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This guide will provide general information about data management, including
- data management plans (DMPs)
- data sharing plans
- file naming conventions
- security and backup
- publication and preservation
Explore the tabs across the top of this page to learn more.
Data Management Glossary
Cornell University's Research Data Management Service has complied a basic glossary of data management terms.
Data Sharing & Management Snafu in 3 Short Acts
A data management horror story by Karen Hanson, Alisa Surkis and Karen Yacobucci. This is what shouldn't happen when a researcher makes a data sharing request! Topics include storage, documentation, and file formats.
MANTRA -Research Data Management Training
MANTRA is a free, non-assessed course with guidelines to help you understand and reflect on how to manage the data you collect throughout your research. The course is particularly appropriate for those who work with digital data.
Why Provide Access to Data?
Wherever research is carried out, data of some kind is created. And where digital or web-based tools and approaches are used, it is likely that the data will be of greater amount and variety than in the past.
There has been a big push since 2008 or so to make research outputs and data more readily available, in part because federal agencies and funders have placed an emphasis on transparency, and partly because computers and the internet can make it easy to do so-- i.e., managing research data with the intention of sharing it. This is already a common practice among some research communities, and principles of Good Lab Practice and Responsible Conduct of Research apply to the practice of sharing data.
- Data Management addresses the lifecycle of research, including its creation, organization, analysis and dissemination.
- Data Sharing addresses how research outputs can be made accessible, especially beyond the life of a research project.
- Both involve activities that can be instituted at various points along the research lifecycle to facilitate access to data after the life of a project.
A Deeper Dive Into Data Management & Sharing
These resources can provide you with more data management information, training, and tools.
Data Sharing Plan Requirements (NIH example)
Highlights NIH policy and related guidance on sharing of research data developed with NIH funding, including FAQ.
Data Management Plan (NSF example)
Presents a scenario involving observation data and responds to the five main points in an NSF DMP.
DataONE Data Management Plannning: Best practices
Click on each activity for recommendations on how to effectively work with data through all stages of the data lifecycle.
Data Curation Lifecycle
The DCC Curation Lifecycle Model provides a graphical, high-level overview of the stages required for successful curation and preservation of data from initial conceptualisation or receipt. You can use the model to plan activities within your organisation or consortium to ensure that all of the necessary steps in the curation lifecycle are covered.
Nature Magazine: Special Issue on Data Sharing (Sept. 2009)
"Sharing data is good. But sharing your own data? That can get complicated. As two research communities who held meetings in May on the issue report their proposals to promote data sharing in biology, a special issue of Nature examines the cultural and technical hurdles that can get in the way of good intentions."
Note: Much of the content of this LibGuide was originally compiled by Lisa Zilinski under Data Management for Undergraduate Researchers