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The topics and concepts section offers topic-specific articles with references to further resources. Important concepts and tools are presented as well as other relevant aspects for digitising chemistry. Basics that are important for research data management for all roles and all domains are explained in detail.

The data life cycle is used to illustrate the handling of research data according to the FAIR principles. Important tools such as electronic laboratory journals (ELNs) help to assign rich metadata. If the ELN is directly connected to a repository, data can be transferred into the repository easily for data publication. In order to ensure that data are permanently referenced and cited, repositories assign unique persistent identifiers (PIDs). By linking the journal publication to the data publication, findability of the data files for re-use is guaranteed. This is one best practice example that can guide you to better understand how efficient data handling works. Data availability statements are also important for publishing data, as they indicate where the data supporting the findings reported in a published article can be found.

Finding relevant articles based on IUPAC names or trivial names of molecules is a challenging task. Having machine-readable chemical structures such as chemical table file formats, InChI identifiers and SMILES structure codes as part of a dataset, associated with a research article, will enhance its findability.

Data format standards are important for all research data. Knowing the core concepts of ontologies in one's research domain is helpful in a FAIR research data management context.

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