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187 results for “TIDE”
NEMO, HIDRA and Tide Gauge Datasets for HIDRA Machine Learning Algorithm Verification
<p>Supporting sea level datasets for paper:</p> <p>"HIDRA 1.0: Deep-Learning-Based Ensemble Sea Level Forecastingin the Northern Adriatic"</p> <p>by Lojze Žust, Anja Fettich, Matej Kristan, and Matjaž Ličer</p>
The sensitivity of British weather to ocean tides.
<p>Data to reproduce the figures in the publication: "The sensitivity of British weather to ocean tides."</p> <p> </p> <p>Details on contents of each file are provided in the filename.</p> <p>An explanation of the variable names:</p> <p>votemper_top = sea surface temperature (SST)</p> <p>votemper_bot = sea bed temperature (SBT)</p> <p>strat = (SST – SBT)</p> <p>ATM_SHELF250_inv_mask.nc: a mask on the atmospheric grid used to define which areas lie over where the bathymetry is over 250m and over land.</p> <p>ATM_shelf_narrow_ESW_mask.nc: a mask on the atmospheric grid used to define the 'GB' area.</p>
Dataset: Thermal tides in the upper cloud layer of Venus as deduced from the emission angle dependence of the brightness temperature by Akatsuki/LIR
<p>The LIR images (Level 2c) used in the figures in the paper "Thermal tides in the upper cloud layer of Venus as deduced from the emission angle dependence of the brightness temperature by Akatsuki/LIR" by Akiba et al. (J. Geophys. Res)</p> <p>The description of Akatsuki/LIR and level definition are in <a href="https://darts.isas.jaxa.jp/planet/project/akatsuki/lir.html.en">https://darts.isas.jaxa.jp/planet/project/akatsuki/lir.html.en</a></p>
GeoJSON files for the MCSC's Trucking Industry Decarbonization Explorer (Geo-TIDE)
<h1>Summary</h1> <p>Geojson files used to visualize geospatial layers relevant to identifying and assessing trucking fleet decarbonization opportunities with the MIT Climate & Sustainability Consortium's Geospatial Trucking Industry Decarbonization Explorer (Geo-TIDE) tool.</p> <h1>Relevant Links</h1> <p>Link to the <a href="https://climatedata.mit.edu/faf5/transportation/">online version of the tool</a> (requires creation of a free user account).</p> <p><a href="https://github.com/mcsc-impact-climate/FAF5-Analysis">Link to GitHub repo</a> with source code to produce this dataset and deploy the Geo-TIDE tool locally.</p> <h1>Funding </h1> <p>This dataset was produced with support from the MIT Climate & Sustainability Consortium.</p> <h1>Original Data Sources</h1> <p>These geojson files draw from and synthesize a number of different datasets and tools. The original data sources and tools are described below:</p> <table> <tbody> <tr> <td><strong>Filename(s)</strong></td> <td><strong>Description of Original Data Source(s)</strong></td> <td><strong>Link(s) to Download Original Data<br></strong></td> <td><strong>License and Attribution for Original Data Source(s)</strong></td> </tr> <tr> <td> <p>faf5_freight_flows/*.geojson</p> <p>trucking_energy_demand.geojson</p> <p>highway_assignment_links_*.geojson</p> <p>infrastructure_pooling_thought_experiment/*.geojson</p> </td> <td> <p>Regional and highway-level freight flow data obtained from the <a href="https://faf.ornl.gov/faf5/">Freight Analysis Framework Version 5</a>. Shapefiles for FAF5 region boundaries and highway links are obtained from the <a href="https://geodata.bts.gov/search?collection=Dataset">National Transportation Atlas Database</a>. Emissions attributes are evaluated by incorporating data from the <a href="https://rosap.ntl.bts.gov/view/dot/42632/dot_42632_DS2.zip">2002 Vehicle Inventory and Use Survey</a> and the <a href="https://greet.anl.gov/">GREET lifecycle emissions tool</a> maintained by Argonne National Lab.</p> </td> <td> <p><a href="https://geodata.bts.gov/datasets/usdot::freight-analysis-framework-faf5-regions">Shapefile for FAF5 Regions</a></p> <p><a href="https://geodata.bts.gov/datasets/usdot::freight-analysis-framework-faf5-network-links">Shapefile for FAF5 Highway Network Links</a></p> <p><a href="https://faf.ornl.gov/faf5/data/download_files/FAF5.5.1_2018-2022.zip">FAF5 2022 Origin-Destination Freight Flow database</a></p> <p><a href="https://ops.fhwa.dot.gov/freight/freight_analysis/faf/faf_highway_assignment_results/FAF5_2022_HighwayAssignmentResults_04_07_2022.zip">FAF5 2022 Highway Assignment Results</a></p> <p> </p> </td> <td> <p><strong>Attribution for Shapefiles:</strong> United States Department of Transportation Bureau of Transportation Statistics National Transportation Atlas Database (NTAD). Available at: https://geodata.bts.gov/search?collection=Dataset. </p> <p><strong>License for Shapefiles:</strong> This NTAD dataset is a work of the United States government as defined in 17 U.S.C. § 101 and as such are not protected by any U.S. copyrights. This work is available for unrestricted public use.</p> <p><strong>Attribution for Origin-Destination Freight Flow database:</strong> <a href="https://www.ornl.gov/ntrc/" target="_blank" rel="noopener">National Transportation Research Center</a> in the <a href="https://www.ornl.gov/" target="_blank" rel="noopener">Oak Ridge National Laboratory</a> with funding from the <a href="https://www.bts.gov/" target="_blank" rel="noopener">Bureau of Transportation Statistics</a> and the <a href="https://www.fhwa.dot.gov/" target="_blank" rel="noopener">Federal Highway Administration</a>. Freight Analysis Framework Version 5: Origin-Destination Data. Available from: https://faf.ornl.gov/faf5/Default.aspx. Obtained on Aug 5, 2024. In the public domain. </p> <p><strong>Attribution for the 2022 Vehicle Inventory and Use Survey Data:</strong> United States Department of Transportation Bureau of Transportation Statistics. Vehicle Inventory and Use Survey (VIUS) 2002 [supporting datasets]. 2024. https://doi.org/10.21949/1506070 </p> <p><strong>Attribution for the GREET tool (original publication):</strong> Argonne National Laboratory Energy Systems Division Center for Transportation Research. GREET Life-cycle Model. 2014. Available from <a href="https://greet.anl.gov/files/greet-model&ved=2ahUKEwiAuryGsd6HAxVMFlkFHaafHNUQFnoECBUQAQ&usg=AOvVaw29kokx-ZurrfBFsjji9UM2">this link</a>.</p> <p><strong>Attribution for the GREET tool (2022 updates):</strong> Wang, Michael, et al. Summary of Expansions and Updates in GREET® 2022. United States. https://doi.org/10.2172/1891644</p> </td> </tr> <tr> <td>grid_emission_intensity/*.geojson</td> <td> <p>Emission intensity data is obtained from the <a href="https://www.epa.gov/egrid/download-data">eGRID database</a> maintained by the United States Environmental Protection Agency.</p> <p>eGRID subregion boundaries are obtained as a shapefile from the <a href="https://www.epa.gov/egrid/egrid-mapping-files">eGRID Mapping Files</a> database.</p> </td> <td> <p><a href="https://www.epa.gov/system/files/documents/2024-01/egrid2022_data.xlsx">eGRID database</a></p> <p><a href="https://www.epa.gov/system/files/other-files/2024-05/egrid2022_subregions_shapefile.zip">Shapefile with eGRID subregion boundaries</a></p> </td> <td> <p><strong>Attribution for eGRID data: </strong>United States Environmental Protection Agency: eGRID with 2022 data. Available from https://www.epa.gov/egrid/download-data. In the public domain.</p> <p><strong>Attribution for shapefile:</strong> United States Environmental Protection Agency: eGRID Mapping Files. Available from https://www.epa.gov/egrid/egrid-mapping-files. In the public domain.</p> </td> </tr> <tr> <td> <p>US_elec.geojson</p> <p>US_hy.geojson</p> <p>US_lng.geojson</p> <p>US_cng.geojson</p> <p>US_lpg.geojson</p> </td> <td>Locations of direct current fast chargers and refueling stations for alternative fuels along U.S. highways. Obtained directly from the <a href="https://afdc.energy.gov/corridors">Station Data for Alternative Fuel Corridors</a> in the Alternative Fuels Data Center maintained by the United States Department of Energy Office of Energy Efficiency and Renewable Energy. </td> <td> <p><a href="https://developer.nrel.gov/api/alt-fuel-stations/v1.geojson?access=public&status=E&country=US&download=true&utf8_bom=true&api_key=srJv3MBMvbZaDHrSssCEnhPK4IijuOLTsvs6l35L&fuel_type=ELEC&ev_charging_level=dc_fast&ev_connector_type=J1772COMBO&beta_min_j1772combo_150plus_port_count=4&response_format=beta_dot_corridors">US_elec.geojson</a></p> <p><a href="https://developer.nrel.gov/api/alt-fuel-stations/v1.geojson?access=public&status=E&country=US&download=true&utf8_bom=true&api_key=srJv3MBMvbZaDHrSssCEnhPK4IijuOLTsvs6l35L&fuel_type=HY&hy_is_retail=true">US_hy.geojson</a></p> <p><a href="https://developer.nrel.gov/api/alt-fuel-stations/v1.geojson?access=public&status=E&country=US&download=true&utf8_bom=true&api_key=srJv3MBMvbZaDHrSssCEnhPK4IijuOLTsvs6l35L&fuel_type=LNG">US_lng.geojson</a></p> <p><a href="https://developer.nrel.gov/api/alt-fuel-stations/v1.geojson?access=public&status=E&country=US&download=true&utf8_bom=true&api_key=srJv3MBMvbZaDHrSssCEnhPK4IijuOLTsvs6l35L&fuel_type=CNG&cng_fill_type=Q&cng_psi=3600">US_cng.geojson</a></p> <p><a href="https://developer.nrel.gov/api/alt-fuel-stations/v1.geojson?access=public&status=E&country=US&download=true&utf8_bom=true&api_key=srJv3MBMvbZaDHrSssCEnhPK4IijuOLTsvs6l35L&fuel_type=LPG&lpg_include_secondary=false">US_lpg.geojson</a></p> </td> <td> <p><strong>Attribution:</strong> U.S. Department of Energy, Energy Efficiency and Renewable Energy. Alternative Fueling Station Corridors. 2024. Available from: <a href="https://afdc.energy.gov/corridors" target="_new" rel="noreferrer">https://afdc.energy.gov/corridors</a>. In the public domain. </p> <p> </p> <p>These data and software code ("Data") are provided by the National Renewable Energy Laboratory ("NREL"), which is operated by the Alliance for Sustainable Energy, LLC ("Alliance"), for the U.S. Department of Energy ("DOE"), and may be used for any purpose whatsoever.</p> </td> </tr> <tr> <td>daily_grid_emission_profiles/*.geojson</td> <td> <p>Hourly emission intensity data obtained from <a href="https://www.electricitymaps.com/data-portal/united-states-of-america">ElectricityMaps</a>.</p> </td> <td> <p>Original data can be downloaded as csv files from the <a href="https://www.electricitymaps.com/data-portal/united-states-of-america">ElectricityMaps United States of America database</a></p> <p><a href="https://raw.githubusercontent.com/electricitymaps/electricitymaps-contrib/master/web/geo/world.geojson">Shapefile with region boundaries used by ElectricityMaps</a></p> </td> <td> <p><strong>License:</strong> <a href="https://opendatacommons.org/licenses/odbl/" target="_blank" rel="noopener">Open Database License (ODbL)</a>. Details here: https://www.electricitymaps.com/data-portal</p> <p><strong>Attribution for csv files:</strong> Electricity Maps (2024). United States of America 2022-23 Hourly Carbon Intensity Data (Version January 17, 2024). Electricity Maps Data Portal. https://www.electricitymaps.com/data-portal.</p> <p><strong>Attribution for shapefile with region boundaries:</strong> ElectricityMaps contributors (2024). electricitymaps-contrib (Version v1.155.0) [Computer software]. https://github.com/electricitymaps/electricitymaps-contrib.</p> </td> </tr> <tr> <td> <p>gen_cap_2022_state_merged.geojson </p> <p>trucking_energy_demand.geojson</p> </td> <td> <p>Grid electricity generation and net summer power capacity data is obtained from the <a href="https://www.eia.gov/electricity/data/state/">state-level electricity database</a> maintained by the United States Energy Information Administration. </p> <p> </p> <p>U.S. state boundaries obtained from <a href="https://www.sciencebase.gov/catalog/item/52c78623e4b060b9ebca5be5">this United States Department of the Interior U.S. Geological Survey ScienceBase-Catalog</a>.</p> </td> <td> <p><a href="https://www.eia.gov/electricity/data/state/annual_generation_state.xls">Annual electricity generation by state</a></p> <p><a href="https://www.eia.gov/electricity/data/state/existcapacity_annual.xlsx">Net summer capacity by state</a></p> <p><a href="https://www.sciencebase.gov/catalog/file/get/52c78623e4b060b9ebca5be5?facet=tl_2012_us_state">Shapefile with U.S. state boundaries</a></p> </td> <td> <p><strong>Attribution for electricity generation and capacity data: </strong>U.S. Energy Information Administration (Aug 2024). Available from: https://www.eia.gov/electricity/data/state/. In the public domain. </p> </td> </tr> <tr> <td>electricity_rates_by_state_merged.geojson</td> <td> <p>Commercial electricity prices are obtained from the <a href="https://www.eia.gov/electricity/data.php">Electricity database</a> maintained by the United States Energy Information Administration.</p> </td> <td> <p><a href="https://www.eia.gov/electricity/data/state/sales_annual_a.xlsx">Electricity rate by state</a></p> <p> </p> </td> <td><strong>Attribution:</strong> U.S. Energy Information Administration (Aug 2024). Available from: https://www.eia.gov/electricity/data.php. In the public domain. </td> </tr> <tr> <td> <p>demand_charges_merged.geojson</p> <p>demand_charges_by_state.geojson</p> </td> <td> <p>Maximum historical demand charges for each state and zip code are derived from a dataset compiled by the National Renewable Energy Laboratory in this <a href="https://data.nrel.gov/submissions/74">this Data Catalog.</a></p> </td> <td><a href="https://data.nrel.gov/system/files/74/Demand%20charge%20rate%20data.xlsm">Historical demand charge dataset</a></td> <td> <p>The original dataset is compiled by the National Renewable Energy Laboratory (NREL), the U.S. Department of Energy (DOE), and the Alliance for Sustainable Energy, LLC ('Alliance').</p> <p><strong>Attribution:</strong> McLaren, Joyce, Pieter Gagnon, Daniel Zimny-Schmitt, Michael DeMinco, and Eric Wilson. 2017. 'Maximum demand charge rates for commercial and industrial electricity tariffs in the United States.' NREL Data Catalog. Golden, CO: National Renewable Energy Laboratory. Last updated: July 24, 2024. DOI: 10.7799/1392982.</p> </td> </tr> <tr> <td> <p>eastcoast.geojson</p> <p>midwest.geojson</p> <p>la_i710.geojson</p> <p>h2la.geojson</p> <p>bayarea.geojson</p> <p>saltlake.geojson</p> <p>northeast.geojson</p> </td> <td> <p>Highway corridors and regions targeted for heavy duty vehicle infrastructure projects are derived from a <a href="https://www.energy.gov/articles/biden-harris-administration-announces-funding-zero-emission-medium-and-heavy-duty-vehicle">public announcement</a> on February 15, 2023 by the United States Department of Energy.</p> <p>The shapefile with Bay area boundaries is obtained from <a href="https://geodata.lib.berkeley.edu/catalog/ark28722-s7hs4j">this Berkeley Library dataset</a>.</p> <p>The shapefile with Utah county boundaries is obtained from <a href="https://gis.utah.gov/products/sgid/boundaries/county/">this dataset</a> from the Utah Geospatial Resource Center. </p> </td> <td> <p><a href="https://spatial.lib.berkeley.edu/public/ark28722-s7hs4j/data.zip">Shapefile for Bay Area country boundaries</a></p> <p><a href="https://opendata.arcgis.com/datasets/90431cac2f9f49f4bcf1505419583753_0.zip">Shapefile for counties in Utah</a></p> <p> </p> </td> <td> <p><strong>Attribution for public announcement:</strong> United States Department of Energy. Biden-Harris Administration Announces Funding for Zero-Emission Medium- and Heavy-Duty Vehicle Corridors, Expansion of EV Charging in Underserved Communities (2023). Available from https://www.energy.gov/articles/biden-harris-administration-announces-funding-zero-emission-medium-and-heavy-duty-vehicle.</p> <p><strong>Attribution for Bay area boundaries:</strong> San Francisco (Calif.). Department Of Telecommunications and Information Services. Bay Area Counties. 2006. In the public domain. </p> <p><strong>Attribution for Utah boundaries:</strong> Utah Geospatial Resource Center & Lieutenant Governor's Office. Utah County Boundaries (2023). Available from https://gis.utah.gov/products/sgid/boundaries/county/. </p> <p><strong>License for Utah boundaries:</strong> <a href="https://gis.utah.gov/documentation/policy/license/#license">Creative Commons 4.0 International License</a>. </p> </td> </tr> <tr> <td>incentives_and_regulations/*.geojson</td> <td> <p>State-level incentives and regulations targeting heavy duty vehicles are collected from the <a href="https://afdc.energy.gov/laws/state">State Laws and Incentives database</a> maintained by the United States Department of Energy's Alternative Fuels Data Center. </p> </td> <td>Data was collected manually from the <a href="https://afdc.energy.gov/laws/state">State Laws and Incentives database</a>.</td> <td> <p><strong>Attribution:</strong> U.S. Department of Energy, Energy Efficiency and Renewable Energy, Alternative Fuels Data Center. State Laws and Incentives. Accessed on Aug 5, 2024 from: https://afdc.energy.gov/laws/state. In the public domain. </p> <p> </p> <p>These data and software code ("Data") are provided by the National Renewable Energy Laboratory ("NREL"), which is operated by the Alliance for Sustainable Energy, LLC ("Alliance"), for the U.S. Department of Energy ("DOE"), and may be used for any purpose whatsoever.</p> </td> </tr> <tr> <td> <p>costs_and_emissions/*.geojson</p> <p>diesel_price_by_state.geojson</p> <p>trucking_energy_demand.geojson</p> </td> <td> <p>Lifecycle costs and emissions of electric and diesel trucking are evaluated by adapting the model developed by <a href="https://chemrxiv.org/engage/chemrxiv/article-details/656e4691cf8b3c3cd7c96810">Moreno Sader et al.</a>, and calibrated to the <a href="https://runonless.com/run-on-less-electric-depot-reports/">Run on Less dataset</a> for the Tesla Semi collected from the 2023 PepsiCo Semi pilot by the North American Council for Freight Efficiency.</p> <p>In addition to the data sources outlined in <a href="https://chemrxiv.org/engage/chemrxiv/article-details/656e4691cf8b3c3cd7c96810">Moreno Sader et al.</a> et al. and the <a href="https://runonless.com/run-on-less-electric-depot-reports/">Run on Less dataset</a>, this dataset incorporates:</p> <ul> <li>Emission intensity data from the <a href="https://www.epa.gov/egrid/download-data">eGRID database</a>, described elsewhere in this metadata. </li> <li>Commercial electricity price data from the US EIA <a href="https://www.eia.gov/electricity/data.php">Electricity database</a>, described elsewhere in this metadata. </li> <li><a href="https://data.nrel.gov/submissions/74">Maximum historical demand charges</a> from the National Renewable Energy Laboratory, described elsewhere in this metadata. </li> <li>Max motor power estimate of 942,900W and frontal area of 10.7 m^s for the Tesla Semi from <a href="https://www.motormatchup.com/catalog/Tesla/Semi-Truck/2022/Empty">motormatchup.com.</a></li> <li>Drag coefficient estimate of 0.36 for the Tesla Semi from <a href="https://www.notateslaapp.com/tesla-reference/963/everything-we-know-about-the-tesla-semi">notateslaapp.com.</a></li> <li>Estimates best-in-class truck rolling resistance of 0.0044 from a <a href="https://www.lrrb.org/pdf/201539.pdf">Rolling Resistance Validation report</a> prepared by the Minnesota Department of Transportation Office of Transportation System Management.</li> <li><a href="https://www.eia.gov/petroleum/gasdiesel/">Historical diesel prices</a> by state from the United States Energy Information Administration.</li> <li>Estimate of best in class diesel powertrain engine efficiency of 44% from a <a href="https://theicct.org/sites/default/files/publications/EU-HDV-Tech-Potential_ICCT-white-paper_14072017_vF.pdf">Fuel Efficiency Technology report</a> by the International Council on Clean Transportation.</li> </ul> </td> <td> <p> </p> <p><a href="https://runonless.com/wp-content/uploads/ROL23-Web-data.zip">NACFE Run on Less dataset</a></p> <p><a href="https://www.eia.gov/petroleum/gasdiesel/xls/psw18vwall.xls">Historical diesel prices</a></p> <p> </p> </td> <td> <p><strong>Attribution for original truck model:</strong> Moreno Sader K, Biswas S, Jones R, Mennig M, Rezaei R, Green WH. Battery Electric Long-Haul Trucking in the United States: A Comprehensive Costing and Emissions Analysis. ChemRxiv. 2023; doi:10.26434/chemrxiv-2023-48zsc (link to <a href="https://colab.research.google.com/drive/124rFu_4vHx4cP6SODtdzCxnUmLY50wbW?usp=sharing">colab notebook</a> included as supplementary material).</p> <p><strong>Attribution for GitHub repository with adapted code for the truck model:</strong> Eamer, D., Moreno-Sader, K., & Biswas, S. (2024). Green_Trucking_Analysis (Version 0.1.0) [Computer software]. https://doi.org/10.5281/zenodo.13205854</p> <p><strong>Attribution for GitHub repository with analysis of the NACFE Run on Less dataset (provides inputs to Eamer, D., Moreno-Sader, K., & Biswas, S. (2024) cited above):</strong> Eamer, D. (2024). PepsiCo_NACFE_Analysis (Version 0.1.0) [Computer software]. https://doi.org/10.5281/zenodo.13173390</p> <p><strong>Attribution for <a href="https://runonless.com/run-on-less-electric-depot-reports/">Run on Less dataset</a>: </strong>North American Countil for Freight Efficiency (2023). Run on Less – Electric DEPOT data. Available from: https://runonless.com/run-on-less-electric-depot-reports/ </p> <p><strong>Attribution for data from MotorMatchup:</strong> 2022 Tesla Semi Truck Empty Specs. Available from: https://www.motormatchup.com/catalog/Tesla/Semi-Truck/2022/Empty. Copyright 2024 by MotorMatchup</p> <p><strong>Attribution for data from Not a Tesla App:</strong> Not a Tesla App. Everything We Know About the Tesla Semi. 2024. Available from: <a href="https://www.notateslaapp.com/tesla-reference/963/everything-we-know-about-the-tesla-semi" target="_new" rel="noreferrer">https://www.notateslaapp.com/tesla-reference/963/everything-we-know-about-the-tesla-semi</a></p> <p><strong>Attribution for historical diesel prices:</strong> U.S. Energy Information Administration (Aug 2024). Available from: https://www.eia.gov/petroleum/gasdiesel/. In the public domain.</p> <p><strong>Attribution for best in class diesel powertrain efficiency:</strong> Delgado O, Rodríguez F, Muncrief R. Fuel Efficiency Technology in European Heavy-Duty Vehicles: Baseline and Potential for the 2020–2030 Time Frame. 2017. Available from: <a href="https://theicct.org/sites/default/files/publications/EU-HDV-Tech-Potential_ICCT-white-paper_14072017_vF.pdf" target="_new" rel="noreferrer">https://theicct.org/sites/default/files/publications/EU-HDV-Tech-Potential_ICCT-white-paper_14072017_vF.pdf</a>.</p> </td> </tr> <tr> <td> <p>electrolyzer_operational.geojson</p> <p>electrolyzer_installed.geojson</p> <p>electrolyzer_planned_under_construction.geojson</p> <p> </p> </td> <td> <p>Data on locations and capacities of planned, under-construction, installed, operational electrolyzers was obtained from <a href="https://www.hydrogen.energy.gov/docs/hydrogenprogramlibraries/pdfs/23003-electrolyzer-installations-united-states.pdf?Status=Master">this DOE Hydrogen Program Record</a>.</p> </td> <td>Data was extracted manually from <a href="https://www.hydrogen.energy.gov/docs/hydrogenprogramlibraries/pdfs/23003-electrolyzer-installations-united-states.pdf?Status=Master">this DOE Hydrogen Program Record</a>.</td> <td><strong>Attribution:</strong> Arjona, Vanessa. DOE Hydrogen Program Record: Electrolyzer Installations in the United States. 2023. Available from https://www.hydrogen.energy.gov/docs/hydrogenprogramlibraries/pdfs/23003-electrolyzer-installations-united-states.pdf?Status=Master. </td> </tr> <tr> <td> <p>grid_emission_intensity/*.geojson</p> <p>gen_cap_2022_state_merged.geojson </p> <p>trucking_energy_demand.geojson</p> <p>electricity_rates_by_state_merged.geojson</p> <p>demand_charges_merged.geojson</p> <p>demand_charges_by_state.geojson</p> <p>trucking_energy_demand.geojson</p> <p>costs_and_emissions/*.geojson</p> <p>diesel_price_by_state.geojson</p> <p>trucking_energy_demand.geojson</p> </td> <td> <p>U.S. state boundaries obtained from <a href="https://www.sciencebase.gov/catalog/item/52c78623e4b060b9ebca5be5">this United States Department of the Interior U.S. Geological Survey ScienceBase-Catalog</a>.</p> </td> <td> </td> <td><strong>Attribution: </strong>U.S. Department of Commerce, U.S. Census Bureau, Geography Division. State boundaries (generalized for mapping). 2011. In the public domain.</td> </tr> <tr> <td> <p>refinery.geojson</p> </td> <td> <p>Locations and production rates of hydrogen from refineries are obtained from the following two complementary datasets on the <a href="https://h2tools.org">Hydrogen Tools Portal</a>:</p> <p><br>1) <a href="https://h2tools.org/hyarc/hydrogen-data/captive-purpose-refinery-hydrogen-production-capacities-individual-us">Captive, On-Purpose, Refinery Hydrogen Production Capacities at Individual U.S. Refineries</a>, and </p> <p><br>2) <a href="https://h2tools.org/hyarc/hydrogen-data/merchant-hydrogen-plant-capacities-north-america">Merchant Hydrogen Plant Capacities in North America</a></p> </td> <td> <p><a href="https://h2tools.org/file/9338/download?token=0IWTving">Dataset for Captive, On-Purpose, Refinery Hydrogen Production Capacities at Individual U.S. Refineries</a></p> <p><a href="https://h2tools.org/file/2050/download?token=Wp-XDY-h">Dataset for Merchant Hydrogen Plant Capacities in North America</a></p> </td> <td> <p><strong>Attribution: </strong>Copyright © 2024 by H2Tools; H2 Tools is intended for public use. It was built, and is maintained, by the Pacific Northwest National Laboratory with funding from the DOE Office of Energy Efficiency and Renewable Energy's Hydrogen and Fuel Cell Technologies Office. All Rights Reserved. </p> </td> </tr> <tr> <td> <p>Truck_Stop_Parking.geojson</p> <p>infrastructure_pooling_thought_experiment/*.geojson</p> </td> <td> <p>Obtained from the DOT Bureau of Transportation Statistics's <a href="https://geodata.bts.gov/datasets/usdot::truck-stop-parking">Truck Stop Parking database</a></p> </td> <td> <p>Original dataset can be downloaded using the Shapefile download link at https://geodata.bts.gov/datasets/usdot::truck-stop-parking (link for hosted download changes regularly). </p> </td> <td> <p><strong>Attribution: </strong>United States Department of Transportation Bureau of Transportation Statistics National Transportation Atlas Database (NTAD). Truck Stop Parking. Available at https://geodata.bts.gov/datasets/usdot::truck-stop-parking. </p> <p><strong>License:</strong> This NTAD dataset is a work of the United States government as defined in 17 U.S.C. § 101 and as such are not protected by any U.S. copyrights. This work is available for unrestricted public use.</p> </td> </tr> <tr> <td> <p>Principal_Port.geojson</p> </td> <td> <p>Obtained from the DOT Bureau of Transportation Statistics's <a href="https://geodata.bts.gov/datasets/usdot::principal-ports-1/about">Principal Ports database</a></p> </td> <td> <p>Original dataset can be downloaded using the Shapefile download link at https://geodata.bts.gov/datasets/usdot::principal-ports-1 (link for hosted download changes regularly). </p> </td> <td> <p><strong>Attribution: </strong>United States Department of Transportation Bureau of Transportation Statistics National Transportation Atlas Database (NTAD). Truck Stop Parking. Available at https://geodata.bts.gov/datasets/usdot::principal-ports-1. </p> <p><strong>License:</strong> This NTAD dataset is a work of the United States government as defined in 17 U.S.C. § 101 and as such are not protected by any U.S. copyrights. This work is available for unrestricted public use.</p> <p><strong> </strong></p> </td> </tr> <tr> <td> <p>ZEF_Corridor_Strategy/*.geojson</p> </td> <td> <p>Visualizes the corridors, facilities, and hubs targeted by the National Zero-Emission Freight Corridor Strategy, a framework developed by the U.S. Joint Office of Energy and Transportation to support the coordinated deployment of medium- and heavy-duty zero-emission vehicle (ZEV) infrastructure along critical freight corridors. The strategy, outlined in the publication <a href="https://driveelectric.gov/files/zef-corridor-strategy.pdf">National Zero-Emission Freight Corridor Strategy</a>, identifies priority corridors and infrastructure investment needs to accelerate the transition to zero-emission medium- and heavy-duty vehicles.</p> </td> <td> <p>Original dataset can be downloaded from https://driveelectric.gov/files/zef-gis-files.zip</p> </td> <td> <p><strong>Attribution:</strong> Chu, K.-C. (J.), Miller, K. G., Schroeder, A., Gilde, A., & Laughlin, M. (2024, September). <em>National Zero-Emission Freight Corridor Strategy: Prioritizing investments, planning, and deployment for medium- and heavy-duty vehicle fueling infrastructure to advance zero-emission freight along our nation’s corridors</em>. Joint Office of Energy and Transportation; U.S. Department of Energy. Accessed from: https://driveelectric.gov/files/zef-corridor-strategy.pdf.</p> </td> </tr> </tbody> </table> <p> </p>
High Tide or Rip-Tide? GJ 486b JWST Transits with NIRSpec G395H - Supplementary Material
<p>Supplementary material for '<em>High Tide or Riptide on the Cosmic Shoreline? A Water-rich Atmosphere or Stellar Contamination for the Warm Super-Earth GJ 486b from JWST Observations</em>' by Moran & Stevenson et al., ApJL (2023).</p> <p>This repository contains four categories of data products:</p> <ol> <li><strong>White light curves</strong> — White light curves produced for Visit 1 and 2, for detectors NRS1 and NRS2 via the <a href="https://ui.adsabs.harvard.edu/abs/2023Natur.614..659R/abstract">FIREFLy pipeline</a> and the <a href="https://github.com/kevin218/Eureka">open-source codes Eureka</a>! and <a href="https://github.com/JamesKirk11/Tiberius">Tiberius</a></li> <li><strong>Transmission spectra</strong> — Reduced transmission spectra from three data reduction codes (Eureka!, FIREFLy, and Tiberius) for both G395H visits.</li> <li><strong>Stellar spectra and models </strong>— Flux calibrated observed stellar spectra of GJ 486 from each G395H visit, along with best-fit PHOENIX stellar models.</li> <li><strong>Atmospheric models</strong> — Atmospheric forward models used to interpret the data, produced from the open source <a href="https://natashabatalha.github.io/picaso/">PICASO</a> and <a href="https://github.com/mrline/CHIMERA">CHIMERA</a> codes.</li> </ol> <p>For any additional data requests or questions, please contact: sarahemoran@arizona.edu or kevin.stevenson@jhuapl.edu</p> <p> </p>
Thames Tide Recorder
A copper octagonal structure at the west end of Westminster Bridge, London (near the boat pier) used for recording the river depth at this location. If you look through the little windows you can see the digital displays of the recording equipment. The box is covered in stickers and other graffiti. Museum of London explanation: https://www.youtube.com/watch?v=YJyaIK_ILZI 186 photos taken in July 2020 with a Sony a6000 and processed in Reality Capture. Source: Objaverse 1.0 / Sketchfab
Zonal and meridional wind tides measured by Kunming meteor radar
<p>Meteor wind radar is operating at Kunming (25.6°N, 103.8°E) since January 2008. This data set represents monthly mean zonal and meridional diurnal tides from 2008 to 2022.</p>
Surface drifters and high resolution global simulations mapping of internal tide surface energy
<p>File " gdp_energy.nc " contains surface semidiurnal internal tides binned-averaged energy levels estimated from the Global Drifter Program dataset. </p> <p>File " <a href="../api/records/10851200/draft/files/energy_SSV_hf_binned_dl1.0_attrs.nc/content" target="_blank" rel="noopener noreferrer">energy_SSV_hf_binned_dl1.0_attrs.nc</a> " contains semidiurnal internal tides squared binned-averaged surface meridional velocity estimated from LLC4320 outputs and simulated drifters. Bins size is 1deg x 1deg .</p> <p>File " <a href="../api/records/10851200/draft/files/energy_SSV_hf_binned_dl1.0_attrs.nc/content" target="_blank" rel="noopener noreferrer">energy_SSU_hf_binned_dl1.0_attrs.nc</a> " contains semidiurnal internal tides squared binned-averaged surface zonal velocity estimated from LLC4320 outputs and simulated drifters. Bins size is 1deg x 1deg .</p> <p>File " <a href="../api/records/10851200/draft/files/energy_SSV_hf_binned_dl1.0_attrs.nc/content" target="_blank" rel="noopener noreferrer">energy_SSV_hf_binned_dl2.0_attrs.nc</a> " contains semidiurnal internal tides squared binned-averaged surface meridional velocity estimated from LLC4320 outputs and simulated drifters. Bins size is 2deg x 2deg .</p> <p>File " <a href="../api/records/10851200/draft/files/energy_SSV_hf_binned_dl1.0_attrs.nc/content" target="_blank" rel="noopener noreferrer">energy_SSU_hf_binned_dl2.0_attrs.nc</a> " contains semidiurnal internal tides squared binned-averaged surface zonal velocity estimated from LLC4320 outputs and simulated drifters. Bins size is 2deg x 2deg .</p> <p>File " <a href="../api/records/10851200/draft/files/energy_SSV_hf_binned_dl1.0_attrs.nc/content" target="_blank" rel="noopener noreferrer">energy_hf_binned_dl1.0_attrs.nc</a> " contains semidiurnal internal tides binned-averaged kinetic energy levels estimated from LLC4320 outputs and simulated drifters. Bins size is 1deg x 1deg .</p> <p>For all files semidiurnal signal is obatined from band-pass filtering.</p>
The Seasonal Variability in the Semidiurnal Internal Tide; A Comparison between Sea Surface Height and Energetics
<p>This dataset contains data from global HYCOM simulations with realistic atmospheric and tidal forcings. The horizontal resolution is 8 km. Data is stored as netcdf4 classic.</p>
Data from: The characteristics of tides and their effects on the general circulation of the Mediterranean Sea
<p>Data for the figures in the paper "The characteristics of tides and their effects on the general circulation of the Mediterranean Sea"</p>
The effect of harbor developments on future high-tide flooding in Miami, Florida. Accompanying data.
<p>List of available data: </p> <p>- Historical documents with tidal range measurements in the Biscayne Bay</p> <p>- Geo-referenced tif files of US Coast Survey chart no. 165 (1895) and NOAA US Coast Survey chart no. 11468 (2017)</p> <p>- Water level series projected through 2100 at the South Florida Water Management District gauge MRMS4 (mat files)</p> <p> </p> <p> </p> <p> </p>
Evaluation of the local sea-level budget at tide gauges since 1958
<p>### Data supplement for 'Evaluation of the local sea-level budget at tide gauges since 1958'</p> <p><br> Authors: Jinping Wang, John A. Church, Xuebin Zhang, Jonathan M. Gregory, Laure Zanna and Xianyao Chen</p> <p>Created 9/5/2020<br> Please email wangjinping@ouc.edu for questions.</p> <p>----------------------------------------------------------------------------------------------------<br> ### PLEASE CITE THE APPROPRIATE PAPERS WHEN USING THIS DATA ###<br> Please cite 'Evaluation of the local sea-level budget at tide gauges since 1958' when using this data set.<br> Our results heavily relies on previous work, and please acknowledge the previous work by citing the original sources of the data.<br> The Data Availability Statement section of 'Evaluation of the local sea-level budget at tide gauges since 1958' contains the full list of sources of all the original data.</p> <p>----------------------------------------------------------------------------------------------------<br> This data supplement contains the following files:</p> <p>separate_components_rsl.mat <br> Spatial patterns of relative sea level trend (mm/yr) are provided for individual component.</p> <p>lon = longitude<br> lat = latitude<br> sdsl = sterodynamic sea level<br> barystatic_grd = the sum of all barystatic-GRD fingerprints<br> glacier = glacier contribution including charted and uncharted glaciers<br> ice_sheet = the sum of Greenland and Antarctic ice sheets<br> gia = glacial isostatic adjustment<br> tws = terrestrial water storage<br> sum = the sum of all contributions</p> <p><br> GMSL.mat<br> Global mean time series of sea level and each components (mm).</p> <p>The variable names are the same with those in separate_components_rsl.mat <br> recons_mean = the ensemble mean of GMSL reconstructions (including Church & White, 2011; Dangendorf et al., 2019; Frederikse et al., 2020; Hay et al., 2015)</p> <p><br> components_TG_locations.mat<br> All tide gauge observations and the individual contribution (mmm/yr).</p> <p>The variable names are the same with those in separate_components_rsl.mat <br> tgobs = tide gauge observations<br> tgobs_ovlm = tide gauge observations with other VLM correction applied</p> <p><br> VLM_components.mat<br> VLM estimate and its individual components (mm/yr).</p> <p>vlm_total = total VLM<br> vlm_bgrd = barystatic-GRD VLM component<br> vlm_gia = GIA-related VLM component<br> vlm_other = other local VLM component</p> <p>vlm_total_uncertianty90 = 90% confidence level for total VLM<br> vlm_bgrd = 90% confidence level for barystatic-GRD VLM component<br> vlm_gia = 90% confidence level for GIA-related VLM component<br> vlm_other = 90% confidence level for other local VLM component</p>
Supporting dataset: "Analysis of tide and offshore storm-induced water table fluctuations for structural characterization of a coastal island aquifer"
<p>Included in this repository are supporting field data and final model input files used to produce the results of the manuscript:</p> <p>Trglavcnik, V., Morrow, D., Weber, K. P., Li, L., & Robinson, C. E. (2017), "Analysis of tide and offshore storm-induced water table fluctuations for structural characterization of a coastal island aquifer."</p> <p>This dataset contains:</p> <ul> <li>SableIsland_data.xlsx <ul> <li>Data used to produce figures in the above manuscript. </li> </ul> </li> <li>Final_SS.zip <ul> <li>Input files for the final model (see Figure 3 in manuscript), steady-state SEAWAT simulation. </li> </ul> </li> <li>Final_PBC.zip <ul> <li>Input files for the final model, transient SEAWAT simulation with a sinusoidal tidal boundary implemented by the Periodic Boundary Condition package (developed for MODFLOW by Post, 2011).</li> </ul> </li> </ul> <p>All data and files are licensed under Creative Commons Attribution Share Alike 4.0 International.</p> <p> </p> <p> </p>
Simulation data of 'Effects of Nonmigrating Diurnal Tides on the Na Layer in the Mesosphere and Lower Thermosphere'
<p>The simulation data of the manuscript 'Effects of Nonmigrating Diurnal Tides on the Na Layer in the Mesosphere and Lower Thermosphere'.</p>
Dynamical tides in highly eccentric binaries: chaos, dissipation, and quasi-steady state
<p>MESA inlists associated with <a href="https://ui.adsabs.harvard.edu/#abs/2018MNRAS.476..482V/abstract">Dynamical tides in highly eccentric binaries: chaos, dissipation, and quasi-steady state</a></p>
Data & Code for Sea-level rise induced threats depend on size of tide-influenced estuaries worldwide
<p>Data & Code for the article: "Data & Code for Sea-level rise induced threats depend on size of tide-influenced estuaries worldwide"</p> <p>- 1D Hydrodynamic model</p> <p>- Along-channel width profiles of the estuaries used in this study</p> <p>- Input data used for the tool and model in this study</p> <p>- Output data used for the figures</p>
The data for "The feasibility of asynchronous rotation via thermal tides for diverse atmospheric compositions"
<p>Supporting code and data for Salazar & Wordsworth, 2024 </p> <p><strong>Description of Jupyter Notebook files:</strong></p> <p>(Figure_X.ipynb) Contains code for recreating all figures appearing in S&W, 2024 (labeled by figure number, X)</p> <p>(ExoTides_analytic.ipynb) Contains functions from the analytic model for calculating torque and rotation states as a function of orbital and atmospheric paramters</p> <p>(LMDZ_Processing.ipynb) Code for processing raw LMDZ data, including centering at the substellar point, spherical harmonic decomposition, and calculation of torque, amplitude, and lag</p> <p>(ExoTides_int.ipynb) Demonstrates solutions by integrating Equation (7) numerically (not done in paper). Also includes a comparison with the analytic model</p> <p><strong>Description of Numpy array files:</strong></p> <p>These files contain numpy arrays of the torque (im_q), amplitude (amp), lag angle (lag), and parameter values (list) for each of the GCM sensitivity tests performed in S&W, 2024. These files were created using the LMDZ_Processing.ipynb file, which takes raw GCM output and outputs the torque information. Full details are available in the publication.</p> <p>The beginning of each .npy file name signifies which sensitivity test the file is (ex: taulw indicates the longwave optical depth sensitivity test). The Figure_2.ipynb file shows how these .npy files are used. </p> <p><strong>Descrition of .csv file:</strong></p> <p>The nasa_exoplanet.csv file contains output from the NASA Exoplanet Archive (DOI: 10.26133/NEA12) and is used in the creation of Figure 7. </p> <p> </p>
The data for "The feasibility of asynchronous rotation via thermal tides for diverse atmospheric compositions"
<p>This repo contains the processed LMD Generic model output data needed to reproduce figures in Salazar & Wordsworth, 2024: "The feasibility of asynchronous rotation via thermal tides for diverse atmospheric compositions". Raw GCM data can be made available upon request.</p> <p> </p> <p>The processed data is saved as numpy arrays. For each sensitivity test detailed in Salazar & Wordsworth, 2024, the GCM data was processed by taking surface temperature and surface pressure output and phase folding the data so that the substellar point is the origin of longitudes. Full details are available in the paper.</p>
Table 1 in Acoustic monitoring reveals the times and tides of harbor porpoise (Phocoena phocoena) distribution off central Oregon, U. S. A.
<p><i>Table 1.</i> Details of digital acoustic monitoring (DMON) deployment sites and recording times over the duration of the study.</p><table><tbody><tr><th>Site</th><th>Coordinates</th><th>Deployment date</th><th>Recovery date</th><th>Deployment duration (time)</th><th>Recorded minutes</th></tr></tbody><tbody><tr><th>Reef Offshore</th><td>44 Ǫ 35.140 N, 124 Ǫ 07.120 W 44 Ǫ 35.121 N, 124 Ǫ 07.000 W 44 Ǫ 34.985 N, 124 Ǫ 07.117 W 44 Ǫ 35.247 N, 124 Ǫ 06.815 W 44 Ǫ 35.221 N, 124 Ǫ 06.859 W 44 Ǫ 34.650 N, 124 Ǫ 13.218 W 44 Ǫ 34.857 N, 124 Ǫ 13.419 W 44 Ǫ 34.929 N, 124 Ǫ 13.215 W 44 Ǫ 34.929 N, 124 Ǫ 13.215 W 44 Ǫ 34.929 N, 124 Ǫ 13.215 W</td><td>16 May 2014 12 Jun 2014 26 Jun 2014 29 Jul 2014 16 Sep 2014 12 Jun 2014 26 Jun 2014 29 Jul 2014 16 Sep 2014 20 Sep 2014</td><td>23 May 2014 20 Jun 2014 7 Jul 2014 8 Aug 2014 18 Sep 2014 20 Jun 2014 7 Jul 2014 8 Aug 2014 29 Sep 2014 13 Oct 2014</td><td>6 d 20 h 00 min 7 d 21 h 30 min 10 d 14 h 40 min 9 d 21 h 40 min 2 d 13 h 20 min 7 d 21 h 40 min 10 d 23 h 10 min 10 d 0 h 30 min 12 d 17 h 40 min 12 d 12 h 00 min</td><td>985 1,138 1,529 1,427 441 1,138 1,580 1,444 1,835 1,801</td></tr></tbody></table>
Table 4 in Acoustic monitoring reveals the times and tides of harbor porpoise (Phocoena phocoena) distribution off central Oregon, U. S. A.
<p><i>Table 4.</i> Predictor environmental variables in generalized additive models (GAM) of harbor porpoise echolocation activity and their significance, with deviance explained of entire model.</p><table><tbody><tr><th></th><th>Reef</th><th>Reef</th><th>Offshore</th><th>Offshore</th></tr></tbody><tbody><tr><th>Predictor</th><td>PPM</td><td>BPM</td><td>PPM</td><td>BPM</td></tr><tr><th>Julian day</th><td><0.001c</td><td><0.001c</td><td><0.001c</td><td><0.001c</td></tr><tr><th>Diel phase:</th><td></td><td></td><td></td><td></td></tr><tr><th>Morning</th><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><th>Day Evening Night</th><td>— — 0.01a</td><td>— — —</td><td>— 0.003b 0.04a</td><td>— <0.001c</td></tr><tr><th>Tidal phase</th><td>0.05</td><td>—</td><td>0.05</td><td>—</td></tr><tr><th>Julian day <i>×</i> Diel</th><td></td><td></td><td></td><td></td></tr><tr><th>phase: Morning Day Evening Night Julian day <i>×</i> Tidal</th><td>— — <0.001c <0.001c 0.02a</td><td>— 0.04a 0.001b <0.001c <0.001c</td><td>— <0.001c <0.001c <0.001c —</td><td><0.001c <0.001c — —</td></tr><tr><th>phase</th><td></td><td></td><td></td><td></td></tr><tr><th>Diel phase <i>×</i> Tidal</th><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><th>phase</th><td></td><td></td><td></td><td></td></tr><tr><th>Deviance explained</th><td>6.9%</td><td>13.7%</td><td>11.5%</td><td>13.2%</td></tr></tbody></table><p><sup>a</sup> Significant at the 0.05 probability level.</p><p><sup>b</sup> Significant at the 0.01 probability level.</p><p><sup>c</sup> Significant at the 0.001 probability level.</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.