Data Management Plan (DMP)

What is a Data Management Plan (DMP)?

A Data Management Plan (DMP) is a document that defines policies for the storage, management, publication, sharing, and reuse of research data generated during the research process. In principle, it is prepared before the start of the research and updated as necessary in accordance with research progress.

Following the issuance of “Basic Principles on the Management and Utilization of Research Data Funded by Public Funds” by the Cabinet Office and the Integrated Innovation Strategy Promotion Council on April 27, 2021, many funding agencies have begun requiring the preparation and submission of a DMP.

Please refer to the websites and materials listed below regarding research data handling policies, guidelines, and DMP requirements of major funding agencies, and prepare your DMP in accordance with the policies and formats specified by each agency. Be sure also to check the application guidelines for the funding program you intend to apply for.

For Grants-in-Aid for Scientific Research (KAKENHI), starting from FY2024, in order to ensure proper management and promote effective use of research data, researchers are required to prepare a DMP for each research project across all research categories. In response to this, our university has established the following policies. Please review them carefully.

University Notices

Research Data Lifecycle

Before Research

Step 1: Securing Research Funding

The process of applying for competitive research funding such as KAKENHI and securing budgets for various resources required for research implementation (personnel costs, equipment, data management).

Step 2: DMP Preparation

A DMP clearly documents, in advance, plans for the generation, sharing, storage, and publication of research data. In recent years, submission of a DMP has become mandatory when applying for competitive research funding.

Step 3: Confirmation of Institutional Infrastructure

This process involves checking and incorporating into the research plan the servers, repositories, databases, and training systems provided by universities or research institutions. Support environments for research data management and utilization are being developed at institutions such as the National Institute of Informatics and universities nationwide, and their use is encouraged.

During Research

Step 4: Research Data Storage

This is the process of storing data obtained from experiments, intermediate processing, or surveys in accordance with appropriate formats, naming conventions, and storage rules. It is considered fundamental to ensuring data quality and reliability.

Step 5: Data Search, Discovery, and Collection

This involves searching for relevant literature and existing datasets as well as collecting and utilizing highly reusable data. It is recognized as an essential step for promoting efficient research.

Step 6: Data Analysis

This is the process of obtaining insights through data processing, aggregation, visualization, and modeling. It also includes documentation and script management to ensure reproducibility of analyses.

Step 7: Management of Data Under Processing and Analysis

This process involves organizing intermediate data and analysis scripts and managing change histories and versions. This ensures reproducibility and transparency and allows for later verification.

Step 8: Updating the DMP

This involves reflecting changes such as data formats or publication policies in the DMP as the research progresses. KAKENHI explicitly requires periodic updates.

After Research

Step 9: Citing Data

This is the process of accurately citing and crediting the data used in research papers produced as research outcomes. Using persistent identifiers such as DOIs increases the value of data as a research output.

Step 10: Decision on Data Publication Policy

This process involves determining whether data will be published, the scope of access (e.g., open or closed strategies), licenses (such as Creative Commons (CC)), and the metadata to be assigned. These decisions are also reflected in the DMP.

Step 11: Registration of Data in Repositories

This is the process of registering curated data and metadata in institutional or disciplinary repositories. This enables long-term preservation and public access. Attention must be paid to interoperability to enhance data reusability.

Step 12: Data Publication through Data Papers

This is the process of publishing data as a Data Paper, which focuses on the data itself. By documenting analysis procedures and metadata, data reusability is enhanced and citation opportunities are increased.

Tohoku University

Open Science Promotion Portal