--- pretty_name: TopVenues Complete Corpus license: other language: - en task_categories: - text-retrieval tags: - cybersecurity - security - bibliography - literature-review - dblp size_categories: - 100K, the home of TopVenues since v1.11.0. This export was made from `topvenues-tool`, which is archived with its history. - **Project page:** - **Profile:** `complete-40` - **Snapshot SHA-256 (gzip):** `6f837953c3cc2d92da74879ad61ef575808f875a2f27d87bab113c698e864dfc` ## Not a paper denominator This corpus is a tool release, not the denominator of any paper. Two separate frozen snapshots back the published work and are **not** replaced by this one: | Snapshot | Records | Cited by | Reproduced by | |---|---|---|---| | `sbseg2026-camera-ready` | 9,925 | the main-track paper | [`topVenues`](https://github.com/sidneibarbieri/topVenues): `git checkout sbseg2026-camera-ready && bash reproduce.sh` | | `security-20` | 20,305 | the tools-track paper | [`topVenues`](https://github.com/sidneibarbieri/topVenues) or the archived [`topvenues-tool`](https://github.com/sidneibarbieri/topvenues-tool) printed in the paper: `bash reproduce.sh --profile security-20` | The current curated release, `security-20-v4` (14,859 records), is the dataset [`sidneibarbieri/topvenues`](https://huggingface.co/datasets/sidneibarbieri/topvenues); it is not the denominator of either paper. Any measurement must name the snapshot that produced its denominator. Numbers from one corpus do not transfer to another merely because the software is shared. ## One record per work Records are deduplicated by DBLP key, field-wise: where the same key was ingested more than once, each field keeps its most complete value, so the surviving record is at least as complete as any copy that fed it. That recovered 2,743 BibTeX entries, 1,483 abstracts, 415 author lists and 13 titles truncated at superscripts. Result: **118,817 rows, 118,817 distinct keys**, so a count of rows is a count of distinct publications. ## Abstract coverage is uneven, by construction 14,289 of 118,817 records carry an abstract (12.0%). That figure is low because the corpus deliberately includes large AI and systems venues whose records were ingested for bibliographic completeness without abstract enrichment. Coverage on the security venues is high. 21 of 40 venues carry abstracts at all: | Venue | Records | Abstracts | Coverage | |---|---:|---:|---:| | USENIX Security | 2,103 | 2,056 | 97.8% | | ACM CCS | 1,972 | 1,971 | 99.9% | | TrustCom | 1,915 | 1,912 | 99.8% | | ACM Computing Surveys | 1,813 | 1,812 | 99.9% | | IEEE S&P | 1,166 | 1,165 | 99.9% | | NDSS | 1,059 | 1,058 | 99.9% | | ACM ASIA CCS | 837 | 827 | 98.8% | | IEEE Communications Surveys & Tutorials | 833 | 804 | 96.5% | | ACSAC | 509 | 509 | 100.0% | | IEEE CNS | 381 | 381 | 100.0% | | IEEE EURO S&P | 339 | 339 | 100.0% | | ACM CODASPY | 316 | 281 | 88.9% | | ACM WiSec | 314 | 270 | 86.0% | | HotNets | 245 | 245 | 100.0% | | RAID | 290 | 222 | 76.6% | | ACM SACMAT | 176 | 176 | 100.0% | | IEEE SaTML | 127 | 127 | 100.0% | | ACM AISec | 77 | 73 | 94.8% | | ESORICS | 598 | 39 | 6.5% | | Foundations and Trends in Privacy and Security | 20 | 20 | 100.0% | | USENIX WOOT | 18 | 2 | 11.1% | | AAAI | 20,085 | 0 | 0.0% | | NeurIPS | 16,527 | 0 | 0.0% | | EMNLP | 12,817 | 0 | 0.0% | | ICML | 11,971 | 0 | 0.0% | | ACL | 11,294 | 0 | 0.0% | | ICLR | 10,680 | 0 | 0.0% | | IJCAI | 6,501 | 0 | 0.0% | | ACM KDD | 4,110 | 0 | 0.0% | | NAACL | 4,107 | 0 | 0.0% | | ACM MobiCom | 993 | 0 | 0.0% | | ACM SenSys | 808 | 0 | 0.0% | | ACM MobiSys | 655 | 0 | 0.0% | | USENIX NSDI | 542 | 0 | 0.0% | | ACM EuroSys | 518 | 0 | 0.0% | | USENIX ATC | 518 | 0 | 0.0% | | ACM IMC | 466 | 0 | 0.0% | | ACM SIGCOMM | 452 | 0 | 0.0% | | ACM SIGMETRICS | 376 | 0 | 0.0% | | ACM HotMobile | 289 | 0 | 0.0% | ## Usage ```python from datasets import load_dataset corpus = load_dataset("sidneibarbieri/topvenues-complete", split="train") security = corpus.filter(lambda r: r["abstract"] and "USENIX" in (r["event"] or "")) ``` ## Fields `paper_id`, `key` (DBLP), `title`, `authors`, `event`, `venue`, `area`, `year`, `paper_type`, `pages`, `url`, `ee` (DOI), `abstract`, `bibtex`. ## Citation If you use this dataset or the tool, cite the tools-track paper and name the dataset and profile you used, for example "TopVenues, `sidneibarbieri/topvenues-complete`, profile `complete-40`", so a reader can reopen the same snapshot. If you use or build on the corpus method or its measurements, also cite the main-track paper. The [project page](https://sidneibarbieri.github.io/topVenues/#cite) carries the same rule and entries. ```bibtex @inproceedings{barbieri2026topvenuestool, author = {Sidnei Barbieri and {\'A}gney Lopes Roth Ferraz and Louren{\c{c}}o Alves {Pereira J{\'u}nior}}, title = {{TopVenues}: An Executable Corpus and Research Tool for Cybersecurity Literature Reviews}, booktitle = {Anais Estendidos do XXVI Simp{\'o}sio Brasileiro de Ciberseguran{\c{c}}a (SBSeg 2026)}, pages = {234--241}, year = {2026}, publisher = {Sociedade Brasileira de Computa{\c{c}}{\~a}o}, doi = {10.5753/sbseg_estendido.2026.33733}, url = {https://sol.sbc.org.br/index.php/sbseg_estendido/article/view/44470} } @inproceedings{barbieri2026topvenues, author = {Sidnei Barbieri and {\'A}gney Lopes Roth Ferraz and Louren{\c{c}}o Alves {Pereira J{\'u}nior}}, title = {{TopVenues}: A Reproducible Corpus and Tooling Substrate for Cybersecurity Literature Reviews}, booktitle = {Anais do XXVI Simp{\'o}sio Brasileiro de Ciberseguran{\c{c}}a (SBSeg 2026)}, pages = {1150--1165}, year = {2026}, publisher = {Sociedade Brasileira de Computa{\c{c}}{\~a}o}, doi = {10.5753/sbseg.2026.29056}, url = {https://sol.sbc.org.br/index.php/sbseg/article/view/44350} } ``` ## License Metadata originates from DBLP (CC0) and open scholarly APIs. Abstracts remain under their publishers' terms and are included for research use.