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15,519 results for “r”
Invader defense traits in France, Japan, and the USA: dataset and R code
<p>This record includes leaf trait data for 44 species sampled in France, Japan, and the USA. R code is associated with analyses for publication.</p>
Dataset and R code for 'Do Morphometric Data Improve Phylogenetic Reconstruction? A Systematic Review and Assessment'
<p>Dataset of tree (.tre) files and R code for running generalized Robinson-Foulds distance (Smith, 2020a;b) analysis. </p> <p>The .tre files can be read into R (R Core Team., 2023) using the ape::read.tree function (Paradis et al., 2003), full details in R code file. </p> <p> </p> <p> </p> <p>Paradis, E., Claude, J., & Strimmer, K. (2004). APE: analyses of phylogenetics and evolution in R language. Bioinformatics, 20(2), 289-290. </p> <p>R Core Team. (2023). R: A Language and Environment for Statistical Computing. (Version 4.2.2). R Foundation for Statistical Computing, Vienna, Austria: https://www.R-project.org/. </p> <p>Smith, M. R. (2020a). Information theoretic generalized Robinson–Foulds metrics for comparing phylogenetic trees. Bioinformatics, 36(20), 5007-5013. https://doi.org/10.1093/bioinformatics/btaa614 </p> <p>Smith, M. R. (2020b). TreeDist: distances between phylogenetic trees. R package version 2.7.0. doi:10.5281/zenodo.3528124. </p>
Semi-leptonic ttbar full-event unfolding R&D dataset
<p>This dataset was generated for the purpose of developing unfolding methods that leverage generative machine learning models. It consists of two pieces: one piece contains events with the Standard Model (SM) production of a top-quark pair in the semi-leptonic decay mode, and the other contains events with top-quark pair production modified by a non-zero EFT operator. The SM dataset contains 15,015,000 events, and the EFT dataset contains 30,000,000. Both datasets store the following event configurations:</p> <ul> <li>Parton level: configurations of all partons that result from the matrix element calculation done using MadGraph</li> <li>Particle level: configurations of all “truth” jets and leptons that result from the parton shower and hadronization modelled using Pythia</li> <li>Detector level: configurations of all reconstruction level jets and leptons, measured by a detector simulated with Delphes and the default CMS detector card.</li> </ul> <p>Each of these configurations is stored in a dedicated group as described below. Throughout, the units of energy and transverse momentum are GeV. For more details on the generation of this dataset, see Ref. [1].</p> <p><strong>Parton level data:</strong></p> <ul> <li>No phase space requirements are placed on the events at parton level. </li> <li>The kinematics of the top, anti-top, W+, W-, and all decay products are contained in groups entitled top, antitop, Wp, and Wm respectively. Each of these contains the kinematics of the parton itself in a group called “particle”, as well as the kinematics of two daughter particles, in groups called “d1” and “d2”. In the case of the tops, these daughters are the W’s and b quarks. In the case of the W’s, these are two light quarks, or a lepton and a neutrino. The “pid” vector contains the PDGID for a given particle, used to identify its type. </li> <li>One detail is that the W’s “particle” description is not always the same as the description of the same W stored as the daughter of the tops. This results from when the W radiates some parton before decaying. </li> </ul> <p><strong>Particle level data:</strong></p> <ul> <li>At particle level all leptons and jets are required to have $p_T > 25$ GeV and absolute pseudo rapidity $|\eta| < 2.5$. </li> <li>Events at particle level are required to have at least one electron or muon and at least 4 jets, of which at least two are b-tagged. Event which pass or fail this criteria are marked by the vector contained in the group “mask”.</li> <li>Electrons and muons are stored in separate groups. Each group contains a vector “mask” which is true only if there is a true particle-level electron or muon in the event, and false if this entry is zero padding. </li> <li>Jets are clustered from stable particle level objects using the anti-kt algorithm with a radius parameter of 0.5. Jet information is stored in the group “jets”, and true jets in the event are again denoted by a true value in the vector “mask”, and zero-padding is marked by a false value. Jets additionally contain a vector “btag” which is 1 if the jet is b-tagged with the default Delphes prescription, and 0 if not.</li> <li>Information on the missing transverse momentum (MET) is contained in the group “met”. The “met” vector gives the magnitude, and the “phi” vector gives the direction in phi of the missing transverse momentum.</li> <li>In addition to the information on the jets, leptons, and MET, the particle level data also contain the configurations for the hadronic top, leptonic top, and ttbar system. These configurations are determined assuming the pseudo-top jet parton assignment algorithm, which is a common method used by LHC experiments when analyzing semileptonic ttbar events.</li> </ul> <p><strong>Detector level data:</strong></p> <ul> <li>Requirements for leptons and jets are the same as for the particle level data.</li> <li>The event selection is the same as the particle level data. Events which pass the selection are again denoted by a true value in the vector “mask”.</li> <li>The data for the leptons, jets, and MET are stored analogously to particle level</li> <li>The configurations of the top quarks and ttbar system are not pre-computed at detector level, since ideally a generative unfolding method would not assume a given jet-carton assignment algorithm when it is being trained. However if the user wishes to pursue such an application, the relevant configurations can be obtained by running the pseudo-top algorithm [2].</li> </ul> <p><strong>Citations:</strong></p> <p>[1] - <a href="https://arxiv.org/abs/2404.14332">https://arxiv.org/abs/2404.14332</a></p> <p>[2] - <a href="https://twiki.cern.ch/twiki/bin/view/LHCPhysics/ParticleLevelTopDefinitions">https://twiki.cern.ch/twiki/bin/view/LHCPhysics/ParticleLevelTopDefinitions</a></p>
Dataset and R script - Non-linear transcriptomic responses to compounded environmental changes across temperature and resources in a pest beetle, Callosobruchus maculatus
<p>This dataset contains data and R script for analysis on life history and transcriptomic responses to single dimensional changes in resource (chickpea-27<span>°</span>C) and temperature (cowpea-35<span>°</span>C) and multi-dimensional environmental changes in resource and temperature (chickpea-35<span>°</span>C) in a pest beetle, <em>Callosobruchus maculatus</em> (control treatment = cowpea-27<span>°</span>C). Dataset contains life history data collected in laboratory conditions<em> </em>(tab 1), logFC data (RNA-sequencing; Novogene Co. Ltd.) for Spearman rank correlation tests between treatments (tabs 2-4), read count data (RNA-sequencing; Novogene Co. Ltd.) for differential expression analysis using edgeR (R1-5 = Four samples at cowpea-27<span>°</span>C; R7-12 = Four samples at cowpea-35<span>°</span>C; R13-17 = Four samples at chickpea-27<span>°</span>C; R25-28 = Four samples at chickpea-35<span>°</span>C; tab 5) and edgeR output data for plotting in R (tab 6). </p>
Do Current Language Models Support Code Intelligence for R Programming Language?
<p>This is the dataset used in the paper: Do Current Language Models Support Code Intelligence for Programming Language?</p> <p> </p> <p>This dataset contains code snippets from R programming language repositories on GitHub, paired with their corresponding natural language (NL) descriptions. It was created for research in software engineering tasks like code summarization and code search. The data was collected using the GitHub REST API and includes over 1,500 public R repositories. To ensure quality, only active, well-structured R packages with proper documentation were included. Roxygen2, a popular documentation framework, was used to extract both the code and its matching NL descriptions.</p> <p>The dataset is organized into three parts: base R functions (Base), functions from the tidyverse (Tidy), and a combined set (RCombine). The dataset follows the CodeSearchNet format, with a split for training, validation, and testing data, ensuring no duplicate functions.</p>
Data and R code supporting Battison et al. (2024) New Phytologist
<p>Data and R code supporting Battison et al. (2024) <span><span>Tracking tree demography and forest dynamics at scale using remote sensing. </span></span>New Phytologist</p> <p>If using these data and/or R code in your work please cite the original publication listed above, as well as this repository using the corresponding DOI.</p> <p> </p>
Low-intensity insect herbivory could have large effects on ecosystem productivity through reduced canopy photosynthesis. R code and data.
<p>R code and data to reproduce analysis in the publication: Visakorpi K., Gripenberg S., Malhi Y. and Riutta T. Low-intensity insect herbivory could have large effects on ecosystem productivity through reduced canopy photosynthesis. Web Ecology, 2024.</p> <p> </p> <p>File named "Visakorpietal2024_litteratureSurvey.csv" contains data collected from literature survey and to perform a meta-analysis. The columns contain the following data:</p> <p>Reference = unique identifier for each study</p> <p>group_id = if a study described several experiments or treatments, this column identifies them from each other.</p> <p>Plant_species = name of the plant species studied</p> <p>Herbivore_species = name of the herbivore species studies</p> <p>Plant_species_phylo and Plant_species_phylo2 = alternative ways to write the plant species name for phylogenetic analysis</p> <p>Plant_clade = Angiosperm or Gymnosperm</p> <p>Growth_form = seedling, sapling or tree</p> <p>Herbivore_order = phylogenetic order of the herbivore species</p> <p>Herbivore_family = phylogenetic family of the herbivore species</p> <p>Type_of_herbivory = chewing, sap-sucking, root-feeding, leafmining or bark-feeding</p> <p>Leaf_type = whether the leaf that was measured was damaged by herbivores, or intact</p> <p>Manipulation = whether herbivores were added (A) or removed (R)</p> <p>Year_published = year when the study was published</p> <p>PN_herb, SD_herb, se_herb, n_herb = photosynthetic rate, standard deviation, standard error, and sample size, of the treatment group (i.e. experiencing herbivory)</p> <p>PN_intact, SD_intact, se_intact, n_intact = photosynthetic rate, standard deviation, standard error, and sample size, of the control group (i.e. not experiencing herbivory)</p> <p>Indirect_effect, SD_indirect = the proportional difference in photosynthetic rates between the treatment and control values, and the standard deviation of the proportional difference</p>
1.Figure cut off value of NLRP3 calculation process by R 2.Normality check results and Variance homogeneity.
Open the record for dataset details and reuse information.
Neutral-current DIS event samples at leading order with $E_e = 27.5\,{\rm GeV}$, $E_p = 920\,{\rm GeV}$, and $\mu_{\rm F}^2 = \mu_{\rm R}^2 = Q^2$
<p>Neutral-current DIS multi-jet event samples at parton level in the HDF5 event format.</p> <p>Beam energies: $E_e = 27.5\,{\rm GeV}$, $E_p = 920\,{\rm GeV}$</p> <p>Renormalisation and factorisation scale: $\mu_{\rm F}^2 = \mu_{\rm R}^2 = Q^2$ with seven-point scale variation</p> <p>PDF: NNPDF40_lo_pch_as_01180</p> <p>Generation cuts: $Q^2 > 2\,{\rm GeV}^2$, $k_{\rm T} > 2\,{\rm GeV}$ in the $k_{\rm T}$-algorithm with $D=1.0$</p> <p>Generated with <a href="https://gitlab.com/hpcgen/me" target="_blank" rel="noopener">Sherpa</a> using the run cards below.</p>
Neutral-current DIS event samples at leading order with $E_e = 27.5\,{\rm GeV}$, $E_p = 820\,{\rm GeV}$, and $\mu_{\rm F}^2 = \mu_{\rm R}^2 = Q^2$
<p>Neutral-current DIS multi-jet event samples at parton level in the HDF5 event format.</p> <p>Beam energies: $E_e = 27.5\,{\rm GeV}$, $E_p = 820\,{\rm GeV}$</p> <p>Renormalisation and factorisation scale: $\mu_{\rm F}^2 = \mu_{\rm R}^2 = Q^2$ with seven-point scale variation</p> <p>PDF: NNPDF40_lo_pch_as_01180</p> <p>Generation cuts: $Q^2 > 2\,{\rm GeV}^2$, $k_{\rm T} > 2\,{\rm GeV}$ in the $k_{\rm T}$-algorithm with $D=1.0$</p> <p>Generated with <a href="https://gitlab.com/hpcgen/me" target="_blank" rel="noopener">Sherpa</a> using the run cards below.</p>
Dataset associated with A. Hallou, R. He, et al. A computational pipeline for spatial mechano-transcriptomics. bioRxiv 2023.08.03.551894
<p>Dataset associated with:</p> <p>Adrien Hallou, Ruiyang He, Benjamin David Simons and Bianca Dumitrascu. A computational pipeline for spatial mechano-transcriptomics. bioRxiv 2023.08.03.551894; doi: <a href="https://doi.org/10.1101/2023.08.03.551894">https://doi.org/10.1101/2023.08.03.551894</a></p> <p>Licence</p> <p>This dataset is licensed under the <a href="https://creativecommons.org/licenses/by-nc/4.0/">Creative Commons Attribution-NonCommercial 4.0 International License</a>.</p>
Data and R code used in Hennecke et al. "Plant species richness and the root economics space drive soil fungal communities"
<p>To investigate how plant diversity and root traits relate to soil fungal communities, in 2021 we collected trait data from plots in the Jena Experiment (https://the-jena-experiment.de; funded by the DFG FOR 5000) and characterized fungal communities by sequencing, respiration and lipid fatty acid quantification. </p>
Dataset and R code for: "Task complexity matters: Investigating behavioural lateralisation in rats"
<h2>Dataset and R code used for the manuscript "Task complexity matters: Investigating behavioural lateralisation in rats"</h2> <p> </p> <p><strong>R project:</strong> Supplementary Task complexity.Rproj R project</p> <p><strong>R code: </strong>Task_Complexity_Stats_updated.Rmd </p> <p><strong>Dataset:</strong> Data_Task_complexity.xlsx </p> <p><strong>Models for repeatability estimation:</strong> mod_..._....rds </p>
Dataset and R code support the manuscript titled "Streamlining Linear Free Energy Relationships of Proteins through Dimensionality Analysis and Linear Modeling"
<p>This dataset and R code support the manuscript titled "Streamlining Linear Free Energy Relationships of Proteins through Dimensionality Analysis and Linear Modeling" submitted to the Journal of Chemical Information and Modeling.</p> <p>Table S 1: Chemicals with their experimental values of logKch , and values of logKow and<span> </span>logKaw used to calibrate chicken muscle protein-water 2p-LFER model.</p> <p>Table S 2: Chemicals with their experimental values of logKfish and values of logKow and<span> </span>logKaw used to calibrate fish muscle protein-water 2p-LFER model.</p> <p>Table S 3: Chemicals with their experimental values of logKBSA and values of logKow and<span> </span>logKaw used to calibrate bovine serum albumin-water 2p-LFER model.</p> <p>Table S 4: Chemicals with their experimental values of logKpw and values of logKow and<span> </span>logKaw used to calibrate combined chicken and fish muscle protein-water 2p-LFER model.</p> <p>Table S 5: Diversity of data for logKpw.</p> <p>Table S 6: Diversity of data for logKBSA.</p> <p>Table S 7: Comparison of Experimental and 2p-LFER Predicted Partition Coefficients for ionizable PFAS Compounds.</p> <p>Table S 8: List of neutral fluorotelomer PFAS Compounds.</p> <p>Table S 9: List of experimental in vivo and in vitro partitioning data for different tissues and species.</p> <p>Table S 10: List of experimental Milk-water partition coefficient and predicted values of Milk-water partitioning.</p> <p>Table S 11: Training set for logKpw</p> <p>Table S 12: Validation set for log Kpw</p> <p>Table S 13: Training set for log KBSA</p> <p>Table S 14: Validation set for log KBSA</p>
Sample Raw (PPG, Accel, and Gyro) and Processed (steps, calories, sleep, HR, HRV, SPO2, Respiratory Rate, R-R) data over 24 hours
<p>Over the course of 24 hours, we collected raw (Photoplethysmography (PPG), Acceleration, and Gyro) and processed (steps, calories, sleep, HR, HRV, SPO2, Respiratory Rate, R-R) data samples. Biostrap approaches health insights from a data-driven perspective. Our clinical-grade hardware enables users to accurately track SpO2, HRV, RHR, and a variety of other biometrics with confidence.</p>
R code to accompany 'Teleconnection-based evaluation of seasonal forecast quality''
<p>R code for the diagnostic published in 'Teleconnection-based evaluation of seasonal forecast quality' DOI: 10.1007/s00382-020-05327-x</p>
R code and dataset to "Monetizing Spillover Effects in the Creative Industries: the Impact of Live Music Performances on Youtube Searches"
<p>Content:</p> <ol> <li>The script<strong> main_script.R</strong> includes code to run a regression discontinuity (RD) design and validation and falsification of estimated results</li> <li>The folder <strong>data</strong> contains two files: <ol> <li>bands_2016_2019.csv: a dataset of performers with additional information for each one.</li> <li>festivals_2016_2019.csv: a dataset of video search activity (as retrieved from Google Trends) for performers in file bands_2016_2019.csv</li> </ol> </li> <li>The folder <strong>source</strong> contains two additional R scripts: <ol> <li>data_preparation.R: generates the long dataset used to estimate RD effects</li> <li>status_simulation.R: randomly assigns treattment status to performers and estimates RD effects. Note this may take a long time to run. Parallel code is used: the number of cores has been set to 4. </li> </ol> </li> <li>The folder simulation_results contains simulated data after running the script status_simulation.R.</li> </ol>
Fig. 2 in Revision of the genus Cryptostegia R. Br. (Apocynaceae, Periplocoideae)
Fig. 2 — Distribution of Cryptostegia: A, C. grandiflora; B, C. madagascariensis.
Aquatic animal antimicrobial resistance trends dataset and R code
<p>First release of aquatic animal AMR R scripts and dataset accompanying "Twenty-year trends in antimicrobial resistance from aquaculture and fisheries in Asia" (doi: 10.1038/s41467-021-25655-8)</p>
Fig. 11 in R K (1902-1946) und seine grundlegenden Arbeiten über Coccinellidae und die Larven der Coleoptera*
Fig. 11: Taf. 2 aus den Bestimmungstabellen der bekanntesten deutschen Dermestidenlarven (1944).
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.