---
title: "Datasets and methods across research fields"
description: "637 of 4,746 tracked papers mention watched datasets; 2,025 mention methods or equations. Compare 12 journal fields with coverage limits and reproducible data."
url: https://scholar4ai.com/research-patterns
markdown_url: https://scholar4ai.com/research-patterns.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "637 of 4,746 tracked papers mention watched datasets; 2,025 mention methods or equations. Compare 12 journal fields with coverage limits and reproducible data. (as of 2026-11-05)"
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-11-05T06:02:37+09:00
window: 2026-10-07 to 2026-11-05
source: OpenAlex (CC0)
---

# Datasets and methods across research fields

In the 30-day window 2026-10-07 – 2026-11-05 (30 days), 637 of 4,746 unique papers (13.4%) name a watched dataset and 2,025 (42.7%) name a watched equation or method.

These are observed name matches in this site’s selected journals, not adoption rates for an entire field. A named method may be discussed or compared rather than used. More searchable text increases the chance of a match. The snapshot measures one window; it does not establish a time trend or explain differences between fields.

*Unique papers with one or more watched names; denominator: all papers in the field*

| Journal field | Papers | Dataset names | Method names | Both | Abstract available | Full text available |
|---|---:|---|---|---|---|---|
| [Finance](#finance) | 447 | 95 / 447 (21.3%) | 203 / 447 (45.4%) | 68 / 447 (15.2%) | 158 / 447 (35.3%) | 91 / 447 (20.4%) |
| [Accounting](#accounting) | 37 | 1 / 37 (2.7%) | 12 / 37 (32.4%) | 0 / 37 (0.0%) | 9 / 37 (24.3%) | 5 / 37 (13.5%) |
| [Economics](#economics) | 898 | 179 / 898 (19.9%) | 394 / 898 (43.9%) | 141 / 898 (15.7%) | 337 / 898 (37.5%) | 281 / 898 (31.3%) |
| [Health Economics and Policy](#health-economics-and-policy) | 320 | 36 / 320 (11.2%) | 105 / 320 (32.8%) | 22 / 320 (6.9%) | 246 / 320 (76.9%) | 110 / 320 (34.4%) |
| [Management](#management) | 371 | 58 / 371 (15.6%) | 131 / 371 (35.3%) | 41 / 371 (11.1%) | 188 / 371 (50.7%) | 126 / 371 (34.0%) |
| [Marketing](#marketing) | 121 | 17 / 121 (14.0%) | 46 / 121 (38.0%) | 13 / 121 (10.7%) | 42 / 121 (34.7%) | 32 / 121 (26.4%) |
| [Information Systems](#information-systems) | 223 | 32 / 223 (14.3%) | 68 / 223 (30.5%) | 17 / 223 (7.6%) | 93 / 223 (41.7%) | 63 / 223 (28.3%) |
| [Tourism and Hospitality](#tourism-and-hospitality) | 108 | 11 / 108 (10.2%) | 35 / 108 (32.4%) | 8 / 108 (7.4%) | 31 / 108 (28.7%) | 21 / 108 (19.4%) |
| [Higher Education and Research](#higher-education-and-research) | 230 | 14 / 230 (6.1%) | 46 / 230 (20.0%) | 5 / 230 (2.2%) | 106 / 230 (46.1%) | 83 / 230 (36.1%) |
| [Operations Research](#operations-research) | 504 | 43 / 504 (8.5%) | 267 / 504 (53.0%) | 38 / 504 (7.5%) | 151 / 504 (30.0%) | 155 / 504 (30.8%) |
| [Statistics](#statistics) | 233 | 20 / 233 (8.6%) | 138 / 233 (59.2%) | 18 / 233 (7.7%) | 122 / 233 (52.4%) | 107 / 233 (45.9%) |
| [AI](#ai) | 1,254 | 131 / 1,254 (10.4%) | 580 / 1,254 (46.3%) | 85 / 1,254 (6.8%) | 186 / 1,254 (14.8%) | 182 / 1,254 (14.5%) |

## Finance

**Dataset families:** [Economic Policy Uncertainty index: 18 / 447 papers](https://scholar4ai.com/datasets/economic-policy-uncertainty-index.md); [S&P 500: 15 / 447 papers](https://scholar4ai.com/datasets/s-p-500.md); [VIX: 11 / 447 papers](https://scholar4ai.com/datasets/vix.md); [Climate Policy Uncertainty index: 8 / 447 papers](https://scholar4ai.com/datasets/climate-policy-uncertainty-index.md); [CSMAR: 8 / 447 papers](https://scholar4ai.com/datasets/csmar.md)

**Methods and equations:** [Difference-in-differences: 32 / 447 papers](https://scholar4ai.com/equations/difference-in-differences.md); [Instrumental variables (two-stage least squares): 28 / 447 papers](https://scholar4ai.com/equations/instrumental-variables-two-stage-least-squares.md); [Ordinary least squares (OLS): 27 / 447 papers](https://scholar4ai.com/equations/ordinary-least-squares-ols.md); [GARCH: 25 / 447 papers](https://scholar4ai.com/equations/garch.md); [Vector autoregression (VAR): 20 / 447 papers](https://scholar4ai.com/equations/vector-autoregression-var.md)

## Accounting

**Dataset families:** [10-K filings: 1 / 37 papers](https://scholar4ai.com/datasets/10-k-filings.md); [Australian Credit (UCI): 1 / 37 papers](https://scholar4ai.com/datasets/australian-credit-uci.md); [BoardEx: 1 / 37 papers](https://scholar4ai.com/datasets/boardex.md); [S&P 500: 1 / 37 papers](https://scholar4ai.com/datasets/s-p-500.md); [SEC EDGAR: 1 / 37 papers](https://scholar4ai.com/datasets/sec-edgar.md)

**Methods and equations:** [Instrumental variables (two-stage least squares): 3 / 37 papers](https://scholar4ai.com/equations/instrumental-variables-two-stage-least-squares.md); [Fuzzy-set QCA (fsQCA): 2 / 37 papers](https://scholar4ai.com/equations/fuzzy-set-qca-fsqca.md); [Structural equation modeling (SEM): 2 / 37 papers](https://scholar4ai.com/equations/structural-equation-modeling-sem.md); [Dechow-Dichev accruals quality: 1 / 37 papers](https://scholar4ai.com/equations/dechow-dichev-accruals-quality.md); [Difference-in-differences: 1 / 37 papers](https://scholar4ai.com/equations/difference-in-differences.md)

## Economics

**Dataset families:** [Eurostat: 22 / 898 papers](https://scholar4ai.com/datasets/eurostat.md); [World Development Indicators (World Bank): 17 / 898 papers](https://scholar4ai.com/datasets/world-development-indicators-world-bank.md); [International Energy Agency (IEA): 16 / 898 papers](https://scholar4ai.com/datasets/international-energy-agency-iea.md); [Economic Policy Uncertainty index: 8 / 898 papers](https://scholar4ai.com/datasets/economic-policy-uncertainty-index.md); [Labour Force Surveys: 8 / 898 papers](https://scholar4ai.com/datasets/labour-force-surveys.md)

**Methods and equations:** [Difference-in-differences: 65 / 898 papers](https://scholar4ai.com/equations/difference-in-differences.md); [Ordinary least squares (OLS): 49 / 898 papers](https://scholar4ai.com/equations/ordinary-least-squares-ols.md); [Instrumental variables (two-stage least squares): 47 / 898 papers](https://scholar4ai.com/equations/instrumental-variables-two-stage-least-squares.md); [Logistic regression (logit): 46 / 898 papers](https://scholar4ai.com/equations/logistic-regression-logit.md); [Two-way fixed-effects panel regression: 27 / 898 papers](https://scholar4ai.com/equations/two-way-fixed-effects-panel-regression.md)

## Health Economics and Policy

**Dataset families:** [Global Burden of Disease: 7 / 320 papers](https://scholar4ai.com/datasets/global-burden-of-disease.md); [CHARLS: 4 / 320 papers](https://scholar4ai.com/datasets/charls.md); [Demographic and Health Surveys (DHS): 4 / 320 papers](https://scholar4ai.com/datasets/demographic-and-health-surveys-dhs.md); [Understanding Society / BHPS: 4 / 320 papers](https://scholar4ai.com/datasets/understanding-society-bhps.md); [WITS / TRAINS tariff data: 4 / 320 papers](https://scholar4ai.com/datasets/wits-trains-tariff-data.md)

**Methods and equations:** [Quality-adjusted life year (QALY): 27 / 320 papers](https://scholar4ai.com/equations/quality-adjusted-life-year-qaly.md); [Incremental cost-effectiveness ratio (ICER): 26 / 320 papers](https://scholar4ai.com/equations/incremental-cost-effectiveness-ratio-icer.md); [Logistic regression (logit): 17 / 320 papers](https://scholar4ai.com/equations/logistic-regression-logit.md); [Two-way fixed-effects panel regression: 12 / 320 papers](https://scholar4ai.com/equations/two-way-fixed-effects-panel-regression.md); [Ordinary least squares (OLS): 7 / 320 papers](https://scholar4ai.com/equations/ordinary-least-squares-ols.md)

## Management

**Dataset families:** [World Development Indicators (World Bank): 9 / 371 papers](https://scholar4ai.com/datasets/world-development-indicators-world-bank.md); [Eurostat: 6 / 371 papers](https://scholar4ai.com/datasets/eurostat.md); [LinkedIn: 6 / 371 papers](https://scholar4ai.com/datasets/linkedin.md); [Twitter / X: 6 / 371 papers](https://scholar4ai.com/datasets/twitter-x.md); [Amazon Mechanical Turk: 3 / 371 papers](https://scholar4ai.com/datasets/amazon-mechanical-turk.md)

**Methods and equations:** [Structural equation modeling (SEM): 36 / 371 papers](https://scholar4ai.com/equations/structural-equation-modeling-sem.md); [Mediation model (indirect effect): 34 / 371 papers](https://scholar4ai.com/equations/mediation-model-indirect-effect.md); [Cronbach's alpha: 19 / 371 papers](https://scholar4ai.com/equations/cronbach-s-alpha.md); [Variance inflation factor: 17 / 371 papers](https://scholar4ai.com/equations/variance-inflation-factor.md); [Fuzzy-set QCA (fsQCA): 15 / 371 papers](https://scholar4ai.com/equations/fuzzy-set-qca-fsqca.md)

## Marketing

**Dataset families:** [Facebook: 4 / 121 papers](https://scholar4ai.com/datasets/facebook.md); [Amazon Mechanical Turk: 2 / 121 papers](https://scholar4ai.com/datasets/amazon-mechanical-turk.md); [Prolific: 2 / 121 papers](https://scholar4ai.com/datasets/prolific.md); [Twitter / X: 2 / 121 papers](https://scholar4ai.com/datasets/twitter-x.md); [Amazon reviews: 1 / 121 papers](https://scholar4ai.com/datasets/amazon-reviews.md)

**Methods and equations:** [Structural equation modeling (SEM): 23 / 121 papers](https://scholar4ai.com/equations/structural-equation-modeling-sem.md); [Mediation model (indirect effect): 10 / 121 papers](https://scholar4ai.com/equations/mediation-model-indirect-effect.md); [Composite reliability: 8 / 121 papers](https://scholar4ai.com/equations/composite-reliability.md); [Average variance extracted (AVE): 5 / 121 papers](https://scholar4ai.com/equations/average-variance-extracted-ave.md); [Cronbach's alpha: 5 / 121 papers](https://scholar4ai.com/equations/cronbach-s-alpha.md)

## Information Systems

**Dataset families:** [Facebook: 7 / 223 papers](https://scholar4ai.com/datasets/facebook.md); [Twitter / X: 4 / 223 papers](https://scholar4ai.com/datasets/twitter-x.md); [Amazon Mechanical Turk: 3 / 223 papers](https://scholar4ai.com/datasets/amazon-mechanical-turk.md); [Weibo: 3 / 223 papers](https://scholar4ai.com/datasets/weibo.md); [YouTube / TikTok: 3 / 223 papers](https://scholar4ai.com/datasets/youtube-tiktok.md)

**Methods and equations:** [Structural equation modeling (SEM): 12 / 223 papers](https://scholar4ai.com/equations/structural-equation-modeling-sem.md); [F1 score: 9 / 223 papers](https://scholar4ai.com/equations/f1-score.md); [Logistic regression (logit): 6 / 223 papers](https://scholar4ai.com/equations/logistic-regression-logit.md); [Maximum likelihood estimation: 6 / 223 papers](https://scholar4ai.com/equations/maximum-likelihood-estimation.md); [ROC curve and AUC: 6 / 223 papers](https://scholar4ai.com/equations/roc-curve-and-auc.md)

## Tourism and Hospitality

**Dataset families:** [TripAdvisor: 3 / 108 papers](https://scholar4ai.com/datasets/tripadvisor.md); [Twitter / X: 2 / 108 papers](https://scholar4ai.com/datasets/twitter-x.md); [YouTube / TikTok: 2 / 108 papers](https://scholar4ai.com/datasets/youtube-tiktok.md); [Airbnb: 1 / 108 papers](https://scholar4ai.com/datasets/airbnb.md); [Amazon Mechanical Turk: 1 / 108 papers](https://scholar4ai.com/datasets/amazon-mechanical-turk.md)

**Methods and equations:** [Structural equation modeling (SEM): 14 / 108 papers](https://scholar4ai.com/equations/structural-equation-modeling-sem.md); [Mediation model (indirect effect): 11 / 108 papers](https://scholar4ai.com/equations/mediation-model-indirect-effect.md); [Bootstrap: 3 / 108 papers](https://scholar4ai.com/equations/bootstrap.md); [Cronbach's alpha: 3 / 108 papers](https://scholar4ai.com/equations/cronbach-s-alpha.md); [Technology acceptance model (TAM): 3 / 108 papers](https://scholar4ai.com/equations/technology-acceptance-model-tam.md)

## Higher Education and Research

**Dataset families:** [PISA: 2 / 230 papers](https://scholar4ai.com/datasets/pisa.md); [USPTO / PatentsView: 2 / 230 papers](https://scholar4ai.com/datasets/uspto-patentsview.md); [World Development Indicators (World Bank): 2 / 230 papers](https://scholar4ai.com/datasets/world-development-indicators-world-bank.md); [YouTube / TikTok: 2 / 230 papers](https://scholar4ai.com/datasets/youtube-tiktok.md); [Bureau of Labor Statistics: 1 / 230 papers](https://scholar4ai.com/datasets/bureau-of-labor-statistics.md)

**Methods and equations:** [Structural equation modeling (SEM): 10 / 230 papers](https://scholar4ai.com/equations/structural-equation-modeling-sem.md); [Technology acceptance model (TAM): 6 / 230 papers](https://scholar4ai.com/equations/technology-acceptance-model-tam.md); [Journal impact factor: 5 / 230 papers](https://scholar4ai.com/equations/journal-impact-factor.md); [Maximum likelihood estimation: 5 / 230 papers](https://scholar4ai.com/equations/maximum-likelihood-estimation.md); [Cronbach's alpha: 4 / 230 papers](https://scholar4ai.com/equations/cronbach-s-alpha.md)

## Operations Research

**Dataset families:** [OpenStreetMap: 6 / 504 papers](https://scholar4ai.com/datasets/openstreetmap.md); [Euro Stoxx / DAX: 4 / 504 papers](https://scholar4ai.com/datasets/euro-stoxx-dax.md); [UCI Machine Learning Repository: 4 / 504 papers](https://scholar4ai.com/datasets/uci-machine-learning-repository.md); [Eurostat: 3 / 504 papers](https://scholar4ai.com/datasets/eurostat.md); [S&P 500: 3 / 504 papers](https://scholar4ai.com/datasets/s-p-500.md)

**Methods and equations:** [Linear and integer programming: 87 / 504 papers](https://scholar4ai.com/equations/linear-and-integer-programming.md); [Robust optimization: 30 / 504 papers](https://scholar4ai.com/equations/robust-optimization.md); [Vehicle routing problem: 29 / 504 papers](https://scholar4ai.com/equations/vehicle-routing-problem.md); [Genetic algorithm: 28 / 504 papers](https://scholar4ai.com/equations/genetic-algorithm.md); [Logistic regression (logit): 25 / 504 papers](https://scholar4ai.com/equations/logistic-regression-logit.md)

## Statistics

**Dataset families:** [S&P 500: 4 / 233 papers](https://scholar4ai.com/datasets/s-p-500.md); [American Community Survey: 3 / 233 papers](https://scholar4ai.com/datasets/american-community-survey.md); [UK Biobank: 2 / 233 papers](https://scholar4ai.com/datasets/uk-biobank.md); [ADNI: 1 / 233 papers](https://scholar4ai.com/datasets/adni.md); [CLHLS: 1 / 233 papers](https://scholar4ai.com/datasets/clhls.md)

**Methods and equations:** [Maximum likelihood estimation: 34 / 233 papers](https://scholar4ai.com/equations/maximum-likelihood-estimation.md); [Markov chain Monte Carlo (Metropolis-Hastings): 17 / 233 papers](https://scholar4ai.com/equations/markov-chain-monte-carlo-metropolis-hastings.md); [Gaussian process regression: 15 / 233 papers](https://scholar4ai.com/equations/gaussian-process-regression.md); [LASSO: 14 / 233 papers](https://scholar4ai.com/equations/lasso.md); [Logistic regression (logit): 14 / 233 papers](https://scholar4ai.com/equations/logistic-regression-logit.md)

## AI

**Dataset families:** [ImageNet: 29 / 1254 papers](https://scholar4ai.com/datasets/imagenet.md); [CIFAR-10 / CIFAR-100: 15 / 1254 papers](https://scholar4ai.com/datasets/cifar-10-cifar-100.md); [MNIST / Fashion-MNIST: 11 / 1254 papers](https://scholar4ai.com/datasets/mnist-fashion-mnist.md); [MS COCO: 11 / 1254 papers](https://scholar4ai.com/datasets/ms-coco.md); [Hugging Face Hub: 9 / 1254 papers](https://scholar4ai.com/datasets/hugging-face-hub.md)

**Methods and equations:** [Scaled dot-product attention (Transformer): 133 / 1254 papers](https://scholar4ai.com/equations/scaled-dot-product-attention-transformer.md); [Graph neural network (graph convolution): 92 / 1254 papers](https://scholar4ai.com/equations/graph-neural-network-graph-convolution.md); [Contrastive loss (InfoNCE): 64 / 1254 papers](https://scholar4ai.com/equations/contrastive-loss-infonce.md); [Convolutional neural network (CNN): 62 / 1254 papers](https://scholar4ai.com/equations/convolutional-neural-network-cnn.md); [F1 score: 61 / 1254 papers](https://scholar4ai.com/equations/f1-score.md)

## Method, missing data and reproduction

Each OpenAlex work ID is counted once in its tracked journal’s category. Within each paper, repeated matches for one normalized name count once; multiple names can occur together, so the top-name rows are not additive. The vocabulary groups aliases into families: for example, a family count may combine several dataset versions. Extraction searches the available title, abstract and retrieved full text; a missing match does not establish non-use. Abstract and full-text flags show availability, not whether a match originated in that source. The comparison is not normalized for journal selection, publication lag or missing text. Coverage flags can overlap. Zero-paper rates are undefined.

- [Field comparison CSV](https://scholar4ai.com/data/research-patterns.csv)
- [Paper-level input JSON](https://scholar4ai.com/data/research-patterns-input.json)
- [Reproduce with Python (standard library)](https://scholar4ai.com/assets/research-patterns.py)
- [Extraction and site methodology](https://scholar4ai.com/about)

```sh
python research-patterns.py research-patterns-input.json > reproduced.csv
```

## Cite this page

Scholar4AI. (2026). Datasets and methods across research fields. Journal and Conference Research Rankings. Retrieved November 5, 2026, from https://scholar4ai.com/research-patterns

```bibtex
@misc{scholar4ai-research-patterns,
  author = {{Scholar4AI}},
  title = {Datasets and methods across research fields},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/research-patterns}},
  urldate = {2026-11-05},
  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/).