---
title: "Code and data repositories cited in research papers"
description: "Since 2017, 13,973 papers in the 480 tracked journals (3.0%) link to at least one code or data repository in their Crossref records, 14,222 repositories in all; 1,456 of these papers link their own code or data. The share of papers linking to code or data went from 0.9% in 2017 to 4.5% in 2025."
url: https://scholar4ai.com/code
markdown_url: https://scholar4ai.com/code.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "Since 2017, 13,973 papers in the 480 tracked journals (3.0%) link to at least one code or data repository in their Crossref records, 14,222 repositories in all; 1,456 of these papers link their own code or data. (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)
---

# Code and data repositories cited in research papers

Which code and data repositories (GitHub, Hugging Face, Zenodo, CRAN and others) the papers of the 480 tracked journals link to, read from their Crossref records, and the lineage the links show: later papers building on an earlier paper's code, forks of linked GitHub repositories and models derived from linked Hugging Face models.

## Key findings

- Since 2017, 13,973 papers in the 480 tracked journals (3.0%) link to at least one code or data repository in their Crossref records, 14,222 repositories in all; 1,456 of these papers link their own code or data.
- The share of papers linking to code or data went from 0.9% in 2017 to 4.5% in 2025.
- By kind, 4,862 papers link to code repositories, 159 papers link to models and apps, 7,434 papers link to data repositories and 2,580 papers link to software packages.
- GitHub is linked most (4,608 papers), followed by CRAN (2,490), Zenodo (2,340) and OSF (2,002).
- The repositories linked by the most papers are ultralytics/ultralytics (173), keras-team/keras (138) and ultralytics/yolov5 (110).
- In 2025, papers linked to code or data most often in Statistics (9.3%), Higher Education and Research (8.9%) and AI (7.1%).
- Pairs of papers where a later paper builds on an earlier one: 679. In 4 the later uses code or data the earlier paper published as its own, in 10 it uses a fork, derived model or archive of a repository the earlier paper used and cites it, and in 665 it cites the earlier paper and links the same repository.
- Linked repositories derived from another linked repository: 53 (GitHub forks: 6; derived models and archives: 47).

## By year

*Papers in the tracked journals by publication year (* this year so far) and those linking to code, models, data or packages*

| Year | Papers | Linking code or data | Share | Code | Models | Data | Packages |
|---|---:|---:|---:|---:|---:|---:|---:|
| 2017 | 34,490 | 323 | 0.9% | 111 | 0 | 49 | 171 |
| 2018 | 35,527 | 424 | 1.2% | 155 | 0 | 100 | 190 |
| 2019 | 37,061 | 521 | 1.4% | 245 | 0 | 147 | 172 |
| 2020 | 42,023 | 821 | 2.0% | 350 | 0 | 339 | 192 |
| 2021 | 47,113 | 1,227 | 2.6% | 476 | 1 | 593 | 265 |
| 2022 | 49,612 | 1,509 | 3.0% | 562 | 0 | 732 | 324 |
| 2023 | 50,278 | 1,625 | 3.2% | 581 | 5 | 892 | 299 |
| 2024 | 56,094 | 1,982 | 3.5% | 596 | 17 | 1,237 | 302 |
| 2025 | 61,849 | 2,804 | 4.5% | 947 | 55 | 1,697 | 349 |
| 2026* | 56,034 | 2,669 | 4.8% | 839 | 81 | 1,648 | 316 |

## By category

*Share of each category's papers linking to code or data, 2017 and 2025*

| Category | Papers linking | 2017 | 2025 | Most-linked repositories |
|---|---:|---:|---:|---|
| [Statistics](https://scholar4ai.com/categories/statistics.md) | 935 | 5.6% | 9.3% | survival (R package), copula (R package), mvtnorm (R package) |
| [Higher Education and Research](https://scholar4ai.com/categories/higher-education-and-research.md) | 882 | 2.1% | 8.9% | psych (R package), dplyr (R package), tidyverse (R package) |
| [AI](https://scholar4ai.com/categories/ai.md) | 5,678 | 1.6% | 7.1% | ultralytics/ultralytics, keras-team/keras, ultralytics/yolov5 |
| [Economics](https://scholar4ai.com/categories/economics.md) | 2,539 | 0.4% | 4.0% | mwnje, 89k47, j23pn |
| [Information Systems](https://scholar4ai.com/categories/information-systems.md) | 454 | 1.2% | 3.8% | ethereum/wiki, cluster (R package), ieee8023/covid-chestxray-dataset |
| [Operations Research](https://scholar4ai.com/categories/operations-research.md) | 1,646 | 1.1% | 3.2% | bstabler/TransportationNetworks, keras-team/keras, jax-ml/jax |
| [Health Economics and Policy](https://scholar4ai.com/categories/health-economics-and-policy.md) | 259 | 0.5% | 2.9% | flexsurv (R package), survival (R package), dplyr (R package) |
| [Marketing](https://scholar4ai.com/categories/marketing.md) | 149 | 0.2% | 2.7% | hqa4n, psych (R package), semTools (R package) |
| [Management](https://scholar4ai.com/categories/management.md) | 613 | 0.2% | 2.2% | psych (R package), NCA (R package), nlme (R package) |
| [Finance](https://scholar4ai.com/categories/finance.md) | 623 | 0.4% | 2.2% | keras (R package), rstudio/keras3, robjhyndman/forecast |
| [Accounting](https://scholar4ai.com/categories/accounting.md) | 29 | 0.0% | 1.0% | nytimes/covid-19-data, v3cgs, arm (R package) |
| [Tourism and Hospitality](https://scholar4ai.com/categories/tourism-and-hospitality.md) | 98 | 0.1% | 0.8% | ergm (R package), sna (R package), ARDL (R package) |

## Hosts

*Where the linked code and data are hosted*

| Host | Papers | Repositories |
|---|---:|---:|
| GitHub | 4,608 | 4,090 |
| CRAN | 2,490 | 1,938 |
| Zenodo | 2,340 | 2,477 |
| OSF | 2,002 | 1,792 |
| Mendeley Data | 1,622 | 1,608 |
| Kaggle | 1,153 | 1,082 |
| figshare | 339 | 324 |
| Harvard Dataverse | 285 | 223 |
| Hugging Face | 200 | 278 |
| SourceForge | 174 | 123 |
| PyPI | 134 | 130 |
| Code Ocean | 97 | 99 |
| Bitbucket | 34 | 31 |
| GitLab | 23 | 24 |
| Gitee | 3 | 3 |

## Code repositories

*Code repositories linked by the most papers since 2017*

| # | Code | Host | Papers | Categories | First linked | Stars | Fork of |
|---:|---|---|---:|---|---|---:|---|
| 1 | [ultralytics/ultralytics](https://scholar4ai.com/code/github-ultralytics-ultralytics.md) | GitHub | 173 | AI, Operations Research | 2023 | 63,051 | – |
| 2 | [keras-team/keras](https://scholar4ai.com/code/github-keras-team-keras.md) | GitHub | 138 | AI, Operations Research | 2017 | 64,413 | – |
| 3 | [ultralytics/yolov5](https://scholar4ai.com/code/github-ultralytics-yolov5.md) | GitHub | 110 | AI, Information Systems | 2021 | 58,246 | – |
| 4 | [bstabler/TransportationNetworks](https://scholar4ai.com/code/github-bstabler-transportationnetworks.md) | GitHub | 76 | Operations Research, AI | 2017 | 1,075 | – |
| 5 | [facebookresearch/detectron2](https://scholar4ai.com/code/github-facebookresearch-detectron2.md) | GitHub | 52 | AI, Economics | 2021 | 34,965 | – |
| 6 | [ieee8023/covid-chestxray-dataset](https://scholar4ai.com/code/github-ieee8023-covid-chestxray-dataset.md) | GitHub | 36 | AI, Information Systems | 2020 | 3,064 | – |
| 7 | [open-mmlab/mmsegmentation](https://scholar4ai.com/code/github-open-mmlab-mmsegmentation.md) | GitHub | 31 | AI | 2022 | 9,987 | – |
| 8 | [jax-ml/jax](https://scholar4ai.com/code/github-jax-ml-jax.md) | GitHub | 28 | Operations Research, AI | 2022 | 36,568 | – |
| 9 | [deepfakes/faceswap](https://scholar4ai.com/code/github-deepfakes-faceswap.md) | GitHub | 27 | AI | 2020 | 57,599 | – |
| 10 | [fastai/imagenette](https://scholar4ai.com/code/github-fastai-imagenette.md) | GitHub | 23 | AI | 2021 | 1,140 | – |
| 11 | [huggingface/pytorch-image-models](https://scholar4ai.com/code/github-huggingface-pytorch-image-models.md) | GitHub | 23 | AI | 2022 | 37,262 | – |
| 12 | [CSSEGISandData/COVID-19](https://scholar4ai.com/code/github-cssegisanddata-covid-19.md) | GitHub | 22 | AI, Economics | 2020 | 28,878 | – |
| 13 | [shap/shap](https://scholar4ai.com/code/github-shap-shap.md) | GitHub | 22 | AI, Economics | 2021 | 25,845 | – |
| 14 | [SheffieldML/GPy](https://scholar4ai.com/code/github-sheffieldml-gpy.md) | GitHub | 22 | AI, Operations Research | 2018 | 2,169 | – |
| 15 | [MarekKowalski/FaceSwap](https://scholar4ai.com/code/github-marekkowalski-faceswap.md) | GitHub | 20 | AI | 2021 | 757 | – |
| 16 | [ethereum/wiki](https://scholar4ai.com/code/github-ethereum-wiki.md) | GitHub | 19 | Information Systems, AI | 2017 | – | – |
| 17 | [meta-llama/llama3](https://scholar4ai.com/code/github-meta-llama-llama3.md) | GitHub | 19 | AI, Information Systems | 2025 | 29,165 | – |
| 18 | [stevengj/nlopt](https://scholar4ai.com/code/github-stevengj-nlopt.md) | GitHub | 19 | Operations Research, Statistics | 2019 | 2,288 | – |
| 19 | [tatsu-lab/stanford_alpaca](https://scholar4ai.com/code/github-tatsu-lab-stanford-alpaca.md) | GitHub | 19 | AI, Operations Research | 2024 | 30,232 | – |
| 20 | [keras-team/keras-tuner](https://scholar4ai.com/code/github-keras-team-keras-tuner.md) | GitHub | 16 | AI, Economics | 2021 | 2,921 | – |
| 21 | [open-mmlab/OpenPCDet](https://scholar4ai.com/code/github-open-mmlab-openpcdet.md) | GitHub | 16 | AI, Operations Research | 2024 | 5,757 | – |
| 22 | [metagol/metagol](https://scholar4ai.com/code/github-metagol-metagol.md) | GitHub | 14 | AI | 2018 | 212 | – |
| 23 | [alibaba/clusterdata](https://scholar4ai.com/code/github-alibaba-clusterdata.md) | GitHub | 13 | AI, Operations Research | 2021 | 2,248 | – |
| 24 | [google-deepmind/dsprites-dataset](https://scholar4ai.com/code/github-google-deepmind-dsprites-dataset.md) | GitHub | 13 | AI | 2019 | 541 | – |
| 25 | [Lightning-AI/pytorch-lightning](https://scholar4ai.com/code/github-lightning-ai-pytorch-lightning.md) | GitHub | 13 | AI | 2022 | 31,439 | – |
| 26 | [nytimes/covid-19-data](https://scholar4ai.com/code/github-nytimes-covid-19-data.md) | GitHub | 13 | Economics, Accounting | 2020 | 6,966 | – |
| 27 | [open-mmlab/mmpose](https://scholar4ai.com/code/github-open-mmlab-mmpose.md) | GitHub | 13 | AI | 2023 | 8,022 | – |
| 28 | [tensorflow/models](https://scholar4ai.com/code/github-tensorflow-models.md) | GitHub | 13 | AI | 2019 | 77,636 | – |
| 29 | [facebookresearch/Detectron](https://scholar4ai.com/code/github-facebookresearch-detectron.md) | GitHub | 12 | AI, Operations Research | 2019 | 26,343 | – |
| 30 | [hill-a/stable-baselines](https://scholar4ai.com/code/github-hill-a-stable-baselines.md) | GitHub | 12 | AI, Operations Research | 2020 | 4,326 | openai/baselines |
| 31 | [HumanSignal/labelImg](https://scholar4ai.com/code/github-humansignal-labelimg.md) | GitHub | 12 | AI, Operations Research | 2019 | 25,030 | – |
| 32 | [open-mmlab/mmdetection3d](https://scholar4ai.com/code/github-open-mmlab-mmdetection3d.md) | GitHub | 12 | AI | 2023 | 6,571 | – |
| 33 | [fxsjy/jieba](https://scholar4ai.com/code/github-fxsjy-jieba.md) | GitHub | 11 | AI, Management | 2018 | 35,228 | – |
| 34 | [robjhyndman/forecast](https://scholar4ai.com/code/github-robjhyndman-forecast.md) | GitHub | 11 | Finance, Operations Research | 2018 | 1,186 | – |
| 35 | [spotify/annoy](https://scholar4ai.com/code/github-spotify-annoy.md) | GitHub | 11 | AI, Management | 2018 | 14,329 | – |
| 36 | [facebookresearch/maskrcnn-benchmark](https://scholar4ai.com/code/github-facebookresearch-maskrcnn-benchmark.md) | GitHub | 10 | AI | 2019 | 9,360 | – |
| 37 | [huggingface/peft](https://scholar4ai.com/code/github-huggingface-peft.md) | GitHub | 10 | AI | 2024 | 21,869 | – |
| 38 | [openai/baselines](https://scholar4ai.com/code/github-openai-baselines.md) | GitHub | 10 | AI | 2019 | 16,775 | – |
| 39 | [pcm-dpc/COVID-19](https://scholar4ai.com/code/github-pcm-dpc-covid-19.md) | GitHub | 10 | Economics, Health Economics and Policy | 2020 | 3,781 | – |
| 40 | [propublica/compas-analysis](https://scholar4ai.com/code/github-propublica-compas-analysis.md) | GitHub | 10 | AI, Operations Research | 2020 | 701 | – |
| 41 | [aleju/imgaug](https://scholar4ai.com/code/github-aleju-imgaug.md) | GitHub | 9 | AI | 2020 | 14,734 | – |
| 42 | [jacobgil/pytorch-grad-cam](https://scholar4ai.com/code/github-jacobgil-pytorch-grad-cam.md) | GitHub | 9 | AI | 2022 | 13,005 | – |
| 43 | [langchain-ai/langchain](https://scholar4ai.com/code/github-langchain-ai-langchain.md) | GitHub | 9 | AI, Information Systems | 2024 | 148,922 | – |
| 44 | [MadryLab/robustness](https://scholar4ai.com/code/github-madrylab-robustness.md) | GitHub | 9 | AI, Operations Research | 2021 | 947 | – |
| 45 | [matterport/Mask_RCNN](https://scholar4ai.com/code/github-matterport-mask-rcnn.md) | GitHub | 9 | AI | 2020 | 25,576 | – |
| 46 | [mjockers/syuzhet](https://scholar4ai.com/code/github-mjockers-syuzhet.md) | GitHub | 9 | Management, AI | 2018 | 350 | – |
| 47 | [pytorch/pytorch](https://scholar4ai.com/code/github-pytorch-pytorch.md) | GitHub | 9 | AI, Higher Education and Research | 2018 | 105,102 | – |
| 48 | [RangiLyu/nanodet](https://scholar4ai.com/code/github-rangilyu-nanodet.md) | GitHub | 9 | AI | 2022 | 6,276 | – |
| 49 | [rstudio/keras3](https://scholar4ai.com/code/github-rstudio-keras3.md) | GitHub | 9 | Finance, Statistics | 2021 | 857 | – |
| 50 | [stellargraph/stellargraph](https://scholar4ai.com/code/github-stellargraph-stellargraph.md) | GitHub | 9 | AI, Finance | 2021 | 3,064 | – |

## Models and apps

*Models and apps linked by the most papers since 2017*

| # | Model | Host | Papers | Categories | First linked | Likes | Base model |
|---:|---|---|---:|---|---|---:|---|
| 1 | [j-hartmann/emotion-english-distilroberta-base](https://scholar4ai.com/code/hf-model-j-hartmann-emotion-english-distilroberta-base.md) | Hugging Face | 6 | Information Systems, AI | 2024 | 505 | – |
| 2 | [black-forest-labs/FLUX.1-dev](https://scholar4ai.com/code/hf-model-black-forest-labs-flux-1-dev.md) | Hugging Face | 5 | AI | 2025 | 15,902 | – |
| 3 | [sentence-transformers/all-mpnet-base-v2](https://scholar4ai.com/code/hf-model-sentence-transformers-all-mpnet-base-v2.md) | Hugging Face | 5 | AI, Management | 2024 | 1,403 | – |
| 4 | [aaditya/Llama3-OpenBioLLM-70B](https://scholar4ai.com/code/hf-model-aaditya-llama3-openbiollm-70b.md) | Hugging Face | 4 | AI | 2025 | 519 | meta-llama/Meta-Llama-3-70B-Instruct |
| 5 | [sentence-transformers/all-MiniLM-L6-v2](https://scholar4ai.com/code/hf-model-sentence-transformers-all-minilm-l6-v2.md) | Hugging Face | 4 | AI | 2025 | 6,059 | nreimers/MiniLM-L6-H384-uncased |
| 6 | [meta-llama/Llama-3.2-1B](https://scholar4ai.com/code/hf-model-meta-llama-llama-3-2-1b.md) | Hugging Face | 3 | AI, Higher Education and Research | 2025 | 2,747 | – |
| 7 | [nlptown/bert-base-multilingual-uncased-sentiment](https://scholar4ai.com/code/hf-model-nlptown-bert-base-multilingual-uncased-sentiment.md) | Hugging Face | 3 | AI, Management | 2025 | 496 | – |
| 8 | [stabilityai/stable-diffusion-2-inpainting](https://scholar4ai.com/code/hf-model-stabilityai-stable-diffusion-2-inpainting.md) | Hugging Face | 3 | AI | 2026 | – | – |
| 9 | [stable-diffusion-v1-5/stable-diffusion-v1-5](https://scholar4ai.com/code/hf-model-stable-diffusion-v1-5-stable-diffusion-v1-5.md) | Hugging Face | 3 | AI | 2026 | 1,356 | – |
| 10 | [open-llm-leaderboard/open_llm_leaderboard](https://scholar4ai.com/code/hf-space-open-llm-leaderboard-open-llm-leaderboard.md) | Hugging Face | 3 | AI | 2025 | – | – |
| 11 | [bertin-project/bertin-roberta-base-spanish](https://huggingface.co/bertin-project/bertin-roberta-base-spanish) | Hugging Face | 2 | AI | 2023 | 39 | – |
| 12 | [cardiffnlp/twitter-roberta-base-sentiment-latest](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment-latest) | Hugging Face | 2 | Management, Operations Research | 2024 | 855 | – |
| 13 | [dbmdz/bert-base-turkish-cased](https://huggingface.co/dbmdz/bert-base-turkish-cased) | Hugging Face | 2 | AI | 2023 | 191 | – |
| 14 | [dbmdz/bert-base-turkish-uncased](https://huggingface.co/dbmdz/bert-base-turkish-uncased) | Hugging Face | 2 | Finance, AI | 2024 | 42 | – |
| 15 | [diffusers/stable-diffusion-xl-1.0-inpainting-0.1](https://huggingface.co/diffusers/stable-diffusion-xl-1.0-inpainting-0.1) | Hugging Face | 2 | AI | 2026 | 393 | stabilityai/stable-diffusion-xl-base-1.0 |
| 16 | [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) | Hugging Face | 2 | Information Systems, Operations Research | 2026 | 3,060 | – |
| 17 | [jy46604790/Fake-News-Bert-Detect](https://huggingface.co/jy46604790/Fake-News-Bert-Detect) | Hugging Face | 2 | AI | 2025 | 35 | – |
| 18 | [meta-llama/Llama-3.1-405B](https://huggingface.co/meta-llama/Llama-3.1-405B) | Hugging Face | 2 | AI | 2026 | 993 | – |
| 19 | [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) | Hugging Face | 2 | AI | 2025 | 8,760 | meta-llama/Llama-3.1-8B |
| 20 | [meta-llama/Llama-3.2-3B](https://huggingface.co/meta-llama/Llama-3.2-3B) | Hugging Face | 2 | AI | 2025 | 1,074 | – |
| 21 | [meta-llama/Llama-Guard-4-12B](https://huggingface.co/meta-llama/Llama-Guard-4-12B) | Hugging Face | 2 | AI | 2026 | 136 | – |
| 22 | [nlpconnect/vit-gpt2-image-captioning](https://huggingface.co/nlpconnect/vit-gpt2-image-captioning) | Hugging Face | 2 | AI | 2023 | 948 | – |
| 23 | [ProsusAI/finbert](https://huggingface.co/ProsusAI/finbert) | Hugging Face | 2 | Information Systems, AI | 2026 | 1,297 | – |
| 24 | [Qwen/Qwen2-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct) | Hugging Face | 2 | AI | 2025 | 1,297 | Qwen/Qwen2-VL-7B |
| 25 | [Salesforce/SFR-Embedding-2_R](https://huggingface.co/Salesforce/SFR-Embedding-2_R) | Hugging Face | 2 | AI | 2025 | 95 | – |
| 26 | [SamLowe/roberta-base-go_emotions](https://huggingface.co/SamLowe/roberta-base-go_emotions) | Hugging Face | 2 | AI, Operations Research | 2026 | 718 | – |
| 27 | [vikhyatk/moondream2](https://huggingface.co/vikhyatk/moondream2) | Hugging Face | 2 | AI, Operations Research | 2026 | 1,450 | – |
| 28 | [huggingfaceh4/open_llm_leaderboard](https://huggingface.co/spaces/huggingfaceh4/open_llm_leaderboard) | Hugging Face | 2 | AI | 2024 | – | – |
| 29 | [abdoelsayed/aradpr](https://huggingface.co/abdoelsayed/aradpr) | Hugging Face | 1 | AI | 2026 | – | – |
| 30 | [ahmedbou/tunibert](https://huggingface.co/ahmedbou/tunibert) | Hugging Face | 1 | AI | 2025 | – | – |
| 31 | [albert/albert-base-v2](https://huggingface.co/albert/albert-base-v2) | Hugging Face | 1 | AI | 2025 | – | – |
| 32 | [alexanderholmes0/fake-news-detector-long](https://huggingface.co/alexanderholmes0/fake-news-detector-long) | Hugging Face | 1 | AI | 2025 | – | – |
| 33 | [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-base-4096) | Hugging Face | 1 | AI | 2025 | – | – |
| 34 | [allenai/molmoe-1b-0924](https://huggingface.co/allenai/molmoe-1b-0924) | Hugging Face | 1 | AI | 2026 | – | – |
| 35 | [ankitai/deberta-v3-small-base-emotions-classifier](https://huggingface.co/ankitai/deberta-v3-small-base-emotions-classifier) | Hugging Face | 1 | AI | 2026 | – | – |
| 36 | [aubmindlab/bert-large-arabertv2](https://huggingface.co/aubmindlab/bert-large-arabertv2) | Hugging Face | 1 | AI | 2026 | – | – |
| 37 | [axelwillekens/centernet_ilvo_example](https://huggingface.co/axelwillekens/centernet_ilvo_example) | Hugging Face | 1 | AI | 2025 | – | – |
| 38 | [baai](https://huggingface.co/baai) | Hugging Face | 1 | Higher Education and Research | 2026 | – | – |
| 39 | [baai/bge-large-en](https://huggingface.co/baai/bge-large-en) | Hugging Face | 1 | Higher Education and Research | 2026 | – | – |
| 40 | [bert-base-cased](https://huggingface.co/bert-base-cased) | Hugging Face | 1 | AI | 2024 | – | – |
| 41 | [bert-base-chinese](https://huggingface.co/bert-base-chinese) | Hugging Face | 1 | Higher Education and Research | 2024 | – | – |
| 42 | [bert-base-uncased](https://huggingface.co/bert-base-uncased) | Hugging Face | 1 | AI | 2021 | – | – |
| 43 | [bioptimus/h-optimus-1](https://huggingface.co/bioptimus/h-optimus-1) | Hugging Face | 1 | AI | 2026 | – | – |
| 44 | [black-forest-labs/flux.1-fill-dev](https://huggingface.co/black-forest-labs/flux.1-fill-dev) | Hugging Face | 1 | AI | 2026 | – | – |
| 45 | [bsc-temu/robertalex](https://huggingface.co/bsc-temu/robertalex) | Hugging Face | 1 | AI | 2023 | – | – |
| 46 | [camel-lab/bert-base-arabic-camelbert-ca](https://huggingface.co/camel-lab/bert-base-arabic-camelbert-ca) | Hugging Face | 1 | AI | 2026 | – | – |
| 47 | [cardiffnlp/twitter-roberta-base-sentiment](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment) | Hugging Face | 1 | Finance | 2026 | – | – |
| 48 | [cognitivecomputations/dolphin3.0-llama3.1-8b](https://huggingface.co/cognitivecomputations/dolphin3.0-llama3.1-8b) | Hugging Face | 1 | AI | 2026 | – | – |
| 49 | [cohereforai/c4ai-command-r-plus-08-2024](https://huggingface.co/cohereforai/c4ai-command-r-plus-08-2024) | Hugging Face | 1 | AI | 2026 | – | – |
| 50 | [cohereforai/c4ai-command-r-v01](https://huggingface.co/cohereforai/c4ai-command-r-v01) | Hugging Face | 1 | AI | 2025 | – | – |

## Data repositories

*Data repositories linked by the most papers since 2017*

| # | Data | Host | Papers | Categories | First linked |
|---:|---|---|---:|---|---|
| 1 | [mlg-ulb/creditcardfraud](https://scholar4ai.com/code/kaggle-mlg-ulb-creditcardfraud.md) | Kaggle | 31 | AI, Operations Research | 2020 |
| 2 | [ultralytics/yolov5: v7.0 - YOLOv5 SOTA Realtime Instance Segmentation](https://scholar4ai.com/code/zenodo-3908559.md) | Zenodo | 31 | AI, Operations Research | 2022 |
| 3 | [paultimothymooney/chest-xray-pneumonia](https://scholar4ai.com/code/kaggle-paultimothymooney-chest-xray-pneumonia.md) | Kaggle | 24 | AI, Information Systems | 2020 |
| 4 | [tawsifurrahman/covid19-radiography-database](https://scholar4ai.com/code/kaggle-tawsifurrahman-covid19-radiography-database.md) | Kaggle | 23 | AI, Information Systems | 2021 |
| 5 | [pandas-dev/pandas: Pandas](https://scholar4ai.com/code/zenodo-3509134.md) | Zenodo | 20 | AI, Operations Research | 2021 |
| 6 | [aptos2019-blindness-detection](https://scholar4ai.com/code/kaggle-competition-aptos2019-blindness-detection.md) | Kaggle | 19 | AI | 2022 |
| 7 | [diabetic-retinopathy-detection](https://scholar4ai.com/code/kaggle-competition-diabetic-retinopathy-detection.md) | Kaggle | 19 | AI, Statistics | 2019 |
| 8 | [rwightman/pytorch-image-models: v0.8.10dev0 Release](https://scholar4ai.com/code/zenodo-4414861.md) | Zenodo | 19 | AI | 2023 |
| 9 | [mwnje](https://scholar4ai.com/code/osf-mwnje.md) | OSF | 18 | Economics, Finance | 2020 |
| 10 | [brain tumor dataset](https://scholar4ai.com/code/figshare-1512427.md) | figshare | 15 | AI | 2021 |
| 11 | [ahmedhamada0/brain-tumor-detection](https://scholar4ai.com/code/kaggle-ahmedhamada0-brain-tumor-detection.md) | Kaggle | 15 | AI | 2022 |
| 12 | [89k47](https://scholar4ai.com/code/osf-89k47.md) | OSF | 15 | Economics, Health Economics and Policy | 2020 |
| 13 | [explosion/spaCy: v3.7.2: Fixes for APIs and requirements](https://scholar4ai.com/code/zenodo-1212303.md) | Zenodo | 15 | AI, Information Systems | 2022 |
| 14 | [rsna-pneumonia-detection-challenge](https://scholar4ai.com/code/kaggle-competition-rsna-pneumonia-detection-challenge.md) | Kaggle | 14 | AI | 2020 |
| 15 | [masoudnickparvar/brain-tumor-mri-dataset](https://scholar4ai.com/code/kaggle-masoudnickparvar-brain-tumor-mri-dataset.md) | Kaggle | 14 | AI | 2024 |
| 16 | [Global assessment report on biodiversity and ecosystem services of the Intergovernmental…](https://scholar4ai.com/code/zenodo-3831673.md) | Zenodo | 14 | Economics, Finance | 2022 |
| 17 | [MaartenGr/KeyBERT: BibTeX](https://scholar4ai.com/code/zenodo-4461265.md) | Zenodo | 14 | AI, Higher Education and Research | 2022 |
| 18 | [j23pn](https://scholar4ai.com/code/osf-j23pn.md) | OSF | 13 | Economics, AI | 2021 |
| 19 | [OpenCLIP](https://scholar4ai.com/code/zenodo-5143773.md) | Zenodo | 13 | AI | 2023 |
| 20 | [painter-by-numbers](https://scholar4ai.com/code/kaggle-competition-painter-by-numbers.md) | Kaggle | 11 | AI | 2020 |
| 21 | [sartajbhuvaji/brain-tumor-classification-mri](https://scholar4ai.com/code/kaggle-sartajbhuvaji-brain-tumor-classification-mri.md) | Kaggle | 11 | AI | 2022 |
| 22 | [5b26t](https://scholar4ai.com/code/osf-5b26t.md) | OSF | 11 | AI, Management | 2024 |
| 23 | [Understanding artificial intelligence ethics and safety: A guide for the responsible…](https://scholar4ai.com/code/zenodo-3240529.md) | Zenodo | 11 | AI, Health Economics and Policy | 2021 |
| 24 | [ultralytics/yolov5: v3.1 - Bug Fixes and Performance Improvements](https://scholar4ai.com/code/zenodo-4154370.md) | Zenodo | 11 | AI, Operations Research | 2021 |
| 25 | [County Presidential Election Returns 2000-2024](https://scholar4ai.com/code/dataverse-voqchq.md) | Harvard Dataverse | 10 | Economics, Health Economics and Policy | 2021 |
| 26 | [navoneel/brain-mri-images-for-brain-tumor-detection](https://scholar4ai.com/code/kaggle-navoneel-brain-mri-images-for-brain-tumor-detection.md) | Kaggle | 10 | AI, Information Systems | 2021 |
| 27 | [Labeled Optical Coherence Tomography (OCT) for Classification](https://scholar4ai.com/code/mendeley-rscbjbr9sj.md) | Mendeley Data | 10 | AI | 2021 |
| 28 | [9yqs8](https://scholar4ai.com/code/osf-9yqs8.md) | OSF | 10 | Economics, Marketing | 2020 |
| 29 | [The Standardized World Income Inequality Database, Versions 8-9](https://scholar4ai.com/code/dataverse-lm4owf.md) | Harvard Dataverse | 9 | Economics | 2020 |
| 30 | [COVID-19 CT Lung and Infection Segmentation Dataset](https://scholar4ai.com/code/zenodo-3757476.md) | Zenodo | 9 | AI | 2020 |
| 31 | [Growth Projections and Complexity Rankings](https://scholar4ai.com/code/dataverse-xtaqmc.md) | Harvard Dataverse | 8 | Economics, Management | 2023 |
| 32 | [uciml/pima-indians-diabetes-database](https://scholar4ai.com/code/kaggle-uciml-pima-indians-diabetes-database.md) | Kaggle | 8 | AI, Information Systems | 2020 |
| 33 | [d3wpk](https://scholar4ai.com/code/osf-d3wpk.md) | OSF | 8 | Higher Education and Research, AI | 2020 |
| 34 | [q43ca](https://scholar4ai.com/code/osf-q43ca.md) | OSF | 8 | Economics, AI | 2020 |
| 35 | [yuq7x](https://scholar4ai.com/code/osf-yuq7x.md) | OSF | 8 | Economics, Health Economics and Policy | 2021 |
| 36 | [Qiskit/qiskit-metapackage: Qiskit 0.44.0](https://scholar4ai.com/code/zenodo-2573505.md) | Zenodo | 8 | AI, Operations Research | 2023 |
| 37 | [Summary for policymakers of the global assessment report on biodiversity and ecosystem…](https://scholar4ai.com/code/zenodo-3553579.md) | Zenodo | 8 | Economics, Accounting | 2021 |
| 38 | [Oil and Gas Data, 1932-2014](https://scholar4ai.com/code/dataverse-ztpw0y.md) | Harvard Dataverse | 7 | Economics | 2016 |
| 39 | [TNO Image Fusion Dataset](https://scholar4ai.com/code/figshare-1008029.md) | figshare | 7 | AI | 2022 |
| 40 | [siim-acr-pneumothorax-segmentation](https://scholar4ai.com/code/kaggle-competition-siim-acr-pneumothorax-segmentation.md) | Kaggle | 7 | AI | 2022 |
| 41 | [johnsmith88/heart-disease-dataset](https://scholar4ai.com/code/kaggle-johnsmith88-heart-disease-dataset.md) | Kaggle | 7 | AI | 2023 |
| 42 | [puneet6060/intel-image-classification](https://scholar4ai.com/code/kaggle-puneet6060-intel-image-classification.md) | Kaggle | 7 | AI, Marketing | 2020 |
| 43 | [sulianova/cardiovascular-disease-dataset](https://scholar4ai.com/code/kaggle-sulianova-cardiovascular-disease-dataset.md) | Kaggle | 7 | AI, Operations Research | 2021 |
| 44 | [Breast Ultrasound Image](https://scholar4ai.com/code/mendeley-wmy84gzngw.md) | Mendeley Data | 7 | AI | 2020 |
| 45 | [PyTorchLightning/pytorch-lightning: 0.7.6 release](https://scholar4ai.com/code/zenodo-3828935.md) | Zenodo | 7 | AI | 2024 |
| 46 | [Untangling Academic Publishing: A history of the relationship between commercial…](https://scholar4ai.com/code/zenodo-546100.md) | Zenodo | 7 | Higher Education and Research, Information Systems | 2017 |
| 47 | [United Nations General Assembly Ideal Points](https://scholar4ai.com/code/dataverse-lejuqz.md) | Harvard Dataverse | 6 | Economics, Management | 2022 |
| 48 | [Chemical reactions from US patents (1976-Sep2016)](https://scholar4ai.com/code/figshare-5104873.md) | figshare | 6 | AI | 2019 |
| 49 | [givemesomecredit](https://scholar4ai.com/code/kaggle-competition-givemesomecredit.md) | Kaggle | 6 | AI, Economics | 2024 |
| 50 | [jigsaw-toxic-comment-classification-challenge](https://scholar4ai.com/code/kaggle-competition-jigsaw-toxic-comment-classification-challenge.md) | Kaggle | 6 | AI, Information Systems | 2021 |

## Software packages

*Software packages linked by the most papers since 2017*

| # | Package | Host | Papers | Categories | First linked |
|---:|---|---|---:|---|---|
| 1 | [psych (R package)](https://scholar4ai.com/code/cran-psych.md) | CRAN | 52 | Higher Education and Research, Management | 2017 |
| 2 | [survival (R package)](https://scholar4ai.com/code/cran-survival.md) | CRAN | 48 | Statistics, Health Economics and Policy | 2017 |
| 3 | [rpart (R package)](https://scholar4ai.com/code/cran-rpart.md) | CRAN | 45 | Statistics, Operations Research | 2016 |
| 4 | [dplyr (R package)](https://scholar4ai.com/code/cran-dplyr.md) | CRAN | 34 | Higher Education and Research, Economics | 2017 |
| 5 | [quantreg (R package)](https://scholar4ai.com/code/cran-quantreg.md) | CRAN | 31 | Statistics, Economics | 2016 |
| 6 | [caret (R package)](https://scholar4ai.com/code/cran-caret.md) | CRAN | 27 | AI, Statistics | 2018 |
| 7 | [xgboost (R package)](https://scholar4ai.com/code/cran-xgboost.md) | CRAN | 27 | AI, Economics | 2018 |
| 8 | [e1071 (R package)](https://scholar4ai.com/code/cran-e1071.md) | CRAN | 26 | AI, Finance | 2016 |
| 9 | [stargazer (R package)](https://scholar4ai.com/code/cran-stargazer.md) | CRAN | 26 | Economics, Operations Research | 2017 |
| 10 | [fpp2 (R package)](https://scholar4ai.com/code/cran-fpp2.md) | CRAN | 25 | AI, Operations Research | 2019 |
| 11 | [tm (R package)](https://scholar4ai.com/code/cran-tm.md) | CRAN | 24 | Higher Education and Research, Management | 2017 |
| 12 | [shiny (R package)](https://scholar4ai.com/code/cran-shiny.md) | CRAN | 23 | Statistics, AI | 2017 |
| 13 | [mvtnorm (R package)](https://scholar4ai.com/code/cran-mvtnorm.md) | CRAN | 22 | Statistics, Operations Research | 2017 |
| 14 | [copula (R package)](https://scholar4ai.com/code/cran-copula.md) | CRAN | 20 | Statistics, Economics | 2016 |
| 15 | [forecast (R package)](https://scholar4ai.com/code/cran-forecast.md) | CRAN | 19 | Economics, AI | 2017 |
| 16 | [semTools (R package)](https://scholar4ai.com/code/cran-semtools.md) | CRAN | 19 | Management, Higher Education and Research | 2019 |
| 17 | [emmeans (R package)](https://scholar4ai.com/code/cran-emmeans.md) | CRAN | 18 | Management, Higher Education and Research | 2021 |
| 18 | [fda (R package)](https://scholar4ai.com/code/cran-fda.md) | CRAN | 18 | Statistics, AI | 2016 |
| 19 | [nlme (R package)](https://scholar4ai.com/code/cran-nlme.md) | CRAN | 18 | Management, Higher Education and Research | 2018 |
| 20 | [cluster (R package)](https://scholar4ai.com/code/cran-cluster.md) | CRAN | 17 | Statistics, Information Systems | 2021 |
| 21 | [keras (R package)](https://scholar4ai.com/code/cran-keras.md) | CRAN | 17 | Finance, Statistics | 2019 |
| 22 | [lme4 (R package)](https://scholar4ai.com/code/cran-lme4.md) | CRAN | 17 | Economics, Management | 2017 |
| 23 | [syuzhet (R package)](https://scholar4ai.com/code/cran-syuzhet.md) | CRAN | 17 | Higher Education and Research, Management | 2018 |
| 24 | [gbm (R package)](https://scholar4ai.com/code/cran-gbm.md) | CRAN | 15 | Economics, Statistics | 2017 |
| 25 | [glmnet (R package)](https://scholar4ai.com/code/cran-glmnet.md) | CRAN | 15 | Operations Research, AI | 2017 |
| 26 | [irace (R package)](https://scholar4ai.com/code/cran-irace.md) | CRAN | 14 | Operations Research, AI | 2016 |
| 27 | [mgcv (R package)](https://scholar4ai.com/code/cran-mgcv.md) | CRAN | 14 | Statistics, Economics | 2017 |
| 28 | [mlogit (R package)](https://scholar4ai.com/code/cran-mlogit.md) | CRAN | 14 | Operations Research, Economics | 2017 |
| 29 | [rugarch (R package)](https://scholar4ai.com/code/cran-rugarch.md) | CRAN | 14 | Economics, Finance | 2017 |
| 30 | [grf (R package)](https://scholar4ai.com/code/cran-grf.md) | CRAN | 13 | Economics, Operations Research | 2021 |
| 31 | [tidyverse (R package)](https://scholar4ai.com/code/cran-tidyverse.md) | CRAN | 13 | Higher Education and Research, Statistics | 2018 |
| 32 | [Hmisc (R package)](https://scholar4ai.com/code/cran-hmisc.md) | CRAN | 12 | Economics, Statistics | 2017 |
| 33 | [NCA (R package)](https://scholar4ai.com/code/cran-nca.md) | CRAN | 12 | Management, Economics | 2021 |
| 34 | [data.table (R package)](https://scholar4ai.com/code/cran-data-table.md) | CRAN | 12 | Statistics, Health Economics and Policy | 2017 |
| 35 | [doParallel (R package)](https://scholar4ai.com/code/cran-doparallel.md) | CRAN | 12 | Statistics, Finance | 2018 |
| 36 | [factoextra (R package)](https://scholar4ai.com/code/cran-factoextra.md) | CRAN | 12 | Economics, AI | 2019 |
| 37 | [ks (R package)](https://scholar4ai.com/code/cran-ks.md) | CRAN | 12 | Statistics, AI | 2016 |
| 38 | [spdep (R package)](https://scholar4ai.com/code/cran-spdep.md) | CRAN | 12 | Economics, Operations Research | 2016 |
| 39 | [C50 (R package)](https://scholar4ai.com/code/cran-c50.md) | CRAN | 11 | AI, Statistics | 2017 |
| 40 | [VGAM (R package)](https://scholar4ai.com/code/cran-vgam.md) | CRAN | 11 | Statistics, Operations Research | 2017 |
| 41 | [MASS (R package)](https://scholar4ai.com/code/cran-mass.md) | CRAN | 10 | AI, Statistics | 2020 |
| 42 | [VineCopula (R package)](https://scholar4ai.com/code/cran-vinecopula.md) | CRAN | 10 | Statistics, Finance | 2017 |
| 43 | [microbenchmark (R package)](https://scholar4ai.com/code/cran-microbenchmark.md) | CRAN | 10 | Statistics, AI | 2017 |
| 44 | [ordinal (R package)](https://scholar4ai.com/code/cran-ordinal.md) | CRAN | 10 | Statistics, AI | 2016 |
| 45 | [pwr (R package)](https://scholar4ai.com/code/cran-pwr.md) | CRAN | 10 | Management, Statistics | 2021 |
| 46 | [tseries (R package)](https://scholar4ai.com/code/cran-tseries.md) | CRAN | 10 | Economics, Finance | 2019 |
| 47 | [Matrix (R package)](https://scholar4ai.com/code/cran-matrix.md) | CRAN | 9 | Statistics, Operations Research | 2017 |
| 48 | [foreach (R package)](https://scholar4ai.com/code/cran-foreach.md) | CRAN | 9 | Statistics, Finance | 2019 |
| 49 | [irr (R package)](https://scholar4ai.com/code/cran-irr.md) | CRAN | 9 | Higher Education and Research, AI | 2016 |
| 50 | [mboost (R package)](https://scholar4ai.com/code/cran-mboost.md) | CRAN | 9 | Statistics, AI | 2016 |

## Lineage: papers building on earlier papers' code

*Pairs of papers where the later builds on the earlier's code (the strongest 100: published code reused first, then forks and derived models, then shared repositories)*

| Earlier paper | Later paper | Repository | How | Cites it |
|---|---|---|---|---|
| [Anomaly detection in streaming data: A comparison and evaluation study](https://doi.org/10.1016/j.eswa.2023.120994) (Expert Systems with Applications, 2023) | [Temporal silhouette: validation of stream clustering robust to concept drift](https://doi.org/10.1007/s10994-023-06462-2) (Machine Learning, 2023) | [Data for Evaluation of Stream Data Analysis Algorithms](https://data.mendeley.com/datasets/c43kr4t7h8) | uses its published code | no |
| [Anomaly detection in streaming data: A comparison and evaluation study](https://doi.org/10.1016/j.eswa.2023.120994) (Expert Systems with Applications, 2023) | [CSTSINR: improving temporal continuity via convolutional structured implicit neural representations for time series anomaly detection](https://doi.org/10.1016/j.neunet.2025.108129) (Neural Networks, 2025) | [SWAN-SF](https://scholar4ai.com/code/dataverse-ebcfkm.md) | uses its published code | no |
| [Anomaly detection in streaming data: A comparison and evaluation study](https://doi.org/10.1016/j.eswa.2023.120994) (Expert Systems with Applications, 2023) | [A noise robust and distribution-adaptive framework for multivariate time series anomaly detection](https://doi.org/10.1016/j.neunet.2026.108695) (Neural Networks, 2026) | [SWAN-SF](https://scholar4ai.com/code/dataverse-ebcfkm.md) | uses its published code | no |
| [Anomaly detection in streaming data: A comparison and evaluation study](https://doi.org/10.1016/j.eswa.2023.120994) (Expert Systems with Applications, 2023) | [Value and shape-aware transformer with prior-enhanced self-attention for multivariate time series classification](https://doi.org/10.1007/s10618-026-01224-9) (Data Mining and Knowledge Discovery, 2026) | [SWAN-SF](https://scholar4ai.com/code/dataverse-ebcfkm.md) | uses its published code | no |
| [A branch-and-cut algorithm for mixed integer bilevel linear optimization problems and its implementation](https://doi.org/10.1007/s12532-020-00183-6) (Mathematical Programming Computation, 2020) | [An exact projection-based algorithm for bilevel mixed-integer problems with nonlinearities](https://doi.org/10.1007/s10898-022-01172-w) (Journal of Global Optimization, 2022) | [coin-or/MibS](https://scholar4ai.com/code/github-coin-or-mibs.md) → [tkralphs/MibS](https://github.com/tkralphs/MibS) | fork | yes |
| [Progressive refined redistribution pyramid network for defect detection in complex scenarios](https://doi.org/10.1016/j.knosys.2022.110176) (Knowledge-Based Systems, 2022) | [Adaptive shape imitation and selective semantic guidance for industrial surface defect detection](https://doi.org/10.1016/j.eswa.2025.127334) (Expert Systems with Applications, 2025) | [ultralytics/yolov5](https://scholar4ai.com/code/github-ultralytics-yolov5.md) → [ultralytics/yolov5: v7.0 - YOLOv5 SOTA Realtime Instance Segmentation](https://scholar4ai.com/code/zenodo-3908559.md) | Zenodo archive | yes |
| [A multi-agent approach to the truck multi-drone routing problem](https://doi.org/10.1016/j.eswa.2022.116604) (Expert Systems with Applications, 2022) | [Truck-multidrone same-day delivery strategies: On-road resupply vs depot return](https://doi.org/10.1016/j.eswa.2025.126757) (Expert Systems with Applications, 2025) | [pcbouman-eur/tsp-d-instances](https://github.com/pcbouman-eur/tsp-d-instances) → [Instances for the TSP with Drone (and some solutions)](https://scholar4ai.com/code/zenodo-1204676.md) | Zenodo archive | yes |
| [Deep learning techniques to classify agricultural crops through UAV imagery: a review](https://doi.org/10.1007/s00521-022-07104-9) (Neural Computing and Applications, 2022) | [QuanCro: a novel framework for quantification of corn crops’ consistency under natural field conditions](https://doi.org/10.1007/s00521-023-08961-8) (Neural Computing and Applications, 2023) | [ultralytics/yolov5](https://scholar4ai.com/code/github-ultralytics-yolov5.md) → [ultralytics/yolov5: v3.1 - Bug Fixes and Performance Improvements](https://scholar4ai.com/code/zenodo-4154370.md) | Zenodo archive | yes |
| [A review of convolutional neural network architectures and their optimizations](https://doi.org/10.1007/s10462-022-10213-5) (Artificial Intelligence Review, 2022) | [Local Concept Embeddings for Analysis of Concept Distributions in Vision DNN Feature Spaces](https://doi.org/10.1007/s11263-025-02446-y) (International Journal of Computer Vision, 2025) | [ultralytics/yolov5](https://scholar4ai.com/code/github-ultralytics-yolov5.md) → [ultralytics/yolov5: v7.0 - YOLOv5 SOTA Realtime Instance Segmentation](https://scholar4ai.com/code/zenodo-3908559.md) | Zenodo archive | yes |
| [Fast forest fire smoke detection using MVMNet](https://doi.org/10.1016/j.knosys.2022.108219) (Knowledge-Based Systems, 2022) | [CEVG-RTNet: A real-time architecture for robust forest fire smoke detection in complex environments](https://doi.org/10.1016/j.neunet.2025.108187) (Neural Networks, 2025) | [ultralytics/yolov5](https://scholar4ai.com/code/github-ultralytics-yolov5.md) → [ultralytics/yolov5: v7.0 - YOLOv5 SOTA Realtime Instance Segmentation](https://scholar4ai.com/code/zenodo-3908559.md) | Zenodo archive | yes |
| [Incomplete pairwise comparison matrices based on graphs with average degree approximately 3](https://doi.org/10.1007/s10479-022-04819-9) (Annals of Operations Research, 2022) | [Optimal sequences for pairwise comparisons: the graph of graphs approach](https://doi.org/10.1007/s10479-025-06752-z) (Annals of Operations Research, 2025) | [szhorvat/IGraphM](https://github.com/szhorvat/IGraphM) → [IGraph/M](https://doi.org/10.5281/zenodo.3739056) | Zenodo archive | yes |
| [The indefinite proximal gradient method](https://doi.org/10.1007/s10589-024-00604-5) (Computational Optimization and Applications, 2024) | [Complexity of trust-region methods with potentially unbounded Hessian approximations for smooth and nonsmooth optimization](https://doi.org/10.1007/s10107-025-02304-2) (Mathematical Programming, 2026) | [JuliaSmoothOptimizers/RegularizedOptimization.jl](https://github.com/JuliaSmoothOptimizers/RegularizedOptimization.jl) → [JuliaSmoothOptimizers/RegularizedOptimization.jl: Release to obtain DOI only](https://doi.org/10.5281/zenodo.6940313) | Zenodo archive | yes |
| [AlignYOLO: A feature-aligned network for object detection](https://doi.org/10.1016/j.eswa.2024.123218) (Expert Systems with Applications, 2024) | [MA-Neck: Mutual attention-based feature enhancement for lightweight object detection](https://doi.org/10.1016/j.patcog.2025.112254) (Pattern Recognition, 2025) | [ultralytics/yolov5](https://scholar4ai.com/code/github-ultralytics-yolov5.md) → [ultralytics/yolov5: v7.0 - YOLOv5 SOTA Realtime Instance Segmentation](https://scholar4ai.com/code/zenodo-3908559.md) | Zenodo archive | yes |
| [MultiSpectral Transformer Fusion via exploiting similarity and complementarity for robust pedestrian detection](https://doi.org/10.1016/j.patcog.2025.111383) (Pattern Recognition, 2025) | [Cross-modal transformer fusion via local sampling for drone RGB-infrared object detection](https://doi.org/10.1016/j.neunet.2026.108997) (Neural Networks, 2026) | [ultralytics/yolov5](https://scholar4ai.com/code/github-ultralytics-yolov5.md) → [ultralytics/yolov5: v6.1 - TensorRT, TensorFlow Edge TPU and OpenVINO Export and Inference](https://scholar4ai.com/code/zenodo-6222936.md) | Zenodo archive | yes |
| [Automatically discriminating and localizing COVID-19 from community-acquired pneumonia on chest X-rays](https://doi.org/10.1016/j.patcog.2020.107613) (Pattern Recognition, 2020) | [Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans](https://doi.org/10.1038/s42256-021-00307-0) (Nature Machine Intelligence, 2021) | [rsna-pneumonia-detection-challenge](https://scholar4ai.com/code/kaggle-competition-rsna-pneumonia-detection-challenge.md) | same repository | yes |
| [Collective Decision for Open Set Recognition](https://doi.org/10.1109/tkde.2020.2978199) (IEEE Transactions on Knowledge and Data Engineering, 2020) | [Recent Advances in Open Set Recognition: A Survey](https://doi.org/10.1109/tpami.2020.2981604) (IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020) | [evtclass (R package)](https://CRAN.R-project.org/package=evtclass) | same repository | yes |
| [COVID19 and Airbnb – Disrupting the Disruptor](https://doi.org/10.1016/j.annals.2020.102961) (Annals of Tourism Research, 2020) | [Competing during a pandemic? Retailers’ ups and downs during the COVID-19 outbreak](https://doi.org/10.1016/j.jbusres.2020.05.036) (Journal of Business Research, 2020) | [t9n6q](https://osf.io/t9n6q/) | same repository | yes |
| [Google Scholar, Web of Science, and Scopus: A systematic comparison of citations in 252 subject categories](https://doi.org/10.1016/j.joi.2018.09.002) (Journal of Informetrics, 2018) | [Google Scholar to overshadow them all? Comparing the sizes of 12 academic search engines and bibliographic databases](https://doi.org/10.1007/s11192-018-2958-5) (Scientometrics, 2018) | [42nkm](https://scholar4ai.com/code/osf-42nkm.md) | same repository | yes |
| [Trading Real-World Assets on Blockchain](https://doi.org/10.1007/s12599-017-0499-8) (Business & Information Systems Engineering, 2017) | [Blockchain technology: Is it hype or real in the construction industry?](https://doi.org/10.1016/j.jii.2020.100125) (Journal of Industrial Information Integration, 2020) | [ethereum/wiki](https://scholar4ai.com/code/github-ethereum-wiki.md) | same repository | yes |
| [Deep learning with long short-term memory networks for financial market predictions](https://doi.org/10.1016/j.ejor.2017.11.054) (European Journal of Operational Research, 2017) | [Forecasting and planning during a pandemic: COVID-19 growth rates, supply chain disruptions, and governmental decisions](https://doi.org/10.1016/j.ejor.2020.08.001) (European Journal of Operational Research, 2020) | [keras-team/keras](https://scholar4ai.com/code/github-keras-team-keras.md) | same repository | yes |
| [Identifying policy challenges of COVID-19 in hardly reliable data and judging the success of lockdown measures](https://doi.org/10.1007/s00148-020-00799-x) (Journal of Population Economics, 2020) | [Working from home and income inequality: risks of a ‘new normal’ with COVID-19](https://doi.org/10.1007/s00148-020-00800-7) (Journal of Population Economics, 2020) | [pcm-dpc/COVID-19](https://scholar4ai.com/code/github-pcm-dpc-covid-19.md) | same repository | yes |
| [Deep learning approaches for COVID-19 detection based on chest X-ray images](https://doi.org/10.1016/j.eswa.2020.114054) (Expert Systems with Applications, 2020) | [Iterative enhancement fusion-based cascaded model for detection and localization of multiple disease from CXR-Images](https://doi.org/10.1016/j.eswa.2024.124464) (Expert Systems with Applications, 2024) | [ieee8023/covid-chestxray-dataset](https://scholar4ai.com/code/github-ieee8023-covid-chestxray-dataset.md) | same repository | yes |
| [Using confirmatory composite analysis to assess emergent variables in business research](https://doi.org/10.1016/j.jbusres.2020.07.026) (Journal of Business Research, 2020) | [Multigroup analysis of more than two groups in PLS-SEM: A review, illustration, and recommendations](https://doi.org/10.1016/j.jbusres.2022.113539) (Journal of Business Research, 2022) | [cSEM (R package)](https://scholar4ai.com/code/cran-csem.md) | same repository | yes |
| [Unassisted noise reduction of chemical reaction datasets](https://doi.org/10.1038/s42256-021-00319-w) (Nature Machine Intelligence, 2021) | [Mapping the space of chemical reactions using attention-based neural networks](https://doi.org/10.1038/s42256-020-00284-w) (Nature Machine Intelligence, 2021) | [Chemical reactions from US patents (1976-Sep2016)](https://scholar4ai.com/code/figshare-5104873.md) | same repository | yes |
| [A dynamic discarding technique to increase speed and preserve accuracy for YOLOv3](https://doi.org/10.1007/s00521-021-05764-7) (Neural Computing and Applications, 2021) | [A fast accurate fine-grain object detection model based on YOLOv4 deep neural network](https://doi.org/10.1007/s00521-021-06651-x) (Neural Computing and Applications, 2022) | [AlexeyAB/darknet](https://scholar4ai.com/code/github-alexeyab-darknet.md) | same repository | yes |
| [Quantum Bridge Analytics I: a tutorial on formulating and using QUBO models](https://doi.org/10.1007/s10288-019-00424-y) (4OR, 2019) | [Quantum bridge analytics I: a tutorial on formulating and using QUBO models](https://doi.org/10.1007/s10479-022-04634-2) (Annals of Operations Research, 2022) | [lanl/qmasm](https://github.com/lanl/qmasm) | same repository | yes |
| [catch22: CAnonical Time-series CHaracteristics](https://doi.org/10.1007/s10618-019-00647-x) (Data Mining and Knowledge Discovery, 2019) | [Forecast evaluation for data scientists: common pitfalls and best practices](https://doi.org/10.1007/s10618-022-00894-5) (Data Mining and Knowledge Discovery, 2022) | [tsfeatures (R package)](https://scholar4ai.com/code/cran-tsfeatures.md) | same repository | yes |
| [Opinion Paper: “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy](https://doi.org/10.1016/j.ijinfomgt.2023.102642) (International Journal of Information Management, 2023) | [Adoption and impacts of generative artificial intelligence: Theoretical underpinnings and research agenda](https://doi.org/10.1016/j.jjimei.2024.100232) (International Journal of Information Management Data Insights, 2024) | [mrz8h](https://scholar4ai.com/code/osf-mrz8h.md) | same repository | yes |
| [Visualising forecasting algorithm performance using time series instance spaces](https://doi.org/10.1016/j.ijforecast.2016.09.004) (International Journal of Forecasting, 2017) | [Kaggle forecasting competitions: An overlooked learning opportunity](https://doi.org/10.1016/j.ijforecast.2020.07.007) (International Journal of Forecasting, 2020) | [ForeCA (R package)](https://scholar4ai.com/code/cran-foreca.md) | same repository | yes |
| [Solidarity and fairness in times of crisis](https://doi.org/10.1016/j.jebo.2021.03.017) (Journal of Economic Behavior & Organization, 2021) | [Prosociality predicts health behaviors during the COVID-19 pandemic](https://doi.org/10.1016/j.jpubeco.2021.104367) (Journal of Public Economics, 2021) | [6ktuz](https://scholar4ai.com/code/osf-6ktuz.md) | same repository | yes |
| [Automated vehicle’s behavior decision making using deep reinforcement learning and high-fidelity simulation environment](https://doi.org/10.1016/j.trc.2019.08.011) (Transportation Research Part C Emerging Technologies, 2019) | [A survey on autonomous vehicle control in the era of mixed-autonomy: From physics-based to AI-guided driving policy learning](https://doi.org/10.1016/j.trc.2021.103008) (Transportation Research Part C Emerging Technologies, 2021) | [torcs](https://scholar4ai.com/code/sourceforge-torcs.md) | same repository | yes |
| [Social pressure in the stadiums: Do agents change behavior without crowd support?](https://doi.org/10.1016/j.joep.2020.102344) (Journal of Economic Psychology, 2020) | [Causal effects of an absent crowd on performances and refereeing decisions during Covid-19](https://doi.org/10.1016/j.econlet.2020.109664) (Economics Letters, 2020) | [hczkj](https://scholar4ai.com/code/osf-hczkj.md) | same repository | yes |
| [FFORMA: Feature-based forecast model averaging](https://doi.org/10.1016/j.ijforecast.2019.02.011) (International Journal of Forecasting, 2019) | [Retail sales forecasting with meta-learning](https://doi.org/10.1016/j.ejor.2020.05.038) (European Journal of Operational Research, 2020) | [tsfeatures (R package)](https://scholar4ai.com/code/cran-tsfeatures.md) | same repository | yes |
| [A deep learning approach for real-time detection of atrial fibrillation](https://doi.org/10.1016/j.eswa.2018.08.011) (Expert Systems with Applications, 2018) | [DENS-ECG: A deep learning approach for ECG signal delineation](https://doi.org/10.1016/j.eswa.2020.113911) (Expert Systems with Applications, 2020) | [keras-team/keras](https://scholar4ai.com/code/github-keras-team-keras.md) | same repository | yes |
| [Multi-output bus travel time prediction with convolutional LSTM neural network](https://doi.org/10.1016/j.eswa.2018.11.028) (Expert Systems with Applications, 2018) | [DENS-ECG: A deep learning approach for ECG signal delineation](https://doi.org/10.1016/j.eswa.2020.113911) (Expert Systems with Applications, 2020) | [keras-team/keras](https://scholar4ai.com/code/github-keras-team-keras.md) | same repository | yes |
| [Forecasting with time series imaging](https://doi.org/10.1016/j.eswa.2020.113680) (Expert Systems with Applications, 2020) | [Improving the accuracy of global forecasting models using time series data augmentation](https://doi.org/10.1016/j.patcog.2021.108148) (Pattern Recognition, 2021) | [gratis (R package)](https://CRAN.R-project.org/package=gratis) | same repository | yes |
| [CHGNet as a pretrained universal neural network potential for charge-informed atomistic modelling](https://doi.org/10.1038/s42256-023-00716-3) (Nature Machine Intelligence, 2023) | [A framework to evaluate machine learning crystal stability predictions](https://doi.org/10.1038/s42256-025-01055-1) (Nature Machine Intelligence, 2025) | [Materials Project Trajectory (MPtrj) Dataset](https://scholar4ai.com/code/figshare-23713842.md) | same repository | yes |
| [Exploring the sources of uncertainty: Why does bagging for time series forecasting work?](https://doi.org/10.1016/j.ejor.2018.01.045) (European Journal of Operational Research, 2018) | [Structural combination of seasonal exponential smoothing forecasts applied to load forecasting](https://doi.org/10.1016/j.ejor.2018.12.013) (European Journal of Operational Research, 2018) | [robjhyndman/forecast](https://scholar4ai.com/code/github-robjhyndman-forecast.md) | same repository | yes |
| [MLDA: A tool for analyzing multi-label datasets](https://doi.org/10.1016/j.knosys.2017.01.018) (Knowledge-Based Systems, 2017) | [Review of ensembles of multi-label classifiers: Models, experimental study and prospects](https://doi.org/10.1016/j.inffus.2017.12.001) (Information Fusion, 2017) | [meka](https://scholar4ai.com/code/sourceforge-meka.md) | same repository | yes |
| [Mapping the space of chemical reactions using attention-based neural networks](https://doi.org/10.1038/s42256-020-00284-w) (Nature Machine Intelligence, 2021) | [Regression Transformer enables concurrent sequence regression and generation for molecular language modelling](https://doi.org/10.1038/s42256-023-00639-z) (Nature Machine Intelligence, 2023) | [Chemical reactions from US patents (1976-Sep2016)](https://scholar4ai.com/code/figshare-5104873.md) | same repository | yes |
| [Deep learning approaches for COVID-19 detection based on chest X-ray images](https://doi.org/10.1016/j.eswa.2020.114054) (Expert Systems with Applications, 2020) | [The Role of Artificial Intelligence in Fighting the COVID-19 Pandemic](https://doi.org/10.1007/s10796-021-10131-x) (Information Systems Frontiers, 2021) | [ieee8023/covid-chestxray-dataset](https://scholar4ai.com/code/github-ieee8023-covid-chestxray-dataset.md) | same repository | yes |
| [Intelligent system for COVID-19 prognosis: a state-of-the-art survey](https://doi.org/10.1007/s10489-020-02102-7) (Applied Intelligence, 2021) | [The Role of Artificial Intelligence in Fighting the COVID-19 Pandemic](https://doi.org/10.1007/s10796-021-10131-x) (Information Systems Frontiers, 2021) | [ieee8023/covid-chestxray-dataset](https://scholar4ai.com/code/github-ieee8023-covid-chestxray-dataset.md) | same repository | yes |
| [A country-level analysis comparing hospital capacity and utilisation during the first COVID-19 wave across Europe](https://doi.org/10.1016/j.healthpol.2021.11.009) (Health Policy, 2021) | [European countries' responses in ensuring sufficient physical infrastructure and workforce capacity during the first COVID-19 wave](https://doi.org/10.1016/j.healthpol.2021.06.015) (Health Policy, 2021) | [COVID-19 Hospitalisation, cases and tests in 18 European countries](https://doi.org/10.7910/DVN/02CFBB) | same repository | yes |
| [PRRNet: Pixel-Region relation network for face forgery detection](https://doi.org/10.1016/j.patcog.2021.107950) (Pattern Recognition, 2021) | [Deepfacelab: Integrated, flexible and extensible face-swapping framework](https://doi.org/10.1016/j.patcog.2023.109628) (Pattern Recognition, 2023) | [deepfakes/faceswap](https://scholar4ai.com/code/github-deepfakes-faceswap.md) | same repository | yes |
| [Learning a deep dual-level network for robust DeepFake detection](https://doi.org/10.1016/j.patcog.2022.108832) (Pattern Recognition, 2022) | [Deepfacelab: Integrated, flexible and extensible face-swapping framework](https://doi.org/10.1016/j.patcog.2023.109628) (Pattern Recognition, 2023) | [deepfakes/faceswap](https://scholar4ai.com/code/github-deepfakes-faceswap.md) | same repository | yes |
| [Watching the BiG artifacts: Exposing DeepFake videos via Bi-granularity artifacts](https://doi.org/10.1016/j.patcog.2022.109179) (Pattern Recognition, 2022) | [Deepfacelab: Integrated, flexible and extensible face-swapping framework](https://doi.org/10.1016/j.patcog.2023.109628) (Pattern Recognition, 2023) | [deepfakes/faceswap](https://scholar4ai.com/code/github-deepfakes-faceswap.md) | same repository | yes |
| [Visualising forecasting algorithm performance using time series instance spaces](https://doi.org/10.1016/j.ijforecast.2016.09.004) (International Journal of Forecasting, 2017) | [A brief history of forecasting competitions](https://doi.org/10.1016/j.ijforecast.2019.03.015) (International Journal of Forecasting, 2019) | [Mcomp (R package)](https://scholar4ai.com/code/cran-mcomp.md) | same repository | yes |
| [Motivated memory in dictator games](https://doi.org/10.1016/j.geb.2019.05.011) (Games and Economic Behavior, 2019) | [Requiem for a Nudge: Framing Effects in Nudging Honesty](https://doi.org/10.1016/j.jebo.2020.02.015) (Journal of Economic Behavior & Organization, 2020) | [7ck25](https://scholar4ai.com/code/osf-7ck25.md) | same repository | yes |
| [Hyper-parameter optimization of deep learning architectures using artificial bee colony (ABC) algorithm for high performance real-time automatic colorectal cancer (CRC) polyp detection](https://doi.org/10.1007/s10489-022-04299-1) (Applied Intelligence, 2022) | [Robust real-time polyp detection system design based on YOLO algorithms by optimizing activation functions and hyper-parameters with artificial bee colony (ABC)](https://doi.org/10.1016/j.eswa.2023.119741) (Expert Systems with Applications, 2023) | [ultralytics/yolov5: v6.1 - TensorRT, TensorFlow Edge TPU and OpenVINO Export and Inference](https://scholar4ai.com/code/zenodo-6222936.md) | same repository | yes |
| [Causal effects of an absent crowd on performances and refereeing decisions during Covid-19](https://doi.org/10.1016/j.econlet.2020.109664) (Economics Letters, 2020) | [Social pressure in the stadiums: Do agents change behavior without crowd support?](https://doi.org/10.1016/j.joep.2020.102344) (Journal of Economic Psychology, 2020) | [hczkj](https://scholar4ai.com/code/osf-hczkj.md) | same repository | yes |
| [Deep learning for face image synthesis and semantic manipulations: a review and future perspectives](https://doi.org/10.1007/s10462-020-09835-4) (Artificial Intelligence Review, 2020) | [Unmasking deepfakes: A systematic review of deepfake detection and generation techniques using artificial intelligence](https://doi.org/10.1016/j.eswa.2024.124260) (Expert Systems with Applications, 2024) | [deepfakes/faceswap](https://scholar4ai.com/code/github-deepfakes-faceswap.md) | same repository | yes |
| [Deepfakes generation and detection: state-of-the-art, open challenges, countermeasures, and way forward](https://doi.org/10.1007/s10489-022-03766-z) (Applied Intelligence, 2022) | [Unmasking deepfakes: A systematic review of deepfake detection and generation techniques using artificial intelligence](https://doi.org/10.1016/j.eswa.2024.124260) (Expert Systems with Applications, 2024) | [deepfakes/faceswap](https://scholar4ai.com/code/github-deepfakes-faceswap.md) | same repository | yes |
| [Exposing deepfakes using a deep multilayer perceptron – convolutional neural network model](https://doi.org/10.1016/j.jjimei.2021.100054) (International Journal of Information Management Data Insights, 2021) | [Unmasking deepfakes: A systematic review of deepfake detection and generation techniques using artificial intelligence](https://doi.org/10.1016/j.eswa.2024.124260) (Expert Systems with Applications, 2024) | [dfaker/df](https://scholar4ai.com/code/github-dfaker-df.md) | same repository | yes |
| [Learning a deep dual-level network for robust DeepFake detection](https://doi.org/10.1016/j.patcog.2022.108832) (Pattern Recognition, 2022) | [Unmasking deepfakes: A systematic review of deepfake detection and generation techniques using artificial intelligence](https://doi.org/10.1016/j.eswa.2024.124260) (Expert Systems with Applications, 2024) | [deepfakes/faceswap](https://scholar4ai.com/code/github-deepfakes-faceswap.md) | same repository | yes |
| [Transfer learning-based convolutional neural networks with heuristic optimization for hand gesture recognition](https://doi.org/10.1007/s00521-019-04427-y) (Neural Computing and Applications, 2019) | [Advances in machine translation for sign language: approaches, limitations, and challenges](https://doi.org/10.1007/s00521-021-06079-3) (Neural Computing and Applications, 2021) | [ardamavi/sign-language-digits-dataset](https://www.kaggle.com/datasets/ardamavi/sign-language-digits-dataset) | same repository | yes |
| [Maize leaf disease classification using deep convolutional neural networks](https://doi.org/10.1007/s00521-019-04228-3) (Neural Computing and Applications, 2019) | [Systematic study on deep learning-based plant disease detection or classification](https://doi.org/10.1007/s10462-023-10517-0) (Artificial Intelligence Review, 2023) | [spMohanty/PlantVillage-Dataset](https://scholar4ai.com/code/github-spmohanty-plantvillage-dataset.md) | same repository | yes |
| [Deep neural network based Rider-Cuckoo Search Algorithm for plant disease detection](https://doi.org/10.1007/s10462-020-09813-w) (Artificial Intelligence Review, 2020) | [Systematic study on deep learning-based plant disease detection or classification](https://doi.org/10.1007/s10462-023-10517-0) (Artificial Intelligence Review, 2023) | [spMohanty/PlantVillage-Dataset](https://scholar4ai.com/code/github-spmohanty-plantvillage-dataset.md) | same repository | yes |
| [Leaf species and disease classification using multiscale parallel deep CNN architecture](https://doi.org/10.1007/s00521-022-07521-w) (Neural Computing and Applications, 2022) | [Systematic study on deep learning-based plant disease detection or classification](https://doi.org/10.1007/s10462-023-10517-0) (Artificial Intelligence Review, 2023) | [A Database of Leaf Images: Practice towards Plant Conservation with Plant Pathology](https://scholar4ai.com/code/mendeley-hb74ynkjcn.md) | same repository | yes |
| [Detecting health misinformation in online health communities: Incorporating behavioral features into machine learning based approaches](https://doi.org/10.1016/j.ipm.2020.102390) (Information Processing & Management, 2020) | [Linguistic characteristics and the dissemination of misinformation in social media: The moderating effect of information richness](https://doi.org/10.1016/j.ipm.2021.102679) (Information Processing & Management, 2021) | [jieba (Python package)](https://scholar4ai.com/code/pypi-jieba.md) | same repository | yes |
| [COVID-19 Infection Detection from Chest X-Ray Images Using Hybrid Social Group Optimization and Support Vector Classifier](https://doi.org/10.1007/s12559-021-09848-3) (Cognitive Computation, 2021) | [Determination of COVID-19 pneumonia based on generalized convolutional neural network model from chest X-ray images](https://doi.org/10.1016/j.eswa.2021.115141) (Expert Systems with Applications, 2021) | [ieee8023/covid-chestxray-dataset](https://scholar4ai.com/code/github-ieee8023-covid-chestxray-dataset.md) | same repository | yes |
| [Convolutional neural network with margin loss for fake news detection](https://doi.org/10.1016/j.ipm.2020.102418) (Information Processing & Management, 2020) | [DSS: A hybrid deep model for fake news detection using propagation tree and stance network](https://doi.org/10.1016/j.eswa.2022.116635) (Expert Systems with Applications, 2022) | [keras-team/keras](https://scholar4ai.com/code/github-keras-team-keras.md) | same repository | yes |
| [Deep learning with long short-term memory networks for financial market predictions](https://doi.org/10.1016/j.ejor.2017.11.054) (European Journal of Operational Research, 2017) | [Forex exchange rate forecasting using deep recurrent neural networks](https://doi.org/10.1007/s42521-020-00019-x) (Digital Finance, 2020) | [keras-team/keras](https://scholar4ai.com/code/github-keras-team-keras.md) | same repository | yes |
| [From distributed machine learning to federated learning: a survey](https://doi.org/10.1007/s10115-022-01664-x) (Knowledge and Information Systems, 2022) | [Trustworthy federated learning: privacy, security, and beyond](https://doi.org/10.1007/s10115-024-02285-2) (Knowledge and Information Systems, 2024) | [OpenMined/PySyft](https://scholar4ai.com/code/github-openmined-pysyft.md) | same repository | yes |
| [Inductive general game playing](https://doi.org/10.1007/s10994-019-05843-w) (Machine Learning, 2019) | [Learning programs by learning from failures](https://doi.org/10.1007/s10994-020-05934-z) (Machine Learning, 2021) | [metagol/metagol](https://scholar4ai.com/code/github-metagol-metagol.md) | same repository | yes |
| [Logical reduction of metarules](https://doi.org/10.1007/s10994-019-05834-x) (Machine Learning, 2019) | [Learning programs by learning from failures](https://doi.org/10.1007/s10994-020-05934-z) (Machine Learning, 2021) | [metagol/metagol](https://scholar4ai.com/code/github-metagol-metagol.md) | same repository | yes |
| [Using confirmatory composite analysis to assess emergent variables in business research](https://doi.org/10.1016/j.jbusres.2020.07.026) (Journal of Business Research, 2020) | [A clarification of confirmatory composite analysis (CCA)](https://doi.org/10.1016/j.ijinfomgt.2021.102399) (International Journal of Information Management, 2021) | [cSEM (R package)](https://scholar4ai.com/code/cran-csem.md) | same repository | yes |
| [European countries' responses in ensuring sufficient physical infrastructure and workforce capacity during the first COVID-19 wave](https://doi.org/10.1016/j.healthpol.2021.06.015) (Health Policy, 2021) | [A country-level analysis comparing hospital capacity and utilisation during the first COVID-19 wave across Europe](https://doi.org/10.1016/j.healthpol.2021.11.009) (Health Policy, 2021) | [COVID-19 Hospitalisation, cases and tests in 18 European countries](https://doi.org/10.7910/DVN/02CFBB) | same repository | yes |
| [Attention based CNN model for fire detection and localization in real-world images](https://doi.org/10.1016/j.eswa.2021.116114) (Expert Systems with Applications, 2021) | [Automated accurate fire detection system using ensemble pretrained residual network](https://doi.org/10.1016/j.eswa.2022.117407) (Expert Systems with Applications, 2022) | [atulyakumar98/test-dataset](https://www.kaggle.com/datasets/atulyakumar98/test-dataset) | same repository | yes |
| [My home is my castle – The benefits of working from home during a pandemic crisis](https://doi.org/10.1016/j.jpubeco.2021.104373) (Journal of Public Economics, 2021) | [Germany’s capacity to work from home](https://doi.org/10.1016/j.euroecorev.2022.104354) (European Economic Review, 2022) | [89k47](https://scholar4ai.com/code/osf-89k47.md) | same repository | yes |
| [How should intelligent agents apologize to restore trust? Interaction effects between anthropomorphism and apology attribution on trust repair](https://doi.org/10.1016/j.tele.2021.101595) (Telematics and Informatics, 2021) | [Trust repair in human-agent teams: the effectiveness of explanations and expressing regret](https://doi.org/10.1007/s10458-021-09515-9) (Autonomous Agents and Multi-Agent Systems, 2021) | [234f5](https://osf.io/234f5/) | same repository | yes |
| [Competence, Confidence, and Gender: The Role of Objective and Subjective Financial Knowledge in Household Finance](https://doi.org/10.1007/s10834-020-09678-9) (Journal of Family and Economic Issues, 2020) | [From Security to Freedom— The Meaning of Financial Well-being Changes with Age](https://doi.org/10.1007/s10834-023-09886-z) (Journal of Family and Economic Issues, 2023) | [q43ca](https://scholar4ai.com/code/osf-q43ca.md) | same repository | yes |
| [Examining characteristics of predictive models with imbalanced big data](https://doi.org/10.1186/s40537-019-0231-2) (Journal Of Big Data, 2019) | [The Effects of Data Sampling with Deep Learning and Highly Imbalanced Big Data](https://doi.org/10.1007/s10796-020-10022-7) (Information Systems Frontiers, 2020) | [gkbrk/slowloris](https://scholar4ai.com/code/github-gkbrk-slowloris.md) | same repository | yes |
| [Severely imbalanced Big Data challenges: investigating data sampling approaches](https://doi.org/10.1186/s40537-019-0274-4) (Journal Of Big Data, 2019) | [The Effects of Data Sampling with Deep Learning and Highly Imbalanced Big Data](https://doi.org/10.1007/s10796-020-10022-7) (Information Systems Frontiers, 2020) | [gkbrk/slowloris](https://scholar4ai.com/code/github-gkbrk-slowloris.md) | same repository | yes |
| [CatBoost for big data: an interdisciplinary review](https://doi.org/10.1186/s40537-020-00369-8) (Journal Of Big Data, 2020) | [Detecting web attacks using random undersampling and ensemble learners](https://doi.org/10.1186/s40537-021-00460-8) (Journal Of Big Data, 2021) | [lightgbm-org/LightGBM](https://scholar4ai.com/code/github-lightgbm-org-lightgbm.md) | same repository | yes |
| [Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectors](https://doi.org/10.1038/s42256-021-00356-5) (Nature Machine Intelligence, 2021) | [Autoencoders on field-programmable gate arrays for real-time, unsupervised new physics detection at 40 MHz at the Large Hadron Collider](https://doi.org/10.1038/s42256-022-00441-3) (Nature Machine Intelligence, 2022) | [google/qkeras](https://scholar4ai.com/code/github-google-qkeras.md) | same repository | yes |
| [The effect of feature extraction and data sampling on credit card fraud detection](https://doi.org/10.1186/s40537-023-00684-w) (Journal Of Big Data, 2023) | [A problem-agnostic approach to feature selection and analysis using SHAP](https://doi.org/10.1186/s40537-024-01041-1) (Journal Of Big Data, 2025) | [mlg-ulb/creditcardfraud](https://scholar4ai.com/code/kaggle-mlg-ulb-creditcardfraud.md) | same repository | yes |
| [Comparative analysis of binary and one-class classification techniques for credit card fraud data](https://doi.org/10.1186/s40537-023-00794-5) (Journal Of Big Data, 2023) | [A problem-agnostic approach to feature selection and analysis using SHAP](https://doi.org/10.1186/s40537-024-01041-1) (Journal Of Big Data, 2025) | [mlg-ulb/creditcardfraud](https://scholar4ai.com/code/kaggle-mlg-ulb-creditcardfraud.md) | same repository | yes |
| [Attention based CNN model for fire detection and localization in real-world images](https://doi.org/10.1016/j.eswa.2021.116114) (Expert Systems with Applications, 2021) | [Fire and smoke detection from videos: A literature review under a novel taxonomy](https://doi.org/10.1016/j.eswa.2024.124783) (Expert Systems with Applications, 2024) | [DeepQuestAI/Fire-Smoke-Dataset](https://scholar4ai.com/code/github-deepquestai-fire-smoke-dataset.md) | same repository | yes |
| [Enhancing the robustness of the disruption metric against noise](https://doi.org/10.1007/s11192-023-04644-2) (Scientometrics, 2023) | [What do we know about the disruption index in scientometrics? An overview of the literature](https://doi.org/10.1007/s11192-023-04873-5) (Scientometrics, 2023) | [d3wpk](https://scholar4ai.com/code/osf-d3wpk.md) | same repository | yes |
| [ClustGeo: an R package for hierarchical clustering with spatial constraints](https://doi.org/10.1007/s00180-018-0791-1) (Computational Statistics, 2018) | [A circular reverse logistics framework for handling e-commerce returns](https://doi.org/10.1016/j.clscn.2022.100080) (Cleaner Logistics and Supply Chain, 2022) | [ClustGeo (R package)](https://CRAN.R-project.org/package=ClustGeo) | same repository | yes |
| [Coronavirus disease (COVID-19) detection in Chest X-Ray images using majority voting based classifier ensemble](https://doi.org/10.1016/j.eswa.2020.113909) (Expert Systems with Applications, 2020) | [Parallel CNN-ELM: A multiclass classification of chest X-ray images to identify seventeen lung diseases including COVID-19](https://doi.org/10.1016/j.eswa.2023.120528) (Expert Systems with Applications, 2023) | [ieee8023/covid-chestxray-dataset](https://scholar4ai.com/code/github-ieee8023-covid-chestxray-dataset.md) | same repository | yes |
| [Diagnosis and quantification of the non-essential collinearity](https://doi.org/10.1007/s00180-019-00922-x) (Computational Statistics, 2019) | [A Redefined Variance Inflation Factor: Overcoming the Limitations of the Variance Inflation Factor](https://doi.org/10.1007/s10614-024-10575-8) (Computational Economics, 2024) | [multiColl (R package)](https://scholar4ai.com/code/cran-multicoll.md) | same repository | yes |
| [The multiColl Package Versus Other Existing Packages in R to Detect Multicollinearity](https://doi.org/10.1007/s10614-021-10154-1) (Computational Economics, 2021) | [A Redefined Variance Inflation Factor: Overcoming the Limitations of the Variance Inflation Factor](https://doi.org/10.1007/s10614-024-10575-8) (Computational Economics, 2024) | [perturb (R package)](https://CRAN.R-project.org/package=perturb) | same repository | yes |
| [PRRNet: Pixel-Region relation network for face forgery detection](https://doi.org/10.1016/j.patcog.2021.107950) (Pattern Recognition, 2021) | [Learning a deep dual-level network for robust DeepFake detection](https://doi.org/10.1016/j.patcog.2022.108832) (Pattern Recognition, 2022) | [deepfakes/faceswap](https://scholar4ai.com/code/github-deepfakes-faceswap.md) | same repository | yes |
| [Forecasting and planning during a pandemic: COVID-19 growth rates, supply chain disruptions, and governmental decisions](https://doi.org/10.1016/j.ejor.2020.08.001) (European Journal of Operational Research, 2020) | [A novel approach based on combining deep learning models with statistical methods for COVID-19 time series forecasting](https://doi.org/10.1007/s00521-021-06548-9) (Neural Computing and Applications, 2021) | [keras-team/keras](https://scholar4ai.com/code/github-keras-team-keras.md) | same repository | yes |
| [Systemic Risk: The Impact of COVID-19](https://doi.org/10.1016/j.frl.2020.101682) (Finance research letters, 2020) | [Systemic risk, Islamic banks, and the COVID-19 pandemic: An empirical investigation](https://doi.org/10.1016/j.ememar.2022.100890) (Emerging Markets Review, 2022) | [TommasoBelluzzo/SystemicRisk](https://scholar4ai.com/code/github-tommasobelluzzo-systemicrisk.md) | same repository | yes |
| [Working from home and income inequality: risks of a ‘new normal’ with COVID-19](https://doi.org/10.1007/s00148-020-00800-7) (Journal of Population Economics, 2020) | [Identifying policy challenges of COVID-19 in hardly reliable data and judging the success of lockdown measures](https://doi.org/10.1007/s00148-020-00799-x) (Journal of Population Economics, 2020) | [pcm-dpc/COVID-19](https://scholar4ai.com/code/github-pcm-dpc-covid-19.md) | same repository | yes |
| [Forecasting realized oil-price volatility: The role of financial stress and asymmetric loss](https://doi.org/10.1016/j.jimonfin.2020.102137) (Journal of International Money and Finance, 2020) | [Forecasting Realized Volatility of Bitcoin: The Role of the Trade War](https://doi.org/10.1007/s10614-020-10022-4) (Computational Economics, 2020) | [robjhyndman/forecast](https://scholar4ai.com/code/github-robjhyndman-forecast.md) | same repository | yes |
| [Instance spaces for machine learning classification](https://doi.org/10.1007/s10994-017-5629-5) (Machine Learning, 2017) | [On normalization and algorithm selection for unsupervised outlier detection](https://doi.org/10.1007/s10618-019-00661-z) (Data Mining and Knowledge Discovery, 2019) | [e1071 (R package)](https://scholar4ai.com/code/cran-e1071.md) | same repository | yes |
| [Motivated memory in dictator games](https://doi.org/10.1016/j.geb.2019.05.011) (Games and Economic Behavior, 2019) | [Ambiguity and excuse-driven behavior in charitable giving](https://doi.org/10.1016/j.euroecorev.2020.103412) (European Economic Review, 2020) | [7ck25](https://scholar4ai.com/code/osf-7ck25.md) | same repository | yes |
| [Dolores: a model that predicts football match outcomes from all over the world](https://doi.org/10.1007/s10994-018-5703-7) (Machine Learning, 2018) | [The Open International Soccer Database for machine learning](https://doi.org/10.1007/s10994-018-5726-0) (Machine Learning, 2018) | [The 2017 Soccer Prediction Challenge](https://scholar4ai.com/code/osf-ftuva.md) | same repository | yes |
| [Modeling outcomes of soccer matches](https://doi.org/10.1007/s10994-018-5741-1) (Machine Learning, 2018) | [The Open International Soccer Database for machine learning](https://doi.org/10.1007/s10994-018-5726-0) (Machine Learning, 2018) | [The 2017 Soccer Prediction Challenge](https://scholar4ai.com/code/osf-ftuva.md) | same repository | yes |
| [Deep learning approaches for COVID-19 detection based on chest X-ray images](https://doi.org/10.1016/j.eswa.2020.114054) (Expert Systems with Applications, 2020) | [A preliminary analysis of AI based smartphone application for diagnosis of COVID-19 using chest X-ray images](https://doi.org/10.1016/j.eswa.2021.115401) (Expert Systems with Applications, 2021) | [paultimothymooney/chest-xray-pneumonia](https://scholar4ai.com/code/kaggle-paultimothymooney-chest-xray-pneumonia.md) | same repository | yes |
| [Learning efficient logic programs](https://doi.org/10.1007/s10994-018-5712-6) (Machine Learning, 2018) | [Inductive logic programming at 30](https://doi.org/10.1007/s10994-021-06089-1) (Machine Learning, 2021) | [metagol/metagol](https://scholar4ai.com/code/github-metagol-metagol.md) | same repository | yes |
| [Inductive general game playing](https://doi.org/10.1007/s10994-019-05843-w) (Machine Learning, 2019) | [Inductive logic programming at 30](https://doi.org/10.1007/s10994-021-06089-1) (Machine Learning, 2021) | [metagol/metagol](https://scholar4ai.com/code/github-metagol-metagol.md) | same repository | yes |
| [Logical reduction of metarules](https://doi.org/10.1007/s10994-019-05834-x) (Machine Learning, 2019) | [Inductive logic programming at 30](https://doi.org/10.1007/s10994-021-06089-1) (Machine Learning, 2021) | [metagol/metagol](https://scholar4ai.com/code/github-metagol-metagol.md) | same repository | yes |
| [Learning programs by learning from failures](https://doi.org/10.1007/s10994-020-05934-z) (Machine Learning, 2021) | [Inductive logic programming at 30](https://doi.org/10.1007/s10994-021-06089-1) (Machine Learning, 2021) | [metagol/metagol](https://scholar4ai.com/code/github-metagol-metagol.md) | same repository | yes |
| [Beneficial and harmful explanatory machine learning](https://doi.org/10.1007/s10994-020-05941-0) (Machine Learning, 2021) | [Inductive logic programming at 30](https://doi.org/10.1007/s10994-021-06089-1) (Machine Learning, 2021) | [metagol/metagol](https://scholar4ai.com/code/github-metagol-metagol.md) | same repository | yes |
| [Social ski driver conditional autoregressive-based deep learning classifier for flight delay prediction](https://doi.org/10.1007/s00521-022-06898-y) (Neural Computing and Applications, 2022) | [Improving flight delays prediction by developing attention-based bidirectional LSTM network](https://doi.org/10.1016/j.eswa.2023.121747) (Expert Systems with Applications, 2023) | [divyansh22/flight-delay-prediction](https://www.kaggle.com/datasets/divyansh22/flight-delay-prediction) | same repository | yes |
| [F-Mapper: A Fuzzy Mapper clustering algorithm](https://doi.org/10.1016/j.knosys.2019.105107) (Knowledge-Based Systems, 2019) | [SFCM: A Fuzzy Clustering Algorithm of Extracting the Shape Information of Data](https://doi.org/10.1109/tfuzz.2020.3014662) (IEEE Transactions on Fuzzy Systems, 2020) | [MLWave/kepler-mapper: 186f](https://scholar4ai.com/code/zenodo-1054444.md) | same repository | yes |

## Lineage: forks and derived models

*Linked repositories derived from another linked repository (GitHub forks, Hugging Face models derived from a base model, Zenodo archives of a GitHub release)*

| Repository | Papers | Derived repository | How | Papers  |
|---|---:|---|---|---:|
| [ultralytics/yolov5](https://scholar4ai.com/code/github-ultralytics-yolov5.md) | 110 | [ultralytics/yolov5: v7.0 - YOLOv5 SOTA Realtime Instance Segmentation](https://scholar4ai.com/code/zenodo-3908559.md) | Zenodo archive | 31 |
| [ultralytics/yolov5](https://scholar4ai.com/code/github-ultralytics-yolov5.md) | 110 | [ultralytics/yolov5: v3.1 - Bug Fixes and Performance Improvements](https://scholar4ai.com/code/zenodo-4154370.md) | Zenodo archive | 11 |
| [ultralytics/yolov5](https://scholar4ai.com/code/github-ultralytics-yolov5.md) | 110 | [ultralytics/yolov5: v6.1 - TensorRT, TensorFlow Edge TPU and OpenVINO Export and Inference](https://scholar4ai.com/code/zenodo-6222936.md) | Zenodo archive | 5 |
| [ultralytics/yolov5](https://scholar4ai.com/code/github-ultralytics-yolov5.md) | 110 | [ultralytics/yolov5: v6.2 - YOLOv5 Classification Models, Apple M1, Reproducibility…](https://scholar4ai.com/code/zenodo-7002879.md) | Zenodo archive | 5 |
| [ultralytics/yolov5](https://scholar4ai.com/code/github-ultralytics-yolov5.md) | 110 | [ultralytics/yolov5: v5.0 - YOLOv5-P6 1280 models, AWS, Supervise.ly and YouTube…](https://scholar4ai.com/code/zenodo-4679653.md) | Zenodo archive | 4 |
| [ultralytics/yolov5](https://scholar4ai.com/code/github-ultralytics-yolov5.md) | 110 | [ultralytics/yolov5: v6.0 - YOLOv5n 'Nano' models, Roboflow integration, TensorFlow…](https://scholar4ai.com/code/zenodo-5563715.md) | Zenodo archive | 4 |
| [ultralytics/yolov5](https://scholar4ai.com/code/github-ultralytics-yolov5.md) | 110 | [ultralytics/yolov5: v4.0 - nn.SiLU() activations, Weights & Biases logging, PyTorch Hub…](https://scholar4ai.com/code/zenodo-4418161.md) | Zenodo archive | 3 |
| [ultralytics/yolov5](https://scholar4ai.com/code/github-ultralytics-yolov5.md) | 110 | [ultralytics/yolov5: v7.0 - YOLOv5 SOTA Realtime Instance Segmentation](https://scholar4ai.com/code/zenodo-7347926.md) | Zenodo archive | 3 |
| [huggingface/pytorch-image-models](https://scholar4ai.com/code/github-huggingface-pytorch-image-models.md) | 23 | [rwightman/pytorch-image-models: v0.8.10dev0 Release](https://scholar4ai.com/code/zenodo-4414861.md) | Zenodo archive | 19 |
| [openai/baselines](https://scholar4ai.com/code/github-openai-baselines.md) | 10 | [hill-a/stable-baselines](https://scholar4ai.com/code/github-hill-a-stable-baselines.md) | fork | 12 |
| [Lightning-AI/pytorch-lightning](https://scholar4ai.com/code/github-lightning-ai-pytorch-lightning.md) | 13 | [PyTorchLightning/pytorch-lightning: 0.7.6 release](https://scholar4ai.com/code/zenodo-3828935.md) | Zenodo archive | 7 |
| [explosion/spaCy](https://github.com/explosion/spaCy) | 2 | [explosion/spaCy: v3.7.2: Fixes for APIs and requirements](https://scholar4ai.com/code/zenodo-1212303.md) | Zenodo archive | 15 |
| [MaartenGr/KeyBERT](https://github.com/MaartenGr/KeyBERT) | 2 | [MaartenGr/KeyBERT: BibTeX](https://scholar4ai.com/code/zenodo-4461265.md) | Zenodo archive | 14 |
| [mlfoundations/open_clip](https://github.com/mlfoundations/open_clip) | 1 | [OpenCLIP](https://scholar4ai.com/code/zenodo-5143773.md) | Zenodo archive | 13 |
| [tensorflow/tensorflow](https://scholar4ai.com/code/github-tensorflow-tensorflow.md) | 9 | [TensorFlow](https://scholar4ai.com/code/zenodo-4724125.md) | Zenodo archive | 3 |
| [tensorflow/tensorflow](https://scholar4ai.com/code/github-tensorflow-tensorflow.md) | 9 | [TensorFlow](https://scholar4ai.com/code/zenodo-4758419.md) | Zenodo archive | 3 |
| [coin-or/Clp](https://scholar4ai.com/code/github-coin-or-clp.md) | 7 | [coin-or/Clp: Version 1.17.6](https://doi.org/10.5281/zenodo.3748677) | Zenodo archive | 2 |
| [pjreddie/darknet](https://scholar4ai.com/code/github-pjreddie-darknet.md) | 3 | [AlexeyAB/darknet](https://scholar4ai.com/code/github-alexeyab-darknet.md) | fork | 6 |
| [bitcoin/bitcoin](https://scholar4ai.com/code/github-bitcoin-bitcoin.md) | 4 | [dashpay/dash](https://scholar4ai.com/code/github-dashpay-dash.md) | fork | 4 |
| [coin-or/Cbc](https://scholar4ai.com/code/github-coin-or-cbc.md) | 5 | [coin-or/Cbc: Version 2.9.9](https://scholar4ai.com/code/zenodo-1317566.md) | Zenodo archive | 3 |
| [open-mmlab/mmdetection](https://scholar4ai.com/code/github-open-mmlab-mmdetection.md) | 6 | [SwinTransformer/Swin-Transformer-Object-Detection](https://github.com/SwinTransformer/Swin-Transformer-Object-Detection) | fork | 2 |
| [coin-or/Cbc](https://scholar4ai.com/code/github-coin-or-cbc.md) | 5 | [coin-or/Cbc: Release releases/2.10.10](https://doi.org/10.5281/zenodo.7843975) | Zenodo archive | 2 |
| [Farama-Foundation/Gymnasium](https://github.com/Farama-Foundation/Gymnasium) | 2 | [Gymnasium: A Standard Interface for Reinforcement Learning Environments](https://scholar4ai.com/code/zenodo-8127025.md) | Zenodo archive | 5 |
| [JuliaSmoothOptimizers/CUTEst.jl](https://scholar4ai.com/code/github-juliasmoothoptimizers-cutest-jl.md) | 3 | [CUTEst.jl](https://scholar4ai.com/code/zenodo-1188851.md) | Zenodo archive | 4 |
| [Lasagne/Lasagne](https://github.com/Lasagne/Lasagne) | 2 | [Lasagne: First release.](https://scholar4ai.com/code/zenodo-27878.md) | Zenodo archive | 4 |
| [pcbouman-eur/tsp-d-instances](https://github.com/pcbouman-eur/tsp-d-instances) | 1 | [Instances for the TSP with Drone (and some solutions)](https://scholar4ai.com/code/zenodo-1204676.md) | Zenodo archive | 5 |
| [stefan-it/turkish-bert](https://github.com/stefan-it/turkish-bert) | 1 | [BERTurk - BERT models for Turkish](https://scholar4ai.com/code/zenodo-3770924.md) | Zenodo archive | 5 |
| [coin-or/MibS](https://scholar4ai.com/code/github-coin-or-mibs.md) | 3 | [tkralphs/MibS](https://github.com/tkralphs/MibS) | fork | 2 |
| [Farama-Foundation/Gymnasium](https://github.com/Farama-Foundation/Gymnasium) | 2 | [Gymnasium](https://scholar4ai.com/code/zenodo-8127026.md) | Zenodo archive | 3 |
| [MaartenGr/KeyBERT](https://github.com/MaartenGr/KeyBERT) | 2 | [MaartenGr/KeyBERT: v0.5](https://scholar4ai.com/code/zenodo-5534341.md) | Zenodo archive | 3 |
| [rdkit/rdkit](https://scholar4ai.com/code/github-rdkit-rdkit.md) | 3 | [rdkit/rdkit: 2019_03_4 (Q1 2019) Release](https://doi.org/10.5281/zenodo.3366468) | Zenodo archive | 2 |
| [8080labs/ppscore](https://github.com/8080labs/ppscore) | 1 | [8080labs/ppscore: zenodo release](https://scholar4ai.com/code/zenodo-4091345.md) | Zenodo archive | 3 |
| [blockchain-group/DIRECTGOLib](https://github.com/blockchain-group/DIRECTGOLib) | 2 | [DIRECTGOLib - DIRECT Global Optimization test problems Library](https://doi.org/10.5281/zenodo.6491951) | Zenodo archive | 2 |
| [eleutherai/lm-evaluation-harness](https://github.com/eleutherai/lm-evaluation-harness) | 1 | [EleutherAI/lm-evaluation-harness: v0.4.3](https://scholar4ai.com/code/zenodo-12608602.md) | Zenodo archive | 3 |
| [explosion/spaCy](https://github.com/explosion/spaCy) | 2 | [explosion/spaCy: v3.3.0: Improved speed, new trainable lemmatizer, and pipelines for…](https://doi.org/10.5281/zenodo.6504092) | Zenodo archive | 2 |
| [geopandas/geopandas](https://github.com/geopandas/geopandas) | 2 | [geopandas/geopandas: v0.8.1](https://doi.org/10.5281/zenodo.3946761) | Zenodo archive | 2 |
| [hidadeng/cntext](https://github.com/hidadeng/cntext) | 1 | [cntext: a Python tool for text mining](https://scholar4ai.com/code/zenodo-7063523.md) | Zenodo archive | 3 |
| [JuliaSmoothOptimizers/Krylov.jl](https://github.com/JuliaSmoothOptimizers/Krylov.jl) | 2 | [Krylov.jl: A Julia basket of hand-picked Krylov methods](https://doi.org/10.5281/zenodo.822073) | Zenodo archive | 2 |
| [JuliaSmoothOptimizers/NLPModels.jl](https://github.com/JuliaSmoothOptimizers/NLPModels.jl) | 2 | [NLPModels.jl](https://doi.org/10.5281/zenodo.2558627) | Zenodo archive | 2 |
| [JuliaSmoothOptimizers/RegularizedOptimization.jl](https://github.com/JuliaSmoothOptimizers/RegularizedOptimization.jl) | 2 | [JuliaSmoothOptimizers/RegularizedOptimization.jl: Release to obtain DOI only](https://doi.org/10.5281/zenodo.6940313) | Zenodo archive | 2 |
| [JuliaStats/Distributions.jl](https://github.com/JuliaStats/Distributions.jl) | 2 | [JuliaStats/Distributions.jl: v0.25.132](https://doi.org/10.5281/zenodo.2647458) | Zenodo archive | 2 |
| [mlco2/codecarbon](https://github.com/mlco2/codecarbon) | 1 | [mlco2/codecarbon: v2.4.1](https://scholar4ai.com/code/zenodo-11171501.md) | Zenodo archive | 3 |
| [szhorvat/IGraphM](https://github.com/szhorvat/IGraphM) | 2 | [IGraph/M](https://doi.org/10.5281/zenodo.3739056) | Zenodo archive | 2 |
| [dswah/pygam](https://github.com/dswah/pygam) | 1 | [dswah/pyGAM: v0.12.0](https://doi.org/10.5281/zenodo.1208723) | Zenodo archive | 2 |
| [eleutherai/lm-evaluation-harness](https://github.com/eleutherai/lm-evaluation-harness) | 1 | [EleutherAI/lm-evaluation-harness: Major refactor](https://doi.org/10.5281/zenodo.10256836) | Zenodo archive | 2 |
| [eleutherai/lm-evaluation-harness](https://github.com/eleutherai/lm-evaluation-harness) | 1 | [A framework for few-shot language model evaluation](https://doi.org/10.5281/zenodo.5371629) | Zenodo archive | 2 |
| [ferhatelmas/sexmachine](https://github.com/ferhatelmas/sexmachine) | 1 | [lead-ratings/gender-guesser](https://github.com/lead-ratings/gender-guesser) | fork | 2 |
| [intellabs/bayesian-torch](https://github.com/intellabs/bayesian-torch) | 1 | [Bayesian-Torch: Bayesian neural network layers for uncertainty estimation](https://doi.org/10.5281/zenodo.5908307) | Zenodo archive | 2 |
| [juliasmoothoptimizers/benchmarkprofiles.jl](https://github.com/juliasmoothoptimizers/benchmarkprofiles.jl) | 1 | [BenchmarkProfiles.jl](https://doi.org/10.5281/zenodo.4630955) | Zenodo archive | 2 |
| [mlco2/codecarbon](https://github.com/mlco2/codecarbon) | 1 | [mlco2/codecarbon: v2.3.1](https://doi.org/10.5281/zenodo.8252426) | Zenodo archive | 2 |
| [wannesm/dtaidistance](https://github.com/wannesm/dtaidistance) | 1 | [wannesm/dtaidistance: v2.3.5](https://doi.org/10.5281/zenodo.5901139) | Zenodo archive | 2 |
| [wannesm/dtaidistance](https://github.com/wannesm/dtaidistance) | 1 | [DTAIDistance](https://doi.org/10.5281/zenodo.7158824) | Zenodo archive | 2 |
| [meta-llama/llama-3.1-8b](https://huggingface.co/meta-llama/llama-3.1-8b) | 1 | [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) | fine-tuned model | 2 |

## 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.

Data: https://scholar4ai.com/data/code.json

## Cite this page

Scholar4AI. (2026). Code and data repositories cited in research papers. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code

```bibtex
@misc{scholar4ai-code,
  author = {{Scholar4AI}},
  title = {Code and data repositories cited in research papers},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code}},
  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/).