Understanding Software in Research: Initial Results from Examining Nature and a Call for Collaboration

This lightning talk paper discusses an initial data set that has been gathered to understand the use of software in research, and is intended to spark wider interest in gathering more data. The initial data analyzes three months of articles in the journal Nature for software mentions. The wider activity that we seek is a community effort to analyze a wider set of articles, including both a longer timespan of Nature articles as well as articles in other journals. Such a collection of data could be used to understand how the role of software has changed over time and how it varies across fields.Comment: lightning talk submitted to WSSSPE5.2 (http://wssspe.researchcomputing.org.uk/wssspe5/

DataCite as a novel bibliometric source: Coverage, strengths and limitations

This paper explores the characteristics of DataCite to determine its possibilities and potential as a new bibliometric data source to analyze the scholarly production of open data. Open science and the increasing data sharing requirements from governments, funding bodies, institutions and scientific journals has led to a pressing demand for the development of data metrics. As a very first step towards reliable data metrics, we need to better comprehend the limitations and caveats of the information provided by sources of open data. In this paper, we critically examine records downloaded from the DataCite's OAI API and elaborate a series of recommendations regarding the use of this source for bibliometric analyses of open data. We highlight issues related to metadata incompleteness, lack of standardization, and ambiguous definitions of several fields. Despite these limitations, we emphasize DataCite's value and potential to become one of the main sources for data metrics development.Comment: Paper accepted for publication in Journal of Informetric

Theory and Practice of Data Citation

Citations are the cornerstone of knowledge propagation and the primary means of assessing the quality of research, as well as directing investments in science. Science is increasingly becoming "data-intensive", where large volumes of data are collected and analyzed to discover complex patterns through simulations and experiments, and most scientific reference works have been replaced by online curated datasets. Yet, given a dataset, there is no quantitative, consistent and established way of knowing how it has been used over time, who contributed to its curation, what results have been yielded or what value it has. The development of a theory and practice of data citation is fundamental for considering data as first-class research objects with the same relevance and centrality of traditional scientific products. Many works in recent years have discussed data citation from different viewpoints: illustrating why data citation is needed, defining the principles and outlining recommendations for data citation systems, and providing computational methods for addressing specific issues of data citation. The current panorama is many-faceted and an overall view that brings together diverse aspects of this topic is still missing. Therefore, this paper aims to describe the lay of the land for data citation, both from the theoretical (the why and what) and the practical (the how) angle.Comment: 24 pages, 2 tables, pre-print accepted in Journal of the Association for Information Science and Technology (JASIST), 201

Energy Research Infrastructures in Europe and Beyond: Mapping an Unmapped Landscape

European research and innovation policy highlights the importance of transnational scientific collaboration, International collaborations in science concentrates and magnifies resources for conducting research and foster innovation. Often, individual institutions, or even individual European countries, cannot provide the right capabilities by themselves. Joint facilities and Research Infrastructures (RIs) are therefore of high importance, and through Horizon 2020 and Horizon Europe nearly €5 bn EU funding is set aside for these institutions. Considering the large-scale funding and the perceived importance of RIs, a better understanding of their roles, functions, and usefulness is highly relevant to of European integration studies. While ‘Research Infrastructures’ has become a fixed terminology of EU-policy, conceptually defining RIs remain a matter of academic debate. We contribute to the “what is an RI?”-discussion by synthesizing existing literature and presenting novel empirical data from the energy domain mapping of the Horizon 2020-project Research Infrastructures in the International Landscape (RISCAPE). We provide insights into the process of mapping an hitherto largely unknown landscape of global Energy RIs. These insights touch upon both definitional issues relevant to the RI-field and methodological concerns for future landscape analyses. Finally, the article suggests that when it comes to energy research, RI-terminology might be misplaced as a catchall modern synonym for “gold standard world-class science”.</p

Metrics for Evaluating the Impact of Data Sets

Research is a social activity, involving a complex array of resources, actors, activities, attitudes, and traditions (Sugimoto & Larivière 2018). There are many norms, including the sharing of new work in the form of books and journal articles and the use of citations and acknowledgments to recognize the influence of earlier work, but what it means to produce impactful scholarship is difficult to define and measure. The goals, methods, metrics, and utility of evaluating the impact of data sets are situated within this broader context of scholarly communication and evaluation. An understanding of the dynamic history, current practices, concepts, and critiques of measuring impact for and beyond research data sets can help researchers navigate the scholarly dissemination landscape more strategically and gain agency in regard to how they and their work are evaluated and described. What is research impact? As Roemer and Borchardt (2015) describe, the concept involves two important ideas: the change a work influences and the strength of this effect. These effects can include, but are not limited to, advances in understanding and decision making, policy creation and change, economic development, and societal benefits. For example, rich documentation of an endangered language might lead to and support community and governmental revitalization efforts. However, the linkages between a specific scholarly product and its effects are rarely direct, there are disciplinary differences between how research is communicated and endorsed, and some outcomes take a very long time to manifest (Greenhalgh et al. 2016). This makes the assessment of research impact very labor intensive, even at a small scale, so researchers and decision makers often rely on data and metrics that are regarded as indicative of certain kinds of impact

How and Why Do Researchers Reference Data ? A Study of Rhetorical Features and Functions of Data References in Academic Articles

La réutilisation des données est une pratique courante dans les sciences sociales. Il peut être difficile de comprendre les motivations pour référencer les données. Cet article étudie comment et pourquoi les chercheurs font référence aux données scientifiques dans leurs écrits universitaires. Nous illustrons l’utilisation de la typologie en codant la recherche multidisciplinaire d’ articles. La typologie offre un moyen systématique de documenter et d’analyser les récits des chercheurs

A Practical Guide

This document, intended for RI policy-makers, funding agencies, RI managers, and other relevant actors, considers the major aspects of assessing the socio-economic impacts of RIs. The main purpose of socio-economic impact assessment is to prove to society that RIs bring benefit to the entire society and that their relevance goes far beyond pure science. It also helps RI managers in setting strategic directions; thus it is recommended an assessment to be conducted every 4-5 years. RI managers and policy-makers, however, face several challenges when organising or commissioning an assessment. First, there is no ‘blueprint’ or ‘easy-to-follow’ manual for assessing RIs: there is no set of methods or indicators that would be automatically appropriate for every RI; each RI needs to be understood first, and then assessed in its own context. Second, ex ante evaluation, monitoring and the assessment of socio-economic impacts are closely interlinked. There are at least two preconditions for a useful assessment exercise. The intervention logic of a given RI – why investment is needed, what impact can be expected and through what mechanisms – needs to be clarified as part of an ex ante evaluation. Further, an appropriate monitoring system should be in place not only for the purpose of monitoring, but also to systematically collect relevant data for socio-economic impact assessment. Third, timing is crucial: to measure certain impacts, one might need to wait. Fourth, some RI managers and/or researchers may be reluctant to engage in assessment exercises. However, assessment is a must, as RIs are funded by public money. Fifth, the evaluation culture in general is weak in quite a few countries, including several in the Danube macro-region, hence the required methodological skills are missing or not yet sufficiently developed. Assessing the socio-economic impacts of RIs is a necessity even in these countries, for the above reason. Learning by doing can contribute to developing missing capacities and skills

Draft list of key international Research Infrastructures, selected methodologies and proposed aspects of complementarities to analyse in the energy sector, RISCAPE deliverable 6.1.

This paper presents a draft list of key international Research Infrastructures within the field of Energy Research. The paper is written as part of the RISCAPE-project, evaluating European Research Infrastructures in the global landscape, and it will be used as a starting point for the upcoming international landscape analysis of Energy Research Infrastructures. This document supplements the European Energy RI sector report which is written in parallel with this document. </p

A literatura sobre Ciência Aberta na Ciência da Informação : um estudo na LISTA e e-LiS.

Dissertação (mestrado)—Universidade de Brasília, Faculdade de Estudos Sociais Aplicados, Departamento de Ciência da Informação e Documentação, 2019.O estudo tem como objetivo identificar a perspectiva da Ciência da Informação sobre a Ciência Aberta, através da literatura. Para isso, foram investigados os movimentos pertencentes à ciência aberta: acesso aberto, dados aberto, código aberto, ciência cidadã, recursos educacionais abertos, cadernos de laboratório abertos e revisão por pares aberta. A pesquisa é caracterizada como descritiva com natureza qualitativa, e o levantamento e análise de dados foram realizados de acordo com as estratégias da Revisão Sistematizada de Literatura. Na análise dos dados, os artigos selecionados foram analisados e classificados de acordo com as cinco escolas de pensamento propostas por Fecher e Friesike (2013). São elas: Pública, Democrática, Pragmática, Infraestrutura e Medição. Os artigos analisados foram retirados da base Library, Information Science & Technology Abstracts with Full Text (LISTA) e do repositório eLiS, ambos especializados em Ciência da Informação. Por meio da estratégia de busca e dos filtros definidos, foram encontrados 101 resultados, somadas as duas fontes de informação. Após a análise dos artigos, pode-se perceber um maior interesse de pesquisadores da Ciência da Informação pelos movimentos de Acesso Aberto e Dados Abertos. As escolas de Infraestrutura e Pública foram as menos abordadas. Além disso, percebe-se a escassez de artigos que englobam todas as escolas de pensamento da ciência abertaCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES).The study aims to identify the perspective of Information Science on Open Science, through the literature. For this, the movements belonging to open Science were investigated: open access, open data, open source, citizen science, open educational resources, open lab notebooks and open peer review. The research is characterized as descriptive with a qualitative nature, and the data collection and analysis will be carried out according to the strategies of the Systematized Literature Review. In the analysis of the data, the selected articles were analyzed and classified according to the five schools of thought proposed by Fecher and Friesike (2013). They are: Public, Democratic, Pragmatic, Infrastructure and Measurement. The articles analyzed were taken from the Library, Information Science & Technology Abstracts with Full Text (LISTA) and from the repository e-LiS, both specialized in Information Science. Through the search strategy and the defined filters, 101 results were found, summing the two sources of information. After the analysis of the articles, one can notice a greater interest of researchers of the Information Science for the movements of Open Access and Open Data. Infrastructure and Public schools were the least addressed. In addition, there is a shortage of articles that encompass all the Open Science schools of thought

Qu’est-ce que le travail scientifique des données ?

Puisant ses analyses et ses exemples dans des champs scientifiques variés, cet ouvrage (dont l’original est paru en 2015 chez MIT Press) offre une étude inédite des utilisations des données au sein des infrastructures de la connaissance – utilisations qui varient largement d’une discipline à l’autre. Bien que le big data ait régulièrement les honneurs de la presse des deux côtés de l’Atlantique, Christine L. Borgman met en évidence qu’il vaut mieux disposer des bonnes données qu’en avoir beaucoup. Elle montre également que les little data peuvent s’avérer aussi précieuses que les big data, et, que, dans bien des cas, il n’y a aucune donnée, parce que les informations pertinentes n’existent pas, sont introuvables ou sont indisponibles… Au travers d’études de cas pratiques issus d’horizons divers, Christine L. Borgman met aussi en lumière que les données n’ont ni valeur ni signification isolément : elles s’inscrivent au sein d’une infrastructure de la connaissance, c’est-à-dire d’un écosystème de personnes, de pratiques, de technologies, d’institutions, d’objets matériels et de relations. Pour l’autrice, gérer les données et les exploiter sur le long terme requiert ainsi des investissements massifs dans ces infrastructures de la connaissance. L’avenir de la recherche, dans un monde en réseau, en dépend