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1,249 results for “R data”
Extended data tables to Haering and Habermann, F1000Res, RNfuzzyApp: an R shiny RNA-seq data analysis app for visualisation, differential expression analysis, time-series clustering and enrichment analysis
<p><b>Background</b> </p> <p>RNA-seq is a widely adopted affordable method for large scale gene expression profiling. However, user-friendly and versatile tools for wet-lab biologists to analyse RNA-seq data beyond standard analyses such as differential expression, are rare. Especially, the analysis of time-series data is difficult for wet-lab biologists lacking advanced computational training. Furthermore, most meta-analysis tools are tailored for model organisms and not easily adaptable to other species.</p> <p><b>Results</b></p> <p>With RNfuzzyApp, we provide a user-friendly, web-based R-shiny app for differential expression analysis, as well as time-series analysis of RNA-seq data. RNfuzzyApp offers several methods for normalization and differential expression analysis of RNA-seq data, providing easy-to-use toolboxes, interactive plots and downloadable results. For time-series analysis, RNfuzzyApp presents the first web-based, automated pipeline for soft clustering with the Mfuzz R package, including methods to aid in cluster number selection, Mfuzz loop computations, cluster overlap analysis, as well as cluster enrichments.</p> <p><b>Conclusion</b></p> <p>RNfuzzyApp is an intuitive, easy to use and interactive R shiny app for RNA-seq differential expression and time-series analysis, offering a rich selection of interactive plots, providing a quick overview of raw data and generating rapid analysis results. Furthermore, its orthology assignment, enrichment analysis, as well as ID conversion functions are accessible to non-model organisms.</p>
Physiological data and R script for running physiology combined model for Drosophila suzukii
<p>This is the dataset that accompanies an article entitled "The use of insect life tables in optimizing invasive pest distributional models" that would be published in Ecography. The dataset include two R script that used to generate physical model and the physiology combined model respectively. Our paper shows that the physiology combined model show good performance when applying ecological niche model in risk assessment. We addressed this by determining whether incorporating physiological data from life table analyses of an invasive insect, Drosophila suzukii, improved predictions of ecological niche models. The dataset also include the physiology data D. suzukii that we assembled for running our physiology combined model.</p>
FIG. 1. — Dioscorea comorensis R in A new edible yam (Dioscorea L.) species endemic to Mayotte, new data on D. comorensis R.Knuth and a key to the yams of the Comoro Archipelago
FIG. 1. — Dioscorea comorensis R.Knuth: A, female inflorescences with basal ovaries reflexed and enlarging; B, female inflorescence with more ovaries reflexed and enlarging; C, immature infructescence; D, part of male inflorescence showing the flowers in cymules; E, male inflorescences; F, male flowers. Photos Nicole Crestey (A, B, D, E) and Jean-Noël Labat (C, F).
Data and scripts for: track2KBA: An R package for identifying important sites for biodiversity from tracking data
<p>Data derivates and analysis scripts (in R) used for the companion paper for the R package track2KBA.</p>
Data for "Training data composition affects performance of protein structure analysis algorithms" by A. Derry, K. A. Carpenter, & R. B. Altman
<p><strong>Description</strong></p> <p>This repository contains all data used in "Training data composition affects performance of protein structure analysis algorithms", published in the Pacific Symposium on Biocomputing 2022 by A. Derry, K. A. Carpenter, & R. B. Altman. </p> <p>The data consists of the following files:</p> <ul> <li>ema_zenodo_data.tar.gz: train, validation, and test splits for Estimation of Model Accuracy task, in LMDB format</li> <li>design_zenodo_data.tar.gz: train, validation, and test splits for Protein Sequence Design task, in JSON format</li> <li>enz_cat_res_zenodo_data.tar.gz: train, validation, and test splits for Catalytic Residue and Enzyme Prediction task, in TF record format</li> </ul> <p>Details on dataset construction can be found in our paper and dataloaders can be found in our <a href="https://github.com/awfderry/ml-structure-bias">Github repo</a>.</p> <p><strong>Reference</strong></p> <p>A. Derry*, K. A. Carpenter*, & R. B. Altman, "Training data composition affects performance of protein structure analysis algorithms", 2021.</p> <p><strong>Dataset References</strong></p> <p>Datasets used were derived from the following works:</p> <p>Kryshtafovych, A., Schwede, T., Topf, M., Fidelis, K., & Moult, J. (2019). Critical assessment of methods of protein structure prediction (CASP)—Round XIII. In <em>Proteins: Structure, Function and Bioinformatics</em> (Vol. 87, Issue 12, pp. 1011–1020). https://doi.org/10.1002/prot.25823</p> <p>Ingraham, J., Garg, V. K., Barzilay, R., & Jaakkola, T. (2019). <em>Generative Models for Graph-Based Protein Design</em>. https://openreview.net/pdf?id=SJgxrLLKOE</p> <p>Furnham, N., Holliday, G. L., de Beer, T. A. P., Jacobsen, J. O. B., Pearson, W. R., & Thornton, J. M. (2014). The Catalytic Site Atlas 2.0: cataloging catalytic sites and residues identified in enzymes. <em>Nucleic Acids Research</em>, <em>42 </em>(Database issue), D485–D489.</p>
Data & R code for: Paquette & Hargreaves 'Biotic interactions are more often important at species' warm vs. cool range edges'
<p>Predicting which ecological factors constrain species distributions is a fundamental ecological question and critical to forecasting geographic responses to global change. Darwin hypothesized that abiotic factors generally impose species' high-latitude and high-elevation (typically cool) range limits, whereas biotic interactions more often impose species' low-latitude/low-elevation (typically warm) limits, but empirical support has been mixed. Here, we clarify three predictions arising from Darwin's hypothesis, and show that previously mixed support is partially due to researchers testing different predictions. Using a comprehensive literature review (885 range limits), we find that biotic interactions, including competition, predation, and parasitism, contributed to >60% of range limits, and influenced species' warm limits more often than cool limits. Abiotic factors contributed more often than biotic interactions to cool range limits, but temperature contributed frequently to both cool and warm limits. Our results suggest that most range limits will be sensitive to climate warming, but warm-limit responses will depend strongly on biotic interactions.</p>
Supplemental Data and Code for "An exact version of Life Table Response Experiment analysis, and the R package exactLTRE"
<p>This dataset enables the user to repeat the analyses presented in the manuscript "An exact version of Life Table Response Experiment analysis, and the R package exactLTRE." It is comprised of two compressed archives: one which contains code, and one which contains data.</p>
Los Alamos National Laboratory (LANL) and Geostationary Operational Environmental Satellite (GOES)–R Geosynchronous Particle Data for Ferradas, C. P., et al. (2023)
<p>This repository contains data from a set of charged particle analyzers onboard the Los Alamos National Laboratory (LANL) and the Geostationary Operational Environmental Satellite (GOES)–R geosynchronous orbit satellites. The data are used in the following open access publication submitted to Frontiers in Astronomy and Space Sciences.</p> <p>Ferradas, C. P., M.-C. Fok, N. Maruyama, M. G. Henderson, S. Califf, S. A. Thaller, and B. T. Kress (2022), <strong>The effects of particle injections on the ring current development during the 7-8 September 2017 geomagnetic storm</strong>, <em>Frontiers in Astronomy and Space Sciences</em>, <em>submitted.</em></p>
Effects of exogenous elevation of corticosterone on immunity and the skin microbiome of Eastern Newts (Notophthalmus viridescens): Data & R scripts
<p>The amphibian chytrid fungus, <em>Batrachochytrium</em> <em>salamandrivorans</em> (<em>Bsal</em>) threatens salamander biodiversity. The factors underlying <em>Bsal</em> susceptibility may include glucocorticoid hormones (GCs). The effects of GCs on immunity and disease susceptibility are well studied in mammals, but less is known in other groups, including salamanders. We used <em>Notophthalmus</em> <em>viridescens</em> (Eastern Newts) to test the hypothesis that GCs modulate salamander immunity. We first determined the dose required to elevate corticosterone (CORT; primary GC in amphibians) to physiologically relevant levels. We then measured immunity (neutrophil-lymphocyte ratios, plasma bacterial killing ability [BKA], skin microbiome, splenocytes, melanomacrophage centers [MMCs]) and overall health in newts following treatment with CORT or an oil vehicle control. Treatments were repeated for a short (2 treatments over 5 days) or long (18 treatments over 26 days) time period. Contrary to our predictions, most immune and health parameters were similar for CORT and oil-treated newts. Surprisingly, differences in BKA, skin microbiome, and MMCs were observed between newts subjected to short and long-term treatments, regardless of treatment type (CORT, oil vehicle). Taken together, CORT does not appear to be a major factor contributing to immunity in Eastern Newts, although more studies examining additional immune factors are necessary.</p>
Data from: aniMotum, an R package for animal movement data: rapid quality control, behavioural estimation and simulation
<p>1. Animal tracking data are indispensable for understanding the ecology, behaviour and physiology of mobile or cryptic species. Meaningful signals in these data can be obscured by noise due to imperfect measurement technologies, requiring rigorous quality control as part of any comprehensive analysis. </p> <p>2. State-space models are powerful tools that separate signal from noise. These tools are ideal for quality control of error-prone location data and for inferring where animals are and what they are doing when they record or transmit other information. However, these statistical models can be challenging and time-consuming to fit to diverse animal tracking data sets. </p> <p>3. The R package <em><span>aniMotum</span></em> eases the tasks of conducting quality control on and inference of changes in movement from animal tracking data. This is achieved via: 1) a simple but extensible workflow that accommodates both novice and experienced users; 2) automated processes that alleviate complexity from data processing and model specification/fitting steps; 3) simple movement models coupled with a powerful numerical optimization approach for rapid and reliable model fitting. </p> <p>4. We highlight <em>aniMotum</em>'s<em> </em>capabilities through three applications to real animal tracking data. Full R code for these and additional applications are included as Supporting Information so users can gain a deeper understanding of how to use <em>aniMotum</em> for their own analyses. </p>
Data and R code for: Survival to weaning in arid-country vervet monkeys
<p>We addressed the relative contributions of maternal rank and sociability to the survival of infant vervet monkeys (Chlorocebus pygerythrus) to nutritional independence (~210 days) in a resource-poor environment. Data from 153 infants across three troops and 10 birth cohorts indicated a pre-weaning mortality of 30% (Range: 9%-85%), with a median age at death of 50 days. In addition to the consequences of resource availability, increased infant survival was independently and equivalently associated with higher maternal rank and a greater number of maternal spatial partners. We use this outcome to suggest that apparent discrepancies in the relative importance of different maternal attributes in determining reproductive outcomes may be resolved by considering more closely local sources of infant mortality.</p>
Data and R code from: Haemosporidian infections influence risk-taking behaviours in young male blackcaps Sylvia atricapilla
<p>This repository contains all data and code necessary to reproduce the results and figures of the paper:</p> <p>Remacha, C., Ramírez, A., Arriero, E. and Pérez-Tris, J. 2023. Haemosporidian infections influence risk-taking behaviours in young male blackcaps <em>Sylvia atricapilla</em>. Animal Behaviour, 196, 113-126. <a href="https://doi.org/10.1016/j.anbehav.2022.12.001">https://doi.org/10.1016/j.anbehav.2022.12.001</a></p> <p>The repository contains a readme file (README_SYAT_MS_ANIBEH_Scripts.txt) with a description of the code and the data. The code is organised in eight R script files. Instructions to run the code are provided in the readme file. The data are organised in two separate files. One file (SYAT_MS_BH_ANIBEHdata.txt) contains data of exploratory and antipredatory behaviours of 43 young male blackcaps. The other one (SYAT_MS_BH_BIOL_ANIBEHdata.txt) contains biological and experimental attributes of the same individuals: status and intensity of parasite infection, experimental treatment, morphology and body mass.</p>
Data for: rtrees: An R package to assemble phylogenetic trees from megatrees
<p>Despite the increasingly available phylogenetic hypotheses for multiple taxonomic groups, most of them do not include all species. In phylogenetic ecology, there is still strong demand to have phylogenies with all species in a study included. The existing software tools to graft species to backbone megatrees, however, are mostly limited to a specific taxonomic group such as plants or fishes. Here, I introduce a new user-friendly R package `rtrees` that can assemble phylogenies from existing or user-provided megatrees. For most common taxonomic groups, users can only provide a vector of species' scientific names to get a phylogeny or a set of posterior phylogenies from megatrees. It is my hope that `rtrees` can provide an easy, flexible, and reliable way to assemble phylogenies from megatrees, facilitating the progress of phylogenetic ecology.</p>
Red deer growth data and R code for analysis
<p>Dataset and R code (Rmd-file) for analysis of seasonal growth of body weight in red deer. </p> <p>Supplementary material for the paper "Shifting seasonality of annual growth through ontogeny for red deer at northern latitudes".</p> <p>This study was part of the AgriDeer project (318575), funded by the Research Council of Norway.</p>
Data from: Rtapas: An R package to assess cophylogenetic signal between two evolutionary histories
<p class="MsoNormal"><span>Cophylogeny represents a framework to understand how ecological and evolutionary process influence lineage diversification. The recently developed algorithm Random Tanglegram Partitions provides a directly interpretable statistic to quantify the strength of cophylogenetic signal and incorporates phylogenetic uncertainty into its estimation, and maps onto a tanglegram the contribution to cophylogenetic signal of individual host-symbiont associations. We introduce </span><span>Rtapas</span><span>, an R package to perform Random Tanglegram Partitions. </span><span>Rtapas</span><span> </span><span>applies a given global-fit method to random partial tanglegrams of a fixed size to identify the associations, terminals, and internal nodes that maximize phylogenetic congruence. This new package extends the original implementation with a new algorithm that examines the contribution to phylogenetic incongruence of each host-symbiont association and adds ParaFit, a method designed to test for topological congruence between two phylogenies, to the list of global-fit methods than can be applied. </span><span>Rtapas</span><span> </span><span>facilitates and speeds up cophylogenetic analysis, as it can handle large phylogenies (100+ terminals) in affordable computational time as illustrated with two real-world examples. </span><span>Rtapas</span><span> </span><span>can particularly cater for the need for causal inference in cophylogeny in two domains: (i) Analysis of complex and intricate host-symbiont evolutionary histories and (ii) assessment of topological (in)congruence between phylogenies produced with different DNA markers and specifically identify subsets of loci for phylogenetic analysis that are most likely to reflect gene-tree evolutionary histories.</span></p>
Input data for the case study reported in "DREAM: an R package for druggability evaluation of human complex diseases".
<p>The data included in this record constituted the input for the case study reported in the manuscript "DREAM: an R package for druggability evaluation of human complex diseases", by Antonio Federico, Michele Fratello, Alisa Pavel, Lena Möbus, Giusy del Giudice, Angela Serra, Dario Greco. The data derive from transcriptomics experiments executed on lesional skin from atopic dermatitis patients and unaffected skin counterparts. The data consists of two files in ".txt" format reporting gene expression data in tabular format, where on the rows are reported genes and on the columns are reported samples. The data is an aggregated and batch-corrected collection of datasets originally downloaded by Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/). The file "GE_Mic_AD_Pamr_MAARS.txt" reports gene expression estimates of lesional skin of atopic dermatitis patients, while the file "GE_Mic_AD_Pamr_nl_MAARS.txt" reports gene expression estimates of non-lesional skin of atopic dermatitis patients.</p>
Data and R scripts for KCC plots in Douville (2023)
<p>Data and R scripts for plotting Fig. 3 and Fig.4 in Douville (2023)</p> <p>Douville H. (2023) Robust and perfectible constraints on human-induced Arctic climate change. Communications Earth & Environment (revised)</p>
Data and R scrips for "Exposure to closed-loop scrubber washwater alters biodiversity, reproduction, and grazing of marine zooplankton"
<p>Research data and scripts associated with the article "Exposure to closed-loop scrubber washwater alters biodiversity, reproduction, and grazing of marine zooplankton" by Jönander et al.</p>
FIG. 3 in New data on the distribution of the genus Roeseliana Zeuner, 1941 (Orthoptera, Tettigoniidae, Tettigoniinae) in the southwestern Balkans, with description of R. epirotica n. sp.
FIG. 3. — Titillators of Roeseliana epirotica n. sp. ♂: A, dorsal view; B, side view on the right. Not to scale.
FIG. 2 in New data on the distribution of the genus Roeseliana Zeuner, 1941 (Orthoptera, Tettigoniidae, Tettigoniinae) in the southwestern Balkans, with description of R. epirotica n. sp.
FIG. 2. — Subgenital plates of females of species with closest distribution: A, Roeseliana epirotica n. sp. ♀ (the plate of the female from Albania is strictly identical); B, Roeseliana ambitiosa (Uvarov, 1924) ♀ from the Republic of North Macedonia; C, Roeseliana ambitiosa ♀ from Paramithia, located at only 10 km from R. epirotica n. sp. type locality. Not to scale. Photo: C, K. G. Heller.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.