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620 results for “sector”
Dissolved trace metal (Fe, Ni, Cu, Zn, Cd, Pb) concentrations in the Indian and Pacific sectors of the Southern Ocean from the Antarctic Circumnavigation Expedition (2016-2017)
<p>Dissolved trace metal (Fe, Ni, Cu, Zn, Cd, Pb) concentrations in the Indian and Pacific sectors of the Southern Ocean from the Antarctic Circumnavigation Expedition, 2016-2017.</p> <p>Dissolved trace metal (Fe, Ni, Cu, Zn, Cd, Pb) concentrations measured on seawater samples from the Southern Ocean. Samples were collected with a trace metal clean rosette system to a maximum depth of 1000 m during Legs 1 and 2 of the Antarctic Circumnavigation Expedition (ACE), 2016-2017. Samples were filtered through Akropak Supor filters (0.2 um) in a class 100 clean container, acidified to pH ≤ 2 and stored until analysis (>6 months). Samples from Leg 1 (TMR Casts 3-7) were collected during a transect from Cape Town, South Africa to Hobart, Australia. Samples from Leg 2 (TMR casts 8-20) were collected during a transect from Hobart, Australia to Punta Arenas, Chile. Data cover environments near subantarctic and Antarctic islands (TMR 3, 4, 13-15), in the Mertz Glacier Polynya (TMR 11-12) and near the Antarctic Peninsula (TMR 18), as well as meridional transects to and from the Antarctic continent (TMR 7-12, TMR 18-20).</p>
Monthly aerosol emissions and GHG concentration projections from 2020-2025: modified SSP2-4.5 to account for COVID-19 impacts on sector activity
<p>This repository holds the netcdf files for emissions and concentrations 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 5 years after 2020. The details of these activity estimates are available from <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/tree/endof2020</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> to have a different timeframe. </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>
Four-year blip emissions changes due to COVID-19: modified SSP2-4.5 to account for sector activity level
<p>This repository holds the netcdf files for aerosol 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 5 years after 2020 before returning to baseline. The details of these activity estimates runs in parallel to those described in <a href="https://github.com/Priestley-Centre/COVID19_emissions">https://github.com/Priestley-Centre/COVID19_emissions</a>, except instead of a 2-year blip, we have done a 4-year blip. Note that it is one year after the blip has finished before things return to baseline.</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. We present only a single scenario (called 4-year blip, featuring a one year recovery after the end of the 4 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>
Satellite-observed surface flow speed within Russell sector, West Greenland, bi-weekly average of 2015-2019
<p>An average horizontal surface ice velocity of Russell sector (Greenland) with 2-week temporal and 150m spatial resolution. Derived from satellite images collected between 2015 and 2019 by Landsat-8, Sentinel-1, and Sentinel-2. The details on the data processing can be found in https://doi.org/10.5194/tc-2021-170.</p> <p><br> Dataset contains 24 independent NetCDF files (one per 2-weeks time step) with maps of vx and vy velocity components, maps of associated uncertainties per velocity component (STD of the 2-weeks averaged raw satellite measurements), and map of number of averaged measurements.</p>
Seasonal evolution of basal conditions within Russell sector, West Greenland, inverted from satellite observations of surface flow
<p>An annual set of model-inferred basal and surface properties of ice flow at Russell Gletcher sector in Western Greenland with half-month temporal resolution. Derived using the Elmer/Ice ice-flow model by inversion of satellite-observed ice surface velocity (10.5281/zenodo.5535532). The details on the data creatoin can be found in 10.5194/tc-15-5675-2021 .</p> <p>Dataset contains 24 independent NetCDF files (one per 2-weeks time step) with:<br> * alpha - inverted be model basal friction coefficient in log10 (log10(MPa m-1 a)<br> * base - basal topography altitude (m)<br> * lithk - ice thickness (m)<br> * orog - surface altitude (m)<br> * strbasemag - magnitude of basal friction tb (MPa)<br> * xvelbase, yvelbase, zvelbase - 3D basal velocity (m/yr)<br> * xvelmean, yvelmean - vertically average mean horizontal velocity (m/yr)<br> * xvelsurf, yvelsurf, zvelsurf - 3D surface velocity (m/yr)<br> * n - effective pressure (MPa)</p> <p>The additional WinterMeanState NetCDF file (inversion from the mean velocity of january, Febriary, Mars) contains the same set of variables (except the effective pressure), and in addition contains the <em>As</em> Weertman sliding coeffitient.</p> <p>The results have been interpolated from the native unstructured model grid to the regular grid used for the observed velocity (10.5281/zenodo.5535624).</p>
Monthly CO2 emissions projections from 2015-2025: modified SSP2-4.5 to account for COVID-19 impacts on sector activity
<p>Monthly CO2 emissions projections 2015-2025, modified by country-specific impacts of COVID-19 lockdown in 2020-2023, with 4 different projections for the period 2024-2025. </p> <p>This repository holds the netcdf files for CO2 emissions from ground-level and aviation sources from the MESSAGE_GLOBIOM 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. Sector activity level in 2020 is based on data up until June, and a fixed estimate is used thereafter. This is the monthly equivalent of <a href="https://zenodo.org/record/3951601#.XxYBsihKhPY">https://zenodo.org/record/3951601#.XxYBsihKhPY</a> for this time period.</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.</p>
Model simulation data used in "The global impact of the transport sectors on atmospheric aerosol in 2030 – Part 2: Aviation" (Righi et al., Atmos. Chem. Phys., 2016)
<p>This dataset contains the output of the EMAC global model simulations analysed and discussed in Righi et al. (<i>Atmos. Chem. Phys.</i>, 2016). For details see the README.md file.</p>
Model simulation data used in "The global impact of the transport sectors on atmospheric aerosol in 2030 – Part 1: Land transport and shipping" (Righi et al., Atmos. Chem. Phys., 2015)
<p>This dataset contains the output of the EMAC global model simulations analysed and discussed in Righi et al. (<i>Atmos. Chem. Phys.</i>, 2015). For details see the README.md file.</p>
Measurements of benzene and toluene in underway surface seawater and ambient air in the Atlantic sector of the Southern Ocean on cruise ANDREXII/JR18005 between February and April 2019.
<p>Benzene and toluene cycling iin the unpolluted marine environment s poorly understood. Due to a paucity of measurements, the role of the ocean in the atmospheric budgets of atmospheric benzene and toluene is unknown. In order to quantify the air-sea fluxes of these gases and obtain insights to their biogeochemical cycling, we measured their seawater concentrations (surface and depth profiles) and air mixing ratios in the Atlantic sector of the Southern Ocean, along a ~11000 km long transect at approximately 60o S in Feb-Apr 2019. The measurements were made using a Proton Transfer Reaction Mass Spectrometer coupled to a Segmented Flow Coil Equilibrator. Concentrations, oceanic saturations and calculated fluxes benzene and toluene are presented here. </p> <p> </p> <p>The data is further presented and discussed in a manuscript: </p> <p>Marine biogenic benzene and toluene emissions and their impact on secondary organic aerosol in the polar regions. Charel Wohl, Qinyi Li, Carlos A. Cuevas, Rafael P. Fernandez, Mingxi Yang, Alfonso Saiz-Lopez, Rafel Simó<span>, </span>Submitted to Atmospheric Atmospheric Chemistry and Physics, 2022</p> <p> </p> <p>Computation of the air-sea gas fluxes is explained in detail in the linked manuscript about benzene and toluene. <br> Positive values indicate oceanic outgassing, thus sea to air flux.</p> <p> </p> <p>Definitions of acronyms, site abbreviations, or other project-specific designations:<br> deg = degree <br> SW = seawater concentration</p> <p>ATM= atmosphere</p> <p>SAT = saturation</p> <p>flux= air-sea flux in (micro)umol_m^(2)_d^(-1)<br> nM = nano Molar seawater concentration defined as nmol dm^(-3)</p> <p>LAT, LONG = Latitude, Longitude. (negative indicates west and south)</p> <p>The timestamp indicates sampling time in UTC, expressed as DD/MM/YYYY_HH:MM</p> <p>Empty data cells/points are listed as an impossible number of -999. Interruptions in the measurements are due to calibrations and other instrument maintenance.Interruptions in the calculated flux are due to missing auxiliary data at those sampling points e.g. no wind speed or underway auxiliary data.</p> <p>Fluxes and saturations computed using the interpolated air mixing ratio (see linked manuscript) are indicated with the suffix "_2"</p> <p> </p> <p>Negative values correspond to readings below the blank and detection limit. <br> They are effectively zero and are included here as the computed negative concentration to avoid skewing the mean.</p> <p> </p> <p>Data last modified 06.05.2022. Version 1 uploaded on that date. No further maintenance planned. This is the final data.</p> <p> </p>
Projected fresh water use from the European energy sector on NUTS2 level by 2050 following EU Energy Reference Scenario 2016
<p>The dataset contains projections of fresh water withdrawal and consumption from the European energy sector on NUTS2 level by 2050 following EU Energy Reference Scenario 2016.</p> <p>The energy sector in this scope includes energy production (production of coal, oil and gas) and energy transformation in oil refineries and power plants (nuclear, solid fuels, oil, gas, biomass and geothermal).</p> <p>The information in provided on NUTS 2 level following the NUTS2 2013 definition.</p> <p>The dataset is explained in more detail in the report <a href="https://ec.europa.eu/jrc/en/publication/projected-fresh-water-use-european-energy-sector">Projected fresh water use from the European energy sector</a>.</p>
Empirical datasets for "Evaluating the impact of lifestyle changes: A scenario-based analysis for Europe's residential buildings sector"
<p>This dataset includes the empirical datasets for the manuscript: Andreas Andreou, Panagiotis Fragkos, Faidra Filippidou, Eleftheria Zisarou, Georgios Avgerinopoulos, Robert Pietzcker, Robin Hasse, Ricarda Rosemann, Evaluating the impact of lifestyle changes: A scenario-based analysis for Europe’s residential buildings sector (under review in Environmetal Research Letters). The dataset contains one CSV file with detailed modelling results for the scenarios presented in the manuscript.</p>
Weather Regime definition for the Euro-Atlantic sector (Daily, DFJM, 1979-2018) used for ACDC-ESM
<p><strong>Weather Regime definition for the Euro-Atlantic sector at daily resolution from 1979-2018 for December to March in CSV format</strong></p> <p> </p> <p><strong>TL;DR</strong>: this is the weather regime assignment based the method as set out by Swinda K.J. Falkena in 'Revisiting the identification of wintertime atmosphericcirculation regimes in the Euro-Atlantic sector' (<a href="https://doi.org/10.1002/qj.3818">10.1002/qj.3818</a>). Daily data is provided for December to March for the period 1979-2018.</p> <p> </p> <p><strong>Method Description </strong><br> The weather regime assignment can be obtained by applying <em>k</em>-means clustering to the full field data of geopotential height data. Following the observed circulation in reanalysis data, the optimal number of clusters is six. By incorporating a weak persistence constraint in the clustering procedure the assignment is stabilized, without changing the weather regime occurrence rates.</p> <p>The six regimes used have been labelled to indicate atmospheric state. Due to their symmetry, a name (Atlantic Ridge (AR), North Atlantic Oscillation (NAO) and Scandinavian Blocking (SB)) and state (positive (+) and negative (-)) are used to label each of the six weather regimes. It should be noted that the naming convention used, does not imply that the weather associated with these six weather regimes is similar to a definition that uses four or two clusters to classify the weather.</p> <p>Full details on the method can be found in: Swinda K.J. Falkena, et al, 'Revisiting the identification of wintertime atmosphericcirculation regimes in the Euro-Atlantic sector' (DOI:<a href="https://doi.org/10.1002/qj.3818">10.1002/qj.3818</a>). The original implementation and source code can be found on gitHub via: <a href="https://github.com/SwindaKJ/Regimes_Public">github.com/SwindaKJ/Regimes_Public</a>.</p> <p> </p> <p><strong>Data structure description</strong><br> The file is provided in CSV (.csv) format with a semicolon (;) as separator. The first row stores the column labels. The columns contain the following:</p> <ul> <li>first column (or A) contains the valid-time <ul> <li>Label: datetime</li> <li>Contents represent time with text as [DD/MM/YYYY])</li> </ul> </li> <li>second column (or B) contains the assigned weather regime <ul> <li>Label: WR</li> <li>Contents represent the assigned cluster as an interger in the range [0,5]</li> <li>Meaning: 0="SB-", 1="AR+", 2="NAO-", 3="SB+", 4="NAO+", 5="AR-"</li> </ul> </li> </ul> <p> </p> <p><strong>DISCLAIMER</strong>: <em>the content of this dataset has been created with the greatest possible care. However, we invite to use the original assignement of weather regimes for critical applications and studies. </em></p>
Regional scale surface of the top of the Variscan basement in some sector of Italy - Supplementary material
<p>The dataset represent the Supplementary material of thew manuscript entitled "Map of the top of the Variscan basement in some sectors of Italy" now under revision.</p> <p>The Supplementary material consist of 9 files:</p> <ul> <li>input data: <ul> <li>dataset_CROP.csv</li> <li>deep_wells.csv</li> <li>domains.geojson</li> <li>thrusts_2.geojson</li> <li>INA_data_point.csv</li> </ul> </li> <li>output data: <ul> <li>INA_depth_1km.csv</li> <li>ONA_OA_ISA_AF_depth_5km.csv</li> <li>INA_contour.geojson</li> <li>ONA_OA_ISA_AF_contour.geojson</li> </ul> </li> </ul>
Open Science: New Challenges and Opportunities for the PV sector
<p>Presentation given at the European PV Solar Energy Conference, Marseille, 2019 about the development of Open Science in the context of photovoltaics</p>
Sectores Tecnológicos asociados a las solicitudes de patente ante la WIPO que vinculen por lo menos a un colombiano
<p>Relación de sectores tecnológicos asociados a los registros de solicitudes de patente presentadas ante la OMPI entre los años 2000 y 2019. que incluyen por lo menos a un colombiano como titular</p>
Determinants of rooftop solar uptake: a comparative analysis of the residential and non-residential sectors in the Basque Country (Spain)
<p>Data for <strong>Determinants of rooftop solar uptake: a comparative analysis of the residential and non-residential sectors in the Basque Country (Spain)</strong></p> <p>Rooftop solar, both in the residential and the non-residential sector, is emerging rapidly as a popular source of clean electricity. Together with utility-scale photovoltaics, its future growth is essential to achieve decarbonization targets. Therefore, understanding adoption determinants for firms and households is key to efficiently promoting its diffusion. There is a gap, however, in the knowledge of non-residential adoption determinants, as less attention has been given to this sector compared to the residential sector. As a result of this gap, there is an absence of comparative analysis across sectors. As determinants of adoption cannot be assumed to be the same in both sectors, the objective of this research is threefold. First, to analyze whether the residential and non-residential sectors share key determinants of rooftop solar investment; second, to compare the sectoral differences in these determinants; and third, to assess the policy implications of the results obtained to further promote distributed solar photovoltaic energy. For this purpose, a regional case study in the Basque Country (Spain) was conducted, applying key theoretical frameworks to both sectors in a way that maximized the comparability of the results obtained across them. The results showed that adoption determinants are very different across sectors and, therefore, sector-specific policy actions need to be taken in each sector to efficiently promote rooftop solar. For the residential sector, policy actions could build upon behavioral aspects; for the non-residential sector, economic incentives are expected to be more successful, especially among medium size businesses, which are identified as the most promising segment.</p> <p> </p>
Global monthly sectoral water withdrawal and allocation datasets (QUAlloc, water use and allocation model) at 10 km spatial resolution
<p>Output data of water withdrawals and water allocation per water source from the sectoral water use and allocation model (QUAlloc).</p> <p>Dataset properties:</p> <ul> <li>spatial resolution: 10 km (global-scale)</li> <li>temporal resolution: monthly time-step</li> <li>period: 1980 - 2019</li> <li>units: m3/month</li> </ul> <p>Output datasets:<br> <data_type>_<sector_name>_allocated_to_<source_type>_monthlyTot_1980_2019.nc</p> <ul> <li><data_type><br> <ul> <li>"withdrawal": refers to the water that is withdrawn at a water source level to satisfy the demands within an allocation zone</li> <li>"demand": refers to the withdrawn water that is supplied to each location (cell) where there are demands to satisfy</li> </ul> </li> <li><sector_name> <ul> <li>"domestic"</li> <li>"irrigation"</li> <li>"livestock"</li> <li>"manufacture"</li> <li>"thermoelectric"</li> </ul> </li> <li><source_type> <ul> <li>"renewable_surfacewater": refers to water obtained from the surface water system components (e.g., direct runoff, base flow, interflow, etc.)</li> <li>"renewable_groundwater": refers to water obtained from aquifers that are recharged by percolation from the upper soil layers</li> <li>"nonrenewable_groundwater": refers to water obtained from aquifers not replenished on a human time scale</li> </ul> </li> </ul> <p>The sectoral water use and allocation model used, QUAlloc, can be found at: https://github.com/SustainableWaterSystems/QUAlloc.</p>
Software-based decision support tools used in the sanitation sector
<p>This dataset includes data used in a scoping review on how decision support tools used in the sanitation sector address resource recovery considerations. The dataset is an accompaniment to the publication "A review of how decision support tools address resource recovery in sanitation systems", which was submitted to the Journal of Cleaner Production.</p>
Online survey of needs and challenges of innovation ecosystems and intermediaries for taking up activity in the EU space sector
<p>The present dataset was generated as part of the "Needs and challenges of innovation ecosystems and intermediaries for taking up activity in the EU space sector" of the H2020 <a href="http://innorbit.eu">InnORBIT project</a>.</p> <p>The aim of this study was to identify and explore the available and missing skills of innovation intermediaries to provide business support services to innovators within their local ecosystems to develop commercial activity in space. The assessment of skills was based on a baseline framework encompassing a wide array of skills and competencies innovation intermediaries are supposed to possess in order to provide effective business support services to space innovators. The skills of the baseline framework are grouped into five broad categories: (i) space industry knowledge, (ii) business assessment knowledge, (iii) business support skills, (iv) organisational and digital skills and (v) soft skills. The baseline framework was originally developed by the InnORBIT consortium through research in related works of EU funded projects and publications and validated through a series of 15 interviews with top-level executives of organisations across the CEE and SEE area, belonging to the two target groups of the study (i.e., innovation intermediaries and innovators). An online survey was deployed from May 26th to June 18th using the EU Survey tool, to innovation intermediaries and innovators across the EU and CEE/SEE countries in particular. Two online questionnaires were developed building on the baseline skills framework - the first intended for innovation intermediaries asking them to perform a self-assessment of their skills in terms of providing business support services and the second targeting innovators, asking them to state their perception on how innovation intermediaries they have worked with, perform in each of the skills.</p> <p>The dataset contains four files:</p> <p>1. Zip file including the transcripts from 6 interviews with space innovators in Eastern Europe for the evaluation of the baseline framework of skills.</p> <p>2. Zip file including the transcripts from 9 interviews with innovation intermediaries in Eastern Europe for the evaluation of the baseline framework of skills.</p> <p>3. a pdf file of the digital questionnaires developed in EU Survey deployed to innovation intermediaries and innovators in the region</p> <p>4. An excel file with 104 valid responses collected from the online survey (56 innovation intermediaries and 48 innovators) across 16 EU countries / 21 countries total.</p> <p>The dataset contains only non-sensitive anonymised information and is in full compliance with the GDPR provisions. Any information leading to the identification of participants in activities (interviews, survey) is either modified or omitted and deonted with brackets.</p>
El sector informal en la Ciudad de México Caso de estudio de la Delegación Iztapalapa
<p><strong>Coordinador del Seminario:</strong> Carlos A. Navarrete Ulloa.<br> <strong>Expositor</strong>: Luis Adolfo Ortega Granados</p> <p><strong>Comité Ejecutivo PRONACE-Vivienda</strong><br> Fernando Córdova Canela, Centro Universitario de Arte, Arquitectura y Diseño, Universidad de Guadalajara (UdeG).<br> Francisco Javier Porras Sánchez, Instituto de Investigaciones Dr. José María Luis Mora.<br> Gabriel Castañeda Nolasco, Universidad Autónoma de Chiapas (UNACH).<br> Carlos A. Navarrete Ulloa, Centro Universitario de Tonalá, (UdeG).</p> <p>Exposición realizada en el marco del PRONACE Vivienda en el cual se comenta la lectura:<br> Martínez-Luis, D., Pérez-Fernández, A., Pat-Fernández, L. A., Caamal-Cauich, I., Franco-Gutiérrez, M. J., & García-Cabrera, L. G. (2019). El sector informal en la Ciudad de México. Caso de estudio de la Delegación Iztapalapa. Estudios sociales. Revista de alimentación contemporánea y desarrollo regional, 29(53). https://www.ciad.mx/estudiosociales/index.php/es/article/view/725</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.