§ GREP — First run report
Software mentioned in 1,356 open-access papers, as extracted by the GREP volunteer crowd
What was done between 28 July and 12 August 2026, and every software mention the three models agreed on, with its confidence score.
Report generated 2026-09-04Server data read 2026-09-04 16:07 UTC
- Papers in corpus
- 2,550
- Papers complete
- 1,356
- Software mentions
- 5,665
- Distinct names
- 1,665
- Volunteer accounts
- 8
§ 01 — What we did
GREP (the Great Research Extraction Project) reads scientific papers and records every piece of software they mention: the name, a type (Application, Plugin, ProgrammingEnvironment or OperatingSystem), a purpose (Usage, Creation, Deposition or Mention) and a confidence score from 0 to 1.
Three separately trained models, v1, f13 and f14, read every paper. A mention enters the final record only when at least two of the three find the same span of text. The confidence score on a final mention is the highest score among the models that agreed on it.
The work ran on Lettuce, a volunteer computing system. Volunteers' own machines downloaded packets of about ten papers, ran one of the models offline, and sent back the mentions. Each packet went to up to three different volunteer accounts and was accepted once two of them returned matching output.
- 2026-07-28Six computations were created on the SciOS Compute server, one per model in a CPU and a GPU version, and a 30-paper test batch was posted to all six.
- 2026-07-29 → 07-30The test batch completed on CPU machines and on GPU machines. The merged output was identical on both.
- 2026-07-31The main batch was posted: 2,520 papers in 276 packets. 1,377 papers went to the three CPU computations and 1,143 to the three GPU computations.
- 2026-07-29 → 08-12Volunteers processed the CPU half. 8 volunteer accounts returned results over the run. The last validation was on 2026-08-12.
- 2026-08-13One GPU volunteer had processed every f13 and f14 GPU packet once. No second GPU account joined, so those results are not validated.
- hourly since 07-30A merge job combined the validated outputs of the three models into the final record.
§ 02 — Where the run stands
| Stage | Papers |
|---|---|
| In the corpus | 2,550 |
| Read by at least one model and validated | 1,406 |
| Complete: all three models validated, final record written | 1,356 |
| Validated by two models, waiting on the third | 50 |
| PDF could not be read by any model | 1 |
| On the GPU half: processed once, not yet validated | 1,143 |
§ 03 — What we found
5,665 software mentions in the 1,356 complete papers, naming 1,665 distinct software names. 4,240 mentions were found by all three models and 1,425 by two of the three. 576 of the complete papers contain no software mention.
Median confidence 0.786. Before the vote, the models individually found: v1 7,098 mentions in 1,406 papers, f13 5,958 in 1,366, f14 6,262 in 1,396.
By type: Application 4,711, ProgrammingEnvironment 619, Plugin 305, OperatingSystem 30. By purpose: Usage 4,662, Creation 572, Mention 393, Deposition 38.
| Field | Papers | Mentions | Distinct software | Papers with no mention |
|---|---|---|---|---|
| Biology | 270 | 2,364 | 836 | 61 |
| Engineering | 236 | 1,194 | 399 | 80 |
| Psychology | 210 | 846 | 225 | 67 |
| Economics | 200 | 461 | 160 | 90 |
| Environmental science | 210 | 367 | 134 | 108 |
| Humanities | 200 | 170 | 75 | 152 |
| Test batch (arts and humanities) | 30 | 263 | 53 | 18 |
| Confidence | Mentions |
|---|---|
| 0.9–1.0 | 1,850 |
| 0.8–0.9 | 855 |
| 0.7–0.8 | 723 |
| 0.6–0.7 | 691 |
| 0.5–0.6 | 697 |
| 0.4–0.5 | 578 |
| 0.3–0.4 | 223 |
| 0.0–0.3 | 48 |
Software named in the most papers · top 25 of 1,665 names
§ 04 — Software list
Every name the models agreed on
Grouped case-insensitively and otherwise as written in the paper. Click a column to sort. The confidence slider hides mentions below the chosen score and recomputes the table.
1,665 names · 5,665 mentions at or above 0.00 · showing 60
| Software | Papers ▾ | Mentions | Median confidence | Highest | Found by all three | Type | Purpose |
|---|---|---|---|---|---|---|---|
| SPSS | 130 | 256 | 0.87 | 1.00 | 90% | Application | Usage |
| R | 77 | 190 | 0.95 | 0.99 | 88% | ProgrammingEnvironment | Usage |
| Excel | 47 | 68 | 0.94 | 0.99 | 97% | Application | Usage |
| Python | 36 | 79 | 0.64 | 0.96 | 79% | ProgrammingEnvironment | Mention |
| MATLAB | 26 | 118 | 0.80 | 0.99 | 95% | ProgrammingEnvironment | Usage |
| Windows | 20 | 30 | 0.65 | 0.97 | 43% | OperatingSystem | Usage |
| GraphPad Prism | 19 | 20 | 0.96 | 0.98 | 85% | ProgrammingEnvironment | Usage |
| SmartPLS | 17 | 44 | 0.95 | 1.00 | 93% | Application | Usage |
| 16 | 24 | 0.65 | 0.95 | 46% | Application | Usage | |
| SPSS Statistics | 16 | 21 | 0.85 | 1.00 | 76% | Application | Usage |
| Stata | 16 | 20 | 0.75 | 0.99 | 85% | ProgrammingEnvironment | Usage |
| BLAST | 15 | 34 | 0.94 | 0.99 | 94% | Application | Usage |
| ChatGPT | 15 | 84 | 0.60 | 0.97 | 73% | Application | Creation |
| 14 | 28 | 0.69 | 0.97 | 75% | Application | Usage | |
| ggplot2 | 11 | 13 | 0.77 | 0.97 | 69% | Application | Usage |
| ImageJ | 11 | 16 | 0.99 | 0.99 | 94% | Application | Usage |
| PROCESS | 11 | 27 | 0.84 | 0.97 | 93% | Plugin | Usage |
| Statistica | 11 | 15 | 0.82 | 0.97 | 87% | Application | Usage |
| Zoom | 10 | 23 | 0.88 | 0.99 | 78% | Application | Usage |
| AMOS | 9 | 17 | 0.96 | 0.99 | 100% | Application | Usage |
| Qualtrics | 9 | 11 | 0.96 | 0.98 | 91% | Application | Usage |
| Bioconductor | 8 | 10 | 0.68 | 0.91 | 60% | ProgrammingEnvironment | Usage |
| lme4 | 8 | 10 | 0.94 | 0.97 | 80% | Plugin | Usage |
| RStudio | 8 | 14 | 0.75 | 0.85 | 79% | Application | Usage |
| script | 8 | 13 | 0.83 | 0.95 | 85% | Plugin | Usage |
| DESeq2 | 7 | 13 | 0.84 | 0.99 | 69% | Application | Usage |
| Ensembl | 7 | 12 | 0.79 | 0.98 | 67% | Application | Usage |
| FastQC | 7 | 12 | 0.96 | 0.99 | 100% | Application | Usage |
| Google Scholar | 7 | 7 | 0.67 | 0.91 | 0% | Application | Usage |
| Google Forms | 7 | 7 | 0.93 | 0.96 | 100% | Application | Usage |
| Mendeley | 7 | 45 | 0.76 | 0.98 | 91% | Application | Mention |
| pandas | 7 | 8 | 0.77 | 0.95 | 88% | Plugin | Usage |
| Android | 6 | 22 | 0.85 | 0.96 | 73% | Application | Usage |
| BioRender | 6 | 9 | 0.90 | 0.99 | 89% | Application | Usage |
| Eviews | 6 | 14 | 0.97 | 0.99 | 100% | Application | Usage |
| 6 | 14 | 0.60 | 0.93 | 71% | Application | Usage | |
| G*Power | 6 | 10 | 0.98 | 1.00 | 90% | Application | Usage |
| Keras | 6 | 9 | 0.64 | 0.90 | 100% | Application | Usage |
| NVivo | 6 | 18 | 0.90 | 0.99 | 56% | Application | Usage |
| SAS | 6 | 12 | 0.61 | 0.97 | 58% | Application | Usage |
| scikit-learn | 6 | 10 | 0.67 | 0.93 | 50% | Application | Usage |
| Web of Science | 6 | 7 | 0.59 | 0.97 | 29% | Application | Usage |
| Word | 6 | 12 | 0.87 | 0.99 | 92% | Application | Usage |
| XGBoost | 6 | 27 | 0.59 | 0.95 | 74% | Application | Usage |
| YouTube | 6 | 11 | 0.62 | 0.93 | 55% | Application | Usage |
| ClustalW | 5 | 6 | 0.86 | 0.97 | 83% | Application | Usage |
| Cutadapt | 5 | 6 | 0.75 | 0.98 | 100% | Application | Usage |
| featureCounts | 5 | 7 | 0.68 | 0.97 | 100% | Application | Usage |
| JASP | 5 | 8 | 0.98 | 0.99 | 100% | Application | Usage |
| Mplus | 5 | 13 | 0.97 | 0.99 | 100% | Application | Usage |
| numpy | 5 | 5 | 0.83 | 0.91 | 60% | Plugin | Usage |
| Smart PLS | 5 | 5 | 0.99 | 0.99 | 100% | Application | Usage |
| Telegram | 5 | 33 | 0.50 | 0.90 | 70% | Application | Usage |
| vegan | 5 | 12 | 0.77 | 0.96 | 83% | Application | Usage |
| Buku | 4 | 4 | 0.28 | 0.28 | 0% | Application | Mention |
| Dimana | 4 | 6 | 0.48 | 0.49 | 83% | Application | Mention |
| edgeR | 4 | 13 | 0.71 | 0.96 | 77% | Application | Usage |
| emmeans | 4 | 8 | 0.76 | 0.96 | 88% | Plugin | Usage |
| Ethereum | 4 | 15 | 0.58 | 0.89 | 40% | Application | Usage |
| figshare | 4 | 4 | 0.76 | 0.90 | 75% | Application | Usage |
§ 05 — By paper
The 1,356 papers with a complete record
Each chip is one mention with its confidence. Three dots mean all three models found it; two dots mean two of three.
Loading the per-paper data…
●●● all three models●●○ two of threenumber = confidence
§ 06 — Technical appendix
The six computations (leaves) on infra.scios.tech
| Leaf | Hardware | Packets | Validated | Queued | Failed | Results | Accounts | First result | Last validation |
|---|---|---|---|---|---|---|---|---|---|
| extract2-student-crowd-v1 | CPU | 142 | 142 | 0 | 0 | 285 | 6 | 2026-07-29 | 2026-08-12 |
| extract2-student-crowd-v1-gpu | GPU | 140 | 3 | 137 | 0 | 30 | 3 | 2026-07-30 | 2026-07-30 |
| extract2-student-crowd-f13 | CPU | 142 | 138 | 1 | 3 | 278 | 5 | 2026-07-29 | 2026-08-12 |
| extract2-student-crowd-f13-gpu | GPU | 140 | 3 | 137 | 0 | 143 | 3 | 2026-07-30 | 2026-07-30 |
| extract2-student-crowd-f14 | CPU | 142 | 141 | 1 | 0 | 284 | 6 | 2026-07-29 | 2026-08-12 |
| extract2-student-crowd-f14-gpu | GPU | 140 | 3 | 137 | 0 | 143 | 3 | 2026-07-30 | 2026-07-30 |
- Totals
- 846 packets · 1,163 results · 8 volunteer accounts
- Images
- ghcr.io/jring-o/extract2-student:2.1-{v1,f13,f14} and -gpu
- Validation
- 3 target copies, quorum 2, numeric tolerance 0.01, timing fields ignored, up to 3 retries
- Packets
- up to 10 papers and under 18 MB each, fetched by volunteers from a public bucket by SHA-256 name
- Models
- DeBERTa-v3-large students distilled from the Extract2 teacher ensemble; same weights on CPU and GPU
Agreement between the three models
- Papers compared
- 1,356
- Spans found by all three
- 4,240
- By two models
- 1,425
- By one model only (dropped by the vote)
- 3,489
- Pairwise span overlap (Jaccard)
- v1-f13 0.5343 · v1-f14 0.531 · f13-f14 0.7292
- Same type on shared spans
- 96%
- Same purpose on shared spans
- 85%
Not in the final record
- 50 papers are validated by two models and wait on the third: 40 on f13 (three failed packets and one queued packet), 10 on f14 (one queued packet).
- 1 paper (
259676743) could not be read by any model: invalid PDF. - 1,143 papers on the GPU half have one unvalidated result each from f13 and f14 (and 210 from v1). They are excluded here.
- 3 results were rejected in validation, all on one paper (
251609378), where the paragraph count differed between machines; the paper's mentions were unaffected.
Download the tables
- software_by_package.csv — every name: papers, mentions, median and highest confidence, share found by all three models, type, purpose
- software_by_paper.csv — every mention with its paper, score and vote count
- papers.csv — all 2,550 papers in the corpus with title, field and status