Faster Qualitative Analysis of Preprints

Evidano is an AI-powered qualitative data analysis platform that, when combined with preprint-fulltext, lets teams build section-aware, reproducible preprint corpora for faster thematic and cross-segment analysis. preprint-fulltext (published to PyPI on 2026-07-18) converts DOIs or IDs into section-labeled chunks ready for embeddings, and Evidano ingests JSONL/Parquet for deterministic coding and audit trails.

• preprint-fulltext was published to PyPI on 2026-07-18 by Min Dai (Broad Institute).
• preprint-fulltext uses a default chunk size of 512 tokens and a 64-token overlap, and it emits deterministic chunk IDs plus license metadata.
• An OpenAlex API key has been required for discovery/search since 2026-02-13.
• Evidano accepts preprint-fulltext JSONL/Parquet and preserves chunk-level provenance for reproducible thematic, frequency, and cross-segment analyses.

preprint-fulltext was added to PyPI on 2026-07-18 by Min Dai (Broad Institute), converting DOIs or IDs into structured sections and exporting JSON/Markdown/JSONL/Parquet suitable for embeddings and retrieval.

• Source: PyPI
• Author / contact: Min Dai, dai@broadinstitute.org
• Why it matters: gives direct access to full methods and results for grounded AI reasoning and reproducible qualitative coding.

preprint-fulltext routes requests to source-specific parsers and outputs deterministic, section-aware chunks with provenance and license metadata. You can fetch one paper's full text as Markdown/JSON, search/discover lists of IDs, and ingest bulk corpora into chunked JSONL or Parquet, with exports that respect TDM and redistribution rules.

• Outputs include section_kind (methods/results/etc.), token_count, chunk_id, source and license.
• Bulk ingest is resumable and idempotent (no duplicate chunks).
• MCP server exposes search/get methods for coding agents.

Structured, chunked full text from preprint-fulltext removes manual scraping and standardizes sections across sources, improving analysis of protocols, tensions between methods and claims, and quote-level evidence. Having section-aware chunks makes Methods and Results first-class inputs to thematic coding, enabling reproducible corpora useful in audits.

• Move from abstract-only sampling to body-aware coding: methods and results often contain decisive detail.
• Create reproducible corpora (JSONL/Parquet) that carry license and version metadata, helpful in audits and reproducibility checks.
• Agent-friendly MCP endpoints let lab automation and code-based pipelines fetch text programmatically for iterative qualitative review.

This checklist shows six steps to move from a DOI list to actionable insights using preprint-fulltext and Evidano.

• 1) Discover: run preprint-fulltext discover/search for your topic or date window and export hits.jsonl.
• 2) Ingest: ingest selected preprints with preprint-fulltext ingest into JSONL/Parquet (Europe PMC for CC subset, S3 for complete corpus).
• 3) Import: upload the JSONL/Parquet into Evidano and map chunk fields to document and segment attributes.
• 4) Code: run AI-assisted codebook generation, review, and lock a codebook for Methods and Results separately.
• 5) Analyze: run thematic, frequency, and cross-segment analyses; produce co-occurrence networks and quote exports with provenance.
• 6) Share: export visuals and quote packs for stakeholders; use Evidano's secure sharing and audit logs.

If your team needs grounded qualitative analysis that cites paper bodies, combine preprint-fulltext to build section-aware corpora and Evidano to run thematic, frequency, and cross-segment analyses at scale.

• Build a pilot: ingest a curated hits.jsonl from preprint-fulltext and import it into Evidano to run a Methods-vs-Results theme comparison in one sprint.
• Try a two-week pilot or get started now: Try Evidano for free.
• References: preprint-fulltext package and docs at PyPI.