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281 results for “community model”
Alaska 2020 update for USGS G19AP00019: Initial Development of Alaska Community Seismic Velocity Models
<p>Seismic velocity model AKEP2020 uses earthquake travel-time and ambient noise group velocity data to update Eberhart-Phillips et al. (2006: AKEP2006)</p> <p> </p> <p>The model is provided in a table: vlAKEP2020xyzltlnSFDRE.tbl.txt</p> <p>and in the simul output from velocity inversion: vlAKEP2020.out.txt</p> <p>Map plots of Vp and Vp/Vs are also provided, with lines denoting limits of adequate data.</p> <p> </p> <p>Velocity Inversion Procedure Notes for AKEP2020 model</p> <p> </p> <p>The 2006 AK model and the 2020 updated model both use Transverse Mercator coordinate transformation with central meridian= -150 and counterclockwise rotation of 162.1. Earth-flattening transformation is used for velocity during ray-tracing. The depths are relative to sea-level and station elevations are used. For the group-velocity data, the surface is taken as the 30-km median filtered topography.</p> <p>Velocity with the 3D gridded model is defined by linearly interpolating between nodes.</p> <p>A gradational inversion approach was used with earthquake and shot travel-time data, and group velocity observations for periods 6-15 s. The table provides, from the computed resolution matrix, the diagonal resolution element (DRE), and the spread function (SF), for each Vp and Vp/Vs node.</p> <p> </p> <p>This material is based upon work supported by the U.S. Geological Survey under Grant No. G19AP00019. Note that an earlier model, AKEP2018, from the first year of this funded project was reported in Eberhart-Phillips et al. (2019).<br> <br> The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the opinions or policies of the U.S. Geological Survey. Mention of trade names or commercial products does not constitute their endorsement by the U.S. Geological Survey</p>
Global atmospheric simulation using the Super-Parameterized Community Atmosphere Model
<p>A 1-month subset from a global atmospheric simulation using the Super-Parameterized Community Atmosphere Model (SP-CAM), which implements the Multi-scale Modeling Framework (MMF) described in Khairoutdinov and Randall (2001) and Khairoutdinov et al. (2005). The version of the SP-CAM used here is described in Marchand et al. (2009) and Ovtchinnikov et al (2006), and was run at Pacific Northwest National Laboratory with DOE support. It is based on CAM 3.0 for the global atmospheric component, and uses the System for Atmospheric Modeling (SAM; Khairoutdinov and Randall 2003). This simulation is configured with CAM running the finite volume dynamical core on a 2x2.5 degree latitude-longitude grid with 26 vertical levels. The embedded CRM (SAM) is configured with 64 horizontal columns at 4 km grid spacing with 24 vertical levels (sharing the bottom 24 levels with the CAM grid), and single-moment microphysics. The simulation was initialized on 1 September 1997 and runs through June 2002, forced with observed monthly-mean sea surface temperatures. Only the month of July 2000 is uploaded here, which is what is required to reproduce the results in Hillman et al. (2018).</p>
Alaska 2018 update for USGSG18AP00017: Initial Development of Alaska Community Seismic Velocity Models
<p>Seismic velocity model AKEP2018 uses earthquake travel-time and ambient noise group velocity data to update the Alaska 3-D model of Eberhart-Phillips et al. (2006: AK2006), for the USGS project on developing Alaska Community Seismic Velocity Models . This 2018 model will be expanded with additional data in 2019 in the second year of the funded project.</p> <p>Velocity within the 3D gridded model is defined by linearly interpolating between nodes. The inversion solved for Vp and Vp/Vs. The model is provided in a table: vlAKEP2018xyzltlnSFDRE.tbl.txt, with velocity at inversion nodes in cartesian and latitude-longitude coordinates. The table provides, from the computed resolution matrix, the diagonal resolution element (DRE), and the spread function (SF), for each Vp and Vp/Vs node.</p> <p>This material is based upon work supported by the U.S. Geological Survey under Grant No. G18AP00017. <br> <br> The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the opinions or policies of the U.S. Geological Survey. Mention of trade names or commercial products does not constitute their endorsement by the U.S. Geological Survey.</p>
Sunburned plankton: Ultraviolet radiation inhibition of phytoplankton photosynthesis in the Community Earth System Model version 2
<p>Climate model output for paper describing CESM2-UVphyto.</p>
Data for publication "Statistical characteristics of extreme daily precipitation during 1501 BCE - 1849 CE in the Community Earth System Model".
<p>Here, the data used in Kim, W. M., Blender, R., Sigl, M., Messmer, M., & Raible, C. C. (2021). "Statistical characteristics of extreme daily precipitation during 1501 BCE–1849 CE in the Community Earth System Model" in <em>Climate of the Past </em>(<a href="https://doi.org/10.5194/cp-2021-61">https://doi.org/10.5194/cp-2021-6</a>) are provided.</p> <p>Two simulations covering the period 1501 BCE - 2008 CE are performed with CESM 1.2.2: the orbital-only and the full-forcing simulations. The full-forcing transient simulation includes the new long record of volcanic eruptions (<a href="https://doi.org/10.1594/PANGAEA.928646">https://doi.org/10.1594/PANGAEA.928646</a>) that covers the last 3500 years. The output from the simulations is used to examine the long-term variability and characteristics of daily extreme precipitation during 1501BCE-1849 CE.</p> <p>The following files are provided:</p> <ul> <li> <strong>CESM122.transient.PRECT.anom.above99th.1501BCE-1849CE_I and II</strong>: Daily precipitation anomalies above the 99th percentiles relative to 1501BCE-1849CE from the full forcing simulation. The file is split into two parts, with the first file containing the first 50% of extremes (I) and the second file containing the rest 50% (II).</li> <li><strong>CESM122.orbital.PRECT.anom.above99th.1501BCE-1849CE I and II:</strong> Daily precipitation anomalies above the 99th percentiles relative to 1501BCE-1849CE from the orbital-only simulation.</li> <li> <strong>CESM122.transient.variables.mon.1979-2008CE:</strong> monthly precipitation, temperature, and geopotential height at 500 hPa for 1979-2008CE from the full-forcing simulation.</li> <li><strong>CESM122.trans.variable_names.years:</strong> Monthly variables from the full-forcing simulation. The simulation starts from the model year 1, which corresponds to the actual year 1501BCE. The variables are solar insolation (SOLIN), clear-sky net surface shortwave radiation (FSNSC), geopotential height at 500hPa (Z500), and surface temperature (TS).</li> <li><strong>CESM122.orbital.variable_names.years:</strong> Monthly variables from the orbital-only simulation. The simulation starts from the model year 1, which corresponds to the actual year 1501BCE.</li> <li><strong>CESM122.*.log-likelihood-GPDmodel-ExtForcing</strong>: Negative log-likelihood for the stationary and non-stationary Generalized Pareto Distribution models for external forcings.</li> <li> <strong>CESM122.*.log-likelihood-GPDmodel-ModesVar</strong>: Negative log-likelihood for the stationary and non-stationary Generalized Pareto Distribution models for modes of variability.</li> <li><strong>Evolk_EVA_distribution_1501BCE-2015CE</strong>: Distribution of volcanic aerosol for CAM5, produced based on Kim et al. (2021).</li> </ul> <p>If you use this dataset, please cite:</p> <p><em>Kim, W. M., Blender, R., Sigl, M., Messmer, M., & Raible, C. C. (2021). Statistical characteristics of extreme daily precipitation during 1501 BCE–1849 CE in the Community Earth System Model. Climate of the Past Discussions, 1-38. <a href="https://doi.org/10.5194/cp-2021-61">https://doi.org/10.5194/cp-2021-61</a></em></p>
Radiocarbon in the land and ocean components of the Community Earth System Model: data to prepare figures
<p>The files contain the data to plot the graphics displayed in the publication by Frischknecht, T., Ekici, A., Joos, F. Radiocarbon in the land and ocean components of the Community Earth System Model, Global Biogeochemical Cycles, 2022, in press.</p>
Simulation Data for "Community-Driven Code Comparisons for Three-Dimensional Dynamic Modeling of Sequences of Earthquakes and Aseismic Slip"
<p>Simulation data from Jiang et al. (2022), "Community-Driven Code Comparisons for Three-Dimensional Dynamic Modeling of Sequences of Earthquakes and Aseismic Slip," <em>Journal of Geophysical Research: Solid Earth</em><em>.</em></p> <p>The archive includes simulation data for 3D SEAS benchmarks BP4-QD and BP5-QD that are analyzed in our paper (descriptions in NOTES.txt) </p> <p><strong>BP4-QD Benchmark Simulations:</strong><br>1000 m: jiang.5, lambert.8, barbot.3, barbot.2, dliu.2, li.4<br>500 m: jiang.3, lambert.3, barbot.5, barbot.7, ozawa</p> <p><strong>BP5-QD Benchmark Simulations:</strong><br>2000 m: jiang.6, lambert.8, liu.4, cattania.5, dli.7, barbot.3, dliu.10, li.3<br>1000 m: jiang.2, lambert.7, liu.5, cattania.3, ozawa, dli.5, barbot, dliu.6, li.2<br>500 m: jiang.4, lambert.9, liu.6, cattania.4, ozawa.2, dli.6, barbot.2, dliu.8<br>250 m: lambert.10, liu.7</p> <p><strong>BP5-QD with Off-Fault Data:</strong><br>1000 m: lambert.7, dli.5, barbot, dliu.6, li.2<br>500 m: lambert.9, dli.6, barbot.2, dliu.8</p> <p>Tables 2–4 in our paper summarizes details of numerical codes and selected simulations.</p> <p>The benchmark descriptions and the full suite of simulation data are available at SEAS online platform https://strike.scec.org/cvws/seas/.</p>
SCEC Community Fault Model (CFM)
<h1>Introduction</h1> <p>The Statewide California Earthquake Center (SCEC) Community Fault Model (CFM) is an object-oriented, fully three-dimensional geometric representation of active faults in California and adjacent offshore basins. For each fault object, the CFM provides triangulated surface representations (t-surfs) in several resolutions, fault traces in several different file formats (shape files, GMT plain text, and GoogleEarth kml), and complete metadata including references used to constrain the surfaces. The CFM faults are defined based on available data including surface traces, seismicity, seismic reflection profiles, well data, geologic cross sections, and various other types of data and models. The CFM serves SCEC as a unified resource for physics-based fault systems modeling, strong ground-motion prediction, probabilistic seismic hazards assessment (e.g., the USGS National Seismic Hazard Model), and many other uses. Together with the Community Velocity Model (CVM-H 15.1.0), the CFM comprises SCEC's Unified Structural Representation of the Southern California crust and upper mantle (Shaw et al., 2015).</p> <h1>Current Model Version: CFM 7.0</h1> <p>The current version of the SCEC CFM is version 7.0 (CFM 7.0), which builds on the previous CFM releases and serves as the latest update to Plesch et al. (2007). CFM 7.0 is a significant update as this is the first CFM to cover the entire state of California, spanning the Pacific-North American plate boundary from northern Mexico to the southern Cascadia subduction zone. This latest version has no changes to the southern California portion of the model, but now includes 113 new fault representations in central and northern California in the preferred model. These new central and northern California fault representations will undergo a community evaluation in 2024-2025, therefore, the central and northern California faults should be considered preliminary representations.</p> <p>CFM 7.0 contains three fully-documented sub models: preferred, ruptures, and alternatives. In total, CFM 7.0 comprises the following components: </p> <ol> <li> <p><strong>CFM 7.0 Preferred</strong>: A set of 556 fault objects that constitute the preferred set of active faults. These faults have attained preferred status based on past community evaluations or are new representations.</p> </li> <li> <p><strong>CFM 7.0 Ruptures</strong>: A set of 13 fault objects assembled from the CFM 7.0 preferred model that ruptured during selected significant historic events. These are not earthquake source models, but are representations of the entire fault surfaces where a significant historic rupture occurred. This model is intended to indicate which CFM fault objects were involved with selected significant historic ruptures.</p> </li> <li> <p><strong>CFM 7.0 Alternatives</strong>: A set of 39 alternative representations where structural differences have been proposed that could potentially significantly impact fault mechanics and associated seismic hazards. These alternative representations were selected based on community rankings following a comprehensive evaluation of the CFM that took place in May of 2022.</p> </li> </ol> <p>Including all sub models, the CFM 7.0 incorporates 608 fully-documented fault objects. If you use the CFM, we would appreciate you citing both Plesch et al. (2007) and the DOI where the archive is stored.</p> <h1>Directory Structure and Contents of the CFM Archive</h1> <p>The CFM archive directory structure is as follows:</p> <p><strong>doc/</strong><br>Documentation and metadata, which include an MS Excel spreadsheet with detailed metadata about each fault surface. Metadata for the preferred, rupture, and alternative models are provided in separate but otherwise identically formatted sheets within the file. All faults contain references to the works that helped to define the 3D fault surface geometry. More information about the metadata columns is provided in doc/README.txt</p> <p><strong>obj/preferred/</strong><br><strong>obj/ruptures/</strong><br><strong>obj/alternatives/</strong><br>These directories contain the model components for the preferred, rupture, and alternative models, respectively. Each model contains an identical directory structure, which is described below using the preferred model as an example.</p> <p><strong>obj/preferred/native/</strong><br>The CFM preferred fault surfaces in gocad tsurf format using the native mesh. The native mesh uses a variable mesh resolution. Smaller triangles generally indicate where a fault is well-constrained by data. All tsurf files are provided in UTM zone 11 using the NAD27 datum (EPSG:26711).</p> <p><strong>obj/preferred/500m/</strong><br>The CFM preferred fault surfaces with a semi-regularized mesh of ~500m resolution in gocad tsurf format. All tsurf files are provided in UTM zone 11 using the NAD27 datum (EPSG:26711).</p> <p><strong>obj/preferred/1000m/</strong><br>The CFM preferred fault surfaces with a semi-regularized mesh of ~1000m resolution in gocad tsurf format. All tsurf files are provided in UTM zone 11 using the NAD27 datum (EPSG:26711).</p> <p><strong>obj/preferred/2000m/</strong><br>The CFM preferred fault surfaces with a semi-regularized mesh of ~2000m resolution in gocad tsurf format. All tsurf files are provided in UTM zone 11 using the NAD27 datum (EPSG:26711).</p> <p><strong>obj/preferred/traces/</strong><br>Fault traces and upper tip lines (for blind faults) of the CFM preferred faults. While the CFM is a 3D model, it is often useful to make map-based visualizations of the model. The traces and blind faults are provided in several different formats described below.</p> <p><strong>obj/preferred/traces/gmt/<br></strong>Fault traces and blind faults in Generic Mapping Tools multiple segment file ASCII format (i.e., plain text).<br> .lonLat - Longitude/Latitude coordinates (WGS84 datum)<br> .utm - UTM zone 11 NAD27 datum (EPSG:26711)</p> <p><strong>obj/preferred/traces/kml/</strong><br>Fault traces and blind faults in Google Earth .kml format (WGS84 datum). The kml files also contain selected metadata as attributes which can be imported into QGIS. When a fault trace is clicked on in the Google Earth interface, a mini-webpage with metadata information will pop up.</p> <p><strong>obj/preferred/traces/shp/</strong><br>Fault traces and blind faults in GIS shapefile format (longitude/latitude coordinates, WGS84 datum).</p> <h1>CFM Contributors</h1> <p>The current and past versions of the CFM would not be possible without contributions from numerous SCEC community members. We would like to thank the following CFM contributors:</p> <p>Christine Benson, <a href="https://central.scec.org/user/bbryant">William Bryant</a>, <a href="https://central.scec.org/user/scarena">Sara Carena</a>, <a href="https://central.scec.org/user/cooke">Michele Cooke</a>, <a href="https://central.scec.org/user/dolan">James Dolan</a>, <a href="https://central.scec.org/user/jessaroni">Jessica Don</a>, <a href="https://central.scec.org/user/fuis">Gary Fuis</a>, <a href="https://central.scec.org/user/gath">Eldon Gath</a>, Russell Graymer, <a href="https://central.scec.org/user/jhubbard">Judith Hubbard</a>, <a href="https://central.scec.org/user/sjanecke">Susanne Janecke</a>, Sam Johnson, <a href="https://central.scec.org/user/ylevy">Yuval Levy</a>, <a href="https://central.scec.org/user/lgrant">Lisa Grant Ludwig</a>, <a href="https://central.scec.org/user/hauksson">Egill Hauksson</a>, <a href="https://central.scec.org/user/tjordan">Thomas Jordan</a>, <a href="https://central.scec.org/user/marc">Marc Kamerling</a>, Keith Knudsen, <a href="https://central.scec.org/user/mrlegg">Mark Legg</a>, <a href="https://central.scec.org/user/lindvall">Scott Lindvall</a>, <a href="https://central.scec.org/user/harold">Harold Magistrale</a>, James Lienkaemper, <a href="https://central.scec.org/user/marshallst">Scott Marshall</a>, <a href="https://central.scec.org/user/nicholson">Craig Nicholson</a>, <a href="https://central.scec.org/user/niemi">Nathan Niemi</a>, Stu Nishenko, <a href="https://central.scec.org/user/oskin">Michael Oskin</a>, <a href="https://central.scec.org/user/perry">Sue Perry</a>, <a href="https://central.scec.org/user/planansky">George Planansky</a>, <a href="https://central.scec.org/user/plesch">Andreas Plesch</a>, <a href="https://central.scec.org/user/rockwell">Thomas Rockwell</a>, David Schwartz, <a href="https://central.scec.org/user/jshaw">John Shaw</a>, <a href="https://central.scec.org/user/pshearer">Peter Shearer</a>, Bob Simpson, <a href="https://central.scec.org/user/sorlien">Christopher Sorlien</a>, M. Peter Süss, <a href="https://central.scec.org/user/suppe">John Suppe</a>, <a href="https://central.scec.org/user/treiman">Jerry Treiman</a>, Jeff Unruh, Janet Watt, <a href="https://central.scec.org/user/wolfe_franklin">Franklin Wolfe</a>, Chris Wills, <a href="https://central.scec.org/user/yeats">Robert Yeats</a>, and every colleague that has participated in a CFM community evaluation. We could not make the CFM without this community effort.</p> <h1>CFM Evaluators</h1> <p>Before assembling CFM 6.0 and subsequently CFM 7.0, a team of SCEC colleagues participated in a rigorous evaluation of CFM 5.3 in April-May of 2022. This evaluation was open to the SCEC community and focused on 23 critical fault representations where different proposed interpretations have the potential to significantly affect seismic hazards. This evaluation resulted in 14 new fault representations in the CFM 6.0 preferred model. The lower ranked representations are now provided in the CFM alternatives. We would like to thank the following CFM evaluators for volunteering their time and expertise to this process:</p> <p><a href="https://central.scec.org/user/sakciz">Sinan Akçiz</a>, <a href="https://central.scec.org/user/scarena">Sara Carena</a>, <a href="https://central.scec.org/user/cooke">Michele Cooke</a>, <a href="https://central.scec.org/user/dawson">Tim Dawson</a>, <a href="https://central.scec.org/user/jessaroni">Jessica Don</a>, <a href="https://central.scec.org/user/ajelliott">Austin Elliot</a>, <a href="https://central.scec.org/user/frost">Erik Frost</a>, <a href="https://central.scec.org/user/fuis">Gary Fuis</a>, <a href="https://central.scec.org/user/aganas">Athanassios Ganas</a>, <a href="https://central.scec.org/user/gath">Eldon Gath</a>, <a href="https://central.scec.org/user/alexhatem">Alex Hatem</a>, <a href="https://central.scec.org/user/sjanecke">Susanne Janecke</a>, <a href="https://central.scec.org/user/marc">Marc Kamerling</a>, <a href="https://central.scec.org/user/christos">Christodoulos Kyriakopoulos</a>, <a href="https://central.scec.org/user/mrlegg">Mark Legg</a>, <a href="https://central.scec.org/user/kluttrell">Karen Luttrell</a>, <a href="https://central.scec.org/user/madden">Chris Madugo</a>, <a href="https://central.scec.org/user/marshallst">Scott Marshall</a>, <a href="https://central.scec.org/user/meigsa">Andrew Meigs</a>, <a href="https://central.scec.org/user/nicholson">Craig Nicholson</a>, <a href="https://central.scec.org/user/nonderdo">Nate Onderdonk</a>, <a href="https://central.scec.org/user/absrp">Alba Rodríguez Padilla</a>, <a href="https://central.scec.org/user/plesch">Andreas Plesch</a>, <a href="https://central.scec.org/user/scharer">Kate Scharer</a>, <a href="https://central.scec.org/user/jshaw">John Shaw</a>, <a href="https://central.scec.org/user/sorlien">Chris Sorlien</a>, <a href="https://central.scec.org/user/wolfe_franklin">Franklin Wolfe</a>, <a href="https://central.scec.org/user/yule">Doug Yule</a>, <a href="https://central.scec.org/user/jzachariasen">Judy Zachariasen</a>.</p> <p> </p>
Using Hydroclimate Modeling and Social Science to Enhance Flood Resilience on Lake Ontario through the Climate Smart Communities Program
<p>This repository contains several data products associated with the New York Sea Grant project R/CHD-15 entitled <em>Using Hydroclimate Modeling and Social Science to Enhance Flood Resilience on Lake Ontario through the Climate Smart Communities Program.</em><strong><em> </em></strong>These products include:</p> <p>1. Estimates of the 25-year, 50-year, and 100-year flood across the New York coastline of Lake Ontario. These design events (reported in feet) are for still water levels that take into account both average water levels across the lake as well as local variations in water level due to storm surge. Wave setup and wave run-up are not considered in these design events. The design events incorporate the effects of water level regulation and the potential impacts of climate change on water supplies to Lake Ontario, and they are tailored for 79 unique locations along the shoreline (identified based on longitude and latitude). These flood levels are presented in an online flood risk assessment tool at: https://kts48.users.earthengine.app/view/lake-ontario-water-level-scenarios</p> <p>2. Protocols and summary of results for a series of focus groups and structured telephone interviews with local officials from communities along the Lake Ontario shoreline to assess barriers to participation in the New York State Climate Smart Communities Program.</p> <p>3. A Crosswalk between activities and administrative requirements of the New York State Climate Smart Communities Program and other federal and state flood resiliency programs. </p> <p>4. A final report summarizing the products above. </p>
Datasets for figures in Implementation and evaluation of Wet Bulb Globe Temperature within non-urban environments in the Community Land Model version 5
<p>The files contain 4 scripts and 6 netcdf files. </p> <p>"laborCap_200400.ncl" uses "Lancet_LRF.nc" to create Figure 1.</p> <p>Script "world_plot_ensemble_Avg.I2000.csh", drives a NCL script, "plot_modern.I2000.WBGT.ncl" to make figures 3 and 4, using the netcdf files, "I2000_PR_22_x1_60_5.exceed.WBGT.20yrs.75_99.nc," "I2000_PR_22_x1_60_5.exceed.WBGT_BG_R.20yrs.75_99.nc," "I2000_PR_22_x1_60_5.exceed.WBGT_BC_R.20yrs.75_99.nc," and "I2000_PR_22_x1_60_5.exceed.WBGT_AC_R.20yrs.75_99.nc."</p> <p>"heatmap.wbgt.v4.org.ncl" uses netcdf "I2000_PR_23_Chicago_x1_60_1.11-17.Chicago.allvars.nc" to create figures 5-7. </p>
Greenland Ice Sheet modeled firn properties from SNOWPACK and the Community Firn Model (1980-2020)
<p>This dataset contains model output from the physics-based SNOWPACK firn model and the semi-empirical Community Firn Model (CFM) over the Greenland Ice Sheet from 1980 through 2020. Included are individual density profiles for locations with firn density observations as well as firn air content (FAC) calculated over different depth intervals. Data for both models are supplied. These data are used in a manuscript to be submitted to The Cryosphere journal (see Thompson-Munson et al., in review).</p>
Genome-scale community modelling reveals key metabolic cross-feedings in epipelagic bacterioplankton communities (Supplementary Materials)
<p>A comprehensive catalog of 19,791 marine prokaryotic isolates (WGS), single-amplified genomes (SAGs) and metagenomic-assembled genomes (MAGs) compiled from MarRef v4.0 (N=943, mostly high-quality WGS), MarDB v4.0 (N=12,963), and the aquatic representative genomes from the ProGenomes database v1.0 (N=566). This collection of well-documented genomes was complemented by 5,319 MAGs assembled from four distinct studies, namely: Parks et al. 2017 (<a href="https://doi.org/10.1038/s41564-017-0012-7">DOI</a>; N=1,765; downloaded from EBI), Tully et al. 2017/2018 (<a href="https://doi.org/10.7717/peerj.3558">DOI</a> and <a href="https://10.1038/sdata.2017.203">DOI</a>; N=2,597; downloaded from EBI), and Delmont et al. 2018 (<a href="https://doi.org/10.1038/s41564-018-0176-9">DOI</a>; N=957; downloaded from FIGSHARE). The Parks et al. study contained genomes reconstructed from non-marine biomes. Thus, a selection of 1,765 genomes was extracted by searching for specific keywords: “tara|marine|sea|ocean|mediterranean” (case insensitive). Note that depending on their study of origin, included MAGs may have been reconstructed using different assembling and binning methods.</p> <p>The archive includes:</p> <ul> <li>a metadata file describing the quality and redundancy of the genomes named `EcoSysMic_metadata.tsv`</li> <li>sequences of the 19,791 (redundant) genomes in `All/WGS`</li> <li>companion files in `All/Data` and `dRep95/Data` (see Methods in the associated paper), including <ul> <li>predicted CDS and EggNOG functional annotations</li> <li>predicted GTDB taxonomy</li> <li>CarveMe reconstructed metabolic models and their MEMOTE quality</li> </ul> </li> </ul> <p>The 7,658 non-redundant species-level genomes (delineated by a 95% ANI threshold over 60% of genome length) that were used in the associated paper are defined by the column `is_drep95` in the metadata file.</p>
Models simulating abrupt changes in the Chilika lagoon fishery, the Easter Island community, forest dieback and lake water quality
<p>This deposit is in support of Willcock et al "Earlier collapse of Anthropocene ecosystems driven by multiple faster and noisier drivers". It covers the following items: (i) A list of the files contained within this data deposit; (ii) How to access and download the specialist software required to view and simulate the system dynamics models (STELLA ‘isee Player’); (iii) How to run isee Player to simulate the models; (iv) How to access and download the standard statistical software ‘R’ to run the R scripts; (v) How to load ‘R’ and modify the standard R script to analyse a subset of the model runs. This file will also details the ‘required content’ (e.g. software versions), as specified in the ‘nr-software-policy.pdf’ document.</p> <p>The full descriptions of each of the four system dynamics models used in this manuscript can be read in the following papers:</p> <ol> <li>Lake Chilika – Cooper, G. S. & Dearing, J. A. Modelling future safe and just operating spaces in regional social-ecological systems. <em>Sci. Total Environ.</em> <strong>651</strong>, 2105–2117 (2019), <a href="https://doi.org/10.1016/j.scitotenv.2018.10.118">https://doi.org/10.1016/j.scitotenv.2018.10.118</a></li> <li>Easter Island – Brandt, G. & Merico, A. The slow demise of Easter Island: Insights from a modeling investigation. <em>Front. Ecol. Evol.</em> <strong>3</strong>, 13 (2015), <a href="https://www.frontiersin.org/article/10.3389/fevo.2015.00013">https://www.frontiersin.org/article/10.3389/fevo.2015.00013</a></li> <li>Lake phosphorus – Wang, R. <em>et al.</em> Flickering gives early warning signals of a critical transition to a eutrophic lake state. <em>Nature</em> <strong>492</strong>, 419–22 (2012), <a href="http://dx.doi.org/10.1038/nature11655">http://dx.doi.org/10.1038/nature11655</a></li> <li>TRIFFID - Ritchie, P. D. L., Clarke, J. J., Cox, P. M. & Huntingford, C. Overshooting tipping point thresholds in a changing climate. <em>Nat. 2021 5927855</em> <strong>592</strong>, 517–523 (2021), <a href="http://dx.doi.org/10.1038/nature11655">http://dx.doi.org/10.1038/nature11655</a></li> </ol>
Model output for five Hmsc models of alpine grassland communities
<p>Model output from five joint species distribution models made with Hmsc in R. One 'global' model with all data, and one model for each of four sites Skjellingahaugen, Gudmedalen, Låvisdalen, and Ulvehaugen.<br> <br> Omegas are species co-occurrence estimates.</p> <p>Models defined by EL, OO, data formatted by EL, model fit by OO.</p> <p>Scripts for model fitting and presenting output are not published here but follow the generic Hmsc pipeline as published in Ovaskainen & Abrego (2020). Joint Species Distribution Modelling With Applications in R. Cambridge university press. DOI: <a href="https://doi.org/10.1017/9781108591720">https://doi.org/10.1017/9781108591720</a></p> <p> </p>
Data and code for gmd-2023-113 "Parameter estimation for ocean background vertical diffusivity coefficients in the Community Earth System Model (v1.2.1) and its impact on ENSO forecast"
<p>Data and code for the paper "Parameter estimation for ocean background vertical diffusivity coefficients in the Community Earth System Model (v1.2.1) and its impact on ENSO forecast"</p> <p>includes: </p> <p>The model is Community Earth System Model (v1.2.1) (provided by www.cesm.ucar.edu)</p> <p>Data assimilation code is initially provided by Data Assimilation Research Testbed (DART) (https://dart.ucar.edu/), some modifications are made to enable parameter estimation function of ocean background vertical diffusivity coefficients. And the programs and scripts for deal with OISST and EN4 profiles are also developed.</p> <p>The parameter sensitivity experiment results are saved as <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/sensitive2008-2012.nc">sensitive2008-2012.nc</a> and <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/sensitive2008-2012salt.nc">sensitive2008-2012salt.nc</a> for temperature and salinity, respectively. And the python script to draw the results is </p> <p>The state estimation results are provided as <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/Temp_05-17.nc">Temp_05-17.nc</a> and <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/Temp_05-17.nc">Salt_05-17.nc</a> for temperature and salinity, respectively.</p> <p>The parameter estimation results are provided as <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/PE_Temp_05-17.nc">PE_Temp_05-17.nc</a> and <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/PE_Temp_05-17.nc">PE_Salt_05-17.nc</a> for temperature and salinity, respectively.</p> <p>the estimated paremeter ensemble is saved in <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/parameters.nc">parameters.nc</a></p> <p>the python script for comparing the SE and PE results is <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/plot_analysis.py">plot_analysis.py</a></p> <p>the nino3.4 indices computed by the forecast experiment is saved in <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/fcst_correlation.nc">fcst_correlation.nc</a></p> <p> </p>
SCEC Community Thermal Model (CTM)
<p>The CTM provides estimates of temperatures and thermal properties of the southern California lithosphere. It is shared as a download archive with data and tools organized into three folders: components and metadata, a Google Colab notebook query tool with associated files, and an alternative (Shinevar et al., 2018) thermal model. README files in each directory describe the contents in detail. </p> <p>Please see <a href="https://www.scec.org/research/ctm">https://www.scec.org/research/ctm</a> for more information.</p>
Accounting for environmental variation in co‐occurrence modelling reveals the importance of positive interactions in root‐associated fungal communities
<p>Understanding the role of interspecific interactions in shaping ecological communities is one of the central goals in community ecology. In fungal communities, measuring interspecific interactions directly is challenging because these communities are composed of large numbers of species, many of which are unculturable. An indirect way of assessing the role of interspecific interactions in determining community structure is to identify the species co-occurrences that are not constrained by the environmental conditions. In this study, we investigated co-occurrences among root-associated fungi, asking whether fungi co-occur more or less strongly than expected based on the environmental conditions and the host plant species examined. For this purpose, we generated molecular data on root-associated fungi of five plant species evenly sampled along an elevational gradient at a high Arctic site. We analysed the data using a joint species distribution modelling approach that allowed us to identify those co-occurrences that could be explained by the environmental conditions and the host plant species, as well as those co-occurrences that remained unexplained and thus more likely reflect interactive associations. Our results indicate that positive interactions play an important role in shaping microbial communities in arctic plant roots. In particular, we found that mycorrhizal fungi are especially prone to positively co-occur with other fungal species. Our results bring new understanding to the structure of arctic interaction networks by suggesting that interactions among root-associated fungi are predominantly positive.</p>
Business Models in Energy Communities: an analysis through legal lenses
<p>This research explores energy communities (EC) and their business models’ attributes. We develop a conceptual framework, which combines and extends the social, economic, environmental, and technological dimensions of value generation to include the legal dimension. The latter has been considered only implicitly in previous studies on this sector. Applying this framework to forty business cases of energy communities allows to identify six business model (BM) archetypes representative of ECs. This study can encourage and support new ventures in this sector to model their strategy and comply with the requirements.</p>
Dataset - Spatio-temporal modeling of the crowding conditions and metabolic variability in microbial communities
<p><strong>Dataset simulated for the manuscript "Spatio-temporal modeling of the crowding conditions and metabolic variability in microbial communities" by Angeles-Martinez and Hatzimanikatis.</strong></p>
Code and data for Bayesian joint species distribution model selection for community-level prediction
<p>Code and data for reproducing the analysis in the manuscript "Bayesian joint species distribution model selection for community-level prediction." Provided data include percent cover observations for 39 modeled vascular plant species within boreal forest understory communities and environmental model covariates. R code is provided to generate model inputs, apply alternative models, generate out-of-sample predictions, and calculate associated community and species log scores and alternative model evaluation metrics. Further, R source code is provided to implement the multinomial joint species distribution model defined in the manuscript. Details on the data, its processing, and the alternative model definitions and structure can be found in the main text of the manuscript. Provided data are currently being used in ongoing analyses and coordination with authors may be warranted to avoid duplicate publication. Potential users are encouraged to consider collaboration with authors when useful and appropriate. Misinterpretation of data may occur if used outside the context of the original analysis. All data are made available in their current state. While significant efforts have been made to ensure data accuracy, complete accuracy cannot be guaranteed. Data may be updated periodically. It is the responsibility of the data user to check for updated versions of the data.</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.