---
title: "mczielinski/bitcoin-historical-data: papers citing it"
description: "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."
url: https://scholar4ai.com/code/kaggle-mczielinski-bitcoin-historical-data
markdown_url: https://scholar4ai.com/code/kaggle-mczielinski-bitcoin-historical-data.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 mczielinski/bitcoin-historical-data in their Crossref records, the first in 2022; most of them (2) in 2024. (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)
---

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

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.

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

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

## 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

*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](https://scholar4ai.com/journals/neural-computing-and-applications.md) | 1 |
| [Statistical Papers](https://scholar4ai.com/journals/statistical-papers.md) | 1 |
| [Annals of the Institute of Statistical Mathematics](https://scholar4ai.com/journals/annals-of-the-institute-of-statistical-mathematics.md) | 1 |

## Family tree

Each paper sits under the earlier paper it builds on.

- [A novel two-sample test within the space of symmetric positive definite matrix distributions and its application in finance](https://doi.org/10.1007/s10463-024-00902-z) (Annals of the Institute of Statistical Mathematics, 2024)
  - [Change-point analysis for matrix data: the empirical Hankel transform approach](https://doi.org/10.1007/s00362-024-01596-4) (Statistical Papers, 2024) — cites the paper above

## Papers

*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](https://doi.org/10.1007/s10463-024-00902-z) | [Annals of the Institute of Statistical Mathematics](https://scholar4ai.com/journals/annals-of-the-institute-of-statistical-mathematics.md) | Žikica Lukić, Bojana Milošević | 6 | cites it |
| 2024-10-28 | [Change-point analysis for matrix data: the empirical Hankel transform approach](https://doi.org/10.1007/s00362-024-01596-4) | [Statistical Papers](https://scholar4ai.com/journals/statistical-papers.md) | Žikica Lukić, Bojana Milošević | 3 | cites it |
| 2022-01-10 | [Improving temporal predictions through time-series labeling using matrix profile and motifs](https://doi.org/10.1007/s00521-021-06744-7) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | 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

```bibtex
@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}
}
```

License: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).