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Survey Paper on the Scientific Use of Patents

This article expands on the considerations on data stewardship in physics the authors have presented as a poster at the “Data Stewardship goes Germany” workshop held in Brunswick in October 2022. We start from the observation that despite the close links between research in physics and scientific computing as a tenet of research data management (RDM), currently, the research data produced by physicists are not as FAIR (findable, accessible, interoperable, and reusable) as they could and should be. Physics research groups in Germany as of now do not feature explicitly designated data stewards. Building on a survey on RDM in physics conducted among researchers in 2020, we lay out a clear case and a mission for more explicitly defined and acknowledged data stewardship in physics. We argue that because of the closeness between data stewardship and genuine research, ample domain knowledge is indispensable: Data stewards in physics should ideally be trained physicists themselves! Data stewards are going to face a heterogeneous research landscape in terms of group size and resources, defined by the pressure to “publish or perish”. We consider that the introduction of data stewardship presents an opportunity to the physics community to self-organize research support infrastructures where they are missing. Data stewards from the physics community would be ideally skilled to transform the existing data handling solutions into the RDM systems needed to achieve a future of FAIR data from physics. We envision them to contribute to scientific projects both as advisors and as active role models of good scientific practice and reproducibility.

This document summarises the results of an online community survey, which was conducted from 2nd June 2020 to 31st July 2020 within the framework of the project "Quality Assurance and Linking of Research Data in Plasma Technology - QPTDat" (funded 2019–2022 by the Federal Ministry of Education and Research (BMBF) in Germany, project numbers 16QK03A, 16QK03B and 16QK03C). The survey aimed at the evaluation of the status quo regarding research data management in the field of low-temperature plasma science as well as the identification of experimental and computational methods with great relevance to be used as a first reference for the design and testing of quality criteria and (meta)data models for research data management., The QPTDat project teams at Leibniz Institute for Plasma Science and Technology (INP), FIZ Karlsruhe – Leibniz-Institut für Informationsinfrastruktur GmbH and Hamburg University of Applied Sciences (HAW Hamburg) supported this work. The work was funded by the Federal Ministry of Education and Research (BMBF) under grant numbers 16QK01A, 16QK01B, 16QK01C. The authors are responsible for the content of this publication.

Implementation of data management standards and adoption of the FAIR data principles are more and more requested by funding agencies in recent years. This results in several new challenges for the scientific community. Research in low-temperature plasma physics is characterized by table-top experiments and a large variety of plasma sources and measurement devices, which are often first developed in the course of the research. Therefore, there are usually no established research data management (RDM) standards. Moreover, the documentation of research results is extremely heterogeneous. This not only complicates the implementation of structured RDM, but also reduces the comparability and reusability of data. To overcome this challenges, research groups at INP, RUB and CAU have joined forces to develop common RDM standards and tools. In monthly workshops, local requirements are discussed and gradually transformed into proposals for community standards, see https://www.plasma-mds.org. The poster reports on the status of the activities and presents how everyone can contribute and use the already developed tools for their own work., The work has been supported by grants 16QK03A (BMBF) and 327886311 (DFG).

Research in low-temperature plasma science is often characterized by small-scale table-top experiments involving diverse methods and devices. As a result, data is extremely heterogeneous and infrastructures are needed to manage and link individual data sets in the sense of making them available for data-driven research. The data platform INPTDAT and the plasma metadata schema Plasma-MDS have recently been developed to address this challenge. The concept underlying these developments is that data obtained in the course of research in a specific subject area by means of a specific experiment and involving specific devices are assigned by the data producers to the respective topic, to a concrete application if applicable, as well as to the related experiment, devices and substrates. In this way, a graph of linked data and further information, e.g. from patents and device descriptions is created, and research data available for specific applications, devices, and/or substrates can be found and re-used immediately. This is particularly beneficial if similar experiments or devices are used in different subject areas and for various applications. The present contribution intents to introduce the FAIR data platform INPTDAT with its underlying metadata schema and it will be discussed how the concept supports cross-domain re-use of research data by making the data directly accessible for data-driven research and development., Funded by the German Federal Ministry of Education and Research (BMBF) under grant 16QK03A.

The need to implement the FAIR data principles and structured research data management in our research workflows is growing. On the one hand, this is due to pressure, e.g. from research funders and publishers. But on the other hand, it is particularly because the advantages thus achieved in the use of data-driven research methods are becoming increasingly obvious. In my presentation I will introduce practical tools and workflows that aim to integrate research data management into the everyday work of scientists and to take the FAIR principles into account as early as possible in the life cycle of research data, avoiding additional work for the researcher as far as possible. While my examples are from practical implementation for plasma technology research, the approaches are generally applicable to any research area involving different research methods and domains., The work was supported by grants 16QK03A, 16QK03B, 16QK03C (BMBF).

The slides introduce recent activities on the development of metadata schemas and tools for research data management in the field of plasma technology., This work was supported by the BMBF under grant 16QK03A.

A few years ago, the FAIR data principles were proposed as a guideline for those wishing to enhance the reusability of their data by making them findable (F), accessible (A), interoperable (I) and reusable (R) [Wilkinson et al., Sci. Data 3:160018 (2016)]. Since then, various activities aiming at implementation of the FAIR principles in different fields of plasma science have been started. Within the project QPTDat, INP works together with partner institutions on research data management (RDM) solutions for low-temperature plasma physics. This includes a close collaboration with the CRC 1316 at RUB, where the focus lays on the establishment of data stewards to support RDM in daily practise. The international project Fair4Fusion addresses the RDM needs in the field of fusion plasmas, while PlasmaFAIR at UoY strives to improve the quality and sustainability of plasma research software. This contribution presents current activities at INP, RUB and UoY and emphasizes the need for collaborations and community involvement to derive real benefits from the FAIR principles for research in plasma science., The work was supported by grants 16QK03A (BMBF), EP/V051822/1 (EPSRC), and 327886311 (DFG).

The web tool Adamant has been developed to systematically collect research metadata as early as the conception of the experiment. Adamant enables a continuous, consistent, and transparent research data management (RDM) process, which is a key element of good scientific practice ensuring the path to Findable, Accessible, Interoperable, Reusable (FAIR) research data. It simplifies the creation of on-demand metadata schemas and the collection of metadata according to established or new standards. The approach is based on JavaScript Object Notation (JSON) schema, where any valid schema can be presented as an interactive web-form. Furthermore, Adamant eases the integration of numerous available RDM methods and software tools into the everyday research activities of especially small independent laboratories. A programming interface allows programmatic integration with other software tools such as electronic lab books or repositories. The user interface (UI) of Adamant is designed to be as user friendly as possible. Each UI element is self-explanatory and intuitive to use, which makes it accessible for users that have little to no experience with JSON format and programming in general. Several examples of research data management workflows that can be implemented using Adamant are introduced. Adamant (client-only version) is available from: https://plasma-mds.github.io/adamant.