The Long and Winding Road towards FAIR Data as an Integral Component of the Computational Modelling and Dissemination of Chemistry

Abstract

Fifty years of the author‘s activities as a “computational chemist” are described, linked by the theme of scientific data. For the first half of this period, data was often handled in a very impoverished way, appearing as in printed form as supporting information or as appendices in e. g. PhD theses and these methods resulted not only in considerable loss of information but caused severe and error‐prone difficulties in the re‐use of the data. The second period charts the explosive growth of the digital information era and describes the author‘s own involvement in developing some of the required infrastructures that are nowadays often taken for granted. This culminates in the present‐day increasing use of data repositories holding FAIR data, which is now firmly regarded as a first‐class citizen of the scientific processes. Many examples of these experiences are included in the essay for the readers to experiment with themselves.

Modelling kinetic isotope effects for Swern oxidation using DFT-based transition state theory

“…Rather than preparing the ESI in the traditional format of a single paginated PDF le used for specifying the coordinates and energies in the original article, 9 we here adopt the FAIR (Findable, Accessible, Interoperable and Reusable) data principles and metadata protocol 13 to allow facile replication and re-use of the data generated as part of this work. [14][15][16] This allows e.g. individual calculations to be identied and cited using a persistent iden-tier (in the form of a DOI).…”

“…These are all identied as components of a hierarchical repository data collection. To facilitate the management of these data and the automated preparation of their associated metadata, we used a computational electronic notebook (CHAMP) 18 coupled to a FAIR data 15 research data repository. 14 3 Results and discussion…”

Open data and algorithms for open science in AI-driven molecular informatics

“…The German government, for example, has recently decided to implement and long-term-fund a national research data infrastructure (Nationale Forschungsdateninfrastruktur, NFDI) [17] with 30 consortia in all areas of science, collaboratively developing open research data management (RDM) e-infrastructures, coordinated by an umbrella process and a joint directorate. One of those consortia is NFDI4Chem which is building an RDM e-infrastructure for chemistry that follows FAIR data principles [18] to make chemical data findable, accessible, interoperable, and reusable [19,20]. One flagship project of NFDI4Chem is nmrXiv, an open and FAIR repository and analysis platform for NMR spectroscopy data [21].…”

Open data and algorithms for open science in AI-driven molecular informatics

“…The German government, for example, has recently decided to implement and long-term-fund a national research data infrastructure (Nationale Forschungsdateninfrastruktur, NFDI) [17] with 30 consortia in all areas of science, collaboratively developing open research data management (RDM) e-infrastructures, coordinated by an umbrella process and a joint directorate. One of those consortia is NFDI4Chem which is building an RDM e-infrastructure for chemistry that follows FAIR data principles [18] to make chemical data findable, accessible, interoperable, and reusable [19,20]. One flagship project of NDFI4Chem is nmrXiv, an open and FAIR repository and analysis platform for NMR spectroscopy data [21].…”