Citation and peer review of data: moving towards formal data publication

This paper discusses many of the issues associated with formally publishing data in academia, focusing primarily on the structures that need to be put in place for peer review and formal citation of datasets. Data publication is becoming increasingly important to the scientific community, as it will provide a mechanism for those who create data to receive academic credit for their work and will allow the conclusions arising from an analysis to be more readily verifiable, thus promoting transparency in the scientific process. Peer review of data will also provide a mechanism for ensuring the quality of datasets, and we provide suggestions on the types of activities one expects to see in the peer review of data. A simple taxonomy of data publication methodologies is presented and evaluated, and the paper concludes with a discussion of dataset granularity, transience and semantics, along with a recommended human-readable citation syntax

Automating Software Citation using GitCite

The ability to cite software and give credit to its authors and contributors is increasingly important. While the number of online open-source software repositories has grown rapidly over the past few years, few are being properly cited when used due to the difficulty of creating appropriate citations and the lack of automated techniques. This paper presents GitCite, a model for software citation with version control which enables citations to be inferred for any project component based on a small number of explicit citations attached to subdirectories/files, and an implementation that integrates with Git and GitHub. The implementation includes a browser extension and a local executable tool, which enable citations to be added/modified/deleted to software project repositories and managed through functions such as fork/merge/copy

Automating Software Citation using GitCite

The ability to cite software and give credit to its authors is increasingly important. This paper presents a model for software citation with version control, and an implementation that integrates with Git and GitHub. The implementation includes a browser extension and a local executable tool, which enable citations to be added/modified/deleted to software project repositories and managed through functions such as fork/merge/copy

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

A Rule-Based Citation System for Structured and Evolving Datasets

We consider the requirements that a citation system must ful\ufb01ll in order to cite structured and evolving data sets. Such a system must take into account variable granularity, context and the temporal dimension. We look at two examples and discuss the possible forms of citation to these data sets. We also describe a rule-based system that generates citations which ful\ufb01ll these requirements