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and analyzed to discover complex patterns through simulations and experiments, and most scientific reference works have been replaced by online curated datasets.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

Data citation and the citation graph

The citation graph is a computational artifact that is widely used to represent the domain of published literature. It represents connections between published works, such as citations and authorship. Among other things, the graph supports the computation of bibliometric measures such as h-indexes and impact factors. There is now an increasing demand that we should treat the publication of data in the same way that we treat conventional publications. In particular, we should cite data for the same reasons that we cite other publications. In this paper we discuss what is needed for the citation graph to represent data citation. We identify two challenges: to model the evolution of credit appropriately (through references) over time and to model data citation not only to a data set treated as a single object but also to parts of it. We describe an extension of the current citation graph model that addresses these challenges. It is built on two central concepts: citable units and reference subsumption. We discuss how this extension would enable data citation to be represented within the citation graph and how it allows for improvements in current practices for bibliometric computations, both for scientific publications and for data

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

Archiving and maintaining curated databases

Curated databases represent a substantial amount of effort by a dedicated group of people to produce a definitive description of some subject area. The value of curated databases lies in the quality of the data that has been manually collected, corrected, and annotated by human curators. Many curated databases are continuously modified and new releases being published on the Web. Given that curated databases act as publications, archiving them becomes a necessity to enable retrieval of particular database versions. A system trying to archive evolving databases on the Web faces several challenges. First and foremost, the systems needs to be able to effciently maintain and query multiple snapshots of ever growing databases. Second, the system needs to be flexible enough to account for changes to the database structure and to handle data of varying quality. Third, the system needs to be robust and invulnerable to local failure to allow reliable long-term preservation of archived information. Our archive management system XArch addresses the first challenge by providing the functionality to maintain, populate, and query archives of database snapshots in hierarchical format. This presentation intends to give an overview of our ongoing efforts of improving XArch regarding (i) archiving evolving databases, (ii) supporting distributed archives, and (iii) using our archives and XArch as the basis of a system to create, maintain, and publish curated databases

Telescope Bibliographies: an Essential Component of Archival Data Management and Operations

Assessing the impact of astronomical facilities rests upon an evaluation of the scientific discoveries which their data have enabled. Telescope bibliographies, which link data products with the literature, provide a way to use bibliometrics as an impact measure for the underlying data. In this paper we argue that the creation and maintenance of telescope bibliographies should be considered an integral part of an observatory's operations. We review the existing tools, services, and workflows which support these curation activities, giving an estimate of the effort and expertise required to maintain an archive-based telescope bibliography.Comment: 10 pages, 3 figures, to appear in SPIE Astronomical Telescopes and Instrumentation, SPIE Conference Series 844

Tracking citations and altmetrics for research data: Challenges and opportunities

Methods for determining research quality have long been debated but with little lasting agreement on standards, leading to the emergence of alternative metrics. Altmetrics are a useful supplement to traditional citation metrics, reflecting a variety of measurement points that give different perspectives on how a dataset is used and by whom. A positive development is the integration of a number of research datasets into the ISI Data Citation Index, making datasets searchable and linking them to published articles. Yet access to data resources and tracking the resulting altmetrics depend on specific qualities of the datasets and the systems where they are archived. Though research on altmetrics use is growing, the lack of standardization across datasets and system architecture undermines its generalizability. Without some standards, stakeholders' adoption of altmetrics will be limited

Using Links to prototype a Database Wiki

Both relational databases and wikis have strengths that make them attractive for use in collaborative applications. In the last decade, database-backed Web applications have been used extensively to develop valuable shared biological references called curated databases. Databases offer many advantages such as scalability, query optimization and concurrency control, but are not easy to use and lack other features needed for collaboration. Wikis have become very popular for early-stage biocuration projects because they are easy to use, encourage sharing and collaboration, and provide built-in support for archiving, history-tracking and annotation. However, curation projects often outgrow the limited capabilities of wikis for structuring and efficiently querying data at scale, necessitating a painful phase transition to a database-backed Web application. We perceive a need for a new class of general-purpose system, which we call a Database Wiki, that combines flexible wiki-like support for collaboration with robust database-like capabilities for structuring and querying data. This paper presents DBWiki, a design prototype for such a system written in the Web programming language Links. We present the architecture, typical use, and wiki markup language design for DBWiki and discuss features of Links that provided unique advantages for rapid Web/database application prototyping