Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
317
datasets available to search
ShareScore release 0.9.0
Dataset results
317 results for “R code”
R code and data for: How much is enough? Minimum sample sizes in community ecology
<p>minimum_sample_sizes_code.R includes the complete set of instructions used to carry out the analyses in the related manuscript, plus save the computed data files and generate the text figures. data_files.tar.gz includes all of the files generated by the R code.</p>
R code for differential gene expression and enrichment analyses
<p>The information about the magnitude of differences in thermal plasticity both between and within populations, as well as identification of the underlying molecular mechanisms are key to understanding the evolution of thermal plasticity. In particular, genes underlying variation in the physiological response to temperature can provide raw material for selection acting on plastic traits. Using RNAseq, we investigate the transcriptional response to temperature in males and females from bulb mite populations selected for the increased frequency of one of two discrete male morphs (fighter- and scrambler-selected populations) that differ in relative fitness depending on temperature. We show that different mechanisms underlie the divergence in thermal response between fighter- and scrambler-selected populations at decreased vs. increased temperatures. Temperature decrease to 18°C was associated with higher transcriptomic plasticity of males with more elaborate armaments, as indicated by a significant selection-by-temperature interaction effect on the expression of 40 genes, 38 of which were upregulated in fighter-selected populations in response to temperature decrease. In response to 28°C, no selection-by-temperature interaction in gene expression was detected. Hence, differences in phenotypic response to temperature increase likely depended on genes associated with their distinct morph-specific thermal tolerance. Selection on males also drove gene expression patterns in females. These patterns could be associated with temperature-dependent fitness differences between females from fighter- vs. scrambler-selected populations reported in previous studies. Our study shows that selection for divergent male sexually selected morphologies and behaviors has the potential to drive divergence in metabolic pathways underlying plastic response to temperature in both sexes.</p>
R code for: Following regulation, imidacloprid persists and flupyradifurone increases in non-target wildlife
<p>After regulation of pesticides, determination of their persistence in the environment is an important indicator of effectiveness of these measures. We quantified concentrations of two types of systemic insecticides: neonicotinoids (imidacloprid, acetamiprid, clothianidin, thiacloprid, and thiamethoxam) and butenolides (flupyradifurone), in off-crop non-target media of hummingbird cloacal fluid, honey bee (<em>Apis mellifera</em>) nectar and honey, and wildflowers before and after regulation of imidacloprid on highbush blueberries in Canada in April 2021. We found that mean total pesticide load increased in hummingbird cloacal fluid, nectar, and flower samples following imidacloprid regulation. On average, we did not find evidence of a decrease in imidacloprid concentrations after regulation. However, there were some decreases, some increases and other cases with no changes in imidacloprid levels depending on the specific media, time point of sampling and site type. At the same time, we found an overall increase in flupyradifurone, acetamiprid, thiamethoxam and thiacloprid, but no change in clothianidin concentrations. In particular, flupyradifurone concentrations observed in biota sampled near to agricultural areas increased by 2-fold in honey bee nectar, 7-fold in hummingbird cloacal fluid, and 8-fold in flowers after the 2021 imidacloprid regulation. The highest residue detected in this study was flupyradifurone at 665 ng/mL (PPB) in honey bee nectar. Mean total pesticide loads were highest in honey samples (84 ± 10 PPB) followed by nectar (56 ± 7 PPB), then hummingbird cloacal fluid (1.8 ± 0.5 PPB), and least, flowers (0.51 ± 0.06 PPB). Our results highlight that limited regulation of imidacloprid does not immediately reduce residue concentrations while other systemic insecticides, possibly replacement compounds, concurrently increase in wildlife.</p>
DataSet & R code used for the analysis of "Mechanisms of mobbing call recognition: Exploring featural decoding in great tits"
<p>Data and R code used in a playback experiment exploring the mechanisms of mobbing call recognition in the great tit, Parus major. Accepted in Animal Behaviour (2024). </p> <p>This experiment aimed at testing the hypothesis of simple featural interpretation in the great tit (i.e., the fact that receivers can focus on specific acoustic features rather than complete note recognition). </p> <p>The experiment is organised with two parts: first, we test the response of great tits to artificial calls that possess either none or all of the characteristics present in their own calls (and shared with other Parids), and compare their level of response to natural mobbing calls. </p> <p>As the 'complete' treatment triggered the same level fo response than the natural calls, we then create artifical calls with only one of the four features used to create our artifical mobbing calls (large frequency range, low frequency, noise and harmonics). </p> <p> </p> <p>More information can be obtained by contacting Ambre SALIS (salis.ambre87[at]gmail.com)</p>
Phenotype variation in Niphargus (Amphipoda: Niphargidae): possible explanations and open challenges: data and R code
<p>Data and R code for performing the analyses of phylogenetic signal presented in the manuscript titled "Phenotype variation in Niphargus (Amphipoda: Niphargidae): possible explanations and open challenges. Data contains phylogenetic tree (Delić et al., 2023) and functional trait data in the RDS format (Premate & Fišer, 2024). The R code is available in the html format.</p> <p>References/data sources:</p> <p>Delić, T., Borko, S., Premate, E., Rexhepi, B., Alther, R., Knuesel, M., ... & Altermatt, F. (2023). Evolutionary origin of morphologically cryptic species imprints co-occurrence and sympatry patterns. <em>bioRxiv</em>, 2023-09.</p> <p>Premate, E., & Fišer, C. (2024). Functional trait dataset of European groundwater Amphipoda: Niphargidae and Typhlogammaridae. <em>Scientific Data</em>, <em>11</em>(1), 188.</p>
Taxon sampling and inferred community phylogenies: R replication code and data.
<p>1 ) Code for simulating community phylogenies:</p> <p>community_simulations_creation.R</p> <p>[taxon].gene</p> <p>[taxon].phy</p> <p>[taxon].RAxML_bestTree.tre</p> <p>[taxon].Simulate.A.Community.pl</p> <p>[taxon].Simulate.B.Community.pl</p> <p>[taxon].Simulate.C.Community.pl</p> <p>[taxon].Simulate.D.Community.pl</p> <p> </p> <p>2) R code for creating and comparing phylogenetic diversity metrics:</p> <p>simulated_metric_calculation_and_comparison.R</p> <p>empirical_metric_calculation_and_comparison.R</p> <p> </p> <p>3) R code and data for statistical analyses:</p> <p>simulated_data_analysis.R</p> <p>empirical_data_analysis.R</p> <p>simulated_interval_individual_lme_data.csv</p> <p>simulated_summary_interval_individual_lme_data.csv</p> <p>empirical_interval_individual_lme_data.csv</p> <p>empirical_summary_interval_lme_data.csv</p> <p> </p>
Questionnaire, R Scripts and Response Data Set of the Survey on Functionally Similar Code Clones
<p>In 2017, we conducted an open online survey regarding functionally similar code clones with practitioners. We make the used questionnaire, the data from the response to the questionnaire and our used R script for the analysis openly available.</p>
R replication code and data for: Do modern hunter-gatherers live in marginal habitats?
<p>Data and R replication code for testing the Marginal Habitat Hypothesis. The R code files contain all models and are organized by the figures they generate for the associated paper.</p> <p>The data are sourced from:</p> <p>1) the Standard Cross Cultural Sample (SCCS).</p> <p>2) NASA Moderate Resolution Imaging Spectroradiometer (MODIS) NPP data (MOD17A3 algorithm) from Numerical Terra Dynamic Simulation Group at the University of Montana.</p> <p>3) Marine Ecoregions Of the World (MEOW): <a href="http://maps.tnc.org/files/metadata/MEOW.xml">http://maps.tnc.org/files/metadata/MEOW.xml</a></p> <p>4) Terrestrial Ecoregions Of the World (TEOW): <a href="http://maps.tnc.org/files/metadata/TerrEcos.xml">http://maps.tnc.org/files/metadata/TerrEcos.xml</a></p>
Data and R code used in Delory et al (2019) The exotic species Senecio inaequidens pays the price for arriving late in temperate European grassland communities
<p>This is the first release of the data and R code used in Delory et al (2019) The exotic species Senecio inaequidens pays the price for arriving late in temperate European grassland communities.</p>
Regeneration Debt R Code
<p>We provide the code used to download and analyze all of the spatial and tabular data used for this analysis.</p>
R code for modeling indexical fields using MSTs
<p>R code for modeling indexical fields using Minimum Spanning Trees</p>
Data and R-code on a cross-sectional study of factors associated with lameness in dairy cows housed in freestall and compost-bedded pack dairy farms in southern Brazil
<p>The data correspond to a cross-sectional study designed to investigate factors associated with lameness in dairy cows on intensive farms in southern Brazil.<br> Farms: 38 freestall and 12 compost-bedded pack visited once in 2016. All lactating cows (n = 13,716) were examined and body condition score (BCS) and gait score were assessed. Additionally, some variables were collected through inspection of facilities and using data from an interview with farmers on routine herd management practices.<br> Additional information is provided in the published paper ("Factors associated with lameness prevalence in lactating cows housed in freestall and compost-bedded pack dairy farms in southern Brazil" https://doi.org/10.1016/j.prevetmed.2019.104773)</p>
R code to accompany 'A novel initialisation technique for decadal climate predictions'
<p>R code for the diagnostic published in 'A novel initialisation technique for decadal climate predictions' <a href="https://doi.org/10.3389/fclim.2021.681127">https://doi.org/10.3389/fclim.2021.681127</a><br> The data loaded follow the cmor standard.</p>
Datasets and R Code for native-invasive shrub analysis
<p>Datasets and R code to reproduce statistical analysis comparing functional traits of native and invasive shrub and liana species of North America.</p>
Source code for R tutorials and dataset for empirical case study on Malurus elegans (red-winged fairy wren)
<p>Biological processes exhibit complex temporal dependencies due to the sequential nature of allocation decisions in organisms' life-cycles, feedback loops, and two-way causality. Consequently, longitudinal data often contain cross-lags: the predictor variable depends on the response variable of the previous time-step. Although statisticians have warned that regression models that ignore such covariate endogeneity in time series are likely to be inappropriate, this has received relatively little attention in biology. Furthermore, the resulting degree of estimation bias remains largely unexplored.</p> <p>We use a graphical model and numerical simulations to understand why and how regression models that ignore cross-lags can be biased, and how this bias depends on the length and number of time series. Ecological and evolutionary examples are provided to illustrate that cross-lags may be more common than is typically appreciated and that they occur in functionally different ways.</p> <p>We show that routinely used regression models that ignore cross-lags are asymptotically unbiased. However, this offers little relief, as for most realistically feasible lengths of time series conventional methods are biased. Furthermore, collecting time series on multiple subjects–such as populations, groups or individuals—does not help to overcome this bias when the analysis focusses on within-subject patterns (often the pattern of interest). Simulations (R tutorial 1 & 2), a literature search and a real-world empirical example on fairy wrens (data archived here with analyses presented in R-tutorial 3) together suggest that approaches that ignore cross-lags are likely biased in the direction opposite to the sign of the cross-lag (e.g. towards detecting density-dependence of vital rates and against detecting life history trade-offs and benefits of group living). Next, we show that multivariate (e.g. structural equation) models can dynamically account for cross-lags, and simultaneously address additional bias induced by measurement error, but only if the analysis considers multiple time series.</p> <p>We provide guidance on how to identify a cross-lag and subsequently specify it in a multivariate model, which can be far from trivial. Our tutorials with data and R code of the worked examples provide step‐by‐step instructions on how to perform such analyses.</p> <p>Our study offers insights into situations in which cross-lags can bias analysis of ecological and evolutionary time series and suggests that adopting dynamical models can be important, as this directly affects our understanding of population regulation, the evolution of life histories and cooperation, and possibly many other topics. Determining how strong estimation bias due to ignoring covariate endogeneity has been in the ecological literature requires further study, also because it may interact with other sources of bias.</p>
Data & R code for: Paquette & Hargreaves 'Biotic interactions are more often important at species' warm vs. cool range edges'
<p>Predicting which ecological factors constrain species distributions is a fundamental ecological question and critical to forecasting geographic responses to global change. Darwin hypothesized that abiotic factors generally impose species' high-latitude and high-elevation (typically cool) range limits, whereas biotic interactions more often impose species' low-latitude/low-elevation (typically warm) limits, but empirical support has been mixed. Here, we clarify three predictions arising from Darwin's hypothesis, and show that previously mixed support is partially due to researchers testing different predictions. Using a comprehensive literature review (885 range limits), we find that biotic interactions, including competition, predation, and parasitism, contributed to >60% of range limits, and influenced species' warm limits more often than cool limits. Abiotic factors contributed more often than biotic interactions to cool range limits, but temperature contributed frequently to both cool and warm limits. Our results suggest that most range limits will be sensitive to climate warming, but warm-limit responses will depend strongly on biotic interactions.</p>
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>
Dataset and R Code for Species-level Avian Influenza Phylogenetic Generalized Least Squares Regression
<p>Dataset for Species-level Avian Influenza Phylogenetic Generalized Least Squares (PGLS) Regression:<br> Variables include taxonomic information for each species, # of IAV-positive individuals, # of IAV-tested individuals, the prevalence of IAV, the proportion of diet made up of different food types, the proportion of foraging time spent in different strata (below water, water surface, ground, understory, etc), sampling-related variables (mean latitude, mean date, the proportion of hatch year individuals), migration and territoriality category, climatologic variables, mean clutch size, and mating system.</p> <p>R Code for PGLS and Avian Influenza Prevalence ContMap.</p>
Data and R code for: Survival to weaning in arid-country vervet monkeys
<p>We addressed the relative contributions of maternal rank and sociability to the survival of infant vervet monkeys (Chlorocebus pygerythrus) to nutritional independence (~210 days) in a resource-poor environment. Data from 153 infants across three troops and 10 birth cohorts indicated a pre-weaning mortality of 30% (Range: 9%-85%), with a median age at death of 50 days. In addition to the consequences of resource availability, increased infant survival was independently and equivalently associated with higher maternal rank and a greater number of maternal spatial partners. We use this outcome to suggest that apparent discrepancies in the relative importance of different maternal attributes in determining reproductive outcomes may be resolved by considering more closely local sources of infant mortality.</p>
Data and R code from: Haemosporidian infections influence risk-taking behaviours in young male blackcaps Sylvia atricapilla
<p>This repository contains all data and code necessary to reproduce the results and figures of the paper:</p> <p>Remacha, C., Ramírez, A., Arriero, E. and Pérez-Tris, J. 2023. Haemosporidian infections influence risk-taking behaviours in young male blackcaps <em>Sylvia atricapilla</em>. Animal Behaviour, 196, 113-126. <a href="https://doi.org/10.1016/j.anbehav.2022.12.001">https://doi.org/10.1016/j.anbehav.2022.12.001</a></p> <p>The repository contains a readme file (README_SYAT_MS_ANIBEH_Scripts.txt) with a description of the code and the data. The code is organised in eight R script files. Instructions to run the code are provided in the readme file. The data are organised in two separate files. One file (SYAT_MS_BH_ANIBEHdata.txt) contains data of exploratory and antipredatory behaviours of 43 young male blackcaps. The other one (SYAT_MS_BH_BIOL_ANIBEHdata.txt) contains biological and experimental attributes of the same individuals: status and intensity of parasite infection, experimental treatment, morphology and body mass.</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.