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

As high-throughput methods become more common, training undergraduates to analyze data must include having them generate informative summaries of large datasets. This flexible case study provides an opportunity for undergraduate students to become familiar with the capabilities of R programming in the context of high-throughput evolutionary data collected using macroarrays. The story line introduces a recent graduate hired at a biotech firm and tasked with analysis and visualization of changes in gene expression from 20,000 generations of the Lenski Lab’s Long-Term Evolution Experiment (LTEE). Our main character is not familiar with R and is guided by a coworker to learn about this platform. Initially this involves a step-by-step analysis of the small Iris dataset built into R which includes sepal and petal length of three species of irises. Practice calculating summary statistics and correlations, and making histograms and scatter plots, prepares the protagonist to perform similar analyses with the LTEE dataset. In the LTEE module, students analyze gene expression data from the long-term evolutionary experiments, developing their skills in manipulating and interpreting large scientific datasets through visualizations and statistical analysis. Prerequisite knowledge is basic statistics, the Central Dogma, and basic evolutionary principles. The Iris module provides hands-on experience using R programming to explore and visualize a simple dataset; it can be used independently as an introduction to R for biological data or skipped if students already have some experience with R. Both modules emphasize understanding the utility of R, rather than creation of original code. Pilot testing showed the case study was well-received by students and faculty, who described it as a clear introduction to R and appreciated the value of R for visualizing and analyzing large datasets.

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.

[Édition 2024] La base de données de référence pour la diffusion infra-communale des résultats du recensement de la population par Iris, de précision décamétrique.Coédition Insee et IGN, Contours... IRIS® est un fond numérisé des îlots Iris définis par l'Insee pour les besoins des recensements sur l'ensemble des communes de plus de 10 000 habitants et la plupart des communes de 5 000 à 10 000 habitants.

There have been very few studies with an evolutionary perspective on eye (iris) color, outside of humans and domesticated animals. Extant members of the family Felidae have a great interspecific and intraspecific diversity of eye colors, in stark contrast to their closest relatives, all of which have only brown eyes. This makes the felids a great model to investigate the evolution of eye color in natural populations. Through machine learning image analysis of publicly available photographs of all felid species, as well as a number of subspecies, five felid eye colors were identified: brown, green, yellow, gray, and blue. Using phylogenetic comparative methods, the presence or absence of these colors was reconstructed on a phylogeny. Additionally, through a new color analysis method, the specific shades of the ancestors’ eyes were quantitatively reconstructed. The ancestral felid population was predicted to have brown-eyed individuals, as well as a novel evolution of gray-eyed individuals, the latter being a key innovation that allowed the rapid diversification of eye color seen in modern felids, including numerous gains and losses of different eye colors. It was also found that the gain of yellow eyes is highly associated with, and may be necessary for, the evolution of round pupils in felids, which may influence the shades present in the eyes in turn. Along with these important insights, the methods presented in this work are widely applicable and will facilitate future research into phylogenetic reconstruction of color beyond irises. Methods Data set In order to sample all felid species, we took advantage of public databases. Images of individuals from 40 extant felid species (all but Felis catus, excluded due to the artificial selection on eye color in domesticated cats by humans), as well as 12 identifiable subspecies and four outgroups (banded linsang, Prionodon linsang; spotted hyena, Crocuta crocuta; common genet, Genetta genetta; and fennec fox, Vulpes zerda), were found using Google Images and iNaturalist using both the scientific name and the common name for each species as search terms. This approach, taking advantage of the enormous resource of publicly available images, allows access to a much larger data set than in the published scientific literature or than would be possible to obtain de novo for this study. Public image-based methods for character state classification have been used previously, such as in a phylogenetic analysis of felid coat patterns (Werdelin and Olsson 1997) and a catalog of iris color variation in the white-browed scrubwren (Cake 2019). However, this approach does require implementing strong criteria for selecting images. Criteria used to choose images included selecting images where the animal was facing towards the camera, at least one eye was unobstructed, the animal was a non-senescent adult, and the eye was not in direct light (causing glare) or completely in shadow (causing unwanted darkening). The taxonomic identity of the animal in each selected image was verified through images present in the literature, as well as the “research grade” section of iNaturalist. When possible, we collected five images per taxon, although some rarer taxa had fewer than five acceptable images available. In addition, some species with a large number of eye colors needed more than five images to capture their variation, determined by quantitative methods discussed below. Each of the 56 taxa and the number of images used are given in Supplementary Table 2. Once the images were selected, they were manually edited using MacOS Preview. This editing process involved choosing the “better” of the two eyes for each image (i.e. the one that is most visible and with the least glare and shadow). Then, the section of the iris for that eye without obstruction, such as glare, shadow, or fur, was cropped out. An example of this is given in Figure S11. The strict selection criteria and image editing eliminated the need to color correct the images, a process that can introduce additional subjectivity; the consistency of the data can be seen in the lack of variation between eyes identified as the same color (Figure S5). This process resulted in a data set of 290 cropped, standardized, irises. These images, along with the original photos, can be found in the Supplementary Material. Eye color identification To impartially identify the eye color(s) present in each felid population, the data set images were loaded by species into Python (version 3.8.8) using the Python Imaging Library (PIL) (Van Rossum and Drake 2009; Clark 2015). For each image, the red, green, and blue (RGB) values for each of its pixels were extracted. Then, they were averaged and the associated hex color code for the average R, G, and B values was printed. The color associated with this code was identified using curated and open source color identification programs (Aerne 2022; Cooper 2022). There is no universally agreed upon list of colors, since exact naming conventions differ on an individual and cultural basis, but these programs offer a workable solution, consisting of tens of thousands of colors names derived from published, corporate, and governmental sources. This data allowed the color of each eye in the data set to be impartially assigned, removing a great deal of the bias inherent in a researcher subjectively deciding the color of each iris. Eye colors were assigned on this basis to one of five fundamental color groups: brown, green (including hazel), yellow (including beige), gray, and blue. The possible color groups were determined before observation of the data based on basic color categories established in the literature: white, black, red, green, yellow, blue, brown, purple, pink, orange, and gray (Berlin and Kay 1991). Of course, not all of the eleven categories ended up being represented by any irises; no irises were observed to be white, black, red, purple, pink, or orange. As an example of this method, if an iris’s color had the RGB values R: 114, G: 160, B: 193, this would correspond to the hex code #72A0C1. This hex code, when put into the color identification programs, results in the identification “Air Superiority Blue”, derived from the British Royal Air Force’s official flag specifications (Cooper 2022; Aerne 2022). Based on the identification, this iris would be added to the “blue” color group, bypassing a researcher having to choose the color themself. If a color’s name did not already contain one of the eleven aforementioned color categories, the name was searched for in the Inter-Society Color Council-National Bureau of Standards (ISCC–NBS) System of Color Designation (Judd and Kelly 1939). For instance, the color with RGB values R: 37, G: 29, B: 14 corresponds to hex code #251D0E, identified as “Burnt Coffee” by the color identification programs. The ISCC–NBS descriptor for this color is “moderate brown”, so the color would be added to the “brown” group. All colors were able to be placed directly from their color name or their ISCC–NBS descriptor and, for colors with both a color category in the name and an ISCC–NBS descriptor, there were no instances in which the two conflicted. While color itself lies on a spectrum, splitting the colors into discrete fundamental groups is the most tractable approach to analyze eye color in a biologically reasonable way. If every eye color was instead taken together on one spectrum and analyzed as a continuous trait, the results would be highly unrealistic. As an example, if there were two sister taxa, one with blue eyes (R: 0, G: 0, B: 139) and one with brown eyes (R: 150, G: 75, B: 0), a continuous reconstruction would assign the ancestor the intermediate eye color in the color space: R: 75, G: 37, B: 69. However, this color is firmly within the “purple” category. It is highly unlikely that a recent ancestor of two taxa with blue and brown eyes had purple eyes, rather than blue eyes, brown eyes, or both, which would be the result if blue and brown were considered as separate categories. Indeed, one would run into the same issue if categories were removed at an earlier stage and each taxon was only considered to have one eye color, determined by averaging all irises. A taxon with blue and brown eyes would again be said to have purple eyes, a color which none of the members of that taxon have. The data being separated into color groups is the most realistic way to investigate this trait, preventing the loss of variation present in the natural populations and simultaneously creating impossible analyses. The lines between color categories are not always clear to an observer (e.g. grayish-blues and bluish-grays can look alike) and, no matter how they are defined, they may still be arbitrary. Nevertheless, this is why we used color identification programs, impartially defining the lines to make the analysis possible. To ensure no data was missed due to low sample size, the first 500 Google Images, as well as all the “research grade” images on iNaturalist, were manually viewed for each species, while referring back to already analyzed data and periodically checked with the color identification programs (Aerne 2022; Cooper 2022). Any missed colors were added to the data set. This method nonetheless has a small, but non-zero, chance to miss rare eye colors that are present in species. However, overall, it provides a robust and repeatable way to identify the general iris colors present in animals. In addition, if, for a given species, one, two, or three eye colors were greatly predominant in the available data online (i.e. the first 500 Google Images, as well as all the “research grade” images on iNaturalist), they were defined as being the most common eye color(s). For three colors to be considered the most common, each color had to be present for >26.6% of the images. For two colors, each had to be present for >40%

Provides de-identified client and matter information related to legal services delivered by Indigenous Legal Assistance Programme service providers\r \r More information about this dataset can be found at: http://www.abs.gov.au/ausstats/abs@.nsf/Lookup/4533.0Main+Features382013

Financial overview and grant giving statistics of Thomas R And Iris B Harrison Foundation Inc

Tidal wetlands are greatly impacted by climate change, and by the invasion of alien plant species that are being exposed to salinity changes and longer inundation periods resulting from sea level rise. To explore the capacity for the invasion of Iris pseudacorus (Yellow flag iris) to persist with sea level rise, we initiated an intercontinental study along estuarine gradients in the invaded North American range and the native European range. Data generated to support this study includes field- and laboratory-derived measurements including a suite of functional plant traits of I. pseudacorus and environmental variables including soil and tidewater inundation data at five native and five introduced populations study sites. These data were used to compare morphological, biochemical, and reproductive plant traits within populations in both ranges to determine if specific functional traits can predict invasion success and if environmental factors explain observed phenotypic differences.

Palmer Penguins

The Palmer penguins dataset by Allison Horst, Alison Hill, and Kristen Gorman was first made publicly available as an R package. The goal of the Palmer Penguins dataset is to replace the highly overused Iris dataset for data exploration & visualization. However, now you can use Palmer penguins on huggingface!

  License

Data are available by CC-0 license in accordance with the Palmer Station LTER Data Policy and the LTER Data Access Policy for Type I data.… See the full description on the dataset page: https://huggingface.co/datasets/SIH/palmer-penguins.

This artifact bundles the five dataset archives used in our private federated clustering evaluation, corresponding to the real-world benchmarks, scaling experiments, ablation studies, and timing performance tests described in the paper. The real_datasets.tar.xz includes ten established clustering benchmarks drawn from UCI and the Clustering basic benchmark (DOI: https://doi.org/10.1007/s10489-018-1238-7); scale_datasets.tar.xz contains the SynthNew family generated to assess scalability via the R clusterGeneration package ; ablate_datasets.tar.xz holds the AblateSynth sets varying cluster separation for ablation analysis also powered by clusterGeneration ; g2_datasets.tar.xz packages the G2 sets—Gaussian clusters of size 2048 across dimensions 2–1024 with two clusters each, collected from the Clustering basic benchmark (DOI: https://doi.org/10.1007/s10489-018-1238-7) ; and timing_datasets.tar.xz includes the real s1 and lsun datasets alongside TimeSynth files (balanced synthetic clusters for timing), as per Mohassel et al.’s experimental framework .

Contents

1. real_datasets.tar.xz

Contains ten real-world benchmark datasets and formatted as one sample per line with space-separated features:

• iris.txt: 150 samples, 4 features, 3 classes; classic UCI Iris dataset for petal/sepal measurements.

• lsun.txt: 400 samples, 2 features, 3 clusters; two-dimensional variant of the LSUN dataset for clustering experiments .

• s1.txt: 5,000 samples, 2 features, 15 clusters; synthetic benchmark from Fränti’s S1 series.

• house.txt: 1,837 samples, 3 features, 3 clusters; housing data transformed for clustering tasks.

• adult.txt: 48,842 samples, 6 features, 3 clusters; UCI Census Income (“Adult”) dataset for income bracket prediction.

• wine.txt: 178 samples, 13 features, 3 cultivars; UCI Wine dataset with chemical analysis features.

• breast.txt: 569 samples, 9 features, 2 classes; Wisconsin Diagnostic Breast Cancer dataset.

• yeast.txt: 1,484 samples, 8 features, 10 localization sites; yeast protein localization data.

• mnist.txt: 10,000 samples, 784 features (28×28 pixels), 10 digit classes; MNIST handwritten digits.

• birch2.txt: (a random) 25,000/100,000 subset of samples, 2 features, 100 clusters; synthetic BIRCH2 dataset for high-cluster‐count evaluation .

2. scale_datasets.tar.xz

Holds the SynthNew_{k}_{d}_{s}.txt files for scaling experiments, where:

• $k \in \{2,4,8,16,32\}$ is the number of clusters,

• $d \in \{2,4,8,16,32,64,128,256,512\}$ is the dimensionality,

• $s \in \{1,2,3\}$ are different random seeds.

These are generated with the R clusterGeneration package with cluster sizes following a $1:2:...:k$ ratio. We incorporate a random number (in $[0, 100]$) of randomly sampled outliers and set the cluster separation degrees randomly in $[0.16, 0.26]$, spanning partially overlapping to separated clusters.

3. ablate_datasets.tar.xz

Contains the AblateSynth_{k}_{d}_{sep}.txt files for ablation studies, with:

• $k \in \{2,4,8,16\}$ clusters,

• $d \in \{2,4,8,16\}$ dimensions,

• $sep \in \{0.25, 0.5, 0.75\}$ controlling cluster separation degrees.

Also generated via clusterGeneration.

4. g2_datasets.tar.xz

Packages the G2 synthetic sets (g2-{dim}-{var}.txt) from the clustering-data benchmarks:

• $N=2048$ samples, $k=2$ Gaussian clusters,

• Dimensions $d \in \{1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024\}$

• Cluster overlap $var \in \{10, 20, 30, 40, 50, 60, 70, 80, 90, 100\}$

5. timing_datasets.tar.xz

Includes:

• s1.txt, lsun.txt: two real datasets for baseline timing.

• timesynth_{k}_{d}_{n}.txt: synthetic timing datasets with balanced cluster sizes C_{avg}=N/K, varying:

  • $k \in \{2,5\}$

  • $d \in \{2,5\}$

  • $N \in \{10000; 100000\}$

Generated similarly to the scaling sets, following Mohassel et al.’s timing experiment protocol .

Usage:

Unpack any archive with tar -xJf

Floral nectar usually functions as a pollinator reward, yet it may also attract herbivores. However, the effects of herbivore consumption of nectar or nectaries on pollination have rarely been tested. We investigated Iris bulleyana, an alpine plant that has showy tepals and abundant nectar, in the Hengduan Mountains of SW China. In this region, flowers are visited mainly by pollen-collecting pollinators and nectarivorous herbivores. We tested the hypothesis that, in I. bulleyana, sacrificing nectar and nectaries to herbivores protects tepals and thus enhances pollinator attraction. We compared rates of pollination and herbivory on different floral tissues in plants with flowers protected from nectar and nectary consumption with rates in unprotected control plants. We found that nectar and nectaries suffered more herbivore damage than did tepals in natural conditions. However, the amount of tepal damage was significantly greater in the flowers with protected nectaries than in the control...

Iris R Company Export Import Records. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

This dataset is about: Time series of coordinates for station IRIS. Please consult parent dataset @ https://doi.org/10.1594/PANGAEA.934034 for more information.

Pearson Correlation Coefficient, R (P value), among Iris-Related Parameters.

*Probable cases. The rest are confirmed cases; N/A: Not available; P: Paradoxical IRIS; U: Unmasking IRIS; R: Recovery; D: Death; IR: IRIS related death.

This data set contains raw data files (NMR, TGA, X-ray) of the publication.

The table displays serum cytokines (pg/ml) at time of IRIS event compared to 2:1 cohort time match controls (n = 86) from weeks 4 to 12 of ART, corresponding to the approximate interquartile range of the timing of IRIS events. Statistical comparison is via Mann Whitney U nonparametric with Benjamini-Hochberg adjustment for multiple comparisons [39], except for CD4, which is not adjusted.aRelative increase is calculated per the relative risk reduction formula  =  (case – control)/control using the geometric mean of log2 transformed cytokines values.

Iris roman yazarı Iris Murdoch ve onun kocası John Bayley ile ilişkisi hakkında 2001 yapımı bir biyografik drama filmi R

Data set and associated R code (RMarkdown) for project assessing factors influencing iris colour in Spotted Towhees (Pipilo maculatus oregonus) in urban greenspaces within Metro Vancouver during the 2022-2024 breeding and 2022-2023 non-breeding seasons. Accepted at the Journal of Field Ornithology.

Summary of paradoxical cryptococcal-IRIS pathogenesis hypothesis.