Open-source research tools

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

Check whether a statistical result reproduces in a second program, or is an artifact of 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. Agreement across the two shows a result is independent of one library's defaults — strong evidence it is reproducible, though not on its own proof the analysis is correct.

• ▪ Compares Python and R statistic by statistic, within tolerance
• ▪ Checks internal consistency and re-run 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   11 pass
  Phase 5  triangulation     11 pass
  Cross-tool: 11/11 statistics matched within tolerance.

Result: PASS