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4,694 results for “Data Analysis”
Data set for the manuscript 'Robustness Analysis of Metasurfaces: Perfect Structures are not always the Best'
<p>In this data set, there are 1 PDF, 3 m-files, and 3 zip files.</p> <p>The manuscript (<strong><em>Readme document<em>.</em>pdf</em></strong>) contains three sections: Quasi-analytical model (<em><strong>analytical_model_EnergyConservation.m</strong></em>), post-processing full-wave simulations (<strong><em>post_processing_from_COMSOL.m</em></strong>), and post-processing experimental data (<strong><em>post_processing_from_experiment.m</em></strong>). Each Matlab code is explained in this manuscript. Corresponding raw data (<em><strong>COMSOL simulation data for reflective metallic metasurfaces.zip</strong>,<strong> COMSOL simulation data for transmitive dielectric metasurfaces.zip</strong>, </em>and <strong><em>experimental data.zip</em></strong>) are attached. One can move the required m-file into the folder and run the m-file directly. In the COMSOL simulation data.zip file, one can find two COMSOL files, which retain the settings for simulation and extracting the required data. </p> <p>The Matlab codes are implemented with version R2018b.</p> <p>The COMSOL files are created with version COMSOL Multiphysics 5.6.</p> <p> </p>
Combined unsupervised and semi-automated supervised analysis of flow cytometry data reveals cellular fingerprint associated with newly diagnosed pediatric type 1 diabetes
<p>Type 1 diabetes is a chronic autoimmune disease resulting in an immune-mediated loss of pancreatic β-cells; however, an unbiased and reproducible profiling of type 1 diabetes-specific circulating immunome at disease onset has yet to be explored. In this study, fresh whole blood was collected from a pediatric cohort of 107 patients with new-onset type 1 diabetes, 85 relatives of patients with type 1 diabetes with 0-1 islet autoantibodies, 58 patients with celiac disease or autoimmune thyroiditis and 76 healthy controls. Up to 6 mL of blood was collected from each subject into a VACUETTE® TUBE 6 ml ACD-B (Greiner). Fresh whole blood underwent red blood cell lysis, was washed and stained with specific monoclonal antibodies. Fresh whole blood samples were stained with five panels of antibodies labelled as T cells, T&NK cells, B cells, Tregs and DCs/monos encompassing main subsets of T cells, NK cells, B cells, Tregs, DCs and monocytes detected using 26 surface markers and the intracellular marker forkhead box P3 (FoxP3); for the Treg panel, intracellular staining was performed after fixation and permeabilization. Cells were acquired on a BD FACSCanto-II flow cytometer equipped with FACSDiva software (Becton Dickinson, Franklin Lakes, NJ). </p>
Data from the parametric analysis of masonry buttressed arches with limit analysis subjected to vertical self-weight plus a proportional horizontal live load
<p>For each one of the simulations performed from the parametric analysis of masonry buttressed arches with limit analysis subjected to vertical self-weight plus a proportional horizontal live load, this database contains a .txt, a .vtk and a .png file. In the .txt file the elapsed time and the collapse multiplier of each simulation can be found. The .vtk file contains all the geometry and displacement values of every masonry buttressed arch. Finally, the .png file presents the collapse mechanism obtained. </p>
Data from the parametric analysis of masonry buttressed arches with limit analysis subjected to vertical self-weight plus a proportional concentrated vertical live load applied at mid-span
<p>For each one of the simulations performed from the parametric analysis of masonry buttressed arches with limit analysis subjected to vertical self-weight plus a proportional concentrated vertical live load applied at mid-span, this database contains a .txt, a .vtk and a .png file. In the .txt file the elapsed time and the collapse multiplier of each simulation can be found. The .vtk file contains all the geometry and displacement values of every masonry buttressed arch. Finally, the .png file presents the collapse mechanism obtained. </p>
Datasets for the CUT&RUN data analysis
<p>The datasets are used for the Galaxy CUT&RUN training material. CUT&RUN data was generated by <a href="https://doi.org/10.1186/s13059-019-1802-4">Zhu et al. 2019</a> and down sampled to speed up the training. ChIP-seq data comes from an experiment for GATA1 by <a href="https://doi.org/10.1038/ng.3793">Canver et al. 2017</a>.</p>
Python Time Normalized Superposed Epoch Analysis (SEAnorm) Example Data Set
<p>Solar Wind Omni and SAMPEX ( Solar Anomalous and Magnetospheric Particle Explorer) datasets used in examples for <a href="https://github.com/samwalton7645/SEA_Code">SEAnorm</a>, a time normalized superposed epoch analysis package in python.</p> <p>Both data sets are stored as either a HDF5 or a compressed csv file (csv.bz2) which contain a Pandas DataFrame of either the Solar Wind Omni and SAMPEX data sets. The data sets where written with pandas.DataFrame.to_hdf() and pandas.DataFrame.to_csv() using a compression level of 9. The DataFrames can be read using pandas.DataFrame.read_hdf( ) or pandas.DataFrame.read_csv( ) depending on the file format. </p> <p>The Solar Wind Omni data sets contains solar wind velocity (V) and dynamic pressure (P), the southward interplanetary magnetic field in Geocentric Solar Ecliptic System (GSE) coordinates (B_Z_GSE), the auroral electrojet index (AE), and the Sym-H index all at 1 minute cadence. </p> <p>The SAMPEX data set contains electron flux from the Proton/Electron Telescope (PET) at two energy channels 1.5-6.0 MeV (ELO) and 2.5-14 MeV (EHI) at an approximate 6 second cadence.</p> <p> </p>
Survey on lattice data analysis, presentation, and curation practices
<p>This repository contains the results of a survey on software workflows and open science in lattice field theory conducted in 2022 by Andreas Athenodorou, Ed Bennett, Julian Lenz, and Elli Papadopolou. These data were collected using <a href="https://www.limesurvey.org/">LimeSurvey</a>, and were first presented in <a href="https://indico.hiskp.uni-bonn.de/event/40/contributions/695/">a talk at Lattice 2022 by Andreas Athenodorou</a>.</p> <p>The analysis is based on Julian Lenz's <a href="https://github.com/chillenzer/limesurvey-parser">LimeSurvey CSV parser</a>.</p> <p>The survey results are included in survey-results-redacted.csv. The survey structure is included in survey-structure.lss. Further details of the structure of the data, setup, see the included README.md file.</p>
Data used in ECLIPSER methods paper and GTEx snRNA-seq cross-tissue reference map analysis
<p>The tables were used in the papers: Rouhana*, Wang* <em>et al.,</em> ECLIPSER: identifying causal cell types and genes for complex traits through single cell enrichment of e/sQTL-mapped genes in GWAS loci, bioRxiv 2021, doi: https://doi.org/10.1101/2021.11.24.469720; and Eraslan <em>et al.,</em> Single-nucleus cross-tissue molecular reference maps to decipher disease gene function, bioRxiv 2021, doi: https://doi.org/10.1101/2021.07.19.452954. '<a href="https://zenodo.org/api/files/1f8d48d0-6bf7-4bec-b6ef-5c7a9ead8079/GTEx_v8_HG38_all_variants.tsv.gz">GTEx_v8_HG38_all_variants.tsv.gz</a>' is an input file for running GWASvar2gene on GTEx v8 eQTLs and sQTLs, and all other files are input files for ECLIPSER.</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>
Nonlinear spectral analysis of ion acoustic solitons arising from a streaming charged object using the numerical inverse scattering transform data
<p>Data files used in the publication: "Nonlinear spectral analysis of ion acoustic solitons arising from a streaming charged object using the numerical inverse scattering transform", submitted to Physics of Plasma August 2022. To be used in conjunction with analysis software KVIST.</p> <p>KVIST can be found at:</p> <ul> <li>https://doi.org/10.5281/zenodo.7017043</li> <li>https://github.com/Planetary-Surfaces-and-Spacecraft-Lab/KVIST</li> </ul> <p>Data files generated with:</p> <p>Truitt, A. (2020). Simulation of Forced Korteweg De Vries Equation as Applied to Small Orbital Debris. Digital Repository at the University of Maryland. https://doi.org/10.13016/FOR0-XJYD</p>
Multi-Sensor Ice Analysis Data: Analysis for Belgica Bank, North East Greenland 2019-20
<p>The intention is that this dataset can be used for machine learning and deep neural network training/validation, and it distinguishes sea ice concentration, type and form derived from manual analysis of a combination of different satellite sensors including ALOS-2, Sentinel-1, COSMO-SkyMed, Sentinel-2, and ICESAT-2. The region chosen for the analysis was the Belgica Bank area offshore of North East Greenland, as this is an area which experiences a wide variety of sea ice, and iceberg, conditions throughout the year. The dataset consists of two parts: 11 days of individual sea ice interpretations, one for each month in the period from April 2019 to March 2020, with the exception of October 2019, and iceberg surveys derived from Sentinel-2 for spring in 2019 and 2020. </p> <p>The dataset includes a user guide issued by MET Norway as report 10/2022 (see https://www.met.no/publikasjoner/met-report) in which the first part describes the data sources, nomenclature, file formats and data in the analysis. A second part of the report compares synthetic aperture radar (SAR) data from both L-band ALOS-2 and C-band Sentinel-1 satellites, and identifies the visible synergies and anomalies. The results confirm that there are variations in backscatter signatures between ALOS-2 and Sentinel-1 data when comparing them for different sea ice situations and conditions. ALOS-2 data in many cases is proven to be a reliable and beneficial source of data when it comes to identifying icebergs, ridges, determining sea ice type, and also distinguishing ice and water compared to standalone Sentinel-1 data.</p>
Data to support the publication "Impact of agricultural management on soil aggregates and associated organic carbon fractions: Analysis of long-term experiments in Europe"
<p><strong>Raw data:</strong> Experimental plot ids and information, mass distribution of all aggregate fractions after wet sieving, Sand content of each fraction to conduct the sand correction, mass distribution of all fractions after isolating the micro-aggregates held within the macroaggregates, yields per treatment, carbon content per fraction (raw data)</p> <p><strong>All data per plot: </strong>SOC content, MAOM and POM content of each fraction presented in the fractionation scheme included in the manuscript, together with the mass of the relative fractions. </p> <p> </p>
Data set: Australia's hidden radiation - phylogenomic analysis reveals rapid Miocene radiation of blindsnakes
<p>This repository contains the additional raw data to accompany our paper entitled "Australia’s hidden radiation: phylogenomic analysis reveals rapid Miocene radiation of blind snakes."</p> <p>This project is part of the AusARG Initiative funded by BioPlatforms Australia.</p> <p>Raw sequences data can be downloaded from the BioPlatforms downloads portal: https://data.bioplatforms.com/dataset?q=ticket%3ABPAOPS-1196</p> <p><strong>Information about files</strong></p> <ol> <li>ASTRAL_tree_SqCL_AHE.tre - output from ASTRAL-III just with SqCL data + outgroups</li> <li>ASTRAL_tree_SqCL_AHE_Ramphotyphlops.tre - same with above but also including additional <em>Ramphotyphlops </em>genes.</li> <li>mcmctree_1.txt - mcmcfile output from MCMCTree analysis using all SkewT or SkewNormal distribution priors.</li> <li>mcmctree_2.txt - mcmcfile output from MCMCTree analysis using SkewT, SkewNormal, and cauchy distribution priors. **This is the tree used in our publication**</li> <li>mcmctree_strategy1.tre - output phylogeny 1</li> <li>mcmctree_strategy2.tre - output phylogeny 2</li> <li>IQTREE_gcf_scf.nex - gene concordance and site factors for mcmctree_strategy2.tre</li> </ol> <p>tree_data/ folder contains concatenated gene trees (IQTREE) and corresponding shortcut coalescent method (ASTRAL-III) tree.</p> <p>Should there be questions regarding the code and data set, please contact the corresponding author.</p>
Data from: Group size and dispersal ploys: An analysis of commuting behaviour of the pond bat (Myotis dasycneme)
<p>This study aimed to provide a description on how Pond bats (<em>Myotis dasycneme</em>) disperse, how to recognize a commuting route, and details about the effort needed to make a complete survey of one commuting route. The study area covered the provinces of Zuid-Holland, Overijssel, Friesland, Noord-Holland, and Utrecht. During 6 years of study between 2002 and 2009, researchers and bat volunteers studied pond bats along several waterways (all waterways wider than 10 m) between known roosts and their hunting areas. All the observations were made between April and September, starting 20 min before sunset. During the entire observation effort, the time (in hours and minutes) and direction of each bat was recorded. The time that each bat passed the observation location was later transformed to minutes after sunset. The number of animals on commuting route was related to the number of animals present in their respective roost.</p> <p> </p> <p>Data are organized in 3 files: <strong>commuting data 10 minutes.csv</strong>, <strong>commuting data.csv</strong> and <strong>observations waddinxveen.csv</strong>. The variables in these data files are explained here:</p> <p>Date: the observation date</p> <p>Location description: description of the location</p> <p>X Y: The coordinates of the location in RD. The RD (Rijks-Driehoek) system is the coordinate system used by the Dutch geographical service.</p> <p>Long Lat: The coordinates of the location in longitude and latitude.</p> <p>Distance over water: commuting distance over water. For each route, the distance (d) over water between roost and observation location was measured from a topographical map and expressed in kilometres.</p> <p>Moon cover: the amount of moon cover, expressed in percentages.</p> <p>Roost location: the assumed location of the roost of the bats passing on their commuting route</p> <p>Max N of bats in roost: the max number of bats observed emerging from a roost.</p> <p>Sum N of bats over 10-minute interval: the sum of all the observed bats passing in one direction within a 10-minute interval</p> <p>Time after sunset in 10 min: the begin time of each interval, measured in minutes after sunset</p> <p>Peak time after sunset: the time of the observed peak in numbers of bats, in minutes after sunset.</p> <p>Area: the municipality near the observation location.</p> <p>Total N of pond bats on route: the total number of pond bats observed on route, in the given observation time. Including foraging and returning bats.</p> <p>Total N of commuting pond bats: the total number of bats observed commuting (excluding all other behaviours).</p> <p>Time of first bat minutes after sunset: the time of the first bat, measured in minutes after sunset.</p> <p>Duration of commuting: the time in hours between the first and the last bat observed commuting.</p> <p>Observation time: the total duration (in minutes) of the observation period.</p> <p>Moon phases: a 1–3 scale, where c1 is the new moon, c2 is the first quarter, c3 half moon, c4 is the last quarter and c5 is the full moon.</p> <p>Cloud cover: estimation of the cover, using the following three categories: c1-0%–25% cover (clear night sky or some isolated clouds), c2-25%–75% cover (several scattered clouds but not covering more than 75% of the night sky), and c3- 75%–100% cover (scattered clouds covering more than 75% of the night sky to a completely overcast night sky</p> <p>Observation type: observation of either emerging bats from a roost (roost) or bats observed on commuting route (commuting).</p> <p> </p> <p>In addition, we also provide 2 pdf’s containing the observation protocols (in Dutch) for counting emerging bats (<strong>Handleiding tellen van een groep meervleermuizen.pdf</strong>) and bats along a commuting route (<strong>Handleiding vliegroute telling.pdf</strong>). The protocols are intended for professionals and citizen scientists.</p>
ACCESS-AM2 Southern Ocean cloud and radiation data for k-means clustering and analysis
<p>The ACCESS-AM2 (Australian Community Climate and Earth-System Simulator - Atmospheric Model Version 2) data and k-means analysis used for the study described in Fiddes et al. 2022 '<em>Southern Ocean cloud and shortwave radiation biases in a nudged climate model simulation: does the model ever get it right?' .</em> </p> <p>Included files: </p> <ul> <li>modis_cluster_centres_2015-2019.nc - kmeans derived cluster centres for MODIS</li> <li>modis_cluster_labels_2015-2019.nc - kmeans derived cluster labels for MODIS </li> <li>bx400_cluster_labels_2015-2019.nc - kmeans fitted cluster label for model </li> <li>COSP_vars_bx400_2015-2019.nc - model data for analysis </li> </ul> <p>The code that performs the analysis/generates this data and has instructions for where to download MODIS data can be found here: https://github.com/sfiddes/code_for_publications_2022/tree/main/ACCESS_cloud_radiation_eval</p>
Synthetic geospatial data for performance analysis of geospatial database systems
<p>This dataset contains a set of synthetic data that can be used to evaluate the efficiency of geosaptial datasbases. </p> <p>The datasets is composed of four json file, characterized by different size. They can be used to analyze the scalability of geospatial datasets with respect to the database size.</p> <p>Each json file contains a set of "points", each one characterized by a set of random attributes (description, url of a picture linked to the point, creation date, delete date, update date, identifier, partition identifier).</p> <p>The synthetically generated points are uniformly distributed among the world.</p>
Early Neolithic polished stone tools analysis: Diploma thesis supplementary data
<p>Supplementary data of a master thesis: Early Neolithic polished stone tools analysis defended at Masaryk Univerzity, Brno, Czech Republic.</p> <p>The repository contains supplementary database and datasets concerning morphometric shape analysis of polished stone tools and related R scripts. The structure is described in attached read me file.</p>
LLODIA (Linguistic Linked Open Data for Diachronic Analysis)
<p>LLODIA (Linguistic Linked Open Data for Diachronic Analysis) model developed within the Nexus Linguarum WG4 UC4.2.1 use case in humanities.</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>
Assessing Quality Variations in Early Career Researchers' Data Management Plans: Quantitative Data of the Content Analysis
<p>The data includes the numerical results of the ranking of the data management plans created during the Basics of Research Data Management (BRDM) courses worth 3 ECTS credits in the years 2020 - 2022. The ranking was made using the Finnish DMP Evaluation Guidance (https://doi.org/10.5281/zenodo.4729831). Additionally, the data contains the results of the analysis of the best RDM practices included in the DMPs.</p> <p>Note 1: The comma-separated coded CSV version 1 (5.2.2024) may not open correctly on MacOS. You can use the comma-delimited CSV file version 2 or 3 (31.5.2024).</p> <p>Note 2: Versions 1 (Quality_variations_in_ECRs_DMPs_data) and 3 (Quality_variations_in_ECRs_DMPs_data_ver_3) contain evaluations of DMPs, best practices for data management, as well as methods for data sharing, storage, and preservation. In version 2 (Quality_variations_in_ECRs_DMPs_data_ver_2), the methods for data sharing, storage, and preservation are missing.</p> <p>Data is related to the research article https://doi.org/10.2218/ijdc.v18i1.873.</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.