📄️ Funder Requirements
Research data management (RDM) has become an integral part of the application procedure of many research funders. Funders increasingly began recognizing the importance of research data management in ensuring transparency, reproducibility, and the long-term impact of research. Many funding organizations have implemented specific requirements and guidelines regarding research data management that researchers must adhere to in order to receive funding. These requirements may vary depending on the funding agency and the type of research being conducted.
📄️ (Re)use of existing data
Instead of acquiring new data, it might be more efficient to reuse existing data. This may be data collected by others but also data you collected previously. Reusing research data offers numerous benefits to both individual researchers and the broader scientific community. In general, reusing research data is a practice that benefits researchers by saving time and resources, reducing data redundancy, improving the rigor of research through validation and reproducibility, enabling comparative and interdisciplinary studies, and promoting a collaborative scientific culture and knowledge exchange. Reusing existing data can sometimes be more ethically sound than collecting new data, especially when dealing with sensitive topics or vulnerable populations. It reduces the need to subject participants to additional data collection processes.
📄️ Data Paragraph (Grant Proposal)
When applying for funding, it has become increasingly common to include a data paragraph, also known as data management paragraph or data management section. A data paragraph is a section within a grant application or research proposal that outlines how the research data will be handled throughout the course of the project. It is a concise yet comprehensive description of the data management practices that the researchers plan to implement to ensure the proper collection, organization, storage, preservation, and sharing of the research data. A more detailed description of data management is included at a later stage (post-award stage) in a Data Management Plan.
📄️ Data Management Plan
A Data Management Plan (DMP) is a formal document that outlines how research data will be collected, organized, documented, stored, preserved, and shared during and after a research project. It is a crucial component of current research practice and is often required by funding agencies as part of grant applications. A funder-approved DMP is often also a requirement to obtain the first round of funding for a research project.
📄️ Costs of Research Data Management
An important part of the data management plan is to cost the resources needed to implement good research data management practices. Most DMPs include a paragraph on the resources that you plan to dedicate to RDM. These costs can be diverse and include things like people’s time, equipment, infrastructure and tools to manage, document, organise, store and provide access to data. You can find a helpful overview of possible RDM costs per research phase and research activity at the Utrecht University website. It is based on the data management costing tool developed by the UK Data Service costing tool and focuses on the social sciences.
📄️ FAIR data
The FAIR principles are the standard for responsible data management and practicing open science. They focus on ensuring that research data are reusable, will actually be reused and will become as valuable as possible. FAIR is not only aimed at human beings but puts emphasis on enhancing the ability of machines to automatically find and use the data. FAIR stands for:
📄️ Ethical Approval
Everyone with TU/e affiliation (including students) must register their research involving human participants and ask for ethical approval. Ethical approval is also necessary when processing data from human participants that were collected by an external party, which is considered the processing of secondary data.