---
title: "hydra-ecosystem/hydra: papers citing it"
description: "4 papers in the tracked journals link to the GitHub repository hydra-ecosystem/hydra in their Crossref records, the first in 2023; most of them (3) in 2025."
url: https://scholar4ai.com/code/github-hydra-ecosystem-hydra
markdown_url: https://scholar4ai.com/code/github-hydra-ecosystem-hydra.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 GitHub repository hydra-ecosystem/hydra in their Crossref records, the first in 2023; most of them (3) in 2025. (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)
---

# hydra-ecosystem/hydra: papers linking to it

Research papers in the 480 tracked journals that link to hydra-ecosystem/hydra (https://github.com/hydra-ecosystem/hydra), 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.

- GitHub
- 10,731 stars
- 1,003 forks
- Python
- license MIT
- created 2019
- https://github.com/hydra-ecosystem/hydra

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

## Key findings

- 4 papers in the tracked journals link to the GitHub repository hydra-ecosystem/hydra in their Crossref records, the first in 2023; most of them (3) in 2025.
- By category: AI (4); the journals with the most are Machine Learning (1), Knowledge and Information Systems (1) and Nature Machine Intelligence (1).

## By year

*Papers linking to hydra-ecosystem/hydra by publication year*

| Year | Papers |
|---|---:|
| 2023 | 1 |
| 2025 | 3 |

## Journals

*Journals whose papers link to hydra-ecosystem/hydra most*

| Journal | Papers |
|---|---:|
| [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | 1 |
| [Knowledge and Information Systems](https://scholar4ai.com/journals/knowledge-and-information-systems.md) | 1 |
| [Nature Machine Intelligence](https://scholar4ai.com/journals/nature-machine-intelligence.md) | 1 |
| [Knowledge-Based Systems](https://scholar4ai.com/journals/knowledge-based-systems.md) | 1 |

## Papers

*Papers linking to hydra-ecosystem/hydra, most cited first*

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 2023-10-19 | [Mitigating the missing-fragmentation problem in de novo peptide sequencing with a two-stage graph-based deep learning model](https://doi.org/10.1038/s42256-023-00738-x) | [Nature Machine Intelligence](https://scholar4ai.com/journals/nature-machine-intelligence.md) | Zeping Mao, Ruixue Zhang, Lei Xin et al. | 42 | cites it |
| 2025-02-24 | [Deep Errors-in-Variables using a diffusion model](https://doi.org/10.1007/s10994-025-06744-x) | [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | Josua Faller, Jörg Martin, Clemens Elster | 2 | cites it |
| 2025-07-21 | [CoSTI: Consistency models for (a faster) spatio-temporal imputation](https://doi.org/10.1016/j.knosys.2025.114117) | [Knowledge-Based Systems](https://scholar4ai.com/journals/knowledge-based-systems.md) | Javier Solí­s-Garcí­a, Belén Vega-Márquez, Juan A. Nepomuceno et al. | 2 | cites it |
| 2025-08-28 | [Focal Sampling: SGD biased towards early important samples for efficient image classification with augmentation selection](https://doi.org/10.1007/s10115-025-02563-7) | [Knowledge and Information Systems](https://scholar4ai.com/journals/knowledge-and-information-systems.md) | Alessio Quercia, Fernanda Nader, Abigail Morrison 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). hydra-ecosystem/hydra: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/github-hydra-ecosystem-hydra

```bibtex
@misc{scholar4ai-code-github-hydra-ecosystem-hydra,
  author = {{Scholar4AI}},
  title = {hydra-ecosystem/hydra: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/github-hydra-ecosystem-hydra}},
  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/).