Sharing Research Data Research Articles

An analysis of the effects of sharing research data, code, and preprints on citations.

Calls to make scientific research more open have gained traction with a range of societal stakeholders. Open Science practices include but are not limited to the early sharing of results via preprints and openly sharing outputs such as data and code to make research more reproducible and extensible. Existing evidence shows that adopting Open Science practices has effects in several domains. In this study, we investigate whether adopting one or more Open Science practices leads to significantly higher citations for an associated publication, which is one form of academic impact. We use a novel dataset known as Open Science Indicators, produced by PLOS and DataSeer, which includes all PLOS publications from 2018 to 2023 as well as a comparison group sampled from the PMC Open Access Subset. In total, we analyze circa 122'000 publications. We calculate publication and author-level citation indicators and use a broad set of control variables to isolate the effect of Open Science Indicators on received citations. We show that Open Science practices are adopted to different degrees across scientific disciplines. We find that the early release of a publication as a preprint correlates with a significant positive citation advantage of about 20.2% (±.7) on average. We also find that sharing data in an online repository correlates with a smaller yet still positive citation advantage of 4.3% (±.8) on average. However, we do not find a significant citation advantage for sharing code. Further research is needed on additional or alternative measures of impact beyond citations. Our results are likely to be of interest to researchers, as well as publishers, research funders, and policymakers.

Public perspectives on increased data sharing in health research in the context of the 2023 National Institutes of Health Data Sharing Policy.

The National Institutes of Health (NIH) is the largest public research funder in the world. In an effort to make publicly funded data more accessible, the NIH established a new Data Management and Sharing (DMS) Policy effective January 2023. Though the new policy was available for public comment, the patient perspective and the potential unintended consequences of the policy on patients' willingness to participate in research have been underexplored. This study aimed to determine: (1) participant preferences about the types of data they are willing to share with external entities, and (2) participant perspectives regarding the updated 2023 NIH DMS policy. A cross-sectional, nationally representative online survey was conducted among 610 English-speaking US adults in March 2023 using Prolific. Overall, 50% of the sample identified as women, 13% as Black or African American, and 7% as Hispanic or Latino, with a mean age of 46 years. The majority of respondents (65%) agreed with the NIH policy, but racial differences were noted with a higher percentage (28%) of Black participants indicating a decrease in willingness to participate in research studies with the updated policy in place. Participants were more willing to share research data with healthcare providers, yet their preferences for data sharing varied depending on the type of data to be shared and the recipients. Participants were less willing to share sexual health and fertility data with health technology companies (41%) and public repositories (37%) compared to their healthcare providers (75%). The findings highlight the importance of adopting a transparent approach to data sharing that balances protecting patient autonomy with more open data sharing.

Promoting Data Sharing: The Moral Obligations of Public Funding Agencies

Sharing research data has great potential to benefit science and society. However, data sharing is still not common practice. Since public research funding agencies have a particular impact on research and researchers, the question arises: Are public funding agencies morally obligated to promote data sharing? We argue from a research ethics perspective that public funding agencies have several pro tanto obligations requiring them to promote data sharing. However, there are also pro tanto obligations that speak against promoting data sharing in general as well as with regard to particular instruments of such promotion. We examine and weigh these obligations and conclude that all things considered funders ought to promote the sharing of data. Even the instrument of mandatory data sharing policies can be justified under certain conditions.

Compartilhamento de Dados de Pesquisa

The article discusses the findings of a thematic analysis of the factors that influence the sharing of open research data during times of crisis. The study involved a literature review and a focus group with researchers and specialists from different fields. The goals were to identify factors affecting data sharing and to propose a theoretical model to help research institutions understand the challenges in sharing their data. The study presents a theoretical model with four dimensions: Contextual, Motivational, Technological/Infrastructure, and Financing, along with categories, factors, definitions, and characteristics. It highlights the crucial role of motivational factors, indicating that researchers' attitudes and beliefs drive data sharing. Understanding these factors can significantly impact decision-making and the development of public policies based on the Open Science model. The article concludes with suggestions for future research to address barriers and resistance to sharing research data, particularly during times of crisis.

Making Mathematical Research Data FAIR: Pathways to Improved Data Sharing

The sharing and citation of research data is becoming increasingly recognized as an essential building block in scientific research across various fields and disciplines. Sharing research data allows other researchers to reproduce results, replicate findings, and build on them. Ultimately, this will foster faster cycles in knowledge generation. Some disciplines, such as astronomy or bioinformatics, already have a long history of sharing data; many others do not. The current landscape of available systems for sharing research data is diverse. In this article, we conduct a detailed analysis of existing web-based systems, specifically focusing on mathematical research data.

Biomedical Data Repository Concepts and Management Principles

The demand for open data and open science is on the rise, fueled by expectations from the scientific community, calls to increase transparency and reproducibility in research findings, and developments such as the Final Data Management and Sharing Policy from the U.S. National Institutes of Health and a memorandum on increasing public access to federally funded research, issued by the U.S. Office of Science and Technology Policy. This paper explores the pivotal role of data repositories in biomedical research and open science, emphasizing their importance in managing, preserving, and sharing research data. Our objective is to familiarize readers with the functions of data repositories, set expectations for their services, and provide an overview of methods to evaluate their capabilities. The paper serves to introduce fundamental concepts and community-based guiding principles and aims to equip researchers, repository operators, funders, and policymakers with the knowledge to select appropriate repositories for their data management and sharing needs and foster a foundation for the open sharing and preservation of research data.

Workflow for detecting biomedical articles with underlying open and restricted-access datasets.

To monitor the sharing of research data through repositories is increasingly of interest to institutions and funders, as well as from a meta-research perspective. Automated screening tools exist, but they are based on either narrow or vague definitions of open data. Where manual validation has been performed, it was based on a small article sample. At our biomedical research institution, we developed detailed criteria for such a screening, as well as a workflow which combines an automated and a manual step, and considers both fully open and restricted-access data. We use the results for an internal incentivization scheme, as well as for a monitoring in a dashboard. Here, we describe in detail our screening procedure and its validation, based on automated screening of 11035 biomedical research articles, of which 1381 articles with potential data sharing were subsequently screened manually. The screening results were highly reliable, as witnessed by inter-rater reliability values of ≥0.8 (Krippendorff's alpha) in two different validation samples. We also report the results of the screening, both for our institution and an independent sample from a meta-research study. In the largest of the three samples, the 2021 institutional sample, underlying data had been openly shared for 7.8% of research articles. For an additional 1.0% of articles, restricted-access data had been shared, resulting in 8.3% of articles overall having open and/or restricted-access data. The extraction workflow is then discussed with regard to its applicability in different contexts, limitations, possible variations, and future developments. In summary, we present a comprehensive, validated, semi-automated workflow for the detection of shared research data underlying biomedical article publications.

What do Australians affected by cancer think about oncology researchers sharing research data? A cross-sectional survey.

Previous research has shown patients and the public in Australia generally support medical researchers in making de-identified research data available to other scientists. However, this research has focussed on certain types of data and recipients. We surveyed Australians affected by cancer to characterize their attitudes toward the sharing of research data with multiple third parties, including the public. A short, anonymous online survey of Australians with a previous diagnosis of cancer was advertised between October 27, 2022, and February 27, 2023. Quantitative responses were analyzed with descriptive statistics. Free-text responses were coded deductively and summarised using content analysis. In total, 551 respondents contributed data to the survey. There was strong support for cancer researchers sharing non-human and de-identified human research data with clinicians (90% and 95%, respectively) and non-profit researchers (both 94%). However, fewer participants supported sharing data with for-profit researchers (both 64%) or publicly (both 61%). When asked if they would hypothetically consent to researchers at their treatment location using and sharing their de-identified data publicly, only half agreed. In contrast, after being shown a visual representation of the de-identified survey data, 80% of respondents supported sharing it publicly. Australians affected by cancer support the sharing of research data, particularly with clinicians and non-profit researchers. Our results also imply that visualization of the data to be shared may enhance support for making it publicly available. These results should help alleviate any concerns about research participants' attitudes toward data sharing, as well as boost researchers' motivation for sharing.

A study on formalizing the knowledge of data curation activities across different fields.

In recent years, with the trend of open science, there have been many efforts to share research data on the internet. To promote research data sharing, data curation is essential to make the data interpretable and reusable. In research fields such as life sciences, earth sciences, and social sciences, tasks and procedures have been already developed to implement efficient data curation to meet the needs and customs of individual research fields. However, not only data sharing within research fields but also interdisciplinary data sharing is required to promote open science. For this purpose, knowledge of data curation across the research fields is surveyed, analyzed, and organized as an ontology in this paper. As the survey, existing vocabularies and procedures are collected and compared as well as interviews with the data curators in research institutes in different fields are conducted to clarify commonalities and differences in data curation across the research fields. It turned out that the granularity of tasks and procedures that constitute the building blocks of data curation is not formalized. Without a method to overcome this gap, it will be challenging to promote interdisciplinary reuse of research data. Based on the analysis above, the ontology for the data curation process is proposed to describe data curation processes in different fields universally. It is described by OWL and shown as valid and consistent from the logical viewpoint. The ontology successfully represents data curation activities as the processes in the different fields acquired by the interviews. It is also helpful to identify the functions of the systems to support the data curation process. This study contributes to building a knowledge framework for an interdisciplinary understanding of data curation activities in different fields.

Investigating the Roles and Responsibilities of Institutional Signing Officials After Data Sharing Policy Reform for Federally Funded Research in the United States: National Survey.

New federal policies along with rapid growth in data generation, storage, and analysis tools are together driving scientific data sharing in the United States. At the same, triangulating human research data from diverse sources can also create situations where data are used for future research in ways that individuals and communities may consider objectionable. Institutional gatekeepers, namely, signing officials (SOs), are therefore at the helm of compliant management and sharing of human data for research. Of those with data governance responsibilities, SOs most often serve as signatories for investigators who deposit, access, and share research data between institutions. Although SOs play important leadership roles in compliant data sharing, we know surprisingly little about their scope of work, roles, and oversight responsibilities. The purpose of this study was to describe existing institutional policies and practices of US SOs who manage human genomic data access, as well as how these may change in the wake of new Data Management and Sharing requirements for National Institutes of Health-funded research in the United States. We administered an anonymous survey to institutional SOs recruited from biomedical research institutions across the United States. Survey items probed where data generated from extramurally funded research are deposited, how researchers outside the institution access these data, and what happens to these data after extramural funding ends. In total, 56 institutional SOs participated in the survey. We found that SOs frequently approve duplicate data deposits and impose stricter access controls when data use limitations are unclear or unspecified. In addition, 21% (n=12) of SOs knew where data from federally funded projects are deposited after project funding sunsets. As a consequence, most investigators deposit their scientific data into "a National Institutes of Health-funded repository" to meet the Data Management and Sharing requirements but also within the "institution's own repository" or a third-party repository. Our findings inform 5 policy recommendations and best practices for US SOs to improve coordination and develop comprehensive and consistent data governance policies that balance the need for scientific progress with effective human data protections.

Cancer researchers’ experiences with and perceptions of research data sharing: Results of a cross-sectional survey

ABSTRACT Background: Despite wide recognition of the benefits of sharing research data, public availability rates have not increased substantially in oncology or medicine more broadly over the last decade. Methods: We surveyed 285 cancer researchers to determine their prior experience with sharing data and views on known drivers and inhibitors. Results: We found that 45% of respondents had shared some data from their most recent empirical publication, with respondents who typically studied non-human research participants, or routinely worked with human genomic data, more likely to share than those who did not. A third of respondents added that they had previously shared data privately, with 74% indicating that doing so had also led to authorship opportunities or future collaborations for them. Journal and funder policies were reported to be the biggest general drivers toward sharing, whereas commercial interests, agreements with industrial sponsors and institutional policies were the biggest prohibitors. We show that researchers’ decisions about whether to share data are also likely to be influenced by participants’ desires. Conclusions: Our survey suggests that increased promotion and support by research institutions, alongside greater championing of data sharing by journals and funders, may motivate more researchers in oncology to share their data.

Researchers’ Willingness and Ability to Openly Share Their Research Data: A Survey of COVID-19 Pandemic-Related Factors

While previous studies show that the drivers and inhibitors for openly sharing research data are diverse and complex, there is a lack of studies empirically examining the influence of the COVID-19 pandemic on researchers’ open data sharing behavior. Using a questionnaire ( n = 135), this study investigates the influence of COVID-19 pandemic-related factors on researchers’ willingness and ability to openly share their research data. Fifty-one respondents (37.8%) stated that factors related to the COVID-19 pandemic increased their willingness and ability to openly share their research data, while 80 (59.3%) reported that various pandemic-related factors did not influence their willingness and ability in this way. As one of the possible influencing factors, this study finds a significant association between the COVID-19-relatedness of researchers’ research discipline and whether or not the COVID-19 pandemic led to a change in their willingness and ability to share their research data openly: χ2 (1) = 5.77, p < .05. Social influences on open data sharing behavior, institutional support for open data sharing, and the fear of potential negative consequences of open data sharing were nearly similar for the respondents who were and were not involved in COVID-19-related research. This study contributes scientifically by going beyond conceptual studies as it provides empirical insights concerning the influence of COVID-19 pandemic-related factors on researchers’ willingness and ability to openly share their data. As a practical contribution, this study discusses recommendations that policymakers can use to sustainably support open research data sharing in post-COVID-19 times.

Development of a model to predict the risk of early graft failure after adult-to-adult living donor liver transplantation: An ELTR study.

Graft survival is a critical end point in adult-to-adult living donor liver transplantation (ALDLT), where graft procurement endangers the lives of healthy individuals. Therefore, ALDLT must be responsibly performed in the perspective of a positive harm-to-benefit ratio. This study aimed to develop a risk prediction model for early (3 months) graft failure (EGF) following ALDLT. Donor and recipient factors associated with EGF in ALDLT were studied using data from the European Liver Transplant Registry. An artificial neural network classification algorithm was trained on a set of 2073 ALDLTs, validated using cross-validation, tested on an independent random-split sample (n=518), and externally validated on United Network for Organ Sharing Standard Transplant Analysis and Research data. Model performance was assessed using the AUC, calibration plots, and decision curve analysis. Graft type, graft weight, level of hospitalization, and the severity of liver disease were associated with EGF. The model ( http://ldlt.shinyapps.io/eltr_app ) presented AUC values at cross-validation, in the independent test set, and at external validation of 0.69, 0.70, and 0.68, respectively. Model calibration was fair. The decision curve analysis indicated a positive net benefit of the model, with an estimated net reduction of 5-15 EGF per 100 ALDLTs. Estimated risks>40% and<5% had a specificity of 0.96 and sensitivity of 0.99 in predicting and excluding EGF, respectively. The model also stratified long-term graft survival ( p <0.001), which ranged from 87% in the low-risk group to 60% in the high-risk group. In conclusion, based on a panel of donor and recipient variables, an artificial neural network can contribute to decision-making in ALDLT by predicting EGF risk.

The role of reuse of research data in research studies

Open Science presents the idea that scientific knowledge should be openly shared, as central values of science are transparency, verifiability, replicability, and openness (Abele-Brehm, Gollwitzer, Steinberg, Schönbrodt, 2019). Its aim is to make "bodies of knowledge that would advance human understanding and benefit humankind" openly available (Willinsky, 2006, 36). The scientific body of knowledge consists of various information entities, including widely popular research data. Similar to other information entities in open science, research data should also achieve several purposes: be openly available to make the existing research more credible; enhance science as a global public good via data patrimonialization, pooling, and synergies; help in verification and trustbuilding; broader engagement; add value to data production; facilitate and accelerate efforts to address the big challenges of our time and other mission-oriented science (Berkowitz, Delacour, 2022). The demand for openly shared research data has risen over the last decade, both at the national and international levels, putting pressure on researchers from funding institutions and journal publishers (Thoegersen, Borlund, 2022). These purposes become evident in numerous scientific publications and different scientific events as the interest in the research process continues growing (Andrew, Winnie Wan, 2018).&#x0D; The purposes also raised a question about whether all research data already stored in, for instance, digital repositories, are suitable for further analysis and can be correctly interpreted and reused in new research. The purpose of this research is to investigate whether researchers in different fields of science in Croatia consider research data as a prerequisite and/or a starting point for their next research, and whether these data are reused just for comparative purposes with their results or as a means to assess the quality of other scientists’ research. The research study is not pre-registered as Croatia does not have a national open science online platform for pre-registration, and there are no ethical concerns with the research questions. The principal research method in this research study is an online questionnaire written in the Croatian language, consisting of 28 questions created for this research study (they will be made available after the conclusion of the study, as is customary). The questionnaire is divided into four parts: aspects of use of research data (sources of research data, importance of research data for planning, executing and quality of research), access to research data for other researchers (quantity of data to share, interaction between researchers, rewards for sharing research data, obstacles when sharing research data, benefits of sharing research data), archiving of research data (where and what) and data about the respondents. The possible participants are invited via e-mail sent to the Croatian scientists mailing list (a central e-mail address) including scientists from all fields of sciences. Therefore, thesample is a convenient one. The results will be analysed using descriptive statistics. It is expected that the participating scientists in Croatia will provide answers that will help clarify their position on the role of use and sharing of research data in their research activities.

The utilisation of open research data repositories for storing and sharing research data in higher learning institutions in Tanzania

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

DataChat: Prototyping a Conversational Agent for Dataset Search and Visualization

ABSTRACTData users need relevant context and research expertise to effectively search for and identify relevant datasets. Leading data providers, such as the Inter‐university Consortium for Political and Social Research (ICPSR), offer standardized metadata and search tools to support data search. Metadata standards emphasize the machine‐readability of data and its documentation. There are opportunities to enhance dataset search by improving users' ability to learn about, and make sense of, information about data. Prior research has shown that context and expertise are two main barriers users face in effectively searching for, evaluating, and deciding whether to reuse data. In this paper, we propose a novel chatbot‐based search system, DataChat, that leverages a graph database and a large language model to provide novel ways for users to interact with and search for research data. DataChat complements data archives' and institutional repositories' ongoing efforts to curate, preserve, and share research data for reuse by making it easier for users to explore and learn about available research data.

Habits and perceptions regarding open science by researchers from Spanish institutions.

The article describes the results of the online survey on open science (OS) carried out on researchers affiliated with universities and Spanish research centres and focused on open access to scientific publications, the publication process, the management of research data and the review of open articles. The main objective was to identify the perception and habits of researchers with regard to practices closely linked to open science and the scientific value added is that offers an in-depth picture of researchers as one of the main actors to whom this transformation and implementation of open science will fall. It focuses on the different aspects of OS: open access, open data, publication process and open review in order to identify habits and perceptions. This is to make possible an implementation of the OS movement. The survey was carried out among researchers who had published in the years 2020-2021, according to data obtained from WoS. It was emailed to a total of 8,188 researchers and obtained a total of 666 responses, of which 554 were complete, the rest being forms with some questions unanswered. The main results showed that open access still requires the diffusion of practices and services provided by the institution, as well as training (library or equivalent service) and institutional support from the competent authorities (vice rectors or equivalent) in specific aspects such as data management. In the case of data, around 50% of respondents stated they had stored data in a repository, and of all the options, the most frequently given was that of an institutional repository, followed by a discipline repository. Among the main reasons for doing this, we found transparency, visibility of data and the ability to validate results. For those who stated they had never stored data, the most frequent reasons for not having done so were privacy and confidentiality, the lack of a mandated data policy or a lack of knowledge of how to do it. In terms of open peer review, participants mentioned a certain reticence to the opening of evaluations due to potential conflicts of interest that may arise or because lower-quality content might be accepted in order to avoid conflicts. In addition, the hierarchical structure of senior researcher versus junior researcher might affect reviews. The main conclusions indicate a need for persuasion of OA to take place; APCs are an economic barrier rather than the main criterion for journal selection; OPR practices may seem innovative and emerging; scientific and evaluation policies seem to have a clear effect on the behaviour of researchers; researchers state that they share research data more for reasons of persuasion than out of obligation. Researchers do question the pathways or difficulties that may arise on a day-to-day basis and seem aware that we are undergoing change, where academic evaluation or policies related to open science, its implementation and habits among researchers may change. In this sense, more and better support is needed on the part of institutions and faculty support services.

The DELAD initiative for sharing language resources on speech disorders

Sharing research data between researchers is becoming a requirement for open science in the field of clinical phonetics and speech and language pathology. An initiative entitled DELAD was started in 2015 to provide researchers a platform to discuss ongoing technical, legal and ethics issues related to sharing recordings of speech disorders, and to assist them in archiving their data for sharing with colleagues in the field. The ultimate goal of data sharing is to enhance research and teaching in the area of speech disorders. This paper first presents DELAD and its history and previous accomplishments. Then the recent work achieved by the DELAD steering group in providing information or recommendations in three areas are discussed: (1) speech annotation and automatic annotation mining, (2) consent and data storage, and (3) a role-play activity for learning data protection assessment. DELAD is an active community and researchers who share similar interests are welcome to join the (ad)venture.

Data sharing upon request and statistical consistency errors in psychology: A replication of Wicherts, Bakker and Molenaar (2011).

Sharing research data allows the scientific community to verify and build upon published work. However, data sharing is not common practice yet. The reasons for not sharing data are myriad: Some are practical, others are more fear-related. One particular fear is that a reanalysis may expose errors. For this explanation, it would be interesting to know whether authors that do not share data genuinely made more errors than authors who do share data. (Wicherts, Bakker and Molenaar 2011) examined errors that can be discovered based on the published manuscript only, because it is impossible to reanalyze unavailable data. They found a higher prevalence of such errors in papers for which the data were not shared. However, (Nuijten et al. 2017) did not find support for this finding in three large studies. To shed more light on this relation, we conducted a replication of the study by (Wicherts et al. 2011). Our study consisted of two parts. In the first part, we reproduced the analyses from (Wicherts et al. 2011) to verify the results, and we carried out several alternative analytical approaches to evaluate the robustness of the results against other analytical decisions. In the second part, we used a unique and larger data set that originated from (Vanpaemel et al. 2015) on data sharing upon request for reanalysis, to replicate the findings in (Wicherts et al. 2011). We applied statcheck for the detection of consistency errors in all included papers and manually corrected false positives. Finally, we again assessed the robustness of the replication results against other analytical decisions. Everything taken together, we found no robust empirical evidence for the claim that not sharing research data for reanalysis is associated with consistency errors.

CHAT BOT DESIGN USING CHATGPT ALGORITHMS AND ITS EFFECTS ON EDUCATIONAL ACADEMIES

AI and GPT technology could have a huge impact on academia and libraries. AI could be used to automate certain tasks, such as cataloguing books, organizing research materials, and providing personalized recommendations to patrons. GPT could be used to generate research articles, abstracts, and literature reviews; and to run simulations, natural language processing, and other sophisticated tasks. It could also be used to facilitate collaboration between researchers and libraries, by providing tools to store, analyze, and share research data. Libraries could use AI and GPT to create virtual interactive collections, to provide personalized guidance to patrons, and to develop new services. In addition, AI and GPT could be used to enhance existing services, such as providing answers to patrons' questions, providing personalized book recommendations, and tracking library usage. Keywords: Artificial Intelligence Algorithms; Chatgpt, Gpt-3, Generative Pre-Trained Transformer, Educational DOI: 10.7176/ALST/97-05 Publication date: April 30 th 2023