Data Management and Sharing
Advantages to Share Data
While the sharing of research data is encouraged and often expected by funding agencies and journals, sharing research data also has many advantages to scientists. Some benefits are listed below:
- Enhancing visibility of research
- Increasing the efficiency of research due to reusability and exposure
- Enabling researchers to ask new research questions and potentially further science
- Promoting scientific integrity and replication
- Enhancing collaboration and community-building
Steps to Sharing Research
Data sharing works best when you have planned for it early in the research process. You will need to think through the following questions before physically sharing data:
- Which of my data can’t I share? Be aware of any legal, ethical and physical constraints to data sharing
- Which of my data should I share? Many funders and journals request you share enough of your research so that it can be verified and reproduced.
- How will I share my data? To make data FAIR, use a repository
- When will I share my data? Your funder, institution, and journal may have requirements as to when data are shared. Read their policies carefully. For a registry of funder data sharing, please see the page "find funder requirements for data sharing".
- What conditions, if any, should I place on my shared data? You need to assign a license to your data. If working with data that cannot be shared publicly, you might need to work with Research Administration to develop data use agreements
Restrictions to Sharing Research Data
Not all research data can or should be shared due to legal, ethical or practical reasons. Your data management plan should address any restrictions to the sharing of your research data with others. The table below outlines some of these restrictions that should be considered. Information on Johns Hopkins University policies, including IRB requirements and intellectual property definitions can be found on the JHU Policies page.
Even with these potential restrictions, there are likely some subsets of your data that you can share after removing identifying information and/or aggregating your data.
|Information that identifies an individual (e.g., HIPPA, FERPA)
|Information that should not be shared (e.g., embargo period, trade secret)
|Threats to something and someone through release of data
|New, intangible creations (e.g., patents)
Physical Barriers: For help with storing, transferring, and sharing large data sets, please talk to your IT department about possible solutions.
Data Repositories for Sharing Data
The best way to make your data FAIR is to share via a repository. These are sometimes called data centers or archives. A data repository is a digital system and actively managed service for providing access to data. Repositories vary in their capabilities, but most include the following to varying degrees:
- Providing a web-accessible interface for discovering and downloading research data collections.
- Managing preservation of digital objects such as file integrity checking and redundant offsite backups.
- Use of identifiers, such as DOIs (digital object identifiers) to give datasets persistent location links and citations similar to journal articles
- Description of projects and files, and ways to include documentation sufficient for using the collection without contacting the researcher.
Please the page "How to Find a Data Repository" to learn more.
Guidance on How to Share Data
- Data Trust Resources on Sharing Johns Hopkins Medicine Data: Data Trust policies and guidance govern the sharing of JHM patient data to support research and operations. JHM data may be shared across JHU or may be shared outside of JHU in many circumstances. The following resources are available to answer questions related to the sharing of data for research, quality, and operations.
- Data Services Guidance and Training Material
- Data management resources: Created by Data Services, a series of online guidance around managing your data for sharing including curating and archiving research code and documenting your data.
- Data Services Webinars: Live webinars including deidentification training, best practices for data management, and reproducible research.
- Self-paced, online trainings: We have a series of modules that can be taken at your convenience including documenting data, how to write a data management and sharing plan, open science, and much more!
- Data Sharing for Next Generation Sequencing: Online guide created by Welch Medical Library and Data Services on how to manage genetic data for sharing.
- Sharing Qualitative Data: Online module from the Qualitative Data Repository on sharing qualitative data including human participant data. This module is one of a number of modules around qualitative data management.