Who this guide is for

This guide is for anybody who has ecological data and wants to improve their data management by making their data more structured and more FAIR, for example:

  • ecologists who want to improve their research data management and by that make their data available for reuse by others

  • students who collect data for their thesis or internship and want to make their data reusable for others after their project is finished

  • institutions and organisations that want to provide their employees with some practical guidance on how to increase the understandability and reuse of their data

  • data stewards and managers who want to assist their colleagues in improving their research data management

  • anyone who wants to learn more about data management, FAIR and structured data and metadata, and best practices for increasing the reusability of data

Why you should use this guide

Data management is the practice of taking care of data throughout its entire lifecycle, from its collection, processing and use to its storage and sharing. Throughout the whole lifecycle, good data management is crucial to ultimately enhance the reusability of the data for yourself and others. For example, by storing your data persistently and implementing versioning, you lower the risk of data loss and have higher traceability of changes and errors. Additionally, by describing, annotating, and organising the data, it becomes better understandable for others, facilitating its reuse and increasing its impact and that of the associated research.

One way of reaching the goals of good data management is to make your data findable, accessible, interoperable and reusable (FAIR). For this, FAIR guiding principles have been developed that provide people with a set of considerations to evaluate whether data can be discovered and reused by others. See this chapter for more info on FAIR.

How to read this guide

This guide is designed in a way that you can only read the chapters that are relevant for enriching your data. To find these chapters, we provide you with a self-evaluation tool that shows to what extent your data already complies with the FAIR principles. Directly based on the outcome of the FAIR and structure evaluation you will be provided with a tailored list of the chapters in this guide you could go through to further enrich your data. You therefore do not have to read the full guide from top to bottom but should easily be able to only go through the chapters most relevant to you.

How this guide came to be

The project “Data for Digital Twins - Piloting a FAIR Data Infrastructure for the Advanced Modelling of Ecological Data”, or FAIR Data for Digital Twins for short, was a collaborative project in 2023-2024 between the Netherlands Institute of Ecology (NIOO-KNAW) and the Dutch Data Archiving and Networked Services (DANS-KNAW) funded by the research fund of the Royal Netherlands Academy of Arts and Sciences (KNAW). The project aimed to explore how to make quantitative ecological data more FAIR and fit for advanced analytical and modelling purposes, such as artificial intelligence and digital twins.

One of the main goals of this project was to use hands-on experience from FAIRifying a range of different ecological datasets to create a manual that guides ecologists step-by-step through the process of FAIRifying their ecological data themselves. Many ecological datasets are not compliant with the FAIR data principles, which makes it difficult to share and exchange biological data. This is also true for the data collected at NIOO and together with the general movement towards open and FAIR data, this initialised this project.