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37 results for “carbon footprint”
COMPAIR carbon footprint calculations and greenhouse gas emissions reduction scenarios
<p>Citizens' carbon footprint calculation results and citizen-created scenarios on how Greenhouse Gas emissions can be reduced by 55% by 2030 are available that were gathered as part of the <a href="https://cordis.europa.eu/project/id/101036563">EU Horizon2020 COMPAIR project</a> in Europe. The pilot cities/regions are Berlin, Athens, Sofia, Plovdiv, and Flanders.</p>
Dataset for Reaction-Induced Formation of Stable Mononuclear Cu(I)Cl Species on Carbon for Low-Footprint Vinyl Chloride Production
<p>This dataset complements the publication entitled "Reaction-Induced Formation of Stable Mononuclear Cu(I)Cl Species on Carbon for Low-Footprint Vinyl Chloride Production" by Dario Faust Akl, Georgios Giannakakis, Andrea Ruiz-Ferrando, Mikhail Agrachev, Juan D. Medrano-García, Gonzalo Guillén-Gosálbez, Gunnar Jeschke, Adam H. Clark, Olga V. Safonova, Sharon Mitchell, Núria López, Javier Pérez-Ramírez.</p>
Estimating the carbon footprint of citizen science biodiversity monitoring
<p>Datasets used in the production of the paper Gillings, S. & Harris, S.J. 2022. Estimating the carbon footprint of citizen science biodiversity monitoring. People & Nature.</p> <p>The dataset comprises a) the estimated round-trip distances from approximate locations of observers to survey locations for the UK Breeding Bird Survey and b) questionnaire responses concerning mode of travel used to access survey locations. Data have been anonymised and locations have been coarsened to preserve anonymity.</p> <p>We would also greatly appreciate if you could fill out <a href="https://forms.gle/DCc58VXpdmqnTmTk8" target="_blank" rel="noopener">this very short form</a> to tell us how you intend to use these data. Thanks in advance!</p> <p> </p> <p> </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>
The Carbon Footprint of Astronomical Research Infrastructures
<p><strong>Content of the directory</strong></p> <p>This record contains code and data that were used for the paper</p> <p><strong>Knoedlseder, J., et al. Estimate of the carbon footprint of astronomical research infrastructures, Nature Astronomy, in press</strong>.</p> <p><strong>Data</strong></p> <p>The file <em>ri-carbon-footprint.xls</em> contains all data that were used for the analysis in the paper.</p> <p>The file is an excel file containing the following tabs:</p> <ul> <li>Description - a description of the excel file</li> <li>Summary - summary of the findings of the carbon footprint estimates</li> <li>Carbon footprint (ground) - Master table for carbon footprint of ground-based observatories</li> <li>Carbon footprint (space) - Master table for carbon footprint of space missions</li> <li>Emission factors - Collection of emission factors used as input to the study</li> <li>Active infrastructures - List of astronomical facilities that were active worldwide in 2019</li> <li>Community - Astronomical community and IAU members for several countries (Ahn, S.H., Economic Power, Population, and Size of Astronomical Community, JKAS, 52, 159 (2019).</li> </ul> <p><strong>Code</strong></p> <p>The record contains three Python scripts.</p> <p><strong><em>adsquery.py</em></strong></p> <p>Script to query the ADS database to extract number of publications for a given facility. The script also determines the number of unique authors. It returns global numbers since the start of the mission or observatory operations, and numbers restricted to IRAP.</p> <p><em><strong>bootstrap.py</strong></em></p> <p>Script to bootstrap the considered facilities to extrapolate the carbon footprint to all infrastructures that exist worldwide. The script also produces Figure 1 of the paper.</p> <p><strong><em>carbonintensity.py</em></strong></p> <p>Script to generate various figures from the excel data, and in particular the carbon intensity Figure 2 of the paper. Running the script requires the "xlrd" Python module that can be installed via conda.</p>
Heavy metal removal from coal fly ash for low carbon footprint cement
<p>Source data for the publication "Heavy metal removal from coal fly ash for low carbon footprint cement"</p>
Replication Data for "Exploring Developer Views on Software Carbon Footprint and its Potential for Automated Reduction"
<p># Replication Data for "Exploring Developer Views on Software Carbon Footprint and its Potential for Automated Reduction"</p> <p>## Overview</p> <p>Reducing software carbon footprint could contribute to efforts to avert climate change. Past research indicates that developers lack knowledge on energy consumption and carbon footprint, and existing reduction guidelines are difficult to apply. Therefore, we propose that automated reduction methods should be explored. However, such tools must be voluntarily adopted and regularly used to have an impact.</p> <p>In this study, we have conducted interviews and a survey (a) to explore developers' existing opinions, knowledge, and practices with regard to carbon footprint and energy consumption, and (b), to identify the requirements that automated reduction tools must meet to ensure adoption. Our findings offer a foundation for future research on practices, guidelines, and automated tools that address software carbon footprint.</p> <p>## Data Contained in This Package</p> <p>- interview_survey_guide.pdf</p> <p>This file contains the interview and survey questions.</p> <p>- interview_responses.docx</p> <p>This file contains relevant material from the interviews.</p> <p>- survey_responses.xlsx</p> <p>This file contains all survey responses.</p> <p>Both interview and survey data has been anonymized to protect the privacy of the participants.</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>
Replication Data for "Exploring Genetic Improvement of the Carbon Footprint of Web Pages"
<p>## Overview</p> <p>In this study, we explore automated reduction of the carbon footprint of web pages through genetic improvement, a process that produces alternative versions of a program by applying program transformations intended to optimize qualities of interest. We introduce a prototype tool that imposes transformations to HTML, CSS, and JavaScript code, as well as image resources, that minimize the quantity of data transferred and memory usage while also minimizing impact to the user experience (measured through loading time and number of changes imposed).</p> <p>In an evaluation, our tool outperforms two baselines---the original page and randomized changes---in the average case on all projects for data transfer quantity, and 80% of projects for memory usage and load time, often with large effect size. Our results illustrate the applicability of genetic improvement to reduce the carbon footprint of web components, and offer lessons that can benefit the design of future tools.</p> <p>## Data Contained in This Package</p> <p>- experiment_data/Subject Project-XX-X.xlsx</p> <p>Each spreadsheet contains data collected as part of our experiments, including the fitness scores of the final solutions.</p>
A before/after intervention study to determine impact on life cycle carbon footprint of converting from single-use to reusable sharps containers in 40 United Kingdom NHS Trusts
<p>The purpose of this study was t<span>o compare Global Warming Potential (GWP) of hospitals converting from single-use to reusable sharps containers (SSC, RSC). Does conversion to RSC result in GWP reduction? </span><span>Using BS PAS 2050:2011 principles, a retrospective, before/after intervention quantitative model together with a purpose-designed, attributional "cradle-to-grave" life cycle tool, were used to determine the annual GHG emissions of the two sharps containment systems. Functional unit was total fill-line litres (FLL) of sharps containers needed to dispose of sharps for one-year period in 40 trusts. Scope 1, 2 and 3 emissions were included. Results were workload-normalised using NHS national hospital patient-workload indicators. A sensitivity analysis examined areas of data variability.</span></p> <p><span><b>Setting</b>. Acute-care hospital trusts in United Kingdom.</span></p> <p><span><b>Participants</b>. 40 NHS hospital Trusts using RSC.</span></p> <p><span><b>Intervention</b>. <span>Conversion from SSC to RSC. SSC and RSC usage details in </span><span>17 base-line trusts immediately prior to 2018 were applied to the RSC usage details of the 40 trusts using RSC in 2019</span><span>.</span></span></p> <p><span><span>The comparison of GWP </span>calculated in carbon dioxide equivalents (CO<sub>2</sub>e)<span> generated in the manufacture, transport, service and disposal of</span> 12 months, hospital-wide usage of both containment systems in the 40 trusts. </span><span>The 40 trusts converting to RSC reduced their combined annual GWP by 3267.4 tonnes CO<sub>2</sub>e (-83.9%); eliminated incineration of 900.8 tonnes of plastic; eliminated disposal/recycling of 132.5 tonnes of cardboard; and reduced container exchanges by 61.1%. GHG as kg CO<sub>2</sub>e/1000 FLL were 313.0 and 50.7 for SSC and RSC systems respectively. A sensitivity analysis showed substantial GHG reductions within unit processes could be achieved, however their impact on relevant final GWP comparison varied <5% from base comparison.</span> Adopting RSC is an example of a sustainable purchasing decision that can assist trusts meet NHS GHG reduction targets and can reduce GWP permanently with minimal staff behaviour-change.</p>
Carbon footprint of synthetic nitrogen under staple crops: A first cradle-to-grave analysis
<p>More than half of the world's population is nourished by crops fertilized with synthetic nitrogen (N). However, N fertilization is a major source of anthropogenic emissions, augmenting the carbon footprint (CF). To date, no global quantification of the CF induced by N fertilization of the main grain crops has been performed, and quantifications at the national scale have neglected the CO<sub>2</sub> assimilated by plants. A first Cradle-Grave life cycle assessment was performed to quantify the CF of the N fertilizers' production, transportation, and application to the field and the uses of the produced biomass in livestock feed, human food, and biofuel production. We quantified direct and indirect inventories emitted or sequestered by the N fertilization of grain crops (wheat, maize, and rice). Grain food produced with N fertilization had a net CF of 7.4 Gt CO<sub>2</sub>eq. in 2019 after excluding the assimilated C in plant biomass, which accounted for a quarter of the total CF. The Cradle (fertilizer production and transportation), Gate (fertilizer application, and soil and plant systems), and Grave (feed, food, biofuel, and losses) stages contributed to the CF by 2, 11, and 87%, respectively. Although Asia was the top grain producer, North America contributed 38% of the CF due to the greatest CF of the Grave stage (2.5 Gt CO<sub>2</sub>eq.). The CF of grain crops will increase to 21.2 Gt CO<sub>2</sub>eq. in 2100, driven by the rise in N fertilization to meet the growing food demand without actions to stop the decline in N use efficiency. To meet the targets of climate change, we introduced an ambitious mitigation strategy, including the improvement of N agronomic efficiency (6% average target for the three crops) and manufacturing technology, reducing food losses, and global conversion to healthy diets, whereby the CF can be reduced to 5.6 Gt CO<sub>2</sub>eq. in 2100.</p>
The cosmic carbon footprint of massive stars stripped in binary systems
<p># Structure</p> <p>## Overview</p> <p>The folder data/ contains the inlists and mod files used in this work. Intermediate data (like history or profile) files must be regenerated from the provided files.</p> <p>The folder plots/ contains a jupyter notebook set-up to remake all plots (assuming the data is saved in the data/ folder). There are also additional scripts and files needed to reproduce this work.</p> <p>The griffith.txt file is the data from https://ui.adsabs.harvard.edu/abs/2021arXiv210309837G/abstract and was accessed from https://github.com/giganano/VICE/blob/master/vice/yields/ccsne/S16/W18F/FeH0/v0/explosive/c.dat</p> <p><br> ## Data folders</p> <p>corehedep - Evolution from ZAMS to end of core helium burning<br> coreodep - Evolution from end of core helium burning to end of core oxygen burning<br> cc - Evolution from end of core oxygen burning up to core collapse<br> ccsn - Evolution from core collapse to shock breakout</p> <p>engmc - Tests variations in the injection energy and mass cut of ccsn explosions<br> spacetime - Test variations in space/time resolution of ccsn explosions<br> ccsn_t_m - Test variations in injection time and injection mass of ccsn explosions</p> <p>## Sub-folders</p> <p>corehedep/base - Base folder with inlists for this set of models<br> corehedep/binary - Contains a folder for each mass for the binary-stripped stars (11-45)<br> corehedep/single - Contains a folder for each mass for the single stars (11-45)<br> corehedep/net/23 - A single star 23msun model ran with a larger nuclear network</p> <p>coreodep/base - Base folder with inlists for this set of models<br> coreodep/binary - Contains a folder for each mass for the binary-stripped stars (11-45)<br> coreodep/single - Contains a folder for each mass for the single stars (11-45)</p> <p>coreodep/mesh - Test variations with respect to space and time during carbon burning<br> coreodep/overshoot - Test variations with respect to overshoot during carbon burning<br> coreodep/net - A single star 23msun model ran with a larger nuclear network</p> <p>cc/base - Base folder with inlists for this set of models<br> cc/binary - Contains a folder for each mass for the binary-stripped stars (11-45)<br> cc/single - Contains a folder for each mass for the single stars (11-45)</p> <p><br> ccsn/base - Base folder with inlists for this set of models<br> ccsn/binary - Contains a folder for each mass for the binary-stripped stars (11-45)<br> ccsn/single - Contains a folder for each mass for the single stars (11-45)<br> ccsn/laplace_binary - Contains core collapse explosions of the binary-stripped models from Laplace et al 2021<br> ccsn/laplace_single - Contains core collapse explosions of the single star models from Laplace et al 2021</p> <p>spacetime/base - Base folder with inlists for this set of models<br> spacetime/binary - Test variations in space/time resolution of ccsn explosions</p> <p>ccsn_t_m/base - Base folder with inlists for this set of models<br> ccsn_t_m/binary - Test variations in injection time and injection mass of ccsn explosions</p> <p>engmc/base - Base folder with inlists for this set of models<br> engmc/binary - Tests variations in the injection energy and mass cut of ccsn explosions</p> <p><br> ## Notes</p> <p>Folders with the name '_k' have enhanced profile output for use in Kippenhan plots.</p> <p>Folders with the name '_v' have enhanced profile output for use in the video of the shock explosion.</p> <p>Each numbered folder contains a set of inlists used (which usually only vary one or two parameters), the rest of the inlists are stored in the base/ folders (See the submit.sh files for how to get MESA to read these files). They also contain a initial.mod file (which is the starting point for this phase of evolution), this is a softlink to the final.mod file from the previous phase (thus coreodep soft links to files in corehedep, corehdep uses MESA's built in ZAMS models to start).</p> <p>The folders that handle the core collapse explosions have additional .mod files that handle each phase of the explosion. See the base/ folders for details on the order.</p> <p>## Files</p> <p>cacheHist.py - Runs mesaplot code to turn history files into a python binary file for faster reading.<br> plotKip.py - Does a quick kippenhan plot for diagnostics</p> <p> </p>
E-storage driven sustainable and resilient city renaissance with lifecycle carbon footprints and levelized costs of carbon abatement
<p>The dataset contains: The simulation results of the energy balance data, battery capacity sizing data and battery degradation data. The data set also includes calculations and results based on the net present value, carbon emission, levelized cost of storage (LCOS), levelized cost of energy (LCOE) and levelized cost of carbon abatement (LCCA).</p>
Carbon footprint of synthetic nitrogen under staple crops: A first cradle-to-grave analysis
Open the record for dataset details and reuse information.
A before/after intervention study to determine impact on life cycle carbon footprint of converting from single-use to reusable sharps containers in 40 United Kingdom NHS Trusts
Open the record for dataset details and reuse information.
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>
Data related to carbon flux footprints
<p>Data related to carbon flux footprints</p> <p>Researchers should preferably obtain access by making a data request at the Semi‐Arid Climate and Environment Observatory of Lanzhou University (SACOL; <a href="http://climate.lzu.edu.cn/English/Data_Sharing/LACMS_data.htm">http://climate.lzu.edu.cn/English/Data_Sharing/LACMS_data.htm</a>)</p>
A hybrid chemical-biological approach can upcycle mixed plastic waste with reduced cost and carbon footprint
<p>Derived from renewable feedstocks, such as biomass, polylactic acid (PLA) is considered a more environmentally-friendly plastic than conventional petroleum-based polyethylene terephthalate (PET). However, PLA must still be recycled and its growing popularity and mixture with PET plastics at the disposal stage poses a cross-contamination threat in existing recycling facilities and results in low-value and low-quality recycled products. Hybrid upcycling has been proposed as a promising sustainable solution for mixed plastic waste; but its techno-economic and lifecycle environmental performance remain understudied. Here we propose a hybrid upcycling approach using a biocompatible ionic liquid (IL) to first chemically depolymerize plastics, then convert the depolymerized stream via biological upgrading with no extra separation. We show that over 95% of mixed PET/PLA was depolymerized into their respective monomers, which then served as the sole carbon source for the growth of <i>Pseudomonas putida</i>, enabling the conversion of the depolymerized plastics into biodegradable polyhydroxyalkanoates (PHA). In comparison to conventional commercial PHA, the estimated optimal production cost and carbon footprint are reduced by 62% and 29%, respectively.</p>
ScienceDex guides
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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.