Definitions of <i>dataset</i>
 in the scientific and technical literature

Renear, Allen H.; Sacchi, Simone; Wickett, Karen M.

doi:10.1002/meet.14504701240

Cited by 50 publications

(40 citation statements)

References 6 publications

(13 reference statements)

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“…As Borgman (2012b) notes, however, the dimensions of data identity are unsettled; there is not yet a clearly agreed upon set of dimensions for data identity, nor for the levels needed to read, interpret, combine, compute upon and trust data. Similarly, Wynholds (2011) states that data are currently "unruly and poorly bounded objects" within scholarly works, and Renear, Sacchi, and Wickett (2010) assert that "Although definitions of data sets do appear to fit a common pattern, with recurring phrases and semantically similar terms, it is clear that there is no single well-defined concept of data set" (p. 3).…”

Section: Identitymentioning

confidence: 99%

Out of Cite, Out of Mind: The Current State of Practice, Policy, and Technology for the Citation of Data

Socha¹

2013

Data Sci. J.

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Edited by Yvonne M. Socha PREFACEThe growth in the capacity of the research community to collect and distribute data presents huge opportunities. It is already transforming old methods of scientific research and permitting the creation of new ones. However, the exploitation of these opportunities depends upon more than computing power, storage, and network connectivity. Among the promises of our growing universe of online digital data are the ability to integrate data into new forms of scholarly publishing to allow peer-examination and review of conclusions or analysis of experimental and observational data and the ability for subsequent researchers to make new analyses of the same data, including their combination with other data sets and uses that may have been unanticipated by the original producer or collector.The use of published digital data, like the use of digitally published literature, depends upon the ability to identify, authenticate, locate, access, and interpret them. Data citations provide necessary support for these functions, as well as other functions such as attribution of credit and establishment of provenance. References to data, however, present challenges not encountered in references to literature. For example, how can one specify a particular subset of data in the absence of familiar conventions such as page numbers or chapters? The traditions and good practices for maintaining the scholarly record by proper references to a work are well established and understood in regard to journal articles and other literature, but attributing credit by bibliographic references to data are not yet so broadly implemented.Recognizing the needs for better data referencing and citation practices and investing effort to address those needs has come at different rates in different fields and disciplines. As competing conventions and practices emerge in separate communities, inconsistencies and incompatibilities can interfere with promoting the sharing and use of research data. In order to reconcile this problem, sharing experiences across communities may be necessary, or at least helpful, to achieving the full potential of published data.Practical and consistent data citation standards and practices are thus important for providing the incentives, recognition, and rewards that foster scientific progress. New requirements from funding agencies to develop data management plans emphasize the need to develop standards and data citation practices.

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Section: Identitymentioning

confidence: 99%

Out of Cite, Out of Mind: The Current State of Practice, Policy, and Technology for the Citation of Data

Socha¹

2013

Data Sci. J.

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“…For instance, a dataset may have set semantics (e.g. "Set of RDF triples") [20], i.e. following the mathematical definition of a set, or it may have collection semantics, which means that deletion and addition of data does not have any effect on the dataset's identity.…”

Section: Dataset (Terminology)mentioning

confidence: 99%

A semi-automatic approach for detecting dataset references in social science texts

Ghavimi

Mayr

Lange

et al. 2017

ISU

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Abstract. Today, full-texts of scientific articles are often stored in different locations than the used datasets. Dataset registries aim at a closer integration by making datasets citable but authors typically refer to datasets using inconsistent abbreviations and heterogeneous metadata (e.g. title, publication year). It is thus hard to reproduce research results, to access datasets for further analysis, and to determine the impact of a dataset. Manually detecting references to datasets in scientific articles is time-consuming and requires expert knowledge in the underlying research domain. We propose and evaluate a semi-automatic three-step approach for finding explicit references to datasets in social sciences articles. We first extract pre-defined special features from dataset titles in the da|ra registry, then detect references to datasets using the extracted features, and finally match the references found with corresponding dataset titles. The approach does not require a corpus of articles (avoiding the cold start problem) and performs well on a test corpus. We achieved an F-measure of 0.84 for detecting references in full-texts and an F-measure of 0.83 for finding correct matches of detected references in the da|ra dataset registry.

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“…This is particularly difficult and problematic for data because of the imprecise and ambiguous notion of what is a "data set". As [39] point out "the notion of 'data set' found in the literature cannot itself be provided with a precise formal definition". Consequently, the decision about an entity/identifier association is necessarily heuristic, user-driven (i.e., what do the users of the system conceptually consider to be a data set, motivated in part by that which they wish to cite), and application-specific rather than technical and algorithmic.…”

Section: Identifier Strategymentioning

confidence: 99%

CED<sup>2</sup>AR: The Comprehensive Extensible Data Documentation and Access Repository

Lagoze

Vilhuber

Williams

et al. 2014

IEEE/ACM Joint Conference on Digital Libraries

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We describe the design, implementation, and deployment of the Comprehensive Extensible Data Documentation and Access Repository (CED 2 AR). This is a metadata repository system that allows researchers to search, browse, access, and cite confidential data and metadata through either a web-based user interface or programmatically through a search API, all the while re-reusing and linking to existing archive and provider generated metadata. CED 2 AR is distinguished from other metadata repository-based applications due to requirements that derive from its social science context. These include the need to cloak confidential data and metadata and manage complex provenance chains.

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Definitions of dataset in the scientific and technical literature

Cited by 50 publications

References 6 publications

Out of Cite, Out of Mind: The Current State of Practice, Policy, and Technology for the Citation of Data

Out of Cite, Out of Mind: The Current State of Practice, Policy, and Technology for the Citation of Data

A semi-automatic approach for detecting dataset references in social science texts

CED<sup>2</sup>AR: The Comprehensive Extensible Data Documentation and Access Repository

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