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864 results for “numerative”
Numerical simulation results of debris flow under different condition in Chutou gully
<p>We release four datasets that reflect the numerical simulation results of debris flow with a recurrence period of 100 years under different check dam conditions, including with and without check dams and the check dam breakage. The simulation parameters and topography were taken from the Chutou gully, Miansi Town, Wenchuan County, Sichuan Province, Southwestern China. The first dataset indicates the final flow depth distribution under the check dam breakage. The second dataset indicates the final flow depth distribution with check dams. The third dataset indicates the final flow depth distribution without check dams. The fourth dataset indicates the flow velocity evolution at different locations, columns A, E, I and M refer to different times, the rest refer to the flow velocity at different locations at corresponding times. The datasets can be used to analyse the effect of the check dam on debris flows or deposit evolution. The first three datasets can be viewed or edited through the ArcGIS software; the last dataset be viewed or edited through the Excel software.</p>
Dataset for Providing Performance Portable Numerics for Intel GPUs
<p>This dataset stores the performance and testing results shown in the HeteroPar21 extension paper "Providing Performance Portable Numerics for Intel GPUs", which is submitted to Concurrency and Computation: Practice and Experience</p>
Drivers of phytoplankton variability in and Near the Pearl River Estuarine, South China Sea during Typhoon Hato (2017): A numerical study
<p>Simulated and Observed data for JGR manuscript: Drivers of phytoplankton variability in and Near the Pearl River Estuarine, South China Sea during Typhoon Hato (2017): A numerical study. Please read the "A Data Interpretation - README.pdf" before loading the dataset.</p>
Dynamics of the euphotic zone in the Black Sea: The synergy of data from profiling floats, machine learning and numerical modeling
<p>The datasets contain input data and data emulated by Neural networks (NN) used in the study 'Dynamics of the euphotic zone in the Black Sea: The synergy of data from profiling floats, machine learning and numerical modeling'</p> <p>- <strong>NN2018_CMEMS_ARGO.tar.gz</strong>: archive contains Matlab binary files consisting of NN-derived BGC variables (Chlorophyll-a, Oxygen and backscatter at 700nm) along ARGO float paths in 2018 with vertical resolution taken from CMEMS (13 depth levels in the depth range studied here); NN was applied either on CMEMS physics ('C') or on ARGO physics ('A'); additionally, CMEMS BGC model data (Chlorophyll-a and Oxygen) along these paths are included; (filenames follow the names of floats given in Table 1:<em> floatname</em>_2018_NNARGOCMEMS.mat)</p> <p>- <strong>NNalongARGO.tar.gz</strong>: archive contains Matlab binary files consisting of NN-derived BGC variables (Chlorophyll-a, Oxygen and backscatter at 700 nm) along ARGO float paths together with input ARGO data (time, latitude, longitude, salinity, temperature, sigma_T and BGC variables); all variables are mapped with 1m vertical resolution; depth range is [1m 150m] ; additionally, float ogs7 data include NO3, float hzg1 data does not contain Chlorophyll-a and backscatter at 700 nm; (filenames follow the names of floats given in Table 1:<em> floatname</em>_euph_1mRes_150mALLINCLNN.mat)</p> <p>- <strong>NNReconBlackSea.tar.gz</strong>: archive contains Matlab binary files consisting of basin wide NN derived BGC variables (Chlorophyll-a, Oxygen and backscatter at 700nm) for the years 2015-19 and 2010/11 (weekly mean data); additionally CMEMS data (time, latitude, longitude, salinity, temperature, sea surface height) are provided for the photic zone; (filenames are reconNNCMEMS_2015_2019.mat and reconNNCMEMS_2010_2011.mat, respectively)</p>
Numerical data for scattering amplitudes of massive scalar particles in d>2
<p>Numerical data for scattering amplitudes of massive scalar particles in d>2 obtained by solving various optimisation problems. The data is stored as lists in Mathematica .m files. Mathematica notebook is provided for loading and plotting the data.</p>
The dataset for an article - An Evaluation of 3D-Printed Materials' Structural Properties Using Active Infrared Thermography and Deep Neural Networks Trained on the Numerical Data
<p>Dataset used in the research presented in the article:</p> <p>Szymanik, Barbara. 2022. "An Evaluation of 3D-Printed Materials’ Structural Properties Using Active Infrared Thermography and Deep Neural Networks Trained on the Numerical Data" <em>Materials</em> 15, no. 10: 3727. https://doi.org/10.3390/ma15103727</p> <p>The database in the .mat (matlab) format contains arrays of double type related to: A - original thermograms obtained for the plate made with the 3D printing technique Ar - thermograms with ROI included FITorg - approximation of original thermograms ImDiff, ImInt, ImProp - data obtained after subtracting the approximation.</p>
PlaSim output data from TSI-related numerical experiments
<p>Dear Visitor,<br> <br> Here you can access all the output of the PlaSim simulations in netCDF format.<br> <br> The form of the file names is as follows:<br> <br> TS_History#_m0##.nc<br> <br> , where TS abbreviates surface temperature, # refers to the particular<br> forcing history (C, B, Ta, Tb, N) and ## denotes numbers from 1 to 40.</p>
PlaSim output data from TSI-related numerical experiments
<p>Dear Visitor,</p> <p>Here you can access all the output of the PlaSim simulations in netCDF format.</p> <p>The form of the file names is as follows:</p> <p>TS_History#_m0##.nc</p> <p>, where TS abbreviates surface temperature, # refers to the particular<br> forcing history (C, B, Ta, Tb, N) and ## denotes numbers from 1 to 40.</p>
PlaSim output data from TSI-related numerical experiments
<p>Dear Visitor,</p> <p>Here you can access all the output of the PlaSim simulations in netCDF format.</p> <p>The form of the file names is as follows:</p> <p>TS_History#_m0##.nc</p> <p>, where TS abbreviates surface temperature, # refers to the particular<br> forcing history (C, B, Ta, Tb, N) and ## denotes numbers from 1 to 40.</p>
PlaSim output data from TSI-related numerical experiments
<p>Dear Visitor,<br> <br> Here you can access all the output of the PlaSim simulations in netCDF format.<br> <br> The form of the file names is as follows:<br> <br> TS_History#_m0##.nc<br> <br> , where TS abbreviates surface temperature, # refers to the particular<br> forcing history (C, B, Ta, Tb, N) and ## denotes numbers from 1 to 40</p>
PlaSim output data from TSI-related numerical experiments
<p>Dear Visitor,<br> <br> Here you can access all the output of the PlaSim simulations in netCDF format.<br> <br> The form of the file names is as follows:<br> <br> TS_History#_m0##.nc<br> <br> , where TS abbreviates surface temperature, # refers to the particular<br> forcing history (C, B, Ta, Tb, N) and ## denotes numbers from 1 to 40.</p>
Data from: A shared numerical magnitude representation evidenced by the distance effect in frequency-tagging EEG
<p>Humans can effortlessly abstract numerical information from various codes and contexts. However, whether the access to the underlying magnitude information relies on common or distinct brain representations remains highly debated. Here, we recorded electrophysiological responses to periodic variation of numerosity (every five items) occurring in rapid streams of numbers presented at 6Hz in randomly varying codes – Arabic digits, number words, canonical dot patterns and finger configurations. Results demonstrated that numerical information was abstracted and generalized over the different representation codes by revealing clear discrimination responses (at 1.2 Hz) of the deviant numerosity from the base numerosity, recorded over parieto-occipital electrodes. Crucially, and supporting the claim that discrimination responses reflected magnitude processing, the presentation of a deviant numerosity distant from the base (e.g., base "2" and deviant "8") elicited larger right-hemispheric responses than the presentation of a close deviant numerosity (e.g., base "2" and deviant "3"). This finding nicely represents the neural signature of the distance effect, an interpretation further reinforced by the clear correlation with individuals' behavioral performance in an independent numerical comparison task. Our results, therefore, provide for the first time unambiguously a reliable and specific neural marker of a magnitude representation that is shared among several numerical codes.</p>
Numerical Simulations on Unconventional Surface Charging within Deep Cavities in the Solar Wind Plasma.
<p>Numerical simulation data presented in Nakazono and Miyake (2022): Unconventional Surface Charging within Deep Cavities in the Solar Wind Plasma. The format of the dataset is described in the PDF document (2022JA_supporting_information.pdf).</p>
cldf-datasets/dryerorder: Dataset of Dryer 2018 "On the order of demonstrative, numeral, adjective, and noun"
<p><strong>Dryer, M.S. (2018). On the order of demonstrative, numeral, adjective, and noun. Language 94(4), 798-833. doi:10.1353/lan.2018.0054.</strong></p>
Figure 2 in Functional and numerical responses of the predatory mite Amblyseius aerialis (Acari: Phytoseiidae) toAceria guerreronis (Acari: Eriophyidae)
Figure 2 Mean number of eggs laid byAmblyeius aerialis females provided with different densities
Figure 3 in Functional and numerical responses of the predatory mite Amblyseius aerialis (Acari: Phytoseiidae) toAceria guerreronis (Acari: Eriophyidae)
Figure 3 Average oviposition rates (eggs/female ± SE) byAmbyseius aerialis females given different
Numerical analysis of working paper "Joint integrated production-maintenance policy of a deteriorating equipment considering random yield and maintenance delay"
<p>This is the numerical analysis code using in the working paper "Joint integrated production-maintenance policy of a</p> <p>deteriorating equipment considering random yield and maintenance delay"</p>
White Gelao (Tianba) numerals
<p>On August 25, 2012, in Tianba, Dongdu Village, Tiechang Township, Malipo County, Yunnan, China, I interviewed a 78-year-old man named Yang Guangsheng 杨光升, whom I recorded some White Gelao words from. Yang spoke White Gelao quite fluently. He reported other White Gelao speakers as (1) Yang Guangpu 杨光甫, a White Gelao speaker in Fengyan, and (2) Yang Guangqing 杨光睛, a White Gelao speaker of Fanpo, Yangwan Township who is the brother of Yang Guangsheng 杨光升.</p>
Bugan (Jiuping) numerals
<p>These Bugan numerals (1-10) were recorded on May 4, 2015 in Jiuping village, Zhuanjiao Township, Guangnan County, Yunnan, China.</p>
Bolyu numerals
<p>In August 2012, a Bolyu man in the Changfa Township center, Longlin County, Guangxi, China whom I recorded some Bolyu numerals from, told me that there are different Bolyu dialects that have different tones. All dialects are mutually intelligible.</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.