages more connection and collaboration between researchers, which can result in important new findings within the field. In a time of reduced monetary investment for science and research, data sharing is more efficient because it allows researchers to share resources.

Data sharing allows researchers to build upon the work of others rather than repeat already existing research. Sharing data also enables researchers to perform meta-analyses on the current research topic. Meta-analyses are important for gathering larger trends over a wider regional or topic area. Therefore data sharing ensures the continued production of these types of analyses.

Better Science & Decisionmaking

Sharing data increases data circulation and use within the scientific community by encouraging better transparency, enabling reproducibility of results, and informing the larger scientific community. This, in turn, can greatly benefit the public as better and more widely disseminated information can lead to informed decisionmaking for environmental planning and policy.

Concerns & Considerations

Despite the many benefits gained from data sharing, there are important considerations that researchers must be aware of when sharing their data. There are concerns that others will use the data inappropriately or out of context from the original purpose of the research. Additionally, data may have sensitive information, and apprehensions about maintaining confidentiality are reasonable.

Lastly, researchers may also be uneasy about the prospect of not receiving acknowledgement by others who use their data, or that others will use their data to gain a competitive advantage. While these are valid concerns, often maintaining good data sharing practices and writing comprehensive metadata can largely address many of these issues.

Best Practices

  • Documentation: Describe the data content and process thoroughly.
    • Good, clear documentation will make it easier for others to see your data, understand its content, and encourage collaboration.
    • Create robust metadata [see Describe > Metadata for more information].
      • Clearly define the purpose of the research and any caveats about the data.
      • Include any security or confidentiality issues related to the data.
      • Describe attributes, geography, and time period of the data.
      • Include links associated with the dataset such as project Web sites and repositories in order to place the dataset in better context.
      • Specify a required data citation for acknowledgement purposes.
      • Create a second version of the data accessible to the public but containing generalized data descriptions.
    • Invite other data contributors to review your metadata to ensure accuracy.
  • Data Storage: Allowing for easy location and access to the data makes them easier to share.
    • Store the data in a repository that can be easily accessed [see Preserve > Repositories for more information].
    • Include archival and reference information.
      • Use properly formatted data citation for the dataset and all other sources.
      • Use persistent identifiers (e.g., Universally Unique Identifiers, UUIDs).
    • Select a format for the data that is intended for long term.
      • i.e., ASCII text files will be readable for a longer time period than Excel 2000 version files.
  • Discovery: Putting it out there.
    • Make the data discoverable by publishing your metadata in data portals and clearinghouses [see Publish/Share > Data Catalogs & Portals for more information].
    • Advertise the data online through social media and blogs.
      • Use specific keywords and tags that make the data more likely to be found in search engines.


  • J. Niu,  Reward and Punishment Mechanisms for Research Data Sharing. IASSIST Quarterly, Winter (2006).
  • H.A. Piwowar, M.J. Becich, H. Bilofsky, R.S. Crowley, Towards a Data Sharing Culture: Recommendations for Leadership from Academic Health Centers. PLoS Med. 5(9), e183 (2008), doi:10.1371/journal.pmed.0050183.