Evidence map›Paper›PMID 40521139›Full record

ArticleData in brief2025

Comprehensive smartphone image dataset for fish species identification in Bangladesh's freshwater ecosystems.

Saieef Sunny, Shiam Prodhan, Nazmuj Shakib, Mohammad Rifat Ahmmad Rashid, Nafees Mansoor

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Data in brief, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

5 authors.

Saieef SunnyDepartment of Computer Science and Engineering, University of Liberal Arts Bangladesh, Mohammadpur, Dhaka, Bangladesh.
Shiam ProdhanDepartment of Computer Science and Engineering, University of Liberal Arts Bangladesh, Mohammadpur, Dhaka, Bangladesh.
Nazmuj ShakibDepartment of Computer Science and Engineering, University of Liberal Arts Bangladesh, Mohammadpur, Dhaka, Bangladesh.
Mohammad Rifat Ahmmad RashidDepartment of Computer Science and Engineering, East West University, Aftabnagar, Dhaka, Bangladesh.
Nafees MansoorDepartment of Computer Science and Engineering, University of Liberal Arts Bangladesh, Mohammadpur, Dhaka, Bangladesh.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The Bangladesh Fish Species Identification dataset encompasses 24,925 images representing 21 freshwater species commonly found in the rivers and ponds of Bangladesh. Photographs were taken using smartphones under carefully controlled conditions to ensure clarity and proper framing, and they include variations in lighting and background for realistic depiction. Species covered in this dataset include Aair, Boal, Chapila, DeshiPuti, Foli, Ilish, KalBaush, Katla, Koi, Magur, Mrigel, Pabda, Pangas, Puti, Rui, Shol, Shorputi, Taki, Tarabaim, Telapiya, and Tengra. By providing an extensive collection of labeled, high-quality images, this dataset serves as a valuable foundation for research on aquatic biodiversity, fishery management, and the development of precise machine-learning models for species recognition. It supports investigations into morphological variations, facilitates the evaluation of deep learning techniques, and assists in understanding fish distribution across diverse habitats.

Indexed as

BangladeshBiodiversityDatasetDeep learningFish species identificationFreshwater fishImage recognitionMachine learning

Identifiers

PMID40521139
PMCPMC12166710

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.