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6,467 results for “process”
Dataset and data processing tools of EPL 139 (2022) 55002 — Harmonic calibration of quadrature phase interferometry
<p>Dataset for <em>EPL</em> <strong>139</strong> (2022) 55002 — Harmonic calibration of quadrature phase interferometry</p> <p><strong>Scripts: </strong>the main Matlab script to analyse all data is <strong>analyseall.m</strong>, and uses all other scripts and functions (<strong>*.m</strong>). See comments in this main script for details. The script <strong>DefineDatasets.m,</strong> and its output <strong>Dataset.mat</strong>, were run before to define the main parameters of the 20 datasets, and define the frequency range where to estimate the background noise to be subtracted from the spectra of the driven cantilever when computing the total harmonic distortion.</p> <p><strong>Data:</strong> the 100 data files (.mat) have the following name scheme: [driving-frequency]-[Amplitude]-(low frequency external phase)-001 to 005.mat. For example 1kHz-10nm-w1Hz150nm-001.mat correspond to a driving of the cantilever at 1kHz, with an oscillation amplitude close to 10nm, with a low frequency driving of the external optical phase at 1Hz and equivalent 150nm amplitude. 001 stand for the first of the five files corresponding to this measurement. Note that the low frequency driving is optional. The main driving frequency is either directly given in kHz, or corresponds to one of the 2 first resonance modes of the cantilever (mode1 at 13.4kHz, and mode2approx at 84.6 kHz). Each of these data files contains 5 variables: Cx and Cy are the 2 outputs of the quadrature phase interferometer (1s of acquisition, 2e6 samples), fs is the sampling frequency (2 MHz), Ix and Iy are the mean total intensities on the photodiodes corresponding to Cx and Cy (units: A, this information can be useful to estimate the expected shot noise floor on the measurement).</p>
Spatio-thermal depth correction of RGB-D sensors based on Gaussian Processes in real-time
<p>This RGB-D dataset is part is part of our publication</p> <p>Heindl, Christoph, et al. "Spatio-thermal depth correction of RGB-D sensors based on Gaussian processes in real-time." <em>Tenth International Conference on Machine Vision (ICMV 2017)</em>. Vol. 10696. SPIE, 2018.</p> <p>Our capture setup consists of a RGB-D sensor looking towards a known planar object. The sensor is coupled with an electronic linear axis to adjust distance. We captured data at distances [40cm, 90cm, 10cm steps] in the temperate range of [25°C, 35°C, 1°C steps]. At each temperature/distance tuple we grabbed 50 images from both RGB and IR (aligned with RGB) sensors. We then created an artificial depth map for all RGB images utilizing the known calibration target in sight.</p> <p>For more information visit https://github.com/cheind/rgbd-correction</p>
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>
High-surface-area corundum nanoparticles by PDC process
<p>Source data for "High-surface-area corundum nanoparticles by resistive hotspot-induced phase transformation".</p>
Neural network models for processing of ultrasound rodents recordings
<p>Neural network models for processing of ultrasound rodents recordings to be used with custom software dedicated to detection and classification of USV.</p>
SRL Curricular integration, Digital technologies & Teaching processes
<p>Systematic literature review data (2018-2021) on "technology integration" and "technology uses in education". ERIC databases.</p>
ARPIP: Ancestral sequence Reconstruction with insertions and deletions under the Poisson Indel Process
<p>Modern phylogenetic methods allow inference of ancestral molecular sequences given an alignment and phylogeny relating present day sequences. This provides insight into the evolutionary history of molecules, helping to understand gene function and to study biological processes such as adaptation and convergent evolution across a variety of applications. Here we propose a dynamic programming algorithm for fast joint likelihood-based reconstruction of ancestral sequences under the Poisson Indel Process (PIP). Unlike previous approaches, our method, named ARPIP, enables the reconstruction with insertions and deletions based on an explicit indel model. Consequently, inferred indel events have an explicit biological interpretation. Likelihood computation is achieved in linear time with respect to the number of sequences. Our method consists of two steps, namely finding the most probable indel points and reconstructing ancestral sequences. First, we find the most likely indel points and prune the phylogeny to reflect the insertion and deletion events per site. Second, we infer the ancestral states on the pruned subtree in a manner similar to FastML. We applied ARPIP on simulated datasets and on real data from the Betacoronavirus genus. ARPIP reconstructs both the indel events and substitutions with a high degree of accuracy. Our method fares well when compared to established state-of-the-art methods such as FastML and PAML. Moreover, the method can be extended to explore both optimal and suboptimal reconstructions, include rate heterogeneity through time and more. We believe it will expand the range of novel applications of ancestral sequence reconstruction.</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>
Supporting information for 'Towards better understanding of the ferrofluid impregnation process and potential artefacts – a prerequisite for reliable interpretation of magnetic pore fabrics'
<p>These tables contain data for the manuscript 'Towards better understanding of the ferrofluid impregnation process and potential artefacts – a prerequisite for reliable interpretation of magnetic pore fabrics'</p>
A Framework for High-throughput Sequence Alignment using Real Processing-in-Memory Systems
<p>Sequence alignment is a fundamentally memory bound computation whose performance in modern systems is limited by the memory bandwidth bottleneck. Processing-in-memory architectures alleviate this bottleneck by providing the memory with computing competencies. We propose Alignment-in-Memory (AIM), a framework for high-throughput sequence alignment using processing-in-memory, and evaluate it on UPMEM, the first publicly-available general-purpose programmable processing-in-memory system.</p>
Spatial phylogenetic and phenotypic patterns reveal ontogenetic shifts in ecological processes of plant community assembly
The analysis of spatial phylogenetic and phenotypic structure of plant communities provides insight into the underlying processes and interactions governing their assembly, and how these may change during plant ontogeny. We used point pattern analysis to find out if saplings and adult plants are surrounded by phylogenetically and phenotypically more similar or dissimilar neighbours than expected by chance, and whether these associations change from the sapling to the adult stage. To this end, we combined information on the phylogenetic structure and eight phenotypic traits of 15 woody plant species in two Mediterranean mixed forests of southeastern Spain. At the community level, we found that the sapling bank at both sites did not show phylogenetic or phenotypic spatial patterns, but adults showed phylogenetic clustering (i.e., heterospecific neighbours were more similar than expected). At the species level, we found frequently repulsive patterns in the sapling bank of less abundant species (i.e., heterospecific sapling or adult neighbours were more dissimilar than expected) in both, phylogenetic and phenotypic analyses. For the adult stage, we found phylogenetic attraction (i.e., more similar neighbours) in just one species and phenotypic clustering in four species. The processes driving the assembly of the communities of saplings and adults leave detectable signals in the spatial phylogenetic and phenotypic structure of our two forest communities. Our findings reinforce the existence of ontogenetic shifts in the mechanisms involved in plant community assembly. Facilitation between phylogenetically distant and phenotypically divergent species favours the recruitment of less abundant species. However, processes acting later in the ontogeny ameliorate the competition between close relatives and determine the spatial structure of adult plants. Nevertheless, the role of phenotype in shaping adult-adult interactions was context- and trait-dependent. The use of spatial point pattern analysis allowed a nuanced interpretation of the phylogenetic and phenotypic structures of the plant community.
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>
WRF-DART/RTTOV input and (processed) output files for GMD-2022-30
<p>The <em><strong>Exp-01.tar.gz ~ Exp-14.tar.gz (including true_processed.tar.gz and ctrl_processed.tar.gz) </strong></em>store the processed output files. Untarrring each of them will generate a list of subdirectories named after UTC time, txt files and netcdf files. (1) The subdirectories named after the UTC time. For example, 202008180200 denotes 02:00 UTC 18 August 2020. The subdirectory 202008180200 includes preassim_mean.nc and postassim_mean.nc files, which denote the prior and posterior estimates of cloud and non-cloud variables (and diagnosed variable). Both preassim_mean.nc and postassim_mean.nc include seven variables: CWP denotes the cloud water path (kg m<sup>-2</sup>). CWC denotes the mixing ratio (kg kg<sup>-1</sup>) of cloud hydrometers, including cloud droplet, rain, snow, ice, and graupel. P denotes the pressure (hPa), Re_CLOUD denotes the effective radius of liquid water clouds, QVAPOR denotes the water vapor mixing ratio, T denotes the perturbation potential temperature, U denotes the u-wind component, and V denotes the v-wind component. (2)Txt files which are the quantitative metrics of cloud and precipitation simulation errors. FSS_pre.txt and FSS_pos.txt store the fraction skill score for the prior and posterior estimates of cloud coverage. MAE_pre.txt and MAE_pos.txt store the Mean Absolute Error for the prior and posterior estimates of CWP. rain_TS_pre.txt and rain_TS_pos.txt store the TS scores for the prior and posterior estimates of precipitation. rain_MAE_pre.txt and rain_MAE_pos.txt store the MAE for the prior and posterior estimates of rain rate. (3) Netcdf files including mpe_mpi.nc, rain_pre.nc, and rain_pos.nc. mpe_mpi.nc includes the Mean Profile Error (MPE) for the prior and posterior estimates of non-cloud variables (Q, T, U, and V). rain_pre.nc and rain_pos.nc store the results for the prior and posterior estimates of rain rate (mm h<sup>-1</sup>).</p> <p>The <em><strong>nature_run.tar.gz</strong></em> file contains the WRF output files for the nature run.</p> <p>The <em><strong>model_codes.tar.gz</strong></em> file includes the source code of WRF-ARW (v4.1.1), WPS (v4.1), RTTOV (v12.3), and DART (Manhattan release v9.8.0).</p> <p>The <em><strong>input_file.tar.gz</strong></em> file includes the input namelist files for the WRF/WPS/DART models (tool) for the nature run, control run, and fourteen cycled data assimilation experiments. In addition, the input observation sequence files for the DART tool at each analysis time are provided.</p> <p>The <em><strong>Figure_script.tar.gz</strong></em> file includes the visualization scripts of Figure 1~Figure 16 in the manuscript.</p>
Kaczmarczyk-et-al.-Search-and-Processing-of-Holliday-Junctions-
<p><strong>Kaczmarczyk-et-al.-Search-and-Processing-of-Holliday-Junctions-</strong></p> <p>scripts and the raw data used in the manuscript by Kaczmarczyk et al. "Search and Processing of Holliday Junctions within Long DNA by Junction-Resolving Enzymes"</p> <p>System requirements:</p> <ul> <li>The analysis was performed on</li> <li>operating system MacOS High Sierra, version 10.13.6</li> <li>Python 3.9.4</li> </ul> <p>The scripts are executed in Jupyter Notebook (Anaconda 4.10.1) using libraries:</p> <ul> <li>numpy 1.20.3</li> <li>matplotlib 3.4.2</li> <li>statistics</li> <li>lumicks pylake 0.10.1 (<a href="https://lumicks-pylake.readthedocs.io/en/stable/install.html#">https://lumicks-pylake.readthedocs.io/en/stable/install.html#</a>)</li> <li>scipy 1.6.3</li> <li>peakutils 1.3.3</li> </ul> <p>To install the software, download Anaconda:</p> <ol> <li>Go to the Anaconda website and download the Python 3.8 (or newer) installer.</li> <li>Create a new environment for Pylake</li> <li>Install additional libraries (scipy, peakutils through Anaconda desktop app or via Terminal)</li> </ol> <p>Detailed instruction on how to install Anaconda and Pylake package is available here: <a href="https://lumicks-pylake.readthedocs.io/en/latest/install.html#installation-instructions">https://lumicks-pylake.readthedocs.io/en/latest/install.html#installation-instructions</a></p> <p>The instalation process of Anaconda and necessary libraries requires ~15 minutes.</p> <p> </p> <p>To run the scripts, put a copy of the .ipynb Jupyter Notebook in the same folder as the .H5 file you wish to analyze.</p> <p> </p> <ol> <li>The script will automatically load the file.</li> <li>Subsequently, it will render a kymograph and the corresponding force measurement.</li> <li>The kymograph can be then cropped and the image's brightness and contrast can be adjusted</li> <li>Within the same script, it is possible to track 1D diffusion and measure the diffusion coefficient.</li> </ol> <p>OUTPUTS: -csv files with data</p> <ul> <li>png images with kymographs</li> <li>raw TIFF file</li> </ul> <p>For questions, contact <a href="mailto:akaczmarczyk88@gmail.com">akaczmarczyk88@gmail.com</a></p>
Deciphering microbial gene function using natural language processing
<p>Dataset for the support of a journal publication. </p> <p>The data include both computed models presented in the paper and all data used for analysis.</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>
Geodetic mass balance of Mýrdalsjökull ice cap, 1999−2021: DEM processing and climate analysis
<p>This repository gathers the data I used and produced during my master thesis at the University of Iceland from April to September 2022 with the financial support of the Landsvirkjun.</p> <p>The geodetic mass of Mýrdalsjökull, the fourth largest Icelandic ice cap, was investigated over the period 1999-2021. The untapped <strong>SPOT5 </strong>archive (2002−2015), the <strong>lidar </strong>data, the <strong>Pléiades </strong>imagery (2011−present), <strong>aerial photographs</strong> from 1999 [1] and the <strong>ArcticDEM </strong>dataset (2010−2019) [2] were used to create Digital Elevation Models (DEMs) of the ice cap. A pre-processing of the DEMs was first performed: co-registration, filtering and interpolation. Then, applying a <strong>Gaussian Process regression</strong> (GP) [3], a state-of-the-art method in DEM processing, a spatially and temporally continuous DEM dataset was created, in 15 x 15 m resolution and 1-month interval from 1999 to 2021. <strong>Volume and mass changes</strong> based on the synthetic GP-generated DEMs were computed and analyzed in 5-year and annual intervals between 1999 and 2019. A local analysis of three glacierized catchments of Mýrdalsjökull (southern catchment, northern catchment and Kötlujökull outlet) was also performed. Errors were estimated using the method from [4]. Tools from the following repositories were used:</p> <ul> <li> <p><em>demcoreg </em>(<a href="https://doi.org/10.5281/zenodo.5733347">https://doi.org/10.5281/zenodo.5733347</a>): DEM co-registration</p> </li> <li> <p><em>xdem </em>(<a href="https://doi.org/10.5281/zenodo.4809698">https://doi.org/10.5281/zenodo.4809698</a>): uncertainties computation and DEM manipulation </p> </li> <li> <p><em>pyddem </em>(<a href="https://pypi.org/project/pyddem/">https://pypi.org/project/pyddem/</a>): Gaussian Process regression</p> </li> </ul> <p>The complete master thesis can be accessed at :</p> <p> </p> <p>The repository contains the following data:</p> <p><strong>1</strong> – <strong>DEM_coregistered</strong></p> <p>All DEMs have been coregistered considering the Islandsdem v1.0 as a reference (atlas.lmi.is/dem)</p> <p>The DEM naming works as follow: </p> <p><em>Glaciername_DEM_date_sensor_resolution_zmae_projection_otherinformation.tif</em></p> <p>The files ending with <strong>*_filtered.tif</strong> (SPOT5, AerialPhotographs) have been filtered using the filtering combination described in 3.1.3.</p> <p>The files ending with<strong> *_mosaic.tif</strong> (SPOT5, Pléiades) are the result of the mosaicking of several DEMs.</p> <p> </p> <p><strong>2</strong> – <strong>Shapefiles</strong></p> <p>Outlines of Mýrdalsjökull in 1999 [1], 2003, 2010 and 2019 [6]</p> <p>Outlines of the three catchments (South, North and Kötlujökull) in 1999.</p> <p>Reference buffer around Mýrdalsjökull used to crop all DEMs to the same extent. </p> <p>Equilibrium Line Altitude (ELA) from 2004-10-05.</p> <p> </p> <p><strong>3</strong> – <strong>Gaussian_Process_regression</strong></p> <p>2 netcdf files: the stack of DEMs (<strong>*_DEMstack_*</strong>) and the result of the Gaussian Process regression (<strong>*_GPregression_*</strong>).</p> <p>Both files were obtained thanks to <em>pyddem </em>tools.</p> <p>The Gaussian Process regression was run at a spatial resolution of 15 x 15m and a temporal resolution of 1 month, starting in January 1999 and ending in December 2022.</p> <p> </p> <p><strong>4</strong> – <strong>Mass_balance_results</strong></p> <p>1 csv file containing mass balance results:</p> <ul> <li> <p>Annual mass balance for the ice cap & the 3 catchments</p> </li> <li> <p>4-year mass balance for the ice cap & the 3 catchments</p> </li> <li> <p>Results from the comparison with survey dates mass balance (Fig 11(a))</p> </li> <li> <p>Results from the comparison with [3] (Fig 11(b))</p> </li> <li> <p>Mass balance overview (Fig 11(c))</p> </li> </ul> <p> </p> <p><strong>References</strong></p> <p>[1] Belart, J., Magnússon, E., Berthier, E., Gunnlaugsson, Á. Þ., Pálsson, F., Aðalgeirsdóttir, G., Jóhannesson, T., Thorsteinsson, T., and Björnsson, H. (2020). Mass balance of 14 Icelandic glaciers, 19452017: spatial variations and links with climate. Frontiers in Earth Science, page 163.</p> <p>[2] Porter, C., Morin, P., Howat, I., Noh, M., Bates, B., Peterman, K., Keesey, S., Schlenk, M., Gardiner, J., et al. (2018). ArcticDEM. Harvard Dataverse, 1.</p> <p>[3] Hugonnet, R., McNabb, R., Berthier, E., Menounos, B., Nuth, C., Girod, L., Farinotti, D., Huss, M., Dussaillant, I., Brun, F., et al. (2021). Accelerated global glacier mass loss in the early twenty-first century. Nature, 592(7856):726731.</p> <p>[4] Hugonnet, R., Brun, F., Berthier, E., Dehecq, A., Mannerfelt, E. S., Eckert, N., and Farinotti, D. (2022). Uncertainty analysis of digital elevation models by spatial inference from stable terrain. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.</p> <p>[5] Hannesdóttir, H., Sigurðsson, O., Þrastarson, R. H., Guðmundsson, S., Belart, J. M., Pálsson, F., Magnússon, E., Víkingsson, S., Kaldal, I., and Jóhannesson, T. (2020). A national glacier inventory and variations in glacier extent in Iceland from the Little Ice Age maximum to 2019. Jökull 2020: 1, 34.</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>Pléiades images were acquired at research price thanks to the CNES ISIS programme (http://www.isiscnes.fr). This study uses the lidar mapping of the glaciers in Iceland, funded by the Icelandic Research Fund, the Landsvirkjun research fund, the Icelandic Road Administration, the Reykjavík Energy Environmental and Energy Research Fund, the Klima- og Luftgruppen research fund of the Nordic Council of Ministers, the Vatnajökull National Park, the organization Friends of Vatnajökull, LMÍ, IMO, and the UI research fund.</p> <p> </p> <p><strong>Dataset Attribution</strong> </p> <p>This dataset is licensed under a <a href="https://creativecommons.org/licenses/by-nc/4.0/">Creative Commons CC BY-NC 4.0 International License</a> (Attribution-NonCommercial).</p>
Supplemental Dataset: Seismological evidence for girdled olivine lattice-preferred orientation in oceanic lithosphere and implications for mantle deformation processes during seafloor spreading
<p>This repository contains supplementary datasets for the manuscript titled "Seismological evidence for girdled olivine lattice-preferred orientation in oceanic lithosphere and implications for mantle deformation processes during seafloor spreading", published in G-Cubed. All files are Microsoft Excel tables containing olivine fabric data.</p> <p>ds01_strain_data_ol60.xlsx: Anisotropy magnitude and fast directions for sample data shown in Figure 3 of the main text, assuming 60% olivine and 40% pyroxene (see methods for details).</p> <p>ds02_strain_data_ol100.xlsx: Anisotropy magnitude and fast directions for sample data shown in Figure 3 of the main text, assuming pure olivine.</p> <p>ds03_fabric_data_ol75.xlsx: Anisotropy fabric data shown in Figure 5 of the main text, assuming 75% olvine and 25% pyroxene (see methods for details).</p> <p>ds04_fabric_data_ol100.xlsx: Anisotropy fabric data shown in Figure 5 of the main text, assuming pure olivine.</p> <p> </p>
Land cover maps: Land Cover Classification with Gaussian Processes using spatio-spectro-temporal features
<p>Land cover maps obtained with Gaussian Processes, Random Forest, Multilayer Perceptron and Lightweight Temporal Self-Attention models for the year 2018 with Sentinel-2 acquisitions.</p> <p>For further details see section VII-A-2 (Results-Performance results in the Southfrance area-Qualitative results) of the 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>
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.