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

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

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 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!

Analysis of ‘Iris.csv’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/saurabh00007/iriscsv on 28 January 2022.

--- No further description of dataset provided by original source ---

--- Original source retains full ownership of the source dataset ---

Dataset

This dataset was created by huiyun zheng

Contents

Dataset

This dataset was created by Ibrahim Serouis 99

Contents

This dataset contains the bibliographic metadata (in Parquet format) coming from the IRIS dump that is not included in OpenCitations Meta. In particular, each line of the CSV file defines a bibliographic resource, and includes the following information:[field "iris_id"] the id of the record as referenced in the IRIS dataset;[field "id"] the Open Citation Identifier (OCI) for the citation;This version of the dataset contains:89,302 bibliographic entities

Dataset

This dataset was created by MITHRA SARAVANAN

Contents

Browse Iris Energy Limited (IREN) market data. Get instant pricing estimates and make batch downloads of binary, CSV, and JSON flat files.

Consolidated last sale, exchange BBO and national BBO across all US equity options exchanges. Includes single name stock options (e.g. TSLA), options on ETFs (e.g. SPY, QQQ), index options (e.g. VIX), and some indices (e.g. SPIKE and VSPKE). This dataset is based on the newer, binary OPRA feed after the migration to SIAC's OPRA Pillar SIP in 2021. OPRA is notable for the size of its data and we recommend users to anticipate several TBs of data per day for the full dataset in its highest granularity (MBP-1).

Origin: Options Price Reporting Authority

Supported data encodings: DBN, JSON, CSV Learn more

Supported market data schemas: MBP-1, OHLCV-1s, OHLCV-1m, OHLCV-1h, OHLCV-1d, TBBO, Trades, Statistics, Definition Learn more

Resolution: Immediate publication, nanosecond-resolution timestamps

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

This dataset contains the bibliographic metadata (in Parquet format) included in OpenCitations Meta. In particular, each line of the CSV file defines a bibliographic resource, and includes the following information:[field "id"] the Open Citation Identifier (OCI) for the citation;[field "citing"] the OMID of the citing entity;[field "cited"] the OMID of the cited entity;This version of the dataset contains:7,890,642 bibliographic entitiesThe zipped dataset weighs 187,1 MB, while, when extracted, it weighs 226,9 MB.

Ce jeu de données décrit en termes de contenu, de précision géométrique et de qualité sémantique les caractéristiques des limites d’IRIS « Îlots Regroupés pour l'Information Statistique ». Ci-après les libellés des champs : ID : Identifiant de l’IRIS INSEE_COM : Code INSEE de la commune NOM_COM : Nom de la commune ( en minuscules accentuées) IRIS : Code de l’IRIS (Valeur particulière de l’attribut [0000] : Code pour les petites communes non découpée) CODE_IRIS : Code complet de l’IRIS. Résultat de la concaténation des attributs INSEE_COM et IRIS NOM_IRIS : Nom de l’IRIS ( en minuscules accentuées). Pour les petites communes non découpées, le nom de l'IRIS est le nom de la commune TYP_IRIS : Type de l'IRIS. Il existe trois types d'IRIS : habitat, activité, divers (H : Habitat ; A : Activité ; D : Divers ; Z : La commune n’est pas divisée en IRIS) INFORMATION : LE JEU DE DONNEES EST DISPONIBLE EN TROIS FORMATS (*.XLSX, *.CSV ET *.GPKG). LE NOMBRE D'ENREGISTREMENTS EST DE L'ORDRE DE : 122

Why this Dataset

This dataset brings to you Iris Dataset in several data formats (see more details in the next sections).

You can use it to test the ingestion of data in all these formats using Python or R libraries. We also prepared Python Jupyter Notebook and R Markdown report that input all these formats:

• Test Data Formats in Python
• Test Data Formats in R

Iris Dataset

Iris Dataset was created by R. A. Fisher and donated by Michael Marshall.

Repository on UCI site: https://archive.ics.uci.edu/ml/datasets/iris

Data Source: https://archive.ics.uci.edu/ml/machine-learning-databases/iris/

The file downloaded is iris.data and is formatted as a comma delimited file.

This small data collection was created to help you test your skills with ingesting various data formats.

Content

This file was processed to convert the data in the following formats: * csv - comma separated values format * tsv - tab separated values format * parquet - parquet format
* feather - feather format * parquet.gzip - compressed parquet format * h5 - hdf5 format * pickle - Python binary object file - pickle format * xslx - Excel format
* npy - Numpy (Python library) binary format * npz - Numpy (Python library) binary compressed format * rds - Rds (R specific data format) binary format

Acknowledgements

I would like to acknowledge the work of the creator of the dataset - R. A. Fisher and of the donor - Michael Marshall.

Inspiration

Use these data formats to test your skills in ingesting data in various formats.

Browse Iris Energy Limited (IREN) market data. Get instant pricing estimates and make batch downloads of binary, CSV, and JSON flat files.

Nasdaq TotalView-ITCH is the proprietary data feed that provides full order book depth for Nasdaq market participants.

Origin: Directly captured at Equinix NY4 (Secaucus, NJ) with an FPGA-based network card and hardware timestamping. Synchronized to UTC with PTP.

Supported data encodings: DBN, CSV, JSON Learn more

Supported market data schemas: MBO, MBP-1, MBP-10, BBO-1s, BBO-1m, TBBO, Trades, OHLCV-1s, OHLCV-1m, OHLCV-1h, OHLCV-1d, Definition, Statistics, Status, Imbalance Learn more

Resolution: Immediate publication, nanosecond-resolution timestamps

CSV 6ec6a9f4-17b6-4945-808f-1f9f0e1f3aef

This dataset describes in terms of content, geometric accuracy and semantic quality the characteristics of the boundaries of IRIS ‘Grouped Islets for Statistical Information’. Following the text of the fields: ID: IRIS identifier INSEE_COM: INSEE code of the municipality COM_NAME: Name of the municipality (in accented lower case) IRIS: IRIS code (Special value of attribute [0000]: Code for small uncut municipalities) IRIS_CODE: Full IRIS code. Result of concatenation of INSEE_COM and IRIS attributes IRIS_NAME: Name of IRIS (in accented lower case). For small uncut municipalities, the name of IRIS is the name of the municipality TYP_IRIS: Type of IRIS. There are three types of IRIS: habitat, activity, miscellaneous (H: Habitat; A: Activity; D: Miscellaneous; Z: The municipality is not divided into IRIS) INFORMATION: THE DATA GAME IS AVAILABLE IN THREE FORMATS (*.XLSX, *.CSV AND *.GPKG). THE NUMBER OF REGISTRATION IS IN THE ORDER OF: 122

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.

This data set contains all the 49 million non-singleton equivalence classes resulting from the transitive closure of over 556 million owl:sameAs statements extracted from the LOD Cloud in the 2015 LOD Laundromat crawl. These equivalence classes are the result of the transitive closure of the owl:sameAs links available in the sameAs.cc data set.

We represent these non-singleton equivalence classes using two CSV files:

1. id2terms.csv: contains in the first column the equivalence class identifier (randomly generated number) and in the rest of the columns all IRIs belonging to this equivalence class, which theoretically should refer to the same real world entity. In the following, we present an example of one row of this file, where "42467584" in the first column represents the ID of this equivalence class, and the 4 other columns represent the IRIs that are identical after transitive closure:

42467584

1. terms2id.csv: contains two columns, representing a mapping between each IRI in the sameAs.cc data set involved in a owl:sameAs link with the equivalence class it belongs to. In the following, we present an example of one row in this file:

   42467584

In addition to the closure of all owl:sameAs links (available in the folder closure_all.zip), this data set contains an additional two closures, with each closure also containing two CSV files with the same structure as presented above. These two additional closures are the following:

• closure_099.zip represents the closure of all owl:sameAs links in the sameAs.cc data set after discarding around 1 million probably erroneous owl:sameAs links (with error degree >0.99). This error degree is computed based on the community structure of the network, described in the approach of [Raad et al., 2018].

• closure_04.zip represents the closure of all owl:sameAs links in the sameAs.cc data set after discarding around 150 million owl:sameAs links (with error degree >0.4). The evaluation conducted in [Raad et al., 2018] shows that the 400M owl:sameAs links with an error degree <= 0.4 have higher probability of correctness compared to other links.

The availability of these 3 different closures allows Linked Data practitioners for the first time to control in practice, the trade-off between (a) using more identity links, possibly not all correct, and benefiting from more contextual information from the LOD Cloud, and (b) using a smaller subset of higher quality identity links for limiting the risk of propagating erroneous identity links and information through the application of owl:sameAs semantics, i.e. transitive, symmetric, reflexive and property sharing.