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307 results for “Model combination”
Numerical model of the Messinian Mediterranean combining hydrological water balance, river erosion, and flexural isostasy: TISC code and input dataset for the Lago-Mare
Open the record for dataset details and reuse information.
Model outputs: Vegetation biomass change in China in the 20th century: An assessment based on a combination of multi-model simulations and field observations
<p>This dataset contains six model outputs of vegetation biomass on plant functional type level and total vegetation biomass on grid cell with and without bias-correction in different sensitivity experiments, as described in Readme.txt.</p>
Data from: Implementation of Option B and a fixed-dose combination antiretroviral regimen for prevention of mother-to-child transmission of HIV in South Africa: a model of uptake and adherence to care
<p><b>Introduction</b></p> <p>Initiating and retaining pregnant women on antiretroviral therapy (ART) to prevent mother-to-child HIV transmission (PMTCT) remains a major challenge facing African HIV programs, particularly during the critical final months prior to delivery. In 2013, South Africa implemented its "Option B" PMTCT regimen (three-drug ART throughout pregnancy and breastfeeding, regardless of maternal CD4 count) and introduced once-daily fixed-dose combinations and lifelong ART. Currently, the uptake of Option B and its possible impact on adherence to PMTCT during the critical final months of pregnancy is unclear.</p> <p><b>Materials and Methods </b></p> <p>We prospectively collected visit data from a cohort of adult, HIV-infected, pregnant women between July 2013-August 2014 to estimate three models of adherence to PMTCT during the final 16 weeks immediately preceding delivery. Adherence was defined according to possession of antiretroviral drugs, which was inferred from clinic visit records under varying assumptions in each model. We describe uptake of the PMTCT regimen, gestational age at initiation, and model possible scenarios of adherence through delivery after the implementation of Option B.</p> <p><b>Results</b></p> <p>Among 138 women enrolled (median (IQR) age 28 years (24-32), median CD4 count 378 cells/mm3), median (IQR) gestational age at initiation was 22 weeks (16-26). Estimates of adherence during the final 16 weeks of pregnancy prior to delivery ranged from 75% (52-89%) under the best case scenario assumptions to 52% (30-75%) under the worst case scenario assumptions. Estimates of the proportion of women who would achieve 80% adherence to PMTCT were <50% across all models.</p> <p><b>Conclusions</b></p> <p>Despite the switch to Option B and once-daily dosing, South African women continue to initiate PMTCT late in pregnancy, and estimations of regimen adherence, as modelled using PMTCT visit attendance data, is poor, with <50% of women reaching 80% adherence during final months of pregnancy across all models. Further guideline changes and interventions are needed to achieve vertical transmission goals.</p> <p><b>Trial Registration</b></p> <p>This study was nested within the original RapIT randomized controlled trial which was registered on ClinicalTrials.gov (Registration number) NCT01710397 on September 7, 2012 and the South African National Clinical Trials Register DOH-27-0213-4177. The first participant in the RapIT study was enrolled on May 8th, 2013.</p>
Accounting for imperfect detection in data from museums and herbaria when modeling species distributions: Combining and contrasting data-level versus model-level bias correction
The digitization of museum collections as well as an explosion in citizen science initiatives has resulted in a wealth of data that can be useful for understanding the global distribution of biodiversity, provided that the well-documented biases inherent in unstructured opportunistic data are accounted for. While traditionally used to model imperfect detection using structured data from systematic surveys of wildlife, occupancy models provide a framework for modelling the imperfect collection process that results in digital specimen data. In this study, we explore methods for adapting occupancy models for use with biased opportunistic occurrence data from museum specimens and citizen science platforms using 7 species of Anacardiaceae in Florida as a case study. We explored two methods of incorporating information about collection effort to inform our uncertainty around species presence: (1) filtering the data to exclude collectors unlikely to collect the focal species and (2) incorporating collection covariates (collection type, time of collection, and history of previous detections) into a model of collection probability. We found that the best models incorporated both the background data filtration step as well as collector covariates. Month, method of collection and whether a collector had previously collected the focal species were important predictors of collection probability. Efforts to standardize meta-data associated with data collection will improve efforts for modeling the spatial distribution of a variety of species.
Development of predictive models of the kinetics of a hydrogen abstraction reaction combining quantum-mechanical calculations and experimental data
<p>The files contain the electronic structure calculations for all the levels of theory tested in this work.</p>
SynProtX: A Large-Scale Proteomics-Based Deep Learning Model for Predicting Synergistic Anticancer Drug Combinations
<h2>SynProtX: A Large-Scale Proteomics-Based Deep Learning Model for Predicting Synergistic Anticancer Drug Combinations</h2> <p>SynProtX is a deep learning model that integrates large-scale proteomics data, molecular graphs, and chemical fingerprints to predict synergistic effects of anticancer drug combinations. It provides robust performance across tissue-specific and study-specific datasets, enhancing reproducibility and biological relevance in drug synergy prediction.</p> <p>This Zenodo repository includes a <code>.tar.gz</code> archive containing all essential components to reproduce the experiments described in the study. This archive is designed to work seamlessly with the coding pipeline available at: <a href="https://github.com/manbaritone/SynProtX" target="_blank" rel="noopener">https://github.com/manbaritone/SynProtX</a>.</p> <h3>License:</h3> <p>Creative Commons Zero v1.0 Universal (CC0)<br>This work is released under CC0, dedicating it to the public domain. You are free to use, modify, and distribute it without restriction.</p> <h3>Archive Contents:</h3> <p>This compressed file includes:</p> <ul> <li>Datasets<br>- Tissue Datasets: <code>ALMANAC-Breast</code>, <code>ALMANAC-Lung</code>, <code>ALMANAC-Ovary</code>, <code>ALMANAC-Skin</code><br>- Study Datasets: <code>FRIEDMAN</code>, <code>ONEIL</code></li> <li>Supporting Files<br>- Raw and preprocessed data<br>- Feature dictionaries<br>- Hyperparameter configurations<br>- Trained model weights</li> </ul> <h3>Folder Structure:</h3> <blockquote> <p><code>SynProtX/</code><br><code>├── data/ # Raw and preprocessed data</code><br><code>│ ├── export/ # Processed protein/gene expression & drug combinations</code><br><code>│ ├── nps/ # Numpy arrays for all datasets</code><br><code>│ ├── nps_intersected/ # Dataset-specific numpy arrays</code><br><code>│ └── raw/ # Original data from DrugComb, CCLE, COSMIC, ChEMBL V31, ProCan-DepMapSanger</code><br><code>├── feature_dicts/ # Feature dictionaries for drug combinations</code><br><code>├── hyperparams/ # Hyperparameter configs for SynProtX-GATFP</code><br><code>│ ├── classification/ # For classification tasks</code><br><code>│ └── regression/ # For regression tasks</code><br><code>├── state_dict/ # Trained model weights</code><br><code>│ ├── classification/ # PyTorch checkpoints for classification</code><br><code>│ └── regression/ # PyTorch checkpoints for regression</code><br><code>└── README_Zenodo.md # This file</code></p> </blockquote> <h3>For more information, please visit:</h3> <p><strong>GitHub:</strong> <a href="https://github.com/manbaritone/SynProtX" target="_blank" rel="noopener">https://github.com/manbaritone/SynProtX</a></p>
Combining bioenergetics and movement models to improve understanding of the population consequences of disturbance
<p>We developed dynamic bioenergetics models to investigate how behavioural responses to anthropogenic disturbance events might affect the population dynamics of three marine mammal species (harbour porpoise, grey seal and harbour seal) with contrasting life-history traits (capital vs income breeders) and movement behaviour (resident vs nomadic). We used these models to analyse how individual vital rates were affected by differences in the probability of disturbance and the duration of any behavioural response, while taking account of uncertainty in the model parameters and heterogeneity in behaviour. The outputs of individual movement models and telemetry data were then used to determine how the probability of exposure might vary among species, individuals, and geographical locations. We then demonstrate how these estimated probabilities of exposure can be translated into probabilities of disturbance. For illustrative purposes, we modelled the potential effects of a temporary decrease in energy assimilation associated with a series of disturbance events that might realistically occur during the construction of an offshore windfarm. Offspring starvation mortality was the vital rate that was most affected by these disturbance events. Monitoring of rates should be considered as standard practice so that populations' responses can be detected as early as possible. Predicted effects on individual vital rates depended on the species' movement behaviour and the likely density of animals where the modelled construction activity was assumed to take place. The magnitude of these effects also depended critically on the assumed duration of the reduction in energy assimilation. No direct estimates of this variable are currently available, but we suggest some ways in which it could be estimated. The described approach could be extended to other species and activities, given sufficient information to parameterise the component models. However, we emphasise the need to account for among-individual heterogeneities and uncertainties in the values of the many model parameters.</p>
Modelling data for: Short-course combination treatment for experimental chronic Chagas disease
<p><span>Chagas disease, caused by the protozoan parasite <em>Trypanosoma</em> <em>cruzi</em>, affects millions of people in the Americas and across the world leading to considerable morbidity and mortality. Current treatment options, benznidazole (BNZ) and nifurtimox, offer limited efficacy and often lead to adverse side effects due to long treatment durations. Better treatment options are therefore urgently required. Here we describe a pyrrolopyrimidine series, identified through phenotypic screening, that offers a clear opportunity to improve on current treatments. In vitro cell-based washout assays demonstrate that compounds in the series are incapable of killing all parasites, however, combining these pyrrolopyrimidines with a sub-efficacious dose of BNZ can clear all parasites in vitro after five days. Importantly, these findings were replicated in a clinically predictive<em> in vivo</em> model of chronic Chagas disease, where five days of treatment with the combination was sufficient to prevent parasite relapse. Comprehensive mechanism of action studies, supported by ligand-structure modelling, show that compounds from this pyrrolopyrimidine series inhibit the Q</span><sub><span>i</span></sub><span> active site of <em>T. cruzi</em> cytochrome <em>b</em>, part of the cytochrome <em>bc1</em> complex of the electron transport chain. Knowledge of the molecular target enabled a cascade of assays to be assembled to evaluate selectivity over the human cytochrome <em>b</em> homologue. As a result, a highly selective and efficacious lead compound was identified. The combination of our lead compound with BNZ rapidly clears<em> T. cruzi</em> parasites, both <em>in vitro</em> and <em>in vivo</em>, and shows great potential to overcome key issues associated with currently available treatments. </span></p>
Data from: Combining mesocosms with models to unravel the effects of global warming and ocean acidification on a temperate marine ecosystem
<p><span>Ocean warming and species exploitation have already caused large-scale reorganization of biological communities across the world. Accurate projections of future biodiversity change require a comprehensive understanding of how entire communities respond to global change. We combined a time-dynamic integrated food web modelling approach (Ecosim) with previous data from community-level mesocosm experiments to determine the independent and combined effects of ocean warming and acidification, and fisheries exploitation, on a well-managed temperate coastal ecosystem. The mesocosm parameters enabled important physiological and behavioural responses to climate stressors to be projected for trophic levels ranging from primary producers to top predators, including sharks. Through model simulations, we show that under sustainable rates of exploitation, near-future warming or ocean acidification in isolation could benefit species biomass at higher trophic levels (e.g., mammals, birds, and demersal finfish) in their current climate ranges, with the exception of small pelagic fish. However, under warming and acidification combined biomass-increases at higher trophic levels will be lower or absent, whilst in the longer term reduced productivity of prey species is unlikely to support the increased biomass at the top of the food web. We also show that increases in exploitation will suppress any positive effects of human-driven climate change, causing individual species biomass to decrease at higher trophic levels. Nevertheless, total future potential biomass of some fisheries species in temperate areas might remain high, particularly under acidification, because unharvested opportunistic species will likely benefit from decreased competition and show an increase in biomass. Ecological indicators of species composition such as the Shannon diversity index declined under all climate change scenarios, suggesting a trade-off between biomass gain and functional diversity. By coupling parameters from multi-level mesocosm food web experiments with dynamic food web models, we were able to simulate the generative mechanisms that drive complex responses of temperate marine ecosystems to global change. This approach, which blends theory with experimental data, provides new prospects for forecasting climate-driven biodiversity change and its effects on ecosystem processes.</span></p>
NOAA PSL thermodynamic profiles retrieved from a combination of active and passive remote sensors and numerical weather prediction models with the optimal estimation physical retrieval TROPoe at Platteville, CO, USA
<p>This dataset contains retrieved profiles of thermodynamic variables obtained using the Tropospheric Remotely Observed Profiling via Optimal Estimation (TROPoe) physical retrieval from various combinations of input data collected by passive and active remote sensing instruments, in-situ surface platforms, and numerical weather prediction models deployed at the Platteville, CO, USA, site in fall 20221-winter 2022. Among the employed instruments are Microwave Radiometers (MWRs), Infrared Spectrometers (IRS), Radio Acoustic Sounding Systems (RASS), ceilometers, surface sensors, and information from the operational Rapid Refresh numerical weather prediction model.</p> <p>The dataset also includes 15 radiosounding launched for assessing the retrievals.</p> <p>For further information, please see:</p> <p>Bianco, L., Adler, B., Bariteau, L., Djalalova, I. V., Myers, T., Pezoa, S., Turner, D. D., and Wilczak, J. M.: Sensitivity of thermodynamic profiles retrieved from ground-based microwave and infrared observations to additional input data from active remote sensing instruments and numerical weather prediction models, Atmos. Meas. Tech. Discuss. [preprint], https://doi.org/10.5194/amt-2023-263, in review, 2024.</p>
The fully executable procedure of the U-Net model combined with the Multi-textRG algorithm to achieve fine ice-water classification ---- another 332 scenes of data-fused SIC labels.
<p>This data source is related to the manuscript titled "Combining the U-Net model and a Multi-textRG algorithm for fine SAR ice-water classification", which will be submitted to the journal---The Cryosphere. </p> <ul> <li>The"ready-to-train-fused_01.zip" to "ready-to-train-fused_10.zip" includes 200 scenes of data-fused SIC labels accessible with doi: 10.5281/zenodo.10973107, https://zenodo.org/records/10973107. </li> <li>The "ready-to-train-fused_11.zip" to "ready-to-train-fused_21.zip" includes another 332 scenes of data-fused SIC labels. </li> </ul>
Data assimilation products by using multiple climate model simulations and different combinations of proxies
<p>This dataset of the climate reconstruction by data assimilation using isotope ratios provides annual surface air temperature, precipitation amount, and other climate variables during 850–2000.</p> <p>Two isotopes-incorporated atmospheric general circulation models and 129 isotopic proxy data (65 corals, 43 ice cores, and 21 tree-ring cellulose) were used in this study. There are nine experiments using three type of simulations and three combinations of proxies.</p> <p>The associated publication: Shoji, S., Okazaki, A., & Yoshimura, K. (2020). Impact of proxies and prior estimates on data assimilation using isotope ratios for the climate reconstruction of the last millennium. (submitted to Earth and Space Science)</p> <p>[Data structure]<br> X(lon) x Y(lat) x Z(2) x Variables(8) x Year(1151)<br> Z(1): analyses<br> Z(2): priors</p>
Study On Hybrid Model Combining Super Learner And Physics-based Models For SHM In Bridges Using Low-cost BWIM
<p>The main objective of this project is to study and develop hybrid models of BWIM physics-based models incorporates with SHM inspection used by artificial intelligence (AI) techniques to predict structural damage. This study presents a comprehensive assessment of FE simulations leveraging contact method verified by vehicle-bridge interaction(VBI) theory and uses machine learning (ML) techniques to identify and predict structural damages from the structural response automatically. Bridges are a fundamental part of infrastructure management. The main challenge that we face is the aging of these transportation infrastructures without a tool to perform accurate structural assessments in a real-time manner. Unfortunately, this topic is still not completely developed due to the lack of study upon BWIM simulations designed with several different severity damages. The Contact method, a new approach to simulating moving-vehicle motion in BWIM simulation, is to carry out actual structural response verified by the VBI with comprehensive parameter studies. In order to simulation the reality complex condition of the bridge, the FE model is designed by four different classes of damages with three different damage locations applied with two different load conditions (e.g., static load and moving load). The responses collected from FE simulation are used for structural damage prediction leveraging the ML damage prediction model. Among ML methods, the XGBoost with assembly decision tree shows the most reliable results. The results in this project indicate that structural damage prediction can be achieved by using the ML technique and BWIM structural response, which provides high accuracy of damage prediction.</p>
VCF datasets and analysis scripts for: The combination of genomic offset and niche modelling provides insights into climate change-driven vulnerability
<p>Global warming is increasingly exacerbating biodiversity loss. Populations locally adapted to spatially heterogeneous environments may respond differentially to climate change, but this intraspecific variation has only recently been considered when modelling vulnerability under climate change. Here, we incorporate intraspecific variation in genomic offset and ecological niche modelling to estimate climate change-driven vulnerability in two bird species in the Sino-Himalayan Mountains. We found that the cold-tolerant populations show higher genomic offset but risk less challenge for niche suitability decline under future climate than the warm-tolerant populations. Based on a genome-niche index estimated by combining genomic offset and niche suitability change, we identified the populations with the least genome-niche interruption as potential donors for evolutionary rescue, i.e., the populations tolerant to climate change. We evaluated potential rescue routes via a landscape genetic analysis. Overall, we demonstrate that the integration of genomic offset, niche suitability modelling, and landscape connectivity can improve climate change-driven vulnerability assessments and facilitate effective conservation management.</p>
Dataset: A phase field model combined with genetic algorithm for polycrystalline hafnium zirconium oxide ferroelectrics
<p>The folder includes generated data MATLAB scripts to read/plot the polarization-electric field (PE) hysteresis curves. The dataset contains phase field generated polycrystalline grain structure, simulated domain structures during polarization reversal, and symmetric PE curves (measured and simulated).</p> <p><strong>Polycrystalline grain structures</strong>: The output files are in the *.txt format, readable by MTEX to generate orientation maps.<br> Column(1) Column(2) Column(3) Column(4) Column(5)<br> X Y φ(rad) θ(rad) 𝜓(rad)<br> ... ... ... ... ...<br> ... ... ... ... ...<br> ... ... ... ... ...</p> <p><strong>Domain structures</strong>: The output files are in the *.csv format, which can be visualized by programs like ParaView.<br> Column(1) Column(2) Column(3) Column(4)<br> X Y Z P<br> ... ... ... ...<br> ... ... ... ...<br> ... ... ... ...</p> <p><br> <strong>PE curves</strong>: The output files are in the *.txt format, readable by MATLAB.<br> Column(1) Column(2)<br> E(MV/cm) P(μC/cm²)<br> ... ...<br> ... ...<br> ... ...</p> <p><strong>List of datasets:-</strong><br> Fig. 1: pecurves/calib_func1.txt (Calibrated p(e)), pecurves/measpe_hf50.txt (Measured PE curve), pecurves/simpe_calib.txt (Simulated PE curve).<br> Fig. 2(a): polcr_struc/xy_col.txt (XY top view), polcr_struc/yz_col.txt (YZ side view), polcr_struc/xz_col.txt (ZX side view)<br> Fig. 2(b): pecurves/simpe_gaopt.txt (Simulated PE curve), pecurves/measpe_hf50.txt (Measured PE curve).<br> Fig. 3: pecurves/calib_func.txt (Calibrated p(e)), pecurves/gaopt_func.txt (GA optimized p(e)).<br> Fig. 5: pecurves/simpe_gaopt.txt (Case 1), pecurves/simpe_elast.txt (Case 2).<br> Fig. 4(e): dom_struc/dom_profile1.csv, (f) dom_struc/dom_profile2.csv, (g) dom_struc/dom_profile3.csv, (h) dom_struc/dom_profile4.csv, (m) dom_struc/dom_profile5.csv, (n) dom_struc/dom_profile6.csv, (o) dom_struc/dom_profile7.csv, (p) dom_struc/dom_profile8.csv<br> Fig. 6: pecurves/simpe_gaopt.txt (GA fit coefficients), pecurves/simpe_ldc1.txt (Set 1), pecurves/simpe_ldc2.txt (Set 2).<br> Fig. 7(a): pecurves/simpe_gaopt.txt (𝝂₀ = 1.0), pecurves/simpe_fr80.txt (𝝂₀ = 0.8), pecurves/simpe_fr50.txt (𝝂₀ = 0.5).<br> Fig. 7(b): pecurves/measpe_hf50.txt (Hf₀.₅Zr₀.₅O₂), pecurves/measpe_hf75.txt (Hf₀.₇₅Zr₀.₂₅O₂).<br> Fig. 8: pecurves/simpe_fr38.txt (Simulated PE curve), pecurves/measpe_hf75.txt (Measured PE curve).<br> Fig. 9: pecurves/simpe_gaopt.txt (Random non-textured), pecurves/simpe_tex001.txt ([001] fiber textured), pecurves/simpe_tex111.txt ([111] fiber textured).<br> Fig. 10(a): polcr_struc/xy_equ.txt (XY top view), polcr_struc/yz_equ.txt (YZ side view), polcr_struc/xz_equ.txt (ZX side view)<br> Fig. 10(b): pecurves/simpe_colmor.txt (Columnar grain microstructure), pecurves/simpe_equmor.txt (Equiaxed grain microstructure).</p> <p><strong>List of MATLAB scripts:</strong><br> Fig 1: matlab_scripts/fig1.m<br> Fig 2(b): matlab_scripts/fig2b.m<br> Fig 3: matlab_scripts/fig3.m<br> Fig 5: matlab_scripts/fig5.m<br> Fig 6: matlab_scripts/fig6.m<br> Fig 7(a): matlab_scripts/fig7a.m<br> Fig 7(b): matlab_scripts/fig7b.m<br> Fig 8: matlab_scripts/fig8.m<br> Fig 9: matlab_scripts/fig9.m<br> Fig 10(b): matlab_scripts/fig10b.m<br> </p>
Modeling the Depletion and Recovery of the Outer Radiation Belt During a Geomagnetic Storm: Combined MHD and Test Particle Simulations
<p>Data associated with JGR: Space Physics paper, "Modeling the Depletion and Recovery of the Outer Radiation Belt During a Geomagnetic Storm: Combined MHD and Test Particle Simulations".</p>
Comparing and Combining Existing and Emerging Data Collection and Modeling Strategies in Support of Signal Control Optimization and Management (Project M2)
<p>For decades, traffic signal management agencies have used signal timing optimization tools combined with fine-tuning of signal timing based on field observations in their updates of time-of-day signal timing plans. These traditional signal optimization methods and tools use very limited amount of data and depend on default values in the signal timing optimization/simulation tools to estimate network performance under different signal optimization strategies. In recent years, new data collection technologies are emerging including high resolution controller data, more advanced detection technologies such as video image detection that are based on vehicle tracking and possible integration with microwave detectors, automatic-vehicle based identification technologies, third party crowdsourcing data, connected vehicles, and connected automated vehicles data. The objective of the proposed study is to propose methods and algorithms to combine data collected from existing and emerging sources with enhanced models and optimization algorithms to optimize and manage signal operations. The results from applying the developed methods and algorithms will be compared with traditional signal timing and optimization methods currently used by transportation agencies. </p>
Dataset for "Bayesian hierarchical models for combining misaligned two-resolution metrology data"
<p>This file contains the datasets used in the paper, Xia, Ding, and Mallick, 2011, “Bayesian hierarchical models for combining misaligned two-resolution metrology data,” <em>IIE Transactions</em>, Vol. 43, pp. 242 – 258.</p>
Data from: Voice efficiency for different voice qualities combining experimentally derived sound signals and numerical modeling of the vocal tract
<p>This dataset contains Stereo-Lithographic (STL) surface models of a human vocal tract, derived Finite-Element-Models, numerical results, and scripts for analyzing these results and (re-)running the computation.</p> <p> </p> <p><strong>In the main folder, this dataset contains:</strong></p> <p>1) Python files (*fig*.py) for the creation of figures and tables (*tab*.py)</p> <p>2) Python files (*.py) for analyzing Finite-Element (FE) calculations (x_resonances.py, x_libs.py, x_fem2excel.py)</p> <p>3) Python-files (*.py) for analyzing stl-data (x_analyzeSTL.py)</p> <p>4) Python files (*.py) for deriving Infinite-Impulse-Response (IIR) filter and their impulse responses (x_IIR.py)</p> <p>5) Excel files (*.xlsx) containing Volume-velocity-transfer-functions (Vlg.xlsx), Pressure-transfer-functions at the lips (Hlg.xlsx), and the glottis (Hgg.xlsx) based on FE, the sound spectra of audio signals (sound_spectra.xlsx), the polynomials describing the IIR (IIR_polynomial.xlsx) and their impulse responses (IIR_impulse_responses.xlsx), and glottal waveforms (glottal_waveform.xlsx) and spectra (glottal_spectra.xlsx)</p> <p>6) Several figures (*.pdf)</p> <p> </p> <p><strong>In folder „x_fenics/x_Subject-1“ (and sub-folders), this data set contains:</strong></p> <p>1) Surface models of the human vocal tract for different voice qualities (glottis.stl, wall.stl, lips.stl)</p> <p>2) Sub-volumes of the vocal tract cavities (*ET.stl, *HPl.stl, *HPu.stl, *OPf.stl, *OPr.stl, *SP.stl, *.VV.stl)</p> <p>3) Derived gmsh volume meshes (*.msh) (www.gmsh.info)</p> <p>3) Derived volume models applicable to FE-Solvers (*.h5, *.xdmf)</p> <p>4) Results of the FE-calculation (*pvtf*.txt, *vvtf*.txt, *pglottis*.txt)</p> <p>5) Formant frequencies computed by inverse filtering (*.for)</p> <p> </p> <p><strong>In folder „x_fenics/x_misc“ the data set contains:</strong></p> <p>1) Python-files (*.py) for (re-)running the calculations using the FE-Method</p>
Combining formal methods and Bayesian approach for inferring discrete-state stochastic models from steady-state data
<p>Model, data, and a script to a paper of respective name</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.