DOI: 10.5281/zenodo.18174813
title: Intuitive datasets: five-level data abstraction transformations

description: Overview

This dataset collection demonstrates systematic transformations of open datasets across five levels of abstraction (L4→L0→L3), enabling users with different data literacy levels to access and understand complex data. The transformations implement a meta-design framework for creating "intuitive datasets" that adapt their complexity to user needs.

Citation: If you use these datasets, please cite: 10.5281/zenodo.18174814

License: CC-BY-4.0 (Creative Commons Attribution 4.0 International)



Related Code : https://github.com/ArthurSrz/intuitiveness


Datasets Included


This collection contains three complete dataset transformation cycles from the French open data platform (data.gouv.fr):






test0_schools: French middle school performance indicators and student enrollment data

test1_ademe: ADEME (French environmental agency) funding allocations




Each dataset includes:

 





raw/: iriginal L4 files (unlinkable multi-level datasets from data.gouv.fr)

descent/: transformed files through L3 (linkable datasets), L2 (categorized table), L1 (feature vector), and L0 (atomic datum)

ascent/: reconstructed datasets from L0 back to L3 with added analytic dimensions

metadata/: transformation metadata, session exports, and join specifications




Five-level abstraction framework


The framework defines five levels of data abstraction:






Level 4 (L4): Unlinkable multi-level datasets - Multiple disconnected CSV files with no apparent structure

Level 3 (L3): Linkable multi-level datasets - Files connected through relationships, forming knowledge graphs

Level 2 (L2): Single dataset with multiple entities and attributes - Categorized or filtered tables

Level 1 (L1): Single entity or single attribute - Feature vectors or entity profiles

Level 0 (L0): Atomic datum - Single entity-attribute-value triplet (e.g., "average school score: 12.5")




Descent Phase (L4→L0)


The descent progressively reduces complexity:





L4→L3: Entity discovery and relationship detection to link disconnected files

L3→L2: Domain isolation through semantic categorization

L2→L1: Feature extraction to create vectors

L1→L0: Aggregation to derive atomic metrics




Ascent Phase (L0→L3)


The ascent intentionally reconstructs complexity:





L0→L1: Expand datum to feature vector with related attributes

L1→L2: Add categorical dimensions (e.g., high/low performance)

L2→L3: Add analytic dimensions to create multi-level structures




File naming convention


All files follow the pattern: `{dataset}_{level}_{description}.{ext}`


Examples:





test0_schools_L4_fr-en-college-effectifs-niveau-sexe-lv.csv - Original L4 raw file

test0_schools_L3_joined_table.csv - Joined table at L3

test0_schools_L0_datum.json - Atomic datum at L0

test0_schools_ascent_L3_table.csv - Reconstructed L3 table during ascent




Data Sources


All datasets originate from data.gouv.fr, France's national open data platform:


test0_schools :

- College enrollment by level, gender, and language : https://www.data.gouv.fr/datasets/effectifs-deleves-par-niveau-sexe-langues-vivantes-1-et-2-les-plus-frequentes-par-college-date-dobservation-au-debut-du-mois-doctobre-chaque-annee

- Middle school performance indicators : https://www.data.gouv.fr/datasets/indicateurs-de-valeur-ajoutee-des-colleges


test1_ademe :

- ADEME financial aid allocations : https://www.data.gouv.fr/datasets/les-aides-financieres-de-lademe-1

- ADEME list of funded projects : https://www.data.gouv.fr/datasets/couts-des-travaux-de-renovation-ecs


Transformation methodology


Transformations were performed using the `intuitiveness` Python package (v0.1.0) with the following dependencies:





Python 3.11

pandas 2.x

networkx 3.x

sentence-transformers (multilingual-e5-small model)




For detailed transformation logic, see the session export files in each dataset's `metadata/` folder.


Reuse examples


For open data platforms





Implement multi-level data access features

Design adaptive interfaces for users with varying data literacy

Test complexity-aware search and navigation




For educators





Teach data literacy concepts through concrete examples

 Demonstrate descent-ascent transformation cycles

Illustrate complexity management principles




For researchers





Study how data structure affects user comprehension

Analyze relationship discovery patterns in open datasets

Investigate semantic categorization effectiveness across domains




Contact


For questions, issues, or suggestions: arthur.sarazin@etu-iepg.fr