Evidence map›Paper›PMID 31819990›Full record

ArticleDatabase : the journal of biological databases and curation2019

Improved standardization of transcribed digital specimen data.

Quentin Groom, Mathias Dillen, Helen Hardy, Sarah Phillips, Luc Willemse, Zhengzhe Wu

Abstract read
In one paragraph

Article in Database : the journal of biological databases and curation, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Article
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  5. Improved standardization of transcribed digital specimen data.Database : the journal of biological databases and curation · 2019
    Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Quentin GroomDepartment of Collections, Meise Botanic Garden, Nieuwelaan 38, 1860 Meise, Belgium.
Mathias DillenDepartment of Collections, Meise Botanic Garden, Nieuwelaan 38, 1860 Meise, Belgium.
Helen HardyDepartment of Life Sciences, Natural History Museum, Cromwell Road London SW7 5BD London, UK.
Sarah PhillipsDepartment of Collections, Royal Botanic Gardens Kew, Richmond TW9 3AB London, UK.
Luc WillemseDepartment of Entomological Collections, Naturalis Biodiversity Center, Darwinweg 2, 2333 CR Leiden, The Netherlands.
Zhengzhe WuFinnish Museum of Natural History, University of Helsinki, Unioninkatu 44, 00170 Helsinki, Finland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

There are more than 1.2 billion biological specimens in the world's museums and herbaria. These objects are particularly important forms of biological sample and observation. They underpin biological taxonomy but the data they contain have many other uses in the biological and environmental sciences. Nevertheless, from their conception they are almost entirely documented on paper, either as labels attached to the specimens or in catalogues linked with catalogue numbers. In order to make the best use of these data and to improve the findability of these specimens, these data must be transcribed digitally and made to conform to standards, so that these data are also interoperable and reusable. Through various digitization projects, the authors have experimented with transcription by volunteers, expert technicians, scientists, commercial transcription services and automated systems. We have also been consumers of specimen data for taxonomical, biogeographical and ecological research. In this paper, we draw from our experiences to make specific recommendations to improve transcription data. The paper is split into two sections. We first address issues related to database implementation with relevance to data transcription, namely versioning, annotation, unknown and incomplete data and issues related to language. We then focus on particular data types that are relevant to biological collection specimens, namely nomenclature, dates, geography, collector numbers and uniquely identifying people. We make recommendations to standards organizations, software developers, data scientists and transcribers to improve these data with the specific aim of improving interoperability between collection datasets.

Indexed as

Data CurationDatabases, FactualGeographyMuseumsReference StandardsSoftwareTerminology as Topic

Identifiers

PMID31819990
PMCPMC6901386

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.