This report provides an overview of our activities and accomplishments concerning machine-actionable Software Management Plans (SMPs) and the Software Management Wizard (SMW) during the ELIXIR BioHackathon Europe 2023. ELIXIR acknowledges the critical role of effective software management in facilitating sustainable and reproducible research outcomes. The Software Best Practices group is actively committed to establishing a robust framework for SMP creation. In this project, our primary focus is on streamlining the SMP creation process for research software within ELIXIR. To achieve this, we are working on developing essential integrators and identifying and reviewing the relevant metadata schema. This effort is closely aligned with various related initiatives such as OpenEBench, FAIR4RS, RDA, maSMPs, among others. The outcomes of the BioHackathon project are now available for immediate use and can be further refined in the future based on community feedback and advancements in research software best practices.

This report describes the integration of RO-Crates into Data Stewardship Wizard and Galaxy during the BioHackathon Europe 2023, aiming to improve data management and sharing in scientific research. By utilizing RO-Crates, researchers can easily create machine-readable metadata for their datasets, ensuring long-term discoverability, accessibility, and reusability. The seamless integration of RO-Crates in these platforms enhances collaboration between researchers and institutions, facilitating data sharing and reuse across projects and domains. Future efforts may focus on enhancing RO-Crate's interoperability with other standards and platforms, as well as promoting wider adoption through outreach and education initiatives to meet the evolving needs of researchers and institutions in data stewardship.

Most research funders require Data Management Plans (DMPs). The review process can be time consuming, since reviewers read text documents submitted by researchers and provide their feedback. Moreover, it requires specific expert knowledge in data stewardship, which is scarce. Machine-actionable Data Management Plans (maDMPs) and semantic technologies increase the potential for automatic assessment of information contained in DMPs. However, the level of automation and new possibilities are still not well-explored and leveraged. This paper discusses methods for the automation of DMP assessment. It goes beyond generating human-readable reports. It explores how the information contained in maDMPs can be used to provide automated pre-assessment or to fetch further information, allowing reviewers to better judge the content. We map the identified methods to various reviewer goals.

Along with the ongoing digitalization of society, we witness a strong movement to make scientific data FAIR, machine-actionable, and available in the form of knowledge graphs. On the other hand, converting machine-actionable data from knowledge graphs back into human-oriented formats, including documents, graphical, or voice user interfaces, poses significant challenges. The solutions often build on various templates tailored to specific platforms on top of the shared underlying data. These templates suffer from limited reusability, making their adaptations difficult. Moreover, the continuous evolution of data or technological advancements requires substantial efforts to maintain these templates over time. In general, these challenges increase software development costs and are error-prone. In this paper, we propose a solution based on Normalized Systems Theory to address this challenge with the aim of achieving evolvability and sustainability in the transformation process of knowledge graphs into human-oriented formats with broad applicability across domains and technologies. We explain the theoretical foundation and design theorems used in our solution and outline the approach and implementation details. We theoretically evaluate our solution by comparing it to the traditional approach, where the systems are crafted manually. The evaluation shows that our solution is more efficient and effective on a large scale, reducing the human labor required to maintain various templates and supported target platforms. Next, we demonstrate the technical feasibility of our solution on a proof-of-concept implementation in a domain of data management planning that may also serve as a basis for future development.

A problem of evolvability is widely discussed in the current world, and still, it has not been fully addressed yet. Our approach tries to improve evolvability in a domain of documents. Our approach is based on principles and recommendations from the Normalized Systems Theory. We try to redefine the process of how the document is created and maintained by involving ontologies. We offer a solution which should increase evolvability for a sort of documents which is created by a template and which is often updated. We demonstrate our solution to an example of a Data Management Plan document.

Data Stewardship Wizard (DSW, ds-wizard.org) is a tool for researchers and data stewards to work together on Smart Data Management Plans that benefit the research project and work towards FAIR Open Science. Thanks to the versatility and elaborated system of guidance DSW is becoming a tool for not just planning but also learning where it is favored over essay-answer-based tools. The Wizard has been developed in cooperation between ELIXIR CZ and ELIXIR NL. It has attracted attention also thanks to the focus on machine actionability, automated prediction of how FAIR the research output will become and extensive possibilities of integration with other services from ELIXIR and beyond. Thanks to ELIXIR, we are also able to develop features directly based on the community feedback. DSW is an integral part of the FAIR tools ecosystem, in which we actively collaborate on several projects opening new possibilities for users and bringing new useful features.

The Data Stewardship Wizard is a tool for data management planning that is focused on getting the most value out of data management planning for the project itself rather than on fulfilling obligations. It is based on FAIR Data Stewardship, in which each data-related decision in a project acts to optimize the Findability, Accessibility, Interoperability and/or Reusability of the data. The background to this philosophy is that the first reuser of the data is the researcher themselves. The tool encourages the consulting of expertise and experts, can help researchers avoid risks they did not know they would encounter by confronting them with practical experience from others, and can help them discover helpful technologies they did not know existed. In this paper, we discuss the context and motivation for the tool, we explain its architecture and we present key functions, such as the knowledge model evolvability and migrations, assembling data management plans, metrics and evaluation of data management plans.

In the current world, low evolvability of documents is a big challenge which has not been fully addressed. This paper focuses on types of documents which have mostly predefined structure, and we use them over and over. Examples of these documents are contracts, applications, legal documents or manuals. The key problem here is that the documents are not modular and evolvable. The problem of modularity and evolvability is addressed by Normalized Systems Theory. This theory is formally proven, and it has great practical results from the first application in a software area. This paper designs a way how to apply principles and recommendations from Normalized Systems Theory in the area of non-evolvable documents.

Presentation for ELIXIR AHM 2019 workshop on usage and deployment of the Data Stewardship Wizard including demonstration of all key features.