techsash/waste-classification-data: papers linking to it

Kaggle · www.kaggle.com/datasets/techsash/waste-classification-data

← Code and data repositories cited in research papers

Research papers in the 480 tracked journals that link to techsash/waste-classification-data (https://www.kaggle.com/datasets/techsash/waste-classification-data), 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 techsash/waste-classification-data in their Crossref records, the first in 2025; most of them (2) in 2025.
By category: AI (2) and Operations Research (1); the journals with the most are Computers & Industrial Engineering (1), Neural Computing and Applications (1) and Expert Systems with Applications (1).

By year#

20252026

Papers linking to techsash/waste-classification-data by publication year
Year	Papers
2025	2
2026	1

Journals#

Journals whose papers link to techsash/waste-classification-data most
Journal	Papers
Computers & Industrial Engineering	1
Neural Computing and Applications	1
Expert Systems with Applications	1

Papers linking to techsash/waste-classification-data, most cited first
Published	Title	Journal	Authors	Citations	Link
2025-01-07	Industry 4.0 technologies in support of circular Economy: A 10R-based integration framework	Computers & Industrial Engineering	Maria Pia Ciano, Mirco Peron, Luigi Panza et al.	54	cites it
2025-10-11	Recognizing recyclable waste materials based on deep transfer learning models	Neural Computing and Applications	Ghada Dahy, Mohamed F. Torky, Václav Snåšel et al.	0	cites it
2026-04-23	Distilling the experts: A new path for plastic waste sorting	Expert Systems with Applications	Samar Daou, Mouna Zouari Mehdi, Jihen Frikha Elleuch 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). techsash/waste-classification-data: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/kaggle-techsash-waste-classification-data

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

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