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15,519 results for “r”
Data and R script for 'Opportunistic food consumption in relation to childhood and adult food insecurity: An exploratory correlational study'
<p>One raw data file and one R script that reproduces all analyses and figures reported in the paper '<strong>Opportunistic food consumption in relation to childhood and adult food insecurity: An exploratory correlational study</strong>' by Nettle et al. </p>
Data and R-script to analyse timing of northward migration of Sanderlings (Calidris alba) through Europe
<p>The data file contains data of observations of individual Sanderlings (<em>Calidris alba</em>) from specific wintering areas observed during migration in different latitude sectors through Europe. This is a subset of the raw data file which also includes observations at the wintering grounds. Each individual is indicated with a unique number in column “Individual”. The column “Winter Area” indicates the winter location of each individual in sectors of 5 degrees latitude (as depicted in Fig. 1 in the manuscript), with 7 indicating Scotland, 8 England, 9 France, 10 North Iberia, 11 Portugal, 13 Canary Islands, 15 Mauritania, 18 Ghana and 23 Namibia, as indicated in the column “Winter_Country”. “Date_DOY” indicates the day of year on which current observation was made. “Year” indicates the year during which an observation was made, “lat” is the latitude and “lon” the longitude of each observation in decimal degrees. “Lat_Sector” specifies in which of the sectors of 6 latitudinal degrees in Europe (see Fig. S1) the observation was made. “Sex” indicates the sex, based on molecular methods of an individual where 0=unknown, 1=female, 2=male. “Age”is the age of an individual, where 50=juvenile (i.e. less than 1 year old), 99=adult (i.e more than 1 year old) and 0 indicates that the age was unknown. “migration” indicates whether an observed individual is conisdered to be on migration (“yes” i.e. seen at least 2 latitudinal degrees north of its average winter location) or not (“no”). Further details can be found in the methods section in the manuscript.</p> <p>The R-script uses this dataset to analyse the variation in timing of migration through Europe for individual Sanderlings from different non-breeding areas. Comments and explanations can also be found in the script and in the methods section of the manuscript.</p> <p> </p>
Manipulation of netCDF data with R for climate change research: Multi-model analysis for CMIP5 models.
<p>Geoscientists now live in a world with an exponential growth in digital data and methods.<br> Climate change studies usually describe computational methods informally. Climate scientists seek to<br> share their information, the justification of reproducible research has received increasing attention in<br> geosciences. To have it in an open-source format makes it easier to interchange not only with fellow<br> scientists but also a variety of sources including funders, publishers, and journalists. R is a open-source<br> computer language powerful and highly extensible that can promotes reproductive science techniques in a<br> easier way. R is highly accessible for non-computational scientists when coupled with packages like<br> ‘raster', ‘netcdf', ´rgdal`and ‘rasterVis', R enables scientists to make sense of their data and to carry out<br> complex data analysis. In this paper we have assessed the power of R language for manipulating climate<br> data from a huge dataset: the Coupled Model Intercomparison Project Phase 5 (CMIP5). Moreover we<br> have proposed an example of best practices to handle model ensembles. This is the first study to our<br> knowledge to promote best practices for CMIP5 ensemble. The NetCDF data accessible to R via raster<br> package capabilities provides efficient access to the multi-model, with crucial applications in climate<br> change research. In recent years more than 100 peer-reviewed scientific publications have used the<br> CMIP5 data sets. We envision that in the near future (5-10 years), scientists will use radically new tools<br> to author papers and disseminate information about the process and products of their research.</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 data objects for the HumanDEU package
<p>This submission contains several R data objects that are part of the R package HumanDEU available through Github (https://github.com/areyesq89/HumanTissuesDEU). The objects correspond to processed data needed to reproduce the statistics, tables and figures presented in the manuscript:<br> <br> A Reyes and W Huber. Alternative start and termination sites of transcription drive most transcript isoform differences across human tissues. Nucleic Acids Research, 2017. doi: https://www.doi.org/10.1093/nar/gkx1165<br> <br> For more details, please visit the Github repository.</p>
Bayesian Methodology: An overview with the help of R software
<p>Bayesian methodology differs from traditional statistical methodology which involves frequentist approach. Bayesian methodology was introduced by Thomas Bayes (Statistician and minister at the Presbyterian Chapel) during the 18<sup>th</sup> Century. Bayesian methodology is now widely being used due to its simple, straightforward and interpretable characteristics of probability values and the efficiency of modern day computer systems.</p> <p>Bayesian methodology is now being used in the field of clinical research, clinical trials, epidemiology, econometrics, statistical process control, marketing research and statistical mechanics. It also used in the emerging field such as data science (machine learning and deep learning) and big data analytics.</p> <p>The book provides an overview of Bayesian methodology, its uses in different fields with the help of R statistical open source software.</p> <p><a href="https://www.amazon.com/dp/B07QCHTR54">https://www.amazon.com/dp/B07QCHTR54</a></p> <p><strong>ISBN-13: 978-1092939898</strong></p> <p> </p> <p><strong>Editor</strong></p> <p><strong>International Journal of Statistics and Medical Informatics</strong></p> <p><a href="http://www.ijsmi.com/book.php"><strong>www.ijsmi.com/book.php</strong></a></p>
R code for modeling indexical fields using MSTs
<p>R code for modeling indexical fields using Minimum Spanning Trees</p>
Mask R-CNN on NYUv2
<p><strong>Mask R-CNN on NYUv2</strong></p> <p>This repository mainly contains information from the execution of the <a href="https://github.com/facebookresearch/maskrcnn-benchmark">Mask R-CNN network </a>[1] on images from the <a href="https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html">NYUv2 dataset</a> [2] as well as additional metadata. It was created for analyzing the output of Mask R-CNN and post-processing it using contextual information for improving its performance. This work has been carried out by <a href="http://mapir.isa.uma.es/jotaraul">Dr. Jose-Raul Ruiz-Sarmiento</a> (MAPIR group, University of Málaga) and <a href="https://lishuda.wordpress.com/">Dr. Shuda Li</a> (AVG group, University of Oxford) in the scope of the European project<a href="http://www.movecare-project.eu/"> MoveCare: Multiple-actOrs Virtual Empathic CARgiver for the Elder</a> (Ref: 732158).</p> <p>Concretely, <strong>this repository includes</strong>:</p> <p>- metadata:<br> + coco_nyu_mapping.txt: Mapping between the categories in COCO dataset and those in NYUv2.<br> + coco_object_categories.txt: Object categories considered in COCO dataset.<br> + nyu_object_categories.txt: Object categories used in NYUv2 dataset.<br> + nyu_scene_categories.txt: Scene categories considered in NYUv2.<br> + objects_and_categories_in_images.txt: For each image in NYUv2, the categories of the appearing objects.</p> <p>- nyu_content:<br> + masks_in_X (Where X is the image index)<br> - Y.png: Where Y is the object index in the image, represents the binary mask of that object.<br> - pixels_labelled.png: Binary mask indicating the labelled pixels in image X.<br> + bboxesX.txt: Where X is the image index, includes the ground truth bounding boxes of the objects in it. Format is: min_x min_y max_x max_y.</p> <p>- preds:<br> + X: Where X is the image index.<br> - Y.png: Where Y is the object index in the image, as detected by Mask R-CNN. Binary image containing the mask of such detected object.<br> + X.txt: Where X is the image index. File containing the objects detected by Mask R-CNN, including: idx class score min_x min_y max_x max_y masks_file, being min_x min_y max_x and max_y bounding box information, while masks_file refers to X/Y.png as described above.<br> + result_X.png: Where X is the image index. Image showing the detections with a socre higher than 0.3.<br> + gt_iou_X: Where X is the image index.<br> - Y: Where Y is the index of the detected object.<br> + Z.png Where Z is the index of the object in the ground truth. Image showing the masks of both objects, Y and Z, for visually checking their overlapping.<br> - Y.txt: Where Y is the index of the detected object. File containing:<br> + The intersection ratio of the object mask Y with the labelled part of the image.<br> + The IoU value for the mask of object Y and those of ground truth objects.<br> <br> <br> <strong>References:</strong></p> <p>[1] He, Kaiming, Georgia Gkioxari, Piotr Dollár, and Ross Girshick. "Mask r-cnn." In Proceedings of the IEEE international conference on computer vision, pp. 2961-2969. 2017.<br> [2] Silberman, Nathan, Derek Hoiem, Pushmeet Kohli, and Rob Fergus. "Indoor segmentation and support inference from rgbd images." In European Conference on Computer Vision, pp. 746-760. Springer, Berlin, Heidelberg, 2012.</p>
Supporting Data for "Refractive index matched, nearly hard polymer colloids" (Proc. R. Soc. A, doi:10.1098/rspa.2018.0763)
<p>SAXS data [Q / Å^{-1}, I(Q) / Arb. unit, error I(Q) / Arb. unit] as *.dat files</p> <p>Data for Figures 1, 2, and 5 [description and units in column headers] as *.csv files</p>
Short sentences on R analyses in a health informatics subject
<p>The dataset contains the list of sentences written by students, with a unique ID, its type (if either given for the hypothesis or normality test), its degree in a range from 0 to 1, and its fail/pass result, flanked with (i) the gold standard, (ii) an alternative gold standard, and (iii) the negated gold standard.</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>
Data and R-Scripts for: Value of crowd-based water level class observations for hydrological model calibration
<p>This dataset corresponds to the study<br> "Value of crowd-based water level class observations for hydrological model calibration"<br> submitted to Water Resources Research in August 2019.</p> <p>Please use the R-Scripts in ascending numbers and adapt the paths to where you stored the files.<br> The helpfunctions.R will be used by some of the scripts and you might<br> want to adapt a path in line 356 for it to be used correctly with the scripts 8a and 8b.</p> <p>The parameter ranges used for the HBV calibration can be found in the "Parameters and parameter ranges.pdf"</p> <p>If you do not wish to calibrate the model, and just perform some statistics<br> start with script 7 and use the<br> - CrossValidation_stats_all.txt in the LUT Tables folder which contains<br> all model performances.<br> - CrossValidation_stats_WP1.txt contains also results of the upper benchmark<br> (only those labelled with no error and hourly).<br> - RandomParamPerformance_Validation.txt contains the results of the random parameters<br> (lower benchmark).<br> - The folders Validation Results and Calibration Results contain the files in HBV-format after the model<br> calibration and validatin were completed. The results of the Calibration and Validation files are also summarized<br> in the aforementioned txt-files within script 6 -HBV CrossValidation.R.<br> Please be aware that for the study only the catchments Murg, Guerbe, Mentue, and Verzasca were used!</p> <p><br> If you run into trouble using the data please contact simon.etter[at]outlook.com.</p> <p>Co-authors are:<br> Prof. Dr. Jan Seibert - jan.seibert[at]geo.uzh.ch<br> Dr. Ilja (H.J.) van Meerveld - ilja.vanmeerveld[at]geo.uzh.ch<br> Barbara Strobl - barbara.strobl[at]geo.uzh.ch</p>
nmatthews323/SustAssessR: An integrated codebase for sustainability assessment using R
<p>An integrated codebase for sustainability assessment using R</p> <p>This codebase was used for and is made available alongside the following publication: Matthews, N. E., Cizauskas, C. A., Layton, D. S., Stamford, L., & Shapira, P. (2019). Collaborating constructively for sustainable biotechnology. Scientific Reports. <a href="https://doi.org/10.1038/s41598-019-54331-7">https://doi.org/10.1038/s41598-019-54331-7</a></p> <p><strong>A note on input data</strong></p> <p>This repository is designed to demonstrate and make available the code used for the above publication. Due to the proprietary nature of some of the process flow modelling the data in the directory "Data/ProcessedFlows" has been averaged and rounded to three significant figures. Therefore, while this codebase will authentically replicate the data processing carried out in the published analysis, the outputs will not be identical due to the changes made to the input data. All output data from the original publication has been made available through the supplementary information of the original publication.</p> <p> </p>
FIG. 3. R in Rapana venosa (Valenciennes, 1846) Залива ДонуЗлав и прилегаюЩей акватории Чёрного морЯ
FIG. 3. R. venosa, SH – 103.4 mm, holding prey – Flexopecten glaber (Linnaeus, 1758) L – 40.0 mm, Donuzlav Bay, depth 3.2 m.
РИС. 1. Карта района исследований с укаЗанием станций отбора проб (А) и плотности поселениЯ R. venosa (ЭкЗ./м²). FIG. 1. Map of the study area with indication the sampling sites and the population density (A) of R. venosa (specimens/m²). in Rapana venosa (Valenciennes, 1846) Залива ДонуЗлав и прилегаюЩей акватории Чёрного морЯ
РИС. 1. Карта района исследований с укаЗанием станций отбора проб (А) и плотности поселениЯ R. venosa (ЭкЗ./м²). FIG. 1. Map of the study area with indication the sampling sites and the population density (A) of R. venosa (specimens/m²).
РИС. 5. Графики Зависимости раЗмера SH, мм (A, B) и веса TW, г (C, D) от воЗраста (годы) особей R. venosa Залива ДонуЗлав (A, C) и прилегаюЩей морской акватории (B, D). in Rapana venosa (Valenciennes, 1846) Залива ДонуЗлав и прилегаюЩей акватории Чёрного морЯ
РИС. 5. Графики Зависимости раЗмера SH, мм (A, B) и веса TW, г (C, D) от воЗраста (годы) особей R. venosa Залива ДонуЗлав (A, C) и прилегаюЩей морской акватории (B, D).
Fig. 6. Letter from Ubirajara R in The Entomological Collection of Ricardo von Diringshofen (1900-1986) and its incorporation to the Museu de Zoologia da Universidade de São Paulo
Fig. 6. Letter from Ubirajara R. Martins to the then director of MZSP Paulo E. Vanzolini requesting an official letter of appreciation to be send to Dirings's family due to the donation of his Entomological Library.
Fig. 4. Letters from Ubirajara R in The Entomological Collection of Ricardo von Diringshofen (1900-1986) and its incorporation to the Museu de Zoologia da Universidade de São Paulo
Fig. 4. Letters from Ubirajara R. Martins de Souza and Paulo E. Vanzolini, emphasizing the importance of Dirings's collection to the knowledge of the Neotropical Insects.
Text-fig. 3. Schema of radial section of G. rudolphii (sample 99/04). t – tracheid, r – ray, bp – bordered pit, tp – taxodioid cross-field pit, gp – glyptostroboid cross-field pit. in New Fossil Woods From The Paleogene Of Doupovské Hory And České Středohoří Mts. (Bohemian Massif, Czech Republic)
Text-fig. 3. Schema of radial section of G. rudolphii (sample 99/04). t – tracheid, r – ray, bp – bordered pit, tp – taxodioid cross-field pit, gp – glyptostroboid cross-field pit.
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.