Evidence map›Paper›PMID 40645987›Full record

ArticleScientific data2025

Visual WetlandBirds Dataset: Bird Species Identification and Behavior Recognition in Videos.

Javier Rodriguez-Juan, David Ortiz-Perez, Manuel Benavent-Lledo, David Mulero-Pérez, Pablo Ruiz-Ponce, Adrian Orihuela-Torres, Jose Garcia-Rodriguez, Esther Sebastián-González

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. Article
  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

8 authors.

Javier Rodriguez-JuanDepartment of Computer Technology, University of Alicante, Alicante, 03690, Spain. jrodriguez@dtic.ua.es.
David Ortiz-PerezDepartment of Computer Technology, University of Alicante, Alicante, 03690, Spain.
Manuel Benavent-LledoDepartment of Computer Technology, University of Alicante, Alicante, 03690, Spain.
David Mulero-PérezDepartment of Computer Technology, University of Alicante, Alicante, 03690, Spain.
Pablo Ruiz-PonceDepartment of Computer Technology, University of Alicante, Alicante, 03690, Spain.
Adrian Orihuela-TorresDepartment of Ecology, University of Alicante, Alicante, 03690, Spain.
Jose Garcia-RodriguezDepartment of Computer Technology, University of Alicante, Alicante, 03690, Spain. jgarcia@dtic.ua.es.
Esther Sebastián-GonzálezDepartment of Ecology, University of Alicante, Alicante, 03690, Spain.

Funding

EC | European Regional Development Fund (Europski Fond za Regionalni Razvoj) HORIZON-MSCA-2021-SE-0101086387EC | European Regional Development Fund (Europski Fond za Regionalni Razvoj) TED2021-130890B-C21Ministerio de Educación, Cultura y Deporte (Ministry of Education, Culture and Sports, Spain) FPU21/00414Ministerio de Educación, Cultura y Deporte (Ministry of Education, Culture and Sports, Spain) FPU22/04200Ministerio de Educación, Cultura y Deporte (Ministry of Education, Culture and Sports, Spain) FPU23/00532Ministerio de Educación, Cultura y Deporte (Ministry of Education, Culture and Sports, Spain) TSI-100121-2024-24Regional Government of Valencia | Conselleria d'Educació, Investigació, Cultura i Esport (Conselleria d'Educació, Investigació, Cultura i Esport de la Generalitat Valenciana) CIACIF/2021/430Regional Government of Valencia | Conselleria d'Educació, Investigació, Cultura i Esport (Conselleria d'Educació, Investigació, Cultura i Esport de la Generalitat Valenciana) CIACIF/2022/175
6 · The paper itself

Abstract

The current biodiversity loss crisis makes animal monitoring a relevant field of study. In light of this, data collected through monitoring can provide essential insights, and information for decision-making aimed at preserving global biodiversity. Despite the importance of such data, there is a notable scarcity of datasets featuring videos of birds, and none of the existing datasets offer detailed annotations of bird behaviors in video format. In response to this gap, our study introduces the first fine-grained video dataset specifically designed for bird behavior detection and species classification. This dataset addresses the need for comprehensive bird video datasets and provides detailed data on bird actions, facilitating the development of deep learning models to recognize these, similar to the advancements made in human action recognition. The proposed dataset comprises 178 videos recorded in Spanish wetlands, capturing 13 different bird species performing 7 distinct behavior classes. In addition, we also present baseline results using state of the art models on two tasks: bird behavior recognition and species classification.

Indexed as

Behavior, AnimalBirdsWetlandsAnimalsBiodiversityDeep LearningSpainVideo Recording

Identifiers

PMID40645987
PMCPMC12254335

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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.