Evidence map›Paper›PMID 32431560›Full record

ArticleBiodiversity data journal2020

Georeferencing the Natural History Museum's Chinese type collection: of plateaus, pagodas and plants.

Krisztina Lohonya, Laurence Livermore, Malcolm G Penn

Abstract read
In one paragraph

Article in Biodiversity data journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
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

3 authors.

Krisztina LohonyaThe Natural History Museum, London, United Kingdom The Natural History Museum London United Kingdom.ORCID https://orcid.org/0000-0002-5313-8674
Laurence LivermoreThe Natural History Museum, London, United Kingdom The Natural History Museum London United Kingdom.ORCID https://orcid.org/0000-0002-7341-1842
Malcolm G PennThe Natural History Museum, London, United Kingdom The Natural History Museum London United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The digitising efforts of herbaria aim to increase access to and impact of scientific collections, by making the data digitally accessible to the global community. Digitising the NHMUK's botanical collection of around 5.1 million specimens is an ongoing process, but the majority of the type collections have already been imaged. The Chinese type collection has also been transcribed; however, during the recent georeferencing process, we realised that much of the data had been transcribed incorrectly, particularly the locality information in which 80% of the collection contained errors. We discovered 154 specimens that were mistakenly filed in China. We corrected the mistakes from the previous transcription and georeferenced the collection which consists of 3,736 records. In this paper, we discuss the problems and errors we encountered during the georeferencing process, detailing why there were mistakes, what made the transcription harder than expected and what could have led to errors. We also give a short description about the Chinese language and its difference from European languages, leading to complex problems for georeferencing. We provide a brief guide on how to georeference a Chinese collection, avoiding errors and making the georeferencing process easier and faster.

Indexed as

botanical sheetsChinese localitiescollectorsdatabasedata cleaningdigitisationdigitizationgeoreferencingherbariaherbarium sheetslinked open datanatural history collectionssitestranscriptiontype specimens

Identifiers

PMID32431560
PMCPMC7217978

What OpenQuestion holds

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LicenceCC BY
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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.