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1,481 results for “data processing”
Ancient Greek Literature for Advanced Data Processing: A Text Fabric Representation of Open Access Texts in TEI XML
<p>This data set contains a full conversion of Greek texts available in the Perseus Digital Library and the Open Greek and Latin Project to the Text Fabric data format. The main advantage of the Text Fabric datatype over the original TEI XML format is that it utilizes a strict separation of text and annotation in a flat data structure. At the same time, it permits multiple distinct formats of the same text as well as an unlimited depth of (embedded) annotations. Because of its flat data structure, it facilitates easy and clean procedures to analyze, transform, and enrich the available data. Many of these processes are very difficult to conduct while departing from the hierarchically organized XML tree representation.</p>
Raw data for "Host-interactor screens of Phytophthora infestans RXLR proteins reveal vesicle trafficking as a major effector-targeted process"
<p>This dataset contains raw and original images, phylogenetic tree files, sequence alignment files used for phylogenetic tree construction and unprocessed data for figures presented in the manuscript titled "Host-interactor screens of <em>Phytophthora infestans</em> RXLR proteins reveal vesicle trafficking as a major effector-targeted process". Each zip file contains raw data for each figure in the manuscript. A version of the manuscript is available on bioRxiv with doi.org/10.1101/2020.09.24.308585.</p>
Data from: DCDC2 READ1 regulatory element: how temporal processing differences may shape language
<p>Classic linguistic theory ascribes language change and diversity to population migrations, conquests, and geographic isolation, with the assumption that human populations have equivalent language processing abilities. We hypothesize that spectral and temporal characteristics make some consonant manners vulnerable to differences in temporal precision associated with specific population allele frequencies. To test this hypothesis, we modeled association between RU1-1 alleles of <i>DCDC2</i> and manner of articulation in 51 populations spanning five continents, and adjusting for geographic proximity, genetic and linguistic relatedness. RU1-1 alleles, acting through increased expression of <i>DCDC2</i>, appear to increase auditory processing precision that enhances stop-consonant discrimination, favoring retention in some populations and loss by others. These findings enhance classical linguistic theories by adding a genetic dimension, which until recently, has not been considered to be a significant catalyst for language change.</p>
Data from Churan et al. Action-dependent processing of self-motion in parietal cortex of macaque monkeys
<p><strong>Animals</strong></p> <p>Two adult male monkeys (macaca mulatta) participated in the study. Single-unit recordings were done using standard tungsten microelectrodes (FHC, Bowdoin, USA) with an impedance of ~2 MΩ at 1 kHz that were positioned by an hydraulic micromanipulator (MO-95, Narishige, Tokyo, Japan). A stainless-steel guiding tube was used for transdural penetration and support of the electrode. The neuronal signal was processed using a commercial system (Alpha Omega, Nof HaGalil, Israel). It was band-pass filtered (cut-off frequencies at 500 Hz and 8000 Hz) and sampled at 44 kHz.</p> <p><strong>Apparatus</strong></p> <p>During recordings, the monkeys were sitting head-fixed in a primate chair in a dark room, and their eye-position was monitored at 1000 Hz using a video-based eye tracker (EyeLink 1000, SR Research, Ottawa, Canada). The chair was positioned at a distance of 97 cm from a semi-transparent screen (size 160 cm x 90 cm, subtending the central 79 deg x 50 deg of the visual field) on which the visual stimuli were back-projected using a PROPixx-projector (VPixx Technologies, St-Bruno de Montarville, Canada) running at a resolution of 1920 x 1080 pixels and at a frame rate of 100 Hz. A custom-made touch sensor (length 10 cm, diameter 1 cm) was integrated into the monkey chair in front of the monkey and its status was monitored online at a sampling rate of 1 kHz.</p> <p><strong>Data processing</strong></p> <p>Single units were isolated using a semi-manual spike sorter (Plexon Inc, Dallas, Texas). To this end we used a threshold on the electrode signal that was set manually to separate the action potentials from noise. The samples that exceeded the threshold were further analyzed using principal components as well as other features that were derived from the signal (like local maxima and minima). Then clusters of samples with similar properties were identified visually and each defined as representing a single unit. For a detailed description of the sorting process see the offline User Guide (Plexon, 2020).</p> <p>Further description of the Methods, see: Churan et al. 2021, doi: 10.1152/jn.00049.2021</p> <p><strong>Data:</strong></p> <p>The file '<strong>data_active_passive.mat</strong>' contains following variables:</p> <p>monkey: code for the tested monkey (1=monkey S, 2=monkey O)</p> <p>baseline: Mean and standard deviation of the activity in a time window of 150 ms to 20 ms before the press of the button.</p> <p>reaction: Mean time between the switch of the color of the fixation point from red to green and the time of the button press.</p> <p>anti_p: Significance of a one sided t-test between the baseline activity and activity 200 ms to 0 ms prior to the onset of stimulus motion.</p> <p>p_win (a (1-3),b (1-3),c (1-3),n(1-110)): 4D matrix containing p-values of t-tests</p> <p>a:</p> <p>1: Was preparatory activity significantly higher in the passive relative to the active condition?</p> <p>2: Was preparatory activity significantly lower in the passive relative to the active condition?</p> <p>3: Was the tonic motion response (200 ms to 500 ms after motion onset) significantly different between the active and the passive conditions?</p> <p>b:</p> <p>1: Calculation was made based on all motion directions</p> <p>2: Calculation was made based on the preferred motion direction</p> <p>3: Calculation was made based on the flanking motion directions</p> <p>c:</p> <p>1: Calculation was made based on all presented delays</p> <p>2: Calculation was made based on the shorter set of delays (500 ms to 700 ms)</p> <p>3: Calculation was made based on the longer set of delays (701 ms to 1000 ms)</p> <p>n: number of the investigated neuron</p> <p>psth_alldir: cell array containing the PSTHs (obtained by convolving each spike with a Gaussian as described in the manuscript) in a time window between 1000 ms before and 800 ms after the onset of motion (in 1 ms steps). PSTHs were calculated based on data from all tested directions. Each cell array consists of 4 elements containing different conditions:</p> <p>1: active condition</p> <p>2: passive condition shorter set of delays (500 ms to 700 ms)</p> <p>3: passive condition longer set of delays (701 ms to 1000 ms)</p> <p>4: passive condition all delays</p> <p>psth_bestdir: same as above - using only the preferred direction</p> <p>psth_nbestdir: same as above - using only the flanking directions</p> <p>d_alldir: cell array containing the continuous d-prime (as described in the manuscript) in a time window between 1000 ms before and 800 ms after the onset of motion (in 1 ms steps). d' were calculated based on data from all tested directions. Each cell array consists of 4 elements containing different conditions:</p> <p>1: active condition</p> <p>2: passive condition shorter set of delays (500 ms to 700 ms)</p> <p>3: passive condition longer set of delays (701 ms to 1000 ms)</p> <p>4: passive condition all delays</p> <p>d_bestdir: same as above - using only the preferred direction</p> <p>d_nbestdir: same as above - using only the flanking directions</p> <p>The file '<strong>timecourse_preparatory.mat</strong>' contains the cell array 'd_alldir_preparatory' that consists of 201 elements. Each of the elements contains PSTHs of 23 neurons that have exhibited significant preparatory activity in the passive condition in a time window 1000 ms to 0 ms before the motion onset. Each of the 201 elements describes a specific range of delays between button press and motion onset. This delay range is always a 100 ms wide sliding window, e.g. the element 1 represents delays between 500 and 600 ms, in element 2, the delays are between 501 and 601 ms and so on with the last element (201) representing delays between 700 and 800 ms.</p> <p>Some example code that re-creates most of the figures from the manuscript and that may serve as a starting point for further exploration of the data is available on request from the corresponding author.</p>
Data from: Spatial processes and evolutionary models: a critical review
Evolution is a fundamentally population level process in which variation, drift, and selection produce both temporal and spatial patterns of change. Statistical model fitting is now commonly used to estimate which kind of evolutionary process best explains patterns of change through time, using models like Brownian motion, stabilizing selection (Ornstein-Uhlenbeck), and directional selection on traits measured from stratigraphic sequences or on phylogenetic trees. But these models assume that the traits possessed by a species are homogeneous. Spatial processes such as dispersal, gene flow, and geographic range changes can produce patterns of trait evolution that do not fit the expectations of standard models, even when evolution at the local-population level is governed by drift or a typical OU model of selection. The basic properties of population level processes (variation, drift, selection, and population size) are reviewed and the relationship between their spatial and temporal dynamics is discussed. Typical evolutionary models used in palaeontology incorporate the temporal component of these dynamics, but not the spatial. Range expansions and contractions introduce rate variability into drift processes, range expansion under a drift model can drive directional change in trait evolution, and spatial selection gradients can create spatial variation in traits that can produce long-term directional trends and punctuation events depending on the balance between selection strength, gene flow, extirpation probability, and model of speciation. Using computational modelling that spatial processes can create evolutionary outcomes that depart from basic population-level notions from these standard macroevolutionary models.
Data for: Scalable and Live Trace Processing with Kieker Utilizing Cloud Computing
<p>Knowledge of the internal behavior of applications often gets lost over the years. This circumstance can arise, for example, from missing documentation. Application-level monitoring, e.g., provided by Kieker, can help with the comprehension of such internal behavior. However, it can have large impact on the performance of the monitored system. High-throughput processing of traces is required by projects where millions of events per second must be processed live. In the cloud, such processing requires scaling by the number of instances.</p> <p>In this paper, we present our performance tunings conducted on the basis of the Kieker monitoring framework to support high-throughput and live analysis of application-level traces. Furthermore, we illustrate how our tuned version of Kieker can be used to provide scalable trace processing in the cloud.</p> <p>This is the dataset containing the results of our conducted benchmarks.</p>
Supplementary data for a study of Open Access Article Processing Charges - 2014
<p>Article Processing Charges levied by a set of Gold Open Access journals and hybrid journals in 2014, as collected from the publishers' web sites. Supplementary data to 10.2314/CERN/C26P.W9DT</p>
Processed BraTS 2013 HG Data
<p>Real high grade glioma patient MRI data from the BraTS 2013 challenge with N3 bias field correction for T1, T1ce, T2, histogram-matched, normalized by mean CSF value and T1ce-T1 subtraction map as fifth channel. Data includes the following features for each of the 5 channels: Gaussian smoothing (0.7; 1.6), Gaussian gradient magnitude (0.7; 1.6), Laplacian of Gaussian (0.7; 1.6), 3 Hessian of Gaussian eigenvalues (0.7; 1.6), 3 structure tensor eigenvalues (0.7; 1.6). Format is HDF5, features along last axis.</p>
Data from Hesse et al. 2017: Preattentive Processing of Numerical Visual Information, Front Hum Neurosci., 11:70, 2017. doi: 10.3389/fnhum.2017.00070.
<p><strong>Data related to the following publication: </strong></p> <p>Hesse Philipp N., Schmitt Constanze, Klingenhoefer Steffen, Bremmer Frank (2017). Preattentive Processing of Numerical Visual Information. Frontiers in Human Neuroscience, 11: 70. doi: 10.3389/fnhum.2017.00070</p> <p><strong>Brief description of dataset:</strong></p> <p>The stimulus was presented on a TFT monitor (size: 41,8° x 24,3°) 52 cm in front of the participants in a dark, sound attenuated and electrically shielded room. During the experiment EEG was recorded continuously. We used 64 Ag/AgCl active electrodes located according to the extended international 10-20 system. </p> <p>The numerosity stimulus consisted of a continuously displayed black fixation target in the center of the gray screen. Additionally in each trial either one, two or three circular white patches were shown 200 ms after trial onset. These were presented for a random duration between 400 ms and 500 ms either in the left or right visual field. Two different types of patches were presented: i) the radius of the patches had the same value (0.65°) and therefore the patch size was the same (“SizeCon”) ii) the total area of the patches was conserved which resulted in the same total luminance independent of the number of patches (“LumCon”). After a random time between 400 ms and 700 ms after stimulus offset the trials ended.</p> <p>In this study we conducted an oddball experiment with an oddball-ratio of 1:4. In each block consisting of 30 trials a standard-amount of patches (one, two or three) was presented in 80% of all trials (24 trials). The two remaining quantities of patches were shown in 10% (3 trials) of the trials each. This presentation scheme allowed us to compare trials with identical physical properties because each amount of patches served as deviant and standard trial in different blocks. Attention of the participants was drawn off the white patches by a demanding detection task at the fixation target. A total number of 432 blocks consisting of 30 trials was presented to each of the 10 participants.</p> <p>EEG data were evaluated offline. The mastoids (TP9 and TP10) were chosen as new reference. A second-order, zero phase shift Butterworth filter with cutoff frequencies 0.5 and 40 Hz was applied to the continuously recorded data before it was sliced in individual trials that had a time range from 200 ms before to 500 ms after stimulus onset. A baseline correction was performed using with the signals from -110 ms to 0 ms. As a last step trials with eye movement artifacts or electrode signals that exceeded a difference of ±100 µV within an interval of 100 ms were excluded in an artifact rejection step.</p> <p> </p>
Eddy covariance data processing workflow example utilizing openeddy and REddyProc R packages
<p>The example dataset is provided within the folder structure required by the workflow files (version 2025-04-27; amended on 2025-07-31) related to the R package openeddy version 0.0.0.9009. Only files needed for successful processing are included. It is shared here as part of a data processing example at <a href="https://github.com/lsigut/EC_workflow">https://github.com/lsigut/EC_workflow</a> to overcome the file size limitation of GitHub.</p>
Processed ERA5, IMERG and TRMM PR/GPM DPR precipitation data for Nicolas & Boos - "Understanding the spatiotemporal variability of tropical orographic rainfall using convective plume buoyancy."
<p>The dataset contains processed data from large datasets that are freely available online. <br>All data cover the period 01/2001 - 12/2020. The file names describe the months & region that each file contains. Variable codes for ERA5 data (all files starting in e5.) are:</p><p> - 228_246_100u : 100m u-wind<br> - 228_247_100v : 100m v-wind<br> - qL : 900-600hPa averaged specific humidity<br> - thetaeb : surface - 900hPa averaged equivalent potential temperature<br> - thetaeL : 900-600hPa averaged equivalent potential temperature<br> - thetaeLstar : 900-600hPa averaged saturation equivalent potential temperature<br> - tL : 900-600hPa averaged temperature<br> - uBL : surface - 900hPa averaged u wind<br> - vBL : surface - 900hPa averaged v wind<br> - 128_034_sstk : sea surface temperature<br> - 162_071_viwve : eastward component of vertically integrated water vapor transport<br> - 162_072_viwvn : northward component of vertically integrated water vapor transport</p><p> </p>
Different spectral sensitivities of ON- and OFF-motion pathways enhance the detection of approaching color objects in Drosophila - Processed Data
<p>Processed data and code for plotting figures for the paper:</p><p>"Different spectral sensitivities of ON- and OFF-motion pathways enhance the detection of approaching color objects in Drosophila", by Kit D. Longden, Edward M. Rogers, Aljoscha Nern, Heather Dionne, Michael B. Reiser.</p><p>Data (compressed results folder) and plotting code (compressed src folder) are MATLAB files (see READ_ME for version information and toolboxes). The Source Data excel file also contains the data plotted in the paper figures.</p>
All-Optical Data Processing with Photon-Avalanching Nanocrystalline Photonic Synapse
<h2>Abstract</h2><p>Data processing and storage in electronic devices are typically performed as a sequence of elementary binary operations. Alternative approaches, such as neuromorphic or reservoir computing, are rapidly gaining interest where data processing is relatively slow, but can be performed in a more comprehensive way or massively in parallel, like in neuronal circuits. Here, time-domain all-optical information processing capabilities of photon-avalanching (PA) nanoparticles at room temperature are discovered. Demonstrated functionality resembles properties found in neuronal synapses, such as: paired-pulse facilitation and short-term internal memory, in situ plasticity, multiple inputs processing, and all-or-nothing threshold response. The PA-memory-like behavior shows capability of machine-learning-algorithm-free feature extraction and further recognition of 2D patterns with simple 2 input artificial neural network. Additionally, high nonlinearity of luminescence intensity in response to photoexcitation mimics and enhances spike-timing-dependent plasticity that is coherent in nature with the way a sound source is localized in animal neuronal circuits. Not only are yet unexplored fundamental properties of photon-avalanche luminescence kinetics studied, but this approach, combined with recent achievements in photonics, light confinement and guiding, promises all-optical data processing, control, adaptive responsivity, and storage on photonic chips.</p>
Data for: New approach for processing recycled carbon staple fiber yarns to unidirectional reinforced recycled carbon staple fiber tape
<p>This data set was generated at Leibniz-Institut für Verbundwerkstoffe GmbH in the project "Process analysis of the pseudo-plastic deformation behavior of unidirectional reinforced staple fiber organo sheets" is funded by the German Research Foundation (DFG) – funding reference 471480678. The goal was to develop a novel process approach for the further processing of staple fiber yarns made from long recycled carbon fibers (rCF) and polyamide 6 (PA6) to highly aligned staple fiber tapes. For this purpose, the input material as well as the manufactured tapes were characterized and examined in thermal analyses. In addition, the influence of the process parameters in the tape manufacturing process on the width and thickness of the tapes and the mechanical properties were investigated. The flexural and tensile properties were then compared with a laminate wound from the staple fiber yarns. The data set is divided in the results of the TGA, DSC, laser profile scans, tensile tests and flexural tests.</p>
data from "Fractal properties of isolines at varying altitude revealing different dominant geological processes on Earth"
<p>The file contains the data used to produce Fig.4 for the paper "Fractal properties of isolines at varying altitude revealing</p><p>different dominant geological processes on Earth", by Andrea Baldassarri, Marco Montuori, Olga Prieto-Ballesteros,</p><p>and Susanna C. Manrubia, Journal of Geophysical Research: PlanetsVolume 113, Issue E9, https://doi.org/10.1029/2007JE003066</p>
Supplementary material for "Exploring Conceptual Data Modeling Processes: Insights from Clustering and Visualizing Modeling Sequences"
<p>This material supplements the following conference publication:</p> <p>Winkler, Rosenthal, Strecker (2024). "Exploring Conceptual Data Modeling Processes: Insights from Clustering and Visualizing Modeling Sequences". Modellierung 2024.</p>
Processed data to accompany "Clonally heritable gene expression imparts a layer of diversity within cell types"
<p>This is the processed data underlying the paper "Clonally heritable gene expression imparts a layer of diversity within cell types" by Mold, Weissman, et al. Data has been gone through preprocessing steps, using the Python Notebooks found at <a href="https://github.com/MartyWeissman/ClonalOmics/tree/main/Data">https://github.com/MartyWeissman/ClonalOmics/tree/main/Data</a>. </p> <p>Smaller files are provided in .csv (comma-separated-value) format and larger files such as expression matrices are provided in .loom format (<a href="https://anndata.readthedocs.io/en/latest/">using the AnnData package</a>).</p> <p> </p> <p> </p>
Processing, Spectroscopic and Laboratory Testing Data from a Medical Grade Hot-Melt Extrusion Process
<p>This dataset contains a collection of raw processing data, spectroscopic data, and laboratory test results of medical-grade polymer extrusion experiments. The data was collected in several experiments conducted in a hot-melt extrusion process. The process involved extruding PLA through a slit die and drawing the extruded strands onto spools to obtain the desired dimensional and mechanical properties. The strands were later knitted to form the final medical implant. Throughout the experiments, the extrusion process and equipment were upgraded and refined. Various operational scenarios were simulated under different nozzle configurations. The experiments start using a single-screw extruder and later progress to a double-screw extruder. Medical Grade PURASORB PLA (PLDLA 96/4) material was used when the hardware upgrades were complete. This dataset contains many variations in experimental conditions. However, enough overlap exists to derive working datasets from this compiled raw data.</p> <p> </p> <p>Two working datasets have been derived from this compiled raw data. Using a double-screw extruder, both working Datasets investigate polymer degradation in the hot-melt extrusion process. Both derived datasets are included in this collection.</p> <p> </p> <p>Two Jupyter notebooks are included in this data collection. The first notebook gives an example of how an initial dataset can be derived from the raw data using data science techniques. The second notebook gives an example of how a final dataset can be created from the initial dataset.</p>
txtools use cases omic references and processed data
<p><strong>Dataset</strong></p> <p>This dataset entry is meant to be downloaded programmatically while rendering the txtools_useCases.Rmd notebooks, to facilitate their replication using the provided genomic references. Processed data is also provided to show ready-to-use examples of data processed by txtools.</p> <p><strong>Abstract</strong></p> <p>We present txtools, an R package that enables the processing, analysis, and visualization of RNA-seq data at the nucleotide-level resolution, seamlessly integrating alignments to the genome with transcriptomic representation. txtools’ main inputs are BAM files and a transcriptome annotation, and the main output is a table, capturing mismatches, deletions, and the number of reads beginning and ending at each nucleotide in the transcriptomic space. txtools further facilitates downstream visualization and analyses. We showcase, using examples from the epitranscriptomic field, how a few calls to txtools functions can yield insightful and ready-to-publish results. txtools is of broad utility also in the context of structural mapping and RNA:protein interaction mapping. By providing a simple and intuitive framework, we believe that txtools will be a useful and convenient tool and pave the path for future discovery. txtools is available for installation from its GitHub repository at <a href="https://github.com/AngelCampos/txtools">https://github.com/AngelCampos/txtools</a>. </p>
Data-Driven Identification and Analysis of Waiting Times in Business Processes: A Systematic Literature Review
<p>Supplementary Material for Systematic Literature Review titled "Data-Driven Identification and Analysis of Waiting Times in<br> Business Processes: A Systematic Literature Review"</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.