Kryštof KomanecData Management Plan is receiving increasing attention among scientific communities. People are realizing the importance of this document and its value for the research data lifecycle, as well as for their own work. However, because this document has previously been perceived primarily as a way to securing funding, some people still fail to grasp the importance of crafting a robust Data Management Plan. Therefore, this article aims to elucidate its significance and provide guidance on its creation.
Most people will associate a Data Management Plan with project requirements set by funders. If a project seeks funding, the project proposers must provide a Data Management Plan, or else the project risks not receiving the funding.
Certainly, it is necessary to include a Data Management Plan when applying for a grant. However, those familiar with Data Management understand that the plan serves as more than just a requirement; it is a valuable tool for clarifying responsibilities, identifying potential cost savings, ensuring effective data management, mitigating or preventing data loss, facilitating data reuse and much more.
In any endeavor where the outputs are intended for practical use, such as reusable data, it's crucial to commence with planning. In the initiation of any research project, researchers don't jump straight into data collection; they first engage in meticulous planning. The same principle should be applied to Data Management, which should also be started with a planning phase.
As there is no strict definition of what a Data Management Plan should contain, institutions such as universities, research institutes, or funding agencies have begun creating their own templates. These templates serve to guide researchers on the necessary information to include in their Data Management Plans. Learn how to navigate the diversity of these templates in our other blog post.
However, in this particular blog post, we aim to outline all the components we deem crucial and some that we would consider optional.
Every Data Management Plan should include a section on Project Metadata. This section should contain basic information such as the name and ID of the research project, its start and finish dates, and the individuals involved in various roles within the project. Additionally, funding information should be included in this section as well as overall project costs, including costs of making the data FAIR.
As research projects extensively engage with data through activities like reuse, collection, transformation, and analysis, it is imperative to include comprehensive details of data collection in every Data Management Plan. This ensures the data's reusability and facilitates effective management throughout the project lifecycle.
The data collected or reused within a project are subject to rules governing their permissible uses. Therefore, it is essential to address various ethical, legal, and security issues within the Data Management Plan. This ensures that all data available for reuse undergo proper anonymization and licensing procedures, thereby promoting compliance and safeguarding data integrity.
After addressing data collection, ethical, and legal considerations, it's imperative to provide corresponding metadata and ensure accessibility to the data under specified conditions, often including availability via the internet. These components should be integral parts of the Data Management Plan to facilitate understanding, compliance, and efficient use of the data. More information on this topic is available in our FAIR vs OPEN blogpost.
In addition to ensuring comprehensive metadata and accessibility, data that is intended for reuse must also be securely stored and preserved. Therefore, the Data Management Plan must address strategies for data storage and preservation to support future reuse, whether for rerunning experiments, usage as reference data, or facilitating use by other researchers for their purposes.
In addition to key components that should be considered mandatory in every Data Management Plan, there are optional, 'nice to have' components. However, for a comprehensive approach to Data Management Planning in accordance with best practices, these components should also be included.
To facilitate data reuse, it is important to include information on potential users of the data and additional instructions on how to work with it effectively.
FAIR Metrics serve as valuable indicators of the extent to which our project's generated data is reusable and available for use in other researchers' projects. It is preferable to include a FAIR assessment within the Data Management Plan to ensure transparency and promote data accessibility and interoperability.
After listing the components, it is essential to include instructions on how to create an actual Data Management Plan. Additionally, institutions typically have Data Stewards responsible for implementing data usage and security policies. These individuals are well-versed in institutional Data Management and Data Management Planning policies. Don't hesitate to reach out to them with any relevant questions or concerns that may arise during the planning process.
The creation of a Data Management Plan should ideally commence at the inception of any research project. While it may not be feasible to address all aspects initially, beginning the planning process allows for addressing some key questions. Moreover, the Data Management Plan should be regarded as a living document, not merely a requirement for securing funding. It should be revisited, edited, and updated as needed throughout the project lifecycle.
Utilizing a tool to facilitate the creation process of a Data Management Plan is highly advantageous. While it is possible to fill out a provided template or create the plan from scratch, these methods entail their own risks and inefficiencies. Fortunately, there are several tools available for this purpose. Here is a comparison of two widely used tools, DMPonline and FAIR Wizard.
Where applicable in a given research field, it is advisable to identify any proprietary, personal and sensitive data in the Data Management Plan prior to data acquisition or collection. This preemptive measure can provide legal justification for withholding such data from public access if necessary.
As previously mentioned, the Data Management Plan should not be a static document but rather subject to regular review and updates. It is advisable to establish a schedule for incorporating new information, revising old information, and possibly editing the plan. These scheduled reviews should coincide with key project events such as funding approval, project reviews, and publication milestones.
Data Management Planning is gaining recognition in scientific communities for its pivotal role in the research data lifecycle. While once perceived as a mere funding requirement, its now understood as vital for project success. This article highlights its significance and provides concise guidance.
A Data Management Plan encapsulates critical aspects of data management, including metadata, ethical and legal considerations, data sharing and re-use protocols, and strategies for data storage and preservation.
Initiating the Data Management Plan at the project's start and treating it as a living document subject to regular updates is crucial. Collaboration with institutional Data Stewards and using available DMP tools can streamline the process.
Prioritizing Data Management Planning optimizes data management practices, enhances accessibility and reusability, and advances scientific knowledge.
