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47 results for “R programming”
Harmfull algae bloom monitoring program dataset; ERDDAP, ERA5 and ONI datasets; and R script for multicriteria analisys in Santa Catarina coastal zone, Brazil.
<p>Project Harmful Algae Bloom (HAB) Monitoring Network in Santa Catarina, Brazil - Database and R script with data analysis. This project was funded by the Foundation for Research Support of the State of Santa Catarina – FAPESC and generated a database combining a HAB monitoring dataset with oceanographic (from ERDDAP) and climatic (from ERA5 and ONI) data which was submitted to multicriteria analysis using R. The HAB monitoring dataset was obtained from Cidasc/SC State Government (http://www.cidasc.sc.gov.br/defesasanitariaanimal/monitoramento-de-algas-nocivas/) and contains results of phytoplankton counts in water samples and toxin levels in shellfish samples obtained from 39 points located in shellfish farms distributed along the SC coastline. Oceanographic data were obtained from the ERDDAP/NOAA website (https://coastwatch.pfeg.noaa.gov/erddap/index.html), including the variables mean chlorophyll concentration (mg.m-3) and mean sea surface temperature (ºC); Climate data were obtained from Copernicus/ERA5 (https://cds.climate.copernicus.eu/) including the variables mean air temperature (ºC), mean pressure (Pasc.), mean cloud cover (%), mean precipitation (kg.m-2), radiation (Einsteins.m-2.day-1), mean U wind (m.s-1), and mean V wind (m.s-1).; Oceanic Niño Index (ONI) data were obtained from the NOAA website (https://origin.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ONI_v5.php); The R script involves a pre-processing routine aimed at summarizing and integrating all datasets and the subsequent data analyses carried out to evidence temporal patterns related to different type of algal blooms. Detailed methods will be provided in a scientific article.</p>
Recreating Fall Risk Appraisal (FRA) matrix using R to support fall prevention programs
Open the record for dataset details and reuse information.
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>
Diversification and change in the R programming language
<p>Languages change over time, driven by creation of new words and cultural pressure to optimise communication. Programming languages resemble written language but communicate primarily with computer hardware rather than a human audience. I tested for changes over time in use of R, a mature, open-source programming language used for scientific computing. Across 393,142 GitHub repositories published between 2014 and 2021, I extracted 143,409,288 R functions, programming "verbs", and paired linguistic and ecological approaches to estimate change in the diversity and composition of function use over time. I found that the number of R functions in use increased and underwent substantial change, driven primarily by the popularity of the "tidyverse" collection of community-written extensions. I provide evidence that users can directly change the nature of programming languages, with patterns that match known processes from natural languages and genetic evolution. In the case of R, patterns suggested there are selective pressures for increased analytic complexity and R functions in decline but not extinct ("extinction debts"). R's evolution towards the tidyverse may also represent the start of a division into two distinct dialects, which may impact the readability and continuity of analytic and scientific inquiries codified in R, as well as the language's future.</p>
Diversification and change in the R programming language
Open the record for dataset details and reuse information.
Data from: Exploring density and frequency dependent interactions experimentally: an R program for generating hexagonal fan designs.
Open the record for dataset details and reuse information.
Patient-Reported Outcome Measures in Lower Extremity Rehabilitation Program PROM_R: Impact on Health Care
ClinicalTrials.gov study NCT06206018. IPD Sharing: NO. Countries: 1. Publications: 5.
Figure 7e from: Briz-Redón Á, Serrano-Aroca Á (2018) Novel pedagogical tool for simultaneous learning of plane geometry and R programming. Research Ideas and Outcomes 4: e25485. https://doi.org/10.3897/rio.4.e25485
Figure 7e Examples of use of the functions Duopoly and Star. - Example of use of the function Star. Parameter angle is set to 20º.
Figure 7b from: Briz-Redón Á, Serrano-Aroca Á (2018) Novel pedagogical tool for simultaneous learning of plane geometry and R programming. Research Ideas and Outcomes 4: e25485. https://doi.org/10.3897/rio.4.e25485
Figure 7b Examples of use of the functions Duopoly and Star. - Example of use of the function Duopoly. Creation of a nefroid.
Figure 6e from: Briz-Redón Á, Serrano-Aroca Á (2018) Novel pedagogical tool for simultaneous learning of plane geometry and R programming. Research Ideas and Outcomes 4: e25485. https://doi.org/10.3897/rio.4.e25485
Figure 6e Examples of different fractals produced with the functions of the LearnGeom package. The examples obtained with FractalSegment show how minimal modifications of the parameters can lead to very different curves. - A modificaction of the first five iterations of the Koch's (c) by changing parameter f from 1 to 2.
Figure 6b from: Briz-Redón Á, Serrano-Aroca Á (2018) Novel pedagogical tool for simultaneous learning of plane geometry and R programming. Research Ideas and Outcomes 4: e25485. https://doi.org/10.3897/rio.4.e25485
Figure 6b Examples of different fractals produced with the functions of the LearnGeom package. The examples obtained with FractalSegment show how minimal modifications of the parameters can lead to very different curves. - First three first iterations of the Koch's curve.
Figure 6a from: Briz-Redón Á, Serrano-Aroca Á (2018) Novel pedagogical tool for simultaneous learning of plane geometry and R programming. Research Ideas and Outcomes 4: e25485. https://doi.org/10.3897/rio.4.e25485
Figure 6a Examples of different fractals produced with the functions of the LearnGeom package. The examples obtained with FractalSegment show how minimal modifications of the parameters can lead to very different curves. - First seven iterations of the Sierpinski triangle.
Figure 5c from: Briz-Redón Á, Serrano-Aroca Á (2018) Novel pedagogical tool for simultaneous learning of plane geometry and R programming. Research Ideas and Outcomes 4: e25485. https://doi.org/10.3897/rio.4.e25485
Figure 5c Different stages of the creation of a beehive structure with the aid of tessellations. - Once the contiguous hexagons are obtained, function Tessellation allows the creation of the structure.
Figure 5b from: Briz-Redón Á, Serrano-Aroca Á (2018) Novel pedagogical tool for simultaneous learning of plane geometry and R programming. Research Ideas and Outcomes 4: e25485. https://doi.org/10.3897/rio.4.e25485
Figure 5b Different stages of the creation of a beehive structure with the aid of tessellations. - Creating two contiguous hexagons to the starting one. These hexagons are derived from the middle points of some of the sides of the initial hexagon.
Figure 5a from: Briz-Redón Á, Serrano-Aroca Á (2018) Novel pedagogical tool for simultaneous learning of plane geometry and R programming. Research Ideas and Outcomes 4: e25485. https://doi.org/10.3897/rio.4.e25485
Figure 5a Different stages of the creation of a beehive structure with the aid of tessellations. - Creating a regular hexagon that works as the start of the tessellation.
Figure 4a from: Briz-Redón Á, Serrano-Aroca Á (2018) Novel pedagogical tool for simultaneous learning of plane geometry and R programming. Research Ideas and Outcomes 4: e25485. https://doi.org/10.3897/rio.4.e25485
Figure 4a Partial results during the process of finding the circumcenter of the triangle of points (-1,0), (0,1) and (1,0). - Triangle creation and obtention of the middle points of the sides and three auxiliary points in the orthogonal direction of each of the sides.
Figure 6d from: Briz-Redón Á, Serrano-Aroca Á (2018) Novel pedagogical tool for simultaneous learning of plane geometry and R programming. Research Ideas and Outcomes 4: e25485. https://doi.org/10.3897/rio.4.e25485
Figure 6d Examples of different fractals produced with the functions of the LearnGeom package. The examples obtained with FractalSegment show how minimal modifications of the parameters can lead to very different curves. - A modificaction of the first five iterations of the Koch's (c) by changing parameter angle from 60º to 90º.
Figure 3e from: Briz-Redón Á, Serrano-Aroca Á (2018) Novel pedagogical tool for simultaneous learning of plane geometry and R programming. Research Ideas and Outcomes 4: e25485. https://doi.org/10.3897/rio.4.e25485
Figure 3e Examples of use of the functions included in the package that represent affine transformations in the plane. In all the pictures, the blue triangle, placed at the points A(0,0), B(2,0) and C(1,1), is the one passed to each of the functions, being the orange triangle the output resulting for each of the transformations. - A shear transformation.
Figure 3f from: Briz-Redón Á, Serrano-Aroca Á (2018) Novel pedagogical tool for simultaneous learning of plane geometry and R programming. Research Ideas and Outcomes 4: e25485. https://doi.org/10.3897/rio.4.e25485
Figure 3f Examples of use of the functions included in the package that represent affine transformations in the plane. In all the pictures, the blue triangle, placed at the points A(0,0), B(2,0) and C(1,1), is the one passed to each of the functions, being the orange triangle the output resulting for each of the transformations. - A homothety.
Figure 3b from: Briz-Redón Á, Serrano-Aroca Á (2018) Novel pedagogical tool for simultaneous learning of plane geometry and R programming. Research Ideas and Outcomes 4: e25485. https://doi.org/10.3897/rio.4.e25485
Figure 3b Examples of use of the functions included in the package that represent affine transformations in the plane. In all the pictures, the blue triangle, placed at the points A(0,0), B(2,0) and C(1,1), is the one passed to each of the functions, being the orange triangle the output resulting for each of the transformations. - A rotation.
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.