Search

In response to Webb and Tangney (2022) we call into question the conclusion that data collected on Amazon's Mechanical Turk (MTurk) was "at best—only 2.6% valid" (p. 1). We suggest that Webb and Tangney made certain choices during the study-design and data-collection process that adversely affected the quality of the data collected. As a result, the anecdotal experience of these authors provides weak evidence that MTurk provides low-quality data as implied. In our commentary we highlight best practice recommendations and make suggestions for more effectively collecting and screening online panel data. [ABSTRACT FROM AUTHOR]

Psychology is moving increasingly toward digital sources of data, with Amazon's Mechanical Turk (MTurk) at the forefront of that charge. In 2015, up to an estimated 45% of articles published in the top behavioral and social science journals included at least one study conducted on MTurk. In this article, I summarize my own experience with MTurk and how I deduced that my sample was—at best—only 2.6% valid, by my estimate. I share these results as a warning and call for caution. Recently, I conducted an online study via Amazon's MTurk, eager and excited to collect my own data for the first time as a doctoral student. What resulted has prompted me to write this as a warning: it is indeed too good to be true. This is a summary of how I determined that, at best, I had gathered valid data from 14 human beings—2.6% of my participant sample (N = 529). [ABSTRACT FROM AUTHOR]

Copyright of ABI Technik is the property of De Gruyter 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.)

The PROSurvival project aims to improve the prediction of recurrencefree survival in prostate cancer by applying federated machine learning to whole slide images combined with selected clinical data. Both the image and clinical data will be aggregated into an anonymized dataset compliant with the General Data Protection Regulation and published under the principles of findable, accessible, interoperable, and reusable data. The DICOM standard will be used for the image data. For the accompanying clinical data, a human-readable, compact and flexible standard is yet to be defined. From the set of existing standards, mostly extendable with varying degrees of modifications, we chose oBDS as a starting point and modified it to include missing data points and to remove mandatory items not applicable to our dataset. Clinical and survival data from clinic-specific spreadsheets were converted into this modified standard, ensuring on-site data privacy during processing. For publication of the dataset, both image and clinical data are anonymized using established methods. The key challenges arose during the clinical data anonymization and in identifying research repositories meeting all of our requirements. Each clinic had to coordinate the publication with their responsible data protection officers, requiring different approval processes due to the individual states' differing interpretations of the legal regulations. The newly established German Health Data Utilization Act is expected to simplify future data sharing in a responsible and powerful way. [ABSTRACT FROM AUTHOR]

Copyright of Arab International Journal of Knowledge Management is the property of Arab Institution of Knowledge Management 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.)

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.

Responsibly managing research data has become increasingly important for researchers, especially within the database research community. Despite significant progress in best practices, the state-of-the-art in research data storage is lacking from a legal perspective. We introduce the stakeholders and the dynamic nature of their relationships (such as researchers changing affiliation) and observe that no existing infrastructure for research data storage fully meets their requirements. Therefore, we emphasize the need to design a comprehensive system architecture for research data storage that is aligned with legal considerations from the start, rather than as an afterthought. [ABSTRACT FROM AUTHOR]

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]

Academic libraries operate in the digital world in a multitude of globally acting information infrastructures. The new ways of use and demands associated with digitization entail a comprehensive development of their tasks and services. As infrastructure and innovation partners, libraries play an important role in the National Research Data Infrastructure (NFDI). [ABSTRACT FROM AUTHOR]

Metadata is vital for information storage and retrieval from a database or repository. In the case of Research Data Repositories (RDRs), metadata can be a potent tool for describing and identifying data. Further, producing the metadata is indispensable for fostering data reuse. A filtered view of the registry of research data repositories, re3data.org, depicts no uniform patterns or standards for metadata in the case of RDRs, and the metadata elements and practices differ from RDR to RDR. The present study describes the features of a select number of RDRs and analyzes their metadata practices: Harvard Dataverse, Dryad, Figshare, Zenodo, and the Open Science Framework (OSF). It further examines the total number of metadata elements, common metadata elements, required metadata elements, and item-level metadata. Results indicate that even though Harvard Dataverse has the most metadata elements, Dryad provides rich metadata concerning item level. This study suggests a common metadata framework, richer metadata elements, and more features to make the research data's interoperability possible from one RDR to another. [ABSTRACT FROM AUTHOR]

The benefits of research data sharing abound in the literature. However, some factors define how researchers and clinicians approach the challenges surrounding sharing human movement health research data. To describe the perceptions of human movement researchers and clinicians on the barriers and facilitators to research data sharing in Africa. A qualitative descriptive design with a purposive sampling method was used. In-depth interviews with human movement researchers and clinicians across Africa were conducted online via Microsoft Teams. Sixteen (n = 16) participants took part in this study. This sample size was representative of East, West, Northern, and Southern Africa. Efforts made to engage with participants in Central Africa were unsuccessful. Five themes emerged: 1) the researcher-clinician gap; 2) technological pros and cons in Africa; 3) cost matters; 4) bureaucracy and ethical factors; and 5) the unique African perspective. Mainly, barriers rather than facilitators to data sharing exist among African human movement researchers and clinicians. There needs to be a societal and psychological shift through reorientation to encourage data sharing among African human movement researchers and clinicians. [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]

In the context of Open Science, the importance of Borgman’s conundrum challenges that have been initially formulated concerning the difficulties to share Research Data is well known: which Research Data might be shared, by whom, with whom, under what conditions, why, and to what effects. We have recently reviewed the concepts of Research Software and Research Data, concluding with new formulations for their definitions, and proposing answers to these conundrum challenges for Research Data. In the present work we extend the consideration of the Borgman’s conundrum challenges to Research Software, providing answers to these questions in this new context. Moreover, we complete the initial list of questions/answers, by asking how and where the Research Software may be shared. Our approach begins by recalling the main issues involved in the Research Software definition, and its production context in the research environment, from the Open Science perspective. Then we address the conundrum challenges for Research Software by exploring the potential similarities and differences regarding our answers for these questions in the case of Research Data. We conclude emphasizing the usefulness of the followed methodology, exploiting the parallelism between Research Software and Research Data in the Open Science environment.

Copyright of ABI Technik is the property of De Gruyter 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.)

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]

Copyright of Métodos de Información is the property of Col·legi Oficial de Bibliotecaris i Documentalistes de la Comunitat Valenciana (COBDCV) 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 Revista General de Información y Documentación is the property of Universidad Complutense de Madrid 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.)

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.

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.

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.