Functional Requirements for Research Data Repositories

RESUMO O artigo investiga a prática da curadoria de dados de pesquisa em repositórios de ensaios clínicos. Propõe reflexões sobre a informação e seus múltiplos formatos no domínio da saúde e sugere uma definição para dados de pesquisa clínicos. Apresenta o conceito de curadoria de dados de pesquisa e fomenta novos estudos que priorizam a discussão sobre a gestão de dados de pesquisa e o movimento da Ciência Aberta no domínio da saúde. Aponta para a necessidade de implantação de políticas de gestão de dados de pesquisa que assegurem critérios de descrição, sistematização, compartilhamento, recuperação, interoperabilidade, preservação e reuso de dados. Como reflexão final, ressalta o desafio de discutir e implementar a prática de curadoria de dados de pesquisa produzidos por ensaios clínicos e evidencia o vasto e oportuno campo a ser explorado, tanto para pesquisadores quanto para profissionais da informação.Palavras-chave: Curadoria de Dados; Dados de Pesquisa; Ensaio Clínico; Ciência Aberta; Reuso de Dados.ABSTRACT The article investigates the practice of curating research data in clinical trial repositories. That proposes reflections on health information and its multiple formats and suggests the definition for clinical research data. It introduces the concept of research data curation and promotes new studies, which prioritize the discussion about research data management and the Open Science movement in the health domain. The article brings points to the need to implement research data management policies that ensure criteria for data description, systematization, sharing, retrieval, interoperability, preservation and reuse. As a final reflection, it highlights the challenge of discussing and implementing the practice of curating research data produced by clinical trials and highlights the vast and timely field to be explored for both researchers and information professionals.Keywords: Data Curator; Research data; Clinical Trial; Open Science; Data Reuse.Lista

Digitisation of research data is widely increasing all around the world because it needs more and development of enormous digital technologies. Data curation services are starting to offer many libraries. Research data curation is the collective invaluable and reusable information of the researchers. Collected data preservation is more important. The majority of the higher education institutes preserved the research data for their students and researchers. It is stored for a long time using various formats. It is called research data preservation. Without proper research data management plan and implementation cannot curate the research data. The aim of the study is to identify the Asian Library and Information Science (LIS) faculties’ experiences in the research data preservation and curation during their research. Data management, curation and preservation all are interlinked. For reuse of the research data; data curation is an essential role. For this research, we adopted a survey method and an online questionnaire was shared with 1400 LIS professionals, belonging to the Asian region but the completed study respondents are 125 university faculties from various Asian countries. The study findings are 76.8 per cent generated statistical data followed by 58.4 per cent textual files. By far, the most preferable data analysis tool is Microsoft Excel 82.4 per cent. Moreover, the result shows that generated data is mostly stored by personal computers and laptop hard disks. This study concludes LIS faculties having adequate skills and knowledge on data curation and preservation even though they are expecting more services from their academic institute libraries.

Research data management includes more than the question how researchers handle their data. In the sense of the FAIR principles, it is also about the sustainable safeguarding and organized reusability of research data. For social science, data-intensive research, research data centers and their data curating staff are therefore becoming increasingly important: data curators usually take on curation-specific tasks such as data preparation, securing research data in suitable archival environments, ensuring data accessibility, and the related control of the conditions of data re-use by third parties. Hence, they are specialized in the entire data curation process and, in particular, take on tasks of archiving and providing research data for reuse. Although the standards of comprehensive research data management are becoming more and more specific, this trend has not yet arrived in the corresponding training and further education measures. As a result, there is a gap between the growing demands on data curators and the development of competencies in the field of research data management with a focus on data curation. The competency framework presented in this article is intended to help close this gap: based on a Data Curation Lifecycle Model, a competency framework has been developed to support the development of targeted training and continuing education programs in the field of data curation, the formulation of learning objectives, and the evaluation of the corresponding trainings. The article points out the necessity to advance the development of competencies for this field, illustrates the schematic substructure of the data curation lifecycle, describes the development as well as the central core elements of the presented competency framework and discusses its perspectives. Overall, this competence framework is aimed in particular at (future) data curators, or as a schematic basis for the training of the relevant personnel. The focus is primarily on the data-intensive discipline of social sciences, although large parts can certainly be adapted for other disciplines and the corresponding data curation. The competency framework and this companion article are thereby intended to assist in advancing the sustainable professionalization of the previously understudied competency field of data curation.

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.

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.

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.

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.

Introduction: The open science movement has grown rapidly since the mid-2010s, and research has been conducted in various disciplines such as public health, medicine, education, and computer science. Research results have mainly been published in the journals of information science, computer science, and multidisciplinary fields. Method: To identify the intellectual structure of open science, we constructed a keyword bibliographic coupling analysis network. We examined a total of 1,000 articles on open science from the Web of Science, extracting and analysing 4,645 keywords. Then, we implemented and visualised the keyword bibliographic coupling network by constructing a keyword dataset and a reference dataset for each keyword. Results: By analysing the backbone keywords and clusters in the network, the study revealed that the most prominent keywords were open access, open data, and reproducibility. The analysis also uncovered nine clusters in open science research: open access, reproducibility, data sharing, preregistrations and registered reports, research data, open peer review, tools and platforms for reproducible research, open innovation, science policy, and preprints. These results indicated that open science research focuses on transparency and reproducibility. Additionally, it is noteworthy that this study revealed a considerable focus on the open innovation and science policy areas, which have not received much attention in previous studies. Conclusions: The findings can help to understand the landscape of open science research and may guide research funding institutes and research policymakers to design their policies to improve the open science scholarly environment.

Globally, the open science movement has as its main principle the openness of publicly funded data and research outputs. It is essential that actors in this movement include the development and implementation of legal, technical and technological infrastructure. The premise of this research was that if the purpose of open science is free access to research products, then technological infrastructure is fundamental to crystallise availability, visibility, reproducibility and unrestricted access. The research aimed to identify actions of the open science movement in Latin America and study technological infrastructure for data repositories developed and implemented there. The methodology comprised a bibliographic review and quantitative methods. For the exploratory analysis, the sources for retrieval of information were official Web portals of library entities, the Registry of Research Data Repositories, and the Dataverse Project. The search was based on defined variables, namely actions, data repository, infrastructure, library collaboration, and repositories in the Registry of Research Data Repositories and Dataverse. Findings were studied through a descriptive content analysis based on defined variables. We found that nine Latin American countries carry out open science actions; eight develop research data repositories; eight implement technological infrastructure to develop repositories; seven collaborate with libraries in open science actions; and seven have their repositories recorded in the Registry of Research Data Repositories and Dataverse, resulting in 62 research data repositories in Latin America. We concluded that these libraries are actors and laboratories with methodological and pragmatic elements that contribute to the organisation, reproducibility, transparency, management and open access that support the paradigm shift which the scholarly communication system requires.

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

Scholarly publishing has rapidly moved towards open access (OA) over the last few decades. However, OA publishing is only one part of a larger open science movement. The recent UNESCO recommendation for open science (UNESCO, 2021) defines open science broadly to cover the openness of scientific knowledge, science infrastructures, engagement with societal actors, and dialogue with other knowledge systems. In its recommendations, open scientific knowledge includes OA to scientific publications but also open research data, metadata, open educational resources, software, and source code and hardware. Earlier research about open scientific knowledge from the point of view of academic publishers has mainly been focused on one element such as OA publishing and neglected other elements.This paper aims to fill this gap by surveying how society publishers in Finland adopted other elements of open scientific knowledge. In Finland, learned societies account for 70% of national journal output (Late et al., 2020) and their publishing model is mainly diamond open access, which excludes author processing charges and relies on publishing subsidies (Pölönen et al., 2020). Furthermore, their activities often go beyond scholarly publishing to include education and research activities such as funding research and collecting and storing research data (Korkeamäki et al., 2019).We conducted an electronic survey addressed to Finnish learned societies in November 2021 to answer the following research questions: “To what degree society publishers take up the elements of open scientific knowledge including open access to publications, open data, and open education?” (RQ1) and “Are elements of open scientific knowledge cumulative?” (RQ2)In total 97 society publishers responded (40% response rate). We analysed their responses through nine variables measuring how they adopted different elements of open scholarly knowledge (Table 1) in view of the UNESCO recommendations for open science. The results show elements related to open scholarly publications prevail. Almost 70% of respondentspublish either gold, green, or hybrid OA publications. Most society publishers reported to support open data policies only some do collect, store, and provide open access to research datasets. Furthermore, only a few societies offer training for opening research data. Even so, a high share of publishers offers open education, and some share their educational materials openly. Although earlier studies have reported differences in adopting open science between disciplines (Rousi & Laakso, 2020), our analysis does not support these findings.However, it has confirmed that adopting the elements of open scholarly knowledge is cumulative, as OA publishers are more likely to take up other elements of open scholarly knowledge. However, adopting all elements is not yet common. Since activities (e.g. collecting research data, offering education, etc.) of publishers other than societies seem to influence the take up of these elements, further research of their activities is needed. For example, it will show how often and how openly these other publishers provide research data or education beyond.

A Ciência Aberta é definida como um ecossistema que abriga os diversos movi- mentos em torno do acesso aberto à informação científica. Os dados científicos são a unidade nuclear básica que suporta as descobertas científicas. Nesse sentido, o objetivo deste trabalho é situar os dados científicos de pesquisa no contexto da Ciência Aberta, apresentando as definições de dados, compartilhamento e reuso de dados científicos. O trabalho apresenta um apanhado sobre os desafios quanto à gestão dos dados científicos, à tipologia de dados e às vantagens do comparti- lhamento e reuso. Apresenta os desafios computacionais para a construção de repositórios de dados de pesquisa. Conclui-se que a diversidade, a tipologia e o volume de dados representam os grandes desafios na implantação dos repositó- rios e indicam-se diretórios de pesquisa de repositórios de dados disciplinares e multidisciplinares.

Purpose For those immersed in the environment of academic and research libraries, the word “data” seems to be everywhere. One hears about linked data, big data, open data, proprietary data, research data, metadata, geospatial data, data repositories, etc. Design/methodology/approach Some libraries even have data librarians and data services departments. Findings The author of this column wonders if she were to collect all of the library and information science literature published in the past three years and plug it into a word cloud app, which of the two, i.e. “data” or “books”, would be displayed in a larger font. Originality/value The author suspects that the chances are more than good that “data” would come out on top.

The publication and utilization of not only evidence data of research results but also many other data associated with research activities are attracting attention as a new indicator for improving research efficiency and evaluating researchers. With the spread of open science, data repositories and data journals for research data have gradually begun to be recognized, and an environment that encourages the reuse of such research data will be further developed in the future. In this study, we focused on data description, which is important for getting an overview of research data. By clarifying the characteristics of data descriptions of highly cited research data, we hope to increase the number of citations, which will lead to the evaluation of researchers and, ultimately, to the evaluation of universities. Specifically, we will analyze the part-of-speech composition ratio of data descriptions and compare the characteristics of the highly cited research data with the low-cited research data.

PurposeBudgeting data curation tasks in research projects is difficult. In this paper, we investigate the time spent on data curation, more specifically on cleaning and documenting quantitative data for data sharing. We develop recommendations on cost factors in research data management.Design/methodology/approachWe make use of a pilot study conducted at the GESIS Data Archive for the Social Sciences in Germany between December 2016 and September 2017. During this period, data curators at GESIS - Leibniz Institute for the Social Sciences documented their working hours while cleaning and documenting data from ten quantitative survey studies. We analyse recorded times and discuss with the data curators involved in this work to identify and examine important cost factors in data curation, that is aspects that increase hours spent and factors that lead to a reduction of their work.FindingsWe identify two major drivers of time spent on data curation: The size of the data and personal information contained in the data. Learning effects can occur when data are similar, that is when they contain same variables. Important interdependencies exist between individual tasks in data curation and in connection with certain data characteristics.Originality/valueThe different tasks of data curation, time spent on them and interdependencies between individual steps in curation have so far not been analysed.