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42 results for “Computational efficiency”
Evaluation datasets and results of the paper "Efficient Online Computation of Business Process State From Trace Prefixes via N-Gram Indexing"
<p>Event logs, process models, and results corresponding to the paper "Efficient Online Computation of Business Process State From Trace Prefixes via N-Gram Indexing".</p> <p><em><strong>Inputs</strong></em>: preprocessed event logs and discovered process models (and their characteristics) used in the evaluation.</p> <ul> <li><em><strong>Real-life</strong></em>: preprocessed event logs (<em>xes</em> and <em>csv</em>) corresponding to the real-life processes used in the evaluation. Process models (<em>pnml</em>) discovered with the Inductive Miner infrequent for thresholds of 10%, 20%, and 50%. Characteristics (<em>txt</em>) of the event logs and process models. Ongoing cases result from splitting each case in the preprocessed event logs (under folder <em>split</em>).</li> <li><em><strong>Synthetic</strong></em>: simulated event logs (<em>csv</em>) corresponding to the synthetic processes used in the evaluation. Designed process models (<em>bpmn</em> and <em>pnml</em>). Ongoing cases result from splitting each case in the preprocessed event logs (under folder <em>split</em>). Ongoing cases with injected noise as described in the publication (under folders <em>noise_1</em>, <em>noise_2</em>, and <em>noise_3</em>).</li> </ul>
Source code and simulation results for the computation of eigenfrequency sensitivities using Riesz projections for efficient optimization of nanophotonic resonators
<p><strong>Summary</strong></p> <p>Data and source code relate to the article "Computation of eigenfrequency sensitivities using Riesz projections for<br> efficient optimization of nanophotonic resonators" [<a href="https://doi.org/10.1038/s42005-022-00977-1">1</a>]. It combines direct differentiation of scattering problems with a contour integral method [<a href="https://doi.org/10.1016/j.jcp.2020.109678">2</a>] to compute eigenfrequency sensitivities. An optimization is used to demonstrate the relevance of the method.</p> <p><strong>Structure</strong></p> <p>The most important elements of this publication are the MATLAB scripts 'sensitivities.m' and 'optimization.m', which can be used to reproduce the most important results of the paper. The directories <strong>code</strong>, <strong>scattering</strong> and <strong>results </strong>contain the software RPExpand [<a href="https://doi.org/10.1016/j.softx.2021.100763">3</a>], input files for JCMsuite [<a href="https://doi.org/10.1002/pssb.200743192">4</a>] and results produced with the scripts, respectively. Furthermore, the latter contains the subfolder <strong>tabulated,</strong> which contains text files tabulating data presented in Figures 2 and 4 of the paper. Eventually, the function 'code/observation.m' evaluates the target for the optimization.</p> <p><strong>Additional Information</strong></p> <p>The applicaton is based on an example from the literature [<a href="https://doi.org/10.1126/science.aaz3985">5</a>]. Using apriori knowledge about the eigenmode of interest, we chose the scalar observable, as defined in Section B of the paper, to be the component of the electric field normal to the plane defining the solid of revolution.</p> <p>The convergence studies are based on the discrete, circular contour <span>\(\tilde{C} = \big\{ c_n~|~ c_n=r_0 e^{2\pi i n/8}, n \in \{0,1,...,7\}\big\}\)</span> with center <span>\(\omega_0 = 2 \pi c/(1600~\mathrm{nm})\)</span> and radius <span>\(r_0 = \omega_0\times10^{-2}\)</span>. For finite element degrees <span>\(d\)</span> higher than 5, the error saturates. For this reason, the differences between results for <span>\(d=5\)</span> and <span>\(d = 6\)</span> may depend on the hardware architecture.</p> <p>A larger radius <span>\(r = 4\times10^{13}\)</span> has been chosen for the optimization to include information from poles located further away from the frequency of interest. The target function <span>\(t(p_1,\dots,p_5) = -q_n \left(1 - \frac{(\omega_n-\omega_0)^2}{r^2} \right)\)</span>is minimized. The first factor is the negative <em>Q-</em>Factor and the second factor ensures that the target is zero at the boundary. If no eigenfrequency <span>\(\omega_n\)</span> is located inside the contour, the target is set to zero. For the purpose of this data publication some numerical parameters have been improved. This resulted in a faster convergence of the optimization.</p> <p><strong>Requirements</strong></p> <ul> <li>JCMsuite (version 5.2.0 or newer)</li> <li>MATLAB (tested with version R2019b)</li> </ul> <p>In order to run the scripts you must replace the corresponding place holders in the files by a path to your installation of JCMsuite. Free trial licenses are available, please refer to the homepage of <a href="https://jcmwave.com/">JCMwave</a>. </p> <p><strong>References</strong></p> <p>[1] Felix Binkowski, Fridtjof Betz, Martin Hammerschmidt, Philipp-Immanuel Schneider, Lin Zschiedrich, Sven Burger, Computation of eigenfrequency sensitivities using Riesz projections for efficient optimization of nanophotonic resonators, Communications Physics <strong>5</strong>, 202 (2022), https://doi.org/10.1038/s42005-022-00977-1</p> <p>[2] Felix Binkowski, Lin Zschiedrich, Sven Burger, A Riesz-projection-based method for nonlinear eigenvalue problems, Journal of Computational Physics <strong>419</strong>, 109678 (2020), https://doi.org/10.1016/j.jcp.2020.109678</p> <p>[3] Fridtjof Betz, Felix Binkowski, Sven Burger, RPExpand: Software for Riesz projection expansion of resonance phenomena, SoftwareX <strong>15</strong>, 100763 (2021), https://doi.org/10.1016/j.softx.2021.100763</p> <p>[4] Jan Pomplun, Sven Burger, Lin Zschiedrich, Frank Schmidt, Adaptive finite element method for simulation of optical nano structures, Physica Status Solidi B <strong>244</strong>, 3419 (2007), http://dx.doi.org/10.1002/pssb.200743192</p> <p>[5] Kirill Koshelev, Sergey Kruk, Elizaveta Melik-Gaykazyan, Jae-Hyuck Choi, Andrey Bogdanov, Hong-Gyu Park, Yuri Kivshar, Subwavelength dielectric resonators for nonlinear nanophotonics, Science <strong>367</strong>, 288 (2020), http://dx.doi.org/%2010.1126/science.aaz3985</p>
A computationally efficient statistically downscaled 100 m resolution Greenland product from the regional climate model MAR: accompanying dataset
<p>Dataset containing surface temperature and surface mass balance datasets generated from the MAR regional climate model over Greenland over two test areas using statistical downscaling tools from 6 km to 100m. The abstract of the accompanying submitted paper follows: </p> <p> </p> <p>The Greenland Ice Sheet (GrIS) has been contributing directly to sea level rise and this contribution is projected to accelerate over next decades. A crucial tool for studying the evolution surface mass loss (e.g., surface mass balance, SMB) consists of regional climate models (RCMs) which can provide current estimates and future projections of sea level rise associated with such losses. However, one of the main limitations of RCMs is the relatively coarse horizontal spatial resolution at which outputs are currently generated. Here, we report results concerning the statistical downscaling of the SMB modeled by the Modèle Atmosphérique Régional (MAR) RCM from the original spatial resolution of 6 km to 100 m building on the relationship between elevation and mass losses in Greenland. To this goal, we developed a geospatial framework that allows the parallelization of the downscaling process, a crucial aspect to increase the computational efficiency of the algorithm. The results obtained in the case of the SMB, assessed through the comparison of the modeled outputs with in-situ SMB measurements, show a considerable improvement in the case of the downscaled product with respect to the original, coarse output. In the case of the downscaled MAR product, the coefficient of determination (R<sup>2</sup>) increases from 0.868 for the original MAR output to 0.935 for the downscaled product. Moreover, the value of the slope and intercept of the linear regression fitting modeled and measured SMB values shifts from 0.865 for the original MAR to 1.015 for the downscaled product in the case of the intercept and from the value -235mm (original) to -57 mm (downscaled) in the case of the slope, considerably improving upon results previously published in the literature.</p>
Data set for the article "Efficient Computation of the Magnetic Polarizability Tensor Spectral Signature using POD"
<p>Data set to accompany the article "Efficient Computation of the Magnetic Polarizability Tensor Spectral Signature using POD" written by B.A. Wilson (Swansea University) and P.D. Ledger (Keele University)</p>
quickSparseM: a library for memory- and time-efficient computation on large, sparse matrices with application to omics data
<p>This page contains the code and datasets used in "quickSparseM: a library for memory- and time-efficient computation on large, sparse matrices with application to omics data".</p> <p>File <strong>test_datasets.zip</strong> containes three datasets:</p> <ul> <li><em>D1.RData</em>: scRNA-seq omics data derived from Salcher et. al (2022)</li> <li><em>D2.RData</em>: scRNA-seq omics data derived from Pineda et al. (2024)</li> <li><em>D3.RData</em>: in silico WGS SNP data.</li> </ul> <p>File <strong>test_scripts.zip</strong> containes the code to reproduce the results.</p>
Data for manuscript "rMATS-turbo: an efficient and flexible computational tool for alternative splicing analysis of large-scale RNA-seq data"
<p>Output files generated by rMATS-turbo for the two example datasets described in the manuscript titled "rMATS-turbo: an efficient and flexible computational tool for alternative splicing analysis of large-scale RNA-seq data".</p> <table> <tbody> <tr> <td>File</td> <td>Description</td> <td>Cell lines</td> <td>BioProject</td> </tr> <tr> <td>PC3E-GS689.tar.gz</td> <td>Compressed folder containing all 36 rMATS-turbo output files for Example 1 described in the manuscript</td> <td>PC3E and GS689 cell lines</td> <td>PRJNA438990</td> </tr> <tr> <td>CCLE.tar.gz</td> <td>Compressed folder containing all 36 rMATS-turbo output files for Example 2 described in the manuscript</td> <td>1,019 CCLE human cancer cell lines</td> <td>PRJNA523380</td> </tr> </tbody> </table> <p>A detailed description of the output files is available in the manuscript and the rMATS-turbo software GitHub repository (https://github.com/Xinglab/rmats-turbo).</p>
Polynomial chaos to efficiently compute the annual energy production in wind farm layout optimization
<p>Data for the Wind Energy Science paper "Polynomial chaos to efficiently compute the annual energy production in wind farm layout optimization".</p> <p>The data includes a file describing the wind direction distribution. The i<sup>th</sup> probability value corresponds to the probability of the wind coming between direction i and i+1.</p> <p>The other data files, corresponding to the wind farm layouts, provide the x,y coordinates of the wind turbines. </p>
Data for "Computational design of developable therapeutic antibodies: efficient traversal of binder landscapes and rescue of escape mutations"
Open the record for dataset details and reuse information.
Dataset for "Light and Mass Transport Computations Guide the Fabrication of 3D-Structured TiO2 and Au/TiO2 Aerogel Photocatalysts for Efficient Hydrogen Production in the Gas Phase"
<p>This dataset is related to "Light and Mass Transport Computations Guide the Fabrication of 3D-Structured TiO<sub>2</sub> and Au/TiO<sub>2</sub> Aerogel Photocatalysts for Efficient Hydrogen Production in the Gas Phase" published in <em> Chemistry of Materials</em> <strong>2023</strong> <em>35</em> (10), 3849-3858.</p> <p>Each file contains the dataset for the respective Figure.</p> <p><strong>File 'Figure 1': </strong>Optical Photograph and SEM images of a 3D printed TiO<sub>2</sub> aerogel.</p> <p><strong>File 'Figure 2': </strong>The subdirectory <em>'absorbed'</em> contains data for the calculation of the light absorption of unstructured, sc-structured, and fcc-structured aerogels. A more detailed description is presented in the <em>'readme</em>' file. The subdirectory <em>'flux_time_resolved'</em> contains data for the calculation of the time-resolved flux in a fcc-structured aerogel. A more detailed description is presented in the readme file.</p> <p><strong>File 'Figure 3': </strong>Measured and calculated data of the pressure drop of unstructured, sc-structured, and fcc-structured aerogels. Images of the velocity profile. Images of simulated velocity profiles of an sc-structured aerogel without and with a surrounding wall. The simulations were performed in COMSOL.</p> <p><strong>File 'Figure 4': </strong>Data of the hydrogen evolution experiments.</p> <p><strong>File 'Figure SI1 and Table SI1': </strong>Data of nitrogen physisorption experiments. <em>'Figure_SI1-sample-identification'</em> contains a list to assign the dataset to the respective subfigures in Figure SI1. <em>'Table_SI1-sample-identification' </em>contains a list to assign the dataset to the respective entry in Table SI1.</p> <p><strong>File 'Figure SI2': </strong>Data of the hydrogen evolution experiments with a gas stream containing pure water and a water/methanol mixture, respectively.</p> <p><strong>File 'Figure SI3': </strong>Data of the UV cleaning experiment.</p> <p><strong>File 'Figure SI4': </strong>Chromatograms recorded during hydrogen evolution experiments to discuss the formation of side products.</p> <p><strong>File 'Figure SI5':</strong> Data of two consecutive hydrogen evolution experiments.</p> <p><strong>File 'Figure SI6': </strong>Data of the hydrogen evolution experiments for an fcc-structured and sc-structured TiO<sub>2</sub> aerogel of similar light absorption. Image of a simulated velocity profiles for an unstructured aerogel. The simulation were performed in COMSOL.</p> <p><strong>File 'Figure SI7': </strong>Data of an hydrogen evolution experiments of an fcc-structured TiO<sub>2</sub> aerogel for flow rates in a range of 1.25 to 20 mL min<sup>-1</sup>.</p> <p><strong>File 'Figure SI8': </strong>TEM/STEM images including EDX mapping of an Au/TiO<sub>2</sub> aerogel fragment.</p> <p><strong>File 'Figure SI9': </strong>Data of the hydrogen evolution, the irradiance of the LED, and the amount of water and methanol.</p> <p><strong>File 'Figure SI10': </strong>Attenuated total reflection infrared spectra of TiO<sub>2</sub> nanoparticle powder and aerogel after UV cleaning.</p> <p><strong>File 'Figure SI11': </strong>XRD pattern of TiO<sub>2</sub> nanoparticles and a reference of anatase TiO<sub>2</sub>.</p> <p><strong>File 'Figure SI12': </strong>Data of hydrogen evoltion for TiO<sub>2</sub> nanoparticle powders.</p> <p><strong>File 'Figure SI13': </strong>Transmission and reflectance spectra of a TiO<sub>2</sub> aerogel.</p> <p><strong>File 'Figure SI14': </strong>Calculated transmission and reflectance for an optical thickness and a scattering albedo in a range of 0 to 5 and 0 to 1, respectively. The data was calculated by solving the radiative transfer equation, as implemented in the DISORT algorithm. A more detailed description of the calculation and data processing is provided in the <em>'readme'</em> file. The code of the DISORT algorithm is provided in the <em>'DISORT'</em> subdirectory.</p> <p><strong>File 'Figure SI16': </strong>Data of the derived absorption and scattering coefficient.</p> <p><strong>File 'Figure SI17': </strong>Data of the light absorption and the scattering coefficient. The <em>'readme'</em> file contains a description of the data processing for the light absorption dataset.</p>
Iterative evaluation of mobile computer-assisted digital chest x-ray screening for TB improves efficiency, yield, and outcomes in Nigeria
<p>Wellness on Wheels (WoW) is a model of mobile systematic tuberculosis (TB) screening of high-risk populations combining digital chest radiography with computer-aided automated detection (CAD) and chronic cough screening to identify presumptive TB clients in communities, health facilities, and prisons in Nigeria. The model evolves to address technical, political, and sustainability challenges.</p> <p>Screening methods were iteratively refined to balance TB yield and feasibility across heterogeneous populations. Performance metrics were compared over time. Screening volumes, risk mix, number needed to screen (NNS), number needed to test (NNT), sample loss, TB treatment initiation and outcomes. Efforts to mitigate losses along the diagnostic cascade were tracked. Participants with high likelihood on CAD4TB (≥80) who tested negative on a single spot GeneXpert were followed-up to assess TB status at six months.</p> <p>An experimental calibration method achieved a viable CAD threshold for testing. High-risk groups and key stakeholders were engaged. Operations evolved in real-time to fix problems. Incremental improvements in mean client volumes (128 to 140/day), target group inclusion (92% to 93%), on-site testing (84% to 86%), TB treatment initiation (87% to 91%), and TB treatment success (71% to 85%). Attention to those as highest risk boosted efficiency (the NNT declined from 8.2 ± SD8.2 to 7.6 ± SD7.7). Clinical diagnosis was added after follow-up among those with ≥ 80 CAD scores initially spot-sputum negative found 11 additional TB cases (6.3%) after 121 person-years of follow-up.</p> <p>Iterative adaptation in response to performance metrics foster feasible, acceptable, and efficient TB case-finding in Nigeria. High CAD scores can identify subclinical TB and those at risk of progression to bacteriologically-confirmed TB disease in the near term.</p> <p>Policy makers, donors, and community advocates are hesitant to invest in the steep infrastructure costs for mobile digital chest x-ray and GeneXpert MTB/RIF (dCXR/GXP) laboratories without a better understanding of how to maximize and sustain their impact. It is rarely possible to conduct the months of local CAD calibration recommended by experts via costly universal testing with a reference standard.4,9 Stakeholder needs and resource limitations require a more rapid and cost-conscious means of setting a sustainable algorithm. Viable, field-robust methodologies are needed, and optimization strategies informed by routine field findings were lacking. A precise assessment of the contribution of routine mobile TB screening has been challenging because few authors fully disaggregate losses along the diagnostic cascade or track TB treatment outcomes. Publication bias has limited access to results of active case finding pilots with suboptimal risk group targeting, community engagement, yield, or treatment outcomes.10–14 Evaluations (and scrutiny) of routine data are needed that make the demands, constraints, costs and choices facing implementers more explicit.</p>
Efficient Implementation of a Novel Decomposition Approach for the Hazmat Network Design Problem with Capacity Constraints in Java Including Computational Results
<p>Supplementary material for the Publication "Solving Multi-Follower Mixed-Integer Bilevel Problems with Binary Linking Variables"</p>
Cloud computing is one of the most popular and sophisticated technologies adopted by organizations worldwide. Some world-leading organizations enhance their efficiency and effectiveness by using cloud computing technology. Working from home (WFH) has been a popular trend among organizations during the coronavirus (COVID-19) pandemic. The COVID-19 saw a breakthrough in work cultures and environments where working from home was a remarkable success in remote working environments, despite being a rare phenomenon in Sri Lanka. Yet, it is argued that the deployment of work from home has not been effective among Sri Lankan business organizations due to a lack of IT infrastructure, facilities, and knowledge. The purpose of the study is to investigate the impact of cloud computing, embracing the service models (Infrastructure as a Service, Platform as a Service, and Software as a Service) as theoretical lenses and testing the COVID-19 as the moderator. The study has been conducted based on a deductive approach and adopted a stratified random sampling method. The sample consisted of 384 IT employees among those who had experienced working from home. The study utilized multiple regression and found that cloud computing service models significantly impact work from home with the moderating effect of COVID-19.
<p>Cloud computing is one of the most popular and sophisticated technologies adopted by organizations worldwide. Some world-leading organizations enhance their efficiency and effectiveness by using cloud computing technology. Working from home (WFH) has been a popular trend among organizations during the coronavirus (COVID-19) pandemic. The COVID-19 saw a breakthrough in work cultures and environments where working from home was a remarkable success in remote working environments, despite being a rare phenomenon in Sri Lanka. Yet, it is argued that the deployment of work from home has not been effective among Sri Lankan business organizations due to a lack of IT infrastructure, facilities, and knowledge. The purpose of the study is to investigate the impact of cloud computing, embracing the service models (Infrastructure as a Service, Platform as a Service, and Software as a Service) as theoretical lenses and testing the COVID-19 as the moderator. The study has been conducted based on a deductive approach and adopted a stratified random sampling method. The sample consisted of 384 IT employees among those who had experienced working from home. The study utilized multiple regression and found that cloud computing service models significantly impact work from home with the moderating effect of COVID-19.</p>
Dataset for the article "Efficient Computation of Magnetic Polarizability Tensor Spectral Signatures for Object Characterisation in Metal Detection"
<p>Datasets to accompany the article "Efficient Computation of Magnetic Polarizability Tensor Spectral Signatures for Object Characterisation in Metal Detection". Written by J. Elgy and P. D. Ledger. </p> <p>The datasets include data files, meshes, and code for recreating the results from the paper. This requires the open source MPT-Calculator software available at <a href="http://github.com/MPT-Calculator/MPT-Calculator">https://github.com/MPT-Calculator/MPT-Calculator</a> (v.1.5.0).</p> <p>J. Elgy and P. D. Ledger gratefully acknowledge the financial support received from EPSRC in the form of grant EP/V009028/1.</p>
Data supporting "A real-time, scalable, fast and resource-efficient decoder for a quantum computer"
<p>Data includes the circuits (stim_circuits.zip) used to create samples to benchmark CC decoder across different noise rates and code sizes. The resulting accuracy and cycle data is in fpga_accuracy_data.csv. The memory footprint (in KB) of the algorithm for different code sizes is in fpga_memory_data.csv.</p> <p>Weights of syndromes for different noise rates for both phenomenological and circuit-level noise at distance d=23 and d=21 are in noise_rate_sampling_full_d23.csv and noise_rate_sampling_full_d21.csv respectively.</p>
Trained Potentials for Article "Computationally Efficient Machine-Learned Model for GST Phase Change Materials via Direct and Indirect Learning"
<p>We provide 8 files here to get started using our trained potentials:</p> <p>1) *.yaml files for each trained potential. These are the outputs of the PACE training process.</p> <p>2) *.yace files for each trained potential. These are read by LAMMPS to use the trained potential. They can be obtained from the *.yaml files using the command line command: "pace_yaml2yace *.yaml".</p> <p>3) GST_config.data -- a starting configuration of GST to be read by LAMMPS. This configuration contains 504 atoms at density 5.85 g/cm^3.</p> <p>4) sample.inp -- a sample LAMMPS input file using the trained potentials. This currently uses "ACE-Indir2.yace" to run the starting configuration "GST_config.data" for 10 ps at 1200 K. When run, it outputs a log file "test.log" and a dump file "test.dump". The choice of trained potential can be changed in the "pair_coeff" section.</p>
Data for "Fully Implicit Time Stepping can be Efficient on Parallel Computers"
<p>Benchmark data and python plotting programs</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>
Data from: Targeted computational design of an interleukin-7 superkine with enhanced folding efficiency and immunotherapeutic efficacy
Open the record for dataset details and reuse information.
Iterative evaluation of mobile computer-assisted digital chest x-ray screening for TB improves efficiency, yield, and outcomes in Nigeria
Open the record for dataset details and reuse information.
A computationally efficient method for parameter sensitivity analysis of microbially-explicit biogeochemical models accounting for long-term behavior
<p>The dataset is for the manuscript entitled "A computationally efficient method for parameter sensitivity analysis of microbially-explicit biogeochemical models accounting for long-term behavior".</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.