Improving machine-readability of research metadata and implementation of RDM workflows with Adamant
Creators: name: Chaerony Siffa, Ihda
name: Plathe, Nick
name: Vilardell Scholten, Laura
name: Schäfer, Jan
name: Becker, Markus M.
Publisher: name: Zenodo
Year: 2022

description: There is an increasing commitment of research communities in recent years with regard to digital documentation of research processes. Many research institutions have initiated the transition from a traditional lab notebook to an electronic lab notebook (ELN). ELNs offer a great number of advantages over traditional lab notebooks, such as easier search through the contents and past records, preventing document loss and damage, research/experiment metadata can be presented with a great clarity (poor handwriting and damaged papers are no longer an issue), etc. Consequently, ELNs are becoming a substantial tool in research data management (RDM). The use of ELNs also supports the implementation of the FAIR data principles that is making research data findable, accessible, interoperable, and reusable. These principles are becoming more relevant in recent years as its implementation is required by more and more funding organizations and publishers. While the clarity of the experiment metadata stored in the ELN system is important for the researchers to re-evaluate and understand the contents, it is also desirable that the contents are readable by machines. Machine-readable research metadata can open the possibility of implementing various automated processes that can further support the management and improve the FAIR-ness of research data. There are a plethora of ELNs where a certain ELN may be more suitable to be used in a specific research area than the others. On the other hand, several ELNs offer a more generic approach of experiment documentation in order to cater to diverse research needs, which in turn sacrifices machine-readability of the contents, i.e., describing the contents in a free text as opposites to using a standardized schema template (community agreed standards). To address this shortcoming, we propose an experiment documentation workflow utilizing a tool called Adamant to improve the machine-readability of research metadata while retaining its human-readability aspect. Adamant is an open-source web-based tool that provides easy use or compilation and creation of domain-specific metadata and metadata templates (or schemas) based on the widely used JavaScript Object Notation (JSON) schema standard. The use of JSON schema in Adamant enables straightforward validation of the metadata, ensuring its quality. Furthermore, storing the metadata in a JSON format maintains the adaptability of the created metadata with other existing RDM tools and processes. Moreover, it increases the findability of the research data to which the metadata is attached due to the machine-readability of JSON documents. Adamant dynamically generates a user-friendly web-form based on an experiment template described in a JSON schema, where researchers can fill it in with relevant information regarding their experiments. Furthermore, researchers may edit the generated web-form such as adding or removing a field, changing the field information, etc., or create an experiment template from scratch. This allows the researchers to use Adamant without having the slightest idea of a JSON document. Adamant can also be integrated with external RDM systems and tools through an Application Programming Interface (API) based integration. As an example, Adamant can be made to work seamlessly with an ELN system that allows storing of the created JSON documents of experiment metadata and template. The possibility of API-based integration with external tools also allows the implementation of other RDM workflows, such as dataset publication and job/operation request of scientific instruments in research labs. The following is a brief explanation of these workflows. One component of the implementation of the FAIR data principles is the findability and accessibility of research data, which requires deposition of the (meta-)data in a data repository. The effort required for this data publishing process on the part of researchers can be greatly reduced if Adamant is used to transfer metadata already in the ELN system to the research data repository. This workflow also improves the reusability of the published data (within its research domain) due to the completeness of the experiment metadata and its machine-readability. As for the latter workflow, Adamant can be used to exchange experiment metadata through an institutional e-mail system between researchers that have different skill sets. For example, there may be only a handful of researchers that are proficient enough to operate a certain advanced lab instrument (denoted as operator) at one institute. An instrument job request workflow can be implemented to make this instrument more accessible to other researchers within the same institute that may not have the necessary skills to operate it (now denoted as requester). A requester can fill in partial information about the experiment using the provided metadata template in Adamant, and submit it to one of the available instrument operators. The operator will fill in the remaining information of the experiment, e.g., the instrument parameters, and proceed to generate an experiment metadata sheet, which is then stored in the ELN system. Both the operator and requester are notified automatically per e-mail for relevant events within the workflow. All things considered, Adamant aims to ease the implementations of various RDM workflows and processes, which can be achieved by improving machine-readability of research metadata, integration of different RDM tools, and providing user-friendly interfaces to the researchers. Adamant (v.1.0.0) is available from: https://github.com/plasma-mds/adamant. It is still being actively developed for new features taking into account the ever-evolving needs from researchers and RDM communities. In future work, we plan to implement features such as ontology and knowledge graph integration. These features can promote and simplify the use of existing terminologies and schemas/templates as well as the re-use of already existing metadata, which further support the implementation of the FAIR principles. The work was funded by the Federal Ministry of Education and Research (BMBF) under the grant mark 16QK03A. The responsibility for the content of this repository lies with the authors.