Evidence map›Paper›PMID 40164648›Full record

ArticleScientific data2025

UrBAN: Urban Beehive Acoustics and PheNotyping Dataset.

Mahsa Abdollahi, Yi Zhu, Heitor R Guimarães, Nico Coallier, Ségolène Maucourt, Pierre Giovenazzo, Tiago H Falk

Abstract readDataset
In one paragraph

Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. 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

7 authors.

Mahsa AbdollahiINRS-EMT, Université du Québec, Montréal, Canada.
Yi ZhuINRS-EMT, Université du Québec, Montréal, Canada.ORCID http://orcid.org/0000-0001-7413-5205
Heitor R GuimarãesINRS-EMT, Université du Québec, Montréal, Canada.
Nico CoallierNectar Technologies Inc., Montréal, Canada.ORCID http://orcid.org/0009-0005-1798-4661
Ségolène MaucourtDépartement de biologie, Université Laval, Quebec City, Canada.
Pierre GiovenazzoDépartement de biologie, Université Laval, Quebec City, Canada.
Tiago H FalkINRS-EMT, Université du Québec, Montréal, Canada. tiago.falk@inrs.ca.ORCID http://orcid.org/0000-0002-5739-2514

Funding

Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada (NSERC Canadian Network for Research and Innovation in Machining Technology) ALLRP 548872-19
6 · The paper itself

Abstract

In this paper, we present a multimodal dataset obtained from a honey bee colony in Montréal, Quebec, Canada, spanning the years of 2021 to 2022. This apiary comprised 10 beehives, with microphones recording more than 3000 hours of high quality raw audio, and also sensors capturing temperature, and humidity. Periodic hive inspections involved monitoring colony honey bee population changes, assessing queen-related conditions, and documenting overall hive health. Additionally, health metrics, such as Varroa mite infestation rates and winter mortality assessments were recorded, offering valuable insights into factors affecting hive health status and resilience. In this study, we first outline the data collection process, sensor data description, and dataset structure. Furthermore, we demonstrate a practical application of this dataset by extracting various features from the raw audio to predict colony population using the number of frames of bees as a proxy.

Indexed as

AcousticsAnimalsBeesPhenotypeQuebecVarroidae

Identifiers

PMID40164648
PMCPMC11958633

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.