Cleaning Collections Data Using OpenRefine

Sterner, Elizabeth

doi:10.29173/istl30

Cited by 4 publications

(3 citation statements)

References 1 publication

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“…It is well suited to disambiguation after specimen labels are transcribed, but before they are imported into a collection management system. There are several manuals available on how to use OpenRefine, but Hill (2016) and Sterner (2019) were specifically written for people working on collections.…”

Section: Relevant Informatics Resourcesmentioning

confidence: 99%

The disambiguation of people names in biological collections

Groom¹,

Bräuchler²,

Cubey³

et al. 2022

BDJ

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Scientific collections have been built by people. For hundreds of years, people have collected, studied, identified, preserved, documented and curated collection specimens. Understanding who those people are is of interest to historians, but much more can be made of these data by other stakeholders once they have been linked to the people’s identities and their biographies. Knowing who people are helps us attribute work correctly, validate data and understand the scientific contribution of people and institutions. We can evaluate the work they have done, the interests they have, the places they have worked and what they have created from the specimens they have collected. The problem is that all we know about most of the people associated with collections are their names written on specimens. Disambiguating these people is the challenge that this paper addresses. Disambiguation of people often proves difficult in isolation and can result in staff or researchers independently trying to determine the identity of specific individuals over and over again. By sharing biographical data and building an open, collectively maintained dataset with shared knowledge, expertise and resources, it is possible to collectively deduce the identities of individuals, aggregate biographical information for each person, reduce duplication of effort and share the information locally and globally. The authors of this paper aspire to disambiguate all person names efficiently and fully in all their variations across the entirety of the biological sciences, starting with collections. Towards that vision, this paper has three key aims: to improve the linking, validation, enhancement and valorisation of person-related information within and between collections, databases and publications; to suggest good practice for identifying people involved in biological collections; and to promote coordination amongst all stakeholders, including individuals, natural history collections, institutions, learned societies, government agencies and data aggregators.

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Section: Relevant Informatics Resourcesmentioning

confidence: 99%

The disambiguation of people names in biological collections

Groom¹,

Bräuchler²,

Cubey³

et al. 2022

BDJ

View full text Add to dashboard Cite

show abstract

“…• OpenRefine: a data cleaning, reconciliation and batch upload tool with a graphical user interface [9];…”

Section: Introductionmentioning

confidence: 99%

Collaborative annotation and semantic enrichment of 3D media

Rossenova

Schubert

Vock

et al. 2022

Proceedings of the 22nd ACM/IEEE Joint Conference on Digital Libraries

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“… OpenRefine: Previously Google Refine, this is "a powerful tool for working with messy data: cleaning it; transforming it from one format into another; and extending it with web services and external data." [4].…”

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confidence: 99%

Open refine to wikibase

Sohmen

Rossenova

2022

Proceedings of the 22nd ACM/IEEE Joint Conference on Digital Libraries

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This tutorial aims to help researchers, digital collection librarians and data managers make their datasets available as linked open data. Participants have the option to either bring their own dataset or work with a sample dataset provided by the organizers. They will take part in practical exercises and learn how to use OpenRefine for data transformation and Wikibase for data storage. This pipeline workflow was developed in the context of NFDI4Culture, a German consortium of research and cultural institutions working towards a shared infrastructure for research data that meets the needs of 21st century data creators, maintainers and end users.

show abstract

Cleaning Collections Data Using OpenRefine

Cited by 4 publications

References 1 publication

The disambiguation of people names in biological collections

The disambiguation of people names in biological collections

Collaborative annotation and semantic enrichment of 3D media

Open refine to wikibase

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