Managing Your Data: Project Checklist
Research Data Services > Data Management > Project Checklist
This Data Management checklist and resource guide will help you set up and maintain robust data management practices and systems through the full life of a project. For additional assistance, contact firstname.lastname@example.org
This guide is modified from “Managing Your Data– Project Start & End Checklists” PDF. Last Updated: 2020.04.17 Created by Phoebe Ayers & Christine Malinowski at MIT Libraries Data Management Services. Last Updated: 2020.04.17
Determine your data management needs & responsibilities
❏ Determine who on the project team is responsible for managing your data.
Make sure you know who is responsible for what. Who collects the data? Who has access? Who is responsible for implementing a data management plan?
❏ Define what data, files, etc. you need to manage.
❏ Create a quick inventory of your data & related files.
❏ Ask what type of files & file formats? What are the estimated file sizes?
❏ Develop & document/share your file organization and naming system.
Create a standard way to name and organize your files to avoid access and version issues. Your system should be useable by everyone in your team.
File naming is important for short and long term success. See our guide on Documenting your Data for more information.
- UC Davis Datalab Toolkit:
Excelling with Excel- Keeping Your Data Tidy
- Stanford Libraries: Best Practices for File Naming
- Princeton University Library: File Naming
❏ Develop a Data Management Plan
These plans are also a great way for you to anticipate your data management needs and establish a shared understanding of the resources available to you.
❏ Be Aware of Funder and Publisher Requirements for Data Management Plans.
Note: Funding agencies and publishers may require specific elements in your Data Management Plan.
- More on Data Management Plans
- Use the DMPTool to help develop your plan
- UC Davis DataLab Tool Kit for Data Management Plans
Store and share your work during the project
❏ Determine your active storage & sharing system.
During the active phase of your work, where do you store your data & files? This is different than your backups; these are the copies you are actively accessing and editing. Consider the amount of space you’ll need for the life of the project, who needs access, what tool integrations are needed, etc.
❏ Set up a backup system.
Follow the Here-Near-Far backup practice of 3 copies in 3 different locations. Set up a ‘Crashplan‘ on your computers as one of your backups.
● Backup Recommendations from UC Davis Information Technology
❏ Establish access & security guidelines for your data.
Do you have sensitive data? Determine what access limits need to be in place for your different data files. Who can access what, when? Some external datasets have explicit access rules that need to be followed.
● See: DataONE Best Practices Working Group, DataONE (July 01, 2010) “Best Practice: Identify data sensitivity“.
Document your project
❏ Determine what you need to document about your data (metadata) & how to capture it.
Check what is standard in your discipline or what a long-term repository may capture about similar data. Think about what you or someone else would need to know to use the data in the future. Start with a simple README (plain text file) for this information.
These sites can help you select the appropriate metadata for your field:
❏ Document your data management system.
Review Your Data Management Plan. Create simple README files that documents your data management system from roles & responsibilities to where your data is stored to how you name and organize it.
❏ Share your system with research group members, collaborators, etc.
Make your READMEs and other project documentation accessible to all your collaborators. Make sure everyone has a shared understanding of these documents.
- Slides from UC Davis Datalab workshop: Readme Write me!
- Cornell University’s Guide to writing “readme” style metadata
Evaluate your system
❏ Test and revise your system as needed over the life of the project.
No system is ever perfect. Every system needs to evolve as projects change and the users of it identify better ways. Fight the urge to abandon an imperfect data-management system. Instead, revise it to better meet your needs.
❏ Think ahead to your longer-term data management & sharing goals and prepare for long-term data management in advance.
❏ Review the “When ending a project” checklist below to start establishing your practices for longer-term storage and sharing.
Determine post-project data sharing & restrictions
❏ Determine what data should be openly shared and how any confidential data will be handled.
❏ Make sure any funder or intellectual property restrictions on the data are documented and followed.
❏ Determine publisher requirements for data sharing.
❏ Evaluate long-term sharing and storage systems.
Store & share your work long term
❏ Ensure those responsible for the project long term have appropriate access to your files and data.
❏ Make sure any needed software is accessible and properly licensed.
❏ Make sure ReadMe files are up to date.
Preserve and Publish Your Data
❏ Adhere to FAIR Principles for Sharing Data
Shareable data are guided by these principles, data are:
- Findable: Data are identified through unique identifiers and clearly cited.
- Accessible: Data are publicly available, for example, in a Open Access repository.
- Interoperable: Data are in made actionable by being available in non-proprietary formats, for instance CSV rather than PDF or Excel files.
- Reusable: Data are properly documented through readme files, file naming protocols, and codebooks.
❏ Finding a Repository
The best place to share your data depends on your discipline. If there is a national or subject-level repository that common to your field, that would be your first choice. To determine if such repositories exist, you can also search the registry of repositories re3data, or check the Open Access Directory of Data Repositories at Simmons University.
Dryad is an open-source, research data curation and publication platform. UC Davis is a partner of Dryad and offers Dryad as a free service for all UC Davis researchers to publish and archive their data. Datasets published in Dryad receive a citation and can be versioned at any time. Dryad is integrated with hundreds of journals and is an easy way to both publish data and comply with funder and publisher mandates.
❏ Citing data
Unique identifiers disambiguate information. They are available for datasets in the form of DOI’s (Digital Object Identifiers). Learn more here.
❏ Get a Unique Author Identifier
Unique identifiers are important for authors too, and can be acquired through ORCID identifiers. Learn more here.
❏ Document the published & unpublished work (manuscripts, published papers, figures) that are based on or related to your data.
Find out more about Preserving and Publishing Your Data
The Library provides services, resources, and assistance to those working with data across the research life-cycle.
This guide provides information on:
- Data Discovery and Selection
- Data Extraction, Formatting, and Visualization
- Data Management, Publishing, and Preservation
- Health Science Data at the Blaisdell Medical Library
For assistance contact: email@example.com