The paper is divided into three parts. Part 1 traces the historical events that led to the modern system of scientific research, funding, knowledge dissemination, and recognition, which largely confines health and medical knowledge production to those in HICs [high income countries]. By understanding our shared past and the rise of structural barriers to global health equity, we can better inform our shared path to dismantle them. Part 2 takes a clear-eyed look at where the scientific community is now. Are the ideals of Open Medicine playing out as envisioned? Are the benefits of Open Medicine shared amongst all of humanity, or with only a select few? Lastly, Part 3 offers ideas and recommendations for all stakeholders to chart a path to bring Open Medicine into alignment with its goals and aspirations.
The results of a survey study of university professors in Canada found 81.1 percent of Canadian faculty would trade all IP for an open-source endowed chair and 34.4 percent of these faculty would require no additional compensation. Surprisingly, even more American faculty (86.7 percent) are willing to accept an open-source endowed professorship.
Synopsis: I have recently adjusted my view to the position that the benefits of Machine learning techniques are more likely to be real and large. This is based on the recent incredible results of LLM (Large Language models) and about a year’s experimenting with some of the newly emerging tools based on such technologies.
If I am right about this, are we academic librarians systematically undervaluing Open Access by not taking this into account sufficiently when negotiating with publishers? Given that we control the purse strings, we are one of the most impactful parties (next to publishers and researchers) that will help decide how fast if at all the transition to an Open Access World occurs.
The article seeks to contribute to this aim by exploring the legal framework in which research data can be accessed and used in EU copyright law. First, it delineates the authors’ understanding of research data. It then examines the protection research data currently receives under EU and Member State law via copyright and related rights, as well as the ownership of these rights by different stakeholders in the scientific community. After clarifying relevant conflict-of-laws issues that surround research data, it maps ways to legally access and use them, including statutory exceptions, the open science movement and current developments in law and practice.
In the WorldFAIR project, CODATA (the Committee on Data of the International Science Council), with the RDA (Research Data Alliance) Association as a major partner, is working with a set of eleven disciplinary and cross-disciplinary case studies to advance implementation of the FAIR principles and, in particular, to improve interoperability and reusability of digital research objects, including data.
To that end, the WorldFAIR project created a range of FAIR Implementation Profiles (FIPs) between July and October 2022 to better understand current FAIR data-related practices. The report, "FAIR Implementation Profiles (FIPs) in WorldFAIR: What Have We Learnt?", is published this week and available at https://doi.org/10.5281/zenodo.7378109.
The report describes the WorldFAIR project, its objectives and its rich set of Case Studies; and it introduces FIPs as a methodology for listing the FAIR implementation decisions made by a given community of practice. Subsequently, the report gives an overview of the initial feedback and findings from the Case Studies, and considers a number of issues and points of discussion that emerged from this exercise. Finally, and most importantly, we describe how we think the experience of using FIPs will assist each Case Study in its work to implement FAIR, and will assist the project as a whole in the development of two key outputs: the Cross-Domain Interoperability Framework (CDIF), and domain-sensitive recommendations for FAIR assessment.
This literature review aims to examine the approach given to open science policy in the different studies. The main findings are that the approach given to open science has different aspects: policy framing and its geopolitical aspects are described as an asymmetries replication and epistemic governance tool. The main geopolitical aspects of open science policies described in the literature are the relations between international, regional, and national policies. There are also different components of open science covered in the literature: open data seems much discussed in the works in the English language, while open access is the main component discussed in the Portuguese and Spanish speaking papers. Finally, the relationship between open science policies and the science policy is framed by highlighting the innovation and transparency that open science can bring into it.
The European research and innovation ecosystem is going through a period of profound change. Researchers, organisations that fund or perform research, and policymakers are reshaping the research process and its outputs based on the opportunities offered by the digital transition. The findability, accessibility, interoperability, and reusability (FAIRness) of research publications, data, and software in the digital space will define research and innovation going forward. Closely related, the transition to an open research process and Open Access of its outputs is becoming the ‘new normal’. One of the most prominent initiatives in the digital and open transition of research is the European Open Science Cloud (EOSC). This federation of existing research data infrastructures in Europe aims to create a web of FAIR data and related services for research.
Currently, the impact of integrating an open and reproducible approach into the curriculum on student outcomes is not well articulated in the literature. Therefore, in this paper, we provide the first comprehensive review of how integrating open and reproducible scholarship into teaching and learning may impact students, using a large-scale, collaborative, team-science approach. Our review highlighted how embedding open and reproducible scholarship may impact: (1) students’ scientific literacies (i.e., students’ understanding of open research, consumption of science, and the development of transferable skills); (2) student engagement (i.e., motivation and engagement with learning, collaboration, and engagement in open research), and (3) students’ attitudes towards science (i.e., trust in science and confidence in research findings). Our review also identified a need for more robust and rigorous methods within evaluations of teaching practice. We discuss implications for teaching and learning scholarship in this area.
Journal policies continuously evolve to enable knowledge sharing and support reproducible science. However, that change happens within a certain framework. Eight modular standards with three levels of increasing stringency make Transparency and Openness Promotion (TOP) guidelines which can be used to evaluate to what extent and with which stringency journals promote open science. Guidelines define standards for data citation, transparency of data, material, code and design and analysis, replication, plan and study pre-registration, and two effective interventions: "Registered reports" and "Open science badges", and levels of adoption summed up across standards define journal’s TOP Factor. In this paper, we analysed the status of adoption of TOP guidelines across two thousand journals reported in the TOP Factor metrics. We show that the majority of the journals’ policies align with at least one of the TOP’s standards, most likely "Data citation" (70%) followed by "Data transparency" (19%). Two-thirds of adoptions of TOP standard are of the stringency Level 1 (less stringent), whereas only 9% is of the stringency Level 3. Adoption of TOP standards differs across science disciplines and multidisciplinary journals (N = 1505) and journals from social sciences (N = 1077) show the greatest number of adoptions. Improvement of the measures that journals take to implement open science practices could be done: (1) discipline-specific, (2) journals that have not yet adopted TOP guidelines could do so, (3) the stringency of adoptions could be increased.
The Open Science movement is a response to the accumulated problems in scholarly communication, like the "reproducibility crisis", "serials crisis", and "peer review crisis". The European Commission defines priorities of Open Science as Findable, Accessible, Interoperable and Reproducible (FAIR) data, infrastructure and services in the European Open Science Cloud (EOSC), Next generation metrics, altmetrics and rewards, the future of scientific communication, research integrity and reproducibility, education and skills and citizen science. Open Science Infrastructure is also one of four key components of Open Science defined by UNESCO.
Mainly represented among Open Science Infrastructures are institutional and thematic repositories for publications, research data, software and code. Furthermore, the Open Science Infrastructure services range may include discovery, mining, publishing, the peer review process, archiving and preservation, social networking tools, training, high-performance computing, and tools for processing and analysis. Successful Open Science Infrastructure should be based on community values and responsive to needed changes. Preferably the Open Science Infrastructure should be distributed, enabling machine-actionable tools and services, supporting reusability and reproducibility, quality FAIR data, interoperability, sustainability, long-term preservation and funding.
Here, we define, categorize and discuss barriers to data and code sharing that are relevant to many research fields. We explore how real and perceived barriers might be overcome or reframed in the light of the benefits relative to costs. By elucidating these barriers and the contexts in which they arise, we can take steps to mitigate them and align our actions with the goals of open science, both as individual scientists and as a scientific community.
In this policy position paper, we outline current open science practices and key bottlenecks in their broader adoption. We propose that national science agencies create a digital infrastructure framework that would standardize open science principles and make them actionable. We also suggest ways of redefining research success to align better with open science, and to incentivize a system where sharing various research outputs is beneficial to researchers.
This paper presents findings from a survey on the status quo of data quality assurance practices at research data repositories.
The personalised online survey was conducted among repositories indexed in re3data in 2021. It covered the scope of the repository, types of data quality assessment, quality criteria, responsibilities, details of the review process, and data quality information and yielded 332 complete responses.
The results demonstrate that most repositories perform data quality assurance measures, and overall, research data repositories significantly contribute to data quality. Quality assurance at research data repositories is multifaceted and nonlinear, and although there are some common patterns, individual approaches to ensuring data quality are diverse. The survey showed that data quality assurance sets high expectations for repositories and requires a lot of resources. Several challenges were discovered: for example, the adequate recognition of the contribution of data reviewers and repositories, the path dependence of data review on review processes for text publications, and the lack of data quality information. The study could not confirm that the certification status of a repository is a clear indicator of whether a repository conducts in-depth quality assurance.
We will discuss seven major open data platforms, such as (1) CKAN (2) DKAN (3) Socrata (4) OpenDataSoft (5) GitHub (6) Google datasets (7) Kaggle. We will evaluate the technological commons, techniques, features, methods, and visualization offered by each tool. In addition, why are these platforms important to users such as providers, curators, and end-users? And what are the key options available on these platforms to publish open data?
Mainly building on our own experience as scholars from different research traditions (life sciences, social sciences and humanities), we describe best-practice approaches for opening up research data. We reflect on common barriers and strategies to overcome them, condensed into a step-by-step guide focused on actionable advice in order to mitigate the costs and promote the benefit of open data on three levels at once: society, the disciplines and individual researchers.
In recent years, United States federal funding agencies, including the National Institutes of Health (NIH) and the National Science Foundation (NSF), have implemented public access policies to make research supported by funding from these federal agencies freely available to the public. Enforcement is primarily through annual and final reports submitted to these funding agencies, where all peer-reviewed publications must be registered through the appropriate mechanism as required by the specific federal funding agency. Unreported and/or incorrectly reported papers can result in delayed acceptance of annual and final reports and even funding delays for current and new research grants. So, it’s important to make sure every peer-reviewed publication is reported properly and in a timely manner. For large collaborative research efforts, the tracking and proper registration of peer-reviewed publications along with generation of accurate annual and final reports can create a large administrative burden. With large collaborative teams, it is easy for these administrative tasks to be overlooked, forgotten, or lost in the shuffle. In order to help with this reporting burden, we have developed the Academic Tracker software package, implemented in the Python 3 programming language and supporting Linux, Windows, and Mac operating systems. Academic Tracker helps with publication tracking and reporting by comprehensively searching major peer-reviewed publication tracking web portals, including PubMed, Crossref, ORCID, and Google Scholar, given a list of authors. Academic Tracker provides highly customizable reporting templates so information about the resulting publications is easily transformed into appropriate formats for tracking and reporting purposes. The source code and extensive documentation is hosted on GitHub (https://moseleybioinformaticslab.github.io/academic_tracker/) and is also available on the Python Package Index (https://pypi.org/project/academic_tracker) for easy installation.
In April of this year, Springer Nature and Figshare announced a new integrated route for data deposition at Nature Portfolio titles to help address this problem and encourage researchers to share data rather than seeing it as a hurdle to article publication.
Following the success of the pilot, this streamlined integration is now being extended. Authors submitting to the Nature Portfolio journals, including Nature, in the fields of life, health, chemical and physical sciences will now be able to easily opt into data sharing, via Figshare, as part of one integrated submission process.
The faculty, staff, and graduate students at Clemson University were surveyed by the library about their RDM needs in the spring of 2021. The survey was based on previous surveys from 2012 and 2016 to allow for comparison, but language was updated, and additional questions were added because the field of RDM has evolved. Survey findings indicated that researchers are overall more likely to back up and share their data, but the process of cleaning and preparing the data for sharing was an obstacle. Few researchers reported including metadata when sharing or consulting the library for help with writing a Data Management Plan (DMP). Researchers want RDM resources; offering and effectively marketing those resources will enable libraries to both support researchers and encourage best practices. Understanding researcher needs and offering time-saving services and convenient training options makes following RDM best practices easier for researchers. Outreach and integrated partnerships that support the research life cycle are crucial next steps for ensuring effective data management.
This Data Primer was collaboratively authored by over 30 Digital Humanities researchers and research assistants, and was peer-reviewed by data professionals. It serves as an overview of the different aspects of data curation and management best practices for digital humanities researchers. Endorsed by the National Training Expert Group of the Digital Research Alliance of Canada.
The residents in this study published 2,637 first-author, PubMed-searchable manuscripts, 555 (21.0%) of which appeared in 138 OA journals. The number of publications in OA journals per resident increased from 0.47 for the class of 2015 to 0.79 for the class of 2019. Publications in OA journals garnered fewer citations than those in non-OA journals (8.9 versus 14.9, p < 0.01). 90.6% of OA journals levy an APC for original research reports (median $1,896), which is positively correlated with their 2019 impact factor (r = 0.63, p < 0.01). Aggregate APCs totaled $900,319.21 and appeared to increase over the study period.
A selection of guides, toolkits, and other resources for librarians working on addressing the NIH Data Management and Sharing Policy.
Microsoft, its subsidiary GitHub, and its business partner OpenAI have been targeted in a proposed class action lawsuit alleging that the companies’ creation of AI-powered coding assistant GitHub Copilot relies on "software piracy on an unprecedented scale". . . .Copilot, which was unveiled by Microsoft-owned GitHub in June 2021, is trained on public repositories of code scraped from the web, many of which are published with licenses that require anyone reusing the code to credit its creators. Copilot has been found to regurgitate long sections of licensed code without providing credit—prompting this lawsuit that accuses the companies of violating copyright law on a massive scale.
Through a nationwide survey of universities and research organizations in Australia and New Zealand, this article investigates the level of confidence that librarians working in scholarly communication have in their current competencies. The results show that, while respondents were generally confident across seven competency areas (institutional repository management, publishing services, research practice, copyright services, open access policies and scholarly communication landscape, data management services, and assessment and impact metrics), the majority combined their scholarly communication tasks with other roles.
The nation’s chief scientist will this year recommend to government a radical departure from the way research is distributed in Australia, proposing a world-first model that shakes up the multi-billion-dollar publishing business so Australian readers don’t pay a cent. . . .The model goes much further than open access schemes in the US and Europe by including existing research libraries and has been designed specifically for Australia’s own challenges.
This paper presents original research about the behaviours, histories, demographics, and motivations of scholars who code, specifically how they interact with version control systems locally and on the Web. By understanding patrons through multiple lenses—daily productivity habits, motivations, and scholarly needs—librarians and archivists can tailor services for software management, curation, and long-term reuse, raising the possibility for long-term reproducibility of a multitude of scholarship.