This is an iris dataset commonly used in machine learning. Accessed on 10-19-2020 from the following URL: http://faculty.smu.edu/tfomby/eco5385_eco6380/data/Iris.xls

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.

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.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('iris', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

The Iris Classification dataset is a well-known dataset in machine learning, commonly used for classification tasks. It contains measurements of various iris flowers, including sepal length, sepal width, petal length, and petal width, as well as the corresponding species label.

Columns Description:

1. Sepal Length (cm): Length of the sepals of the iris flower.

2. Sepal Width (cm): Width of the sepals of the iris flower.

3. Petal Length (cm): Length of the petals of the iris flower.

4. Petal Width (cm): Width of the petals of the iris flower.

5. Species: The species of the iris flower, which is the target variable to be predicted.

This dataset is commonly used for practicing classification algorithms and exploring data analysis techniques.

Context

There's a story behind every dataset and here's your opportunity to share yours.

Content

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

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.

Dataset

This dataset was created by Deepak Thakur

Released under Data files © Original Authors

Contents

It contains the following files:

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

Data for Nature manuscript titled

“Environmental drivers of increased ecosystem respiration in a warming tundra”

Corresponding author Dr. Sybryn Maes – sybryn.maes@gmail.com

Part A. Meta-analysis

The bold names refer to scripts (see the Github repository https://github.com/mjalava/tundraflux) and names in italics refer to files in this repository

df_0

-Study design Figure 1 and Extended Fig. 1 from main text

df_1a

-Effect size calculations of response (ER)

-Links to df_1.csv file with raw flux and environmental data

-Only the experiments that state ‘Open Access’ in the excel file Authors_Datasets (sheet 2). For experiments stating ‘Available Upon Request’, you need to contact the authors for the -raw flux data.

df_1b

-Effect size calculations of environmental drivers

-Links to df_1.csv file with raw flux data data (see above) and Dataset_ID.csv (this file includes all dataset IDs to merge the drivers into one dataframe)

df_2a-f

-Meta-analysis (2a) and meta-regression models (2b-f) (ER, N=136)

-Links to df_2.csv file with effect size data and context-dependencies and Forestplot_horiz_weights_fig.csv (this file includes the mean pooled Hedges SMD as well as the individual dataset Hedges SMD to plot figure 2)

-Contains code for Figs. 2-4 and Extended Figs 2-3

df_3

-Meta-regression for experimental warming duration

-Contains code for Fig. 5

df_4a

-Effect size calculations of autotrophic-heterotrophic respiration partitioning (Ra, Rh, N=9)

-Links to df_3.csv file with raw partitioning data of subset experiments (output file df_4.csv)

df_4b

-Sub-meta-analysis models (ER, Ra, Rh)

-Links to df_4.csv (input file)

NOTES

· All additional input files for the meta-analysis R-scripts are included within the folders.

· ER, Ra, Rh = ecosystem, autotrophic, and heterotrophic respiration

· N = sample size (number of datasets)

Part B. Upscaling results

For upscaling, the input data is described in the code files (see the Github repository) and the accompanying Readme.txt.

percentageChangeResp_tundraAlpine.tif: modelled change in respiration

baseResp_tundraAlpine.tif: baseline respiration (calculated from the data from literature)

modResp_tundraAlpine.tif: modelled respiration after warming (our calculations: (percentageChangeResp_tundraAlpine+1) * baseResp_tundraAlpine)

changeResp_tundraAlpine.tif: modResp-baseResp

standError_tundraAlpine.tif: standard error of modelled respiration (

standError_tundraAlpine_onlyDataUncertainty.tif: standard error of modelled respiration where only data uncertainty is taken into account

This dataset consists of three files: PF_ChoiceDataM.csv: Text file with choice data of male pied flycatchers Samplonius_etal_Rscript.R: The R script used for statistical analysis of the data and creating the graph used in the paper Samplonius_Data_legend_key.pdf: Metadata explaining the variables in the data file

Existing facial rPPG datasets (e.g., for heart rate or oxygen saturation monitoring) primarily capture full-face regions, focusing on skin perfusion signals from the forehead, cheeks, or periorbital areas. These datasets lack specialized annotations for ocular structures (pupil, iris, sclera) and direct IOP measurements, as their core goal is general physiological parameter estimation rather than eye-specific pressure analysis. In contrast, EVIP is uniquely designed to link ocular physiological signals with IOP values: it provides high-resolution eye-region videos (isolating pupil-iris complexes) paired with clinically validated IOP measurements, enabling the extraction of IOP-related BVP features from eye regions.The folder contains two subfolders, EVIP-1 and EVIP-2, representing the two versions of the EVIP dataset. Each subfolder contains eye videos recorded in MP4 format, along with data files containing the actual IOP values recorded and stored in CSV file. For the EVIP-1 folder, there were 60 eye videos from 30 participants, named in Arabic numeral order, along with an “EVIP-1.CSV” data file. The data included a column named “Actual Value” containing the actual IOP records, which corresponded to the order of the video files from top to bottom. EVIP-2 was stored in the same manner, containing 122 eye videos from 60 participants, along with a synchronously recorded actual IOP value data file named “EVIP-2.CSV”.

One csv file contain colour measures of 24 facial stimuli (iris, hair, and skin colour in CIE La*b* colour space).The other csv file contains the proportion of warm colours chosen by 87 participants in a 2 alternative forced choice between a warm vs cool colour pair at a high colour saturation and mid-level colour value (lightness), or a high colour value and mid level colour saturation. Facial stimuli vary in eye lightness and in skin lightness.