---
title: "Datasets and code used together in research"
description: "41,608 papers in the tracked journals use at least one of the 438 datasets and repositories in this network (350 datasets, 88 code and data repositories and packages); 15,324 use two or more, which make its 5,078 links. The Louvain method finds 11 groups of three or more; the largest are around World Development…, Eurostat, International Energy… (115), S&P 500, Economic Policy…, Compustat (88) and ImageNet, CIFAR-10 / CIFAR-100, MNIST / Fashion-MNIST (87)."
url: https://scholar4ai.com/code/network
markdown_url: https://scholar4ai.com/code/network.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "41,608 papers in the tracked journals use at least one of the 438 datasets and repositories in this network (350 datasets, 88 code and data repositories and packages); 15,324 use two or more, which make its 5,078 links. (as of 2026-11-04)"
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-11-04T18:03:26+09:00
window: 2026-10-06 to 2026-11-04
source: OpenAlex (CC0)
---

# Code and data network: datasets and repositories used together

Which datasets and which code, models and data repositories research papers use together, as a network: its groups, the nodes that bridge them, the strongest and the most distinctive pairs, and the flow from datasets to code. A node is a dataset that papers name in their title, abstract or full text (as on the dataset rankings), used by at least 10 papers, or a code or data repository or software package that papers link to in their Crossref records (as on /code), linked by at least 5. Two nodes are joined when at least 3 papers use both; a paper using more than 40 of them (a survey or a benchmark list) joins none. Groups are the communities the Louvain method finds (nodes more often used with each other than with the rest). Centrality is the betweenness: the share of shortest paths between other nodes that pass through a node, so a high value marks a link between groups. Bridging is the participation coefficient: how evenly a node's links spread over the groups (0: all in one group). Lift compares the papers using both with what the two nodes' sizes alone would give (1: no more than chance). Datasets are found by name and repositories by links, so both are lower bounds.

All repositories: https://scholar4ai.com/code.md · Dataset rankings: https://scholar4ai.com/rankings/datasets.md

## Key findings

- 41,608 papers in the tracked journals use at least one of the 438 datasets and repositories in this network (350 datasets, 88 code and data repositories and packages); 15,324 use two or more, which make its 5,078 links.
- The Louvain method finds 11 groups of three or more; the largest are around World Development…, Eurostat, International Energy… (115), S&P 500, Economic Policy…, Compustat (88) and ImageNet, CIFAR-10 / CIFAR-100, MNIST / Fashion-MNIST (87).
- The most central nodes, which link otherwise separate groups, are Twitter / X, Facebook and Kaggle.
- The dataset and code used together most often are MS COCO and ultralytics/ultralytics (22 papers).
- The most distinctive pair is doParallel (R package) with foreach (R package): 9 papers, 3,467 times what chance would give.

## Groups

*Groups of datasets and repositories used together (Louvain communities, largest first)*

| # |  | Group around | Nodes | Datasets | Code and data | Fields | Members |
|---:|---|---|---:|---:|---:|---|---|
| 1 |  | World Development…, Eurostat, International Energy… | 115 | 113 | 2 | economics, management | [World Development Indicators (World Bank)](https://scholar4ai.com/datasets/world-development-indicators-world-bank.md), [Eurostat](https://scholar4ai.com/datasets/eurostat.md), [International Energy Agency (IEA)](https://scholar4ai.com/datasets/international-energy-agency-iea.md), [Worldwide Governance Indicators](https://scholar4ai.com/datasets/worldwide-governance-indicators.md), [Bureau of Labor Statistics](https://scholar4ai.com/datasets/bureau-of-labor-statistics.md), [European Patent Office / WIPO](https://scholar4ai.com/datasets/european-patent-office-wipo.md), [WITS / TRAINS tariff data](https://scholar4ai.com/datasets/wits-trains-tariff-data.md), [OECD statistics](https://scholar4ai.com/datasets/oecd-statistics.md) |
| 2 |  | S&P 500, Economic Policy…, Compustat | 88 | 87 | 1 | finance, economics | [S&P 500](https://scholar4ai.com/datasets/s-p-500.md), [Economic Policy Uncertainty index](https://scholar4ai.com/datasets/economic-policy-uncertainty-index.md), [Compustat](https://scholar4ai.com/datasets/compustat.md), [Marketization index (Fan Gang)](https://scholar4ai.com/datasets/marketization-index-fan-gang.md), [VIX](https://scholar4ai.com/datasets/vix.md), [CRSP](https://scholar4ai.com/datasets/crsp.md), [WTI crude oil](https://scholar4ai.com/datasets/wti-crude-oil.md), [Refinitiv Eikon / LSEG Workspace](https://scholar4ai.com/datasets/refinitiv-eikon-lseg-workspace.md) |
| 3 |  | ImageNet, CIFAR-10 / CIFAR-100, MNIST / Fashion-MNIST | 87 | 66 | 21 | AI, operations research | [ImageNet](https://scholar4ai.com/datasets/imagenet.md), [CIFAR-10 / CIFAR-100](https://scholar4ai.com/datasets/cifar-10-cifar-100.md), [MNIST / Fashion-MNIST](https://scholar4ai.com/datasets/mnist-fashion-mnist.md), [MS COCO](https://scholar4ai.com/datasets/ms-coco.md), [Kaggle](https://scholar4ai.com/datasets/kaggle.md), [PASCAL VOC](https://scholar4ai.com/datasets/pascal-voc.md), [SVHN](https://scholar4ai.com/datasets/svhn.md), [UCI Machine Learning Repository](https://scholar4ai.com/datasets/uci-machine-learning-repository.md) |
| 4 |  | Twitter / X, Facebook, YouTube / TikTok | 69 | 55 | 14 | AI, management | [Twitter / X](https://scholar4ai.com/datasets/twitter-x.md), [Facebook](https://scholar4ai.com/datasets/facebook.md), [YouTube / TikTok](https://scholar4ai.com/datasets/youtube-tiktok.md), [Wikipedia](https://scholar4ai.com/datasets/wikipedia.md), [Reddit](https://scholar4ai.com/datasets/reddit.md), [LinkedIn](https://scholar4ai.com/datasets/linkedin.md), [Hugging Face Hub](https://scholar4ai.com/datasets/hugging-face-hub.md), [Amazon Mechanical Turk](https://scholar4ai.com/datasets/amazon-mechanical-turk.md) |
| 5 |  | CSMAR, CNRDS, National Bureau of… | 29 | 29 | 0 | finance, economics | [CSMAR](https://scholar4ai.com/datasets/csmar.md), [CNRDS](https://scholar4ai.com/datasets/cnrds.md), [National Bureau of Statistics of China](https://scholar4ai.com/datasets/national-bureau-of-statistics-of-china.md), [Wind](https://scholar4ai.com/datasets/wind.md), [China Family Panel Studies (CFPS)](https://scholar4ai.com/datasets/china-family-panel-studies-cfps.md), [Bloomberg ESG](https://scholar4ai.com/datasets/bloomberg-esg.md), [China Statistical Yearbook](https://scholar4ai.com/datasets/china-statistical-yearbook.md), [China patent office (CNIPA / SIPO)](https://scholar4ai.com/datasets/china-patent-office-cnipa-sipo.md) |
| 6 |  | dplyr (R package), psych (R package), readxl (R package) | 10 | 0 | 10 | higher education and research, management | [dplyr (R package)](https://scholar4ai.com/code/cran-dplyr.md), [psych (R package)](https://scholar4ai.com/code/cran-psych.md), [readxl (R package)](https://scholar4ai.com/code/cran-readxl.md), [stargazer (R package)](https://scholar4ai.com/code/cran-stargazer.md), [tidyr (R package)](https://scholar4ai.com/code/cran-tidyr.md), [semTools (R package)](https://scholar4ai.com/code/cran-semtools.md), [emmeans (R package)](https://scholar4ai.com/code/cran-emmeans.md), [ggplot2 (R package)](https://scholar4ai.com/code/cran-ggplot2.md) |
| 7 |  | ieee8023/covid-chestxray-da…, tawsifurrahman/covid19-radi…, paultimothymooney/chest-xra… | 7 | 0 | 7 | AI, information systems | [ieee8023/covid-chestxray-dataset](https://scholar4ai.com/code/github-ieee8023-covid-chestxray-dataset.md), [tawsifurrahman/covid19-radiography-database](https://scholar4ai.com/code/kaggle-tawsifurrahman-covid19-radiography-database.md), [paultimothymooney/chest-xray-pneumonia](https://scholar4ai.com/code/kaggle-paultimothymooney-chest-xray-pneumonia.md), [agchung/Actualmed-COVID-chestxray-dataset](https://scholar4ai.com/code/github-agchung-actualmed-covid-chestxray-dataset.md), [rsna-pneumonia-detection-challenge](https://scholar4ai.com/code/kaggle-competition-rsna-pneumonia-detection-challenge.md), [agchung/Figure1-COVID-chestxray-dataset](https://scholar4ai.com/code/github-agchung-figure1-covid-chestxray-dataset.md), [andrewmvd/convid19-x-rays](https://scholar4ai.com/code/kaggle-andrewmvd-convid19-x-rays.md) |
| 8 |  | rpart (R package), caret (R package), e1071 (R package) | 5 | 0 | 5 | AI, operations research | [rpart (R package)](https://scholar4ai.com/code/cran-rpart.md), [caret (R package)](https://scholar4ai.com/code/cran-caret.md), [e1071 (R package)](https://scholar4ai.com/code/cran-e1071.md), [rpart.plot (R package)](https://scholar4ai.com/code/cran-rpart-plot.md), [xgboost (R package)](https://scholar4ai.com/code/cran-xgboost.md) |
| 9 |  | ahmedhamada0/brain-tumor-de…, brain tumor dataset, masoudnickparvar/brain-tumo… | 5 | 0 | 5 | AI, information systems | [ahmedhamada0/brain-tumor-detection](https://scholar4ai.com/code/kaggle-ahmedhamada0-brain-tumor-detection.md), [brain tumor dataset](https://scholar4ai.com/code/figshare-1512427.md), [masoudnickparvar/brain-tumor-mri-dataset](https://scholar4ai.com/code/kaggle-masoudnickparvar-brain-tumor-mri-dataset.md), [sartajbhuvaji/brain-tumor-classification-mri](https://scholar4ai.com/code/kaggle-sartajbhuvaji-brain-tumor-classification-mri.md), [navoneel/brain-mri-images-for-brain-tumor-detection](https://scholar4ai.com/code/kaggle-navoneel-brain-mri-images-for-brain-tumor-detection.md) |
| 10 |  | deepfakes/faceswap, MarekKowalski/FaceSwap, iperov/DeepFaceLab | 4 | 0 | 4 | AI, information systems | [deepfakes/faceswap](https://scholar4ai.com/code/github-deepfakes-faceswap.md), [MarekKowalski/FaceSwap](https://scholar4ai.com/code/github-marekkowalski-faceswap.md), [iperov/DeepFaceLab](https://scholar4ai.com/code/github-iperov-deepfacelab.md), [shaoanlu/faceswap-GAN](https://scholar4ai.com/code/github-shaoanlu-faceswap-gan.md) |
| 11 |  | keras (R package), rstudio/keras3, tensorflow (R package) | 3 | 0 | 3 | finance, statistics | [keras (R package)](https://scholar4ai.com/code/cran-keras.md), [rstudio/keras3](https://scholar4ai.com/code/github-rstudio-keras3.md), [tensorflow (R package)](https://scholar4ai.com/code/cran-tensorflow.md) |

## Central nodes

*The most central datasets and repositories (betweenness: the share of shortest paths through them)*

| # |  | Dataset or repository | Type | Papers | Partners | Centrality | Main fields |
|---:|---|---|---|---:|---:|---:|---|
| 1 |  | [Twitter / X](https://scholar4ai.com/datasets/twitter-x.md) | Dataset | 3,870 | 211 | 16.8% | AI, management |
| 2 |  | [Facebook](https://scholar4ai.com/datasets/facebook.md) | Dataset | 2,581 | 167 | 8.8% | management, AI |
| 3 |  | [Kaggle](https://scholar4ai.com/datasets/kaggle.md) | Dataset | 569 | 86 | 8.3% | AI, operations research |
| 4 |  | [ImageNet](https://scholar4ai.com/datasets/imagenet.md) | Dataset | 2,282 | 120 | 8.1% | AI, operations research |
| 5 |  | [S&P 500](https://scholar4ai.com/datasets/s-p-500.md) | Dataset | 2,253 | 143 | 4.6% | finance, economics |
| 6 |  | [Eurostat](https://scholar4ai.com/datasets/eurostat.md) | Dataset | 1,429 | 129 | 4.4% | economics, management |
| 7 |  | [dplyr (R package)](https://scholar4ai.com/code/cran-dplyr.md) | Software package | 34 | 9 | 3.8% | higher education and research, economics |
| 8 |  | [International Energy Agency (IEA)](https://scholar4ai.com/datasets/international-energy-agency-iea.md) | Dataset | 932 | 124 | 3.7% | economics, operations research |
| 9 |  | [World Development Indicators (World Bank)](https://scholar4ai.com/datasets/world-development-indicators-world-bank.md) | Dataset | 933 | 122 | 3.4% | economics, finance |
| 10 |  | [Wikipedia](https://scholar4ai.com/datasets/wikipedia.md) | Dataset | 962 | 113 | 3.2% | AI, economics |
| 11 |  | [Economic Policy Uncertainty index](https://scholar4ai.com/datasets/economic-policy-uncertainty-index.md) | Dataset | 2,355 | 150 | 2.8% | finance, economics |
| 12 |  | [YouTube / TikTok](https://scholar4ai.com/datasets/youtube-tiktok.md) | Dataset | 1,663 | 109 | 2.8% | AI, management |
| 13 |  | [rsna-pneumonia-detection-challenge](https://scholar4ai.com/code/kaggle-competition-rsna-pneumonia-detection-challenge.md) | Data repository | 14 | 5 | 2.5% | AI |
| 14 |  | [MODIS](https://scholar4ai.com/datasets/modis.md) | Dataset | 404 | 76 | 2.1% | AI, economics |
| 15 |  | [CIFAR-10 / CIFAR-100](https://scholar4ai.com/datasets/cifar-10-cifar-100.md) | Dataset | 1,305 | 84 | 2.0% | AI, operations research |
| 16 |  | [CSMAR](https://scholar4ai.com/datasets/csmar.md) | Dataset | 436 | 71 | 1.9% | finance, management |
| 17 |  | [Hugging Face Hub](https://scholar4ai.com/datasets/hugging-face-hub.md) | Dataset | 460 | 76 | 1.9% | AI, information systems |
| 18 |  | [Compustat](https://scholar4ai.com/datasets/compustat.md) | Dataset | 629 | 119 | 1.9% | finance, accounting |
| 19 |  | [Marketization index (Fan Gang)](https://scholar4ai.com/datasets/marketization-index-fan-gang.md) | Dataset | 670 | 123 | 1.7% | finance, economics |
| 20 |  | [ahmedhamada0/brain-tumor-detection](https://scholar4ai.com/code/kaggle-ahmedhamada0-brain-tumor-detection.md) | Data repository | 15 | 5 | 1.7% | AI |

## Bridges between groups

*Datasets and repositories whose links spread most evenly over the groups (participation, at least 5 partners)*

| # |  | Dataset or repository | Type | Papers | Partners | Bridging | Main fields |
|---:|---|---|---|---:|---:|---:|---|
| 1 |  | [China Family Panel Studies (CFPS)](https://scholar4ai.com/datasets/china-family-panel-studies-cfps.md) | Dataset | 376 | 42 | 0.76 | economics, finance |
| 2 |  | [MODIS](https://scholar4ai.com/datasets/modis.md) | Dataset | 404 | 76 | 0.73 | AI, economics |
| 3 |  | [LendingClub](https://scholar4ai.com/datasets/lendingclub.md) | Dataset | 88 | 13 | 0.72 | finance, AI |
| 4 |  | [M3 / M4 / M5 competitions](https://scholar4ai.com/datasets/m3-m4-m5-competitions.md) | Dataset | 146 | 12 | 0.72 | economics, AI |
| 5 |  | [Credit bureaus (Equifax, Experian, TransUnion)](https://scholar4ai.com/datasets/credit-bureaus-equifax-experian-transunion.md) | Dataset | 105 | 25 | 0.71 | finance, economics |
| 6 |  | [China Household Finance Survey (CHFS)](https://scholar4ai.com/datasets/china-household-finance-survey-chfs.md) | Dataset | 394 | 51 | 0.68 | finance, economics |
| 7 |  | [Traffic (PeMS, METR-LA)](https://scholar4ai.com/datasets/traffic-pems-metr-la.md) | Dataset | 215 | 33 | 0.67 | AI, operations research |
| 8 |  | [Indonesia Family Life Survey](https://scholar4ai.com/datasets/indonesia-family-life-survey.md) | Dataset | 68 | 7 | 0.65 | economics, AI |
| 9 |  | [Baidu Index](https://scholar4ai.com/datasets/baidu-index.md) | Dataset | 117 | 28 | 0.65 | finance, economics |
| 10 |  | [Lightcast (Burning Glass)](https://scholar4ai.com/datasets/lightcast-burning-glass.md) | Dataset | 22 | 8 | 0.64 | economics, management |
| 11 |  | [Google Patents](https://scholar4ai.com/datasets/google-patents.md) | Dataset | 48 | 12 | 0.64 | economics, AI |
| 12 |  | [National Bureau of Statistics of China](https://scholar4ai.com/datasets/national-bureau-of-statistics-of-china.md) | Dataset | 164 | 53 | 0.64 | economics, finance |
| 13 |  | [USPTO / PatentsView](https://scholar4ai.com/datasets/uspto-patentsview.md) | Dataset | 282 | 49 | 0.63 | economics, management |
| 14 |  | [China patent office (CNIPA / SIPO)](https://scholar4ai.com/datasets/china-patent-office-cnipa-sipo.md) | Dataset | 115 | 23 | 0.63 | management, finance |
| 15 |  | [University of Michigan Surveys of Consumers](https://scholar4ai.com/datasets/university-of-michigan-surveys-of-consumers.md) | Dataset | 194 | 54 | 0.63 | economics, finance |

## Datasets and code used together

*Datasets and code used together: the pairs with the most papers*

|  | Used with | Papers using both | Lift |
|---|---|---:|---:|
| [MS COCO](https://scholar4ai.com/datasets/ms-coco.md) | [ultralytics/ultralytics](https://scholar4ai.com/code/github-ultralytics-ultralytics.md) | 22 | 6.2 |
| [Credit card fraud data (Kaggle, PaySim, IEEE-CIS)](https://scholar4ai.com/datasets/credit-card-fraud-data-kaggle-paysim-ieee-cis.md) | [mlg-ulb/creditcardfraud](https://scholar4ai.com/code/kaggle-mlg-ulb-creditcardfraud.md) | 13 | 260.4 |
| [ImageNet](https://scholar4ai.com/datasets/imagenet.md) | [ultralytics/ultralytics](https://scholar4ai.com/code/github-ultralytics-ultralytics.md) | 11 | 1.2 |
| [ImageNet](https://scholar4ai.com/datasets/imagenet.md) | [fastai/imagenette](https://scholar4ai.com/code/github-fastai-imagenette.md) | 9 | 7.1 |
| [Kaggle](https://scholar4ai.com/datasets/kaggle.md) | [mlg-ulb/creditcardfraud](https://scholar4ai.com/code/kaggle-mlg-ulb-creditcardfraud.md) | 9 | 21.2 |
| [MS COCO](https://scholar4ai.com/datasets/ms-coco.md) | [facebookresearch/detectron2](https://scholar4ai.com/code/github-facebookresearch-detectron2.md) | 9 | 8.5 |
| [nuScenes / Waymo Open / Argoverse](https://scholar4ai.com/datasets/nuscenes-waymo-open-argoverse.md) | [open-mmlab/OpenPCDet](https://scholar4ai.com/code/github-open-mmlab-openpcdet.md) | 9 | 84.2 |
| [ImageNet](https://scholar4ai.com/datasets/imagenet.md) | [facebookresearch/detectron2](https://scholar4ai.com/code/github-facebookresearch-detectron2.md) | 8 | 2.8 |
| [MS COCO](https://scholar4ai.com/datasets/ms-coco.md) | [ultralytics/yolov5](https://scholar4ai.com/code/github-ultralytics-yolov5.md) | 8 | 3.6 |
| [KITTI](https://scholar4ai.com/datasets/kitti.md) | [open-mmlab/OpenPCDet](https://scholar4ai.com/code/github-open-mmlab-openpcdet.md) | 7 | 46.2 |
| [KITTI](https://scholar4ai.com/datasets/kitti.md) | [ultralytics/ultralytics](https://scholar4ai.com/code/github-ultralytics-ultralytics.md) | 7 | 4.3 |
| [syuzhet (R package)](https://scholar4ai.com/code/cran-syuzhet.md) | [Twitter / X](https://scholar4ai.com/datasets/twitter-x.md) | 6 | 3.8 |
| [Twitter / X](https://scholar4ai.com/datasets/twitter-x.md) | [JustAnotherArchivist/snscrape](https://scholar4ai.com/code/github-justanotherarchivist-snscrape.md) | 6 | 9.2 |
| [tm (R package)](https://scholar4ai.com/code/cran-tm.md) | [Twitter / X](https://scholar4ai.com/datasets/twitter-x.md) | 5 | 2.2 |
| [CIFAR-10 / CIFAR-100](https://scholar4ai.com/datasets/cifar-10-cifar-100.md) | [ultralytics/ultralytics](https://scholar4ai.com/code/github-ultralytics-ultralytics.md) | 5 | 0.9 |
| [ImageNet](https://scholar4ai.com/datasets/imagenet.md) | [MadryLab/robustness](https://scholar4ai.com/code/github-madrylab-robustness.md) | 5 | 10.1 |
| [KITTI](https://scholar4ai.com/datasets/kitti.md) | [open-mmlab/mmdetection3d](https://scholar4ai.com/code/github-open-mmlab-mmdetection3d.md) | 5 | 44.0 |
| [nuScenes / Waymo Open / Argoverse](https://scholar4ai.com/datasets/nuscenes-waymo-open-argoverse.md) | [open-mmlab/mmdetection3d](https://scholar4ai.com/code/github-open-mmlab-mmdetection3d.md) | 5 | 62.4 |
| [Twitter / X](https://scholar4ai.com/datasets/twitter-x.md) | [mjockers/syuzhet](https://scholar4ai.com/code/github-mjockers-syuzhet.md) | 5 | 6.0 |
| [Twitter / X](https://scholar4ai.com/datasets/twitter-x.md) | [twintproject/twint](https://scholar4ai.com/code/github-twintproject-twint.md) | 5 | 10.8 |

## Datasets used together

*Datasets used together: the pairs with the most papers*

|  | Used with | Papers using both | Lift |
|---|---|---:|---:|
| [Facebook](https://scholar4ai.com/datasets/facebook.md) | [Twitter / X](https://scholar4ai.com/datasets/twitter-x.md) | 893 | 3.7 |
| [CIFAR-10 / CIFAR-100](https://scholar4ai.com/datasets/cifar-10-cifar-100.md) | [ImageNet](https://scholar4ai.com/datasets/imagenet.md) | 768 | 10.7 |
| [Facebook](https://scholar4ai.com/datasets/facebook.md) | [YouTube / TikTok](https://scholar4ai.com/datasets/youtube-tiktok.md) | 560 | 5.4 |
| [CIFAR-10 / CIFAR-100](https://scholar4ai.com/datasets/cifar-10-cifar-100.md) | [MNIST / Fashion-MNIST](https://scholar4ai.com/datasets/mnist-fashion-mnist.md) | 515 | 15.8 |
| [Twitter / X](https://scholar4ai.com/datasets/twitter-x.md) | [YouTube / TikTok](https://scholar4ai.com/datasets/youtube-tiktok.md) | 460 | 3.0 |
| [ImageNet](https://scholar4ai.com/datasets/imagenet.md) | [MS COCO](https://scholar4ai.com/datasets/ms-coco.md) | 356 | 7.6 |
| [S&P 500](https://scholar4ai.com/datasets/s-p-500.md) | [VIX](https://scholar4ai.com/datasets/vix.md) | 328 | 6.4 |
| [ImageNet](https://scholar4ai.com/datasets/imagenet.md) | [MNIST / Fashion-MNIST](https://scholar4ai.com/datasets/mnist-fashion-mnist.md) | 322 | 5.6 |
| [Facebook](https://scholar4ai.com/datasets/facebook.md) | [LinkedIn](https://scholar4ai.com/datasets/linkedin.md) | 295 | 5.9 |
| [Compustat](https://scholar4ai.com/datasets/compustat.md) | [CRSP](https://scholar4ai.com/datasets/crsp.md) | 273 | 42.6 |
| [Economic Policy Uncertainty index](https://scholar4ai.com/datasets/economic-policy-uncertainty-index.md) | [VIX](https://scholar4ai.com/datasets/vix.md) | 256 | 4.8 |
| [Reddit](https://scholar4ai.com/datasets/reddit.md) | [Twitter / X](https://scholar4ai.com/datasets/twitter-x.md) | 252 | 5.0 |
| [Marketization index (Fan Gang)](https://scholar4ai.com/datasets/marketization-index-fan-gang.md) | [S&P 500](https://scholar4ai.com/datasets/s-p-500.md) | 250 | 6.9 |
| [Twitter / X](https://scholar4ai.com/datasets/twitter-x.md) | [Wikipedia](https://scholar4ai.com/datasets/wikipedia.md) | 249 | 2.8 |
| [LinkedIn](https://scholar4ai.com/datasets/linkedin.md) | [Twitter / X](https://scholar4ai.com/datasets/twitter-x.md) | 240 | 3.2 |
| [Brent crude oil](https://scholar4ai.com/datasets/brent-crude-oil.md) | [WTI crude oil](https://scholar4ai.com/datasets/wti-crude-oil.md) | 218 | 32.3 |
| [Economic Policy Uncertainty index](https://scholar4ai.com/datasets/economic-policy-uncertainty-index.md) | [S&P 500](https://scholar4ai.com/datasets/s-p-500.md) | 217 | 1.7 |
| [World Development Indicators (World Bank)](https://scholar4ai.com/datasets/world-development-indicators-world-bank.md) | [Worldwide Governance Indicators](https://scholar4ai.com/datasets/worldwide-governance-indicators.md) | 181 | 18.3 |
| [MS COCO](https://scholar4ai.com/datasets/ms-coco.md) | [PASCAL VOC](https://scholar4ai.com/datasets/pascal-voc.md) | 174 | 27.4 |
| [Climate Policy Uncertainty index](https://scholar4ai.com/datasets/climate-policy-uncertainty-index.md) | [Economic Policy Uncertainty index](https://scholar4ai.com/datasets/economic-policy-uncertainty-index.md) | 169 | 5.4 |

## Code and data repositories used together

*Code and data repositories used together: the pairs with the most papers*

|  | Used with | Papers using both | Lift |
|---|---|---:|---:|
| [ultralytics/ultralytics](https://scholar4ai.com/code/github-ultralytics-ultralytics.md) | [ultralytics/yolov5](https://scholar4ai.com/code/github-ultralytics-yolov5.md) | 35 | 76.5 |
| [deepfakes/faceswap](https://scholar4ai.com/code/github-deepfakes-faceswap.md) | [MarekKowalski/FaceSwap](https://scholar4ai.com/code/github-marekkowalski-faceswap.md) | 19 | 1,464.0 |
| [ultralytics/ultralytics](https://scholar4ai.com/code/github-ultralytics-ultralytics.md) | [ultralytics/yolov5: v7.0 - YOLOv5 SOTA Realtime Instance Segmentation](https://scholar4ai.com/code/zenodo-3908559.md) | 18 | 139.7 |
| [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) | 15 | 183.0 |
| [ieee8023/covid-chestxray-dataset](https://scholar4ai.com/code/github-ieee8023-covid-chestxray-dataset.md) | [paultimothymooney/chest-xray-pneumonia](https://scholar4ai.com/code/kaggle-paultimothymooney-chest-xray-pneumonia.md) | 10 | 481.6 |
| [doParallel (R package)](https://scholar4ai.com/code/cran-doparallel.md) | [foreach (R package)](https://scholar4ai.com/code/cran-foreach.md) | 9 | 3,467.3 |
| [keras (R package)](https://scholar4ai.com/code/cran-keras.md) | [rstudio/keras3](https://scholar4ai.com/code/github-rstudio-keras3.md) | 9 | 2,447.5 |
| [huggingface/pytorch-image-models](https://scholar4ai.com/code/github-huggingface-pytorch-image-models.md) | [rwightman/pytorch-image-models: v0.8.10dev0 Release](https://scholar4ai.com/code/zenodo-4414861.md) | 9 | 856.9 |
| [deepfakes/faceswap](https://scholar4ai.com/code/github-deepfakes-faceswap.md) | [iperov/DeepFaceLab](https://scholar4ai.com/code/github-iperov-deepfacelab.md) | 7 | 1,348.4 |
| [ieee8023/covid-chestxray-dataset](https://scholar4ai.com/code/github-ieee8023-covid-chestxray-dataset.md) | [tawsifurrahman/covid19-radiography-database](https://scholar4ai.com/code/kaggle-tawsifurrahman-covid19-radiography-database.md) | 7 | 351.8 |
| [dplyr (R package)](https://scholar4ai.com/code/cran-dplyr.md) | [readxl (R package)](https://scholar4ai.com/code/cran-readxl.md) | 6 | 917.8 |
| [keras (R package)](https://scholar4ai.com/code/cran-keras.md) | [tensorflow (R package)](https://scholar4ai.com/code/cran-tensorflow.md) | 6 | 2,097.9 |
| [brain tumor dataset](https://scholar4ai.com/code/figshare-1512427.md) | [ahmedhamada0/brain-tumor-detection](https://scholar4ai.com/code/kaggle-ahmedhamada0-brain-tumor-detection.md) | 6 | 1,109.5 |
| [ieee8023/covid-chestxray-dataset](https://scholar4ai.com/code/github-ieee8023-covid-chestxray-dataset.md) | [andrewmvd/convid19-x-rays](https://scholar4ai.com/code/kaggle-andrewmvd-convid19-x-rays.md) | 6 | 1,155.8 |
| [paultimothymooney/chest-xray-pneumonia](https://scholar4ai.com/code/kaggle-paultimothymooney-chest-xray-pneumonia.md) | [tawsifurrahman/covid19-radiography-database](https://scholar4ai.com/code/kaggle-tawsifurrahman-covid19-radiography-database.md) | 6 | 452.3 |
| [dplyr (R package)](https://scholar4ai.com/code/cran-dplyr.md) | [psych (R package)](https://scholar4ai.com/code/cran-psych.md) | 5 | 117.7 |
| [dplyr (R package)](https://scholar4ai.com/code/cran-dplyr.md) | [stargazer (R package)](https://scholar4ai.com/code/cran-stargazer.md) | 5 | 235.3 |
| [dplyr (R package)](https://scholar4ai.com/code/cran-dplyr.md) | [tidyr (R package)](https://scholar4ai.com/code/cran-tidyr.md) | 5 | 1,019.8 |
| [agchung/Actualmed-COVID-chestxray-dataset](https://scholar4ai.com/code/github-agchung-actualmed-covid-chestxray-dataset.md) | [tawsifurrahman/covid19-radiography-database](https://scholar4ai.com/code/kaggle-tawsifurrahman-covid19-radiography-database.md) | 5 | 1,130.7 |
| [agchung/Figure1-COVID-chestxray-dataset](https://scholar4ai.com/code/github-agchung-figure1-covid-chestxray-dataset.md) | [tawsifurrahman/covid19-radiography-database](https://scholar4ai.com/code/kaggle-tawsifurrahman-covid19-radiography-database.md) | 5 | 1,292.2 |

## Distinctive pairs

*The most distinctive pairs: highest lift among pairs used together in at least 6 papers*

|  | Used with | Papers using both | Lift |
|---|---|---:|---:|
| [doParallel (R package)](https://scholar4ai.com/code/cran-doparallel.md) | [foreach (R package)](https://scholar4ai.com/code/cran-foreach.md) | 9 | 3,467.3 |
| [keras (R package)](https://scholar4ai.com/code/cran-keras.md) | [rstudio/keras3](https://scholar4ai.com/code/github-rstudio-keras3.md) | 9 | 2,447.5 |
| [keras (R package)](https://scholar4ai.com/code/cran-keras.md) | [tensorflow (R package)](https://scholar4ai.com/code/cran-tensorflow.md) | 6 | 2,097.9 |
| [deepfakes/faceswap](https://scholar4ai.com/code/github-deepfakes-faceswap.md) | [MarekKowalski/FaceSwap](https://scholar4ai.com/code/github-marekkowalski-faceswap.md) | 19 | 1,464.0 |
| [deepfakes/faceswap](https://scholar4ai.com/code/github-deepfakes-faceswap.md) | [iperov/DeepFaceLab](https://scholar4ai.com/code/github-iperov-deepfacelab.md) | 7 | 1,348.4 |
| [ieee8023/covid-chestxray-dataset](https://scholar4ai.com/code/github-ieee8023-covid-chestxray-dataset.md) | [andrewmvd/convid19-x-rays](https://scholar4ai.com/code/kaggle-andrewmvd-convid19-x-rays.md) | 6 | 1,155.8 |
| [brain tumor dataset](https://scholar4ai.com/code/figshare-1512427.md) | [ahmedhamada0/brain-tumor-detection](https://scholar4ai.com/code/kaggle-ahmedhamada0-brain-tumor-detection.md) | 6 | 1,109.5 |
| [dplyr (R package)](https://scholar4ai.com/code/cran-dplyr.md) | [readxl (R package)](https://scholar4ai.com/code/cran-readxl.md) | 6 | 917.8 |
| [huggingface/pytorch-image-models](https://scholar4ai.com/code/github-huggingface-pytorch-image-models.md) | [rwightman/pytorch-image-models: v0.8.10dev0 Release](https://scholar4ai.com/code/zenodo-4414861.md) | 9 | 856.9 |
| [Financial PhraseBank](https://scholar4ai.com/datasets/financial-phrasebank.md) | [FiQA](https://scholar4ai.com/datasets/fiqa.md) | 6 | 821.2 |
| [Indian Pines](https://scholar4ai.com/datasets/indian-pines.md) | [Pavia University / Pavia Centre](https://scholar4ai.com/datasets/pavia-university-pavia-centre.md) | 12 | 768.1 |
| [ieee8023/covid-chestxray-dataset](https://scholar4ai.com/code/github-ieee8023-covid-chestxray-dataset.md) | [paultimothymooney/chest-xray-pneumonia](https://scholar4ai.com/code/kaggle-paultimothymooney-chest-xray-pneumonia.md) | 10 | 481.6 |
| [paultimothymooney/chest-xray-pneumonia](https://scholar4ai.com/code/kaggle-paultimothymooney-chest-xray-pneumonia.md) | [tawsifurrahman/covid19-radiography-database](https://scholar4ai.com/code/kaggle-tawsifurrahman-covid19-radiography-database.md) | 6 | 452.3 |
| [GSM8K / MATH](https://scholar4ai.com/datasets/gsm8k-math.md) | [HumanEval / MBPP](https://scholar4ai.com/datasets/humaneval-mbpp.md) | 16 | 395.6 |
| [Call Reports](https://scholar4ai.com/datasets/call-reports.md) | [FR Y-9C](https://scholar4ai.com/datasets/fr-y-9c.md) | 6 | 384.1 |
| [ieee8023/covid-chestxray-dataset](https://scholar4ai.com/code/github-ieee8023-covid-chestxray-dataset.md) | [tawsifurrahman/covid19-radiography-database](https://scholar4ai.com/code/kaggle-tawsifurrahman-covid19-radiography-database.md) | 7 | 351.8 |
| [Call Reports](https://scholar4ai.com/datasets/call-reports.md) | [FDIC Summary of Deposits](https://scholar4ai.com/datasets/fdic-summary-of-deposits.md) | 12 | 349.2 |
| [BIG-bench](https://scholar4ai.com/datasets/big-bench.md) | [HumanEval / MBPP](https://scholar4ai.com/datasets/humaneval-mbpp.md) | 8 | 325.4 |
| [FactSet](https://scholar4ai.com/datasets/factset.md) | [Supplier-customer links (FactSet Revere, Bloomberg SPLC)](https://scholar4ai.com/datasets/supplier-customer-links-factset-revere-bloomberg-splc.md) | 9 | 300.1 |
| [BIG-bench](https://scholar4ai.com/datasets/big-bench.md) | [GSM8K / MATH](https://scholar4ai.com/datasets/gsm8k-math.md) | 11 | 289.5 |

Data: https://scholar4ai.com/data/code.json (key "network")

## Cite this page

Scholar4AI. (2026). Code and data network: datasets and repositories used together. Journal and Conference Research Rankings. Retrieved November 4, 2026, from https://scholar4ai.com/code/network

```bibtex
@misc{scholar4ai-code-network,
  author = {{Scholar4AI}},
  title = {Code and data network: datasets and repositories used together},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/network}},
  urldate = {2026-11-04},
  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/).