Although Open Science and research data management are part of a pan-disciplinary movement, they take various forms depending on the fields of study, disciplines, academic cultures, and national evaluation systems: thus, research data in the Life Sciences and in the STEM (Science, Technology, Engineering, and Mathematics) disciplines requires appropriate management.
Data on people, for example, must be appropriately shared, archived and published in order to avoid legal and ethical problems. Ethical issues may arise also when working with AI and machine learning data. Similarly, working, for instance, with animal, environmental, biological and chemical data requires different skills and knowledge of the legislation on sensitive data and potential danger exposure. Importantly, it is also crucial to ensure that you are generating or re-using data which is coherent with the FAIR principles (which are standard principles and which can be applied to all disciplinary fields) and, where possible, with open data.
This page illustrates a list of useful links and resources to guide you when working with specific and different kinds of data:
- Different guides for working within different domain of human data, health data, marine data, plant science data, genomic data, machine learning data, proteomics data
- EU's Open Data, Software and Code Guidelines for Physical Sciences and Engineering
- Direttiva Habitat
- Life Sciences and Bio-informatics resources
- Climate research resources
- A guide on how to use R