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1,481 results for “data processing”
Processed data from Svalbard shelf moorings 2010-2014
<p>Data from five moorings deployed in 2010-2014 on the south-western Svalbard shelf in Matlab-format. Data subset (from DJFM winter periods) was used and described in Goszczko et al. 2018 (published in JGR Oceans, see table S2 in Supporting Information). Some processing was modified compared to the original analysis (see read-me for details). Raw data are available from the Institute of Oceanology, Polish Academy of Sciences (IOPAN), Sopot, Poland.</p> <p>These deployments took place during IO PAN Arctic Experiments AREX carried out since 1987 and were supported by the Norway Grants from the Polish-Norwegian Research Fund project AWAKE (PNRF-22-A I-1/07), from the Polish-Norwegian Research Programme: AWAKE-2 (POL-NOR/198675/17/2013) and from Narodowe Centrum Nauki, Preludium MIXAR grant (2012/05/N/ST10/03643).</p>
HGG-oncohistones processed data
<p>The sample names in this dataset match supplementary tables 1 and 2. </p> <p>The tarball contains 4 directories: bulk, scRNA, scATAC, scMultiome</p> <p>The bulk directory contains tsv files.</p> <p>The scRNA, scATAC and scMultiome directories contain subdirectories for each sample containing their respective processed files.</p> <p>See the README.md for a description of all the files.</p>
Experimental data linked to publication "Process optimization and study of the co-sintering behaviour of Cu-Ni multi-material 3D structures fabricated by spark plasma sintering (SPS)"
<p>Those are all the experimental data used to produce the plots in the article</p>
Example of Surabaya COPACR Business Process Architecture and Conceptual Data Model
<p>In this repository consist of three diagrams. Two diagrams are part of Surabaya COPACR Business Process Architecture (BPA), they are:</p> <p>1. Fig 1. Shows six value chains that become BPA Level 0 of the Surabaya COPACR. <br> 2. Fig 2. Shows Electronic ID card printing services as PBA Level 5</p> <p>And one diagram, Fig. 3 show Conceptual Data Model for Surabaya COPACR Core Process</p>
Processed High Frequency Radar (HFR) Surface Current Data for the Processes Driving Exchange at Cape Hatteras (PEACH) Program
<p>These hourly surface current velocities are a combined product derived from 8 monostatic radars (4 CODAR and 4 WERA) operated as part of the PEACH program. Level 2 data have been quality-controlled and gridded to an hourly time-base. A detailed description of processing methods and analysis is provided by Seim, <em>et al. </em>(2022) and a brief outline in the documentation uploaded with this dataset.</p> <p>Seim, H., Savidge, D., Muglia, M., Haines, S., & Han, L. (2022). Surface current observations from a combined CODAR/WERA high-frequency radar array along the North Carolina coast during the Processes Driving Exchange at Cape Hatteras (PEACH) Project. <em>IEEE/MTS Proceedings Oceans 2022</em>.</p>
Process simulation-based inventory data for the perovskite single-junction, Silicon (PERC) and four-terminal perovskite/silicon tandem solar photovoltaic system life cycles
<p>Process simulation-based inventory data (mass and energy balances) for the perovskite single-junction, silicon (PERC architecture), and four-terminal perovskite/silicon tandem solar photovoltaic system life cycles. The file "0 Overview of simulation flowsheets.xlsx" contains images of the 11 flowsheets that constitute the perovskite/silicon tandem simulation model, which encompasses the perovskite single-junction and silicon (PERC) simulation models. For each unit process shown in each of the flowsheet images, the corresponding Excel file in this repository (with the same name) contains the detailed mass and energy balances, as well as full compositions and thermochemical properties of all streams and the compounds in them. That is, streams are not assumed to consist of pure elements simply moving through the system together, but rather taking into account that streams consist of compounds in solution, which have different thermochemical properties than simple mixtures of the elements involved.</p> <p>Nine additional data files, the names of which start with "Inventory - " contain summarized inventory data for the production of 1000 perovskite single-junction, silicon (PERC), and silicon/perovskite tandem PV modules, each with no Si recycling (i.e. zero circularity), 50% Si recycling, and 100% Si recycling (i.e. full Si circularity).</p>
Classification Data set : Land Cover Classification with Gaussian Processes using spatio-spectro-temporal features
<p>Classification data set (train, validation, test) from the study area based on 27 tiles on the south of the France. Data set are provided for each eco-climatic region. The size corresponds to the data set DS-A. Only one random pixel sampling is provided: seed 0. This data set was used to train Gaussian Processes, Random Forest, Multilayer Perceptron and Lightweight Temporal Self-Attention models.</p> <p>For further details see section VI-A-1 of the pre-print article "Land Cover Classification with Gaussian Processes using spatio-spectro-temporal features ". This article is available <a href="https://hal.archives-ouvertes.fr/hal-03781332">here</a>.</p> <p>The implementation of the models is available in the <a href="https://gitlab.cesbio.omp.eu/belletv/land_cover_southfrance_gp">open source repository</a>.</p>
Processed data for the study on "Observation of site-selective chemical bond changes via ultrafast chemical shifts"
<p>This repository contains the underlying data for the paper entitled "Observation of site-selective chemical bond changes via ultrafast chemical shifts". The uploaded data are preprocessed data measured at the LCLS SLAC facility. Each individual file contains the measurements at a set delay time and spectrometer setting on a shot-by-shot basis.</p> <p>The files contain the following keys:</p> <pre>['Ebeam_energy_MeV', 'Electron_orbit_probe_x', 'Electron_orbit_probe_y', 'Electron_orbit_pump_x', 'Electron_orbit_pump_y', 'Fiducial', 'Fit_quality', 'Gas_monitor_mJ', 'Group', 'N_electrons', 'Photon_energy_FEL_eV', 'Probe_energy_xtcav', 'Probe_pulse_energy_mJ', 'Probe_width_fs', 'Pump_energy_xtcav', 'Pump_pulse_energy_mJ', 'Pump_width_fs', 'TOF_CO_yield', 'TOF_Ne_yield', 'Time_delay_XTCAV']</pre> <p>Description of the individual entries is found in the supplementary materials of the publication. The following table assigns the time delay and measure electron kinetic energy for each dataset.</p> <table> <tbody> <tr> <td>Time delay</td> <td>Ek = 234eV</td> <td>Ek = 220eV</td> <td>Ek = 210eV</td> </tr> <tr> <td>40fs</td> <td>Run_210</td> <td>Run_212</td> <td>Run_213</td> </tr> <tr> <td>20fs</td> <td>Run_231</td> <td>Run_236</td> <td>Run_235</td> </tr> <tr> <td>15fs</td> <td>Run_239</td> <td>Run_242</td> <td>Run_241</td> </tr> <tr> <td>10fs</td> <td>Run_238</td> <td>Run_233</td> <td>Run_234</td> </tr> <tr> <td>5fs</td> <td>Run_228</td> <td>Run_226</td> <td>Run_225</td> </tr> <tr> <td>0</td> <td>Run_221</td> <td>Run_223</td> <td>Run_224</td> </tr> <tr> <td>-5fs</td> <td>Run_217</td> <td>Run_219</td> <td>Run_220</td> </tr> </tbody> </table> <p> </p>
Data to reproduce the results presented in Lake et al. 2022. Hydrological processes, https://doi.org/10.1002/hyp.14726 ("Using particle size distributions to fingerprint suspended sediment sources – evaluation at laboratory and catchment scales")
<p>This repository contains data on particle size distribution data obtained from the laboratory and field experiments as described in Lake et al., 2022. </p> <p>The data contains the input files as needed for the modelling:</p> <p>- In the excel files the particle size distribution data for the target SS</p> <p>- In the text file the particle size distribution data from the sources.</p> <p> </p> <p>Furthermore, the data contains the resulting output files.</p> <p> </p>
Source data for "Lonely individuals process the world in idiosyncratic ways"
<p>The following includes the source data for the manuscript titled "Lonely individuals process the world in idiosyncratic ways".</p>
Raw and processed data for 'Purification-based quantum error mitigation of pair-correlated electron simulations'
<p>Experimental data for 'Demonstration of purification-based error mitigation for quantum simulation'</p> <p>This directory contains two folders - 'final_data_sets' and 'plots_for_paper'.<br> 'final_data_sets' contains directories with raw experimental data, either in the form of raw shots, or accumulated expectation values. Everything is stored in either json or pickle format. This data was processed to generate the datasets in subfolders of 'plots_for_paper', which also contains notebooks for generating plots in the paper (data processing scripts to appear soon). Note that the python file to generate Fig.3 is in 'plots_for_paper/paper_plot_ring_opening_sixq_10q'.</p> <p>For any further questions, please email teobrien@google.com.</p>
Patient-reported outcomes via electronic health record portal vs. telephone: process and retention data in a pilot trial of anxiety or depression symptoms in epilepsy
<p>Objective: To close gaps between research and clinical practice, tools are needed for efficient pragmatic trial recruitment and patient-reported outcome(PROM) collection. The objective was to assess feasibility and process measures for patient-reported outcome collection in a randomized trial comparing electronic health record(EHR) patient portal questionnaires to telephone interview among adults with epilepsy and anxiety or depression symptoms.</p> <p>Results: Participants were 60% women, 77% White/non-Hispanic, with mean age 42.5 years. Among 15 individuals randomized to EHR portal, 10(67%, CI 41.7-84.8%) met the 6-month retention endpoint, versus 100%(CI 79.6-100%) in the telephone group(p=0.04). EHR outcome collection at 6 months required 11.8 minutes less research staff time per participant than telephone (5.9, CI 3.3-7.7 vs. 17.7, CI 14.1-20.2). Subsequent telephone contact after unsuccessful EHR attempts enabled near complete data collection and still saved staff time.</p> <p>Discussion: Data from this randomized pilot study of pragmatic outcome collection methods for patients with anxiety or depression symptoms in epilepsy includes baseline participant characteristics, recruitment flow resulting from a novel EHR-based, care-embedded recruitment process, and data on retention along with various process measures at 6-months.</p>
Supplementary data for "Upward leaders from instrumented lightning rods competing to connect a downward leader during a lightning attachment process"
<p>High-speed videos of the lightning attachment to lightning rods observed by the Vision Reserch Phantom V12 and V711 cameras. See Instructions file to play the videos.</p>
CS2900 - Multi-dimensional Data Processing Slidecasts Royal Holloway, University of London
<p>Slidecasts of CS2900 (Multi-dimensional Data Processing - but essentially Linear Algebra for Computer Scientists) 2018/2019. For undergraduate CS students.</p>
Landsat 8 and SRTM Data Processed for Soil Classification (Yuri Coelho's Thesis)
<p>This dataset was processed for the Senior Thesis of Yuri Coelho.</p> <p>The dataset is composed by the processed rasters that are used in the Senior Thesis. The objective of the dataset is to classify satellite data into Soil Classes.</p>
Data for: Evolution Reinforces Cooperation with the Emergence of Self-Recognition Mechanisms: an empirical study of the Moran process for the iterated Prisoner's dilemma using reinforcement learning
<p>This contains data used for a paper titled: Evolution Reinforces Cooperation with the Emergence of Self-Recognition Mechanisms: an empirical study of the Moran process for the iterated Prisoner's dilemma using reinforcement learning.</p> <p>Numerous data sets are included, the main being `main.csv` which includes the fixation counts for a number of Moran processes between pairs of players from the Axelrod library.</p> <p>The source code and explanation of the data is here: https://github.com/Axelrod-Python/axelrod-moran. </p> <p> </p>
Data from: seasonal patterns and processes of migration in a long-distance migratory bird: energy or time minimization?
<p>Optimal migration theory prescribes adaptive strategies of energy, time or mortality minimization. To test alternative hypotheses of energy and time minimization migration we used multisensory data loggers recording time-resolved flight activity and light for positioning by geolocation in a long-distance migratory shorebird, little ringed plover Charadrius dubius. We could reject the hypothesis of energy minimization based on a relationship between stopover duration and subsequent flight time as predicted for a time minimizer. We found seasonally diverging slopes between stopover and flight durations in relation to the progress (time) of migration, which follows for a time minimizing policy if resource gradients increase and decrease, respectively. Total flight duration did not differ significantly between autumn and spring migration, although spring migration was 6% shorter. Overall duration of autumn migration was longer than that in spring, mainly due to a mid-migration stop in most birds, when they likely initiated moult. Overall migration speed was not significantly different between autumn and spring. Migratory flights often occurred as runs of 2-7 nocturnal flights on adjacent days, which may be countering a time minimization strategy. Other factors may influence a preference for nocturnal migration, such as avoiding flight in turbulent conditions, heat stress, and diurnal predators.</p>
Benchmark datasets for testing AIRR-seq data processing with pyIgMap pipeline
<p>This is a set of raw FASTQ files produced by various AIRR-seq protocols, stored here for benchmark conveinience and for reference purposes.</p> <p>Currently it contains the following datasets:</p> <table> <tbody> <tr> <td><strong>id</strong></td> <td><strong>fastq</strong></td> <td><strong>reference</strong></td> <td><strong>method</strong></td> <td><strong>description</strong></td> </tr> <tr> <td>allergy</td> <td> <p>ERR7425614_1.fastq.gz,</p> <p>ERR7425614_2.fastq.gz</p> </td> <td>https://doi.org/10.7554/eLife.79254</td> <td>5'RACE, UMI, long MiSeq reads, IGH w/ isotype</td> <td>Longitudinal full-length IGH repertoire profiling and clonal lineage dynamics in memory B cells, plasmablasts and plasma cells of human peripheral blood</td> </tr> <tr> <td>covid</td> <td> <p>fmba_TRAB_R1.fastq.gz,</p> <p>fmba_TRAB_R2.fastq.gz</p> </td> <td> <p>https://doi.org/10.1101/2023.11.08.566227</p> </td> <td>DNA multiplex, UMI, TRA+TRB mix, NextSeq</td> <td>TCR sequencing in COVID-19 convalescent and healthy donors</td> </tr> <tr> <td>natprot</td> <td> <p>PMID27490633_R1.fastq.gz,</p> <p>PMID27490633_R2.fastq.gz</p> </td> <td> <p>https://doi.org/10.1038/nprot.2016.093</p> </td> <td>5'RACE, UMI, long MiSeq reads, high-quality overlap, IGH no isotype</td> <td>High-quality full-length immunoglobulin profiling with unique molecular barcoding</td> </tr> <tr> <td>brnaseq</td> <td> <p>SRR3743469_R1.fastq.gz,</p> <p>SRR3743469_R2.fastq.gz</p> </td> <td> <p>https://doi.org/10.1016/j.immuni.2016.08.012</p> </td> <td>RNA-Seq, all chains, B-cells</td> <td>Primary human mature naïve B-cells (IgD+CD38lo; NB) and GCB-cells (CD77+CD38hi; GCB) were purified from tonsils of healthy individuals. RNA-seq libraries were prepared using the Illumina TruSeq RNA sample kits according to the manufacturer.</td> </tr> <tr> <td>uhrr</td> <td> <p>UHRR_full_R1.fastq.gz,</p> <p>UHRR_full_R2.fastq.gz</p> </td> <td> <p>https://doi.org/10.1038/s41598-021-04583-z</p> </td> <td>RNA-Seq, bulk</td> <td>Universal Human Reference RNA</td> </tr> <tr> <td>10x</td> <td> <p>10x_bcr_R1.fastq.gz,</p> <p>10x_bcr_R2.fastq.gz,</p> <p>10x_tcr_R1.fastq.gz,</p> <p>10x_tcr_R2.fastq.gz</p> </td> <td> <p>https://www.10xgenomics.com/datasets/human-pbmc-from-a-healthy-donor-10-k-cells-v-2-2-standard-5-0-0</p> </td> <td>10x Genomics vdj</td> <td>See reference. AIRR-seq is split into TCR and BCR parts</td> </tr> </tbody> </table> <p> </p>
Raw Data for Control of Ferroelectricity in Solution-Processed Hafnia Films through Annealing Atmosphere
<p>The following raw data are the basis of the paper "Control of Ferroelectricity in Solution-Processed Hafnia Films through Annealing Atmosphere" published in Advanced Electronic Materials in 2024 (DOI 10.1002/aelm.202300893).</p> <p>BM and SG acknowledge Luxembourg National Research Fund (FNR) for supporting this work through the project TRICOLOR (INTER/NWO/20/15079143/TRICOLOR). We would like to thank Prof. Beatriz Noheda, Prof. Monica Acuautla, and Dr. Miguel Badillo for their inputs on the growth process used in this work and electrical characterization of the thin films.</p>
Data from: Versatile MRI Acquisition and Processing Protocol for Population-Based Neuroimaging
<p>This data repository contains an example of the acquired MRI sequences and the output of the post-processing pipelines in the Rhineland Study. An overview and information about the MRI sequences and post-processing pipelines can be found in our work 'Versatile MRI Acquisition and Processing Protocol for Population-Based Neuroimaging.' (under-submission)</p>
ScienceDex guides
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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.