---
title: "data-science-bowl-2017: papers citing this dataset"
description: "3 papers in the tracked journals link to the Kaggle dataset data-science-bowl-2017 in their Crossref records, the first in 2020; most of them (1) in 2025."
url: https://scholar4ai.com/code/kaggle-competition-data-science-bowl-2017
markdown_url: https://scholar4ai.com/code/kaggle-competition-data-science-bowl-2017.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "3 papers in the tracked journals link to the Kaggle dataset data-science-bowl-2017 in their Crossref records, the first in 2020; 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-2017: papers linking to it

Research papers in the 480 tracked journals that link to data-science-bowl-2017 (https://www.kaggle.com/competitions/data-science-bowl-2017), 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-2017

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

## Key findings

- 3 papers in the tracked journals link to the Kaggle dataset data-science-bowl-2017 in their Crossref records, the first in 2020; most of them (1) in 2025.
- By category: AI (3); the journals with the most are Applied Intelligence (1), Neural Computing and Applications (1) and Cognitive Computation (1).
- 2 of the papers build on one another: a later paper cites an earlier one that also links data-science-bowl-2017.

## By year

*Papers linking to data-science-bowl-2017 by publication year*

| Year | Papers |
|---|---:|
| 2020 | 1 |
| 2022 | 1 |
| 2025 | 1 |

## Journals

*Journals whose papers link to data-science-bowl-2017 most*

| Journal | Papers |
|---|---:|
| [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | 1 |
| [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | 1 |
| [Cognitive Computation](https://scholar4ai.com/journals/cognitive-computation.md) | 1 |

## Family tree

Each paper sits under the earlier paper it builds on.

- [Automatic prognosis of lung cancer using heterogeneous deep learning models for nodule detection and eliciting its morphological features](https://doi.org/10.1007/s10489-020-01990-z) (Applied Intelligence, 2020)
  - [Deep Learning Innovations in the Detection of Lung Cancer: Advances, Trends, and Open Challenges](https://doi.org/10.1007/s12559-025-10408-2) (Cognitive Computation, 2025) — cites the paper above

## Papers

*Papers linking to data-science-bowl-2017, most cited first*

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 2022-02-01 | [Literature review: efficient deep neural networks techniques for medical image analysis](https://doi.org/10.1007/s00521-022-06960-9) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Mohamed A. Abdou | 315 | cites it |
| 2020-11-05 | [Automatic prognosis of lung cancer using heterogeneous deep learning models for nodule detection and eliciting its morphological features](https://doi.org/10.1007/s10489-020-01990-z) | [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | Weilun Wang, Goutam Chakraborty | 44 | cites it |
| 2025-02-17 | [Deep Learning Innovations in the Detection of Lung Cancer: Advances, Trends, and Open Challenges](https://doi.org/10.1007/s12559-025-10408-2) | [Cognitive Computation](https://scholar4ai.com/journals/cognitive-computation.md) | Helena Liz, Áurea Anguera de Sojo Hernández, Sergio D’Antonio Maceiras et al. | 39 | 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-2017: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/kaggle-competition-data-science-bowl-2017

```bibtex
@misc{scholar4ai-code-kaggle-competition-data-science-bowl-2017,
  author = {{Scholar4AI}},
  title = {data-science-bowl-2017: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/kaggle-competition-data-science-bowl-2017}},
  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/).