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 columns in this dataset are:

• Id
• SepalLengthCm
• SepalWidthCm
• PetalLengthCm
• PetalWidthCm
• Species

It includes three iris species with 50 samples each as well as some properties of each flower. One flower species is linearly separable from the other two, but the other two are not linearly separable from each other.

FIle name: iris.csv

IRIS tracks the status of safety deficiencies identified during Occupational Safety and Health Administration (OSHA) and Safety & Environmental Management (SEM) survey inspections.

Iris Flower Dataset

This is a classic and very widely used dataset in machine learning and statistics, often serving as a first dataset for classification problems. Introduced by the British statistician and biologist Ronald Fisher in his 1936 paper "The use of multiple measurements in taxonomic problems," it is a foundational resource for learning classification algorithms.

Overview:

The dataset contains measurements for 150 samples of iris flowers. Each sample belongs to one of three species of iris:

• Iris setosa
• Iris versicolor
• Iris virginica

For each flower, four features were measured:

• Sepal length (in cm)
• Sepal width (in cm)
• Petal length (in cm)
• Petal width (in cm)

The goal is typically to build a model that can classify iris flowers into their correct species based on these four features.

File Structure:

The dataset is usually provided as a single CSV (Comma Separated Values) file, often named iris.csv or similar. This file typically contains the following columns:

1. sepal_length (cm): Numerical. The length of the sepal of the iris flower.
2. sepal_width (cm): Numerical. The width of the sepal of the iris flower.
3. petal_length (cm): Numerical. The length of the petal of the iris flower.
4. petal_width (cm): Numerical. The width of the petal of the iris flower.
5. species: Categorical. The species of the iris flower (either 'setosa', 'versicolor', or 'virginica'). This is the target variable for classification.

Content of the Data:

The dataset contains an equal number of samples (50) for each of the three iris species. The measurements of the sepal and petal dimensions vary between the species, allowing for their differentiation using machine learning models.

How to Use This Dataset:

1. Download the iris.csv file.
2. Load the data using libraries like Pandas in Python.
3. Explore the data through visualization and statistical analysis to understand the relationships between the features and the different species.
4. Build classification models (e.g., Logistic Regression, Support Vector Machines, Decision Trees, K-Nearest Neighbors) using the sepal and petal measurements as features and the 'species' column as the target variable.
5. Evaluate the performance of your model using appropriate metrics (e.g., accuracy, precision, recall, F1-score).
6. The dataset is small and well-behaved, making it excellent for learning and experimenting with various classification techniques.

Citation:

When using the Iris dataset, it is common to cite Ronald Fisher's original work:

Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7(2), 179-188.

Data Contribution:

Thank you for providing this classic and fundamental dataset to the Kaggle community. The Iris dataset remains an invaluable resource for both beginners learning the basics of classification and experienced practitioners testing new algorithms. Its simplicity and clear class separation make it an ideal starting point for many data science projects.

If you find this dataset description helpful and the dataset itself useful for your learning or projects, please consider giving it an upvote after downloading. Your appreciation is valuable!

Iris Dataset

The classic Iris dataset in .parquet format. Useful for ML demos, classification tasks, and model testing.

EPA?s Integrated Risk Information System (IRIS) is a compilation of electronic reports on specific substances found in the environment and their potential to cause human health effects.

Fisher's Iris dataset is a multivariate dataset introduced by Sir Ronald Fisher in his 1936 paper "The use of multiple measurements in taxonomic problems". It contains 150 samples from three species of iris flowers (Iris setosa, Iris virginica, and Iris versicolor). Each sample is described by 4 features: the length and width of the sepal and petal.

To support the USArray TA infrasound dataset, EarthScope DS developed two infrasound data products: the TA Infrasound Reference Event Database ("TAIRED":/ds/products/infrasound-taired) and TA Infrasound Detections ("TAID":/ds/products/infrasound-taid). These products are designed to provide researchers with insights and tools to begin working with this extensive and somewhat unique dataset.

✓ https://ds.iris.edu/spud/resources/images/spud.png" style="width:30px;"/> A query interface for TA Infrasound Event Database is available on "SPUD":https://ds.iris.edu/spud/infrasoundevent

✓ ☁️ The TAID data files are also available from the "EarthScope Data Archive":https://data.earthscope.org/archive/seismology/products/infrasound/README.html.

Eye Iris Dataset

## Overview

Eye Iris Dataset is a dataset for object detection tasks - it contains Iris UW69 Rf2D HB0U annotations for 5,685 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

1) Data Introduction • The Iris Species Dataset is a classic multi-class classification data that collected a total of 150 samples, 50 for each of the three iris species (Setosa, Versicolor, Virginica), consisting of four numerical characteristics and species labels, including calyx length, width, petal length, and width.

2) Data Utilization (1) The Iris Species Dataset has characteristics that: • This dataset consists of a total of six columns and is labeled as one of three types, making it suitable for class division and basic statistical analysis. (2) The Iris Species Dataset can be used to: • Classification Algorithm Practice: You can easily practice various machine learning classification models such as logistic regression, SVM, and decision tree by inputting four characteristics: calyx and petal length and width. • Visualize data and analyze basic statistics: Visualize the distribution of characteristics by variety into scatterplots, boxplots, etc. to explore differences between classes and correlations between characteristics.

it can be used in applications such as access control systems

IRIS

## Overview

IRIS is a dataset for object detection tasks - it contains Flip annotations for 347 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

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.

The iris_dataset.rds serialisation is a replication of datasets::iris_dataset as dataset s3 class.

The iris_dataset.csv serialisation is an incomplete replication of the iris_dataset because the CSV file does not contain important semantic information; that is exported to iris_dataset.json (in a not standardised form) and the dataset-level metadata into the iris_dataset.bib BibLatex text file.

IRIS Work Item Module supports Real Property Asset Management (RPAM) and the Financial Operation Division. IRIS manages the estimated cost of building projects related to repairs and alterations, and new construction

BIT/iris-dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

Here are a few use cases for this project:

1. Iris-based biometric authentication: Utilize the "Iris detection" model for secure access to devices or facilities by identifying individuals based on the unique patterns in their irises, thereby enhancing security for personal or professional applications.

2. Ophthalmic research and diagnosis: Employ the model to analyze collected eye images in order to study iris variations among different populations, detect potential eye diseases, or monitor the progression of eye-related disorders, thereby contributing to better eye health.

3. Customized eye-wear and contact lens manufacturing: Make use of the "Iris detection" model to accurately measure and identify iris patterns in customers, thus enabling the production of tailor-made eye-wear or contact lenses that fit perfectly and offer improved visual experiences.

4. Personalized advertising and marketing: Leverage the "Iris detection" system to identify iris colors and generate customized ads or marketing campaigns targeting people with specific iris features, such as matching make-up or fashion recommendations based on iris color and patterns.

5. Robotics and virtual assistants: Implement the model into advanced robotic systems or virtual assistant devices to enable face and eye recognition for improved user interaction, empowering robots to better identify and adapt to individual users through iris detection.

Iris dataset in json format

The Incorporated Research Institutions for Seismology (IRIS) is a consortium of United States Universities that have research programs in seismology. The purpose of IRIS is to develop and operate the infrastructure needed for the acquisition and distribution of high quality seismic data.

IRIS was formed in 1984 by twenty-six universities to serve a national focus for the development, deployment, and support of modern digital seismic instrumentation. Today, membership in this nonprofit consortium numbers over 91 institutions , and IRIS supports the research needs of earth scientists in the U.S. and around the world. IRIS consists of four management programs: - The Data Management System (DMS), "http://www.iris.washington.edu/HTM/dms.htm" - The Global Seismographic Network (GSN), "http://www.iris.washington.edu/GSN/" - The Program for Array Seismic Studies of the Continental Lithosphere, (PASSCAL), "http://www.passcal.nmt.edu/iris/passcal/passcal.htm" - Education & Outreach Program, "http://www.iris.washington.edu/EandO/"

The IRIS DMS presently consists of seven components or "nodes". These nodes work together to insure the smooth flow of GSN data from the stations to the seismological research community.

The IRIS Global Seismographic Network (GSN) is one of the four major components of the IRIS Consortium. The goal of the GSN is to deploy 128 permanent seismic recording stations uniformly over the earth's surface. The GSN provides funding to two network operators: - IRIS/ASL Network Operations Center Albuquerque, New Mexico, operated by the United States Geological Survey; and - IRIS/IDA Network Operations Center La Jolla, California, operated by personnel from Scripps Institution of Oceanography.

PASSCAL is one of two major instrumentation programs of IRIS (the other being the Global Seismic Network or GSN). PASSCAL operates a pool of over 400 portable seismic instruments to record active source reflection data, active source refraction data or natural source recordings of earthquakes (see Instrumentation for more details). The instrumentation is currently housed and supported by an instrument center at New Mexico Tech, Socorro, New Mexico. PASSCAL Instruments and support are available to the academic research community according to the rules and policies set by the IRIS Executive Committee. All data from PASSCAL supported experiments are made available through the IRIS DMC in Seattle, Washington.

The IRIS Education & Outreach (E&O) program, in collaboration with the seismological and educational communities, develops and implements IRIS programs designed to enhance seismology and Earth Science education in K-12 schools, colleges and universities, and in adult education. Our goal is to foster within the next generation of research scholars, educators, policy-makers, business leaders and benefactors an appreciation for and an understanding of seismology and related study of the Earth.

1. Title: Iris Plants Database

2. Sources: (a) Creator: R.A. Fisher (b) Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov) (c) Date: July, 1988

3. Past Usage:

   • Publications: too many to mention!!! Here are a few.
   • Fisher,R.A. "The use of multiple measurements in taxonomic problems" Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions to Mathematical Statistics" (John Wiley, NY, 1950).
   • Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis. (Q327.D83) John Wiley & Sons. ISBN 0-471-22361-1. See page 218.
   • Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System Structure and Classification Rule for Recognition in Partially Exposed Environments". IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. PAMI-2, No. 1, 67-71. -- Results: -- very low misclassification rates (0% for the setosa class)
   • Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule". IEEE Transactions on Information Theory, May 1972, 431-433. -- Results: -- very low misclassification rates again
   • See also: 1988 MLC Proceedings, 54-64. Cheeseman et al's AUTOCLASS II conceptual clustering system finds 3 classes in the data.

4. Relevant Information: --- This is perhaps the best known database to be found in the pattern recognition literature. Fisher's paper is a classic in the field and is referenced frequently to this day. (See Duda & Hart, for example.) The data set contains 3 classes of 50 instances each, where each class refers to a type of iris plant. One class is linearly separable from the other 2; the latter are NOT linearly separable from each other. --- Predicted attribute: class of iris plant. --- This is an exceedingly simple domain. --- This data differs from the data presented in Fishers article

5. Number of Instances: 150 (50 in each of three classes)

6. Number of Attributes: 4 numeric, predictive attributes and the class

7. Attribute Information:

   1. sepal length in cm
   2. sepal width in cm
   3. petal length in cm
   4. petal width in cm
   5. class: -- Iris Setosa -- Iris Versicolour -- Iris Virginica

8. Missing Attribute Values: None

Summary Statistics:

sepal length: 4.3 7.9 5.84 0.83 0.7826
sepal width: 2.0 4.4 3.05 0.43 -0.4194 petal length: 1.0 6.9 3.76 1.76 0.9490 (high!) petal width: 0.1 2.5 1.20 0.76 0.9565 (high!)

1. Class Distribution: 33.3% for each of 3 classes.

The family violence measures produced from the Family Violence Database (FVDB) are derived from administrative information recorded by agencies that are funded by DHHS to provide Women and Children’s Family Violence Services and Men’s Behaviour Change Programs. The information is input by the agencies that are providing the services, and de-identified data is extracted from the IRIS database by DHHS and provided to the Crime Statistics Agency for input into the FVDB.