add full text in noow

README.md

@@ -36,7 +36,67 @@ tags:
 
 # PostNauka
 
-Some measurable characteristics of the dataset:
+## Origin and credits
+
+This Russian science communication corpus originates from [PostNauka](https://postnauka.ru/), its authors and editors. The historical corpus was received by the dataset owner with the description “publications up to February 2017”; individual publication dates have not been verified for this export.
+
+Related research includes N. A. Chirkova and K. V. Vorontsov, *Additive Regularization for Hierarchical Multimodal Topic Modeling*, Journal of Machine Learning and Data Analysis, 2(2), 187–200 (2016), [DOI: 10.21469/22233792.2.2.05](https://doi.org/10.21469/22233792.2.2.05), [primary PDF](https://publications.hse.ru/pubs/share/direct/210878218.pdf). That paper describes an earlier PostNauka corpus of 1,728 documents; that count must not be substituted for the populations below.
+
+The dataset owner also credits Irina V. Efimova's 2017 Master's thesis at Moscow Institute of Physics and Technology, *Иерархическая мультимодальная тематическая модель коллекции научно-популярных текстов* ([thesis link](http://www.machinelearning.ru/wiki/images/2/24/Efimova_Diploma.pdf)). This bibliographic information was supplied by the owner; the linked PDF was unavailable for verification during preparation of this export.
+
+## Configurations and text fields
+
+Both configurations retain the three columns `id`, `raw_text`, and `vw_text` and the historical row order. `bag-of-words` remains the default.
+
+| Configuration | Rows | `raw_text` | `vw_text` |
+|:--|--:|:--|:--|
+| `bag-of-words` | 3,404 | Existing title/snippet display text, unchanged | Existing multimodal BOW features and counts, unchanged |
+| `natural-order-of-words` | 3,446 | Full text from the original cleaned document with the same NOOW ID | Existing natural-order prepared tokens, unchanged |
+
+Each NOOW `raw_text` is the corresponding original cleaned file decoded once as UTF-8. Headings, snippets, separators, line breaks and other whitespace are retained. There is no further normalization or inferred character-offset alignment between `raw_text` and prepared `vw_text`.
+
+## Reconstructed document correspondence
+
+[data/document-correspondence.json](data/document-correspondence.json) contains 3,403 one-to-one BOW-to-NOOW pairs reconstructed with the existing PostNauka adapter. For each BOW document, every NOOW document sharing at least one `@word` token is a candidate. The matcher compares token-set Jaccard scores, accepts a unique maximum without a threshold, and checks the resulting assignments for collisions. There are zero collisions. This is a reproducible token-based reconstruction, not exact human ground truth.
+
+BOW document `8.txt` remains unresolved: NOOW documents `7810.txt` and `9419.txt` tie at score `0.2`. The JSON also lists all 43 unmapped NOOW IDs. The two configurations therefore have different populations, and a BOW ID must not be used directly as a NOOW ID.
+
+The original `data/noow/topics.json` and `data/noow/topic_documents.json` remain unchanged. They describe the historical reference topic model with 19 topics plus one background topic and use NOOW IDs; any transfer to BOW must pass through the reconstructed mapping and retain the unresolved case.
+
+For local, offline use, run this example from the downloaded dataset repository (requires `datasets`):
+
+```python
+import json
+from pathlib import Path
+from datasets import load_dataset
+
+root = Path(".")
+bow = load_dataset(str(root.resolve()), "bag-of-words", split="train")
+noow = load_dataset(str(root.resolve()), "natural-order-of-words", split="train")
+correspondence = json.loads((root / "data/document-correspondence.json").read_text(encoding="utf-8"))
+mapping = correspondence["bow_to_noow"]
+noow_by_id = {row["id"]: row for row in noow}
+for bow_row in bow:
+    noow_id = mapping.get(bow_row["id"])
+    if noow_id is None:
+        continue  # Unresolved BOW document; no match is guessed.
+    full_text = noow_by_id[noow_id]["raw_text"]
+    # Use full_text alongside bow_row's unchanged multimodal features.
+```
+
+## Integrity and provenance
+
+[EXPORT_PROVENANCE.json](EXPORT_PROVENANCE.json) pins the immutable original CSVs, all 3,446 full-text files by SHA-256, the canonical inventory hash, the matching identity, the conversion method and the code used. It records only repository-relative source locations and includes no private audit selections. [SHA256SUMS](SHA256SUMS) covers the packaged data, documentation and provenance; run `sha256sum -c SHA256SUMS` from this directory.
+
+## Rights
+
+The existing card metadata links to the TopicNet license. That metadata concerns the TopicNet distribution and does not establish permission to redistribute the underlying full PostNauka articles. The [PostNauka about page](https://postnauka.org/about) states that all rights are reserved. Full-article redistribution rights have not been established for this export; no public redistribution permission is asserted.
+
+## Historical BOW statistics
+
+The following tables describe the 3,404-document BOW representation and are retained from the original dataset card without recalculation. They do not describe the 3,446-document NOOW population.
+
+Some measurable characteristics of the BOW dataset:
 
 * D — number of documents
 * <modality name> W — modality dictionary size (number of unique tokens)
@@ -64,5 +124,31 @@ Information about document lengths in modality tokens:
 | 75%  |           5       |            67      |            2       |             15      |          563     |              6       |           1        |          5       |           52      |           2       |           15       |         343     |             6       |          1        |
 | max  |          22       |           778      |           69       |             28      |         4892     |             18       |           4        |         22       |          444      |          27       |           28       |        2277     |            18       |          4        |
 
+There exists a reference "gold-standard" topic model for 19 + 1 (background) topics.
+Information about the topics is available for the [natural word order version of the dataset](https://huggingface.co/datasets/TopicNet/PostNauka/tree/main/data/noow).
+
+## Citations
+
+```bibtex
+@article{chirkova2016hierarchical,
+  author = {Chirkova, N. A. and Vorontsov, K. V.},
+  title = {Additive Regularization for Hierarchical Multimodal Topic Modeling},
+  journal = {Journal of Machine Learning and Data Analysis},
+  year = {2016},
+  volume = {2},
+  number = {2},
+  pages = {187--200},
+  doi = {10.21469/22233792.2.2.05},
+  url = {https://publications.hse.ru/pubs/share/direct/210878218.pdf}
+}
 
-There exists a reference "gold-standard" topic model for 19 + 1 (background) topics. Information about topics is available for the [natural word order version of the dataset](https://huggingface.co/datasets/TopicNet/PostNauka/tree/main/data/noow). (At the same time, [bag-of-words dataset](https://huggingface.co/datasets/TopicNet/PostNauka/tree/main/data/bow) with `@snippet`-s includes the same documents as the dataset with a natural order of words, but under different names — and information about the correspondence of document names, alas, is lost 🙃)
+@mastersthesis{efimova2017hierarchical,
+  author = {Efimova, Irina V.},
+  title = {Иерархическая мультимодальная тематическая модель коллекции научно-популярных текстов},
+  school = {Moscow Institute of Physics and Technology},
+  year = {2017},
+  language = {Russian},
+  url = {http://www.machinelearning.ru/wiki/images/2/24/Efimova_Diploma.pdf},
+  note = {Bibliographic information supplied by the dataset owner; linked PDF not verified for this export}
+}
+```

SHA256SUMS

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+3904c9867fd8bbc9dee6435e2d365678c515f1fce776bb4652897ca3b9b89384  EXPORT_PROVENANCE.json
+ccce17538394478d680c19b60bdf8e4bf62829da0d553b25093c88f1daa13828  README.md
+333f1a4bbd32bb6b75ff7a1daa1e92326ce2e6f2da18d4ead08822b6e0d0d467  data/bow/postnauka.csv.gz
+e2284ad705d192763efc837b05e290588e0f83a7e9c0ec8d77556262c00ee715  data/document-correspondence.json
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+b9b67f4020a1c3c18d327797ddf60b655f544c02eb1ad390656b18ed7389164a  data/noow/topic_documents.json
+923be0c27aa8f61643e29414964071482e7f2ddffb48c4c1578db50e685db9fe  data/noow/topics.json

data/noow/postnauka.csv.gz

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