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608 results for “ensembles”
REVEL (Rare Exome Variant Ensemble Learner) Scores
<p>REVEL is an ensemble method for predicting the pathogenicity of missense variants in the human genome. For more information, see <a href="https://sites.google.com/site/revelgenomics/">https://sites.google.com/site/revelgenomics/</a> and <a href="https://dx.doi.org/10.1016/j.ajhg.2016.08.016">https://dx.doi.org/10.1016/j.ajhg.2016.08.016</a>.</p>
Ensembl TSS dataset for GRCh38
<p>We used the human genome reference sequence in its GRCh38.p13 version in order to have a reliable source of data in which to carry out our experiments. We chose this version because it is the most recent one available in Ensemble at the moment. However, the DNA sequence by itself is not enough, the specific TSS position of each transcript is needed. In this section, we explain the steps followed to generate the final dataset. These steps are: raw data gathering, positive instances processing, negative instances generation and data splitting by chromosomes.</p> <p>First, we need an interface in order to download the raw data, which is composed by every transcript sequence in the human genome. We used Ensembl release 104 (Howe et al., 2020) and its utility BioMart (Smedley et al., 2009), which allows us to get large amounts of data easily. It also enables us to select a wide variety of interesting fields, including the transcription start and end sites. After filtering instances that present null values in any relevant field, this combination of the sequence and its flanks will form our raw dataset. Once the sequences are available, we find the TSS position (given by Ensembl) and the 2 following bases to treat it as a codon. After that, 700 bases before this codon and 300 bases after it are concatenated, getting the final sequence of 1003 nucleotides that is going to be used in our models. These specific window values have been used in (Bhandari et al., 2021) and we have kept them as we find it interesting for comparison purposes. One of the most sensitive parts of this dataset is the generation of negative instances. We cannot get this kind of data in a straightforward manner, so we need to generate it synthetically. In order to get examples of negative instances, i.e. sequences that do not represent a transcript start site, we select random DNA positions inside the transcripts that do not correspond to a TSS. Once we have selected the specific position, we get 700 bases ahead and 300 bases after it as we did with the positive instances.</p> <p>Regarding the positive to negative ratio, in a similar problem, but studying TIS instead of TSS (Zhang135<br> et al., 2017), a ratio of 10 negative instances to each positive one was found optimal. Following this136<br> idea, we select 10 random positions from the transcript sequence of each positive codon and label them137<br> as negative instances. After this process, we end up with 1,122,113 instances: 102,488 positive and 1,019,625 negative sequences. In order to validate and test our models, we need to split this dataset into three parts: train, validation and test. We have decided to make this differentiation by chromosomes, as it is done in (Perez-Rodriguez et al., 2020). Thus, we use chromosome 16 as validation because it is a good example of a chromosome with average characteristics. Then we selected samples from chromosomes 1, 3, 13, 19 and 21 to be part of the test set and used the rest of them to train our models. Every step of this process can be replicated using the scripts available in https://github.com/JoseBarbero/EnsemblTSSPrediction.</p>
Revised transcript annotations for GRCh38 reference genome and Ensembl v87.
<p>Custom transcript annotations generated using the reviseAnnotations package. </p> <p>Reference genome: GRCh38<br> Ensembl version: 87</p> <p>See the GitHub page of reviseAnnotations for more details:<br> https://github.com/kauralasoo/reviseAnnotations</p>
Revised transcript annotations for GRCh37 (hg19) reference genome and Ensembl v90.
<p>Custom transcript annotations generated using the reviseAnnotations package. </p> <p>Reference genome: GRCh37<br> Ensembl version: 90</p> <p>See the GitHub page of reviseAnnotations for more details:<br> https://github.com/kauralasoo/reviseAnnotations</p>
Computing the Committor with the Committor: an Anatomy of the Transition State Ensemble
<p>The study of the kinetic bottlenecks that hinder the rare transitions between long-lived metastable states is a major challenge in atomistic simulations. We propose a method to explore the transition state ensemble, which is the distribution of configurations that the system passes as it translocates from one metastable basin to another. We base our method on the committor function and the variational principle to which it obeys. We find its minimum through a self-consistent procedure that starts from information limited to the initial and final states. Right from the start, our procedure allows sampling very many transition state configurations. With the help of the variational principle, we perform a detailed analysis of the transition state ensemble, ranking quantitatively the degrees of freedom mostly involved in the transition and enabling for a systematic approach for the interpretation of simulation results and the construction of efficient physics-informed collective variables.</p>
ConceptNet Vector Ensemble 16.04 input data
<p>This is the data required to build the paper "An Ensemble Method to Build High-Quality Word Embeddings", by Robyn Speer and Joshua Chin.</p> <p>The input data itself comes from:</p> <ul> <li> <p><a href="http://conceptnet5.media.mit.edu/">ConceptNet 5.4</a>, which contains data from Wiktionary, WordNet, and many contributors to Open Mind Common Sense projects, edited by Robyn Speer</p> </li> <li> <p><a href="http://nlp.stanford.edu/projects/glove/">GloVe</a>, by Jeffrey Pennington, Richard Socher, and Christopher Manning</p> </li> <li> <p><a href="https://code.google.com/archive/p/word2vec/">word2vec</a>, by Tomas Mikolov and Google Research</p> </li> <li> <p><a href="http://www.cis.upenn.edu/~ccb/ppdb/">PPDB</a>, by Juri Ganitkevitch, Benjamin Van Durme, and Chris Callison-Burch</p> </li> </ul>
Snowpack ensemble simulations at the Kühtai snow monitoring station
<p>This dataset presents an ensemble of 230000 point-scale snowpack simulations at the snow monitoring station Kühtai (Tyrol, Austria) over the course of 25 winter seasons. The dataset splits in simulations for various sensitivity analysis designs: i.e. to assess the sensitivity of 1) forcing data error, model structure, and parametrization, 2) different forcing variables while perturbing model structure and parametrization, 3) model structures while perturbing forcing errors and parametrization.</p>
Snowpack ensemble simulation at the Kühtai snow monitoring station
<p>This dataset presents an ensemble of 230000 point-scale snowpack simulations at the snow monitoring station Kühtai (Tyrol, Austria) over the course of 25 winter seasons. We provide time series of simulated snow water equivalent, errors of these simulations and the corresponding sensitivity indices. The dataset splits in simulations for various sensitivity analysis designs: i.e. to assess the sensitivity of a) forcing data error, model structure, and parametrization, b) different forcing variables while perturbing model structure and parametrization, c) model structures while perturbing forcing errors and parametrization.</p>
Sea level trends over 1993-2015 and 2005-2015 from the OCCIPUT ensemble simulation
<p><strong>Contributions of atmospheric forcing and chaotic ocean variability to regional sea level trends over 1993-2015</strong></p> <p>William Llovel, Thierry Penduff, Benoit Meyssignac, Jean-Marc Molines, Laurent Terray, Laurent Bessières and Bernard Barnier</p> <p> </p> <p>This data set contains 50 sea level trend fields computed globally over 1993-2015 and 2005-2015 from the OCCIPUT ensemble hindcast. These fields are studied in the paper “Contribution of atmospheric forcing and chaotic ocean variability to regional sea level trends over 1993-2015” in revision in <em>Geophysical Research Letters</em>.</p> <p>These sea level trends come from the OceaniC Chaos – ImPacts, structure, predictability (OCCIPUT) ensemble of 1/4° ocean/sea-ice simulations (Penduff et al, 2014; Bessières et al., 2017). This ensemble consists of 50 global hindcasts at ¼° horizontal resolution performed over 1960-2015. The configuration is based on the NEMO 3.5 model and implemented on an eddy-permitting quasi-isotropic horizontal mesh whose grid spacing is about 27 km at the equator and decreases poleward. The 50 members are initialized on January 1<sup>st</sup> 1960 from the final state of a 21-year one-member spinup. A small stochastic perturbation is applied within each ensemble member during the first year (1960) and switched off at the end of 1960, yielding 50 different oceanic states on January 1<sup>st</sup> 1961. Each member is then integrated until the end of 2015 with the same atmospheric forcing (DSF5.2) based on the ERA-Interim atmospheric reanalysis. We therefore obtain an ensemble of 50 simulations with the same numerical model and forcing, but different initial conditions.</p> <p>A one-member 327-year climatological simulation based the exact same code and setup is used to estimate the impact of spurious model drift on sea level trends. This simulation was forced each year with the same annual atmospheric cycle derived from DFS5.2. The spurious trends of simulated sea level was estimated at every grid point by computing sea level trends in the climatological simulation over the corresponding years of the 1993-2015 OCCIPUT simulations. This spurious trend map was then removed from the 50 trend maps derived from the ensemble simulation (as also done in Penduff et al., 2018).</p> <p>As NEMO conserves volume rather than mass, the global mean steric effect is missing and the global mean sea level is not properly computed (Greatbatch, 1994). The global mean sea level trends were thus subtracted from the regional sea level trends within each member: the trend provided in the present dataset are anomalies respective to their global mean sea level trend (and corrected for the model drift).</p> <p> </p> <p> </p> <p> </p> <p>Here is an example of the file header. </p> <p><strong><em>dimensions:</em></strong></p> <p><strong><em> y = 1021 ;</em></strong></p> <p><strong><em> x = 1442 ;</em></strong></p> <p><strong><em>variables:</em></strong></p> <p><strong><em> float nav_lat(y, x) ;</em></strong></p> <p> <strong><em>nav_lat:axis = "Y" ;</em></strong></p> <p><strong><em> nav_lat:standard_name = "latitude" ;</em></strong></p> <p><strong><em> nav_lat:long_name = "Latitude" ;</em></strong></p> <p><strong><em> nav_lat:units = "degrees_north" ;</em></strong></p> <p><strong><em> nav_lat:nav_model = "grid_T" ;</em></strong></p> <p> <strong><em>float nav_lon(y, x) ;</em></strong></p> <p> <strong><em>nav_lon:axis = "X" ;</em></strong></p> <p><strong><em> nav_lon:standard_name = "longitude" ;</em></strong></p> <p><strong><em> nav_lon:long_name = "Longitude" ;</em></strong></p> <p><strong><em> nav_lon:units = "degrees_east" ;</em></strong></p> <p> <strong><em>nav_lon:nav_model = "grid_T" ;</em></strong></p> <p><strong><em> double trend(y, x) ;</em></strong></p> <p> <strong><em>trend:units = "m/yr" ;</em></strong></p> <p><strong><em> trend:_FillValue = 0. ;</em></strong></p> <p> </p> <p><strong><em>// global attributes:</em></strong></p> <p><strong><em> :_NCProperties = "version=1|netcdflibversion=4.4.1|hdf5libversion=1.8.14" ;</em></strong></p> <p><strong><em> :Conventions = "CF" ;</em></strong></p> <p><strong><em> :title = "NCL Efficient Approach by W. Llovel" ;</em></strong></p> <p>nav_lat and nav_lon represent the latitude and longitude of the NEMO model whereas trend represents the trend anomalies. The trend anomaly estimates are in m.yr<sup>-1</sup>.</p> <p> </p> <p><strong>References</strong></p> <p>Bessières, L., Leroux, S., Brankart, J.-M., Molines, J.-M., Moine, M.-P., Bouttier, P.-A., Penduff, T., Terray, L., Barnier, B., and Sérazin, G., 2017: Development of a probabilistic ocean modelling system based on NEMO 3.5: application at eddying resolution, Geosci. Model Dev., 10, 1091-1106, doi:10.5194/gmd-10-1091-2017</p> <p>Greatbatch, R. J. , 1994: A note on the representation of steric sea level in models that conserve volume rather than mass, J. Geophys. Res., 99(C6), 12767–12771.</p> <p>Penduff T, Juza M, Barnier B, Zika J, Dewar WK, Treguier A-M, Molines JM, Audiffren N (2011) Sea-level expression of intrinsic and forced ocean variabilities at interannual time scales. J Clim 24:5652–5670.</p> <p>Penduff, T., Barnier, B., Terray, L., Bessières, L., Sérazin, G., Gregorio, S., Brankart, J., Moine, M., Molines, J., and Brasseur, P.: Ensembles of eddying ocean simulations for climate, CLIVAR Exchanges, Special Issue on High Resolution Ocean Climate Modelling, 19, 2014</p> <p>Penduff, T., W. Llovel, S. Close, B.-I. Garcia-Gomez, G. Sérazin, L. Bessières, S. Leroux, Trends of coastal sea level between 1993 and 2015: roles of atmospheric forcing and oceanic chaos, in prep.</p>
Ensemble and single cell data for primary Ovis Aries pulmonary artery endothelial cells response to IGF-1 administration
<p>Data for IGF-1 administration of healthy and persistent pulmonary hypertension of the newborn primary Ovis Aries pulmonary artery endothelial cells.</p> <p>Ensemble.zip:<br> Dose-response to IGF-1<br> Proliferation<br> Western blots for VEGF and eNOS<br> IGF-1R and mTOR inhibition<br> Branch points in tube formation assay<br> <br> IF.zip:<br> Cellprofiler 3.1 analysis of snapshot data for the time course of IGF-1 administration with nuclei, actin, VEGF, and eNOS</p> <p>Translation.zip:<br> CellProfiler 3.1 analysis of snapshot data for the time course of IGF-1 administration with nuclei and total protein production</p>
Atmospheric and sea ice model fields from the perturbed parameter ensemble E3SMv0-HILAT used to examine emergent relationships among climate variables in the Arctic
<p>These files contain time series of several sea ice ad atmospheric fields produced in an ensemble of perturbed parameter simulations using the E3SMv0-HiLAT model. The time series are used to produced seasonal means, which are used to examine emerging relationships in the ensemble discussed in our manuscript, as of September 2019, in review at JGR</p>
Figure 2 in Ensemble distribution modeling of the Mesopotamian spiny-tailed lizard, Saara loricata (Blanford, 1874), in Iran: an insight into the impact of climate change
Figure 2. The habitat suitability map of the Mesopotamian spiny-tailed lizard in southwestern Iran.
Figure 1 in The potential effects of future climate change on suitable habitat for the Taiwan partridge (Arborophila crudigularis): an ensemble-based forecasting method
Figure 1. Modeled range and presence records for Arborophila crudigularis.
Float+SOCAT sampling masks for ML reconstruction of surface ocean pCO2 using the Large Ensemble Testbed
<p>Here we provide sampling masks used in the study "The importance of adding unbiased Argo observations to the ocean carbon observing system" (Heimdal & McKinley, 2024, Scientific Reports). In this paper, we reconstruct surface ocean pCO2 using the Large Ensemble Testbed (Gloege et al., 2021, https://doi.org/10.1029/2020GB006788) and the pCO2-Residual method (Bennington et al., 2022, https://doi.org/10.1029/2021MS002960). We provide 2 different sampling masks used in the experiments presented in Heimdal & McKinley (2024). These masks represent two different float sampling schemes (+SOCAT) including 500 floats, corresponding to historical Argo float observations (https://fleetmonitoring.euro-argo.eu/dashboardpatterns) and potential optimized float sampling (following Chamberlain et al., 2023, <a href="https://doi.org/10.1175/JTECH-D-22-0093.1" target="_blank" rel="noopener">https://doi.org/10.1175/JTECH-D-22-0093.1</a>). </p>
ensemble_graphs_fmri_2024
<p>Correlation and ensemble graphs constructed from fMRI data of 100 individuals performing 7 different cognitive tasks. Original fMRI data were taken from the Human Connectome Project. More details on the ensemble graph construction method can be found on github (https://github.com/Daniil-Vlasenko/ensemble_graphs_fmri_2024.git) and our paper (to be published soon).</p> <p>Individuals with the following indices were selected for graph construction: 102109, 102614, 102715, 103212, 106824, 108020, 113316, 118831, 119025, 125222, 127226, 130114, 130720, 135629, 137532, 138332, 144933, 151324, 151930, 152225, 161832, 165436, 169545, 191235, 193845, 194443, 200513, 206525, 206727, 206828, 210112, 211619, 211821, 213017, 213522, 219231, 281135, 300719, 314225, 325129, 329844, 342129, 349244, 378756, 392447, 421226, 454140, 516742, 519647, 541640, 111211, 115724, 117021, 120414, 123723, 125424, 126426, 127832, 135124, 139435, 143224, 146735, 147636, 152427, 153126, 167440, 168947, 175136, 176845, 180230, 186545, 186848, 188145, 192237, 198047, 199352, 206323, 255740, 274542, 350330, 368753, 376247, 394956, 419239, 453542, 463040, 481042, 510225, 513130, 558960, 634748, 654552, 680452, 694362, 788674, 814548, 825654, 828862, 869472, 911849.</p>
Datasets and Code: Revealing the Role of Redox Reaction Selectivity and Mass Transfer in Current–Voltage Predictions for Ensembles of Photocatalysts
<ul> <li>Raw datasets (.mat and .fig files) and codes (.mlx and .m files) used in our manuscript of the same title. </li> <li>Figure numbers correspond with the figure numbers in the corresponding manuscript. <ul> <li>Figure 4: Effects of kinetic parameters on Solar-to-chemical (STC) efficiencies and reaction selectivity</li> <li>Figure 5: Solar-to-chemical (STC) efficiencies for a model incorporating competing undesired redox reactions implemented for different redox shuttle pairs</li> <li>Figure 7: Solar-to-chemical efficiencies for an ensemble of light absorbers</li> <li>Figure 8: Maximum solar-to-chemical (STC) efficiencies and corresponding number of light absorbers as a function of asymmetry factors in limiting current density for redox shuttle reduction</li> <li>Figure 9: Solar-to-chemical efficiencies for an increasing number of light absorbers for different total absorptance values (99%, 75%, 50%).</li> <li>Figure 10: Qualitative comparisons between experimental measurements and model predictions for a photocatalytic suspension reactor</li> </ul> </li> <li>The main piece of the code developed is provided as an interactive .mlx file; not all subfunction calls within the main code is included, and can be shared upon reasonable request via email from the lead (luisab@umich.edu) and the corresponding authors (rbchan@umich.edu) of this paper. </li> </ul> <p> </p>
Varèse_dataset_PoD: Dataset containing results from the research project Points of Discontinuity concerning Edgard Varèse, Ionisation for percussion ensemble of 13 players (1931), excerpt
<p>The complete datasets resulting from the research project <em>Points of Discontinuity</em> contain 23 datasets for the musical works or excerpts that were part of the online listening experiment, with each dataset containing seven or eight files (all audio files are stored in a dataset with restricted access), as well as a dataset (PoD_general_dataset) with five additional files.</p> <p>This dataset<strong> Varese_dataset_PoD </strong>contains eight files:</p> <ul> <li>Varese_01_ReadMe.pdf</li> <li>Varese_02_data.xlsx (processed data for this excerpt)</li> <li>Varese_03_individual_data.xlsx (raw data for each participant obtained from the experiment)</li> <li>Varese_04_audio.mp3 <strong>[non-public] </strong>(audio recording used in the experiment) [stored in the restricted dataset <a href="https://doi.org/10.5281/zenodo.13981214" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13981214</a>]</li> <li>Varese_05_model_results.sv (graphical representation of results and the model in Sonic Visualiser) [requires audio file Varese_04_audio.mp3 to display correctly]</li> <li>Varese_06_SV-data_model+results.zip (text files with the marker locations for all layers in Sonic Visualiser)</li> <li>Varese_07_model+results_SV-screenshot.pdf (a screenshot of the full-screen display of the SV-file)</li> <li>Varese_08_annotated_score.pdf (model analysis annotated in the score)</li> </ul>
Spahlinger_dataset_PoD: Dataset containing results from the research project Points of Discontinuity concerning Mathias Spahlinger, furioso für Ensemble (1991–92), excerpt
<p>The complete datasets resulting from the research project <em>Points of Discontinuity</em> contain 23 datasets for the musical works or excerpts that were part of the online listening experiment, with each dataset containing seven or eight files (all audio files are stored in a dataset with restricted access), as well as a dataset (PoD_general_dataset) with five additional files.</p> <p>This dataset<strong> Spahlinger_dataset_PoD </strong>contains eight files:</p> <ul> <li>Spahlinger_01_ReadMe.pdf</li> <li>Spahlinger_02_data.xlsx (processed data for this excerpt)</li> <li>Spahlinger_03_individual_data.xlsx (raw data for each participant obtained from the experiment)</li> <li>Spahlinger_04_audio.mp3 <strong>[non-public] </strong>(audio recording used in the experiment) [stored in the restricted dataset <a href="https://doi.org/10.5281/zenodo.13981214" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13981214</a>]</li> <li>Spahlinger_05_model_results.sv (graphical representation of results and the model in Sonic Visualiser) [requires audio file Spahlinger_04_audio.mp3 to display correctly]</li> <li>Spahlinger_06_SV-data_model+results.zip (text files with the marker locations for all layers in Sonic Visualiser)</li> <li>Spahlinger_07_model+results_SV-screenshot.pdf (a screenshot of the full-screen display of the SV-file)</li> <li>Spahlinger_08_annotated_score.pdf (model analysis annotated in the score)</li> </ul>
Structural models of overlapping dinucleosome for SAXS-based ensemble modeling
<p>Around transcription start sites, chromatin remodelers form overlapping dinucleosomes (OLDNs). OLDNs contain two structural units, octasome and hexasome. In the octasome, the same histone octamer as a canonical nucleosome is wrapped by DNA. In the hexasome, a histone hexamer that lacks an H2A-H2B dimer is wrapped by DNA. Changes in positional relationship between structural units of a biomolecular complex resulting from molecular motion or fluctuation often have functional significance. To understand fluctuation of OLDNs, we conducted SAXS-based ensemble modeling. Here structural models of OLDNs for the ensemble modeling are uploaded. The structures were obtained by performing coarse-grained molecular dynamics simulations followed by reverse-mapping of the coarse-grained models to atomistic models.</p>
Downscaled North American Multi-Model Ensemble Forecast for the Pacific Northwest USA
<h1>Downscaled North American Multi-Model Ensemble Forecast of Meteorological Variables for the Pacific Northwest</h1> <p>Monthly retrospective hindcasts (1982-2010) and forecasts (2011-2020) of temperature and precipitation are acquired for the Pacific Northwest region of the United States from five models (CFSv2, NASA GEOS5v2, CanCM4i, GEM-NEMO, and NCAR-CCSM) participating in the North American Multi-Model Ensemble project <a href="https://www.zotero.org/google-docs/?WlFE7n">(Kirtman et al., 2014)</a>. These models, detailed in Table 1 with more recent information available in <a href="https://www.zotero.org/google-docs/?PjZZHw">(Becker et al., 2022)</a>, are initialized monthly to provide a forecast of 0-9 months at a 1.0̊ × 1.0̊ spatial resolution. The multi-model ensemble mean (ENSMEAN) is then generated for each initialization by simply averaging all considered models and their ensemble members. Monthly ENSMEAN forecast is bias-corrected and spatially downscaled to 1/24th degree using the methodology described in <a href="https://www.zotero.org/google-docs/?jwPLzP">Wood et al. (2002)</a> and <a href="https://www.zotero.org/google-docs/?2b3lkj">Barbero et al. (2017)</a> using historical meteorological data <a href="https://www.zotero.org/google-docs/?zN8gKh">(gridMET; Abatzoglou, 2013)</a> as the baseline. Then, the downscaled ENSMEAN data are temporally disaggregated to daily timescales using an analog approach. The closest analog month for the ENSMEAN forecast is found from the gridMET dataset by minimizing the root mean square error (RMSE) of monthly gridMET and forecast precipitation (excluding gridMET data for the target month). Other daily meteorological variables (such as maximum and minimum temperature, maximum and minimum relative humidity, wind speed, and specific humidity) are extracted from the same analog month to use as input for the coupled crop-hydrology model. As a last step to the analog approach, the process corrects the bias between the forecast and analog month to ensure that monthly mean temperature and accumulated precipitation match those of the original forecast. </p> <p>Table 1. List of NMME models used to create Ensemble Mean.</p> <div> <table> <tbody> <tr> <td>Model </td> <td>Model Expansion </td> <td>Ensemble Size </td> <td>References</td> </tr> <tr> <td>NCEP- CFSv2 </td> <td>Climate Forecast System, version 2 </td> <td>24 </td> <td><a href="https://www.zotero.org/google-docs/?XTr15U">(Saha et al., 2014)</a></td> </tr> <tr> <td>NASA GEOS5v2</td> <td>Goddard Earth Observing System, version 5 </td> <td>4</td> <td><a href="https://www.zotero.org/google-docs/?fQzQZL">(Molod et al., 2020)</a></td> </tr> <tr> <td>CanCM4i </td> <td>Fourth Generation Canadian Coupled Global Climate Model </td> <td>10 </td> <td><a href="https://www.zotero.org/google-docs/?AoXCO8">(Merryfield et al., 2013)</a></td> </tr> <tr> <td>GEM - NEMO </td> <td>Global Environmental Multiscale Model – Nucleus for European Modelling of the Ocean </td> <td>10 </td> <td><a href="https://www.zotero.org/google-docs/?N4JwM9">(Lin et al., 2020)</a></td> </tr> <tr> <td>NCAR - CCSM </td> <td>Community Climate System Model </td> <td>10 </td> <td><a href="https://www.zotero.org/google-docs/?NMkImR">(Kirtman & Min, 2009)</a></td> </tr> </tbody> </table> </div> <p>The dataset has *.mat files which are MATLAB data files. </p> <h3>References</h3> <ol> <li> <p>Abatzoglou, J. T. (2013). Development of gridded surface meteorological data for ecological applications and modelling. International Journal of Climatology, 33(1), 121–131. <a href="https://doi.org/10.1002/joc.3413">https://doi.org/10.1002/joc.3413</a></p> </li> <li> <p>Barbero, R., Abatzoglou, J. T., & Hegewisch, K. C. (2017). Evaluation of Statistical Downscaling of North American Multimodel Ensemble Forecasts over the Western United States. Weather and Forecasting, 32(1), 327–341. https://doi.org/10.1175/WAF-D-16-0117.1</p> </li> <li> <p>Becker, E. J., Kirtman, B. P., L’Heureux, M., Muñoz, Á. G., & Pegion, K. (2022). A Decade of the North American Multimodel Ensemble (NMME): Research, Application, and Future Directions. Bulletin of the American Meteorological Society, 103(3), E973–E995. <a href="https://doi.org/10.1175/BAMS-D-20-0327.1">https://doi.org/10.1175/BAMS-D-20-0327.1</a></p> </li> <li> <p>Kirtman, B. P., & Min, D. (2009). Multimodel Ensemble ENSO Prediction with CCSM and CFS. Monthly Weather Review, 137(9), 2908–2930. https://doi.org/10.1175/2009MWR2672.1</p> </li> <li> <p>Kirtman, B. P., Min, D., Infanti, J. M., Kinter, J. L., Paolino, D. A., Zhang, Q., Dool, H. van den, Saha, S., Mendez, M. P., Becker, E., Peng, P., Tripp, P., Huang, J., DeWitt, D. G., Tippett, M. K., Barnston, A. G., Li, S., Rosati, A., Schubert, S. D., … Wood, E. F. (2014). The North American Multimodel Ensemble: Phase-1 Seasonal-to-Interannual Prediction; Phase-2 toward Developing Intraseasonal Prediction. Bulletin of the American Meteorological Society, 95(4), 585–601. <a href="https://doi.org/10.1175/BAMS-D-12-00050.1">https://doi.org/10.1175/BAMS-D-12-00050.1</a></p> </li> <li> <p>Lin, H., Merryfield, W. J., Muncaster, R., Smith, G. C., Markovic, M., Dupont, F., Roy, F., Lemieux, J.-F., Dirkson, A., Kharin, V. V., Lee, W.-S., Charron, M., & Erfani, A. (2020). The Canadian Seasonal to Interannual Prediction System Version 2 (CanSIPSv2). 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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.