Linking to Data - Effect on Citation Rates in Astronomy

Henneken, Edwin A.; Accomazzi, Alberto

doi:10.48550/arxiv.1111.3618

Cited by 9 publications

(13 citation statements)

References 8 publications

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“…Integral to supporting astronomical research, the Astrophysics Data System (ADS) is a robust open-access tool, indexing nearly all astronomical publications and associated metadata, including links to data when available (Accomazzi & Dave 2011;Accomazzi et al 2011), and with capabilities for visualizing citation and collaboration patterns (Henneken et al 2009). Using the ADS, Henneken & Accomazzi (2011) show that publications with links to data are more highly cited than publications that do not link to data, an important finding for open data and data sharing initiatives (Henneken 2015).…”

Section: Towards Open Data and Open Sciencementioning

confidence: 97%

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

Heidorn¹,

Stahlman²,

Steffen³

2018

ApJS

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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.

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Section: Towards Open Data and Open Sciencementioning

confidence: 97%

“…As noted by Henneken & Accomazzi (2011), publications based on datasets are essentially expressions of data. Journal publishers are innovating new methods of digital publication that provide rich scientific data beneath a text publication itself.…”

Section: Focus On Low-hanging Fruitmentioning

confidence: 99%

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

Heidorn¹,

Stahlman²,

Steffen³

2018

ApJS

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“…A significant amount of research has shown that freely releasing the code, data, and published text (either through a preprint or open-access article) leads to increased citations (Frachtenberg, 2022;Dorch et al, 2015;Henneken & Accomazzi, 2011;Piwowar & Vision, 2013;Piwowar et al, 2007;Colavizza et al, 2020;Fu & Hughey, 2019;Christensen et al, 2019). In some domains, the increase was 2-fold (Wahlquist et al, 2018).…”

Section: Increased Exposurementioning

confidence: 99%

Computational Reproducibility Within Prognostics and Health Management

Hahn¹,

Mechefske²

2022

Preprint

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Scientific research frequently involves the use of computational tools and methods. Providing thorough documentation, open-source code, and data -the creation of reproducible computational research -helps others understand a researcher's work. Here, we explore computational reproducibility, broadly, and from within the field of prognostics and health management (PHM). The adoption of reproducible computational research practices remains low across scientific disciplines and within PHM. Our text mining of more than 300 articles, from publications engaged in PHM research, showed that fewer than 4% of researchers made their code and data available to others. Although challenges remain, there are also clear opportunities, and benefits, for engaging in reproducible computational research. Highlighting an opportunity, we introduce an open-source software tool, called PyPHM, to assist PHM researchers in accessing and preprocessing common industrial datasets.

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“…A small number of studies have found positive relationships between data sharing and increased citations in a range scientific fields (e.g. Piwowar et al, 2007;Henneken and Accomazzi, 2011;Dorch et al, 2015). Furthermore, Gleditsch et al (2003) studied the Journal of Peace Research, and concluded that making data available seemed to serve authors well in terms of citations.…”

Section: Fear Of the Scoopmentioning

confidence: 99%

The Stark Realities of Reproducible Statistically Orientated Sociological Research: Some Newer Rules of the Sociological Method

Connelly¹,

Gayle²

2022

Preprint

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There is increasing concern that research is not transparent and that empirical results are often impossible to reproduce. Guidelines for undertaking reproducible research have been proposed by a number of academic areas (e.g. computational economics, psychology and medical research), however currently there are no guidelines for sociological research. This methodological paper provides guidance for undertaking reproducible statistically oriented sociological research. We provide an extended demonstration of the issues associated with reproducing results and undertaking transparent analyses. We draw on suitable concepts and techniques from open research, e-research and computing. We propose a set of Newer Rules of the Sociological Method, for undertaking transparent statistically oriented sociological research that supports reproducibility.

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Linking to Data - Effect on Citation Rates in Astronomy

Cited by 9 publications

References 8 publications

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

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

Computational Reproducibility Within Prognostics and Health Management

The Stark Realities of Reproducible Statistically Orientated Sociological Research: Some Newer Rules of the Sociological Method

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