Open Science Initiatives Research Articles

Open science: demand and prospects

Problem setting. Since the European Commission has emphasized openness in science and innovation, it has been natural for relevant mandatory requirements to appear in EU research grant programs, including Horizon Europe, the main one. In principle, the very need to meet these requirements is a sufficient motivation for the introduction of open science in Ukraine and the development of the relevant National Plan, an important European integration tool. analysis of recent researches and publications. Domestic scholars who have studied this issue include: Drach I.I., Vasylenko A.Y., Bruy O. and others. Foreign researchers are: Lichten C.A., Hafner M., Wooding S., Cooper S.F., KhatibA., Treuille J., Barbero J. Lee, Beenen M., Leaver-Fay A., Baker D., Popović Z., Eiben C.B., Siegel J.B., Bale J.B., Cooper S., Khatib F., Shen B.W., Brownstein J.S., Freifeld C.C., Madoff L.C., and others. Purpose of the research is to analyze the essence of open science, its goals, advantages and disadvantages, as well as the demand for and prospects of large-scale implementation of open science policy. article’s main body. For the international community, the role of open science has become even more apparent during the global crisis caused by the coronavirus pandemic, which has shown humanity the key role of open access to scientific information, rapid knowledge sharing, and collaboration between scientists in overcoming global challenges. Today, it is no exaggeration to say that the effective and rapid implementation of open science in Ukraine can become the basis for restarting the national scientific system, which is not fully competitive in the global dimension and suffers from the vestiges of the past. In particular, this concerns the development of a renewed national scientific culture based on academic integrity, cooperation, and a focus on the needs of society. The emergence of a national plan for open science in Ukraine is the beginning of systematic work at the state level, which is to be welcomed. Obviously, we need to act quickly, because every day the gap with the EU countries will increase and may become critical, complicating European integration processes. The bad news is that the implementation of open science always means additional efforts and/or costs (for new research infrastructure, payment for open access publications, awareness raising and training, etc.) that Ukraine may not be able to afford, especially given that a huge share of the national budget will be spent on defense in the coming years (almost 50% in 2023). It is also worth remembering the need to focus on young scientists, who will need additional support but can become effective agents of change, as open science will provide them with many tangible benefits, and the ideal time to develop the relevant skills is at the beginning of their scientific career. conclusions and prospects for the development. Given the benefits of open science, compliance by Ukrainian scientists with certain requirements will facilitate their integration into the global scientific community. Since open science is still in the early stages of its evolution, consolidated information on trends and practices will help to show where policy development can be useful and where participants in open science initiatives have already been able to create their own useful practices. However, open science can also have a negative impact on the research system, and this should also be highlighted and investigated. It is advisable to review the systems for evaluating research and professional performance in order to bring them in line with the principles of open science.

Contrast-agent-based perfusion MRI code repository and testing framework: ISMRM Open Science Initiative for Perfusion Imaging (OSIPI).

Software has a substantial impact on quantitative perfusion MRI values. The lack of generally accepted implementations, code sharing and transparent testing reduces reproducibility, hindering the use of perfusion MRI in clinical trials. To address these issues, the ISMRM Open Science Initiative for Perfusion Imaging (OSIPI) aimed to establish a community-led, centralized repository for sharing open-source code for processing contrast-based perfusion imaging, incorporating an open-source testing framework. A repository was established on the OSIPI GitHub website. Python was chosen as the target software language. Calls for code contributions were made to OSIPI members, the ISMRM Perfusion Study Group, and publicly via OSIPI websites. An automated unit-testing framework was implemented to evaluate the output of code contributions, including visual representation of the results. The repository hosts 86 implementations of perfusion processing steps contributed by 12 individuals or teams. These cover all core aspects of DCE- and DSC-MRI processing, including multiple implementations of the same functionality. Tests were developed for 52 implementations, covering five analysis steps. For T1 mapping, signal-to-concentration conversion and population AIF functions, different implementations resulted in near-identical output values. For the five pharmacokinetic models tested (Tofts, extended Tofts-Kety, Patlak, two-compartment exchange, and two-compartment uptake), differences in output parameters were observed between contributions. The OSIPI DCE-DSC code repository represents a novel community-led model for code sharing and testing. The repository facilitates the re-use of existing code and the benchmarking of new code, promoting enhanced reproducibility in quantitative perfusion imaging.

NeuroBridge: a prototype platform for discovery of the long-tail neuroimaging data.

Open science initiatives have enabled sharing of large amounts of already collected data. However, significant gaps remain regarding how to find appropriate data, including underutilized data that exist in the long tail of science. We demonstrate the NeuroBridge prototype and its ability to search PubMed Central full-text papers for information relevant to neuroimaging data collected from schizophrenia and addiction studies. The NeuroBridge architecture contained the following components: (1) Extensible ontology for modeling study metadata: subject population, imaging techniques, and relevant behavioral, cognitive, or clinical data. Details are described in the companion paper in this special issue; (2) A natural-language based document processor that leveraged pre-trained deep-learning models on a small-sample document corpus to establish efficient representations for each article as a collection of machine-recognized ontological terms; (3) Integrated search using ontology-driven similarity to query PubMed Central and NeuroQuery, which provides fMRI activation maps along with PubMed source articles. The NeuroBridge prototype contains a corpus of 356 papers from 2018 to 2021 describing schizophrenia and addiction neuroimaging studies, of which 186 were annotated with the NeuroBridge ontology. The search portal on the NeuroBridge website https://neurobridges.org/ provides an interactive Query Builder, where the user builds queries by selecting NeuroBridge ontology terms to preserve the ontology tree structure. For each return entry, links to the PubMed abstract as well as to the PMC full-text article, if available, are presented. For each of the returned articles, we provide a list of clinical assessments described in the Section "Methods" of the article. Articles returned from NeuroQuery based on the same search are also presented. The NeuroBridge prototype combines ontology-based search with natural-language text-mining approaches to demonstrate that papers relevant to a user's research question can be identified. The NeuroBridge prototype takes a first step toward identifying potential neuroimaging data described in full-text papers. Toward the overall goal of discovering "enough data of the right kind," ongoing work includes validating the document processor with a larger corpus, extending the ontology to include detailed imaging data, and extracting information regarding data availability from the returned publications and incorporating XNAT-based neuroimaging databases to enhance data accessibility.

Papers with code or without code? Impact of GitHub repository usability on the diffusion of machine learning research

Open Science initiatives prompt machine learning (ML) researchers and experts to share source codes - "scientific artifacts" - alongside research papers via public repositories such as GitHub. Here we analyze the extent to which 1) the availability of GitHub repositories influences paper citation and 2) the popularity trend of ML frameworks (e.g., PyTorch and TensorFlow) affects article citation rates. To accomplish this, we connect ML research publications indexed by Papers with Code (PwC) to Microsoft Academic Graph (MAG) and collect repository-level metadata using the GitHub API. Applying nearest-neighbor matching and econometric considerations, we estimate that papers enjoy approximately 20% advantages in monthly citation rates after the creation of the first GitHub repositories, accounting for paper-level fixed effects and ages. We also find that the temporal popularity trends for frameworks used in the first associated repositories could influence the monthly citation rate for papers. The results highlight the importance of technological artifacts and infrastructure latent to the diffusion of research.

The usability gap in water resources open data and actionable science initiatives

AbstractThe open data movement represents a major advancement for informed water management. Data that are findable, accessible, interoperable, and reusable—or FAIR—are now prerequisite to responsible data stewardship. In contrast to FAIR, accessibility and usability case studies and guidelines designed around human access and understanding are lacking in the literature, especially for water resources. Such decision support guidelines are critical because (i) inherent visual design trade‐offs are not best made using intuition or feedback (perceived preference), and (ii) choosing designs requires a nuanced understanding of why and how the design works (revealed effectiveness). Thus, the goal of this commentary is to highlight knowledge gaps and discuss a general usability testing method which can be applied to any water resources decision support product. The user‐testing approach includes (i) interviews about visualization goals, audiences, and the uses and decisions made with the data products, (ii) diagnosis of usability challenges, and (iii) redesign of decision support products given best practices and control versus treatment with intended end‐user audiences. We illustrate the method using high‐profile U.S. Geological Survey water science products. In sum, optimizing and testing for usability and understandability are as central to stakeholder use as FAIR standards are, and warrant being part of the development of data products and geovisualizations.

The library and open access resources: Threats vs opportunities

The open science initiatives, growing number of open resources (digital collections, institutional repositories, open educational resources, scientific data, etc.), changing user information needs and preferences hang like a sword of Damocles over the libraries and threaten with the absence of demand for science and education support. The purpose of the study is to identify librarians’ awareness of the world open access resources (OAR) and their role in developing library and information products and services. The author analyzes OAR structure, Russian librarians’ awareness of 50 largest OAR structured by document type (books and articles, dissertation, preprints, data sets, learning materials, patents and standards, encyclopedias and dictionaries, scientific information retrieval systems). She also emphasizes the problems of mastering global OAR by Russian academic librarians, their interest toward practical use of OAR in library and information user services. The findings of the study demonstrate the possibility for the libraries to build their value in the market of information OAR and to become essential part of the modern science information ecosystem, to assist in managing scientific content and document retrieval, and to provide subject-oriented teaching and consulting of users.

Open Science Drone Toolkit: Open source hardware and software for aerial data capture.

Despite the increased access to scientific publications and data as a result of open science initiatives, access to scientific tools remains limited. Uncrewed aerial vehicles (UAVs, or drones) can be a powerful tool for research in disciplines such as agriculture and environmental sciences, but their use in research is currently dominated by proprietary, closed source tools. The objective of this work was to collect, curate, organize and test a set of open source tools for aerial data capture for research purposes. The Open Science Drone Toolkit was built through a collaborative and iterative process by more than 100 people in five countries, and comprises an open-hardware autonomous drone and off-the-shelf hardware, open-source software, and guides and protocols that enable the user to perform all the necessary tasks to obtain aerial data. Data obtained with this toolkit over a wheat field was compared to data from satellite imagery and a commercial hand-held sensor, finding a high correlation for both instruments. Our results demonstrate the possibility of capturing research-grade aerial data using affordable, accessible, and customizable open source software and hardware, and using open workflows.

FAIR human neuroscientific data sharing to advance AI driven research and applications: Legal frameworks and missing metadata standards.

Modern AI supported research holds many promises for basic and applied science. However, the application of AI methods is often limited because most labs cannot, on their own, acquire large and diverse datasets, which are best for training these methods. Data sharing and open science initiatives promise some relief to the problem, but only if the data are provided in a usable way. The FAIR principles state very general requirements for useful data sharing: they should be findable, accessible, interoperable, and reusable. This article will focus on two challenges to implement the FAIR framework for human neuroscience data. On the one hand, human data can fall under special legal protection. The legal frameworks regulating how and what data can be openly shared differ greatly across countries which can complicate data sharing or even discourage researchers from doing so. Moreover, openly accessible data require standardization of data and metadata organization and annotation in order to become interpretable and useful. This article briefly introduces open neuroscience initiatives that support the implementation of the FAIR principles. It then reviews legal frameworks, their consequences for accessibility of human neuroscientific data and some ethical implications. We hope this comparison of legal jurisdictions helps to elucidate that some alleged obstacles for data sharing only require an adaptation of procedures but help to protect the privacy of our most generous donors to research … our study participants. Finally, it elaborates on the problem of missing standards for metadata annotation and introduces initiatives that aim at developing tools to make neuroscientific data acquisition and analysis pipelines FAIR by design. While the paper focuses on making human neuroscience data useful for data-intensive AI the general considerations hold for other fields where large amounts of openly available human data would be helpful.

Increasing Efficiency of Nautical Chart Production and Accessibility to Marine Environment Data through an Open-Science Compilation Workflow

Electronic Navigational Chart (ENC) data are essential for safe maritime navigation and have multiple other uses in a wide range of enterprises. Charts are relied upon to be as accurate and as up-to-date as possible by the vessels moving vast amounts of products to global ports each year. However, cartographic generalization processes for updating and creating ENCs are complex and time-consuming. Increasing the efficiency of the chart production workflow has been long sought by the nautical charting community. Toward this effort, approaches must consider intended scale, data quality, various chart features, and perform consistently in different scenarios. Additionally, supporting open-science initiatives through standardized open-source workflows will increase marine data accessibility for other disciplines. Therefore, this paper reviews, improves, and integrates available open-source software, and develops new custom generalization tools, for the semi-automated processing of land and hydrographic features per nautical charting specifications. The robustness of this approach is demonstrated in two areas of very different geographic configurations and the effectiveness for use in nautical charting was confirmed by winning the first prize in an international competition. The presented rapid data processing combined with the ENC portrayal of results as a web-service provides new opportunities for applications such as the development of base-maps for marine spatial data infrastructures.

Mobile researchers, immobile data: Managing data (producers).

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.

Current landscape and future directions of synthetic biology in South America.

Synthetic biology (SynBio) is a rapidly advancing multidisciplinary field in which South American countries such as Chile, Argentina, and Brazil have made notable contributions and have established leadership positions in the region. In recent years, efforts have strengthened SynBio in the rest of the countries, and although progress is significant, growth has not matched that of the aforementioned countries. Initiatives such as iGEM and TECNOx have introduced students and researchers from various countries to the foundations of SynBio. Several factors have hindered progress in the field, including scarce funding from both public and private sources for synthetic biology projects, an underdeveloped biotech industry, and a lack of policies to promote bio-innovation. However, open science initiatives such as the DIY movement and OSHW have helped to alleviate some of these challenges. Similarly, the abundance of natural resources and biodiversity make South America an attractive location to invest in and develop SynBio projects.

The Chinese Open Science Network (COSN): Building an Open Science Community From Scratch

Open Science is becoming a mainstream scientific ideology in psychology and related fields. However, researchers, especially early-career researchers (ECRs) in developing countries, are facing significant hurdles in engaging in Open Science and moving it forward. In China, various societal and cultural factors discourage ECRs from participating in Open Science, such as the lack of dedicated communication channels and the norm of modesty. To make the voice of Open Science heard by Chinese-speaking ECRs and scholars at large, the Chinese Open Science Network (COSN) was initiated in 2016. With its core values being grassroots-oriented, diversity, and inclusivity, COSN has grown from a small Open Science interest group to a recognized network both in the Chinese-speaking research community and the international Open Science community. So far, COSN has organized three in-person workshops, 12 tutorials, 48 talks, and 55 journal club sessions and translated 15 Open Science-related articles and blogs from English to Chinese. Currently, the main social media account of COSN (i.e., the WeChat Official Account) has more than 23,000 subscribers, and more than 1,000 researchers/students actively participate in the discussions on Open Science. In this article, we share our experience in building such a network to encourage ECRs in developing countries to start their own Open Science initiatives and engage in the global Open Science movement. We foresee great collaborative efforts of COSN together with all other local and international networks to further accelerate the Open Science movement.

PISA-tree - a data management framework for life science research projects using a standardised directory tree

We developed pISA-tree, a straightforward and flexible data management solution for organisation of life science project-associated research data and metadata. pISA-tree was initiated by end-user requirements thus its strong points are practicality and low maintenance cost. It enables on-the-fly creation of enriched directory tree structure (project/Investigation/Study/Assay) based on the ISA model, in a standardised manner via consecutive batch files. Templates-based metadata is generated in parallel at each level enabling guided submission of experiment metadata. pISA-tree is complemented by two R packages, pisar and seekr. pisar facilitates integration of pISA-tree datasets into bioinformatic pipelines and generation of ISA-Tab exports. seekr enables synchronisation with the FAIRDOMHub repository. Applicability of pISA-tree was demonstrated in several national and international multi-partner projects. The system thus supports findable, accessible, interoperable and reusable (FAIR) research and is in accordance with the Open Science initiative. Source code and documentation of pISA-tree are available at https://github.com/NIB-SI/pISA-tree.

Public Servants’ Perception Towards Publishing Quality and Impactful Open Data to Support Open Science Initiatives in Malaysia

Open Data Portal is the key intervention of e-Government initiative for information management. The Government of Malaysia has introduced the initiatives since 2015 to facilitate open data portal and strengthen transparency governance. This Open Data is crucial because it provides significant benefits to government in term of political, social, economic, operational, and technical. However, World Bank’s Open Data Readiness Assessment (ODRA) for Malaysia stated that despite the clear evidence of readiness and the country’s data-rich environment, it was significantly difficult to access high- quality data from Malaysia. As a result, lower scores on impact criterion in three consecutive years has landed Malaysia on 53rd of the ranking of the Global Open Data Barometer. Therefore, the objectives of this research re to determine the general perception of public servants toward open data initiatives and to identify determinants that impede the publication of open government data by the existing government entity. This research adopted qualitative method through semi-structured interview. There are six common themes of perception identified and ten common themes emerged as determinants. The result indicated that that Malaysian public servant demonstrates highly positive perception toward the initiative. It is very useful in enhancing public policy towards effective data publishing and sharing management. Subsequently may serve as input to to devise strategies to improve perceptions by addressing the perceptual biases or translating it into actionable policy to promote the success factors onto the non-providing agencies which would escalate the number of providing agencies, and consequently boost the number of data set, and eventually enhance the availability of impactful dataset to support Malaysia Government Data Sharing and Open Science initiatives.

Factors that determine open data readiness among scholars: experience from selected universities in Tanzania

PurposeThis study aims to investigate the factors determining the readiness for uptake of open data (OD) in Tanzania. Specifically, this study intended to answer the question that sought to find out the factors that influence the implementation of OD in universities under study in a view to aligning with recommended strategies for optimizing the use of data in the open science era.Design/methodology/approachThis study used a cross-sectional survey design whereby data were collected using quantitative and qualitative research approaches. A sample size of 212 respondents was drawn from the sampling frame of a population of 1,846 researchers from the participating universities using both probability and nonprobability sampling techniques. A self-administered questionnaire was used to collect data from researchers while interviews were administered to decision-makers. These two groups were believed to have the necessary knowledge for this study.FindingsThe findings indicate low or lack of skills and awareness on issues related to OD among researchers and decision-makers. This study also reveals inadequate infrastructure to support open science initiatives including OD. Moreover, this study shows a lack of supportive institutional strategies and policies that trigger the implementation of OD initiatives in Tanzania. This study recommends that universities should uplift the level of confidence of researchers by ensuring all necessary factors determining the uptake of OD are in place before and during the implementation of OD initiatives.Research limitations/implicationsThis study was conducted during the time when researchers in universities are in an infant stage of adopting the concept from the developed world. In this regard, it is important to carry out a tracer study on establishing the OD phenomena after a number of universities have implemented OD initiatives in the country.Practical implicationsThe researchers recommend the establishment of institutional policy and strategies to guide the implementation of OD among universities in Tanzania. Including awareness creation awareness through providing training among researchers and academics in universities. The results shed light to decision-makers on the understanding of the role of sharing research data in enhancing openness and validation of findings to increase the authenticity of results among researchers.Social implicationsThe authors have revealed the factors affecting the implementation of OD among scholars in universities. This study reveals the level of acceptance of OD initiatives and the factors that could trigger adoption of OD.Originality/valueThis paper presents factors that determine the readiness for the uptake of OD in universities in Tanzania from the researcher’s perspective. This study was conducted to fill the knowledge gap that sought to establish understanding of researchers about OD. The gap was established through literature and it was found that none of the presented studies researched on this phenomenon in Tanzania.

The Portiloop: A deep learning-based open science tool for closed-loop brain stimulation.

Closed-loop brain stimulation refers to capturing neurophysiological measures such as electroencephalography (EEG), quickly identifying neural events of interest, and producing auditory, magnetic or electrical stimulation so as to interact with brain processes precisely. It is a promising new method for fundamental neuroscience and perhaps for clinical applications such as restoring degraded memory function; however, existing tools are expensive, cumbersome, and offer limited experimental flexibility. In this article, we propose the Portiloop, a deep learning-based, portable and low-cost closed-loop stimulation system able to target specific brain oscillations. We first document open-hardware implementations that can be constructed from commercially available components. We also provide a fast, lightweight neural network model and an exploration algorithm that automatically optimizes the model hyperparameters to the desired brain oscillation. Finally, we validate the technology on a challenging test case of real-time sleep spindle detection, with results comparable to off-line expert performance on the Massive Online Data Annotation spindle dataset (MODA; group consensus). Software and plans are available to the community as an open science initiative to encourage further development and advance closed-loop neuroscience research [https://github.com/Portiloop].

Open Science in Experimental Autism Research: A Replication Study of Information Transfer Within and Between Autistic and Non-Autistic People

Information sharing depends on successful communication. Because autism is clinically defined by social communication deficits, autistic people may be expected to be less effective at sharing information, particularly with one another. 
 In 2020, we published a paper indicating that it is the mismatch between autistic and non-autistic people, rather than autism itself, that degrades information sharing. We used an experimental diffusion chain methodology to examine information transfer in groups of autistic people, groups of non-autistic people, and mixed groups of autistic and non-autistic people. The first participant in each group was told a story which they recounted to a second participant, who recounted it to the third participant and so on, along a “diffusion chain” of eight participants (n = 72). Retention of details within the mixed chain showed a significantly steeper decline than the autistic and mixed chains, which did not significantly differ from each other. 
 These results challenge the diagnostic criterion of inherent social deficits in autism, demonstrate that autistic social behaviour can include effective communication and social interaction, and suggest that social difficulties in autism are contextual rather than absolute. Although these findings have profound implications for practitioners, educators, clinicians and psychologists, pushing for dramatic change in policy and practice is premature without a replication establishing these effects as robust and meaningful. Importantly, a failure to replicate would still yield novel insights into interactions between autistic and non-autistic people that could be applied to practice across services. 
 We are embarking on an open-science replication of this study in a larger (n = 324) and more diverse sample, across three international sites, to determine whether these findings are replicable in new samples. We hypothesise that we will replicate the original finding, showing that social interactions are facilitated in an autistic group of participants. Our increased sample size will allow for the identification of moderating demographic factors, and potential communicative mechanisms that facilitate or impede interaction between and within diagnostic groups. Additionally, we will explore whether informing participants of the diagnostic status of participants in their chain affects information transfer and rapport, and whether transfer differs for fictional and factual information. 
 This study was funded as part of an Open Science Initiative; our protocols will be pre-registered on the Open Science Framework, and we have a Registered Report currently under review. It is the first Open Science endeavour of this team, and we look forward to the opportunity to discuss this work with the Edinburgh Open Science community. We will present a short summary of the results from the first study and a detailed account of the planned replication, drawn from our Registered Report (currently under review at Nature Human Behaviour)

A Survey of Visualization and Analysis in High‐Resolution Connectomics

AbstractThe field of connectomics aims to reconstruct the wiring diagram of Neurons and synapses to enable new insights into the workings of the brain. Reconstructing and analyzing the Neuronal connectivity, however, relies on many individual steps, starting from high‐resolution data acquisition to automated segmentation, proofreading, interactive data exploration, and circuit analysis. All of these steps have to handle large and complex datasets and rely on or benefit from integrated visualization methods. In this state‐of‐the‐art report, we describe visualization methods that can be applied throughout the connectomics pipeline, from data acquisition to circuit analysis. We first define the different steps of the pipeline and focus on how visualization is currently integrated into these steps. We also survey open science initiatives in connectomics, including usable open‐source tools and publicly available datasets. Finally, we discuss open challenges and possible future directions of this exciting research field.

Roles and responsibilities in stem cell research: a focus group study with stem cell researchers and patients.

Background: The perspectives of researchers and patients regarding roles and responsibilities in stem cell research are rarely studied, but thesecould offer insights about responsible research conduct. Method: We have conducted a qualitative study consisting of focus groups with bothearly- (n=7) and late-career stem cell researchers (n=11) that are primarily based in Europe, and with Dutch patients with chronic lower back pain (n=9). These focus groups have been analyzed thematically. Results: Four themes were identified: 1) roles and responsibilities in the laboratory, 2) responsibilities of and toward patients and the public, 3) the role of regulation and 4) structural hurdles for responsibility. Discussion: The results suggest that responsible research conduct could be improved by addressing grant application procedures, publication pressure and by providing support of dissemination activities for researchers. Conclusion: Responsibility in stem cell research could be enhanced by embracing open science initiatives and targeted training.

Research Data in Astronomy: Assessing its Impact and Visibility through a Bibliometric Analysis

In the context of the Open Access and Open Science initiatives, Research Data Management becomes relevant and of current interest to researchers, libraries, and institutions that contribute to the development of scientific knowledge. Particularly in Astronomy, it is a challenge to convert, standardize, process, and arrange large volumes of information and data (estimated in several petabytes), coming from the results of astronomical observations, astronomical catalogs, computational codes, among others, which constitute a fundamental initial step in scientific research . Due to this, and the limited studies on this subject, a bibliometric methodology was applied to analyze indexed datasets in the Data Citation Index (Web of Science, Clarivate Analytics) in the Astronomy & Astrophysics category, for the period 2010 - 2019, allowing to identify annual evolutions, countries, and institutions with higher productivity, main repositories, and hosting platforms used in publications indexed in Web of Science. The analysis was complemented with InCites (Clarivate Analytics) bibliometric tools to determine the linkage of the publications with the datasets and their impact. The results from this study show a substantial increase in the number of indexed datasets provided by institutions in the United States, United Kingdom, Spain, and Chile, during the analyzed period. In turn, the publications that cite the main datasets show superior performances in the number of citations and the standardized impact, among other productivity and impact metrics.