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Dataset results
317 results for “R code”
Data and R code from: GC-MS analysis of murine oestrous odours
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Data and R code for What you see is where you go: visibility influences movement decisions of a forest bird navigating a 3D structured matrix
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The performance of permutations and exponential random graph models when analysing animal networks (R code and data)
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Unvalidated R source code
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Evidence that male sea lamprey increase pheromone release after perceiving a competitor: raw data, R-code, R analyses
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Systematic characterization of the HOXA9 downstream targets in MLL-r leukemia by non-coding CRISPR screens [RNA-Seq sgCDK6]
GEO Series GSE239684. Homo sapiens. 5 samples. Type: Expression profiling by high throughput sequencing.
Systematic characterization of the HOXA9 downstream targets in MLL-r leukemia by non-coding CRISPR screens [RNA-Seq]
GEO Series GSE216017. Homo sapiens. 12 samples. Type: Expression profiling by high throughput sequencing.
Systematic characterization of the HOXA9 downstream targets in MLL-r leukemia by non-coding CRISPR screens [ATAC-Seq]
GEO Series GSE216010. Homo sapiens. 4 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Data and R code for the preprint "Downscaling digital soil maps using electromagnetic induction and aerial imagery"
<p>Data and R code used in the preprint "Downscaling digital soil maps using electromagnetic induction and aerial imagery" (Møller, 2020). This is the data and code for the preprint before submission for peer review. The data and code for the revised manuscript are available at <a href="https://doi.org/10.5281/zenodo.3959005">https://doi.org/10.5281/zenodo.3959005</a>.</p> <p>Code originally written for R version 3.6.2.</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> <p> </p>
Soil microbes and plant phenology data and R code
<p>Datasets and R code associated with a manuscript by M. Van Nuland et al. that details how natural soil microbiome variation affects plant biomass growth by mediating spring foliar phenology. Included are bacterial and fungal community and taxonomic datasets, experimental plant phenology datasets, and code to reproduce all data analysis and figures in the associated manuscript.</p>
Data and R-Code from: How to account for behavioral states in step-selection analysis: a model comparison
<p>This repository provides the R-code and data used for the simulation and case study of the research paper: "How to account for behavioral states in step-selection analysis: a model comparison".</p><p>The folder "<strong>Pohle_et_al_2023_BehavioralStates_iSSA_Data</strong>" contains the landscape rasters used for data generation in the simulation study, and the bank vole (<i>Myodes glareolus</i>) movement data used in the case study on bank vole interactions:</p><ul><li>landscape10.RData and landscape50.RData: Landscape rasters for the simulation study.</li><li>Vole_case_control.rds: Case-control bank vole data for the case study.</li><li>Info_replicates.rds: Information about bank vole indiviuals and corresponding replicates for the case study.</li><li>Codebook_case_study.xlsx: Codebook for the case study data sets.</li><li>Read_me.txt</li></ul><p>The folder "<strong>Pohle_et_al_2023_BehavioralStates_iSSA_RCode</strong>" contains the R-scripts for the simulation and case study:</p><ul><li>Functions.R: Functions to apply HMMs, TS-iSSAs, and HMM-iSSAs to movement data; used for the simulation and case study.</li><li>Simulation_study.R: R-Code to run the simulation study. Parallel computation is used.</li><li>Results_simulation_study.R: R-Code to create the result figures and tables for the simulation study.</li><li>Case_study.R: R-Code to run the bank vole interaction case study. Parallel computation is used.</li><li>Results_case_study.R: R-Code to create the result figures and tables for the case study.</li><li>Read_me.txt</li></ul><p>Besides the simulation and case study from the paper, the included functions (<i>Functions.R</i>) can generally be used to perform an HMM-iSSA analysis.</p><p>For the bank vole movement data without control locations, see: Schlägel, U.E. et al. (2019). Data from: Estimating interactions between individuals from concurrent animal movements [Dataset]. Dryad. <a href="https://doi.org/10.5061/dryad.rt535m8">https://doi.org/10.5061/dryad.rt535m8</a>.</p><p><strong>Acknowledgements</strong></p><p>We thank Sophie Eden, Angela Puschmann and Pauline Lange for help with the bank vole data collection and maintenance of the outdoor enclosures.</p><p> </p><p> </p>
data and R code (including explanations) used in the preprint
<p>Contents:</p> <p>data_model_selection2.txt - used for analysing within-year variation in parasite loads via model selection and plotting Figure 1 (see R code)</p> <p>before_year_seasonal_weather2.txt - used for analysing inter-annual variation in parasite loads and plotting Figures 2-4</p> <p>Accompanying R code</p>
2023_ Datasets and R source code of "Effect of physiological hyperthermia on mitochondrial fuel selection in skeletal muscle of birds and mammals"
<p>GENERAL INFORMATION</p> <p>Title of Dataset: 2023_ Datasets and R source code of "Effect of physiological hyperthermia on mitochondrial fuel selection in skeletal muscle of birds and mammals" </p> <p>METHODOLOGICAL INFORMATION</p> <p>Datasets contain mitochondrial bioenergetic data of our comparative study from the skeletal muscle, of 8 pigeons and 8 rats with similar body mass.<br> Methodology: mitochondrial isolation, respiration (oxygen consumption measurements with assay temperature and substrate effects)</p> <p>## Description of the Data <br> First dataset "RatPigeon_Oxy_Flux"<br> ### Individual: subject number (1-8 for pigeons and 11-18 for rats)<br> ### Species: Rat or Pigeon<br> ### Temperature : assay temperature for the mitochondrial respiration(37°C, 40°C, 43°C)<br> ### Substrate: available substrate utilization, Pyruvate/Malate (PM) or PalmitoylCarnitine/Malate (PCM)<br> ### OXPHOS: phosphorylating respiration <br> ### LEAK: basal non-phosphorylating respiration rate <br> ### Coupling: control efficiency, flux control of ADP on substrate oxidation</p> <p>Second dataset "RatPigeon_Oxy_Ratio"<br> ### Individual: subject number (1-8 for pigeons and 11-18 for rats)<br> ### Species: Rat or Pigeon<br> ### Temperature : assay temperature for the mitochondrial respiration(37°C, 40°C, 43°C)<br> ### PCM.PM: fuel selection index (OXPHOSPCM/OXPHOSPM ratio). </p> <p>R Source code "code RatPigeon(Oxy).R". Complete analysis as one single R script. All analyses were performed in R version 4.2.1 (R Core Team 2022) using Linear Mixed Effect Model and Effect sizes.</p>
Systematic characterization of the HOXA9 downstream targets in MLL-r leukemia by non-coding CRISPR screens
GEO Series GSE215928. Homo sapiens. 86 samples. Type: Other; Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
Systematic characterization of the HOXA9 downstream targets in MLL-r leukemia by non-coding CRISPR screens [CRISPR]
GEO Series GSE215926. Homo sapiens. 27 samples. Type: Other.
Systematic characterization of the HOXA9 downstream targets in MLL-r leukemia by non-coding CRISPR screens [ATAC-Seq II]
GEO Series GSE237661. Homo sapiens. 16 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Data and R code for: "Nineteenth-century land use shape the current occurrence of some plant species, but weakly affects richness and total composition of Central European grasslands"'
<ol> <li> <p><strong><code>IndVal.all.habitats.csv</code></strong>: the results of the IndVal statistics (<a href="https://doi.org/10.1111/j.1600-0706.2010.18334.x">De Cáceres et al. 2013</a>) for 1,498 species for the historical land use categories calculated across the entire dataset;</p> </li> <li> <p><code><strong>IndVal.separate.habitats.csv</strong></code>: the results of the IndVal statistics for 1,498 species for the historical land use categories calculated for each habitat type (dry grasslands, mesic grasslands, wet grasslands) separately;</p> </li> <li> <p><code><strong>ecological.and.disturbance.values.csv</strong></code>: the original Ellenberg-type and disturbance indicator values, and the varimax-rotated components (‘RC’) used in the analysis (data obtained from <a href="https://doi.org/10.1111/jvs.13168">Tichý et al. 2023</a> and <a href="http://dx.doi.org/10.1111/geb.13603">Midolo et al. 2023</a>; accessible at the FloraVeg.eu website <a href="https://floraveg.eu/download/" target="_new" rel="noreferrer">https://floraveg.eu/download/</a>);</p> </li> <li> <p><strong>R code and data for reproducibility</strong>. The R code is for illustration purposes only and is based on a subset of 1,184 mesic grassland vegetation plots located in the Czech Republic and in the study area. This is part of the Czech National Phytosociological Database (<a href="https://www.preslia.cz/article/387">Chytrý & Rafajová 2003</a>) and the European Vegetation Archive (<a href="https://doi.org/10.1111/avsc.12191">Chytrý et al. 2016</a>). The data includes the following:</p> <ul> <li> <p> <code>data</code> folder:</p> </li> </ul> </li> </ol> <ul> <li> <ul> <li> <ul> <li>i. <code>indicator.values.csv</code>: the original indicator values for 831 species;</li> <li>ii. <code>plot.data.csv</code>: data for each of the 1,184 vegetation plots, including their historical land use, plot size, bioclimatic variables (‘bio’; <a href="http://dx.doi.org/10.1038/sdata.2017.122">Karger et al. 2017</a>), and soil pH (<a href="https://doi.org/10.1371%2Fjournal.pone.0169748">Hengl et al. 2017</a>);</li> <li>iii. <code>species.matrix.csv</code>: community matrix reporting the relative abundance of species (columns) and plot sites (rows).</li> </ul> </li> <li>R scripts for species richness, species composition, and species indicator analyses. R script are also rendered in .html with R Markdown.</li> </ul> </li> </ul>
Raw data and R code: A major spatial reorganization of the North Atlantic Oscillation around 4000 BP
<p>The raw data as well as the R code from the study 'A major spatial reorganization of the North Atlantic Oscillation around 4000 BP' by J. Schirrmacher and M. Weinelt in review at Nature Communications Earth & Environment is archived. The final plots presented in the paper have beenmade with Grapher16 and QGIS 3.10.</p>
Systematic characterization of the HOXA9 downstream targets in MLL-r leukemia by non-coding CRISPR screens [Capture-C]
GEO Series GSE215927. Homo sapiens. 2 samples. Type: Other.
Systematic characterization of the HOXA9 downstream targets in MLL-r leukemia by non-coding CRISPR screens [ChIP-seq]
GEO Series GSE216031. Homo sapiens. 20 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
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.