How open science helps researchers succeed.

Introduction: The open science movement has grown rapidly since the mid-2010s, and research has been conducted in various disciplines such as public health, medicine, education, and computer science. Research results have mainly been published in the journals of information science, computer science, and multidisciplinary fields. Method: To identify the intellectual structure of open science, we constructed a keyword bibliographic coupling analysis network. We examined a total of 1,000 articles on open science from the Web of Science, extracting and analysing 4,645 keywords. Then, we implemented and visualised the keyword bibliographic coupling network by constructing a keyword dataset and a reference dataset for each keyword. Results: By analysing the backbone keywords and clusters in the network, the study revealed that the most prominent keywords were open access, open data, and reproducibility. The analysis also uncovered nine clusters in open science research: open access, reproducibility, data sharing, preregistrations and registered reports, research data, open peer review, tools and platforms for reproducible research, open innovation, science policy, and preprints. These results indicated that open science research focuses on transparency and reproducibility. Additionally, it is noteworthy that this study revealed a considerable focus on the open innovation and science policy areas, which have not received much attention in previous studies. Conclusions: The findings can help to understand the landscape of open science research and may guide research funding institutes and research policymakers to design their policies to improve the open science scholarly environment.

Scholarly publishing has rapidly moved towards open access (OA) over the last few decades. However, OA publishing is only one part of a larger open science movement. The recent UNESCO recommendation for open science (UNESCO, 2021) defines open science broadly to cover the openness of scientific knowledge, science infrastructures, engagement with societal actors, and dialogue with other knowledge systems. In its recommendations, open scientific knowledge includes OA to scientific publications but also open research data, metadata, open educational resources, software, and source code and hardware. Earlier research about open scientific knowledge from the point of view of academic publishers has mainly been focused on one element such as OA publishing and neglected other elements.This paper aims to fill this gap by surveying how society publishers in Finland adopted other elements of open scientific knowledge. In Finland, learned societies account for 70% of national journal output (Late et al., 2020) and their publishing model is mainly diamond open access, which excludes author processing charges and relies on publishing subsidies (Pölönen et al., 2020). Furthermore, their activities often go beyond scholarly publishing to include education and research activities such as funding research and collecting and storing research data (Korkeamäki et al., 2019).We conducted an electronic survey addressed to Finnish learned societies in November 2021 to answer the following research questions: “To what degree society publishers take up the elements of open scientific knowledge including open access to publications, open data, and open education?” (RQ1) and “Are elements of open scientific knowledge cumulative?” (RQ2)In total 97 society publishers responded (40% response rate). We analysed their responses through nine variables measuring how they adopted different elements of open scholarly knowledge (Table 1) in view of the UNESCO recommendations for open science. The results show elements related to open scholarly publications prevail. Almost 70% of respondentspublish either gold, green, or hybrid OA publications. Most society publishers reported to support open data policies only some do collect, store, and provide open access to research datasets. Furthermore, only a few societies offer training for opening research data. Even so, a high share of publishers offers open education, and some share their educational materials openly. Although earlier studies have reported differences in adopting open science between disciplines (Rousi & Laakso, 2020), our analysis does not support these findings.However, it has confirmed that adopting the elements of open scholarly knowledge is cumulative, as OA publishers are more likely to take up other elements of open scholarly knowledge. However, adopting all elements is not yet common. Since activities (e.g. collecting research data, offering education, etc.) of publishers other than societies seem to influence the take up of these elements, further research of their activities is needed. For example, it will show how often and how openly these other publishers provide research data or education beyond.

Open data is becoming more and more a recognised research output in the research life cycle. Research validation and replication depend on open data which enriches the research output. Open data goes beyond the traditional article and book publishing format. As such open data reinforces Open Access (OA) publishing, and OA publishing reinforces open data. Open data and OA publishing stimulate collaboration and increase both the impact of research findings and the visibility of researchers and research groups. In the past years, funders and institutions developed independently policies that support open data and OA publishing. Researchers are increasingly required to publish OA and make their data open. The question is how we can evaluate the impact of Open data on OA publishing. Subsequently, to what extent does OA publishing influence open data? At the TU Delft, 82% of peer-reviewed articles and 71% of conference papers are published OA in 2021, but how many of those publications refer to open data? We have no records and no traceability processes in place to answer this question. As a consequence, few datastewards took on themselves to count manually the number of OA publications that include open data. It is nevertheless clear some changes need to occur to help open data and OA publishing joining forces. The current challenges and barriers faced could be reduced with an increase in OA research outputs (OA books or educational resources), a change in research culture and a change in reviewers’ practices. Recognising and rewarding data publishing; credit data review activities; developing a different workflow of the review process if including data review and requiring different expertise from the reviewers on the data review (data management and research insights) are realistic options. In parallel, new features of innovation for the publishing platform (open data review workflow, link to datasets for download) and for data repositories (quality badge on reviewed datasets, flexibility of incorporating review changes) are worth exploring. There are few possible solutions or workaround available that could help all. We can make it easy for the researchers by connecting different blocks of open science and optimising the benefits with the Data Access Statement (DAS) as a standard section of the article template and with publishers providing a DAS template with basic requirements. For institutions, having a dashboard connecting all research outputs with DOIs together will be very beneficial. Another option is giving authors control of their publications by publishing their findings in an open peer-reviewed community-driven journal. Our framework for connecting open data to OA publishing includes an OA publishing platform, data and publication repositories, a funding policy for OA publishing, expertise in Open Science and research lifecycle; and collaboration.

Focuses on open science practices in suicide research. Specifically, the authors use examples from their own and others' work to demonstrate the opportunities for future-proofing research by implementing open science practices, and they discuss some of the challenges and their potential solutions. They cover implementing open science practices in new, ongoing, and concluded studies, and discuss practices in order of being 'low' to 'high' threshold to implement (based on Kathawalla et al., 2021). Space constraints preclude an extensive presentation of all open science practices although they briefly describe the strategies of providing preprints, sharing study materials and codes, preregistration of studies and registered reports, and sharing research data. To highlight the open science work of as many researchers as possible, examples are provided in Electronic Supplementary Material 1 (ESM 1) rather than in the text. The authors ask for readers' help in adding to these examples via their 'living' reading list (https://osf.io/v6y3t/).

Should research institutions from developing countries provide open access to their data? Who would benefit? These are highly relevant questions for environmental research. In the backdrop of increasing discussions regarding open data, attention should be paid to guarantee that research institutions from developing countries do not become mere data providers. At the same time, restricted access to data should not become a barrier for advancing our knowledge of critical environmental issues. Open access policies for environmental data must be developed not only for advancing science, but also to provide growing opportunities for researchers in developing countries.

Objectives:To identify the engagement of health sciences librarians (HSLs) in open science (OS) through the delivery of library services, support, and programs for researchers.Methods:We performed a scoping review guided by Arksey and O'Malley's framework and Joanna Briggs' Manual for Scoping Reviews. Our search methods consisted of searching five bibliographic databases (MEDLINE, Embase, CINAHL, LISTA, and Web of Science Core Collection), reference harvesting, and targeted website and journal searching. To determine study eligibility, we applied predetermined inclusion and exclusion criteria and reached consensus when there was disagreement. We extracted data in duplicate and performed qualitative analysis to map key themes.Results:We included fifty-four studies. Research methods included descriptive or narrative approaches (76%); surveys, questionnaires, and interviews (15%); or mixed methods (9%). We labeled studies with one or more of FOSTER's six OS themes: open access (54%), open data (43%), open science (24%), open education (6%), open source (6%), and citizen science (6%). Key drivers in OS were scientific integrity and transparency, openness as a guiding principle in research, and funder mandates making research publicly accessible.Conclusions:HSLs play key roles in advancing OS worldwide. Formal studies are needed to assess the impact of HSLs' engagement in OS. HSLs should promote adoption of OS within their research communities and develop strategic plans aligned with institutional partners. HSLs can promote OS by adopting more rigorous and transparent research practices of their own. Future research should examine HSLs' engagement in OS through social justice and equity perspectives.

Public science is critical to the economy and to society. However, much of the beneficial impact of scientific research only occurs when scientific knowledge is disseminated broadly and is used by others. This thesis examines the emerging policy, law, and practice of facilitating open access to scientific research data. One particular focus is to examine the open data policies recently introduced by research funders and publishers, and the potential in these for driving the practice of open scientific data into the future. This thesis identifies five major stumbling blocks to sustainable open scientific data. Firstly, the prevailing ‘mindset’ that facilitating open access to data is analogous to facilitating open access to publications and, therefore, research data can easily be shared, with research funders and librarians effectively leading the process. Secondly, the unclear meaning of the term ‘data’, which causes confusion among stakeholders. Thirdly, ‘misunderstood incentives’ for data sharing and the additional inputs required from researchers. Fourthly, ‘data privacy’—an issue that only applies to selected research datasets, and yet appears to dominate the discussion about open research data. Finally, there is ‘copyright law’, which poses challenges at different stages of data release and reuse. In this thesis, I argue that the above problems can be addressed using a staged model for open scientific data. I draw specifically on the practice with open scientific data at CERN (the European Organization for Nuclear Research) and the practice of sharing clinical trial data to argue that open data can be shared at various stages of processing and diversification. This model is supplemented by recommendations proposing changes to existing open data mandates and the introduction of a text and data mining exemption into Australian copyright law.

How can we address the complexity and cost of applying science to societal challenges? Open science and collaborative R&D may help [1]–[3]. Open science has been described as “a research accelerator” [4]. Open science implies open access [5] but goes beyond it: “Imagine a connected online web of scientific knowledge that integrates and connects data, computer code, chains of scientific reasoning, descriptions of open problems, and beyond …. tightly integrated with a scientific social web that directs scientists' attention where it is most valuable, releasing enormous collaborative potential.” [1]. Open science and collaborative approaches are often described as open source, by analogy with open-source software such as the operating system Linux which powers Google and Amazon—collaboratively created software which is free to use and adapt, and popular for Internet infrastructure and scientific research [6], [7]. However, this use of “open source” is unclear. Some people use “open source” when a project's results are free to use, others when a project's process is highly collaborative [4]. It is clearer to classify open source and open science within a broader class of collaborative R&D, which can be defined as scalable collaboration (usually enabled by information technology) across organizational boundaries to solve R&D challenges [8]. Many approaches to open science and collaborative R&D have been tried [1], [9]. The Gene Wiki has created over 10,000 Wikipedia articles, and aims to provide one for every notable human gene [10]. The crowdsourcing platform InnoCentive has reportedly facilitated solutions to roughly half of the thousands of technical problems posed on the site, including many in life sciences such as the $1 million ALS Biomarker Prize [11]. Other examples include prizes (X-Prize [12]), scientific games (FoldIt [13]), and licensing schemes inspired by open-source software (BIOS [14]). Collaborative R&D approaches vary in openness [15]. In some approaches, the R&D process and outputs are open to all—for example, open-science projects like the Gene Wiki described above. In other approaches which demonstrate what might be called controlled collaboration, there are strong controls on who contributes and benefits—for example, computational platforms like Collaborative Drug Discovery or InnoCentive that support both commercial and nonprofit research [9], [11]. Collaborative approaches can unleash innovation from unforeseen sources, as with crowdsourcing health technologies [11]–[13], [16]. They may help in global challenges like drug development [17], as with India's OSDD (Open Source Drug Discovery) project that recruited over 7,000 volunteers [16] and an open-source drug synthesis project that improved an existing drug without increasing its cost [18]. If you want to apply open science and collaborative R&D, what principles are useful? We suggest Ten Simple Rules for Cultivating Open Science and Collaborative R&D. We also offer eight conversational interviews exploring life experiences that led to these rules (Box 1). Box 1. Conversations on Open Science and Collaborative R&D Many commentators have considered challenges in translating open science and collaborative methods to biomedical research [2]–[4], [9], [17], [20], [24], [26], [28], [29]. How can protecting intellectual property be balanced with freeing researchers to build on previous knowledge? If R&D results are collaboratively created and freely available, who will take responsibility for costly clinical trials and quality control? What will be the Linux of open-source R&D? To explore such challenges and convey life experiences in biomedical open science and collaborative R&D, we offer eight conversational interviews by the first author of this article as supplementary material. The conversations were done on behalf of the Results for Development Institute and are with: Alph Bingham, cofounder of InnoCentive (Text S1) Barry Bunin, CEO of Collaborative Drug Discovery (Text S2) Leslie Chan, open access pioneer and director of Bioline International (Text S3) Aled Edwards, director of the Structural Genomics Consortium (Text S4) Benjamin Good, coleader of the Gene Wiki initiative (Text S5) Bernard Munos, pharmaceutical innovation thought leader (Text S6) Zakir Thomas, director of India's Open Source Drug Discovery (OSDD) project (Text S7) Matt Todd, open science and drug development pioneer (Text S8)

Supervision is one important way to socialize Ph.D. candidates into open and responsible research. We hypothesized that one should be more likely to identify open science practices (here publishing open access and sharing data) in empirical publications that were part of a Ph.D. thesis when the Ph.D. candidates' supervisors engaged in these practices compared to those whose supervisors did not or less often did. Departing from thesis repositories at four Dutch University Medical centers, we included 211 pairs of supervisors and Ph.D. candidates, resulting in a sample of 2062 publications. We determined open access status using UnpaywallR and Open Data using Oddpub, where we also manually screened publications with potential open data statements. Eighty-three percent of our sample was published openly, and 9% had open data statements. Having a supervisor who published open access more often than the national average was associated with an odds of 1.99 to publish open access. However, this effect became nonsignificant when correcting for institutions. Having a supervisor who shared data was associated with 2.22 (CI:1.19-4.12) times the odds to share data compared to having a supervisor that did not. This odds ratio increased to 4.6 (CI:1.86-11.35) after removing false positives. The prevalence of open data in our sample was comparable to international studies; open access rates were higher. Whilst Ph.D. candidates spearhead initiatives to promote open science, this study adds value by investigating the role of supervisors in promoting open science.

The Open Science movement can be broadly summarised as aiming to promote integrity, repeatability and transparency across all aspects of research, from data collection to publication. Systematic reviews and systematic maps aim to provide a reliable synthesis of the evidence on a particular topic, making use of methods that seek to maximise repeatability and comprehensives whilst minimising subjectivity and bias. The central tenet of repeatability is operationalised by transparently reporting methodological activities in detail, such that all actions could be replicated and verified. To date, evidence synthesis has only partially embraced Open Science, typically striving for Open Methodology and Open Access, and occasionally providing sufficient information to be considered to have Open Data for some published reviews. Evidence synthesis communities needs to better embrace Open Science not only to balance knowledge access and increase efficiency, but also to increase reliability, trust and reuse of information collected and synthesised within a review: concepts fundamental to systematic reviews and maps. All aspects of Open Science should be embraced: Open Methodology, Open Data, Open Source and Open Access. In doing so, evidence synthesis can be made more equal, more efficient and more trustworthy. I provide concrete recommendations of how CEE and others can fully embrace Open Synthesis.

This study is a systematic examination of the open access status of research in two flagship language testing and assessment journals: Language Testing and Language Assessment Quarterly. Coding and analysing 898 articles, we investigated (a) the prevalence of open access in four aspects—open manuscripts, open materials, open data, and open code, and (b) the relationship between open access and various characteristics of research, tests, and researchers. Our study revealed a positive trend in the adoption of open access over time, with open manuscripts and materials showing notable increases. Open code and data have remained scarce, though with a recent uptick from a low base. Notably, logistic regression results suggest inequitable participation in open access as authors from the Global South were less likely to have open manuscripts. Recognising the potential role of flagship journals as trend and standard setters, we call on the field to (a) shift towards more equitable open access models, (b) balance intellectual property concerns with validation needs, (c) recognise open code and open data with protected access via dedicated badges, and (d) adopt Research Transparency Statements, a new reporting structure inclusive of methodological and epistemological differences in open research practices.

<strong> Open Scholarship (my preferred definition) </strong> Open Scholarship as a paradigm consists of three dimensions: <strong> Open output </strong> , comprising specific content / products from the fields of open publishing, open data, open source, open education, open access, and similar open movements.

Principles and applications of open science (also referred to as open research or open scholarship) in psychology have emerged in response to growing concerns about the replicability, transparency, reproducibility, and robustness of psychological research alongside global moves to open science in many fields. Our objective in this paper is to inform ways of collectively constructing open science practices and systems that are appropriate to, and get the best out of, the full range of qualitative and mixed‐method approaches used in psychology. We achieve this by describing three areas of open research practice (contributorship, pre‐registration, and open data) and explore how and why qualitative researchers might consider engaging with these in ways that are compatible with a qualitative research paradigm. We argue it is crucial that open research practices do not (even inadvertently) exclude qualitative research, and that qualitative researchers reflect on how we can meaningfully engage with open science in psychology.

This paper presents an overview of several efforts towards reproducible research in the field of medical imaging and visualization. In the first section, the components of Open Science are presented: open access, open data and open source. In the sec

The study is devoted to a systematic analysis of the implementation of open science principles in Ukrainian higher education institutions and research organizations. The methodological basis is the content analysis of official websites of universities, research insti-tutes, and libraries, which made it possible to assess the level of openness of scientific commu-nications and the availability of research outputs. Particular attention is paid to the evaluation of the National Open Science Plan and the provisions reflected in the draft law “On Amendments to Certain Laws of Ukraine Regarding the Implementation of Open Science Principles.”The purpose of the article is to determine the degree of integration of open access policies, research data management, and FAIR principles (Findable, Accessible, Interoperable, Reus-able) into the activities of Ukrainian universities and research institutions. The study focuses on key terms, criteria, and indicators that make it possible to assess progress in implementing open science at the institutional level and to compare Ukrainian practices with international standards.The results highlight the uneven nature of open science implementation: leading universi-ties and institutions engaged in international projects demonstrate higher levels of adaptation, while the majority of organizations remain at the initial stage of establishing open access and research data management policies. Both positive practices (creation of institutional reposito-ries, introduction of copyright transfer agreements, organization of seminars and training courses) and barriers (technical, organizational, and regulatory) were identified.Thus, this research has practical significance for developing recommendations on harmo-nizing national science policy with European requirements, as well as for defining priorities for the further development of Ukraine’s institutional open science infrastructure