Datasets & Software

Our datasets:

  • The SHAP-based explanation data set presented and anlysed in “Contextual Explanations for Decision Support in
    Predictive Maintenance”
  • Data sets describing fuel sales at 25 petrol stations (part 1 and part 2).
  • The synthetic data set used in “Sensor-Based Predictive Maintenance with Reduction of False Alarms – A Case Study in Heavy Industry”
  • The data set describing symptoms of patients tested for COVID-19. This data set is referred in “Screening Support System Based on Patient Survey Data—CaseStudy on Classification of Initial, Locally CollectedCOVID-19 Data” and in “Enhancement of COVID-19 symptom-based screening with quality-based classifier optimisation”
  • The data set describing pediatric patients with several hematologic diseases, who were subject to the unmanipulated allogeneic unrelated donor hematopoietic stem cell transplantation. Data is available on github or kaggle.
  • Data which are referred to in “Predicting presence of amphibian species using features obtained from GIS and satellite images”
  • Data which are referred to in “Boolean Representation for Exact Biclustering”
  • Methane data which are refered to in “A Framework for Learning and Embedding Multi-Sensor Forecasting Models into a Decision Support System: A Case Study of Methane Concentration in Coal Mines”
  • HGR – Database for hand gesture recognition
  • Methane – Datasets analysed in “Decision rule learning from stream of measurements — a case study in methane hazard forecasting in coal mines”. The data set was collected within the DISESOR project
  • Seismic-bumps  – The data describe the problem of high energy (higher than 10^4 J) seismic bumps forecasting in a coal mine
  • Gene data – Datasets analysed in “Relatives-based granular Gene Ontology term similarity measures – broad comparison and analysis”

Our software:

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