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830 results for “Industrialization”
Disability and Industrial Society 1780-1948: A Comparative Cultural History of British Coalfields: Statistical Compendium
<p>This statistical compendium gives information about accidents and injuries in the British coal industry from 1780 to 1948. It provides in tabular form statistics about the occurrence of non-fatal accidents, and various welfare and medical responses. It was produced as part of a Wellcome Trust Programme Grant in Medical History, 'Disability and Industrial Society: A Comparative Cultural History of British Coalfields 1780-1948' (095948/Z/11/Z)</p>
Software Evolution and Quality Data from Controlled, Multiple, Industrial Case Studies
<p>This data was obtained from a controlled, multiple case study involving six professional developers and four real-life, industrial systems. The study was designed to control for the moderator factors: programmer skill, maintenance task and learning effect. The primary data set contains multiple sets of defects, in the form of reports (excel files) extracted from six issue tracking systems. The secondary data consists of a series of attributes extracted from the software systems (i.e., code smells) and their evolution (i.e., code churn), and a log specifying the dates on which developers worked on each of the systems/tasks, in the form of excel files. Details on the controlled, multiple case study can be found in the doctoral dissertation by Yamashita titled: "Assessing the Capability of Code Smells to Support Software Maintainability Assessments: Empirical Inquiry and Methodological Approach" (online) Available at: https://www.duo.uio.no/handle/10852/34525</p>
Causes and importance of new particle formation in the present-day and pre-industrial atmospheres: supporting data
<p>Data presented in the manuscript "Causes and importance of new particle formation in the present-day and pre-industrial atmospheres" currently in review.</p> <p>Particle number concentrations (files with initial word "CCN" or "N3") have units of particles per cubic centimetre, calculated at ambient temperature and pressure. Files with initial word "solar" have units of percent. Ion production rates have units ion pairs per cubic centimetre per second.</p> <p>The simulation data presented here was generated with the GLOMAP aerosol model, https://www.see.leeds.ac.uk/research/icas/research-themes/atmospheric-chemistry-and-aerosols/groups/aerosols-and-climate/the-glomap-model/ running on a T42 grid.</p> <p>The manuscript associated with this data was written using results from the CLOUD experiment at CERN, and the author list is a subset of the CLOUD collaboration.</p> <p> </p> <p> </p>
SYMBA project Industrial Symbiosis mapping
<p>The dataset includes the infomation collected during the elaboration of the Industrial Symbiosis (IS) mapping carried out in the framework of the SYMBA project (101135562). Concretely, it compiles the data included in the deliverable D3.1 Report on current Industrial Symbiosis solutions in Europe.</p> <p>The updated v3.0 of the dataset includes the prioritisation of IS solutions, resulting from the application of the Multicriteria Decision Analysis (MCDA) methodology developed under the SYMBA project. This prioritisation, detailed in Deliverable D3.3: Evaluation of IS Prioritised Solutions, applies weighted scoring factors across five readiness dimensions: Symbiosis, Environmental, Societal, Organisational, and Legal/Ethical. The weighting factors used for prioritisation are presented in the adjacent table. For more in-depth insight into the evaluation and prioritisation approach, consult Deliverable D3.3.</p>
Monitoring Continuous Integration Practices in Industry: A Case Study
<p><span>In our previous studies </span><span>have demonstrated </span><span>that only executing automatic builds and checking test coverage </span><span>have been considered to measure the level of Continuous Integra</span><span>tion maturity in several projects. In this paper we seek for under</span><span>stand the the benefits and challenges of monitoring CI practices in </span><span>day-to-day software development.</span><span> </span><span>We aim to evaluate the </span><span>impact of monitoring seven CI practices in a real-world scenario on </span><span>three public organizations in Brazil.</span><span> </span><span>We first developed a </span><span>CI practices monitoring suite tool and conducted a multiple-case </span><span>study applying a mixed-methods strategy, combining surveys, in</span><span>terviews, log data, and mining data from CI services.</span><span> </span><span>We </span><span>verified organization’ interest in monitoring CI practices. Moni</span><span>toring provided an overview of the organization’s CI status, not </span><span>covered by other tools, motivated constant improvement in these </span><span>practices, a perception of software quality, improve the communica</span><span>tion and and it is easy to adopt.</span><span> </span><span>Conclusions:</span><span> </span><span>We recommend that </span><span>companies adopt monitoring of CI practices and that CI services </span><span>integrate this monitoring into their dashboards.</span></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>
Fig. 2 in Bird Diversity Differs Between Industrial Tree Plantations On Borneo: Implications For Conservation Planning
Fig. 2. Species accumulation curves for (A) Sabah Softwoods and (B) Sarawak Planted Forest Project.
Correspondence table between UNFCCC CRF and EXIOBASE industry sectors
<p>This repository contains a correspondence table (CT) that maps the UNFCCC Common Reporting Format (CRF) to the EXIOBASE v3 industry classification. The CT was compiled for our study on "Estimating the uncertainty of the greenhouse gas emission accounts<br>in Global Multi-Regional Input-Output analysis" submitted to the Journal of Earth System Science Data (ESSD): <a href="https://essd.copernicus.org/preprints/essd-2023-473/">https://essd.copernicus.org/preprints/essd-2023-473/</a></p> <p>In the UNFCCC CRF (2019 revision) emission sources are grouped in <strong>categories</strong> and <strong>classifications</strong>, in a hierarchical order. The highest ranked categories - also called sectors - are 1) Energy, 2) Industrial Processes and Product Use, 3) Agriculture, 4) Land use, land-use change, and forestry (LULUCF), 5) Waste and 6) Other. Those sectors are further broken down into sub-categories, e.g. 1.A.1.a.i. The sub-categories of the sectors Energy, Agriculture and LULUCF are further broken down by the classification. The classification distinguishes different fuel types (in case of emission from "Energy') and animal types (in case of emissions from "Agriculture"). Like the categories, the classification also follows a hierarchical structure.</p> <p>Parties report their emissions to the UNFCCC at different levels of resolution depending on national characteristics and data availability. </p> <p>Here, we provide two tables: </p> <ul> <li><a href="../api/records/10046372/draft/files/correspondence_CRFdetailed_to_EXIOBASE_root.xlsx/content">correspondence_CRFdetailed_to_EXIOBASE_root.xlsx</a>: Lists the correspondences only for the most detailed level (with regard to both category and classfication).</li> <li><a href="../api/records/10046372/draft/files/correspondence_CRFdetailed_to_EXIOBASE_coherent.xlsx/content">correspondence_CRFdetailed_to_EXIOBASE_coherent.xlsx</a>: List the correspondences for all levels (automatically compiled from the 'root' table).</li> </ul> <p><strong>Collaboration in checking, revising and refining the correspondence table is appreciated. Please contact me if you have a revised version so that I can upload it to this repository. </strong></p> <p> </p> <p> </p> <p> </p>
Dataset of spontaneous design examples from industrial shopfloors
<p><strong>Background and motivation</strong></p><p>There is a common association of industrial work at shopfloor level with jobs that are repetitive, devoid of creativity and of leeway to make changes to the job itself, the products or the equipment. In a project funded by the <i>Portuguese Foundation for Science and Technology</i>, entitled <i>Craft - Spontaneous design in shopfloors as a source for inclusive design</i>, we documented examples of shopfloor industrial workers' inventiveness, which counteracts these preconceptions.</p><p>This dataset contains 696 photographs of spontaneous design examples, i.e. designs made by non-designers, created by industrial workers. The photographs in the dataset were collected in eight industrial units in Portugal in the sectors of ceramics, textiles, metallurgy, packaging, and injection moulding, and they document examples of artifacts created by industrial shopfloor workers to improve ergonomics, processes and product quality.</p><p> </p><p><strong>About how the dataset is organised</strong></p><p>Each picture has metadata on the country where the photograph was taken, a description of the worker's intervention and/or purpose of the resulting artifact, alternative text, copyright status, and keywords categorising the industrial sector, as well as the type of spontaneous design. For the latter, we have used Madeleine Akrich's categories of displacement, adaptation, extension, and detour (Akrich, 1998).</p><p>Each folder in the dataset is an industrial unit. Photograph naming standard is as follows: <i>yyyymmdd_CompanyCode_##.png</i> or <i>yyyymmdd_CompanyCode_P##_##.png.</i> The latter is for photographs in which a worker (P##) has been identified as a creator of that spontaneous design example and has interacted with the researchers collecting the data. Therefore, we can group each worker's spontaneous designs.</p><p> </p><p><strong>Reference: </strong></p><p>Akrich M (1998). Les utilisateurs, acteurs de l'innovation. Éducation permanente, Paris: Documentation française, 79–90</p>
Software Development Waste amidst COVID-19 Pandemic: An Industry Study
<p>The dataset is to support the publication "Software Development Waste amidst COVID-19 Pandemic: An Industry Study" in ISEC 2024. </p>
LoDoInd: A Benchmark Low-dose Industrial CT Dataset - 1 of 3
<h2>Summary</h2> <p>This dataset accompanies the paper "LoDoInd: Introducing A Benchmark Low-dose Industrial CT Dataset and Enhancing Denoising with 2.5D Deep Learning Techniques". We are releasing the dataset with five different dose levels, including a reference set. All datasets are pre-registered, making them immediately suitable for deep learning applications in industrial CT.</p> <h2>Description</h2> <p>The uploaded content includes reconstructed images for noise levels 1 and 2. Each level comprises 4000 slices, with each slice being 1250x1250 pixels. Due to the 50 GB space limitation per submission on Zenodo, the rest of the dataset is available through separate links listed below:</p> <ul> <li>Noise1 and Noise2 (this one) <a href="../records/10356955" target="_blank" rel="noopener">https://zenodo.org/records/10356955</a></li> <li>Noise3 and Noise4 <a href="../records/10391277" target="_blank" rel="noopener">https://zenodo.org/records/10391277</a></li> <li>Noise5 and Reference <a href="../records/10391412" target="_blank" rel="noopener">https://zenodo.org/records/10391412</a></li> </ul> <p>The scanning parameters for all noise levels are summarized in the table below:</p> <table> <tbody> <tr> <td> </td> <td>Averaged Projs</td> <td>Exposure Time/ms</td> <td>Scan Time/min</td> <td>Voltage/kV</td> <td>Current/uA</td> </tr> <tr> <td>Reference</td> <td>6</td> <td>333</td> <td>59.3</td> <td>140</td> <td>180</td> </tr> <tr> <td>Noise Level 1</td> <td>1</td> <td>333</td> <td>9.9</td> <td>140</td> <td>180</td> </tr> <tr> <td>Noise Level 2</td> <td>1</td> <td>333</td> <td>9.9</td> <td>140</td> <td>90</td> </tr> <tr> <td>Noise Level 3</td> <td>1</td> <td>333</td> <td>9.9</td> <td>140</td> <td>45</td> </tr> <tr> <td>Noise Level 4</td> <td>1</td> <td>333</td> <td>9.9</td> <td>140</td> <td>23</td> </tr> <tr> <td>Noise Level 5</td> <td>1</td> <td>333</td> <td>9.9</td> <td>140</td> <td>12</td> </tr> </tbody> </table> <h2>Additional link</h2> <p>The code for supervised learning-based denoising is available <a href="https://github.com/jiayangshi/LoDoInd">code</a> .</p> <h2>Acknowledgment</h2> <p>This research was co-financed by the European Union H2020-MSCA-ITN-2020 under grant agreement no. 956172 (xCTing). </p>
Data to support: Anthropogenic influence on tropospheric reactive bromine since the pre-industrial: Implications for ice-core bromine trends
<p>Tropospheric reactive bromine (Br<sub>y</sub>) influences the oxidation capacity of the atmosphere by acting as a sink for ozone and nitrogen oxides. Aerosol acidity plays a crucial role in Br<sub>y</sub> abundances through acid-catalyzed debromination from sea-salt-aerosol, the largest global source. Bromine concentrations in a Russian Arctic ice-core, Akademii Nauk, show a 3.5-fold increase from pre-industrial (PI) to the 1970s (peak acidity, PA), and decreased by half to 1999 (present day, PD). Ice-core acidity mirrors this trend, showing robust correlation with bromine, especially after 1940 (<em>r</em>=0.9). Model simulations considering anthropogenic emission changes alone show that atmospheric acidity is the main driver of Br<sub>y</sub> changes, consistent with the observed relationship between acidity and bromine. The influence of atmospheric acidity and Br<sub>y</sub> should be considered in interpretation of ice-core bromine trends.</p>
Shipwreck 15563- Possibly Whaling Brig Industry
This is the first model of shipwreck 15563. This was made using the high-resolution photogrammetry passes from Deep Discoverer (D2) remotely operated vehicle (ROV) on the Research Vessel Okeanos Explorer. These data were from NOAA Cruise EX2201 Dive 02. Much credit goes to the skillful ROV pilots on the Okeanos. Protocols for data collection and processing by BOEM's Marine Archaeologist Scott Sorset. The BOEM Virtual Archaeology Museum can be accessed at https://www.boem.gov/Virtual-Archaeology-Museum/. Source: Objaverse 1.0 / Sketchfab
Old industrial ruins and tree
The remains of an old brick kiln with a pine tree growing on it. Near Auckland, New Zealand. Branches cropped off in scan. My 3D reconstruction generated with photogrammetry software 3DF Zephyr v5.019 processing 293 images Source: Objaverse 1.0 / Sketchfab
''Do you have time for a quick call?": Exploring Remote and Hybrid Requirements Engineering Practices and Challenges in Industry
<p>This replication package contains the survey and interview questions, list of codes derived from the analysis, and the survey respondent demographics from the paper titled <em><strong>''Do you have time for a quick call?": Exploring Remote and Hybrid Requirements Engineering Practices and Challenges in Industry </strong></em>accepted to International Requirements Engineering Conference 2024. </p>
Data from: Cryptic diversity of cellulose-degrading gut bacteria in industrialized humans
<p>Humans, like all mammals, depend on the gut microbiome for digestion of cellulose, the main component of plant fiber, but evidence for cellulose fermentation in the human gut is scarce. We have identified ruminococcal species in the gut microbiota of human populations that assemble functional multi-enzymatic cellulosome systems capable of degrading plant cell wall polysaccharides. One of these species, which is strongly associated with humans, likely originated in the ruminant gut and was subsequently transferred to the human gut potentially during domestication, where it underwent diversification and diet-related adaptation through the acquisition of genes from other gut microbes. Collectively, these species are abundant and widespread among ancient humans, hunter-gatherers, and rural populations, but are extremely rare in populations from industrialized societies, suggesting potential disappearance in response to the westernized lifestyle.</p>
Dataset of 'Unequal Impacts of Urban Industrial Land Expansion on Economic Growth and Carbon Dioxide Emissions'
<p>This dataset is an integral component of the research presented in 'Unequal Impacts of Urban Industrial Land Expansion on Economic Growth and Carbon Dioxide Emissions' by Yoo et al., 2024. It encompasses:</p> <p><strong>1. Links to two publicly accessible datasets:</strong></p> <ul> <li>Industrial land mapping data derived from Google Earth Engine.</li> <li>Data employed in a longitudinal analysis to evaluate the effects of urban industrial land expansion on CO2 emissions and economic growth.</li> </ul> <p><strong>2. Comprehensive input data </strong>utilized in Mixed Effects Random Forest (MERF) longitudinal modeling to assess the separate impacts on CO2 emissions and economic growth in developing and developed regions.</p> <p><strong>3. Python scripts provided for:</strong></p> <ul> <li>Executing MERF longitudinal modeling.</li> <li>Computing SHAP (SHapley Additive exPlanations) values to interpret the contributions of each predictor variable.</li> <li>Generating figures that visually summarize the findings for both developing and developed regions.</li> </ul> <p> </p> <p><strong>The manuscript is available</strong> at: <a href="https://doi.org/10.1038/s43247-024-01375-x"><span>https://doi.org/10.1038/s43247-024-01375-x</span></a></p> <div> <div> </div> </div>
Reference Energy System for the Industry Sector
<p>This diagram illustrates a reference energy system designed for the industry sector.</p> <p>This visual aid can support the development of models on OSeMOSYS, LEAP, TIMES, or other energy modelling tools, as well as facilitate the integration of energy planning models like MAED and OSeMOSYS, or others.</p> <p>This material has been produced with support from the Climate Compatible Growth (CCG) programme. CCG is funded by UK aid from the UK government. However, the views expressed herein do not necessarily reflect the UK government's official policies. </p>
Impact of open bioinformatics resources on industry (worked example for an ELIXIR deposition database)
Open the record for dataset details and reuse information.
Code and data to "The shifting of buffer crop repertoires in pre-industrial north-eastern Europe "
<p>This code and data can be used to replicate the plots and figures of the paper and to trace the correlation and tests of climate variability and crop development in the study area.</p> <p> </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.