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149 results for “R package”
Key Biodiversity Areas (KBAs) R package, KBAscope, application to Greece
<p>Key Biodiversity Areas (KBAs) represent the largest global network of sites critical to the persistence of biodiversity, which have been identified against standardised quantitative criteria. Sites that hold very high biodiversity value or potential are given specific attention on site-based conservation targets of the Kunming-Montreal Global Biodiversity Framework (GBF), and KBAs are already used in indicators for the GBF and the Sustainable Development Goals. However, most of the species that trigger KBA status are birds and to maximise benefits for biodiversity under the actions taken to fulfil the GBF, countries need to update their KBAs to represent important sites across multiple taxa. Here we introduce KBAscope, an R package to identify potential KBAs using multiple taxonomic groups. KBAscope provides flexible, user-friendly functions to edit species data (population, range maps, area of occupancy, area of habitat and localities); apply KBA criteria; and generate outputs to support the delineation and validation of KBAs. The details of the analysis - such as the spatial units tested or the KBA criteria applied - can be decided according to the scope of the analysis. We demonstrate the functionality of KBAscope by using it to identify potential KBAs in Greece based on multiple terrestrial taxonomic groups and four sizes of grid cells (4 km<sup>2</sup>, 25 km<sup>2</sup>, 100 km<sup>2</sup>, 225 km<sup>2</sup>).</p>
R data objects for the HumanDEU package
<p>This submission contains several R data objects that are part of the R package HumanDEU available through Github (https://github.com/areyesq89/HumanTissuesDEU). The objects correspond to processed data needed to reproduce the statistics, tables and figures presented in the manuscript:<br> <br> A Reyes and W Huber. Alternative start and termination sites of transcription drive most transcript isoform differences across human tissues. Nucleic Acids Research, 2017. doi: https://www.doi.org/10.1093/nar/gkx1165<br> <br> For more details, please visit the Github repository.</p>
-0.2 0.0 0.2 0.4 0.6 PC1 (29.8% of total variance) Fig. 8. Morphospace plot of the first two PCO axes generated in the R statistical environment (Claddis package). Branches are superimposed from a single representative topology selected from amongst the 48 MPTs. in The sauropodomorph biostratigraphy of the Elliot Formation of southern Africa: Tracking the evolution of Sauropodomorpha across the Triassic-Jurassic boundary
-0.2 0.0 0.2 0.4 0.6 PC1 (29.8% of total variance) Fig. 8. Morphospace plot of the first two PCO axes generated in the R statistical environment (Claddis package). Branches are superimposed from a single representative topology selected from amongst the 48 MPTs.
Replication Data for the retroharmonize R Package Case Study: Working With Arab Barometer Surveys
<p>Replication datasets for the <a href="https://retroharmonize.dataobservatory.eu/articles/arabbarometer.html">retroharmonize Case Study: Working With Arab Barometer Surveys</a></p>
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>
The R package enerscape: A general energy landscape framework for terrestrial movement ecology
<ol> <li> <p class="western"><span>Ecological processes and biodiversity patterns are strongly affected by how animals move through the landscape. However, it remains challenging to predict animal movement and space use. Here we present our new R package <i>enerscape</i> to quantify and predict animal movement in real landscapes based on energy expenditure. </span></p> </li> <li> <p class="western"><span><i>Enerscape</i> integrates a general locomotory model for terrestrial animals with GIS tools in order to map energy costs of movement in a given environment, resulting in energy landscapes that reflect how energy expenditures may shape habitat use. <i>Enerscape</i> only requires topographic data (elevation) and the body mass of the studied animal. To illustrate the potential of <i>enerscape</i>, we analyze the energy landscape for the Marsican bear (<i>Ursus arctos marsicanus</i>) in a protected area in central Italy in order to identify least-cost paths and high-connectivity areas with low energy costs of travel.</span></p> </li> <li> <p class="western"><span><i>Enerscape</i> allowed us to identify travel routes for the bear that minimize energy costs of movement and regions that have high landscape connectivity based on movement efficiency, highlighting potential corridors. It also identifies areas where high energy costs may prevent movement and dispersal, potentially exacerbating human-wildlife conflicts in the park. A major strength of <i>enerscape</i> is that it requires only widely available topographic and body size data. As such, <i>enerscape</i> permits a first cost-effective way to estimate landscape use and movement corridors even when telemetry data is not readily available, such as for the example with the bear. </span></p> </li> <li> <p class="western"><span><i>Enerscape</i> is built in a modular way and other movement modes and ecosystem types can be implemented when appropriate locomotory models are available. In summary, <i>enerscape</i> is a new general tool that quantifies, using minimal and widely available data, the energy costs of moving through a landscape. This can clarify how and why animals move in real landscapes and inform practical conservation and restoration decisions.</span></p> </li> </ol>
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>
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 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>
Dataset od: "Towards a Taxonomy of Roxygen Documentation in R Packages"
<p>Replication package for the paper titled "Towards a Taxonomy of Roxygen Documentation in R Packages"</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>
Fig. 2 in A new R package and web application for detecting bilateral asymmetry in parasitic infections
Fig. 2. Histogram showing distribution of fold differences in abundance of Diplostomum spp. metacercariae between left and right eyes (excluding lenses). For each host the number of parasites in the right eye was divided by the number of parasites in the left eye, and the result was binary log transformed. The log 2 ratio will be negative if there are more parasites in the left than right eye, and positive if there are more parasites in the right than left eye. A log 2 ratio of one corresponds to a one-fold difference, i.e. double the number of parasites. Perfect symmetry is a log 2 ratio of zero.
Fig. 1 in A new R package and web application for detecting bilateral asymmetry in parasitic infections
Fig. 1. Screenshot of the web application. The panel on the left contains the controls for the application, including file upload, selection of test, choice of multiplicity correction and significance threshold. Results are displayed on the four tabbed pages of the main panel: summary, individual hosts, histogram and volcano plot.
mapspamc_db: a database with global spatial datasets to support the implementation of the mapspamc R package.
<p>This repository contains the mapspamc database (mapspamc_db), a collection of global spatial datasets to support the implementation of the <a href="https://github.com/michielvandijk/mapspamc">mapspamc</a> R package. The database also includes subnational crop statistics and matching country shapefiles for several country examples. For more information on how to use the mapspamc package in combination with mapspamc_db, see the <a href="https://michielvandijk.github.io/mapspamc/">mapspamc documentation</a>. Detailed information on the contents of mapspam_db, such as the sources of information and pre-processing is described in the mapspamc_db documentation (pdf file) that is part of the repository.</p>
Data for: rtrees: An R package to assemble phylogenetic trees from megatrees
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Key Biodiversity Areas (KBAs) R package, KBAscope, application to Greece
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Data from: Ppgm: an R package for integrating neontological, palaeontological, and climate data in a phylogenetic comparative framework
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SSP: An R package to estimate sampling effort in studies of ecological communities
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funspace: an R package to build, analyze and plot functional trait spaces
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ScienceDex guides
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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.