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[
[
[
"core",
"BBC3.tab"
],
{
"collection": "",
"description": "A subset of BBC news articles, containing categories business, entertainment, and sport from 2004-2005. These datasets are made available for non-commercial and research purposes only. All rights, including copyright, in the content of the original articles are owned by the BBC.",
"name": "BBC3",
"title": "BBC3",
"instances": 1407,
"variables": 3,
"missing": false,
"target": "categorical",
"size": 2754486,
"year": 2006,
"version": "2.1",
"tags": [
"text",
"classification",
"news"
],
"references": [
"D. Greene and P. Cunningham. [Practical Solutions to the Problem of Diagonal Dominance in Kernel Document Clustering](http://mlg.ucd.ie/files/publications/greene06icml.pdf), Proc. ICML 2006."
],
"source": "<a href='http://mlg.ucd.ie/datasets/bbc.html'>ML Resources</a>",
"url": "https://datasets.biolab.si/core/BBC3.tab",
"seealso": []
}
],
[
[
"core",
"GDS360.tab"
],
{
"collection": "NCBI",
"description": "Breast cancer core biopsies taken from patients found to be resistant (greater than 25% residual tumor volume) or sensitive (less than 25% residual tumor volume) to docetaxel treatment.",
"name": "GDS360",
"title": "Breast Cancer and Docetaxel Treatment",
"instances": 24,
"variables": 9486,
"missing": false,
"target": "categorical",
"size": 1870893,
"year": 2006,
"version": "1.0",
"tags": [
"biology"
],
"references": [
"Chang JC, Wooten EC, Tsimelzon A, Hilsenbeck SG et al. (2005) Patterns of resistance and incomplete response to docetaxel by gene expression profiling in breast cancer patients. J Clin Oncol, 23(6): 1169-77."
],
"source": "<a href='https://www.ncbi.nlm.nih.gov/sites/GDSbrowser/?acc=GDS360'>NCBI</a>",
"url": "https://datasets.biolab.si/core/GDS360.tab",
"seealso": []
}
],
[
[
"core",
"GDS3713-small.tab"
],
{
"collection": "GEO",
"description": "Gene expression data from peripheral circulating B cells from smoking (39 samples) and non-smoking healthy US white females (40 samples). Only 3,000 randomly selected genes (features) were retained from original data set to reduce the data size.",
"name": "GDS3713-small",
"title": "Smoking effect on B lymphocytes",
"instances": 79,
"variables": 3000,
"missing": false,
"target": "categorical",
"size": 1887679,
"year": 2009,
"version": "1.0",
"tags": [
"genomics"
],
"references": [],
"source": "<a href='https://www.ncbi.nlm.nih.gov/sites/GDSbrowser/?acc=GDS3713'>Gene Expression Omnibus GDS3713</a>",
"url": "https://datasets.biolab.si/core/GDS3713-small.tab",
"seealso": []
}
],
[
[
"core",
"HDI.tab"
],
{
"collection": "UNDP Human Development Reports",
"description": "The Human Development Index (HDI) is a summary measure of average achievement in key dimensions of human development: a long and healthy life, knowledge, and decent living. The data includes HDI as reported in 2015 and selected related socioeconomic from countries worldwide. We have removed HDI-related variables and countries with a missing HDI score.",
"name": "HDI",
"title": "HDI",
"instances": 188,
"variables": 53,
"missing": true,
"target": null,
"size": 46317,
"year": 2015,
"version": "3.1",
"tags": [
"economy",
"geo"
],
"references": [],
"source": "<a href='https://www.kaggle.com/datasets/undp/human-development'>Human Development Data (1990-2015)</a>",
"url": "https://datasets.biolab.si/core/HDI.tab",
"seealso": [
"<a href='https://blog.biolab.si/2018/07/17/data-mining-and-machine-learning-for-economists/'>Data Mining and Machine Learning for Economists</a>."
]
}
],
[
[
"core",
"ParlaMint-1000.tab"
],
{
"name": "ParlaMint-1000",
"title": "ParlaMint",
"description": "A sample of 1000 parliamentary debates from the ParlaMint-GB 2.1 corpus. ParlaMint-GB features speeches from both houses of the parliament for years 2019 and 2020. It comes with rich metadata, such as speaker name, party affiliation, and speaker role. A column with GPT-generated summaries is added.",
"references": [
"Toma\u017e Erjavec et al. The ParlaMint corpora of parliamentary proceedings. Language Resources and Evaluation, 2022. https://doi.org/10.1007/s10579-021-09574-0"
],
"tags": [
"text",
"classification",
"time",
"politics"
],
"target": "categorical",
"version": "2.2",
"year": 2021,
"instances": 1000,
"missing": true,
"variables": 18,
"source": "https://github.com/clarin-eric/ParlaMint/blob/main/Samples/ParlaMint-GB/ParlaMint-GB.ana.xml",
"url": "https://datasets.biolab.si/core/ParlaMint-1000.tab",
"size": 1795759
}
],
[
[
"core",
"SentiNews-SI.tab"
],
{
"collection": "CLARIN",
"description": "A subset of 2000 documents from AutoSentiNews data set. The original corpus contains 256,567 documents from the Slovenian news portals 24ur, Dnevnik, Finance, Rtvslo, and \u017durnal24, which contain political, business, economic and financial content. The subset covers only Dnevnik and 24ur. The news articles are annotated as positive, negative or neutral at the document level.",
"name": "SentiNews-SI",
"title": "SentiNews",
"instances": 2000,
"variables": 7,
"missing": false,
"target": "categorical",
"size": 5203086,
"year": 2017,
"version": "1.0",
"tags": [
"text",
"sentiment"
],
"references": [
"Bu\u010dar, Jo\u017ee, 2017, Automatically sentiment annotated Slovenian news corpus AutoSentiNews 1.0, Slovenian language resource repository CLARIN.SI, http://hdl.handle.net/11356/1109."
],
"source": "<a href='http://hdl.handle.net/11356/1109'>CLARIN Repository</a>",
"url": "https://datasets.biolab.si/core/SentiNews-SI.tab",
"seealso": []
}
],
[
[
"core",
"TKI-resistance-spectroscopy.tab"
],
{
"collection": "spectral",
"description": "An infrared spectroscopy dataset on Tyrosine Kinase Inhibitor resistance. Chronic Myeloid Leukemia (CML) is a common blood disease caused by a chromosomal translocation creating a chimeric tyrosine kinase (BCR-ABL) that is permanently active and causes uncontrolled proliferation of blood progenitor cells. CML crises can be very effectively treated by using drugs called Tyrosine Kinase Inhibitors (TKI), but TKI resistance appears in some patients that prompted the development of new generation TKIs. However, the T315I mutation renders BCR-ABL kinases resistant to all generations of TKI (resistance to imatinib, dasatinib or nilotinib). The dataset is from the Sandt et al. (2018) study where murine embryonic stem cells were used to evaluate the T315I mutation in a stem cell context, mimicking the blood progenitor context. The question was whether we could separate the cells expressing the mutated BCR-ABL from the cells expressing the wild type BCR-ABL. Murine embryonic GS2 cells (mES GS2) were transduced either with MIGR, MIGR-BCR-ABL or MIGR-T315I vectors and grown on low-e slides. The dataset contains spectra of individual mES GS2 cells measured by synchrotron radiation FTIR microspectroscopy in transflection mode at 12x12 \u00b5m\u00b2 spatial resolution. Data was collected and compiled by Dr. Christophe Sandt.",
"name": "TKI-resistance-spectroscopy",
"title": "TKI resistance",
"instances": 280,
"variables": 467,
"missing": false,
"target": "categorical",
"year": 2018,
"version": "1.0",
"tags": [
"spectral"
],
"references": [
"Sandt C, Feraud O, Bonnet ML, Desterke C, Khedhir R, Flamant S, Bailey CG, Rasko JE, Dumas P, Bennaceur-Griscelli A, Turhan AG. Direct and rapid identification of T315I-Mutated BCR-ABL expressing leukemic cells using infrared microspectroscopy. Biochemical and biophysical research communications. 2018. 503(3):1861-7."
],
"source": "",
"url": "https://datasets.biolab.si/core/TKI-resistance-spectroscopy.tab",
"size": 1310515
}
],
[
[
"core",
"abalone.tab"
],
{
"collection": "UCI",
"description": "Abalone is marine snails. This data set is about predicting abalone age from physical measurements. The age of abalone is determined by cutting the shell through the cone, staining it, and counting the number of rings through a microscope - a boring and time-consuming task. Other measurements, which are easier to obtain, may be used to predict the age. The data set contains the attributes that report on sex, size and weight measurements. The age is reported as the number of rings, which increased by 1.5 gives the age in years.",
"name": "abalone",
"title": "Abalone",
"instances": 4177,
"variables": 8,
"missing": false,
"target": "numeric",
"size": 191993,
"year": 1994,
"version": "1.1",
"tags": [
"biology"
],
"references": [],
"source": "<a href='http://archive.ics.uci.edu/ml/datasets/Abalone'>UCI ML Repository</a>",
"url": "https://datasets.biolab.si/core/abalone.tab",
"seealso": []
}
],
[
[
"core",
"adult.tab"
],
{
"collection": "UCI",
"description": "Data extracted by Barry Becker from the 1994 Census database so that ((AAGE>16) & (AGI>100) & (AFNLWGT>1) & (HRSWK>0)). The prediction task is determining whether a person makes over 50K yearly. Regarding fairness, \"sex\" is frequently considered the protected attribute, with \"Male\" as the privileged value. Similarly, \"race\" and \"age\" can be treated as protected attributes. For \"race,\" \"Caucasian\" is the privileged value, and for \"age,\" the specified group is between 25 and 60 years.",
"name": "adult",
"title": "Adult Census Income",
"instances": 48842,
"variables": 15,
"missing": true,
"target": "categorical",
"size": 5583317,
"year": 1996,
"version": "1.4",
"tags": [
"economy",
"fairness"
],
"references": [
"Ron Kohavi (1996) Scaling Up the Accuracy of Naive-Bayes Classifiers: a Decision-Tree Hybrid. Proceedings of the Second International Conference on Knowledge Discovery and Data Mining."
],
"source": "<a href='https://archive.ics.uci.edu/ml/datasets/adult'>UCI ML Repository</a>",
"url": "https://datasets.biolab.si/core/adult.tab",
"seealso": []
}
],
[
[
"core",
"ames-iowa-housing.xlsx"
],
{
"collection": "kaggle",
"description": "The Ames Housing dataset contains 79 explanatory variables describing (almost) every aspect of housing in Ames, Iowa, with the goal of predicting sales prices. This dataset is a great alternative to the popular but older Boston Housing dataset. The data was compiled by Dean De Cock in 2011 for use in data science education.",
"name": "ames-iowa-housing",
"title": "Ames Iowa Housing",
"target": "numeric",
"year": 2011,
"version": "1.0",
"instances": 2930,
"missing": true,
"variables": 81,
"tags": [
"economy"
],
"source": "<a href='https://www.kaggle.com/datasets/marcopale/housing'>Ames Iowa Housing Data on kaggle</a>",
"url": "https://datasets.biolab.si/core/ames-iowa-housing.xlsx",
"size": 851185
}
],
[
[
"core",
"amphorae.tab"
],
{
"collection": "Archaeology Data Service",
"description": "An introductory resource for the study of Roman amphorae. There are many types of amphorae - we kept the most well-documented ones, Dressel, Gauloise, and Keay. Each subtype has the corresponding metadata.",
"name": "amphorae",
"title": "Roman Amphorae",
"instances": 164,
"variables": 16,
"missing": true,
"target": "categorical",
"size": 24289,
"year": 2005,
"version": "1.0",
"tags": [
"archaeology",
"image analytics"
],
"references": [],
"source": "<a href='https://archaeologydataservice.ac.uk/archives/view/amphora_ahrb_2005/index.cfm'>Archaeology Data Service</a>",
"url": "https://datasets.biolab.si/core/amphorae.tab",
"seealso": []
}
],
[
[
"core",
"attrition-predict.tab"
],
{
"collection": "IBM",
"description": "Complimentary synthetic data set to be used for prediction with Attrition - Train. Data instances were created at Biolab for education purposes.",
"name": "attrition-predict",
"title": "Attrition - Predict",
"instances": 3,
"variables": 18,
"missing": false,
"target": "categorical",
"size": 838,
"year": 2017,
"version": "1.0",
"tags": [
"economy",
"synthetic",
"education"
],
"references": [],
"source": "",
"url": "https://datasets.biolab.si/core/attrition-predict.tab",
"seealso": [
"<a href='http://www.planetgv.si/hrm/iz-revije/hrm-revija-februar-marec-2017/kratek-primer-iz-napovedane-analitike-v-hr-u'>Kratek primer iz napovedne analitike v HR-u</a>."
]
}
],
[
[
"core",
"attrition-train.tab"
],
{
"collection": "IBM",
"description": "Uncover the factors that lead to employee attrition and explore important questions such as \u2018show me a breakdown of distance from home by job role and attrition\u2019 or \u2018compare average monthly income by education and attrition\u2019. This is a fictional data set created by IBM data scientists.",
"name": "attrition-train",
"title": "Attrition - Train",
"instances": 1470,
"variables": 18,
"missing": false,
"target": "categorical",
"size": 186525,
"year": 2015,
"version": "1.0",
"tags": [
"economy",
"synthetic"
],
"references": [],
"source": "<a href='https://www.ibm.com/communities/analytics/watson-analytics-blog/hr-employee-attrition/'>Watson Analytics Sample Data</a>",
"url": "https://datasets.biolab.si/core/attrition-train.tab",
"seealso": [
"<a href='http://www.planetgv.si/hrm/iz-revije/hrm-revija-februar-marec-2017/kratek-primer-iz-napovedane-analitike-v-hr-u'>Kratek primer iz napovedne analitike v HR-u</a>."
]
}
],
[
[
"core",
"auto-mpg.tab"
],
{
"collection": "UCI",
"description": "This dataset is a slightly modified version of the dataset provided in the StatLib library. In line with the use by Ross Quinlan (1993) in predicting the attribute 'mpg', 8 of the original instances were removed because they had unknown values for the 'mpg' attribute. 'The data concerns city-cycle fuel consumption in miles per gallon, to be predicted in terms of 3 multivalued discrete and 5 continuous attributes.' (Quinlan, 1993)",
"name": "auto-mpg",
"title": "Auto MPG",
"instances": 398,
"variables": 9,
"missing": true,
"target": "numeric",
"size": 17760,
"year": 1993,
"version": "1.0",
"tags": [],
"references": [
"Quinlan, R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann."
],
"source": "<a href='https://archive.ics.uci.edu/ml/datasets/auto+mpg'>UCI ML Repository</a>",
"url": "https://datasets.biolab.si/core/auto-mpg.tab",
"seealso": []
}
],
[
[
"core",
"bank-additional.tab"
],
{
"collection": "UCI",
"description": "Data from direct marketing campaigns (phone calls) of a Portuguese bank. The classification goal is to predict if the client will subscribe a term deposit given the profile of a client that contains attributes such as age, job type, martial status, education, information on previous loans, and other.",
"name": "bank-additional",
"title": "Bank Marketing",
"instances": 4119,
"variables": 20,
"missing": true,
"target": "categorical",
"size": 477308,
"year": 2014,
"version": "1.0",
"tags": [
"economy"
],
"references": [
"Moro S, Cortez P, and Rita P (2014) A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems 62:22-31."
],
"source": "<a href='http://archive.ics.uci.edu/ml/datasets/Bank+Marketing'>UCI ML Repository</a>",
"url": "https://datasets.biolab.si/core/bank-additional.tab",
"seealso": []
}
],
[
[
"core",
"banking-crises.tab"
],
{
"collection": "The World Bank",
"description": "This is a transformed STATA file reporting on the banking crises per year per country.",
"name": "banking-crises",
"title": "Banking Crises",
"instances": 211,
"variables": 73,
"missing": false,
"target": null,
"size": 32083,
"year": "2017",
"version": "1.0",
"tags": [
"time",
"economy"
],
"references": [],
"source": "<a href='https://datacatalog.worldbank.org/dataset/wps5016-banking-crisis-and-exports-dataset'>The World Bank</a>",
"url": "https://datasets.biolab.si/core/banking-crises.tab",
"seealso": []
}
],
[
[
"core",
"bicycle-gravel-vs-mountain.xlsx"
],
{
"collection": "",
"description": "Donated by Janez Dem\u0161ar, this dataset records his bike rides in the year 2023. Each ride is characterized by five different features and labeled with the type of bike Janez used.",
"name": "bicycle-gravel-vs-mountain",
"title": "Gravel vs Mountain Bike Rides: Data from Strava",
"instances": 98,
"variables": 6,
"missing": false,
"target": "categorical",
"size": 13691,
"year": 2024,
"version": "1.0",
"tags": [
"sport"
],
"references": [],
"source": "",
"url": "https://datasets.biolab.si/core/bicycle-gravel-vs-mountain.xlsx",
"seealso": []
}
],
[
[
"core",
"bicycle-time-distance.xlsx"
],
{
"collection": "",
"description": "Donated by Janez Dem\u0161ar, this dataset records his bike rides in 2023, and includes the total time of the ride, the distance traveled, and the type of bike used.",
"name": "bicycle-time-distance",
"title": "Gravel vs Mountain Bike Rides: Distance and Time",
"instances": 98,
"variables": 3,
"missing": false,
"target": "categorical",
"size": 10859,
"year": 2024,
"version": "1.0",
"tags": [
"sport"
],
"references": [],
"source": "",
"url": "https://datasets.biolab.si/core/bicycle-time-distance.xlsx",
"seealso": []
}
],
[
[
"core",
"body-fat-brozek.xlsx"
],
{
"collection": "kaggle",
"description": "Lists estimates of the percentage of body fat determined by underwater and includes various body circumference measurements for 252 men. Use the data to determine if body fat can be estimated from body measurements. The data were generously provided by Dr. A. Garth Fisher, Human Performance Research Center, Brigham Young University, Provo, Utah 84602, who granted permission for the data to be freely distributed and used for non-commercial purposes.",
"name": "body-fat-brozek",
"title": "Body Fat Prediction",
"target": "numeric",
"year": 1985,
"version": "1.0",
"instances": 252,
"missing": false,
"variables": 14,
"tags": [
"health"
],
"source": "<a href='https://www.kaggle.com/datasets/fedesoriano/body-fat-prediction-dataset'>Body Fat Prediction Dataset on kaggle</a>",
"url": "https://datasets.biolab.si/core/body-fat-brozek.xlsx",
"size": 66394
}
],
[
[
"core",
"bone-healing.xlsx"
],
{
"collection": "",
"description": "Microscope images of bone-fracture repair that highlight the involvement from skeletal stem cells. Images are from laboratory of Dongsu Park at Baylor College of Medicine, Houston.",
"name": "bone-healing",
"title": "Bone Healing",
"instances": 37,
"variables": 0,
"missing": true,
"target": "categorical",
"size": 11922,
"year": 2018,
"version": "1.0",
"tags": [
"image analytics",
"biology"
],
"references": [],
"source": null,
"url": "https://datasets.biolab.si/core/bone-healing.xlsx",
"seealso": []
}
],
[
[
"core",
"brca_metabric.pkl.gz"
],
{
"name": "brca_metabric",
"title": "METABRIC: Molecular Taxonomy of Breast Cancer International Consortium",
"description": "Survival data for 1904 patients with primary breast tumors. Contains 35 clinical features and expression data for 24368 genes. Log-transformed mRNA z-Scores compared to the expression distribution of all samples (Illumina Human v3 microarray)",
"references": [
"Curtis, Christina et al. \u201cThe genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups.\u201d Nature vol. 486,7403 346-52. 18 Apr. 2012, doi:10.1038/nature10983",
"Rueda, Oscar M et al. \u201cDynamics of breast-cancer relapse reveal late-recurring ER-positive genomic subgroups.\u201d Nature vol. 567,7748 (2019): 399-404. doi:10.1038/s41586-019-1007-8",
"Pereira, Bernard et al. \u201cThe somatic mutation profiles of 2,433 breast cancers refines their genomic and transcriptomic landscapes.\u201d Nature communications vol. 7 11479. 10 May. 2016, doi:10.1038/ncomms11479"
],
"tags": [
"gene expression",
"survival analysis",
"censoring"
],
"target": "",
"version": "1.2",
"year": 2021,
"instances": 1904,
"missing": true,
"variables": 24403,
"source": "<a href='https://www.cbioportal.org/study/summary?id=brca_metabric'>cBioPortal</a>",
"seealso": [
"Survival Outcomes are Associated with Genomic Instability in LuminalBreast Cancers: <a href='https://figshare.com/articles/journal_contribution/S1_File_-/13708393'>Supplementary Material</a>."
],
"url": "https://datasets.biolab.si/core/brca_metabric.pkl.gz",
"size": 142871615
}
],
[
[
"core",
"breast-cancer-wisconsin.tab"
],
{
"collection": "UCI",
"description": "Features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. They describe characteristics of the cell nuclei present in the image. Separating plane described above was obtained using Multisurface Method-Tree (MSM-T), a classification method which uses linear programming to construct a decision tree. Relevant features were selected using an exhaustive search in the space of 1-4 features and 1-3 separating planes.",
"name": "breast-cancer-wisconsin",
"title": "Breast Cancer Wisconsin",
"instances": 683,
"variables": 10,
"missing": false,
"target": "categorical",
"size": 35787,
"year": 1992,
"version": "1.0",
"tags": [
"biology"
],
"references": [
"Wolberg, W.H., & Mangasarian, O.L. (1990). Multisurface method of pattern separation for medical diagnosis applied to breast cytology. In Proceedings of the National Academy of Sciences, 87, 9193-9196."
],
"source": "<a href='https://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+(original)'>UCI ML Repository</a>",
"url": "https://datasets.biolab.si/core/breast-cancer-wisconsin.tab",
"seealso": []
}
],
[
[
"core",
"breast-cancer.tab"
],
{
"collection": "UCI",
"description": "This is one of three domains provided by the Oncology Institute that has repeatedly appeared in the machine learning literature. This data set includes 201 instances of one class and 85 instances of another class. The instances are described by 9 attributes, some of which are linear and some are nominal.",
"name": "breast-cancer",
"title": "Breast Cancer",
"instances": 286,
"variables": 10,
"missing": true,
"target": "categorical",
"size": 18801,
"year": 1992,
"version": "1.0",
"tags": [
"biology"
],
"references": [
"Michalski, R.S., Mozetic, I., Hong, J., & Lavrac, N. (1986). The Multi-Purpose Incremental Learning System AQ15 and its Testing Application to Three Medical Domains. In Proceedings of the Fifth National Conference on Artificial Intelligence, 1041-1045, Philadelphia, PA: Morgan Kaufmann."
],
"source": "<a href='https://archive.ics.uci.edu/ml/datasets/breast+cancer'>UCI ML Repository</a>",
"url": "https://datasets.biolab.si/core/breast-cancer.tab",
"seealso": []
}
],
[
[
"core",
"bridges.tab"
],
{
"collection": "UCI",
"description": "This is a design domain where 5 properties (design description) need to be predicted based on 7 specification properties.",
"name": "bridges",
"title": "Pittsburg Bridges",
"instances": 108,
"variables": 11,
"missing": true,
"target": null,
"size": 6251,
"year": 1990,
"version": "1.0",
"tags": [
"design"
],
"references": [
"Reich & Fenves (1989). Incremental Learning for Capturing Design Expertise. Technical Report: EDRC 12-34-89, Engineering Design Research Center, Carnegie Mellon University, Pittsburgh, PA."
],
"source": "<a href='https://archive.ics.uci.edu/ml/datasets/Pittsburgh+Bridges'>UCI ML Repository</a>",
"url": "https://datasets.biolab.si/core/bridges.tab",
"seealso": []
}
],
[
[
"core",
"brown-selected.tab"
],
{
"collection": "",
"description": "Gene expression of baker's yeast.",
"name": "brown-selected",
"title": "Baker's Yeast",
"instances": 186,
"variables": 81,
"missing": true,
"target": "categorical",
"size": 98010,
"year": null,
"version": "1.0",
"tags": [
"biology"
],
"references": [
"Brown, M.P., Grundy, W.N., Lin, D., Cristianini, N., Sugnet, C., Furey, T.S., Ares, M., Haussler, D. (2000) Knowledge-based analysis of microarray gene expression data by using support vector machines, Proceedings of the National Academy of Sciences, 1 , 262-267."
],
"source": "<a href='http://www.biolab.si/supp/bi-vizrank/use.html'>Brown for Orange</a>",
"url": "https://datasets.biolab.si/core/brown-selected.tab",
"seealso": [
"<a href='https://blog.biolab.si/2016/10/02/intro-to-data-mining-for-life-scientists/'>Intro to Data Mining for Life Scientists</a>."
]
}
],
[
[
"core",
"bupa.tab"
],
{
"collection": "UCI",
"description": "The BUPA dataset contains 345 single male patients with 6 numeric attributes. Five of these attributes are blood tests which are thought to be sensitive to liver disorders that might arise from excessive alcohol consumption. Each line in the dataset constitutes the record of a single male individual.",
"name": "bupa",
"title": "Liver Disorders",
"instances": 345,
"variables": 11,
"missing": false,
"target": "categorical",
"size": 7369,
"year": 1990,
"version": "1.0",
"tags": [
"biology"
],
"references": [
"McDermott & Forsyth 2016, Diagnosing a disorder in a classification benchmark, Pattern Recognition Letters, Volume 73."
],
"source": "<a href='https://archive.ics.uci.edu/ml/datasets/liver+disorders'>UCI ML Repository</a>",
"url": "https://datasets.biolab.si/core/bupa.tab",
"seealso": []
}
],
[
[
"core",
"car.tab"
],
{
"collection": "UCI",
"description": "This is a synthetic data set derived from a simple hierarchical decision model to demonstrate decision support system DEX (see Bohanec and Rajkovic). The decision model included six attributes, including buying and maintenance price, the number of passengers, size of the luggage booth, and evaluated the utility of the car from a buyer's perspective. All attributes were discrete, having from three to four values. The data set provides car's utility for all possible combinations of attribute values. The data set was originally created to showcase the ability of machine learning by function decomposition to recreate the hierarchy of the decision model.",
"name": "car",
"title": "Car Evaluation",
"instances": 1728,
"variables": 6,
"missing": false,
"target": "categorical",
"size": 51939,
"year": 1999,
"version": "1.0",
"tags": [
"synthetic"
],
"references": [
"Bohanec M, Rajkovic V (1988) Knowledge acquisition and explanation for multi-attribute decision making. In 8th International Workshop on Expert Systems and their Applications, Avignon, France. pages 59-78.",
"Zupan B, Bohanec M, Demsar J, Bratko I (1999) <a href='http://file.biolab.si/papers/1999-Zupan-AIJ.pdf'>Learning by discovering concept hierarchies</a>, Artificial Intelligence 109: 211-242."
],
"source": "<a href='http://archive.ics.uci.edu/ml/datasets/Car+Evaluation'>UCI ML Repository</a>",
"url": "https://datasets.biolab.si/core/car.tab",
"seealso": []
}
],
[
[
"core",
"climate-europe.tab"
],
{
"collection": "UCI",
"description": "Database of European cities, their climatic characteristics and associated climate. The database contains 11 variables describing the climatic characteristics of cities: record high temperature [\u00b0C], average high temperature [\u00b0C], daily average temperature [\u00b0C], average low temperature [\u00b0C], record low temperature [\u00b0C], average precipitation [mm], number of rainy days, number of snowy days, average relative humidity (%), average monthly sunshine hours, average ultraviolet index. For each city, it is then noted in which climate it is classified. Each city is also given its geographical longitude and latitude.",
"name": "climate-europe",
"title": "Climate of European cities",
"instances": 41,
"variables": 16,
"missing": true,
"target": null,
"size": 4478,
"year": 2015,
"version": "1.0",
"tags": [
"geography"
],
"references": [],
"source": "",
"url": "https://datasets.biolab.si/core/climate-europe.tab",
"seealso": []
}
],
[
[
"core",
"compas-scores-two-years.tab"
],
{
"collection": "ProPublica",
"description": "With the COMPAS dataset, we can predict the likelihood of a defendant becoming a recidivist. The data contains various attributes: age, race, sex, priors count, charge degree, and more. The data was, in detail, analyzed by ProPublica, which identified racial disparities in its predictions. We have included all date/time features as meta attributes. When addressing fairness, \"race\" is often considered the protected attribute, with \"Caucasian\" being the privileged value. In some studies, \"sex\" is also treated as the protected attribute, designating \"female\" as the privileged value.",
"name": "compas-scores-two-years",
"title": "COMPAS Analysis",
"instances": 7214,
"variables": 52,
"missing": true,
"target": "categorical",
"size": 2779697,
"year": 2014,
"version": "1.0",
"tags": [
"criminal justice",
"fairness"
],
"references": [
"Angwin, J., Larson, J., Mattu, S., & Kirchner, L. (2016). <a href='https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing/'>Machine Bias.</a> ProPublica.",
"Larson, J., Mattu, S., Kirchner, L., & Angwin, J. (2016). <a href='https://www.propublica.org/article/how-we-analyzed-the-compas-recidivism-algorithm/'>How We Analyzed the COMPAS Recidivism Algorithm.</a> ProPublica."
],
"source": "<a href='https://github.com/propublica/compas-analysis'>ProPublica GitHub Repository</a>",
"url": "https://datasets.biolab.si/core/compas-scores-two-years.tab",
"seealso": []
}
],
[
[
"core",
"conferences.tab"
],
{
"collection": "",
"description": "Machine learning publications with author and paper counts.",
"name": "conferences",
"title": "Conferences",
"instances": 42,
"variables": 5,
"missing": false,
"target": null,
"size": 2337,
"year": null,
"version": "1.0",
"tags": [],
"references": [],
"source": "",
"url": "https://datasets.biolab.si/core/conferences.tab",
"seealso": [
"<a href='https://blog.biolab.si/2016/07/18/network-analysis-with-orange'>Network Analysis with Orange</a>, a blog about how to plot ML publications in a network."
]
}
],
[
[
"core",
"cyber-security-breaches.tab"
],
{
"collection": "PRC",
"description": "Reports on security breaches in the US states between 1997 and 2014. Data reports on type, individuals affects, location of the breach and entity involved.",
"name": "cyber-security-breaches",
"title": "Cyber Security Breaches",
"instances": 1055,
"variables": 10,
"missing": true,
"target": null,
"size": 230445,
"year": 2014,
"version": "1.0",
"tags": [
"security",
"time",
"geo"
],
"references": [],
"source": "<a href='https://academic.oup.com/cybersecurity/article/2/1/3/2736315'>Hype and Heavy Tails: a closer look at data breaches</a>",
"url": "https://datasets.biolab.si/core/cyber-security-breaches.tab",
"seealso": []
}
],
[
[
"core",
"dermatology.tab"
],
{
"collection": "UCI",
"description": "The differential diagnosis of erythemato-squamous diseases is a real problem in dermatology. They all share the clinical features of erythema and scaling, with very little differences. The diseases in this group are psoriasis, seboreic dermatitis, lichen planus, pityriasis rosea, chronic dermatitis, and pityriasis rubra pilaris. Usually a biopsy is necessary for the diagnosis but unfortunately these diseases share many histopathological features as well. Another difficulty for the differential diagnosis is that a disease may show the features of another disease at the beginning stage and may have the characteristic features at the following stages. Patients were first evaluated clinically with 12 features. Afterwards, skin samples were taken for the evaluation of 22 histopathological features. The values of the histopathological features are determined by an analysis of the samples under a microscope. In the dataset constructed for this domain, the family history feature has the value 1 if any of these diseases has been observed in the family, and 0 otherwise. The age feature simply represents the age of the patient. Every other feature (clinical and histopathological) was given a degree in the range of 0 to 3. Here, 0 indicates that the feature was not present, 3 indicates the largest amount possible, and 1, 2 indicate the relative intermediate values.",
"name": "dermatology",
"title": "Dermatology",
"instances": 366,
"variables": 35,
"missing": true,
"target": "categorical",
"size": 31682,
"year": 1998,
"version": "1.0",
"tags": [
"biology",
"medical"
],
"references": [
"G. Demiroz, H. A. Govenir, and N. Ilter, Learning Differential Diagnosis of Eryhemato-Squamous Diseases using Voting Feature Intervals, Aritificial Intelligence in Medicine."
],
"source": "<a href='https://archive.ics.uci.edu/ml/datasets/dermatology'>UCI ML Repository</a>",
"url": "https://datasets.biolab.si/core/dermatology.tab",
"seealso": []
}
],
[
[
"core",
"dicty-development.xlsx"
],
{
"collection": "",
"description": "Images of strains of the social amoeba Dictyostelium discoideum from Gad Shaulsky's Lab at Baylor College of Medicine, Houston, USA.",
"name": "dicty-development",
"title": "Development of Social Amoeba",
"instances": 152,
"variables": 0,
"missing": true,
"target": "categorical",
"size": 15904,
"year": 2018,
"version": "1.1",
"tags": [
"image analytics",
"biology"
],
"references": [
"Li CL, Santhanam B, Webb AN, Zupan B, Shaulsky G (2016) Gene discovery by chemical mutagenesis and whole-genome sequencing in Dictyostelium. Genome Res 26(9):1268-76."
],
"source": "",
"url": "https://datasets.biolab.si/core/dicty-development.xlsx",
"seealso": []
}
],
[
[
"core",
"ewba-slovenia-illegal-dumpsites.tab"
],
{
"collection": "",
"description": "Illegal waste dumpsites in Slovenia as tracked by Ecologists Without Borders Association. The data contains geographical location of the site and its description, including information about accessibility, access type, waste area and volume and types of waste deposited.",
"name": "ewba-slovenia-illegal-dumpsites",
"title": "Illegal waste dumpsites in Slovenia",
"instances": 13165,
"variables": 25,
"missing": true,
"target": null,
"size": 2907714,
"year": 2017,
"version": "1.0",
"tags": [
"geo",
"timeseries",
"ecology"
],
"references": [],
"source": "<a href='http://ocistimo.si/register-divjih-odlagalisc.html'>Dumpsite Registry of Slovenia</a>",
"url": "https://datasets.biolab.si/core/ewba-slovenia-illegal-dumpsites.tab",
"seealso": []
}
],
[
[
"core",
"food-nutrition-info.tab"
],
{
"collection": "data.word",
"description": "Nutritional information for selected raw fruits, vegetables and seafood. The data is borrowed from data.world, where it was created by Adam Helsinger. Where values are given in both mass and percent of daily value, the former is retained and the latter is moved to the meta-attribute.",
"name": "food-nutrition-info",
"title": "Food Nutrition Information",
"instances": 61,
"variables": 15,
"missing": false,
"target": "categorical",
"size": 6672,
"year": 2017,
"version": "1.0",
"tags": [
"biology"
],
"references": [],
"source": "<a href='https://data.world/adamhelsinger/food-nutrition-information'>Food Nutrition Information @ data.world</a>",
"url": "https://datasets.biolab.si/core/food-nutrition-info.tab",
"seealso": []
}
],
[
[
"core",
"foodmart.basket"
],
{
"collection": "",
"description": "Foodmart 2000 is a market based dataset that came with Microsoft Analysis Services. For every transaction (rows) it contains tuples of item names and number of items bought. Every transaction also contains the store ID.",
"name": "foodmart",
"title": "Foodmart 2000",
"instances": 62560,
"variables": 126,
"missing": true,
"target": null,
"size": 4212566,
"year": 2005,
"version": "1.0",
"tags": [
"economy",
"associate",
"basket"
],
"references": [],
"source": "<a href='https://github.com/neo4j-examples/neo4j-foodmart-dataset/tree/master/data'>neo4j data repository</a>",
"url": "https://datasets.biolab.si/core/foodmart.basket",
"seealso": [