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35 results for “forward model”
Forward-modelled reflectance from spring and summer Baltic Sea specific inherent optical properties
<p>An extensive dataset of remote-sensing reflectance (R<sub>rs</sub>, units sr<sup>-1</sup>) spectra based on forward modelling of mean concentration-specific inherent optical properties (SIOPs) for both spring and summer optical conditions in the open Baltic Sea. The spectra are modelled using Hydrolight 5.2 for a wide range of Chlorophyll-a (Chla), Coloured Dissolved Organic Matter (CDOM), and Total Suspended Matter (TSM) concentrations as well as solar and viewing angles. The primary aim of providing this supplementary dataset is to aid evaluation of remote sensing algorithms for the Baltic Sea in future studies.</p>
Benchmark protocol for exoplanet forward model and retrieval
<p>Benchmark protocol for giant exoplanet atmosphere tools, presented in Baudino et al. 2017 <a href="https://doi.org/10.3847/1538-4357/aa95be">https://doi.org/10.3847/1538-4357/aa95be</a></p> <p>The original data to reproduice the protocol are used in a jupyter notebook "Tutorial.ipynb" including all the plot routines to help to compare with you own models</p>
Electronic Supplementary for: Refining patterns of melt with forward stratigraphic models on stable Pleistocene coastlines
<p>This zipped repository accompanies the paper, "Refining patterns of melt with forward stratigraphic models on stable Pleistocene coastlines". Within the file are two sub-folders. "Model_Input" and "SELEN". "Model_Input" contains the parameters needed to re-run the model runs described in the manuscript in the model environement "OpenFlowSuit" (Beicip Franlab). The "SELEN" sub-folder contains three additional sub-folders: "Background", "Full", and "G2A5". Each of these sub-folders has three .csv files containing the GIA driven sea level curve under three different mantel viscosities, MV1, MV2, and MV3.</p>
Asymmetry in kinematic generalization between visual and passive lead-in movements are consistent with a forward model in the sensorimotor system
<p><span><span>In our daily life we often make complex actions comprised of linked movements, such as reaching for a cup of coffee and bringing it to our mouth to drink. Recent work has highlighted the role of such linked movements in the formation of independent motor memories, affecting the learning rate and ability to learn opposing force fields. In these studies, distinct prior movements (lead-in movements) allow adaptation of opposing dynamics on the following movement. Purely visual or purely passive lead-in movements exhibit different angular generalization functions of this motor memory as the lead-in movements are modified, suggesting different neural representations. However, we currently have no understanding of how different movement kinematics (distance, speed or duration) affect this recall process and the formation of independent motor memories. Here we investigate such kinematic generalization for both passive and visual lead-in movements to probe their individual characteristics. After participants adapted to opposing force fields using training lead-in movements, the lead-in kinematics were modified on random trials to test generalization. For both visual and passive modalities, recalled compensation was sensitive to lead-in duration and peak speed, falling off away from the training condition. However, little reduction in force was found with increasing lead-in distance. Interestingly, asymmetric transfer between lead-in movement modalities was also observed, with partial transfer from passive to visual, but very little vice versa. Overall these tuning effects were stronger for passive compared to visual lead-ins demonstrating the difference in these sensory inputs in regulating motor memories. Our results suggest these effects are a consequence of state estimation, with differences across modalities reflecting their different levels of sensory uncertainty arising as a consequence of dissimilar feedback delays. </span></span></p>
Ellipsoidal Harmonic Forward Model derived from Earth2014 topographies up to d/o 7200: EHFM_Earth_7200
<p>The model named EHFM_Earth_7200 was derived by layer-based forward modeling technique in ellipsoidal harmonics, the maximum degree of this model reaches 7200. The relief information was provided by Earth2014 relief model. EHFM_Earth_7200 provides very detailed (~3 km) information for the Earth’s short-scale gravity field, and it is expected to be able to augment or refine existing global gravity models. To meet the existing standard, here we provide spherical harmonic coefficients, which are transformed from original ellipsoidal harmonic coefficients.</p>
Crustal structure of the Volgo-Uralian subcraton revealed by inverse and forward gravity modeling [dataset]
<p>This collection contains data that were used to build a 3D crustal model of the Volgo-Uralian subcraton through inverse and forward gravity modeling.</p> <p>The dataset is subdivided into two folders: (1) Gravity field inversion; (2) Forward gravity modeling. </p>
Yerrida Basin 3D 'Noddy' geological and forward models
<p>This dataset contains an archive for kinematic 3D geological models of the Yerrida Basin, southern Capricorn region, Western Australia. These models were used to investigate the influence of adding high density material (the mafic Killara Formation) on the calculated gravitational response.</p> <p><strong><em>Yerrida_Basin_3D_Noddy.zip </em></strong>is a set of three dimensional 'Noddy' geological models.</p> <p>1. <strong>Yerrida_gravity_response_noKillara.his </strong>A model with no Killara Formation.</p> <p>2. <strong>Yerrida_gravity_response-500mKillara.his</strong> A model with 500 m thick Killara Formation.</p> <p>3. <strong>Yerrida_gravity_response-2000mKillara.his</strong> A model with 2000 m thick Killara Formation.</p> <p>Corresponding gravity responses (*.grv) files are supplied.</p> <p>A Noddy executable installation file in Windows format is supplied in this archive or can be downloaded from <a href="http://tectonique.net/noddy/">http://tectonique.net/noddy/</a></p> <p>This is a companion dataset for the paper submitted to the scientific journal Solid Earth: Mapping undercover: integrated geoscientific interpretation and 3D modelling of a Proterozoic basin.<em> </em>Mark D Lindsay, Sandra Occhipinti, Crystal LaFlamme, Alan Aitken, Lara Ramos.</p>
Forward-Looking Climate Modelling for Western Athens
<p>We produced actionable data on heat stress in cities to inform analysis and client dialogue on the part of World Bank teams. We applied an urban-scale climate modeling framework to generate datasets describing modeled heat stress exposure for present-day and future conditions under selected climate scenarios. The study domain focus on the metropolitan area of Athens, Greece.</p> <p>More details about the dataset: </p> <ul> <li>The dataset includes calculations for each indicator across three scenarios (<strong>present, SSP1-1.9, SSP3-7.0</strong>) and three twenty-year periods (<strong>2001-2020, 2021-2040, and 2041-2060</strong>). The present period refers to 2001-2020, while the other two periods correspond to the two SSP scenarios.</li> <li>All indicators are available in both <strong>NetCDF</strong> and <strong>GeoTiff</strong> formats.</li> <li>The indicators are calculated at a resolution of <strong>150 m</strong>, consistent with the UrbClim and WBGT simulations. Additionally, downscaled versions of the indicators are provided at a resolution of <strong>30 m</strong>.</li> <li>The UrbClim and WBGT simulations, as well as the postprocessing, are conducted using the regional projection <strong>EPSG 32634</strong>. The NetCDF and GeoTiff data also adopt this projection. Furthermore, a GeoTiff data file with <strong>EPSG 4326</strong> projection is included.</li> <li>All indicators are calculated as <strong>yearly averages</strong>. Some indicators also have additional calculations for <strong>seasonal averages</strong>, including Spring (MAM), Summer (JJA), Autumn (SON), and Winter (DJF).</li> <li>Images for <strong>quick viewing</strong> <strong>in</strong> <strong>png</strong> format visualizing the results for each indicator. Present denotes the period 2001-2020; 2030 denotes the period 2021-2040; & 2050 denotes the period 2041-2060.</li> <li>The images are also clipped to focus on the ASDA municipalities, in which case the png files will be ended with ‘_clip.png’</li> <li>The NetCDF and GeoTiff data can be found in the <a href="https://zenodo.org/api/files/76dbc341-075d-49aa-9b73-8378f6410038/data.zip?versionId=df51b0aa-9cb0-4be0-afe6-46fa30372e91">data.zip</a>; The png files for quick viewing can be found in <a href="https://zenodo.org/api/files/76dbc341-075d-49aa-9b73-8378f6410038/quickview.zip?versionId=199b62b8-bba3-4ec0-a3f4-753d784972eb">quickview.zip</a>; more information about the dataset, including the methodology, all available data list, contact information, etc. can be found in the <a href="https://zenodo.org/api/files/76dbc341-075d-49aa-9b73-8378f6410038/Technical_Annex_Athens_ver4.docx?versionId=695aac25-2b99-47ab-867f-721331eb9bfe">Technical_Annex_Athens_ver4.docx</a></li> </ul>
Urban Heat: Forward-Looking Climate Modeling for Nis, Serbia
<p>We produced actionable data on heat stress in cities to inform analysis and client dialogue on the part of World Bank teams. We applied an urban-scale climate modeling framework to generate datasets describing modeled heat stress exposure for <strong>present-day and future conditions</strong> under selected climate scenarios (<strong>present, SSP1-1.9, SSP3-7.0</strong>). The study domain focuses on Nis, Serbia.</p> <p>More details about the dataset: </p> <ul> <li>The dataset includes calculations for each indicator across three scenarios (<strong>present, SSP1-1.9, SSP3-7.0</strong>) and three twenty-year periods (<strong>2001-2020, 2021-2040, and 2041-2060</strong>). The present period refers to 2001-2020, while the other two periods correspond to the two SSP scenarios.</li> <li>All indicators are available in both <strong>NetCDF</strong> and <strong>GeoTiff</strong> formats.</li> <li>The indicators are calculated at a resolution of <strong>150 m</strong>, consistent with the UrbClim and WBGT simulations. Additionally, downscaled versions of the indicators are provided at a resolution of <strong>30 m</strong>.</li> <li>The UrbClim and WBGT simulations, as well as the postprocessing, are conducted using the regional projection <strong>E</strong><strong>PSG 32634</strong>. The NetCDF and GeoTiff data also adopt this projection. Furthermore, a GeoTiff data file with <strong>EPSG 4326</strong> projection is included.</li> <li>All indicators are calculated as <strong>yearly averages</strong>. Some indicators also have additional calculations for <strong>seasonal averages</strong>, including Spring (MAM), Summer (JJA), Autumn (SON), and Winter (DJF).</li> <li>Images for <strong>quick viewing</strong> <strong>in</strong> <strong>png</strong> format visualizing the results for each indicator. Present denotes the period 2001-2020; 2030 denotes the period 2021-2040; & 2050 denotes the period 2041-2060.</li> <li>The NetCDF and GeoTiff data can be found in the data.zip; The PNG files for quick viewing can be found in quickview.zip; more information about the dataset, including the methodology, all available data list, contact information, etc. can be found in the Technical_Annex_Nis.docx</li> </ul>
Asymmetry in kinematic generalization between visual and passive lead-in movements are consistent with a forward model in the sensorimotor system
Open the record for dataset details and reuse information.
Molodensky's truncation coefficients for cap integration in spectral gravity forward modelling
<p>Provided are Molodensky's truncation coefficients for cap-modified spectral gravity forward modelling from the <a href="https://doi.org/10.1007/s00190-019-01277-3">Bucha et al. (2019)</a> study. The coefficients are evaluated for</p> <ul> <li>the spherical distance of <em><span>\(\psi_0 = 100000\ \mathrm{m} / 6378137\ \mathrm{m}\)</span> </em>(100 km integration radius from the evaluation point),</li> <li>the reference sphere having the radius <span>\(R = 6378137\ \mathrm{m}\)</span>,</li> </ul> <ul> <li>the radius of the evaluation point<em> <span>\(r = 6378137\ \mathrm{m} + 7000\ \mathrm{m}\)</span></em>,</li> <li>harmonic degrees <span>\(n=0,\dots,21600\)</span>,</li> <li>topography powers <span>\(p=1,\dots,30\)</span>,</li> <li>radial derivatives <span>\(k=0,\dots,40\)</span>, and</li> <li>the first- and second-order horizontal derivatives.</li> </ul> <p>The coefficients were computed using 256 significant digits, ensuring 24-digit accuracy or better. After the evaluation, the coefficients were converted to double precision with 16 significant digits. Importantly, in some cases, the loss of significance errors may be encountered during the spherical harmonic synthesis when using the coefficients (see the reference below).</p> <p>Bucha, B., Hirt, C., Kuhn, M., 2019. <em>Cap integration in spectral gravity forward modelling up to the full gravity tensor</em>. Journal of Geodesy, <a href="https://doi.org/10.1007/s00190-019-01277-3">https://doi.org/10.1007/s00190-019-01277-3</a>.</p>
Data products associated with "Probabilistic Forward Modeling of Galaxy Catalogs with Normalizing Flows"
<p>These are the data products associated with "Probabilistic Forward Modeling of Galaxy Catalogs with Normalizing Flows" by J. F. Crenshaw, et. al. This includes the input catalog and the outputs of the workflow described here https://github.com/jfcrenshaw/pzflow-paper, as well as a gzip of the github repo.</p>
Forward Asteroseismic Modeling of Stars with a Convective Core from Gravity-mode Oscillations: Parameter Estimation and Stellar Model Selection
<p>MESA inlists associated with <a href="https://ui.adsabs.harvard.edu/#abs/arXiv:1806.06869">Aerts et al. (2018)</a>. MESA version 10108.</p> <p>Publication DOI: <a href="https://doi.org/10.3847/1538-4365/aaccfb">10.3847/1538-4365/aaccfb</a></p>
A Forward Modeling Approach To Analyzing Galaxy Clustering with SimBIG
<p>Data sets accompanying the publication: "A Forward Modeling Approach to Analyzing Galaxy Clustering" (https://arxiv.org/abs/2211.00723). </p> <ul> <li><a href="https://zenodo.org/api/files/136695d6-4319-4b74-9b7c-8a6b2f2df956/mcmc.v4.plk.cmass.npy">mcmc.v4.plk.cmass.npy</a> : MCMC samples drawn from the SimBIG posterior of the cosmological and HOD parameters given galaxy power spectrum multipoles to k_max = 0.5</li> <li><a href="https://zenodo.org/api/files/136695d6-4319-4b74-9b7c-8a6b2f2df956/mcmc.v4.plk.kmax0.25.cmass.npy">mcmc.v4.plk.kmax0.25.cmass.npy</a> : MCMC samples drawn from the SimBIG posterior of the cosmological and HOD parameters given galaxy power spectrum multipoles to k_max = 0.5</li> <li><a href="https://zenodo.org/api/files/136695d6-4319-4b74-9b7c-8a6b2f2df956/obs.cmass_sgc.k.dat">obs.cmass_sgc.</a>* : k, number density, and power spectrum measurements of galaxies in the BOSS CMASS-SGC sample. </li> <li><a href="https://zenodo.org/api/files/136695d6-4319-4b74-9b7c-8a6b2f2df956/simbig.cmass_sgc.v3.test.p2k.dat">simbig.cmass_sgc.v3.test</a>* : test data sets used to validate the accuracy of the SimBIG posteriors. The test data sets are described in detail in <a href="https://ui.adsabs.harvard.edu/abs/2023JCAP...04..010H/abstract">Hahn et al. (2023)</a></li> <li><a href="https://zenodo.org/api/files/136695d6-4319-4b74-9b7c-8a6b2f2df956/simbig.cmass_sgc.v4.k.dat">simbig.cmass_sgc.v4.</a>* : training data used to train the normalizing flows used to infer the posterior. </li> </ul>
Urban Heat: Forward-Looking Climate Modeling for Skopje, North Macedonia
<p>We produced actionable data on heat stress in cities to inform analysis and client dialogue on the part of World Bank teams. We applied an urban-scale climate modeling framework to generate datasets describing modeled heat stress exposure for present-day and future conditions under selected climate scenarios. The study domain focuses on Skopje, North Macedonia.</p> <p>More details about the dataset: </p> <ul> <li>The dataset includes calculations for each indicator across three scenarios (<strong>present, SSP1-1.9, SSP3-7.0</strong>) and three twenty-year periods (<strong>2001-2020, 2021-2040, and 2041-2060</strong>). The present period refers to 2001-2020, while the other two periods correspond to the two SSP scenarios.</li> <li>All indicators are available in both <strong>NetCDF</strong> and <strong>GeoTiff</strong> formats.</li> <li>The indicators are calculated at a resolution of <strong>100 m</strong>, consistent with the UrbClim and WBGT simulations. Additionally, downscaled versions of the indicators are provided at a resolution of <strong>30 m</strong>.</li> <li>The UrbClim and WBGT simulations, as well as the postprocessing, are conducted using the regional projection <strong>E</strong><strong>PSG 32634</strong>. The NetCDF and GeoTiff data also adopt this projection. Furthermore, a GeoTiff data file with <strong>EPSG 4326</strong> projection is included.</li> <li>All indicators are calculated as <strong>yearly averages</strong>. Some indicators also have additional calculations for <strong>seasonal averages</strong>, including Spring (MAM), Summer (JJA), Autumn (SON), and Winter (DJF).</li> <li>Ten representative locations within the study domain have been selected to retrieve the WBGT profile on a chosen date (2017-07-11). The results and the locations are stored in wbgt_profile.xlsx.</li> <li>Images for <strong>quick viewing</strong> <strong>in</strong> <strong>png</strong> format visualizing the results for each indicator. Present denotes the period 2001-2020; 2030 denotes the period 2021-2040; & 2050 denotes the period 2041-2060.</li> <li>The NetCDF and GeoTiff data can be found in the data.zip; The png files for quick viewing can be found in quickview.zip; more information about the dataset, including the methodology, all available data list, contact information, etc. can be found in the Technical_Annex_Skopje.docx</li> </ul>
Data citation for a forward stratigraphic-based porosity and permeability model developed for the Volve field, Norway.
<p>The data, models, and script presented here are those used for developing a forward stratigraphic simulation. The data include: 24 suits of well logs, seismic data, forward stratigraphic simulation scenarios of the shallow marine depositional setting, synthetic wells derived from the stratigraphic model, and 3-D reservoir models in Eclipse and RMS formats. In addition, a short script from the property calculator tool in Petrel, which is was used to classify lithofacies-associations in the stratigraphic model is also provided. The Petrel software license and code used in GPM software to undertake these forward stratigraphic simulations cannot be provided, because Schlumberger, who are the developers of the software do not allow its code to be shared in any publication.</p>
Integrating univariate niche dynamics in species distribution models: a step forward for marine research on biological invasions
<p>Aim The development of approaches to predict the distribution and potential expansion of invasive species is still an open challenge. Here our goal is to improve the modelling procedure for marine invaders by coupling Species Distribution Models (SDMs) with an analysis of their univariate niche dynamics. In particular, we tested for the first time whether choosing model predictors among the stable niche dimensions was effective in improving predictions of invasive species expansion.<br> Location Mediterranean Sea<br> Taxon Dusky spinefoot, Siganus luridus.<br> Methods We analysed the univariate niche dynamics for S. luridus across its native and invaded ranges, by applying a standardized framework that allowed the identification of cases of niche stability or shift. We compared inter-range transferability of SDMs fitted with different combinations of labile or stable predictors. Finally, we evaluated interactions in SDM settings (calibration area, model technique and predictors set) on models' predictive ability, using independent data from the most recent phase of invasion.<br> Results We detected a pattern of niche stability for several variables, especially salinity and bathymetry, which positively influenced model inter-ranges transferability: when the models calibrated in the native range include only stable niche axes, predictive ability is improved. We also identified a shift toward lower surface temperatures in the introduced range, which were almost never experienced by the species before invasion. The model calibrated within the combined ranges was the most ecologically congruent. Also, models calibrated in the invaded range allowed a correct prediction of range expansion, with the predicted suitable areas only slightly underestimated.<br> Main conclusions We provide the first evidence that using conserved predictors in SDMs improves inter-range projections of expanding invasive species. Variable selection, calibration area and modelling technique all matter when modelling invasive species, with important interaction effects. We provide guidelines on how to improve SDMs applications in biological invasion research.</p>
GHGF-Flux forward model outputs
<p>Outputs from a tagged tracer simulation using the Greenhouse Gas Framework – Flux (GHGF-Flux) forward model for the four regions of interest, including Northeast China, Southeast China, North Bangladesh, and North India.</p>
The synthetic and field seismic datasets for "ClinoformNet-1.0: stratigraphic forward modeling and deep learning for seismic clinoform delineation"
<p>This is the synthetic and field siesmic dataset used in manuscript "Three-Dimensional Implicit Structural Modeling Using Convolutional Neural Network". The dimensions of the large-scale and small-scale synthetic seismic datasets are 1600×256 pixels and 900×256 pixels. The field seismic data contains the subsets of the F3 Block, Australia Poseidon, Alaska North Slope seismic data.</p>
Urban Heat: Forward-Looking Climate Modelling for West-Africa : Togolese Cities
<p>We produced actionable data on heat stress in cities to inform analysis and client dialogue on the part of World Bank teams. We applied an urban-scale climate modeling framework to generate datasets describing modeled heat stress exposure for present-day and future conditions under selected climate scenarios. The study focuses on five cities in Togo: Tsevie, Dapaong, Kara, Sokode and Atakpame.</p> <p>More details about the dataset: </p> <ul> <li>The dataset includes calculations for each indicator across three scenarios (<strong>present, SSP2-4.5, SSP3-7.0</strong>) and three twenty-year periods (<strong>2001-2020, 2031-2050, and 2051-2070</strong>). The present period refers to 2001-2020, while the other two periods correspond to the two SSP scenarios.</li> <li>All indicators are available in both <strong>NetCDF</strong> and <strong>GeoTiff</strong> formats.</li> <li>The indicators are calculated at a resolution of <strong>100 m</strong>, consistent with the UrbClim and WBGT simulations. Additionally, downscaled versions of the indicators are provided at a resolution of <strong>30 m</strong>.</li> <li>The UrbClim and WBGT simulations, as well as the postprocessing, are conducted using the regional projection <strong>E</strong><strong>PSG 32631</strong>. The NetCDF and GeoTiff data also adopt this projection. Furthermore, a GeoTiff data file with <strong>EPSG 4326</strong> projection is included.</li> <li>All indicators are calculated as <strong>yearly averages</strong>. </li> <li>Images for <strong>quick viewing</strong> <strong>in</strong> <strong>png</strong> format visualizing the results for each indicator. </li> <li>The indicators in NetCDF and GeoTiff format as well as the visualized PNG files can be found in the <strong>{city}_indicators.zip</strong>.</li> <li>Three representative locations within the study domain have been selected to retrieve the WBGT profile on a chosen date (a hot day in 2020). The results are stored in WBGT_data.xlsx and visualized as WBGT_{date}.png. The shapefile is named as selected_locations.shp. These data together with the visualization of the land use map is compressed in <strong>{city}_landuse_wbgt.zip</strong>. </li> <li>More information about the dataset, including the methodology, all available data list, contact information, etc. can be found in the <strong>Technical_Annex_Togo.docx</strong></li> </ul>
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.