Evidence map›Paper›PMID 40235701›Full record

ArticleData in brief2025

A comprehensive dataset of soccer event images for advancing automatic recognition systems.

Jamal Hussain Shah, Maira Afzal, Samia Riaz, Mussarat Yasmin, Seifedine Kadry, Fahad Ahmed Khokhar

Abstract read
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. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Jamal Hussain ShahDepartment of Computer Science, COMSATS University Islamabad, Wah Campus, Pakistan.
Maira AfzalDepartment of Computer Science, COMSATS University Islamabad, Wah Campus, Pakistan.
Samia RiazDepartment of Computer Science, COMSATS University Islamabad, Wah Campus, Pakistan.
Mussarat YasminDepartment of Computer Science, COMSATS University Islamabad, Wah Campus, Pakistan.
Seifedine KadryDepartment of Applied Data Science, Noroff University College, Kristiansand, Norway.
Fahad Ahmed KhokharDepartment of Computer Science, COMSATS University Islamabad, Wah Campus, Pakistan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This article presents a detailed overview of a dataset, created for in-depth analysis of soccer events. This dataset will serve as a foundation for researchers and practitioners in the field, providing a perspective on different soccer events under various views. This soccer dataset is designed to categorize soccer matches into various events and contains 187,151 instances divided across 14 groups. To make this dataset simple, it is separated into two main datasets. The first dataset is known as the "View-Based Dataset." which is divided into four categories: Long view, Medium view, Short view, and Outer view, for a total of 137,196 images. The second dataset is the "Event-Based Dataset," which has 10 separate classes that highlight multiple soccer events Red card, Spectator, Yellow card, Plenty stock, Player celebration, Offside, Goal attempt, Goal, and Free kick for a total of 38,728 images. Each class in both datasets helps to provide a full understanding of soccer events. This dataset can serve as a foundation for future video analysis studies, promoting progress in soccer analytics and related domains.

Indexed as

Event-based datasetSoccer classificationSoccer eventsSoccer trendsSoccer visualizationSports dataView-based dataset

Identifiers

PMID40235701
PMCPMC11999477

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.