Iris Species Dataset

The Iris dataset was used in R.A. Fisher's classic 1936 paper, The Use of Multiple Measurements in Taxonomic Problems, and can also be found on the UCI Machine Learning Repository. It includes three iris species with 50 samples each as well as some properties about each flower. One flower species is linearly separable from the other two, but the other two are not linearly separable from each other. The dataset is taken from UCI Machine Learning Repository's… See the full description on the dataset page: https://huggingface.co/datasets/scikit-learn/iris.

This famous (Fisher's or Anderson's) iris data set gives the measurements in centimeters of the variables sepal length and width and petal length and width, respectively, for 50 flowers from each of 3 species of iris. The species are Iris setosa, versicolor, and virginica.

Iris Petal and Sepal Dataset Description The Iris dataset is one of the most famous datasets in the field of machine learning and statistical classification. It was first introduced by British biologist and statistician Ronald Fisher in 1936 as an example of linear discriminant analysis. The dataset is widely used for educational purposes and model building in machine learning due to its simplicity and versatility.

Dataset Overview The dataset contains 150 observations of Iris flowers from three species:

Iris Setosa Iris Versicolor Iris Virginica Each observation includes four numerical features:

Sepal Length (cm) Sepal Width (cm) Petal Length (cm) Petal Width (cm) Additionally, the dataset provides a class label for the species of the Iris flower.

Feature Descriptions: Sepal Length: The length of the flower’s sepal in centimeters. Sepal Width: The width of the flower’s sepal in centimeters. Petal Length: The length of the flower’s petal in centimeters. Petal Width: The width of the flower’s petal in centimeters. Species: The class label that classifies the flower into one of three species (Setosa, Versicolor, Virginica). Data Summary: 150 instances (50 samples per species) 4 features (numeric data) 1 target variable (categorical – species of the flower) Applications: The dataset is often used for:

Classification tasks: Building models to classify the species of Iris flowers. Exploratory data analysis (EDA): Exploring relationships between features. Data visualization: Plotting petal and sepal dimensions to understand patterns. Predictive modeling: Training and testing machine learning algorithms such as k-nearest neighbors (KNN), support vector machines (SVM), and decision trees. Why This Dataset? The Iris dataset is ideal for beginners and experts alike, as it provides an easy introduction to supervised learning. It is perfect for understanding basic classification algorithms and exploring key concepts such as:

Multiclass classification Feature correlation Data visualization techniques This description is tailored for the Kaggle community and provides a clear overview of the dataset’s content and potential use cases. You can customize it further if needed!

Dataset

This dataset was created by Sumit Roy

Contents

Files to run the small dataset experiments used in the preprint "Self-Supervised Spatio-Temporal Representation Learning Of Satellite Image Time Series" available here. This .csv files enables to generate balanced small dataset from the PASTIS dataset. These files are required to run the experiment with a small training data-set, from the open source code ssl_ubarn. In the .csv file name selected_patches_fold_{FOLD}_nb_{NSITS}_seed_{SEED}.csv :

• FOLD: id which corresponds to one of the 5 experiments run due to PASTIS K-fold.
• NSITS: Number of SITS selected to construct this training data-set
• SEED: the randomness used to create this small dataset

Comparative studies have shown that the eye morphology of primates has been shaped by a variety of selection pressures (e.g. communication, environmental factors). To comprehensively elucidate the complex links between ocular morphology and its evolutionary drive, attention should be paid to other phylogenetic groups. Here, we address a new question regarding the evolution of eye colour patterns in the oldest domesticated animal, namely, the domestic dog (Canis familiaris). In this study, we conducted an image analysis of dogs and their closest relatives, grey wolves (Canis lupus), to compare the colours of their irises, with the aim of assessing whether eye colours of dogs affect how humans perceived dogs. We found that the irises of dogs were significantly darker than those of wolves. We also found that facial images of dark-eyed dogs were perceived as more friendly and immature, potentially eliciting caregiving responses from humans. Our findings are consistent with our expectation that humans favour dark-eyed dogs over light-eyed ones and provide an updated hypothesis that dogs with dark eyes may have evolved by acquiring a facial trait that sends a non-threatening gaze signal to humans.

Summary tabular data relating to Natura 2000 SAC sites in Ireland, providing Natura 2000 site-related details, including lists of the habitats and species listed in Annex I and Annex II of the Habitats Directive for which each Natura 2000 site is selected. Data is accurate up to March 2023. Please check the Iris Oifigiúil, Irish, Irish Statute Book for more recently published Statutory Instrument (S.I.) regulations. Data is provided in a single zip file containing sub folders holding MS Excel, CSV and JSON formats, each accompanied by a ‘readme’ file.

Context

This is a curated pack of the dataset for learning data visualization. Refer notebook on how to learn visualization using these datasets.

Content

1. altcoins.csv
2. barchart-divorcerates.csv
3. barcharts-us-music-sales-by-genre.csv
4. df_weed.csv
5. index.csv
6. iris.csv
7. mtcars_csv-Copy1.csv
8. sales.csv
9. setosa-versicolor.csv
10. tips.csvcalendar_view_week

Catalogue of seismic events from Nabro volcano (Sep 2011 - Oct 2012). Data format is a csv file.

Events were detected by U-GPD phase arrival picking model. See following paper for details on event detection and location procedure: A Little Data Goes A Long Way Way: Automating Seismic Phase Arrival Picking at Nabro Volcano With Transfer Learning by Lapins et al., 2021, https://doi.org/10.1029/2021JB021910).

Original seismic waveforms are from the Nabro Urgency Array (Hammond et al., 2011; https://doi.org/10.7914/SN/4H_2011), which is publicly available through IRIS Data Services (http://service.iris.edu/fdsnws/dataselect/1/). See Hammond et al. (2011) for further details on waveform data access and availability.

Full code to reproduce our U-GPD transfer learning model, perform model training, run the U-GPD model over continuous sections of data and use model picks to locate events in NonLinLoc (Lomax et al., 2000) are available at https://github.com/sachalapins/U-GPD, with the release (v1.0.0) associated with this study also archived and available through Zenodo (Lapins, 2021; https://doi.org/10.5281/zenodo.4558121).

Dataset column key:

time = Origin time of seismic event (UTC)

lat = Hypocentre latitude in decimal degrees

lon = Hypocentre longitude in decimal degrees

depth = Hypocentre depth in km

rms = RMS error for phase arrival picks and hypocentre (sec)

erh = Estimate of horizontal Gaussian error (km)

erz = Estimate of vertical Gaussian error (km)

azgap = Azimuthal gap (maximum angle separating two adjacent seismic stations, measured from earthquake epicentre)

cluster = HDBSCAN cluster number (see Chapter 6 of Lapins, 2021 doctoral thesis: Detecting and characterising seismicity associated with volcanic and magmatic processes through deep learning and the continuous wavelet transform. Persistent URL: https://hdl.handle.net/1983/ea90148c-a1b2-47ae-afad-5dd0a8b5ebbd)

nab*_p_time = P-wave arrival time for station NAB* (UTC)

nab*_p_prob = Maximum detection 'probability' around P-wave phase arrival from U-GPD model (between 0 and 1)

nab*_s_time = S-wave arrival time for station NAB* (UTC)

nab*_s_prob = Maximum detection 'probability' around S-wave phase arrival from U-GPD model (between 0 and 1)

Station csv column key:

Network = Seismic network name

Station = Seismic station name

Latitude = Latitude in decimal degrees

Longitude = Longitude in decimal degrees

Elevation_asl_km = Station elevation in km above sea level