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620 results for “sector”
Job Ads in Retail and E-commerce sectors for INAIR project countries
<p><strong>INAIR</strong> (<a href="https://www.ai4retail.eu/en/">Increasing the Uptake of AI in Retail</a>) is a Coordination and Support Action funded by the European Union'’s Horizon Europe Research and Innovation programme - Grant Agreement No. 101133847. The project aims to contribute to reducing the AI skills gap of European MSMEs in Retail, to let them exploit the potential of AI for greening their businesses, support their competitiveness in the global market and ultimately contribute to reaching the digital decade target of 75%+ European companies adopting AI technologies by 2030. </p> <p>This dataset contains job advertisements from the retail and e-commerce sectors in Cyprus, Germany, Italy, Poland, and Romania, collected as part of the INAIR Horizon Europe project. This resource supports analyses of digital skill requirements and helps identify trends and gaps in the labor market for the retail and e-commerce sectors.</p> <p>Data was collected three times during the period from March to May 2024, resulting in a total of 44,494 job offers.</p>
Risks of Transition Pathways of the Steel and Electricity Sectors – The Case of Austria
<p>This dataset contains the underlying data for the following publication: <strong>Risks of Transition Pathways of the Steel and Electricity Sectors – The Case of Austria], [upcoming in TRANSrisk Special Issue in Environmental Innovation and Societal Transitions (EIST)], [https://www.journals.elsevier.com/environmental-innovation-and-societal-transitions/].</strong></p>
SET-NAV: WP5: Invert modelling output for the building sector final energy demand and cost data
<p>This data set contains the Invert modelling results for final energy demand for space heating, cooling and hot water in buildings; hourly data for district heating and electricity (for different technologies) for 3-4 building types; annual data for the other energy carriers.</p> <p>It also contains all annual cost (Annuity of investments, O&M, fuel cost ) of electricity generation and / or heat generation and considered efficiency measures.</p> <p>This data is also available and visualised in our dedicated SET-NAV open data platform: The SET-NAV Scenario Explorer: https://data.ene.iiasa.ac.at/set-nav/#/workspaces</p>
Dataset: Global Electricity Sector Decarbonization Modelling
<p>This dataset contains the supporting materials for the PhD thesis "Global Electricity Sector Decarbonization Modelling". The following list of data packages and files are compiled from this research outputs.</p> <p><strong>Data Package 1: The main output datasets</strong><br> File 1: Definition of the zones in this study<br> File 2: Statistics on land cover pixels updated from OSM<br> File 3: Validation of the eligibility masks with existing sites<br> File 4: Validation of the estimated CF profile for EU countries<br> File 5: Summary of the available area, technical potential and capacity potential in this study<br> File 6: Aggregation of the estimated 10-year hourly CF into 288 month-hour profile<br> File 7: The projection of electricity demand profile for each zone in 288 month-hour profile<br> File 8: The overall aggregated output covariance matrices for each country</p> <p><strong>Data Package 2: The estimated 10-years hourly capacity factor of all available technologies and capacity factor tranches in every zone in the world</strong></p> <p><strong>Data Package 3: The solutions of optimal climate-related energy mix with different objectives in global electricity systems</strong></p> <p><strong>Data Package 4: The modelling results of electricity sector decarbonization pathways by country/district</strong></p> <p><strong>Data Package 5: Python scripts of the electricity sector decarbonization modelling framework</strong></p>
Fig. 4 in Predatory behaviors: Pristimantis savagei (Anura: Craugastoridae) as prey of Trechalea sp. spiders (Araneae: Trechaleidae) in a sector of the Piedemonte Llanero, Villavicencio, Colombia
Fig. 4. Trechalea sp. individual secreting its digestive juices.
Network files and Python code used in "Designing a sector-coupled European energy system robust to 60 years of historical weather data"
<p><strong>Description</strong></p> <p>This repository contains data presented in the paper <a href="https://www.nature.com/articles/s41467-024-54853-3" target="_blank" rel="noopener">Designing a sector-coupled European energy system robust to 60 years of historical weather data</a>. It contains the derived metrics (.csv) files from a:</p> <ol> <li>joint capacity and dispatch optimization with weather years (design years) from 1960 to 2021 as input</li> <li>dispatch optimization of the 62 capacity layouts using weather years (operational years) different from the design year.</li> </ol> <p>All results from (1) are found in "Capacity_optimization.zip" and results from (2) are found in "Dispatch_optimization.zip".</p> <p>The resulting network files (both from the capacity and dispatch optimization) are located <a href="https://anon.erda.au.dk/cgi-sid/ls.py?share_id=DuGvDWlkeI">here</a>.</p> <p>We also provide the Python code used to derive the metrics and to create the visualizations included in the paper. This is located in "Jupyter_notebooks". The Jupyter notebooks refer to Python scripts located <a href="https://github.com/ebbekyhl/multi-weather-year-assessment">here</a>.</p> <p><strong>Revisions:</strong></p> <p>This version includes the following additions compared to the previous versions: </p> <ul> <li>Timeseries of nodal loads for all years</li> <li>Timeseries of nodal heat pump Coefficient of Performance (COP) </li> <li>Nodal capacity and hourly capacity factors </li> </ul>
EXPLORING THE CAPABILITIES AND LIMITATIONS OF DEEP LEARNING IN HEALTHCARE SECTORS IN INDIA: A REVIEW OF CURRENT TRENDS, page 1-10
<p> </p> <p><strong>Learnet Publishing ( Publisher) </strong></p> <p>Conference Volume : ARTIFICIAL INTELLIGENCE (AI) – APPLICATIONS & MISAPPLICATIONS 2024" (ISBN No.: 978-81-957591-7-0) </p> <p>Paper title : <em><strong>EXPLORING THE CAPABILITIES AND LIMITATIONS OF DEEP LEARNING IN HEALTHCARE SECTORS IN INDIA: A REVIEW OF CURRENT TRENDS</strong></em></p> <p><strong>Authors </strong><br>Suvendu Narayan Roy<br>Director, Knowgen Education Services Private Limited<br>Researcher, Faculty of Finance & Management<br>Orcid id : https://orcid.org/0000-0002-2801-0740<br>Soham Roy<br>Researcher & Partner, E & O Learnet and Learnet Manpower Solutions<br>Orcid id : https://orcid.org/0000-0002-3127-608</p>
Índices de acciones mundiales por sector - PR1 Tipología de datos - Máster Ciencia de Datos UOC
<p>Generación de datos mediante webscraping para <strong>obtener el estado de diferentes mercados financieros en tiempo real</strong>, centrándonos en acciones de diferentes países. <strong>Histórico de datos sobre el que testear algoritmos de predicción</strong> de evolución de mercado.</p>
Exploring the needs of institutional stakeholders involved in the cultural heritage and social innovation sectors
<p>This data set was produced to gather information and lessons learnt from institutional stakeholders directly or indirectly involved in cultural approaches and/or promoting social integration of disadvantaged groups in order to understand what they need to facilitate participatory and inclusive initiatives. </p> <p>The data was gathered across Europe in early 2019 and consists of 90 surveys administered and 17 interviews. 90 surveys were administered to actors working at the intersection of cultural heritage and social innovation. The survey’s goal was to gather insights about the barriers these actors face in the implementation of participatory approaches and the measures adopted to overcome them. Semi-structured interviews (n=17) expanded upon the survey data and were carried out through an opportunistic sample of the survey respondents. The findings provided relevant insights in relation to the configuration of approaches for participatory projects, the main faced barriers, and the achieved level of impact.</p> <p>The original dataset has been processed by removing details and parts that might compromise the anonymity of the participant. </p> <p>This work is supported by the European Union’s Horizon2020 project CultureLabs: Recipes for Social Innovation, under Grant Agreement 770158. The sole responsibility of this work lies with the authors. The European Union is not responsible for any use that may be made of the information contained therein.</p> <p> </p>
Benthic and benthopelagic Ostracoda from a wide depth range (189-6224 m) in the Atlantic Sector of the Southern Ocean
<p>This dataset contains abundance (i.e., absolute number of speicmens/individuals) of ostracod genera collected during three cruises of the German Research Vessel Polarstern in the Atlantic Sector of the Southern Ocean. The cruises are EASIZ II (PS 48/ANT XV-3), ANDEEP I (PS 61/ANT XIX-3), ANDEEP II (PS 67/ANTXXII-3), and ANDEEP III (PS 67/ANTXXII-4). The samples related to the present file were all collected using an epibenthic sledge, described by (Brandt & Barthel, 1995; Brenke, 2005, see compelte references in Brandão et al., 2021, Zoological Journal of the Linnean Society). These ostracods are both benthic (most specimens collected) and bentho-pelagic (Halocyprididae, which in the case of the present dataset, swam just above the sea floor).</p>
Replication files for "The role of actors' issue and sector specialization for policy integration in the parliamentary arena: An analysis of Swiss biodiversity policy using text as data"
<p>The ZIP file contains all data and code to replicate the analyses reported in the following paper.</p> <p>Reber, U., Ingold, K., & Fischer, M. (2023). The role of actors' issue and sector specialization for policy integration in the parliamentary arena: An analysis of Swiss biodiversity policy using text as data. <em>Policy Sciences</em>. <a href="https://doi.org/10.1007/s11077-022-09490-2">https://doi.org/10.1007/s11077-022-09490-2</a></p> <p>If you use any of the material included in this repository, please refer to the paper.</p>
Modeling Decarbonization Pathways in the Power Sector in Developing Countries: The case of Colombia (EMP-LAC 2023) - Dataset
<p>Dataset of the project Modeling Decarbonization Pathways in the Power Sector in Developing Countries: The case of Colombia (EMP-LAC 2023) - Dataset</p>
Factors influencing open government data post-adoption in the public sector: The perspective of data providers
Providing access to non-confidential government data to the public is one of the initiatives adopted by many governments today to embrace government transparency practices. The initiative of publishing non-confidential government data for the public to use and re-use without restrictions is known as Open Government Data (OGD). Nevertheless, after several years after its inception, the direction of OGD implementation remains uncertain. The extant literature on OGD adoption concentrates primarily on identifying factors influencing adoption decisions. Yet, studies on the underlying factors influencing OGD after the adoption phase are scarce. Based on these issues, this study investigated the post-adoption of OGD in the public sector, particularly the data provider agencies. The OGD post-adoption framework is crafted by anchoring the Technology–Organization–Environment (TOE) framework and the innovation adoption process theory. The data was collected from 266 government agencies in the Malaysian public sector. This study employed the partial least square-structural equation modeling as the statistical technique for factor analysis. The results indicate that two factors from the organizational context (top management support, organizational culture) and two from the technological context (complexity, relative advantage) have a significant contribution to the post-adoption of OGD in the public sector. The contribution of this study is threefold: theoretical, conceptual, and practical. This study contributed theoretically by introducing the post-adoption framework of OGD that comprises the acceptance, routinization, and infusion stages. As the majority of OGD adoption studies conclude their analysis at the adoption (decisions) phase, this study gives novel insight to extend the analysis into unexplored territory, specifically the post-adoption phase. Conceptually, this study presents two new factors in the environmental context to be explored in the OGD adoption study, namely, the data demand and incentives. The fact that data providers are not influenced by data requests from the agency's external environment and incentive offerings is something that needs further investigation. In practicality, the findings of this study are anticipated to assist policymakers in strategizing for long-term OGD implementation from the data provider's perspective. This effort is crucial to ensure that the OGD initiatives will be incorporated into the public sector's service thrust and become one of the digital government services provided to the citizen.
MAED template Reconstruction Base Year - Manufacturing (Industry Sector) and Household Sector (Urban & Rural)
<p>MAED template Reconstruction Base Year - Manufacturing (Industry Sector) and Household Sector (Urban & Rural) for the Hands-on 6 of the Open Learn Create an online course on MAED. The Template for "MAED template Reconstruction Base Year - Agriculture, Construction, Mining (Industry Sector)" is available for download here: https://doi.org/10.5281/zenodo.7750256. </p>
MAED template Reconstruction Base Year - Agriculture, Construction, Mining (Industry Sector)
<p>MAED template Reconstruction Base Year - Agriculture, Construction, Mining (Industry Sector) for Hands-on 5 of the Open Learn Create Course on MAED. </p>
Коэффициенты корреляции повторяемости блокирования между секторами для северного и южного режимов для двух периодов. Blocking frequency correlation coefficients between sectors for northern and southern regimes for two periods.
<p>Представлены коэффициенты корреляции (выше порога 0.4) повторяемости блокирования между секторами для северного (NM) и южного режимов (SM) для ноября (nov), декабря (dec), января (jan) и февраля (feb) .</p> <p>Секторы показаны в https://doi.org/10.5281/zenodo.7489504</p> <p>Correlation coefficients (above a threshold of 0.4) of blocking frequency between sectors for northern (NM) and southern (SM) modes for November (nov), December (dec), January (jan) and February (feb) are presented.</p> <p>Sectors are shown at https://doi.org/10.5281/zenodo.7489504</p>
ODYM-RECC Copper dataset, used for sector-level estimates for global future copper demand and the potential for resource efficiency
<p>Complete Model database with 105 parameter files used in the ODYM-RECC Copper model (github: https://github.com/SteffiKlose/ODYM-RECC-Copper.git) used for Sector-level estimates for global future copper demand and the potential for resource efficiency (<a href="https://doi.org/10.1016/j.resconrec.2023.106941">https://doi.org/10.1016/j.resconrec.2023.106941</a>)</p> <p>This dataset is based on the Database of the ODYM-RECC v2.4 model, used for the GLOBAL case study on material efficiency and climate change mitigation (https://doi.org/10.5281/zenodo.4671644)</p>
THE GROWTH IMPACT ON COMPANY VALUE: EVIDENCE FROM THE E-COMMERCE SECTOR
<p>Thid dataset contains the information about e-commerce companies globally.</p> <p>The indicators include: region of operations (developed / developing), business-model (1st party trade / 3d party trade), key financial indicators</p> <p> </p> <p>Detailed description of variables:</p> <p>1. Year: the period covers 2017-2021, annual data</p> <p>2. Index: number of the company</p> <p>3. EV/GMV_t: trailing mulriple EV/GMV</p> <p>4. EV/GMV_f: forward multiple EV/GMV</p> <p>5. EV/Revenue: trailing multiple EV/Revenue</p> <p>6. EV/Gross Profit: trailing multiple EV/Gross Proift</p> <p>7. EV/EBITDA: trailing multiple EV/EBITDA</p> <p>8. EV/EBIT: trailing multiple EV/EBIT</p> <p>9. Dummy_Dev: dummy variable, which equals 1 for companies, operating on developed markets, 0 - for developing</p> <p>10. Dummy_Ind: dummy variable, which equals 1 for companies which operating model combines a marketplace and retailing, 0 - for retailers</p> <p>11. Dummy_GP: dummy variable, which equals 1 for companies with positive Gross Profit at a given year</p> <p>11. Dummy_EBITDA: dummy variable, which equals 1 for companies with positive EBITDA at a given year</p> <p>12. GMV_g_1yH: GMV growth rate for 1 historical year</p> <p>13. GMV_g_2yH: GMV CAGR rate for 2 historical years</p> <p>14. GMV_g_1yF: GMV growth for 1 forecasted year</p> <p>15. Rev_g_1yH, Rev_g_2yH, Rev_g_1yF, GP_g_1yH, GP_g_2yH, GP_g_1yF, EBITDA_g_1yH, EBITDA_g_2yH, EBITDA_m_1yF: the same as p.12-14 for Revenue, Gross Profit, and EBITDA</p> <p>16. GP_m_1yH: Gross Margin for 1 historical year</p>
CCG: Beyond the Dams: Combatting Hydropower Over-reliance & Securing Pathways for a Low-carbon Future for Laos' Electricity Sector using OSeMOSYS (Open-Source Energy Modelling System)
<p>Seven clicSAND scenario files for <strong>Beyond the Dams: Combatting Hydropower Over-reliance & Securing Pathways for a Low-carbon Future for Laos' Electricity Sector using OSeMOSYS (Open-Source Energy Modelling System).</strong> </p> <p><strong>How to Visualise Results Online and Offline</strong> outline the steps required to re-run the scenarios on OSeMOSYS Cloud</p> <p><strong>Scenario Short Note</strong> outlines the steps to replicate the analysis and rebuild the scenarios</p> <p><strong>Annex - Input Data and Assumptions</strong> listing the data sources and assumptions in the scenarios</p>
Spatially and temporally continuous reconstruction of Antarctic Amundsen Sea sector ice sheet surface velocities: 1996-2018
<p>Spatially and temporally continuous reconstruction of ice sheet surface velocities for the Amundson Sea Sector of the Antarctica. The reconstruction is derived from the synthesis of annual published InSAR (R14: Rignot et al. 2014) and optical (G18: Gardner et al., 2018 & Gardner et al., 2022) surface velocities. Data are posted on a uniform 240 m by 240 m grid in Antarctic Polar Stereographic (EPSG:3031) coordinates. The temporal posting is every 2.4 months or 1/5 of a year.</p> <p>R14 and G18 annual velocity data have large errors and data gaps in both space and time that make the data challenging to work with. For this reason, a Spatially and temporally continuous reconstruction was made. These are the preprocessing steps that were applied to create the reconstruction:</p> <ol> <li>R14 component velocities [vx/vy] are mapped to the same 240-m grid as G18 for the Amundson Sea sector.</li> <li>Velocities falling outside of mapped ice extents (see Paolo et al., 2022) are set to no data values.</li> <li>A reference velocity is defined as the 1996 velocity field or the earliest valid measurement thereafter. The average of both velocities is taken if multiple observations exist for the first year of data.</li> <li>For areas moving faster than 200 m/yr., the percentage anomalies are calculated for all years relative the reference velocity. This was done for both G18 and R14 velocities separately.</li> <li>Annual velocity anomalies are then filter with a 5-km windowed moving median.</li> <li>G18 and R14 filtered anomalies are merge by taking the mean of each year. Years with less than 30% coverage for fast moving ice (>= 200 m/yr.) were discarded.</li> <li>If missing annual values were within 25 km of a valid datapoint they are filled using natural neighbor interpolation, otherwise anomalies were set to zero.</li> <li>Outside of fast-moving areas, annual anomalies are tapered to zero using a 10-km cosine taper.</li> <li>Merged and filled annual anomalies are then smoothed one last time using a 5-km windowed moving mean.</li> <li>To create a continuous record of velocity, annual anomalies are interpolated in time to every 1/5 of a year for every 240 m pixel using a spline interpolant and multiplied by the reference velocity.</li> </ol> <p>All x and y component velocities [vx/vy] and velocity magnitudes [v] are stored as individual geotiff files and are contained in the .zip included file. A visualization of the velocity magnitudes is included as an animated gif. </p> <p> </p> <p>References:</p> <p>Gardner, A., M. Fahnestock, and T. Scambos. (2022). MEaSUREs ITS_LIVE Regional Glacier and Ice Sheet Surface Velocities, Version 1 [Data Set]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. https://doi.org/10.5067/6II6VW8LLWJ7. Date Accessed 04-07-2019.<br> <br> Gardner, A. S., Moholdt, G., Scambos, T., Fahnstock, M., Ligtenberg, S., van den Broeke, M., & Nilsson, J. (2018). Increased West Antarctic and unchanged East Antarctic ice discharge over the last 7 years. <em>The Cryosphere</em>, <em>12</em>(2), 521–547. https://doi.org/10.5194/tc-12-521-2018</p> <p>Paolo, F., Gardner, A., Greene, C., Nilsson, J., Schodlok, M., Schlegel, N., & Fricker, H. (2022). Widespread slowdown in thinning rates of West Antarctic Ice Shelves. <em>EGUsphere</em>, <em>2022</em>, 1–45. https://doi.org/10.5194/egusphere-2022-1128</p> <p>Rignot, E., J. Mouginot, and B. Scheuchl. (2014). MEaSUREs InSAR-Based Ice Velocity of the Amundsen Sea Embayment, Antarctica, Version 1 [Data Set]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. https://doi.org/10.5067/MEASURES/CRYOSPHERE/nsidc-0545.001. Date Accessed 04-07-2019.</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.