PaperTrail v0.2.0 Data — 1,072 papers enriched + embedded

Data Release: April 2026

Full pipeline run on the Koo Lab Slack workspace.

Contents

• all_papers_merged.json.gz — 1,072 unique papers from 6 Slack channels
• all_papers_enriched_v2.json.gz — All papers with OpenAlex/S2 metadata (692 with full metadata)
• papers_dashboard_ready.json.gz — Enriched + TF-IDF embeddings + UMAP/t-SNE/PCA projections + k-means clusters (12 clusters)

Stats

• Channels: papers-dl (559), papers-genomics (283), papers-protein (83), papers-ai-agents (62), papers-phenomics (53), papers-health+digest (37)
• Enrichment: 692/1,072 papers with title/abstract (65%)
• Embeddings: TF-IDF (5000 features) + SVD-128, 44% variance explained
• Projections: PCA, t-SNE, UMAP
• Clusters: 12 k-means clusters with auto-generated labels

Usage

from papertrail.data import download_release, load_papers
download_release(tag="v0.2.0-data-2026-04-04")
papers = load_papers(which="enriched")

Data Snapshot: 1,072 papers from 7 Slack channels

PaperTrail Data Snapshot — April 4, 2026

Comprehensive scrape of all paper-sharing channels in the Koo Lab Slack workspace.

Contents

File	Description	Size
all_papers_merged.json.gz	1,072 unique papers, merged & deduplicated across all channels, with extracted IDs (arxiv, DOI, PMID)	39 KB
enrich_checkpoint.json.gz	Partial enrichment (599 papers processed, 100 with full metadata from OpenAlex + Semantic Scholar)	81 KB
channel_scrapes.tar.gz	Per-channel raw scrapes (6 JSON files)	51 KB

Channels scraped

Channel	Papers	Date range
papers-dl	629	Apr 2020 – Mar 2026
papers-genomics	316	Jun 2020 – Dec 2022
papers-health + paper_digest_moon	124	2023 – 2026
papers-protein	102	2020 – 2023
papers-ai-agents	86	2024 – 2026
papers-phenomics	77	2024 – 2026

Usage

# Download and decompress
gh release download v0.1.0-data-2026-04-04
gunzip all_papers_merged.json.gz

# Continue enrichment
pip install papertrail-lab
python -c "
from papertrail.enricher import PaperEnricher
import json

with open('all_papers_merged.json') as f:
    papers = json.load(f)

enricher = PaperEnricher(email='you@example.com', openalex_first=True)
enriched = enricher.enrich_papers(papers)

with open('papers_enriched.json', 'w') as f:
    json.dump(enriched, f, indent=2)
"

Notes

• Enrichment is partial (100/1,072) due to Semantic Scholar rate limiting. Run PaperEnricher with your own email to complete.
• All compressed with gzip (~85% compression ratio).