Evidence map›Paper›PMID 37709808›Full record

ArticleScientific data2023

A dataset on African bats' functional traits.

Francesca Cosentino, Giorgia Castiello, Luigi Maiorano

Abstract readDataset
In one paragraph

Article in Scientific data, 2023. 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. Review
  2. Review
  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.

Francesca CosentinoDepartment of Biology and Biotechnologies "Charles Darwin", Sapienza University of Rome, Rome, Italy. francesca.cosentino@uniroma1.it.ORCID http://orcid.org/0000-0001-9064-2696
Giorgia CastielloDepartment of Biology and Biotechnologies "Charles Darwin", Sapienza University of Rome, Rome, Italy.ORCID http://orcid.org/0000-0001-7488-1312
Luigi MaioranoDepartment of Biology and Biotechnologies "Charles Darwin", Sapienza University of Rome, Rome, Italy.ORCID http://orcid.org/0000-0002-2957-8979

Funding

Sapienza Università di Roma (Sapienza University of Rome) AR12117A5D86252A
6 · The paper itself

Abstract

Trait-based approaches are becoming extremely common in ecological modeling and the availability of traits databases is increasing. However, data availability is often biased towards particular regions and taxa, with many taxa (e.g., bats) often under-represented. Here, we present the AfroBaT dataset, a compilation of trait data on 320 African bat species containing 76,914 values for 86 traits focusing on morphology, reproduction, life-history, trophic ecology, and species distributions. All data were gathered from published literature following the ecological trait-data standard procedure. Missing data for both numerical and categorical traits were imputed with a machine learning approach including species phylogeny. Trophic ecology traits showed the highest coverage in the literature (72% of the species averaged over all traits), while reproductive traits the lowest. Our data imputation improved the coverage of AfroBaT especially for reproductive traits, going from 27% to 58% of the species covered. AfroBaT has a range of potential applications in macroecology and community ecology, and the availability of open-access data on African bats will enable collaboration and data-sharing among researchers.

Indexed as

ChiropteraAnimalsDatabases, FactualMachine LearningNutritional Status

Identifiers

PMID37709808
PMCPMC10502069

What OpenQuestion holds

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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.