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6,467 results for “process”
BriTROC-1 study: genomic landscape of recurrent ovarian high grade serous carcinoma - pre processed data
<p>Dataset containing pre-processed data files required to replicate analysis performed in the publication "<strong>The genomic landscape of recurrent ovarian high grade serous carcinoma: the BriTROC-1 study</strong>" (<a href="https://doi.org/10.1038/s41467-023-39867-7">Smith & Bradley et al. 2023</a>).</p>
Dataset for PNAS: Adiabatic computing for optimal thermodynamic efficiency of information processing
<p>Data set for the article to be published in PNAS "Adiabatic computing for optimal thermodynamic efficiency of information processing"</p> <p>This data set contains:</p> <ul> <li><strong>optimal_1ms.zip</strong> : raw data files of the response to an optimal erasure protocol of characteristic duration 1ms. This Matlab file includes all necessary quantitites to compute the average work and heat during the procedure ( ie position trajectory and driving parameters).</li> <li><strong>optimal_3ms_part1 & _part2.zip</strong>: same for optimal protocols of 3ms.</li> <li><strong>Analysis_code_optimal.m</strong>: Analysis Matlab code to be run on the mat files above. The code run the full analysis and output the work, heat, kinetic and potential energy and some calibration parameters. The processed data is saved in <strong>optimal_1 & 3ms_result.mat</strong>.</li> <li><strong>Fast_erasure_v1_0.3 & _0.12.zip</strong>: same for basic protocol at v<sub>1</sub>=0 & v<sub>1</sub>=0.12. The processed data is saved in <strong>Fast_erasure_v1_0.3 & _0.12.mat</strong>. This data corresponds tp the two speeds used to draw the heating kinetic energy curves of Fig.4</li> <li> <p><strong>BasicAndOptimalTranslations.mat:</strong> Mat file containing the data used to create Fig. 3:</p> <ul> <li> <p>tprotocol (ms), x1basprotocol and x1optprotocol (sigma) define the ramps of the well center for the basic and optimal protocols (time and value of x1 for each ramp).</p> </li> <li> <p>t (ms), xbas and xopt (sigma) are the recording from the experiment (time, and arrays corresponding to 2000 measurements with 10ms of data sampled at 2Mhz each). The data is post processed with a low pass filter at 8kHz (filtfilt function in Matlab, using a 4th order butterworth filter). Fig. 3 correspond to the average of those 2000 trajectories.</p> </li> <li> <p>f0 (Hz) is the resonance frequency of the cantilever.</p> </li> </ul> </li> <li><strong>FigX.fig: </strong>Matlab format figure source used to draw all of the plots of the article, embedding the all data.</li> </ul>
SatTerm experience: vocabulary control and facet analysis help improve the software requirement solicitation process
<p>One of the most difficult steps in the software development process is moving from requirements written in natural, uncontrolled language, to the formalisms required by the design modelling languages. To solve this issue, practitioners should pay attention to the possibility of applying vocabulary control and knowledge representation techniques to produce better specifications. The use of controlled vocabularies and the modelling of the conceptual relationships between concepts in a specific domain are expected to improve the quality of the specifications.<br> Vocabulary control and semantic modelling are promising tools to avoid the most frequent problems in the requirements specification process: lack of consistency and ambiguity. This paper provides a detailed description of the development process of an ontology used for requirements modelling in the area of satellite control systems. The process applied is based on well-established practices and guidelines applied for the construction of controlled vocabularies and faceted classifications schemas. Engineers can use the ontology when writing system specifications using predefined templates. The use of this ontology ensures the consistency of the specifications written by different engineers improves the communication with other parties involved in the system construction activities and sets the foundations for a semi-automated generation of models for subsequent design activities.</p>
Replication Package - From Research to Practice: A Survey of XAI Process Frameworks
<p>Replication package for the ICSE NIER Submission "From Research to Practice: A Survey of XAI Process Frameworks." Provides details about the methods and data used in our analysis. For more information, please take a look at the README.</p>
EEG Dataset and Processing script Associated with the Manuscript, "Using the Time-varying Drift Rate, the Signal Suppression and the Utility Maximisation to Account for Road Crossing Decisions".
<p>The repository archived the behavioural data (zip files) associated with the EEG experiment and the MATLAB script for EEG processing. BDFs stored the down-sampled EEG. </p>
Dataset from the paper "When rage is not feminist: Processes of Cooptation and Delegitimization"
<p>ID of the tweets composing the final corpus of messages used in the paper "When rage is not feminist: Processes of Cooptation and Delegitimization"</p>
Sims et al. A Tale of Two Pools: The Dynamic Influence of Subsurface Geological Processes on the Assembly and Diversification of Thermophilic Microbial Communities in Hydrothermal Systems - Supplementary Datasets
<p>These datasets comprise the supplementary datasets found in Sims et al. A Tale of Two Pools: The Dynamic Influence of Subsurface Geological Processes on the Assembly and Diversification of Thermophilic Microbial Communities in Hydrothermal Systems, <strong><em>Geochimica et Cosmiochimica Act </em></strong></p>
Cybervetting in the Recruitment Process Dataset
<p>The dataset used in the bibliometric analysis using VOSviewer in the paper "Bibliometric Analysis of Cybervetting in the Recruitment Process"</p>
Vibrational-EELS Dataset and data processing routine (Jupyter Notebook) (Laforet et al.)
<p>Contains all the vibrational-EELS data presented in the article, accompanied with the python HyperSpy processing routine used (Jupyter Notebook). </p>
Sainfoin Fruit Processing - Object Detection Dataset
<p>This dataset consists of 500 images of sainfoin (Onobrychis viciifolia) seed pods, seed, and split seeds. The images were taken as a part of an experiment to determine minimum sample size of seed pods needed to accurately estimate pod threshing trait heritability within sainfoin breeding lines.</p><p>The experiment was a complete factorial design with the following factors:</p><ul><li>Sainfoin named varieties: AAC Mountainview, Delaney, Eski , Rocky Mountain Remont, and Shoshone</li><li>Sample Size: 1, 2, 3, 4, and 5 grams of dried seed pods</li><li>Two different threshing types: Belt thresher processed 3X, Haldrup Impact Thresher (35sec @ Speed 9)</li></ul><p>This makes for a total factorial combination set of 5 varieties X 5 sample sizes X 2 threshing types = 50.</p><p>Each combination was comprised of 10 individual replicates where each replicate in a combination was a unique, random sample of seeds of the same mass (So, 10 random, 2g samples of Eski seed, processed by belt thresher; 10 random, 5g samples of Delaney seed processed by the Haldrup thresher, etc.). This makes for a total of 500 experimental units that comprise the sample set.</p><p>Once the seeds were sampled, weighed, and processed through the threshing equipment, they were weighed again and imaged.</p><p>The threshed seeds were scattered onto an imaging platform with a blue background, lit by 2 LED panels, and photographed with a Sony ILCE-7RM2 at the following settings:</p><ul><li>ISO: 100</li><li>Exposure: 1/40s</li><li>Focal Length: 55mm</li><li>Format: TIFF</li><li>Size: 7968x5320</li></ul><p>The raw images were converted from TIFF files to JPEG format and annotated in image labeling software. The seed objects were annotated with bounding boxes classified as the following classes</p><ol><li>pod: an enclosed seed pod</li><li>seed: a seed which was successfully threshed from the legume pod carpel</li><li>split: a seed threshed from the pod, but which split in two halves during the threshing process</li></ol><p>All image annotations were exported into the convenient <a href="https://docs.aws.amazon.com/rekognition/latest/customlabels-dg/md-coco-overview.html">COCO format</a>.</p><p>No further image processing was performed.</p><p>The image set was split into a 80/20 training and validation step using `scikit-learn` in Python 3.11 stratifying the datasets equally over the various experimental factor levels.</p><p>The zip file 'train_val_images.zip' contains a 'train' folder with 400 training images, 'val' containing 100 validation images, an image taken with a color correction card named 'color_test.jpg', and a json file with all the annotations.</p><p>Another file called 'seed_weights.csv' contains the image_name to global-key mapping in tabular format as well as the before and after threshing seed weights for each experimental sample.</p><p><strong>Labeling Metrics:</strong></p><ul><li>Pod (48.58%)<ul><li>36,599 objects</li></ul></li><li>Seed (33.83%)<ul><li>25,488 object</li></ul></li><li>Split (17.59%)<ul><li>13,255 objects</li></ul></li><li><strong>TOTAL (100%)</strong><ul><li><strong>75,342 objects</strong></li></ul></li></ul>
Neural and behavioral data from: A dynamic sequence of visual processing initiated by gaze shifts
<p>Animals move their head and eyes as they explore and sample the visual scene. Previous studies have demonstrated neural correlates of head and eye movements in rodent primary visual cortex (V1), but the sources and computational roles of these signals are unclear. We addressed this by combining measurement of head and eye movements with high density neural recordings in freely moving mice. V1 neurons responded primarily to gaze shifts, where head movements are accompanied by saccadic eye movements, rather than to head movements where compensatory eye movements stabilize gaze. A variety of activity patterns immediately followed gaze shifts, including units with positive, biphasic, or negative responses, and together these responses formed a temporal sequence following the gaze shift. These responses were greatly diminished in the dark for the vast majority of units, replaced by a uniform suppression of activity, and were similar to those evoked by sequentially flashed stimuli in head-fixed conditions, suggesting that gaze shift transients represent the temporal response to the rapid onset of new visual input. Notably, neurons responded in a sequence that matches their spatial frequency preference, from low to high spatial frequency tuning, consistent with coarse-to-fine processing of the visual scene following each gaze shift. Recordings in foveal V1 of freely gazing head-fixed marmosets revealed a similar sequence of temporal response following a saccade, as well as the progression of spatial frequency tuning. Together, our results demonstrate that active vision in both mice and marmosets consists of a dynamic temporal sequence of neural activity associated with visual sampling.</p>
Model version, input data, results, and processing scripts for Speizer et al., "Rapid implementation of mitigation measures can facilitate decarbonization of the global steel sector in 1.5°C-consistent pathways"
<p>Includes the files needed to run the scenarios, analyze the outputs, and produce the figures for Speizer et al., "Rapid implementation of mitigation measures can facilitate decarbonization of the global steel sector in 1.5°C-consistent pathways." </p>
Supplementary Files: processed model results for freshwater tracer concentration and filling/flushing time
<p>FWdye_avg_241yr_300yr: monthly and inter-annual mean tracer concentrations for model years 241 to 300.</p> <p>FWdye_outflux_avg_271yr_300yr: freshwater tracer sinks fluxes averaged for model years 271 to 300.</p> <p>FW_flux_avg_001yr_060yr: monthly averaged freshwater fluxes with seasonal and inter-annual variability for one cycle of COREII.</p> <p>filltime_basinWE_001yr_300yr: processed river water tracer filling time on shelf seas for model years 1 to 300.</p> <p>filltime_basinWE_01yr_60yr_1yrimpulse: processed flushing time for one year released impulse test for model years 1 to 60.</p>
climate-processes/tobac_example_data: tobac example datasets
<p>Data used in the tobac (<a href="https://github.com/climate-processes/tobac">https://github.com/climate-processes/tobac</a>) example notebooks. The datasets are based on model simulations for the ACPC deep convection intercomparison (<a href="http://acpcinitiative.org/Docs/ACPC_DCC_Roadmap_171019.pdf">http://acpcinitiative.org/Docs/ACPC_DCC_Roadmap_171019.pdf</a>) and GOES-13 geostationary satellite retrievals (<a href="https://www.avl">https://www.avl</a>. class.noaa.gov/saa/products/search?datatype_family=GVAR_IMG).</p>
A More Physiological Feeding Process in ICU:the Intermittent Infusion With Semi-solidification of Nutrients
ClinicalTrials.gov study NCT03017079. IPD Sharing: NO. Countries: 1. Publications: 8.
Hearing Aid Processing and Working Memory in Realistic Spatial Conditions
ClinicalTrials.gov study NCT04521166. IPD Sharing: NO. Countries: 1. Publications: 1.
Efficacy and Safety of BIIB019 (Daclizumab High Yield Process) Versus Interferon β 1a in Participants With Relapsing-Remitting Multiple Sclerosis
ClinicalTrials.gov study NCT01064401. IPD Sharing: Not stated. Countries: 28. Publications: 7.
Safety and Efficacy Extension Study of Daclizumab High Yield Process (DAC HYP) (BIIB019) in Participants Who Have Completed Study 205MS202 (NCT00870740) to Treat Relapsing Remitting Multiple Sclerosis
ClinicalTrials.gov study NCT01051349. IPD Sharing: Not stated. Countries: 8. Publications: 3.
Neuroimaging the Effects of Intravenous Anesthetic on Amygdala Dependent Memory Processes
ClinicalTrials.gov study NCT00504894. IPD Sharing: NO. Countries: 1. Publications: 1.
Speed of Processing Training for Cognitive Deficits After Delirium in Older Adults
ClinicalTrials.gov study NCT03342989. IPD Sharing: YES. Countries: 1. Publications: 2.
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.