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1,093 results for “scripts”
The true colour of water at Upper Penticton Creek -- data and scripts
<p>These files contain data and scripts used in the analysis for an article titled " Streamwater colour in snow-dominated headwater catchments: natural variability and the effects of forest harvesting," by R.D. Moore, R.D. Winkler and G.D. Hope, to be published in <em>Hydrological Processes</em>.</p> <p>The file <em>upc_water_colour.csv</em> contains the colour data as expressed in true colour units (TCU). The first line is a comment that should be skipped, noting that entries of "creek dry" have been manually edited out of this version of the data. All other editing was performed in the script named <em>0_wrangle_data.r</em>. The columns are as follows:</p> <ul> <li><em>Year</em> - year of observation as four-digit value (e.g., 2005)</li> <li><em>Date</em> - date as dd-Mmm (e.g., 15-May)</li> <li><em>Day</em> - day of year (e.g., 1-Jan = 1)</li> <li><em>Cut241</em> - cumulative area harvested in 241 Creek as a percentage of catchment area</li> <li><em>Cut242</em> - cumulative area harvested in 241 Creek as a percentage of catchment area</li> <li><em>wc_240</em> - water colour (TCU) in 240 Creek</li> <li><em>wc_241</em> - water colour (TCU) in 241 Creek</li> <li><em>wc_242</em> - water colour (TCU) in 242 Creek</li> </ul> <p>The scripts are numbered in the order of dependency. For example, a script beginning <em>0_</em> should be run before running a script beginning <em>1_</em>. The scripts are set up to be run within an R project on the local hard drive. The project directory should contain a folder named <em>data</em> that contains <em>upc_water_colour.csv. </em>All other data sets are accessed programmatically within the scripts.</p> <p>Brief descriptions of the scripts follow:</p> <ul> <li><em>0_wrangle_data.r</em> - Uses functions in the <strong>tidyhydat</strong> package to access streamflow data; corrects some erroneous entries for the water colour data; merges streamflow and colour data sets for further analysis.</li> <li><em>0_wrangle_spatial_data.r</em> - Accesses digital elevation models (DEMs) catchment boundaries and soil map from the Upper Penticton Creek data repository (zenodo); computes various topographic indices from the DEMS; saves processed files on the local hard drive in a folder named <em>dem</em>, located within the project root folder.</li> <li><em>1_soil_maps.r </em>- Generates a map of the gleyed soil units (Figure 2).</li> <li><em>1_q_pca_trimonthly.r </em>- Performs a paired-catchment analysis of the streamflow response to logging using a tri-monthly time step; generates plots of observed and predicted streamflow for 241 and 242 Creeks (Figure 3).</li> <li><em>1_wc_analysis_post_140.r</em> - Analyses water colour variations and response to logging; generates figures used in the article; analysis focuses on days 145 and on each year due to lack of data for earlier dates in the pre-harvest period.</li> <li><em>1_catchment_characteristics.r</em> - Computes topographic indices for each catchment and generates a table (Table 1) that contains a summary of catchment characteristics.</li> <li><em>ch_saga_functions.r</em> - Contains functions that use RSAGA package to process the digital elevation models to remove sinks and calculate contributing area grids.</li> </ul>
Neural network dataset, script and model
<p>This data was collected from several sources and compiled into a single text file called NN.txt</p> <p>Ten quiet and five disturbed days in each month from 2009 to 2019 were used to develop the dataset. These days were got from the list of International Q and D days accessed on the website of the World Data Centre of Geomagnetism, Kyoto (<em><a href="https://wdc.kugi.kyoto-u.ac.jp/qddays/index.html">https://wdc.kugi.kyoto-u.ac.jp/qddays/index.html</a>). </em>The dataset has seventeen columns defined as Date, Year (Y), Day of the year (DOY), hour of the day (HH), cosine and sine components of the day of the year for annual variation (DAC and DAS), cosine and sine components of the day of the year for semi-annual variation (DSC and DSS), cosine and sine components of the hour for daily variation (HRC and HRS), geographical coordinates (Lat, Lon), geomagnetic coordinates (Glat, Glon), Dst Index, F10.7 Index, TEC and sunspot number (SSN). The geographical coordinates were converted to geomagnetic coordinates using quasi dipole coordinates (Emmert et al., 2010; Richmond, 1995). Dst index, F10.7 index, and SSN were downloaded from the OmniWeb database (<em>https://omniweb.gsfc.nasa.gov/form/dx1.html</em>). Jason TEC data was downloaded from the FTP access of the CEDAR Madrigal database (<em>http://cedar.openmadrigal.org/ftp/</em>). </p> <p>The data resolution was an 18-second interval. </p> <p>It should be noted that any day that had a missing value was eliminated from the database. The data downloaded from Omni web was on a resolution of 1 hour and it was put at an 18-second interval by repeating the same value. The TEC data from the CEDAR Madrigal database is in a second interval and therefore it was averaged at an 18-second interval. </p> <p>The dataset was trained with MATLAB software, the MATLAB script (NN script) is attached. The output was a Neural network model also attached </p>
Model output and analysis scripts for "High-latitude precipitation as a driver of multicentennial variability of the AMOC in a climate model of intermediate complexity"
<p>Here, we provide annually averaged model output from a 3000-year control simulation of PlaSim–LSG, a climate model of intermediate complexity. Processed variables and a Jupyter notebook to reproduce all figures of the manuscript (Mehling et al.: "High-latitude precipitation as a driver of multicentennial variability of the AMOC in a climate model of intermediate complexity") can also be found in this repository.</p> <p>In addition, a Python implementation of the three-box model proposed in the manuscript can be found in the notebook <em>boxmodel.ipynb</em>.</p>
Scripts for quantifying the effect of diamond nano-pillars on the fluorescence of NV centers
<p><strong>Summary</strong></p> <p>Scripts and data can be used to reproduce and build on the numerical results published under the title: "<a href="http://doi.org/10.3390/nano12091516">Optical and Spin Properties of NV Center Ensembles in Diamond Nano-Pillars</a>" by Kseniia Volkova, Julia Heupel, Sergei Trofimov, Fridtjof Betz, Rémi Colom, Rowan W. MacQueen, Sapida Akhundzada, Meike Reginka, Arno Ehresmann, Johann P. Reithmaier, Sven Burger, Cyril Popov, and Boris Naydenov (Nanomaterials 12(9), 1516, 2022).</p> <p><strong>Method</strong></p> <p>The dipole emitters are assumed to be distributed uniformly 30 nm below the top surface of the nano-pillars. They are first integrated with a trapezoidal rule along the azimuth (because of the periodicity this results in a geometrical convergence) and with a 15 point Gauss-Kronrod quadrature rule in radial direction.</p> <p>The main source of error results from the dipole positions being integrated only from 0 to R - min_dist, as it is challenging to model a dipole emitter located only few nanometers from the curved material interface. Further numerical parameters can be adjusted in the input files for JCMsuite. Both, a 3D setup and a 2D setup are provided. The letter exploits the rotational symmetry which results in a smaller memory footprint. Yet, as the distance of the dipole from the symmetry axis increases, many Fourier components are required which leads to long computation times.</p> <p>For further quantitative studies we propose the 3D setup that is the default in the script 'integration.m', which allows to integrate closer to the side walls without increasing the costs. Furthermore in the second data set, shipped together with the data published in the paper, the height has been kept constant. In the paper the height has been chosen according to the fabricated samples. The 90° angle has been assigned to the [111] samples and the correspondingt height of 1400 nm and the 35.3° angle was assigned to the [100] samples and a height of 2200 nm.</p> <p><strong>Structure</strong></p> <p>The directories <strong>scattering2D</strong>, <strong>scattering3D </strong>and <strong>scatteringFlat</strong> contain input files for JCMsuite. The script 'integration.m' can be used to produce new data. With 'plotresults.m' you can either plot the results produced with 'integration.m' or those which were published in the related paper. Please note that the provided example produced with the script 'integration.m' differs from the published data, which has been computed with slightly different parameters.</p> <p><strong>Requirements</strong></p> <ul> <li>JCMsuite 5.2.0</li> <li>Matlab R2019b</li> </ul> <p>In order to produce new data, you must replace the corresponding place holders in the files by a path to your installation of JCMsuite. Free trial licenses are available, please refer to the homepage of <a href="https://jcmwave.com/">JCMwave</a>.</p>
Data and script: Reduced enumeration effort, but not coarse taxonomic resolution, is sufficient to represent beta diversity patterns of stream benthic diatoms
<p>This is a dataset on benthic diatom communities sampled in 90 riffles (the local communities) within nine near-pristine subtropical streams (each stream represented a metacommunity) in southeast subtropical Brazil. </p> <p>In addition to the dataset, we also provide the R code used to investigate whether reduced enumeration efforts (i.e., subsets of counted valves per sample) and the identification to the genus level are sufficient to recover patterns in the species composition and in beta diversity of benthic diatom metacommunities.</p>
Data, scripts, and figures of the article: Processing weights of chickens determined by Dual-Energy X-Ray Absorptiometry. 2. Developing prediction models
<p>This data set contains the data, JMP scripts, and figures of the article titled "Processing weights of chickens determined by Dual-Energy X-Ray Absorptiometry. 2. Developing prediction models" to be published in the journal Animal - Open Space.</p>
Data, scripts, and figures of the article: Processing weights of chickens determined by Dual-Energy X-Ray Absorptiometry. 1. Weight changes due to fasting, bleeding, and chilling
<p>This data set contains the data, JMP scripts, and figures of the article titled "Processing weights of chickens determined by Dual-Energy X-Ray Absorptiometry. 1. Weight changes due to fasting, bleeding, and chilling" to be published in the journal Animal - Open Space. </p>
Data, scripts and model output to perform spatiotemporal analysis of plankton drivers in the Belgian part of the North Sea
<p>This archive contains the input data, R scripts and final results of a mechanistic model that uses near real-time data from the Belgian Part of the North Sea (2011-2017) to quantify the relative contributions of the bottom-up and top-down drivers in phytoplankton dynamics. Input data are zooplankton and phytoplankton abundances, nutrients, Sea Surface Temperature (SST), photosynthetically active radiation (PAR); from the LifeWatch data and infrastructure, funded by Research Foundation - Flanders (FWO). Water temperature data for one of the locations was obtained from Flemish Banks Monitoring Network at https://meetnetvlaamsebanken.be/. The R scripts are presented in a R Markdown file that can be executed in the Blue-Cloud Zoo and Phytoplankton EOV products Vlab at https://blue-cloud.d4science.org/web/zoo-phytoplankton_eov, operated by D4Science.org, www.d4science.org (Assante et al., 2019). </p>
Data, scripts, and figure of the article: The fasting heat production of broilers is a function of their body composition
<p>This data set contains the data, JMP scripts, and figures of the article titled "The fasting heat production of broilers is a function of their body composition" to be published in the journal Animal - Open Space.</p>
Data set and scripts - Influence of Festive Periods on Road Safety: Multidimensional Analysis (Road Accidents in Colombia 2017-2021)
<p>This dataset comprises historical information about road accidents in Colombia from 2017 to 2021, titled 'Road Accidents 2017-2021', containing 18,600 records of accident events on roads managed by the National Roads Institute (INVÍAS, 2021). The dataset includes 41 descriptors and was last updated on July 15, 2022. It has been published under the Open Data initiative (Law 1712 of 2014 on Transparency and Access to National Public Information).</p> <p>In addition to accident information, the dataset integrates a database with holiday dates and road identifiers, ensuring data coherence and quality for data analysis purposes. Statistical analysis is conducted through exploratory data analysis focusing on the years 2017 to 2021, utilizing Python (version 3.10) within the Jupyter Notebooks execution environment and specialized libraries (Pandas, NumPy, Matplotlib, and Seaborn), due to their ease of application for this dataset. After data normalization, the dataset comprises 18,554 records, with 46 excluded due to inconsistent data formats.</p>
Scripts and datas for "Climate-driven projections of future global wetlands extent"
<p>Computations scripts (1, 2), associated input dataset (3), and output datasets for wetland fractions (4, 5) used and presented in the study:</p> <p><em><strong>L. Hardouin, B. Decharme, J. Colin, C. Delire: </strong>Climate-driven projections of future global wetlands extent.</em></p> <p>The calculation and input scripts include:<br><em>1_var_comput </em>: Calculation of the main variables used to diagnose wetlands: depth of the "active" layer d_wtl, liquid water content w_l, ice content and maximum content in the layer d_wtl.</p> <p><em>2_TOPMODEL </em>: The scripts used to diagnose the wetland fraction and to calibrate the models using the TOPMODEL approach. In this folder, the mean, maximum, minimum, standard deviation and skewness datasets of the topographic indices at the grid-cell level are also included.</p> <p><em>3_alpha_and_beta </em>: Calibrated alpha and beta parameters used to obtain the historical and projected wetland fractions with the calibrated version.</p> <p>The outputs datasets contain:</p> <p><em>4_fwtl_model_period </em>: The fraction of wetlands computed from each model in the calibrated version, for the historical period and the 4 SSPs scenarios presented in the submitted work.</p> <p><em>5_not_calibrated_fwtl_model_period </em>: The fraction of wetlands computed from each model in the uncalibrated version with alpha=0.65, for the historical period and the 4 SSPs scenarios, where only the historical period is used in the submitted work.</p> <p> </p> <p>Additional data not created by the authors are needed to reproduce the study (see the Open research section in the submitted article). Feel free to contact the authors (lucas.hardouin@meteo.fr) for any help or questions.</p>
Experimental data, analysis scripts and simulations for "Emittance preservation in a plasma-wakefield accelerator"
<p>This dataset presents the experimental data, the analysis scripts and the accompanying simulations for the article <em>"Emittance preservation in a plasma-wakefield accelerator"</em> by C. A. Lindstrøm <em>et al</em>. [<a href="https://doi.org/10.1038/s41467-024-50320-1">Nat. Commun. 15, 6097 (2024)</a>].</p> <p>The data was collected at the FLASHForward facility at DESY (Hamburg, Germany). Simulations were performed using <a href="https://doi.org/10.5281/zenodo.5639467" target="_blank" rel="noopener">HiPACE++ v23.11</a>.</p> <p><strong>Folder structure:</strong></p> <ul> <li>Folders containing experimental data: <ul> <li>Folder <code>1A_DATA__OBJECT_PLANE_SCANS</code> contains all data from object-plane scans (emittance measurements).</li> <li>Folder <code>1B_DATA__SPECTRUM_MEASUREMENT</code> contains all data from energy-spectrum measurements.</li> <li>Folder <code>1C_DATA__TWO_BPM_TOMOGRAPHY</code> contains all data from two-BPM tomography measurements.</li> <li>Folder <code>1D_DATA__BEAM_RECONSTRUCTION</code> contains all data from beam-reconstruction measurements (including longitudinal-phase-space measurements).</li> <li>Folder <code>1E_DATA__PLASMA_DENSITY</code> contains all data from plasma-density measurements (spectral-line broadening).</li> </ul> </li> <li>Folder <code>2_ANALYSIS</code> contains all the data-analysis scripts, required for plotting experimental figures.</li> <li>Folder <code>3_SIMULATION</code> contains all simulation scripts, required for generating 6D beam phase spaces and plotting simulation figures.</li> <li>Folder <code>4_FIGURES</code> contains all figure-plotting scripts (17 figures total).</li> </ul> <p><br><strong>Dataset structure:</strong></p> <ul> <li>Each dataset is identified by a 5-digit number (e.g., <code>14275</code>)</li> <li>Metadata and beam-synchronous scalar values are contained in a <code>.mat</code> dataset file (e.g., <code>14275.mat</code>).</li> <li>The dataset file has the following fields: <ul> <li><code>.metadata</code> containing all the generic metadata</li> <li><code>.state</code> containing all the <em>non-beam-synchronous</em> data (once per dataset; magnet settings etc.)</li> <li><code>.scalars</code> containing all the <em>beam-synchronous scalar</em> data (once per shot; BPM readings etc.)</li> <li><code>.vectors</code> containing all the <em>beam-synchronous vector</em> data (once per shot; scope traces etc.)</li> <li><code>.images</code> containing all the <em>beam-synchronous image</em> data, with relative URLs (once per shot; spectrometer images etc.)</li> </ul> </li> <li>The corresponding images (linked from the <code>.mat</code> file) are contained in the <code>images</code> folder, sorted by scan step.</li> </ul> <p><br><strong>Instructions for plotting all figures*:</strong></p> <ol> <li>Change directory to <code>4_FIGURES/</code></li> <li>In MATLAB, run <code>plot_all_figures();</code></li> <li>The 4 main figures and 13 supplementary figures will be plotted</li> </ol> <p><strong>Instructions for generating the 6D phase space for simulations*:</strong></p> <ol> <li>Change directory to<code> 3_SIMULATION/input_beam_generation/</code></li> <li>In MATLAB, run <code>generate_beam_and_plasma();</code></li> <li>The full analysis will up to several minutes (the files are stored in the <code>_files</code> folder)</li> </ol> <p><strong>Instructions for performing HiPACE++ simulations*:</strong></p> <ol> <li>Change directory to e.g. <code>3_SIMULATION/simulations/experimental_cell_50mm/</code></li> <li>The HiPACE++ input file is called <code>input_file</code></li> <li>This file refers to the plasma profile (<code>plasma_short.csv</code>) and beam files (<code>beam.h5</code> and <code>driver.h5</code>) found in <code>3_SIMULATION/run_notebooks/inputs/</code></li> </ol> <p><strong>Instructions for re-performing all the analysis*:</strong></p> <ol> <li>Change directory to <code>2_ANALYSIS/</code></li> <li>In MATLAB, run <code>run_all_analyses();</code></li> <li>The full analysis will up to several hours (the files are stored in various <code>_files</code> folders)</li> </ol> <p><em>* The scripts use UNIX system calls and are only compatible with Linux and Mac, but not Windows.</em></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>
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>
Data processing scripts and images from e-MERLIN project CY6213 used in Ghirlanda et al. 2019, Science
<p>Data processing scripts and images from e-MERLIN project CY6213 used in Ghirlanda et al. 2019, Science</p> <p> </p> <ul> <li>info.txt contains a general description of how the data was processed and the main results.</li> <li>pipeline.tar contains the data pipeline used to process the e-MERLIN observations</li> <li>imaging.py is the script used to produce the final images</li> <li>CY6213_images.tar contain the final images of the target source (not corrected by calibration factor, described in the imaging script).</li> </ul> <p> </p>
Supplementary data and scripts for Willemsen and Bravo 2019 "Origin and evolution of papillomavirus (onco)genes and genomes"
<p>Supplementary data for Willemsen and Bravo 2019 "Origin and evolution of papillomavirus (onco)genes and genomes". The data set consists of two folders: “Bali-Phy” and “RandomPermutationTests”. The “Bali-Phy” folder contains the final results and convergence diagnostics of the Common Ancestry tests obtained by using the Bali-Phy software. The “RandomPermutationTests” folder contains all the data and scripts to repeat the random permutation tests described in the manuscript. Please see the corresponding README files for more information.</p>
Supplementary data and scripts for Willemsen et al., 2019 "Genome plasticity in Papillomaviruses and de novo emergence of E5 oncogenes"
<p>Supplementary data for Willemsen et al., 2019 "Genome plasticity in Papillomaviruses and <em>de novo</em> emergence of E5 oncogenes". The data set consists of three folders: “Alignments”, “Bali-Phy” and “RandomPermutationTests”. The “Alignments” folder contains the different alignments used for phylogenetic tree construction and comparison. The “Bali-Phy” folder contains the final results and convergence diagnostics of the Common Ancestry tests obtained by using the Bali-Phy software. The “RandomPermutationTests” folder contains all the data and scripts to repeat the random permutation tests described in the manuscript. Please see the corresponding README files for more information.</p>
caseysaenger/ForamMgCa_PSM: files and scripts for revised version of manuscript "Calibration and validation of environmental controls on planktic foraminifera Mg/Ca using global core-top data".
<p>files and scripts for revised version of manuscript "Calibration and validation of environmental controls on planktic foraminifera Mg/Ca using global core-top data". Saenger, C. and M. N. Evans. Resubmitted to Paleoceanography and Paleoclimatology, May 3, 2019.</p>
Source Data and Scripts - MultiMatch: Geometry-Informed Colocalization in Multi-Color Super-Resolution Microscopy
<p>Experimental and simulated STED data and scripts associated with Naas et al. "<em>MultiMatch: Geometry-Informed Colocalization in Multi-Color Super-Resolution Microscopy.</em>" <em>bioRxiv</em> (2024): 2024-02. </p> <p>The MultiMatch Python package and further illustrative examples are available on GitHub repository <a href="https://github.com/gnies/multi_match">https://github.com/gnies/multi_match</a>.</p>
R scripts for analyzing LiDAR data to assess forest canopy structure and perform Principal Component Analysis (PCA) on derived metrics
<p>This repository contains R scripts for analyzing LiDAR data to assess forest canopy structure and perform Principal Component Analysis (PCA) on spectral and LiDAR-derived metrics. The scripts cover LiDAR data processing, canopy height model (CHM) generation, calculation of forest canopy metrics, and PCA analysis.</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.