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38 results for “filter model”
RRR/RAPID input and output files corresponding to "Underlying Fundamentals of Kalman Filtering for River Network Modeling"
<p><strong>Corresponding peer-reviewed publication</strong></p> <p>This dataset corresponds to all the RRR/RAPID input and output files that were used in the study reported in:</p> <ul> <li> <p>Emery, C. M., C. H. David, K. M. Andreadis, M. J. Turmon, J. T. Reager, and J. M. Hobbs (2020), Underlying Fundamentals of Kalman Filtering for River Network Modeling, Journal of Hydrometeorology, 21, 453-474, DOI: 10.1175/JHM-D-19-0084.1.</p> </li> </ul> <p>When making use of any of the files in this dataset, please cite both the aforementioned article and the dataset herein. </p> <p><strong>Known bugs and limitations in this dataset or the associated manuscript.</strong></p> <p>In the final version of the published manuscript, Figure 5a, Figure 5b, Figure 5c, and Figure SF1 are inaccurate. The issue in these figures is that they were all prepared with an incorrect indexing relating observed and simulated discharge, hence observations at any one location were consistently being compared to simulations at another different location. As a result, all values of "measured" discharge errors (i.e. Bias, STDE, and RMSE) are incorrect. This issue did not affect the values of "estimated" errors, nor did it affect all values of the Nash-Sutcliffe effeciency that are presented. The figures published in the manuscript can all be recreated using the files in which "BUG_DO_NOT_USE" was appended to the name. Correct figures can also be created using corresponding file names that were not so appended. </p> <p>Note that corrected versions of Figure 5a, Figure 5b, Figure 5c, and Figure SF1 all retain the same strong linear relationships that are discussed in the paper. The slope of the daily discharge STDE trend initially reported as <span class="math-tex">\(\alpha = 0.3876\)</span> in Figure 5c changes to <span class="math-tex">\(\alpha = 0.4507\)</span> after correction. The resulting value of the ideal inflation factor hence changes from <span class="math-tex">\(I = {1 \over 0.3876} \approx 2.58\)</span> to <span class="math-tex">\(I = {1 \over 0.4507} \approx 2.22\)</span>. This updated ideal inflation factor has no impact on the conclusions reached in the manuscript because it remains closer to <span class="math-tex">\(I = 2.58\)</span> than to <span class="math-tex">\(I = 1\)</span> or <span class="math-tex">\(I = 5\)</span>, <em>i.e.</em> the three values that were evaluated.</p> <p>Additionally, a faulty version 1.3.1 of the Python toolbox netCDF4 led to incorrect interpretation of _FillValue in which every data point of value greater than _FillValue was interpreted as masked. This created discrepancies in the following three files, which were updated between V1 and V2 of this dataset: "timeseries_rap_exp01.csv", "timeseries_rap_exp18.csv", and "stats_rap_exp18.csv". Faulty versions of the same files have "BUG_NETCDF4" appended to their names. Correct files have been recreated with file names that were not so appended. </p>
Training CNNs with Low-Rank Filters for Efficient Image Classification: Trained Models
<p>Models from experiments referenced in the paper "Training CNNs with Low-Rank Filters for Efficient Image Classification", https://arxiv.org/abs/1511.06744</p> <p>Model names differ from those in the paper, but the csv files for each set of experiments relates the paper's name for the model and the real name of the model here:</p> <ul> <li>cifarma.csv: Network-in-Network CIFAR10 Models</li> <li>mitma.csv: MIT Places Models</li> <li>googlenetma.csv: GoogLeNet ILSVRC2012 Models</li> <li>vggma.csv: VGG-11 ILSVRC2012 Models</li> </ul> <p> </p> <p> </p>
NetCDF data used in analysis presented in "Assessment of the z~ time-filtered Arbitrary Lagrangian-Eulerian coordinate in a global eddy-permitting ocean model"
<p>NetCDF data used in analysis presented in "Assessment of the z~ time-filtered Arbitrary Lagrangian-Eulerian coordinate in a global eddy-permitting ocean model", submitted to Journal of Advances in Modelling the Earth System.</p> <p>The data are produced from an ensemble of six experiments based on the GO8p0 configuration of NEMO v4.0.1 on a global 1/4° grid, as described in the paper. The ensemble is intended to test the z~ vertical coordinate, and includes a control with the default "z-star" fixed coordinate, and five experiments with the z-tilde vertical coordinate, using a selection of values for the two z-tilde timescale parameters. The data includes time series of global mean ocean and ice fields; large-scale transports; and fields from diapycnal mixing analysis.</p> <p>The first part of each filename refers to the experiment from the ensemble ("zstar", "ztilde_5_30", "ztilde_10_30", "ztilde_20_30", ztilde_20_60" and "ztilde_40_60"); the following five-character string identifies the respective suite on the Met Office Rose system and the MASS archive system; and the rest of the name specifies the type of data contained in the file.</p>
Supplementary material for "Including filter-feeding gelatinous macrozooplankton in a global marine biogeochemical model: model-data comparison and impact on the ocean carbon cycle"
<p>Supplementary material for "Including filter-feeding gelatinous macrozooplankton in a global marine biogeochemical model: model-data comparison and impact on the ocean carbon cycle". </p> <p>Clerc, C., Bopp, L., Benedetti, F., Vogt, M., and Aumont, O.: Including filter-feeding gelatinous macrozooplankton in a global marine biogeochemical model: model-data comparison and impact on the ocean carbon cycle, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-1282, 2022.</p> <p>Three directories can be downloaded:</p> <p><strong>DataOBS</strong> : AtlantECO [WP2] – Traditional microscopy dataset – Thaliacea (Salpida+Doliolida+Pyromosomatida) abundance and biomass concentration data, presented in Clerc et al. (2022). </p> <p><strong>FigPaper </strong>: Source code and .nc files for the figures presented in Clerc et al. (2022) (https://doi.org/10.5194/egusphere-2022-1282). </p> <p><strong>MY_SRC_PISCES_NEMO_3.6 :</strong> Additional fortran routines for the compilation of PISCES-FFGM, the model developed for Clerc et al. (2022), from NEMO-3.6 (https://www.nemo-ocean.eu)</p>
Wind farm power short-term prediction using WRF model and Kalman filtering
<p>This repository contains the data used and generated in the paper:</p> <p>Mamani, R., & Hendrick, P. (2019). Wind farm power short-term prediction using WRF model and Kalman filtering. ECOS 2019</p>
Realistic 3D avian vocal tract model demonstrates how shape affects sound filtering (Passer domesticus)
<p><span>Despite the complex geometry of songbird's vocal system, it was typically modelled as a tube or with simple mathematical parameters to investigate sound filtering. Here, we developed an adjustable computational acoustic model of a sparrow's upper vocal tract (<em>Passer domesticus</em>), derived from micro-CT scans. We discovered that a 20% tracheal shortening or a 20° beak gape increase caused the vocal tract harmonic resonance to shift towards higher pitch (11.7% or 8.8%, respectively), predominantly in the mid-range frequencies (3-6 kHz). The oropharyngeal-esophageal cavity (OEC), known for its role in sound filtering, was modelled as an adjustable 3D cylinder. For a constant OEC volume, an elongated cylinder induced a higher frequency shift than a wide cylinder (70% versus 37%). We found that the OEC volume adjustments can modify the OEC first harmonic resonance at low frequencies (1.5–3 kHz) and the OEC third harmonic resonance at higher frequencies (6-8 kHz). This work demonstrates the need to consider the realistic geometry of the vocal system to accurately quantify its effect on sound filtering and show that sparrows can tune the entire range of produced sound frequencies to their vocal system resonances, by controlling the vocal tract shape, especially through complex OEC volume adjustments.</span></p>
Data from: Filtering ground noise from LiDAR returns produces inferior models of forest aboveground biomass in heterogenous landscapes
<p>Airborne LiDAR has become an essential data source for large-scale, high-resolution modeling of forest aboveground biomass and carbon stocks, enabling predictions with much higher resolution and accuracy than can be achieved using optical imagery alone. Ground noise filtering -- that is, excluding returns from LiDAR point clouds based on simple height thresholds -- is a common practice meant to improve the 'signal' content of LiDAR returns by preventing ground returns from masking useful information about tree size and condition contained within canopy returns. However, ground returns may be helpful for making accurate aboveground biomass predictions in heterogeneous landscapes that include a patchy mosaic of vegetation heights and land cover types.<br> <br> In this paper, we applied several ground noise filtering thresholds while mapping forest AGB across New York State (USA), a heterogenous landscape composed of both contiguously forested and highly fragmented areas with mixed land cover types. We fit random forest models to predictor sets derived from each filtering intensity threshold and compared model accuracies, paying attention to how changes in accuracy correlated with landscape structure. We observed that removing ground noise via any height threshold systematically biases many of the LiDAR-derived variables used in AGB modeling, with mean correlation (Spearman's $\rho$) between variables increasing from 0.183 to 0.266. We found that that ground noise filtering yields models of forest AGB with lower accuracy than models trained using predictors derived from unfiltered point clouds, with RMSE increasing by up to 2.2 Mg ha^-1^ statewide. Although we only modeled AGB for forest cover types, models fit to predictors derived from filtered point clouds performed worse as landscape heterogeneity (as measured by patch density and edge density) increased, suggesting ground returns are particularly useful when modeling edge forests. Our results suggest that ground filtering should be a carefully considered decision when mapping forest AGB, particularly when mapping heterogeneous and highly fragmented landscapes, as ground returns are more likely to represent useful 'signal' than extraneous 'noise' in these cases.</p>
Trained Random Forest Model for PNW Seismic Event Classification Trained on 150s waveforms (P-50, P+100), 50 Hz, and 1-10 Hz BP Filtered
<p>This dataset contains three trained random forest models named as following - </p> <ul> <li>P_10_100_F_1_10_50.joblib - This is a model trained on 110s long waveforms (origin time - 10, origin time +100) in case of earthquakes and explosions and (first arrival pick -10, first arrival pick + 100) in case of surface events, the waveforms are tapered using 10% cosine taper, bandpass filtered between 1-10 Hz using Butterworth four corner filter, normalized and resampled to 50 Hz. </li> <li>P_50_100_F_1_10_50.joblib </li> <li>P_10_30_F_1_15_50.joblib. </li> </ul> <p>And also the standard scaler parameters for each features that will be used to normalize them. </p>
Filtered GNPS dataset for MSBERT and retrain Spec2Vec model
<p>Embedding Tandem Mass Spectra into Chemically Rational Space by Mask Learning and Contrastive Learning</p>
Can short-term data accurately model long-term environmental exposures? Investigating the multigenerational adaptation potential of Daphnia magna to environmental concentrations of organic ultraviolet filters
<p>Organic ultraviolet filters (UVFs) are contaminants of concern, ubiquitously found in many aquatic environments due to their use in personal care products to protect against ultraviolet radiation. Research regarding the toxicity of UVFs such as avobenzone, octocrylene and oxybenzone indicates that these chemicals may pose a threat to invertebrate species; however, minimal long-term studies have been conducted to determine how these UVFs may affect continuously exposed populations. The present study modeled the effects of a 5-generation exposure of <em>Daphnia</em> <em>magna</em> to these UVFs at environmental concentrations. Avobenzone and octocrylene resulted in minor, transient decreases in reproduction and wet mass. Oxybenzone exposure resulted in > 40% mortality, 46% decreased reproduction and 4-fold greater reproductive failure over the F0 and F1 generations; however, normal function was largely regained by the F2 generation. These results indicate that <em>Daphnia</em> are able to acclimate over long-term exposures to concentrations of 6.59 μg/L avobenzone, ~0.6 μg/L octocrylene or 16.5 μg/L oxybenzone. This suggests that short-term studies indicating high toxicity may not accurately represent long-term outcomes in wild populations, adding additional complexity to risk assessment practices at a time when many regions are considering or implementing UVF bans in order to protect these most sensitive invertebrate species.</p>
High-rate GNSS Raw Doppler Positive Impact on Cascading Filter-based Approach for Improving Real-time Transient Coseismic Velocities Modeling
<p>The high-rate GNSS average and instantaneous coseismic velocity waveforms for the 2016 Mw 6.6 Norcia earthquake and the 2011 Mw 9.1 Tohoku earthquake are included in this repository.</p>
Can short-term data accurately model long-term environmental exposures? Investigating the multigenerational adaptation potential of Daphnia magna to environmental concentrations of organic ultraviolet filters
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Realistic 3D avian vocal tract model demonstrates how shape affects sound filtering (Passer domesticus)
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Data from: Environmental filtering improves ecological niche models across multiple scales
1. A clear challenge for ecological niche modeling is determining how to best mitigate the effects of sampling bias from commonly collected biodiversity data. Recent approaches have focused on filtering occurrences in overrepresented regions based on geographic or environmental proximity. 2. We tested the efficacy of filtering in geographic and environmental space using occurrence data from four species. Our evaluation strategies examined 14 distance measures in geographic and environmental spaces and eight combinations of environmental variables and their ordinations. This resulted in 78 datasets for each species, which we evaluated using area under the curve (AUC), the difference between training and testing AUC, omission rate, the true skill statistic, and Schoener's D to examine the effects of different filtering schemes. 3. The degree of change produced by filtering on predicted suitability and evaluation statistics increased with increasing range size. Environmental filtering resulted in higher model fit at larger extents and retained more occurrences than geographic filtering. 4. Our results indicate that models should be evaluated using multiple evaluation statistics at multiple thresholds. The use of bin sizes when filtering in environmental space allows for simple comparison between species and filter types and makes for an easily reportable and repeatable distance metric. We specifically recommend that ecological niche models using natural history collection data filter in environmental space with variables derived from permutation importance or the first few axes of a principal components ordination.
Data from: Using filter-based community assembly models to improve restoration outcomes
1. Ecological filter models derived from community assembly theory can inform restoration planning by highlighting management actions most likely to affect community composition. Despite growing interest in these models, many restoration studies solely manipulate a single filter—the biotic filter by altering interspecific competition in studies—while ignoring abiotic and dispersal filters that may also influence restoration success. 2. To examine how manipulating all three filters (biotic, abiotic, dispersal) affected restoration in an annual-type grassland, we seeded native forbs from the same functional group as a target invader to increase biotic resistance to invasion (biotic filter), cut standing biomass and either removed it or returned it to plots as litter to alter light conditions (abiotic filter), and added native forbs at different seeding rates to alter density of establishing native populations (dispersal filter). We measured restoration success by recording native species and invader cover in plots. 3. The addition of native species with phenological and morphological traits similar to the target invader reduced invasion and increased native populations, but only in litter-free plots when high-densities of native seed were added. 4. Seeding two species with functional traits similar to the invader was more effective at reaching restoration goals than seeding just one functionally similar species. As such, trait differences among restoration species, even species belonging to the same functional group, may increase biotic resistance to invasion in restored communities. 5. Litter removal altered native-invader interactions. When litter was kept, added natives did not reduce invader cover. However, when litter was removed, added natives led to declines in invader cover. 6. Increasing native seeding rates led to larger native populations and increased invasion resistance. 7. Synthesis and applications. In this study, simultaneously manipulating biotic, abiotic and dispersal filters was necessary to optimize restoration outcomes. In particular, the biotic filter only contributed to successful restoration outcomes under abiotic and dispersal conditions that were created through management actions specifically targeting these two additional filters. Restoration planning based on filter models should incorporate actions that target all three filters, rather than solely focusing on the biotic filter.
Model Output and Validation Data for Online Determination of GNSS Differential Code Biases using Rao-Blackwellized Particle Filtering
<p>This dataset contains the outputs of 10 test runs of the A-CHAIM data assimilation model used to evaluate the performance of the bias estimation procedure used by the model. The 5-minute output files for each test run are included in a separate folder.</p> <p>The IGS DCBs in SINEX format and the various global ionospheric maps used in the study are included.</p> <p>This dataset also includes all of the processed GNSS data used in the study, which were used to generate the DCBs used in each test run.<br> <br> The in-situ electron density measurements from DMSP as well as the ionosonde data from GIRO which were used to measure the performance are also included.</p>
Data for the paper "Longitudinal Filtering, Sponge Layers, and Equatorial Jet Formation in a General Circulation Model of Gaseous Exoplanets"
<p>GCM data for the paper "Longitudinal Filtering, Sponge Layers, and Equatorial Jet Formation in a General Circulation Model of Gaseous Exoplanets" published in Monthly Notices of the Royal Astronomical Society. Data are in the pp format used by the Met Office Unified Model GCM and can be loaded using the Iris python package.<br><br>The individual files correspond to simulations done in the following parts of the paper<br><br>eta75_study.tgz - Simulations from Section 3.1.1 investigating changing the value of t_K with eta_s=0.75<br>Keff_flat.tgz - The simulation with a flattened Keff, as outlined in Section 3.1.2<br>eta9_study.tgz - Simulations from Section 3.2 investigating increasing eta_s to 0.9<br>resolution_study.tgz - The simulations from the resolution study found in Appendix B.</p>
Data accompanying the article "Arctic sea ice data assimilation combining an ensemble Kalman filter with a novel Lagrangian sea ice model for the winter 2019–2020"
<p>The .zip file contains temporal-spatial averaged metrics for evaluating simulations against observed ice thickness, concentration, volume, and drift. These quantities are presented in the manuscript "Arctic sea ice data assimilation combining an ensemble Kalman filter with a novel Lagrangian sea ice model for the winter 2019–2020"</p> <p>Subfolders are named by the experiment IDs, including metrics obtained from the relevant experimental results and observations.</p> <p>In case information is missing, do not hesitate to contact chengsukun@hotmail.com</p> <p>We thank Pavel Sakov for helpful discussions and improvement regarding the EnKF-C code and Jiping Xie for contributing the TOPAZ interface to sea ice observations. We are grateful for the support from Timothy Williams and Anton Korosov regarding the environments of neXtSIM and its analysis tools. The work is funded by the DASIM-II grant from ONR (grant nos. N00014-18-1-2493 and N00014-18-1-2204). Alberto Carrassi, Christopher K. R. T. Jones, Ali Aydo ̆gdu, and Pierre Rampal acknowledge the support of the project SASIP funded by Schmidt Futures – a philanthropic initiative that seeks to improve societal outcomes through the development of emerging science and technologies. Sukun Cheng and Laurent Bertino were co-funded by the FOCUS project from the Research Council of Norway (grant no. 301450), and Alberto Carrassi and Yumeng Chen are also supported by the UK National Centre for Earth Observation (grant no. NCEO02004). Computations were carried out on the Norwegian Supercomputing InfrastructureSigma2 (grants nn2993k for computing and NS2993K for data storage)</p>
Data from: Environmental filtering improves ecological niche models across multiple scales
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Data from: Using filter-based community assembly models to improve restoration outcomes
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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)
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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.