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1,481 results for “data processing”
Replication Data for: Interpretable machine learning prediction of fire emission and comparison with FireMIP process-based models
<p>The target and predictor variables used in the developed ML model.</p>
Figure 3 from: Remsen D, Knapp S, Georgiev T, Stoev P, Penev L (2012) From text to structured data: Converting a word-processed floristic checklist into Darwin Core Archive format. PhytoKeys 9: 1-13. https://doi.org/10.3897/phytokeys.9.2770
Figure 3 - An updated database with final column titles and unique identifier added for each record.
Recharging process of commercial floating-gate MOS transistor in dosimetry application (raw data from journal article)
<p>This upload contains raw data from the manuscript "Recharging process of commercial floating-gate MOS transistor in dosimetry application". The manuscript was published in Microelectronics Reliability, vol. 126, p.114322, 2021; DOI: https://doi.org/10.1016/j.microrel.2021.114322</p> <p>The upload consists of a .pdf file of the manuscript and .vsz files with raw data related to the figures in the manuscript. Each .vsz file is linked with raw data from the text files (.txt) and placed in a folder with the name and an ordinal number of the figure in the publication. Additionally, a .pdf output file of the Veusz program (freely available) is placed in each folder.</p> <p>This work was partly supported by the European Union’s Horizon 2020 research and innovation programme (Grant No. 857558) and the Ministry of Education, Science and Technology Development of the Republic of Serbia (Project No. 43011 and Project No.451-03-9/2021-14/200026).</p>
Processed data with small file sizes [individual subject files]
<p>Processed data with small file sizes [individual subject files]</p>
Processed data for RCPANN model
<p>Processed RBSP/RBSPICE data, satellite coordinates and geomagnetic indices</p>
AOPS topic_raw and processed data_11_2022
<p>This dataset contains collection of validated raw and processed data related to the paper:</p> <p>Kehu Zhang, Silövia Moreno, Xueyi Wang, Yang Zhou, Susanne Boye, Dagmar Voigt, Briogitte Voit and Dietmar Appelhans</p> <p>"Biomimetic cell structures: Probing induced pH-feedback loops and pH self-monitoring in cytosol using binary enzyme-loaded polymersomes in proteinosome"</p> <p>Biomacromolecules 2023, https://doi.org/10.1021/acs.biomac.3c00010</p>
Processed MEG data used for revealing dynamic brain reconfiguration using MAPPER
<p>This is MEG data used for topological data analysis for revealing dynamic brain reconfiguration.</p>
Water quality data and processing scripts
<ul> <li>Python scripts used to process eight water quality databases.</li> <li>Output files for Glorich (Global), Waterbase (EU), WQP (USA) and DWS (South Africa).</li> </ul> <p>References</p> <p>Graham, D.J., Bierkens, M.F.P., van Vliet, M.T.H., 2024. Impacts of droughts and heatwaves on river water quality worldwide. Journal of Hydrology 629, 130590, https://doi.org/10.1016/j.jhydrol.2023.130590</p> <p>Hartmann, J., Lauerwald, R., Moosdorf, N., 2014. A Brief Overview of the GLObal RIver Chemistry Database. GLORICH. Procedia Earth Planet. Sci. 10, 23–27. https://doi.org/10.1016/J.PROEPS.2014.08.005</p> <p>European Environment Agency: Waterbase – Water Quality ICM, available at: https://www.eea.europa.eu/data-and-maps/data/waterbase-water-quality-icm-2, Accessed 01/01/2022</p> <p>Water Quality Portal. Washington (DC): National Water Quality Monitoring Council, United States Geological Survey (USGS), Environmental Protection Agency (EPA); 2021. doi: 10.5066/P9QRKUVJ</p> <p>Department of Water and Sanitation, South Africa. Research Quality Information Services, available at: https://www.dws.gov.za/iwqs/report.aspx, Accessed 01/01/2022</p>
Processed Data and Inversion Results for Constraining the Io Plasma Torus
<p>The processed and used data for constraining the plasma torus.</p> <p> </p> <p>FieldLines.txt are the mapped positions along the magnetic field lines calculated with the JRM33 model. The lines start at Io's orbit at 5.9 Jupiter Radii and are mapped towards the flattened Jupiter surface. The longitudinal resolution is 0.5°, resulting in 1440 total field lines, 720 for each the northern and the southern hemisphere. Therefore, the file contains of 1440 blocks, each introduced with the starting Easter System III longitude and the corresponding hemisphere. The block afterwards contains of 4 columns, three for the Cartesian position of the mapping and the last for the total field strength, which are formatted as</p> <p>X[RJ], Y[RJ], Z[RJ], B[nT]</p> <p> </p> <p>TravelTimesNorth.txt and TravelTimesSouth.txt contain the calculated travel times of the Alfven waves corresponding to the northern and southern Io Footprint (IFP), respectively. The travel times are calculated by using the observed positions of the IFP by Bonfond et al. (2017) and mapping along the magnetic field lines of the JRM33 model to Io's orbit. Then, the longitudinal distance to Io's position at the time of the observation is divided by Io's synodic orbital frequency. The file is formatted as</p> <p>Lon[°], Travel Time [s], Error [s]</p> <p>Lon is the system 3 eastern longitude of the footprint. Travel Time is the calculated travel time as described above and Error is the propagated uncertainty given by Bonfond et al. (2017)</p> <p> </p> <p>Dipole.txt and Multipole.txt are the CSV inversion results. For N = 10000 randomly chosen scale heights (between 0.4 and 1.6 Jupiter radii [RJ]) and peak densities (between 500 and 3500 particles per cm³), the resulting misfit for a torus centered at the dipole or multipole centrifugal equator has been calculated, respectively. The files have 10000 CSV lines which are formatted as</p> <p>Peak Density [1/cm³], Scale Height [RJ], Misfit</p> <p>Peak Density is the maximum particle density at the center of the torus and Scale Height is the scale height H of the torus with the density profile decreasing with distance z to the center of the torus corresponding to</p> <p><span class="math-tex">\(\rho = \rho_0 e^{-z^2 \over H^2}\)</span></p> <p> </p>
Data for article entitled 'Prediction in SVO and SOV languages: Processing and typological considerations', published in Linguistics
<p>see the publication for the description of the data and analysis</p>
Dopant network processing units as tuneable extreme learning machines - Data Sheet 1
<p>Inspired by the highly efficient information processing of the brain, which is based on the chemistry and physics of biological tissue, any material system and its physical properties could in principle be exploited for computation. However, it is not always obvious how to use a material system’s computational potential to the fullest. Here, we operate a dopant network processing unit (DNPU) as a tuneable extreme learning machine (ELM) and combine the principles of artificial evolution and ELM to optimise its computational performance on a non-linear classification benchmark task. We find that, for this task, there is an optimal, hybrid operation mode (“tuneable ELM mode”) in between the traditional ELM computing regime with a fixed DNPU and linearly weighted outputs (“fixed-ELM mode”) and the regime where the outputs of the non-linear system are directly tuned to generate the desired output (“direct-output mode”). We show that the tuneable ELM mode reduces the number of parameters needed to perform a formant-based vowel recognition benchmark task. Our results emphasise the power of analog in-matter computing and underline the importance of designing specialised material systems to optimally utilise their physical properties for computation.</p>
Linking evapotranspiration, boundary-layer processes and atmospheric moisture using isotope tracer modeling and data
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Data from: The role of spatial averaging scale in leaf-to-canopy scaling of non-linear processes in homogeneous canopies
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Data and code for simulation study and case study in "A Bayesian Dirichlet process community occupancy model to estimate community structure and species similarity"
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Data from: What is in a general plan? using natural language processing to read 461 California city general plans
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Data from: Neutral processes forming large clones during colonization of new areas
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Data from: Fragility of foot process morphology in kidney podocytes arises from chaotic spatial propagation of cytoskeletal instability
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Data from: Integrated biomarker responses of the submerged macrophyte Vallisneria spiralis via hydrological processes from Lake Poyang, China
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Data from: Phylogenetic analysis using Lévy processes: finding jumps in the evolution of continuous traits
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Data from: The evolution of sexual and parthenogenetic Warramaba: a window onto Plio-Pleistocene diversification processes in an arid biome
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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.