Update README.md

README.md

@@ -35,27 +35,53 @@ configs:
 
 ## Topic breakdown
 
-The corpus is weighted heavily toward GitHub repository documentation (curated from the 10,000 highest-starred security-related repos). The smaller structured sources — vulnerability databases, kernel commits, detection rules — each contribute fewer documents but typically denser per-document signal.
-
-| % | Documents | Topic |
-|---:|---:|---|
-| 85.64% | 3,469,231 | GitHub Security Repos (curated 10K repos, markdown extraction) |
-| 11.06% | 448,176 | Vulnerability Databases (NVD, GHSA, vendor advisories, ExploitDB) |
-| 1.27% | 51,256 | Stack Exchange Q&A (security, reverse engineering, cryptography) |
-| 0.96% | 38,771 | Linux Kernel Security Commits |
-| 0.68% | 27,574 | Detection Rules (YARA, Sigma) |
-| 0.25% | 9,995 | Offensive Frameworks & Techniques (Metasploit, ATT&CK, etc.) |
-| 0.04% | 1,653 | OWASP Guides |
-| 0.04% | 1,500 | Vulnerability Taxonomy (CWE, CAPEC) |
-| 0.02% | 936 | Penetration Testing Methodology |
-| 0.02% | 779 | Network & Protocol Security |
-| 0.01% | 388 | AI/ML Security |
-| 0.01% | 331 | Forensics & Reverse Engineering |
-| 0.01% | 243 | Mobile Security |
-| <0.01% | 149 | Cloud & Container Security |
-| <0.01% | 128 | Cryptography |
-| <0.01% | 16 | Academic Papers (partial — see [Limitations](#known-limitations)) |
-| <0.01% | 13 | Other Code Snippets (GitHub Gists) |
+Documents are listed by primary topic. Note that the largest source — `github_deep` (3.47M documents, 86% of the corpus) — consists of markdown documentation from 10,000 curated security-focused GitHub repositories spanning all topics below; it is shown as a single broad-coverage bucket rather than allocated across topics.
+
+| Rank | % | Documents | Topic |
+|---:|---:|---:|---|
+| 1 | 85.64% | 3,469,231 | **Broad security documentation** — curated GitHub repo content (spans all topics below) |
+| 2 | 7.91% | 320,557 | **Vulnerability disclosure (CVE tracking)** — NVD full CVE database |
+| 3 | 1.27% | 51,256 | **Practitioner Q&A** — Stack Exchange (security, RE, cryptography) |
+| 4 | 1.16% | 47,021 | **Exploit code & writeups** — ExploitDB |
+| 5 | 1.06% | 43,063 | **Linux distribution security advisories** — Red Hat Security |
+| 6 | 0.96% | 38,771 | **Linux kernel security commits** — security-relevant kernel git history |
+| 7 | 0.53% | 21,458 | **Windows / Microsoft security advisories** — MSRC Patch Tuesday entries |
+| 8 | 0.50% | 20,226 | **Malware detection rules** — YARA |
+| 9 | 0.39% | 15,757 | **Package ecosystem vulnerabilities** — GHSA (7 ecosystems) |
+| 10 | 0.18% | 7,348 | **SIEM / log-based detection rules** — Sigma |
+| 11 | 0.11% | 4,569 | **Offensive frameworks** — Metasploit modules |
+| 12 | 0.06% | 2,354 | **Threat intelligence & attack modeling** — MITRE ATT&CK techniques |
+| 13 | 0.05% | 2,138 | **Detection validation test cases** — Atomic Red Team |
+| 14 | 0.04% | 1,653 | **Web/API/Mobile security testing guides** — OWASP MASTG, API Security, WSTG |
+| 15 | 0.04% | 1,500 | **Vulnerability & attack pattern taxonomy** — CWE + CAPEC |
+| 16 | 0.02% | 936 | **Pentest methodology & reports** — PTES, pentest reports, secure coding, IR playbooks |
+| 17 | 0.02% | 779 | **Network & protocol security** — Nmap, Wireshark, PacketLife, wireless |
+| 18 | 0.01% | 388 | **AI/ML security** — ML-focused security guides and repositories |
+| 19 | 0.01% | 372 | **Heap exploitation** — How2Heap |
+| 20 | 0.01% | 331 | **Forensics & incident response** — DFIR tools, Windows artifacts, Linux forensics |
+| 21 | 0.01% | 320 | **Ubuntu security advisories** (partial — see Limitations) |
+| 22 | 0.01% | 245 | **Kernel exploitation training** — Nightmare CTF series |
+| 23 | 0.01% | 243 | **Mobile application security** — MASTG, hacktricks_mobile, mobile repos |
+| 24 | 0.01% | 237 | **Living-off-the-land binaries** — LOLBAS |
+| 25 | <0.01% | 149 | **Cloud & container security** — Azure, GCP, Kubernetes, CloudGoat, FLAWS |
+| 26 | <0.01% | 128 | **Cryptography** — Cryptopals, post-quantum, CryptoHack, Dan Boneh course |
+| 27 | <0.01% | 57 | **Reverse engineering training** — Ghidra course |
+| 28 | <0.01% | 16 | **Academic security papers** — arXiv (partial — see Limitations) |
+| 29 | <0.01% | 13 | **Miscellaneous code snippets** — GitHub Gists |
+
+### Topic coverage at a glance (excluding the broad GitHub bucket)
+
+Among the ~582K **structured** documents — the 14% that isn't broad GitHub markdown — the corpus emphasizes:
+
+- **Vulnerability disclosure & tracking** (NVD + GHSA + vendor advisories): ~401K docs, ~69% of structured content
+- **Practitioner Q&A** (Stack Exchange): 51K docs, ~9%
+- **Exploit & offensive technique documentation** (ExploitDB + Metasploit + How2Heap + Nightmare + LOLBAS + Atomic Red Team): ~54K docs, ~9%
+- **Linux kernel security**: 39K docs, ~7%
+- **Detection engineering** (YARA + Sigma): 28K docs, ~5%
+- **Threat modeling & taxonomy** (ATT&CK + CWE + CAPEC): ~3.9K docs
+- **Everything else** (cloud, mobile, crypto, forensics, network, AI/ML, web): ~3K docs combined
+
+This shape — heavy on vulnerability disclosure plus broad practitioner content, lighter on specific defensive subfields — reflects what's openly available at scale in cybersecurity. Sparser topics (cryptography, mobile security, cloud security) are documented but represent priority areas for v1.x expansion.
 
 ## What's in it
 
@@ -63,19 +89,18 @@ The corpus is weighted heavily toward GitHub repository documentation (curated f
 - **NVD (CVE database):** 320,557 entries — full NIST National Vulnerability Database from 2002 to present, including descriptions, CVSS scores, CWE mappings, and references
 - **ExploitDB:** 47,021 exploits with descriptions and metadata
 - **Red Hat Security:** 43,063 advisories with CVE mappings and affected packages
-- **MSRC (Microsoft Security):** 21,458 monthly advisory entries
-- **GHSA (GitHub Security Advisories):** 15,757 advisories across 7 ecosystems (PIP, Maven, RubyGems, Go, Rust, Composer, NuGet)
+- **MSRC (Microsoft Security Response Center):** 21,458 monthly advisory entries
+- **GHSA (GitHub Security Advisories):** 15,757 advisories across 7 ecosystems (pip, Maven, RubyGems, Go, Rust, Composer, NuGet)
 - **Ubuntu Security Notices:** 320 (partial — see [Limitations](#known-limitations))
 
-### Threat intelligence & taxonomy (12K documents)
+### Threat intelligence & taxonomy (~6K documents)
 - **MITRE ATT&CK:** 2,354 techniques, tactics, and procedures
 - **Atomic Red Team:** 2,138 test definitions
 - **CWE:** 944 weakness classifications
 - **CAPEC:** 556 attack patterns
 - **LOLBAS:** 237 living-off-the-land binaries
-- **MITRE D3FEND:** included in mitre_attack source
 
-### Detection & rule corpora (28K documents)
+### Detection & rule corpora (~32K documents)
 - **YARA rules:** 20,226 from multiple public repos
 - **Sigma rules:** 7,348 SIEM detection rules
 - **Metasploit Framework:** 4,569 modules with documentation
@@ -90,7 +115,7 @@ The corpus is weighted heavily toward GitHub repository documentation (curated f
 - **github_deep:** Markdown documentation extracted from the 10,000 highest-starred security-related GitHub repositories matching 108 security-focused search queries. Includes READMEs, in-repo documentation, methodology notes, technique writeups, and tool documentation. 96% unique content; median document length ~1,500 characters.
 
 ### Specialty sources
-OWASP MASTG, OWASP API Security, OWASP WSTG, How2Heap, Nightmare, Nmap docs, Wireshark docs, PacketLife, OWASP Mobile (HackTricks), Azure Security, GCP Security, Kubernetes Container Security, post-quantum cryptography, Cryptopals, CryptoHack, Forensic Tools docs, Windows Artifact Guide, Ghidra Course, IR Playbooks, SIEM Implementation, Secure Coding guides, Mobile Security Repos, CloudGoat, PTES, Pentest Reports, Dan Boneh's crypto course, FLAWS.cloud, AI/ML Security guides.
+OWASP MASTG, OWASP API Security, OWASP WSTG, How2Heap, Nightmare, Nmap docs, Wireshark docs, PacketLife, OWASP Mobile (HackTricks), Azure Security, GCP Security, Kubernetes Container Security, post-quantum cryptography, Cryptopals, CryptoHack, Forensic Tools docs, Windows Artifact Guide, Ghidra Course, IR Playbooks, SIEM Implementation, Secure Coding guides, Mobile Security Repos, CloudGoat, PTES, Pentest Reports, Dan Boneh's Cryptography course, FLAWS.cloud, AI/ML Security guides.
 
 A complete machine-readable source breakdown is included as `sources_manifest.json` in this dataset.
 
@@ -104,7 +129,7 @@ A complete machine-readable source breakdown is included as `sources_manifest.js
 | `text` | string | The primary content of the document |
 | `url` | string | The original URL or canonical reference |
 
-Source-specific fields commonly include `id`, `cve_id`, `severity`, `cvss_score`, `published`, `repo`, `file`, `subject`, `date`, `technique_id`, `attack_ids`, etc. See `sources_manifest.json` and the [scraper code](https://github.com/yourdeardaniel/secdata-scrapers) for per-source schemas.
+Source-specific fields commonly include `id`, `cve_id`, `severity`, `cvss_score`, `published`, `repo`, `file`, `subject`, `date`, `technique_id`, `attack_ids`, and others. See `sources_manifest.json` and the [scraper code](https://github.com/yourdeardaniel/secdata-scrapers) for per-source schemas.
 
 **The HuggingFace dataset viewer is disabled** because the multi-schema structure cannot be cleanly cast to a single Arrow schema. Load the data directly:
 
@@ -112,7 +137,7 @@ Source-specific fields commonly include `id`, `cve_id`, `severity`, `cvss_score`
 import json
 
 docs = []
-for chunk_idx in range(8):  # adjust to actual chunk count
+for chunk_idx in range(8):  # adjust to actual chunk count in this dataset
     with open(f'raw_docs_chunk_{chunk_idx:03d}.jsonl') as f:
         for line in f:
             docs.append(json.loads(line))
@@ -128,8 +153,7 @@ For streaming use (recommended for the full dataset):
 from huggingface_hub import hf_hub_download
 import json
 
-# Download one chunk at a time
-for i in range(8):
+for i in range(8):  # adjust to actual chunk count
     path = hf_hub_download(
         repo_id="deardaniel/secdata-raw",
         filename=f"raw_docs_chunk_{i:03d}.jsonl",
@@ -186,7 +210,7 @@ When using this dataset, please cite:
 ## How to cite
 
 ```bibtex
-@dataset{durante_secdata_raw_2026,
+@dataset{yourdeardaniel_secdata_raw_2026,
   author       = {yourdeardaniel},
   title        = {secdata-raw: A multi-source raw corpus for cybersecurity language models},
   year         = {2026},
@@ -243,7 +267,7 @@ The exact scraper version that produced this dataset is tagged as `v1.0.0` on Gi
 ## Contact & contributions
 
 For technical questions, bug reports, or contributions:
-- Open an issue: [secdata-scrapers/issues](https://github.com/yourdeardaniel/secdata-scrapers/issues) (for scraper/collection issues) or [secdata-pipeline/issues](https://github.com/yourdeardaniel/secdata-pipeline/issues) (for pipeline/conversion issues)
+- Open an issue: [secdata-scrapers/issues](https://github.com/yourdeardaniel/secdata-scrapers/issues) for scraper/collection issues, or [secdata-pipeline/issues](https://github.com/yourdeardaniel/secdata-pipeline/issues) for pipeline/conversion issues
 - For dataset-specific issues, use the Community tab on this dataset page
 
 If you identify specific documents that should be removed for safety or licensing reasons, please open an issue with the document content (or its URL/hash) and a description of the concern. Examples that are genuinely harmful or improperly licensed will be removed from future dataset versions.