MetaCheck Report

To Err is Human: An Empirical Investigation

MetaCheck version 0.0.1.0
Report Created: 2026-06-04
DOI: 10.32614/10.5281/zenodo.2669586

Metacheck is a tool that screens scientific manuscripts and aims to identify potential issues for improvement, thereby guiding researchers towards best practices. Metacheck is developed to help researchers correctly and completely report statistical results, automatically retrieve possible relevant information about citations, and improve how researchers share data, code, and preregistrations.

Metacheck combines existing and new checks in a module-based tool. It mainly relies on text search or retrieving information from external sources through API’s or web-scraping, but some modules also incorporate tools that use machine learning classifiers or large language models. The use of LLM’s is always optional and opt-in. The development of Metacheck is guided by our values statement.

Our modules are validated on sets of open-access papers. In each validated module, there will be a sentence explaining the prevalence of false positives (incorrect detection/classification) and false negatives (incorrect omission). For example, if a hypothetical module detects the inappropriate practice of “woozling”, the sentence might read:

In a sample of 250 papers from the Journal of X, there were 350 instances of woozling. This module correctly detected 340 of them, and incorrectly identified 7. Therefore 3% of true instances are missed, and 2% of detections are false positives.

There is an inherent tradeoff between false positives and false negatives. Many of our modules are designed like “smoke detectors”, where they are more likely to detect a practice that needs attention, but also more likely to incorrectly flag something. Therefore, you need to check the output of each module, keeping the validated error rates in mind.

Metacheck is under continuous development. Issues can be submitted on Github, and suggestions for improvement or feedback can be sent to metacheck@scienceverse.org.

Summary

  • ✅️ Open Practices Check: Shared data and code detected.
  • ✅️ COI Check: A conflict of interest statement was detected.
  • ⚠️ Funding Check: No funding statement was detected.
  • ℹ️ Preregistration Check: We found 2 preregistrations.
  • ⚠️ Power Analysis Check: We detected 3 potential power analyses.
  • ⚠️ Exact P-Values: We found 1 imprecise p value out of 3 detected p values.
  • 🔍 Non-Significant P Value Check: We found 2 non-significant p values that should be checked for appropriate interpretation.
  • ⚠️ Marginal Significance: You described 2 effects with terms related to ‘marginally significant’.
  • 🔍 Effect Sizes in t-tests and F-tests: We found 1 t-test and/or F-test where effect sizes are not reported.
  • ⚠️ StatCheck: 1 possible error in t-tests or F-tests
  • 🔍 Repository Check:
    • We found 14 files in 3 repositories.
    • We found 1 README file and 2 repositories without READMEs.
    • We found 1 archive file.
  • 🔍 Code Check:
    • We found 4 R, 0 SAS, 0 SPSS, and 0 Stata code files.
    • All your code files had comments.
    • 4 files loaded in the code were missing in the repository.
    • Absolute file paths were found.
    • Libraries/imports were loaded in multiple places.
    • No parsing issues of R-type files were found.
  • 🔍 Reference Accuracy: We checked 5 references in CrossRef and found entries for 3.
  • ℹ️ Replication Check: We found 1 replication for 1 original you cited.
  • ℹ️ RetractionWatch: You cited 1 article in the RetractionWatch database.
  • ℹ️ PubPeer Comments: You cited 1 reference with comments in PubPeer.
  • ℹ️ Summarise References: Summary information provided for 5 references

General Modules

✅️ Open Practices Check

Shared data and code detected.

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Data was openly shared for this article, based on the following text:

The paper shows examples of (1) open and closed OSF links; (2a) citation of retracted papers, (2b) citations without a doi, (2c) citations with Pubpeer comments, (2d) citations in the FLoRA replication database, and (2e) missing/mismatched/incorrect citations and references; (3a) R files with code on GitHub that do not load libraries in one location, (3b) load files that are not shared in the repository, (3c) lack comments, and (3d) have absolute file paths; (4) imprecise reporting of non-significant p-values; (5) tests with and without effect sizes; (6) use of “marginally significant” to describe non-significant findings; (7) a power analysis reporting some of the essential attributes; and (8) retrieving information from preregistrations.

Data and analysis code is available on GitHub from https://github.com/Lak-ens/to_err_is_human and from https://researchbox.org/4377.

Data is also available from https://osf.io/5tbm9 and code is also available from https://osf.io/629bx.

Code was openly shared for this article, based on the following text:

The paper shows examples of (1) open and closed OSF links; (2a) citation of retracted papers, (2b) citations without a doi, (2c) citations with Pubpeer comments, (2d) citations in the FLoRA replication database, and (2e) missing/mismatched/incorrect citations and references; (3a) R files with code on GitHub that do not load libraries in one location, (3b) load files that are not shared in the repository, (3c) lack comments, and (3d) have absolute file paths; (4) imprecise reporting of non-significant p-values; (5) tests with and without effect sizes; (6) use of “marginally significant” to describe non-significant findings; (7) a power analysis reporting some of the essential attributes; and (8) retrieving information from preregistrations.

Data and analysis code is available on GitHub from https://github.com/Lak-ens/to_err_is_human and from https://researchbox.org/4377.

Data is also available from https://osf.io/5tbm9 and code is also available from https://osf.io/629bx.

This module searches for open data, code, materials, and registration statements.

It is much faster than the previous ODDPub version of this module, and has a lower false negative rate, but also a higher false positive rate.

This module was developed by Lisa DeBruine

✅️ COI Check

A conflict of interest statement was detected.

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The following conflict of interest statement was detected.

Identify and extract Conflicts of Interest (COI) statements.

The COI Check module uses regular expressions to check sentences for words related to conflict of interest statements. It will return the sentences in which the conflict of interest statement was found.

The function incorporates code from rtransparent, which is no longer maintained. For their validation, see the paper.

This module was developed by Daniel Lakens

⚠️ Funding Check

No funding statement was detected.

No funding statement was detected. Consider adding one.

Identify and extract funding statements.

The Funding Check module uses regular expressions to check sentences for words related to funding statements. It will return the sentences in which the conflict of interest statement was found.

The function incorporates code from rtransparent, which is no longer maintained. For their validation, see the paper.

This module was developed by Daniel Lakens

Method Modules

ℹ️ Preregistration Check

We found 2 preregistrations.

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We found 2 preregistrations.

Meta-scientific research has shown that deviations from preregistrations are often not reported or checked, and that the most common deviations concern the sample size. We recommend manually checking the full preregistration at the links above, and have provided the preregistered sample size.

For metascientific articles demonstrating the rate of deviations from preregistrations, see:

van den Akker O, Bakker M, van Assen M, Pennington C, Verweij L, Elsherif M, Claesen A, Gaillard S, Yeung S, Frankenberger J, Krautter K, Cockcroft J, Kreuer K, Evans T, Heppel F, Schoch S, Korbmacher M, Yamada Y, Albayrak-Aydemir N, Wicherts J (2024). “The potential of preregistration in psychology: Assessing preregistration producibility and preregistration-study consistency.” Psychological Methods. doi:10.1037/met0000687.

For educational material on how to report deviations from preregistrations, see:

Lakens, Daniël (2024). “When and How to Deviate From a Preregistration.” Collabra: Psychology, 10(1), 117094. doi:10.1525/collabra.117094.

Retrieve information from preregistrations in a standardised way, and make them easier to check.

The Preregistration Check module identifies preregistrations on the OSF and AsPredicted based on links in the manuscript, retrieves the preregistration text, and organizes the information into a template. The module then uses regular expressions to identify text from AsPredicted, and the API to retrieve text from the OSF. The information in the preregistration is returned.

The module can’t extract information from non-structured preregistration templates (i.e., where the preregistration is uploaded in a single text field) and it can’t retrieve information in preregistrations that are stored as text documents on the OSF.

If you want to extend the package to be able to download information from other preregistration sites, reach out to the Metacheck development team.

This module was developed by Daniel Lakens and Lisa DeBruine

⚠️ Power Analysis Check

We detected 3 potential power analyses.

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We used the LLM model ‘ollama/qwen2.5:3b’ to check the contents of 3 paragraphs that contained words suggesting they might contain power analyses.

Some essential information could not be detected: alpha_level, effect_size, effect_size_metric, software

Power analyses need to contain the following information to be interpretable: the type of power analysis, the statistical test, the software used, sample size, critical alpha criterion, power level, effect size, and an effect size metric. In addition, it is recommended to make sure the power analysis is reproducible (by sharing the code, or a screenshot, of the power analysis), and to provide good arguments for why the study was designed to detect an effect of this size.

For an a-priori power analysis, where the sample size is determined, reporting all information would look like:

An a priori power analysis for an independent samples t-test, conducted using the pwr.t.test function from pwr (Champely, 2020), indicated that for a Cohen’s d = 0.5, an alpha level of 0.05, and a desired power level of 80% required at least 64 participants in each group.

For a sensitivity power analysis, this sentence would look like:

A sensitivity power analysis for an independent samples t-test, conducted using the pwr.t.test function from pwr (Champely, 2020), indicated that with 64 participants in each group, and an alpha level of 0.05, a desired power level of 80% was reached for an effect size of d = 0.5.

This module uses uses regular expressions to identify sentences that contain a statistical power analysis. If specified by the user, it also uses a large language module (LLM) to extract information reported in power analyses, including the statistical test, sample size, alpha level, desired level of power, and magnitude and type of effect size.

The Power Analysis Check module uses regular expressions to identify sentences that contain a statistical power analysis. Without the use of an LMM, the module uses regular expressions to classify the power analysis as a-priori, sensitivity or post-hoc. With the use of an LMM, it checks if the power analysis is reported with all required information.

The regular expressions can miss power analyses, or fail to classify them correctly. The type of power analysis is often difficult to classify, which can easily be solved by explicitly specifying the type of power analysis as ‘a-priori’, ‘sensitivity’, or ‘post-hoc’. Note that ‘post-hoc’ or ‘observed’ power is rarely useful. The LMM can fail to identify information in the paper, and will not have access to information in paragraphs in the paper other than those that contain the word ‘power’. This package was validated by the Metacheck team on articles in Psychological Science.

This module was developed by Lisa DeBruine, Daniel Lakens and Cristian Mesquida

Validation: In a sample of 128 papers with 246 instances of power statements, 203 were correctly detected (true positives), 22 were missed (false negatives) and 21 were incorrectly detected (false positives). Overall, among all instances flagged as power statements, 90.6% were correct (positive prediction value).

Results Modules

⚠️ Exact P-Values

We found 1 imprecise p value out of 3 detected p values.

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Reporting p values imprecisely (e.g., p < .05) reduces transparency, reproducibility, and re-use (e.g., in p value meta-analyses). Best practice is to report exact p-values with three decimal places (e.g., p = .032) unless p values are smaller than 0.001, in which case you can use p < .001.

The APA manual states: Report exact p values (e.g., p = .031) to two or three decimal places. However, report p values less than .001 as p < .001. However, 2 decimals is too imprecise for many use-cases (e.g., a p value meta-analysis), so report p values with three digits.

American Psychological Association (2020). Publication manual of the American Psychological Association, 7 edition. American Psychological Association.

List any p-values reported with insufficient precision (e.g., p < .05 or p = n.s.) or reported as exactly zero (e.g., p = .000).

This module uses regular expressions to identify p-values. It will flag any values reported as p > ? or p < numbers greater than .001. It will also flag p-values reported as exactly zero (e.g., p = .000, p = 0.00), which are mathematically impossible — p-values are never exactly zero and should instead be reported as p < .001.

We try to exclude figure and table notes like “* p < .05”, but may not succeed at excluding all false positives.

This module was developed by Lisa DeBruine

Validation: In a sample of 225 papers containing 405 instances of non-exact p-values, th module correctly detected 269 cases (true positives) and incorrectly identified 78 (false positives). It missed 136 instances of imprecisely reported p-values (false negatives) and correctly identified 4557 cases of precisely reported p-values (true negative). Additionally, 78% of positive detections were correct (positive predictive value).

🔍 Non-Significant P Value Check

We found 2 non-significant p values that should be checked for appropriate interpretation.

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Meta-scientific research has shown nonsignificant p values are commonly misinterpreted. It is incorrect to infer that there is ‘no effect’, ‘no difference’, or that groups are ‘the same’ after p > 0.05.

It is possible that there is a true non-zero effect, but that the study did not detect it. Make sure your inference acknowledges that it is possible that there is a non-zero effect. It is correct to include the effect is ‘not significantly’ different, although this just restates that p > 0.05.

Metacheck does not yet analyze automatically whether sentences which include non-significant p-values are correct, but we recommend manually checking the sentences below for possible misinterpreted non-significant p values.

For metascientific articles demonstrating the rate of misinterpretations of non-significant results is high, see:

Aczel B, Palfi B, Szollosi A, Kovacs M, Szaszi B, Szecsi P, Zrubka M, Gronau Q, van den Bergh D, Wagenmakers E (2018). “Quantifying Support for the Null Hypothesis in Psychology: An Empirical Investigation.” Advances in Methods and Practices in Psychological Science, 1(3), 357–366. doi:10.1177/2515245918773742.

Murphy S, Merz R, Reimann L, Fernández A (2025). “Nonsignificance misinterpreted as an effect’s absence in psychology: Prevalence and temporal analyses.” Royal Society Open Science, 12(3), 242167. doi:10.1098/rsos.242167.

For educational material on preventing the misinterpretation of p values, see Improving Your Statistical Inferences.

This module checks for imprecisely reported p values. If p > .05 is detected, it warns for misinterpretations.

The nonsignificant p-value check searches for regular expressions that match a predefined pattern. The module identifies all p-values in a manuscript and selects those that are not reported to be smaller than or equal to 0.05. It returns all sentences containing non-significant p-values.

In the future, the Metacheck team aims to incorporate a machine learning classifier to only return sentences likely to contain misinterpretations. If you want to help to improve the module, reach out to the Metacheck development team.

This module was developed by Daniel Lakens

Validation: In a sample of 194 papers with 1602 instances of non-significant p-values, this module correctly detected 1486 of them, and incorrectly identified 153. Additionally, 91% of detections were true instances (positive predictive value). That is, when this module flags non-significant p-values in a paper, it correctly identifies an issue 91% of the time.

⚠️ Marginal Significance

You described 2 effects with terms related to ‘marginally significant’.

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You described effects with terms related to ‘marginally significant’. If p values above 0.05 are interpreted as an effect, you inflate the alpha level, and increase the Type 1 error rate. If a p value is higher than the prespecified alpha level, it should be interpreted as a non-significant result.

For metascientific articles demonstrating the rate at which non-significant p-values are interpreted as marginally significant, see:

Olsson-Collentine, A., van Assen, M. MAL, Hartgerink &, J. CH (2019). “The Prevalence of Marginally Significant Results in Psychology Over Time.” Psychological Science, 30, 576–586. doi:10.1177/0956797619830326.

For the list of terms used to identifify marginally significant results, see this blog post by Matthew Hankins.

List all sentences that describe an effect as ‘marginally significant’.

The marginal module searches for regular expressions that match a predefined pattern. The list of terms is a subset of those listed in a blog post by Matthew Hankins. The module returns all sentences that match terms describing ‘marginally significant’ results.

Some of the terms identified might not be problematic in some contexts, and there are ways to describe ‘marginal significance’ that are not detected by the module.

This module was developed by Daniel Lakens

Validation: In a sample of 51 papers with 87 statements, this module correctly identified 38 statements (true positives) and incorrectly flagged 22 statements (false positives). It failed to detect 27 statements. Thus, among all statements flagged by the module, 63% were genuine cases (positive predictive value). However, the module missed 42% of all true statements (false negative rate).

🔍 Effect Sizes in t-tests and F-tests

We found 1 t-test and/or F-test where effect sizes are not reported.

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We recommend checking the sentences below, and add any missing effect sizes.

For metascientific articles demonstrating that effect sizes are often not reported:

  • Peng, C.-Y. J., Chen, L.-T., Chiang, H.-M., & Chiang, Y.-C. (2013). The Impact of APA and AERA Guidelines on Effect Size Reporting. Educational Psychology Review, 25(2), 157–209. doi:10.1007/s10648-013-9218-2.

For educational material on reporting effect sizes:

The Effect Size module checks for effect sizes in t-tests and F-tests.

The Effect Size check searches for regular expressions that match a predefined pattern. The module was validated on APA reported statistical tests, and might miss effect sizes that were reported in other reporting styles. It was validated by the Metacheck team on papers published in Psychological Science.

If you want to extend the package to detect effect sizes for additional tests, reach out to the Metacheck development team.

This module was developed by Daniel Lakens and Lisa DeBruine

Validation: In a sample of 161 papers with 1469 tests, this module correctly detected 1106 reported effect sizes (true positives) and correctly identified 295 cases where no effect size was present (true negatives). However, it missed 23 that were reported (false negatives), and incorrectly identified 45 effect sizes when none were reported (false positives). Among all instances detected by the module, 96% were true cases (positive predictive value).

⚠️ StatCheck

1 possible error in t-tests or F-tests

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We detected possible errors in test statistics. Note that as the accuracy of statcheck has only been validated for t-tests and F-tests. As Metacheck only uses validated modules, we only provide statcheck results for t tests and F-tests.

For metascientific research on the validity of statcheck, and it’s usefulness to prevent statistical reporting errors, see:

Nuijten M, van Assen M, Hartgerink C, Epskamp S, Wicherts J (2017). “The validity of the tool "statcheck" in discovering statistical reporting inconsistencies.” doi:10.31234/osf.io/tcxaja. Preprint.

Nuijten M, Wicherts J (2023). “The effectiveness of implementing statcheck in the peer review process to avoid statistical reporting errors.” doi:10.31234/osf.io/bxau9. Preprint.

Check consistency of p-values and test statistics

The Statcheck module runs Statcheck. Statcheck searches for regular expressions that match a predefined pattern, and identifies APA reported statistical tests. More information on the package can be found at https://github.com/cran/statcheck. The module only returns Statcheck results for t-tests and F-tests, as these are the only tests which have been validated, see https://osf.io/preprints/psyarxiv/tcxaj_v1/.

Statcheck was developed by Michèle Nuijten and Sascha Epskamp.

Statcheck considers p = 0.000 an error, as you should report p < 0.001. Furthermore, p < 0.03 is an error if the p-value was 0.031, and one should simply report exact p-values (p = 0.031). Statcheck might miss one-sided tests, and falsely assume the p-value is incorrect. For more information, see StatCheck.

This module was developed by Daniel Lakens and Lisa DeBruine

Validation: In a sample of 685 tests with 34 instances of inconsistent reporting, Statcheck correctly detected 34 of them, and incorrectly identified 26. Therefore 0% of true instances were missed, and 43% of detections were false positives. See https://osf.io/preprints/psyarxiv/tcxaj_v1/ for more details of the validation.

🔍 Repository Check

  • We found 14 files in 3 repositories.
  • We found 1 README file and 2 repositories without READMEs.
  • We found 1 archive file.
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Repositories

Files

README Files

README files are a way to document the contents and structure of a folder, helping users locate the information they need. You can use a README to document changes to a repository, and explain how files are named. Please consider adding a README to each repository or including ‘README’ in the name of your overview document.

Archive Files

The following files are archives: Archive.zip. We did not examine their content. Consider uploading these individually to improve discoverability and re-use.

This module retrieves information from repositories.

The Repository Check module lists files on the OSF, GitHub, ResearchBox, and Zenodo based on links in the manuscript.

If you want to extend the package to be able to download files from additional data repositories reach out to the Metacheck development team.

This module was developed by Daniel Lakens and Lisa DeBruine

🔍 Code Check

  • We found 4 R, 0 SAS, 0 SPSS, and 0 Stata code files.
  • All your code files had comments.
  • 4 files loaded in the code were missing in the repository.
  • Absolute file paths were found.
  • Libraries/imports were loaded in multiple places.
  • No parsing issues of R-type files were found.
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Below, we describe some best coding practices and give the results of automatic evaluation of these practices in the code files below. This check may miss things or produce false positives if your scripts are less typical.

Code Comments

Best programming practice is to add comments to code, to explain what the code does (to yourself in the future, or peers who want to re-use your code). All your code files had comments.

Missing Files

The scripts load files, but 4 scripts loaded 4 files that could not be automatically identified in the repository. Check if the following files are made available, so that others can reproduce your code, or that the files are missing:

Absolute Paths

Best programming practice is to use relative file paths (e.g., ‘./files’) instead of absolute file paths (e.g., ‘C://Lakens/project_dir/files’) as these folder names do not exist on other computers. The following absolute file paths were found in 4 code files. However, these may be false positives in code like paste0(dir, '/file.csv').

Libraries / Imports

Best programming practice is to load all required libraries/imports in one block near the top of the code. In 2 code files, libraries/imports were at multiple places (i.e., with more than 3 non-comment lines in between).

Parsable code

All R-type code files (.R, .Rmd, .qmd) could be read in. There were no parsing issues.

This module retrieves information from repositories checked by repo_check about code files (R, SAS, SPSS, Stata).

The Code Check module checks R, Rmd, Qmd, SAS, SPSS, and Stata files, using regular expressions to check the code. The regular expression search will detect the number of comments, the lines at which libraries/imports are loaded, attempts to detect absolute paths to files, and lists files that are loaded, and checks if these files are in the repository. The module will return suggestions to improve the code if there are no comments, if libraries/imports are loaded in lines further than 4 lines apart, if files that are loaded are not in the repository, and if absolute file paths are found.

The regular expressions can miss information in code files, or falsely detect parts of the code as a fixed file path. Libraries/imports might be loaded in one block, even if there are more than 3 intermittent lines. The package was validated internally on papers published in Psychological Science. There might be valid reasons why some loaded files can’t be shared, but the module can’t evaluate these reasons, and always gives a warning.

If you want to extend the package to perform additional checks on code files, or make the checks work on other types of code files, reach out to the Metacheck development team.

This module was developed by Daniel Lakens and Raphael Merz

Reference Modules

🔍 Reference Accuracy

We checked 5 references in CrossRef and found entries for 3.

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Double check any references listed in the tables below. This module has a high false positive rate.

Title mismatches often happen because of errors reading text from PDFs. Author mismatches often happen because of errors in parsing author lists. Year mismatches often happen because of differences between date of first publication and date of print publication.

This module checks references for mismatches with CrossRef.

This module uses the bib_match table from each paper (this can be added or refreshed using add_bib_match()) to detect possible problems in the reference section.

We check that the title from your reference section is the same as the retrieved title (ignoring differences in capitalisation) and that all author last names in your reference section are also in the retrieved author list (we do not check first names or order yet). This check is done for all references with crossref entries in the bib_match table.

Mismatches may be because of problems with our parsing of references from your PDF (we’re working on improving this), incorrect formatting in CrossRef, or minor differences in punctuation.

This module was developed by Daniel Lakens and Lisa DeBruine

ℹ️ Replication Check

We found 1 replication for 1 original you cited.

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We checked 5 references with DOIs. We found 1 replication for 1 original you cited.

Check if you are aware of the replication studies, and cite them where appropriate.

This module checks references and warns for citations of original studies for which replication or reproduction studies exist in the FLoRA database.

The Replication Check module compares the reference list against studies in the FLoRA (FORRT Library of Replication Attempts) database based on the DOI. If a study in the database is found, a reminder is provided that a replication or reproduction of the original study exists, and should be cited (currently, a warning is provided regardless of whether the replication/reproduction study is already cited).

The module requires that the reference has a DOI. If you run the ref_doi_check module in a pipeline before this, it will use the enhanced DOI list from that module, otherwise it will only run on references with existing DOIs.

It is possible the original study was cited for other reasons than the empirical claim tested, or that the replication/reproduction in the FLoRA database is for only one of the studies in the paper, and not the study the authors discuss.

The database can be manually updated with the FLoRA_update() function. For more information, see https://forrt.org/FLoRA/.

This module was developed by Daniel Lakens, Lisa DeBruine and Lukas Wallrich

ℹ️ RetractionWatch

You cited 1 article in the RetractionWatch database.

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We checked 5 references with DOIs. You cited 1 article in the RetractionWatch database.

Check if you are aware of the replication studies, and cite them where appropriate.

This module checks references and warns for citations in the RetractionWatch Database.

The RetractionWatch Check module compares the reference list against studies in the RetractionWatch database based on the DOI. If a study in the database is found, a reminder is provided that the study was retracted, has an expression of concern, or a correction.

The module requires that the reference has a DOI. If you run the ref_doi_check module in a pipeline before this, it will use the enhanced DOI list from that module, otherwise it will only run on references with existing DOIs.

It is possible the authors are already aware that a study was retracted, but the module can’t evaluate this.

The database can be manually updated with the rw_update function. For more information, see https://gitlab.com/crossref/retraction-watch-data.

This module was developed by Daniel Lakens and Lisa DeBruine

ℹ️ PubPeer Comments

You cited 1 reference with comments in PubPeer.

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We checked 5 references with DOIs. You cited 1 reference with comments in PubPeer.

Pubpeer is a platform for post-publication peer review. We have filtered out Pubpeer comments by ‘Statcheck’. You can check out the comments by visiting the URLs below:

This module checks references and warns for citations that have comments on pubpeer (excluding Statcheck comments).

The PubPeer module uses the PubPeer API to check for each reference that has a DOI whether there are comments on the post-publication peer review platform. If comments are found, a link to the comments is provided. Comments by ‘Statcheck’ on PubPeer are ignored, see https://retractionwatch.com/2016/09/02/heres-why-more-than-50000-psychology-studies-are-about-to-have-pubpeer-entries/.

The module requires that the reference has a DOI. If you run the doi_check module in a pipeline before this, it will use the enhanced DOI list from that module, otherwise it will only run on references with existing DOIs.

For more information, see PubPeer.

This module was developed by Daniel Lakens and Lisa DeBruine

ℹ️ Summarise References

Summary information provided for 5 references

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See the specific reports above for details.

Summarise information about each reference in a paper.

This module summarises previously-run reference section modules: ref_accuracy, ref_pubpeer, ref_replication, and ref_retraction.

This module was developed by Lisa DeBruine