Evidence map›Paper›PMID 36305606›Full record

ArticleGigaScience2022

A dataset of ant colonies' motion trajectories in indoor and outdoor scenes to study clustering behavior.

Meihong Wu, Xiaoyan Cao, Ming Yang, Xiaoyu Cao, Shihui Guo

Abstract read
In one paragraph

Article in GigaScience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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.

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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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.

Meihong WuSchool of Informatics, Xiamen University, Xiamen, 361000, China.ORCID 0000-0002-1489-4039
Xiaoyan CaoSchool of Informatics, Xiamen University, Xiamen, 361000, China.ORCID 0000-0002-4980-2050
Ming YangSchool of Informatics, Xiamen University, Xiamen, 361000, China.
Xiaoyu CaoChemistry and Chemical Engineering, Xiamen University, Xiamen, 361000, China.ORCID 0000-0003-0219-1798
Shihui GuoSchool of Informatics, Xiamen University, Xiamen, 361000, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe motion and interaction of social insects (such as ants) have been studied by many researchers to understand clustering mechanisms. Most studies in the field of ant behavior have focused only on indoor environments (a laboratory setup), while outdoor environments (natural environments) are still underexplored.

findingsIn this article, we collect 10 videos of 3 species of ant colonies from different scenes, including 5 indoor and 5 outdoor scenes. We develop an image sequence marking software named VisualMarkData, which enables us to provide annotations of the ants in the videos. (i) It offers comprehensive annotations of states at the individual-target and colony-target levels. (ii) It provides a simple matrix format to represent multiple targets and multiple groups of annotations (along with their IDs and behavior labels). (iii) During the annotation process, we propose a simple and effective visualization that takes the annotation information of the previous frame as a reference, and then a user can simply click on the center point of each target to complete the annotation task. (iv) We develop a user-friendly window-based GUI to minimize labor and maximize annotation quality. In all 5,354 frames, the location information and the identification number of each ant are recorded for a total of 712 ants and 114,112 annotations. Moreover, we provide visual analysis tools to assess and validate the technical quality and reproducibility of our data.

conclusionsWe provide a large-scale ant dataset with the accompanying annotation software. It is hoped that our work will contribute to a deeper exploration of the behavior of ant colonies.

Indexed as

AntsAnimalsCluster AnalysisReproducibility of ResultsSoftwarecomputer visionimage sequence annotation softwaremultiobject trackingoutdoor scenessocial insects

Identifiers

PMID36305606
PMCPMC9614923

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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.