Evidence map›Paper›PMID 38293577›Full record

ArticleData in brief2024

Comprehensive image dataset for enhancing object detection in chemical experiments.

Ryosuke Sasaki, Mikito Fujinami, Hiromi Nakai

Abstract read
In one paragraph

Article in Data in brief, 2024. 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

3 authors.

Ryosuke SasakiDepartment of Chemistry and Biochemistry, School of Advanced Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan.
Mikito FujinamiWaseda Research Institute for Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan.
Hiromi NakaiDepartment of Chemistry and Biochemistry, School of Advanced Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The application of image recognition in chemical experiments has the potential to enhance experiment recording and risk management. However, the current scarcity of suitable benchmarking datasets restricts the applications of machine vision techniques in chemical experiments. This data article presents an image dataset featuring common chemical apparatuses and experimenter's hands. The images have been meticulously annotated, providing detailed information for precise object detection through deep learning methods. The images were captured from videos filmed in organic chemistry laboratories. This dataset comprises a total of 5078 images including diverse backgrounds and situations surrounding the objects. Detailed annotations are provided in accompanying text files. The dataset is organized into training, validation, and test subsets. Each subset is stored within independent folders for easy access and utilization.

Indexed as

Chemical apparatusDeep learningImage classificationImage recognition

Identifiers

PMID38293577
PMCPMC10827390

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.