lick text

README.md

@@ -38,11 +38,11 @@ tags:
 
 ## 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.
+This Russian science communication corpus contains texts from [PostNauka](https://postnauka.ru/), whose authors and editors created the original articles. The historical corpus description says “publications up to February 2017”; individual publication dates have not been verified.
 
-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.
+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, rather than the larger 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.
+Related work also includes Irina V. Efimova's 2017 Master's thesis at Moscow Institute of Physics and Technology, *Иерархическая мультимодальная тематическая модель коллекции научно-популярных текстов* ([thesis](http://www.machinelearning.ru/wiki/images/2/24/Efimova_Diploma.pdf)).
 
 ## Configurations and text fields
 
@@ -50,8 +50,8 @@ Both configurations retain the three columns `id`, `raw_text`, and `vw_text` and
 
 | 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 |
+| `bag-of-words` | 3,404 | Title/snippet display text | Multimodal BOW features and counts |
+| `natural-order-of-words` | 3,446 | Full text from the original cleaned document with the same NOOW ID | Prepared tokens in natural order |
 
 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`.
 
@@ -61,7 +61,7 @@ Each NOOW `raw_text` is the corresponding original cleaned file decoded once as
 
 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.
+The files `data/noow/topics.json` and `data/noow/topic_documents.json` 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`):
 
@@ -84,13 +84,15 @@ for bow_row in bow:
     # Use full_text alongside bow_row's unchanged multimodal features.
 ```
 
-## Integrity and provenance
+## Preparation and integrity
 
-[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.
+This package combines the historical BOW and NOOW representations with the cleaned source documents. NOOW `raw_text` is decoded directly from those documents; `vw_text` contains the prepared features. The token-based correspondence above links the two populations where a unique match is available.
+
+Run `sha256sum -c SHA256SUMS` from this directory to check the files listed in [SHA256SUMS](SHA256SUMS).
 
 ## 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.
+The [TopicNet license](https://github.com/machine-intelligence-laboratory/TopicNet/blob/master/LICENSE.txt) concerns the TopicNet distribution and does not establish permission to redistribute the underlying PostNauka articles. The [PostNauka about page](https://postnauka.org/about) states that all rights are reserved. Permission to redistribute the full articles remains unresolved.
 
 ## Historical BOW statistics
 
@@ -99,9 +101,9 @@ The following tables describe the 3,404-document BOW representation and are reta
 Some measurable characteristics of the BOW dataset:
 
 * D — number of documents
-* <modality name> W — modality dictionary size (number of unique tokens)
-* <modality name> len D — average document length in modality tokens (number of tokens)
-* <modality name> len D uniq — average document length in unique modality tokens (number of unique tokens)
+* `<modality name>` W — modality dictionary size (number of unique tokens)
+* `<modality name>` len D — average document length in modality tokens (number of tokens)
+* `<modality name>` len D uniq — average document length in unique modality tokens (number of unique tokens)
 
 
 |       |    D |   @title W |   @title len D |   @title len D uniq |   @2gramm W |   @2gramm len D |   @2gramm len D uniq |   @3gramm W |   @3gramm len D |   @3gramm len D uniq |   @snippet W |   @snippet len D |   @snippet len D uniq |   @word W |   @word len D |   @word len D uniq |   @post_tag W |   @post_tag len D |   @post_tag len D uniq |   @author W |   @author len D |   @author len D uniq |
@@ -148,7 +150,6 @@ Information about the topics is available for the [natural word order version of
   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}
+  url = {http://www.machinelearning.ru/wiki/images/2/24/Efimova_Diploma.pdf}
 }
 ```

SHA256SUMS

@@ -1,6 +1,5 @@
 f4e703ea6e44bbebe53aceed2a89c11e40b88b7ae130c480c89860ab805ffc8f  .gitattributes
-3904c9867fd8bbc9dee6435e2d365678c515f1fce776bb4652897ca3b9b89384  EXPORT_PROVENANCE.json
-ccce17538394478d680c19b60bdf8e4bf62829da0d553b25093c88f1daa13828  README.md
+7782b79bfe87f1d5309c96a8cf8c9109102697ef5aee22afaeb3d64585213009  README.md
 333f1a4bbd32bb6b75ff7a1daa1e92326ce2e6f2da18d4ead08822b6e0d0d467  data/bow/postnauka.csv.gz
 e2284ad705d192763efc837b05e290588e0f83a7e9c0ec8d77556262c00ee715  data/document-correspondence.json
 73a6717fc90034e54628515c15b51f8d48bc04b877cab31789890c9cc3b362fa  data/noow/postnauka.csv.gz