What This Does
Support the creation of research data management plans (DMPs) required by competitive research funds, joint research, and institutional policies. Organize the types of research data, storage locations, access permissions, whether data can be made public, retention periods, and role assignments, and put them into a form that can be operated after the research begins.
- Target Users: Researchers, URAs, research support staff, library and information infrastructure staff
- Issues to Solve: DMPs remain formal entries and cannot be used for actual data management or disclosure decisions
- Expected Effects: Clarify how data will be handled before the research begins, reducing loss, failure to disclose, contract violations, and personal information risks
Required Inputs
- Overview of the Research Project: Research objectives, period, research structure
- Types of Data: Experimental data, survey data, images, audio, source code, secondary-use data, etc.
- Data Volume and Format: File formats, estimated capacity, update frequency
- Constraints: Personal information, contracts, intellectual property, export control, ethics review, conditions of joint research partners
- DMP Format of the Funding Program or Institution(Optional): If a specified format exists
- Planned Disclosure(Optional): Repository, disclosure timing, licensing policy
What It Produces
- DMP Draft: Draft descriptions conforming to the funding program or institutional format
- Data Inventory Table: Data type, responsible person, storage location, whether disclosure is possible, retention period
- Risk Checklist: Issues related to personal information, contracts, intellectual property, ethics, and security
- Operational Task Table: Who checks and updates what, and when
Procedure
1. Take inventory of the data
Organize the data generated and used in the research in the following table.
| Item | Example Entry |
|---|---|
| Data Name | Interview audio, analysis code, experiment logs |
| Generation Method | Experimental equipment, questionnaire, existing DB, simulation |
| Format | CSV、JSON、MP4、TIFF、Jupyter Notebook |
| Volume | 1GB, 500GB, 2TB per year, etc. |
| Sensitivity | Personal information, trade secrets, unpublished inventions, no restrictions |
| Reuse Value | High, medium, low |
2. Confirm constraints
Confirm the following for each dataset.
- Whether it contains personal information or special care-required personal information
- Whether it conforms to the approval conditions of ethics review and the scope of use in the consent document
- Whether there are constraints from joint research contracts, data provision agreements, NDAs, or outsourcing contracts
- Whether there is a risk of disclosure before patent filing
- Is confirmation required for security export control, the Foreign Exchange and Foreign Trade Act, and sensitive technology management?
- Is disclosure or preservation required by the call for proposals guidelines or institutional policy?
3. Decide on a preservation and backup policy
Decide on the management method during the research period.
- Primary storage location: Institutional storage, laboratory server, cloud, repository
- Backup: Frequency, storage destination, method of restoration verification
- Access permissions: Permissions of the principal investigator, co-investigators, students, and external collaborators
- Naming convention: Naming that includes date, data type, and version
- Change history: Tracked via Git, electronic lab notebooks, data catalogs, etc.
4. Determine whether data is public or non-public
Separate public availability for each dataset.
| Determination | Typical example | Response |
|---|---|---|
| Public | Reusable anonymized data, analysis code | Prepare repository, license, and metadata |
| Restricted public access | Application-based data, data restricted by collaborative research agreements | Specify terms of use, application destination, and review procedure |
| Non-public | Data that can identify individuals, unpublished inventions, data that cannot be disclosed under contract | Specify the reason for non-disclosure and the retention period |
| Requires confirmation | Data whose constraints have not been organized | Set the contact point for confirmation and the deadline |
Do not stop at merely saying "disclose to the extent possible in accordance with the FAIR principles"; also organize the reasons why disclosure is not possible in practical terms.
5. Describe it in the DMP
In accordance with the format of the funding call or institution, include the following elements.
- Overview and format of the data
- Preservation and management method during the research period
- Policy for creating metadata and documentation
- Policy and planned timing for disclosure and sharing
- Reasons for data that will not be disclosed
- Retention period and disposal policy
- Management supervisor and division of roles
- Security measures and access control
6. Break it down into operational tasks
Do not let the DMP end as a document for submission; incorporate it into research operations.
| Task | Person in charge | Deadline | Frequency |
|---|---|---|---|
| Update the data list | Principal investigator or data manager | End of month | Monthly |
| Backup check | IT infrastructure staff or laboratory staff | End of quarter | Quarterly |
| Reconfirm public availability | PI, URA, intellectual property staff | Before manuscript submission | As needed |
| Repository registration | Data manager | After manuscript acceptance | Per outcome |
Judgment criteria
- Publication policy is judged not by "whether we want to publish" but by consent, contracts, intellectual property, security, and reuse value
- Data storage locations are chosen not merely for convenience but based on institutional regulations, access management, backup, and continuity upon retirement or transfer
- Source code, analysis procedures, README, and metadata are also treated as subjects of research data management
- The DMP is updated at the start of research, when the research plan changes, before manuscript submission, and at the end of research
Common mistakes and countermeasures
| Mistake | Countermeasure |
|---|---|
| Writing only "manage appropriately" in the DMP | Specify storage locations, responsible persons, permissions, and retention periods |
| Overestimating the anonymization of personal information | Check re-identifiability, free-text responses, and combination risks |
| Starting publication preparation after the manuscript is accepted | Organize the file structure and metadata from the start of research |
| Publishing collaborative research partners' data without permission | Check whether contracts, consent, and collaborative research partner approval are required |
| Data is lost after a student graduates | Decide on institutionally managed storage locations and handover procedures |
Quality standards
- Storage location, responsible person, publication availability, and retention period are specified for each dataset
- There is a practically explainable reason for data that will not be published
- Ethical review, contracts, intellectual property, and information security issues are separated
- The contents of the DMP are converted into daily operational tasks
- It is described completely and without excess in accordance with the formats of the funding call and affiliated institution
Available Tools
- Read: Reading the funding call guidelines, DMP format, research plan, and contract terms
- Write: Creating the DMP, data inventory, and operational task table
- WebSearch / WebFetch: Checking the latest requirements of public institutions, affiliated institutions, and repositories
- Bash: Formatting the data inventory CSV and checking the file structure