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Investment in large neuroscience initiatives is starting to show results, but questions remain over whether they can solve the most fundamental questions about cognition. [ABSTRACT FROM AUTHOR]

Research data is increasingly recognised as an essential output type in the context of research documentation. This paper aims to examine the possible use cases for showcasing research datasets in research information systems. The analysis indicates that three primary use cases play a key role in the implementation of a dataset module in an institutional or in a regional/national CRIS. The paper describes the use cases and places them in the specific context of the University of Vienna, while also referring to existing case studies of other institutions. [ABSTRACT FROM AUTHOR]

This article discusses the involvement of Polish medical libraries in the field of research data management (RDM). It provides an overview of RDM’s development and its introduction in medical libraries. On the basis of a survey conducted among the staff of Polish medical libraries, the authors outline the librarians’ level of competence, the extent of services they provide and the organisational context of their work. It is stressed that it is crucial to further develop librarians’ qualifications related to RDM, as well as to increase the awareness of libraries’ role in this area among researchers. The authors present conclusions concerning the formal acknowledgement of librarians’ role in the process of RDM, which contributes to the development of both libraries and research institutions.

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. [ABSTRACT FROM AUTHOR]

Artificial intelligence could help speedily summarize research. But it comes with risks. [ABSTRACT FROM AUTHOR]

Poorly performing antibodies have plagued biomedical sciences for decades. Several fresh initiatives hope to change this. [ABSTRACT FROM AUTHOR]

With efforts to promote sustainability on the rise, researchers are making gains — but doing science in a green way isn’t always easy. [ABSTRACT FROM AUTHOR]

This paper explores the landscape of research data in the humanities in the European context, delving into their diversity and the challenges of defining and sharing them. It investigates three aspects: the types of data in the humanities, their representation in repositories, and their alignment with the FAIR principles (Findable, Accessible, Interoperable, Reusable). By reviewing datasets in repositories, this research determines the dominant data types, their openness, licensing, and compliance with the FAIR principles. This research provides important insight into the heterogeneous nature of humanities data, their representation in the repository, and their alignment with FAIR principles, highlighting the need for improved accessibility and reusability to improve the overall quality and utility of humanities research data. [ABSTRACT FROM AUTHOR]

Too often, journal assessments of potentially unreliable research are superficial, opaque and prolonged. Changes to the guidance given by the Committee on Publication Ethics could tighten up the process. [ABSTRACT FROM AUTHOR]

Researchers have tried a bunch of strategies to get more negative results into the literature. Nature asks whether they are working. [ABSTRACT FROM AUTHOR]

Researchers need to be able to find, access, and use data to participate in open science. To understand how users search for research data, we analyzed textual queries issued at a large social science data archive, the Inter-university Consortium for Political and Social Research (ICPSR). We collected unique user queries from 988,475 user search sessions over four years (2012-16). Overall, we found that only 30% of site visitors entered search terms into the ICPSR website. We analyzed search strategies within these sessions by extending existing dataset search taxonomies to classify a subset of the 1,554 most popular queries. We identified five categories of commonly-issued queries: keyword-based (e.g., date, place, topic); name (e.g., study, series); identifier (e.g., study, series); author (e.g., institutional, individual); and type (e.g., file, format). While the dominant search strategy used short keywords to explore topics, directed searches for known items using study and series names were also common. We further distinguished exploratory browsing from directed search queries based on their page views, refinements, search depth, duration, and length. Directed queries were longer (i.e., they had more words), while sessions with exploratory queries had more refinements and associated page views. By comparing search interactions at ICPSR to other natural language interactions in similar web search contexts, we conclude that dataset search at ICPSR is underutilized. We envision how alternative search paradigms, such as those enabled by recommender systems, can enhance dataset search. [ABSTRACT FROM AUTHOR]

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The Digitalia MUNI ARTS - a local node of the LINDAT/CLARIAH-CZ research infrastructure at the Faculty of Arts of Masaryk University constitutes a repository in the Islandora system. It is used for long-term preservation of research data together with their research environment in the form of digital platforms. We transfer the digital outputs of humanities scholars research to the repository according to a set plan, which is based on best practice recommendations for project management and digital curation. In this paper, we present how the results of interviews with platform developers and infrastructure stakeholders translate into the curation workflow, and a resulting model for migrating digital platforms to the repository. Reflecting on three types of problems we encountered during the implementation of platforms into the repository - communication problems, problems of external dependence, and management problems - we describe a modification of the migration process. We present six recommendations for repository administrators and curators in an academic setting - holding an introductory meeting with developers, researching significant and relevant theories of knowledge domain, consulting license experts, prioritizing requirements, and preparing handover protocol and progress reports. [ABSTRACT FROM AUTHOR]

Background. Open Science seeks to render research outputs visible, accessible, reusable. The Open Science framework is currently evolving vigorously due, among others reasons, to the UNESCO Open Science Recommendation adopted in November 2021. In this context, it is relevant to better visualize and describe the relationships that hold among the direct protagonists of this changing landscape: research teams and their research outputs, namely: articles, software and data, as their comprehension will certainly contribute to foster better Open Science practices. Method. In this work we review and describe, through the information collected in a large number of bibliographic references, the current changing trends involving some essential, defining, characteristics and behaviors of the main components of the scientific production, namely, research teams and three kinds of research outputs they produce in many scientific areas. This comparative study is based, among others, in our recent work on the evolving concepts of research software, research data in the context of Open Science. Results. In this work we observe and document some key features in this evolving landscape such as the changing and extended roles of research team members; the need to develop a new citing and referencing culture for articles, but specially for research software and data; the rising relevance of open access (to publications, software, data) policies all over the world; the existence of some barriers and difficulties like the regulations concerning academic research close to industry, or other technological applications; the need to develop standards for the “right to be forgotten”; the need to consider the impact of Open Science costs for less favored communities, countries, institutions. . . Conclusions. This calls for the urgent need to observe and depict further this changing Open Science ecosystem, and to propose –as we have partially attempted in this work– new concepts to analyze this context as well as to contribute to ongoing research-on-research and to improve the implementation of Open Science practices, in order to foster better ways towards a sound, inclusive and fairer Open Science landscape. [ABSTRACT FROM AUTHOR]

Objective: Increases in data-intensive research at colleges and universities is driving demand for data services provided by academic libraries. The current work investigates the distribution of library data services, how such services are offered, and the effect of resourcing on the amount of services offered by a library. Methods: We used a web-based inventory of 25 academic libraries at U.S. Research 1 (R1) Carnegie institutions to assess the state of data services at university libraries. We categorized and quantified services, and tested for an effect of library resourcing on the size of library data service portfolios.Results: Support for data management and geospatial services was relatively widespread, with increasing support in areas of data analyses and data visualization. There was significant variation among services in the modality in which they were offered (web, consult, instruction) and library resourcing had a significant effect on the number of data services a library offered.Conclusions: While a core subset of these data services are offered at most academic libraries, more specialized topics are restricted to well-resourced libraries. In light of the influence of resource scarcity on the number of services a library can offer, intra- and inter-campus partnerships will be critical to ensure campus support for data service needs.

Objective: This study was aimed at analyzing and disseminating the fossil wood collection data stored in the thin slide sections of the paleobotany collection of the Department of Paleontology and Stratigraphy of the Institute of Geosciences of a university in Southern Brazil. Thin sheets of petrified wood are described. Research data used in geosciences specifically seek to develop a model for the use of this type of sheet in paleobotany, enabling visualization of its representativeness in studies published over 40 years to obtain the anatomical characteristics of fossil woods and define their systematic affinities as a specific typology of research data in geosciences. Methods: The methodology involved interviewing a paleobotanical specialist and using different techniques applied in metric studies to map scientific production. Thus, a dataset of (20) thin slide sections of petrified fossil wood used in “Stressing environmental conditions in the “petrified forest” from the Mata Sequence in the Triassic context of the Paraná Basin,” a paper published by the Journal of South American Earth Sciences (DOI:10.1016/j.jsames.2023.104415), was used. Depending on the methodology, these thin petrified wood sheets can reveal paleoclimatic signatures based on the anatomical characteristics of the fossil wood. In addition to this case study, which represents a collection of more than (2.000) thousand blades of fossil wood and approximately 40 years of research, the paleobotanical collection of the department is reused in methodology classes. The collection comprises a database created by research physicists that provides information on anatomical features, systematic affinities, paleoclimatic conditions, and paleoenvironmental insights. Potential for reuse: The reuse, registration, storage, identification, and preservation of thin slide sections as a type of research data used in paleobotany aims to improve the methodology associated with the organization of the physical database of the institution. The research data and blades of fossil wood are being digitized, and soon will be available under the CC BY 4.0 license in the ZENODO repository, according to the sample described here in this data paper (DOI Mendeley:10.17632/b8phfcsync.1). Furthermore, the data can be reused in optical and electronic scanning microscopy software.

Researchers need to be able to find, access, and use data to participate in open science. To understand how users search for research data, we analyzed textual queries issued at a large social science data archive, the Inter-university Consortium for Political and Social Research (ICPSR). We collected unique user queries from 988,475 user search sessions over four years (2012-16). Overall, we found that only 30% of site visitors entered search terms into the ICPSR website. We analyzed search strategies within these sessions by extending existing dataset search taxonomies to classify a subset of the 1,554 most popular queries. We identified five categories of commonly-issued queries: keyword-based (e.g., date, place, topic); name (e.g., study, series); identifier (e.g., study, series); author (e.g., institutional, individual); and type (e.g., file, format). While the dominant search strategy used short keywords to explore topics, directed searches for known items using study and series names were also common. We further distinguished exploratory browsing from directed search queries based on their page views, refinements, search depth, duration, and length. Directed queries were longer (i.e., they had more words), while sessions with exploratory queries had more refinements and associated page views. By comparing search interactions at ICPSR to other natural language interactions in similar web search contexts, we conclude that dataset search at ICPSR is underutilized. We envision how alternative search paradigms, such as those enabled by recommender systems, can enhance dataset search.

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.

Data gaps are a significant lack of data about marginalized groups existing due to unequal power relations (D’Ignazio and Klein, 2020). They both perpetuate and result in a dominance of male, white, hetero, and cis perspectives in how we make sense of and interact with the world. The most prominent data gap is the gender data gap notably described by Criado-Perez (2020). However, not only women, but all marginalized groups are affected by such gaps, as data about them are frequently not collected due to a disregard on behalf of those in power of the need to do so. LGBTIQ+ people, considered a ‘hidden population’ by demographers, are a case in point. The acronym is used to refer to lesbian, gay, bisexual, trans, intersex, and queer people, as well as all people with non-normative sexual or gender identities, including asexual and agender people, who do not consider themselves as falling under one of these labels. A first step towards identifying and closing data gaps is to take stock of data that already exist. In this paper we give an overview of LGBTIQ+ data in European social science archives. We researched all data archives of CESSDA ERIC, the Consortium of European Social Science Data Archives, and found 66 LGBTIQ+ datasets in 9 of the 34 member and associated archives and 1 former member archive. We discuss characteristics, coverages, and findability of the identified datasets and approach the question of potential data gaps by analyzing the keywords assigned to each dataset by the archive.

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.

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]

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]

[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.

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]