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15,520 results for “r”
GOES-R Land Surface Products at AmeriFlux and NEON Eddy Covariance Tower Locations
The terrestrial carbon cycle varies dynamically over short periods that can be difficult to observe. Geostationary (“weather”) satellites like the Geostationary Operational Environmental Satellite - R Series (GOES-R) deliver near-hemispheric imagery at a ten-minute cadence, and its Advanced Baseline Imager (ABI) measures visible and near-infrared spectral bands that can be used to estimate land surface properties and carbon dioxide flux. GOES-R data are designed for real-time dissemination and are difficult to link with eddy covariance time series of land-atmosphere carbon dioxide exchange. We compiled three-year time series of GOES-R land surface attributes including visible and near-infrared reflectances, land surface temperature, and downwelling shortwave radiation (DSR) at 318 ABI fixed grid pixels containing eddy covariance towers for years 2020-2022. We demonstrate how to best combine satellite and in-situ datasets, and show how ABI attributes useful for carbon cycle science vary across space and time. By connecting observation networks that infer rapid changes to the carbon cycle, we can gain a richer understanding of the processes that control it.
Course Materials for Environmental Data Science in R: Introduction to Data Integration and Machine Learning (ENV 730)
In today's world, understanding environmental data and making informed decisions based on it is crucial for addressing complex environmental challenges. Yale School of the Environment's Environmental Data Science in R: Introduction to Data Integration and Machine Learning (ENV 730) course serves as an introduction to the integration of environmental data using R programming language, coupled with machine learning techniques. This dataset contains a zip file with all the data files used in this course, along with a README that has the metadata for those files.
HURRECON Model for Estimating Hurricane Wind Speed, Direction, and Damage (R and Python)
The HURRECON model estimates wind speed, wind direction, enhanced Fujita scale wind damage, and duration of EF0 to EF5 winds as a function of hurricane location and maximum sustained wind speed. Results may be generated for a single site or an entire region. Hurricane track and intensity data may be imported directly from the US National Hurricane Center's HURDAT2 database. HURRECON is available in R and Python. The R version is available on CRAN as HurreconR. The model is an updated version of the original HURRECON model written in Borland Pascal for use with Idrisi (see HF025). New features include support for: (1) estimating wind damage on the enhanced Fujita scale, (2) importing hurricane track and intensity data directly from HURDAT2, (3) creating a land-water file with user-selected geographic coordinates and spatial resolution, and (4) creating plots of site and regional results. The model equations for estimating wind speed and direction, including parameter values for inflow angle, friction factor, and wind gust factor (over land and water), are unchanged from the original HURRECON model. For more details and sample datasets, see the project website on GitHub (https://github.com/hurrecon-model).
EXPOS Model for Estimating Topographic Exposure to Wind (R and Python)
The EXPOS model uses a digital elevation model (DEM) to estimate exposed and protected areas for a given hurricane wind direction and inflection angle. The resulting topograhic exposure maps can be combined with output from the HURRECON model to estimate hurricane wind damage across a region. EXPOS is available in R and Python. The R version is available on CRAN as ExposR. The model is an updated version of the original EXPOS model written in Borland Pascal for use with Idrisi (see HF024). For more details and sample datasets, see the project website on GitHub (https://github.com/expos-model).
Data and R Code from "A novel approach to sustainability assessment of food supply chains using networks of ecosystem services"
<p>Data and R code from this paper applying network analysis (iGraph) to two case studies pre and post agroecological transitions in Central America and Tanzania, Africa from the IPES-Food report. Further descriptions of this data and code can be found within the extended manuscript. R Code relies on the data from the scenarios (e.g., Nodes and Relations CSVs) and creates the output network metrics (e.g., Node Metric CSVs). </p>
Dataset and images for "Instantaneous R calculation for COVID-19 epidemic in Brazil"
<p>This dataset was generated from raw data obtained at </p> <ul> <li>Ceará State - <a href="https://indicadores.integrasus.saude.ce.gov.br/api/casos-coronavirus/export-csv">https://indicadores.integrasus.saude.ce.gov.br/api/casos-coronavirus/export-csv</a></li> <li>São Paulo State - <a href="http://www.seade.gov.br/wp-content/uploads/2020/04/Dados-covid-19-estado.csv">http://www.seade.gov.br/wp-content/uploads/2020/04/Dados-covid-19-estado.csv</a></li> <li>Brazil - <a href="https://covid.saude.gov.br/">https://covid.saude.gov.br/</a></li> </ul> <p>Data was processed with R package EpiEstim (methodology in the associated preprint). Briefly, instantaneous R was estimated within a 5 day time window. Prior mean and standard deviation values for R were set at 3 and 1. Serial interval was estimated using a parametric distribution with uncertainty (offset gamma). We compared the results at two time points (day 7 and day 21 after the first case was registered at each region) from different brazillian states in order to make inferences about the epidemic dynamics.</p>
Data and code to perform the"Target deformation" workflow in R: virtual reconstruction of the Equus stenonis holotype skulll
<p>Data and code to perform the"Target deformation" workflow in R: virtual reconstruction of the Equus stenonis holotype skulll.</p> <p>TargetDeformation.R: R code with for the Target Deformation procedure.<br> IGF560.ply: 3D mesh of the holotype IGF560 in ply extension.<br> IGF560_set.txt: landmark set of the holotype IGF560.<br> Dm. 5/154.3/4.A4.5.ply: 3D mesh of Dm 5/154.3/4.A4.5 in .ply extension.<br> Dm_set.txt: landmark set of the Dm 5/154.3/4.A4.5 sample.<br> IGF11023: 3D mesh of IGF11023 in.ply extension.<br> IGF11023_set.txt: landmark set on the IGF11023 sample.<br> IGF560R: 3D mesh of IGF560R in.ply extension.<br> IGF560W: 3D mesh of IGF560W in.ply extension.<br> IGF560R-s: 3D mesh of IGF560R-s in.ply extension.<br> IGF560W-s: 3D mesh of IGF560W-s in.ply extension.<br> IGF560_IGF560R_IGF560W.html: file that contain WebGL code to reproduce the 3D meshes of IGF560, IGF560R and IGF560W in a browser.<br> IGF560Rs_IGF560Ws.html: file that contain WebGL code to reproduce the 3D meshes of IGF560R-S and IGF560W-S in a browser.<br> IGF560W Mesh area variation.html: file that contain WebGL code to reproduce two 3d meshes of IGF560W using localmeshDist() and meshdist() in a browser.<br> </p>
Data and R-Scripts for "Quality and timing of crowd-based water level class observations"
<p>This are the data and the R-scripts used for the manuscript "Quality and timing of crowd-based water level class observations" accepted for publication in the journal Hydrological Processes in July 2020 as a Scientific Briefing. To run the code, just run the R-script with the name "RunThisForResults.R". Results will be written to the "Figures" and the "Results" folder.</p>
Designing Types for R, Empirically (Dataset)
<p>This dataset is intended to accompany the paper "Designing Types for R, Empirically" (@ OOPSLA'20, <a href="https://2020.splashcon.org/details/splash-2020-oopsla/57/Designing-Types-for-R-Empirically">link to paper</a>). This data was obtained by running the Typetracer (aka propagatr) dynamic analysis tool (<a href="https://github.com/PRL-PRG/propagatr">link to tool</a>) on the test, example, and vignette code of a corpus of >400 extensively used R packages.<br> <br> Specifically, this dataset contains:</p> <ol> <li>function type traces for >400 R packages (raw-traces.tar.gz);</li> <li>trace data processed into a more readable/usable form (processed-traces.tar.gz), which was used in obtaining results in the paper;</li> <li>inferred type declarations for the >400 R packages using various strategies to merge the processed traces (see type-declarations-* directories), and finally;</li> <li>contract assertion data from running the reverse dependencies of these packages and checking function usage against the declared types (contract-assertion-reverse-dependencies.tar.gz).</li> </ol> <p> A preprint of the paper is also included, which summarizes our findings.</p> <p><strong>Fair warning Re: data size:</strong> the raw traces, once uncompressed, take up nearly 600GB. The already processed traces are in the 10s of GB, which should be more manageable for a consumer-grade computer.</p>
Data and R Code for 'Domestication via the commensal pathway in a fish-invertebrate mutualism'
<p>This document contains all data and R code required to replicate the analyses in 'Domestication via the commensal pathway in a fish-invertebrate mechanism' as published in Nature Communications. The R Markdown provided includes descriptions of all variables and the code used for the analysis of the following eight datasets:</p> <p>1. Transects<br> 2. Census of farms<br> 3. Paired choice experiments<br> 4. Predation experiment 1<br> 5. Predation experiment 2<br> 6. Timed observations<br> 7. Farm algae composition<br> 8. Longfin damselfish body condition</p> <p>In addition, the R Markdown also includes descriptions of all variables for two additional datasets:</p> <p>9. Estimates of mysid swarm density<br> 10. Mysid waste excretion and nutrient availability</p> <p>A PDF version of the R Markdown with all output is also provided. Please see the methods section of the associated manuscript for further information on data collection and analysis procedures.</p> <p>Author contributions to data collection and analysis: RMB, JMC, ZLC, TLS & WEF collected the data; WEF, RMB, JMC, ZLC & AM implemented the analyses. </p> <p>Correspond with: rohan.m.brooker@gmail.com</p>
Datasets associated with Agostini, S., Houlbreque, F., Biscéré, T., Harvey, B. P., Heitzman, J. M., Takimoto, R., et al. (2020). Greater mitochondrial energy production provides resistance to ocean acidification in 'winning' hermatypic corals. Front. Mar. Sci. 7. doi:10.3389/fmars.2020.600836.
<p>Datasets associated with Agostini, S., Houlbreque, F., Biscéré, T., Harvey, B. P., Heitzman, J. M., Takimoto, R., et al. (2020). Greater mitochondrial energy production provides resistance to ocean acidification in ‘winning’ hermatypic corals. Front. Mar. Sci. 7. doi:10.3389/fmars.2020.600836.</p>
Compiled datasets and R codes for enzymatic activities in Atlantic salmon tissues
<p>Folder with two datasets of enzyme activities (CS and LDH) with associated details of fish, incuding genotypes, body size, and metabolic rates, and R codes for linear mixed models as described in the manuscript Prokkola et al (submitted 2023). See README file for more information.</p>
R Code and Re-analyzed Datasets for: Robust approaches for the quantitative analysis of genome formula variation in multipartite and segmented viruses
<p>This submission includes all the scripts and data analyzed in the manuscript "Robust approaches for the quantitative analysis of genome formula variation in multipartite and segmented viruses". This manuscript is a technical note on how genome formula data can be analyzed. There are no new experimental data in the manuscript, as published datasets are re-analyzed. Here we reproduce those datasets as formatted for our analysis, for the convenience of the reader. Please consult the README.txt file first.</p> <p>The corresponding paper was published in Viruses <em>16</em>(2): 270. (<a href="https://doi.org/10.3390/v16020270">https://doi.org/10.3390/v16020270</a>).</p> <p>This is the second version of the code, corresponding to the final version of the paper. The intial restricted version for review had a DOI 10.5281/zenodo.10355273.</p> <p> </p>
R package n2khab: providing preprocessed reference data for Flemish Natura 2000 habitat analyses
The n2khab package is an R package with preprocessing functions and standard reference data, useful for analyses regarding Flemish Natura 2000 habitats and regionally important biotopes (RIBs). URL: <a href="https://inbo.github.io/n2khab">https://inbo.github.io/n2khab</a>.
R code for archaeological examples of calculating isotopic niche space and overlap using the rKIN package
<p>This R code was written to apply the tools of the rKIN package to calculate isotopic niche space and overlap for the three archaeological case studies for the manuscript Investigating Isotopic Niche Space: Using rKIN for Stable Isotope Studies in Archaeology published in the Journal of Archaeological Method and Theory. Raw data for the case studies are available in the supplemental Excel file.</p>
Dataset and R code: Above and belowground functional trait coordination in the Neotropical understory genus Costus
<p>Dataset and R code accompanying the paper "Above and belowground functional trait coordination in the Neotropical understory genus <em>Costus</em>" published by AoB Plants. </p>
Example code and data for ubms: An R package for fitting hierarchical occupancy and N-mixture abundance models in a Bayesian framework
<p>This repository contains an R script (grouse_example.R) and data (grouse_data.csv) used to reproduce the grouse abundance analysis described in Kellner, K. F., et al. (2021) ubms: An R package for fitting hierarchical occupancy and N-mixture abundance models in a Bayesian framework. Methods in Ecology and Evolution. The R script requires installation of the ubms R package, which can be obtained from CRAN (https://cran.r-project.org/package=ubms).</p> <p>The repository also contains an additional example occupancy analysis (occupancy_example.R) using the crossbill dataset included with the unmarked R package.</p>
Perception and evaluation of (modified) wood by older adults from Slovenia and Norway (Datasets, R analysis code, and supplementary tables)
<p>This entry contains datasets, R analysis code, and supplementary tables for the article <em>Perception and evaluation of (modified) wood by older adults from Slovenia and Norway.</em></p> <p>The article investigates human perception and evaluation of handrails made of different materials. Our goal was to identify how older adults perceive handrails made of unmodified wood, modified wood, and steel. We examined if certain materials are more preferred than others, which material properties might be associated with differences in human preference, and what are the roles of tactile and tactile-visual domains in material perception. Our analysis is based on the results from an 11-item rating scale and a ranking task.</p>
Hoofprints in the Sand Supplement S4: R Code for LSI, ADI, GMM Analyses
<p>R code to reproduce the statistics and graphic plots shown in our study on the maritime mobility of sheep in the Iron Age eastern Mediterranean, as demonstrated in Harding, S. et al. Hoofprints in the Sand: A Metric Study of Livestock on the Southern Phoenician Coast. In preparation for <em>Quaternary International</em>. </p>
Data & R-Code for "Weather and food availability additively affect reproductive output in an expanding raptor population"
<p><strong>Abstract</strong></p> <p>The joint effects of interacting environmental factors on key demographic parameters can exacerbate or mitigate the separate factors’ effects on population dynamics. Given ongoing changes in climate and land use, assessing interactions between weather and food availability on reproductive performance is crucial to understand and forecast population dynamics. By conducting a feeding experiment in 4 years with different weather conditions, we were able to disentangle the effects of weather, food availability and their interactions on reproductive parameters in an expanding population of the red kite (<em>Milvus milvus</em>), a conservation-relevant raptor known to be supported by anthropogenic feeding. Brood loss occurred mainly during the incubation phase, and was associated with rainfall and low food availability. In contrast, brood loss during the nestling phase occurred mostly due to low temperatures. Survival of last-hatched nestlings and nestling development was enhanced by food supplementation and reduced by adverse weather conditions. However, we found no support for interactive effects of weather and food availability, suggesting that these factors affect reproduction of red kites additively. The results not only suggest that food-weather interactions are prevented by parental life-history trade-offs, but that food availability and weather conditions are crucial separate determinants of reproductive output, and thus population productivity. Overall, our results suggest that the observed increase in spring temperatures and enhanced anthropogenic food resources have contributed to the elevational expansion and the growth of the study population during the last decades.</p>
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.