Mr. Isaac Tai
Assistant Librarian (User Services)
Email: cytai@eduhk.hk
Tel: (852) 2948-6681
We warmly welcome faculty members, researchers, and academic departments to partner with us in organizing tailored workshops that support students' research skills development and academic success.
A strategic document outlining how research data will be collected, organized, stored, shared, and preserved throughout a project's lifecycle.
Ensures data integrity, enables reproducibility, meets funding requirements, and maximizes research impact through proper data stewardship.
Ideally before data collection begins, often required at grant application stage, and updated throughout the research lifecycle.
Recognizing the importance of data planning but not sure where to begin? Fortunately, free, established online platforms can guide you every step of the way. These tools walk you through the process of crafting a Data Management Plan—whether you need to meet specific funder requirements or prefer a flexible, generic template—making them especially valuable for researchers new to DMP preparation. Two of the most popular and user-friendly options are DMPTool and DMPonline, both of which offer intuitive interfaces, customizable templates, and clear, step-by-step instructions to help you get started with confidence.
This checklist provides comprehensive guidance for creating a Data Management Plan (DMP). Each section contains essential questions to consider and detailed guidance to help you develop a robust data management strategy for your research project.
A pertinent ID as determined by the funder and/or institution.
State research funder if relevant.
Enter grant reference number if applicable [POST-AWARD DMPs ONLY].
If applying for funding, state the name exactly as in the grant proposal.
Briefly summarise the type of study (or studies) to help others understand the purposes for which the data are being collected or created.
Name of Principal Investigator(s) or main researcher(s) on the project.
E.g. ORCID: https://orcid.org/0000-0002-1825-0097
Name (if different to above), telephone and email contact details.
List any other relevant funder, institutional, departmental or group policies on data management, data sharing and data security. Some of the information you give in the remainder of the DMP will be determined by the content of other policies. If so, point/link to them here.
Give a brief description of the data, including any existing data or third-party sources that will be used, in each case noting its content, type and coverage. Outline and justify your choice of format and consider the implications of data format and data volumes in terms of storage, backup and access.
Outline how the data will be collected/created and which community data standards (if any) will be used. Consider how the data will be organised during the project, mentioning naming conventions, version control and folder structures. Explain how consistency and quality will be controlled and documented. This may include calibration, repeat samples, standardised capture, entry validation, peer review, or controlled vocabularies.
Describe the documentation that will accompany the data to help secondary users understand and reuse it. Include details such as creator, title, date, conditions of access.
Documentation may also cover methodology, procedural info, variable definitions, vocabularies, units, assumptions, file formats. Identify how and where this will be recorded, using community standards where possible.
Address ethical issues: anonymisation, ethics committee referrals, consent agreements. Show awareness and planning. For human participant research, ensure consent for sharing and reuse.
State IPR ownership and licences. For multi-partner projects, cover IPR in consortium agreements. Consider funder/institutional policies and permissions for third-party data.
State backup frequency and locations. How many copies? Avoid only local drives. Prefer managed storage and automated backups. If third-party, ensure compliance with policies and data jurisdiction.
For confidential data (personal, trade secrets), outline security measures and standards (e.g. ISO 27001).
Consider reuse value, validation, new studies, teaching. Balance obligations, value, cost, and preparation effort (e.g. format conversion).
Outline preservation beyond grant lifetime: curation, documentation, and resources if not using an established repository.
Choose sharing method based on data type, size, complexity, sensitivity. Cite past sharing examples. Consider citation and acknowledgement.
Explain expected sharing difficulties (confidentiality, consent, IPR) and mitigation (e.g. NDA for confidentiality).
Define roles for data capture, metadata, quality, storage, backup, archiving, sharing. Name individuals where possible and note policy compliance roles.
List software, hardware, technical expertise needed. Outline and justify dedicated resources.
Source: DCC. (2013). Checklist for a Data Management Plan. v.4.0. Edinburgh: Digital Curation Centre. Available online: http://www.dcc.ac.uk/resources/data-management-plans