mczielinski/bitcoin-historical-data: papers linking to it

Kaggle · www.kaggle.com/datasets/mczielinski/bitcoin-historical-data

← Code and data repositories cited in research papers

Research papers in the 480 tracked journals that link to mczielinski/bitcoin-historical-data (https://www.kaggle.com/datasets/mczielinski/bitcoin-historical-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 mczielinski/bitcoin-historical-data in their Crossref records, the first in 2022; most of them (2) in 2024.
By category: Statistics (2) and AI (1); the journals with the most are Neural Computing and Applications (1), Statistical Papers (1) and Annals of the Institute of Statistical Mathematics (1).
2 of the papers build on one another: a later paper cites an earlier one that also links mczielinski/bitcoin-historical-data.

By year#

20222024

Papers linking to mczielinski/bitcoin-historical-data by publication year
Year	Papers
2022	1
2024	2

Journals#

Journals whose papers link to mczielinski/bitcoin-historical-data most
Journal	Papers
Neural Computing and Applications	1
Statistical Papers	1
Annals of the Institute of Statistical Mathematics	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.

A novel two-sample test within the space of symmetric positive definite matrix distributions and its application in finance Annals of the Institute of Statistical Mathematics, 2024
- Change-point analysis for matrix data: the empirical Hankel transform approach Statistical Papers, 2024 — cites the paper above

Papers linking to mczielinski/bitcoin-historical-data, most cited first
Published	Title	Journal	Authors	Citations	Link
2024-04-08	A novel two-sample test within the space of symmetric positive definite matrix distributions and its application in finance	Annals of the Institute of Statistical Mathematics	Žikica Lukić, Bojana Milošević	6	cites it
2024-10-28	Change-point analysis for matrix data: the empirical Hankel transform approach	Statistical Papers	Žikica Lukić, Bojana Milošević	3	cites it
2022-01-10	Improving temporal predictions through time-series labeling using matrix profile and motifs	Neural Computing and Applications	Pratik Saha, Pritthijit Nath, Asif Iqbal Middya et al.	2	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). mczielinski/bitcoin-historical-data: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/kaggle-mczielinski-bitcoin-historical-data

@misc{scholar4ai-code-kaggle-mczielinski-bitcoin-historical-data,
  author = {{Scholar4AI}},
  title = {mczielinski/bitcoin-historical-data: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/kaggle-mczielinski-bitcoin-historical-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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