Jupyter notebooks as discovery mechanisms for open science: Citation practices in the astronomy community

Citing data and software is a means to give scholarly credit and to facilitate access to research objects. Citation principles encourage authors to provide full descriptions of objects, with stable links, in their papers. As Jupyter notebooks aggregate data, software, and other objects, they may facilitate or hinder citation, credit, and access to data and software. We report on a study of references to Jupyter notebooks in astronomy over a 5-year period (2014-2018). References increased rapidly, but fewer than half of the references led to Jupyter notebooks that could be located and opened. Jupyter notebooks appear better suited to supporting the research process than to providing access to research objects. We recommend that authors cite individual data and software objects, and that they stabilize any notebooks cited in publications. Publishers should increase the number of citations allowed in papers and employ descriptive metadata-rich citation styles that facilitate credit and discovery

Astrolabe: Curating, Linking and Computing Astronomy's Dark Data

Where appropriate repositories are not available to support all relevant astronomical data products, data can fall into darkness: unseen and unavailable for future reference and re-use. Some data in this category are legacy or old data, but newer datasets are also often uncurated and could remain "dark". This paper provides a description of the design motivation and development of Astrolabe, a cyberinfrastructure project that addresses a set of community recommendations for locating and ensuring the long-term curation of dark or otherwise at-risk data and integrated computing. This paper also describes the outcomes of the series of community workshops that informed creation of Astrolabe. According to participants in these workshops, much astronomical dark data currently exist that are not curated elsewhere, as well as software that can only be executed by a few individuals and therefore becomes unusable because of changes in computing platforms. Astronomical research questions and challenges would be better addressed with integrated data and computational resources that fall outside the scope of existing observatory and space mission projects. As a solution, the design of the Astrolabe system is aimed at developing new resources for management of astronomical data. The project is based in CyVerse cyberinfrastructure technology and is a collaboration between the University of Arizona and the American Astronomical Society. Overall the project aims to support open access to research data by leveraging existing cyberinfrastructure resources and promoting scientific discovery by making potentially-useful data in a computable format broadly available to the astronomical community.Comment: Accepted for publication in the Astrophysical Journal Supplement Series, 22 pages, 2 figure

Scientific data citation : scoping review

Objetivo: Para acompanhar a evolução dos estudos relacionados a dados científicos, investigou-se o significado das citações a eles, buscando responder: 1) Quais as motivações dos pesquisadores para citar dados científicos?; 2) Quais as práticas de citação de dados apresentadas nas áreas cobertas pelo presente estudo?; 3) Quais as análises métricas para citação de dados? Método: Caracteriza-se como pesquisa do tipo qualitativa e descritiva, sendo uma revisão de literatura do tipo Scoping Review, com busca às bases de dados Emerald, LISA, LISTA, Scopus e Web of Science. Resultados: Como motivação, identificaram-se estudos sobre a correlação do incremento de citações às publicações tradicionais ao citarem os dados que as embasavam, muitos confirmaram a correlação, outros não, surgindo também a hipótese de causa comum: qualidade da pesquisa associada a mais recursos. Quanto às práticas, a comunidade está ciente que as citações atuais a dados não estão padronizadas, surgindo a tendência para a adoção de um padrão de citação que atenda às demandas de diferentes tipos de dados. Esta falta de padrão dificulta a análise métrica de citação a dados científicos, que ainda precisa ser explorada em pesquisas, tendo em vista que há uma repetição em utilizar as mesmas técnicas da citação tradicional para essa nova fonte de informação. Conclusões: Promover o avanço da ciência é a principal vantagem em disponibilizar dados, mas existem dificuldades técnicas e de atribuição de crédito que precisam ser enfrentadas em conjunto pelos pesquisadores, instituições, agências de fomento, repositórios de dados e equipes editoriais de publicações.Objective: This paper investigates the meaning assigned to data citation in order to follow the evolution of studies related to data citation, it tries to answer: 1) What are the motivations of researchers to cite scientific data?; 2) What are the data citation practices presented by the areas covered by this study?; 3) What are the metric analysis for data citation? Methods: It is a qualitative and descriptive research, being a scoping review of literature, by searching the Emerald, LISA, LIST, Scopus and Web of Science databases. Results : The studies investigated the correlation of citations increment to traditional publications by citing the data that supported them, many studies confirmed the correlation, others did not, and a common cause hypothesis arose: research quality associated with more resources. As for practices, the community is aware that current citations to data are not standardized, and there is a tendency to adopt a citation standard that meets the demands of different types of data. This lack of standard hinders the metric analysis of citation to scientific data that still needs to be explored in research, given that there is a repetition in using the same techniques of traditional citation for this new source of information. Conclusions : Promoting the progress of science is the main advantage in making data available, but there are credit and technical difficulties that need to be tackled together by researchers, institutions, funding agencies, data repositories, and publishing editorial teams