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620 results for “sector”
Bodø - Sankey and indicator data for construction sector
<p>This excel sheet was made by the CityLoops course participants for Bodø, Norway and contains the data needed for the Sankey diagram and indicators for the Sector-wide Circularity Assessment for the Biomass and Construction sectors. </p> <p>"CityLoops - Closing the loop for urban material flows" is a EU Horizon2020 project with <a href="https://cityloops.metabolismofcities.org/partners/">28 partners</a>. <em>(Official project website: <a href="https://cityloops.eu/">https://cityloops.eu/</a>)</em>. CityLoops brings together six ambitious European cities to demonstrate a series of innovative tools and urban planning approaches, aimed at closing the loops of urban material flows and increasing their regenerative capacity.</p> <p>(For detailed information please visit: https://cityloops.metabolismofcities.org/dashboards/bodo/datasets/1004599/) </p>
Options to improve the carbon balance of the harvested wood products sector in four EU countries
<p>Harvested Wood Products (HWP) may contribute to climate change mitigation by storing carbon and by replacing energy-intensive materials and fossil energy, reducing Greenhouse Gas (GHG) emissions. However, when assessing improved HWP utilizations, interactions between wood use pathways, the carbon stock dynamics, and the resulting effect on the GHG balance are still not well understood. This research aims to assess the carbon sequestration effects of alternative wood product utilizations in four EU countries. We conducted a material flow analysis of wood uses in France, Finland, Germany, and Spain for 2017 taking into account national production, imports, and exports. Then, we quantified the future dynamics of carbon stock in the HWP through time, assuming the same as in 2017 input and ignoring the forest sink. We then ran six alternative scenarios: two energy-focused (Energy, Energy+), two material-focused (Cascading, Material), one with extended half-life of the wood products (HL) and one as business as usual (BAU). For the simulation period (2020–2050), the Material scenario leads to the highest mitigation benefits with a cumulative HWP net carbon accumulation of -502 Mt CO<sub>2</sub> for Germany, -290 Mt CO<sub>2</sub> for France, -118 Mt CO<sub>2</sub> for Spain, and -116 Mt CO<sub>2</sub> for Finland over the 30 years. The Energy+ scenario with an increase in wood usage for bioenergy generates a loss of the HWP pool of 351, 80, 77, and 6 Mt CO<sub>2</sub> for the same countries, not accounting for energy substitution effects. Overall, our results suggest that the HWP carbon stock can be increased in the short-medium term by prioritising the use of wood for material purposes, while maintaining constant harvest. The HWP mitigation potential differed greatly according to national wood industry characteristics. Hence, tailoring the HWP mitigation strategies to the specific characteristics of the national wood chain would enhance the HWP climate benefits.</p>
Figure 6 in The benthic and pelagic phases of Muraenolepis marmorata (Muraenolepididae) off the Kerguelen Plateau (Indian sector of the Southern Ocean)
Figure 6. – Length Frequency Distributions (LFD) of Muraenolepis marmorata fingerlings during early (January 2014, MYCTO 3 D) or late summer (March 1995, IPEKER) pelagic surveys. Photo: Muraenolepis marmorata, fingerling TL 35 mm (photo ©MYCTO 3D).
Figure 5 in The benthic and pelagic phases of Muraenolepis marmorata (Muraenolepididae) off the Kerguelen Plateau (Indian sector of the Southern Ocean)
Figure 5. – Depth range occurrence of Muraenolepis marmorata by-catch in the total bottom longlines deployed in the fishery (2006-2016) off the Kerguelen Islands (500-2000 m).
Figure 2 in The benthic and pelagic phases of Muraenolepis marmorata (Muraenolepididae) off the Kerguelen Plateau (Indian sector of the Southern Ocean)
Figure 2. – Geographical pelagic survey coverage off the Kerguelen Islands during the "IPEKER" (1995), "ICHTYOKER 1, 2 & 3" (1998-2000) and "MYCTO 3 D" (2014) cruises. Each station includes four depths hauls (subsurface, 50, 150, 300 m). Yellow circles are day stations, deep blue diamond- shaped are night stations.
Figure 4 in The benthic and pelagic phases of Muraenolepis marmorata (Muraenolepididae) off the Kerguelen Plateau (Indian sector of the Southern Ocean)
Figure 4. – Length Frequency Distributions (LFD) of Muraenolepis marmorata from POKER 1 (2006), POKER 2 (2010) Kerguelen Islands fish bottom biomass surveys and by-catch in the bottom longline fishery (2006-2016) off the Kerguelen Islands. Photo: Muraenolepis marmorata (photo ©POKER 2).
Options to improve the carbon balance of the harvested wood products sector in four EU countries
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Physical properties of summer sea ice in the Pacific sector of the Arctic in 2008-2018
<p>This dataset includes the data of physical properties (ice temperature, salinity, density, and crystal structure) of summer sea ice in the Pacific sector of the Arctic collected by the Chinese National Arctic Research Expedition (CHINARE) programs in 2008/10/12/14/16/18.</p> <p>The files contain the following data fields:</p> <p><strong>readme</strong> – a detailed description of all data</p> <p><strong>Ice station information</strong> – specific information (location, date, air temperature, etc.) of ice stations</p> <p><strong>Ice temperature</strong> – ice temperature along ice depth for individual ice cores</p> <p><strong>Ice salinity</strong> – ice salinity along ice depth for individual ice cores</p> <p><strong>Ice density</strong> – ice density along ice depth for individual ice cores</p> <p><strong>Ice crystal structure</strong> – Picture sequences of ice crystal structure along ice depth for individual ice cores</p>
CO2 emissions changes due to COVID-19: modified SSP2-4.5 to account for sector activity level
<p>Monthly CO2 emission projections, modified by the country-specific impacts of COVID-19 lockdown. </p> <p>This repository holds the netcdf files for CO2 emissions projected by the scenario SSP2-4.5, from the Scenario4MIPs database ( <a href="https://esgf-node.llnl.gov/search/input4mips/">https://esgf-node.llnl.gov/search/input4mips/</a>), modified by the country and sector activity levels associated with lockdown, projected out for 3 years after 2020 before returning to baseline. The details of these activity estimates can be found in <a href="https://github.com/Priestley-Centre/COVID19_emissions">https://github.com/Priestley-Centre/COVID19_emissions</a>.</p> <p>The methodology behind these calculations is based on <a href="https://github.com/Rlamboll/modify_COVID19_netCDF_Emissions/">https://github.com/Rlamboll/modify_COVID19_netCDF_Emissions/</a>, a slight modification of the approach used in <a href="https://zenodo.org/record/3947917#.XxR_qyhKhPZ">https://zenodo.org/record/3947917#.XxR_qyhKhPZ</a> for aerosols emissions, and version numbers used here are consistent with the data seen in that database. We present only a single scenario (called 2-year blip, featuring a one year recovery after the end of the 2 years) compared to the baseline. </p> <p>Funding was provided by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement nos. 820829 (CONSTRAIN) <a href="http://constrain-eu.org/">http://constrain-eu.org/</a> </p>
Weekly NOx aviation emissions changes due to COVID-19: modified SSP2-4.5 to account for sector activity level
<p>Weekly NOx aviation emissions estimates for 2020 until 21/07/2020, modified by the country-specific impacts of COVID-19 lockdown. </p> <p>This repository holds the netcdf files for NOx emissions projected by the scenario SSP2-4.5, from the Scenario4MIPs database (<a href="https://esgf-node.llnl.gov/search/input4mips/">https://esgf-node.llnl.gov/search/input4mips/</a>), modified by the country and sector activity levels associated with lockdown for 2020, with observation-based data up until the 5th of July and a fixed estimate thereafter. This is the weekly equivalent of the aviation file in <a href="https://zenodo.org/record/3951601#.XxYBsihKhPY">https://zenodo.org/record/3951601#.XxYBsihKhPY</a> for a shorter time period, although the version number is different since we have more information available and the normalisation process has been improved. </p> <p>Funding was provided by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement nos. 820829 (CONSTRAIN) <a href="http://constrain-eu.org/">http://constrain-eu.org/</a> </p> <p>see <a href="https://github.com/Priestley-Centre/COVID19_emissions">https://github.com/Priestley-Centre/COVID19_emissions</a> for more details on the methodology.</p>
Non-State Actors and Energy Transition. Prospects on the Role of Private Sector in the UN Agenda 2030.
<p>Conference on January 27th, 2020.</p> <p>International Environmental Law and Climate Change Lecture.</p> <p>Room BLB 1_University of Houston Law Center.</p> <p>Houston, Texas.</p>
CSO Networked Gas Consumption by Dublin Postal District for Residential & Non-Residential Sector 2011-2019 in Parquet & CSV Form
<p>Metered network gas data on a Dublin postal district level from 2010-2019, translated from a HTML to parquet files. </p> <p>Original dataset can be found at https://www.cso.ie/en/releasesandpublications/er/ngc/networkedgasconsumption2019/ </p>
Data Documentation Erdgas-BRidGE – Input data for modeling the power, building, and gas sector
<p>This data documentation provides data representing parts of the power, the building and gas sector, which have been compiled within the research project Erdgas-BRidGE (Erdgas - Bedeutung und zukünftige Rolle in der deutschen (German) Energiewende). The aim of this documentation is to increase the transparency of input data for energy modeling in the German context.</p> <p>Therefore, the report Erdgas-BRidGE_Data_Documentation_Report (2021).pdf documents the data collected and processed in the course of the project. Furthermore, the data set Erdgas-BRidGE_Dataset (2021).xlsx provides the compiled or further processed data with separate table sheets. The script available under the file name PythonCodeToProcessCapacityBookings.zip has been used to process historical capacity bookings.</p> <p>The modifications of the version 1.1.0 – compared to the previous version 1.0.0 – include the following updates:</p> <ul> <li>Minor formal corrections in the report</li> <li>An adjustment in the calculation basis of the district heating profiles</li> <li>Correction of an error in the calculation of the absolute number of individual type buildings in the database for the German building stock.</li> </ul> <p>Erdgas-BRidGE is a joined effort by the Energiewirtschaftliches Insitut an der Universität zu Köln (ewi) and the Chair of Energy Economics at the Technische Universität Dresden (TUD-EE2). The project was funded by the Federal Ministry for Economic Affairs and Energy through the grant "Erdgas-BRidGE", FKZ: 03ET4055A and FKZ: 03ET4055B.</p> <ul> </ul>
8. Sector 1000. Walls partial model (2016)
[Scientific Project](http://www.icac.cat/recerca/projectes-de-recerca/projecte/20942/) [Archaeological report 2016](https://www.researchgate.net/publication/311705780_Archaeological_report_-2016-_of_excavation_in_the_visigothic_settlement_of_Valencia_la_Vella_Riba-roja_del_Turia_Valencian_Country_Spain?_iepl%5BviewId%5D=XzB9abVr2fOR1kpvvnyc8cX4&_iepl%5Bcontexts%5D%5B0%5D=projectUpdatesLog&_iepl%5BtargetEntityId%5D=PB%3A311705780&_iepl%5BinteractionType%5D=publicationTitle) Source: Objaverse 1.0 / Sketchfab
Old model ( sector 1000-2016)
[Scientific Project](http://www.icac.cat/recerca/projectes-de-recerca/projecte/20942/) [Archaeological report 2016](https://www.researchgate.net/publication/311705780_Archaeological_report_-2016-_of_excavation_in_the_visigothic_settlement_of_Valencia_la_Vella_Riba-roja_del_Turia_Valencian_Country_Spain?_iepl%5BviewId%5D=XzB9abVr2fOR1kpvvnyc8cX4&_iepl%5Bcontexts%5D%5B0%5D=projectUpdatesLog&_iepl%5BtargetEntityId%5D=PB%3A311705780&_iepl%5BinteractionType%5D=publicationTitle) Source: Objaverse 1.0 / Sketchfab
Kharkov random private sector yard.
Энгельс Маркс Ленин Сталин, в земле на территории случайного дворика Харькова. Source: Objaverse 1.0 / Sketchfab
Niveles del síndrome de Burnout en profesoras de educación inicial sector Callao
<p>La matriz de datos contiene las siguientes variables: Institución educativa, Edad, Tiempo de servicio, Síndrome de Burnout, Agotamiento emocional, Despersonalización, Baja realización personal y 153 casos.</p>
Programa "Jugando en los sectores" para desarrollar capacidades matemáticas en niños de 4 años de una institución educativa del Callao
<p>La matriz de datos contiene las siguientes variables: Género, Edad, Grupo, Precontrol, Postcontrol, Resultados y 48 casos.</p>
Global Sectoral GDP map at 30'' resolution (SectGDP30) v2.0
<p>- This dataset provides global sector-specific GDP distribution maps (in GeoTIFF format) with a 30-second spatial resolution. It allocates GDP at the 30-arcsecond grid level for three sectors (services, industry, and agriculture) by the distribution of country-level GDP data using high-resolution land cover map.<br>- The source GDP data for allocation is based on nominal GDP for the years 2010, 2015, and 2020, obtained from the World Bank. As the high-resolution land cover map, it uses the built-up area and non-residential area data by the Global Human Settlement Layer (Pesaresi and Politis, 2022) for the service and industrial sectors and the Global cropland map by Potapov et al. (2022) for the agriculture sector. Detailed descriptions of the data creation methodology can be found in Shoji et al. (In Review).<br>- Each pixel represents the monetary value of added value generated by economic activity hypothetically occurring within that pixel. The unit of each pixel value is in millions of USD (current prices for 2010, 2015, and 2020).</p> <p>(Updated to v2.0 on July 1, 2025)</p> <p>This update includes a major change to the spatial allocation method for service and agricultural GDP. For details on the new GDP mapping methodology, please refer to Shoji et al. (In Review). There are no changes to the industrial GDP map.</p> <p> </p> <p>Reference:<br>- Pesaresi M, Politis P.: GHS-BUILT-S R2022A: GHS built-up surface grid, derived from Sentinel2 composite and Landsat, multitemporal (1975–2030). European Commission, Joint Research Centre (JRC), 2022.<br>- Potapov P, Svetlana T, Matthew CH, Alexandra T, Viviana Z, Ahmad K, Xiao-Peng S, Amy P, Quan S, Jocelyn C.: Global maps of cropland extent and change show accelerated cropland expansion in the twenty-first century. Nature Food 3: 19–28, 2022.<br>- Shoji T, Kajiyama K, Yamazaki D, Kita Y, Watanabe M.: Global spatially-distributed sectoral GDP map for disaster risk analysis. In Review.</p>
Data for "Low- and mid-latitude ionospheric response to the 2013 St Patrick's Day geomagnetic storm in the American Sector: GITM simulation"
<p>The dataset stores the GITM simulation results for the study "Low- and mid-latitude ionospheric response to the 2013 St Patrick's Day geomagnetic storm in the American Sector: GITM simulation"</p>
ScienceDex guides
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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.