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Open source
Every line is on GitHub under an MIT license. You can read exactly what each tool does, adapt it to your work, and cite it.
Tools
Open-source research tools
Small, free tools I build for the day-to-day work of research — finding the right peer reviewers, and checking that a statistical result is sound. Each one runs on your own machine.
Good research depends on small, unglamorous tasks done well: matching a manuscript to reviewers who can actually judge it, or confirming that a result is real before it reaches a journal. I build tools for tasks like these and release them openly, so other researchers can use them, see exactly how they work, and trust what comes out.
For HRD & adjacent-field editors
peer-reviewer-finder
Find well-matched, conflict-free, and diverse peer reviewers for a manuscript in human resource development and adjacent fields.
Python · JavaScript · OpenAlex · MIT
Given a manuscript's title, abstract, and a few keywords, along with the submitting authors' institutions, it searches a registry of 97 journals spanning human resource development and six adjacent disciplines — adult and continuing education, management and organizational behavior, industrial-organizational psychology, higher education, career and workforce development, and international and comparative education — on OpenAlex for scholars whose published work genuinely matches. It screens out conflicts of interest, then returns a relevance-ranked panel with institutional and national diversity built in. The editor still decides; the tool supplies the evidence and a defensible shortlist.
- Searches 97 journals across HRD and six adjacent disciplines via OpenAlex
- Screens conflicts of interest by shared institution and co-authorship
- Proposes a diverse, relevance-ranked shortlist with alternates
- Runs locally or in your browser — sends no manuscript text to any AI
For quantitative researchers
cross-tool-statistical-verification
Confirm a statistical result holds up in a second program, not only the one you ran it in.
Python · R · MIT
You write your analysis twice, once in Python and once in R, and the tool reconciles them. It runs a six-phase protocol that inspects the data, checks every reported number for internal consistency, re-runs the analysis to confirm it reproduces, and compares the two implementations statistic by statistic. It then writes the evidence: a verification log, a side-by-side comparison table, and a methodology paragraph you can adapt for a manuscript.
- Compares Python and R statistic by statistic, within tolerance
- Checks internal consistency and exact reproducibility
- Produces a verification log and a comparison table as evidence
- Drafts a methodology paragraph for your paper
crossverify — OLS regression: mpg ~ wt + hp (mtcars) Phase 3 consistency 8 pass Phase 4 reproducibility 9 pass Phase 5 triangulation 9 pass Cross-tool: 9/9 statistics matched within tolerance. Result: PASS
How they work
Shared principles
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Runs locally
Your data stays on your own computer. The tools make no calls to any AI service and keep your files out of version control by default.
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Built for trust
Each tool produces a record you can hand to a reviewer, an editor, or a co-author: a log, a table, a shortlist with its reasoning attached.
Get the tools
Free to use, and free to inspect
Both tools are free and open source under the MIT license. Read exactly how they work, adapt them to your own research, or open an issue if something could be better. Setup instructions live in each repository.
Citation
How to cite
If a tool supports your work, a citation is appreciated. APA 7th edition:
Crocco, O. S. (2026). peer-reviewer-finder (Version 0.2.0) [Computer software]. GitHub. https://github.com/olivercrocco/peer-reviewer-finder
Crocco, O. S. (2026). cross-tool-statistical-verification [Computer software]. GitHub. https://github.com/olivercrocco/cross-tool-statistical-verification