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608 results for “ensembles”
Dataset for "Assessing Storm Surge Multi-Scenarios based on Ensemble Tropical Cyclone Forecasting" paper
<p>1000 ensemble track forecast of tropical cyclone Hagibis (2019) is provided in NetCDF format and the computed storm surge forecast is provided in the Excel file.</p>
Molecular Gas Phase Conformational Ensembles
<p>Accurately determining molecular structures' lowest energy states is crucial for fields like drug design and materials science. Conformational search engines, like Auto3D and RDKit, are valuable tools for this. We evaluate their effectiveness alongside CREST and Balloon, using collisional cross section data to validate. Our findings enhance molecular structure determination, aiding fields dependent on precise structures.</p>
META DATA FOR ENSEMBLE PROJECT
<p>### META DATA FOR ENSEMBLE PROJECT ###</p> <p>This dataset contains raw field measurements and laboratory results of field surveys and experiments performed in the ENSEMBLE project.</p> <p><br> For more information on the ENSEMBLE Project and related publications please see: https://www.epfl.ch/labs/river/ensemble/</p> <p>This dataset contains data and metadata for field surveys conducted in June and August 2019 (ENSEMBLE_2019_Campaign) and data and metadata on a global-change microbiology experiment performed during summer 2020 (ENSEMBLE_2020_Experiment).</p> <p>### ENSEMBLE Field Campaign ###<br> - Sensor data contains a continuous record of key environmental parameter (Temperature, Electrical Conductivity, pH, Turbidity) measured at the the three field sites (Val Sorey, Valroseg and Otemma)<br> - BA contains flow cytometric measurements of bacterial abundance (in cells g-1 dry sediment).<br> - BP contains measurements of bacterial carbon production measured as 3H Leucine incorporation.<br> - Chla-1 and Chla-2 contain data on the spectrophotometric measurement of chlorophyl-a (after extraction in EtOH).<br> - DOC contains data on the concentration of Dissolved Organic Carbon.<br> - EEA contains data on the measurements of Extracellular Enzyme Activities.<br> - EPS contains data on the quantification of Extracellular Polymeric Substances measured as carbohydrate equivalents (Dubois assay).<br> - Field Data contains metadata (ID, coordinates, sampling time, etc) for the field sites.<br> - NUT contains measurements of main water nutrients (NO3, NO2, NH4, and SRP)<br> - SI contains data on water stable isotopes<br> - Water Metadata contains measurements of key environmental data for a subset of sites.</p> <p><br> ### ENSEMBLE Experiment ###</p> <p>- The folder Pictures contains drone images and close-up pictures of the field experiment setup.<br> - CHLA_BA contains measurements of chl-a concentration and bacterial cell counts<br> - metadata contains measurements of key environmental parameter for each treatment over the course of the experiment<br> - NUT contains measurement of main water nutrients (NO3, NO2, NH4, and SRP)<br> </p>
Terrain variables used for ensemble distribution modelling of vulnerable marine ecosystems indicator taxa on data-limited seamounts of Cabo Verde (NW Africa)
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Huge ensembles part I design of ensemble weather forecasts with spherical Fourier neural operators; Huge ensembles part II properties of a huge ensemble of hindcasts generated with spherical Fourier neural operators
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Data from: Gesture encoding in human left precentral gyrus neuronal ensembles
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Intrinsic excitability mechanisms of neuronal ensemble formation
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Accumbal calcium-permeable AMPA receptors orchestrate neuronal ensembles underlying social attachment
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Hippocampal ensembles represent sequential relationships among an extended sequence of nonspatial events
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ClepsHresEns-HbvRhein134-SbkReRhein Medium Range Waterlevel Ensemble Forecasts for Waterway Rhine
<p>The datasets provided here were produced as part of the IMPREX project for work package 9, task 3 “<em>Case studies</em>”. Analysis of the datasets are published in Deliverable 9.2 “<em>Framework for the assessment of forecast quality and value in the navigation sector</em>“(Klein & Meißner 2017) and Deliverable 9.4 “<em>Semi-operational forecasting system for Rhine, Danube and Elbe to support improved transport cost planning</em>“ (Klein & Meißner 2019). The aim of the dataset was to apply the statistical post-processing method Ensemble Model Output Statistics EMOS (Gneiting et al. 2005) to estimate the predictive uncertainty of the waterlevel ensemble forecasts, in order to provide probabilistic water level forecasts to the end users (Klein & Meißner 2019).</p> <p>Meteorological forcing data used to calculate water-level forecasts with an extended forecast horizon are based on a 68 member multi-model ensemble: 51 ensemble members from ECMWF ENS (1 control forecast and 50 perturbed members) as well as the control forecast ECMWF HRES with a higher spatial resolution (Leutbecher & Palmer 2008, Owens & Hewson 2018), and the 16 members of the limited-area ensemble prediction system by the consortium for small-scale modelling COSMO LEPS (Montani et al. 2011, Marsigli et al. 2014). Archived meteorological real-time forecasts of the period January 2008 to December 2015 have been used to produce this comprehensive water level re-forecast data set.</p> <p>The conceptual, semi-distributed rainfall-runoff model HBV-96 (Bergström 1995, Lindstrom et al. 1997) is applied to calculate the flow forecasts used as boundary conditions and lateral inflows of the hydrodynamic model SOBEK (Deltares 2012) used to calculate water level forecasts along the river Rhine. The river Rhine basin is divided into 134 subbasins which are further subdivided into hydrological response units (HRU) according to land use and elevation classes. The flow formation processes are calculated on those HRUs. The model calculates flow with a temporal resolution of 1 h using temperature and precipitation fields that have been interpolated over the subbasins as meteorological input.</p> <p>The hydrodynamic model suite SOBEK is used as one-dimensional model, which uses cross-section information of the River Rhine as well as its main tributaries. The distance between the cross-sections, which cover the river bathymetry as well as its floodplains, is non-equidistant and ranges between 100 m and 800 m. As the main tributaries of the River Rhine are impounded rivers (e.g. Moselle, Main) the SOBEK-model includes several weirs with their specific control rules in order to simulate the real behaviour of these elements, too.</p> <p>The flow and water level forecasts were initialized each day at 06:00 UTC, which means that observed real-time meteorological data, interpolated to the subbasins of the hydrological model, up to the forecast date were used as forcings of the hydrological model and observed flow was used as input for the hydrodynamic model to initialize the model states. For the forecast period meteorological ensemble runs from the different Numerical Weather Prediction (NWP) models interpolated to the subbasins were used as forcings of the hydrological model. Flow forecasts of the large tributaries of the river Rhine simulated with HBV were then used as input for the hydrodynamic model. To reduce the error of the input to the hydrodynamic model autoregressive error correction models (Broersen & Weerts 2005) was applied using the differences between the simulation of the model using meteorological observations as forcings and the actually past flow observations as training data. This error correction reduces the error of the hydrological model at the forecast initialization time to zero. To reduce the error of the waterlevel forecasts obtained by running the hydrodynamic model, again autoregressive error correction models were applied using the differences between the water level simulation using observed flow as input and the water-level observations of the past.</p> <p><strong>Dataset H_OBS_RHINE.nc:</strong></p> <p>Hourly observed water levels of the gauges Kaub, Koeln, Ruhrort / Rhine for the period 2008–2016 stored as variable <em><strong>h_obs(time=78912, stations=3).</strong></em></p> <p>Data originate from the database of gauge measurements of the Federal Waterways and Shipping Administration (WSV). These data were quality checked and published by the gauge-operating WSV offices. Nevertheless, data errors and inconsistencies cannot be ruled out completely, so that neither the WSV nor the BfG do accept any liability for the correctness and completeness of the data. Data source: "German Federal Waterways and Shipping Administration (WSV)", provided by the German Federal Institute of Hydrology (BfG).</p> <pre><code>float h_obs(time=78912, stations=3); :units = "cm"; :_FillValue = -9999.0f; // float :long_name = "observed waterlevel"; :coordinates = "lat lon";</code></pre> <p><strong>Dataset H_MM_HBV134_SOBEK.nc</strong></p> <p>Hourly forecasted water level of the hydrodynamic mode SOBEK forced by flow forecasts of the hydrological model HBV134 forced by a multi-model meteorological ensemble. Daily forecasts initialized at 06:00 UTC of the period 2008-01-01 to 2015-12-31 with a lead time of 240 hours. Gauges Kaub, Koeln, Ruhrort / Rhine.</p> <p>Forecast values are stored in the variable <em><strong>h_fcast_ens(time=2869, lead_time=241, realization=68, stations=3)</strong></em>, first dimension forecast dates, second dimension lead time, third dimension realization, fourth dimension station. ECMWF-HRES first realization, COSMO-LEPS realization 2 – 17, ECMWF-ENS realization 18- 68.</p> <pre><code>float h_fcast_ens(time=2869, lead_time=241, realization=68, stations=3); :_FillValue = -9999.0f; // float :long_name = "forecast waterlevel ensemble"; :units = "cm"; :coordinates = "lat lon";</code></pre> <p><strong>Literature</strong></p> <p>Bergström, S. (1995): The HBV model. In: V. P. Singh (Ed.): Computer models of watershed hydrology. Water Resources Publications, Colorado, USA, 443-476</p> <p>Broersen, P. & A. Weerts (2005): Automatic Error Correction of Rainfall-Runoff models in Flood Forecasting Systems. Conference Proceedings: IMTC 2005 – Instrumentation and Measurement Technology Conference, Ottawa, Canada, 17-19 May 2005.</p> <p>Deltares (2012): Technical Reference SOBEK-RE. Deltares, Delft, The Netherlands</p> <p>Gneiting, T., A. E. Raftery, A. H. Westveld & T. Goldman (2005): Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation. Monthly Weather Review 133(5), 1098-1118</p> <p>Klein, B. & D. Meissner (2017): Framework for the assessment of forecast quality and value in the navigation sector. Deliverable 9.2, IMPREX - Improving Predictions of Hydrological Extremes - Grant Agreement Number 641811, <a href="http://www.imprex.eu/system/files/generated/files/resource/d9-2-imprex-v2-0.pdf">http://www.imprex.eu/system/files/generated/files/resource/d9-2-imprex-v2-0.pdf</a></p> <p>Klein, B. & D. Meissner (2019): Semi-operational forecasting system for Rhine, Danube and Elbe to support improved transport cost planning. Deliverable 9.4, IMPREX - Improving Predictions of Hydrological Extremes - Grant Agreement Number 641811, <a href="https://imprex.eu/system/files/generated/files/resource/deliverable9-4-imprex-v1-0.pdf">https://imprex.eu/system/files/generated/files/resource/deliverable9-4-imprex-v1-0.pdf</a></p> <p>Leutbecher, M. & T. N. Palmer (2008): Ensemble forecasting. Journal of Computational Physics 227(7), 3515-3539</p> <p>Lindstrom, G., B. Johansson, M. Persson, M. Gardelin & S. Bergstrom (1997): Development and test of the distributed HBV-96 hydrological model. Journal of Hydrology 201(1-4), 272-288</p> <p>Marsigli, C., A. Montani & T. Paccagnella (2014): Perturbation of initial and boundary conditions for a limited-area ensemble: multi-model versus single-model approach. Quarterly Journal of the Royal Meteorological Society 140(678), 197-208</p> <p>Montani, A., D. Cesari, C. Marsigli & T. Paccagnella (2011): Seven years of activity in the field of mesoscale ensemble forecasting by the COSMO-LEPS system: main achievements and open challenges. Tellus Series a-Dynamic Meteorology and Oceanography 63(3), 605-624</p> <p>Owens, R. & T. R. E. Hewson (2018): ECMWF Forecast User Guide. ECMWF, Reading, doi: 10.21957/m1cs7h</p>
Ensemble calculations of "torro17" from EURO-CORDEX data for Europe
<p><strong>Climate Index: </strong>torro17</p> <p><strong>Definition:</strong> Number of days per year with daily maximum wind speed equal or greater than 17 m/s, average over a 30-year time-period.</p> <p><strong>Additional information:</strong> The dataset is based on an ensemble of EURO-CORDEX model simulations of daily maximum near-surface wind speed (sfcWindmax).</p> <p>Results (ensemble mean and ensemble standard deviation) are available for historical (1971-2000) and future (2011-2040, 2041-2070, 2071-2100) time periods and for the representative concentration pathways RCP2.6, RCP4.5 and RCP8.5.</p> <p>The EURO-CORDEX climate model simulations used are:</p> <ul> <li>SMHI-RCA4/ ICHEC-EC-EARTH, SMHI-RCA4/ MOHC-HadGEM2-ES</li> <li>CLMcom-CCLM4-8-17/ ICHEC-EC-EARTH, CLMcom-CCLM4-8-17/ MOHC-HadGEM2-ES</li> <li>DMI-HIRHAM5/ ICHEC-EC-EARTH</li> <li>KNMI-RACMO22E/ ICHEC-EC-EARTH, KNMI-RACMO22E/ MOHC-HadGEM2-ES</li> </ul>
Data of the paper: Ensemble daily simulations for elucidating cloud–aerosol interactions under a large spread of realistic environmental conditions
<p>Data of the paper: Ensemble daily simulations for elucidating cloud–aerosol interactions under a large spread of realistic environmental conditions</p> <p> </p> <p>The name of variable are as in the paper</p>
Efficient ensemble data assimilation for coupled models with the Parallel Data Assimilation Framework: Example of AWI-CM - output files and plot scripts
<p>This archive outputs_plotting.zip contains the raw output files (STDOUT) from the scaling runs performed for the paper "Efficient ensemble data assimilation for coupled models with the Parallel Data Assimilation Framework: Example of AWI-CM" submitted to GMD (gmd-2019-167). Further the scripts to extract timing information from the raw output files and plot scripts are included.</p> <p>The archive SST-DA_plotting.zip contains the scripts to compute RMS errors for the free ensemble run (output file in gmd_N46_free.zip) and the SST assimilation run (gmd_N46_sst.zip) and to plot these. The two output files contain each a Netcdf file with the ensemble mean state information and the stdout file from the model run.</p>
Data from: Bat ensembles differ in response to use zones in a tropical biosphere reserve
<p>Biosphere reserves, designated under The United Nations Education, Scientific and Cultural Organization's (UNESCO) Man and Biosphere Programme, aim to sustainably integrate protected areas into the biological and economic landscape around them by buffering strictly protected habitats with zones of limited use. However, the effectiveness of biosphere reserves and the contribution of the different zones of use to protection is poorly known. We assessed the diversity and activity of bats in the Crocker Range Biosphere Reserve (CRBR) in Sabah, Malaysia, using harp traps, mist nets and acoustic surveys in each zone—core, buffer, transition and in agricultural plots outside of the reserve. We captured 30 species, bringing the known bat fauna of CRBR to 50 species, half of Borneo's bat species. Species composition and acoustic activity varied among zones and by foraging ensemble, with the core and buffer showing particular importance for conserving forest-dependent insectivorous bats. Frugivorous bats were found in all zones but were the most abundant and most species-rich ensemble within agricultural sites. Although sampling was limited, bat diversity and activity was low in the transition zone compared to other zones, indicating potential for management practices that increase food availability and enhance biodiversity value. We conclude that, collectively, the zones of the CRBR effectively protect diversity, but the value of the transition zone can be improved.</p>
Models from: Exploring ensemble applications for multi-sequence myocardial pathology segmentation
<p>Trained models for the MyoPS2020 challenge http://www.sdspeople.fudan.edu.cn/zhuangxiahai/0/MyoPS20/</p> <p>As described in the publication: "Exploring ensemble applications for multi-sequence myocardial pathology segmentation"</p> <p>Source code available at: https://github.com/chfc-cmi/miccai2020-myops</p>
Results of ISMIP6 Antarctica: a multi-model ensemble of the Antarctic ice sheet evolution over the 21st century
<p>This archive provides the ice sheet model outputs produced as part of the publication "ISMIP6 Antarctica: a multi-model ensemble of the Antarctic ice sheet evolution over the 21st century", published in The Cryosphere, <a href="https://tc.copernicus.org/articles/14/3033/2020/">https://tc.copernicus.org/articles/14/3033/2020/</a></p> <p>Seroussi, H., Nowicki, S., Payne, A. J., Goelzer, H., Lipscomb, W. H., Abe-Ouchi, A., Agosta, C., Albrecht, T., Asay-Davis, X., Barthel, A., Calov, R., Cullather, R., Dumas, C., Galton-Fenzi, B. K., Gladstone, R., Golledge, N. R., Gregory, J. M., Greve, R., Hattermann, T., Hoffman, M. J., Humbert, A., Huybrechts, P., Jourdain, N. C., Kleiner, T., Larour, E., Leguy, G. R., Lowry, D. P., Little, C. M., Morlighem, M., Pattyn, F., Pelle, T., Price, S. F., Quiquet, A., Reese, R., Schlegel, N.-J., Shepherd, A., Simon, E., Smith, R. S., Straneo, F., Sun, S., Trusel, L. D., Van Breedam, J., van de Wal, R. S. W., Winkelmann, R., Zhao, C., Zhang, T., and Zwinger, T.: ISMIP6 Antarctica: a multi-model ensemble of the Antarctic ice sheet evolution over the 21st century, The Cryosphere, 14, 3033–3070, https://doi.org/10.5194/tc-14-3033-2020, 2020.</p> <p>Contact: Helene Seroussi, Helene.seroussi@jpl.nasa.gov</p> <p>Further information on ISMIP6 and ISMIP6 Antarctica Projections can be found here:<br> http://www.climate-cryosphere.org/activities/targeted/ismip6<br> http://www.climate-cryosphere.org/wiki/index.php?title=ISMIP6-Projections-Antarctica</p> <p>Users should cite the original publication when using all or part of the data. <br> In order to document CMIP6’s scientific impact and enable ongoing support of CMIP, users are also obligated to acknowledge CMIP6, ISMIP6 and the participating modeling groups.</p> <p>About the dataset:</p> <p>- The results are based on model output computed from the ISMIP6 native grids that vary between models. <br> - The results are calculated over the ice-covered area of Antarctica, corrected for map projection errors, ice sheet model specific densities taken into account.<br> - Results for the experiments 'exp*' are provided both as raw results and calculated as differences to the control experiment (ctrl_proj_open or ctrl_proj_std depending on the experiment). The later files are named with "minus_ctrl_proj" to indicate that the control run is substracted.<br> - Results for ctrl_proj_open, ctrl_proj_std, hist_open and hist_std are not corrected to remove the control run.</p> <p><br> ------------------------------------------------</p> <p>Directory structure:</p> <p>groupname1<br> modelname1<br> expid<br> computed_iareafl_AIS_groupname1_modelname1_expid.nc<br> computed_iareafl_minus_ctrl_proj_AIS_groupname1_modelname1_expid.nc<br> computed_iareagr_AIS_groupname1_modelname1_expid.nc<br> computed_iareagr_minus_ctrl_proj_AIS_groupname1_modelname1_expid.nc<br> computed_icearea_AIS_groupname1_modelname1_expid.nc<br> computed_icearea_minus_ctrl_proj_AIS_groupname1_modelname1_expid.nc<br> computed_ivol_AIS_groupname1_modelname1_expid.nc<br> computed_ivol_minus_ctrl_proj_AIS_groupname1_modelname1_expid.nc<br> computed_ivaf_AIS_groupname1_modelname1_expid.nc<br> computed_ivaf_minus_ctrl_proj_AIS_groupname1_modelname1_expid.nc<br> computed_smb_AIS_groupname1_modelname1_expid.nc<br> computed_smb_minus_ctrl_proj_AIS_groupname1_modelname1_expid.nc<br> computed_smbgr_AIS_groupname1_modelname1_expid.nc<br> computed_smbgr_minus_ctrl_proj_AIS_groupname1_modelname1_expid.nc<br> computed_bmbfl_AIS_groupname1_modelname1_expid.nc<br> computed_bmbfl_minus_ctrl_proj_AIS_groupname1_modelname1_expid.nc<br> ...</p> <p>-------------------------------------------------</p> <p><br> Description of variables:</p> <p>icearea - ice area [m^2]<br> iareafl - floating ice area [m^2]<br> iareagr - grounded ice area [m^2]<br> ivol - ice volume [m^3]<br> ivaf - ice volume above floatation [m^3]<br> smb - spatially integrated surface mass balance [kg/s]<br> smbgr - spatially integrated surface mass balance over grounded ice [kg/s]<br> bmbfl - spatially integrated basal melt rate under floating ice (negative for melting ice) [kg/s]</p> <p>Variables per file:</p> <p>rhoi - model specific ice density [kg m-3]<br> rhow - model specific ocean water density [kg m-3]</p> <p>time - time, in years</p> <p>[variable] - global variable integrated over the Antarctica ice sheet<br> [variable]_region_1 - variable integrated over West Antarctica<br> [variable]_region_2 - variable integrated over East Antarctica<br> [variable]_region_3 - variable integrated over the Antarctic Peninsula<br> [variable]_sector_X - variable integrated over the X sector of the Antarctic ice sheet (18 sectors, from 1 to 18)</p> <p>--------------------------------------------------</p> <p><br> Data usage notice:<br> If you use any of these results, please acknowledge the work of the people involved in producing them. Acknowledgements should have language similar to the below.</p> <p>"We thank the Climate and Cryosphere (CliC) effort, which provided support for ISMIP6 through sponsoring of workshops, hosting the ISMIP6 website and wiki, and promoted ISMIP6. We acknowledge the World Climate Research Programme, which, through it's Working Group on Coupled Modelling, coordinated and promoted CMIP5 and CMIP6. We thank the climate modeling groups for producing and making available their model output, the Earth System Grid Federation (ESGF) for archiving the CMIP data and providing access, the University at Buffalo for ISMIP6 data distribution and upload, and the multiple funding agencies who support CMIP5 and CMIP6 and ESGF. We thank the ISMIP6 steering committee, the ISMIP6 model selection group and ISMIP6 dataset preparation group for their continuous engagement in defining ISMIP6."</p> <p>You should also refer to and cite the following papers:</p> <p>Seroussi, H., Nowicki, S., Payne, A. J., Goelzer, H., Lipscomb, W. H., Abe-Ouchi, A., Agosta, C., Albrecht, T., Asay-Davis, X., Barthel, A., Calov, R., Cullather, R., Dumas, C., Galton-Fenzi, B. K., Gladstone, R., Golledge, N. R., Gregory, J. M., Greve, R., Hattermann, T., Hoffman, M. J., Humbert, A., Huybrechts, P., Jourdain, N. C., Kleiner, T., Larour, E., Leguy, G. R., Lowry, D. P., Little, C. M., Morlighem, M., Pattyn, F., Pelle, T., Price, S. F., Quiquet, A., Reese, R., Schlegel, N.-J., Shepherd, A., Simon, E., Smith, R. S., Straneo, F., Sun, S., Trusel, L. D., Van Breedam, J., van de Wal, R. S. W., Winkelmann, R., Zhao, C., Zhang, T., and Zwinger, T.: ISMIP6 Antarctica: a multi-model ensemble of the Antarctic ice sheet evolution over the 21st century, The Cryosphere, 14, 3033–3070, https://doi.org/10.5194/tc-14-3033-2020, 2020.</p> <p>Nowicki, S., Goelzer, H., Seroussi, H., Payne, A. J., Lipscomb, W. H., Abe-Ouchi, A., Agosta, C., Alexander, P., Asay-Davis, X. S., Barthel, A., Bracegirdle, T. J., Cullather, R., Felikson, D., Fettweis, X., Gregory, J. M., Hattermann, T., Jourdain, N. C., Kuipers Munneke, P., Larour, E., Little, C. M., Morlighem, M., Nias, I., Shepherd, A., Simon, E., Slater, D., Smith, R. S., Straneo, F., Trusel, L. D., van den Broeke, M. R., and van de Wal, R.: Experimental protocol for sea level projections from ISMIP6 stand-alone ice sheet models, The Cryosphere, 14, 2331–2368, https://doi.org/10.5194/tc-14-2331-2020, 2020.</p>
X-ray structure ensemble refinement of the second bromodomain of Pleckstrin homology domain interacting protein (PHIP) (space group P21212)
<p>X-ray structure ensemble refinement of the second bromodomain of Pleckstrin homology domain interacting protein (PHIP) (space group P21212). Raw diffraction images are available on Zenodo: 10.5281/zenodo.4086066. A single conformer model was deposited in the Protein Data Bank under accession code <a href="https://www.ebi.ac.uk/pdbe/entry/pdb/7AV8">7AV8</a>. Refinement was carried out with <a href="https://www.phenix-online.org/documentation/reference/ensemble_refinement.html">phenix.ensemble_refinement</a> and the repository contains all input and output files:</p> <p>Input:</p> <ul> <li>mx8421v63_xPHIPAx1521_free.mtz</li> <li>refine9.pdb</li> </ul> <p>Output:</p> <ul> <li>PHIPA-P21212_ensemble_refinement_ensemble.geo</li> <li>PHIPA-P21212_ensemble_refinement_ensemble.pdb</li> <li>PHIPA-P21212_ensemble_refinement_ensemble.mtz</li> <li>PHIPA-P21212_ensemble_refinement_ensemble.log</li> </ul>
RNA Ensembles From Solvent Accessibility Data: Application to the SAM-I Riboswitch Aptamer Domain
<p>SASA-derived ensembles of the -SAM and +SAM states of the SAM-responsive riboswitch.</p>
SAXS calculations for Refinement of 𝛼-synuclein ensembles against SAXS data: Comparison of force fields and methods
<p>SAXS curves calculated from MD simulations used as input to reweighting as described in preprint Refinement of 𝛼-synuclein ensembles against SAXS data: Comparison of force fields and methods</p>
Metainference ensemble of α-synuclein ensembles against SAXS data
<p>Metainference metadynamcs ensemble from "Refinement of <em>α</em>-synuclein ensembles against SAXS data: Comparison of force fields and methods" https://doi.org/10.1101/2021.01.15.426794</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.