| --- |
| pretty_name: MicrobeKG |
| language: |
| - en |
| license: other |
| license_name: microbekg-source-specific-terms |
| license_link: LICENSE |
| size_categories: |
| - 1M<n<10M |
| tags: |
| - knowledge-graph |
| - microbiome |
| - biology |
| - graph-machine-learning |
| - link-prediction |
| configs: |
| - config_name: edges |
| default: true |
| data_files: |
| - split: full |
| path: data/edges/*.parquet |
| - config_name: nodes |
| data_files: |
| - split: full |
| path: data/nodes/*.parquet |
| --- |
| |
| # MicrobeKG |
|
|
| MicrobeKG connects microorganisms, metabolites, substrates, diseases, host genes, |
| and interventions in a heterogeneous knowledge graph. Records retain source and |
| evidence fields for graph querying, resource analysis, and hypothesis generation. |
|
|
| This package contains the **audited-20260928** graph: **3,647,004 assertion rows**, |
| **67,485 typed nodes**, **25 relation labels**, and **31 typed relation patterns**. |
| It is a lossless Parquet export prepared on 2026-09-29. |
|
|
| **Data terms:** the graph incorporates third-party sources with different terms. |
| The `other` label refers to [source-specific terms](LICENSE), not a blanket open |
| license. See [SOURCE_TERMS.md](SOURCE_TERMS.md) for source attribution, current |
| contribution counts, review dates, and unresolved redistribution permissions. |
| The software repository's MIT license does not license these third-party data. |
|
|
| ## Contents and loading |
|
|
| | Configuration | Split | Rows | Files | |
| |---|---|---:|---| |
| | `edges` | `full` | 3,647,004 | 8 Parquet shards | |
| | `nodes` | `full` | 67,485 | 1 Parquet file | |
|
|
| `full` means the complete table. It is not a training or evaluation partition. |
| Shards preserve the original row order and contain at most 500,000 rows. The files |
| use Zstandard compression and row groups of at most 65,536 rows. |
|
|
| ```python |
| from datasets import load_dataset |
| |
| repo_id = "YOUR_HF_USERNAME/MicrobeKG" # replace with the actual dataset repository |
| edges = load_dataset(repo_id, "edges", split="full") |
| nodes = load_dataset(repo_id, "nodes", split="full") |
| |
| # Read progressively without materializing the entire table. |
| edge_stream = load_dataset(repo_id, "edges", split="full", streaming=True) |
| print(next(iter(edge_stream))) |
| ``` |
|
|
| For a private repository, first run `hf auth login` with an account that has access. |
| For reproducible work, pass `revision="<dataset-commit-sha>"` to `load_dataset`. |
| To use downloaded Parquet directly: |
|
|
| ```python |
| import pyarrow.dataset as ds |
| |
| edges = ds.dataset("data/edges", format="parquet") |
| subset = edges.to_table( |
| columns=["head_id", "relation", "tail_id", "source", "evidence"], |
| filter=(ds.field("head_type") == "microbe") |
| & (ds.field("tail_type") == "disease"), |
| ) |
| ``` |
|
|
| ## Schema |
|
|
| All columns are UTF-8 strings. Empty cells remain empty strings, and identifiers |
| retain their original prefixes and formatting. See [schema.json](schema.json). |
|
|
| | Table | Column | Meaning | |
| |---|---|---| |
| | edges | `head_id`, `head_type` | Identifier and type of the subject node | |
| | edges | `relation` | Directed relation label | |
| | edges | `tail_id`, `tail_type` | Identifier and type of the object node | |
| | edges | `confidence` | Source-specific score or label, retained verbatim; not a calibrated probability | |
| | edges | `species_source` | Source organism/context label, retained verbatim | |
| | edges | `source` | Source labels; multiple labels can be separated by `\|` | |
| | edges | `evidence` | Source evidence, identifiers, and provenance, retained verbatim | |
| | edges | `evidence_type` | Evidence-class labels, potentially combined with `\|` | |
| | nodes | `node_id` | Original canonical identifier | |
| | nodes | `node_type` | One of the six entity types below | |
| | nodes | `node_name` | Recorded display label; may be an identifier-derived label | |
| | nodes | `source_databases` | Source labels associated with the node | |
|
|
| **Node identity is `(node_type, node_id)`.** The same chemical identifier can occur |
| as both a substrate and a metabolite. Join edges to nodes using both the identifier |
| and type, rather than `node_id` alone. Evidence text may contain delimiters with |
| different meanings; it should not be interpreted as a single list of source labels. |
|
|
| | Node type | Count | |
| |---|---:| |
| | metabolite | 25,366 | |
| | host_gene | 19,907 | |
| | microbe | 15,821 | |
| | disease | 5,212 | |
| | substrate | 1,057 | |
| | intervention | 122 | |
| |
| The microbe count includes taxonomic ranks and genome bins; it is not a species |
| count. The build audit flags identifier-derived display labels for 19,907 host |
| genes, 252 metabolites, and 14 substrates. These cells are populated, not missing; |
| the original labels and typed graph connections are retained without name imputation. |
| |
| ## Sources and preparation |
| |
| The graph integrates 18 upstream source labels, including curated association |
| databases, metabolic resources, taxonomy/ontology resources, and literature-derived |
| records. `cross_source_conflict` is an additional derived label. Source-labelled |
| counts overlap when a row cites multiple sources and should not be summed as |
| distinct graph assertions. `Lit44` is a historical source identifier; this snapshot |
| contains retained assertions from 18 studies under that label. |
| |
| The audited snapshot harmonizes typed identifiers and relation labels and preserves |
| evidence and disagreement records. This export does not change, filter, rescore, |
| or impute any graph field. Source TSV hashes, Parquet hashes, file sizes, and counts |
| are recorded in [manifest.json](manifest.json). Independently checked row equality, |
| typed endpoint integrity, and statistics are recorded in [validation.json](validation.json) |
| and [statistics.json](statistics.json). |
| |
| ## Scope and appropriate use |
| |
| Use the graph for evidence-aware retrieval, graph exploration, and development of |
| research methods. Distinguish observed associations, computationally inferred |
| metabolic capabilities, curated biochemical records, and ontology relations. |
| Associations and graph paths alone do not establish causation or clinical efficacy. |
| Source coverage, research attention, organism resolution, and evidence density are |
| uneven; missing edges should not be assumed to be confirmed negative findings. |
| |
| This package contains graph tables, not raw participant-level clinical records, |
| sequencing reads, upstream database dumps, model checkpoints, or benchmark splits. |
| Previously reported benchmark results use the frozen reference graph and splits; |
| they are not new measurements on this audited export. Define and document suitable |
| splits and leakage controls when evaluating methods on this snapshot. |
| |
| ## Attribution and related code |
| |
| Code: [MicrobeKG-dataset_split_task](https://github.com/ZachGu-00/MicrobeKG-dataset_split_task). |
| The code repository documents the scope of its reference graph construction and |
| evaluation tools; this export is not a claim of complete upstream reconstruction. |
| |
| When citing the resource, include **MicrobeKG, audited-20260928**, the actual |
| Hugging Face repository URL, and the immutable dataset commit used. Also acknowledge |
| the relevant original data providers listed in [SOURCE_TERMS.md](SOURCE_TERMS.md). |
| No DOI or publication identifier has been assigned by this packaging operation. |
| Questions about this package can be filed in the linked code repository's Issues. |
| |
