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.

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

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%

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

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.

Description

The VRBiom (Virtual Reality Dataset for Biometric Applications) dataset has been acquired using a head-mounted display (HMD) to benchmark and develop various biometric use-cases such as iris and periocular recognition and associated sub-tasks such as detection and semantic segmentation. The VRBiom dataset consists of 900 short videos acquired from 25 individuals recorded in the NIR spectrum. To encompass real-world variations, the dataset includes recordings under three gaze conditions: steady, moving, and partially closed eyes. Additionally, it has maintained an equal split of recordings without and with glasses to facilitate the analysis of eyewear. These videos, characterized by non-frontal views of the eye and relatively low spatial resolutions (400 × 400). The dataset also includes 1104 presentation attacks constructed from 92 PA instruments. These PAIs fall into six categories constructed through combinations of print attacks (real and synthetic identities), fake 3D eyeballs, plastic eyes, and various types of masks and mannequins.

Reference

If you use this dataset, please cite the following publication(s) depending on the use:

@article{vrbiom_dataset_arxiv2024,
author = {Kotwal, Ketan and Ulucan, Ibrahim and \”{O}zbulak, G\”{o}khan and Selliah, Janani and Marcel, S\'{e}bastien},
title = {VRBiom: A New Periocular Dataset for Biometric Applications of HMD},
year = {2024},
month = {Jul},
journal = {arXiv preprint arXiv:2407.02150},
DOI = {https://doi.org/10.48550/arXiv.2407.02150}
}

@inproceedings{vrbiom_pad_ijcb2024,
author = {Kotwal, Ketan and \”{O}zbulak, G\”{o}khan and Marcel, S\'{e}bastien},
title = {Assessing the Reliability of Biometric Authentication on Virtual Reality Devices},
booktitle = {Proceedings of IEEE International Joint Conference on Biometrics (IJCB2024)},
month = {Sep},
year = {2024}   
}

time series and allele frequency dataThe file contains data used to estimate the population growth rate (time series data) and inbreeding (allele frequency data).Inbreeding_Ibex_dataDryad_Bozzuto_etal_NEE2019.xlsxdata for custom R-codeData for custom R-code used for the regression analysis.ibexData.RDatacustom R-codeCustom R-code used for the regression analysisInbreeding_Ibex_Bozzuto_etal_NEE2019.r

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

Author: H. Altay Guvenir, Burak Acar, Haldun Muderrisoglu
Source: UCI
Please cite: UCI

Cardiac Arrhythmia Database
The aim is to determine the type of arrhythmia from the ECG recordings. This database contains 279 attributes, 206 of which are linear valued and the rest are nominal.

Concerning the study of H. Altay Guvenir: "The aim is to distinguish between the presence and absence of cardiac arrhythmia and to classify it in one of the 16 groups. Class 01 refers to 'normal' ECG classes, 02 to 15 refers to different classes of arrhythmia and class 16 refers to the rest of unclassified ones. For the time being, there exists a computer program that makes such a classification. However, there are differences between the cardiologist's and the program's classification. Taking the cardiologist's as a gold standard we aim to minimize this difference by means of machine learning tools.

The names and id numbers of the patients were recently removed from the database.

Attribute Information

  1 Age: Age in years , linear
  2 Sex: Sex (0 = male; 1 = female) , nominal
  3 Height: Height in centimeters , linear
  4 Weight: Weight in kilograms , linear
  5 QRS duration: Average of QRS duration in msec., linear
  6 P-R interval: Average duration between onset of P and Q waves
   in msec., linear
  7 Q-T interval: Average duration between onset of Q and offset
   of T waves in msec., linear
  8 T interval: Average duration of T wave in msec., linear
  9 P interval: Average duration of P wave in msec., linear
 Vector angles in degrees on front plane of:, linear
 10 QRS
 11 T
 12 P
 13 QRST
 14 J
 15 Heart rate: Number of heart beats per minute ,linear
 Of channel DI:
  Average width, in msec., of: linear
  16 Q wave
  17 R wave
  18 S wave
  19 R' wave, small peak just after R
  20 S' wave
  21 Number of intrinsic deflections, linear
  22 Existence of ragged R wave, nominal
  23 Existence of diphasic derivation of R wave, nominal
  24 Existence of ragged P wave, nominal
  25 Existence of diphasic derivation of P wave, nominal
  26 Existence of ragged T wave, nominal
  27 Existence of diphasic derivation of T wave, nominal
 Of channel DII: 
  28 .. 39 (similar to 16 .. 27 of channel DI)
 Of channels DIII:
  40 .. 51
 Of channel AVR:
  52 .. 63
 Of channel AVL:
  64 .. 75
 Of channel AVF:
  76 .. 87
 Of channel V1:
  88 .. 99
 Of channel V2:
  100 .. 111
 Of channel V3:
  112 .. 123
 Of channel V4:
  124 .. 135
 Of channel V5:
  136 .. 147
 Of channel V6:
  148 .. 159
 Of channel DI:
  Amplitude , * 0.1 milivolt, of
  160 JJ wave, linear
  161 Q wave, linear
  162 R wave, linear
  163 S wave, linear
  164 R' wave, linear
  165 S' wave, linear
  166 P wave, linear
  167 T wave, linear
  168 QRSA , Sum of areas of all segments divided by 10,
    ( Area= width * height / 2 ), linear
  169 QRSTA = QRSA + 0.5 * width of T wave * 0.1 * height of T
    wave. (If T is diphasic then the bigger segment is
    considered), linear
 Of channel DII:
  170 .. 179
 Of channel DIII:
  180 .. 189
 Of channel AVR:
  190 .. 199
 Of channel AVL:
  200 .. 209
 Of channel AVF:
  210 .. 219
 Of channel V1:
  220 .. 229
 Of channel V2:
  230 .. 239
 Of channel V3:
  240 .. 249
 Of channel V4:
  250 .. 259
 Of channel V5:
  260 .. 269
 Of channel V6:
  270 .. 279

Class code - class - number of instances:

  01       Normal        245
  02       Ischemic changes (Coronary Artery Disease)  44
  03       Old Anterior Myocardial Infarction      15
  04       Old Inferior Myocardial Infarction      15
  05       Sinus tachycardy    13
  06       Sinus bradycardy    25
  07       Ventricular Premature Contraction (PVC)    3
  08       Supraventricular Premature Contraction    2
  09       Left bundle branch block     9 
  10       Right bundle branch block    50
  11       1. degree AtrioVentricular block    0 
  12       2. degree AV block        0
  13       3. degree AV block        0
  14       Left ventricule hypertrophy        4
  15       Atrial Fibrillation or Flutter        5
  16       Others         22

Molecular pathology reports of patients with colorectal carcinoma were collected from PALGA using specific queries from 1 October 2017 to 30 June 2019 (for details see: Figure 1A PMID: 34675090 / DOI: 10.1136/jclinpath-2021-207865). Manual curation of these reports showed 4060 patients with CRC undergoing predictive mutation analyses in this 21-month study period. Details ofthe mutation analyses (ie, technique, gene panel, diagnostic yield) were manually extracted from these reports and shown in the current dataset.

PsyCorona is an ad hoc, multinational collaborative study in response to the COVID-19 pandemic. Broadly speaking, we study the psychological factors that predict how people respond to the coronavirus and to the associated public health measures. The ultimate goal is to provide actionable knowledge that can serve to enhance pandemic response. To achieve this goal, PsyCorona was designed with three distinct phases: (1) a cross-sectional survey, (2) follow-up surveys, and (3) integrative data science. The Dataset codebook can be found at: https://osf.io/qhyue/.

The link between human ocular morphology and attractiveness, especially in the context of its potential adaptive function, is an underexplored area of research. In our study, we examined the association between facial attractiveness and three sexually dimorphic measures of ocular morphology in White Europeans: the sclera size index, width-to-height ratio, and relative iris luminance. Sixty participants (30 women) assessed the attractiveness of the opposite-sex photographs of 50 men and 50 women. Our results show that in both men and women, none of the three measures was linked to the opposite sex ratings of facial attractiveness. We conclude that those ocular morphology measures may play a limited role in human mate preferences.

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

IRIS Data Set and R code. (ZIP 291 KB)

Apalachicola Bay and St. George Sound contain the largest oyster fishery in Florida, and the growth and distribution of the numerous oyster reefs here are the combined product of modern estuarine conditions and the late Holocene evolution of the bay. A suite of geophysical data and cores were collected during a cooperative study by the U.S. Geological Survey, the National Oceanic and Atmospheric Administration Coastal Services Center, and the Apalachicola National Estuarine Research Reserve to refine the geology of the bay floor as well as the bay's Holocene stratigraphy. Sidescan-sonar imagery, bathymetry, high-resolution seismic profiles, and cores show that oyster reefs occupy the crests of sandy shoals that range from 1 to 7 kilometers in length, while most of the remainder of the bay floor is covered by mud. The sandy shoals are the surficial expression of broader sand deposits associated with deltas that advanced southward into the bay between 6,400 and 4,400 years before present. The seismic and core data indicate that the extent of oyster reefs was greatest between 2,400 and 1,200 years before present and has decreased since then due to the continued input of mud to the bay by the Apalachicola River. The association of oyster reefs with the middle to late Holocene sandy delta deposits indicates that the present distribution of oyster beds is controlled in part by the geologic evolution of the estuary. For more information on the surveys involved in this project, see http://woodshole.er.usgs.gov/operations/ia/public_ds_info.php?fa=2005-001-FA and http://woodshole.er.usgs.gov/operations/ia/public_ds_info.php?fa=2006-001-FA.

Apalachicola Bay and St. George Sound contain the largest oyster fishery in Florida, and the growth and distribution of the numerous oyster reefs here are the combined product of modern estuarine conditions and the late Holocene evolution of the bay. A suite of geophysical data and cores were collected during a cooperative study by the U.S. Geological Survey, the National Oceanic and Atmospheric Administration Coastal Services Center, and the Apalachicola National Estuarine Research Reserve to refine the geology of the bay floor as well as the bay's Holocene stratigraphy. Sidescan-sonar imagery, bathymetry, high-resolution seismic profiles, and cores show that oyster reefs occupy the crests of sandy shoals that range from 1 to 7 kilometers in length, while most of the remainder of the bay floor is covered by mud. The sandy shoals are the surficial expression of broader sand deposits associated with deltas that advanced southward into the bay between 6,400 and 4,400 years before present. The seismic and core data indicate that the extent of oyster reefs was greatest between 2,400 and 1,200 years before present and has decreased since then due to the continued input of mud to the bay by the Apalachicola River. The association of oyster reefs with the middle to late Holocene sandy delta deposits indicates that the present distribution of oyster beds is controlled in part by the geologic evolution of the estuary. For more information on the surveys involved in this project, see http://woodshole.er.usgs.gov/operations/ia/public_ds_info.php?fa=2005-001-FA and http://woodshole.er.usgs.gov/operations/ia/public_ds_info.php?fa=2006-001-FA.

Apalachicola Bay and St. George Sound contain the largest oyster fishery in Florida, and the growth and distribution of the numerous oyster reefs here are the combined product of modern estuarine conditions and the late Holocene evolution of the bay. A suite of geophysical data and cores were collected during a cooperative study by the U.S. Geological Survey, the National Oceanic and Atmospheric Administration Coastal Services Center, and the Apalachicola National Estuarine Research Reserve to refine the geology of the bay floor as well as the bay's Holocene stratigraphy. Sidescan-sonar imagery, bathymetry, high-resolution seismic profiles, and cores show that oyster reefs occupy the crests of sandy shoals that range from 1 to 7 kilometers in length, while most of the remainder of the bay floor is covered by mud. The sandy shoals are the surficial expression of broader sand deposits associated with deltas that advanced southward into the bay between 6,400 and 4,400 years before present. The seismic and core data indicate that the extent of oyster reefs was greatest between 2,400 and 1,200 years before present and has decreased since then due to the continued input of mud to the bay by the Apalachicola River. The association of oyster reefs with the middle to late Holocene sandy delta deposits indicates that the present distribution of oyster beds is controlled in part by the geologic evolution of the estuary. For more information on the surveys involved in this project, see http://woodshole.er.usgs.gov/operations/ia/public_ds_info.php?fa=2005-001-FA and http://woodshole.er.usgs.gov/operations/ia/public_ds_info.php?fa=2006-001-FA.

Apalachicola Bay and St. George Sound contain the largest oyster fishery in Florida, and the growth and distribution of the numerous oyster reefs here are the combined product of modern estuarine conditions and the late Holocene evolution of the bay. A suite of geophysical data and cores were collected during a cooperative study by the U.S. Geological Survey, the National Oceanic and Atmospheric Administration Coastal Services Center, and the Apalachicola National Estuarine Research Reserve to refine the geology of the bay floor as well as the bay's Holocene stratigraphy. Sidescan-sonar imagery, bathymetry, high-resolution seismic profiles, and cores show that oyster reefs occupy the crests of sandy shoals that range from 1 to 7 kilometers in length, while most of the remainder of the bay floor is covered by mud. The sandy shoals are the surficial expression of broader sand deposits associated with deltas that advanced southward into the bay between 6,400 and 4,400 years before present. The seismic and core data indicate that the extent of oyster reefs was greatest between 2,400 and 1,200 years before present and has decreased since then due to the continued input of mud to the bay by the Apalachicola River. The association of oyster reefs with the middle to late Holocene sandy delta deposits indicates that the present distribution of oyster beds is controlled in part by the geologic evolution of the estuary. For more information on the surveys involved in this project, see http://woodshole.er.usgs.gov/operations/ia/public_ds_info.php?fa=2005-001-FA and http://woodshole.er.usgs.gov/operations/ia/public_ds_info.php?fa=2006-001-FA.