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1,249 results for “R data”
R scripts for analyzing LiDAR data to assess forest canopy structure and perform Principal Component Analysis (PCA) on derived metrics
<p>This repository contains R scripts for analyzing LiDAR data to assess forest canopy structure and perform Principal Component Analysis (PCA) on spectral and LiDAR-derived metrics. The scripts cover LiDAR data processing, canopy height model (CHM) generation, calculation of forest canopy metrics, and PCA analysis.</p>
Data and R-scripts for "Land-use trajectories for sustainable land system transformations: identifying leverage points in a global biodiversity hotspot" (V2)
<p>Sustainable land system transformations are necessary to avert biodiversity and climate collapse. However, it remains unclear where entry points for transformations exist in complex land systems. Here, we conceptualize land systems along land-use trajectories, which allows us to identify and evaluate leverage points; i.e., entry points on the trajectory where targeted interventions have particular leverage to influence land-use decisions. We apply this framework in the biodiversity hotspot Madagascar. In the Northeast, smallholder agriculture results in a land-use trajectory originating in old-growth forests, spanning forest fragments, and reaching shifting hill rice cultivation and vanilla agroforests. Integrating interdisciplinary empirical data on seven taxa, five ecosystem services, and three measures of agricultural productivity, we assess trade-offs and co-benefits of land-use decisions at three leverage points along the trajectory. These trade-offs and co-benefits differ between leverage points: two leverage points are situated at the conversion of old-growth forests and forest fragments to shifting cultivation and agroforestry, resulting in considerable trade-offs, especially between endemic biodiversity and agricultural productivity. Here, interventions enabling smallholders to conserve forests are necessary. This is urgent since ongoing forest loss threatens to eliminate these leverage points due to path-dependency. The third leverage point allows for the restoration of land under shifting cultivation through vanilla agroforests and offers co-benefits between restoration goals and agricultural productivity. The co-occurring leverage points highlight that conservation and restoration are simultaneously necessary. Methodologically, the framework shows how leverage points can be identified, evaluated, and harnessed for land system transformations under the consideration of path-dependency along trajectories.</p>
Simulated data from abmAnimalMovement: An R package for simulating animal movement using an agent-based model
<p>Contained here are the data simulated as part of the manuscript: "abmAnimalMovement: An R package for simulating animal movement using an agent-based model" that can be found at: https://github.com/BenMMarshall/abmAnimalMovement (and archived at: https://doi.org/10.5281/zenodo.6951937).</p> <p>- BADGER_locations.csv: A csv file that contains the realised locations of the example badger simulation, where each row is equal to a timestep. Columns include: timestep, the timestep as an integer; x, the x coordinate of the animal; y, the y coordinate of the animal; sl, the step length between locations used during the simulation; sl_rescale the rescale factor required to return step lengths back to the input scale; ta, turning angle between locations in degrees; behave, the behavioural mode the animal was in at a given timestep; chosen, the location chosen out of the number of options available; destination_x and destination_y the point the animal was attracted to at that time (note exploratory behaviour is not subject attraction).</p> <p>- BADGER_options.csv: A csv file that contains the options available to the example badger simulation over the entire simulation duration, where each row is equal to an option repeated for each timestep. Columns include: timestep, the timestep as an integer; oall_x, and oall_y show the x and y coordinates of all the options available to an animal at a timestep; oall_steplengths are the step lengths from the current location compared to all the options.</p> <p>- completelist.RDS: This RDS file contains a list object of length three, where the full simulation outputs from each three examples are stored. Each species slot contains the “locations” dataframe (see description of locations.csv), the "options" dataframe (see description of options.csv), and a nested list containing all the "inputs" used to generate the simulated results (split into subsections: inputs_basic that contains inputs linked to simulation duration and intensity, inputs_destination that contains inputs linked to destination and attraction aspects, inputs_movement that contains inputs linked to movement capacity and behavioural switching, inputs_cycle that contains inputs linked to activity cycling, inputs_layerSeed that contains the environmental matrices and seed). A fourth object is returned called "others" that captures all other outputs, mainly used internally for debugging and checking.</p> <p>- KINGCOBRA_locations.csv: A csv file that contains the realised locations of the example king cobra simulation, where each row is equal to a timestep. The file structure follows the same as the BADGER_options.csv file.<br>KINGCOBRA_options.csv: A csv file that contains the options available to the example king cobra simulation over the entire simulation duration, where each row is equal to an option repeated for each timestep. The file structure follows the same as the BADGER_options.csv file.</p> <p>- VULTURE_locations.csv: A csv file that contains the realised locations of the example vulture simulation, where each row is equal to a timestep. The file structure follows the same as the BADGER_locations.csv file.<br>VULTURE_options.csv: A csv file that contains the options available to the example vulture simulation over the entire simulation duration, where each row is equal to an option repeated for each timestep. The file structure follows the same as the BADGER_locations.csv file.</p> <p>- eg_landscapedata_completelist.RDS: This RDS file contains the landscape matrices required for recreating the simulated outputs described in the manuscript named above. It is a list of three objects ("shelter", "forage", "movement"), each a numeric matrix of equal size, with values describing the quality of each landscape characteristic.</p> <p>- argument_table.csv: Descriptive table of the simulation inputs used in the walk-through manuscript.</p>
R code and data for "Flake selection and scraper retouch probability: an alternative model for explaining Middle Paleolithic assemblage retouch variability"
<p>R code and data used for "Flake selection and scraper retouch probability: an alternative model for explaining Middle Paleolithic assemblage retouch variability" (Archaeological and Anthropological Sciences, Volume 10, Issue 7, pp 1791–1806)</p>
Supporting Data -- Evaluating Mask R-CNN Models to Extract Terracing across Oceanic High Islands: an example from Sāmoa.
<p>This dataset provides supplemental information for the manuscript, "Diverse terracing practices revealed by automated lidar analysis across the Sāmoan islands", submitted to Archaeological Prospection. The dataset contains a trained Mask R-CNN deep learning model designed for detecting archaeological terracing features on the islands of American Samoa, associated training data, and the raw and cleaned output of detected terraces.</p>
Spectral data associated to the publication: "Near-infrared reflectance spectroscopy of sublimating salty ice analogues. Implications for icy moons" by R. Cerubini et al. (Planetary and Space Science 211, 2022)
<p>This is the complete set of experimental NIR reflectance data collected by R. Cerubini and co-authors for the article "Near-infrared reflectance spectroscopy of sublimating salty ice analogues. Implications for icy moons" published in Planetary and Space Science 211 (2022). doi: https://doi.org/10.1016/j.pss.2021.105391.</p> <p>The article itself is published in open-access and provides the methodology for the spectral aquisitions, discussion of the errors and uncertainties, analysis of the spectra and implications for the composition of Solar System surfaces.</p> <p>The data are contained in ASCII files (columns separated by comma). The first column is the wavelength (in micrometers) and the other columns contain the reflectance data (in unit of reflectance factor). The different compositions are indicated in the filenames and correspond directly to the figures in the published paper.</p> <p> </p> <p> </p>
Refined Cropland Data Layer (R-CDL)
<p>A decision tree algorithm was employed to refine Cropland Data Layer (CDL) using spatial and temporal information. The Refined Cropland Data Layer (R-CDL) could be used as an alternative to researchers as it provides more accurate cropland information. Annual RCDL maps were produced for the contiguous United States from the year 2017 to 2021.</p> <p>To explore the data online and access the web-based services, please visit the project homepage: https://cloud.csiss.gmu.edu/icrop/</p> <p>To read our full paper on RCDL, please visit: https://www.nature.com/articles/s41597-022-01169-w</p> <p>Cite this article:</p> <p>Lin, L., Di, L., Zhang, C. <em>et al.</em> Validation and refinement of cropland data layer using a spatial-temporal decision tree algorithm. <em>Sci Data</em> <strong>9, </strong>63 (2022). https://doi.org/10.1038/s41597-022-01169-w</p> <p>Note:</p> <p>2020 RCDL was reproduced with the Re-released CDL on February 1, 2022. More information could be found at: https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php</p> <p>2021 RCDL was released with the official release of 2021 CDL on February 14, 2022</p> <p>2022 RCDL was released on March 24, 2022</p>
Data and R scripts for KCC plots in Douville and Willett (2023)
<p>CMIP6 data (historical and scenario SSP5-8.5, run 1 only), observation and R scripts to plot Fig.1 and Fig.2 of Douville and Willett (Sc. Adv., 2023)</p>
Open-population models for estimating roadkill rates - Data and R Code
<p>Roadkill carcass capture-recapture data, capture histories for four and eight-occasion designs, and R code (with JAGS code) for roadkill rates estimation.</p>
Data dan R Markdown Notebook untuk "PENONJOLAN PERAN SEMANTIS DAN KONSTRUKSI GRAMATIKAL PASANGAN VERBA -I DAN -KAN: KAJIAN GRAMATIKA KONSTRUKSIONAL BERBASIS KORPUS ATAS MENAWARI/MENAWARKAN"
<p>Repositori <a href="https://r4ds.had.co.nz/workflow-projects.html">RStudio Project</a> yang mengandung data dan kode pemrograman R untuk analisis data dan penulisan makalah berjudul <strong>"Penonjolan Peran Semantis dan Konstruksi Gramatikal Pasangan Verba -<em>i</em> dan -<em>kan</em>: Kajian Gramatika Konstruksional Berbasis Korpus atas <em>Menawari</em>/<em>Menawarkan</em>"</strong>. Makalah ini diterbitkan pada jurnal <a href="https://ojs.linguistik-indonesia.org/index.php/linguistik_indonesia/index"><em>Linguistik Indonesia</em></a> di bulan Agustus, 2023.</p> <ol> <li> <p>Kode pemrograman R dan narasi teks makalah terintegrasi dalam berkas <a href="https://rmarkdown.rstudio.com">R Markdown</a> Notebook dengan nama <a href="https://github.com/gederajeg/profiled-participant-roles/blob/main/manuskrip-2.Rmd"><code>manuskrip-2.Rmd</code></a>.</p> </li> <li> <p>Data konkordansi terdapat pada berkas <a href="https://github.com/gederajeg/profiled-participant-roles/blob/main/menawari.txt"><code>menawari.txt</code></a> dan <a href="https://github.com/gederajeg/profiled-participant-roles/blob/main/menawarkan.txt"><code>menawarkan.txt</code></a>.</p> </li> <li> <p>Luaran visualisasi tersimpan pada direktori <a href="https://github.com/gederajeg/profiled-participant-roles/tree/main/plots"><code>plot</code></a>.</p> </li> </ol>
Data and R code for “Individual-level variation in reproductive effort in chestnut oak (Quercus montana Willd.) and black oak (Q. velutina Lam.)”, Forest Ecology and Management, 2022
Masting is a population-level reproductive strategy, where individuals synchronize large but intermittent seed production. Despite the high degree of synchrony at the population level, there can be considerable variation in reproduction among individuals (intraspecific variation). Here, we use 18 years of acorn production data from individual chestnut oak and black oak from control and thinned stands, to understand what factors influence individual differences in reproductive effort and variability. We included a variety of tree-level measurements, environmental characteristics, and measurements from tree cores to determine if certain characteristics were associated variations in reproduction. We considered both mean annual acorn production per m2 crown and interannual variation in acorn production (CV) as response variables. We also classified individuals as super producers (i.e., those that consistently produce more acorns than others), good, fair and poor producers (i.e., those that consistently produce less or have a higher number of failure years). In chestnut oak, 14% of the individuals were classified as super producers and contributed 34% of the total acorns, while poor producers made up 35% of the trees and contributed only 16% to total acorn production. In black oak, super producers (14% of the individuals) contributed 31% of total acorns and poor producers (24% of the individuals) contributed only 9% of the acorns. Diameter at breast height (DBH) was the most consistent variable for explaining intraspecific variation in reproductive effort and variability (i.e., larger individuals had higher mean acorn production for both chestnut oak and black oak, and lower CV for black oak). Other variables that influenced reproduction and variation included elevation and clay content for chestnut oak, and slope for black oak. We found no significant effect from the thinning treatment on acorn production. Our results illustrate how tree-level and environmental characte
Data and R scripts for analyses of declines in invertebrate species from the Gulf of Maine, USA, 1997 - 2018
Data files and R scripts are for the analyses that are presented in an article that is under revision for Biology Communications. The title of the article is: Declines over the last two decades of five key invertebrate species found on rocky intertidal shores throughout the western North Atlantic. The three data files contain the abundances of four gastropod species and the recruitment of barnacles and mussels from 1997 to 2018, monthly temperature data from three buoys from 2001 to 2018, and pH and aragonite saturation state from 1997 to 2014. R scripts include details of Bayesian estimates for Poisson regressions of species over time, clean-up of environmental data, imputation of missing environmental data and analyses of species versus environmental parameters.
R code and data to reproduce figures from the "Multivariate autoregressive modelling and conditional simulation for temporal uncertainty analysis of an urban water system in Luxembourg" paper
<p>This repository contains the R code and data to reproduce figures from the "Multivariate autoregressive modelling and conditional simulation for temporal uncertainty analysis of an urban water system in Luxembourg" paper.</p>
Data and R code for the revised manuscript "Downscaling digital soil maps using electromagnetic induction and aerial imagery"
<p>Data and R code for the revised manuscript "Downscaling digital soil maps using electromagnetic induction and aerial imagery". This is the code for the revised version of the manuscript, after adressing comments from reviewers. The data and code for the preprint, before submission to peer review (Møller et al., 2020), is available at <a href="https://doi.org/10.5281/zenodo.3699130">https://doi.org/10.5281/zenodo.3699130</a>.</p> <p>The R code was written for R version 3.6.3.</p> <p>References<br> Møller, A.B., Koganti, T., Beucher, A., Iversen, B.V. and Greve, M.H., 2020. Downscaling digital soil maps using electromagnetic induction and aerial imagery. EarthArXiv. <a href="http://dx.doi.org/10.31223/osf.io/a7xz6">http://dx.doi.org/10.31223/osf.io/a7xz6</a>. [preprint]</p>
Data and R-script for a tutorial that explains how to convert spreadsheet data to tidy data.
<p>Data and R-script for a tutorial that explains how to convert spreadsheet data to tidy data. The tutorial is published in a blog for The Node (https://thenode.biologists.com/converting-excellent-spreadsheets-tidy-data/education/)</p>
Data and R code from: Pollination interactions reveal direct costs and indirect benefits of plant–plant facilitation for ecosystem engineers
Ecosystem engineers substantially modify the environment via their impact on abiotic conditions and the biota, resulting in facilitation of associated species that would not otherwise grow. Yet, reciprocal effects are poorly understood as studies of plant–plant interactions usually estimate only benefits for associated species while hardly considering how another trophic level may mediate direct and indirect effects for ecosystem engineers. We run a field experiment with ecosystem engineers blooming either alone or with associated plants to decompose net effects and to test the hypothesis that pollinator-mediated interactions provide benefits which balance costs of facilitation by ecosystem engineers. We found that net costs of facilitation are accompanied by pollinator-mediated benefits. Despite ecosystem engineers producing less flowers per plant, they were visited by more and more diverse pollinators per flower when blooming with associated plants than when blooming alone. However, fruit set was unaffected by the presence of associated plants and seed production per plant was higher when ecosystem engineers bloomed alone. Our findings suggest that besides experiencing direct costs, ecosystem engineers can also benefit from facilitating other species via increasing their own visibility to pollinators. This study illuminates how the outcome of direct plant–plant interactions might be mediated by indirect interactions including third players.
Supplementary files for Silva et al. 2020 "Reptiles on the wrong track?": R Code, data and figures
<p>Datasets, R code and figures pertaining to the manuscript: Silva, I., Crane, M., Marshall, B.M., & Strine, C.T. (2020).<em> </em> <em>Reptiles on the wrong track? Moving beyond traditional estimators with dynamic Brownian Bridge Movement Models.</em> Movement Ecology 8, 43 DOI: 10.1186/s40462-020-00229-3</p> <p>Article available at: <a href="https://movementecologyjournal.biomedcentral.com/articles/10.1186/s40462-020-00229-3">https://movementecologyjournal.biomedcentral.com/articles/10.1186/s40462-020-00229-3</a></p>
Supplementary files for Crane et al., "On a wing and a prayer": R Code, data and figures
<p>Datasets, R code and figures pertaining to the manuscript: Crane M, Silva I, Grainger MJ, & Gale GA.<em> On a wing and a prayer: limitations and gaps in global bat wing morphology trait data</em></p>
Replication R code and data for "Geometric morphometric investigation of craniofacial morphological change in domesticated silver foxes"
<p>This repository holds various files and R code used in the publication of the manuscript entitled "Geometric morphometric investigation of craniofacial morphological change in domesticated silver foxes".</p> <p>Data files include: The 3D landmark coordinates of each individual specimen (Fox_data_Morphologika.txt), the linear measurement data associated with those foxes (fox_linear_volume_data.csv), and replication data measurements. </p> <p>The following files include the R code used to perform the analyses contained within the paper:</p> <p>1_Procrustes_analysis - details the Geometric morphometrics analyses performed</p> <p>2_linear_models - details the model specification for the GLS models employed in the paper</p> <p>3_graph_code - contains R script for the creation of the graphs displayed in the paper</p> <p>4_repeatability_script - contains R code that details the statistical calculations made with the repeatability measurements indicated above. </p> <p> </p> <p> </p> <p> </p>
Anthropoid morphometric and phylogenetic data, with R replication code.
<p>This repository contains four files: 1) a NEXUS phylogeny of 100 anthropoid primates; 2) a CSV text file of anthropoid primate lower molar areas, body mass, and primary dietary category; 3) a CSV text file of modern human lower molar area proportions; and, 4) an R script containing replication code for fitting Bayesian phylogenetic generalized linear mixed models to the morphometric data. These files are associated with the paper "The Evolution of Anthropoid Molar Proportions" in BMC Evolutionary Biology (2016).</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.