surveys/2017: papers linking to it

Kaggle · www.kaggle.com/datasets/surveys/2017

← Code and data repositories cited in research papers

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.

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#

20192020

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	2
Data Mining and Knowledge Discovery	1

Family tree#

Each paper sits under the earlier paper it builds on; papers that cite no other paper here start a branch of their own.

From local explanations to global understanding with explainable AI for trees Nature Machine Intelligence, 2020
- Learning with explainable trees Nature Machine Intelligence, 2020 — cites the paper above

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	Nature Machine Intelligence	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	Data Mining and Knowledge Discovery	G. J. J. van den Burg, A. Nazábal, C. Sutton	41	cites it
2020-01-17	Learning with explainable trees	Nature Machine Intelligence	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

@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}
}

Free to reuse for non-commercial purposes with attribution (CC BY-NC 4.0).