add: first draft of the readme

README.md

@@ -16,35 +16,881 @@ tags:
 - with_text
 pretty_name: BeyondArena Datasets
 size_categories:
-- 100K<n<1M
+- 100<n<1M
+configs:
+- config_name: 5g_energy_consumption
+  data_files:
+  - split: train
+    path: 5g_energy_consumption/*/dataset.parquet
+- config_name: acquire_valued_shoppers_challenge
+  data_files:
+  - split: train
+    path: acquire_valued_shoppers_challenge/*/dataset.parquet
+- config_name: airfoil_self_noise
+  data_files:
+  - split: train
+    path: airfoil_self_noise/*/dataset.parquet
+- config_name: allstate_claims_severity
+  data_files:
+  - split: train
+    path: allstate_claims_severity/*/dataset.parquet
+- config_name: amazon_employee_access
+  data_files:
+  - split: train
+    path: amazon_employee_access/*/dataset.parquet
+- config_name: amex_non_iid
+  data_files:
+  - split: train
+    path: amex_non_iid/versions/*/dataset.parquet
+- config_name: anes_voting_2026
+  data_files:
+  - split: train
+    path: anes_voting_2026/*/dataset.parquet
+- config_name: aps_failure
+  data_files:
+  - split: train
+    path: aps_failure/*/dataset.parquet
+- config_name: asp_potassco_classification
+  data_files:
+  - split: train
+    path: asp_potassco_classification/*/dataset.parquet
+- config_name: audiology_diagnosis
+  data_files:
+  - split: train
+    path: audiology_diagnosis/*/dataset.parquet
+- config_name: bad_customer_detection
+  data_files:
+  - split: train
+    path: bad_customer_detection/*/dataset.parquet
+- config_name: bank_customer_churn
+  data_files:
+  - split: train
+    path: bank_customer_churn/*/dataset.parquet
+- config_name: bank_marketing
+  data_files:
+  - split: train
+    path: bank_marketing/*/dataset.parquet
+- config_name: biogeographical_ancestry_prediction
+  data_files:
+  - split: train
+    path: biogeographical_ancestry_prediction/*/dataset.parquet
+- config_name: biomechanical_orthopaedic_prediction
+  data_files:
+  - split: train
+    path: biomechanical_orthopaedic_prediction/*/dataset.parquet
+- config_name: bioresponse
+  data_files:
+  - split: train
+    path: bioresponse/*/dataset.parquet
+- config_name: blood_tests_drink_prediction
+  data_files:
+  - split: train
+    path: blood_tests_drink_prediction/*/dataset.parquet
+- config_name: blood_transfusion
+  data_files:
+  - split: train
+    path: blood_transfusion/*/dataset.parquet
+- config_name: body_density_prediction
+  data_files:
+  - split: train
+    path: body_density_prediction/*/dataset.parquet
+- config_name: california_house_prices_2020
+  data_files:
+  - split: train
+    path: california_house_prices_2020/*/dataset.parquet
+- config_name: cardiotocography
+  data_files:
+  - split: train
+    path: cardiotocography/*/dataset.parquet
+- config_name: churn
+  data_files:
+  - split: train
+    path: churn/*/dataset.parquet
+- config_name: cirrhosis_patient_survival_prediction
+  data_files:
+  - split: train
+    path: cirrhosis_patient_survival_prediction/*/dataset.parquet
+- config_name: climate_model_weather_forecasting
+  data_files:
+  - split: train
+    path: climate_model_weather_forecasting/versions/*/dataset.parquet
+- config_name: clock_protein_toxicity
+  data_files:
+  - split: train
+    path: clock_protein_toxicity/*/dataset.parquet
+- config_name: coffee_rating_prediction
+  data_files:
+  - split: train
+    path: coffee_rating_prediction/*/dataset.parquet
+- config_name: coil_2000
+  data_files:
+  - split: train
+    path: coil_2000/*/dataset.parquet
+- config_name: concrete_compressive_strength
+  data_files:
+  - split: train
+    path: concrete_compressive_strength/*/dataset.parquet
+- config_name: consumer_complaints
+  data_files:
+  - split: train
+    path: consumer_complaints/versions/*/dataset.parquet
+- config_name: cooking_time
+  data_files:
+  - split: train
+    path: cooking_time/versions/*/dataset.parquet
+- config_name: covertype
+  data_files:
+  - split: train
+    path: covertype/*/dataset.parquet
+- config_name: credit_approval
+  data_files:
+  - split: train
+    path: credit_approval/*/dataset.parquet
+- config_name: credit_card_clients_default
+  data_files:
+  - split: train
+    path: credit_card_clients_default/*/dataset.parquet
+- config_name: credit_g
+  data_files:
+  - split: train
+    path: credit_g/*/dataset.parquet
+- config_name: customer_satisfaction_in_airline
+  data_files:
+  - split: train
+    path: customer_satisfaction_in_airline/*/dataset.parquet
+- config_name: delivery_eta
+  data_files:
+  - split: train
+    path: delivery_eta/versions/*/dataset.parquet
+- config_name: dementia_prediction
+  data_files:
+  - split: train
+    path: dementia_prediction/*/dataset.parquet
+- config_name: diabetes_130_us
+  data_files:
+  - split: train
+    path: diabetes_130_us/*/dataset.parquet
+- config_name: diamonds
+  data_files:
+  - split: train
+    path: diamonds/*/dataset.parquet
+- config_name: drug_induced_autoimmunity_prediction
+  data_files:
+  - split: train
+    path: drug_induced_autoimmunity_prediction/*/dataset.parquet
+- config_name: early_learning_predictors
+  data_files:
+  - split: train
+    path: early_learning_predictors/*/dataset.parquet
+- config_name: early_stage_diabetes_risk_prediction
+  data_files:
+  - split: train
+    path: early_stage_diabetes_risk_prediction/*/dataset.parquet
+- config_name: ecoli_proteins
+  data_files:
+  - split: train
+    path: ecoli_proteins/*/dataset.parquet
+- config_name: ecommerce_shipping
+  data_files:
+  - split: train
+    path: ecommerce_shipping/*/dataset.parquet
+- config_name: electric_motor_temperature_prediction
+  data_files:
+  - split: train
+    path: electric_motor_temperature_prediction/*/dataset.parquet
+- config_name: emscad
+  data_files:
+  - split: train
+    path: emscad/*/dataset.parquet
+- config_name: eryhemato_squamous_disease
+  data_files:
+  - split: train
+    path: eryhemato_squamous_disease/*/dataset.parquet
+- config_name: fiat_500
+  data_files:
+  - split: train
+    path: fiat_500/*/dataset.parquet
+- config_name: fitness_club
+  data_files:
+  - split: train
+    path: fitness_club/*/dataset.parquet
+- config_name: food_delivery_time
+  data_files:
+  - split: train
+    path: food_delivery_time/*/dataset.parquet
+- config_name: forensic_glass_identification
+  data_files:
+  - split: train
+    path: forensic_glass_identification/*/dataset.parquet
+- config_name: forest_fires
+  data_files:
+  - split: train
+    path: forest_fires/*/dataset.parquet
+- config_name: gallstone_disease
+  data_files:
+  - split: train
+    path: gallstone_disease/*/dataset.parquet
+- config_name: garments_worker_productivity
+  data_files:
+  - split: train
+    path: garments_worker_productivity/*/dataset.parquet
+- config_name: ghanas_indigenous_intel
+  data_files:
+  - split: train
+    path: ghanas_indigenous_intel/*/dataset.parquet
+- config_name: give_me_some_credit
+  data_files:
+  - split: train
+    path: give_me_some_credit/*/dataset.parquet
+- config_name: hazelnut_spread_contaminant_detection
+  data_files:
+  - split: train
+    path: hazelnut_spread_contaminant_detection/*/dataset.parquet
+- config_name: healthcare_insurance_expenses
+  data_files:
+  - split: train
+    path: healthcare_insurance_expenses/*/dataset.parquet
+- config_name: heart_disease_cleveland
+  data_files:
+  - split: train
+    path: heart_disease_cleveland/*/dataset.parquet
+- config_name: heart_disease_hungary
+  data_files:
+  - split: train
+    path: heart_disease_hungary/*/dataset.parquet
+- config_name: heart_disease_va_long_beach
+  data_files:
+  - split: train
+    path: heart_disease_va_long_beach/*/dataset.parquet
+- config_name: heart_failure_followup_survival
+  data_files:
+  - split: train
+    path: heart_failure_followup_survival/*/dataset.parquet
+- config_name: heloc
+  data_files:
+  - split: train
+    path: heloc/*/dataset.parquet
+- config_name: hepatitis_c_prediction
+  data_files:
+  - split: train
+    path: hepatitis_c_prediction/*/dataset.parquet
+- config_name: hepatitis_survival_prediction
+  data_files:
+  - split: train
+    path: hepatitis_survival_prediction/*/dataset.parquet
+- config_name: hiva_agnostic
+  data_files:
+  - split: train
+    path: hiva_agnostic/*/dataset.parquet
+- config_name: home_credit_default_risk
+  data_files:
+  - split: train
+    path: home_credit_default_risk/*/dataset.parquet
+- config_name: home_credit_default_stability
+  data_files:
+  - split: train
+    path: home_credit_default_stability/versions/*/dataset.parquet
+- config_name: homeq_default_prediction
+  data_files:
+  - split: train
+    path: homeq_default_prediction/*/dataset.parquet
+- config_name: homesite_quote_conversion
+  data_files:
+  - split: train
+    path: homesite_quote_conversion/*/dataset.parquet
+- config_name: horse_colic_survival
+  data_files:
+  - split: train
+    path: horse_colic_survival/*/dataset.parquet
+- config_name: hotel_booking_demand
+  data_files:
+  - split: train
+    path: hotel_booking_demand/*/dataset.parquet
+- config_name: houses
+  data_files:
+  - split: train
+    path: houses/*/dataset.parquet
+- config_name: hr_analytics
+  data_files:
+  - split: train
+    path: hr_analytics/*/dataset.parquet
+- config_name: ieee_fraud_detection
+  data_files:
+  - split: train
+    path: ieee_fraud_detection/*/dataset.parquet
+- config_name: immoscout_german_house_prices
+  data_files:
+  - split: train
+    path: immoscout_german_house_prices/*/dataset.parquet
+- config_name: in_vehicle_coupon_recommendation
+  data_files:
+  - split: train
+    path: in_vehicle_coupon_recommendation/*/dataset.parquet
+- config_name: indian_liver_patient_dataset
+  data_files:
+  - split: train
+    path: indian_liver_patient_dataset/*/dataset.parquet
+- config_name: iranian_churn
+  data_files:
+  - split: train
+    path: iranian_churn/*/dataset.parquet
+- config_name: jm1
+  data_files:
+  - split: train
+    path: jm1/*/dataset.parquet
+- config_name: kdd_cup_09_appetency
+  data_files:
+  - split: train
+    path: kdd_cup_09_appetency/*/dataset.parquet
+- config_name: kick
+  data_files:
+  - split: train
+    path: kick/*/dataset.parquet
+- config_name: kickstarter
+  data_files:
+  - split: train
+    path: kickstarter/*/dataset.parquet
+- config_name: labour_inspection_compliance
+  data_files:
+  - split: train
+    path: labour_inspection_compliance/*/dataset.parquet
+- config_name: lending_club
+  data_files:
+  - split: train
+    path: lending_club/versions/*/dataset.parquet
+- config_name: ljubljana_breast_cancer
+  data_files:
+  - split: train
+    path: ljubljana_breast_cancer/*/dataset.parquet
+- config_name: ljubljana_primary_tumor
+  data_files:
+  - split: train
+    path: ljubljana_primary_tumor/*/dataset.parquet
+- config_name: lung_cancer
+  data_files:
+  - split: train
+    path: lung_cancer/*/dataset.parquet
+- config_name: lung_cancer_epithelial_genexp
+  data_files:
+  - split: train
+    path: lung_cancer_epithelial_genexp/*/dataset.parquet
+- config_name: maps_router_eta
+  data_files:
+  - split: train
+    path: maps_router_eta/versions/*/dataset.parquet
+- config_name: marketing_campaign
+  data_files:
+  - split: train
+    path: marketing_campaign/*/dataset.parquet
+- config_name: maternal_health_risk
+  data_files:
+  - split: train
+    path: maternal_health_risk/*/dataset.parquet
+- config_name: mercari_price_suggestion
+  data_files:
+  - split: train
+    path: mercari_price_suggestion/versions/*/dataset.parquet
+- config_name: mercedes_benz_greener_manufacturing
+  data_files:
+  - split: train
+    path: mercedes_benz_greener_manufacturing/*/dataset.parquet
+- config_name: miami_housing
+  data_files:
+  - split: train
+    path: miami_housing/*/dataset.parquet
+- config_name: mic
+  data_files:
+  - split: train
+    path: mic/*/dataset.parquet
+- config_name: mice_protein_trisomy_discriminant
+  data_files:
+  - split: train
+    path: mice_protein_trisomy_discriminant/*/dataset.parquet
+- config_name: micro_mass
+  data_files:
+  - split: train
+    path: micro_mass/*/dataset.parquet
+- config_name: musk
+  data_files:
+  - split: train
+    path: musk/*/dataset.parquet
+- config_name: mutual_funds_india
+  data_files:
+  - split: train
+    path: mutual_funds_india/*/dataset.parquet
+- config_name: naticusdroid_android_permissions_dataset
+  data_files:
+  - split: train
+    path: naticusdroid_android_permissions_dataset/*/dataset.parquet
+- config_name: obesity_estimation
+  data_files:
+  - split: train
+    path: obesity_estimation/*/dataset.parquet
+- config_name: online_shoppers_purchasing_intention_dataset
+  data_files:
+  - split: train
+    path: online_shoppers_purchasing_intention_dataset/*/dataset.parquet
+- config_name: otto_group_product_classification_challenge
+  data_files:
+  - split: train
+    path: otto_group_product_classification_challenge/*/dataset.parquet
+- config_name: pancreatic_cancer_mouse_detection
+  data_files:
+  - split: train
+    path: pancreatic_cancer_mouse_detection/*/dataset.parquet
+- config_name: parkinsons_biomedical_voice_measurements
+  data_files:
+  - split: train
+    path: parkinsons_biomedical_voice_measurements/*/dataset.parquet
+- config_name: physiochemical_protein
+  data_files:
+  - split: train
+    path: physiochemical_protein/*/dataset.parquet
+- config_name: polish_companies_bankruptcy
+  data_files:
+  - split: train
+    path: polish_companies_bankruptcy/*/dataset.parquet
+- config_name: porto_seguro
+  data_files:
+  - split: train
+    path: porto_seguro/*/dataset.parquet
+- config_name: predict_students_dropout_and_academic_success
+  data_files:
+  - split: train
+    path: predict_students_dropout_and_academic_success/*/dataset.parquet
+- config_name: prostate_cancer_detection
+  data_files:
+  - split: train
+    path: prostate_cancer_detection/*/dataset.parquet
+- config_name: pva_revenue_prediction_kddcup98
+  data_files:
+  - split: train
+    path: pva_revenue_prediction_kddcup98/*/dataset.parquet
+- config_name: qsar_aquatic_toxicity
+  data_files:
+  - split: train
+    path: qsar_aquatic_toxicity/*/dataset.parquet
+- config_name: qsar_biodeg
+  data_files:
+  - split: train
+    path: qsar_biodeg/*/dataset.parquet
+- config_name: qsar_fish_toxicity
+  data_files:
+  - split: train
+    path: qsar_fish_toxicity/*/dataset.parquet
+- config_name: qsar_tid_11
+  data_files:
+  - split: train
+    path: qsar_tid_11/*/dataset.parquet
+- config_name: regensburg_pediatric_appendicitis
+  data_files:
+  - split: train
+    path: regensburg_pediatric_appendicitis/*/dataset.parquet
+- config_name: rossmann_store_sales
+  data_files:
+  - split: train
+    path: rossmann_store_sales/*/dataset.parquet
+- config_name: santander_customer_satisfaction
+  data_files:
+  - split: train
+    path: santander_customer_satisfaction/*/dataset.parquet
+- config_name: santander_customer_transaction_prediction
+  data_files:
+  - split: train
+    path: santander_customer_transaction_prediction/*/dataset.parquet
+- config_name: santander_transaction_value
+  data_files:
+  - split: train
+    path: santander_transaction_value/*/dataset.parquet
+- config_name: sat11_hand_algo_runtime
+  data_files:
+  - split: train
+    path: sat11_hand_algo_runtime/*/dataset.parquet
+- config_name: sberbank_housing_market_forecasting
+  data_files:
+  - split: train
+    path: sberbank_housing_market_forecasting/*/dataset.parquet
+- config_name: sdss_17
+  data_files:
+  - split: train
+    path: sdss_17/*/dataset.parquet
+- config_name: seismic_bumps
+  data_files:
+  - split: train
+    path: seismic_bumps/*/dataset.parquet
+- config_name: sepsis_prediction
+  data_files:
+  - split: train
+    path: sepsis_prediction/versions/*/dataset.parquet
+- config_name: sepsis_survival_minimal_clinical_records
+  data_files:
+  - split: train
+    path: sepsis_survival_minimal_clinical_records/*/dataset.parquet
+- config_name: sf_permit_time
+  data_files:
+  - split: train
+    path: sf_permit_time/*/dataset.parquet
+- config_name: south_africa_coronary_heart_disease
+  data_files:
+  - split: train
+    path: south_africa_coronary_heart_disease/*/dataset.parquet
+- config_name: splice
+  data_files:
+  - split: train
+    path: splice/*/dataset.parquet
+- config_name: student_portuguese_performance
+  data_files:
+  - split: train
+    path: student_portuguese_performance/*/dataset.parquet
+- config_name: superconductivity
+  data_files:
+  - split: train
+    path: superconductivity/*/dataset.parquet
+- config_name: taiwanese_bankruptcy_prediction
+  data_files:
+  - split: train
+    path: taiwanese_bankruptcy_prediction/*/dataset.parquet
+- config_name: telemonitoring_parkinsons_biomedical_voice_measurements
+  data_files:
+  - split: train
+    path: telemonitoring_parkinsons_biomedical_voice_measurements/*/dataset.parquet
+- config_name: thyroid_discordant
+  data_files:
+  - split: train
+    path: thyroid_discordant/*/dataset.parquet
+- config_name: tour_travels_churn
+  data_files:
+  - split: train
+    path: tour_travels_churn/*/dataset.parquet
+- config_name: video_game_fps_prediction
+  data_files:
+  - split: train
+    path: video_game_fps_prediction/*/dataset.parquet
+- config_name: video_transcoding_time_prediction
+  data_files:
+  - split: train
+    path: video_transcoding_time_prediction/*/dataset.parquet
+- config_name: website_phishing
+  data_files:
+  - split: train
+    path: website_phishing/*/dataset.parquet
+- config_name: wids_diabetes_mellitus
+  data_files:
+  - split: train
+    path: wids_diabetes_mellitus/*/dataset.parquet
+- config_name: wine_quality
+  data_files:
+  - split: train
+    path: wine_quality/*/dataset.parquet
+- config_name: wine_world_cost
+  data_files:
+  - split: train
+    path: wine_world_cost/*/dataset.parquet
 ---
+
 # BeyondArena Datasets
 
 Datasets from BeyondArena, a unified, holistic benchmark for tabular data that supports diverse task types (IID, temporal, grouped),
-across sample size and feature dimensionality scales, with diverse feature types (with text, with high cardinality) from a 
+across sample size and feature dimensionality scales, with diverse feature types (with text, with high cardinality) from a
 broad range of disciplines.
 
-We introduce BeyondArena and its datasets in: [TODO link] 
+We introduce BeyondArena and its datasets in: [TODO link to paper]
+
+<details>
+<summary><b>Click for BibTeX!</b></summary>
+
+```text
+@article{X,
+  title   = {X},
+  author  = {X},
+  year    = {2026}
+}
+```
+</details>
 
-**BibTeX:** [More Information Needed]
+More details:
+- **Project page and leaderboard:** http://tabarena.ai/
+- **Code / Benchmark repository:** https://tabarena.ai/code
 
+## Quickstart
 
 We suggest using the dataset via DataFoundry ([TODO LINK]):
-[TODO add code example from data foundry here]
 
+```python
+# TODO: add code example from DataFoundry here
+```
+
+### Loading a single dataset directly
+
+Each per-dataset config in this card's frontmatter routes only `dataset.parquet`, which is enough to get
+the table but **not** the sibling metadata files (`dtypes.json`, `task_metadata.*`, `experiment_metadata.*`
+with the CV folds, `dataset_metadata.*`, `container_metadata.json`). Because the benchmark protocol depends
+on those files, the recommended path is to download the whole dataset folder with `huggingface_hub`:
+
+```python
+from huggingface_hub import snapshot_download
+
+local_dir = snapshot_download(
+    repo_id="TabArena/BeyondArena",
+    repo_type="dataset",
+    allow_patterns=["churn/**"],                # one or more <dataset_name>/** globs
+)
+# local_dir/<dataset_name>/<uuid>/ now contains all six files for that dataset.
+```
+
+For the 10 datasets that use the `versions/` wrapper (see [Dataset Structure](#dataset-structure)), the layout 
+is `<dataset_name>/versions/<uuid>/...` — the `<dataset_name>/**` glob already covers both layouts.
+
+If you only need the table (no folds, no metadata), the `datasets` library shortcut works:
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("<org>/BeyondArena", name="churn")   # any per-dataset config_name
+```
+
+### Downloading the full bundle
 
-## Dataset Selection Overview
+```python
+from huggingface_hub import snapshot_download
 
-We build on top of the dataset curation protocol of TabArena-v0.1 (https://arxiv.org/abs/2506.16791) 
+local_dir = snapshot_download(
+    repo_id="<org>/BeyondArena",
+    repo_type="dataset",
+)
+```
+
+## Datasets
+
+BeyondArena comes with 142 datasets. BeyondArena covers tabular classification and regression tasks.
+And the following types of datasets:
+
+- **IID tabular data**
+- **Non-IID temporal tabular data**
+- **Non-IID grouped tabular data**
+- **IID and non-IID tabular data with text features** 
+- **Tabular data with high-cardinality categoricals**
+
+### Dataset Selection Overview
+
+We build on top of the dataset curation protocol of TabArena-v0.1 (https://arxiv.org/abs/2506.16791)
 and curate 142 tiny to large-sized, tabular IID and non-IID tasks. For details, see the paper.
 
 ![image](https://cdn-uploads.huggingface.co/production/uploads/677d00ac70b9142c01cc90f9/9PtFwqNy14dBavZtFyu6Y.png)
 
-## Dataset Dashboard
+### Dataset Dashboard
 
 We curated a diverse set of datasets. We share the dataset sizes (w.r.t. rows, columns, and cells), their age distribution,
-the distribution of feature types per dataset, and the share of datasets from a specific problem type, 
+the distribution of feature types per dataset, and the share of datasets from a specific problem type,
 task type, dataset source, or application domain.
 
-
 ![image](https://cdn-uploads.huggingface.co/production/uploads/677d00ac70b9142c01cc90f9/3hNSmDFA2O-Q0HYkahP0c.png)
+
+### Per-Dataset Index
+
+Per-dataset metadata for the BeyondArena benchmark, sorted by number of rows (`N`).
+
+<details>
+<summary><b>Click for expand all 142 Datasets!</b></summary>
+
+
+**Columns.** `N` = rows · `d` = columns (before preprocessing) · `C` = classes (regression: —) · **Prob.** = problem type (Binary classification / Multiclass / Regression) · **Task** = task type (IID / Temporal / Grouped) · `Age` = years since publication at release time.
+
+**Domain abbreviations.** M & H = Medical & Healthcare · B & M = Business & Marketing · B & L = Biology & Life Sciences · T & I = Technology & Internet · I & M = Industry & Manufacturing · C & M = Chemistry & Material Science · E & C = Environmental Science & Climate · P & A = Physics & Astronomy.
+
+Each dataset has an `academic_reference_bibtex_key` in its `dataset_metadata.dataset-mold-v1.json`; the matching BibTeX entries are collected in [`dataset_references.bib`](./dataset_references.bib). The `BibKey(s)` column below lists the keys to look up in that file (some datasets cite multiple sources).
+
+| Dataset | Domain | Source | Year | Age | N | d | C | Prob. | Task | BibKey(s) |
+| --- | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | --- | --- |
+| hepatitis_survival_prediction | M & H | UCI | 1981 | 45 | 155 | 19 | 2 | Binary | IID | efron1981statistical |
+| cirrhosis_patient_survival_prediction | M & H | UCI | 1984 | 42 | 161 | 17 | — | Reg | IID | dickson1989prognosis |
+| clock_protein_toxicity | B & L | UCI | 2021 | 5 | 171 | 1,117 | 2 | Binary | IID | gul2021structure |
+| pancreatic_cancer_mouse_detection | M & H | Other | 2003 | 23 | 181 | 6,771 | 2 | Binary | Grouped | hingorani2003preinvasive |
+| lung_cancer_epithelial_genexp | M & H | GOV Website | 2006 | 20 | 187 | 22,215 | 2 | Binary | IID | spira2007airway |
+| parkinsons_biomedical_voice_measurements | M & H | UCI | 2007 | 19 | 195 | 23 | 2 | Binary | Grouped | little2007exploiting |
+| lung_cancer | M & H | Other | 2001 | 25 | 197 | 12,600 | 4 | Multi | IID | bhattacharjee2001classification |
+| audiology_diagnosis | M & H | UCI | 1987 | 39 | 199 | 68 | 3 | Multi | IID | bareiss1990protos |
+| heart_disease_va_long_beach | M & H | UCI | 1989 | 37 | 200 | 13 | 2 | Binary | IID | detrano1989international |
+| forensic_glass_identification | C & M | UCI | 1987 | 39 | 214 | 9 | 6 | Multi | IID | German1987glass |
+| early_stage_diabetes_risk_prediction | M & H | UCI | 2019 | 7 | 251 | 16 | 2 | Binary | IID | islam2019likelihood |
+| body_density_prediction | M & H | Kaggle | 1985 | 41 | 252 | 13 | — | Reg | IID | penrose1985generalized |
+| ljubljana_breast_cancer | M & H | UCI | 1988 | 38 | 286 | 9 | 2 | Binary | IID | Zwitter1988BreastCancer |
+| heart_disease_hungary | M & H | UCI | 1989 | 37 | 294 | 13 | 2 | Binary | IID | detrano1989international |
+| heart_failure_followup_survival | M & H | UCI | 2020 | 6 | 299 | 12 | 2 | Binary | IID | chicco2020machine |
+| ljubljana_primary_tumor | M & H | UCI | 1987 | 39 | 302 | 17 | 11 | Multi | IID | Zwitter1987primarytumor |
+| heart_disease_cleveland | M & H | UCI | 1989 | 37 | 303 | 13 | 2 | Binary | IID | detrano1989international |
+| biomechanical_orthopaedic_prediction | M & H | UCI | 2011 | 15 | 310 | 6 | 3 | Multi | IID | Barreto2005Vertebral |
+| gallstone_disease | M & H | UCI | 2023 | 3 | 319 | 38 | 2 | Binary | IID | esen2024early |
+| prostate_cancer_detection | M & H | Other | 2002 | 24 | 322 | 15,154 | 2 | Binary | IID | petricoin2002serum |
+| ecoli_proteins | B & L | UCI | 1996 | 30 | 327 | 6 | 5 | Multi | IID | horton1996probabilistic |
+| horse_colic_survival | B & L | UCI | 1989 | 37 | 344 | 20 | 3 | Multi | IID | McLeish1989HorseColic |
+| blood_tests_drink_prediction | M & H | UCI | 1996 | 30 | 345 | 5 | — | Reg | IID | UCILiverDisorders2016 |
+| eryhemato_squamous_disease | M & H | UCI | 1997 | 29 | 366 | 34 | 6 | Multi | IID | guvenir1998learning |
+| dementia_prediction | M & H | Other | 2010 | 16 | 370 | 8 | 3 | Multi | Grouped | marcus2010open |
+| south_africa_coronary_heart_disease | M & H | Kaggle | 1983 | 43 | 462 | 9 | 2 | Binary | IID | rossouw1983coronary |
+| obesity_estimation | M & H | UCI | 2019 | 7 | 498 | 14 | — | Reg | IID | palechor2019dataset |
+| telemonitoring_parkinsons_biomedical_voice_measurements | M & H | UCI | 2007 | 19 | 502 | 19 | — | Reg | Grouped | tsanas2009accurate |
+| forest_fires | E & C | UCI | 2008 | 18 | 517 | 12 | — | Reg | IID | cortez2007data |
+| qsar_aquatic_toxicity | B & L | UCI | 2014 | 12 | 546 | 8 | — | Reg | IID | cassotti2014prediction |
+| micro_mass | B & L | UCI | 2013 | 13 | 571 | 1,082 | 20 | Multi | Grouped | mahe2014automatic |
+| indian_liver_patient_dataset | M & H | UCI | 2012 | 14 | 583 | 10 | 2 | Binary | IID | ramana2012critical |
+| drug_induced_autoimmunity_prediction | M & H | UCI | 2025 | 1 | 597 | 177 | 2 | Binary | IID | huang2025interdia |
+| hepatitis_c_prediction | M & H | UCI | 2018 | 8 | 608 | 12 | 4 | Multi | IID | hoffmann2018using |
+| biogeographical_ancestry_prediction | B & L | GitHub | 2025 | 1 | 635 | 104 | 10 | Multi | IID | heinzel2025advancing, ruiz2023development, xavier2020development |
+| student_portuguese_performance | Education | UCI | 2008 | 18 | 649 | 30 | — | Reg | IID | silva2008using |
+| credit_approval | Finance | UCI | 1987 | 39 | 690 | 15 | 2 | Binary | IID | quinlan1987simplifying |
+| blood_transfusion | M & H | UCI | 2008 | 18 | 748 | 4 | 2 | Binary | IID | yeh2009knowledge |
+| regensburg_pediatric_appendicitis | M & H | Other | 2021 | 5 | 763 | 51 | 2 | Binary | IID | marcinkevivcs2024interpretable |
+| mutual_funds_india | Finance | Kaggle | 2023 | 3 | 793 | 12 | — | Reg | IID | Barnawal2022MutualFundsIndiaDetailed |
+| qsar_fish_toxicity | B & L | UCI | 2015 | 11 | 908 | 6 | — | Reg | IID | cassotti2015similarity |
+| tour_travels_churn | B & M | Kaggle | 2021 | 5 | 954 | 6 | 2 | Binary | IID | Tejashvi2023TourTravelsCustomerChurnPrediction |
+| credit_g | Finance | UCI | 1994 | 32 | 1,000 | 20 | 2 | Binary | IID | hofmann1994statlog |
+| maternal_health_risk | M & H | UCI | 2020 | 6 | 1,014 | 6 | 3 | Multi | IID | ahmed2020review |
+| concrete_compressive_strength | C & M | UCI | 1998 | 28 | 1,030 | 8 | — | Reg | IID | yeh1998modeling |
+| qsar_biodeg | B & L | UCI | 2013 | 13 | 1,054 | 41 | 2 | Binary | IID | mansouri2013quantitative |
+| mice_protein_trisomy_discriminant | B & L | UCI | 2015 | 11 | 1,080 | 76 | 8 | Multi | Grouped | higuera2015self |
+| garments_worker_productivity | I & M | UCI | 2020 | 6 | 1,197 | 15 | — | Reg | Temporal | imran2021mining |
+| asp_potassco_classification | T & I | ASlib | 2014 | 12 | 1,212 | 136 | 11 | Multi | Grouped | hoos2014claspfolio, bischl_aslib_2016 |
+| wine_world_cost | B & M | Kaggle | 2023 | 3 | 1,279 | 14 | — | Reg | IID | Rustamov2023WineDataset |
+| healthcare_insurance_expenses | M & H | Kaggle | 2023 | 3 | 1,338 | 6 | — | Reg | IID | arunjangir2452023insurance |
+| website_phishing | T & I | UCI | 2014 | 12 | 1,353 | 9 | 3 | Multi | IID | abdelhamid2014phishing |
+| fitness_club | B & M | Kaggle | 2023 | 3 | 1,500 | 6 | 2 | Binary | IID | ddosad2023fitness |
+| airfoil_self_noise | P & A | UCI | 2014 | 12 | 1,503 | 5 | — | Reg | IID | brooks1989airfoil |
+| fiat_500 | T & I | Kaggle | 2020 | 6 | 1,538 | 7 | — | Reg | IID | paolocons2020fiat |
+| mic | M & H | UCI | 2020 | 6 | 1,699 | 111 | 8 | Multi | IID | golovenkin2020trajectories |
+| bad_customer_detection | B & M | Kaggle | 2020 | 6 | 1,723 | 13 | 2 | Binary | IID | Podsyp2020IsThisAGoodCustomer |
+| cardiotocography | M & H | UCI | 2010 | 16 | 2,126 | 22 | 3 | Multi | Grouped | campos2010cardiotocography |
+| marketing_campaign | B & M | Kaggle | 2020 | 6 | 2,240 | 25 | 2 | Binary | IID | saldanha2020marketing |
+| coffee_rating_prediction | B & M | Kaggle | 2023 | 3 | 2,369 | 12 | — | Reg | Temporal | AlIrsyad2023CoffeeDataCoffeeReview |
+| hazelnut_spread_contaminant_detection | B & L | OpenML | 2020 | 6 | 2,400 | 30 | 2 | Binary | IID | ricci2021machine |
+| seismic_bumps | E & C | UCI | 2013 | 13 | 2,584 | 15 | 2 | Binary | IID | sikora2010application |
+| iranian_churn | B & M | UCI | 2011 | 15 | 2,850 | 13 | 2 | Binary | IID | keramati2011churn |
+| sat11_hand_algo_runtime | T & I | ASlib | 2011 | 15 | 2,960 | 169 | — | Reg | Grouped | xu-sat12a, sat12, bischl_aslib_2016 |
+| splice | B & L | UCI | 1991 | 35 | 3,190 | 60 | 3 | Multi | IID | towell1994knowledge |
+| thyroid_discordant | M & H | UCI | 1986 | 40 | 3,711 | 26 | 2 | Binary | IID | quinlan1987simplifying |
+| bioresponse | B & L | Kaggle | 2012 | 14 | 3,751 | 1,776 | 2 | Binary | IID | bioresponse2012hamner |
+| hiva_agnostic | C & M | Other | 2007 | 19 | 3,845 | 1,518 | 2 | Binary | IID | guyon2007agnostic |
+| mercedes_benz_greener_manufacturing | I & M | Kaggle | 2017 | 9 | 4,204 | 371 | — | Reg | Temporal | Novy2017MercedesBenzGreenerManufacturing |
+| predict_students_dropout_and_academic_success | Education | UCI | 2021 | 5 | 4,424 | 36 | 3 | Multi | IID | martins2021early |
+| santander_transaction_value | Finance | Kaggle | 2018 | 8 | 4,447 | 540 | — | Reg | IID | McDonald2018SantanderValuePredictionChallenge |
+| churn | T & I | OpenML | 2005 | 21 | 5,000 | 19 | 2 | Binary | IID | marcoulides2005churn |
+| homeq_default_prediction | B & M | Other | 2016 | 10 | 5,708 | 12 | 2 | Binary | IID | baesens2016credit |
+| qsar_tid_11 | C & M | OpenML | 2015 | 11 | 5,741 | 1,024 | — | Reg | IID | olier2018meta |
+| polish_companies_bankruptcy | Finance | UCI | 2010 | 16 | 5,790 | 64 | 2 | Binary | IID | zikeba2016ensemble |
+| wine_quality | C & M | UCI | 2009 | 17 | 6,497 | 12 | — | Reg | IID | cortez2009modeling |
+| musk | C & M | UCI | 1994 | 32 | 6,598 | 166 | 2 | Binary | Grouped | dietterich1993comparison |
+| taiwanese_bankruptcy_prediction | Finance | UCI | 2009 | 17 | 6,819 | 92 | 2 | Binary | IID | liang2016financial |
+| naticusdroid_android_permissions_dataset | T & I | UCI | 2021 | 5 | 7,491 | 85 | 2 | Binary | IID | mathur2021naticusdroid |
+| coil_2000 | B & M | UCI | 2000 | 26 | 9,822 | 85 | 2 | Binary | IID | van2000coil |
+| bank_customer_churn | B & M | Kaggle | 2020 | 6 | 10,000 | 10 | 2 | Binary | IID | Topre2022BankCustomerChurn |
+| immoscout_german_house_prices | B & M | Kaggle | 2019 | 7 | 10,317 | 23 | — | Reg | IID | Shritech2019GermanHousingPricePrediction, OpenML43342Dataset |
+| heloc | Finance | Kaggle | 2021 | 5 | 10,459 | 23 | 2 | Binary | IID | averkiyoliabev2021heloc |
+| jm1 | T & I | OpenML | 2004 | 22 | 10,885 | 21 | 2 | Binary | IID | menzies2004good |
+| ghanas_indigenous_intel | E & C | Zindi | 2025 | 1 | 10,928 | 10 | 4 | Multi | Temporal | zindi_ghana_indigenous_intel_2025 |
+| ecommerce_shipping | B & M | Kaggle | 2021 | 5 | 10,999 | 10 | 2 | Binary | IID | gopalani2021ecommerce |
+| video_game_fps_prediction | T & I | OpenML | 2020 | 6 | 12,288 | 38 | — | Reg | Grouped | peeters2021performance |
+| online_shoppers_purchasing_intention_dataset | B & M | UCI | 2017 | 9 | 12,330 | 17 | 2 | Binary | IID | sakar2019real |
+| in_vehicle_coupon_recommendation | B & M | UCI | 2017 | 9 | 12,684 | 24 | 2 | Binary | IID | wang2017bayesian |
+| miami_housing | Finance | Kaggle | 2016 | 10 | 13,776 | 15 | — | Reg | IID | bourassa2021big |
+| emscad | B & M | Other | 2014 | 12 | 17,460 | 17 | 2 | Binary | IID | vidros2017automatic |
+| early_learning_predictors | Education | Other | 2023 | 3 | 18,874 | 743 | — | Reg | Grouped | DataDrive2030_2024_elom_thrivebyfive |
+| hr_analytics | B & M | Kaggle | 2021 | 5 | 19,158 | 12 | 2 | Binary | IID | arashnic2021hr |
+| houses | B & M | Other | 1990 | 36 | 19,675 | 8 | — | Reg | IID | pace1997sparse |
+| superconductivity | P & A | UCI | 2018 | 8 | 21,263 | 81 | — | Reg | IID | hamidieh2018data |
+| sberbank_housing_market_forecasting | B & M | Kaggle | 2017 | 9 | 27,195 | 386 | — | Reg | Temporal | Herman2024HomeCreditCreditRiskModelStability |
+| credit_card_clients_default | Finance | UCI | 2009 | 17 | 30,000 | 23 | 2 | Binary | IID | yeh2009comparisons |
+| amazon_employee_access | B & M | Kaggle | 2010 | 16 | 32,769 | 9 | 2 | Binary | IID | hamner2013amazon |
+| california_house_prices_2020 | B & M | Kaggle | 2021 | 5 | 41,528 | 41 | — | Reg | Temporal | d2lcourse2021california_house_prices |
+| bank_marketing | Finance | UCI | 2012 | 14 | 45,211 | 13 | 2 | Binary | IID | moro2014bank-marketing |
+| food_delivery_time | B & M | Kaggle | 2023 | 3 | 45,451 | 9 | — | Reg | IID | rajatkumar302023food |
+| physiochemical_protein | C & M | UCI | 2013 | 13 | 45,730 | 9 | — | Reg | IID | rana2013protein |
+| anes_voting_2026 | Social Science | Other | 2026 | 0 | 48,587 | 318 | 2 | Binary | Temporal | anes2026timeseries |
+| kdd_cup_09_appetency | B & M | Other | 2008 | 18 | 50,000 | 212 | 2 | Binary | IID | guyon2009analysis |
+| diamonds | B & M | Other | 2015 | 11 | 53,940 | 9 | — | Reg | IID | wickham2016data |
+| otto_group_product_classification_challenge | B & M | Kaggle | 2015 | 11 | 61,878 | 93 | 9 | Multi | IID | Bossan2015OttoGroupProductClassificationChallenge |
+| labour_inspection_compliance | I & M | Other | 2019 | 7 | 63,634 | 376 | 2 | Binary | IID | flogard2022dataset |
+| video_transcoding_time_prediction | T & I | UCI | 2015 | 11 | 68,784 | 18 | — | Reg | Grouped | deneke2014video |
+| santander_customer_satisfaction | B & M | Kaggle | 2016 | 10 | 71,080 | 307 | 2 | Binary | IID | Jimenez2016SantanderCustomerSatisfaction |
+| diabetes_130_us | M & H | UCI | 2014 | 12 | 71,518 | 44 | 2 | Binary | IID | strack2014impact |
+| kick | B & M | Kaggle | 2011 | 15 | 72,983 | 32 | 2 | Binary | Temporal | DontGetKicked |
+| aps_failure | I & M | UCI | 2016 | 10 | 76,000 | 170 | 2 | Binary | IID | ida2016challenge |
+| sdss_17 | P & A | Kaggle | 2022 | 4 | 78,053 | 11 | 3 | Multi | IID | accetta2022seventeenth |
+| hotel_booking_demand | B & M | Other | 2019 | 7 | 81,418 | 31 | 2 | Binary | Temporal | antonio2019hotel |
+| 5g_energy_consumption | T & I | HuggingFace | 2023 | 3 | 92,629 | 20 | — | Reg | Grouped | huawei_netop_5g_energy_consumption |
+| sepsis_survival_minimal_clinical_records | M & H | UCI | 2020 | 6 | 110,204 | 3 | 2 | Binary | IID | chicco2020survival |
+| sf_permit_time | B & M | GOV Website | 2025 | 1 | 116,954 | 37 | — | Reg | Temporal | SanFrancisco2026BuildingPermits |
+| wids_diabetes_mellitus | M & H | Kaggle | 2021 | 5 | 127,358 | 181 | 2 | Binary | IID | Matthys2021WiDSDatathon2021 |
+| customer_satisfaction_in_airline | B & L | Kaggle | 2023 | 3 | 129,880 | 21 | 2 | Binary | IID | yakhyojon2023airlinesatisfaction |
+| pva_revenue_prediction_kddcup98 | B & M | Other | 1997 | 29 | 144,095 | 477 | 2 | Binary | IID | Parsa1998KDDCup1998 |
+| give_me_some_credit | Finance | Kaggle | 2011 | 15 | 150,000 | 10 | 2 | Binary | IID | cukierski2011credit |
+| acquire_valued_shoppers_challenge | B & M | Kaggle | 2014 | 12 | 160,057 | 111 | 2 | Binary | Temporal | DMDave2014AcquireValuedShoppersChallenge |
+| kickstarter | B & M | Other | 2025 | 1 | 187,118 | 15 | 2 | Binary | Temporal | webrobots2026kickstarter |
+| allstate_claims_severity | Insurance | Kaggle | 2016 | 10 | 188,317 | 130 | — | Reg | IID | Ferguson2016AllstateClaimsSeverity |
+| santander_customer_transaction_prediction | Finance | Kaggle | 2019 | 7 | 200,000 | 600 | 2 | Binary | IID | Piedra2019SantanderCustomerTransactionPrediction |
+| homesite_quote_conversion | Insurance | Kaggle | 2015 | 11 | 260,753 | 295 | 2 | Binary | IID | Darrel2015HomesiteQuoteConversion |
+| home_credit_default_risk | Finance | Kaggle | 2018 | 8 | 307,507 | 504 | 2 | Binary | IID | Montoya2018HomeCreditDefaultRisk |
+| covertype | E & C | UCI | 1998 | 28 | 512,625 | 13 | 3 | Multi | Grouped | blackard1999comparative |
+| ieee_fraud_detection | Finance | Kaggle | 2019 | 7 | 590,540 | 435 | 2 | Binary | Temporal | ieee-fraud-detection |
+| porto_seguro | Insurance | Kaggle | 2017 | 9 | 595,206 | 37 | 2 | Binary | IID | Howard2017PortoSegurosSafeDriverPrediction |
+| rossmann_store_sales | B & M | Kaggle | 2015 | 11 | 844,392 | 15 | — | Reg | Temporal | kaggle_rossmann_store_sales |
+| lending_club_1m | Finance | Kaggle | 2018 | 8 | 1,064,751 | 96 | 2 | Binary | Temporal | sanz2025credit |
+| home_credit_default_stability_1m | Finance | Kaggle | 2024 | 2 | 1,224,927 | 711 | 2 | Binary | Temporal | Herman2024HomeCreditCreditRiskModelStability |
+| consumer_complaints_1m | Finance | GOV Website | 2025 | 1 | 1,226,140 | 12 | 3 | Multi | Temporal | cfpb2025ConsumerComplaintDatabase |
+| sepsis_prediction_1m | M & H | Other | 2019 | 7 | 1,228,686 | 42 | 2 | Binary | Grouped | reyna2020early |
+| amex_non_iid_1m | Finance | Kaggle | 2022 | 4 | 1,249,605 | 189 | 2 | Binary | Grouped | howard2022amex |
+| delivery_eta_1m | I & M | Kaggle | 2024 | 2 | 1,250,000 | 225 | — | Reg | Temporal | rubachev2025tabred |
+| cooking_time_1m | I & M | Kaggle | 2024 | 2 | 1,250,000 | 196 | — | Reg | Temporal | rubachev2025tabred |
+| climate_model_weather_forecasting_1m | E & C | Kaggle | 2024 | 2 | 1,250,000 | 100 | — | Reg | Temporal | rubachev2025tabred |
+| maps_router_eta_1m | I & M | Kaggle | 2024 | 2 | 1,250,000 | 988 | — | Reg | Temporal | rubachev2025tabred |
+| mercari_price_suggestion_1m | B & M | Kaggle | 2018 | 8 | 1,250,000 | 6 | — | Reg | IID | Howard2017MercariPriceSuggestionChallenge |
+| electric_motor_temperature_prediction | I & M | Kaggle | 2021 | 5 | 1,296,316 | 109 | — | Reg | Grouped | kirchgassner2020estimating |
+
+
+</details>
+
+## Dataset Structure
+
+The release ships as a flat bundle of 142 datasets. Each dataset lives in its own top-level directory named by `unique_name`, with a UUID-named version subdirectory holding all artifacts. Two layout variants exist:
+
+```
+<dataset_name>/<uuid>/...                 # default (132 datasets)
+<dataset_name>/versions/<uuid>/...        # versioned wrapper (10 large non-IID datasets)
+```
+
+Each directory contains exactly six files:
+
+```
+<uuid>/
+├── dataset.parquet                                       # the table (rows × columns)
+├── dtypes.json                                           # column name → pandas dtype
+├── container_metadata.json                               # uuid + sha256 checksum
+├── dataset_metadata.dataset-mold-v1.json                 # provenance & curation notes
+├── task_metadata.predictive-ml-task-mold-v1.json         # target, problem type, metric, split keys
+└── experiment_metadata.predictive-ml-splits-mold-v1.json # CV fold indices
+```
+
+For details on files and the metadata structure, checkout [DataFoundry](https://github.com/TabArena/data-foundry)!
+
+## Licensing
+
+This collection is released under the terms in [LICENSE](LICENSE) (`copyright-at-original-authors`).
+Individual datasets retain their original licenses; see each dataset metadata for their source-specific terms.
+
+## Citation
+
+If you use BeyondArena, please cite:
+
+**BibTeX:**
+
+```bibtex
+TODO
+```
+
+
+### Per-Dataset References
+
+If you use individual datasets, please also cite their original authors. BibTeX for every dataset in the benchmark is shipped alongside this card in [`dataset_references.bib`](./dataset_references.bib) (one entry per unique `academic_reference_bibtex_key` referenced by the dataset metadata files).
+
+## Changelog
+
+- **[XXX]** — Initial release: 142 curated IID and non-IID tasks.

dataset_references.bib

@@ -0,0 +1,1263 @@
+@misc{AlIrsyad2023CoffeeDataCoffeeReview,
+  author = {Hanif Al Irsyad},
+  title  = {Coffee Data CoffeeReview},
+  year   = {2023},
+  howpublished = {\url{https://www.kaggle.com/datasets/hanifalirsyad/coffee-scrap-coffeereview}},
+  note   = {Kaggle dataset}
+}
+
+@misc{Barnawal2022MutualFundsIndiaDetailed,
+  author = {Ravi Barnawal},
+  title  = {Mutual Funds India Detailed},
+  year   = {2022},
+  howpublished = {\url{https://www.kaggle.com/datasets/ravibarnawal/mutual-funds-india-detailed}},
+  note   = {Kaggle dataset}
+}
+
+@misc{Barreto2005Vertebral,
+  author       = {Barreto, Guilherme and Neto, Ajalmar},
+  title        = {{Vertebral Column}},
+  year         = {2005},
+  howpublished = {UCI Machine Learning Repository},
+  note         = {{DOI}: https://doi.org/10.24432/C5K89B}
+}
+
+@misc{Bossan2015OttoGroupProductClassificationChallenge,
+  author = {Benjamin Bossan and Josef Feigl and Wendy Kan},
+  title  = {Otto Group Product Classification Challenge},
+  year   = {2015},
+  howpublished = {\url{https://kaggle.com/competitions/otto-group-product-classification-challenge}},
+  note   = {Kaggle competition}
+}
+
+@misc{DMDave2014AcquireValuedShoppersChallenge,
+  author = {DMDave and Todd B and Will Cukierski},
+  title  = {Acquire Valued Shoppers Challenge},
+  year   = {2014},
+  howpublished = {\url{https://kaggle.com/competitions/acquire-valued-shoppers-challenge}},
+  note   = {Kaggle competition}
+}
+
+@misc{Darrel2015HomesiteQuoteConversion,
+  author = {Darrel and Stephen D. Stayton and Will Cukierski},
+  title  = {Homesite Quote Conversion},
+  year   = {2015},
+  howpublished = {\url{https://kaggle.com/competitions/homesite-quote-conversion}},
+  note   = {Kaggle competition}
+}
+
+@misc{DataDrive2030_2024_elom_thrivebyfive,
+    author       = {{DataDrive2030}},
+    title        = {ELOM and Thrive by Five Index 2016--2023, Merged Data},
+    version      = {1},
+    year         = {2024},
+    address      = {Cape Town},
+    publisher    = {DataDrive2030},
+    note         = {[dataset]. Producer: DataDrive2030; Distributor: DataFirst},
+    doi          = {10.25828/WG0D-Y909},
+    url          = {https://doi.org/10.25828/WG0D-Y909}
+}
+
+@misc{DontGetKicked,
+    author = {faysal and Will Adams and Will Cukierski},
+    title = {Don't Get Kicked!},
+    year = {2011},
+    howpublished = {\url{https://kaggle.com/competitions/DontGetKicked}},
+    note = {Kaggle}
+}
+
+@misc{Ferguson2016AllstateClaimsSeverity,
+  author = {Dana Ferguson and Meg Risdal and NoTrick and Sara R. Sillah and Tim Emmerling and Will Cukierski},
+  title  = {Allstate Claims Severity},
+  year   = {2016},
+  howpublished = {\url{https://kaggle.com/competitions/allstate-claims-severity}},
+  note   = {Kaggle competition}
+}
+
+@misc{German1987glass,
+  author       = {German, B.},
+  title        = {{Glass Identification}},
+  year         = {1987},
+  howpublished = {UCI Machine Learning Repository},
+  note         = {{DOI}: https://doi.org/10.24432/C5WW2P}
+}
+
+@misc{Herman2024HomeCreditCreditRiskModelStability,
+  author = {Daniel Herman and Tomas Jelinek and Walter Reade and Maggie Demkin and Addison Howard},
+  title  = {Home Credit - Credit Risk Model Stability},
+  year   = {2024},
+  howpublished = {\url{https://kaggle.com/competitions/home-credit-credit-risk-model-stability}},
+  note   = {Kaggle competition}
+}
+
+@misc{Howard2017MercariPriceSuggestionChallenge,
+  author = {{Kaggle} and Addison Howard and kaoriiida and Kei Otagaki and Mark McDonald and mueno and Wendy Kan and Zhang and zyaga},
+  title  = {Mercari Price Suggestion Challenge},
+  year   = {2017},
+  howpublished = {\url{https://kaggle.com/competitions/mercari-price-suggestion-challenge}},
+  note   = {Kaggle competition}
+}
+
+@misc{Howard2017PortoSegurosSafeDriverPrediction,
+  author = {Addison Howard and Adriano Moala and Walter Reade},
+  title  = {Porto Seguro’s Safe Driver Prediction},
+  year   = {2017},
+  howpublished = {\url{https://kaggle.com/competitions/porto-seguro-safe-driver-prediction}},
+  note   = {Kaggle competition}
+}
+
+@misc{Jimenez2016SantanderCustomerSatisfaction,
+  author = {Soraya Jimenez and Will Cukierski},
+  title  = {Santander Customer Satisfaction},
+  year   = {2016},
+  howpublished = {\url{https://kaggle.com/competitions/santander-customer-satisfaction}},
+  note   = {Kaggle competition}
+}
+
+@misc{Matthys2021WiDSDatathon2021,
+  author = {Karen Matthys and Meredith Lee and Neha Goel and Sharada Kalanidhi and Valerie and Vani M.},
+  title  = {WiDS Datathon 2021},
+  year   = {2021},
+  howpublished = {\url{https://kaggle.com/competitions/widsdatathon2021}},
+  note   = {Kaggle competition}
+}
+
+@misc{McDonald2018SantanderValuePredictionChallenge,
+  author = {Mark McDonald and Mercedes Piedra and Sohier Dane and Soraya Jimenez},
+  title  = {Santander Value Prediction Challenge},
+  year   = {2018},
+  howpublished = {\url{https://kaggle.com/competitions/santander-value-prediction-challenge}},
+  note   = {Kaggle competition}
+}
+
+@misc{McLeish1989HorseColic,
+  author       = {McLeish, Mary and Cecile, Matt},
+  title        = {{Horse Colic}},
+  year         = {1989},
+  howpublished = {UCI Machine Learning Repository},
+  note         = {{DOI}: https://doi.org/10.24432/C58W23}
+}
+
+@misc{Montoya2018HomeCreditDefaultRisk,
+  author = {Anna Montoya and inversion and Kirill Odintsov and Martin Kotek},
+  title  = {Home Credit Default Risk},
+  year   = {2018},
+  howpublished = {\url{https://kaggle.com/competitions/home-credit-default-risk}},
+  note   = {Kaggle competition}
+}
+
+@misc{Novy2017MercedesBenzGreenerManufacturing,
+  author = {Alexander Novy and CH1Mercedes and Christian Drescher and Christian Pfaundler and KOESIM and Will Cukierski},
+  title  = {Mercedes-Benz Greener Manufacturing},
+  year   = {2017},
+  howpublished = {\url{https://kaggle.com/competitions/mercedes-benz-greener-manufacturing}},
+  note   = {Kaggle competition}
+}
+
+@misc{OpenML43342Dataset,
+  author = {{OpenML}},
+  title  = {ImmoScout24 OpenML Dataset 43342},
+  year   = {2023},
+  howpublished = {\url{https://www.openml.org/d/43342}},
+  note   = {OpenML dataset}
+}
+
+@misc{Parsa1998KDDCup1998,
+  author = {Ismail Parsa},
+  title  = {KDD Cup 1998},
+  year   = {1998},
+  howpublished = {\url{https://kdd.ics.uci.edu/databases/kddcup98/kddcup98.html}},
+  note   = {Dataset, UCI Machine Learning Repository. DOI: 10.24432/C5401H}
+}
+
+@misc{Piedra2019SantanderCustomerTransactionPrediction,
+  author = {Mercedes Piedra and Sohier Dane and Soraya Jimenez},
+  title  = {Santander Customer Transaction Prediction},
+  year   = {2019},
+  howpublished = {\url{https://kaggle.com/competitions/santander-customer-transaction-prediction}},
+  note   = {Kaggle competition}
+}
+
+@misc{Podsyp2020IsThisAGoodCustomer,
+  title={Is This a Good Customer?},
+  author={Podsyp},
+  year={2020},
+  publisher={Kaggle},
+  url={https://www.kaggle.com/datasets/podsyp/is-this-a-good-customer}
+}
+
+@misc{Rustamov2023WineDataset,
+  author = {Elvin Rustamov},
+  title  = {Wine Dataset},
+  year   = {2023},
+  howpublished = {\url{https://www.kaggle.com/datasets/elvinrustam/wine-dataset}},
+  note   = {Kaggle dataset}
+}
+
+@misc{SanFrancisco2026BuildingPermits,
+  author = {{City and County of San Francisco}},
+  title  = {Building Permits},
+  year   = {2026},
+  howpublished = {\url{https://data.sfgov.org/Housing-and-Buildings/Building-Permits/i98e-djp9/about_data}},
+  note   = {DataSF Open Data Portal dataset, Accessed: 2026-02-05}
+}
+
+@misc{Shritech2019GermanHousingPricePrediction,
+  author = {shritech1404},
+  title  = {German Housing Price Prediction},
+  year   = {2019},
+  howpublished = {\url{https://www.kaggle.com/code/shritech1404/german-housing-price-prediction}},
+  note   = {Kaggle notebook}
+}
+
+@misc{Tejashvi2023TourTravelsCustomerChurnPrediction,
+  author = {Tejashvi},
+  title  = {Tour \& Travels Customer Churn Prediction},
+  year   = {2023},
+  howpublished = {\url{https://www.kaggle.com/datasets/tejashvi14/tour-travels-customer-churn-prediction}},
+  note   = {Kaggle dataset}
+}
+
+@misc{Topre2022BankCustomerChurn,
+  title={Bank Customer Churn Dataset},
+  author={Gaurav Topre},
+  year={2022},
+  publisher={Kaggle},
+  url={https://www.kaggle.com/datasets/gauravtopre/bank-customer-churn-dataset}
+}
+
+@misc{UCILiverDisorders2016,
+  title        = {Liver Disorders},
+  author       = {{UCI Machine Learning Repository}},
+  year         = {2016},
+  howpublished = {\url{https://doi.org/10.24432/C54G67}},
+  note         = {Dataset}
+}
+
+@misc{Zwitter1987primarytumor,
+  author       = {Zwitter, M. and Soklic, M.},
+  title        = {{Primary Tumor}},
+  year         = {1987},
+  howpublished = {UCI Machine Learning Repository},
+  note         = {{DOI}: https://doi.org/10.24432/C5WK5Q}
+}
+
+@misc{Zwitter1988BreastCancer,
+  author       = {Zwitter, Matjaz and Soklic, Milan},
+  title        = {{Breast Cancer}},
+  year         = {1988},
+  howpublished = {UCI Machine Learning Repository},
+  note         = {{DOI}: https://doi.org/10.24432/C51P4M}
+}
+
+@article{abdelhamid2014phishing,
+  title={Phishing detection based associative classification data mining},
+  author={Abdelhamid, Neda and Ayesh, Aladdin and Thabtah, Fadi},
+  journal={Expert Systems with Applications},
+  volume={41},
+  number={13},
+  pages={5948--5959},
+  year={2014},
+  publisher={Elsevier}
+}
+
+@article{accetta2022seventeenth,
+  title={The seventeenth data release of the Sloan Digital Sky Surveys: Complete release of MaNGA, MaStar, and APOGEE-2 data},
+  author={Accetta, Katherine and Aerts, Conny and Aguirre, Victor Silva and Ahumada, Romina and Ajgaonkar, Nikhil and Ak, N Filiz and Alam, Shadab and Prieto, Carlos Allende and Almeida, Andres and Anders, Friedrich and others},
+  journal={The Astrophysical Journal Supplement Series},
+  volume={259},
+  number={2},
+  pages={35},
+  year={2022},
+  publisher={IOP Publishing}
+}
+
+@inproceedings{ahmed2020review,
+  title={Review and analysis of risk factor of maternal health in remote area using the Internet of Things (IoT)},
+  author={Ahmed, Marzia and Kashem, Mohammod Abul and Rahman, Mostafijur and Khatun, Sabira},
+  booktitle={InECCE2019: Proceedings of the 5th International Conference on Electrical, Control \& Computer Engineering, Kuantan, Pahang, Malaysia, 29th July 2019},
+  pages={357--365},
+  year={2020},
+  organization={Springer}
+}
+
+@misc{anes2026timeseries,
+    author       = {{American National Election Studies}},
+    title        = {{ANES Time Series Cumulative Data File [dataset and documentation]}},
+    year         = {2026},
+    month        = feb,
+    note         = {February 5, 2026 version},
+    howpublished = {\url{https://www.electionstudies.org}}
+}
+
+@article{antonio2019hotel,
+  title={Hotel booking demand datasets},
+  author={Antonio, Nuno and de Almeida, Ana and Nunes, Luis},
+  journal={Data in brief},
+  volume={22},
+  pages={41--49},
+  year={2019},
+  publisher={Elsevier}
+}
+
+@misc{arashnic2021hr,
+  author       = {Kaggle User Arashnic},
+  title        = {HR Analytics: Job Change of Data Scientists},
+  year         = {2021},
+  howpublished = {\url{https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists}},
+  note         = {Kaggle dataset},
+}
+
+@misc{arunjangir2452023insurance,
+  author       = {Kaggle User Arunjangir245},
+  title        = {Healthcare Insurance Expenses},
+  year         = {2023},
+  howpublished = {\url{https://www.kaggle.com/datasets/arunjangir245/healthcare-insurance-expenses/}},
+  note         = {Kaggle dataset},
+}
+
+@misc{averkiyoliabev2021heloc,
+  author       = {Kaggle User Averkiyoliabev},
+  title        = {Home Equity Line of Credit (HELOC)},
+  year         = {2021},
+  howpublished = {\url{https://www.kaggle.com/datasets/averkiyoliabev/home-equity-line-of-creditheloc}},
+  note         = {Kaggle dataset},
+}
+
+@book{baesens2016credit,
+  title={Credit risk analytics: Measurement techniques, applications, and examples in SAS},
+  author={Baesens, Bart and Roesch, Daniel and Scheule, Harald},
+  year={2016},
+  publisher={John Wiley \& Sons}
+}
+
+@incollection{bareiss1990protos,
+  title={Protos: An exemplar-based learning apprentice},
+  author={Bareiss, E Ray and Porter, Bruce W and Wier, Craig C},
+  booktitle={Machine learning},
+  pages={112--127},
+  year={1990},
+  publisher={Elsevier}
+}
+
+@article{bhattacharjee2001classification,
+  title={Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses},
+  author={Bhattacharjee, Arindam and Richards, William G and Staunton, Jane and Li, Cheng and Monti, Stefano and Vasa, Priya and Ladd, Christine and Beheshti, Javad and Bueno, Raphael and Gillette, Michael and others},
+  journal={Proceedings of the National Academy of Sciences},
+  volume={98},
+  number={24},
+  pages={13790--13795},
+  year={2001},
+  publisher={The National Academy of Sciences}
+}
+
+@misc{bioresponse2012hamner,
+    author = {Ben Hamner and dcthompson and Jorg},
+    title = {Predicting a Biological Response},
+    year = {2012},
+    howpublished = {https://kaggle.com/competitions/bioresponse},
+    note = {Kaggle}
+}
+
+@article{bischl_aslib_2016,
+  title={Aslib: A benchmark library for algorithm selection},
+  author={Bischl, Bernd and Kerschke, Pascal and Kotthoff, Lars and Lindauer, Marius and Malitsky, Yuri and Fr{\'e}chette, Alexandre and Hoos, Holger and Hutter, Frank and Leyton-Brown, Kevin and Tierney, Kevin and others},
+  journal={Artificial Intelligence},
+  volume={237},
+  pages={41--58},
+  year={2016},
+  publisher={Elsevier}
+}
+
+@article{blackard1999comparative,
+  title={Comparative accuracies of artificial neural networks and discriminant analysis in predicting forest cover types from cartographic variables},
+  author={Blackard, Jock A and Dean, Denis J},
+  journal={Computers and electronics in agriculture},
+  volume={24},
+  number={3},
+  pages={131--151},
+  year={1999},
+  publisher={Elsevier}
+}
+
+@article{bourassa2021big,
+  title={Big data, accessibility and urban house prices},
+  author={Bourassa, Steven C and Hoesli, Martin and Merlin, Louis and Renne, John},
+  journal={Urban Studies},
+  volume={58},
+  number={15},
+  pages={3176--3195},
+  year={2021},
+  publisher={SAGE Publications Sage UK: London, England}
+}
+
+@misc{brooks1989airfoil,
+  title={Airfoil self-noise and prediction},
+  author={Brooks, Thomas F and Pope, D Stuart and Marcolini, Michael A},
+  year={1989}
+}
+
+@misc{campos2010cardiotocography,
+  author       = {Campos, D. and Bernardes, J.},
+  title        = {{Cardiotocography}},
+  year         = {2000},
+  howpublished = {UCI Machine Learning Repository},
+  note         = {{DOI}: https://doi.org/10.24432/C51S4N}
+}
+
+@article{cassotti2014prediction,
+  title={Prediction of acute aquatic toxicity toward daphnia magna by using the ga-k nn method},
+  author={Cassotti, Matteo and Ballabio, Davide and Consonni, Viviana and Mauri, Andrea and Tetko, Igor V and Todeschini, Roberto},
+  journal={Alternatives to Laboratory Animals},
+  volume={42},
+  number={1},
+  pages={31--41},
+  year={2014},
+  publisher={SAGE Publications Sage UK: London, England}
+}
+
+@article{cassotti2015similarity,
+  title={A similarity-based QSAR model for predicting acute toxicity towards the fathead minnow (Pimephales promelas)},
+  author={Cassotti, Matteo and Ballabio, Davide and Todeschini, Roberto and Consonni, Viviana},
+  journal={SAR and QSAR in Environmental Research},
+  volume={26},
+  number={3},
+  pages={217--243},
+  year={2015},
+  publisher={Taylor \& Francis}
+}
+
+@misc{cfpb2025ConsumerComplaintDatabase,
+  author       = {{Consumer Financial Protection Bureau}},
+  title        = {Consumer Complaint Database},
+  year         = {2025},
+  howpublished = {\url{https://www.consumerfinance.gov/data-research/consumer-complaints/}},
+  note         = {Accessed: 2026-01-23},
+}
+
+@article{chicco2020machine,
+  title={Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone},
+  author={Chicco, Davide and Jurman, Giuseppe},
+  journal={BMC medical informatics and decision making},
+  volume={20},
+  number={1},
+  pages={16},
+  year={2020},
+  publisher={Springer}
+}
+
+@article{chicco2020survival,
+  title={Survival prediction of patients with sepsis from age, sex, and septic episode number alone},
+  author={Chicco, Davide and Jurman, Giuseppe},
+  journal={Scientific reports},
+  volume={10},
+  number={1},
+  pages={17156},
+  year={2020},
+  publisher={Nature Publishing Group UK London}
+}"
+
+@misc{cortez2007data,
+  title={A data mining approach to predict forest fires using meteorological data},
+  author={Cortez, Paulo and Morais, An{\'\i}bal de Jesus Raimundo},
+  year={2007},
+  publisher={Associa{\c{c}}{\~a}o Portuguesa para a Intelig{\^e}ncia Artificial (APPIA)}
+}
+
+@article{cortez2009modeling,
+  title={Modeling wine preferences by data mining from physicochemical properties},
+  author={Cortez, Paulo and Cerdeira, Ant{\'o}nio and Almeida, Fernando and Matos, Telmo and Reis, Jos{\'e}},
+  journal={Decision support systems},
+  volume={47},
+  number={4},
+  pages={547--553},
+  year={2009},
+  publisher={Elsevier}
+}
+
+@misc{cukierski2011credit,
+    author = {Credit Fusion and Will Cukierski},
+    title = {Give Me Some Credit},
+    year = {2011},
+    howpublished = {url{https://kaggle.com/competitions/GiveMeSomeCredit}},
+    note = {Kaggle}
+}
+
+@misc{d2lcourse2021california_house_prices,
+  author       = {d2lcourse},
+  title        = {California House Prices},
+  year         = {2021},
+  howpublished = {\url{https://kaggle.com/competitions/california-house-prices}},
+  note         = {Kaggle}
+}
+
+@misc{ddosad2023fitness,
+  author       = {Kaggle User Ddosad},
+  title        = {Fitness Club Dataset for ML Classification},
+  year         = {2023},
+  howpublished = {\url{https://www.kaggle.com/datasets/ddosad/datacamps-data-science-associate-certification}},
+  note         = {Kaggle dataset},
+}
+
+@inproceedings{deneke2014video,
+  title={Video transcoding time prediction for proactive load balancing},
+  author={Deneke, Tewodors and Haile, Habtegebreil and Lafond, S{'e}bastien and Lilius, Johan},
+  booktitle={2014 IEEE International Conference on Multimedia and Expo (ICME)},
+  pages={1--6},
+  year={2014},
+  organization={IEEE}
+}
+
+@article{detrano1989international,
+  title={International application of a new probability algorithm for the diagnosis of coronary artery disease},
+  author={Detrano, Robert and Janosi, Andras and Steinbrunn, Walter and Pfisterer, Matthias and Schmid, Johann-Jakob and Sandhu, Sarbjit and Guppy, Kern H and Lee, Stella and Froelicher, Victor},
+  journal={The American journal of cardiology},
+  volume={64},
+  number={5},
+  pages={304--310},
+  year={1989},
+  publisher={Elsevier}
+}
+
+@article{dickson1989prognosis,
+  title={Prognosis in primary biliary cirrhosis: model for decision making},
+  author={Dickson, E Rolland and Grambsch, Patricia M and Fleming, Thomas R and Fisher, Lloyd D and Langworthy, Alice},
+  journal={Hepatology},
+  volume={10},
+  number={1},
+  pages={1--7},
+  year={1989},
+  publisher={Wiley Online Library}
+}
+
+@article{dietterich1993comparison,
+  title={A comparison of dynamic reposing and tangent distance for drug activity prediction},
+  author={Dietterich, Thomas and Jain, Ajay and Lathrop, Richard and Lozano-Perez, Tomas},
+  journal={Advances in neural information processing systems},
+  volume={6},
+  year={1993}
+}
+
+@inproceedings{efron1981statistical,
+  title={Statistical theory and the computer},
+  author={Efron, Bradley and Gong, Gail},
+  booktitle={Computer science and statistics: Proceedings of the 13th Symposium on the Interface},
+  pages={3--7},
+  year={1981},
+  organization={Springer}
+}
+
+@article{esen2024early,
+  title={Early prediction of gallstone disease with a machine learning-based method from bioimpedance and laboratory data},
+  author={Esen, {\.I}rfan and Arslan, Hilal and Esen, Selin Akt{\"u}rk and G{\"u}l{\c{s}}en, Mervenur and K{\"u}ltekin, Nimet and {\"O}zdemir, O{\u{g}}uzhan},
+  journal={Medicine},
+  volume={103},
+  number={8},
+  pages={e37258},
+  year={2024},
+  publisher={LWW}
+}
+
+@inproceedings{flogard2022dataset,
+  title={A dataset for efforts towards achieving the sustainable development goal of safe working environments},
+  author={Flogard, Eirik Lund and Mengshoel, Ole Jakob},
+  booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
+  year={2022}
+}
+
+@article{golovenkin2020trajectories,
+  title={Trajectories, bifurcations, and pseudo-time in large clinical datasets: applications to myocardial infarction and diabetes data},
+  author={Golovenkin, Sergey E and Bac, Jonathan and Chervov, Alexander and Mirkes, Evgeny M and Orlova, Yuliya V and Barillot, Emmanuel and Gorban, Alexander N and Zinovyev, Andrei},
+  journal={GigaScience},
+  volume={9},
+  number={11},
+  pages={giaa128},
+  year={2020},
+  publisher={Oxford University Press}
+}
+
+@misc{gopalani2021ecommerce,
+  author       = {Prachi Gopalani},
+  title        = {E-Commerce Shipping Data},
+  year         = {2021},
+  howpublished = {\url{https://www.kaggle.com/datasets/prachi13/customer-analytics}},
+  note         = {Kaggle dataset},
+}
+
+@article{gul2021structure,
+  title={Structure-based design and classifications of small molecules regulating the circadian rhythm period},
+  author={Gul, Seref and Rahim, Fatih and Isin, Safak and Yilmaz, Fatma and Ozturk, Nuri and Turkay, Metin and Kavakli, Ibrahim Halil},
+  journal={Scientific reports},
+  volume={11},
+  number={1},
+  pages={18510},
+  year={2021},
+  publisher={Nature Publishing Group UK London}
+}
+
+@article{guvenir1998learning,
+  title={Learning differential diagnosis of erythemato-squamous diseases using voting feature intervals},
+  author={G{\"u}venir, H Altay and Demir{\"o}z, G{\"u}l{\c{s}}en and Ilter, Nilsel},
+  journal={Artificial intelligence in medicine},
+  volume={13},
+  number={3},
+  pages={147--165},
+  year={1998},
+  publisher={Elsevier}
+}
+
+@inproceedings{guyon2007agnostic,
+  author={Isabelle Guyon and Amir Saffari and Gideon Dror and Gavin Cawley},
+  booktitle={2007 International Joint Conference on Neural Networks}, 
+  title={Agnostic Learning vs. Prior Knowledge Challenge}, 
+  year={2007},
+  pages={829-834},
+  doi={10.1109/IJCNN.2007.4371065}
+}
+
+@inproceedings{guyon2009analysis,
+  title={Analysis of the kdd cup 2009: Fast scoring on a large orange customer database},
+  author={Guyon, Isabelle and Lemaire, Vincent and Boull{\'e}, Marc and Dror, Gideon and Vogel, David},
+  booktitle={KDD-Cup 2009 Competition},
+  pages={1--22},
+  year={2009},
+  organization={PMLR}
+}
+
+@article{hamidieh2018data,
+  title={A data-driven statistical model for predicting the critical temperature of a superconductor},
+  author={Hamidieh, Kam},
+  journal={Computational Materials Science},
+  volume={154},
+  pages={346--354},
+  year={2018},
+  publisher={Elsevier}
+}
+
+@misc{hamner2013amazon,
+  author       = {Ben Hamner and kenmonta and Will Cukierski},
+  title        = {Amazon.com - Employee Access Challenge},
+  year         = {2013},
+  howpublished = {\url{https://www.kaggle.com/competitions/amazon-employee-access-challenge}},
+  note         = {Kaggle competition},
+}
+
+@article{higuera2015self,
+  title={Self-organizing feature maps identify proteins critical to learning in a mouse model of down syndrome},
+  author={Higuera, Clara and Gardiner, Katheleen J and Cios, Krzysztof J},
+  journal={PloS one},
+  volume={10},
+  number={6},
+  pages={e0129126},
+  year={2015},
+  publisher={Public Library of Science San Francisco, CA USA}
+}
+
+@article{hingorani2003preinvasive,
+  title={Preinvasive and invasive ductal pancreatic cancer and its early detection in the mouse},
+  author={Hingorani, Sunil R and Petricoin, Emanuel F and Maitra, Anirban and Rajapakse, Vinodh and King, Catrina and Jacobetz, Michael A and Ross, Sally and Conrads, Thomas P and Veenstra, Timothy D and Hitt, Ben A and others},
+  journal={Cancer cell},
+  volume={4},
+  number={6},
+  pages={437--450},
+  year={2003},
+  publisher={Elsevier}
+}"
+
+@article{hoffmann2018using,
+  title={Using machine learning techniques to generate laboratory diagnostic pathways—a case study},
+  author={Hoffmann, Georg and Bietenbeck, Andreas and Lichtinghagen, Ralf and Klawonn, Frank},
+  journal={Journal of Laboratory and Precision Medicine},
+  volume={3},
+  number={6},
+  year={2018},
+  publisher={AME Publishing Company}
+}
+
+@misc{hofmann1994statlog,
+  author       = {Hofmann, H.},
+  title        = {Statlog (German Credit Data) [Dataset]},
+  year         = {1994},
+  howpublished = {\url{https://doi.org/10.24432/C5NC77}},
+  note         = {UCI Machine Learning Repository},
+}
+
+@article{hoos2014claspfolio,
+  title={claspfolio 2: Advances in algorithm selection for answer set programming},
+  author={Hoos, Holger and Lindauer, Marius and Schaub, Torsten},
+  journal={Theory and Practice of Logic Programming},
+  volume={14},
+  number={4-5},
+  pages={569--585},
+  year={2014},
+  publisher={Cambridge University Press}
+}
+
+@inproceedings{horton1996probabilistic,
+  title={A probabilistic classification system for predicting the cellular localization sites of proteins.},
+  author={Horton, Paul and Nakai, Kenta},
+  booktitle={Ismb},
+  volume={4},
+  pages={109--115},
+  year={1996},
+  organization={St. Louis, Missouri, USA}
+}
+
+@misc{howard2022amex,
+  author       = {Howard, Addison and AritraAmex and Xu, Di and Vashani, Hossein and inversion and Negin and Dane, Sohier},
+  title        = {American Express -- Default Prediction},
+  year         = {2022},
+  howpublished = {Kaggle Competition},
+  url          = {https://kaggle.com/competitions/amex-default-prediction},
+  note         = {Accessed via Kaggle}
+}
+
+@article{huang2025interdia,
+  title={InterDIA: Interpretable prediction of drug-induced autoimmunity through ensemble machine learning approaches},
+  author={Huang, Lina and Liu, Peineng and Huang, Xiaojie},
+  journal={Toxicology},
+  volume={511},
+  pages={154064},
+  year={2025},
+  publisher={Elsevier}
+}
+
+@misc{huawei_netop_5g_energy_consumption,
+  author       = {{HUAWEI Netop Team}},
+  title        = {5G Network Energy Consumption Dataset},
+  year         = {n.d.},
+  howpublished = {\url{https://huggingface.co/datasets/netop/5G-Network-Energy-Consumption}},
+  note         = {Dataset hosted on Hugging Face, accessed 2026-04-15}
+}
+
+@misc{ida2016challenge,
+  author       = {{IDA2016Challenge}},
+  title        = {IDA2016Challenge [Dataset]},
+  year         = {2016},
+  howpublished = {\url{https://doi.org/10.24432/C5V60Q}},
+  note         = {UCI Machine Learning Repository},
+}
+
+@misc{ieee-fraud-detection,
+    author = {Addison Howard and Bernadette Bouchon-Meunier and IEEE CIS and inversion and John Lei and Lynn@Vesta and Marcus2010 and Prof. Hussein Abbass},
+    title = {IEEE-CIS Fraud Detection},
+    year = {2019},
+    howpublished = {\url{https://kaggle.com/competitions/ieee-fraud-detection}},
+    note = {Kaggle}
+}
+
+@article{imran2021mining,
+    title={Mining the productivity data of the garment industry},
+    author={Imran, Abdullah Al and Rahim, Md Shamsur and Ahmed, Tanvir},
+    journal={International Journal of Business Intelligence and Data Mining},
+    volume={19},
+    number={3},
+    pages={319--342},
+    year={2021},
+    publisher={Inderscience Publishers (IEL)}
+}
+
+@inproceedings{islam2019likelihood,
+  title={Likelihood prediction of diabetes at early stage using data mining techniques},
+  author={Islam, MM Faniqul and Ferdousi, Rahatara and Rahman, Sadikur and Bushra, Humayra Yasmin},
+  booktitle={Computer Vision and Machine Intelligence in Medical Image Analysis: International Symposium, ISCMM 2019},
+  pages={113--125},
+  year={2019},
+  organization={Springer}
+}
+
+@misc{kaggle_rossmann_store_sales,
+    title        = {Rossmann Store Sales},
+    author       = {{Kaggle}},
+    howpublished = {\url{https://www.kaggle.com/competitions/rossmann-store-sales/overview}},
+    note         = {Kaggle competition page. Accessed: 2026-03-19},
+    year         = {2015}
+}
+
+@article{keramati2011churn,
+  title={Churn analysis for an Iranian mobile operator},
+  author={Keramati, Abbas and Ardabili, Seyed MS},
+  journal={Telecommunications Policy},
+  volume={35},
+  number={4},
+  pages={344--356},
+  year={2011},
+  publisher={Elsevier}
+}
+
+@article{kirchgassner2020estimating,
+  title={Estimating electric motor temperatures with deep residual machine learning},
+  author={Kirchg{"a}ssner, Wilhelm and Wallscheid, Oliver and B{"o}cker, Joachim},
+  journal={IEEE Transactions on Power Electronics},
+  volume={36},
+  number={7},
+  pages={7480--7488},
+  year={2020},
+  publisher={IEEE}
+}
+
+@article{liang2016financial,
+  title={Financial ratios and corporate governance indicators in bankruptcy prediction: A comprehensive study},
+  author={Liang, Deron and Lu, Chia-Chi and Tsai, Chih-Fong and Shih, Guan-An},
+  journal={European journal of operational research},
+  volume={252},
+  number={2},
+  pages={561--572},
+  year={2016},
+  publisher={Elsevier}
+}
+
+@article{little2007exploiting,
+  title={Exploiting nonlinear recurrence and fractal scaling properties for voice disorder detection},
+  author={Little, Max and Mcsharry, Patrick and Roberts, Stephen and Costello, Declan and Moroz, Irene},
+  journal={Nature Precedings},
+  pages={1--1},
+  year={2007},
+  publisher={Nature Publishing Group UK London}
+}
+
+@article{mahe2014automatic,
+  title={Automatic identification of mixed bacterial species fingerprints in a MALDI-TOF mass-spectrum},
+  author={Mahe, Pierre and Arsac, Maud and Chatellier, Sonia and Monnin, Val{\'e}rie and Perrot, Nadine and Mailler, Sandrine and Girard, Victoria and Ramjeet, Mahendrasingh and Surre, J{\'e}r{\'e}my and Lacroix, Bruno and others},
+  journal={Bioinformatics},
+  volume={30},
+  number={9},
+  pages={1280--1286},
+  year={2014},
+  publisher={Oxford University Press}
+}
+
+@article{mansouri2013quantitative,
+  title={Quantitative structure--activity relationship models for ready biodegradability of chemicals},
+  author={Mansouri, Kamel and Ringsted, Tine and Ballabio, Davide and Todeschini, Roberto and Consonni, Viviana},
+  journal={Journal of chemical information and modeling},
+  volume={53},
+  number={4},
+  pages={867--878},
+  year={2013},
+  publisher={ACS Publications}
+}
+
+@article{marcinkevivcs2024interpretable,
+  title={Interpretable and intervenable ultrasonography-based machine learning models for pediatric appendicitis},
+  author={Marcinkevi{\v{c}}s, Ri{\v{c}}ards and Wolfertstetter, Patricia Reis and Klimiene, Ugne and Chin-Cheong, Kieran and Paschke, Alyssia and Zerres, Julia and Denzinger, Markus and Niederberger, David and Wellmann, Sven and Ozkan, Ece and others},
+  journal={Medical image analysis},
+  volume={91},
+  pages={103042},
+  year={2024},
+  publisher={Elsevier}
+}
+
+@misc{marcoulides2005churn,
+  title={Discovering knowledge in data: An introduction to data mining},
+  author={Marcoulides, George A},
+  year={2005},
+  publisher={Taylor \& Francis}
+}
+
+@article{marcus2010open,
+  title={Open access series of imaging studies: longitudinal MRI data in nondemented and demented older adults},
+  author={Marcus, Daniel S and Fotenos, Anthony F and Csernansky, John G and Morris, John C and Buckner, Randy L},
+  journal={Journal of cognitive neuroscience},
+  volume={22},
+  number={12},
+  pages={2677--2684},
+  year={2010},
+}
+
+@inproceedings{martins2021early,
+  title={Early prediction of student’s performance in higher education: A case study},
+  author={Martins, M{\'o}nica V and Tolledo, Daniel and Machado, Jorge and Baptista, Lu{\'\i}s MT and Realinho, Valentim},
+  booktitle={Trends and Applications in Information Systems and Technologies: Volume 1 9},
+  pages={166--175},
+  year={2021},
+  organization={Springer}
+}
+
+@article{mathur2021naticusdroid,
+  title={NATICUSdroid: A malware detection framework for Android using native and custom permissions},
+  author={Mathur, Akshay and Podila, Laxmi Mounika and Kulkarni, Keyur and Niyaz, Quamar and Javaid, Ahmad Y},
+  journal={Journal of Information Security and Applications},
+  volume={58},
+  pages={102696},
+  year={2021},
+  publisher={Elsevier}
+}
+
+@inproceedings{menzies2004good,
+  title={How good is your blind spot sampling policy},
+  author={Menzies, Tim and Di Stefano, Justin S},
+  booktitle={Eighth IEEE International Symposium on High Assurance Systems Engineering, 2004. Proceedings.},
+  pages={129--138},
+  year={2004},
+  organization={IEEE}
+}
+
+@article{moro2014bank-marketing,
+  title={A data-driven approach to predict the success of bank telemarketing},
+  author={Moro, S{\'e}rgio and Cortez, Paulo and Rita, Paulo},
+  journal={Decision Support Systems},
+  volume={62},
+  pages={22--31},
+  year={2014},
+  publisher={Elsevier}
+}
+
+@article{olier2018meta,
+  title={Meta-QSAR: a large-scale application of meta-learning to drug design and discovery},
+  author={Olier, Ivan and Sadawi, Noureddin and Bickerton, G Richard and Vanschoren, Joaquin and Grosan, Crina and Soldatova, Larisa and King, Ross D},
+  journal={Machine Learning},
+  volume={107},
+  pages={285--311},
+  year={2018},
+  publisher={Springer}
+}
+
+@article{pace1997sparse,
+  title={Sparse spatial autoregressions},
+  author={Pace, R Kelley and Barry, Ronald},
+  journal={Statistics \& Probability Letters},
+  volume={33},
+  number={3},
+  pages={291--297},
+  year={1997},
+  publisher={Elsevier}
+}
+
+@article{palechor2019dataset,
+    title={Dataset for estimation of obesity levels based on eating habits and physical condition in individuals from Colombia, Peru and Mexico},
+    author={Palechor, Fabio Mendoza and De la Hoz Manotas, Alexis},
+    journal={Data in brief},
+    volume={25},
+    pages={104344},
+    year={2019},
+    publisher={Elsevier}
+}
+
+@misc{paolocons2020fiat,
+  author       = {Kaggle User Paolocons},
+  title        = {Another Dataset on Used Fiat 500 (1538 Rows)},
+  year         = {2020},
+  howpublished = {\url{https://www.kaggle.com/datasets/paolocons/another-fiat-500-dataset-1538-rows}},
+  note         = {Kaggle dataset},
+}
+
+@inproceedings{peeters2021performance,
+  title={Performance Prediction for Hardware-Software Configurations: A Case Study for Video Games},
+  author={Peeters, Sven and Melnikov, Vitalik and H{""u}llermeier, Eyke},
+  booktitle={International Symposium on Intelligent Data Analysis},
+  pages={222--234},
+  year={2021},
+  organization={Springer}
+}
+
+@article{penrose1985generalized,
+  title={Generalized body composition prediction equation for men using simple measurement techniques},
+  author={Penrose, Keith W and Nelson, Arnold G and Fisher, Arnold Garth},
+  journal={Medicine \& Science in Sports \& Exercise},
+  volume={17},
+  number={2},
+  pages={189},
+  year={1985},
+  publisher={Ovid Technologies (Wolters Kluwer Health)}
+}
+
+@article{petricoin2002serum,
+  title={Serum proteomic patterns for detection of prostate cancer},
+  author={Petricoin III, Emanuel F and Ornstein, David K and Paweletz, Cloud P and Ardekani, Ali and Hackett, Paul S and Hitt, Ben A and Velassco, Alfredo and Trucco, Christian and Wiegand, Laura and Wood, Kamillah and others},
+  journal={Journal of the National Cancer Institute},
+  volume={94},
+  number={20},
+  pages={1576--1578},
+  year={2002},
+  publisher={Oxford University Press}
+}
+
+@article{quinlan1987simplifying,
+  title={Simplifying decision trees},
+  author={Quinlan, J. Ross},
+  journal={International journal of man-machine studies},
+  volume={27},
+  number={3},
+  pages={221--234},
+  year={1987},
+  publisher={Elsevier}
+}
+
+@misc{rajatkumar302023food,
+  author       = {Kaggle User Rajatkumar30},
+  title        = {Food Delivery Time},
+  year         = {2023},
+  howpublished = {\url{https://www.kaggle.com/datasets/rajatkumar30/food-delivery-time}},
+  note         = {Kaggle dataset},
+}
+
+@article{ramana2012critical,
+  title={A critical comparative study of liver patients from USA and INDIA: an exploratory analysis},
+  author={Ramana, Bendi Venkata and Babu, M Surendra Prasad and Venkateswarlu, NB},
+  journal={International Journal of Computer Science Issues (IJCSI)},
+  volume={9},
+  number={3},
+  pages={506},
+  year={2012},
+  publisher={International Journal of Computer Science Issues (IJCSI)}
+}
+
+@misc{rana2013protein,
+  author       = {Rana, Prashant},
+  title        = {Physicochemical Properties of Protein Tertiary Structure},
+  year         = {2013},
+  howpublished = {\url{https://doi.org/10.24432/C5QW3H}},
+  note         = {UCI Machine Learning Repository},
+}
+
+@article{reyna2020early,
+  title={Early prediction of sepsis from clinical data: the PhysioNet/Computing in Cardiology Challenge 2019},
+  author={Reyna, Matthew A and Josef, Christopher S and Jeter, Russell and Shashikumar, Supreeth P and Westover, M Brandon and Nemati, Shamim and Clifford, Gari D and Sharma, Ashish},
+  journal={Critical care medicine},
+  volume={48},
+  number={2},
+  pages={210--217},
+  year={2020},
+  publisher={LWW}
+}
+
+@article{ricci2021machine,
+  title={Machine-learning-based microwave sensing: A case study for the food industry},
+  author={Ricci, Marco and {\v{S}}titi{\'c}, Bernardita and Urbinati, Luca and Di Guglielmo, Giuseppe and Vasquez, Jorge A Tob{\'o}n and Carloni, Luca P and Vipiana, Francesca and Casu, Mario R},
+  journal={IEEE Journal on Emerging and Selected Topics in Circuits and Systems},
+  volume={11},
+  number={3},
+  pages={503--514},
+  year={2021},
+  publisher={IEEE}
+}
+
+@article{rossouw1983coronary,
+  title={Coronary risk factor screening in three rural communities. The CORIS baseline study.},
+  author={Rossouw, JE and Du Plessis, JP and Benad{\'e}, AJ and Jordaan, PC and Kotze, JP and Jooste, PL and Ferreira, JJ},
+  journal={South African medical journal= Suid-Afrikaanse tydskrif vir geneeskunde},
+  volume={64},
+  number={12},
+  pages={430--436},
+  year={1983}
+}
+
+@inproceedings{rubachev2025tabred,
+  title={TabReD: Analyzing Pitfalls and Filling the Gaps in Tabular Deep Learning Benchmarks},
+  author={Rubachev, Ivan and Kartashev, Nikolay and Gorishniy, Yury and Babenko, Artem},
+  booktitle={The Thirteenth International Conference on Learning Representations},
+  year={2025},
+}
+
+@article{ruiz2023development,
+  title={Development and evaluations of the ancestry informative markers of the VISAGE Enhanced Tool for Appearance and Ancestry},
+  author={Ruiz-Ram{\'\i}rez, Jorge and de La Puente, M and Xavier, Catarina and Ambroa-Conde, Adri{\'a}n and {\'A}lvarez-Dios, J and Freire-Aradas, A and Mosquera-Miguel, Ana and Ralf, Arwin and Amory, Christina and Katsara, Maria Alexandra and others},
+  journal={Forensic Science International: Genetics},
+  volume={64},
+  pages={102853},
+  year={2023},
+  publisher={Elsevier}
+}
+
+@article{sakar2019real,
+  title={Real-time prediction of online shoppers’ purchasing intention using multilayer perceptron and LSTM recurrent neural networks},
+  author={Sakar, C Okan and Polat, S Olcay and Katircioglu, Mete and Kastro, Yomi},
+  journal={Neural Computing and Applications},
+  volume={31},
+  number={10},
+  pages={6893--6908},
+  year={2019},
+  publisher={Springer}
+}
+
+@misc{saldanha2020marketing,
+  author       = {Saldanha, Rodolfo},
+  title        = {Marketing Campaign},
+  year         = {2020},
+  howpublished = {\url{https://www.kaggle.com/datasets/rodsaldanha/arketing-campaign}},
+  note         = {Kaggle dataset},
+}
+
+@article{sanz2025credit,
+  title={Credit Risk Meets Large Language Models: Building a Risk Indicator from Loan Descriptions in P2P Lending},
+  author={Sanz-Guerrero, Mario and Arroyo, Javier},
+  journal={Inteligencia Artificial},
+  volume={28},
+  number={75},
+  pages={220--247},
+  year={2025}
+}
+
+@article{sikora2010application,
+  title={Application of rule induction algorithms for analysis of data collected by seismic hazard monitoring systems in coal mines},
+  author={Sikora, Marek and Wr{\'o}bel, {\L}ukasz},
+  journal={Archives of Mining Sciences},
+  volume={55},
+  number={1},
+  pages={91--114},
+  year={2010}
+}
+
+@misc{silva2008using,
+  title={Using data mining to predict secondary school student performance},
+  author={Silva, Alice},
+  year={2008}
+}
+
+@article{spira2007airway,
+  title={Airway epithelial gene expression in the diagnostic evaluation of smokers with suspect lung cancer},
+  author={Spira, Avrum and Beane, Jennifer E and Shah, Vishal and Steiling, Katrina and Liu, Gang and Schembri, Frank and Gilman, Sean and Dumas, Yves-Martine and Calner, Paul and Sebastiani, Paola and others},
+  journal={Nature medicine},
+  volume={13},
+  number={3},
+  pages={361--366},
+  year={2007},
+  publisher={Nature Publishing Group US New York}
+}
+
+@article{strack2014impact,
+  title={Impact of HbA1c measurement on hospital readmission rates: analysis of 70,000 clinical database patient records},
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+  journal={BioMed research international},
+  volume={2014},
+  number={1},
+  pages={781670},
+  year={2014},
+  publisher={Wiley Online Library}
+}
+
+@article{towell1994knowledge,
+  title={Knowledge-based artificial neural networks},
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+  journal={Artificial intelligence},
+  volume={70},
+  number={1-2},
+  pages={119--165},
+  year={1994},
+  publisher={Elsevier}
+}
+
+@article{tsanas2009accurate,
+  title={Accurate telemonitoring of Parkinson’s disease progression by non-invasive speech tests},
+  author={Tsanas, Athanasios and Little, Max and McSharry, Patrick and Ramig, Lorraine},
+  journal={Nature Precedings},
+  pages={1--1},
+  year={2009},
+  publisher={Nature Publishing Group UK London}
+}
+
+@techreport{van2000coil,
+  title={CoIL challenge 2000: The insurance company case},
+  author={Van Der Putten, Peter and van Someren, Maarten and others},
+  year={2000},
+  institution={Technical Report 2000--09, Leiden Institute of Advanced Computer Science}
+}
+
+@article{vidros2017automatic,
+  title={Automatic detection of online recruitment frauds: Characteristics, methods, and a public dataset},
+  author={Vidros, Sokratis and Kolias, Constantinos and Kambourakis, Georgios and Akoglu, Leman},
+  journal={Future Internet},
+  volume={9},
+  number={1},
+  pages={6},
+  year={2017},
+  publisher={MDPI}
+}
+
+@article{wang2017bayesian,
+  title={A bayesian framework for learning rule sets for interpretable classification},
+  author={Wang, Tong and Rudin, Cynthia and Doshi-Velez, Finale and Liu, Yimin and Klampfl, Erica and MacNeille, Perry},
+  journal={Journal of Machine Learning Research},
+  volume={18},
+  number={70},
+  pages={1--37},
+  year={2017}
+}
+
+@misc{webrobots2026kickstarter,
+  title        = {Kickstarter Datasets},
+  author       = {{Web Robots}},
+  howpublished = {\url{https://webrobots.io/kickstarter-datasets/}},
+  note         = {Accessed: 2026-01-25},
+  year         = {2026},
+  organization = {Web Robots}
+}
+
+@incollection{wickham2016data,
+  title={Data analysis},
+  author={Wickham, Hadley},
+  booktitle={ggplot2: Elegant graphics for data analysis},
+  pages={189--201},
+  year={2016},
+  publisher={Springer}
+}
+
+@article{xavier2020development,
+  title={Development and validation of the VISAGE AmpliSeq basic tool to predict appearance and ancestry from DNA},
+  author={Xavier, Catarina and de la Puente, Maria and Mosquera-Miguel, Ana and Freire-Aradas, Ana and Kalamara, Vivian and Vidaki, Athina and Gross, Theresa E and Revoir, Andrew and Po{\'s}piech, Ewelina and Kartasi{\'n}ska, Ewa and others},
+  journal={Forensic Science International: Genetics},
+  volume={48},
+  pages={102336},
+  year={2020},
+  publisher={Elsevier}
+}
+
+@misc{yakhyojon2023airlinesatisfaction,
+    author = {Kaggle User Yakhyojon},
+    title = {Customer Satisfaction in Airline.},
+    year = {2023},
+    howpublished = {url{https://www.kaggle.com/datasets/yakhyojon/customer-satisfaction-in-airline}},
+    note = {Kaggle}
+}
+
+@article{yeh1998modeling,
+  title={Modeling of strength of high-performance concrete using artificial neural networks},
+  author={Yeh, I-C},
+  journal={Cement and Concrete research},
+  volume={28},
+  number={12},
+  pages={1797--1808},
+  year={1998},
+  publisher={Elsevier}
+}
+
+@article{yeh2009comparisons,
+  title={The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients},
+  author={Yeh, I-Cheng and Lien, Che-hui},
+  journal={Expert systems with applications},
+  volume={36},
+  number={2},
+  pages={2473--2480},
+  year={2009},
+  publisher={Elsevier}
+}
+
+@article{yeh2009knowledge,
+  title={Knowledge discovery on RFM model using Bernoulli sequence},
+  author={Yeh, I-Cheng and Yang, King-Jang and Ting, Tao-Ming},
+  journal={Expert Systems with applications},
+  volume={36},
+  number={3},
+  pages={5866--5871},
+  year={2009},
+  publisher={Elsevier}
+}
+
+@article{zikeba2016ensemble,
+  title={Ensemble boosted trees with synthetic features generation in application to bankruptcy prediction},
+  author={Zi{\k{e}}ba, Maciej and Tomczak, Sebastian K and Tomczak, Jakub M},
+  journal={Expert systems with applications},
+  volume={58},
+  pages={93--101},
+  year={2016},
+  publisher={Elsevier}
+}
+
+@misc{zindi_ghana_indigenous_intel_2025,
+    author       = {{Zindi}},
+    title        = {Ghana's Indigenous Intel Challenge [BEGINNERS ONLY]: Data},
+    year         = {2025},
+    howpublished = {\url{https://zindi.africa/competitions/ghana-indigenous-intel-challenge/data}},
+    note         = {Zindi dataset page. Accessed 2026-04-11}
+}