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1,773 results for “Supplementary data,”
Supplementary Data for "Identification of Neighborhood Hotspots via the Cumulative Hazard Index: Results from a Community-Partnered Low-cost Sensor Deployment"
<p>These are the underlying data sets needed to build the kriging maps and calculate dissemination block cumulative hazard indices described in the paper. There are three data sets:</p> <ol> <li><strong>"Sampling location names and coordinates.csv"</strong>: locations and IDs of the low-cost sensors and the regulatory monitoring stations used in this work.<strong> [NOTE: </strong>latitudes and longitudes for the sensor deployments have been intentionally rounded to protect the location of volunteer sensor hosts.]</li> <li><strong>"Dissemination Block Populations.csv"</strong>: These are the relevant dissemination blocks in the study domain and their associated populations. This information was originally extracted from: https://censusmapper.ca/#13/49.2430/-123.1252</li> <li><strong>"Daily average concentrations by site and pollutant.csv"</strong>: This contains the PM2.5, NO2 and O3 daily averages for the entire study period across all low-cost sensor sites and regulatory monitoring stations. Refer to "Sampling location names and coordinates.csv" to parse the labels in this data set.</li> </ol> <p>There is also a sample code in Python to construct the kriging maps provided in 2 formats. <strong>[NOTE: </strong>we have intentionally excluded uploading the exact data sets imported by this code; our original data contains exact locations of sensor host volunteers and thus cannot be shared.]</p> <ol> <li><strong>"Jain et al - GeoHealth - Kriging Script.ipynb"</strong>: A Jupyter notebook script to import the data, build kriging maps, calculate CHIs, and export the data.</li> <li><strong>" Jain et al - GeoHealth - Kriging Script.pdf"</strong>: A PDF export of the Jupyter notebook so that you can read the Python scripts even if you are not a Jupyter notebooks user.</li> </ol>
Supplementary data for "Ecological assessment of combined sewer overflow management practices through the analysis of benthic and hyporheic sediment bacterial assemblages of an intermittent stream"
<p><strong>Supplementary data for the Pozzi <em>et al.</em> paper entitled "Ecological assessment of combined sewer overflow management practices through the analysis of benthic and hyporheic microbial assemblages and a tracking of exogenous bacterial taxa in a peri-urban intermittent stream".</strong></p> <p># Created by Dr Adrien C. MEYNIER POZZI on June, 29th, 2023<br> # Part of DOmic research project funded by the Agence de l’Eau - Rhône Méditerranée Corse [AE-RMC, Project 2020 0702 DOmic, 2020-2023], and of the DOmic extension funded by the EUR H2O'Lyon [ANR-17-EURE-0018] of Université de Lyon<br> # Part of the Chaudanne river long-term experiment site belonging to the Observatoire de Terrain en Hydrologie Urbaine (OTHU)<br> # Part of the work conducted in the team on Opportinistic Bacterial Pathogen in the Environment (BPOE) led by Dr. Benoit Cournoyer<br> # Samples were obtained in 2 campaigns, corresponding to periods before (2010-2011) or after (2018) the implementation of the 91/271/EEC European Directive that limited Combined-Sewer Overflow (CSO) discharges to the Chaudanne river<br> # Samples consisted in surface water, benthic and hyporheic sediments taken in run, riffle and pool geomorphologic features, either upstream or downstream the CSO outlet, plus positive and negative controls</p> <table> <tbody> <tr> <td><strong>Metadata. Name and description of data tables provided as supplementary information</strong></td> </tr> <tr> <td><strong>Data Name</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>Data S1. River hydrology variables and hydraulic gradients at surveyed transects</td> <td>Array to describe the hydrologic variables and gradients at the studied transects. Top line is header, second line is metadata for each recorded variable, and third line is the unit of the variable, if any.</td> </tr> <tr> <td>Data S2. Environmental variables (water physical-chemistry, nutrients, FIBs, MTEs, PAHs) with metadata</td> <td>An array to list environmental variables for all true samples (n=90) included in the study. Sample identifiers and dates are provided. First 8 rows list the CAS number, SANDRE number, unit, method, limit of quantification and norm for each variable, if any.</td> </tr> <tr> <td>Data S3. Hydrological indices and synthetic variables computed with ClustOfVar</td> <td>Hydrological indices computed for the river flow, precipitations and CSO overflows computed over a 3-week period preceding each sampling date.</td> </tr> <tr> <td>Data S4. Discharge events selected to compute CSO dilution ratios</td> <td>An array to describe CSO events included for the computation of the CSO dilution ratio (SI Data 6A) together with 6 tables and 3 figures (SI Data 6B to 6J) describing the CSO event ratio all year round over the studied period, as well as for events that occurred before or after the CSO was modified and during low flow or high flow season. In SI Data 6A, top line is header and second line is metadata for each recorded variable.</td> </tr> <tr> <td>Data S5. Raw environmental matrix for use in R</td> <td>An array to list experimental design and environmental variables for all true samples and controls. Several environmental variables were synthetized using the ClustOfVar method (Chavent et al (2012) 10.18637/jss.v050.i13). Format is directly usable in R software.</td> </tr> </tbody> </table> <p> </p>
Supplementary data to "Destruction and regrowth of lithospheric mantle beneath large igneous provinces"
<p>Database files to accompany "<em>Destruction and regrowth of lithospheric mantle beneath large igneous provinces</em>", By <a href="http://www.science.org/doi/10.1126/sciadv.adf6216">Stephenson et al. (2023)</a>. The article can be accessed by following <a href="http://www.science.org/doi/10.1126/sciadv.adf6216">this permanent link</a>.</p> <p>The primary resources in this database are (i) estimates of melt equilibration pressure and temperature calculated using the scheme of <a href="https://github.com/fmcnab/meltPT">McNab & Ball (2023)</a>; (ii) a database of lithospheric thickness estimates beneath modern intraplate magmatic provinces using geochemical and seismological techniques; (iii) a database of the outlines and ages of large igneous provinces, substantially updated from <a href="http://https://doi.org/10.1029/93RG02508">Coffin & Eldholm (1994)</a>, and <a href="https://doi.org/10.5670/oceanog.2006.13">Coffin et al. (2006)</a>; (iv) a database of large igneous province eruption centres; and (v) a document of references used to build these databases. Files are numbered as in the Supplementary Information of <a href="http://www.science.org/doi/10.1126/sciadv.adf6216">the paper.</a> Please see below for more details.</p> <ol> <li><strong>Data S1</strong>. A database of global geochemical compositions of mafic intraplate magmatic rocks compiled by <a href="http://doi.org/10.1038/s41467-021-22323-9">Ball et al (2021)</a>, and corresponding estimates of melt equilibration pressure and temperature P<sub>eq</sub> and T<sub>eq</sub>, respectively; <a href="http://www.science.org/doi/10.1126/sciadv.adf6216">this study</a>). Note that authors should cite <a href="http://doi.org/10.1038/s41467-021-22323-9">Ball et al. (2021)</a> in reference to the global geochemical database. They should cite <a href="http://www.science.org/doi/10.1126/sciadv.adf6216">Stephenson et al (2023)</a> in reference to the global equilibration pressure and temperature estimates, in which case they should also cite <a href="http://github.com/fmcnab/meltPT">McNab & Ball (2023)</a>, whose software was used to calculate P<sub>eq</sub> and T<sub>eq</sub>.</li> <li><strong>Data S2</strong>. A spreadsheet containing modern-day lithospheric thickness estimates beneath modern intraplate provinces. For complete references to geochemical analyses contained in this database, please see <a href="https://doi.org/10.1038/s41467-021-22323-9">Ball et al (2021)</a>. The database includes lithospheric thickness estimates obtained <ul> <li>by exploiting melt equilibration pressure and temperature <a href="http://www.science.org/doi/10.1126/sciadv.adf6216">(this study)</a>;</li> <li>by inverse modelling of rare earth element compositions <a href="https://doi.org/10.1038/s41467-021-22323-9">(Ball et al., 2021)</a>; and</li> <li>from the lithospheric thickness model of<a href="https://doi.org/10.1038/s41561-020-0593-2"> Hoggard et al. (2020)</a>, which is based upon the tomographic model of <a href="https://doi.org/10.1093/gji/ggt095">Schaeffer & Lebedev (2013)</a>.</li> </ul> </li> <li><strong>Data S3</strong> & <strong>S4</strong>. A database containing outlines of magmatic provinces dating back to 750 Ma, including <ul> <li>a directory (Data_S3.zip) containing the unfiltered database shape files (lips.shp, lips.shx, lips.dbf, lips.cpg). This directory also contains the same data in a multisegment text file for plotting in the Generic Mapping Tools (polys_ID_age_unfiltered.dat) in which each polygon is separated by '>' where the header indicates polygon ID and time since eruption. And</li> <li>a database filtered for final magmatic event in a given location (Data_S4.dat), where each polygon header also contains '>' ID age polygon_area'. See <a href="http://www.science.org/doi/10.1126/sciadv.adf6216">the paper</a> for methodological details.</li> </ul> </li> <li><strong>Data S5</strong>. A database of located LIP eruption centres.</li> <li><strong>Data S6</strong>. A pdf document of references. The document includes <ul> <li>references used to update locations and ages of the large igneous province database of <a href="http://doi.org/10.5670/oceanog.2006.13">Coffin et al. (2006)</a>;</li> <li>references for existing lithospheric thickness models used test our observed LAB depth as a function of time relationship; and</li> <li>references used to locate the eruption centres of mantle plumes (i.e. Data S5; <a href="http://www.science.org/doi/10.1126/sciadv.adf6216">this study</a>).</li> </ul> </li> </ol>
Supplementary data: Geometry-preserving Expansion Microscopy microplates enable high fidelity nanoscale distortion mapping
<p>--- Data supplement---</p> <p>--- Title: Geometry-preserving Expansion Microscopy microplates enable high fidelity nanoscale distortion mapping</p> <p>The zip file contains the following directories and subdirectories. The files, recommended software, calling code and relevance to the main manuscript (preprint found at https://doi.org/10.1101/2023.02.20.529230) are included in the notes below:</p> <p>(i) “STL files” – Includes .stl file formats of the Expansion Microscopy microplate components. These files can be opened with any CAD software (e.g. Fusion 360) or any opensource 3D printer slicer programme (e.g. Chitubox v1.9.5)</p> <p>(ii) “Image alignment & distortion analysis code” – Directory containing the ImageJ macros and custom-written Python scripts for the analysis pipeline described in the main manuscript, from image scaling and alignment to for distortion plotting and RMS Error analysis and plotting. Subdirectories include:</p> <ul> <li>a.“Distortion analysis and RMSE plotting code” – Directory contains custom-written Python scripts for updated distortion analysis, plotting, RMS error calculation, and code for combining the plots. Each .py file can be run with any Python distribution. Dependency libraries and modules (all opensource) include numpy, os, cv2, skimage, and tifffile</li> <li>b.“Basic Align Macro.ijm” – ImageJ macro for basic image alignment of pre- and post-Expansion Microscopy images. Refer to <a href="https://imagej.nih.gov/ij/developer/macro/macros.html#tools">https://imagej.nih.gov/ij/developer/macro/macros.html#tools</a> on how to install and run ImageJ macros.</li> </ul> <p> </p> <p>(iii)“Example data and analysis scripts” – Directory containing example datasets and worked examples of analysis. Subdirectories include:</p> <ul> <li>a.“Single-channel_HeLa_cells”. Worked example of single-colour dataset of a cluster of HeLa cells stained with NHS-AZ488. Folder includes pre-Expansion and post-Expansion images (.tif format), overlays of the pre- and post-Expansion images along with distortion vector maps, and plots of normalised RMS error (in % values) against measurement length scale (in micrometers) saved as numpy arrays (.npy format). This can be called in using a Python code similar to: numpy.load("file name")</li> <li>b.“Multiplexed_Drosophila_wing”. Worked example of two-colour dataset of Drosophila fly wing tissue images stained with NHS-Alexa647 and anti-E-cad-GFP/Alexa488. Folder includes pre-Expansion and post-Expansion images (.tif format), overlays of the pre- and post-Expansion images along with distortion vector maps, and plots of normalised RMS error (in % values) against measurement length scale (in micrometers) saved as numpy arrays (.npy format).</li> <li>c.“Multiplexed_HeLa_cells”. Worked example of two-colour dataset of cultured HeLa cell images stained with NHS-AZ488 and antibodies. Each folder includes pre-Expansion and post-Expansion images (.tif format), overlays of the pre- and post-Expansion images along with distortion vector maps, and plots of normalised RMS error (in % values) against measurement length scale (in micrometers) saved as numpy arrays (.npy format): <ul> <li>I.“KDELAlexa594_NHSAZ488” – antibody staining against KDEL</li> <li>II.“NUP98Alexa594_NHSAZ488” – antibody staining against Nups98</li> </ul> </li> </ul> <p> </p> <ul> <li>d.“Distortion Correction”. Folder contains two subfolders of output images from distortion corrections using the Linear Stack Alignment with SIFT plugin in ImageJ v1.54f. Each example consists of a pre- and post-Expansion image file, and the ‘corrected’ image file generated with 5, 10, and 50 voxel B-spline sampling.</li> </ul> <p> </p> <p>These files are placed in public domain under Creative Commons license CC BY-ND 4.0</p>
CoBaHMA domains: supplementary Table and Data
<p>Supplementary Table and Supplementary Data 1 to 4 to the article "<strong>AlphaFold2-guided description of CoBaHMA, a novel family of bacterial domains within the Heavy-Metal Associated superfamily"</strong></p> <p> </p> <p><strong>Supplementary Table 1 </strong>(.xlsx file):</p> <ol> <li><strong>Structural and functional annotations of the large communities of CobaHMA domain proteins.</strong> The number of community sequences matching a given profile (IPR or ECOD) is indicated. The color code corresponds to the following functional categories. For each IPR accession, the type of profile is indicated between brackets (D for domain, F for family, H for superfamily).</li> <li><strong>Structural and functional annotations of small communities.</strong> Only the 20 small communities with at least one new annotation (e.g. not shared with the large communities, in grey and black) are detailed here. For these 20 communities, some annotations are shared with the large communities, corresponding only to P-type (cyan) and HMA (yellow). For a complete view of the annotations of the small communties, see <strong>Supplementary Figure 5</strong>. The number of community sequences matching a given profile (IPR or ECOD) is indicated. For each IPR accession, the type of profile is indicated between brackets (D for domain, F for family, H for superfamily).</li> </ol> <p> </p> <p><strong>Supplementary Data 1 </strong>(.zip file):</p> <p>Multiple sequence alignments (MSA) of the sequences of the most populated communities, build using MAFFT and rendered using EspPript3. The first two lines report the 2D structures of the AF2 model of the representative sequence of the community, and its amino acid sequence. The initial MSA (fasta format) is also provided, together with the coordinates of the 3D structure model (pdb format) and the HCA plot (postcript file) of the representative sequence of the considered community.</p> <p> </p> <p><strong>Supplementary Data 2 </strong>(.zip file):</p> <p>Annotations by communities: Modular organization of the full-length CoBaHMA domain proteins within the 48 large community (Functional annotations are indicated along the sequence by shaded areas. Each functional category of domain is highlighted by the following color code: CoBaHMA (green), HMA_2 (lime), HMA (yellow), P-type (cyan) and Serca (darkblue), ABC (orange), Calcyanin Gly-Zip (red), PAP2 (magenta). Membrane regions as identified by deepTMHMM are indicated by gray areas.</p> <p> </p> <p><strong>Supplementary Data 3 </strong>(.tsv file)</p> <p>Main features of communities (e.g. representative sequence, community size, mean amino acid length, annotations, …).</p> <p> </p> <p><strong>Supplementary Data 4 </strong>(.tsv file)</p> <p>Mean features of sequences possessing CoBaHMA domain(s).</p>
Online supplementary data linked to the publication "Aubenas-les-Alpes (S-E France). Part III – Last and final part of the mammalian assemblage with some comments on the palaeoenvironment and palaeobiogeography" doi:10.1016/j.annpal.2019.03.001
<p>Online supplementary appendix including the list of Oligocene localities and associated faunal lists compared to Aubenas-les-Alpes, and the size estimation of the non-predatory species for the construction of Fig.10.</p>
Supplementary data and scripts for "An Atlas of Human Metabolism"
<p>This repository contains the models, data, and scripts associated with the publication "An Atlas of Human Metabolism".</p> <p>The content is divided into three main directories, each containing their own README file containing instructions.</p> <ol> <li>tINIT_GEMs - Contains the genome-scale metabolic models (GEMs) generated in the study using the tINIT algorithm, as well as many scripts and datasets necessary to reproduce model generation, analyses, and figures.</li> <li>ec_GEMs - Contains the enzyme-constrained models (ecGEMs) generated in the study using the GECKO framework, as well as many scripts and datasets necessary to reproduce model generation, analyses, and figures.</li> <li>GEM_PRO - Contains the GEM-PRO dataframe with the protein structure information associated with the Human1 model.</li> </ol>
Supplementary Data S1 for Rosen et al. Li and Ca enrichment in the Bristol Dry Lake brine, California
<p>Supplementary file S1: Chemical groundwater data used for analysis in the Journal article entitled "<strong>Li and Ca enrichment in the Bristol Dry Lake brine compared to brines from Cadiz and Danby Dry Lakes, Barstow-Bristol Trough, California, USA" </strong>by Michael R. Rosen, Lisa L. Stillings, Tyler Kane, Kate Campbell-Hay, and Matthew Vitale, P.G., Ray Spanjers, P.G.in the Journal Minerals</p> <p>Supplementary file S2: Chemical data from extractions from 10 clay samples from Bristol Dry lake by acid digestion. used in "<strong>Li and Ca enrichment in the Bristol Dry Lake brine compared to brines from Cadiz and Danby Dry Lakes, Barstow-Bristol Trough, California, USA" </strong>by Michael R. Rosen, Lisa L. Stillings, Tyler Kane, Kate Campbell-Hay, and Matthew Vitale, P.G., Ray Spanjers, P.G.in the Journal Minerals.</p> <p>Methods for all data are in the journal article.</p> <p>Data is for Bristol, Cadiz and Danby playas (Dry Lakes). Most data has been previously published either online in the California Division of Water Resources website, or in other USGS publications or journal articles. Some data from Cadiz Dry lake has not been previously published and publication of these data has been granted by Tetra Technologies Inc. and Standard Lithium LLC.</p>
Table S3. List of Locustella sound recordings included in bioacoustic analysis surrounding description of the Taliabu Grasshopper-Warbler. The table provides information on sound library sources and sampling localities of recordings as well as raw data on all 11 bioacoustic parameters measured (see Supplementary Materials section SM3 for more details on parameters). Recordings whose source is labeled as "private recording" were obtained by colleagues and are available upon demand from the corresponding author.
<p>supplement to Rheindt, Frank E., Prawiradilaga, Dewi M., Ashari, Hidayat, Suparno, Gwee, Chyi Yin, Lee, Geraldine W. X., Wu, Meng Yue, Ng, Nathaniel S. R. (2020): A lost world in Wallacea: Description of a montane archipelagic avifauna. Science 367: 167-170, DOI: 10.1126/science.aax2146</p>
Supplementary data: Computational analysis of mechanical stress in colonic diverticulosis
<p>The data set contains code, source data, and derivatives data for the results presented in our research paper titled "Computational analysis of mechanical stress in colonic diverticulosis".</p> <p>The "code" contains Abaqus (SIMULIA, Providence, RI) files for the simulations presented in the paper (tested with Abaqus version 6.13) and jupyter notebooks developed to analyze simulation results.</p> <p>The "sourcedata" folder contains Excel files with parameters used for the simulations as well as data directly extracted from the simulation results.</p> <p>The "derivatives" folder contains secondary data calculated based on the files from "sourcedata".</p> <p>The README document included in the dataset contains a more detailed description of the files and folders.</p>
Forensic Exchange Analysis of Contact Artifacts on Data Hiding Timestamps-ADS Experiment Supplementary Files
<p>Da-Yu Kao is an Associate Professor at the Department of Information Management, Central Police University, Taiwan. He was a detective and forensic police officer at Taiwan's Criminal Investigation Bureau (under the National Police Administration). With a Master's degree in Information Management and a Ph.D. degree in Crime Prevention and Correction, he had led several investigations in cooperation with police agencies from other countries for the past 20 years. He is now the director of Computer Crime Investigation Lab at Central Police University and the webmaster of Cybercrime Investigation and Digital Forensics in the Facebook Group.</p>
Supplementary Data: A Frequentist Analysis of Three Right-Handed Neutrinos with GAMBIT
<p><strong>Supplementary Data</strong><br> <em> A Frequentist Analysis of 3 Right-Handed Neutrinos with GAMBIT.</em></p> <p>The files in this record contain data for the frequentist global fit of a model with three right-handed neutrinos using the <a href="http://gambit.hepforge.org">GAMBIT</a> tool.</p>
Offshore wind competitiveness in mature markets without subsidy - Supplementary Data
<p>This is the data set named "Supplementary Data 1" for the research paper "Offshore wind competitiveness in mature markets without subsidy". This data set also contains the raw data for reproducing Figure 1 through to Figure 4. The paper is currently under review and access is for peer-review purposes only.</p>
Data and supplementary material in support of article "Can robots express facial emotions dominantly enough for use in dementia care?"
<p>Data and supplementary material in support of article "Vlachos, E. and Tan, Z. H. (2020). Can robots express facial emotions dominantly enough for use in dementia care?, <em>International Psychogeriatrics</em>, Cambridge University Press". </p> <p>Our objective is to evaluate the recognition, and denomination of the six basic emotional facial expressions as displayed by a social robot to persons with dementia, and to compare it with the results from the evaluation of static photographs of humans from the Paul Ekman database in order to investigate the differences in recognition rates among the two stimuli.</p>
Supplementary Data: Strengthening the bound on the mass of the lightest neutrino with terrestrial and cosmological experiments (arXiv:2009.03287)
<p><strong>Supplementary Data</strong></p> <p><em>Strengthening the bound on the mass of the lightest neutrino with terrestrial and cosmological experiments (arXiv:2009.03287)</em></p> <p>The files in this record contain data from the scans of the models considered in the <a href="http://gambit.hepforge.org">GAMBIT</a> paper on neutrino masses.</p> <p>The files consist of</p> <ul> <li>21 <code>.yaml</code> files corresponding to different models, sampling parameters and/or priors</li> <li>11 final <code>.hdf5</code> files, containing the results of running GAMBIT with each yaml file</li> <li>10 <code>.margestats</code> files containing 1D credible regions for parameters and observables, obtained by running <a href="https://github.com/cmbant/getdist">getdist</a> on the hdf5 files</li> <li>An example file 3-NHB_Neff2_PC500_pp.pip file for plotting the results of a single hdf5 file with <a href="github.com/patscott/pippi">pippi</a></li> <li>A tarball including all files in this record except the hdf5 files.</li> </ul> <p>The different yaml, hdf5 and margestats files corresponding to different models, priors or setttings follow the naming scheme <code>[scan index]-[hierarchy][m_nu0 prior]_[Neff prior]_[scanner]_[extra]_[step].[extension]</code>, where</p> <ul> <li>scan index = <code>1</code>-<code>11</code></li> <li>hierarchy = <code>NH</code> (normal hierarchy), <code>IH</code> (inverted hierarchy)</li> <li>m_nu0 pior = <code>A</code> (linear-log prior on m_nu0), <code>B</code> (linear prior on m_nu0)</li> <li>Neff prior = <code>0</code> (Delta N_eff = 0), <code>1</code> (Delta N_eff > 0), <code>2</code> (Delta N_eff free)</li> <li>scanner = <code>PC500</code> (Polychord with 500 live points), <code>DIV10k</code> (Diver with NP=1e4)</li> <li>extra = blank (standard likelihood combination), <code>Lyalpha</code> (likelihood also includes eBOSS DR14 Lyman-alpha BAO scale measurements)</li> <li>step = blank (main scan), <code>pp</code> (postprocessing of outputs of main scan).</li> <li>extension = <code>yaml</code>, <code>margestats</code>, <code>hdf5/hdf5.tar.gz</code></li> </ul> <p>A few caveats to keep in mind:</p> <ol> <li> <p>The YAML files are designed to work with the tagged release of GAMBIT 1.5.0, and the pip file is tested with pippi 2.1. They may or may not work with later versions of either software (but you can of course always obtain the version that they do work with via the git history).</p> </li> <li> <p>The pip file is an example only. Users wishing to reproduce the more advanced plots in any of the GAMBIT papers should contact us for tips or scripts, or experiment for themselves. Many of these scripts are in multiple parts and require undocumented manual interventions and steps in order to implement various plot-specific customisations, so please do not expect the same level of polish as for files provided here or in the GAMBIT repo.</p> </li> </ol>
Supplementary data: Quantifying and numerically representing recharge and flow components in a karstified carbonate aquifer
<p>The following data and information is related to the paper "Quantifying and numerically representing recharge and flow components in a karstified carbonate aquifer":</p> <p>Observed and estimated hydrological time series (hourly spring discharge, hourly rainfall, daily minimum and maximum air temperature and evapotranspiration) and modelling results (hourly discharges and flows) using an InfoWorks ICM pipe network model, as well as R code to compute Fourier transform / power spectrum of time series.</p> <p>See 'readme_Schuler_etal_WRR_supplementary data.pdf' for more information.</p>
Supplementary files for Silva et al. 2020 "Reptiles on the wrong track?": R Code, data and figures
<p>Datasets, R code and figures pertaining to the manuscript: Silva, I., Crane, M., Marshall, B.M., & Strine, C.T. (2020).<em> </em> <em>Reptiles on the wrong track? Moving beyond traditional estimators with dynamic Brownian Bridge Movement Models.</em> Movement Ecology 8, 43 DOI: 10.1186/s40462-020-00229-3</p> <p>Article available at: <a href="https://movementecologyjournal.biomedcentral.com/articles/10.1186/s40462-020-00229-3">https://movementecologyjournal.biomedcentral.com/articles/10.1186/s40462-020-00229-3</a></p>
Mehrabi et al. 2020. The global divide in data-driven farming. Supplementary Information.
<p>This repository contains data, data sources, and code for reproducing the results of the paper Mehrabi et al. 2020. The global divide in data-driven farming. Nature Sustainability. https://doi.org/10.1038/s41893-020-00631-0.</p> <p>There are five directories included here, listed in alphabetical order. Directory SI_A contains the script for reproducing the analysis presented in the main text of the paper. Directories SI_B to SI_E contain the scripts for creating the input data sets used in the analysis.</p> <p>This is a baseline study, that is intended to be built upon and improved as and when new data become available. For issues and suggestions, please contact zia.mehrabi@ubc.ca.</p>
Supplementary material (buffet data set): Egeler, G.-A., von Rickenbach, F., & Baur, P. (2020). Menüwahl in der Hochschulmensa: Design & Durchführung Feldexperiment (NOVANIMAL Kurzbericht). ZHAW. https://doi.org/10.21256/zhaw-1408
<p>During the fieldexperiment in the NOVANIMAL Project several informations about the offer of the buffet were collected. The report of the fieldexperiment see:</p> <p><a href="https://zenodo.org/deposit/4115429">Egeler, G.-A. & Baur, P. (2020). Menüwahl in der Hochschulmensa: Fleisch oder Vegi? Ergebnisse eines 12-wöchigen Feldexperiments (NOVANIMAL Working Paper No. 5). ZHAW. https://doi.org/10.21256/zhaw-1405</a></p>
Supplementary data to *Benchmarking of numerical integration methods for ODE models of biological systems*
<p>This archive contains supplementary data and code for the manuscript <strong>Benchmarking of numerical integration methods for ODE models of biological systems </strong>by<strong> Städter et al. 2020</strong>. It contains</p> <ul> <li>scripts to automatically download and install all required packages and models,</li> <li>scripts to compile the models and to perform the study,</li> <li>value files containing all data underlying the analyses in the manuscript,</li> <li>scripts to generate the manuscript figures.</li> </ul> <p>There is a <strong>README.md </strong>file with further information, in particular on what scripts to execute to reproduce the study.</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.