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Research Data Management at SUNY Geneseo

Data management includes the processes of collecting, organizing, describing, sharing, and preserving data. This guide will help you plan for and prepare a data management plan and learn about the data lifecycle and research data management.


Data lifecycle graphicUnderstanding the entire data lifecycle enables a researcher to plan ahead, make informed decisions, and best set up their research project so collected data can more easily be published and made accessible.

This guide provides contextual information; including standards, guidelines, and resources for more comprehensive information; to help researchers learn and understand how data documentation, organization, storage, and sharing are critical pieces to effective research data management. 

Due to current federal mandates, researchers are now required to take additional steps in organizing and documenting their data so that data produced by federally funded research projects are prepared to be published openly to meet grant requirements. These steps are interwoven into existing workflows that researchers likely already perform, however, extra care has to be given so that the data can be discovered and used by other interested parties and stakeholders.

Librarians are well practiced in organizing and presenting information. Members of the KnightScholar Services Team can assist researchers in the following ways:

  • Writing of the data management plan
  • Consultants to find solutions that meet your data management needs
  • Research appropriate storage solutions and workflows
  • Recommend tools for data management
  • Librarians can share expertise on metadata creation, file organization, and preservation, as well as available tools and platforms to manage data and potential costs associated with the management plan

Contact the KnightScholar Services Team by using the form located below. It’s most helpful and appreciated if researchers make contact early in the research project so team members can best advise, learn about the research project, and conduct any further investigation.

Data Lifecycle with Definitions and Services

Did you know that data has a lifecycle, and it starts with planning?  Keep your data safe for future use by learning to identify the different data lifecycle stages, as well as the important elements that need to be addressed in each stage, and at what stages the services that the KnightScholar team offers are best suited, below.

  • Plan
    • Planning for a project involves making decisions about data management, potential products, as well as data stewardship roles and responsibilities. It is important to document all stages of the data management life cycle and quality control prior to beginning a new project.
    • Members of the KnightScholar Services Team can assist with planning by sharing expertise on metadata creation, file organization, and preservation, as well as available tools and platforms to manage data and potential costs associated with the management plan.
  • Acquire
    • Data can be acquired by collecting new data, processing old or legacy data, collaborating with partners, and contracting others to collect data.
  • Maintain
    • Data maintenance includes processing data for analysis, creating metadata, and making sure data are in a format that can be accessed by others in the future.
    • Members of the KnightScholar Services Team can share expertise on metadata creation and file organization.
  • Access
    • The ability to prepare, release, and share quality data to the public, other agencies, and internally is an important part of the life cycle process.
    • Members of the KnightScholar Services Team can research appropriate solutions and workflows to make data accessible. KnightScholar Repository is the College's institutional repository. Current members of the College community are welcomed to deposit their data in KnightScholar Repository to make data openly accessible. Please contact the KnightScholar Services Team to learn more:
  • Evaluate
    • Evaluate represents steps associated with processing and analyzing data. Important goals include maximizing accuracy and productivity while minimizing costs.
  • Archive
    • Data archiving supports the long-term storage of scientific data and the methods used to read or interpret them.
    • Members of the KnightScholar Services Team can research appropriate archiving and storage solutions and workflows.

(creditData Management Life Cycle from the U.S. Fish & Wildlife Service)

Why Manage Research Data?

Why manage research data?

  • Find and understand data when needed
  • Project continuity through researcher or staff changes
  • Organized data saves time
  • Reduces risk of lost, stolen, or mis-used data
  • Comply with funder and journal requirements
  • Allows for easier validation of results
  • Data can be shared, leading to collaboration and greater impact

In addition, well managed and documented data make it easier to write up research results for publication.

Advantages to planning your research data management practices in advance

  • Save time: planning ahead for data management needs will let you anticipate what you'll need and organize your data from the start
  • Maintain data integrity: managing and documenting your data throughout the entire project will allow you and others to understand and more easily use your data in the future.
  • Meet grant requirements: many funding agencies require that researchers create and follow a data management and/or data sharing plan.

Advantages to publishing your datasets

  • Publishing your dataset in an indexed repository that provides a unique, persistent identifier allows it to be:
  • More easily discovered
  • Cited in its own right, increasing its visibility and impact
  • Able to promote the research that created it

More generally, sharing data:

  • Can lead to new, unanticipated discoveries
  • Provides research material for those with little or no funding
  • Promotes innovation and potential new data uses
  • Can lead to new collaborations
  • Maximizes transparency and accountability
  • Encourages improvement and validation of research methods
  • Reduces the cost of duplicating data collection​​​​​​​

(credit: Princeton Research Data Service)