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317 results for “R code”
Data, Metadata, R-codes and R data files for publication "Comparative ungulate diversity and biomass change with human use and drought: implications for community stability and protected area prioritization in African savannas" by Bartzke et al. in Ecology and Evolution
<p>These files contain data and metadata for modeling ungulate diversity and biomass in the Maasai Mara ecosystem in Kenya in the drought year of 1999 and a year with normal rainfall, 2002. The files also contain R codes and R data files.</p> <p>Metadata.pdf: Metadata for files "mc_333m.csv" and "mc_1km.csv"</p> <p>mc_333m.csv: A data file for 333-meter-by-333-meter sub-blocks.</p> <p>prepare_data.r: R code to impute missing vegetation records in 333-meter-by-333-meter subblocks and summarize the data over 1-kilometer-by-1-kilometer blocks for analysis.</p> <p>krige_vegetation.RData: An R data file containing the imputed vegetation records.</p> <p>mc_1km.csv: A data file for 1-kilometer-by-1-kilometer blocks for analysis.</p> <p>mc_1km.r: R code for modeling ungulate diversity and biomass; mc_1km_mod.RData: An R data file for loading the ungulate diversity and biomass models.</p> <p>mc_1km.RData: An R data file containing model predictions of ungulate diversity and biomass.</p> <p>mc_1km_plots.r: R code for plotting model predictions of ungulate diversity and biomass.</p> <p>MMNR_boundary.shp: A shapefile of the Maasai Mara National Reserve boundary in Kenya and associated files. These files are used for plotting the predictions of ungulate diversity and biomass.</p> <p>MMNR_border.zip: A shapefile and associated files for the Maasai Mara National Reserve border with Tanzania. These files are also used for plotting predictions of ungulate diversity and biomass.</p>
R code and data for "Intraspecific and intraindividual trait variability decrease with tree species richness in a subtropical tree diversity experiment"
<p>R codes and dataset for tha statistical analyses and production of figures in "Intraspecific and intraindividual trait variability decrease with tree species richness in a subtropical tree diversity experiment" by Castro Sánchez-Bermejo et al.</p>
Turek Laboratory Sleep-Wake Dataset and R Code for Analysis
<p>Sleep-wake dataset for MCI-Park mice and R code used for statistical analysis. </p>
R code of the mechanism controlling soil bacterial α and β diversity under N addition
Open the record for dataset details and reuse information.
R code and supplementary data for : "A framework for mapping conservation agricultural fields using time-series optical and radar imagery"
<p>Source code and cover crop maps for the paper "A framework for mapping conservation cropland using optical and radar time series imagery." (Zhou et al., 2025)</p> <p>https://doi.org/10.1016/j.rse.2025.114858</p> <p> </p> <p>The entire workflow consists of these steps:</p> <p>1. Obtain satellite data from Google Earth Engine platform. script path: (<a href="https://code.earthengine.google.com/?scriptPath=users%2Fyuez9466%2FCApractice%3ANDVI">https://code.earthengine.google.com/?scriptPath=users%2Fyuez9466%2FCApractice%3ANDVI</a>). You need to obtain the NDVI, NBR2, Sentinel-1 Radar dataset and Precipitation data for your research area and seltected time interval. Download .csv data from Google Cloud, then convert the format of the data for following calculations.(see 1_import_transfer_data.R)</p> <p>2. Obtain the annual crop types in your study area, either through agricultural census data or remote sensing predictions (not mentioned in this paper), calculate organic carbon input based on the crop types. Extracting seasons based on time-series NDVI values using phenofit package. (see 2_NDVI_Smooth_Divide_seasons.R)</p> <p>3. Calculating the length of the cover crop growing season and periods of bare soil, also get the nessasary covariates for tillage model meanwhile. (see 3_CC_BS_length_add_Tillage.R)</p> <p>4. Build a tillage model. (see 4_Build_Tillage_model)</p> <p>Build your own conservation agriculture fields model.</p>
An evidence map of research assessing the effects of timber harvesting on water quality, biotic and biodiversity indicators in running waters: Data and R code
<p>These are the data and R code that accompany the Forest Ecology and Management publication titled "An evidence map of research assessing the effects of timber harvesting on water quality, biotic and biodiversity indicators in running waters". </p>
Compendium of R code and data for Mycenaean roads in the Peloponnese, Greece: least-cost path modelling using R and Movecost
<p><strong>Abstract</strong></p> <p><em>This study evaluates multiple methodologies and their variants for Least Cost Path (LCP) modelling, applied in combination with different Digital Elevation Models (DEMs), to explore the broader applicability of the Movecost package, using the Mycenaean road networks of the Peloponnese (Greece) as case studies. Using a geographic information system (GIS) and the R programming environment, this paper employs the Movecost package for the R statistical package to simulate ancient routes based on existing road segments. By integrating a variety of functions and parameters, this study evaluates their effectiveness across different DEMs, including both Shuttle Radar Topography Mission DEM (SRTM-DEM) and Copernicus DEM (COP-DEM) at 30 m spatial resolution. The study also examines how varying these parameters can lead to different modelling outcomes, underscoring the necessity of calibrating least-cost analysis to specific regional contexts. The road segments around Nichoria (Messenia), Ayios Ioannis Kazarma (Argolis), and in the Berbati Valley (Argolis), provide a historical canvas against which these methodological innovations are tested with the ultimate aim of exploring the capabilities of the Movecost package and how different combinations of DEM, function, parameter, and path points can effectively model the route through the existing road remains, highlighting the variability and context-specific nature of LCP modelling. The results suggest that the 'Wheeled-vehicle critical cost function' (WCS) was effective in modelling the roads through the extant remains based on start and endpoints suggested by previous research and posited by this paper. These results further suggest that Mycenaean roads likely served as key infrastructure links between major centres and ports or harbours, underscoring their role in facilitating regional trade and communication. However, this outcome represents one of several possible results, as the appropriateness of functions and the parameters tested depend on the specific landscape and archaeological context. This underscores the importance of careful parameter selection, providing insights into the economic and social landscapes of Mycenaean Greece, while also highlighting the potential of integrating spatial data with robust computational tools to enhance our understanding of ancient infrastructure.</em></p>
Raw data and R code for the stone artifact analysis in "Lithic miniaturization in South China since the terminal Pleistocene: a multivariate analysis of lithic reduction from Fodongdi, Fulin and Xiqiaoshan"
Open the record for dataset details and reuse information.
R vignette of Toothnroll: full data and R code
<p>R vignette of Toothnroll: full data and R code</p>
R code and data for running models in "Rapid Growth of the Swainson's Hawk Population in California since 2005"
<p>By 1979 Swainson's Hawks (<em>Buteo swainsoni)</em> had declined to as low as 375 breeding pairs throughout their summer range in California. Shortly thereafter the species was listed as threatened in the state. To evaluate the hawk's population trend since then, we analyzed data from 1,038 locations surveyed throughout California in either 2005, 2006, 2016, or 2018. We estimated a total statewide population of 18,810 breeding pairs (95CI: 11,353–37,228) in 2018, and found that alfalfa (<em>Medicago sativa</em>, lucerne) cultivation, agricultural crop diversity, and the occurrence of non-agricultural trees for nesting were positively associated with hawk density. We also concluded that California's Swainson's Hawk summering population grew rapidly between 2005 and 2018 at a rate of 13.9% per year (95CI: 7.8–19.2%). Despite strong evidence that the species has rebounded overall in California, Swainson's Hawks remain largely extirpated from Southern California where they were historically common. Further, we note that the increase in Swainson's Hawks has been coincident with expanded orchard and vineyard cultivation which is not considered suitable for nesting. Therefore, we recommend more frequent, improved surveys to monitor the stability of the species' potential recovery and to better understand the causes. Our results are consistent with increasing raptor populations in North America and Europe that contrast with overall global declines, especially in the tropics.</p>
Chong-Montenegro_and_Kindsvater_2022_R_code
<p>Supporting R code for: Chong-Montenegro C and Kindsvater HK (2022) Demographic Consequences of Small-Scale Fisheries for Two Sex-Changing Groupers of the Tropical Eastern Pacific. Front. Ecol. Evol. 10:850006. DOI: 10.3389/fevo.2022.850006</p> <p> </p> <p>Note: If you use this data or code please city the paper accordingly.</p>
Accuracy of bird identifications in citizen science data: a quantification of errors using photographic records [R code]
<p><strong>Appendix S1</strong>. The full dataset used in this study with a reproducible R code to perform data quality and network analyses. R code archived to Zenodo for publication. </p>
Data and R-code belonging to Manuscript: "Prior experience of captivity affects behavioural responses to 'novel' environments"
<p>Data and R-code belonging to Manuscript:"Prior experience of captivity affects behavioural responses to 'novel' environments" </p> <p>1) the data can be found in an excel file named: datafile_explorationGT2022.xlsx</p> <p>2) the R-code used to do the analysis in the MS can be found in an R-markdown file: Rmd_file_MS_GTexploration_R2.Rmd</p>
International data (WB, UNDP, JMP) Nov. 2017. Preprocessed with R (code included).
<p>Preprocess of the following data for use with R:</p> <ul> <li>JMP, http://washdata.org/data [Nov. 2017]</li> <li>UNDP, http://hdr.undp.org/en/data [Nov. 2017]</li> <li>WB, http://data.worldbank.org/data-catalog/world-development-indicators [Nov. 2017]</li> </ul> <p> </p> <p>R files:</p> <p>- DATA2017_Part0.R : It reads the .csv files and writes three .Rdata with the tables with country data and description of variables, one for each data source. The WB pairs indicator-year are dropped if they do not have a minimum of records available. The selection is time consuming. Because of this, an auxiliary table with the number of records for all indicator-years has been saved separately from main results. Also, specific outputs for two thresholds have been already computed (200 and 250). A correspondence between country names used by UNDP and JMP-WB is provided in a .csv file.</p> <p>- DATA2017_Part0B.R : Here, the three tables of data and the three ones with description of the indicators are merged. The countries are kept in the final list only if they are present in the three sources. </p> <p>- DATA2017_Part0C.R : First, data available for year 2015 are collected.. The WB records are kept only if they have less than 40 empty cases. Second, the indicators with time evolution data from 2000-2010 are merged in a single table (WB and JMP data). </p> <p> </p> <p>Directories:</p> <ul> <li>csv/</li> <li>data_org/</li> <li>data_org/JMP/</li> <li>data_org/UNDP/</li> <li>data_org/WB/</li> <li>Rdata/ </li> </ul>
Dataset and R code for phyllosphere microbial diversity during rubber tree leaf senescence
Open the record for dataset details and reuse information.
R_code_FE-2024-00264
<p>This is an R script for the data analyses performed on field-collected data of plants and associated flower visitors. It involves a mix of multivariate analyses and generalised-linear mixed modeling approaches. The script is provided as an Rmarkdown file and can only be run with installation of Rtools due to the requirement of a github package installation for one of the data analyses. </p>
Dataset and R code for "Relationship between wind speed and plant hydraulics at the global scale"
<p><strong><span>Data collection</span></strong><strong><span> </span></strong></p> <p><span> Plant hydraulic traits and height data were obtained from three sources: (1) field measurements of plant hydraulics for 210 forest species in China; (2) the TRY Plant Traits Database (https://www.try-db.org/TryWeb/Home.php; Kattge et al., 2020); and (3) published literature. For the latter we conducted searches on Web of Science, Google Scholar, and China National Knowledge Infrastructure (http://www.cnki.net) using keywords such as “hydraulic traits,” “xylem hydraulic conductivity,” “xylem vulnerability,” “water potential at 50% loss of hydraulic conductivity,” “xylem embolism resistance,” and “plant water conductivity.” A substantial portion of data in our study were obtained from published literature (Choat et al., 2012; Gleason et al., 2016) and the Xylem Functional Traits Database (XFT; </span><span><a href="https://xylemfunctionaltraits.org/"><span>https://xylemfunctionaltraits.org</span></a></span><span>).</span></p> <p><span>To minimize ontogenetic and methodological variation, we only included data that met the following criteria: (a) plants were grown in natural ecosystems, excluding greenhouse and common garden experiments; (b) measurements were made on adult plants and not on seedlings; (c) hydraulic traits were measured on terminal stem or branch segments in the sapwood at the crown; (d) trait data were calculated as the mean value for each species at the same site when data were from multiple sources; and (e) data values > 3 SD (standard deviation) were removed to reduce the effect of outliers (Carmona et al., 2021); (f) <span>height data were reported at the same site where plant hydraulic traits were measured.</span> </span></p> <p><span>Climate data were obtained either from the original reports or from WorldClim version 2 (http://worldclim.org/version2; Fick & Hijmans, 2017; Table 1) if the original data were not available. The following variables measured at ~1 km<sup>2</sup> scale were extracted from WorldClim: mean annual wind speed (<span>μ</span>), mean annual precipitation, mean annual temperature, precipitation seasonality, temperature seasonality, wind seasonality (<span>μS; </span>coefficient of variation across monthly measurements × 100), precipitation of driest month, and minimum temperature of coldest month. The VPD data were extracted from the TerraClimate dataset (http://www.climatologylab.org/terraclimate.html; Abatzoglou et al., 2018). Annual PET (potential evapotranspiration) data were extracted from the CGIAR-CSI consortium (http://www.cgiar-csi.org/data; Zomer et al., 2008). Moisture index (MI), which is the ratio of precipitation to PET. </span></p> <p><strong><span>Data analysis</span></strong></p> <p><span>Trait and environment data were log<sub>10</sub>-transformed to achieve approximate normality, except for <em>P</em><sub>50</sub> and temperature data. We first calculated correlations among all climatic variables and for subsequent analyses retained only those variables with correlation coefficients lower than |0.7| (Dormann et al., 2013). We then ran independent multiple linear models for each trait of interest using the retained climatic variables. Model selection based on a corrected Akaike information criterion and using the R package glmulti (Calcagno & de Mazancourt, 2010), identified the best linear model for each trait. The R package ‘visreg’ (Breheny & Burchett, 2017) was used to visualize the partial relationships between wind speed and hydraulic traits. Two-dimensional contour plots were then used to explore and visualise how plant hydraulic traits varied simultaneously with wind speed and moisture index.</span></p> <p><span>To quantify the strength of wind effects on plant hydraulics, models with wind parameters μ and μS included were compared to those without these wind parameters. </span></p> <p><span>To test for differences in the relationship between hydraulic traits and wind speed among species grouped into different climatic regions (i.e., dry <em>vs</em>. wet sites, and tropical <em>vs</em>. temperate regions), we used standardized major axis (SMA) analyses using the R package ‘smatr’ (Warton et al., 2012).</span><span> </span><span>A grouping factor was added in each SMA to test whether species groups share a common slope, with <em>p</em> > 0.05 indicating species groups share a common slope. </span></p> <p><span>Variance partitioning analysis was performed using the ‘rdacca. hp’ R package to quantify the degree to which the effect of wind speed was independent from other climatic variables (Lai et al., 2022). The individual contribution of each predictor was estimated in this analysis. This analysis also helped to illustrate the significant values of climatic variables on plant hydraulics. </span></p> <p><span>A Random Forest </span><span>machine-learning algorithm (implemented using the R package ‘randomForest’) was utilized to further assess the relative importance of environmental variables for each plant hydraulic trait (Breiman, 2001). To avoid multicollinearity, this analysis only included variables with correlation coefficients lower than |0.7|. A higher value of the mean decrease in accuracy (%IncMSE) indicates the increased importance of a variable (e.g., a %IncMSE value of 50 indicates that the overall mean square error would increase by 50% if that variable were to be excluded from the analysis). This provides a measure of a variable's importance in estimating the value of the target variable across the trees in the forest. </span></p> <p> </p>
Data and R code from: Fire-induced loss of the world's most biodiverse forests in Latin America
<p>Fire plays a dominant role in deforestation, particularly in the tropics, but the relative extent of transformations and influence of fire frequency on eventual forest loss remain unclear. Here we analyze the frequency of fire and its influence on post-fire forest trajectories between 2001-2018. We account for ~1.1% of Latin American forests burnt in 2002-2003 (8,465,850 ha). Although 40.1% of forests (3,393,250 ha) burned only once, by 2018~48% of the evergreen forests converted to other, primarily grass-dominated uses. While greater fire frequency yielded more transformation, our results reveal the staggering impact of even a single fire. Increasing fire frequency imposes greater risks of irreversible forest loss, transforming forests into ecosystems increasingly vulnerable to disturbance and degradation. Reversing this trend is indispensable to both mitigate and adapt to climate change globally. As climate change transforms fire regimes across the region, key actions are needed to conserve Latin American forests.</p>
Supplementary Material (dataset and R code) paper "Evidences of branching and blending phenomena in the pottery decoration during the dispersal of the Early Neolithic across Western Europe"
<p>Dataset and R code of the paper "Evidences of branching and blending phenomena in the pottery decoration during the dispersal of the Early Neolithic across Western Europe" published in Journal of Archaeological Sciences: Report 23, 252-264</p>
16s rRNA sequences, R code used for amplicon analysis and example code for NMGS analysis
<p>This submission contains the following data presented in: "Selection processes of Arctic seasonal glacier snowpack bacterial communities" by Keuschnig et al.</p> <p>the R code used to analyze the 16S rRNA amplicon data</p> <p>the script used for NMGS analysis</p> <p>the sequences obtained from snow samples</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.