---
title: "givemesomecredit: papers citing this dataset"
description: "6 papers in the tracked journals link to the Kaggle dataset givemesomecredit in their Crossref records, the first in 2024; most of them (4) in 2026."
url: https://scholar4ai.com/code/kaggle-competition-givemesomecredit
markdown_url: https://scholar4ai.com/code/kaggle-competition-givemesomecredit.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "6 papers in the tracked journals link to the Kaggle dataset givemesomecredit in their Crossref records, the first in 2024; most of them (4) in 2026. (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)
---

# givemesomecredit: papers linking to it

Research papers in the 480 tracked journals that link to givemesomecredit (https://www.kaggle.com/competitions/givemesomecredit), 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/givemesomecredit

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

## Key findings

- 6 papers in the tracked journals link to the Kaggle dataset givemesomecredit in their Crossref records, the first in 2024; most of them (4) in 2026.
- By category: AI (4), Economics (1) and Operations Research (1); the journals with the most are Expert Systems with Applications (2), Computational Economics (1) and Journal of Optimization Theory and Applications (1).

## By year

*Papers linking to givemesomecredit by publication year*

| Year | Papers |
|---|---:|
| 2024 | 1 |
| 2025 | 1 |
| 2026 | 4 |

## Journals

*Journals whose papers link to givemesomecredit most*

| Journal | Papers |
|---|---:|
| [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | 2 |
| [Computational Economics](https://scholar4ai.com/journals/computational-economics.md) | 1 |
| [Journal of Optimization Theory and Applications](https://scholar4ai.com/journals/journal-of-optimization-theory-and-applications.md) | 1 |
| [Information Fusion](https://scholar4ai.com/journals/information-fusion.md) | 1 |
| [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | 1 |

## Papers

*Papers linking to givemesomecredit, most cited first*

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 2024-06-27 | [Secure and fast asynchronous Vertical Federated Learning via cascaded hybrid optimization](https://doi.org/10.1007/s10994-024-06541-y) | [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | Ganyu Wang, Qingsong Zhang, Xiang Li et al. | 6 | cites it |
| 2026-02-06 | [Breaking barriers in federated transfer learning: A systematic review on federated transfer learning for non-overlapping domains](https://doi.org/10.1016/j.eswa.2026.131514) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Tasnim Binte Shiraj, Sobhana Jahan, Md. Rawnak Saif Adib et al. | 6 | cites it |
| 2026-01-16 | [The impact of machine learning uncertainty on the robustness of counterfactual explanations](https://doi.org/10.1016/j.eswa.2026.131198) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Leonidas Christodoulou, Chang Sun | 1 | cites it |
| 2025-12-19 | [RA-MD: An RKHS-based adaptive Mahalanobis distance to enhance counterfactual explanations for neural networks](https://doi.org/10.1016/j.inffus.2025.104067) | [Information Fusion](https://scholar4ai.com/journals/information-fusion.md) | Ao Xu, Yukai Zhang, Tieru Wu | 0 | cites it |
| 2026-01-07 | [LightGBM Scorecard based on SHAP values](https://doi.org/10.1007/s10614-025-11194-7) | [Computational Economics](https://scholar4ai.com/journals/computational-economics.md) | Yutae Choi, Eunji Cha | 0 | cites it |
| 2026-06-30 | [Mixed-Integer Optimization for Cross-Validated Feature Selection in Linear Classification](https://doi.org/10.1007/s10957-026-03043-w) | [Journal of Optimization Theory and Applications](https://scholar4ai.com/journals/journal-of-optimization-theory-and-applications.md) | Robert Purcaru, David Islip, Roy H. Kwon | 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). givemesomecredit: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/kaggle-competition-givemesomecredit

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