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620 results for “sector”
Рис. 2. Раскоп 1: А – сектор 5, профиль бровки по линии меЖду квадратами 20–21; В – северная стенка, квадраты 21–22. Fig. 2. Excavation 1: A – sector 5, the profile of the edge along the line between the squares 20–21; B – the northern wall, squares 21–22. in Mollusks from the shell-midden of the Telyakovskogo 2 site in southern Primorye (Yankovskaya culture), their paleoecology and role in paleoeconomy
Рис. 2. Раскоп 1: А – сектор 5, профиль бровки по линии меЖду квадратами 20–21; В – северная стенка, квадраты 21–22. Fig. 2. Excavation 1: A – sector 5, the profile of the edge along the line between the squares 20–21; B – the northern wall, squares 21–22.
Structural breaks in greenhouse gas emissions for OECD countries in 1995-2022 and 37 sectors
<p>The quantitative assessment of policy instruments aimed at climate change mitigation requires the rigorous identification of abnormal changes in greenhouse gas emissions. We present a new dataset of robust level changes in greenhouse gas emissions that cannot be explained by aggregate socioeconomic fluctuations. Modern methods of structural break identification based on two-way fixed effects models are employed to estimate the size of significant level changes in emissions. The resulting dataset spans information for all OECD countries and all 37 IPCC sectors, ranging from 1995 to 2022. The data unveils large differences in abnormal changes in emissions across gases, countries and sectors, as well as over time. Our resulting data can be applied to a broad range of research questions, including the analysis of the comparative efficacy of policy instruments to mitigate climate change.</p>
Analysis of policy measures on designing a renewable fuel supply chain in the transport sector- Supplementary materials
<p><strong>This repository contains supporting data for: "Analysis of policy measures on designing a renewable fuel supply chain in the transport sector"</strong></p> <ol> <li> <p><strong>Supplementary Materials</strong>: This collection encompasses the model input data, alongside a detailed formulation of the objective functions.</p> </li> <li> <p><strong>Pareto_Table</strong>: This file contains the Pareto frontier results. </p> </li> <li><strong>DoE_Results_Table</strong>: This file presents the results of various solution scenarios, each characterized by differing levels of policy measures.</li> </ol>
Self-Assessed Radiation Safety Culture Across Sectors
<p>The aim of this work is to compare radiation safety culture in the medical and nuclear sectors, by means of a radiation protection self-assessment tool.</p> <p>This is a programme level assessment, considering the department’s culture to the extent that it exhibits the traits of a healthy safety culture described by the US Institute of Nuclear Power Operations (INPO)<sup>1</sup>. It draws on the levels of radiation safety self-assessment described in Report 162 of the National Council on Radiation Protection and Measurements (NCRP)<sup>2</sup>.</p> <p>The tool consists of fifteen good practice statements, against which users assess themselves on a five point scale. Each statement is accompanied by prompt questions, to provide evidence for the choice of response.</p> <p>The purpose of the self-assessment tool is to assist employers in taking ownership of radiation protection by recognising the common management tools and processes that apply across departments and modalities. It is also expected to aid recognition of the elements of day-to-day working that contribute to an organisation’s culture and how these express the importance of radiation safety.</p> <p>Data will be presented demonstrating how perceived safety culture varies both within and between sectors, and suggestions made as to how this approach could contribute to improving safety culture in the UK.</p> <p>The work is in keeping with the new ICRP general recommendations in that it incorporates aspects of communications and stakeholder engagement, set out in the overarching considerations in section 3 of the draft report.</p>
scatterbrain: TESS Sector 2 Backdrop Files
<p>This is a preliminary upload of the driver files for scatterbrain, for TESS sector 2, camera1. </p>
Dataset- Decarbonization of the Indian Electricity Sector: Technology Choices and Policy Trade-Offs
<p>Input and output data for 40 scenarios for the 2040 Indian electricity sector.</p> <p>Compatible with old versions of GenX: https://github.com/GenXProject/GenX, available upon request.</p> <p> </p>
Data for Deciphering-the-CO2-emissions-and-emission-intensity-of-cement-sector-in-China-through-decomposition-analysis
<p>The dataset contains data for Figure 7-13 in our article "<em>Deciphering the CO<sub>2</sub> emissions and emission intensity of cement sector in China through decomposition analysis</em>", and data for part of<em> China Cement Industry Dataset (CCID)</em>. </p>
Survey questionnaire and results on Structural Barriers to Investment in Energy Efficiency Policies in the Private Rented Sector
<p>The online survey was designed and conducted in the framework of the EU H2020 project ENPOR ("Actions to Mitigate Energy Poverty in the Private Rented Sector). The aim of the survey was to receive statistically sound insights on structural factors that affect the implementation of energy efficiency policies for the alleviation of energy poverty in the European Private Rented Sector. We developed it as an explorative, semi-quantitative, self-completion online questionnaire, using the online tool “EUSurvey”. We performed the online survey among different stakeholders from academia, policy, NGO’s, landlords and tenant associations, etc.</p>
Model output data and code for Zhang et al., Cross-cutting scenarios and strategies for designing decarbonization pathways in the transport sector toward carbon neutrality
<p>Model output data and code for "Zhang et al., Cross-cutting scenarios and strategies for designing decarbonization pathways in the transport sector toward carbon neutrality" in Nature Communications.</p>
Supporting Data for "Decarbonization pathways for the residential sector in the United States"
<p>This repository contains input and processed data for the analysis presented in "Decarbonization pathways for the residential sector in the United States" by Berrill et al. (2022), in Nature Climate Change. </p> <p>Description of the files can be found in the 'Data_Descriptors.docx' file.</p> <p>Please direct any enquiries relating to this dataset to peter.berrill@aya.yale.edu</p>
Customised pre-built Sector-coupled Euro-Calliope Model - Focus on the power sector and additional SPORES options
<p><strong>Customised pre-built Sector-coupled Euro-Calliope Model - Focus on the power sector and additional SPORES options</strong></p> <p><em>Based on the <a href="https://zenodo.org/record/5774988#.YqwqYDJByUk">pre-built Sector-coupled Euro-Calliope model</a> developed by Bryn Pickering</em></p> <p>This model is pre-packaged and ready to be loaded into Calliope, based on 2015 input data. To run the model as done in the associated publication you will need to do the following:</p> <ol> <li>Install a specific conda environment to be working with the correct version of Calliope ( <code>conda env create -f requirements.yml</code> )</li> <li>Run the model including only those scenarios that relate to the power sector and SPORES</li> </ol> <p> </p> <p><strong>Main and parallel batches of SPORES</strong></p> <p>To facilitate this second point and the reproduction of results, you'll find some pre-packaged python script with all and only those model scenarios that allow you to run either the "main batch" of SPORES (<code>spores_model_run.py</code>) or any of the "parallel batches" of SPORES (e.g., <code>excl_bio</code> and <code>max_bio</code>, which generate SPORES while minimising and, respectively, maximising bioenergy deployment).</p> <p> </p> <p><strong>Strength of the anchoring to extremes of the decision space</strong></p> <p>To tweak the strength of the anchoring to a specific technology feature, as we do in the paper, you need to modify the <code>euro_calliope/spores.yaml</code> override file. More precisely, you need to change the <code>excl_score</code> parameter in the objective function at the end of the file:</p> <pre><code class="language-bash">max_mode.run.spores_options.objective_cost_class: {'spores_score': 1, 'monetary': 0, 'excl_score': -1} excl_mode.run.spores_options.objective_cost_class: {'spores_score': 1, 'monetary': 0, 'excl_score': 1}</code></pre> <p>A value of 1 (for maximisation) or -1 (for minimisation) is the default by which we generate the primary results in the paper. By changing it to 0.1, you can reproduce as well the secondary results that we use as a sensitivity for a "weaker anchoring" to extreme technology features of the decision space.</p> <p><br> <strong>Weight-assignment method</strong></p> <p>Finally, to change the weight-assignment method, you need to modify the <code>euro_calliope/eurospores/model.yaml</code> file. More precisely, the <code>scoring_method</code> parameter, which can be one of the following: <code>integer</code>, <code>relative_deployment</code>, <code>random</code> or <code>evolving_average</code>.</p> <pre><code class="language-bash">run.spores_options.scoring_method: integer</code></pre> <p> </p> <p><strong>Hard-coded changes to be aware of</strong></p> <p>The files in this model theoretically allow accounting for all energy sectors (power, heat, transport, industry). Yet, we subset the analysis in the associated publication to only the power sector. To this end, we have modified the original electricity demand file (<code>euro_calliope/eurospores/electricity-demand.csv</code>).</p> <p>In fact, the original file did not account for the fraction of electricity associated with heat, transport or industry consumption, which was instead allocated to sector-specific demand files. In such a way, the model was free to decide whether to electrify these sectoral demands or not. In the present study, instead, we wanted to run our analysis based on the current electricity demand, inclusive of the currently electrified sector-specific demand. Therefore, we have replaced the original file with a new one that includes the present-day electricity demand, with no subtractions.</p> <p>If you want to run the analysis for all sectors, unlike we do in the study, you'll first need to recover the original file. You'll quickly find it in the same folder, named as <code>__electricity-demand.csv</code>.</p> <p><br> <strong>Summary of results from the paper</strong></p> <p>The folder <code>paper_summary_results</code> features some CSV files that summarise the results we obtained for our study across all the different tested search strategies.</p>
GENeSYS-MOD Transport Sensitivities: Data and model code for Hainsch (preprint): Identifying policy areas for the transition of the transportation sector
<p>This dataset contains all GENeSYS-MOD input data for Hainsch (preprint): Identifying policy areas for the transition of the transportation sector. doi: 10.5281/zenodo.6919452.</p> <p>With the input data files and the GAMS files, the model results presented in the preprint can be replicated.</p> <p>Furthermore, the output folder contains the result files for the base case and all sensitivities as well as the Tableau files which were used to generate the result figures.</p>
Manual material handling in the supermarket sector: full dataset
<p><strong>Manual material handling in the supermarket sector: full dataset</strong></p> <p><strong>Sup. figures 1-50:</strong> Trunk flexion/extension (T8 relative to pelvis), lateral bending and rotation, knee flexion/extension and shoulder flexion/extension joint angles over the complete lifting cycles for all 50 analyzed manual material handling tasks (listed in Tables 1a and 1b).</p> <p><strong>Sup. figures 51-76:</strong> Knee and shoulder (glenohumeral) resultant joint reaction forces, as well as L4-L5 and L5-S1 axial compression, anteroposterior shear and mediolateral shear forces over the complete lifting cycles for the 26 manual material handling tasks included in the musculoskeletal model analysis (listed in Table 9) .</p> <p><strong>Tables 1-8 (a and b):</strong> Peak, 90<sup>th</sup> and 50<sup>th</sup> percentile muscle activity of trapezius descendens and erector spinae longissimus, bilateral peak joint angle and ROM for knee flexion-extension and shoulder flexion-extension for the 50 manual material handling tasks (description in Table 1a and 1b). The tasks are ranked from 1 to 50 (highest to lowest) for each outcome variable. </p> <p><strong>Tables 9-12:</strong> L5-S1 axial compression peak force and impulse, anteroposterior shear and mediolateral shear peak forces (Table 10), bilateral knee and shoulder (glenohumeral) peak resultant joint reaction forces (Table 11) as well as bilateral peak net total shoulder joint and knee flexion/extension moment (Table 12) for the 26 manual material handling tasks included in the musculoskeletal model analysis (description in Table 9). The tasks are ranked from 1 to 26 (highest to lowest) for each variable. </p>
Text-fig. 7. Geology of the Muaredzi-Muanza sector of the Cheringoma Plateau showing the location of fossil occurrences. White stars – fossiliferous localities mapped by Pickford (2012, 2013), Black stars – fossil sites mapped by Habermann et al. (2019) and d'Oliveira Coelho et al. (2021) (GPL 12 and GPL 12b correspond to the White Patch sites). TTI – Cheringoma Formation, TTs1 – Mazamba Formation, TTs1a – Palaeopan facies, TTs2 – Inhaminga Formation, Qc – Quaternary sediments. The base map is modified from Google Earth. in Stratigraphy, Chronology And Palaeontology Of The Tertiary Rocks Of The Cheringoma Plateau, Mozambique
Text-fig. 7. Geology of the Muaredzi-Muanza sector of the Cheringoma Plateau showing the location of fossil occurrences. White stars – fossiliferous localities mapped by Pickford (2012, 2013), Black stars – fossil sites mapped by Habermann et al. (2019) and d'Oliveira Coelho et al. (2021) (GPL 12 and GPL 12b correspond to the White Patch sites). TTI – Cheringoma Formation, TTs1 – Mazamba Formation, TTs1a – Palaeopan facies, TTs2 – Inhaminga Formation, Qc – Quaternary sediments. The base map is modified from Google Earth.
Supplementary Data: Full Results: Synergies of sector coupling and transmission extension in a cost-optimised, highly renewable European energy system
<p>Supplementary Data</p> <p><a href="https://arxiv.org/abs/1801.05290"><strong>Synergies of sector coupling and transmission extension in a cost-optimised, highly renewable European energy system</strong></a></p> <p>Authors: T. Brown, D. Schlachtberger, A. Kies, S. Schramm, M. Greiner</p> <p><a href="https://arxiv.org/abs/1801.05290">arXiv:1801.05290</a></p> <p>The files in this record contain the full output data from each of the scenarios considered in the above publication. They also include the post-processed input data, which might be useful if you want to rerun the scenarios with only small changes to the input data.</p> <p>The scripts to build the model, input data and result summaries can be found in a <a href="https://zenodo.org/record/1146665">companion Zenodo repository</a>. (The supplementary data was split because of the size of the full results.)</p> <p>For each scenario, there is a <a href="https://github.com/PyPSA/PyPSA">PyPSA</a> network file in <a href="https://en.wikipedia.org/wiki/Hierarchical_Data_Format">HDF5 format</a> and a CSV of shadow prices.</p> <p>To read in a network file do:</p> <pre><code class="language-python">import pypsa network = pypsa.Network("network_file_name.h5")</code></pre> <p>All data is released under the <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International Licence</a> (CC BY 4.0).</p>
Comparing long-term power sector pathways in Viet Nam: a simple cost-optimization approach with OSeMOSYS
<p>Six clicSAND files for OSeMOSYS Modelling in Viet Nam. The six files depict six different scenarios:</p> <p>1. Power Development Plan 7-based scenario</p> <p>2. Power Development Plan 8-based scenario</p> <p>3. Renewable Energy Development Strategy-based scenario</p> <p>4. Renewables-Led Pathway-based scenario</p> <p>5. Net Zero</p> <p>6. Clean Efficient Transition</p> <p>The Reference Energy System (RES) is also included to represent the relationship between estimated energy demands, energy conversion technologies, fuel mixes, and the resources required to satisfy the demands in the study.</p>
Dataset: Cabana Target Leading Sector Moderate ETF (CLSM) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: First Trust NASDAQ-100-Technology Sector Index Fund (QTEC) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: First Trust NASDAQ-100 Ex-Technology Sector Index Fund (QQXT) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: PeakShares Sector Rotation ETF (PSTR) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
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.