Workflows Allowing Creation of Journal Article Supporting Information and Findable, Accessible, Interoperable, and Reusable (FAIR)-Enabled Publication of Spectroscopic Data

Abstract

There is an increasing focus on the part of academic institutions, funding agencies, and publishers, if not researchers themselves, on preservation and sharing of research data. Motivations for sharing include research integrity, replicability, and reuse. One of the barriers to publishing data is the extra work involved in preparing data for publication once a journal article and its supporting information have been completed. In this work, a method is described to generate both human and machine-readable supporting information directly from the primary instrumental data files and to generate the metadata to ensure it is published in accordance with findable, accessible, interoperable, and reusable (FAIR) guidelines. Using this approach, both the human readable supporting information and the primary (raw) data can be submitted simultaneously with little extra effort. Although traditionally the data package would be sent to a journal publisher for publication alongside the article, the data package could also be published independently in an institutional FAIR data repository. Workflows are described that store the data packages and generate metadata appropriate for such a repository. The methods both to generate and to publish the data packages have been implemented for NMR data, but the concept is extensible to other types of spectroscopic data as well.

A data‐oriented approach to making new molecules as a student experiment: artificial intelligence‐enabling FAIR publication of NMR data for organic esters

“…The landing page for a fileset, showing files and selected metadata present for the item with https://doi.org/ 10.14469/hpc/6873 to the collection is appended any other associated information, which must also have an assigned persistent identifier as a DOI. In this example, it is a formal publication describing the FAIR-data processes associated with this laboratory technique [1] and ultimately any DOI resulting from the present article. Shown in Figure 3 is an individual entry in the collection.…”

“…One commercially available solution which can access the data in these formats is MestreNova, [6] which can absorb the primary data and automatically transform this into a spectrum to produce a composite .mnova file, a process which ensures no overall loss of data. [1] The format can also hold information about the molecule itself, along with a selection of rich metadata derived from the instrumental parameters and weighting functions, which allows automated reprocessing of the primary data. An even closer correspondence between the data and the molecule can be achieved using formats such as NMReDATA annotations, [7,8] which captures extracted features like peak lists, coupling constants and other inferred correlations between the spectrum and the molecule, as produced either by human assignment or AI techniques.…”

“…The first of these is the presence of additional files created by the submission process and not uploaded by the depositor, taking the form of an .mnpub file. [1] These are a feature unique to the repository we use and which contain in effect a single-use licence, which allows anyone downloading specifically this .mnpub file to access the dataset associated with it using the Mestrenova programme in fully functional form. This is available to any external user and overcomes the aspect that MestreNova is a commercial programme, normally requiring a pre-purchased licence to unlock functionality such as providing Fourier transform capability that converts the primary data (the FID) into a spectrum.…”

“…In this article, we explore these themes using a case study based on one emerging modern model for the acquisition and management [1] of what is called primary data from an NMR instrument (a spectrometer) and its subsequent life cycle. This cycle includes transformation into a processed dataset to enable information to be extracted and derived for the system being studied (the 'molecule') and then its management as a so-called Findable, Accessible, Interoperable and Reusable (FAIR) [2] data set to enable its re-use for AI applications.…”

FAIR enough?

“…The student adds further core metadata to the record and initiates workflows which include generating a chemical identifier (InChI string and key) and a free-to-use MestreNova dataset Access license. 15 Publication produces a metadata record registered against a persistent identifier (DOI), 16 the latter eventually appearing in the student's ORCID researcher profile. The metadata has sufficient information to allow a variety of complex Finding searches to be undertaken 12 and includes Access information allowing potentially unsupervised machine Interoperation and Reuse of the data for e.g.…”