Data Management Plan
In certain research fields, planning data management in advance has been a common practice since the 1970s. Over time, this approach extended to other scientific and engineering disciplines, eventually becoming a requirement by funding bodies to ensure proper oversight of data management. Why? Developing a data management plan is considered to mark the best practice.
| Benefits of devising a DMP | |
| Enhancing the data management quality |
Planning ahead allows for early identification of weak spots in data management, enabling timely solutions. All researchers and students involved in the project refer to the DMP as the key reference document, outlining the strategies for managing data. E.g., everyone must adhere to established file/data naming conventions with required detail on additional information. This practice helps to preserve the data’s value long after the author has left the project. |
| Understanding what support is required |
IOCB is committed to supporting research data management at the institutional level, as stated in the data policy. Developing a DMP may lead to addressing the gaps and issues right in time. |
| Budget Planning | The costs associated with data management are considered justifiable expenses (e.g., new disk storage, senior team member overseeing the data management in the project). See this link for more ideas on what is applicable. |
Data Stewardship Wizard
All funding bodies recommend the Horizon Europe template for Data Management Plans (DMP). In case you would rather avoid the trap of falling victim to another administrative burden, you may choose to work in a DMP-making tool, Data Stewardship Wizard (DSW). Within the next six months, we plan to introduce an institutional instance of the DSW - a migration of existing DMPs created in the free version into the institutional one will be possible.
The FAIR metric, embedded in the DSW, has been integrated in the software to raise awareness of better options. Its primary goal is to promote good
data management practice in line with FAIR principles. Do not choose options in the DMP simply based on the FAIR parameters - that would invalidate
this particular benefit for you. Rather, use the indicators to enhance your awareness.
When answering questions, bear in mind that not all questions will apply to your project. Some follow-up questions, despite their inclusion of FAIR indicators, may not be relevant. For example, questions regarding minimum metadata information or procedures for ensuring data quality might not apply in every case.
Rest assured, the first DMP created in the DSW is the most time-consuming and challenging. However, using the tool will save you significant time and effort for subsequent plans.
| Benefits of using the DSW | |
| 1. TIME-SAVING | The tool significantly reduces the time needed for creating and managing DMPs, especially for future projects. |
| 2. REUSABLE TEMPLATES | You can clone existing DMPs and easily adapt them to new projects by adjusting details specific to each project's requirements. |
| 3. SIMPLE UPDATES | Revisions and updates are straightforward; a customizable versioning feature projects a version log directly in the document. |
| 4. COLLABORATION-FRIENDLY | DMPs can be shared in review or editing mode, enabling seamless collaboration with others. |
Access the tool on the following link.
Select the version “For Researchers” via “Get Started” button and log in via the Life Science login (IOCB login credentials) or G-mail.
FAQ
| I am reusing existing data? | If your project is built on existing data, whether from a researcher within your field or from your own (or your group’s) previous project, you are reusing data in the new project. |
| Who owns the data? | IOCB is the owner, researcher is the author. |
| Do I have to open my data? | Yes, although some funding bodies, such as MŠMT and national-level TAČR projects, currently allow data to be made open upon request. Exceptions, including data subject to intellectual property rights, are valid reasons for not making data publicly accessible. |
| What are the repositories? | Sharing your data through a trustworthy repository is the best way to open your data. A reliable repository offers a well-defined policy covering storage quality, backup procedures, and data retention. The repository you choose should also support assigning unique identifiers (such as DOIs), selecting appropriate licenses for data usage, and ensuring that provided metadata can be indexed and harvested. Storing your data in such a repository guarantees its archival in accordance with the repository’s policies. |
| What repository should I choose? | Field-specific (also known as domain-specific) repositories are the most suitable choice if one exists for your data type. If you’re unsure, you may choose to search for a repository according to the data type/field in the repository database. IOCB researchers will soon (next year) have access to the institutional data repository ASEP. The details on its benefits and use will follow. In addition, when a domain-specific repository is not available, a general repository is a good alternative. Our researchers commonly use the trusted Zenodo for this purpose. |
| What form should the data be opened in? | Field-specific repositories provide guidelines on the format in which datasets should be deposited. If such information is not available, consider what best supports data interpretation and reproducibility. Is it raw data or a combination of both, the raw and processed data? |
| What to use for long-term archival? | If not sure, repositories serve this purpose. |
| What is the best data format for preservation? | While proprietary data formats may be commonly used today, the goal of effective data management is to ensure data longevity
and future usability (= interoperability). For this reason, open, standard, non-proprietary formats should be the preferred choice for retaining data.
It’s worth exploring whether the lab equipment software allows for configuring data in these formats.
A helpful decision-making guide for choosing data formats is available here. |
| Is there a minimum metadata information standard/metadata standard available about my data? | Some experimental methods have a standardized metadata minimum standard, while others do not. A researcher knows the best what within their discipline and method is needed to know about their data to ensure their data may be correctly interpreted (e.g., lab equipment type, software used and its version, buffer details,..). If you use a trusted repository for data sharing/archiving, there is a metadata standard. Field-specific repositories request a method-specific metadata standard, while institutional and general repositories utilize a less specific metadata standard. E.g., a general repository Zenodo follows the DataCite minimum metadata scheme with additional features. |
| Do I use any vocabularies/ontologies? | You may already use your own vocabularies, ontologies, or both to describe your experiments. In general, data acquired via lab instruments include metadata annotations describing values. These annotations frequently draw on terms from ontologies, which are structured frameworks that define concepts and their relationships. Vocabularies provide the standardized terms within these ontologies, ensuring clarity and consistency in the description of your data. |
| Who does the data peer review? | There may be several people involved in the process, e.g.:
|