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Open science is a fundamental pillar to promote scientific progress and collaboration, based on the principles of open data, open source and open access. However, the requirements for publishing and sharing open data are in many cases difficult to meet in compliance with strict data protection regulations. Consequently, researchers need to rely on proven methods that allow them to anonymize their data without sharing it with third parties. To this end, this paper presents the implementation of a Python library for the anonymization of sensitive tabular data. This framework provides users with a wide range of anonymization methods that can be applied on the given dataset, including the set of identifiers, quasi-identifiers, generalization hierarchies and allowed level of suppression, along with the sensitive attribute and the level of anonymity required. The library has been implemented following best practices for integration and continuous development, as well as the use of workflows to test code coverage based on unit and functional tests. [ABSTRACT FROM AUTHOR]

Open science, accessibility and knowledge sharing, especially of articles and monographs stemming from publicly funded research, seem to be moving in quite a positive direction toward scientific development and have received almost unanimous approval from the scientific community. However, when it comes to data sharing, the existing practice reveals a different picture, and not exclusively a discipline-dependent one. FAIR principles are developed and promoted as guiding tools for creating contextualized standards. The fact that data obtained by a qualitative methodology deserve special attention and treatment regarding the accessibility principle is recognized. Although FAIR principles provide ways to anonymize the data and interlocutors, individuals coming from smaller communities or even communities of practice can sometimes be easily recognized by members of the same community if data are openly accessed. Sometimes the interlocutors might agree with these terms, but sometimes they do not. According to the disciplinary code of ethics, a researcher is obliged to thoroughly describe the ways of the raw data management and usage, and in the case of mandatory raw data sharing (e.g., for receiving funding), it can inevitably impact the narratives collected. The prerogative to make all data open inevitably leads to autocensorship among interlocutors, i.e., resulting in a kind of FAIRy tale being collected. The article discusses the results obtained from the survey carried out among Croatian ethnologists and cultural anthropologists about the currently practiced data sharing, their attitudes about data sharing and their perceived behavior in hypothetical situations connected with data sharing. The results show mixed opinions about data sharing and a desire to follow the disciplinary code of ethics first, i.e., to follow the interlocutors' wishes in the case of data management and usage. [ABSTRACT FROM AUTHOR]

Open data is increasingly a priority for research funders and data availability statements are commonly required for submissions to peer-reviewed journals. Although much of the recent momentum for data-sharing has arisen from the ‘replication crisis’ affecting experimental psychology, qualitative data can also be shared, although different considerations apply to this practice. Despite considerable discussion about qualitative data-sharing, there is less discussion of how to share novel qualitative interview data in psychology. In this article, we describe our motivations for and experiences of archiving qualitative interview data from a study exploring loneliness in young adulthood. We discuss the ethical issues that pertained to our process, particularly relating to consent, and the practical issues that arose as relative novices in data-sharing. We conclude with reflections that we hope will be instructive for qualitative researchers attempting to reconcile arguments for open data with the epistemological, ethical, and pragmatic realities of qualitative research. [ABSTRACT FROM AUTHOR]

Striving to build an exhaustive guidebook of the types and properties of human cells, the Human Cell Atlas' (HCA) success relies on the sampling of diverse populations, developmental stages, and tissue types. Its open science philosophy preconizes the rapid, seamless sharing of data – as openly as possible. In light of the scope and ambition of such an international initiative, the HCA Ethics Working Group (EWG) has been working to build a solid foundation to address the complexities of data collection and sharing as part of Atlas development. Indeed, a particular challenge of the HCA is the diversity of sampling scenarios (e.g., living participants, deceased donors, pediatric populations, culturally diverse backgrounds, tissues from various developmental stages, etc.), and associated ethical and legal norms, which vary across countries contributing to the effort. Hence, to the extent possible, the EWG set out to provide harmonised, international and interoperable policies and tools, to guide its research community. This paper provides a high-level overview of the types of challenges and approaches proposed by the EWG. The human cell atlas (HCA) is intended as an exhaustive guidebook of human cell types and their properties. Here Kirby et al. outline how the HCA Ethics Working Group is working to build a solid foundation to address the complexities of data collection and sharing. [ABSTRACT FROM AUTHOR]

Purpose: This study investigates the multifaceted barriers and facilitators affecting research data sharing across the research data lifecycle. It aims to broaden the understanding of data sharing beyond the publication phase, emphasizing the continuous nature of data sharing from generation to reuse. Design/methodology/approach: Employing a mixed-methods approach, the study integrates the Theory of Planned Behavior, the Technology Acceptance Model, and the Institutional Theory to hypothesize the influence of various factors on data sharing behaviors across the lifecycle. A questionnaire survey and structural equation modeling are utilized to empirically test these hypotheses. Findings: This study identifies critical factors influencing data sharing at different lifecycle stages, including perceived behavioral control, perceived effort, journal and funding agency pressures, subjective norms, perceived risks, resource availability, and perceived benefits. The findings highlight the complex interplay of these factors and their varying impacts at different stages of data sharing. Research limitations/implications: This study illuminates the dynamics of research data sharing, offering insights while recognizing its scope might not capture all disciplinary and cultural nuances. It highlights pathways for stakeholders to bolster data sharing, suggesting a collaborative push towards open science, reflecting on how strategic interventions can bridge existing gaps in practice. Practical implications: This study offers actionable recommendations for policymakers, journals, and institutions to foster a more conducive environment for data sharing, emphasizing the need for support mechanisms at various lifecycle stages. Originality/value: This study contributes to the literature by offering a comprehensive model of the research data lifecycle, providing empirical evidence on the factors influencing data sharing across this continuum. [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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The increasing technical complexity of all aspects involving bioimages, ranging from their acquisition to their analysis, has led to a diversification in the expertise of scientists engaged at the different stages of the discovery process. Although this diversity of profiles comes with the major challenge of establishing fruitful interdisciplinary collaboration, such collaboration also offers a superb opportunity for scientific discovery. In this Perspective, we review the different actors within the bioimaging research universe and identify the primary obstacles that hinder their interactions. We advocate that data sharing, which lies at the heart of innovation, is finally within reach after decades of being viewed as next to impossible in bioimaging. Building on recent community efforts, we propose actions to consolidate the development of a truly interdisciplinary bioimaging culture based on open data exchange and highlight the promising outlook of bioimaging as an example of multidisciplinary scientific endeavour. [ABSTRACT FROM AUTHOR]

This study is an analysis of the international debate on open science that took place during the pandemic. It addresses the question, how did the COVID-19 pandemic impact the debate on open science? The study takes the form of a qualitative analysis of a large corpus of key articles, editorials, blogs and thought pieces about the impact of COVID on open science, published during the pandemic in English, German, Portuguese, and Spanish. The findings show that many authors believed that it was clear that the experience of the pandemic had illustrated or strengthened the case for open science, with language such as a "stress test", "catalyst", "revolution" or "tipping point" frequently used. It was commonly believed that open science had played a positive role in the response to the pandemic, creating a clear 'line of sight' between open science and societal benefits. Whilst the arguments about open science deployed in the debate were not substantially new, the focuses of debate changed in some key respects. There was much less attention given to business models for open access and critical perspectives on open science, but open data sharing, preprinting, information quality and misinformation became most prominent in debates. There were also moves to reframe open science conceptually, particularly in connecting science with society and addressing broader questions of equity. [ABSTRACT FROM AUTHOR]

Open science (OS) is widely recognized as a new method of knowledge sharing using digital technologies. Despite the potential benefits OS offers, there is a low level of inspiration toward adopting it among researchers in higher learning institutions. The purpose of this study was to assess the faculty adoption of OS and their practices toward data sharing at Health Sciences University in Tanzania. The study employed a cross-sectional design, whereby a total of 252 faculties were systematically sampled from two purposefully selected health universities. Using a survey, the study employed SPSS to generate descriptive statistics and a coding strategy using ATLAS.ti software to analyze qualitative resultant data. Moreover, regression analysis determined the predicting factors that hindered faculty adoption of OS and data-sharing practices. The findings demonstrate that faculty had poor data-sharing practices triggered by a lack of institutional data-sharing policies, fear of data misinterpretation, and parasitic use of clinical research datasets with other researchers. However, the presence of a data repository, journal guidelines, and intellectual property rights are instrumental variables that allow faculty to practice OS and data sharing. Therefore, university libraries should take affirmative action to create awareness of the importance of practicing OS and data sharing among faculty and researchers. Such undertakings will allow other researchers to carry out comprehensive follow-up research to meet the increasing demand for innovative health research output in Tanzania. [ABSTRACT FROM AUTHOR]

Functional traits offer an informative framework for understanding ecosystem functioning and responses to global change. Trait data are abundant in the literature, yet many communities of practice lack data standards for trait measurement and data sharing, hindering data reuse that could reveal large‐scale patterns in functional and evolutionary ecology. Here, we present a roadmap toward community data standards for trait‐based research on bees, including a protocol for effective trait data sharing. We also review the state of bee functional trait research, highlighting common measurement approaches and knowledge gaps. These studies were overwhelmingly situated in agroecosystems and focused predominantly on morphological and behavioral traits, while phenological and physiological traits were infrequently measured. Studies investigating climate change effects were also uncommon. Along with our review, we present an aggregated morphological trait dataset compiled from our focal studies, representing more than 1600 bee species globally and serving as a template for standardized bee trait data presentation. We highlight obstacles to harmonizing this trait data, especially ambiguity in trait classes, methodology, and sampling metadata. Our framework for trait data sharing leverages common data standards to resolve these ambiguities and ensure interoperability between datasets, promoting accessibility and usability of trait data to advance bee ecological research. [ABSTRACT FROM AUTHOR]

Computer code plays a vital role in modern science, from the conception and design of experiments through to final data analyses. Open sharing of code has been widely discussed as being advantageous to the scientific process, allowing experiments to be more easily replicated, helping with error detection, and reducing wasted effort and resources. In the case of psychology, the code used to present stimuli is a fundamental component of many experiments. It is not known, however, the degree to which researchers are sharing this type of code. To estimate this, we conducted a survey of 400 psychology papers published between 2016 and 2021, identifying those working with the open-source tools Psychtoolbox and PsychoPy that openly share stimulus presentation code. For those that did, we established if it would run following download and also appraised the code's usability in terms of style and documentation. It was found that only 8.4% of papers shared stimulus code, compared to 17.9% sharing analysis code and 31.7% sharing data. Of shared code, 70% ran directly or after minor corrections. For code that did not run, the main error was missing dependencies (66.7%). The usability of the code was moderate, with low levels of code annotation and minimal documentation provided. These results suggest that stimulus presentation code sharing lags behind other forms of code and data sharing, potentially due to less emphasis on such code in open-science discussions and in journal policies. The results also highlight a need for improved documentation to maximize code utility. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Data sharing is increasingly an expectation in health research as part of a general move toward more open sciences. In the United States, in particular, the implementation of the 2023 National Institutes of Health Data Management and Sharing Policy has made it clear that qualitative studies are not exempt from this data sharing requirement. Recognizing this trend, the Palliative Care Research Cooperative Group (PCRC) realized the value of creating a de-identified qualitative data repository to complement its existing de-identified quantitative data repository. The PCRC Data Informatics and Statistics Core leadership partnered with the Qualitative Data Repository (QDR) to establish the first serious illness and palliative care qualitative data repository in the U.S. We describe the processes used to develop this repository, called the PCRC-QDR, as well as our outreach and education among the palliative care researcher community, which led to the first ten projects to share the data in the new repository. Specifically, we discuss how we co-designed the PCRC-QDR and created tailored guidelines for depositing and sharing qualitative data depending on the original research context, establishing uniform expectations for key components of relevant documentation, and the use of suitable access controls for sensitive data. We also describe how PCRC was able to leverage its existing community to recruit and guide early depositors and outline lessons learned in evaluating the experience. This work advances the establishment of best practices in qualitative data sharing. [ABSTRACT FROM AUTHOR]

The Journal of Statistics & Data Science Education (JSDSE) has implemented requirements for authors to share deidentified data and code to promote reproducibility and open science. This document discusses the importance of sharing data in research and provides guidelines for selecting appropriate repositories for data sharing. It also addresses the challenges of sharing deidentified data, particularly in relation to privacy concerns and the potential for reidentification. The document suggests options such as removing sensitive demographic variables or creating synthetic data to protect privacy while still allowing for reproducibility. It concludes by emphasizing the importance of balancing privacy and sharing to foster better science. [Extracted from the article]

FishSounds is an online portal that provides open and user-friendly access to academic scholarship regarding the sounds made by fish species. It is the result of an international collaboration between students, scientists, and information professionals, and has become a resource used around the globe for research, education, journalism, and general interest. This website is just the first instance of a new approach to sharing knowledge and an emerging cyberinfrastructure for open scholarship. The codebase behind FishSounds was designed to be reusable with other datasets, and in the coming years, additional portals will connect users to knowledge from varied subject areas across academic disciplines. This discussion examines the development and reception of FishSounds as a case study for the creation of these websites, called searchable online catalogues of knowledge or SOCKs. As it is publicly released, the SOCK platform will continue to evolve and develop new strategies based on the lessons learned from different audiences accessing FishSounds. [ABSTRACT FROM AUTHOR]

Research transparency and reproducibility can improve the credibility of scientific evidence on development effectiveness and the utility of this evidence for decision-making. As a funder and producer of research on development effectiveness, the International Initiative for Impact Evaluation (3ie) has several policies and programmes that aim to improve research transparency and reproducibility. This manuscript provides a descriptive overview of the history-to-date of research transparency and reproducibility policies and programmes at 3ie. In 2012, 3ie launched its Replication Programme to incentivise replication of impact evaluations in international development. In 2014, 3ie created the Registry for International Development Impact Evaluations to provide infrastructure for the prospective registration of impact evaluations of development interventions. In 2018, 3ie published its first Research Transparency Policy articulating requirements on the use of open science practices in research activities. In 2022, 3ie created the Transparent, Reproducible, and Ethical Evidence (TREE) Review Framework, which integrates best practices for research transparency and reproducibility into evaluation workflows. This manuscript provides stakeholders in development effectiveness specifically – as well as research grant managers and other organisations in the scientific ecosystem more generally – a descriptive example of institutional efforts to continuously improve research transparency and reproducibility policies and programmes. [ABSTRACT FROM AUTHOR]

Biologists increasingly rely on computer code to collect and analyze their data, reinforcing the importance of published code for transparency, reproducibility, training, and a basis for further work. Here, we conduct a literature review estimating temporal trends in code sharing in ecology and evolution publications since 2010, and test for an influence of code sharing on citation rate. We find that code is rarely published (only 6% of papers), with little improvement over time. We also found there may be incentives to publish code: Publications that share code have tended to be low‐impact initially, but accumulate citations faster, compensating for this deficit. Studies that additionally meet other Open Science criteria, open‐access publication, or data sharing, have still higher citation rates, with publications meeting all three criteria (code sharing, data sharing, and open access publication) tending to have the most citations and highest rate of citation accumulation. [ABSTRACT FROM AUTHOR]

Open science (OS) awareness and skills are increasingly becoming an essential part of everyday scientific work as e.g., many journals require authors to share data. However, following an OS workflow can seem challenging at first. Thus, instructions by journals and other guidelines are important. But how comprehensive are they in the field of ecology and evolutionary biology (Ecol Evol)? To find this out, we reviewed 20 published OS guideline articles aimed for ecologists or evolutionary biologists, together with the data policies of 17 Ecol Evol journals to chart the current landscape of OS guidelines in the field, find potential gaps, identify field‐specific barriers for OS and discuss solutions to overcome these challenges. We found that many of the guideline articles covered similar topics, despite being written for a narrow field or specific target audience. Likewise, many of the guideline articles mentioned similar obstacles that could hinder or postpone a transition to open data sharing. Thus, there could be a need for a more widely known, general OS guideline for Ecol Evol. Following the same guideline could also enhance the uniformity of the OS practices carried on in the field. However, some topics, like long‐term experiments and physical samples, were mentioned surprisingly seldom, although they are typical issues in Ecol Evol. Of the journals, 15 out of 17 expected or at least encouraged data sharing either for all articles or under specific conditions, e.g. for registered reports and 10 of those required data sharing at the submission phase. The coverage of journal data policies varied greatly between journals, from practically non‐existing to very extensive. As journals can contribute greatly by leading the way and making open data useful, we recommend that the publishers and journals would invest in clear and comprehensive data policies and instructions for authors. [ABSTRACT FROM AUTHOR]

This study empirically examined the promotion and implementation of open science measures among high‐performing journals of Brazil, Mexico, Portugal, and Spain. Journal policy related to data sharing, materials sharing, preregistration, open peer review, and consideration of preprints and replication studies was gathered from the websites of the journals. Four hundred articles were coded for the inclusion of data availability statements, conflict of interest disclosures, funding disclosures, DOI, ORCID, and continuous publishing. Analyses found a higher promotion of open science measures among Brazilian journals than their Portuguese counterparts, and higher promotion of open science measures among international journals than their domestic counterparts. Analyses found higher implementation of open science measures among Brazilian journals than their Portuguese and Mexican counterparts. One journal out of 40 encouraged preregistration of studies; none encouraged replication studies and none had implemented open peer review. These findings reveal reasonably strong implementation of secondary open science measures (e.g., DOI, ORCID, conflict of interest and funding source disclosure) among the sample, but weaker implementation of primary measures (e.g., open data, open materials, replication studies and open peer review). The implications of these findings are considered and suggestions are made to bolster the adoption of open science measures among Ibero‐American scientific journals. [ABSTRACT FROM AUTHOR]

The "Cubes and Clouds" Massive Open Online Course (MOOC) addresses the growing challenges and opportunities in Earth Observation (EO) data analysis posed by the exponential growth of satellite missions and data volumes. This course introduces cloud-native EO concepts and open science principles, facilitating collaboration and data sharing within the EO community.Key topics covered in the MOOC include data discovery, processing workflows, and data sharing using real-world examples like snow cover mapping in the Alps. Participants engage with interactive lessons, videos, and hands-on coding exercises, leveraging freely available geospatial data and emphasizing open data principles and interoperability.The course infrastructure seamlessly integrates with cloud platforms like the Copernicus Data Space Ecosystem, enabling learners to apply concepts in a practical, cloud-based environment. Initial user statistics indicate strong interest, particularly among early-career professionals and researchers, with participant surveys suggesting increased confidence in using EO cloud platforms and embracing open science practices upon course completion.Overall, "Cubes and Clouds" serves as a valuable resource for the EO community, fostering transparency, collaboration, and innovation in geospatial science. Future developments aim to expand course content and attract diverse user groups, ensuring broader accessibility and long-term impact in advancing cloud-native EO and open science initiatives. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Preprints are non-peer-reviewed and publicly available articles for open and transparent research communication. Preprint servers host the submission of such manuscripts, and despite the presence of established preprint servers, their numbers have continued to rise in recent times. A steep increasing pattern in posted preprints and their accommodating servers has been observed over the last decade. In this article, we explored the global trends in the preprint adoption and its involvement in promoting open and transparent research findings across various domains. We further emphasized the importance of preprinting, highlighting its significant impact during the pandemic through effective information sharing, and advocating for its broader integration in scholarly communication. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Citizen science has been studied intensively in recent years. Nonetheless, the voice of citizen scientists is often lost despite their altruistic and indispensable role. To remedy this deficiency, a survey on the overall experiences of citizen scientists was undertaken. Dimensions investigated include activities, open science concepts, and data practices. However, the study prioritizes knowledge and practices of data and data management. When a broad understanding of data is lacking, the ability to make informed decisions about consent and data sharing, for example, is compromised. Furthermore, the potential and impact of individual endeavors and collaborative projects are reduced. Findings indicate that understanding of data management principles is limited. Furthermore, an unawareness of common data and open science concepts was observed. It is concluded that appropriate training and a raised awareness of Responsible Research and Innovation concepts would benefit individual citizen scientists, their projects, and society. [ABSTRACT FROM AUTHOR]

Background: The analysis of large and complex biological datasets in bioinformatics poses a significant challenge to achieving reproducible research outcomes due to inconsistencies and the lack of standardization in the analysis process. These issues can lead to discrepancies in results, undermining the credibility and impact of bioinformatics research and creating mistrust in the scientific process. To address these challenges, open science practices such as sharing data, code, and methods have been encouraged. Results: CREDO, a Customizable, REproducible, DOcker file generator for bioinformatics applications, has been developed as a tool to moderate reproducibility issues by building and distributing docker containers with embedded bioinformatics tools. CREDO simplifies the process of generating Docker images, facilitating reproducibility and efficient research in bioinformatics. The crucial step in generating a Docker image is creating the Dockerfile, which requires incorporating heterogeneous packages and environments such as Bioconductor and Conda. CREDO stores all required package information and dependencies in a Github-compatible format to enhance Docker image reproducibility, allowing easy image creation from scratch. The user-friendly GUI and CREDO's ability to generate modular Docker images make it an ideal tool for life scientists to efficiently create Docker images. Overall, CREDO is a valuable tool for addressing reproducibility issues in bioinformatics research and promoting open science practices. [ABSTRACT FROM AUTHOR]

This paper explores how the COVID-19 pandemic affected science and tourism activities and their governance in the Antarctic and Southern Ocean. The pandemic reduced the ability of Antarctic Treaty Parties to make decisions on policy issues and placed a considerable burden on researchers. Tourism was effectively suspended during the 2020-2021 Antarctic season and heavily reduced in 2021-2022 but rebounded to record levels in 2022-2023. The pandemic stimulated reflection on practices to facilitate dialog, especially through online events. Opportunities arose to integrate innovations developed during the pandemic more permanently into Antarctic practices, in relation to open science, reducing operational greenhouse gas footprints and barriers of access to Antarctic research and facilitating data sharing. However, as well as the long-term impacts arising directly from the pandemic, an assemblage of major geopolitical drivers are also in play and, combined, these signal a considerable weakening of Antarctic exceptionalism in the early Anthropocene. [ABSTRACT FROM AUTHOR]

Sabina Leonelli's book, "Philosophy of Open Science," provides an insightful examination of the relationship between the ideals of the open science movement and the realities of scientific practice. Leonelli argues that the prevailing emphasis on sharing research outcomes in open science initiatives may overlook the diversity and complexity of the scientific inquiry process. She advocates for a shift towards a more process-oriented focus that prioritizes holistic collaboration and inclusivity. The book explores concrete examples and theoretical foundations to support Leonelli's argument and highlights the potential ethical and epistemological challenges of object-oriented approaches in open science. While the book may be more relevant to policymakers and those in positions of influence, it serves as a valuable resource for researchers looking to critically assess the assumptions and implications of open science projects. [Extracted from the article]

The article confidently explores the broadening of the demographic contingent of science. Since the end of the twentieth century, the problems of attracting the younger generation to science, studying international cooperation of scientists and migration patterns in science have become relevant issues. Also important are the issues of the influence of interdisciplinary education on obtaining breakthrough scientific results, determining the impact of the favorable scientific environment on the productive work of a scientist, the problems of inclusiveness of the scientific environment and the expansion of racial and ethnic representation in science. It is shown that although academic mobility of scientists is globally viewed as a process of internationalization of science, which contributes to the dissemination and exchange of knowledge and ideas, and the growth of scientists' productivity. However, given that in certain contexts mobility is associated with the loss of human resources in science, it should be viewed as a complex political problem of attracting and retaining scientists. This problem is exacerbated in times of military conflicts and socio-political crises. It is emphasized that despite the usefulness of scientists using the benefits of Open Science and participating in international research projects, attention should be paid to national and regional problems that require scientific support. [ABSTRACT FROM AUTHOR]

A review of 593 pharmacogenomic genome-wide association studies (GWAS) articles found that only 2% of them contained links to publicly available GWAS summary statistics. This lack of data sharing hinders research in the field of pharmacogenomics, which relies on large sample sizes for meaningful results. The use of GWAS summary statistics offers advantages such as decreased resource utilization, simplified data collection, and protection of participant privacy. The authors recommend that authors include mandatory reporting elements in their summary statistics, standardize and share data through established platforms, and that publishers and funding organizations promote data sharing and open science initiatives. [Extracted from the article]

To understand how open research data sharing and reuse can be further improved in the field of Epidemiology, this study explores the facilitating role that infrastructural and institutional arrangements play in this research discipline. It addresses two research questions: 1) What influence do infrastructural and institutional arrangements have on open research data sharing and reuse practices in the field of Epidemiology? And 2) how could infrastructural and institutional instruments used in Epidemiology potentially be useful to other research disciplines? First, based on a systematic literature review, a conceptual framework of infrastructural and institutional instruments for open research data facilitation is developed. Second, the conceptual framework is applied in interviews with Epidemiology researchers. The interviews show that two infrastructural and institutional instruments have a very high influence on open research data sharing and reuse practices in the field of Epidemiology, namely (a) access to a powerful search engine that meets open data search needs and (b) support by data stewards and data managers. Third, infrastructural and institutional instruments with a medium, high, or very high influence were discussed in a research workshop involving data stewards and research data officers from different research fields. This workshop suggests that none of the influential instruments identified in the interviews are specific to Epidemiology. Some of our findings thus seem to apply to multiple other disciplines. This study contributes to Science by identifying field-specific facilitators and challenges for open research data in Epidemiology, while at the same time revealing that none of the identified influential infrastructural and institutional instruments were specific to this field. Practically, this implies that open data infrastructure developers, policymakers, and research funding organizations may apply certain infrastructural and institutional arrangements to multiple research disciplines to facilitate and enhance open research data sharing and reuse. [ABSTRACT FROM AUTHOR]

Neuroscience has made significant strides over the past decade in moving from a largely closed science characterized by anemic data sharing, to a largely open science where the amount of publicly available neuroscience data has increased dramatically. While this increase is driven in significant part by large prospective data sharing studies, we are starting to see increased sharing in the long tail of neuroscience data, driven no doubt by journal requirements and funder mandates. Concomitant with this shift to open is the increasing support of the FAIR data principles by neuroscience practices and infrastructure. FAIR is particularly critical for neuroscience with its multiplicity of data types, scales and model systems and the infrastructure that serves them. As envisioned from the early days of neuroinformatics, neuroscience is currently served by a globally distributed ecosystem of neuroscience-centric data repositories, largely specialized around data types. To make neuroscience data findable, accessible, interoperable, and reusable requires the coordination across different stakeholders, including the researchers who produce the data, data repositories who make it available, the aggregators and indexers who field search engines across the data, and community organizations who help to coordinate efforts and develop the community standards critical to FAIR. The International Neuroinformatics Coordinating Facility has led efforts to move neuroscience toward FAIR, fielding several resources to help researchers and repositories achieve FAIR. In this perspective, I provide an overview of the components and practices required to achieve FAIR in neuroscience and provide thoughts on the past, present and future of FAIR infrastructure for neuroscience, from the laboratory to the search engine. [ABSTRACT FROM AUTHOR]

The article offers information on Open Science, a movement promoting transparency, collaboration, and accessibility in scientific research. Topics include four Open Research options: preregistration, registered reports, preprints, and data sharing. The Journal of Sustainable Tourism (JoST) aims to be diverse, inclusive, empowering, rigorous, and accessible through Open Science.

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: ω² (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. [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.)

Ethnographers can and should not just do or not do open science, but study the push to share data, instruments, and other research materials as an important moment of change and contest in contemporary knowledge-making and knowledge politics. Following ethnographers of science and technology who have demonstrated the analytic opportunities afforded by moments of scientific controversy, we should treat the places where these calls are made, debated, and taken up as important field sites for ethnographic inquiry. Whenever and wherever the sharing of data, instruments, and research is discussed, planned, done, measured, judged, or regulated, there are powerful claims, visions, and action concerning what makes for facticity, legitimacy, and credibility in both research and politics. From these sites, I argue, we can observe changes to disciplinary and popular understandings of epistemic virtue, or what makes for reliable, factual, or adequately transparent knowledge production. Attention to these sites can also yield important perspectives on the ways that visions of proper research conduct are imbricated with visions of governance. I argue that turning ethnographic methods to studying the open science movement can enable us to do timely scholarship about shifting understandings of facticity, knowledge, information, and governance. [ABSTRACT FROM AUTHOR]

This article presents a discussion of the emerging ethical issue of geodata privacy in geographical research. The paper highlights the importance of considering challenges to privacy when working with geographically explicit data and explores explicit ways in which researchers and practitioners can be conscious of these issues. Through summarising the key problems in this area and presenting outstanding open research areas and questions from a seminar series on geodata privacy, we highlight important considerations for future research in this field. We focus on the specific topics of appropriate anonymization, responsible data dissemination, the balance between data sharing and privacy, and the challenges posed by working across international contexts. We conclude by recommending approaches to manage various legal and ethical frameworks, raise the importance of the international context, and inspire future research to address the challenges of safeguarding sensitive geodata while promoting openness and transparency. [ABSTRACT FROM AUTHOR]

In order to produce the most reliable syntheses of the effectiveness of educational interventions, systematic reviews need to adhere to rigorous methodological standards. This meta‐review investigated risk of bias occurring while conducting a systematic review and the presence of open science practices like data sharing and reproducibility of the review procedure, in recently published reviews in education. We included all systematic reviews of educational interventions, instructions and methods for all K‐12 student populations in any school form with experimental or quasi‐experimental designs (an active manipulation of the intervention) with comparisons and where the outcome variables were academic performance of any kind. We searched the database Education Resources Information Center (ERIC) through the years 2019–2021. In parallel we hand‐searched four major educational review journals for systematic reviews: Educational Research Review (Elsevier), Educational Review (Taylor & Francis), Review of Education (Wiley), and Review of Educational Research (AERA). Systematic reviews were assessed with the risk of bias tool ROBIS and whether the studies had pre‐registered protocols, shared primary research data, and whether a third party could reproduce search strings and details of where exactly primary research data were extracted. A total of 88 studies that matched our PICOS were included in this review; of these, 10 educational systematic reviews were judged as low risk of bias (approximately 11%). The rest were classified as high risk of bias during a shortened ROBIS assessment or assessed as high risk or unclear risk of bias following a full ROBIS assessment. Of the 10 low risk of bias reviews, 6 had detailed their search sufficiently enough for a third party to reproduce, 3 reviews shared the data from primary studies, however none had specified how and from where exactly data from primary studies were extracted. The study shows that at least a small part of systematic reviews in education has a low risk of bias, but most systematic reviews in our set of studies have high risk of bias in their methodological procedure. There are still improvements in this field to be expected as even the low risk of bias reviews are not consistent regarding pre‐registered protocols, data sharing, reproducibility of primary research data and reproducible search strings. Context and implicationsRationale for this studyRigorous systematic review is the method to use to evaluate and provide synthesis of methods and interventions in educational research.Why the new findings matterAlarmingly few systematic reviews were judged with low risk of bias and few reviews utilise recent open science practices. This might undermine the reliability of study findings.Implications for journals and review researchersThis meta‐review highlights the need for review researchers and journals to better adopt best practices in systematic reviews. We therefore urge for better dissemination and awareness of systematic reviews standards and for more transparency in the review process, which may lead to more reliable synthesis of what works best in education. In the long run this is a necessary change and way forward to provide evidence‐based practices in the classroom. [ABSTRACT FROM AUTHOR]

PLOS Pathogens, a leader in the pathogens research community, is committed to advancing Open Science practices that promote transparency, reproducibility, and trust in research. Open Science, which involves the unrestricted sharing of research components, has numerous benefits, including increased equity, access to research findings, and the ability to build on existing science. PLOS Pathogens has long advocated for Open Science and is already compliant with funder policies. The journal has also implemented experimental features, such as the Accessible Data icon, to encourage data sharing and engagement. The pathogens research community has demonstrated its commitment to cooperative Open Science through initiatives like community-curated virus databases and the sharing of research materials. However, there is still a need to address barriers, such as the cost of research, to achieve truly equitable Open Science. PLOS Pathogens remains dedicated to promoting open, high-quality, and peer-reviewed science in collaboration with the research community. [Extracted from the article]

Over the last 30 years, there has been substantial debate about the practical, ethical and epistemological issues uniquely associated with qualitative data sharing. In this paper, we contribute to these debates by examining established data sharing practices in Conversation Analysis (CA). CA is an approach to the analysis of social interaction that relies on audio/video recordings of naturally occurring human interactions and moreover works at a level of detail that presents challenges for assumptions about participant anonymity. Nonetheless, data sharing occupies a central position in both the methodology and the wider academic culture of CA as a discipline and a community. Despite this, CA has largely been ignored in qualitative data sharing debates and discussions. We argue that the methodological traditions of CA present a strong case for the value of qualitative data sharing and offer open data sharing practices that might be usefully adopted in other qualitative approaches. [ABSTRACT FROM AUTHOR]

Conversations about open science have reached the mainstream, yet many open science practices such as data sharing remain uncommon. Our efforts towards openness therefore need to increase in scale and aim for a more ambitious target. We need an ecosystem not only where research outputs are openly shared but also in which transparency permeates the research process from the start and lends itself to more rigorous and collaborative research. To support this vision, this Essay provides an overview of a selection of open science initiatives from the past 2 decades, focusing on methods transparency, scholarly communication, team science, and research culture, and speculates about what the future of open science could look like. It then draws on these examples to provide recommendations for how funders, institutions, journals, regulators, and other stakeholders can create an environment that is ripe for improvement. What might Open Science look like in the future? By examining past initiatives, this Essay offers recommendations to funders, institutions, journals, and stakeholders to create an environment conducive to open science, emphasizing its value in fostering better science. [ABSTRACT FROM AUTHOR]

This article explores researchers' different open science practices in the process for generating knowledge in basic sciences (biology, chemistry, and physics), using a qualitative approach with eight semi-directed interviews. The main perceived effect of open science is the access to research inputs, i.e., open access to articles, open research data - especially the reuse of data and paradata -, and the reuse of codes as main sources. Another topic is the increased and accelerated collaboration with other colleagues, as well as the promotion of the exchange of ideas, i.e., it is a cultural change caused by the exchange of information. However, this work also explores the fact that the implementation of open science policies has unintended consequences, such as the creation of new inequalities or the reproduction of those that already exist in science. Finally, the issue of scientific communication as part of the conceptualization of open science is also mentioned by different researchers, although it is a subject that remains to be explored. [ABSTRACT FROM AUTHOR]

Federal mandates, publishing requirements, and an interest in open science have all generated renewed attention on research data management and, in particular, data sharing practices. Due to the size and types of data they produce, bioimaging researchers confront specific challenges in aligning their data with FAIR principles, ensuring that it is findable, accessible, interoperable, and reusable. Although not always recognized by researchers, libraries can, and have been, offering support for data throughout its lifecycle by assisting with data management planning, acquisition, processing and analysis, and sharing and reuse of data. Libraries can educate researchers on best practices for research data management and sharing, facilitate connections to experts by coordinating sessions using peer educators and appropriate vendors, help assess the needs of different researcher groups to identify challenges or gaps, recommend appropriate repositories to make data as accessible as possible, and comply with funder and publisher requirements. As a centralized service within an institution, health sciences libraries have the capability to bridge silos and connect bioimaging researchers with specialized data support across campus and beyond. [ABSTRACT FROM AUTHOR]

Purpose: In the study, the algorithms of operation of digital formed scientific information objects through machines, based on the open science ecosystem, are discussed. How the basic working logic of this algorithm is and which protocols, software and standards it operates and interacts with are discussed with the support of literature within the open science/open access ecosystem, and the predictions of change in the near future are presented together with the final findings. By study, knowledge objects, digital forms, contents, interacting structures and systems were evaluated within the "Open Science / Open Access" ecosystem. Method: The descriptive method was used in the study. In this context, the reasons for the existence of digital information objects, the structures and systems they interact with, and how they interact, together with their common components, have been tried to be explained with the support of the literature. Findings: The research findings are stated at the end of the study by gathering under six items as the original "result" and "suggestion" supported by the literature, which are evidence of scientific reality, according to the data observed and obtained at the end of the study. Conclusion and Suggestions: The conclusion and suggestion section of the study consists of the entire structure that covers the content of the study. With the findings, the topicality and importance of the research topic, how developing information technologies, artificial intelligence software, open science and open access platforms and digitalized information objects communicate and exchange information among themselves within the structures and systems in which these components interact, the importance of structuralism for information, information centers and information users were emphasized. Originality: This study is an original evaluation study that includes hybrid and autonomous communications and information exchange among themselves in computer/network supported structure on OAI-PMH-AI-GPT artificial intelligence infrastructure platforms of the developing digital information objects in the world. [ABSTRACT FROM AUTHOR]

Background: We aimed to assess the adherence to five transparency practices (data availability, code availability, protocol registration and conflicts of interest (COI), and funding disclosures) from open access Coronavirus disease 2019 (COVID-19) related articles. Methods: We searched and exported all open access COVID-19-related articles from PubMed-indexed journals in the Europe PubMed Central database published from January 2020 to June 9, 2022. With a validated and automated tool, we detected transparent practices of three paper types: research articles, randomized controlled trials (RCTs), and reviews. Basic journal- and article-related information were retrieved from the database. We used R for the descriptive analyses. Results: The total number of articles was 258,678, of which we were able to retrieve full texts of 186,157 (72%) articles from the database Over half of the papers (55.7%, n = 103,732) were research articles, 10.9% (n = 20,229) were review articles, and less than one percent (n = 1,202) were RCTs. Approximately nine-tenths of articles (in all three paper types) had a statement to disclose COI. Funding disclosure (83.9%, confidence interval (CI): 81.7–85.8 95%) and protocol registration (53.5%, 95% CI: 50.7–56.3) were more frequent in RCTs than in reviews or research articles. Reviews shared data (2.5%, 95% CI: 2.3–2.8) and code (0.4%, 95% CI: 0.4–0.5) less frequently than RCTs or research articles. Articles published in 2022 had the highest adherence to all five transparency practices. Most of the reviews (62%) and research articles (58%) adhered to two transparency practices, whereas almost half of the RCTs (47%) adhered to three practices. There were journal- and publisher-related differences in all five practices, and articles that did not adhere to transparency practices were more likely published in lowest impact journals and were less likely cited. Conclusion: While most articles were freely available and had a COI disclosure, adherence to other transparent practices was far from acceptable. A much stronger commitment to open science practices, particularly to protocol registration, data and code sharing, is needed from all stakeholders. [ABSTRACT FROM AUTHOR]

This paper offers an overview of some of the highlights of the 2023 NISO Plus Annual Conference that was held virtually from February 14–February 16, 2023. This was the fourth such conference and the third to be held in a completely virtual format due to the global impact of COVID-19. These conferences have resulted from the merger of NISO and the National Federation of Abstracting and Information Services (NFAIS) in June 2019, replacing the NFAIS Annual Conferences and offering a new, more interactive format. As with prior years, there was no general topical theme (although the importance of metadata was a common thread throughout), but there were topics of interest for everyone working in the information ecosystem - from the practical subjects of standards, archiving, digital preservation, Open Science, and Open Access. With speakers and attendees from around the world and across multiple time zones and continents, it truly was a global conversation! [ABSTRACT FROM AUTHOR]

Scientific institutions have increasingly embraced formalized research data management strategies, which involve complex social practices of codifying the tacit dimensions of data practices. Several guidelines to facilitate these practices have been introduced in recent years, for example, the FAIR guiding principles. The aim of these practices is to foster transparency and reproducibility through 'data sharing,' the public release of data for unbounded reuse. However, a closer look suggests that many scientists' practices of data release might be better described as what I call data handovers. These practices are not rooted in the lofty ideals of good scientific practice and global data reuse but in the more mundane necessities of research continuity, which have become more urgent in light of increasing academic mobility. The Austrian scientists interviewed for this study reinterpreted defining features of research data management – such as ensuring findability – as techniques for managing the effects of researcher mobility. This suggests that the adoption of Open Science practices might be dissociated from its stated epistemic goals, and explains why many Open Science initiatives at present are administratively strong but normatively weak. [ABSTRACT FROM AUTHOR]

The authors discuss the results of a survey of academic librarians' perceptions of a data sharing policy and their preferred data management support, as well as the steps to be taken by "College & Research Libraries" with regard to data guidance for authors. They explain the journal's decision regarding the sharing of data by authors. They also describe the open science movement and developments in the practice of data sharing.

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.)