add note about publication

README.md

@@ -59,7 +59,7 @@ NOOW means occurrence order after preprocessing, not untouched or reversible pro
 
 ## Reproduce from source
 
-The [preprocessing folder](preprocessing/) contains the standalone code shared by all five prepared corpora. Its [README](preprocessing/README.md) explains how to download only that folder at a full commit revision, create the locked Linux x86-64 environment with Python 3.11.15, and record a full server build and verification. Folder access follows this repository's permissions. No parent project checkout, BigARTM or TopicNet installation is needed.
+The [preprocessing folder](preprocessing/) contains the standalone code shared by all five prepared corpora. Its [README](preprocessing/README.md) explains how to download only that folder at a full commit revision, create the locked Linux x86-64 environment with Python 3.11.15, and record a full server build and verification. No parent project checkout, BigARTM or TopicNet installation is needed.
 
 Obtain approved access to the original `dongqi-me/SciNews` dataset and authenticate using standard Hugging Face credentials before downloading sources. Access to this preparation does not grant access to the original source. From the directory containing `preprocessing/`, run:
 
@@ -109,7 +109,7 @@ annotated = corpus.map(lambda row: {"labels": labels[row["id"]],
 fit = annotated.filter(lambda row: row["research_split"] == "fit")
 ```
 
-For remote metadata, download `data/labels.json` and `data/splits.json` with `huggingface_hub.hf_hub_download(repo, filename, repo_type="dataset")`, then read the same direct mappings. Private repositories require authorized authentication.
+For remote metadata, download `data/labels.json` and `data/splits.json` with `huggingface_hub.hf_hub_download(repo, filename, repo_type="dataset")`, then read the same direct mappings.
 
 ## Files and integrity
 
@@ -117,10 +117,33 @@ The package contains this card, `.gitattributes`, [SHA256SUMS](SHA256SUMS), two
 
 ## Rights, limitations and citation
 
-The [official dataset card](https://huggingface.co/datasets/dongqi-me/SciNews) declares CC BY 4.0 and also imposes gated access terms restricting use to non-commercial academic research or education. Both statements are retained in the verbatim [SOURCE_README.md](SOURCE_README.md); the CC BY tag does not erase the gate conditions. Their applicability to redistribution needs clarification, and redistribution remains unresolved. News articles can carry third-party copyright and other obligations.
+This topic-modeling version is published with permission from Dongqi Liu, one of the original SciNews authors. Please cite the original SciNews publication using the full BibTeX entry below when using this dataset.
+
+The [official dataset card](https://huggingface.co/datasets/dongqi-me/SciNews) declares CC BY 4.0 and specifies non-commercial academic research or educational use in its access terms. These source notices are preserved in [SOURCE_README.md](SOURCE_README.md). Permission to publish this preparation does not replace the stated source terms or third-party rights in the news articles. The original source repository remains gated; separate approval is needed to download its files for a rebuild.
 
 Source selection, historical coverage, class imbalance and automatic preprocessing affect the labels and topic distributions. Family grouping reduces known duplicate leakage, but some semantic duplicates may remain. The articles may contain personal information and outdated claims. Assess the corpus's suitability for your application before relying on it for decisions.
 
-Dongqi Liu, Yifan Wang, Jia Loy and Vera Demberg (2024), [*SciNews: From Scholarly Complexities to Public Narratives – A Dataset for Scientific News Report Generation*](https://aclanthology.org/2024.lrec-main.1258/), LREC-COLING. See the [project page](https://dongqi.me/projects/SciNews).
+```bibtex
+@inproceedings{pu-etal-2024-scinews,
+    title = "{S}ci{N}ews: From Scholarly Complexities to Public Narratives {--} a Dataset for Scientific News Report Generation",
+    author = "Liu, Dongqi  and
+      Wang, Yifan  and
+      Loy, Jia  and
+      Demberg, Vera",
+    editor = "Calzolari, Nicoletta  and
+      Kan, Min-Yen  and
+      Hoste, Veronique  and
+      Lenci, Alessandro  and
+      Sakti, Sakriani  and
+      Xue, Nianwen",
+    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
+    month = may,
+    year = "2024",
+    address = "Torino, Italia",
+    publisher = "ELRA and ICCL",
+    url = "https://aclanthology.org/2024.lrec-main.1258",
+    pages = "14429--14444"
+}
+```
 
-When citing this preparation, identify its retained population and repository revision as well as the source publication.
+Please also identify this TopicNet preparation and its repository revision so readers can distinguish it from the original SciNews release.

SHA256SUMS

@@ -1,5 +1,4 @@
 947f03cf79a99d7f9061220c575021c918ae95f64ab75c4112917be804fb51cd  .gitattributes
-735681b0b89f7ece379a3c17c12d446d072e8690e88a80da0aa626605b903d52  README.md
 dffd02d522c1cb894d4fd1eebe0e4b6098d045fac77755fc8f49341388b32750  SOURCE_README.md
 45f086796ca39994272e11a31a601ac2b8e91536f9bd812ce849be4c55042537  data/bow.csv
 b9959f976605f8b418f6565fec8228e1bfbb0e8a7c314f0d1cf9b79258e9ac7c  data/labels.json
@@ -7,7 +6,6 @@ b9959f976605f8b418f6565fec8228e1bfbb0e8a7c314f0d1cf9b79258e9ac7c  data/labels.js
 d2e221cca3c7297d5b84f0d18dc2a7ba448e3775e33ff96a5e4bd30b33d13400  data/splits.json
 6feaded4e28ea86e001a6751b4e3d960e8b7c31b9616ff9936b79297086e809b  preprocessing/.gitignore
 15eb97925fe33f7fe096b36f95a5b19e66041101ba35d0dd4948cc8f3e0528e0  preprocessing/LICENSE
-1c947d9d405f7031654d0ca900eba7c71b1adbb8675d1939c3dd1b9aa4be46e7  preprocessing/README.md
 85821876745568dee4cfb46b7d49843ace0390b89d761fa2fc76e21d5b65efee  preprocessing/_pipeline.py
 d6d1374d3ea144edc3fd5285ba87dac4daa9dea2857d0f165c6d0943a69a43fc  preprocessing/conda-linux-64.lock.txt
 7e301b934c9abe47e404e0935bc83129023a0ee31a9edbec2ea039d1c322ce22  preprocessing/payload-pins.json

preprocessing/README.md

@@ -8,16 +8,16 @@ BigARTM, TopicNet installation or model training.
 
 The code is distributed under [MIT](LICENSE). This license covers the code only.
 Source texts and prepared data retain their own rights and access conditions;
-consult the corresponding dataset card and original source notices. In
-particular, SciNews requires approved access to the original dataset, and BBC
-redistribution rights remain unresolved.
+consult the corresponding dataset card and original source notices.
+In particular, rebuilding SciNews requires approved access to the original dataset,
+and BBC redistribution rights remain unresolved.
 
 ## Obtain only the code
 
 The common code lives in `preprocessing/` in the `TopicNet/SciNews` dataset
-repository. Access to this folder follows access to that repository. Authenticate
-with an authorized Hugging Face account if required; downloading the folder does
-not change repository visibility or grant access to the original SciNews source.
+repository. Downloading this folder does not require access to the original
+SciNews source. Separate source approval is needed only to rebuild SciNews;
+it is not needed to run the code for BBC-News or WOS.
 
 Choose a full commit hash from the repository history that contains this folder.
 Set `CODE_REVISION` to that hash, rather than relying on a moving `main` branch.