---
title: "surveys/2017: papers citing this dataset"
description: "3 papers in the tracked journals link to the Kaggle dataset surveys/2017 in their Crossref records, the first in 2019; most of them (2) in 2020."
url: https://scholar4ai.com/code/kaggle-surveys-2017
markdown_url: https://scholar4ai.com/code/kaggle-surveys-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 surveys/2017 in their Crossref records, the first in 2019; most of them (2) in 2020. (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)
---

# surveys/2017: papers linking to it

Research papers in the 480 tracked journals that link to surveys/2017 (https://www.kaggle.com/datasets/surveys/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/datasets/surveys/2017

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

## Key findings

- 3 papers in the tracked journals link to the Kaggle dataset surveys/2017 in their Crossref records, the first in 2019; most of them (2) in 2020.
- By category: AI (3); the journals with the most are Nature Machine Intelligence (2) and Data Mining and Knowledge Discovery (1).
- 2 of the papers build on one another: a later paper cites an earlier one that also links surveys/2017.

## By year

*Papers linking to surveys/2017 by publication year*

| Year | Papers |
|---|---:|
| 2019 | 1 |
| 2020 | 2 |

## Journals

*Journals whose papers link to surveys/2017 most*

| Journal | Papers |
|---|---:|
| [Nature Machine Intelligence](https://scholar4ai.com/journals/nature-machine-intelligence.md) | 2 |
| [Data Mining and Knowledge Discovery](https://scholar4ai.com/journals/data-mining-and-knowledge-discovery.md) | 1 |

## Family tree

Each paper sits under the earlier paper it builds on.

- [From local explanations to global understanding with explainable AI for trees](https://doi.org/10.1038/s42256-019-0138-9) (Nature Machine Intelligence, 2020)
  - [Learning with explainable trees](https://doi.org/10.1038/s42256-019-0142-0) (Nature Machine Intelligence, 2020) — cites the paper above

## Papers

*Papers linking to surveys/2017, most cited first*

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 2020-01-17 | [From local explanations to global understanding with explainable AI for trees](https://doi.org/10.1038/s42256-019-0138-9) | [Nature Machine Intelligence](https://scholar4ai.com/journals/nature-machine-intelligence.md) | Scott Lundberg, Gabriel Erion, Hugh Chen et al. | 10,493 | cites it |
| 2019-07-26 | [Wrangling messy CSV files by detecting row and type patterns](https://doi.org/10.1007/s10618-019-00646-y) | [Data Mining and Knowledge Discovery](https://scholar4ai.com/journals/data-mining-and-knowledge-discovery.md) | G. J. J. van den Burg, A. Nazábal, C. Sutton | 41 | cites it |
| 2020-01-17 | [Learning with explainable trees](https://doi.org/10.1038/s42256-019-0142-0) | [Nature Machine Intelligence](https://scholar4ai.com/journals/nature-machine-intelligence.md) | Wojciech Samek | 25 | 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). surveys/2017: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/kaggle-surveys-2017

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