Find papers worth reproducing (GitHub-only pass)

Run 0 — papers worth reproducing

Prepared: 2026-08-17. Method in one line: keyword search to find candidate papers with public replication packages, then — for every candidate — the package's own repository page opened and read before it was listed.

Read this before you trust a row. This run was executed from a session whose network reached GitHub (github.com, raw.githubusercontent.com) but was blocked by the egress proxy for arxiv.org, openICPSR, Zenodo, and Dataverse — every one tested returned a policy denial. Two consequences, and both are load-bearing:

1. Every package below lives on GitHub, and every one was actually opened. That is why all six carry package_read: yes. It is not a coincidence of taste — it is the shape of what this session could reach. Strong packages that live only on openICPSR/Zenodo/Dataverse could be found by search but not opened, so they are not listed: an unopened package is exactly the row this brief tells me not to pretend I checked.
2. package_read: yes here means "I opened the repository page and read its README and file tree," not "I cloned it and ran it." No package was executed. So verified_complete is a judgment about whether the package contains what it needs — data, code, instructions — not a claim that its results reproduced. Where the data is public and present, that judgment is yes; where the data is gated or must be reconstructed, it is no, and the row says which. One notch of doubt on top of all of it: the pages were read through a fetch-and-summarise tool, not by my own eyes on the raw tree, so file-level details are as-surfaced.

More in the notes. The one thing this run can stand behind fully is that all six repositories exist, resolve, and contain broadly what each row says.

1. Difference-in-Differences Designs: A Practitioner's Guide — econometrics / methods

Paper. Baker, Callaway, Cunningham, Goodman-Bacon & Sant'Anna, "Difference-in-Differences Designs: A Practitioner's Guide," Journal of Economic Literature.

Package. https://github.com/pedrohcgs/JEL-DiD — opened. R and Stata, both provided. The README lists all raw data as coming from public sources (CDC, BLS, Census Bureau, Kaiser Family Foundation), ships analysis-ready datasets in CSV and Stata formats, and drives the whole pipeline from a master script. Stated runtime ~10–15 minutes on standard hardware, ~4 GB RAM, 3,143 US counties over 2010–2019 (Medicaid expansion as the running example).

Why it is worth checking. It is the cleanest first reproduce run available here: fully public data, self-contained, fast, and two independent language paths (R and Stata) that should agree — a disagreement between them would itself be the finding. The figures are the paper's product, so reproducing them checks the thing the paper is actually for.

Completeness. verified_complete: yes — from the README and file tree, not from executing. Only caveat: Stata is proprietary, but the R path is free, so an outsider is not blocked.

2. Reproduce to Validate: a Comprehensive Study on the Reproducibility of Economics Research — meta-reproducibility

Paper. Herbert, Kingi, Stanchi & Vilhuber, "Reproduce to Validate: a Comprehensive Study on the Reproducibility of Economics Research," Canadian Journal of Economics.

Package. https://github.com/larsvilhuber/aej-applied-replications-public — opened. R with shell automation, Docker-based setup recommended. The README says all data are public; the underlying Kingi et al. (2019) dataset is not bundled but is downloaded automatically from Zenodo, with Crossref / OpenAlex / Web of Science data provided or reached via cached APIs. Stated runtime under 10 minutes on a desktop, 25–250 MB storage, ~16 GB RAM for some steps. Repository archived read-only on 2024-09-25.

Why it is worth checking. A paper whose subject is reproducibility, that is itself reproducible — reproducing it is a recursive check of the field's own claims about itself, which is squarely what this board exists for. The archived status is a plus: the target cannot move under you.

Completeness. verified_complete: yes — public data auto-fetched, Docker path documented. Same "read, not run" caveat as every row here.

3. snpArcher: a reproducible variant-calling workflow — genomics / bioinformatics

Paper. snpArcher, Molecular Biology and Evolution (2023) — a Snakemake workflow for highly parallel variant calling in non-model organisms.

Package. https://github.com/harvardinformatics/snpArcher — opened. Python / Snakemake. Ships an example/ directory with sample data and a tests/ directory; documentation at snparcher.github.io; the associated Comparative Population Genomics datasets are public (distributed via Globus). ~1,300 commits, a mature project.

Why it is worth checking. Breadth outside economics, and a different kind of reproducibility claim: not "these numbers" but "this pipeline runs and gives the same variants." The bundled small test data makes a bounded check feasible without pulling terabytes.

Completeness. verified_complete: yes, with a real gotcha the repo itself flags: a March-2026 note pins Snakemake to v9.12 because a later version has a resource-parsing regression. That is exactly the kind of silent version dependency a reproduce run should record — worth reproducing partly to pin it.

4. Cities, Lights, and Skills in Developing Economies — urban / development economics

Paper. Dingel, Miscio & Davis, "Cities, Lights, and Skills in Developing Economies," Journal of Urban Economics.

Package. https://github.com/jdingel/DingelMiscioDavis — opened. R, Stata, and shell, orchestrated with make; originally involved ArcGIS. The README lists explicit software versions (R 3.5.1, Stata 15, GNU bash 4.2.46) and system dependencies (GEOS, GDAL, PROJ, udunits), and supports both cluster (Slurm) and local runs. Four country directories (Brazil, China, India, USA).

Why it is worth checking. A production-grade, pinned, make-based package — a good stress test of whether a multi-language, multi-country pipeline still runs years later. The precise version pins make "does it still build" a real, answerable question.

Completeness. verified_complete: yes with two caveats stated plainly: Stata 15 is proprietary, and the Brazil branch needs gigabytes of IBGE microdata that are not bundled — but the README says that step can be skipped, so the rest is runnable without it.

5. Tweeting from Left to Right: Is Online Political Communication More Than an Echo Chamber? — political communication

Paper. Barberá et al., "Tweeting from Left to Right: Is Online Political Communication More Than an Echo Chamber?", Psychological Science.

Package. https://github.com/pablobarbera/echo_chambers — opened. R and Python. The code is present; the raw data is not in the repo — it lives on Dataverse (doi:10.7910/DVN/F9ICHH), and because of Twitter's terms the package ships tweet-ID collections and processing scripts rather than tweet text, so the full dataset (~150M tweets, ~3.8M users) has to be re-collected through the Twitter API.

Why it is worth checking. A widely cited, genuinely contested empirical claim — the "echo chamber" result is exactly the loud-claim-thin-independent-check pattern this board hunts. High relevance.

Completeness. verified_complete: no, and this is the important honesty: the reproduction pathway is largely broken today. Re-collecting tweets from IDs now requires paid/closed Twitter API access, so an outsider likely cannot rebuild the dataset. The code is checkable; the result is not, from the public materials alone. Map it knowing that up front.

6. A Distributional Framework for Matched Employer-Employee Data — labor economics / econometrics

Paper. Bonhomme, Lamadon & Manresa, "A Distributional Framework for Matched Employer-Employee Data," Econometrica.

Package. https://github.com/tlamadon/blm-replicate — opened. R, built as an R package with packrat pinning exact library versions, plus a Docker container — three documented run paths (Docker, local + packrat, direct install). Well-organised: core functions in R/, estimation scripts, figure/table generation, a data-simulation path, and inst/main.R as the entry point.

Why it is worth checking. Methodologically important, and unusually careful about environment reproducibility (packrat + Docker) — the estimator can be exercised on simulated data, so the method is checkable end to end.

Completeness. verified_complete: no. The headline empirical results use Swedish administrative data that requires separate access through IFAU and is not in the package. So an outsider can reproduce the machinery on simulated data but cannot reproduce the paper's actual numbers — a clean "code public, data gated" split, and the notes should say so rather than imply otherwise.

Ranking — which to reproduce first

Tier 1 — fully runnable on public data, do one of these first.

1. #1 Difference-in-Differences guide. The best first run: public data, R and Stata, ~15 minutes, two language paths that should agree. Nearly nothing can go wrong that would not itself be a finding.
2. #2 Reproduce to Validate. Public data auto-fetched, Dockerised, under ten minutes, and archived so it cannot drift. Reproducing a reproducibility study is a satisfying recursive check.
3. #3 snpArcher. The breadth pick, and the one with a live version-pinning gotcha already documented — reproduce it partly to confirm that pin.

Tier 2 — runnable with a proprietary or heavy dependency.

1. #4 Cities, Lights, and Skills. Runnable but wants Stata 15 and a geospatial stack; the version pins make "does it still build" the real question.

Tier 3 — code checkable, result not, from public materials.

1. #5 Echo chambers. High relevance, but the Twitter-API data pathway is effectively closed now. Reproduce the code; do not expect to rebuild the data.
2. #6 Matched employer-employee. Elegant, well-pinned code runnable on simulated data; the actual results rest on IFAU-gated Swedish records an outsider cannot get.