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Dataset results
10 results for “Energy Footprint”
Data for - The environmental footprint of transport by car using renewable energy
<p>Replacing fossil fuels in the transport sector by renewable energy will help combat climate change. However, lowering greenhouse gas emissions by switching to alternative fuels or electricity can come at the expense of land and water resources. To understand the scale of this possible tradeoff we compare and contrast carbon, land and water footprints per driven km in midsize cars utilizing conventional gasoline, biofuels, bioelectricity, solar electricity and solar-based hydrogen. Results show that solar-powered electric cars have the smallest environmental footprints per km, followed by solar-based hydrogen cars, and that biofuel-driven cars have the largest footprints.</p>
How much data about the data centres' energy footprint is currently available globally?
<p><strong>By scraping the open database "Datacenter.rs", the candidate analysed and mapped more than 6.000 data centres worldwide on April 9, 2022.</strong></p>
How do Google News' top 100 sources visually represent the data centres' energy footprint?
<p><strong>By querying "data centres' energy footprint" on Google News in incognito mode, the candidate has selected and mapped the top 100 results according to the ranking on May 15, 2022. </strong></p>
Dataset associated with Schyns & Vanham (2019) "The water footprint of wood for energy consumed in the European Union"
<p>Input and output datasets related to the paper Schyns & Vanham (2019) The water footprint of wood for energy consumed in the European Union. <em>Water</em>, 11(2): 206.</p>
Source data for "Halving the North Sea's offshore wind energy carbon footprint"
<p>This dataset provides source data for the paper "Halving the North Sea’s offshore wind energy carbon footprint". It contains basic geographical factors, including wind speed, water depth, and distance from shore, and environmental impact intensities, including steel, Cu, and Al use, climate change, marine ecotoxicity, and marine eutrophication impacts. For more details, please refer to https://pubs.acs.org/doi/full/10.1021/acs.est.2c02183 and https://www.sciencedirect.com/science/article/pii/S1364032122004993. </p>
LOFAR Carbon Footprint and Energy Consumption
<p>The LOw Frequency ARray (LOFAR) is a European radio telescope operating since 2010 in the frequency bands 10 - 80 MHz and 110 - 250 MHz. This Excel model provides an analysis of the energy consumption and the carbon footprint of LOFAR. The analysis uses a Life Cycle Analysis following the Green House Gas protocol. Results include the footprint stemming from operations of all LOFAR stations and central processing. The impact of a number of typical science projects is analyzed as well. This model provides a transparent baseline to the sustainability of LOFAR and can serve as a blueprint for the analysis of other research infrastructures.</p>
The Energy consumption and Carbon Footprint of the LOFAR Telescope V2.0
<p>The LOw Frequency ARray (LOFAR) is a European radio telescope operating since 2010 in the frequency bands 10 - 80 MHz and 110 - 250 MHz. This article provides an analysis of the energy consumption and the carbon footprint of LOFAR. The approach used is a Life Cycle Analysis (LCA). We find that one year of LOFAR operations requires 3,627 MWh of electricity, 48,714 m3 gas and 135,497 liters of fuel. The associated carbon emission is 2,624 tCO2e/year. Results include the footprint stemming from operations of all LOFAR stations and central processing, but exclude scientific post-processing and activities. The potential recovery of embodied footprint in construction materials at the end of life equals 17%. The electrical energy required for scientific processing is assessed separately. It ranges from 1% (standard The Energy Consumption and Carbon Footprint of the LOFAR Telescope imaging and time-domain), to 40% (wide field long baseline imaging) of the energy consumption for the observation. The outcome provides<br> a transparent baseline in making LOFAR more sustainable and can serve as a blueprint for the analysis of other research infrastructures.</p>
Global Energy Virtual Water Trade Network and Country Electricity Water Footprints
<p>Global energy trade accounts for between 22 and 32% of total energy consumption, approximately 2.0 x 10<sup>11</sup> GJ of energy. Based on concepts of the energy-water nexus and water footprinting, we determine the water intensity of the energy trade for 11 different energy commodities, including primary (i.e., fossil fuels) and secondary (i.e., electricity) energy. In this data description, we present a database on the water footprint of country-to-country energy trade for 2010--2018 and water intensity values of electricity for each country. The database includes the country of origin, trade partner, type of energy commodity, quantity traded, value of the energy trade, a mean water footprint value in m<sup>3</sup>, and an estimated range of uncertainty water footprint. These data provide the basis for assessing international virtual water trade, water scarcity concerns, and the environmental implications for a changing global energy system. </p>
Photovoltaic Windows to Offset the Intensive Energy and Carbon Footprints of Highly Glazed Buildings
<p>Data generated by extensive building energy simulations to determine the impact of next-generation glazing technologies.</p>
Data for National carbon footprint estimations of an electrified internet of energy with circular economy under future EV market share prediction in China
<p>This dataset is created for National carbon footprint estimations of an electrified internet of energy with circular economy under future EV market share prediction in China.</p> <p>The dataset includes the energy demand for buildings of each province in China, the Centralized and Distributed PV-battery system design of each province in China, The EV and ICEV carbon emission comparison of each province in China, the electricity price in China, The Carbon footprint and NPV calculation of current and future building-transportation system in China.</p>
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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.