---
title: "data-science-bowl-2018: papers citing this dataset"
description: "5 papers in the tracked journals link to the Kaggle dataset data-science-bowl-2018 in their Crossref records, the first in 2021; most of them (1) in 2025."
url: https://scholar4ai.com/code/kaggle-competition-data-science-bowl-2018
markdown_url: https://scholar4ai.com/code/kaggle-competition-data-science-bowl-2018.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "5 papers in the tracked journals link to the Kaggle dataset data-science-bowl-2018 in their Crossref records, the first in 2021; most of them (1) in 2025. (as of 2026-11-03)"
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-11-03T14:43:26+09:00
window: 2026-10-05 to 2026-11-03
source: OpenAlex (CC0)
---

# data-science-bowl-2018: papers linking to it

Research papers in the 480 tracked journals that link to data-science-bowl-2018 (https://www.kaggle.com/competitions/data-science-bowl-2018), read from their Crossref records: by year, category and journal, the family tree of papers building on one another, and the linked repositories derived from it or that it derives from.

- Kaggle
- https://www.kaggle.com/competitions/data-science-bowl-2018

All repositories: https://scholar4ai.com/code.md

## Key findings

- 5 papers in the tracked journals link to the Kaggle dataset data-science-bowl-2018 in their Crossref records, the first in 2021; most of them (1) in 2025.
- By category: AI (5); the journals with the most are Expert Systems with Applications (1), Knowledge-Based Systems (1) and Neural Computing and Applications (1).
- 2 of the papers build on one another: a later paper cites an earlier one that also links data-science-bowl-2018.

## By year

*Papers linking to data-science-bowl-2018 by publication year*

| Year | Papers |
|---|---:|
| 2021 | 1 |
| 2022 | 1 |
| 2023 | 1 |
| 2024 | 1 |
| 2025 | 1 |

## Journals

*Journals whose papers link to data-science-bowl-2018 most*

| Journal | Papers |
|---|---:|
| [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | 1 |
| [Knowledge-Based Systems](https://scholar4ai.com/journals/knowledge-based-systems.md) | 1 |
| [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | 1 |
| [Pattern Recognition](https://scholar4ai.com/journals/pattern-recognition.md) | 1 |
| [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | 1 |

## Family tree

Each paper sits under the earlier paper it builds on.

- [A segmentation network for generalized lesion extraction with semantic fusion of transformer with value vector enhancement](https://doi.org/10.1016/j.eswa.2024.126098) (Expert Systems with Applications, 2024)
  - [A carving hierarchical information integration network for medical image segmentation](https://doi.org/10.1016/j.patcog.2025.112291) (Pattern Recognition, 2025) — cites the paper above

## Papers

*Papers linking to data-science-bowl-2018, most cited first*

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 2021-09-01 | [MC-Net: Multiple max-pooling integration module and cross multi-scale deconvolution network](https://doi.org/10.1016/j.knosys.2021.107456) | [Knowledge-Based Systems](https://scholar4ai.com/journals/knowledge-based-systems.md) | Hongfeng You, Long Yu, Shengwei Tian et al. | 76 | cites it |
| 2022-08-22 | [BT-Unet: A self-supervised learning framework for biomedical image segmentation using barlow twins with U-net models](https://doi.org/10.1007/s10994-022-06219-3) | [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | Narinder Singh Punn, Sonali Agarwal | 54 | cites it |
| 2023-06-22 | [DRI-UNet: dense residual-inception UNet for nuclei identification in microscopy cell images](https://doi.org/10.1007/s00521-023-08729-0) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Ajay Sharma, Pramod Kumar Mishra | 13 | cites it |
| 2024-12-10 | [A segmentation network for generalized lesion extraction with semantic fusion of transformer with value vector enhancement](https://doi.org/10.1016/j.eswa.2024.126098) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Yuefei Wang, Yuanhong Wei, Xi Yu et al. | 3 | cites it |
| 2025-08-12 | [A carving hierarchical information integration network for medical image segmentation](https://doi.org/10.1016/j.patcog.2025.112291) | [Pattern Recognition](https://scholar4ai.com/journals/pattern-recognition.md) | Yutong Zhang, Yuefei Wang, Yan Wan et al. | 0 | cites it |

## How this is measured

Links are read from each paper's Crossref record as its publisher deposited it: the URLs and repository DOIs in its reference list (the software and data it cites) and its supplementary links (“is-supplemented-by”: its own code or data). A paper that gives its code only in the text, a footnote or a data availability statement is not counted, and some publishers deposit reference lists without their URLs, so every count here is a lower bound. Repository details come from the GitHub REST API (stars, forks, and the repository a fork was made from), the Hugging Face Hub API (likes, downloads, and the base model a model was derived from) and DataCite (Zenodo and other data DOIs, and the GitHub release a Zenodo record archives), read again every month.

## Cite this page

Scholar4AI. (2026). data-science-bowl-2018: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/kaggle-competition-data-science-bowl-2018

```bibtex
@misc{scholar4ai-code-kaggle-competition-data-science-bowl-2018,
  author = {{Scholar4AI}},
  title = {data-science-bowl-2018: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/kaggle-competition-data-science-bowl-2018}},
  urldate = {2026-11-03},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}
```

License: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).