Datasets:

mdpi-di
/

taxonomy

license: cc0-1.0
language:
  - en
pretty_name: MDPI Taxonomy
tags:
  - taxonomy
  - article-classification
  - research-topics
  - openalex
  - hierarchical
  - scholarly
task_categories:
  - text-classification
size_categories:
  - 100K<n<1M
configs:
  - config_name: taxonomy_flat
    data_files: data/taxonomy_flat.parquet
    default: true
  - config_name: taxonomy_hierarchy
    data_files: data/taxonomy_hierarchy.parquet
  - config_name: domain
    data_files: data/domain.parquet
  - config_name: field
    data_files: data/field.parquet
  - config_name: subfield
    data_files: data/subfield.parquet
  - config_name: topic
    data_files: data/topic.parquet
  - config_name: concept
    data_files: data/concept.parquet

MDPI Taxonomy

Visualization: Atlantis is an interactive visualization of this taxonomy with MDPI article classification.

What is it?

MDPI Taxonomy is a five-level hierarchical taxonomy for classifying scholarly content across scientific domains. It builds on the OpenAlex hierarchy (Domain → Field → Subfield → Topic) and extends it with an MDPI Concept layer (~113k nodes) for finer article classification and topic tagging.

Level	Name	Count	Source
1	Domain	4	OpenAlex
2	Field	26	OpenAlex
3	Subfield	252	OpenAlex
4	Topic	4,516	OpenAlex
5	Concept	113,892	MDPI

Domains: Health Sciences · Life Sciences · Physical Sciences · Social Sciences

Each node has a name and a short explanation. Explanations are AI-generated (concise glosses, not expert-curated definitions).

Worked example

A concept can sit under more than one valid path. Example for Industry 5.0:

Physical Sciences → Engineering → Electrical and Electronic Engineering → Advanced Data and IoT Technologies → Industry 5.0

What is being released

One dataset, multiple configs (one Parquet file each):

Config	What it is	Best for
`taxonomy_flat` (default)	Denormalized paths: IDs, names, explanations at every level	Browsing and training without joins
`taxonomy_hierarchy`	ID links for the full hierarchy	Building trees / graphs
`domain` · `field` · `subfield` · `topic` · `concept`	Normalized level tables	Custom joins

How to download and use it

🤗 Datasets

from datasets import load_dataset

ds = load_dataset("mdpi-di/taxonomy", "taxonomy_flat")
print(ds["train"][0])

# Or a single level
concepts = load_dataset("mdpi-di/taxonomy", "concept")

Polars

import polars as pl

flat = pl.read_parquet("data/taxonomy_flat.parquet")

# Recommended: full latest taxonomy (all rows through v5)
latest = flat  # v5 is the latest release; this file is the full snapshot as of v5

health = latest.filter(pl.col("domain_name") == "Health Sciences")
print(health.select(["field_name", "subfield_name", "topic_name", "concept_name"]).head())

Join normalized tables

import polars as pl

domain = pl.read_parquet("data/domain.parquet")
field = pl.read_parquet("data/field.parquet")
subfield = pl.read_parquet("data/subfield.parquet")
topic = pl.read_parquet("data/topic.parquet")
concept = pl.read_parquet("data/concept.parquet")

tree = (
    concept
    .join(topic, on="topic_id", how="left", suffix="_topic")
    .join(subfield, on="subfield_id", how="left", suffix="_subfield")
    .join(field, on="field_id", how="left", suffix="_field")
    .join(domain, on="domain_id", how="left", suffix="_domain")
)

Dataset statistics

Property	Value
Levels	5
Concepts	113,892
Full paths (`taxonomy_flat`)	113,947

Versions

Rows are tagged with version (v1, v3, v4, v5).

v5 is the latest. We recommend using the full current taxonomy in this release (all rows — v1 through v5). Most nodes were added in v1; later versions add incremental concepts/paths up to v5.

version	rows	notes
v1	112,133	bulk of the taxonomy
v3	344	incremental
v4	501	incremental
v5	969	latest increment

Filter to a single tag only if you need that increment alone, e.g. flat.filter(pl.col("version") == "v5").

Dataset structure

data/
  domain.parquet
  field.parquet
  subfield.parquet
  topic.parquet
  concept.parquet
  taxonomy_hierarchy.parquet
  taxonomy_flat.parquet

Table	Columns
`domain`	`domain_id`, `domain_name`, `domain_explanation`, `version`, `created_at`
`field`	`field_id`, `domain_id`, `field_name`, `field_explanation`, `version`, `created_at`
`subfield`	`subfield_id`, `field_id`, `subfield_name`, `subfield_explanation`, `version`, `created_at`
`topic`	`topic_id`, `subfield_id`, `topic_name`, `topic_explanation`, `version`, `created_at`
`concept`	`concept_id`, `topic_id`, `concept_name`, `concept_explanation`, `version`, `created_at`
`taxonomy_hierarchy`	`id`, `domain_id`, `field_id`, `subfield_id`, `topic_id`, `concept_id`, `version`, `created_at`
`taxonomy_flat`	All IDs, names, and explanations for every level, plus `version`, `created_at`

Dataset creation

Levels 1–4 come from the OpenAlex topic hierarchy.
Level 5 (Concept) is produced by MDPI Data Intelligence.
Explanations are AI-generated short texts for browsing and labeling — not authoritative definitions.

Considerations for using the data

Intended uses

Article / topic classification and labeling
Hierarchical retrieval over scientific concepts
Training or evaluating topic-tagging models

Limitations

AI-generated explanations may be inaccurate
Multiple version values coexist; prefer the full table as of v5 (all rows). Filter by version only for incremental slices
Some hierarchy rows may have null concept_id (path ends at topic)
A concept may appear under more than one parent path

Additional information

License

CC0 1.0 Universal

Citation

@dataset{mdpi_taxonomy,
  title        = {MDPI Taxonomy},
  author       = {{MDPI Data Intelligence}},
  year         = {2025},
  note         = {Hierarchical research topic taxonomy (Domain → Field → Subfield → Topic → Concept), extending OpenAlex},
  url          = {https://huggingface.co/datasets/mdpi-di/taxonomy},
  license      = {CC0-1.0}
}

Acknowledgements

Built on the OpenAlex topic hierarchy. Concept-level extensions and packaging by MDPI Data Intelligence.

Contact

Questions about this dataset or attribution: MDPI Data Intelligence team.