What is research data management?

Research Data Management (RDM) refers to all the practices used to organise, document, store, protect, preserve and, whenever possible, share the data collected, produced or used as part of a research project.

RDM follows the research project lifecycle and covers data throughout their entire lifespan: from project design and the collection of the first data through to their archiving, publication or reuse.

Not sure whether you are working with research data? → [Identify research data]

Why manage your data effectively ?

Managing research data requires sustained effort throughout a project, but it brings significant benefits. Good data management helps you to:

Save time in your day-to-day work by making it easier to find the files, information and versions you need through appropriate file naming and documentation without having to reconstruct how your data were organised or processed later on.

Prevent data loss or corruption by using appropriate storage solutions, performing regular backups and clearly managing different versions.

Facilitate teamwork by establishing shared rules for organising, naming and documenting files. This helps ensure that data remain understandable when new people join the project, others leave, or the data are reused by someone else.

Improve the quality and reliability of research by keeping a record of the methods, decisions and transformations applied to the data. This documentation makes it possible to verify analyses, support published results and establish when discoveries were made during a project.

Protect people whose data are collected by taking confidentiality, data processing and protection and ethical requirements into account from the outset of the project.

Preserve valuable data over the long term so that they do not become unreadable, incomprehensible or unusable once the project has ended.

Increase the visibility and impact of your research by making data easier to find, understand and cite. Publishing a dataset can also foster new collaborations and contribute to recognition of the work carried out.

Meet the requirements that apply to your project, including those of the University, funders and academic publishers regarding data management, preservation and sharing.

RDM throughout the research project

Research Data Management ideally begins at the project design stage and continues until the research project is fully completed. RDM activities can be divided into several stages that together form the research data lifecycle. These stages are not always strictly sequential: they may overlap or be repeated over the course of a research project.

UNIGE_Data-lifecycle_2025.png

“UNIGE Data Life Cycle” by University of Geneva is licensed under CC BY 4.0. It is adapted from "Data Life Cycle" by ELIXIR RDMkit licensed under CC BY 4.0.

Before the research begins: anticipate the data that will be used or produced, including their nature and volume. Define responsibilities, resources and the tools required, and identify any legal, ethical or contractual constraints. In practice, this planning is generally documented in a Data Management Plan (DMP).

During the research: produce, collect or select the data needed for the research using appropriate tools and according to the methods defined. Document their provenance and the conditions under which they were collected, while ensuring their quality and, where applicable, obtaining the consent of the people concerned. Laboratory notebooks, including electronic lab notebooks (ELNs), can make this process easier to track.

During the research: organise, clean, convert and quality-check the data to make them suitable for analysis. Document the methods, transformations, software and scripts used and, where necessary, anonymise or otherwise protect the data.

During the research: store data in appropriate storage environments, manage access rights and perform regular backups. Select the data that should be preserved over the long term and ensure that they remain understandable and usable by using appropriate file formats and documentation.

At the end of the research: select and prepare the data that can be made available, then deposit them in an appropriate data repository together with their metadata and documentation. Define the conditions governing access and reuse and make the data easier to identify and cite.

After the research: use existing data to address new research questions, verify results or complement other work. Before reusing data, assess their quality, provenance and documentation, as well as any legal or ethical conditions that apply.

To learn more

UNIGE resources


Data Science for all  video - The challenges specific to data management

  • Draw.io – a tool developed by the Faculty of Medicine to support research data planning and management

Training at UNIGE

8 August 2026