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New attention to the power of data in research has brought new kinds of data questions to the university research library. This study seeks to understand the character of research data questions in order to help research libraries develop the structures, technologies, collections, and professional skills needed to meet the needs of the research communities. It employs two original metrics to analyze 42 cases: The Data Question Typology, which allows for the organization of data questions into categories based on researcher objectives, and the Modified READ Scale for Data Questions (MRSD), which is used to record the magnitude of difficulty presented by each case. It finds that data questions differ significantly across academic fields and that successful research assistance often requires partnerships between subject specialists librarians and technological or computational experts. It concludes with a recommendation on how research libraries can facilitate a collaborative process and workflow for handling a diversity of data questions from across the university.

IntroductionThe COVID-19 pandemic has prompted global research efforts to reduce infection impact, highlighting the potential of cross-disciplinary collaboration to enhance research quality and efficiency.MethodsAt the FMUSP-HC academic health system, we implemented innovative flow management routines for collecting, organizing and analyzing demographic data, COVID-related data and biological materials from over 4,500 patients with confirmed SARS-CoV-2 infection hospitalized from 2020 to 2022. This strategy was mainly planned in three areas: organizing a database with data from the hospitalizations; setting-up a multidisciplinary taskforce to conduct follow-up assessments after discharge; and organizing a biobank. Additionally, a COVID-19 curated collection was created within the institutional digital library of academic papers to map the research output.ResultsOver the course of the experience, the possible benefits and challenges of this type of research support approach were identified and discussed, leading to a set of recommended strategies to enhance collaboration within the research institution. Demographic and clinical data from COVID-19 hospitalizations were compiled in a database including adults and a minority of children and adolescents with laboratory confirmed COVID-19, covering 2020–2022, with approximately 350 fields per patient. To date, this database has been used in 16 published studies. Additionally, we assessed 700 adults 6 to 11 months after hospitalization through comprehensive, multidisciplinary in-person evaluations; this database, comprising around 2000 fields per subject, was used in 15 publications. Furthermore, thousands of blood samples collected during the acute phase and follow-up assessments remain stored for future investigations. To date, more than 3,700 aliquots have been used in ongoing research investigating various aspects of COVID-19. Lastly, the mapping of the overall research output revealed that between 2020 and 2022 our academic system produced 1,394 scientific articles on COVID-19.DiscussionResearch is a crucial component of an effective epidemic response, and the preparation process should include a well-defined plan for organizing and sharing resources. The initiatives described in the present paper were successful in our aim to foster large-scale research in our institution. Although a single model may not be appropriate for all contexts, cross-disciplinary collaboration and open data sharing should make health research systems more efficient to generate the best evidence.

As research misconduct has created reproducibility and researcher reputation concerns, there is an opportunity to recommend data management techniques to assist researchers as they seek to prevent these issues. Also central to the discussion are issues of power in the conduct of research, particularly in upholding the values of honesty and accountability. This commentary discusses how data professionals can engage in practical strategies to protect against allegations of misconduct.

The open scientific data platform is the information infrastructure for open data activities. construction of relevant platforms in universities is an important step in strengthening scientific data services. By investigating the current situation both domestically and internationally, the paper proposes issues to be explored in the construction logic, data collaboration functions and data harmonization standards of domestic platforms. Based on the practice of Zhejiang University, it explains the overall framework of independent platform developing path, the major issues of construction about collaborative processes and data harmonization, as well as the effectiveness and reflection of the practice, in order to provide reference for domestic universities to carry out relevant practices and promote the further implementation of domestic open scientific data activities. [ABSTRACT FROM AUTHOR]

A study on scientific data citation is crucial to promote data sharing and is the basis for the examination of scientific data measurement and analysis. To this end, it is necessary to identify and label data reference information. Currently, there are many supervised methods for entity recognition and relationship extraction of diseases, drugs, proteins, symptoms, etc., but they have not discussed the effectiveness of scientific data recognition. To fill this gap, the effectiveness of the classical machine learning model and the deep learning model on recognizing scientific data citation are discussed in this study. In experiments, this study took the full text of scientific and technical papers as the research object, conducted annotated citation classification based on rules and manual recognition of their references to form a dataset. The results of the empirical study showed that: (1) the methods used in this paper can achieve automatic identification and extraction of data citations and can address the problem of automating the construction of citation relationships between scientific and technical literature and scientific data; (2) the BERT-based models have the optimal effectiveness in the recognition task of scientific data citation, especially the BioBERT and SciBERT; (3) the full-text information has a crucial impact on the recognition results. [ABSTRACT FROM AUTHOR]

Reproducibility is a preferred aim in any scientific research, including occupational health research. Datamanagement is an important and essential step in marching towards reproducibility. A good datamanagement helps us stay organized, improve transparency, quality and fosters collaboration. Here we discuss how to organize and prepare for data management, how data management facilitates interoperability and accessibility, followed by storing and dissemination of data. We wrap up by providing pointers on what needs to be included in the data management plans. [ABSTRACT FROM AUTHOR]

The paper is devoted to the problem of generating artificial datasets for data envelopment analysis (DEA), which can be used for testing DEA models and methods. In particular, the papers that applied DEA to big data often used synthetic data generation to obtain large-scale datasets because real datasets of large size, available in the public domain, are extremely rare. This paper proposes the algorithm which takes as input some real dataset and complements it by artificial efficient and inefficient units. The generation process extends the efficient part of the frontier by inserting artificial efficient units, keeping the original efficient frontier unchanged. For this purpose, the algorithm uses the assurance region method and consistently relaxes weight restrictions during the iterations. This approach produces synthetic datasets that are closer to real ones, compared to other algorithms that generate data from scratch. The proposed algorithm is applied to a pair of small real-life datasets. As a result, the datasets were expanded to 50K units. Computational experiments show that artificially generated DMUs preserve isotonicity and do not increase the collinearity of the original data as a whole. [ABSTRACT FROM AUTHOR]

Copyright of Teorija in Praksa is the property of Teorija in Praksa and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Artificial-intelligence tools that enable companies to share data about drug candidates while keeping sensitive information safe can unleash the potential of machine learning and cutting-edge lab techniques, for the common good. [ABSTRACT FROM AUTHOR]

Virtual platforms have revolutionized distance education, making it accessible worldwide and empowering scientists, academicians, and researchers to access knowledge effortlessly. These platforms provide flexibility, allowing the users to tailor their learning experience to their needs and integrating knowledge into their work. Reproducibility is crucial in cancer research, and researchers integrate data analysis into virtual or electronic learning (e-Learning) platforms to facilitate replication and verification, promoting transparency and reliability. This integration enhances accessibility and enables collaboration among scientists and stakeholders in the fight against cancer. Virtual learning offers written and audio-visual communication benefits facilitated by electronic and web-enabling advancements. In the dynamic virtual realm, researchers transcend limitations, exchange knowledge, and push the boundaries of cancer research. Virtual platforms provide time efficiency and financial freedom, while advanced tools support data analysis and facilitate new insights. These tools unlock hidden patterns and accelerate the pace of discovery. The digital ecosystem generates new ideas, improves research methodology, and enhances research quality. Limitless collaboration and advanced tools propel cancer research, unravelling complex data with precision and innovation. The potential of cyberspace to revolutionize scientific research in the future, therefore, is promising. [ABSTRACT FROM AUTHOR]

Copyright of Publizistik: Vierteljahreshefte für Kommunikationsforschung is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

[Purpose/Significance] In 2021, the Ministry of Education of China issued the "Norms for the Construction of Digital Campuses in Higher Education Institutions (Trial)", which mentioned that universities should attempt to form a new model of academic exchange and resource sharing through the construction of institutional repositories (IRs). The construction of knowledge resources in university IRs is gradually developing towards the next generation IRs. Research data resources are an important component of the future resources. From the perspective of the generation, acquisition, and existence of information resources in universities, the institutional resources in universities primarily consist of resources generated by "industry, academia, research and application" activities, including teaching resources, scientific research resources, design resources, scientific activity resources, etc., which are specifically reflected in the activity process such as scholar resources, research activity resources, research process resources, and research achievements resources. Research data resources are studied as the research object in this article. [Method/Process] This paper analyzes the formation mechanism of institutional knowledge resources, and conduct research on the integration, management and reuse of research data resources, including: 1) internal factors of research data, data generation, source, type, structure, and dependency mode; 2) research on external elements of data, such as data standards, unique identifiers, data registration, data protocols, data rights, data reuse, and data sharing Through the study of internal and external factors, the influencing factors, functions and roles, responsibilities and rights, permissions and attribution of data integration management and data reuse were sorted and interpreted and feasible methods were designed for research data management, collection, implementation, and registration. In order to ensure the effectiveness of data management, a series of standards and schemes have been developed, such as data type and format standards, metadata schemes, and data guardianship demand survey templates. The purpose is to achieve data discovery, interoperability, and reuse through continuous monitoring of scientific data. The basic rules of reuse are mainly divided into: 1) reuse: the concept of reuse, reuse, sharing, and incomplete equivalence in reuse; 2) sharing: possible to be used; 3) protocol usage: discussion about how to use it; and 4) rights use: complying with the data copyright agreement. [Results/Conclusions] Through research courses, the development and implementation of favorable data management and reuse strategies have clarified the data objects, management set management services, and data reuse permissions of research data in university IRs. We have clarified the current situation and urgent issues to integrate research data into the long-term preservation, management, and sharing and reuse system of knowledge resources in university IRs. We have defined the responsibilities, management mechanisms, standardized business processes, permission attributes, data exchange platforms, data registration, and data reuse of research data subjects by category, region, and field, and provided some suggestions and guarantee measures for the establishment of a data management center in China.

This paper presents the results of an empirical research study of Croatian scientists’ use and management of research data. This research study was carried out from 28 June 2023 until 31 August 2023 using an online questionnaire consisting of 28 questions. The answers of 584 respondents working in science were filtered out for further analysis. About three-quarters of the respondents used the research data of other scientists successfully. Research data were mostly acquired from colleagues from the same department or institution. Roughly half of the respondents did not ask other scientists directly for their research data. Research data are important to the respondents mostly for raising the quality of research. Repeating someone else’s research by using their research data is still a problem. Less than one-third of the respondents provided full access to their research data mostly due to their fear of misuse. The benefits of research data sharing were recognized but few of the respondents received any reward for it. Archiving research data is a significant problem for the respondents as they dominantly use their own computers prone to failure for that activity and do not think about long-term preservation. Finally, the respondents lacked deeper knowledge of research data management.

The special collection “Data for Psychological Research on COVID-19” presents selected datasets collected during the COVID-19 pandemic. In selecting the data papers, care was taken to ensure that the described datasets not only represent the full range of psychological topics addressed during the pandemic but also reflect its global nature, in that diverse nationalities were included in the investigated samples. As these datasets are shared according to the FAIR Principles (Wilkinson et al., 2016), they are findable, accessible, interoperable and reusable. The special collection comprises 12 data papers presenting quantitative studies on the impact of COVID-19 on various psychological constructs, including socio-political attitudes, beliefs in conspiracy theories, emotional reactions, and control behaviour.

In medical research, there are vast amounts of health data that can be used in a variety of ways. But what about this kind of data at Helios? This article describes the existing infrastructure and the institutions who are important for the research at Helios. Furthermore, it will be shown what the library of Helios Kliniken is currently doing for researchers and how it wants to position itself as a partner for research data.

The Open Science movement is gaining tremendous popularity and tries to initiate changes in science, for example the sharing and reuse of data. The new requirements that come with Open Science poses researchers with several challenges. While most of these challenges have already been addressed in several studies, little attention has been paid so far to the underlying Open Science practices (OSP). An exploratory study was conducted focusing on the OSP relating to sharing and using data. 13 researchers from the Weizenbaum Institute were interviewed. The Weizenbaum Institute is an interdisciplinary research institute in Germany that was founded in 2017. To reconstruct OSP a grounded theory methodology (Strauss in Qualitative Analysis for Social Scientists, Cambridge University Press, Cambridge, 1987) was used and classified OSP into open production, open distribution and open consumption (Smith in Openness as social praxis. First Monday, 2017). The research shows that apart from the disciplinary background and research environment, the methodological approach and the type of research data play a major role in the context of OSP. The interviewees' self-attributions related to the types of data they work with: qualitative, quantitative, social media and source code. With regard to the methodological approach and type of data, it was uncovered that uncertainties and missing knowledge, data protection, competitive disadvantages, vulnerability and costs are the main reasons for the lack of openness. The analyses further revealed that knowledge and established data infrastructures as well as competitive advantages act as drivers for openness. Because of the link between research data and OSP, the authors of this paper argue that in order to promote OSP, the methodological approach and the type of research data must also be considered. [ABSTRACT FROM AUTHOR]

Investigations suggest that, in some fields, at least one-quarter of clinical trials might be problematic or even entirely made up, warn some researchers. They urge stronger scrutiny. [ABSTRACT FROM AUTHOR]

While data sharing has received research interest in recent times, its real status remains unclear, owing to its ambiguous concept. To understand the current status of data sharing, this study examined primary reuse, data integration, and dataset release as the actual practices of data sharing. A total of 963 articles, chosen from those published in 2018 and registered in the Web of Science global citation database, were manually checked. Existing data were reused in the mode of data integration (13.3%) as frequently as they were for the mode of primary reuse (12.1%). Dataset release was the least common mode (9.0%). The results show the variation in data sharing and indicate the need for standardization of data description in articles based on thorough registration and expansion in public data archives to close the loop that results in the virtuous cycle of research data. [ABSTRACT FROM AUTHOR]

The explosion of information and the open access movement have posed significant challenges in data management for laboratories and research groups. The Laboratory of Studies and Research in Social Economy (Laboratório de Estudos e Pesquisas em Economia Social - LEPES) at the University of São Paulo focuses on developing research and impact assessments of social policies in Brazil, generating a substantial amount of research data. However, storing this data on a server with restricted access has led to various issues, including difficulties in accessing data, lack of standardization, security concerns, and version management problems. To address these challenges, this work aims to propose a comprehensive data management solution for LEPES. The proposal encompasses multiple actions, such as education, expertise, curation, indexing, and data privacy, all integrated into an open repository for research data. The primary objective is to facilitate data storage and enable public access, promoting data sharing. The approach taken in this study is an applied research design that utilizes the case study as a method. Additionally, the study draws support from relevant bibliographical and documentary literature. The proposal's innovation lies in its meticulous alignment of data management principles within a laboratory that has a direct impact on Brazilian educational public policies. As for the results, the management group outlines the pathways they followed to ensure secure sharing of research data, while adhering to the existing legal framework. Furthermore, the proposed solution not only benefits LEPES but also offers valuable insights for other groups facing similar data management challenges. Overall, this research endeavours to create a robust and practical data management framework that facilitates safe data sharing, fosters collaboration, and contributes significantly to the advancement of social policy research in Brazil. [ABSTRACT FROM AUTHOR]

The growing role of research data and the rise of open science has also led to a demand for data literacy, which also serves managing, critically sharing and reusing research, business and citizen data. Focusing on these areas, this paper presents the characteristics of information literacy and data literacy, including their critical versions approaches. [ABSTRACT FROM AUTHOR]

The 1st Austrian Library Congress took place in Innsbruck from May 2 to 5, 2023. The main topic of the conference was „Working environments – Realities and visions“. This congress report presents presentations on the topics of data guidelines in German-language library science journals, bibliometrics as a service of the university library and information science consulting services by liaison librarians. [ABSTRACT FROM AUTHOR]

This article discusses the outcome of the final project carried out within the framework of the course „Library and Information Studies“ on the topic of research data management in children’s and youth literature research. It offers a condensed overview of the initial project idea and design, the applied research strategy, and a discussion of the results. The project’s objective was to evaluate the availability of freely accessible research data in the field of children’s and youth literature research and, if possible, to create a collection of reusable research data. The research results show that such a collection is currently not feasible due to insufficient data in this area and suggest that research data management in children’s and youth literature research is not in a stage of advanced development. A more targeted research approach and seeking direct contact with researchers is recommended as a more practical approach for obtaining research data in this field. Additionally, it is advisable to concentrate resources on further developing data management practices in the field. [ABSTRACT FROM AUTHOR]

Copyright of Investigación Bibliotecológica is the property of UNAM, Centro Universitario de Investigaciones Bibliotecologicas and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Medical Library Forum is the property of Warszawski Uniwersytet Medyczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This article evaluates the new EU regulatory policy concerning research data. To this end, it seeks to determine to what extent the demands of open science have been implemented by the EU and national legislators. The article examines the provisions of Directive (EU) 2019/1024 of the European Parliament and of the Council of 20 June 2019 on open data and the re-use of public sector information (recast) and those of the Polish Act of 11 August 2021 on open data and the re-use of public sector information. [ABSTRACT FROM AUTHOR]

• P-values can deny genuine beneficial effects of an intervention. • Bradford Hill created a rounded assessment to determine causality. • Incorporates previous literature, study design, mechanism and secondary outcomes. • Rounded assessment can be more reflective of true clinical impact of intervention. • Alternative methods to evaluate the clinical benefit of an intervention are required. Medical practice relies on reliable research observations. Whether such observations are true is traditionally tested by hypotheses and expressed with P-values. A strict P-value driven interpretation could potentially deny benefits of treatment. A strict P-value driven interpretation was compared to a context driven causality interpretation using the Bradford Hill Criteria to determine the clinical benefit of an intervention. We searched all randomised controlled trials in Women's Health, published in five leading medical journals since January 2014. These were then scored using the 10 Bradford Hill Criteria for causation. Each component of the Bradford Hill Criteria was given a score from zero to three, resulting in a total score between zero and 30 for each article, converted into a decimal value. These scores were then compared to conclusions based on the p-value and conclusions drawn by the authors. For results discordant between Bradford Hill Criteria and P-values, we compared results with meta -analysis. We found 68 articles for extraction of data. Of these, 49 (72%) showed concordance between Bradford Hill criteria and p-value driven interpretation, 25 (37%) of the articles reporting effectiveness (true positive), and 24 (35%) reporting no effectiveness (true negative). In eight (12%) articles, Bradford Hill criteria scores suggested effetiveness while p-values driven interpretation did not. Seven of those eight articles had p-values between 0.05 and 0.10. Out of these eight articles, six had a subsequent meta-analysis' published on the intervention being studied. All six meta -analysis demonstrated effetiveness of the intervention. In the interpretation of clinical trials, a context driven interpretation of causality may be more clinically informative than a strict P-value driven approach. [ABSTRACT FROM AUTHOR]

Today, one can observe shifts in the research landscape, which is formed by digitization and open science principles. The open science movement continues to gain momentum, attention and debate. In parallel with the principle of unity, open science gives rise to a taxonomy of several related ideas, guidelines and concepts, such as open access, open replicable research and open data. Over the past fifteen years, research institutions have focused on open access to publications. However, recently the focus of attention has shifted to research data as a "new currency" in research activities and their distribution in open access, and the guiding principles of data management are becoming crucial for the wide implementation of open science practices and the effective use of data in research, industry, business and other sectors of the economy. In this context, it is relevant to carry out a thorough study of primary scientific works on open science issues and to study the role of the concept of "open research data" in the paradigm of a holistic ecosystem of open science and business ecosystem. In this work, it is proposed to use the methods of quantitative and qualitative bibliometric analysis, which allows to identify the main trends and form the basis for further research. The information base for this work was the international scientometric database Scopus, which enables to analyze bibliographic data using built-in tools and import them for external use in the VOSviewer software. The study revealed an increasing trend in the number of publications on the subject under study, with the highest annual growth rate in 2017 (76%) and 2019 (66%). Qualitative bibliographic analysis made it possible to analyze the most cited and, therefore, trending works on the selected topic. In terms of the number of citations per year, the results show that the studies with such directions in open science as open program code (open source); data/research reproducibility, research data management; open access to publications (open access) are most popular. In addition, a cluster analysis of the co-prevalence of keywords was conducted. It formed clusters dedicated to both institutional and infrastructural problems of the development of open science and research data. Separately, the results of the analysis create a scientific basis for further research into the key determinants of the effectiveness of the implementation of a proper research data management system at the micro, meso, and macro levels. It will improve the effectiveness of the implementation of scientific developments from one field of knowledge to another, while achieving increased interdisciplinary research. In parallel with this, interested persons of the real sector of the economy get the opportunity to analyze scientific results, determining the possibility of their adoption in their own activities.