Update README.md

README.md

@@ -27,24 +27,19 @@ configs:
 ---
 
 <div style="display: flex; gap: 8px; align-items: center; flex-wrap: wrap;">
-  <a href="https://doi.org/10.5334/johd.520"><img src="https://img.shields.io/badge/DOI-10.5334%2Fjohd.520-blue" alt="DOI"></a>
-  <a href="https://doi.org/10.5281/zenodo.18402099"><img src="https://zenodo.org/badge/DOI/10.5281/zenodo.18402099.svg" alt="DOI"></a>
+  <a href="https://doi.org/10.5334/johd.520"><img src="https://img.shields.io/badge/DOI-10.5334%2Fjohd.520-blue" alt="Paper DOI"></a>
+  <a href="https://doi.org/10.5281/zenodo.18402099"><img src="https://img.shields.io/badge/Zenodo-10.5281%2Fzenodo.18402099-blue" alt="Zenodo DOI"></a>
   <a href="https://graph.openaire.eu/docs/"><img src="https://img.shields.io/badge/docs-OpenAIRE%20Graph-informational" alt="OpenAIRE Graph Docs"></a>
 </div>
 
-[![DOI](https://img.shields.io/badge/DOI-10.5334%2Fjohd.520-blue)](https://doi.org/10.5334/johd.520) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.18402099.svg)](https://doi.org/10.5281/zenodo.18402099) [![OpenAIRE Graph Docs](https://img.shields.io/badge/docs-OpenAIRE%20Graph-informational)](https://graph.openaire.eu/docs/)
-
-
 # Compact OpenAIRE Citation Graph
 
-A distilled, compact representation of the complete OpenAIRE citation graph, making a
-very large scholarly citation network easily accessible. The core graph is shared as two
-files totalling ~11 GB; a larger node file with additional publication metadata is also
-provided.
+A the complete OpenAIRE citation graph available as simple parquet files (edge list). The core graph is shared as an edge list
+files totalling ~8.8 GB; a larger node file with additional publication features (full list below) is also provided.
+
+This is a working mirror for convenient loading. **Please cite the accompanying data paper (see Citation below).**
 
-This is a working mirror for convenient loading. The **archival record of
-record is on Zenodo** ([10.5281/zenodo.18402099](https://doi.org/10.5281/zenodo.18402099)).
-Please cite the accompanying data paper (see Citation below).
+The nodes are publications, the edges are citations.
 
 ## Dataset structure
 
@@ -55,67 +50,45 @@ The dataset is a directed citation graph: publications are nodes, citations are
 | `citations.parquet` | Edges — the citation links between publications | 8.8 GB | ~2B |
 | `publications_large.parquet` | Nodes with additional metadata fields (see below) | 72.5 GB | ~200M |
 
-### Fields in `publications_large`
-
-| Field | Type | Description | Filled |
-| --- | --- | --- | --- |
-| `nodeId` | int32 | Unique internal node identifier | 100.00% |
-| `openaireId` | str | OpenAIRE platform identifier | 100.00% |
-| `title` | str | Publication title | 99.41% |
-| `authors` | list[str] | Authors | 83.84% |
-| `description` | str | Abstract / short description | 57.17% |
-| `date` | datetime | Publication date | 97.33% |
-| `container` | str | Journal / conference / repository | 68.45% |
-| `citations` | int | Number of times cited | 97.33% |
-| `language` | str | Language of the publication | 99.99% |
-| `pid_dois` | list[str] | DOI identifiers | 80.70% |
-| `pid_mag_ids` | list[str] | MAG IDs | 44.27% |
-| `pid_pmids` | list[str] | PubMed IDs | 18.18% |
-| `pid_handles` | list[str] | Persistent handles | 8.33% |
-| `pid_pmcs` | list[str] | PubMed Central IDs | 4.77% |
-| `pid_arxiv_ids` | list[str] | ArXiv IDs | 1.38% |
+### Features/columns in `publications_large`
+
+| Field | Type | Description | Memory (GB) | Filled |
+| --- | --- | --- | --- | --- |
+| `nodeId` | int32 | Unique internal node identifier | 0.8 | 100.00% |
+| `openaireId` | str | OpenAIRE platform identifier | 9.6 | 100.00% |
+| `title` | str | Publication title | 16.5 | 99.41% |
+| `authors` | list[str] | Authors | 11.0 | 83.84% |
+| `description` | str | Abstract / short description | 131.3 | 57.17% |
+| `date` | datetime | Publication date | 0.8 | 97.33% |
+| `container` | str | Journal / conference / repository | 2.2 | 68.45% |
+| `citations` | int | Number of times cited | 1.6 | 97.33% |
+| `language` | str | Language of the publication | 0.2 | 99.99% |
+| `pid_dois` | list[str] | DOI identifiers | 5.6 | 80.70% |
+| `pid_mag_ids` | list[str] | MAG IDs | 2.0 | 44.27% |
+| `pid_pmids` | list[str] | PubMed IDs | 1.2 | 18.18% |
+| `pid_handles` | list[str] | Persistent handles | 1.1 | 8.33% |
+| `pid_pmcs` | list[str] | PubMed Central IDs | 0.9 | 4.77% |
+| `pid_arxiv_ids` | list[str] | ArXiv IDs | 0.9 | 1.38% |
 
 ## Usage
-
-The core node and edge files load comfortably in memory. Use the PyArrow backend:
-
 ```python
 import pandas as pd
+from huggingface_hub import hf_hub_download
 
-df_pubs = pd.read_parquet(
-    "publications.parquet",
-    engine="pyarrow",
-    dtype_backend="pyarrow",
-)
+REPO = "Zmeos/Compact_OpenAIRE_citation_graph"
 
-df_cites = pd.read_parquet(
-    "citations.parquet",
-    engine="pyarrow",
-    dtype_backend="pyarrow",
+# citations (edges)
+cites = pd.read_parquet(
+    hf_hub_download(REPO, "citations.parquet", repo_type="dataset"),
+    engine="pyarrow", dtype_backend="pyarrow",
 )
-```
 
-`publications_large.parquet` will not fit in memory on most machines. Select only the
-columns you need:
-
-```python
-df_large = pd.read_parquet(
-    "publications_large.parquet",
+# publications_large (nodes + metadata) — may not fit in memory;
+# select only the columns you need
+large = pd.read_parquet(
+    hf_hub_download(REPO, "publications_large.parquet", repo_type="dataset"),
     columns=["nodeId", "title", "pid_dois"],
-    engine="pyarrow",
-    dtype_backend="pyarrow",
-)
-```
-
-You can also pull files directly from the Hub:
-
-```python
-from huggingface_hub import hf_hub_download
-
-path = hf_hub_download(
-    repo_id="USERNAME/compact-openaire-citation-graph",
-    filename="citations.parquet",
-    repo_type="dataset",
+    engine="pyarrow", dtype_backend="pyarrow",
 )
 ```