From mess to method

The vision of NFDI4Chem is the digitalisation of all key steps in chemical research to support scientists in their efforts to collect, store, process, analyse, disclose and re-use research data. Measures to promote Open Science and Research Data Management (RDM) in agreement with the FAIR data principles are fundamental aims of NFDI4Chem to serve the chemistry community with a holistic concept for access to research data. To this end, the overarching objective is the development and maintenance of a national research data infrastructure for the research domain of chemistry in Germany, and to enable innovative and easy to use services and novel scientific approaches based on re-use of research data. NFDI4Chem intends to represent all disciplines of chemistry in academia. We aim to collaborate closely with thematically related consortia. In the initial phase, NFDI4Chem focuses on data related to molecules and reactions including data for their experimental and theoretical characterisation. This overarching goal is achieved by working towards a number of key objectives: Key Objective 1: Establish a virtual environment of federated repositories for storing, disclosing, searching and re-using research data across distributed data sources. Connect existing data repositories and, based on a requirements analysis, establish domain-specific research data repositories for the national research community, and link them to international repositories. Key Objective 2: Initiate international community processes to establish minimum information (MI) standards for data and machine-readable metadata as well as open data standards in key areas of chemistry. Identify and recommend open data standards in key areas of chemistry, in order to support the FAIR principles for research data. Finally, develop standards, if there is a lack. Key Objective 3: Foster cultural and digital change towards Smart Laboratory Environments by promoting the use of digital tools in all stages of research and promote subsequent Research Data Management (RDM) at all levels of academia, beginning in undergraduate studies curricula. Key Objective 4: Engage with the chemistry community in Germany through a wide range of measures to create awareness for and foster the adoption of FAIR data management. Initiate processes to integrate RDM and data science into curricula. Offer a wide range of training opportunities for researchers. Key Objective 5: Explore synergies with other consortia and promote cross-cutting development within the NFDI. Key Objective 6: Provide a legally reliable framework of policies and guidelines for FAIR and open RDM.

PurposeThe study aims to investigate the utilisation of open research data repositories (RDRs) for storing and sharing research data in higher learning institutions (HLIs) in Tanzania.Design/methodology/approachA survey research design was employed to collect data from postgraduate students at the Nelson Mandela African Institution of Science and Technology (NM-AIST) in Arusha, Tanzania. The data were collected and analysed quantitatively and qualitatively. A census sampling technique was employed to select the sample size for this study. The quantitative data were analysed using the Statistical Package for the Social Sciences (SPSS), whilst the qualitative data were analysed thematically.FindingsLess than half of the respondents were aware of and were using open RDRs, including Zenodo, DataVerse, Dryad, OMERO, GitHub and Mendeley data repositories. More than half of the respondents were not willing to share research data and cited a lack of ownership after storing their research data in most of the open RDRs and data security. HILs need to conduct training on using trusted repositories and motivate postgraduate students to utilise open repositories (ORs). The challenges for underutilisation of open RDRs were a lack of policies governing the storage and sharing of research data and grant constraints.Originality/valueResearch data storage and sharing are of great interest to researchers in HILs to inform them to implement open RDRs to support these researchers. Open RDRs increase visibility within HILs and reduce research data loss, and research works will be cited and used publicly. This paper identifies the potential for additional studies focussed on this area.

Implementation and adoption plan “23 Things Revisited: Field Guides to Research data Management” is a project funded by RDA Europe following its Adoption Open Call in 2019. Research support staff, such as data stewards, IT support staff, librarians or policy officers, often have different levels of understanding of Research Data Management (RDM). However, they need to collaborate closely to offer state-of-the-art support for researchers wishing to do responsible RDM. The 23 Things: Libraries for Research Data resource that has been developed by the RDA's Libraries for Research Data Interest Group can act as a shared reference tool that enhances mutual understanding and improves collaboration in the Netherlands. It can also be used for quick reference and as a guideline for training. We propose to update and adjust the 23 Things to the above-mentioned audiences and stimulate nationwide adoption. The implementation plan, together with the workplan and Gantt chart describe the goals, activities, stakeholders involved in the adoption, implementation and dissemination of the '23 things' in The Netherlands.

Purpose: The purpose of this paper was to examine the potential opportunities and risks of sharing agricultural research data in Tanzania identified in the existing research literature. Design/methodology/approach: The study involved a review of the literature on research data sharing practices. Findings: The findings indicate that, research data sharing have significant positive benefits among researchers such as increase high research impact; enhancing international community collaboration among researchers with same interests; improving scientific transparency and accuracy of data (Rappert and Bezuidenhout, 2016); increasing research output whereby a single dataset can be used to generate more than one article by different authors; and many more. The risks hampering data sharing practices includes researchers’ fears that data will be scooped, poached or misused (Onyancha, 2016); unreliable electric power; lack of fund to support research data sharing activities; absence of institutional governmental support for data management; perceived lack of evidence benefits (Leonelli, Rappert and Bezuidenhout, 2018); and others. However, in Tanzania research data sharing is relatively new, thus, no any governmental agency mandating or encouraging research data sharing; therefore, there is no research data management; no research open data repositories and no research data sharing policy at any agricultural institution in Tanzania. The study recommends that agricultural researchers should be sensitized to share their data, research data policy and data repositories should also be established to support data sharing practices in Tanzania. Originality and usefulness: From the available literature, this has been the first time that an effort has been made to examine the potential opportunities and risks of sharing agricultural research data in Tanzania. The study could be used by agricultural institutions and other institutions to assess the researchers’ needs in supporting research data sharing. Also, it can be used by the government and institutions to see the need of establishing open data repositories and open data policies to support research data sharing.

La gestión de datos de investigación ha implicado acciones tempranas e inmediatas en las instituciones de educación superior en Colombia y en el mundo entero. En agosto del 2022, el Ministerio de Ciencias, Tecnologías e Innovación de Colombia publicó la Política Nacional de Ciencia Abierta, en cual, se expresan en sus metas y estrategias la implementación de servicios de gestión de datos de investigación y repositorio de datos. En la presente investigación se define el servicio de gestión de datos de investigación en la Universidad Distrital Francisco José de Caldas, universidad pública con una población cercana a los 28.000 estudiantes y con más de 200 grupos de investigación. A través de la metodología referencia TOPE (Tecnology, Organization, People, Environment) se identificó una necesidad urgente de definir políticas, formación y estrategias de acompañamiento para la gestión de datos de investigación, así como el acompañamiento de las instancias de investigación y bibliotecas dentro de la propia institución; el diagnóstico de datos de investigación publicados por investigadores de la institución permitió identificar 599 datasets publicados en los últimos 12 años en diferentes repositorios como Mendeley Data, Zenodo, SiB Colombia - GBIF Colombia para, finalmente, definir, mediante la metodología Business Process Management Notation, el planteamiento del servicio de gestión de datos de investigación para toda la institución. Esta pesquisa plantea un escenario de respuesta a la política nacional y establece nuevas rutas orientadas a desarrollar un nivel de madurez a nivel institucional respecto a la gestión de datos de investigación en un contexto de ciencia abierta.

The research data repository RADAR is designed to support the secure management, archiving, publication and dissemination of digital research data from completed scientific studies and projects. Developed as a collaborative project funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) (2013-2016), the system is operated by FIZ Karlsruhe - Leibniz Institute for Information Infrastructure - and currently serves as a generic cloud service for about 20 universities and non-university research institutions. Since its launch, RADAR has witnessed significant changes in the landscape of research data repositories and the evolving needs of researchers, research communities and institutions. In our presentation within the “Enabling RDM” Track, we will show how RADAR is responding to these dynamic changes. In order to create a sufficiently large user base for the sustainable operation of the system, we have moved RADAR away from its previous single focus on a discipline-agnostic cloud service and towards a demand-driven functional optimisation. In 2021, we introduced an additional operating model for institutions (RADAR Local), where we operate a separate RADAR instance locally at the institution site exclusively using the institutional IT-infrastructure. In 2022 we opened up RADAR to new target groups with community-specific service offerings, in particular in the context of the National Research Data Infrastructure (NFDI). Beside the expansion of the functional scope, our ongoing development work focuses also on strengthening the system's support for the FAIR principles [1] and the concepts of FAIR Digital Objects (FDO) [2] and Schema.org. Our presentation will outline recent RADAR developments and achievements as well as future plans thus providing solutions and synergy potential for the scientific community and for other service providers.

Research data have been increasingly published. A search function on a research data repository is crucial to improve the accessibility. Generally, the search is executed based on metadata written by the creators of the research data. However, such the metadata may not be sufficiently descriptive because there can exist features or usages that the creators did not expect. The information about features or usages generated by users might appear in citation contexts of research data in their scholarly papers. In this study, we set and discuss the hypothesis that citation contexts in scholarly papers are useful for research data search. First, we investigated whether adding citation contexts to the metadata can enrich information about the research data. Concretely, the existing metadata and the citation contexts were collected, and their overlap was examined. Furthermore, a retrieval experiment was conducted to confirm the effectiveness of the citation contexts. The results indicated the usefulness of the citation contexts in scholarly papers.

In June 2020, ETH Zurich’s Research Collection celebrated its third anniversary. The Research Collection serves as an institutional repository for ETH Zurich that can host both publications and research data and is operated by the E-Publishing team at the ETH Library. Publishing research data and advising customers on research-data-specific questions in the publishing workflow has emerged as a new field of activity for the team. With over 800 research data items published over the last few years, we have now gained a good understanding of the actual use cases for publishing data in an institutional repository at a large university for science and technology. We regularly talk to researchers about the incentives and requirements for publishing their data and monitor what kind of data they deposit. In this paper, we share and discuss our insights. We present statistics on the types of deposited datasets and explain how “FAIR” they are in terms of accessibility, licences and metadata. We also discuss our workflows for checking datasets for formal quality criteria and compliance with institutional policies and how to bridge publishing and preservation requirements in a research data repository. Finally, we give an overview of two ongoing development projects. The first one aims to enable ETH researchers to deposit datasets directly from the data management tool openBIS, while the second one will deliver a solution for publishing large datasets via the Research Collection.

The aim of the RADAR (Research Data Repository) project is to set up and establish an infrastructure that facilitates research data management: the infrastructure will allow researchers to store, manage, annotate, cite, curate, search and find scientific data in a digital platform available at any time that can be used by multiple (specialized) disciplines. While appropriate and innovative preservation strategies and systems are in place for the big data communities (e.g., environmental sciences, space, and climate), the stewardship for many other disciplines, often called the “long tail research domains”, is uncertain. Funded by the German Research Foundation (DFG), the RADAR collaboration project develops a service oriented infrastructure for the preservation, publication and traceability of (independent) research data. The key aspect of RADAR is the implementation of a two-stage business model for data preservation and publication: clients may preserve research results for up to 15 years and assign well-graded access rights, or to publish data with a DOI assignment for an unlimited period of time. Potential clients include libraries, research institutions, publishers and open platforms that desire an adaptable digital infrastructure to archive and publish data according to their institutional requirements and workflows.

Results of scientific work in chemistry can usually be obtained in the form of materials and data. A big step towards transparency and reproducibility of the scientific work can be gained if scientists publish their data in research data repositories in a FAIR manner. Nevertheless, in order to make chemistry a sustainable discipline, obtaining FAIR data is insufficient and a comprehensive concept that includes preservation of materials is needed. In order to offer a comprehensive infrastructure to find and access data and materials that were generated in chemistry projects, we combined the infrastructure Chemotion repository with an archive for chemical compounds. Samples play a key role in this concept: we describe how FAIR metadata of a virtual sample representation can be used to refer to a physically available sample in a materials’ archive and to link it with the FAIR research data gained using the said sample. We further describe the measures to make the physically available samples not only FAIR through their metadata but also findable, accessible and reusable.

Purpose In the present era, research data is a concern for researchers, as they are trying to find new ways to communicate their research findings and conclusions to other researchers in order to increase visibility and credibility. BRICS nations are fast emerging economies and contribute significantly in research output. This study makes an effort to analyze and explore the role of BRICS nations towards open access research data repository registered with Registry of Research Data Repositories. Design/methodology/approach The data were gathered from re3data repository, and the search was limited to BRICS nations. The data were further analyzed and tabulated as per set parameters, namely, country-wise distribution, types of contents, subject coverage and language diversity. Findings The findings depict that in terms of strength, India has the highest number of data repositories, thereby achieved the first rank among BRICS nations, and South Africa has the least number of data repositories, whereas in terms of content type and subject coverage, India again is leading among BRICS nations. The English language is used by repositories as the main language of the interface. Practical implications The study helps to understand the development of research data repositories by BRICS nations. The study is further beneficial to researchers, as Registry of Research Data Repository provides a single platform to access repositories from various disciplines. Readily available data saves time, money and efforts of researchers and helps the researcher in completing their research activity in a very short span of time. Originality/value The paper has investigated open access data repositories of BRICS nation that has not been attempted earlier. This gives readers comprehensive overview of research data repositories developed in fast emerging economies of the global. The paper can be very helpful for information managers, OA promoters and education and research policy makers to devise plans and policy bearing in mind the evolving research channels in emerging economies.

Research on historical manuscripts is increasingly supported by technical disciplines, which are used, for example, to digitally restore degraded text or to analyze the composition of inks, pigments, binders, and the materials constituting the manuscript’s support. The Centre of Image and Material Analysis in Cultural Heritage (CIMA) in Vienna is an inter-university research institution that fosters collaboration between philology and the natural sciences. CIMA has generated a large amount of diverse research data and findings concerning numerous manuscripts. These data exist in various formats and are distributed across different storage media at the participating institutions. This paper describes the conceptual framework and technical implementation of a digital research data repository designed for the long-term preservation and dissemination of CIMA’s large volumes of heterogeneous data. The repository provides the research data related to the examined manuscripts for download and reuse by interested users, in accordance with open-access principles. Furthermore, the diverse data are visualized through a user interface, presenting different research insights derived from a single physical manuscript. This multimodal representation of a manuscript is intended to serve as an accessible starting point for interdisciplinary investigation, data exploration, and teaching. Graphical abstract

Research data must be testable. Science is all about verification and testing. To make data testable, tools used to produce, collect, and examine data during the research must be available. Quite often, however, these data become inaccessible once the work is over and the results being published. Hence, information and the related context must be provided on how research data are preserved and how they can be reproduced. Open Science is the international movement for making scientific research data properly accessible for research community. One of its major goals is building data repositories to foster wide dissemination of open data. The objectives of this research are to examine the features of research data, common repository platforms, and community requests for the purpose of designing functional requirements for research data repositories. To analyze the features of the research data, we use data curation profiles available from the Data Curation Center of the Purdue University, USA. For common repository platforms we examine Fedora Commons, iRODS, DataONE, Dataverse, Open Science Data Cloud (OSDC), and Figshare. We also analyze the requests from research community. To design a technical solution that would meet public needs for data accessibility and sharing, we take the requirements of RDA Repository Interest Group and the requests for the DataNest Community Platform developed by the Korea Institute of Science and Technology Information (KISTI). As a result, we particularize 75 requirement items grouped into 13 categories (metadata; identifiers; authentication and permission management; data access, policy support; publication; submission/ingest/management, data configuration, location; integration, preservation and sustainability, user interface; data and product quality). We hope that functional requirements set down in this study will be of help to organizations that consider deploying or designing data repositories. URL: http://ijkcdt.net/xml/13281/13281.pdf

Objective – In consideration of emerging national Research Data Management (RDM) policy and infrastructure, this literature review seeks answers to the following questions: 1) What is the most effective way for a Canadian research university to build capacity among library and campus-wide research support staff, with a view towards providing coordinated RDM support services for our researcher community?2) What international training models and course offerings are available and appropriate for a local context?3) What national guidelines and best practices for pedagogical design and delivery can be adapted for a local context? Methods – This literature review synthesizes a total of 13 sources: 9 articles, 2 book chapters, and 2 whitepapers. The whitepapers were selected for a narrative literature review because of their focus on case studies detailing train-the-trainer models. Within the 13 sources we found 14 key case studies. This review serves as a supplement to the 2017 CARL Portage Training Expert Group white paper, “Research Data Management Training Landscape in Canada,” the focus of which was to identify RDM training gaps in order to recommend a coordinated approach to RDM training in a national environment. Results – The narrative review of case studies revealed three thematic areas. Firstly, pedagogical challenges were identified, including the need to target training to RDM support staff such as librarians and researchers, as they comprise distinct groups of trainees with divergent disciplinary vocabularies and incentives for training. Secondly, the case studies cover a broad range of pedagogical models including single or multiple sessions, self-directed or instructor-led, in-person or online instruction, and a hybrid of the two. Finally, RDM training also emerged as a key factor in community building within library staff units, among service units on campus, and with campus research communities. Conclusion – RDM training programs at local institutions should be guided by a set of principles aligned with the training methods, modes of assessment, and infrastructure development timeline outlined in a national training strategy. When adapting principles and training strategies to a local context, the following trends in the literature should be considered: librarians and researchers must have meaningful incentives to undertake training in RDM or to join a community of practice; disciplinary-specific instruction is preferable to general instruction; a librarian’s own training opportunities will influence their ability to provide discipline-specific RDM instruction to researchers; in-person training opportunities improve learning retention and produce beneficial secondary effects, whereas online instruction is most effective when paired with an in-person component; generalized third-party RDM training should be adapted to local context to be meaningful. Future directions for RDM training will integrate into open access and digital scholarship training, and into cross-disciplinary, open science communities of practice.

Research data sharing has become an expected component of scientific research and scholarly publishing practice over the last few decades, due in part to requirements for federally funded research. As part of a larger effort to better understand the workflows and costs of public access to research data, this project conducted a high-level analysis of where academic research data is most frequently shared. To do this, we leveraged the DataCite and Crossref application programming interfaces (APIs) in search of Publisher field elements demonstrating which data repositories were utilized by researchers from six academic research institutions between 2012-2022. In addition, we also ran a preliminary analysis of the quality of the metadata associated with these published datasets, comparing the extent to which information was missing from metadata fields deemed important for public access to research data. Results show that the top 10 publishers accounted for 89.0% to 99.8% of the datasets connected with the institutions in our study. Known data repositories, including institutional data repositories hosted by those institutions, were initially lacking from our sample due to varying metadata standards and practices. We conclude that the metadata quality landscape for published research datasets is uneven; key information, such as author affiliation, is often incomplete or missing from source data repositories and aggregators. To enhance the findability, interoperability, accessibility, and reusability (FAIRness) of research data, we provide a set of concrete recommendations that repositories and data authors can take to improve scholarly metadata associated with shared datasets.