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36 results for “economic development”
Database of water, agriculture and economic development in Huang-Huai-ai region of China
<p>The database of water, agriculture and economic development contains 61 prefecture-level cities in the Huang-Huai-Hai region from 2010 to 2019.</p> <p>Firstly, we summarize the city-level agricultural dataset from the Provincial Bureau of Statistics, which contains the annual agricultural output, total planting area, labor, fertilizer, and machinery of each prefecture-level city. </p> <p>Secondly, we collect agricultural output (total land value per hectare) as the output and four main types of inputs: labor, fertilizer, machinery, and agricultural water consumption.</p> <p>Thirdly, we also collect city-level unbalanced panel data from the Water Resources Bulletin database, which contains annual data on agricultural water consumption, groundwater supply, precipitation, and groundwater resources.</p>
Organisation for Economic Co-operation and Development (OECD) data for Antalya (Turkey), Antwerp (Belgium), Cork (Ireland), Thessaloniki (Greece) (source: OECD)
<p>The data have been collected via the official OECD Application Programming Interface (API)<strong> </strong>and<strong> </strong>includes the following indicators:</p> <ul> <li>EmpPlaRes - Employment at place of residence</li> <li>LfPartRa - Labour Force and Participation rate</li> <li>UnemReg - Unemployment in regions </li> <li>RegGdpTL2 - Regional Gross Domestic Product (Large regions TL2)</li> <li>GDPLT3 - Gross Domestic Product (Small regions TL3)</li> <li>RegEmIndu - Regional Employment by industry (ISIC rev 4)</li> <li>RegGVAWorker - Regional GVA per worker</li> <li>RegIncPC - Regional income per capita</li> </ul> <p>Source: https://data.oecd.org/api/</p>
Driving social and economic development in the MENA region: the role of international organizations - Stakeholders' engagement meeting report
<p>On 15 November 2018, the Center for Public Policy and Democracy Studies (PODEM) hosted a stakeholders’ meeting in Istanbul with twenty-nine participants, including academics, experts, and representatives from civil society and international humanitarian and development organizations from Europe and the MENA region, as part of the Middle East North Africa Regional Architecture Project (MENARA). Through two panels of expert presentations and facilitated discussion, the participants addressed present challenges, ongoing transformations and future opportunities facing societies across the region, as well as the role of international organizations in addressing these challenges and opportunities. This report will summarize the discussion during that meeting in two broad parts, focusing on the region’s challenges and positive developments respectively and is further divided into sections by issue subject. Two sets of policy recommendations are provided at the end of the report.</p>
Socio-economic development of global river deltas from gridded data
<p>Crop, population, and GDP values in the world's major river deltas, derived from publicly available gridded datasets. </p> <p>v0: Dec. 2022</p> <p>v1: Jan 2023 (added Metadata)</p>
Geospatial Analysis of Economic Development in kenya by Province
<p>This dataset presents both vector and raster data combinations for pm2.5, elevation, nightlight data, population density, area, and population that can be used to estimate the economic development of Kenya using distribution of banks as a proxy.</p>
A dataset of regional operational programmes (ROP) and rural development program (PROW) expenditures and socio-economic features in 2007-2013, Poland (source: Bank of Local Data)
<p>Dataset prepared on the bases of Polish Central Statistical Office (Statistics Poland) Bank of Local Data system https://bdl.stat.gov.pl/BDL/dane/podgrup/tablica [access: 1.07.2018]. The data set the expenditure of funds for individual priority axes in the programmes of both policies in the 2007-2013 programming period and the change in socio-economic features at the local (<em>poviat</em>, NUTS4) level. The Pearson correlation coefficients are used to assess the relationship between the level of expenditure for RDP and ROP <em>per capita</em> and selected indicators describing the level of economic, social and demographic development of local government units. The results of the analysis (the article <strong>Regional approach to rural development? A case of regional and rural programs 2007-2015 in Poland) </strong>will consist of tables, texts and of numerical data. Article with data is available here: OI: 10.5604/01.3001.0012.2934 GICID: 01.3001.0012.2934 Available language versions: en. <strong>Issue: </strong>Annals PAAAE 2018; XX (4): 22-28, https://rnseria.com/resources/html/article/details?id=176837</p>
Database of indicators of socio-economic development of the Great Altai regions in the post-Soviet period
<p>As part of the research work "Altai vector of Eurasian economic integration: cross-border challenges, effects, strategic objectives and priorities for the Altai Krai" (FZMW-2023-0015), the task was to develop a database of indicators of development of cross-border regions of Russia, Kazakhstan, Mongolia and China for the period 1990-2022. To solve the task, statistical data from official sources were used. The database contains 68 indicators of socio-economic development of the Great Altai region, grouped into 10 structural blocks. The temporal resolution of the data is 1 year. The data were preliminarily analyzed for outliers and inconsistencies. On the basis of econometric methods, processing was performed to restore short-term gaps in the time series of the data. The generated database represents a unique set of socio-economic and climatic indicators for the transboundary study area, allowing to solve the problems of modeling and analysis of agricultural production dynamics in the post-Soviet period.</p>
Survey of Local Organisations and Economic Development in the Czech Republic, Hungary and Poland (2007).
This survey, undertaken in 2007, covers 1,200 local organisations in the Czech Republic, Hungary and Poland. Data cover two regions per country - one above and one below the average level of countrywide economic development (GDP, unemployment rate, rate of agriculture in GDP). 400 organisations per country (200 per region) are surveyed. Organisations include firms, NGOs, municipalities and universities.
Thermal requirements for egg development of two endemic Wiseana pest species (Lepidoptera: Hepialidae) of economic importance
<p>Raw and analyzed data for our paper title "Thermal requirements for egg development of two endemic <em>Wiseana</em> pest species (Lepidoptera: Hepialidae) of economic importance". </p>
Data and code of Land use scenario for 'Development of common socio-economic scenarios for climate change impact assessments in Japan'
<p>Land use scenario calculation: Executable files, source code files and data files<br> This dataset contains program codes and input data used for reproducing land use scenarios explained in Chapter 5.2 in Yoshikawa et al. (submitted to GMDD).</p> <p>We found a few fatal errors in the following code.<br> These code were fixed from version 2 (http://dx.doi.org/10.5281/zenodo.7090670).<br> /Step3/a01_calc_land_use.py<br> /Step3/a01_calc_land_use_std.py<br> /Step3/a01_calc_land_use_rate.py<br> /Step3/run03.bat</p>
Data to estimate energy efficiency and environmental friendliness of Ukrainian GDP from indicators of structural, economic, and social development in Ukraine
<p><span>Data to estimate regression dependences of energy efficiency and environmental friendliness of GDP from indicators of structural, economic, and social development in Ukraine </span></p>
Replication data for Temperature variability and long-run economic development
<p><strong>Description</strong></p> <p>This dataset contains the processed data used for the statistical analysis in Linsenmeier, M. (2023): Temperature variability and long-term economic development, published in the Journal of Environmental Economics and Management.</p> <p>The main data on nightlights stem from the satellites of the Visible Infrared Imaging Radiometer Suite (VIIRS). The data are downloaded as annual composites (vcm) of version V1 (Elvidge et al., 2017). For robustness tests, also annual composites of version V2 are used (Elvidge et al., 2021). Additional data on nightlights are taken from the U.S. Air Force Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) of Version 4. Data on the global distribution of crop land and pasture land (Ramankutty et al., 2008) are taken from NASA (Ramankutty et al., 2010). Data on population are from the Gridded Population of the World (GPW) dataset version 4.0 (CIESIN, 2018). Data on elevation are from the Global Land One-kilometer Base Elevation (GLOBE) dataset in version 1 provided by the National Oceanic and Atmospheric Administration (NOAA) (Hastings et al., 1999). Data on terrain ruggedness are from a global dataset with a resolution of 1 km (Shaver et al., 2018). Data on distances from the nearest coast are from NASA. Distance from inland water bodies are from the GloboLakes dataset (Carrea et al., 2015). Finally, data on weather are from ERA5 reanalysis (Hersbach et al. 2018). All datasets are spatially aggregated to the grid cells of the ERA5 reanalysis.</p> <p><strong>Acknowledgements</strong></p> <p>The data contain modified Copernicus Climate Change Service information 2020. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains.</p> <p>Image and data processing by Earth Observation Group, Payne Institute for Public Policy, Colorado School of Mines. DMSP data collected by US Air Force Weather Agency.</p> <p><strong>Bibliography</strong></p> <ul> <li>Carrea, L., Embury, O., andMerchant, C.(2015). GloboLakes: High-resolution global limnology data. Center for Environmental Data Analysis. http://catalogue.ceda.ac.uk/uuid/06cef537c5b14a2e871a333b9bc0b482.</li> <li>CIESIN (2018). Gridded Population of the World, Version 4 (GPWv4): Population Count, Revision 11.</li> <li>Elvidge, C. D., Baugh, K., Zhizhin, M., Hsu, F. C., and Ghosh, T. (2017). VIIRS night-time lights. International Journal of Remote Sensing, 38(21):5860–5879.</li> <li>Elvidge, C.D, Zhizhin, M., Ghosh T., Hsu FC, Taneja J. Annual time series of global VIIRS nighttime lights derived from monthly averages:2012 to 2019. Remote Sensing 2021, 13(5), p.922, doi:10.3390/rs13050922.</li> <li>Hastings, D. A., Dunbar, P. K., Elphingstone, G. M., Bootz, M., Murakami, H., Maruyama, H., Masaharu, H., Holland, P., Payne, J., Bryant, N. A., et al. (1999). The global land one-kilometer base elevation (GLOBE) digital elevation model, version 1.0. National Oceanic and Atmospheric Administration, National Geophysical Data Center, 325.</li> <li>Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Thépaut, J-N. (2018): ERA5 hourly data on single levels from 1959 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed in January 2021), 10.24381/cds.adbb2d47</li> <li>Ramankutty, N., Evan, A., Monfreda, C., and Foley, J. (2010). Global Agricultural Lands: Pastures, 2000. Global Agricultural Lands Dataset.</li> <li>Ramankutty, N., Evan, A. T., Monfreda, C., and Foley, J. A. (2008). Farming the Planet: 1. Geographic Distribution of Global Agricultural Lands in the Year 2000: Global Agricultural Lands in 2000. Global Biogeochemical Cycles, 22(1).</li> <li>Shaver, A., Carter, D. B., and Shawa, T. W. (2018). Terrain ruggedness and land cover: Improved data for most research designs. Terrain Ruggedness and Land Cover Dataset.</li> </ul>
Reversing the great degradation of nature through economic development
<p>We analyze past and anticipated future trends in crop yields, per capita consumption, and population to estimate agricultural land requirements globally by 2050 and 2100. Assuming "business as usual," higher-income countries are expected to show little or no net growth in cropland by the end of the century, even in the face of moderate climate change. In contrast, in lower-income countries, we project that land requirements will grow dramatically, and climate change will likely double this expansion. Although economic growth is often considered to work in opposition to conservation, accelerating economic development in lower-income countries, which would help alleviate poverty and increase standards of living, would also greatly reduce potential cropland expansion in lower-income countries, even with climate change, owing to slower population growth and improved crop yields that more than offset increased per capita consumption. Combining economic development in low-income countries with reduced consumption in high-income countries could dramatically shrink global cropland requirements by the year 2100 even with moderate climate change. Such a remarkable reduction in cropland area would have enormous benefits for both biodiversity and global climate change. </p>
Reversing the great degradation of nature through economic development
Open the record for dataset details and reuse information.
Where do Gyps fulvus (Griffon Vultures) feed? Combining biologging with socio-economic analysis can guide sustainable ecotourism development
Open the record for dataset details and reuse information.
Data set: Resource footprints, quality of life and economic development
<p>This data set accompanies the publication "Towards a comprehensive framework of the relationships between resource footprints, quality of life and economic development" by Stefan Cibulka and Stefan Giljum from the Institute for Ecological Economics at the Vienna University of Economics and Business (WU).</p> <p>The file provides data on resource footprints (carbon footprint, material footprint), Human Development Index, Happiness Index as well as GDP for countries world-wide in the time span from 1990 to 2015. In addition, it provides all specifications of the regression analyses undertaken in the course of this study and the detailed regression results.</p>
Raw Economic Census data for full replication of Burlig and Preonas, "Out of the Darkness and Into the Light? Development Effects of Rural Electrification"
<p>These raw data are intended to be copy-pasted into the folder "data/Economic Census" in Burlig and Preonas's replication archive</p>
Economic, institutional and environmental drivers of SME development for 21 EU states
<p><span>SMEs are seen as important actors of national and regional development in many countries. This is a panel<span> </span>data set on economic, institutional and environmental drivers of SME development for 21 EU states (Austria, Republic of Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Hungary, Italy, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Poland, Portugal, Romania, Slovakia, Slovenia and Spain) during 2011-2020 . </span></p>
Datasets supporting the publication: Evaluating night-time light sources and correlation with socio-economic development using high-resolution multi-spectral Jilin-1 satellite imagery of Quito, Ecuador
<p>##################################</p> <p><strong>Datasets supporting the publication:</strong></p> <p><em>Evaluating night-time light sources and correlation with socio-economic development using high-resolution multi-spectral Jilin-1 satellite imagery of Quito, Ecuador.</em></p> <p>International Journal of Remote Sensing. <a href="https://doi.org/10.1080/01431161.2023.2205983">https://doi.org/10.1080/01431161.2023.2205983</a></p> <p>C. Scott Watson<sup>a</sup>*, John R. Elliott<sup>a</sup>, Marco Córdova<sup>b</sup>, Jonathan Menoscal<sup>b</sup>, Santiago Bonilla-Bedoya<sup>c</sup></p> <p><em><sup>a</sup>COMET, School of Earth and Environment, University of Leeds, LS2 9JT, UK</em></p> <p><em><sup>b</sup>Facultad Latinoamericana de Ciencias Sociales, FLACSO, Quito, Ecuador</em></p> <p><em><sup>c</sup>Research Center for the Territory and Sustainable Habitat, Universidad Tecnológica Indoamérica,Machala y Sabanilla, 170301, Quito, Ecuador</em></p> <p><strong>-Please refer to the publication for details on the production of each dataset.<br> -Please cite the publication and this dataset repository when using the data.</strong></p> <p>##################################</p> <p><strong>Data:</strong></p> <table> <tbody> <tr> <td><strong>File</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>J1_mosaic_max.tif</td> <td>Mosaicked Jilin-1 multi-spectral night-time image of Quito, Ecuador. Acquisition: 8th July 2021 at ~10:30 UTC (05:30 local time)</td> </tr> <tr> <td>corine_landcover_S2_20210705T153621_20210705T154215_T17MQV.tif</td> <td>Land cover classification applied to a Sentinel-2 image (5th July 2021)</td> <td> </td> </tr> <tr> <td>corine_landcover_symbology_qgis.txt</td> <td>Land cover classification symbology for QGIS</td> </tr> <tr> <td>light_type_classification.tif</td> <td>Light type classification: class 1 = LED, class 10 = HPS.</td> </tr> <tr> <td>classified_light_locations.shp</td> <td>Classified light source (point) locations</td> </tr> </tbody> </table>
An Observational Pilot Study to Develop a Behavioral Economics Electronic Health Record Module to Guide the Care of Older Adults With Diabetes
ClinicalTrials.gov study NCT03409523. IPD Sharing: Not stated. Countries: 1. Publications: 1.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.