arxiv_id float64 1.61k 2k | title stringclasses 7
values | authors stringclasses 7
values | year int64 2.02k 2.02k | venue stringclasses 5
values | doi stringclasses 5
values | field stringclasses 7
values | outcome stringclasses 1
value | outcome_label stringclasses 1
value | headline_held int64 1 8 | headline_held_earlier_code int64 0 0 | headline_total int64 1 8 | claims_checked int64 2 16 | checked stringdate 2026-10-18 00:00:00 2026-10-19 00:00:00 | page_url stringclasses 7
values | evidence_url stringclasses 7
values | badge_url stringclasses 7
values | log_head_hash stringclasses 7
values | log_events int64 26 67 | bundle_url stringclasses 7
values | bundle_sha256 stringclasses 7
values | summary stringclasses 7
values | attempt_id stringclasses 7
values | qualifiers stringclasses 4
values | qualifier_text stringclasses 4
values | qualifiers_source stringclasses 1
value | coverage_note float64 | assurance_level stringclasses 1
value | release_on stringdate 2026-10-19 00:00:00 2026-10-19 00:00:00 | report_json_sha256 stringclasses 7
values | superseded_attempts float64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1,708.05031 | Neural Collaborative Filtering | Xiangnan He; Lizi Liao; Hanwang Zhang; Liqiang Nie; Xia Hu; Tat-Seng Chua | 2,017 | null | null | Recommender systems | reproduced | Reproduced | 4 | 0 | 4 | 6 | 2026-10-19 | https://openrepro.org/papers/1708.05031/ | https://openrepro.org/evidence/arxiv-1708-05031-20261019T135218Z.html | 04ac3824830007550ce23545b33c04ec2f8c290dd7107cca03355bd8ce4ddfc6 | 67 | https://openrepro.org/bundles/arxiv-1708-05031-20261019T135218Z.tar.gz | 526c1df742b41900055aa6ca2ab00d065b10fd744f3fb163204fd9df964561cd | The authors Keras code (commit 312aff0, identical in code to the current default branch) was run on MovieLens-1M with the exact README environment (Python 2.7.15, Keras 1.0.7, Theano 0.8.0) on CPU, using the README commands with 8 predictive factors and 20 epochs. All six pre-registered Table 2/3/4 values at 8 factors ... | arxiv-1708-05031-20261019T135218Z | claims_registered_after_output; plan_not_sealed; evidence_not_bound; agent_selected_extraction | some claims were registered after command output had been observed; legacy attempt: the plan was never sealed; claims were frozen only by their first result; at least one value was read from an excerpt the agent chose after seeing the output, not by a rule fixed before it | report | null | Legacy | 2026-10-19 | 9415526a0cc89ac96f5f036ec663c59c3ffed64312cb6452053aeba2ad3f724f | null | |
1,606.00931 | DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network | Jared Katzman; Uri Shaham; Alexander Cloninger; Jonathan Bates; Tingting Jiang; Yuval Kluger | 2,018 | BMC Medical Research Methodology | 10.1186/s12874-018-0482-1 | Medical statistics | reproduced | Reproduced | 4 | 0 | 4 | 6 | 2026-10-19 | https://openrepro.org/papers/1606.00931/ | https://openrepro.org/evidence/arxiv-1606-00931-20261018T225505Z.html | b6c148f9a6936f84ea439e56d1c64f0a7dcb49e564e06bcc17ae75d7dd7f225f | 63 | https://openrepro.org/bundles/arxiv-1606-00931-20261018T225505Z.tar.gz | a3ec5893321c321b834852634486c0dfe6d3c97bdcdac039cbf2375a469fbe07 | Rerunning the first author code at the commit matching arXiv v3 (079dee7, 2017-08-08), with the documented command for each experiment in a reconstructed Python 2.7 / Theano 0.9 / Lasagne environment on CPU, reproduces all six DeepSurv C-index values of Table 1 within 0.01: mean of 10 unseeded runs SUPPORT 0.6186 vs 0.... | arxiv-1606-00931-20261018T225505Z | claims_registered_after_output; plan_not_sealed; evidence_not_bound; agent_selected_extraction | some claims were registered after command output had been observed; legacy attempt: the plan was never sealed; claims were frozen only by their first result; at least one value was read from an excerpt the agent chose after seeing the output, not by a rule fixed before it | report | null | Legacy | 2026-10-19 | 4618c43c82c309831531bc345505686abff25b5cf1658e4033b8c092f0f678d0 | null | |
1,711.10561 | Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations | Maziar Raissi; Paris G. Perdikaris; George Em Karniadakis | 2,017 | null | null | Scientific machine learning | reproduced | Reproduced | 1 | 0 | 1 | 2 | 2026-10-19 | https://openrepro.org/papers/1711.10561/ | https://openrepro.org/evidence/arxiv-1711-10561-20261018T234638Z.html | 450dad5c4edbfc2ab0c9dce568dcddd793d19978beba85e4a64f26f6724759da | 26 | https://openrepro.org/bundles/arxiv-1711-10561-20261018T234638Z.tar.gz | ec1fdbada7591b44cf36e0de3e4bc1798cf1dfc98d745f9fcf3fb9bb8401092d | Running the authors Burgers.py (the earliest version in their repository, from five months after arXiv v1) with its fixed seeds on CPU gives a relative L2 error of 2.15e-3 for the continuous-time Burgers example, against 6.7e-4 reported. That meets the pre-registered order-of-magnitude headline check (at most 6.7e-3) b... | arxiv-1711-10561-20261018T234638Z | bound_checks_only; broad_bound; other_checks_failed; plan_not_sealed; evidence_not_bound; agent_selected_extraction | Burgers error 3.21× the published value; passes a registered 10× ceiling; a stricter 2× check did not hold; legacy attempt: the plan was never sealed; claims were frozen only by their first result; at least one value was read from an excerpt the agent chose after seeing the output, not by a rule fixed before it | report | null | Legacy | 2026-10-19 | c0df97b17edb7b847d7e7b5cf3dc788c18d3f4960563ba5ff3ce186d721e7037 | null | |
1,607.01759 | Bag of Tricks for Efficient Text Classification | Armand Joulin; Édouard Grave; Piotr Bojanowski; Tomáš Mikolov | 2,017 | Conference of the European Chapter of the Association for Computational Linguistics | 10.18653/v1/e17-2068 | Natural language processing | reproduced | Reproduced | 8 | 0 | 8 | 16 | 2026-10-18 | https://openrepro.org/papers/1607.01759/ | https://openrepro.org/evidence/arxiv-1607-01759-20261018T223713Z.html | 87b5ac6197ec5f210cb74ba6aaa5412fe8e2237bdf845f1afee95be9043de61d | 56 | https://openrepro.org/bundles/arxiv-1607-01759-20261018T223713Z.tar.gz | 31c2f0bbfa19009868e934d406a85c850c9e430dd90b79022e1977909a00cad4 | With fastText at the commit of arXiv v3 and its classification-results.sh, test accuracy with 10 hidden units and bigrams reproduces Table 1 on all eight datasets: 92.2, 96.7, 98.6, 95.7, 63.9, 72.4, 60.2 and 94.6 against 92.5, 96.8, 98.6, 95.7, 63.9, 72.3, 60.2 and 94.6. The unigram row also reproduces, within 0.2 poi... | arxiv-1607-01759-20261018T223713Z | plan_not_sealed; evidence_not_bound; agent_selected_extraction | legacy attempt: the plan was never sealed; claims were frozen only by their first result; at least one value was read from an excerpt the agent chose after seeing the output, not by a rule fixed before it | report | null | Legacy | 2026-10-19 | d4c54196bf7f87246ae7f1b2123994691f4b3adb69a4f9def94e8ba9c76e3b9a | null | |
2,004.01105 | Inferring change points in the spread of COVID-19 reveals the effectiveness of interventions | Jonas Dehning; Johannes Zierenberg; F. Paul Spitzner; Michael Wibral; João Pinheiro Neto; Michael Wilczek; Viola Priesemann | 2,020 | Science | 10.1126/science.abb9789 | Epidemiology | reproduced | Reproduced | 4 | 0 | 4 | 7 | 2026-10-18 | https://openrepro.org/papers/2004.01105/ | https://openrepro.org/evidence/arxiv-2004-01105-20261018T033025Z.html | 90e4d597839451c42440893ea4dea7451ef11eaedc3887da05aa3c80dcc11e78 | 44 | https://openrepro.org/bundles/arxiv-2004-01105-20261018T033025Z.tar.gz | 133120d2279425834b961d4304c7e6d626fbf8f190ff67d6e32ec166bc6ce68f | Fitting the authors three-change-point SIR model with weekly reporting modulation (repository state on the date of arXiv v3) to German case data until 21 April 2020, using the JHU snapshot bundled with the repository, reproduces all registered values: spreading rates 0.43, 0.25, 0.15 and 0.09 (posterior medians 0.428, ... | arxiv-2004-01105-20261018T033025Z | plan_not_sealed; evidence_not_bound; agent_selected_extraction | legacy attempt: the plan was never sealed; claims were frozen only by their first result; at least one value was read from an excerpt the agent chose after seeing the output, not by a rule fixed before it | report | null | Legacy | 2026-10-19 | 4f7abfc274235e7f7089ee79e78a693b99a8e4c736292bb44e7f806cb6dfae3d | null | |
1,810.08473 | From Louvain to Leiden: guaranteeing well-connected communities | V. A. Traag; L. Waltman; Nees Jan van Eck | 2,019 | Scientific Reports | 10.1038/s41598-019-41695-z | Network science | reproduced | Reproduced | 4 | 0 | 4 | 6 | 2026-10-18 | https://openrepro.org/papers/1810.08473/ | https://openrepro.org/evidence/arxiv-1810-08473-20261018T013946Z.html | 578432e9062b72e8e2576db5eeed0bf5ac96e6f5b649204a2e77964f446edc6c | 42 | https://openrepro.org/bundles/arxiv-1810-08473-20261018T013946Z.tar.gz | 44a9835769081eadd17b22c481374d07d8050ab73869c91bdc82b5ea64e9bca3 | Using the authors Java implementation at the release tag matching the paper (networkanalysis 1.0.0) on the SNAP DBLP and Amazon networks, with the paper protocol of 10 random starts of 10 iterations each, all tested Table II values reproduced within 0.0005: DBLP Louvain 0.8267 vs 0.8262 and Leiden 0.8384 vs 0.8387; Ama... | arxiv-1810-08473-20261018T013946Z | broad_bound; plan_not_sealed; evidence_not_bound; agent_selected_extraction | DBLP Leiden gain over Louvain 0.938× the published value; passes a registered floor of 0, a sign check; Amazon Leiden gain over Louvain 1.04× the published value; passes a registered floor of 0, a sign check; legacy attempt: the plan was never sealed; claims were frozen only by their first result; at least one value wa... | report | null | Legacy | 2026-10-19 | 7578c2dcc994acbddf7fe022f261bda0a1b18de84da38b67c401c0f5cee29cfd | null | |
1,812.0997 | Synthetic Difference-in-Differences | Dmitry Arkhangelsky; Susan Carleton Athey; David A. Hirshberg; Guido W. Imbens; Stefan Wager | 2,021 | American Economic Review | 10.1257/aer.20190159 | Economics | reproduced | Reproduced | 4 | 0 | 4 | 10 | 2026-10-18 | https://openrepro.org/papers/1812.09970/ | https://openrepro.org/evidence/arxiv-1812-09970-20261018T010328Z.html | 8b90233f1aba9384bcb8d580e05cbaaf9a5947ebf64d2d62c4f0f4b157f1f7cb | 59 | https://openrepro.org/bundles/arxiv-1812-09970-20261018T010328Z.tar.gz | b5ba1fe35f4c450bce1c3d66546b117baf1d15a8633fec07e878292fe03fd96a | Running the authors own Table 1 code (vignettes/paper-results.Rmd) at the synthdid commit named in their session-info, with R 4.0.5 and the pinned MCPanel and glmnet commits, reproduced every entry of Table 1 at the reported precision: SDID -15.6 (8.4), SC -19.6 (9.9), DID -27.3 (17.7), MC -20.2 (11.5), DIFP -11.1 (9.5... | arxiv-1812-09970-20261018T010328Z | plan_not_sealed; evidence_not_bound; agent_selected_extraction | legacy attempt: the plan was never sealed; claims were frozen only by their first result; at least one value was read from an excerpt the agent chose after seeing the output, not by a rule fixed before it | report | null | Legacy | 2026-10-19 | 1ea4c3c995f66424b59dccd235fa8015529c6ea76038251bc7b787f6a292a9ae | null |
OpenRepro Ledger
OpenRepro re-runs published computational research and publishes hash-chained evidence of whether the
results hold. This dataset is a mirror of the ledger's machine-readable files from
openrepro.org. The site is the source of record. Every value here comes from an
attempt's report.json and is the same as on the site.
Release 2026-10-20. 7 papers and 53 registered claims (52 held within tolerance), from 7 released attempts. Only attempts listed in release.json, the release manifest, are included; a paper without a due release record is absent.
| arXiv | Paper | Outcome | Headline claims held | Qualifiers | Assurance | Report |
|---|---|---|---|---|---|---|
| 1606.00931 | DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network | Reproduced | 4 of 4 | some claims were registered after command output had been observed; legacy attempt: the plan was never sealed; claims were frozen only by their first result; at least one value was read from an excerpt the agent chose after seeing the output, not by a rule fixed before it | Legacy | report |
| 1607.01759 | Bag of Tricks for Efficient Text Classification | Reproduced | 8 of 8 | legacy attempt: the plan was never sealed; claims were frozen only by their first result; at least one value was read from an excerpt the agent chose after seeing the output, not by a rule fixed before it | Legacy | report |
| 1708.05031 | Neural Collaborative Filtering | Reproduced | 4 of 4 | some claims were registered after command output had been observed; legacy attempt: the plan was never sealed; claims were frozen only by their first result; at least one value was read from an excerpt the agent chose after seeing the output, not by a rule fixed before it | Legacy | report |
| 1711.10561 | Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations | Reproduced | 1 of 1 | Burgers error 3.21× the published value; passes a registered 10× ceiling; a stricter 2× check did not hold; legacy attempt: the plan was never sealed; claims were frozen only by their first result; at least one value was read from an excerpt the agent chose after seeing the output, not by a rule fixed before it | Legacy | report |
| 1810.08473 | From Louvain to Leiden: guaranteeing well-connected communities | Reproduced | 4 of 4 | DBLP Leiden gain over Louvain 0.938× the published value; passes a registered floor of 0, a sign check; Amazon Leiden gain over Louvain 1.04× the published value; passes a registered floor of 0, a sign check; legacy attempt: the plan was never sealed; claims were frozen only by their first result; at least one value was read from an excerpt the agent chose after seeing the output, not by a rule fixed before it | Legacy | report |
| 1812.09970 | Synthetic Difference-in-Differences | Reproduced | 4 of 4 | legacy attempt: the plan was never sealed; claims were frozen only by their first result; at least one value was read from an excerpt the agent chose after seeing the output, not by a rule fixed before it | Legacy | report |
| 2004.01105 | Inferring change points in the spread of COVID-19 reveals the effectiveness of interventions | Reproduced | 4 of 4 | legacy attempt: the plan was never sealed; claims were frozen only by their first result; at least one value was read from an excerpt the agent chose after seeing the output, not by a rule fixed before it | Legacy | report |
Files
| File | Contents |
|---|---|
ledger.csv (config ledger) |
One row per listed paper. |
ledger.json |
The same records under papers, plus an envelope: schema_version, version (the build date), generated, license, documentation, citation, feed, mcp, papers_listed. |
claims.csv (config claims) |
One row per registered claim of every listed paper. |
claims.json |
The same records under claims, with the same envelope. |
release.json |
The release manifest: every released attempt (current or superseded) with its release record and the SHA-256 of its report files and evidence bundle. Nothing else is in these files. |
In the CSV files, list fields such as authors are joined with "; ". Empty cells are nulls.
from datasets import load_dataset
ledger = load_dataset("openrepro/openrepro-ledger", "ledger", split="train")
claims = load_dataset("openrepro/openrepro-ledger", "claims", split="train")
Ledger fields (one record per paper)
| Field | Meaning |
|---|---|
arxiv_id |
The paper's arXiv id, without a version suffix. It is the stable key for a paper in every file and on the site (/papers/<arxiv_id>/). |
title, authors, year, venue, doi |
Citation metadata from the report. Display-only corrections are sourced in the site's editorial file. |
field |
A browsing label assigned by OpenRepro. |
outcome, outcome_label |
The computed outcome, as a key and as printed: reproduced, reproduced_with_earlier_code, partially_reproduced, not_reproduced, evidence_incomplete ("Not verified: evidence incomplete": OpenRepro's evidence failed, not the paper) or not_attemptable. |
qualifiers, qualifier_text, qualifiers_source |
Qualifier codes (bound_checks_only, broad_bound, other_checks_failed, untested_for_budget), the same in plain words (show it next to the outcome), and whether they come from the report or were derived by the site from a legacy report. |
coverage_note |
What the report says, or cannot say, about results left untested for budget. |
assurance_level (assurance in JSON) |
Legacy, Partial, Sealed plan, Sealed and bound, or Sealed, bound and re-derived. The JSON has the plan seal, evidence binding, execution manifest, re-derivation and engine version. |
attempt_id, superseded_attempts (attempts in JSON) |
The released attempt, and the lineage: every released attempt of the paper with its status (released or superseded), outcome, superseded_by and reason. |
release_on, report_json_sha256 |
The release date from the release record, and the SHA-256 of the released report.json. |
headline_held, headline_held_earlier_code, headline_total |
How many headline claims held, how many of those held only with a declared earlier version of the code, and how many headline claims were scored. |
claims_checked |
Every claim registered, headline or not. |
checked |
The date the attempt ended (UTC). |
page_url, evidence_url, badge_url |
The report page, the full evidence report, and the SVG badge. |
log_head_hash, log_events |
The head hash and the number of events in the attempt's hash-chained log. The report page prints this as the seal. |
bundle_url, bundle_sha256 |
The downloadable evidence bundle and its SHA-256, when published. |
summary |
The report's summary paragraph. |
Claims fields (one record per claim)
| Field | Meaning |
|---|---|
arxiv_id, claim_id |
The paper and the claim. Together they are the stable key for a claim; the report page anchor is #claim-<claim_id>. |
paper |
The paper's short name. |
headline |
True when the claim counts toward the outcome. |
statement, location, metric, unit |
What was registered: the claim in words, where it is in the paper, and what was measured. |
comparison, published, tolerance, accepted_if |
The rule registered before any result was recorded: within a band, at most, or at least; the published value; the tolerance; and the rule as printed. |
check_kind, broad_bound, ratio_to_published |
Equivalence or bound; whether the bound admits more than twice (or less than half) the published value; observed ÷ published. From the report when present, else derived by the site. |
attempt_id |
The attempt the claim belongs to. |
observed |
The value obtained by re-running the authors' code. Null when untested. |
verdict |
consistent, consistent_earlier_code, inconsistent or untested. |
earlier_code_observed |
The value under the declared earlier-code fallback, when one was tried. |
registered_event, first_result_event |
The log events at which the claim was registered and its first result was recorded, so the order can be checked. |
deviation_over_tolerance |
(observed − published) ÷ tolerance. Null when untested; inf or -inf when the tolerance was zero and the value missed. |
How it is produced
For each paper, an AI agent (Claude, made by Anthropic) works in a sandbox with OpenRepro's engine:
- It takes the authors' own code and data.
- Before results are recorded, it registers the claims to check, each with a quote from the paper and a
numeric tolerance. In current (legacy) attempts this does not show that no output had been seen first;
each record's
assurancesays what applies. - It rebuilds the environment as of the paper's date and re-runs the experiments.
Every command and output goes into a hash-chained log. The engine computes the verdicts by fixed rules from the registered claims, tolerances and headline choices, which the agent makes; the qualifiers show when a headline check is a broad bound or another check missed. The report is generated from the log; the one-file verifier checks log and artifact integrity and the report's head reference, not the report's contents. A person oversees the project: choosing papers and setting the rules the agent works under. The full method is at openrepro.org/methods/. The evidence bundles and a one-file verifier are at openrepro.org/verify/.
Limits
- AI-run, no expert review. The re-runs are done by an AI agent. No domain expert has reviewed the reports, and nothing here is peer reviewed.
- An outcome is not a judgment of the paper. It says what happened when the authors' code was re-run under the recorded conditions. "Could not reproduce" means the published artifacts did not produce the published numbers under OpenRepro's protocol. It is not a finding about the authors' honesty or the idea's merit.
- Release is fail-closed. For any outcome other than "reproduced", the authors get 14 days to reply before
publication, and every attempt needs an explicit release record. Without a due one, the paper is absent from
these files and from the site;
release.jsonlists exactly what is included. - Small and selective. The ledger covers only the papers OpenRepro has chosen and finished. It is not a sample of any field and should not be used to estimate a field's reproducibility rate.
- Snapshot. Each version is the ledger as of its build date. A correction is published as a new attempt that links to the old one; ids never change.
Update cadence
The files are rebuilt with the site whenever an attempt's release record falls due. This
mirror is updated after the site, so it can lag by a few days. version in the JSON envelope is the build
date. The live files are at openrepro.org/data/.
License and citation
The reports, verdicts, evidence logs and these files are published under CC BY 4.0. Attribute "OpenRepro, https://openrepro.org" and link the report URL so a reader can reach the evidence.
@misc{openrepro_ledger,
author = {{OpenRepro}},
title = {{OpenRepro}: a public ledger of re-run computational research},
year = {2026},
howpublished = {\url{https://openrepro.org/}},
note = {Dataset, version 2026-10-20. CC BY 4.0. Mirrored at https://huggingface.co/datasets/openrepro/openrepro-ledger}
}
The site's CITATION.cff has the same details. Each report page has its own citation block.
Contact and right of reply
Write to hello@openrepro.org to reply to a report or to report an error. Authors may reply at any time, and their notes can be published unedited alongside the report.
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