Datasets:

maximerivest
/

openalex-denseon-embeddings

@@ -1,12 +1,215 @@
 ---
 pretty_name: OpenAlex paper embeddings (DenseOn, English titles and abstracts)
 ---
-# OpenAlex paper embeddings (upload in progress)
-203.7 million OpenAlex works with English abstracts, embedded with
-[lightonai/DenseOn](https://huggingface.co/lightonai/DenseOn), plus a ready-to-use search index.
-The files are being uploaded now (about 310 GB). Documentation and licence come with the last file:
-please wait for them before using the data.
-Made by [ScienceMadeReadable](https://sciencemadereadable.com).

 ---
+license: cc0-1.0
 pretty_name: OpenAlex paper embeddings (DenseOn, English titles and abstracts)
+language:
+- en
+size_categories:
+- 100M<n<1B
+task_categories:
+- feature-extraction
+- sentence-similarity
+- text-retrieval
+tags:
+- openalex
+- scientific-papers
+- scholarly
+- embeddings
+- semantic-search
+- dense-retrieval
+- science-map
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path:
+    - data/*/*.parquet
+    - data/updates/*/*.parquet
 ---
+# OpenAlex paper embeddings: 203.7 million English titles + abstracts, ready to search
+One row per work in [OpenAlex](https://openalex.org) that has an English abstract:
+its OpenAlex id, DOI, title, date, type, venue, field, a 2D position on a map of science, and a
+768-number **embedding** of its title and abstract made with
+[lightonai/DenseOn](https://huggingface.co/lightonai/DenseOn). Papers about the same thing get
+nearby vectors, so you can search by meaning, find similar papers, cluster, or map all of science.
+These are the vectors behind the search on [sciencemadereadable.com](https://sciencemadereadable.com)
+and its [free search API for people and AI agents](https://api.sciencemadereadable.com/api/data/docs).
+| | |
+|---|---|
+| Rows (embedded works) | **203,725,202** |
+| …of which in the search index | 125,130,657 publications, one per distinct text ([`in_search_index`](#columns)) |
+| Size | 297 GB in 502 Parquet files (0.74 GB each at most), split by publication year |
+| Text embedded | `title + " " + abstract`, English only, first 512 tokens |
+| Vector | 768 × float16, L2-normalised (cosine similarity = dot product) |
+| Metadata as of | OpenAlex snapshot 2026-09-23 + daily OpenAlex API additions to 2026-10-12 |
+| Data release | `20261012T073823Z` |
+## Pick your path
+| You have | Do this |
+|---|---|
+| Nothing to download | Use the hosted API: `curl https://ask.sciencemadereadable.com/coral+reef+recovery` ([docs](https://api.sciencemadereadable.com/api/data/docs)) |
+| A laptop | Query the metadata remotely with DuckDB (reads only the columns you ask for), or download one year |
+| ~20 GB of RAM, ~16 GB of disk | Download `index/` and search **all 125 million** papers by meaning ([below](#search-everything-on-your-own-machine)) |
+| ~350 GB of SSD | Download everything: exact re-scoring, your own index, clustering, maps |
+### Look without downloading (DuckDB)
+Parquet lets DuckDB fetch only the columns and row groups a query needs, straight from the Hub:
+```sql
+-- duckdb
+SELECT year, count(*) AS papers
+FROM 'hf://datasets/maximerivest/openalex-denseon-embeddings/data/**/*.parquet'
+WHERE year BETWEEN 2015 AND 2026
+GROUP BY year ORDER BY year;
+```
+### Download part of it
+Files are grouped by publication year (`data/2025/`, `data/1999/`, …, plus `data/before-1900/`,
+`data/after-2026/`, `data/no-year/` and the daily additions in `data/updates/`):
+```sh
+pip install -U huggingface_hub
+hf download maximerivest/openalex-denseon-embeddings --repo-type dataset --include "data/2025/*" --local-dir openalex-denseon
+hf download maximerivest/openalex-denseon-embeddings --repo-type dataset --local-dir openalex-denseon          # everything, ~297 GB
+```
+### Use the vectors
+```python
+import numpy as np, pyarrow.parquet as pq
+from sentence_transformers import SentenceTransformer
+t = pq.read_table("openalex-denseon/data/2025/part-00000.parquet")
+E = t["embedding"].combine_chunks().values.to_numpy().reshape(-1, 768)   # float16, one row per paper
+model = SentenceTransformer("lightonai/DenseOn", revision="cb9947ebccb33862d24e3c7ca2edb25e51acd887")
+q = model.encode("coral reef recovery after bleaching", prompt_name="query", normalize_embeddings=True)
+scores = E.astype(np.float32) @ q
+for i in np.argsort(-scores)[:5]:
+    print(f"{scores[i]:.3f}", t["title"][i], "https://openalex.org/" + str(t["openalex_id"][i]))
+```
+Questions are encoded with `prompt_name="query"` (`"query: "` in front), papers with
+`prompt_name="document"` (`"document: "`). To put **your own texts** in the same space, encode
+`title + " " + abstract` with the document prompt: re-encoding papers on a CPU in float32 gives a
+cosine ≥ 0.999 with the stored vectors (we checked).
+With 🤗 Datasets, stream rather than load: a plain `load_dataset` converts all 297 GB into its
+cache first.
+```python
+from datasets import load_dataset
+ds = load_dataset("maximerivest/openalex-denseon-embeddings", split="train", streaming=True)
+print(next(iter(ds))["title"])
+```
+### Search everything on your own machine
+`index/` holds the search index the live site uses for this release: FAISS `IVF32768_HNSW32,RaBitQ`
+over the 125,130,657 rows with `in_search_index = true` (14.1 GB), and
+`index/work_ids.npy` (row number → OpenAlex W-number, 1.6 GB). Its ids are the `row` column.
+```sh
+hf download maximerivest/openalex-denseon-embeddings --repo-type dataset --include "index/*" --include "*.py" --local-dir openalex-denseon
+cd openalex-denseon
+uv run search.py "coral reef recovery after bleaching"          # needs ~20 GB of RAM
+```
+| What you downloaded | Quality of the top 10 | Time per question |
+|---|---|---|
+| `index/` only | ~75% of an exact search's top 10 (approximate scores) | ~5–30 ms, after a ~20 s load |
+| `index/` + all of `data/` + `uv run make_vectors.py` (builds a 313 GB `vectors.npy`; use an SSD) | ~99% (300 candidates re-scored exactly) | ~10–50 ms |
+| `data/` for some years, no index | exact, over those years | ~1 minute per recent year from an SSD (each is 15–40 GB) |
+(Measured on 200 paper-vector questions against a deeper exact re-scoring, release `20261012T073823Z`;
+not a relevance judgement. The index was written by `faiss-cpu==1.15.1`, which `search.py` pins.)
+The live site ranks a little differently: it also demotes likely junk records (spam, paratext),
+which this dataset does not include.
+## Columns
+| Column | Type | What it is |
+|---|---|---|
+| `row` | int64 | Row number, 0 … 203,725,201. The index's ids. New versions add rows at the end ([Versions](#versions)) |
+| `openalex_id` | string | `W…` id; `https://openalex.org/<id>` is the record, `https://api.openalex.org/works/<id>` its full metadata |
+| `doi` | string | DOI without `https://doi.org/`, null if none |
+| `title` | string | Title as embedded |
+| `publication_date`, `year` | date, int16 | From OpenAlex. A few are wrong in OpenAlex (years like 1007 or 2050) |
+| `type` | string | OpenAlex type: `article`, `book-chapter`, `preprint`, `review`, `paratext`, `erratum`, … |
+| `venue` | string | Journal, conference or repository |
+| `field`, `subfield`, `topic` | string | OpenAlex primary topic and its subfield and field |
+| `cited_by_count` | int32 | Citations counted by OpenAlex at that time |
+| `is_retracted`, `is_open_access`, `license` | bool, bool, string | From OpenAlex (`license` is the work's licence, e.g. `cc-by`) |
+| `in_search_index` | bool | In the search index: a publication type (article, review, preprint, book, chapter, conference paper, thesis, report, letter, editorial, …) and the smallest W-number among works with byte-identical title + abstract |
+| `atlas_x`, `atlas_y` | float32 | Position (0–1) on the [map of science](https://sciencemadereadable.com): openTSNE layout of a reference sample, every paper placed from its 10 nearest reference papers. Atlas `20261009T190750-tsne-mode`. Projections distort distances: use it to look, not to measure |
+| `text_hash` | binary(16) | BLAKE2b-128 of the UTF-8 text embedded; equal hash = identical text |
+| `embedding` | float16[768] | The vector |
+Every Parquet file also carries the model, revision, prompts and input format in its metadata.
+## How it was made
+- **Selection.** All OpenAlex works whose language is `en` and whose abstract is not empty, with no
+  filter on type, open access or completeness (203,725,202 rows: 203,486,035 from the OpenAlex snapshot
+  of 2026-09-23, 239,167 new papers from the daily API run since).
+- **Text.** `((title or "") + " " + abstract).strip()`, abstract rebuilt from OpenAlex's inverted
+  index (words put back in order). DenseOn reads the first 512 tokens.
+- **Model.** `lightonai/DenseOn` at revision `cb9947ebccb33862d24e3c7ca2edb25e51acd887` (149M parameters, Apache-2.0),
+  sentence-transformers 5.3.0, bfloat16 on an RTX 3090, `document: ` prompt, then L2-normalised
+  in float32 and stored as float16.
+- **Checks.** For every year folder, the title + abstract of a full shard of rows was hashed again
+  from the OpenAlex records and compared with the hash stored next to each vector: all matched.
+  Every row was joined to exactly one OpenAlex record.
+## Limits, said plainly
+- **Not all of science.** Only works OpenAlex labels English *and* gives an abstract. Many papers
+  (older ones, many books, much non-English research) have no abstract in OpenAlex, and its language
+  labels make mistakes in both directions.
+- **Not only papers.** 73,731,213 rows (36%) are not publications by the definition
+  above (`dataset` 57.3M, `other` 11.8M, `paratext` 0.8M, `software` 0.7M, …), and 4,863,332 publications repeat another record's exact title and abstract.
+  `in_search_index` removes both; `type` and `text_hash` let you choose differently.
+- **Metadata ages.** Titles, citation counts, topics and retractions are as of 2026-09-23 (daily
+  additions: as of the day they were fetched). OpenAlex re-ran its topic classifier after that
+  snapshot, so recent additions carry newer topic labels than older rows.
+- **No abstracts here.** Abstracts belong to their publishers, and this dataset does not redistribute
+  them. Get them from OpenAlex (free snapshot or API) by `openalex_id`, or through our
+  [API](https://api.sciencemadereadable.com/api/data/docs).
+- **Search quality** of the vectors is DenseOn's (BEIR nDCG@10 56.2 per its authors). We have not
+  published a relevance evaluation of this release.
+## Versions
+New versions add files for new papers and may refresh metadata columns; an existing row keeps its
+`row` number and vector. A change that would renumber rows or change vectors (a new model, a new
+way to build the text) will be a separate dataset, not a silent update. Each version is a tagged
+revision of this repository named after its data release; `export.json` describes it.
+## Licence
+CC0 1.0: no rights reserved. Use, share and build on it for anything, commercial or not, without asking. Credit is appreciated, not required (see Cite).
+The metadata comes from [OpenAlex](https://openalex.org) (CC0). DenseOn is Apache-2.0.
+## Cite
+```bibtex
+@misc{rivest_openalex_denseon_2026,
+  author = {Rivest, Maxime},
+  title  = {OpenAlex paper embeddings (DenseOn, English titles and abstracts)},
+  year   = {2026},
+  publisher = {Hugging Face},
+  url    = {https://huggingface.co/datasets/maximerivest/openalex-denseon-embeddings}
+}
+```
+Also cite OpenAlex (Priem, Piwowar & Orr 2022, [arXiv:2205.01833](https://arxiv.org/abs/2205.01833)) and DenseOn.
+Made by [ScienceMadeReadable](https://sciencemadereadable.com); code: [srl-search](https://github.com/MaximeRivest/srl-search),
+[pipeline](https://github.com/MaximeRivest/ScholarsReadingList-v2/tree/main/pipeline).