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1,249 results for “R data”
AMOC reconstruction between 1981 and 2016 from hydrographic data using an empirical linear regression model from Worthington, E. L., Moat, B. I., Smeed, D. A., Mecking, J. V., Marsh, R., and McCarthy, G. D.: A 30-year reconstruction of the Atlantic meridional overturning circulation shows no decline, Ocean Sci., 17, 285–299, https://doi.org/10.5194/os-17-285-2021, 2021.
<p>Dataset used to create Figure 8 in Worthington et al., 2021 (https://doi.org/10.5194/os-17-285-2021). Details of the data and methods can be found in the journal article.<br> <br> Worthington, E. L., Moat, B. I., Smeed, D. A., Mecking, J. V., Marsh, R., and McCarthy, G. D.: A 30-year reconstruction of the Atlantic meridional overturning circulation shows no decline, Ocean Sci., 17, 285–299, <a href="https://doi.org/10.5194/os-17-285-2021">https://doi.org/10.5194/os-17-285-2021</a>, 2021.</p>
Data and R code for Tansley review New Phytologist 2021: "An integrated framework of plant form and function: The belowground perspective"
<p>The files in this archive are related to the paper of Weigelt, Mommer, Andraczek et al. (2021) An integrated framework of plant form and function: The belowground perspective. Tansley Review New Phytologist. The paper developed and tested a new conceptual framework of plant form and function linking above and belowground traits of 2510 species. We found that an integrated, whole-plant trait space required as much as four axes. The two main axes represented the fast-slow ‘conservation’ gradient on which leaf and fine-root traits were well aligned, and the ‘collaboration’ gradient in roots. The two additional axes were separate, orthogonal plant size axes for height and rooting depth.</p> <p>This archives contains four files:</p> <ol> <li><strong>Weigelt et al.2021RCode.DataCleaning.txt</strong> - RCode for the complete data processing starting with the downloaded database files from the Plant Trait Database version 5.0 (TRY, Kattge et al. 2020), the Global Root Trait database (GRooT, Guerrero-Ramirez et al. 2020) and a small number of additional data files listed in Table S2 of the original paper. Additional information was later incorporated using FungalRoot Database (Soudzilovkaia et al. 2020), nodDB Database (Tedersoo et al. 2018) and a compiled dataset on rooting depth (Fan et al. 2017). The code processes, cleans and merges the data and produces a final table for PCA analysis of species specific mean traits. This final table is provided as a second file in this archive (Weigelt_et_al_2021_Main.PCA.Matrix.xlsx). A second part of the RCode.DataCleaning extracts species-specific individual trait data where root and shoot traits were measured on the same plant individual or plot. This data was compiled from 43 studies identified in Table S2 of the original publication. The final table for individual trait data is the third file in this archive (Weigelt_et_al_2021_Individual.PCA.Matrix.xlsx).</li> <li><strong>Weigelt_et_al_2021_Main.PCA.Matrix.xlsx</strong> – Datafile with species-specific global mean trait data for 17 traits of 2510 species with at least one root and one shoot trait available. Meta-data is provided in the data file.</li> <li><strong>Weigelt_et_al_2021_Individual.PCA.Matrix.xlsx</strong> – Datafile with species-specific trait data where root and shoot traits were measured on the same individual or plot for 6 traits of 455 species. Meta-data is provided in the data file.</li> <li><strong>Weigelt et al.2021RCode.Analysis.txt – </strong>RCode for all analyses and figures provided in the paper for both the species mean and individual based dataset. The Code is annotated to help reproducibility of the analysis.</li> </ol>
Hail Event on 2022-06-28 in Locarno-Monti (TI), Switzerland: Drone Photogrammetry Imagery, Mask R-CNN Model and Analysis Data of Hailstones
<p>This hail data collection belongs to a drone hail survey performed on 2022-06-28 in Locarno-Monti (TI, Switzerland). The supercell reached the location around 07:50 UTC in the morning. Only one photogrammetry flight could be performed and thus no estimation of the hail melting process is available. The orthophoto is masked to ignore parts where detection of hail is unwanted.</p> <p> </p> <p>Expert 1 (lai, mlainer), Expert 2 (jtm), Expert 3 (por, jportmann)</p>
Data, scripts, and R Notebook for Carneiro et al 2023. Flight performance and wing morphology in the bat Carollia perspicillata: biophysical models and energetics. Integrative Zoology DOI:10.1111/1749-4877.12707
<p>Files provided as supporting information for the paper by Carneiro et al. 2023. Flight performance and wing morphology in the bat <em>Carollia perspicillata</em>: biophysical models and energetics. Integrative Zoology. DOI:10.1111/1749-4877.12707</p> <p>File descriptions</p> <p>ArmTA.txt - Temperature and surface areas for arms of <em>C. perspicillata</em> after flight experiment<br> BodyTA.txt - Temperature and surface areas for body of <em>C. perspicillata</em> after flight experiment<br> HeadTA.txt - Temperature and surface areas for head of <em>C. perspicillata</em> after flight experiment<br> WingTA.txt - Temperature and surface areas for wings (patagium) of <em>C. perspicillata</em> after flight experiment<br> WingMorph.txt - Morphological variables measured in the body and wings of <em>C. perspicillata</em><br> HeatLoss.R - Function to estimate heat loss (Qt)<br> PowFlight.R - Function to estimate minimum power required to fly<br> Script-HeatLoss-FlightPerformance.R - R script with set of analyses performed<br> SupportingInformationFile.docx - R notebook with set of analyses performed, word format<br> SupportingInformationFile.nb.html - R notebook with set of analyses performed, html format<br> SupportingInformationFile.Rmd - R notebook with set of analyses performed (R markdown)</p> <p>For the R scripts (Script-HeatLoss-FlightPerformance.R) and notebook (<br> SupportingInformationFile.Rmd) to work and be compiled, all files need to be copied to the same folder.</p>
R script and data files for Oakley et al (2017) Journal of Proteome Research. DOI: 10.1021/acs.jproteome.6b00797
<p>This R script and data replicates the analysis of Oakley et al (2017) Thermal shock induces host proteostasis disruption and endoplasmic reticulum stress in the model symbiotic Cnidarian <em>Aiptasia</em>. <em>Journal of Proteome Research</em>. 16:2121-2134. DOI: 10.1021/acs.jproteome.6b00797. </p>
Example data set for the R package riversCentralAsia
<p>This data set contains example data for demonstrating the functionality of the R package riversCentralAsia. riversCentralAsia (https://github.com/hydrosolutions/riversCentralAsia) includes several functions for pre-processing hydrological data to facilitate hydrological modelling with RS MINERVE (https://crealp.github.io/rsminerve-releases/). The package is used extensively in the open-source teaching course Modeling of Hydrological Systems in Semi-Arid Central Asia (https://hydrosolutions.github.io/caham_book/). </p>
HRV-ACC: a dataset with R-R intervals and accelerometer data for the diagnosis of psychotic disorders using a Polar H10 wearable sensor
<p><strong>ABSTRACT</strong></p> <p>The issue of diagnosing psychotic diseases, including schizophrenia and bipolar disorder, in particular, the objectification of symptom severity assessment, is still a problem requiring the attention of researchers. Two measures that can be helpful in patient diagnosis are heart rate variability calculated based on electrocardiographic signal and accelerometer mobility data. The following dataset contains data from 30 psychiatric ward patients having schizophrenia or bipolar disorder and 30 healthy persons. The duration of the measurements for individuals was usually between 1.5 and 2 hours. R-R intervals necessary for heart rate variability calculation were collected simultaneously with accelerometer data using a wearable Polar H10 device. The Positive and Negative Syndrome Scale (PANSS) test was performed for each patient participating in the experiment, and its results were attached to the dataset. Furthermore, the code for loading and preprocessing data, as well as for statistical analysis, was included on the corresponding GitHub repository.</p> <p><strong>BACKGROUND</strong></p> <p>Heart rate variability (HRV), calculated based on electrocardiographic (ECG) recordings of R-R intervals stemming from the heart's electrical activity, may be used as a biomarker of mental illnesses, including schizophrenia and bipolar disorder (BD) [Benjamin et al]. The variations of R-R interval values correspond to the heart's autonomic regulation changes [Berntson et al, Stogios et al]. Moreover, the HRV measure reflects the activity of the sympathetic and parasympathetic parts of the autonomous nervous system (ANS) [Task Force of the European Society of Cardiology the North American Society of Pacing Electrophysiology, Matusik et al]. Patients with psychotic mental disorders show a tendency for a change in the centrally regulated ANS balance in the direction of less dynamic changes in the ANS activity in response to different environmental conditions [Stogios et al]. Larger sympathetic activity relative to the parasympathetic one leads to lower HRV, while, on the other hand, higher parasympathetic activity translates to higher HRV. This loss of dynamic response may be an indicator of mental health. Additional benefits may come from measuring the daily activity of patients using accelerometry. This may be used to register periods of physical activity and inactivity or withdrawal for further correlation with HRV values recorded at the same time.</p> <p><strong>EXPERIMENTS</strong></p> <p>In our experiment, the participants were 30 psychiatric ward patients with schizophrenia or BD and 30 healthy people. All measurements were performed using a Polar H10 wearable device. The sensor collects ECG recordings and accelerometer data and, additionally, prepares a detection of R wave peaks. Participants of the experiment had to wear the sensor for a given time. Basically, it was between 1.5 and 2 hours, but the shortest recording was 70 minutes. During this time, evaluated persons could perform any activity a few minutes after starting the measurement. Participants were encouraged to undertake physical activity and, more specifically, to take a walk. Due to patients being in the medical ward, they received instruction to take a walk in the corridors at the beginning of the experiment. They were to repeat the walk 30 minutes and 1 hour after the first walk. The subsequent walks were to be slightly longer (about 3, 5 and 7 minutes, respectively). We did not remind or supervise the command during the experiment, both in the treatment and the control group. Seven persons from the control group did not receive this order and their measurements correspond to freely selected activities with rest periods but at least three of them performed physical activities during this time. Nevertheless, at the start of the experiment, all participants were requested to rest in a sitting position for 5 minutes. Moreover, for each patient, the disease severity was assessed using the PANSS test and its scores are attached to the dataset.</p> <p>The data from sensors were collected using Polar Sensor Logger application [Happonen]. Such extracted measurements were then preprocessed and analyzed using the code prepared by the authors of the experiment. It is publicly available on the GitHub repository [Książek et al].</p> <p>Firstly, we performed a manual artifact detection to remove abnormal heartbeats due to non-sinus beats and technical issues of the device (e.g. temporary disconnections and inappropriate electrode readings). We also performed anomaly detection using Daubechies wavelet transform. Nevertheless, the dataset includes raw data, while a full code necessary to reproduce our anomaly detection approach is available in the repository. Optionally, it is also possible to perform cubic spline data interpolation. After that step, rolling windows of a particular size and time intervals between them are created. Then, a statistical analysis is prepared, e.g. mean HRV calculation using the RMSSD (Root Mean Square of Successive Differences) approach, measuring a relationship between mean HRV and PANSS scores, mobility coefficient calculation based on accelerometer data and verification of dependencies between HRV and mobility scores.</p> <p><strong>DATA DESCRIPTION</strong></p> <p>The structure of the dataset is as follows. One folder, called <em>HRV_anonymized_data</em> contains values of R-R intervals together with timestamps for each experiment participant. The data was properly anonymized, i.e. the day of the measurement was removed to prevent person identification. Files concerned with patients have the name <em>treatment_X.csv</em>, where <em>X</em> is the number of the person, while files related to the healthy controls are named <em>control_Y.csv</em>, where <em>Y</em> is the identification number of the person. Furthermore, for visualization purposes, an image of the raw RR intervals for each participant is presented. Its name is <em>raw_RR_{control,treatment}_N.png</em>, where <em>N</em> is the number of the person from the control/treatment group. The collected data are raw, i.e. before the anomaly removal. The code enabling reproducing the anomaly detection stage and removing suspicious heartbeats is publicly available in the repository [Książek et al]. The structure of consecutive files collecting R-R intervals is following:</p> <table> <tbody> <tr> <td><strong>Phone timestamp</strong></td> <td><strong>RR-interval [ms]</strong></td> </tr> <tr> <td>12:43:26.538000</td> <td>651</td> </tr> <tr> <td>12:43:27.189000</td> <td>632</td> </tr> <tr> <td>12:43:27.821000</td> <td>618</td> </tr> <tr> <td>12:43:28.439000</td> <td>621</td> </tr> <tr> <td>12:43:29.060000</td> <td>661</td> </tr> <tr> <td>...</td> <td>...</td> </tr> </tbody> </table> <p>The first column contains the timestamp for which the distance between two consecutive R peaks was registered. The corresponding R-R interval is presented in the second column of the file and is expressed in milliseconds. <br> The second folder, called <em>accelerometer_anonymized_data</em> contains values of accelerometer data collected at the same time as R-R intervals. The naming convention is similar to that of the R-R interval data: <em>treatment_X.csv </em>and <em>control_X.csv</em> represent the data coming from the persons from the treatment and control group, respectively, while <em>X </em>is the identification number of the selected participant. The numbers are exactly the same as for R-R intervals. The structure of the files with accelerometer recordings is as follows:</p> <table> <tbody> <tr> <td><strong>Phone timestamp</strong></td> <td><strong>X [mg]</strong></td> <td><strong>Y [mg]</strong></td> <td><strong>Z [mg]</strong></td> </tr> <tr> <td>13:00:17.196000</td> <td>-961</td> <td>-23</td> <td>182</td> </tr> <tr> <td>13:00:17.205000</td> <td>-965</td> <td>-21</td> <td>181</td> </tr> <tr> <td>13:00:17.215000</td> <td>-966</td> <td>-22</td> <td>187</td> </tr> <tr> <td>13:00:17.225000</td> <td>-967</td> <td>-26</td> <td>193</td> </tr> <tr> <td>13:00:17.235000</td> <td>-965</td> <td>-27</td> <td>191</td> </tr> <tr> <td>...</td> <td>...</td> <td>...</td> <td>...</td> </tr> </tbody> </table> <p>The first column contains a timestamp, while the next three columns correspond to the currently registered acceleration in three axes: X, Y and Z, in milli-g unit.</p> <p>We also attached a file with the PANSS test scores (<em>PANSS.csv</em>) for all patients participating in the measurement. The structure of this file is as follows:</p> <table> <tbody> <tr> <td><strong>no_of_person</strong></td> <td><strong>PANSS_P</strong></td> <td><strong>PANSS_N</strong></td> <td><strong>PANSS_G</strong></td> <td><strong>PANSS_total</strong></td> </tr> <tr> <td>1</td> <td>8</td> <td>13</td> <td>22</td> <td>43</td> </tr> <tr> <td>2</td> <td>11</td> <td>7</td> <td>18</td> <td>36</td> </tr> <tr> <td>3</td> <td>14</td> <td>30</td> <td>44</td> <td>88</td> </tr> <tr> <td>4</td> <td>18</td> <td>13</td> <td>27</td> <td>58</td> </tr> <tr> <td>...</td> <td>...</td> <td>...</td> <td>...</td> <td>..</td> </tr> </tbody> </table> <p><br> The first column contains the identification number of the patient, while the three following columns refer to the PANSS scores related to positive, negative and general symptoms, respectively.</p> <p><strong>USAGE NOTES</strong></p> <p>All the files necessary to run the HRV and/or accelerometer data analysis are available on the GitHub repository [Książek et al]. HRV data loading, preprocessing (i.e. anomaly detection and removal), as well as the calculation of mean HRV values in terms of the RMSSD, is performed in the <em>main.py</em> file. Also, Pearson's correlation coefficients between HRV values and PANSS scores and the statistical tests (Levene's and Mann-Whitney U tests) comparing the treatment and control groups are computed. By default, a sensitivity analysis is made, i.e. running the full pipeline for different settings of the window size for which the HRV is calculated and various time intervals between consecutive windows. Preparing the heatmaps of correlation coefficients and corresponding p-values can be done by running the <em>utils_advanced_plots.py</em> file after performing the sensitivity analysis. Furthermore, a detailed analysis for the one selected set of hyperparameters may be prepared (by setting <em>sensitivity_analysis = False</em>), i.e. for 15-minute window sizes, 1-minute time intervals between consecutive windows and without data interpolation method. Also, patients taking quetiapine may be excluded from further calculations by setting <em>exclude_quetiapine = True</em> because this medicine can have a strong impact on HRV [Hattori et al].</p> <p>The accelerometer data processing may be performed using the <em>utils_accelerometer.py</em> file. In this case, accelerometer recordings are downsampled to ensure the same timestamps as for R-R intervals and, for each participant, the mobility coefficient is calculated. Then, a correlation coefficient between mean HRV values and mobility coefficient is computed. The plotting of the pure accelerometer signal may be done by running the <em>utils_loading.py </em>file.</p> <p>The comparison of age distribution between the tested groups can be made by the histogram plotted with the use of the <em>utils_basic_plots.py</em> file.</p>
Course Materials for Environmental Data Science in R: Introduction to Data Integration and Machine Learning (ENV 730)
In today's world, understanding environmental data and making informed decisions based on it is crucial for addressing complex environmental challenges. Yale School of the Environment's Environmental Data Science in R: Introduction to Data Integration and Machine Learning (ENV 730) course serves as an introduction to the integration of environmental data using R programming language, coupled with machine learning techniques. This dataset contains a zip file with all the data files used in this course, along with a README that has the metadata for those files.
Data and R Code from "A novel approach to sustainability assessment of food supply chains using networks of ecosystem services"
<p>Data and R code from this paper applying network analysis (iGraph) to two case studies pre and post agroecological transitions in Central America and Tanzania, Africa from the IPES-Food report. Further descriptions of this data and code can be found within the extended manuscript. R Code relies on the data from the scenarios (e.g., Nodes and Relations CSVs) and creates the output network metrics (e.g., Node Metric CSVs). </p>
Data and code to perform the"Target deformation" workflow in R: virtual reconstruction of the Equus stenonis holotype skulll
<p>Data and code to perform the"Target deformation" workflow in R: virtual reconstruction of the Equus stenonis holotype skulll.</p> <p>TargetDeformation.R: R code with for the Target Deformation procedure.<br> IGF560.ply: 3D mesh of the holotype IGF560 in ply extension.<br> IGF560_set.txt: landmark set of the holotype IGF560.<br> Dm. 5/154.3/4.A4.5.ply: 3D mesh of Dm 5/154.3/4.A4.5 in .ply extension.<br> Dm_set.txt: landmark set of the Dm 5/154.3/4.A4.5 sample.<br> IGF11023: 3D mesh of IGF11023 in.ply extension.<br> IGF11023_set.txt: landmark set on the IGF11023 sample.<br> IGF560R: 3D mesh of IGF560R in.ply extension.<br> IGF560W: 3D mesh of IGF560W in.ply extension.<br> IGF560R-s: 3D mesh of IGF560R-s in.ply extension.<br> IGF560W-s: 3D mesh of IGF560W-s in.ply extension.<br> IGF560_IGF560R_IGF560W.html: file that contain WebGL code to reproduce the 3D meshes of IGF560, IGF560R and IGF560W in a browser.<br> IGF560Rs_IGF560Ws.html: file that contain WebGL code to reproduce the 3D meshes of IGF560R-S and IGF560W-S in a browser.<br> IGF560W Mesh area variation.html: file that contain WebGL code to reproduce two 3d meshes of IGF560W using localmeshDist() and meshdist() in a browser.<br> </p>
Data and R-Scripts for "Quality and timing of crowd-based water level class observations"
<p>This are the data and the R-scripts used for the manuscript "Quality and timing of crowd-based water level class observations" accepted for publication in the journal Hydrological Processes in July 2020 as a Scientific Briefing. To run the code, just run the R-script with the name "RunThisForResults.R". Results will be written to the "Figures" and the "Results" folder.</p>
Data and R Code for 'Domestication via the commensal pathway in a fish-invertebrate mutualism'
<p>This document contains all data and R code required to replicate the analyses in 'Domestication via the commensal pathway in a fish-invertebrate mechanism' as published in Nature Communications. The R Markdown provided includes descriptions of all variables and the code used for the analysis of the following eight datasets:</p> <p>1. Transects<br> 2. Census of farms<br> 3. Paired choice experiments<br> 4. Predation experiment 1<br> 5. Predation experiment 2<br> 6. Timed observations<br> 7. Farm algae composition<br> 8. Longfin damselfish body condition</p> <p>In addition, the R Markdown also includes descriptions of all variables for two additional datasets:</p> <p>9. Estimates of mysid swarm density<br> 10. Mysid waste excretion and nutrient availability</p> <p>A PDF version of the R Markdown with all output is also provided. Please see the methods section of the associated manuscript for further information on data collection and analysis procedures.</p> <p>Author contributions to data collection and analysis: RMB, JMC, ZLC, TLS & WEF collected the data; WEF, RMB, JMC, ZLC & AM implemented the analyses. </p> <p>Correspond with: rohan.m.brooker@gmail.com</p>
R package n2khab: providing preprocessed reference data for Flemish Natura 2000 habitat analyses
The n2khab package is an R package with preprocessing functions and standard reference data, useful for analyses regarding Flemish Natura 2000 habitats and regionally important biotopes (RIBs). URL: <a href="https://inbo.github.io/n2khab">https://inbo.github.io/n2khab</a>.
Example code and data for ubms: An R package for fitting hierarchical occupancy and N-mixture abundance models in a Bayesian framework
<p>This repository contains an R script (grouse_example.R) and data (grouse_data.csv) used to reproduce the grouse abundance analysis described in Kellner, K. F., et al. (2021) ubms: An R package for fitting hierarchical occupancy and N-mixture abundance models in a Bayesian framework. Methods in Ecology and Evolution. The R script requires installation of the ubms R package, which can be obtained from CRAN (https://cran.r-project.org/package=ubms).</p> <p>The repository also contains an additional example occupancy analysis (occupancy_example.R) using the crossbill dataset included with the unmarked R package.</p>
Data & R-Code for "Weather and food availability additively affect reproductive output in an expanding raptor population"
<p><strong>Abstract</strong></p> <p>The joint effects of interacting environmental factors on key demographic parameters can exacerbate or mitigate the separate factors’ effects on population dynamics. Given ongoing changes in climate and land use, assessing interactions between weather and food availability on reproductive performance is crucial to understand and forecast population dynamics. By conducting a feeding experiment in 4 years with different weather conditions, we were able to disentangle the effects of weather, food availability and their interactions on reproductive parameters in an expanding population of the red kite (<em>Milvus milvus</em>), a conservation-relevant raptor known to be supported by anthropogenic feeding. Brood loss occurred mainly during the incubation phase, and was associated with rainfall and low food availability. In contrast, brood loss during the nestling phase occurred mostly due to low temperatures. Survival of last-hatched nestlings and nestling development was enhanced by food supplementation and reduced by adverse weather conditions. However, we found no support for interactive effects of weather and food availability, suggesting that these factors affect reproduction of red kites additively. The results not only suggest that food-weather interactions are prevented by parental life-history trade-offs, but that food availability and weather conditions are crucial separate determinants of reproductive output, and thus population productivity. Overall, our results suggest that the observed increase in spring temperatures and enhanced anthropogenic food resources have contributed to the elevational expansion and the growth of the study population during the last decades.</p>
Data for Introduction to R for Biologists
<p>This dataset is supplementary to the workshop designed for the MSc Students in UCL Cancer Institute to introduce R (statistical-) programming language.</p>
R-IBU: A basic ice breakup data set for the Aura and Kokemäki rivers
<p>This data set is related to the article ‘Tricentennial trends in spring ice break-ups on three rivers in Northern Europe’ which was published in <em>The Cryosphere</em> in 2022. The data set includes the ice-off dates (the first day the river is ice-free) for Aura River in Turku (60°45’N, 22°27’E) over the period 1749–2020 and the break-up (the date when the ice started breaking up and/or moving) dates for Kokemäki River in Pori (61°48’N, 21°79’E) over the period 1793–2020. Both rivers are in Finland.</p> <p>The data set has been developed for climate research purposes. The aim was to create two series with homogenized break-up dates with regard to site and event. The series will be updated if new observations are obtained. See the Readme_txt for more information about the data sets.</p>
Data and R script for 'Evaluating the cyclic ratio schedule as an assay of feeding behaviour in the European starling (Sturnus vulgaris)'
<p>Data files and R script for Dunn et al. "Evaluating the cyclic ratio schedule as an assay of feeding behaviour in the European starling (<em>Sturnus vulgaris</em>)"</p> <p>Includes a single R script that produces all the analyses in the paper. The script makes use of three different .csv data files.</p>
Body size convergence in Sturnira - R Code and supporting data
<p>R Code and supporting data for: Co-occurrence and character convergence in two Neotropical bats. Journal of Mammalogy</p>
SLAFEEL: R scripts and reformatted data analyzed by Alamil et al. (2019)
<p>SLAFEEL: Statistical Learning Approach For Estimating Epidemiological Links from deep sequencing data</p> <p>This archive contains R scripts for running analyses proposed by Alamil et al. (2019; Inferring epidemiological links from deep sequencing data: a statistical learning approach for human, animal and plant diseases), namely<br> - functions.R that contains R functions required for computations,<br> - influenza.R, ebola.R and potyvirus.R where the analyses are implemented for each case study, and<br> - influenza-format-genomic-data.R giving an example of how to format data to be used in the statistical learning approach.</p> <p>This archive also contains the reformatted data analyzed by Alamil et al. (2019). The datasets that are provided concern swine influenza virus (reformatted from Murcia et al., 2012), Ebola virus (reformatted from Gire et al., 2014) and a wild salsify potyvirus. Two rds files are provided for swine influenza, the first one for the naive chain, the second one for the vaccinated chain. Ebola rds files are compressed into the archive ebolaRDS.zip. rds files can be loaded in the R statistical software with the command "readRDS(filename)", which returns a list. The list contains a "readme" item describing the contents of the list, as well as a "host.table" item providing metadata about host units and a "set.of.sequences" item providing sequencing data formatted in numeric matrices.</p> <p>Murcia PR, Hughes J, Battista P, Lloyd L, Baillie GJ, Ramirez-Gonzalez RH, et al. Evolution of an Eurasian avian-like influenza virus in naive and vaccinated pigs. PLoS Pathogens. 2012;8(5):e1002730.</p> <p>Gire SK, Goba A, Andersen KG, Sealfon RS, Park DJ, Kanneh L, et al. Genomic surveillance elucidates Ebola virus origin and transmission during the 2014 outbreak. Science. 2014;345:1369–1372</p> <p> </p> <p>Funded by the ANR - Project name: SMITID (2016-2020) - Grant number: ANR-16-CE35-0006</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.