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608 results for “ensembles”
Results of ISMIP6 CMIP6 forced simulations: a multi-model ensemble of the Greenland and Antarctic ice sheet evolution over the 21st century
<p>This archive provides the ice sheet model outputs produced as part of the publication "Payne et al. 2021 Future sea level change under CMIP5 and CMIP6 scenarios from the Greenland and Antarctic ice sheets", published in GRL</p> <p>Contact: Tony Payne a.j.payne@bristol.ac.uk, Sophie Nowicki sophien@buffalo.edu, ismip6@gmail.com </p> <p><br> Further information on ISMIP6 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<br> http://www.climate-cryosphere.org/wiki/index.php?title=ISMIP6-Projections-Greenland</p> <p>Data usage notice:<br> If you use any of these results, please acknowledge the work of the people involved in the process producing this data set. Acknowledgements should have language similar to the below (if you only use CMIP5 forcing, remove CMIP6 and vice versa).</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>For Greenland datasets </p> <p>Heiko Goelzer, Sophie Nowicki, Anthony Payne, Eric Larour, Helene Seroussi, William H. Lipscomb, Jonathan Gregory, Ayako Abe-Ouchi, Andy Shepherd, Erika Simon, Cecile Agosta, Patrick Alexander, Andy Aschwanden, Alice Barthel, Reinhard Calov, Christopher Chambers, Youngmin Choi, Joshua Cuzzone, Christophe Dumas, Tamsin Edwards, Denis Felikson, Xavier Fettweis, Nicholas R. Golledge, Ralf Greve, Angelika Humbert, Philippe Huybrechts, Sebastien Le clec'h, Victoria Lee, Gunter Leguy, Chris Little, Daniel P. Lowry, Mathieu Morlighem, Isabel Nias, Aurelien Quiquet, Martin Rückamp, Nicole-Jeanne Schlegel, Donald Slater, Robin Smith, Fiamma Straneo, Lev Tarasov, Roderik van de Wal, and Michiel van den Broeke: The future sea-level contribution of the Greenland ice sheet: a multi-model ensemble study of ISMIP6 , The Cryosphere, 2020. doi:10.5194/tc-2019-319</p> <p>Slater, D. A., Felikson, D., Straneo, F., Goelzer, H., Little, C. M., Morlighem, M., Fettweis, X., and Nowicki, S.: Twenty-first century ocean forcing of the Greenland ice sheet for modelling of sea level contribution , The Cryosphere, 14, 985–1008, https://doi.org/10.5194/tc-14-985-2020, 2020.</p> <p>Sophie Nowicki, Antony Payne, Heiko Goelzer, Helene Seroussi, William Lipscomb, Ayako Abe-Ouchi, Cecile Agosta, Patrick Alexander, Xylar Asay-Davis, Alice Barthel, Thomas Bracegirdle, Richard Cullather, Denis Felikson, Xavier Fettweis, Jonathan Gregory, Tore Hatterman, Nicolas Jourdain, Peter Kuipers Munneke, Eric Larour, Christopher Little, Mathieu Morlinghem, Isabel Nias, Andrew Shepherd, Erika Simon, Donald Slater, Robin Smith, Fiammetta Straneo, Luke Trusel, Michiel van den Broeke, and Roderik van de Wal: <br> Experimental protocol for sea level projections from ISMIP6 standalone ice sheet models, The Cryosphere, doi:10.5194/tc-2019-322, 2020.</p> <p>For Antarctica datasets</p> <p>Seroussi, H., Nowicki, S., Simon, E., Abe-Ouchi, A., Albrecht, T., Brondex, J., Cornford, S., Dumas, C., Gillet-Chaulet, F., Goelzer, H., Golledge, N. R., Gregory, J. M., Greve, R., Hoffman, M. J., Humbert, A., Huybrechts, P., Kleiner, T., Larour, E., Leguy, G., Lipscomb, W. H., Lowry, D., Mengel, M., Morlighem, M., Pattyn, F., Payne, A. J., Pollard, D., Price, S. F., Quiquet, A., Reerink, T. J., Reese, R., Rodehacke, C. B., Schlegel, N.-J., Shepherd, A., Sun, S., Sutter, J., Van Breedam, J., van de Wal, R. S. W., Winkelmann, R., and Zhang, T.: initMIP-Antarctica: an ice sheet model initialization experiment of ISMIP6, The Cryosphere, 13, 1441–1471, https://doi.org/10.5194/tc-13-1441-2019, 2019.</p> <p>Jourdain, N. C., Asay-Davis, X., Hattermann, T., Straneo, F., Seroussi, H., Little, C. M., and Nowicki, S.: A protocol for calculating basal melt rates in the ISMIP6 Antarctic ice sheet projections, The Cryosphere, 14, 3111–3134, https://doi.org/10.5194/tc-14-3111-2020, 2020.</p> <p><br> Sophie Nowicki, Antony Payne, Heiko Goelzer, Helene Seroussi, William Lipscomb, Ayako Abe-Ouchi, Cecile Agosta, Patrick Alexander, Xylar Asay-Davis, Alice Barthel, Thomas Bracegirdle, Richard Cullather, Denis Felikson, Xavier Fettweis, Jonathan Gregory, Tore Hatterman, Nicolas Jourdain, Peter Kuipers Munneke, Eric Larour, Christopher Little, Mathieu Morlinghem, Isabel Nias, Andrew Shepherd, Erika Simon, Donald Slater, Robin Smith, Fiammetta Straneo, Luke Trusel, Michiel van den Broeke, and Roderik van de Wal: Experimental protocol for sea level projections from ISMIP6 standalone ice sheet models, The Cryosphere, doi:10.5194/tc-2019-322, 2020.</p> <p> </p>
Neural ensemble reactivation in REM and SWS coordinate with muscle activity to promote rapid motor skill learning
<p>Neural activity patterns of recent experiences are reactivated during sleep in structures critical for memory storage, including hippocampus and neocortex. This reactivation process is thought to aid memory consolidation. Although synaptic rearrangement dynamics following learning involve an interplay between slow-wave sleep (SWS) and rapid eye movement sleep (REM), most physiological evidence implicates SWS directly following experience as a preferred window for reactivation. Here we show that reactivation occurs in both REM and SWS, and that coordination of REM and SWS activation on the same day is associated with rapid learning of a motor skill. We performed 6-hour recordings from cells in rats' motor cortex as they were trained daily on a skilled reaching task. In addition to SWS following training, reactivation occurred in REM, primarily during the pre-task rest period, and REM and SWS reactivation occurred on the same day in rats that acquired the skill rapidly. Both pre-task REM and posttask SWS activation were coordinated with muscle activity during sleep, suggesting a functional role for reactivation in skill learning. Our results provide the first demonstration that reactivation in REM sleep occurs during motor skill learning, and that coordinated reactivation in both sleep states on the same day, although at different times, is beneficial for skill learning.</p>
Ensemble synchronization in the reassembly of Hydra's nervous system
<p class="Default">Although much is known about how the structure of the nervous system develops, it is still unclear how its functional modularity arises. A dream experiment would be to observe the entire development of a nervous system, correlating the emergence of functional units with their associated behaviors. This is possible in the cnidarian <i>Hydra vulgaris</i>, which, after its complete dissociation into individual cells, can reassemble itself back together into a normal animal. We used calcium imaging to monitor the complete neuronal activity of dissociated <i>Hydra </i>as they re-aggregated over several days. Initially uncoordinated neuronal activity became synchronized into coactive neuronal ensembles. These local modules then synchronized with others, building larger functional ensembles that eventually extended throughout the entire reaggregate, generating neuronal rhythms similar to those of intact animals. Global synchronization was not due to neurite outgrowth but to strengthening of functional connections between ensembles. We conclude that <i>Hydra's</i> nervous system achieves its functional reassembly through the hierarchical modularity of neuronal ensembles.</p>
Supplementary video content for "Three-dimensional visualization of ensemble weather forecasts", Parts 1 and 2 (Geoscientific Model Development, 2015)
<p>Supplementary video content (full resolution) for the papers "Three-dimensional visualization of ensemble weather forecasts - Part 1: The visualization tool Met.3D (version 1.0)" and "Three-dimensional visualization of ensemble weather forecasts - Part 2: Forecasting warm conveyor belt situations for aircraft-based field campaigns", Geoscientific Model Development, 2015. The corresponding papers can be found on http://geosci-model-dev.net/.</p>
Supporting datasets PubFig05 for: "Heterogeneous Ensemble Combination Search using Genetic Algorithm for Class Imbalanced Data Classification"
<p><strong>Faces Dataset: PubFig05</strong></p> <p>This is a subset of the ''PubFig83'' dataset [1] which provides 100 images each of 5 most difficult celebrities to recognise (referred as class in the classification problem). For each celebrity persons, we took 100 images and separated them into training and testing sets of 90 and 10 images, respectively:</p> <p><strong>Person: </strong>Jenifer Lopez; Katherine Heigl; Scarlett Johansson; Mariah Carey; Jessica Alba</p> <p> </p> <p><strong>Feature Extraction</strong></p> <p>To extract features from images, we have applied the HT-L3-model as described in [2] and obtained 25600 features.</p> <p><strong>Feature Selection</strong></p> <p>Details about feature selection followed in brief as follows:</p> <ol> <li> <p><strong>Entropy Filtering:</strong> First we apply an implementation of Fayyad and Irani's [3] entropy base heuristic to discretise the dataset and discarded features using the minimum description length (MDL) principle and only 4878 passed this entropy based filtering method.</p> </li> <li> <p><strong>Class-Distribution Balancing:</strong> Next, we have converted the dataset to binary-class problem by separating into 5 binary-class datasets using one-vs-all setup. Hence, these datasets became <em>imbalanced</em> at a ratio of 1:4. Then we converted them into <em>balanced binary-class</em> datasets using random sub-sampled method. Further processing of the dataset has been described in the paper.</p> </li> <li> <p><strong>(alpha,beta)-k Feature selection:</strong> To get a good feature set for training the classifier, we select the features using the approach based on the (alpha,beta)-k feature selection [4] problem. It selects a minimum subset of features that maximise both within class similarity and dissimilarity in different classes. We applied the entropy filtering and (alpha,beta)-k feature subset selection methods in three ways and obtained different numbers of features (in the Table below) after consolidating them into binary class dataset.</p> </li> </ol> <ul> <li> <p><strong>UAB:</strong> We applied (alpha,beta)-k feature set method on each of the balanced binary-class datasets and we took the <em>union</em> of selected features for each binary-class datasets. Finally, we applied the (alpha,beta)-k feature set selection method on each of the binary-class datasets and get a set of features.</p> </li> <li> <p><strong>IAB:</strong> We applied (alpha,beta)-k feature set method on each of the balanced binary-class datasets and we took the <em>intersection</em> of selected features for each binary-class datasets. Finally, we applied the (alpha,beta)-k feature set selection method on each of the binary-class datasets and get a set of features.</p> </li> <li> <p><strong>UEAB:</strong> We applied (alpha,beta)-k feature set method on each of the balanced binary-class datasets. Then, we applied the entropy filtering and (alpha,beta)-k feature set selection method on each of the balanced binary-class datasets. Finally, we took the <em>union</em> of selected features for each <em>balanced binary-class</em> datasets and get a set of features.</p> </li> </ul> <p>All of these datasets are inside the compressed folder. It also contains the document describing the process detail.</p> <p> </p> <p><strong>References</strong></p> <p>[1] Pinto, N., Stone, Z., Zickler, T., & Cox, D. (2011). Scaling up biologically-inspired computer vision: A case study in unconstrained face recognition on facebook. In Computer Vision and Pattern Recognition Workshops (CVPRW), 2011 IEEE Computer Society Conference on (pp. 35–42).</p> <p>[2] Cox, D., & Pinto, N. (2011). Beyond simple features: A large-scale feature search approach to unconstrained face recognition. In Automatic Face Gesture Recognition and Workshops (FG 2011), 2011 IEEE International Conference on (pp. 8–15).</p> <p>[3] Fayyad, U. M., & Irani, K. B. (1993). Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning. In International Joint Conference on Artificial Intelligence (pp. 1022–1029).</p> <p>[4] Berretta, R., Mendes, A., & Moscato, P. (2005). Integer programming models and algorithms for molecular classification of cancer from microarray data. In Proceedings of the Twenty-eighth Australasian conference on Computer Science - Volume 38 (pp. 361–370). 1082201: Australian Computer Society, Inc.</p> <p> </p>
Ensemble Metatone Agent and App Study Data (2014-07-19)
<p>This repository contains recordings of performances made by Ensemble Metatone (Christina Hopgood, Yvonne Lam, and Jonathan Griffiths) as part of a formal study of touch-screen apps and ensemble director agents. Video recordings as well as touch-data logs are included for each of the 18 performances.</p> <p>Three apps were used in the sessions: Snow Music (SM), Bird’s Nest (BN), and Singing Bowls (SB). Two agents were used: a gesture classifying agent (CLA), and an agent that generated similar signals from a statistical model (GEN) that was not connected to the performers’ actions.</p>
Proper modelling of ligand binding requires an ensemble of bound and unbound states
<p>Crystallographic data for structures described in the manuscript "Proper modelling of ligand binding requires an ensemble of bound and unbound states".</p>
Data for manuscript "Modes of Variability in E3SM and CESM Large Ensembles"
An adequate characterization of internal modes of climate variability (MoV) is prerequisite for both accurate seasonal predictions and the attribution and detection of forced climate change in nature. Assessing the fidelity of climate models in simulating MoV is therefore essential; however, doing so is complicated by the large intrinsic variations in MoV and the limited span of the observational record. Large ensembles (LEs) provide a unique opportunity to assess model fidelity in simulating MoV and quantify inter-model contrasts. In this work, these goals are pursued in four recently produced LEs: the Energy Exascale Earth System Model (E3SM) versions 1 and 2 LEs, and the Community Earth System Model (CESM) versions 1 and 2 LEs. In general, the representation of MoV is found to improve across successive E3SM and CESM versions concurrent with improved simulation of the base state climate. The patterns of global coupled modes and many extratropical modes are well simulated by both E3SM2 and CESM2, though various persistent shortcomings are identified. The results both demonstrate the successes of these recent model versions and suggest the potential for continued improvement in the representation of MoV with advances in model physics.
Supplementary Data for "Comparison of Ensemble-Based Data Assimilation Methods for Sparse Oceanographic Data"
<p>This data set represents the supplementary data for the paper Comparison of Ensemble-Based Data Assimilation Methods for Sparse Oceanographic Data (Section 4) by Florian Beiser, Håvard Heitlo Holm, and Jo Eidsvik.</p><p>It contains the data that is plotted in the manuscript. The code for plotting is provided in the supplementary software.</p>
Ensemble experiment to investigate Antarctic meltwater input under global warming by GFDL CM2.1
<p>Dataset for "Non-monotonic responses of Atlantic Meridional Overturning Circulation to Antarctic meltwater forcing" submitted to GRL.</p><p>GW indicates global_warming experiments without meltwater input whereas MW is with meltwater input.</p><p>atlantic, global, surface_density, integrated_density.mat is preprocessed ocean dataset (structure) by MATLAB.</p>
Ensembles for neutron-star-matter equation-of-state interpolations
<p>Ensembles for neutron-star-matter equation-of-state interpolations to reproduce the figures in arXiv:2303.11356</p>
Supplemental Movie Files for "Agent-Based Modeling of a Nuclear Chromosome Ensemble Identifies Determinants of Homolog Pairing During Meiosis" by Chriss et al.
<p>This set of Supplemental Information contains two movies made from the simulations from the model developed in the manuscript "<strong>Agent-Based Modeling of a Nuclear Chromosome Ensemble Identifies Determinants of Homolog Pairing During Meiosis</strong>" by A. Chriss, G. V. Börner, and S. D. Ryan. </p> <p> </p> <p>Supplemental Movie S1: <strong>WT Chromosome Trajectories during Prophase I. </strong>The first file "movie_WT..." contains the file for the results of simulations for the wild-type chromosomes and the exact parameter values can be found in Table 1 of the manuscript. The movie shows one realization of the agent-based model. The simulation movie covers the homology search process from <em>t = 3h </em>to <em>t = 9h</em>. Matching colors correspond to homologous pairs. True chromosome lengths are incorporated and scale the relevant interaction radii. The radius represents the attractive and non-homologous repulsive region.</p> <p> </p> <p>Supplemental Movie S2: <strong>WT Chromosome Trajectories during Prophase I with active dumbbell model. </strong> The second file "movie<em>WT</em>_activedumbbell..." contains the file for the results of the simulations for the modeling of chromosomes as active dumbbells (from polymers) to allow for the study of the effects of elongation, orientation, and flexibility. The movie shows one realization of the agent-based active dumbbell model which is closer to modeling a chromosome as a polymer. The simulation movie covers the homology search process from <em>t = 3h </em>to <em>t = 9h</em>. Matching colors correspond to homologous pairs. True chromosome lengths are incorporated and scale the relevant interaction radii, but are allowed to change in time as the two beads expand and contract. The radius represents the attractive and non-homologous repulsive region.</p> <p> </p> <p>Supplemental Movie S3: <strong><em>spo11</em> hypomorph (30% WT DSB levels) Chromosome Trajectories during Prophase I (parameters from Fig 7B)</strong>. The third file "movie_spo11..." contains the file for the results of the simulations for the spo-11 hypomorph and the associated parameter values can be found in Table 1 of the manuscript. The movie depicts one realization of the agent-based model for the {\it spo11} hypomorphic mutant. The simulation movie covers the homology search process from <em>t = 3h</em> to <em>t = 9h</em> where mutant <em>spo11</em> is associated with a weaker attractive and repulsive force (e.g., reduction to 77% of WT values). True chromosome lengths are incorporated and scale the relevant interaction radii. Matching colors correspond to homologous pairs. The radii represent the homologous attractive and the non-homologous repulsive region. Note that the reduction in interaction strength delays homologous pairing consistent with experimental observations in [13]. </p> <p> </p> <p> </p> <p>The codes that generated these movies were written in Matlab and freely available via GitHub: <a href="https://github.com/sdryan/ChromosomeDynamicsProphase1">https://github.com/sdryan/ChromosomeDynamicsProphase1</a></p> <p> </p> <p>For questions please contact the corresponding authors: G. Valentin Börner <a href="mailto:g.boerner@csuohio.edu">g.boerner@csuohio.edu</a> (Biology) or Shawn D. Ryan <a href="mailto:s.d.ryan@csuohio.edu">s.d.ryan@csuohio.edu</a> (Math).</p>
Customized gtf file from Ensembl version 109 GRCz11 (danRer11)
<p>The gtf from Ensembl version 109 GRCz11 (danRer11) was filtered to remove transcripts on alternative chromosomes, readthrough transcripts and all non-coding transcripts from a protein-coding gene. In addition, all genes with the same gene name which overlaps were merged under the same gene id to avoid ambiguous reads.<br>The procedure to generate the gtf is described in the bash file attached.<br> </p>
An LES perturbed parameter ensemble of free-tropospheric cloud-controlling factors on stratocumulus
<p>This dataset contains a perturbed parameter ensemble of large-eddy simulations to assess the effect of two free-tropospheric cloud-controlling factors on stratocumulus clouds properties. The simulations were run on the UK Met Office/NERC cloud model (MONC) for the DYCOMS-II RF01 nocturnal stratocumulus case (Stevens et. al., 2005). Each simulation had the same initial conditions except for the two perturbed parameters, which were the jumps in moisture and temperature at the temperature inversion at cloud top. The corresponding analysis code can be found at this <a href="https://github.com/eers1/dycoms_analysis">dycoms_analysis Github page</a>. </p><p> </p><p>Stevens, B., Moeng, C. H., Ackerman, A. S., Bretherton, C. S., Chlond, A., de Roode, S., . . . Zhu, P. (2005). Evaluation of large-eddy simulations via observations of nocturnal marine stratocumulus. Monthly Weather Review , 133 (6), 1443–1462. doi: 10.1175/MWR2930.1</p>
Model checkpoints for "SEEDS: Emulation of Weather Forecast Ensembles with Diffusion Models"
<p>Checkpoints for all SEEDS models in the paper <a href="https://arxiv.org/abs/2306.14066" rel="nofollow">https://arxiv.org/abs/2306.14066</a>, including all SEEDS-GEE and SEEDS-GPP models in the main manuscript, and the additional models trained in the Supplemental Material.</p> <p>Checkpoint naming convention:</p> <ul> <li><code>gee_c2_s7</code>: SEEDS-GEE trained conditioning on 2 seeds for 7-day lead time.</li> <li><code>gpp_c2_s7_g3_r4</code>: SEEDS-GPP trained conditioning on 2 seeds for 7-day leadtime, where the label mixture is 3 GEFS members and 4 ERA reanalyses.</li> </ul>
Data from Uncertainty Ensembles of the MIT EPPA Model
<p>This data repository is created by the MIT Joint Program on the Science and Policy of Global Change (https://globalchange.mit.edu/) and makes available data resulting from ensembles of simulations of the MIT Economic Projection and Policy Analysis (EPPA) Model that were designed to quantify socio-economic uncertainties. </p>
UFLUX European ensemble 0.25deg daily carbon, water, and energy fluxes from 2000 - 2020
<h3>UFLUX Ensemble Europe025ddaily (European 0.25° Daily)</h3> <p><strong>Overview</strong><br>The <strong>UFLUX ensemble dataset</strong> offers <strong>European daily fluxes at 0.25° spatial resolution</strong>, generated using <strong>Deep Forest machine learning models</strong>. It integrates <strong>satellite-based vegetation proxies </strong>— including MODIS NIRv, GOME-2 SIF, and OCO-2 SIF — with <strong>ERA5 climate reanalysis</strong>, and is trained against <strong>ICOS eddy covariance observations</strong>. The dataset includes five core flux components:</p> <ul> <li> <p>Gross Primary Production (<strong>GPP</strong>)</p> </li> <li> <p>Ecosystem Respiration (<strong>RECO</strong>)</p> </li> <li> <p>Net Ecosystem Exchange (<strong>NEE</strong>)</p> </li> <li> <p>Sensible Heat Flux (<strong>H</strong>)</p> </li> <li> <p>Latent Energy Flux (<strong>LE</strong>)</p> </li> </ul> <p><strong>Background and Methodology</strong><br>The <strong>Unified FLUXes (UFLUX)</strong> initiative is a data-driven, machine learning-based platform designed to upscale eddy covariance (EC) flux measurements from tower sites to the global scale. It aims to answer pressing questions about how effectively terrestrial ecosystems are managed under climate change.</p> <p>Key innovations of UFLUX include:</p> <ol> <li> <p><strong>Consistent Upscaling Framework</strong>: Harmonizes flux upscaling across spatial/temporal scales and multiple flux types (GPP, RECO, etc.) using deep decision tree-based methods, better suited than conventional neural networks for EC flux data.</p> </li> <li> <p><strong>Hybrid Explainable ML</strong>: Combines black-box ML with ecological interpretability through residual learning, offering both predictive power and new scientific insight (UFLUXv2).</p> </li> <li> <p><strong>Uncertainty Quantification</strong>: Employs sampling space completeness to assess model uncertainty in a transparent, robust manner.</p> </li> <li> <p><strong>Multisource Integration</strong>: Leverages complementary strengths of vegetation proxies (e.g., NIRv, SIF) and climate data (e.g., ERA5) to represent carbon dynamics more comprehensively than single-source approaches.</p> </li> <li> <p><strong>Superior Gap-Filling</strong>: Originally developed as a global EC flux gap-filling tool, UFLUX improves accuracy by up to 30% and reduces uncertainty by as much as 70% compared to traditional methods.</p> </li> <li> <p><strong>High Performance</strong>: Achieves strong predictive accuracy, with global-scale R² > 0.8 for RECO and ≈0.9 for GPP, while being computationally efficient enough to run on a standard laptop.</p> </li> <li> <p><strong>Community Adoption</strong>: Already used by other global upscaling projects, highlighting its reliability and impact.</p> </li> </ol> <p><strong>Applications</strong><br>UFLUX is ideal for studying the interactions between land management, climate change, and carbon fluxes, particularly in improving global estimates of GPP and RECO by addressing biases in EC measurements.</p> <p><strong>Resources</strong></p> <ul> <li><strong>UFLUX Website: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://sites.google.com/view/uflux</a></strong></li> <li> <p><strong>Code Repository</strong>: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://github.com/soonyenju/uflux</a></p> </li> <li> <p><strong>Technical & Descriptive Publication</strong>: <a href="https://doi.org/10.1080/01431161.2024.2312266" target="_new" rel="noopener">https://doi.org/10.1080/01431161.2024.2312266</a></p> </li> </ul>
UFLUX global ensemble 0.25deg monthly carbon, water, and energy fluxes from 2001 - 2021
<p> </p> <h3>UFLUX Ensemble Globe025dmonthly (Global 0.25° Monthly, 13 Members)</h3> <p><strong>Overview</strong><br>The <strong>UFLUX ensemble dataset</strong> provides <strong>global monthly fluxes at 0.25° spatial resolution</strong>, incorporating <strong>13 ensemble members</strong> derived from different combinations of satellite-based vegetation proxies and climate reanalysis data. The dataset includes five key ecosystem flux components:</p> <ul> <li> <p>Gross Primary Production (<strong>GPP</strong>)</p> </li> <li> <p>Ecosystem Respiration (<strong>RECO</strong>)</p> </li> <li> <p>Net Ecosystem Exchange (<strong>NEE</strong>)</p> </li> <li> <p>Sensible Heat Flux (<strong>H</strong>)</p> </li> <li> <p>Latent Energy Flux (<strong>LE</strong>)</p> </li> </ul> <p><strong>Ensemble Members:</strong><br>Each member combines unique satellite vegetation indices with climate datasets:</p> <ol> <li> <p>MODIS-NIRv-CFSV2</p> </li> <li> <p>MODIS-NIRv-ERA5</p> </li> <li> <p>OCO-2-CSIF-ERA5</p> </li> <li> <p>GOME-2-SIF-ERA5</p> </li> <li> <p>GOSAT-755-SIF-ERA5</p> </li> <li> <p>GOSAT-772-SIF-ERA5</p> </li> <li> <p>MODIS-NDVI-ERA5</p> </li> <li> <p>MODIS-EVI2-ERA5</p> </li> <li> <p>AVHRR-NIRv-ERA5</p> </li> <li> <p>AVHRR-NDVI-ERA5</p> </li> <li> <p>AVHRR-EVI2-ERA5</p> </li> <li> <p>MODIS-NIRv-ERA5-WY</p> </li> <li> <p>MODIS-NIRv-ERA5-NT</p> </li> </ol> <p><strong>Background and Methodology</strong><br>The <strong>Unified FLUXes (UFLUX)</strong> initiative is a data-driven, machine learning-based platform designed to upscale eddy covariance (EC) flux measurements from tower sites to the global scale. It aims to answer pressing questions about how effectively terrestrial ecosystems are managed under climate change.</p> <p>Key innovations of UFLUX include:</p> <ol> <li> <p><strong>Consistent Upscaling Framework</strong>: Harmonizes flux upscaling across spatial/temporal scales and multiple flux types (GPP, RECO, etc.) using deep decision tree-based methods, better suited than conventional neural networks for EC flux data.</p> </li> <li> <p><strong>Hybrid Explainable ML</strong>: Combines black-box ML with ecological interpretability through residual learning, offering both predictive power and new scientific insight (UFLUXv2).</p> </li> <li> <p><strong>Uncertainty Quantification</strong>: Employs sampling space completeness to assess model uncertainty in a transparent, robust manner.</p> </li> <li> <p><strong>Multisource Integration</strong>: Leverages complementary strengths of vegetation proxies (e.g., NIRv, SIF) and climate data (e.g., ERA5) to represent carbon dynamics more comprehensively than single-source approaches.</p> </li> <li> <p><strong>Superior Gap-Filling</strong>: Originally developed as a global EC flux gap-filling tool, UFLUX improves accuracy by up to 30% and reduces uncertainty by as much as 70% compared to traditional methods.</p> </li> <li> <p><strong>High Performance</strong>: Achieves strong predictive accuracy, with global-scale R² > 0.8 for RECO and ≈0.9 for GPP, while being computationally efficient enough to run on a standard laptop.</p> </li> <li> <p><strong>Community Adoption</strong>: Already used by other global upscaling projects, highlighting its reliability and impact.</p> </li> </ol> <p><strong>Applications</strong><br>UFLUX is ideal for studying the interactions between land management, climate change, and carbon fluxes, particularly in improving global estimates of GPP and RECO by addressing biases in EC measurements.</p> <p><strong>Resources</strong></p> <ul> <li><strong>UFLUX Website: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://sites.google.com/view/uflux</a></strong></li> <li> <p><strong>Code Repository</strong>: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://github.com/soonyenju/uflux</a></p> </li> <li> <p><strong>Technical & Descriptive Publication</strong>: <a href="https://doi.org/10.1080/01431161.2024.2312266" target="_new" rel="noopener">https://doi.org/10.1080/01431161.2024.2312266</a></p> </li> </ul>
Neural network ensembles and FEFF spectra for multi-modal small molecule chemical motif prediction
<p><strong>Data</strong></p> <ul> <li><strong>22-12-05-data</strong>: original molecular XANES data created from <a href="https://doi.org/10.1103/PhysRevResearch.5.013180">Ghose <em>et al.</em></a></li> <li><strong>23-04-26-ml-data</strong>: machine learning-ready data which is prepared in the format required by <a href="https://github.com/matthewcarbone/Crescendo">Crescendo</a>.</li> <li><strong>23-05-03-hp</strong>: hyper-parameter tuning results from 23-04-26-ml-data.</li> <li><strong>23-05-05-ensembles</strong>: ensemble results from 23-04-26-ml-data.</li> <li><strong>23-05-11-ml-data-CUTOFF8</strong>: a special machine learning-ready dataset constructed by a unique partitioning: only molecules with less than or equal to 8 atoms/molecule are used for training/validation, the rest are used for testing.</li> <li><strong>23-12-06_torch_models</strong>: torch only models which can be easily used independently of our ML helper repository, Crescendo. Instead, it can be used with a few lines of code found in multimodal_molecules/core.py, in our <a href="https://github.com/AI-multimodal/multimodal-molecules">GitHub respository</a>.</li> </ul> <p><strong>Funding</strong></p> <p>This research is based upon work supported by the U.S. Department of Energy, Office of Science, Office Basic Energy Sciences, under Award Number FWP PS-030. This research also used theory and computational resources of the Center for Functional Nanomaterials, which is a U.S. Department of Energy Office of Science User Facility, and the Scientific Data and Computing Center, a component of the Computational Science Initiative, at Brookhaven National Laboratory under Contract No. DE-SC0012704.</p>
Uncovering Protein Ensembles: Automated Multiconformer Model Building for X-ray Crystallography and Cryo-EM
<p>This respository corresponds to the following paper: Wankowicz et al. Uncovering Protein Ensembles: Automated Multiconformer Model Building for X-ray Crystallography and Cryo-EM (2024). These are the qFit models. MTZ and deposited models cna be downloaded from the PDB. </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.