---
title: "Effsize - a package for efficient effect size computation"
description: "4 papers in the tracked journals link to the Zenodo dataset Effsize - a package for efficient effect size computation in their Crossref records, the first in 2020; most of them (2) in 2026."
url: https://scholar4ai.com/code/zenodo-1480624
markdown_url: https://scholar4ai.com/code/zenodo-1480624.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "4 papers in the tracked journals link to the Zenodo dataset Effsize - a package for efficient effect size computation in their Crossref records, the first in 2020; most of them (2) in 2026. (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)
---

# Effsize - a package for efficient effect size computation: papers linking to it

Research papers in the 480 tracked journals that link to Effsize - a package for efficient effect size computation (https://doi.org/10.5281/zenodo.1480624), 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.

- Zenodo
- Software
- 2016
- archives github.com/mtorchiano/effsize
- https://doi.org/10.5281/zenodo.1480624

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

## Key findings

- 4 papers in the tracked journals link to the Zenodo dataset Effsize - a package for efficient effect size computation in their Crossref records, the first in 2020; most of them (2) in 2026.
- By category: AI (2), Management (1) and Health Economics and Policy (1); the journals with the most are Journal of Business and Psychology (1), Expert Systems with Applications (1) and PharmacoEconomics (1).

## By year

*Papers linking to Effsize - a package for efficient effect size computation by publication year*

| Year | Papers |
|---|---:|
| 2020 | 1 |
| 2024 | 1 |
| 2026 | 2 |

## Journals

*Journals whose papers link to Effsize - a package for efficient effect size computation most*

| Journal | Papers |
|---|---:|
| [Journal of Business and Psychology](https://scholar4ai.com/journals/journal-of-business-and-psychology.md) | 1 |
| [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | 1 |
| [PharmacoEconomics](https://scholar4ai.com/journals/pharmacoeconomics.md) | 1 |
| [Information Fusion](https://scholar4ai.com/journals/information-fusion.md) | 1 |

## Papers

*Papers linking to Effsize - a package for efficient effect size computation, most cited first*

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 2024-01-18 | [Psychometric Performance Comparison of the Adapted versus Original Versions of the EQ-5D-Y-3L and -Y-5L in Proxy Respondents for 2- to 4-Year-Olds](https://doi.org/10.1007/s40273-024-01350-6) | [PharmacoEconomics](https://scholar4ai.com/journals/pharmacoeconomics.md) | Alexander van Heusden, Oliver Rivero‐Arias, Michael Herdman et al. | 14 | cites it |
| 2020-12-24 | [Stratified opposition-based initialization for variable-length chromosome shortest path problem evolutionary algorithms](https://doi.org/10.1016/j.eswa.2020.114525) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Aiman Ghannami, Jing Li, Ammar Hawbani et al. | 7 | cites it |
| 2026-02-23 | [Document classification pattern recognition via information fusion: A systematic review of multimodal and multiview representation approaches](https://doi.org/10.1016/j.inffus.2026.104247) | [Information Fusion](https://scholar4ai.com/journals/information-fusion.md) | Marcin Mirończuk | 2 | cites it |
| 2026-07-23 | [One Mistake, Two Standards: An Investigation of Gendered Evaluations of Leaders’ Task and Relational Mistakes](https://doi.org/10.1007/s10869-026-10137-9) | [Journal of Business and Psychology](https://scholar4ai.com/journals/journal-of-business-and-psychology.md) | Stefanie Krauth, Martin Paul Fladerer, Melissa M. Hehnen 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). Effsize - a package for efficient effect size computation: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/zenodo-1480624

```bibtex
@misc{scholar4ai-code-zenodo-1480624,
  author = {{Scholar4AI}},
  title = {Effsize - a package for efficient effect size computation: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/zenodo-1480624}},
  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/).