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Research Data Management: Home

Effective research data management is an ongoing process that involves collecting, organizing, describing, sharing, and preserving data throughout the course of your research project.

Why Research Data Management Matters

  • Ensure that your research data is easily navigable and allows others to understand your methodology.
  • Safeguard your intellectual property rights.
  • Maintain the ability to find specific iterations of your data.
  • Enhance your chances of getting published or securing funding.
  • Simplify and promote collaborative efforts

What is Research Data?

One definition of research data is: "the recorded factual material commonly accepted in the scientific community as necessary to validate research findings."
(OMB Circular 110).

Note that properly managing data (and records) does not necessarily equate to sharing or publishing that data.

Types of Information
  • Documents (text, Word), spreadsheets
  • Laboratory notebooks, field notebooks, diaries
  • Questionnaires, transcripts, codebooks
  • Audiotapes, videotapes
  • Photographs, films
  • Protein or genetic sequences
  • Spectra
  • Test responses
  • Slides, artifacts, specimens, samples
  • Collection of digital objects acquired and generated during the process of research
  • Database contents (video, audio, text, images)
  • Models, algorithms, scripts
  • Contents of an application (input, output, logfiles for analysis software, simulation software, schemas)
  • Methodologies and workflows
  • Standard operating procedures and protocols

Data Lifecycle

Biomedical Data Lifecycle Infographic

Image and Biomedical Data Lifecycle created by LMA Research Data Management Working Group at Harvard Medical School licensed under a CC BY-NC 4.0 DEED License.

Plan & Design

Research Data Management is a continuum of practices. It continues throughout the course of a research project. You will likely jump around and move between phases in the lifecycle, but you should always start at the Plan & Design phase. In this phase you will plan processes from onboarding, to project closure and data resources.

During the Plan & Design phase, you will ask:

  • What does your research project look like from start to (anticipated) finish?
  • How will data be handled during the project and after the project is completed?

During the Plan & Design phase, you will need to know:

  • your research project,
  • research stakeholders,
  • roles and responsibilities,
  • funder requirements,
  • data goals,
  • and challenges. 

Use checklists to help plan and design your work:

Collect & Create

Before launching a research project, design a model for capturing, storing, and organizing your data. 

During the Collect & Create phase, you will ask:

  • What types of data will be produced?
  • What standards will be used for data documentation and metadata?

Consider project

  • workflows (record data procedures, workflows, protocols, and responsibilities),
  • data types (document what types of data will be produced in the project),
  • metadata standards (use common standards including Common Data Elements or other FAIR metadata standards), 
  • formats (chose non-proprietary formats when available or convert to open, non-proprietary, widely used formats for sharing),
  • volume (understand how much data will be created as part of the project),
  • access (document any specialized tools required).

Design how you will store your data:

Analyze & Collaborate

Processing and analyzing data should be collaborative and documented.

During the Analyze & Collaborate phase, you will ask:

  • What software or tools will be used for data analysis?
  • How will you collaborate and document the process over time?

Data analysis is a process of inspecting, cleansing, transforming and modeling data with the goal of discovering useful information, informing conclusions and supporting decision-making.

The choices you make while analyzing your data can also contribute to effectively managing your research data:

  • Document your steps: Consider the software you use for analysis, and whether those applications automatically generate information about your data files and process steps. Keeping track of your steps can save you time when you want to recreate your work, or share your methodology with others! Use Electronic Lab NotebooksCollaborative Tools & Software, and Image Management platforms.

  • Keep your data safe: Describe your data as you capture it, organize your files, and make smart choices about where you store your data. Since some software programs produce files that are proprietary and can only be opened in their applications, consider saving data in formats that can be opened by different software programs. Ensure you are working with Analysis Ready Datasets.


Evaluate & Archive

Identify essential research records and evaluate for retention.

During the Evaluate & Archive phase, you will ask:

  • How will the data be archived for preservation and long-term access?
  • What are the retention requirements for your type of data, your funder, and/or university?

A small percentage of data and related records might be identified for permanent storage as a part of the historical record of a discipline or institution, or as intellectual property. 

Records eligible for permanent retention maybe those that:

  •  document a breakthrough
  • are generated by a lab or individual who had a great impact on the field
  • are highly reusable in a particular area of research.

Follow required retention and preservation requirements as established by your institution or funding agency.

The Data Curation Network has developed extensive guidance on working with and keeping research data:

Share & Disseminate

In the last decade, it has become increasingly common for researchers to make their data available to others when they complete a study. This is usually referred to as data sharing or data publishing. Data sharing is growing mostly due to recent data policies from journals and funders. 

During the Share & Disseminate phase, you will ask:

  • What data sharing policies do you need to consider?
  • What repository is best for your data?

Find a repository for sharing and publishing:

  • SUNY Dryad Instance: SUNY OLIS and some SUNY Campuses are members of Dryad and will be experimenting with Dryad as an open data publishing platform and community.  
  • SUNY SOAR  is an institutional repository, but not designed for data storage and sharing.

Data publishing repositories should follow FAIR principles 

Access & Reuse

Promote sharing and use of your data by making it available under appropriate licenses to ensure proper use and attribution. There are many licenses available that represent the range of rights for the create and licensee of the data. 

During the Access & Reuse phase, you will ask:

  • How will the data be accessed?
  • How can the data be reused?

In general, raw data are considered facts and cannot be copyrighted. Community norms for data attribution and scholarly communication are often more successful in documenting origins of data than licensing restrictions when possible.

Data license considerations include the following: 


Please send any questions to Research and Information Literacy Services Librarian, Christina Hilburger at

This guide was heavily adapted from the SUNY Office of Library and Information Services Research Data Management Guide, the LMA Research Data Management Working Group at Harvard Medical School, and the Defining Research Data page by NC State University Libraries.

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