Dataset Reuse: Toward Translating Principles to Practice

Koesten, Laura; Vougiouklis, Pavlos; Simperl, Elena; Groth, Paul

doi:10.1016/j.patter.2020.100136

Cited by 25 publications

(21 citation statements)

References 88 publications

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“…This importance will only grow as researchers increasingly reuse data. 48 However, the impact of data handling on computational models is, as of yet, a poorly studied area.…”

Section: Discussionmentioning

confidence: 99%

The non-linear impact of data handling on network diffusion models

2021

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“…This importance will only grow as researchers increasingly reuse data. 48 However, the impact of data handling on computational models is, as of yet, a poorly studied area.…”

Section: Discussionmentioning

confidence: 99%

The non-linear impact of data handling on network diffusion models

2021

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“…Given the proximity of such users to actual databases, GitHub is a rich source for heterogeneous tables. Prior analyses of CSV files from GitHub also found that these files have diverse formatting and the tables extracted from them have relatively large dimensions [35,19]. These properties are common across database contexts [35,20], so that we consider CSV files from GitHub a suitable resource for database-like tables (C2).…”

Section: Design Principles Of Gittablesmentioning

confidence: 99%

GitTables: A Large-Scale Corpus of Relational Tables

Hulsebos¹,

Demiralp²,

Groth³

2021

Preprint

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The practical success of deep learning has sparked interest in improving relational table tasks, like data search, with models trained on large table corpora. Existing corpora primarily contain tables extracted from HTML pages, limiting the capability to represent offline database tables. To train and evaluate high-capacity models for applications beyond the Web, we need additional resources with tables that resemble relational database tables.Here we introduce GitTables, a corpus of currently 1.7M relational tables extracted from GitHub. Our continuing curation aims at growing the corpus to at least 20M tables. We annotate table columns in GitTables with more than 2K different semantic types from Schema.org and DBpedia. Our column annotations consist of semantic types, hierarchical relations, range types and descriptions. The corpus is available at https://gittables.github.io. Our analysis of GitTables shows that its structure, content, and topical coverage differ significantly from existing table corpora. We evaluate our annotation pipeline on hand-labeled tables from the T2Dv2 benchmark and find that our approach provides results on par with human annotations. We demonstrate a use case of GitTables by training a semantic type detection model on it and obtain high prediction accuracy. We also show that the same model trained on tables from the Web generalizes poorly.Preprint. Under review.

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“…With the increasing digitisation of research processes, there has been a significant call for the wider adoption of interoperable sharing of data and its associated metadata. We refer to [72] for a comprehensive overview and recommendations, in particular for data; notably that review highlights the wide variety of metadata and documentation that the literature prescribes for enabling data reuse. Likewise, we suggest [82] that covers the importance of metadata standards in reproducible computational research.…”

Section: Related Workmentioning

confidence: 99%

Packaging research artefacts with RO-Crate

Soiland‐Reyes

Sefton

Crosas

et al. 2022

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An increasing number of researchers support reproducibility by including pointers to and descriptions of datasets, software and methods in their publications. However, scientific articles may be ambiguous, incomplete and difficult to process by automated systems. In this paper we introduce RO-Crate, an open, community-driven, and lightweight approach to packaging research artefacts along with their metadata in a machine readable manner. RO-Crate is based on Schema.org annotations in JSON-LD, aiming to establish best practices to formally describe metadata in an accessible and practical way for their use in a wide variety of situations. An RO-Crate is a structured archive of all the items that contributed to a research outcome, including their identifiers, provenance, relations and annotations. As a general purpose packaging approach for data and their metadata, RO-Crate is used across multiple areas, including bioinformatics, digital humanities and regulatory sciences. By applying “just enough” Linked Data standards, RO-Crate simplifies the process of making research outputs FAIR while also enhancing research reproducibility. An RO-Crate for this article11 https://w3id.org/ro/doi/10.5281/zenodo.5146227 is archived at https://doi.org/10.5281/zenodo.5146227.

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Dataset Reuse: Toward Translating Principles to Practice

Cited by 25 publications

References 88 publications

The non-linear impact of data handling on network diffusion models

The non-linear impact of data handling on network diffusion models

GitTables: A Large-Scale Corpus of Relational Tables

Packaging research artefacts with RO-Crate

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