NFDI MatWerk Ontology (MWO): A BFO‐Compliant Ontology for Research Data Management in Materials Science and Engineering

Open research data practices are a relatively new, thus still evolving part of scientific work, and their usage varies strongly within different scientific domains. In the literature, the investigation of open research data practices covers the whole range of big empirical studies covering multiple scientific domains to smaller, in depth studies analysing a single field of research. Despite the richness of literature on this topic, there is still a lack of knowledge on the (open) research data awareness and practices in materials science and engineering. While most current studies focus only on some aspects of open research data practices, we aim for a comprehensive understanding of all practices with respect to the considered scientific domain. Hence this study aims at 1) drawing the whole picture of search, reuse and sharing of research data 2) while focusing on materials science and engineering. The chosen approach allows to explore the connections between different aspects of open research data practices, e.g. between data sharing and data search. In depth interviews with 13 researchers in this field were conducted, transcribed verbatim, coded and analysed using content analysis. The main findings characterised research data in materials science and engineering as extremely diverse, often generated for a very specific research focus and needing a precise description of the data and the complete generation process for possible reuse. Results on research data search and reuse showed that the interviewees intended to reuse data but were mostly unfamiliar with (yet interested in) modern methods as dataset search engines, data journals or searching public repositories. Current research data sharing is not open, but bilaterally and usually encouraged by supervisors or employers. Project funding does affect data sharing in two ways: some researchers argue to share their data openly due to their funding agency’s policy, while others face legal restrictions for sharing as their projects are partly funded by industry. The time needed for a precise description of the data and their generation process is named as biggest obstacle for data sharing. From these findings, a precise set of actions is derived suitable to support Open Data, involving training for researchers and introducing rewards for data sharing on the level of universities and funding bodies.

The Open Biomedical Ontology (OBO) Foundry initiative is a collaborative effort for developing interoperable, science-based ontologies. The Basic Formal Ontology (BFO) serves as the upper ontology for the domain-level ontologies of OBO. BFO is an upper ontology of types as conceived by defenders of realism. Among the ontologies developed for OBO use, there are those which have been ratified, and those currently holding the status of candidate. To maintain consistency between ontologies, it is important to establish formal principled criteria that a candidate ontology must meet for ratification. Members of the OBO Foundry have expressed interest in using OntoClean to help construct BFO compliant domain ontologies. OntoClean is a system that decomposes the notion of sortal into criteria for deciding when subsumption can hold between classes. OntoClean primarily includes three components, based on the notions of Rigidity, Identity, and Unity. The methodology for integrating the OntoClean and BFO approaches to constructing consistent ontologies has been clarified by this dissertation. A formal integration between BFO, the Relation Ontology of BFO (RO), and OntoClean is given. The informal aspects and differing formalisms within and between the theories are analyzed and integrated within the axioms and theorems of one first-order system put forth in the dissertation. To set the foundation for this work, the categorical units of type and property of BFO and OntoClean, respectively, are unified under class. The modal logic axioms that OntoClean's theory of Rigidity is expressed within are interpreted and reformulated in our system, where axioms connecting it with BFO's categorical unit type is given. Central to this work is the axiom that a type is a class that is Rigid, i.e., a class whose definition is fundamental to the existence of the members of the class. A unity criterion for a class is a way in which certain parts of a member of the class are related such that they form the whole member. Our reformulation of this work focuses on the notion of the underlying unifying relation. As opposed to the informal approach taken by OntoClean, we express the notion that a class is unified under a relation as a meta-predicate defined by a definition schema. An identity criterion for a class is a test by which a member of the class can be re-identified. However as given in OntoClean, the notion of identity criteria is ontologically ambiguous. A formalization is given that provides a mapping to processes during which identity is confirmed. The reformulations of Unity and Identity are discussed in terms of Material Entity subtypes of BFO's theory. The integration work and resulting formal system affords a theoretically sound ontological foundation. A method for evaluating and standardizing candidate OBO Foundry ontologies is developed atop this foundation, where the method focuses on BFO's integration with OntoClean's notion of Rigidity. The method is given as a decision tree algorithm that evaluates one class at a time as they are introduced into an ontology, asking a prospective modeler questions that map to specific integration axioms. Finally, an implementation of the decision tree is provided in the form of an interactive Wizard plugin for the ontology editor Protege. In an iterative approach informal user testing was applied to improve the questions and error messages. The plugin serves to facilitate ontologists and subject matter experts in making explicit what is implicit in, or unclearly specified for, the classes they choose to introduce into an ontology. Ultimately though, the integration axioms serves as a software platform-independent foundation for future software designed to evaluate and standardize candidate domain ontologies for the OBO Foundry.

Since presenting a paper at the International Digital Curation Conference 2010 conference entitled ‘An Institutional Approach to Developing Research Data Management Infrastructure’, the University of Oxford has come a long way in developing research data management (RDM) policy, tools and training to address the various phases of the research data lifecycle. Work has now begun on integrating these various elements into a unified infrastructure for the whole university, under the aegis of the Data Management Roll-out at Oxford (Damaro) Project.This paper will explain the process and motivation behind the project, and describes our vision for the future. It will also introduce the new tools and processes created by the university to tie the individual RDM components together. Chief among these is the ‘DataFinder’ – a hierarchically-structured metadata cataloguing system which will enable researchers to search for and locate research datasets hosted in a variety of different datastores from institutional repositories, through Web 2 services, to filing cabinets standing in department offices. DataFinder will be able to pull and associate research metadata from research information databases and data management plans, and is intended to be CERIF compatible. DataFinder is being designed so that it can be deployed at different levels within different contexts, with higher-level instances harvesting information from lower-level instances enabling, for example, an academic department to deploy one instance of DataFinder, which can then be harvested by another at an institutional level, which can then in turn be harvested by another at a national level.The paper will also consider the requirements of embedding tools and training within an institution and address the difficulties of ensuring the sustainability of an RDM infrastructure at a time when funding for such endeavours is limited. Our research shows that researchers (and indeed departments) are at present not exposed to the true costs of their (often suboptimal) data management solutions, whereas when data management services are centrally provided the full costs are visible and off-putting. There is, therefore, the need to sell the benefits of centrally-provided infrastructure to researchers. Furthermore, there is a distinction between training and services that can be most effectively provided at the institutional level, and those which need to be provided at the divisional or departmental level in order to be relevant and applicable to researchers. This is being addressed in principle by Oxford’s research data management policy, and in practice by the planning and piloting aspects of the Damaro Project.

Science is changing: the volume and complexity of data are increasing, the number of studies is growing and the goal of achieving reproducible results requires new solutions for scientific data management. In the field of neuroscience, the German National Research Data Infrastructure (NFDI-Neuro) initiative aims to develop sustainable solutions for research data management (RDM). To obtain an understanding of the present RDM situation in the neuroscience community, NFDI-Neuro conducted a comprehensive survey among the neuroscience community. Here, we report and analyze the results of the survey. We focused the survey and our analysis on current needs, challenges, and opinions about RDM. The German neuroscience community perceives barriers with respect to RDM and data sharing mainly linked to (1) lack of data and metadata standards, (2) lack of community adopted provenance tracking methods, (3) lack of secure and privacy preserving research infrastructure for sensitive data, (4) lack of RDM literacy, and (5) lack of resources (time, personnel, money) for proper RDM. However, an overwhelming majority of community members (91%) indicated that they would be willing to share their data with other researchers and are interested to increase their RDM skills. Taking advantage of this willingness and overcoming the existing barriers requires the systematic development of standards, tools, and infrastructure, the provision of training, education, and support, as well as additional resources for RDM to the research community and a constant dialogue with relevant stakeholders including policy makers to leverage of a culture change through adapted incentivization and regulation.

Experimentalresearch data in the materials science domain is often insufficiently described with regard to metadata and frequently displays an incoherent form of documentation. These circumstances often hinder current and future researchers significantly in reuse and comprehension of data. To support researchers during the archiving and provision of materials science research data (incl. supplementing material), a Quality Management Manual (QMM) approach as an established QM tool is proposed in this paper. Quality-assurance of experimental research data and the perpetuation of good scientific practice in provision and archiving research data are examined before the QMM approach is applied in a case study. The preliminary results indicate that QMM allows to provide practitioners basic guidelines which support integrity, availability and reusability of experimental research data in materials science for subsequent reuse.

As a partner in several NFDI consortia, the Bundesanstalt für Materialforschung und -prüfung (BAM, German federal institute for materials science and testing) contributes to research data standardization efforts in various domains of materials science and engineering (MSE). To implement a central research data management (RDM) infrastructure that meets the requirements of MSE groups at BAM, we initiated the Data Store pilot project in 2021. The resulting infrastructure should enable researchers to digitally document research processes and store related data in a standardized and interoperable manner. As a software solution, we chose openBIS, an open-source framework that is increasingly being used for RDM in MSE communities. The pilot project was conducted for one year with five research groups across different organizational units and MSE disciplines. The main results are presented for the use case “nanoPlattform”. The group registered experimental steps and linked associated instruments and chemicals in the Data Store to ensure full traceability of data related to the synthesis of ~400 nanomaterials. The system also supported researchers in implementing RDM practices in their workflows, e.g., by automating data import and documentation and by integrating infrastructure for data analysis. Based on the promising results of the pilot phase, we will roll out the Data Store as the central RDM infrastructure of BAM starting in 2023. We further aim to develop openBIS plugins, metadata standards, and RDM workflows to contribute to the openBIS community and to foster RDM in MSE.

Research data management (RDM) in Jordanian public university libraries: Present status, challenges and future perspectives

Driven by funding and publishing requirements to open and reuse data, Research Data Management (RDM) has become a crucial part of a researcher’s role. However, this key task is often completed by researchers, who sometimes make decisions, without having the necessary support or know-how, resulting in few research datasets being shared. The objective of this study is to identify the challenges in researcher RDM practices that impact the sharing/reusing of research data. Four thematic areas emerge from our coding of the selected literature: (i) alignment of research management and data management, (ii) resourcing, (iii) researcher openness, and (iv) research data governance. Despite the growing field of RDM, there is a limited understanding of RDM practiceshighlighting a requirement for further investigation together with practical tools, decision aids and training to assuage clearly unmet needs. Indeed, this provides an opportunity for the Information Systems (IS) community to better support researchers to implement good RDM practices.

Research data management is a major necessity for the digital transformation in material science. Material science is multifaceted and experimental data, especially, is highly diverse. We demonstrate an adjustable approach to a group level data management based on a customizable document management software. Our solution is to continuously transform data management workflows from generalized to specialized data management. We start up fast with a relatively unregulated base setting and adapt continuously over the period of use to transform more and more data procedures into specialized data management workflows. By continuous adaptation and integration of analysis workflows and metadata schemes, the amount and the quality of the data improves. As an example of this process, in a period of 36 months, data on over 1800 samples, mainly materials libraries with hundreds of individual samples, were collected. The research data management system now contains over 1700 deposition processes and more than 4000 characterization documents. From initially mainly user-defined data input, an increased number of specialized data processing workflows was developed allowing the collection of more specialized, quality-assured data sets.

Desde a antiguidade, com a introducao da aplicacao do metodo cientifico para validar o conhecimento, sua producao e os resultados de pesquisas passaram a ser pautados na troca de ideias e sugestoes entre os pares, no compartilhamento de informacoes que necessitavam passar pelo crivo dos membros da comunidade cientifica. A partir da evolucao da ciencia e dos aparatos tecnologicos que passaram a coletar maior quantidade de informacoes para as pesquisas cientificas, os dados emergem como produto essencial para o avanco do conhecimento cientifico necessarios para a validacao dos resultados de qualquer estudo. Os dados de pesquisa se apresentam em varias formas e devem ser contextualizados dentro das disciplinas ou areas as quais pertencem. Nesse sentido, esta pesquisa tem como objetivo investigar as praticas e necessidades informacionais dos pesquisadores (docentes, discentes e tecnico-administrativos em Educacao vinculados ao Mestrado ou ao Doutorado) dos cursos de Pos-Graduacao da Universidade Federal do Ceara (UFC), concernentes ao gerenciamento de dados de pesquisa e a Ciencia Aberta. Para tal, delinearam-se os seguintes objetivos especificos: analisar a percepcao dos pesquisadores sobre a gestao de dados de pesquisa e a Ciencia Aberta; averiguar quais as praticas e as necessidades informacionais destes pesquisadores referentes a estas tematicas; propor um Programa de Gestao de Dados de Pesquisa (PGDP) para a UFC com o objetivo de sugerir uma Politica de Gestao de Dados de Pesquisa; sugerir a criacao de servicos de apoio e suporte ao pesquisador na UFC; desenvolver acoes de educacao e informacao com vistas a testar um piloto de curso online como parte integrante do programa. Realizou-se um levantamento exaustivo a partir de buscas realizadas por meio do software Publish or Perish, Portal de Periodicos da CAPES, Wizdom.ai e Twitter. Como estrategia metodologica, utilizou-se a triangulacao de metodos – Teoria Fundamentada em Dados e a Netnografia, alem das tecnicas de pesquisa analise documental e a observacao participante. Para coletar os dados adotou-se o questionario e a entrevista, bem como o uso do diario de campo eletronico e o caderno de laboratorio eletronico para as anotacoes, registros de notas de campo e na construcao de memorandos. Os dados foram tratados por uma abordagem qualitativa com o uso do software Atlas.ti para a construcao das categorias. Os resultados demonstram que em relacao as praticas e estrategias de armazenamento dos pesquisadores, o computador pessoal e a nuvem sao os mais utilizados para manter seus arquivos e dados de pesquisas, embora a maioria tenha revelado nao ter uma frequencia de backup de seus arquivos por usar o servico de sincronizacao automatica da nuvem. Sobre as praticas de documentacao da pesquisa com a elaboracao de um Plano de Gestao de Dados (PGD), entre todos os respondentes do questionario apenas uma pessoa elaborou um PGD, enquanto no grupo de entrevistados nenhum chegou a usar o PGD para essa finalidade. Sobre o compartilhamento, os entrevistados afirmaram ter realizado algum tipo de compartilhamento, seja de informacoes ou dados de pesquisa, e, quando nao compartilham, os motivos declarados foram: desconhecimento, por nao saber como fazer ou por esbarrarem em questoes eticas, legais e de integridade da pesquisa. Diante do exposto, conclui-se que o pesquisador tem um papel fundamental na Gestao dos Dados de Pesquisa, pois adotar essa postura representa garantia da qualidade e integridade da pesquisa, alem de colaborar para as boas praticas na ciencia. Ademais, a literatura mostra que o bibliotecario tem sido o profissional mais recomendado para auxiliar os pesquisadores nesse processo. Finalmente, esta pesquisa traz como contribuicao a percepcao dos pesquisadores sobre os dados de pesquisa e a Ciencia Aberta, alem da sugestao de uma proposta de Programa de Gestao de Dados de Pesquisa (PGDP) para a UFC que se concentra no desenvolvimento de politicas, diretrizes, acoes de educacao e informacao, produtos, servicos e gestao dos dados de pesquisa na universidade.

PurposeThe purpose of this paper is to investigate the knowledge and attitude about research data management, the use of data management methods and the perceived need for support, in relation to participants’ field of research.Design/methodology/approachThis is a quantitative study. Data were collected by an email survey and sent to 792 academic researchers and doctoral students. Total response rate was 18% (N = 139). The measurement instrument consisted of six sets of questions: about data management plans, the assignment of additional information to research data, about metadata, standard file naming systems, training at data management methods and the storing of research data.FindingsThe main finding is that knowledge about the procedures of data management is limited, and data management is not a normal practice in the researcher's work. They were, however, in general, of the opinion that the university should take the lead by recommending and offering access to the necessary tools of data management. Taken together, the results indicate that there is an urgent need to increase the researcher's understanding of the importance of data management that is based on professional knowledge and to provide them with resources and training that enables them to make effective and productive use of data management methods.Research limitations/implicationsThe survey was sent to all members of the population but not a sample of it. Because of the response rate, the results cannot be generalized to all researchers at the university. Nevertheless, the findings may provide an important understanding about their research data procedures, in particular what characterizes their knowledge about data management and attitude towards it.Practical implicationsAwareness of these issues is essential for information specialists at academic libraries, together with other units within the universities, to be able to design infrastructures and develop services that suit the needs of the research community. The findings can be used, to develop data policies and services, based on professional knowledge of best practices and recognized standards that assist the research community at data management.Originality/valueThe study contributes to the existing literature about research data management by examining the results by participants’ field of research. Recognition of the issues is critical in order for information specialists in collaboration with universities to design relevant infrastructures and services for academics and doctoral students that can promote their research data management.

Training up and reaching out : library strategies to coordinate research data management on campus. -- As scholarly products beyond traditional publications are increasingly curated and shared, academic libraries are playing a growing role in supporting research data management (RDM) needs at their institutions. We are helping build collaborations and infrastructure across campus that faculty and students require for modern, increasingly open scholarship. But even with these efforts underway, the question remains: how can we efficiently and effectively integrate RDM into research activities on campus? In this session, a panel of current and recent CLIR postdoctoral fellows working in RDM at five different institutions will share how they are working in a variety of ways to embed RDM practices and support in research workflows. Researchers by training and members of library service groups, CLIR fellows are particularly well situated to understand the scholarly operations of these institutions and the challenges of implementing research support services. Daniels will speak about Vanderbilt's efforts to build connections within the library in order to alert faculty and students to RDM tools and services through workshops and tool-specific trainings. Pickle from Penn State will address her library's Research Working Group, comprising diverse library faculty and staff and aiming to become a community of practice where participants learn from each other and produce formal RDM guidance. Van Gulick will discuss the Carnegie Mellon's Libraries'-coordinated Management Steering Committee, which brings together stakeholders from offices across campus to prioritize RDM at the university and develop optimal resources for their researchers. Calvert will present research on the current level of staff expertise and skill in RDM services at UCLA. Lastly, Simms will present on the California Digital Library's (University of California Curation Center) approach to offering system-wide RDM tools and services to many diverse campuses, which has involved pairing these resources with library-based coordination efforts specific to local needs. Presenters: Morgan Daniels (Vanderbilt University), Ana Van Gulick (Carnegie Mellon University), Sarah Pickle (The Claremont Colleges), Scout Calvert (University of California, Los Angeles), Stephanie Simms (California Digital Library). management plans as a research tool -- As funding agencies increasingly require evidence of sharing and archiving research data, many academic libraries are developing or modifying research data management (RDM) services. These services include outreach regarding funder requirements, assistance with planning for data management, and digital curation services to help researchers manage, share and archive data. These service developments are driving an increasing demand for mechanisms to better understand researcher needs and practices. An analysis of data management plans (DMPs) can uncover important insights into local RDM practices. As a document produced by researchers themselves, DMPs provide a window into researchers' data knowledge, practices, and needs—a formal analysis of DMPs can provide a means to develop data services responsive to the needs of local data producers. To assist librarians in a review of DMPs, the IMLS- funded Data management plans as A Research Tool (DART) Project has developed an analytic rubric to standardize the review of NSF data management plans. Our rubric allows librarians to utilize DMPs as a research tool that can shape decisions about the provision of research data services. It enables librarians who may have no direct experience in applied research or RDM to become better informed about researchers' data. The rubric can be used to identify strengths, gaps and weaknesses in researchers' understanding of data management concepts and practices, as well as existing opportunities and barriers in applying best practices. This panel will consist of data specialists from the project's five research partners. We will describe our methodology for developing the analytic rubric, share the results of DMP analyses at our respective academic institutions, discuss broader trends and observations across institutions, and describe how the results are informing the evolution of services at our respective libraries. Presenters: Susan Wells Parham (Georgia Institute of Technology), Patricia Hswe (Penn State University), Brian Westra (University of Oregon), Amanda Whitmire (Oregon State University)

Purpose This study aims to conceptualize the application and management of research data in academic libraries through institutional repositories. The objectives of the study are to determine the role of academic libraries in managing research data, to explore the ethical issues related to research data management (RDM) services and to determine stakeholders involved in the success of RDM. Design/methodology/approach The study employs a qualitative research design within the interpretive paradigm, using content analysis to explore RDM in academic libraries and institutional repositories. The research aims to determine the role of academic libraries in managing research data, explore ethical issues related to RDM services and identify key stakeholders. Literature was sourced from databases like Emerald Insight, Scopus and Google Scholar, focusing on publications from 2020 to 2024. Case studies from institutions such as the University of Pretoria and Stellenbosch University illustrated practical RDM implementations. Ethical considerations were strictly adhered to, ensuring proper citation and adherence to RDM guidelines. Findings The reviewed literature established the significance of managing research data through institutional repositories while highlighting the research data lifecycle, stakeholders involved in the success of RDM and ethical issues related to RDM services. RDM involves stakeholders such as institutional researchers, government and funding agencies, university leadership and research support units. Research limitations/implications This study demonstrated the importance of effective RDM practices in enhancing transparency, reproducibility and efficiency in academic research. Institutional repositories play a crucial role in preserving and making research data accessible, thereby promoting interdisciplinary collaboration and increasing citation rates. Practical implications The study provided actionable recommendations for academic libraries to support researchers in complying with RDM policies through training, clear guidelines and user-friendly repository interfaces. These strategies enhance the effectiveness of RDM practices and ensure regulatory compliance. Social implications The study underscores the need for regulatory frameworks that promote open science and data sharing while ensuring ethical guidelines for data privacy and informed consent. It also highlights well-managed research data’s economic and commercial benefits, such as facilitating industry–-academia collaboration. Originality/value This study is significant as it contributed to the body of knowledge and theoretically motivated how institutional repositories can be of value in reserving research data by highlighting the benefits and significance of sharing research data. A proper RDM increases the opportunities for funders, institutions, publishers and libraries to redesign policies that govern research data sharing.

To ensure the quality and integrity of data and the reliability of research, data must be well documented, organised, and described. This calls for research data management (RDM) education for researchers. In light of 3 ECTS Basics of Research Data Management (BRDM) courses held between 2019 and 2021, we aim to find how a generic level multi-stakeholder training can improve STEM and HSS disciplines’ doctoral students’ and postdoc researchers’ competencies in RDM. The study uses quantitative, descriptive and inferential statistics to analyse respondents’ self-ratings of their competencies, and a qualitative grounded theory-inspired approach to code and analyse course participants’ feedback. Results: On average, based on the post-course surveys, respondents’ (n = 123) competencies improved one point on a four-level scale, from “little competence” (2) to “somewhat competent” (3). Participants also reported that the training would change their current practices in planning research projects, data management and documentation, acknowledging legal and data privacy viewpoints, and data collecting and organising. Participants indicated that it would be helpful to see legal and data privacy principles and regulations presented as concrete instructions, cases, and examples. The most requested continuing education topics were metadata and description, discipline specific cultures, and backup, version management, and storage. Conclusions: Regarding to the widely used criteria for successful training containing 1) active participation during training; 2) demand for RDM training; 3) increased participants’ knowledge and understanding of RDM and confidence in enacting RDM practices; and 4) positive post-training feedback, BRDM meets the criteria. This study shows that although reaching excellent competence in a RDM basics training is improbable, participants become aware of RDM and its contents and gain the elementary tools and basic skills to begin applying sound RDM practices in their research. Furthermore, participants are introduced to the academic and research support professionals and vice versa: Stakeholders will get to know the challenges that young researchers and research students encounter when applying RDM. The study reveals valuable information on doctoral students’ and postdoc researchers’ competencies, the impact of education on competencies, and further learning needs in RDM.

PurposeThe purpose of this study is to investigate the current research data management practices among researchers in Ghana and their impact on data reuse and collaborative research. The study aims to identify the methods used by researchers to store and preserve their research data, as well as to determine the extent to which researchers share their data with others.Design/methodology/approachThe study uses a mixed-method research strategy to blend qualitative and quantitative data and is conducted at two public and two private universities in Ghana.FindingsThe study revealed that researchers in Ghana currently store and preserve their research data using personal devices, such as laptops, CDs and external flash drives, rather than keeping the data in university data repositories. They also do not share their research data with others, which negatively affects collaborative research. The current practice of storing data on personal devices and not sharing data with others hinders collaborative research. The study recommends that universities in Ghana revise their research policy documents to address RDM-related issues such as data storage, data preservation, data sharing and data reuse.Research limitations/implicationsThe study was conducted at two public and two private universities in Ghana, but the findings were placed in a wider context through appropriate references.Practical implicationsThis study emphasises the need for sound research data management procedures to support research collaboration and data reuse in Ghana. Universities should provide incentives to academics to disclose their data to encourage data sharing and collaboration.Social implicationsThe government and management of universities should consciously invest in the needed technologies and equipment to implement research data management in their universities.Originality/valueThis study looks at how researchers in Ghana manage their research data and how it affects data reuse and collaborative research.