What is the research data lifecycle?
The research data lifecycle refers to the process of conducting research, from the initial planning and design of a project to the publication or sharing of its results.
This process is not necessarily linear, and researchers often move back and forth between steps with fluidity over the duration of a project.
Planning Stage
During the planning stage, you will identify the data that will be collected or generated to answer your research question. You can define roles and responsibilities for the different people working on the project, set file naming and documentation conventions, and choose secure data storage. You might be applying for grants to fund your project and will need to know funder requirements. You might create a Data Management and Sharing Plan, which outlines how you will store, manage, and share your data during and after your project. Making a plan for how you will manage your data at the beginning of your project will save you a lot of work later on!
Our tips and tricks:
- Cornell Data Services offers free Data Management Plan review services. You can also generate a customized plan with DMPTool, a free online tool for creating, reviewing, and sharing data management plans.
- Learn more about data storage options supported by Cornell University at the Data Storage Finder tool.
- Check out our guidance for file management!
Active Stage
The majority of hands-on data work happens during the active stage. At this point, you will collect or generate raw data, process and prepare data for analysis, and conduct analyses. Documentation of methods and processes are very important at this stage. Various tools may be used for gathering, preparing, and analyzing data, and collaborate tools may be used to improve workflows.
Our tips and tricks:
- Need help with statistical analysis? The Cornell Statistical Consulting Unit provides statistical expertise to the Cornell research community through consulting, instruction, and contract services.
- The Cornell Center for Social Sciences offers consultations, workshops, and trainings for qualitative data analysis, web scraping, and data extraction.
- Are you working with humanities data? The Digital Co-Lab offers walk-in consultations and weekly co-working hours and assistance with data processing, analysis, and visualization tools
- Electronic lab notebooks are a great tool for documentation, collaboration, and project management. RDOS provides support and instruction for LabArchives and Open Science Framework (OSF) – for you or your lab group!
Concluding Stage
As you wrap up your project, you will start preparing to share it with a wider audience. This may involve writing and publishing an article about your findings. If you received funding for your research, you will need to comply with funder requirements to make the data publicly accessible for a certain amount of time. You may need to follow your institution’s data sharing and data retention policies. You’ll also need to be mindful of whether you have permission from all of your research subjects to share the data. That’s a lot to keep track of! The great news is that there are lots of librarians at Cornell to help you share your research, archive your data safely, and maximize the impact and visibility of your work.
Our tips and tricks:
- Persistent identifiers like ORCIDs and DOIs make research and scholarship more findable, accessible, interoperable, and reusable. We can issue a DOI for your dataset and help you set up your ORCID profile!
- eCommons is Cornell’s institutional repository, which provides long-term access to Cornell-related digital content, including documents, research papers, images, data, code and software, electronic theses and dissertations (ETDs), and more. If you need a repository to publish your dataset in, consider using eCommons!
- In order to encourage proper re-use of your data, you should define the terms of use by using an attribution license. RDOS librarians are happy to help you choose a license for your dataset!
- Learn more about sharing and archiving data!
This guide was created in 2025 by Gabby Evergreen and Lencia McKee and is shared under a Creative Commons CC BY 4.0 license.