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Fully structured semantic resources representing facts in the form of triples (i.e., knowledge graphs) have a major function in driving computer applications, particularly the ones related to biomedicine, to library and information science and to digital humanities (Haslhofer et al., 2018; Sargsyan et al., 2020). They can be easily processed using Application Programming Interfaces (APIs, like REST APIs) and query languages (mainly SPARQL) to assess the reference semantic information and to generate accurate and precise interpretations and predictions, particularly when the analyzed data is multifactorial and ever-changing such as the COVID-19 knowledge (Turki et al., 2021c), information about the laureates of Nobel Prize in Literature (Lebuda and Karwowski, 2016), and the findings of scholarly publications (Fathalla et al., 2017). In particular, the role of open knowledge graphs to facilitate scientific collaboration has been stressed against the backdrop of the COVID-19 pandemic (Anteghini et al., 2020; Colavizza et al., 2021; Turki et al., 2021a). Effectively, the information included in textual or semi-structured resources such as electronic health records, scholarly publications, encyclopedic entries, and citation indexes can be converted into fully structured Research Description Framework (RDF) triples and included in knowledge graphs and then processed in near real-time using computer methods to obtain evolving research outputs that are automatically updated as the knowledge graphs feeding them is regularly curated. These living research outputs include systematic reviews (Wang and Lo, 2021), clinical trials (Servant et al., 2014), scientometric studies (Nielsen et al., 2017), and epidemiological studies (Turki et al., 2021b). However, the construction of knowledge graphs is a complex effort including the recognition of scholarly publications related to the scope of the semantic resource (Turki, 2018), the retrieval of abbreviations and terms for every concept (Turki et al., 2021a), and the extraction and validation of semantic relations (Turki et al., 2018a). Many projects depend on advanced neural network-driven machine-learning techniques for applying these tasks as these methods contribute to higher quality (Asada et al., 2021; Fei et al., 2021). However, these techniques are considered as black boxes and cannot be debugged to identify the reasons behind returned false results and consequently to solve these limitations in a transparent way (Turki et al., 2021b). What is more, the quality of these techniques is considered imperfect in some cases, requiring more time to achieve the same results as specific well-defined algorithms (Turki et al., 2021b). Here, Bibliometric-Enhanced Information Retrieval (BIR) has evolved as a novel field that utilizes bibliographic metadata to efficiently drive the extraction and refinement of semantic data from scholarly publications (Cabanac et al., 2018). This field contributed to the development of many intuitive and explainable algorithms for knowledge engineering. On the one hand, this has been achieved through the restriction of the analysis of full texts to the publications including a particular value of a metadata to reveal the bibliographic settings where assessed algorithms perform well or bad (Safder and Hassan, 2019). On the other hand, this could be done thanks to the analysis of the bibliographic information using taxonomies like MeSH and Wikipedia Category Graph (Hadj Taieb et al., 2020) or using the probabilistic heuristics and constraints inferred from publications using statistical models including TF*IDF (Ramos, 2003) or extracted from knowledge graphs using inference engines, particularly HySpirit (Fuhr and Rölleke, 1998) and F-OWL (Zou et al., 2004). In this opinion article, we explain how each type of bibliographic metadata can provide useful insights to enhance the automatic enrichment and fact-checking of knowledge graphs from scholarly publications based on the outcomes of research efforts about BIR.

Science and Innovation Diplomacy (S&ID) has emerged in recent years as a relevant scholarly movement and interdisciplinary research agenda internationally. This field is promoting a significant impact on the understanding of the cultural and political dynamics of Science, Technology and Innovation (ST&I), implementing initiatives from local to global level. Notwithstanding, S&ID is growing asymmetrically around the world, setting up over a particular configuration in the so-called Global South (GS) societies. In Latin America (LA), although S&ID is a recent, unequal and intra-nationally fragmented process, there are important achievements that have been able to create a favorable mix of approaches, agendas, and practices in this field. Addressing the scope of the special issue “Science Diplomacy and Sustainable Development: Perspectives from Latin America,” this article aims to present a comprehensive analytical typology to the study of the emerging experiences of S&ID in LA, catching the diversity of this research agenda. This is a qualitative merged method-based study, sustained by a literature review, documentary research, online data analysis, and typology building. We understand S&ID in LA as a tentative re-organization of different states and subnational actors around the study and institutionalization of the governance of contemporary transformations on the systems of ST&I, Frontiers in Research Metrics and Analytics, 6, ISSN:2504-0537

Scientific knowledge should be shared beyond academic circles in order to promote science in policymaking. Science communication increases the understanding of how the natural world works and the capacity to make informed decisions. However, not every researcher has the ability to master the art of communicating, and even less in a clear, concise, and easy to understand language that society representatives appreciate. Within the huge and extraordinarily diverse Latin American region, science communication has been going on for at least 200 years, when the first science stories appeared in the newspapers, as well as the first science museums and botanical gardens were founded. Nevertheless, resources are limited, and notably time, which researchers spend mostly in mentoring, ensuring funding, publication of their results and laboratory work, while science journalists are an endangered species. This perspective article aims at providing some recommendations to build bridges between science and decision-making parties through communication, by exploring how Latin American diplomats and policymakers engage with scientific knowledge.

Cloud computing has the capacity to transform many parts of the research ecosystem, from particular research areas to overall strategic decision making and policy. Scientometrics sits at the boundary between research and the decision making and evaluation processes of research. One of the biggest challenges in research policy and strategy is having access to data that allows iterative analysis to inform decisions. Many of these decisions are based on "global" measures such as benchmark metrics that are hard to source. In this article, Cloud technologies are explored in this context. A novel visualisation technique is presented and used as a means to explore the potential for scaling scientometrics by democratising both access to data and compute capacity using the Cloud., Comment: 12 pages, 5 figures

Ocean science is central in providing evidence for the implementation of the United Nations Law of the Sea Convention. The Convention’s provisions on transfer of marine technology to developing countries aim at strengthening scientific capabilities to promote equitable opportunities for these countries to exercise rights and obligations in managing the marine environment. Decades after the adoption of the Convention, these provisions are under implemented, despite the efforts of international organizations, such as IOC-UNESCO. Latin America and the Caribbean struggle to conduct marine scientific research and seize the opportunities of blue economy due to the limited access to state-of-the-art technology. Ocean science communities in these countries are subject to constraints not foreseeing in international treaties, such as unstable exchange rates, taxation, fees for transportation, costs of maintenance and calibration of technology, challenges to comply with technical standards, and intellectual property rights. Action is needed to overcome these challenges by promoting a closer tie between science and diplomacy. We discuss that this interplay between science and international relations, as we frame science diplomacy, can inform on how to progress in allowing countries in this region to develop relevant research and implement the Convention. We provide concrete examples of this transfer of marine technology and ways forward, in particular in the context of the UN Decade of Ocean Science for Sustainable Development (2021–2030).

Our work analyzes the artificial intelligence and machine learning (AI/ML) research portfolios of six large research funding organizations from the United States [National Institutes of Health (NIH) and National Science Foundation (NSF)]; Europe [European Commission (EC) and European Research Council (ERC)]; China [National Natural Science Foundation of China (NNSFC)]; and Japan [Japan Society for the Promotion of Science (JSPS)]. The data for this analysis is based on 127,000 research clusters (RCs) that are derived from 1.4 billion citation links between 104.8 million documents from four databases (Dimensions, Microsoft Academic Graph, Web of Science, and the Chinese National Knowledge Infrastructure). Of these RCs, 600 large clusters are associated with AI/ML topics, and 161 of these AI/ML RCs are expected to experience extreme growth between May 2020 and May 2023. Funding acknowledgments (in the corpus of the 104.9 million documents) are used to characterize the overall AI/ML research portfolios of each organization. NNSFC is the largest funder of AI/ML research and disproportionately funds computer vision. The EC, RC, and JSPS focus more efforts on natural language processing and robotics. The NSF and ERC are more focused on fundamental advancement of AI/ML rather than on applications. They are more likely to participate in the RCs that are expected to have extreme growth. NIH funds the largest relative share of general AI/ML research papers (meaning in areas other than computer vision, natural language processing, and robotics). We briefly describe how insights such as these could be applied to portfolio management decision-making.

PubMed is the largest resource of curated biomedical knowledge to date, entailing more than 25 million documents. Large quantities of novel literature prevent a single expert from keeping track of all potentially relevant papers, resulting in knowledge gaps. In this article, we present CHEMMESHNET, a newly developed PubMed-based network comprising more than 10,000,000 associations, constructed from expert-curated MeSH annotations of chemicals based on all currently available PubMed articles. By learning latent representations of concepts in the obtained network, we demonstrate in a proof of concept study that purely literature-based representations are sufficient for the reconstruction of a large part of the currently known network of physical, empirically determined protein–protein interactions. We demonstrate that simple linear embeddings of node pairs, when coupled with a neural network–based classifier, reliably reconstruct the existing collection of empirically confirmed protein–protein interactions. Furthermore, we demonstrate how pairs of learned representations can be used to prioritize potentially interesting novel interactions based on the common chemical context. Highly ranked interactions are qualitatively inspected in terms of potential complex formation at the structural level and represent potentially interesting new knowledge. We demonstrate that two protein–protein interactions, prioritized by structure-based approaches, also emerge as probable with regard to the trained machine-learning model.

This paper presents a methodological proposal based on the identification of highly cited papers (HCPs) at domestic-level in the Spanish Public University System (SUPE), in order to find the most outstanding publications in the local context. The principal aim is to detect different activity and impact profiles among Spanish universities and differentiate those institutions that play a more significant role. To determine which and how many are the highly cited papers at the domestic level (HCP-DL) collected in the Web of Science, three citation thresholds (1, 5, and 10%) were established. Thematic classification in Incites/Essential Science Indicators areas is used. The results show a preponderance of HCPs in the field of Space Science, while the polytechnic universities have high visibility in the Computer Science area. It has been observed that the presence of HCPs in a given area is involved with universities specialized in teaching and research activities. In absolute terms, the big non-specialized universities are major producers of HCPs and hold the leading positions in our results. However, when efficiency is analyzed in relative terms, some small, specialized universities reveal themselves to be more efficient at producing HCPs (% of HCPs or citations per HCP). We think that this methodology, due to its simplicity, its ease of calculation, and the knowledge it provides, can be very useful to analyze the national systems of any country, in order to know the impact and visibility of the research carried out in its scientific institutions or research areas.

This article consists of a conceptual analysis—from the perspective of communication sciences—of the relevant aspects that should be considered during operational steps in data visualization. The analysis is performed taking as a reference the components that integrate the communication framework theory—the message, the form, the encoder, the context, the channel, and the decoder—which correspond to six elements in the context of data visualization: content, graphic representation, encoding setup, graphic design and approach, media, and user. The study is focused accordingly on the dimensions that these elements describe: the degrees of abstraction of the information, the functionalities of the tool for the graphical representation, the specifications for the setup of the visualization, the approach modes to the context by the graphic design, the levels of communication efficiency in the media, and the requirements of the visualization perceived as values from the user experience side. The unfolding of these dimensions is undertaken following a common pattern of six organizational layers of complexity—basic, extended, synthetic, dynamic, interactive, and integrative—according to the analytical criteria. The results of the detailed study, based on an extensive scientific literature review, allow the design of a dimensional taxonomy of data visualization built on a matrix structure where these elements act as factors of completeness and the layers act as factors of complexity. As a conclusion, an object-centered model constituted by an ordered series of phases and achievements is proposed as a guide to complete a systematic process of data visualization.

Scientometric indicators are useful to evaluate the relevance of scientific research, to prepare rankings, and to evaluate and inform research policies. That is why the choice of appropriate indicators is a matter of primary concern. This article aims to introduce a framework to decide the appropriate type of indicator for assessing the citation-based performance of complex innovation systems. The framework is two-fold: First, it brings the methodology to decide when the use of standard average based indicators is granted, and when scale-invariant indicators are mandatory. Second, it provides the procedures to build scale-invariant indicators to assess the relative impact of complex innovation systems. The framework is validated empirically through the evaluation of the relative impact of the Chilean science system in 2017. The result suggests that the Chilean science system has characteristics of a complex innovation system such as the distribution of citations fits to a power law with an exponential cutoff −2.77±0.09 and a power-law correlation between the size of the system and its impact 1.29±0.11. Furthermore, the framework shows to be efficient to compare fields of vastly different sizes.

Little has been investigated about Science Diplomacy (SD) in emerging economies, more specifically on governance schemes useful for organizing intersecting actors, practices, conceptions and suggestions of the future in foreign affairs and Science, Technology and Innovation (STI) in public administration sectors. This paper contributes to a better understanding of the “texture and nature” of SD initiatives in emerging economies through the eyes of relevant actors involved or reflecting about them in Colombia. The aim of this paper is to propose a general governance scheme for SD in emerging economies and its potential instrumentation for a policy-mix. In Colombia, SD initiatives are very fragmented, and are not part of the priorities of the Colombian state, however the increasing interest of an embryonic practitioner and scholar community working in the topic make necessary this work. A phenomenological perspective combined with a single case study research methodology is used to gain a very accurate description of the state of the situation of SD in Colombia. Policy document review and semi-structured interviews were conducted with 18 relevant actors to understand the conceptions, practices, and suggestions for the future of SD in Colombia. The study results show that SD actors in Colombia are scattered, practices strongly related to traditional cooperation diplomatic activities and the need to give a function to SD for capacity building, better global intermediation and the development of new knowledge, in particular promoting SD abilities in the scientific community. In addition, data expresses the need to cultivate a multi-stakeholder working group for such a purpose. The study reflects on the need of a policy mix for SD in emerging economies. It proposes a general governance scheme for it, a potential instrumentation founded on research participant future suggestions, and a set of practical recommendations and policy implications. Conclusions and further research questions are set, pointing out the importance of including non-conventional diplomacy actors and knowledge, and the need to inquire rationales behind possible SD policy mixes in the southern world.

Scientific communication has been growing strong worldwide in the past decades. The use of modern data analysis tools to fine-tune its content, strategy, and effectiveness, together with the significative rise of social media, have contributed to such significative growth (Kappel and Holmen, 2019). Social media (such as blogs and microblogging) are powerful engines greatly incorporated into our daily lives for capturing information and as a social tool. As such, they are already being exploited for learning, discovering, searching, storing, and sharing knowledge (Lopez-Goni and Sanchez-Angulo, 2018). Research has shown that online media use increases scientific knowledge (Cacciatore et al., 2014; Su et al., 2015) and positive attitudes toward science (Dudo et al., 2011) therefore enhancing the learning and science process skills. Such scientific knowledge is a critical resource that enables political actors to inform and legitimate political decisions, and it is also important for non-scientific audiences in terms of forming public opinion about important political issues (Huber et al., 2019). Moreover, previous work has demonstrated that democratic societies that are scientifically literate make equitable choices regarding science-related policy issues (European Commission, 1995; Rudolph and Horibe, 2016). Thus, according to Marquez and Porras (2020), effective science communication and science literacy are socioeconomically imperative for all societies. At the same time, Science Communication can serve various diplomatic purposes. Particularly, science popularization initiatives, even when not targeted to policymakers or diplomats, can both raise awareness about international scientific cooperation and about locally produced science and technology which could be highly overlooked (Leach, 2015). English is currently the lingua franca of science. Currently, 98% of publications in science are written in English (Ramirez-Castaneda, 2020). This has facilitated the dissemination of knowledge across boundaries, but at the same time, the hegemony of English in science promotes and enforces the imposition of just one cultural point of view over others (Marquez and Porras, 2020). Because of that, generating science communication multilinguistic alternatives promotes diversity and creates culturally relevant content. Science communication in Spanish is especially imperative in Latin America. The intrinsic functional illiteracy, framed in the lack of economic and educational resources, inequality, poverty, political and social instability, are historical challenges that keep this region from unleashing its full potential (UNESCO, 2020). Moreover, and due to these issues, Latin America faces a human capital flight crisis, in which a high percentage of the individuals pursuing higher academic education end up emigrating and learning a second language. This is reflected in a marked lack of availability of educational resources in Spanish addressed to Latin American communities. Nevertheless, there have been efforts to build remote networks of Latin American scientists and science communicators that come together to counteract this effect. Three renown projects, different in nature, can be used as examples. First, the RedPop (Latin American and Caribbean Network for the Popularization of Science and Technology https://www.redpop.org) is a network of centers and programs created in 1990 at the request of UNESCO's program for Science, Technology and Society (Massarani et al., 2015). It encompasses around 80 science communication projects in different media platforms, but also science museums, interactive science centers, natural history museums, environmental parks, zoos, botanical gardens, and aquariums. Second, the bilingual science communication portal Latin American Science (www.latinamericanscience.org) publishes pieces written by scientist and science writers for the public both in Spanish and English-speaking countries. It focuses on regionally produced research, science policy and science-related stories from the region. And third, the Journal of Science Communication JCOM America Latina (https://jcomal.sissa.it/jcomal/index.jsp), an open access journal focused on science communication in Latin America and publishing contributions in Spanish and Portuguese (Weitkamp and Massarani, 2018). Still, more opportunities need to exist in term of communicating science with regional relevance. Blogs and social media platforms, which are especially open and easily accessible resources, have fantastic potential to address this gap since it is allowing information and education to reach every home to an unprecedented extent. One of these regional initiatives, the Latin American Network for Scientific Culture (RedLCC), brings together regional scientists that communicate science for Latin American communities and, in consequence, also nurtures the “Science for Diplomacy” dimension of Science Diplomacy.

Authorship represents a highly discussed topic in nowadays academia. The share of co-authored papers has increased substantially in recent years allowing scientists to specialize and focus on specific tasks. Arising from this, social scientific literature has especially discussed author orders and the distribution of publication and citation credits among co-authors in depth. Yet only a small fraction of the authorship literature has also addressed the actual underlying question of what actually constitutes authorship. To identify social scientists' motives for assigning authorship, we conduct an empirical study surveying researchers around the globe. We find that social scientists tend to distribute research tasks among (individual) research team members. Nevertheless, they generally adhere to the universally applicable Vancouver criteria when distributing authorship. More specifically, participation in every research task with the exceptions of data work as well as reviewing and remarking increases scholars' chances to receive authorship. Based on our results, we advise journal editors to introduce authorship guidelines that incorporate the Vancouver criteria as they seem applicable to the social sciences. We further call upon research institutions to emphasize data skills in hiring and promotion processes as publication counts might not always depict these characteristics.

With the growing unstructured data in healthcare and pharmaceutical, there has been a drastic adoption of natural language processing for generating actionable insights from text data sources. One of the key areas of our exploration is the Medical Information function within our organization. We receive a significant amount of medical information inquires in the form of unstructured text. An enterprise-level solution must deal with medical information interactions via multiple communication channels which are always nuanced with a variety of keywords and emotions that are unique to the pharmaceutical industry. There is a strong need for an effective solution to leverage the contextual knowledge of the medical information business along with digital tenants of natural language processing (NLP) and machine learning to build an automated and scalable process that generates real-time insights on conversation categories. The traditional supervised learning methods rely on a huge set of manually labeled training data and this dataset is difficult to attain due to high labeling costs. Thus, the solution is incomplete without its ability to self-learn and improve. This necessitates techniques to automatically build relevant training data using a weakly supervised approach from textual inquiries across consumers, healthcare professionals, sales, and service providers. The solution has two fundamental layers of NLP and machine learning. The first layer leverages heuristics and knowledgebase to identify the potential categories and build an annotated training data. The second layer, based on machine learning and deep learning, utilizes the training data generated using the heuristic approach for identifying categories and sub-categories associated with verbatim. Here, we present a novel approach harnessing the power of weakly supervised learning combined with multi-class classification for improved categorization of medical information inquiries.

The COVID-19 pandemic constitutes an ongoing worldwide threat to human society and has caused massive impacts on global public health, the economy and the political landscape. The key to gaining control of the disease lies in understanding the genetics of SARS-CoV-2 and the disease spectrum that follows infection. This study leverages traditional and intelligent bibliometric methods to conduct a multi-dimensional analysis on 5,632 COVID-19 genetic research papers, revealing that 1) the key players include research institutions from the United States, China, Britain and Canada; 2) research topics predominantly focus on virus infection mechanisms, virus testing, gene expression related to the immune reactions and patient clinical manifestation; 3) studies originated from the comparison of SARS-CoV-2 to previous human coronaviruses, following which research directions diverge into the analysis of virus molecular structure and genetics, the human immune response, vaccine development and gene expression related to immune responses; and 4) genes that are frequently highlighted includeACE2,IL6,TMPRSS2, andTNF. Emerging genes to the COVID-19 consist ofFURIN,CXCL10,OAS1,OAS2,OAS3, andISG15. This study demonstrates that our suite of novel bibliometric tools could help biomedical researchers follow this rapidly growing field and provide substantial evidence for policymakers’ decision-making on science policy and public health administration.

When a knowledge system importantly loses integrity, ceasing to provide the kinds of trusted knowledge expected of it, we can label thisepistemic corruption. Epistemic corruption often occurs because the system has been co-opted for interests at odds with some of the central goals thought to lie behind it. There is now abundant evidence that the involvement of pharmaceutical companies corrupts medical science. Within the medical community, this is generally assumed to be the result of conflicts of interest. However, some important ways that the industry corrupts are not captured well by standard analyses in terms of conflicts of interest. It is not just that there is a body of medical science perverted by industry largesse. Instead, much of the corruption of medical science via the pharmaceutical industry happens through grafting activities: Pharmaceutical companies do their own research and smoothly integrate it with medical science, taking advantage of the legitimacy of the latter.

The COVID-19 pandemic forced healthcare systems globally to handle a dramatic surge in healthcare utilization while also taxing available testing resources. In the context of healthcare systems in Latin America and the Caribbean, COVID-19 added to the existing burden of infectious diseases related to endemic infections such as arboviruses and HIV. In the Dominican Republic, testing is supplied mostly by the private sector and a national public laboratory. The surge in testing demands laid bare a lack of installed capacities both in laboratory facilities and equipment and trained staff in molecular biology laboratory procedures. This article discusses a case of how science diplomacy and a relatively new law fostering public-private partnerships allowed a university to play a major role in public health response while generating knowledge to inform public policy decisions in an unprecedented manner in the country. Science diplomacy is discussed in the context of decolonization and the importance of the local gaze when creating academic partnerships in the context of global health emergencies.

Collaboration has become an essential paradigm in sustainable development research and in strategies for meeting the United Nations Sustainable Development Goals (SDGs). This study uses bibliometric methods and network analysis to examine research output and collaboration supporting the SDGs and explores means to detect and analyze research collaboration beyond the traditional definition of multiple, one-time co-authorship. We employed two additional lenses of collaboration: repeat collaboration and collaboration time point to quantify and visualize co-authorship data sourced from Microsoft Academic Graph. Our results show an increased collaboration rate over time at the author and institutional levels; however they also indicate that the majority of collaborations in SDG-related research only happened once. We also found out that on average, repeat collaboration happens more frequently, but after a longer duration, at the institutional level than at the author level. For this reason, we further analyzed institutions and identified core institutions that could help influence more consistent collaboration and sustain or grow the SDG-related research network. Our results have implications for understanding sustainable partnerships in research related to SDGs and other global challenges.

Science diplomacy is a fast-growing field of research, policy, and practice dedicated to understanding and reinforcing the connections between science and international affairs to tackle national, regional, and global issues. By aligning science and diplomacy, countries can attract talent, strengthen their national research ecosystems, provide avenues for participation of scientists in policy, and coordinate integrated solutions to challenges with technical dimensions. While Latin America has a long tradition of bilateral and regional cooperation, science still plays a marginal role in foreign policy, as has become evidenced by the response to the COVID-19 pandemic. With few exceptions, Latin American nations have a relatively immature science, technology, and innovation ecosystem, compounded by low public and private investments in research, coexisting with profound socio-economic inequalities, and large vulnerable populations. Such challenging conditions have created barriers to a fluid relationship between science and diplomacy, fundamentally characterized by inefficient communication between scientists and policymakers, weak collaboration channels, and duplicated roles, which altogether perpetuate siloed mentalities and a lack of trust between the two communities. Over the last decade, a first influential wave of Latin American scientists, diplomats, and other professionals, including five of the co-authors, have undertaken science diplomacy training provided by specialized organizations. Through these experiences, we recognized the need to elevate awareness and build capacities in science diplomacy in our respective countries and overall, across Latin America. Here, we describe emerging efforts and mechanisms to bridge the gap between scientists and policymakers at the national and regional level. Furthermore, we offer recommendations to amplify the impact of those pioneering initiatives toward consolidating a robust science diplomacy practice across the region. The national experiences described from Costa Rica, Mexico, and Panama can serve as a roadmap for other Latin American nations in the early process of developing a science diplomacy strategy, so they can also align themselves to a collective pathway. Most critically, we propose a way forward so that Latin America can leapfrog beyond disjointed training of individuals into integrated institutional strategies that can harness the tools of science diplomacy to enhance science-informed multilateral cooperation and enable more effective science-informed policymaking.

Many academic analyses of good practice in the use of bibliometric data address only technical aspects and fail to account for and appreciate user requirements, expectations, and actual practice. Bibliometric indicators are rarely the only evidence put before any user group. In the present state of knowledge, it is more important to consider how quantitative evaluation can be made simple, transparent, and readily understood than it is to focus unduly on precision, accuracy, or scholarly notions of purity. We discuss how the interpretation of ‘performance’ from a presentation using accurate but summary bibliometrics can change when iterative deconstruction and visualization of the same dataset is applied. From the perspective of a research manager with limited resources, investment decisions can easily go awry at governmental, funding program, and institutional levels. By exploring select real-life data samples we also show how the specific composition of each dataset can influence interpretive outcomes.

Subject categories of scholarly papers generally refer to the knowledge domain(s) to which the papers belong, examples being computer science or physics. Subject category information can be used for building faceted search for digital library search engines. This can significantly assist users in narrowing down their search space of relevant documents. Unfortunately, many academic papers do not have such information as part of their metadata. Existing methods for solving this task usually focus on unsupervised learning that often relies on citation networks. However, a complete list of papers citing the current paper may not be readily available. In particular, new papers that have few or no citations cannot be classified using such methods. Here, we propose a deep attentive neural network (DANN) that classifies scholarly papers using only their abstracts. The network is trained using 9 million abstracts from Web of Science (WoS). We also use the WoS schema that covers 104 subject categories. The proposed network consists of two bi-directional recurrent neural networks followed by an attention layer. We compare our model against baselines by varying the architecture and text representation. Our best model achieves micro-F1 measure of 0.76 with F1 of individual subject categories ranging from 0.50-0.95. The results showed the importance of retraining word embedding models to maximize the vocabulary overlap and the effectiveness of the attention mechanism. The combination of word vectors with TFIDF outperforms character and sentence level embedding models. We discuss imbalanced samples and overlapping categories and suggest possible strategies for mitigation. We also determine the subject category distribution in CiteSeerX by classifying a random sample of one million academic papers., Comment: submitted to "Frontiers Mining Scientific Papers Volume II: Knowledge Discovery and Data Exploitation"

Science diplomacy and science–policy interfaces are tools that science has to address the biggest challenges that the world faces today. The scientific community needs to develop the capacity to bring this scientific knowledge to society and decision-makers for the purposes of new governance of the Earth System and thus a more resilient society. Climate change is one of the most challenging issues the world is currently facing, and the Latin America and Caribbean (LAC) region is highly vulnerable to its consequences, as it is expected to exacerbate environmental, social, and economic problems in the LAC region. In this context, and as an emergency call to address the climate crisis with the latest available science in the region, this paper collects a series of examples of the progress, best practices, gaps, challenges, and solutions. We do so from the perspective of Early Careers Researchers (ECRs) and undergraduate and graduate students, highlighting what we are doing to engage scientists in society–policy–science interaction for the sustainable development agenda and climate action in Latin America and the Caribbean.

Open science (OS) is of paramount importance for the improvement of science worldwide and across research fields. Recent years have witnessed a transition toward open and transparent scientific practices, but there is still a long way to go. Early career researchers (ECRs) are of crucial relevance in the process of steering toward the standardization of OS practices, as they will become the future decision makers of the institutional change that necessarily accompanies this transition. Thus, it is imperative to gain insight into where ECRs stand on OS practices. Under this premise, the Open Science group of the Max Planck PhDnet designed and conducted an online survey to assess the stance toward OS practices of doctoral candidates from the Max Planck Society. As one of the leading scientific institutions for basic research worldwide, the Max Planck Society provides a considerable population of researchers from multiple scientific fields, englobed into three sections: biomedical sciences, chemistry, physics and technology, and human and social sciences. From an approximate total population of 5,100 doctoral candidates affiliated with the Max Planck Society, the survey collected responses from 568 doctoral candidates. The survey assessed self-reported knowledge, attitudes, and implementation of different OS practices, namely, open access publications, open data, preregistrations, registered reports, and replication studies. ECRs seemed to hold a generally positive view toward these different practices and to be interested in learning more about them. Furthermore, we found that ECRs’ knowledge and positive attitudes predicted the extent to which they implemented these OS practices, although levels of implementation were rather low in the past. We observed differences and similarities between scientific sections. We discuss these differences in terms of need and feasibility to apply these OS practices in specific scientific fields, but additionally in relation to the incentive systems that shape scientific communities. Lastly, we discuss the implications that these results can have for the training and career advancement of ECRs, and ultimately, for the consolidation of OS practices.

This paper presents a large-scale document-level comparison of two major bibliographic data sources: Scopus and Dimensions. The focus is on the differences in their coverage of documents at two levels of aggregation: by country and by institution. The main goal is to analyze whether Dimensions offers as good new opportunities for bibliometric analysis at the country and institutional levels as it does at the global level. Differences in the completeness and accuracy of citation links are also studied. The results allow a profile of Dimensions to be drawn in terms of its coverage by country and institution. Dimensions’ coverage is more than 25% greater than Scopus which is consistent with previous studies. However, the main finding of this study is the lack of affiliation data in a large fraction of Dimensions documents. We found that close to half of all documents in Dimensions are not associated with any country of affiliation while the proportion of documents without this data in Scopus is much lower. This situation mainly affects the possibilities that Dimensions can offer as instruments for carrying out bibliometric analyses at the country and institutional level. Both of these aspects are highly pragmatic considerations for information retrieval and the design of policies for the use of scientific databases in research evaluation., The Scimago group annually receives a raw data copy in XML format through a contract with Elsevier. The Scimago group has the possibility of downloading a copy of Dimensions in JSON format through an agreement with Digital Science. We are not allowed to redistribute the Scopus and Dimensions data used in this paper.We thank the Dimensions and Scopus development teams for their availability to provide us with access to the information necessary to carry out this analysis.

Chemical patents represent a valuable source of information about new chemical compounds, which is critical to the drug discovery process. Automated information extraction over chemical patents is, however, a challenging task due to the large volume of existing patents and the complex linguistic properties of chemical patents. The Cheminformatics Elsevier Melbourne University (ChEMU) evaluation lab 2020, part of the Conference and Labs of the Evaluation Forum 2020 (CLEF2020), was introduced to support the development of advanced text mining techniques for chemical patents. The ChEMU 2020 lab proposed two fundamental information extraction tasks focusing on chemical reaction processes described in chemical patents: (1)chemical named entity recognition, requiring identification of essential chemical entities and their roles in chemical reactions, as well as reaction conditions; and (2)event extraction, which aims at identification of event steps relating the entities involved in chemical reactions. The ChEMU 2020 lab received 37 team registrations and 46 runs. Overall, the performance of submissions for these tasks exceeded our expectations, with the top systems outperforming strong baselines. We further show the methods to be robust to variations in sampling of the test data. We provide a detailed overview of the ChEMU 2020 corpus and its annotation, showing that inter-annotator agreement is very strong. We also present the methods adopted by participants, provide a detailed analysis of their performance, and carefully consider the potential impact of data leakage on interpretation of the results. The ChEMU 2020 Lab has shown the viability of automated methods to support information extraction of key information in chemical patents.

The experience of COVID-19 has highlighted the strategic role of local administrations, in all areas of service, in directing and coordinating actions to contain the pandemic. In this brief research report, we have interpreted the theme of the issue Open when, why, to whom? Changing challenges, perspectives, and practices in a new research culture by transferring it into a local context, namely in Italy’s inner areas, whose communities had already endured the 2016–2017 seismic swarm. We will look into the issue pragmatically, because we think that in front of a COVID-19 induced fast-changing institutional environment, science and technology studies researchers have some ideas to offer. These days, we are learning important lessons in citizen science. Today, local administrators must equip themselves with the management of infrastructures (unimaginable before COVID-19) for enforcing social distance and tracking positive cases. One of the tasks that we wish to take up is determining the levels of societal readiness and the levels of integration in society of new technologies, products, and services. The pandemic requires social and cultural innovation policies that make communities ready to respond to catastrophic events on their territory—our case-study is Italy’s inner areas—through access to data, communities of practice, co-creation, reflection, and inclusion. Finally, COVID-19 ought not to undermine the work done so far to achieve Sustainable Development Goal 1 (Poverty), 3 (Health), 4 (Education), 5 (Gender), 6 (Water), 8 (Work), 10 (Inequalities) and 16 (Peace). Pope Francis has made it clear: “This is the moment to see the poor.”

In this paper we examine how the process of collaboration works in science and literature. In the first part, we discuss the features of scientific collaboration and literary collaboration and the differences between them. In the second part, we analyze two processes of collaboration, each from a different field: the case of CERN and high-energy physics and the case of Scrittura Industriale Collettiva and its Great Open Novel. Lastly, we try to compare those two processes and deduce the common traits of a successful collaboration.

Background: Alongside the COVID-19 pandemic, government authorities around the world have had to face a growing infodemic capable of causing serious damages to public health and economy. In this context, the use of infoveillance tools has become a primary necessity.Objective: The aim of this study is to test the reliability of a widely used infoveillance tool which is Google Trends. In particular, the paper focuses on the analysis of relative search volumes (RSVs) quantifying their dependence on the day they are collected.Methods: RSVs of the query coronavirus + covid during February 1—December 4, 2020 (period 1), and February 20—May 18, 2020 (period 2), were collected daily by Google Trends from December 8 to 27, 2020. The survey covered Italian regions and cities, and countries and cities worldwide. The search category was set to all categories. Each dataset was analyzed to observe any dependencies of RSVs from the day they were gathered. To do this, by calling i the country, region, or city under investigation and j the day its RSV was collected, a Gaussian distribution Xi=X(σi,x¯i) was used to represent the trend of daily variations of xij=RSVsij. When a missing value was revealed (anomaly), the affected country, region or city was excluded from the analysis. When the anomalies exceeded 20% of the sample size, the whole sample was excluded from the statistical analysis. Pearson and Spearman correlations between RSVs and the number of COVID-19 cases were calculated day by day thus to highlight any variations related to the day RSVs were collected. Welch’s t-test was used to assess the statistical significance of the differences between the average RSVs of the various countries, regions, or cities of a given dataset. Two RSVs were considered statistical confident when t<1.5. A dataset was deemed unreliable if the confident data exceeded 20% (confidence threshold). The percentage increase Δ was used to quantify the difference between two values.Results: Google Trends has been subject to an acceptable quantity of anomalies only as regards the RSVs of Italian regions (0% in both periods 1 and 2) and countries worldwide (9.7% during period 1 and 10.9% during period 2). However, the correlations between RSVs and COVID-19 cases underwent significant variations even in these two datasets (Max |Δ| = + 625% for Italian regions, and Max |Δ|= +175% for countries worldwide). Furthermore, only RSVs of countries worldwide did not exceed confidence threshold. Finally, the large amount of anomalies registered in Italian and international cities’ RSVs made these datasets unusable for any kind of statistical inference.Conclusion: In the considered timespans, Google Trends has proved to be reliable only for surveys concerning RSVs of countries worldwide. Since RSVs values showed a high dependence on the day they were gathered, it is essential for future research that the authors collect queries’ data for several consecutive days and work with their RSVs averages instead of daily RSVs, trying to minimize the standard errors until an established confidence threshold is respected. Further research is needed to evaluate the effectiveness of this method.

Bibliometric indicators such as the number of published articles and citations received are subject to a strong ambiguity. A high numerical value of bibliometric indicators may not measure the quality of scientific production, but only a high level of activity of a researcher. There may be cases of good researchers who do not produce a high number of articles, but have few research products of high quality. The sociology of science relies on the so-called “Matthew effect,” which is inspired by Matthew’s Gospel on Talents. “Those that have more will have more” seems to support the idea that those that publish more, merit to have higher bibliometric indicators, and to be recognized for their major results. But is this really the case? Can bibliometric indicators be considered a measure of the merit of scholars or they come from luck and chance? The answer is of fundamental importance to identify best practices in research assessment. In this work, using philosophical argumentation, we show how Christian theology, in particular St. Thomas Aquinas, can help us to clarify the concept of merit, overcoming the conceptual ambiguities and problems highlighted by the existing literature. By doing this, Christian theology, will allow us to introduce the evaluation framework in a broader perspective better suited to the interpretation of the complexity of research evaluation.

Although established forms of peer review are often criticized for being slow, secretive, and even unfair, they are repeatedly mentioned by academics as the most important indicator of quality in scholarly publishing. In many countries, the peer review of books is a less codified practice than that of journal articles or conference papers, and the processes and actors involved are far from uniform. In Sweden, the review process of books has seldom been formalized. However, more formal peer review of books has been identified as a response to the increasing importance placed on streamlined peer-reviewed publishing of journal articles in English, which has been described as a direct challenge to more pluralistic publication patterns found particularly in the humanities. In this study, we focus on a novel approach to book review, Kriterium, where an independent portal maintained by academic institutions oversees the reviewing of academic books. The portal administers peer reviews, providing a mark of quality through a process which involves reviewers, an academic coordinator, and an editorial board. The paper studies how this process functions in practice by exploring materials concerning 24 scholarly books reviewed within Kriterium. Our analysis specifically targets tensions identified in the process of reviewing books with a focus on three main themes, namely the intended audience, the edited volume, and the novel role of the academic coordinator. Moreover, we find that the two main aims of the portal–quality enhancement (making research better) and certification (displaying that research is of high quality)–are recurrent in deliberations made in the peer review process. Consequently, we argue that reviewing procedures and criteria of quality are negotiated within a broader discussion where more traditional forms of publishing are challenged by new standards and evaluation practices.

The health and life science domains are well known for their wealth of named entities found in large free text corpora, such as scientific literature and electronic health records. To unlock the value of such corpora, named entity recognition (NER) methods are proposed. Inspired by the success of transformer-based pretrained models for NER, we assess how individual and ensemble of deep masked language models perform across corpora of different health and life science domains—biology, chemistry, and medicine—available in different languages—English and French. Individual deep masked language models, pretrained on external corpora, are fined-tuned on task-specific domain and language corpora and ensembled using classical majority voting strategies. Experiments show statistically significant improvement of the ensemble models over an individual BERT-based baseline model, with an overall best performance of 77% macro F1-score. We further perform a detailed analysis of the ensemble results and show how their effectiveness changes according to entity properties, such as length, corpus frequency, and annotation consistency. The results suggest that the ensembles of deep masked language models are an effective strategy for tackling NER across corpora from the health and life science domains.

Objective: In 2016, the International Agency for Research on Cancer, part of the World Health Organization, released the Exposome-Explorer, the first database dedicated to biomarkers of exposure for environmental risk factors for diseases. The database contents resulted from a manual literature search that yielded over 8,500 citations, but only a small fraction of these publications were used in the final database. Manually curating a database is time-consuming and requires domain expertise to gather relevant data scattered throughout millions of articles. This work proposes a supervised machine learning pipeline to assist the manual literature retrieval process.Methods: The manually retrieved corpus of scientific publications used in the Exposome-Explorer was used as training and testing sets for the machine learning models (classifiers). Several parameters and algorithms were evaluated to predict an article’s relevance based on different datasets made of titles, abstracts and metadata.Results: The top performance classifier was built with the Logistic Regression algorithm using the title and abstract set, achieving an F2-score of 70.1%. Furthermore, we extracted 1,143 entities from these articles with a classifier trained for biomarker entity recognition. Of these, we manually validated 45 new candidate entries to the database.Conclusion: Our methodology reduced the number of articles to be manually screened by the database curators by nearly 90%, while only misclassifying 22.1% of the relevant articles. We expect that this methodology can also be applied to similar biomarkers datasets or be adapted to assist the manual curation process of similar chemical or disease databases.

In psychology, a Registered Reports system is key to preventing questionable research practices. Under this system, manuscripts, including their detailed protocols (i.e., hypothesis, experimental design, sample size, and methods of statistical analysis), are reviewed prior to data collection. If a protocol manuscript is accepted, publication of the full manuscript including the results and discussion is guaranteed in principle regardless of whether the collected data support the registered hypothesis. However, this assurance of publication might be broken under the impact of the COVID-19 pandemic: Begrudging withdrawal of an accepted protocol manuscript due to a difficulty to meet the deadline by compelling reasons (e.g., pandemic) has occurred. The present paper reports the first author’s real-life experience related to the collapse of the assurance of publication in the Registered Reports system and discusses the disbenefits of this collapse. Furthermore, we propose the implementation of a journal section specific to protocol manuscripts as a solution to the crisis of the Registered Reports system.

On the behest of the Office of Science and Technology Policy in the White House, six institutions, including ours, have created an open research dataset called COVID-19 Research Dataset (CORD-19) to facilitate the development of question-answering systems that can assist researchers in finding relevant research on COVID-19. As of May 27, 2020, CORD-19 includes more than 100,000 open access publications from major publishers and PubMed as well as preprint articles deposited into medRxiv, bioRxiv, and arXiv. Recent years, however, have also seen question-answering and other machine learning systems exhibit harmful behaviors to humans due to biases in the training data. It is imperative and only ethical for modern scientists to be vigilant in inspecting and be prepared to mitigate the potential biases when working with any datasets. This article describes a framework to examine biases in scientific document collections like CORD-19 by comparing their properties with those derived from the citation behaviors of the entire scientific community. In total, three expanded sets are created for the analyses: 1) the enclosure set CORD-19E composed of CORD-19 articles and their references and citations, mirroring the methodology used in the renowned “A Century of Physics” analysis; 2) the full closure graph CORD-19C that recursively includes references starting with CORD-19; and 3) the inflection closure CORD-19I, that is, a much smaller subset of CORD-19C but already appropriate for statistical analysis based on the theory of the scale-free nature of the citation network. Taken together, all these expanded datasets show much smoother trends when used to analyze global COVID-19 research. The results suggest that while CORD-19 exhibits a strong tilt toward recent and topically focused articles, the knowledge being explored to attack the pandemic encompasses a much longer time span and is very interdisciplinary. A question-answering system with such expanded scope of knowledge may perform better in understanding the literature and answering related questions. However, while CORD-19 appears to have topical coverage biases compared to the expanded sets, the collaboration patterns, especially in terms of team sizes and geographical distributions, are captured very well already in CORD-19 as the raw statistics and trends agree with those from larger datasets.

The present study examines the intellectual structure of research on coronavirus, as revealed from an author co-citation analysis using citation data retrieved from the Web of Science Core Collection and mapped to the PubMed database. Four major dimensions are identified: I) outbreaks, II) viral structure and function, III) vaccine and therapeutic development, and IV) coronaviruses found in a range of animals. The "outbreaks" dimension is by far the most prominent, dominated by reports on the three recent major outbreaks: COVID-19, severe acute respiratory syndrome, and Middle East respiratory syndrome. The focus of research on major outbreaks is on public health and clinical research, with focus on disease characterization, diagnosis, transmission, and clinical course. Notably, certain clinically important areas, such as mental health during outbreaks and viral surveillance, among others, did not stand out as identifiable specialties or topics in the coronavirus research landscape. Results from this study should contribute to the understanding of the coronavirus research landscape and to the identification of strengths and weaknesses of current research on COVID-19.

Los conflictos de intereses, los casos de fraude científico y las limitaciones a la investigación por las leyes de propiedad intelectual han llevado a cuestionar la idoneidad epistémica y de justicia social de la investigación financiada por la industria. El ideal de Ciencia Abierta –que promueve la transparencia, la colaboración y la rendición de cuentas– parece abordar las limitaciones de la investigación impulsada comercialmente. Sin embargo, el movimiento de Ciencia Abierta se centra en la investigación financiada con fondos públicos, alienta los lazos con el sector privado y crea nuevas estrategias para comercializar la ciencia. Así, la Ciencia Abierta termina contribuyendo a la comercialización de la ciencia y no a superar sus limitaciones; y la asimetría entre ciencia privada y pública, presente en la actual apelación a la ciencia abierta, termina comprometiendo los valores que promueve el ideal. Conflicts of interest, cases of scientific fraud, and limitations on research by intellectual property laws have led to questions about the epistemic and social justice adequacy of industry-funded research. The Open Science ideal - which promotes transparency, collaboration, and accountability-seems to address the limitations of commercially driven research. However, the Open Science movement focuses on publicly funded research, encourages ties to the private sector, and creates new strategies for commercializing science. Thus, Open Science ends up contributing to the commercialization of science rather than overcoming its limitations; and the asymmetry between private and public science, present in the current plea to Open Science, ends up compromising the values that this ideal promotes.

Scholarly organizations bring together experts to move forward specific areas of scientific endeavor. More than 5,000 scholarly societies exist world-wide yet little is known about the composition, evolution, and collaboration of experts within these organizations. This study presents general methods to study the evolution of a biomedical organization and its impact over time. Methods are illustrated in a case study that aims to capture the "Then and Now" science of the North American Vascular Biology Organization (NAVBO). Publications authored by the founding members who came together to create NAVBO in 1994 are compared with publications by those representing the society 25 years later. Google Scholar data was compiled for NAVBO members registered in 1994 (n = 70) and in 2019 (n = 465), some members being present in both data sets (n = 22). The 501 unique NAVBO members had more than 76,000 papers cited over 4,400,000 times. Several characteristics associated with the NAVBO members' output were revealed, including their high productivity, the high impact of their publications, and the predominant contribution of relatively small laboratories, as suggested by a low average number of authors per publication. To understand how NAVBO fostered scientific collaborations and exchanges of expertize, data was analyzed and visualized to show the evolution of member co-author networks. The UCSD Map of Science was used to communicate the evolution of scientific topics covered by NAVBO members helping to create a global picture of NAVBO's science "then and now." All workflows are available online in case other scholarly organizations would want to use them to map their own evolution and impact; and meta studies that communicate the inner workings of these important scholarly efforts can be conducted.

Comparing scientific impact among countries is fundamental for the development of effective strategies for science financing and assessment (Vinkler, 1986; King, 2004; Hermes-Lima et al., 2007a; Wagner et al., 2018). Such comparisons allow the definition of funding strategies based on inputs and outputs of the Research and Development (RD Leydesdorff et al., 2019). These top performer countries, in turn, can be emulated by less-developed nations. Usually, the number of scientific papers and the number of citations of a country are the main variables used for these comparisons. These metrics are frequently combined in a single indicator, such as the Citations Per Publication (CPP) (Lehmann et al., 2003; Moed, 2005; Waltman and van Eck, 2009; Vinkler, 2010) or the well-known h-index (Hirsch, 2005; Egghe, 2006; Glanzel, 2006; Hermes-Lima et al., 2007b; Franceschini and Maisano, 2010). However, each index has its caveats, and identifying simple, meaningful metrics for scientific productivity evaluation at country level has been a challenge (King, 2004; Leydesdorff et al., 2019). The limitations of the h-index have been vastly discussed in the literature (Bornmann and Daniel, 2005; Waltman and van Eck, 2012); and inter-country comparisons using the CPP have problems related to the time frame of an analysis. Thus, early publications from a given country will have more citations (therefore resulting in a higher CPP) than later publications, simply because papers published earlier had more time to be cited. For instance, Belgium was the country with the highest research impact in Medicine in 2019 (Scimago data), with a CPP value of 1.38 (12,768 publications; 17,595 citations), whereas its CPP value for 2016 is 17.73 (11,766 publications; 208,622 citations) and for 2013, 30.91 (22 times higher than in 2019). This limitation leads to the question: How can one avoid this caveat and meaningfully compare research impact among countries in a year-per-year basis? In this essay we present two simple methods to standardize CPP values along time, thus allowing for comparisons among countries in a time-independent fashion. Therefore, two metrics are presented: The Relative CPP and the Impact-Relative Rank Score (IRRS). Both of these procedures are based on the principle of normalizing the metric according to maximum-minimum values in the common set of observational units (countries) used for comparison.

Smart grid (SG) offers great advantages in renewable energy integration and has become a popular trend of modern power development recently; meanwhile China is the second most prolific country using SG. Hence the purpose of this study is to get access to the research status, development, and trends of SG in China based on the 3,558 published papers obtained from the WOS core library and application of the bibliometric method and visualization analysis software VOSviewer and alluvial diagrams. The results consequently demonstrate some valuable insights. Firstly, the volume of publications in China's SG is on the rise, and the cooperation between countries and institutions is getting closer. Besides, the research hotspots have obvious interdisciplinary characteristics. Taking into consideration the impact of the information and communication field on SG, the major current research hotspots include wireless sensor network (WSN), internet of things (IoT), smart meter, big data, and security. Taking into consideration the impact of SG on traditional power systems, the main hotspots cover demand response, micro-grid, distributed generation, and electric vehicle (EV). Furthermore, China's SG research shows a trend from a single theme to diversified development. The research themes during 2010–2018 have deepened with most studies focusing on the traditional power system. The findings of this paper provide some enlightenment on China's SG research, which can present scholars with an overview of the macro perspective, help them understand the latest development of the SG field in China and offer useful guidance for future research in this subject as well.

The disciplinary profiles of the mean citation rates across 22 research areas were analyzed for 107 countries/territories that published at least 3,000 papers that exceeded the entrance thresholds for the Essential Science Indicators (ESI; Clarivate Analytics) during the period from January 1, 2009 to December 31, 2019. The matrix of pairwise differences between any two profiles was analyzed with a non-metric multidimensional scaling (MDS) algorithm, which recovered a two-dimensional geometric space describing these differences. These two dimensions, Dim1 and Dim2, described 5,671 pairwise differences between countries' disciplinary profiles with a sufficient accuracy (stress = 0.098). A significant correlation (r = 0.81, N = 107, p < 0.0001) was found between Dim1 and the Indicator of a Nation's Scientific Impact (INSI), which was computed as a composite of the average and the top citation rates. The scientific impact ranking of countries derived from the pairwise differences between disciplinary profiles seems to be more accurate and realistic compared with more traditional citation indices.

This paper explores how cultural understandings of the autonomy and responsibility of science in modern society are manifested in two contemporary science novels about research misconduct in biomedical research. In doing so, it looks at several facets of the societal impact of and on public and private biomedical research, especially with respect to changing authority relations and their epistemic and institutional consequences. The analysis focuses on the multi-layered ways in which social and epistemic interests are treated in Allegra Goodman’s Intuition and Jennifer Rohn’s The Honest Look. Goodman’s novel demonstrates how, intensified by the economization of science, internal cultural and institutional aspects of the scientific field enable social configurations that, among others, encourage scientific malpractice and lead to the delay of research projects epistemically and socially worth pursuing. In contrast, Rohn’s novel exemplifies the corrosion of the ideal scientific ethos by profit-driven practices in private-sector biomedical sciences. The concluding discussion juxtaposes these findings with pertinent contemporary phenomena in modern science systems to provide a more substantial understanding of the interpenetration between science and other social spheres.

In this article, we aimed to analyze whether coronavirus disease 2019 (COVID-19)-associated articles were being subjected to the same standards of peer-review as non-COVID-19 articles. In order to do this, we taught eight PhD students manuscript reviewing skills and analyzed eight papers published in valued journals, five of them on COVID-19. Each selected publication was reviewed by at least two graduate students from a Scientific English class and two scientists in charge of the course at the Faculty of Medicine, Universidad de Chile. Several shortcomings were identified in the revised studies, particularly on those related to COVID-19, which led us to conclude that the emergency imposed by the COVID-19 has endangered the quality of the accepted studies. The COVID-19 pandemic caused by the novel Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has rapidly spread throughout the world. This virus is killing many people, and taking a massive physical, as well as mental toll, on the lives of all those that have been infected (Yi et al., 2020). The COVID-19 pandemic has severely depressed every country’s economy because governments have been obliged to apply quarantine measures to control the disease. Thus, the impact has not only been on people’s health, but also on their lifestyle and economic situation (Nicola et al., 2020). For these same reasons, many people, including politicians and leaders from different countries, have turned to the scientific community for answers regarding actions that need to be taken to control and treat the disease. Nevertheless, many scientific studies published these days concerning the COVID-19 virus, even those reported by important journals, fall short on experimental evidence to support their conclusions. It is clear that great pressure exists to rapidly know more about this virus and how to stop the pandemic. Notwithstanding, we believe that this is leading editors and reviewers to accept manuscripts that would have never been considered for publication under different circumstances. It is not bad science, it is just not the complete story; the story that the good, high impact journals would normally ask for, when peer reviewing manuscripts for publication.

Background: Many academic institutions and journals disseminate research through social media to increase accessibility and reach a wider audience. “Visual Abstracts” are well-suited for social media dissemination, and have been adopted by some as a novel approach to increase engagement with academic content. Visual abstracts are a visual representation of key methods and findings from a traditional peer-reviewed publication. This study expands on previous research by examining the impact of visual abstracts compared to traditional text abstracts to disseminate research produced in a national research center focused on preventing Veteran suicide. Methods: A prospective, randomized crossover design was utilized to compare Twitter posts with a visual abstract to those with a simple screen grab of the PubMed abstract (n = 50 journal publications). Outcomes were measured using native Twitter Analytics to track impressions, retweets, total engagements, and link clicks about 28 days post-tweet, and Altmetric It to track additional alternative metric outcomes. Results: Visual abstract tweets were associated with a significantly higher number of impressions (p < 0.001), retweets (p < 0.001), and link clicks (p = 0.02) compared with text abstract tweets. Conclusions: In line with results from prior studies, we found that visual abstracts resulted in significantly greater research dissemination and social media engagement via retweets and link clicks compared with text tweets. These findings provide further evidence that visual abstracts increase awareness and readership of journal publications, and that Twitter is an effective platform for research dissemination beyond the traditional academic researcher audience. Implications highlight the importance of social media for suicide prevention advocates, Veteran health researchers and other stakeholders to communicate research findings.

This study analyzes how characteristics in the careers of star researchers affect the outcomes of research and development (R&D), based on a case study in a Japanese semiconductor company. By analyzing the collaboration network of patent coinventors in the company, we observe that long-term exposition and collaboration with other high-achieving researchers play a significant role in determining a successful career, that is, in terms of productivity and impact. Also, a deeper exploration of the characteristics of a selected group of star researchers in a company's R&D division helped to identify that it takes 10–15 years to generate remarkable achievements in the form of filing patents that are widely cited at a later stage. This period is followed by low productivity, thereby revealing productivity peaks such as those observed in the artistic and scientific careers but at different times. Industry researchers tend to follow a more fixed pattern. Additionally, we analyzed the influence of having star researchers in coinventor teams. Our results suggest that staying aligned in one research direction, long-term exposure to a diverse group of researchers, and early mentorship helped the researchers in our study to attain their achievements.

Domain analysis by means of scientific collaboration enables evidencing aspects that are involved in the establishment of relationships between researchers and institutions, such as the influence of institutional management models for the development of collaborative networks. This article aims to analyze the domain through the scientific collaboration network of the National Institute of the Atlantic Forest (INMA), a research unit currently affiliated to the Brazilian Ministry of Science, Technology and Innovation (MCTI), formerly known as the Professor Mello Leitão Museum of Biology (MBML), in order to acknowledge the institutional research identity in its historical journey as a public institution. It is thus analyzed how co-authorship constitutes this network and what research profile it reveals. Co-authorship analysis is adopted as a methodology, as well as the analysis of administrative documents with the survey and categorization of employees, regarding their types of ties to the institution, combined with searches in the Scopus database for the corroboration of institutional affiliations. A corpus of 138 articles published by 41 researchers from 1993 to 2019 is consolidated in this base, which represents 44% of the Institute’s total research collaborators (93 collaborators). Of these 41, 92.5% have temporary links, such as scholarship holders and/or volunteers, with the remaining being public workers. It is recognized that the citation impact of the scientific production of scholarship holders, consigned to the Institute, is less than the citation impact of the volunteers' and public workers' production. It is evidenced that eight of the ten publications with the greatest impact and thematic prominence correspond to the field of zoology, with emphasis on the fields of herpetology and primatology. Macro-level collaborative relations are more intense with the United States, in both areas mentioned, covering 16% of the total corpus of articles in cooperation with that country. Zoology, besides its greater impact, accounts for more than half of the corpus production (65.9%).On the other hand, botany is responsible for 30.4% of the corpus, with its dispersed international cooperation in a broad variety of countries. Individual authorship articles are 57% consigned to botany. In summary, the accomplished analysis will contribute to the development of institutional domain analysis methodologies that present scientific collaboration as a basic procedure.

In recent years, the science of science policy has been facilitated by the greater availability of and access to digital data associated with the science, technology, and innovation enterprise. Historically, most of the studies from which such data are derived have been econometric or "scientometric" in nature, focusing on the development of quantitative data, models, and metrics of the scientific process as well as outputs and outcomes. Broader definitions of research impact, however, necessitate the use of qualitative case-study methods. For many years, U.S. federal science agencies such as the National Institutes of Health have demonstrated the impact of the research they support through tracing studies that document critical events in the development of successful technologies. A significant disadvantage and barrier of such studies is the labor-intensive nature of a case study approach. Currently, however, the same data infrastructures that have been developed to support scientometrics may also facilitate historical tracing studies. In this paper, we describe one approach we used to discover long-term, downstream outcomes of research supported in the late 1970's and early 1980's by the National Institute of General Medical Sciences, a component of the National Institutes of Health.

Expectations of fair competition underlie the assumption that academia is a meritocracy. However, bias may reinforce gender inequality in peer review processes, unfairly eliminating outstanding individuals. Here, we ask whether applicant gender biases peer review in a country top ranked for gender equality. We analyzed peer review assessments for recruitment grants at a Swedish medical university, Karolinska Institutet (KI), during four consecutive years (2014–2017) for Assistant Professor (n = 207) and Senior Researcher (n = 153). We derived a composite bibliometric score to quantify applicant productivity and compared this score with subjective external (non-KI) peer reviewer scores of applicants' merits to test their association for men and women, separately. To determine whether there was gender segregation in research fields, we analyzed publication list MeSH terms, for men and women, and analyzed their overlap. There was no gendered MeSH topic segregation, yet men and women with equal merits are scored unequally by reviewers. Men receive external reviewer scores resulting in stronger associations (steeper slopes) between computed productivity and subjective external reviewer scores, meaning that peer reviewers “reward” men's productivity with proportional merit scores. However, women applying for assistant professor or senior researcher receive only 32 or 92% of the score men receive, respectively, for each additional composite bibliometric score point. As productivity increases, the differences in merit scores between men and women increases. Accumulating gender bias is thus quantifiable and impacts the highest tier of competition, the pool from which successful candidates are ultimately chosen. Track record can be computed, and granting organizations could therefore implement a computed track record as quality control to assess whether bias affects reviewer assessments.

The unprecedented, explosive growth of the COVID-19 domain presents challenges to researchers to keep up with research knowledge within the domain. This article profiles this research to help make that knowledge more accessible via overviews and novel categorizations. We provide websites offering means for researchers to probe more deeply to address specific questions. We further probe and reassemble COVID-19 topical content to address research issues concerning topical evolution and emphases on tactical vs. strategic approaches to mitigate this pandemic and reduce future viral threats. Data suggest that heightened attention to strategic, immunological factors is warranted. Connecting with and transferring in research knowledge from outside the COVID-19 domain demand a viable COVID-19 knowledge model. This study provides complementary topical categorizations to facilitate such modeling to inform future Literature-Based Discovery endeavors.

Background: Concerns with problematic research are primarily attributed to statistics and methods used to support data. Language, as an extended component of problematic research in published work, is rarely given the same attention despite language's equally important role in shaping the discussion and framings of presented data. Purpose: This study uses a topic modeling approach to study language as a predictor of potential bias among collected publication histories of several health research areas. Methods: We applied Latent Dirichlet Allocation (LDA) topic models to dissect publication histories disaggregated by three factors commonly cited as language influencers: (1) time, to study ADHD pharmacotherapy; (2) funding source, to study sugar consumption; and (3) nation of origin, to study Pediatric Highly-Active Anti-Retroviral Therapy (P-HAART). Results: We found that, for each factor, there were notable differences in language among each corpus when disaggregated by each factor. For time, article content changed to reflect new trends and research practices for the commonly prescribed ADHD medication, Ritalin. For funding source, industry and federally funded studies had differing foci, despite testing the same hypothesis. For nation of origin, regulatory structures between the United States and Europe seemingly influenced the direction of research. Conclusion: This work presents two contributions to ethics research: (1) language and language framing should be studied as carefully as numeric data among studies of rigor, reproducibility, and transparency; and (2) the scientific community should continue to apply topic models as mediums to answer hypothesis-driven research questions.