add base data

.gitattributes

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 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+*.csv filter=lfs diff=lfs merge=lfs -text
+*.jsonl filter=lfs diff=lfs merge=lfs -text
+*.vw filter=lfs diff=lfs merge=lfs -text
+*.native filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+language:
+  - en
+license: cc-by-4.0
+task_categories:
+  - text-classification
+tags:
+  - topic-modeling
+  - topic-modelling
+  - text-clustering
+  - document-representation
+size_categories:
+  - 10K<n<100K
+configs:
+  - config_name: noow
+    default: true
+    data_files:
+      - split: corpus
+        path: data/noow.csv
+  - config_name: bow
+    data_files:
+      - split: corpus
+        path: data/bow.csv
+---
+
+# WOS-11967
+
+This TopicNet derived preparation contains 11,965 retained scientific abstracts from the WOS-11967 release, with 33 observed integer labels serialized as strings. The source has 11,967 rows. The source README declares 35 categories and seven parents, but Y.txt actually contains 33 observed labels (0–32). Names for those 33 labels are not inferred from other WOS subsets. This is a derived research preparation, not the original release.
+
+## Population, labels and research splits
+
+Raw duplicate identity is body text after casefold and whitespace collapse. There are no raw duplicate groups or cross-label conflicts. Two identical lemma-sequence groups remove two rows; five near-duplicate pairs join families without removing their members. Y.txt is the authoritative global class. YL1.txt/YL2.txt remain provenance only: global label 6 has child values 2 and 3, label 10 has child values 2 and 3, and parent/child pair (2,3) maps to global labels 10 and 11. These ambiguities are preserved without repairing Y or inventing class names.
+
+Family groups combine raw/lemma duplicate relations, source URL/DOI relations where present, and body five-token shingle Jaccard similarity ≥0.9. Groups are assigned by iterative multilabel stratification with seeds 20260824 and 20260825; all retained members of a group stay together. The resulting disjoint, exhaustive research splits are **fit 7,182, calibration 2,391, validation 2,392**. The Hugging Face split `corpus` contains every retained document; it is not `fit`. Original source membership is preserved in accepted audit/documents metadata and does not define these research splits.
+
+Retained class supports come directly from `audit.json.output_rows`:
+
+| Label | Documents |
+| --- | ---: |
+| 0 | 298 |
+| 1 | 301 |
+| 2 | 300 |
+| 3 | 300 |
+| 4 | 299 |
+| 5 | 300 |
+| 6 | 406 |
+| 7 | 426 |
+| 8 | 389 |
+| 9 | 397 |
+| 10 | 392 |
+| 11 | 393 |
+| 12 | 387 |
+| 13 | 371 |
+| 14 | 402 |
+| 15 | 346 |
+| 16 | 420 |
+| 17 | 386 |
+| 18 | 410 |
+| 19 | 423 |
+| 20 | 384 |
+| 21 | 441 |
+| 22 | 449 |
+| 23 | 309 |
+| 24 | 357 |
+| 25 | 368 |
+| 26 | 321 |
+| 27 | 262 |
+| 28 | 351 |
+| 29 | 340 |
+| 30 | 401 |
+| 31 | 335 |
+| 32 | 301 |
+
+## Representations and pinned preparation
+
+The source adapter reads aligned X/Y/YL1/YL2 lines with zero-based row indices. IDs are `wos11967_<six-digit-row-index>`. The CSV body is the saved X.txt line without its line terminator; no title is present.
+
+Both configurations read a single UTF-8 CSV with exactly `id,raw_text,vw_text`, sorted lexicographically by ID. `raw_text` is only the saved `documents.jsonl.raw_text` body. Title text is separate metadata. NOOW VW preserves the saved prepared line exactly, excluding its line terminator: document ID followed by only `|@word`. It retains every prepared occurrence, including terms outside the fitted vocabulary. BOW counts those same terms separately within each namespace as `token:count`, with lexicographic token ordering and the same namespace ordering. Class labels never appear as VW namespaces.
+
+NOOW means occurrence order after preprocessing, not untouched or reversible prose. The accepted English preprocessing pins Python 3.11.15, NLTK 3.9.2 and NLTK-data commit `550b6625bcef1f2abff2ff770a5a0d272c9c6b2a`: WordPunctTokenizer, lowercase per original span, minimum token length 2, averaged_perceptron_tagger_eng, WordNet POS mapping J/V/N/R with noun fallback, English stopword removal and `lemma.isalpha()` filtering. The copied audit records the exact stopwords and resource hashes. Names are not masked.
+
+The research model uses **only @word**, with vocabulary fitted on `fit` only, `min_df=5` and `max_df=0.9`. The saved `*-model.vw` and `*.vw` inputs project prepared text to @word; the separate fitted dictionary controls the vocabulary while the sequence retains OOV positions. The CSVs preserve full prepared sequences, including the title namespace where present. Occurrence offsets and chains are separate saved artifacts, not CSV columns.
+
+`data/accepted/documents.jsonl` stores `offsets` and `title_offsets` as `[start, end]` arrays: zero-based Python character positions with an exclusive end (not byte offsets), relative to `raw_text` and `raw_title`, respectively. WOS has an empty `raw_title` and empty title arrays; no additional title text is included. The corresponding `occurrences` and `title_occurrences` records contain `lemma`, `start`, `end` and `occurrence_index`, aligned in occurrence order with `tokens`/`offsets` and `title_tokens`/`title_offsets`; a lemma need not equal the raw substring. Per-split `data/accepted/wos-11967/*-occurrences.jsonl` files contain TF-IDF decision records keyed by `document_id` and `occurrence_index`, rather than raw spans. Join `document_id` to the document's `id` and use `occurrence_index` to select its body occurrence and raw span.
+
+`tfidf-bank.json` and `*-chains.jsonl` preserve the immutable accepted TF-IDF reference bank and extracted chains. These are automatic reference constructions, not gold human annotations. This packaging converts saved representations and does not rerun preprocessing, dictionary creation, reference fitting, scoring or model training. Candidate model results and selected human-audit examples are not included.
+
+## Loading and metadata joins
+
+Authenticate with an account that has access if the intended remote repository is private:
+
+```python
+from datasets import load_dataset
+
+repo = "TopicNet/WOS-11967"
+corpus = load_dataset(repo, "noow", split="corpus", token=True)
+bow = load_dataset(repo, "bow", split="corpus", token=True)
+```
+
+Labels and research splits are auxiliary JSON files. Their actual schema is a direct ID-to-label-list object and a direct split-name-to-ID-list object, respectively; there are no `labels` or `splits` wrappers:
+
+```python
+import json
+from huggingface_hub import hf_hub_download
+
+def metadata_path(name):
+    return hf_hub_download(repo, "data/accepted/wos-11967/" + name,
+                           repo_type="dataset", token=True)
+
+with open(metadata_path("labels.json"), encoding="utf-8") as handle:
+    labels = json.load(handle)
+with open(metadata_path("splits.json"), encoding="utf-8") as handle:
+    splits = json.load(handle)
+membership = {doc_id: split for split, ids in splits.items() for doc_id in ids}
+annotated = corpus.map(lambda row: {"labels": labels[row["id"]],
+                                   "research_split": membership[row["id"]]})
+fit = annotated.filter(lambda row: row["research_split"] == "fit")
+```
+
+For offline access, obtain the complete package including actual LFS contents and set `HF_DATASETS_OFFLINE=1` and `HF_HUB_OFFLINE=1` before starting Python. Use a writable cache:
+
+```python
+import json
+from pathlib import Path
+from datasets import load_dataset
+
+package = Path("/path/to/WOS-11967")
+corpus = load_dataset(str(package), "noow", split="corpus",
+                      cache_dir="/writable/cache")
+bow = load_dataset(str(package), "bow", split="corpus",
+                   cache_dir="/writable/cache")
+with (package / "data/accepted/wos-11967/labels.json").open(encoding="utf-8") as handle:
+    labels = json.load(handle)
+with (package / "data/accepted/wos-11967/splits.json").open(encoding="utf-8") as handle:
+    splits = json.load(handle)
+membership = {doc_id: split for split, ids in splits.items() for doc_id in ids}
+fit = corpus.filter(lambda row: membership[row["id"]] == "fit")
+```
+
+An explicit CSV reader is also available:
+`load_dataset("csv", data_files={"corpus": str(package / "data/noow.csv")}, split="corpus", cache_dir="/writable/cache")`.
+For independent Python CSV inspection, raise `csv.field_size_limit` when needed for long bodies.
+
+## Accepted files and integrity
+
+The byte-preserving accepted tree totals **537,238,307 bytes** before adding export CSVs. NOOW CSV is **28,197,299 bytes** and BOW CSV is **26,653,958 bytes**.
+
+All 34 files from the accepted source subtree are preserved byte for byte under `data/accepted/`, including the original nested `wos-11967/` directory. Root accepted files include documents, audit, config, spec, diagnostics, manifest, checksum manifest and TF-IDF bank. The nested directory retains labels, splits, vocabulary, eligible labels, dictionary exports/native dictionary and per-split prepared/model VW, occurrences, summaries and chains. Saved machine paths are provenance, not loading requirements.
+
+[EXPORT_PROVENANCE.json](EXPORT_PROVENANCE.json) records each original file size/hash, input source pins, the pinned source manifest digest, verbatim source README identity, conversion rules and both CSV sizes/hashes. The original inner checksum manifests are unchanged. Root [SHA256SUMS](SHA256SUMS) covers every package file except itself and Git internals; run `sha256sum -c SHA256SUMS` from this directory. Git LFS attributes apply to CSV, JSONL, VW and native dictionaries; small JSON metadata and cards remain ordinary Git files.
+
+## Rights, limitations and citation
+
+The [Mendeley release, version 6](https://data.mendeley.com/datasets/9rw3vkcfy4/6) declares CC BY 4.0. The verbatim [SOURCE_README.txt](SOURCE_README.txt) retains Copyright (c) 2017 Kamran Kowsari and the permission notice requiring the copyright and permission notices in copies or substantial portions. Preserve those notices and cite the source. The release license is not a blanket guarantee that abstracts or other third-party content have no additional rights.
+
+These local packages are intended for private research storage; remote visibility has not been verified or changed. Private storage does not supply redistribution permission. Labels and topic distributions reflect source selection, historical coverage, class imbalance and automatic preprocessing. Family grouping limits known leakage but does not establish that every semantic duplicate was detected. Normalized representations do not guarantee current facts, absence of personal information or suitability for downstream decisions.
+
+Kamran Kowsari, Donald E. Brown, Mojtaba Heidarysafa, Kiana Jafari Meimandi, Matthew S. Gerber and Laura E. Barnes (2017), *HDLTex: Hierarchical Deep Learning for Text Classification*, IEEE ICMLA.
+
+Report this TopicNet derived population and the source manifest hash in [EXPORT_PROVENANCE.json](EXPORT_PROVENANCE.json). The [research repository](https://github.com/Alvant/Alekseev2026IntraTextCoherence) contains the [source adapters and preprocessing](https://github.com/Alvant/Alekseev2026IntraTextCoherence/blob/master/work/wintra/lib/dataset_adapters.py) and [model input preparation](https://github.com/Alvant/Alekseev2026IntraTextCoherence/blob/master/work/wintra/experiments/prepare_model_inputs.py). It is currently private and access-controlled; viewing those links requires permission. Code is linked rather than bundled, and this package does not promise public standalone reproducibility.

data/bow.csv

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data/noow.csv

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