Datasets

This catalogue collects 45 estimate-level datasets from the empirical meta-analyses on this site, each in a standard format.

There are two products here. The archive publishes each paper's original file with automatic CSV and Parquet conversions, a codebook and a DOI. The harmonised table pools those datasets into one file with a common set of columns, so they can be compared across literatures, and it is stable as of version 1.1.1. For one paper's data use the archive; to work across literatures use the harmonised table, and read its caveats below.

One file, every literature

49,669 estimate-level rows from 41 literatures in a single table, with the effect, its standard error, the sample size, and the characteristics that recur across literatures.

Also as Parquet, which is smaller and keeps column types.

df <- read.csv("https://meta-analysis.cz/data/v1/estimates_harmonised.csv")

import pandas as pd
df = pd.read_parquet("https://meta-analysis.cz/data/v1/estimates_harmonised.parquet")

Raw effect levels are not comparable across literatures. An elasticity, a partial correlation and a dollar value per tonne of carbon all sit in the same column; effect_units records which is which. Analyse within each literature. Comparing across them needs an explicitly standardised measure, and relative changes are meaningful only where the baseline is safely away from zero.

The harmonised table is version 1.1.1: 49,669 observations from 41 literatures. Every row records the file and the columns it came from, so any value can be traced back to its published dataset.

All 41 pooled literatures are verified: 21 domain-reviewed against the paper's own replication code or, where a paper ships none, against its published results, and 20 code-traced. Every dataset publishes an audit_status saying which it is.

One literature at a time

The converted source files hold 66,683 rows, every one keeping the variables coded for its original paper. After applying each paper's analysis selection and, where necessary, reshaping the source data into estimate-level observations, the catalogue below represents 55,274 estimates. The CSV and Parquet are automatic conversions carrying one data sheet per source workbook, the one named in datasets.json; any other sheet is absent, and no column is independently re-checked. For anything load-bearing, work from the original file.

Each dataset is also published on its own, with all of the variables coded for its paper rather than the shared subset, and a codebook describing every column.

The Estimates column counts what each literature contributes to the pooled table; for the datasets that are not pooled, it counts the rows in the published file after the paper's own analysis filters. Neither is always the number in the paper's abstract: a published file is usually the analysis subset rather than the full collection, a pooled contribution can be smaller still where estimates overlap another literature (trust contributes the 284 rows size does not already carry, out of 1,613 in its file), and two literatures contribute a row per impulse-response horizon. datasets.json carries both counts and a reconciliation for every dataset where they differ.

45 datasets, 55,274 estimates in their analysis samples (66,683 rows in the source files). 41 literatures are pooled into the harmonised table.

Published but not pooled

Four datasets are published but stay out of the pooled table: two carry no per-estimate standard error, and two duplicate a dataset already included, which would otherwise be counted twice.

fdi

no per-estimate precision exists: the file has no standard error, t-statistic, weight or inverse-SE column, so SE-based synthesis is impossible

hedge

duplicate of alphas: identical estimates, row for row

lags

outcome is the transmission lag (mon_bot, months to the price trough); it has no sampling standard error, so it cannot enter an effect/SE table

substitution

duplicate of eis: identical estimates, row for row

Machine-readable

The whole collection is available as JSON: an index of every dataset with paper, DOI, file URLs and column roles, a Frictionless data package, and a Croissant record. Full documentation is in the API README.

Licence and citation

Everything here is CC BY 4.0, including for training, provided you give credit. The licence file states the terms, and the citation for the collection is in the footer of every page.

Each dataset was assembled for a specific paper. When you use one, cite that paper. The index carries every paper's title, authors and DOI. That is how attribution under CC BY is satisfied here.

The Zenodo identifier in that footer is the concept DOI: it always resolves to the newest version. In a replication package, where the exact files matter, cite this release instead: 10.5281/zenodo.22050272, version 1.1.1.