13:30 - 15:00
We live in an increasingly digital era, where more and more electronic research data is being generated through scientific research, and scientific publications are frequently underpinned by large quantities of data and code. The amount of data being generated is too much for humans to manage and analyse on their own, therefore we need machine-actionable data to enable us to exploit AI and machine learning technologies to make the most use of this data. Therefore, the requirements and methods for implementing successful research data management (RDM) are being explored.
Which file formats should be used in the long term? How and where should research data be stored? What data and information should be included in the supplementary information of journal papers? What information about workflows, experiments or calculations should be included in the metadata (the data used to describe your data)? How can people in your group and people outside your group access the data? How can the research data be easily found by people and computer systems?
Finding the best answers to these questions is part of the implementation of the FAIR data principles (findable, accessible, interoperable and reusable) [1]. These are particularly important to increase the reproducibility of scientific research and to enable the reuse of data and code, and the replication of scientific experiments. Furthermore, with the advent of artificial intelligence and machine learning, FAIR data and the principles surrounding it are becoming even more important.
There are already initiatives around the world (such as the Physical Sciences Data Infrastructure (PSDI) in the UK and NFDI4Chem in Germany) working on the practical implementation of the FAIR principles, for example through the use of software tools such as open source Electronic Lab Notebooks (ELNs), through providing training and educational resources, and through developing free services and tools for the scientific community [2-5]. These initiatives are also involved in the definition of standards, for example, in collaboration with IUPAC (International Union of Pure and Applied Chemistry).
This symposium will comprise three talks with time for discussion. The first talk will introduce the national initiatives of PSDI and NFDI and provide an introduction to the current landscape for research data management, introducing key concepts such as RDM, metadata and the FAIR principles; highlighting the state of play, why it matters and the current issues that the scientific community is facing. The second and third talks will focus in more detail on the activities being undertaken by both PSDI and NFDI, providing an overview of the free tools, training, educational resources, data infrastructures and services available through their and other research data management initiatives as well as giving chemists an insight into journal and funder requirements with respect to FAIR data.
[1] Mons, B. et al., The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 2016, 3, 160018.
[2] Herres-Pawlis, S. et al., NFDI4Chem: Shaping a Digital and Cultural Change in Chemistry. Angew. Chem. Int. Ed., 2019, 58, 10766 – 10768.
[3] Steinbeck, C. et al., NFDI4Chem - Towards a National Research Data Infrastructure for Chemistry in Germany, Research Ideas and Outcomes, 2020, 6, e55852.
[4] Website: https://www.nfdi4chem.de
[5] Website: https://www.psdi.ac.uk/