This data was prepared as input for the Selkie GIS-TE tool. This GIS tool aids site selection, logistics optimization and financial analysis of wave or tidal farms in the Irish and Welsh maritime areas. Read more here: https://www.selkie-project.eu/selkie-tools-gis-technoeconomic-model/

This research was funded by the Science Foundation Ireland (SFI) through MaREI, the SFI Research Centre for Energy, Climate and the Marine and by the Sustainable Energy Authority of Ireland (SEAI). Support was also received from the European Union's European Regional Development Fund through the Ireland Wales Cooperation Programme as part of the Selkie project.

File Formats

Results are presented in three file formats:

tif Can be imported into a GIS software (such as ARC GIS) csv Human-readable text format, which can also be opened in Excel png Image files that can be viewed in standard desktop software and give a spatial view of results

Input Data

All calculations use open-source data from the Copernicus store and the open-source software Python. The Python xarray library is used to read the data.

Hourly Data from 2000 to 2019

• Wind - Copernicus ERA5 dataset 17 by 27.5 km grid
  10m wind speed

• Wave - Copernicus Atlantic -Iberian Biscay Irish - Ocean Wave Reanalysis dataset 3 by 5 km grid

Accessibility

The maximum limits for Hs and wind speed are applied when mapping the accessibility of a site.
The Accessibility layer shows the percentage of time the Hs (Atlantic -Iberian Biscay Irish - Ocean Wave Reanalysis) and wind speed (ERA5) are below these limits for the month.

Input data is 20 years of hourly wave and wind data from 2000 to 2019, partitioned by month. At each timestep, the accessibility of the site was determined by checking if
the Hs and wind speed were below their respective limits. The percentage accessibility is the number of hours within limits divided by the total number of hours for the month.

Environmental data is from the Copernicus data store (https://cds.climate.copernicus.eu/). Wave hourly data is from the 'Atlantic -Iberian Biscay Irish - Ocean Wave Reanalysis' dataset.
Wind hourly data is from the ERA 5 dataset.

Availability

A device's availability to produce electricity depends on the device's reliability and the time to repair any failures. The repair time depends on weather
windows and other logistical factors (for example, the availability of repair vessels and personnel.). A 2013 study by O'Connor et al. determined the
relationship between the accessibility and availability of a wave energy device. The resulting graph (see Fig. 1 of their paper) shows the correlation between accessibility at Hs of 2m and wind speed of 15.0m/s and availability. This graph is used to calculate the availability layer from the accessibility layer.

The input value, accessibility, measures how accessible a site is for installation or operation and maintenance activities. It is the percentage time the
environmental conditions, i.e. the Hs (Atlantic -Iberian Biscay Irish - Ocean Wave Reanalysis) and wind speed (ERA5), are below operational limits.
Input data is 20 years of hourly wave and wind data from 2000 to 2019, partitioned by month. At each timestep, the accessibility of the site was determined
by checking if the Hs and wind speed were below their respective limits. The percentage accessibility is the number of hours within limits divided by the total
number of hours for the month. Once the accessibility was known, the percentage availability was calculated using the O'Connor et al. graph of the relationship between the two. A mature technology reliability was assumed.

Weather Window

The weather window availability is the percentage of possible x-duration windows where weather conditions (Hs, wind speed) are below maximum limits for the
given duration for the month.

The resolution of the wave dataset (0.05° × 0.05°) is higher than that of the wind dataset
(0.25° x 0.25°), so the nearest wind value is used for each wave data point. The weather window layer is at the resolution of the wave layer.

The first step in calculating the weather window for a particular set of inputs (Hs, wind speed and duration) is to calculate the accessibility at each timestep.
The accessibility is based on a simple boolean evaluation: are the wave and wind conditions within the required limits at the given timestep?

Once the time series of accessibility is calculated, the next step is to look for periods of sustained favourable environmental conditions, i.e. the weather
windows. Here all possible operating periods with a duration matching the required weather-window value are assessed to see if the weather conditions remain
suitable for the entire period. The percentage availability of the weather window is calculated based on the percentage of x-duration windows with suitable
weather conditions for their entire duration.The weather window availability can be considered as the probability of having the required weather window available
at any given point in the month.

Extreme Wind and Wave

The Extreme wave layers show the highest significant wave height expected to occur during the given return period. The Extreme wind layers show the highest wind speed expected to occur during the given return period.

To predict extreme values, we use Extreme Value Analysis (EVA). EVA focuses on the extreme part of the data and seeks to determine a model to fit this reduced
portion accurately. EVA consists of three main stages. The first stage is the selection of extreme values from a time series. The next step is to fit a model
that best approximates the selected extremes by determining the shape parameters for a suitable probability distribution. The model then predicts extreme values
for the selected return period. All calculations use the python pyextremes library. Two methods are used - Block Maxima and Peaks over threshold.

The Block Maxima methods selects the annual maxima and fits a GEVD probability distribution.

The peaks_over_threshold method has two variable calculation parameters. The first is the percentile above which values must be to be selected as extreme (0.9 or 0.998). The second input is the time difference between extreme values for them to be considered independent (3 days). A Generalised Pareto Distribution is fitted to the selected
extremes and used to calculate the extreme value for the selected return period.

General Mills reported $33.07B in Assets for its fiscal quarter ending in May of 2025. Data for General Mills | GIS - Assets including historical, tables and charts were last updated by Trading Economics this last July in 2025.

General Mills stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

General Mills reported $0.74 in EPS Earnings Per Share for its fiscal quarter ending in May of 2025. Data for General Mills | GIS - EPS Earnings Per Share including historical, tables and charts were last updated by Trading Economics this last August in 2025.

The dataset is used for estimating Phillips curve using regional data of Indonesia with dynamic spatial Durbin model.

Based on the establishment data of provincial-provincial, city-city, provincial-city, city-provincial Hometown Chambers of Commerce (HCC) in China by the end of 2022, this paper combines social network analysis and exponential random graph model to explore the network spatial patterns and determinants of China’s HCC.The primary data on HCC establishments as of the end of 2022 were obtained from the Tianyancha platform (https://www.tianyancha.com/), a widely used enterprise credit information database in China. Given the possibility of registration inconsistencies, missing information, or duplicate records, we conducted a multi-step validation process to ensure data reliability.GDP, per capita GDP, and local general public budget expenditure data were all sourced from the China Statistical Yearbook and China Urban Statistical Yearbook. The dialect data were derived from the Atlas of Languages in China, including nine dialects: Xiang, Gan, Hui, Wu, Zhongyuan Mandarin, Jianghuai Mandarin, Southwest Mandarin, Hakka, and others. The urban agglomeration data were obtained from the 14th Five-Year Plan for National Economic and Social Development of the People's Republic of China, which mentions 19 urban agglomerations. The road distance data were calculated based on the shortest intercity highway distances from the 2022 Amap (Gaode Map) database.

General Mills reported $294M in Net Income for its fiscal quarter ending in May of 2025. Data for General Mills | GIS - Net Income including historical, tables and charts were last updated by Trading Economics this last August in 2025.

As a new type of economic format, digital economy has three major characteristics: technical, innovative, energy-saving and environmentally friendly. Acting on various sectors of the national economy, it is beneficial for improving carbon emission efficiency and is of great significance for achieving China’s two major goals of carbon peak and carbon neutrality. Firstly, theoretical analysis of the impact mechanism of digital economy on carbon emission efficiency, proposing research hypotheses on the direct effect, mediating effect, and spatial effect of digital economy on carbon emission efficiency. Secondly, based on panel data from 279 cities in China from 2011 to 2020, the econometric models are constructed to empirically analyze the direct, mediating, and spatial effects of digital economy on carbon emission efficiency. The results show that: 1) Digital economy can improve carbon emission efficiency; 2) The impact of digital economy on carbon emission efficiency has a “U”-shaped relationship, which is consistent with the "Environmental Kuznets Curve" hypothesis; 3) The impacts of digital economy on carbon emission efficiency exist in urban heterogeneity, specifically manifested as regional heterogeneity and urban scale heterogeneity; 4) Technological innovation is an important mediator for improving carbon emission efficiency in digital economy, and promoting technological innovation in digital economy can improve carbon emission efficiency; 5) Digital economy has spatial effect on carbon emission efficiency, which can improve the carbon emission efficiency of neighboring cities. Finally, based on the above results, suggestions are proposed from three aspects: promoting important industries and key areas for deep cultivation of carbon emission in digital economy, emphasizing regional balance in the development of digital economy, and strengthening regional cooperation in the development of digital economy, in order to continue to play a positive role in improving carbon emission efficiency through digital economy.

Post Disaster Needs Assessment, Environment & Economic Analysis of Loss and Damage to Environmental Goods and Services in Vanuatu, related to Severe Category 5 Tropical Cyclone Harold (April 2020).

This dataset contains: - the final PDNA report - the economic analysis for the Environment sector - the post cyclone mobile data collection survey form (developed in XLSForms and deployed with KoBoToolbox) - all data collected during the post cyclone field expeditions using these forms (consolidated into 1 data file, including data analysis, graphs, etc) - results of a GIS analysis to calculate forest and vegetation change - maps of forest and vegetation change - a separate mobile data collection form for the Waste sector - estimates of waste and damaged waste facilities

General Mills reported $27.87B in Market Capitalization this August of 2025, considering the latest stock price and the number of outstanding shares.Data for General Mills | GIS - Market Capitalization including historical, tables and charts were last updated by Trading Economics this last August in 2025.

As a new type of economic format, digital economy has three major characteristics: technical, innovative, energy-saving and environmentally friendly. Acting on various sectors of the national economy, it is beneficial for improving carbon emission efficiency and is of great significance for achieving China’s two major goals of carbon peak and carbon neutrality. Firstly, theoretical analysis of the impact mechanism of digital economy on carbon emission efficiency, proposing research hypotheses on the direct effect, mediating effect, and spatial effect of digital economy on carbon emission efficiency. Secondly, based on panel data from 279 cities in China from 2011 to 2020, the econometric models are constructed to empirically analyze the direct, mediating, and spatial effects of digital economy on carbon emission efficiency. The results show that: 1) Digital economy can improve carbon emission efficiency; 2) The impact of digital economy on carbon emission efficiency has a “U”-shaped relationship, which is consistent with the "Environmental Kuznets Curve" hypothesis; 3) The impacts of digital economy on carbon emission efficiency exist in urban heterogeneity, specifically manifested as regional heterogeneity and urban scale heterogeneity; 4) Technological innovation is an important mediator for improving carbon emission efficiency in digital economy, and promoting technological innovation in digital economy can improve carbon emission efficiency; 5) Digital economy has spatial effect on carbon emission efficiency, which can improve the carbon emission efficiency of neighboring cities. Finally, based on the above results, suggestions are proposed from three aspects: promoting important industries and key areas for deep cultivation of carbon emission in digital economy, emphasizing regional balance in the development of digital economy, and strengthening regional cooperation in the development of digital economy, in order to continue to play a positive role in improving carbon emission efficiency through digital economy.

As a new type of economic format, digital economy has three major characteristics: technical, innovative, energy-saving and environmentally friendly. Acting on various sectors of the national economy, it is beneficial for improving carbon emission efficiency and is of great significance for achieving China’s two major goals of carbon peak and carbon neutrality. Firstly, theoretical analysis of the impact mechanism of digital economy on carbon emission efficiency, proposing research hypotheses on the direct effect, mediating effect, and spatial effect of digital economy on carbon emission efficiency. Secondly, based on panel data from 279 cities in China from 2011 to 2020, the econometric models are constructed to empirically analyze the direct, mediating, and spatial effects of digital economy on carbon emission efficiency. The results show that: 1) Digital economy can improve carbon emission efficiency; 2) The impact of digital economy on carbon emission efficiency has a “U”-shaped relationship, which is consistent with the "Environmental Kuznets Curve" hypothesis; 3) The impacts of digital economy on carbon emission efficiency exist in urban heterogeneity, specifically manifested as regional heterogeneity and urban scale heterogeneity; 4) Technological innovation is an important mediator for improving carbon emission efficiency in digital economy, and promoting technological innovation in digital economy can improve carbon emission efficiency; 5) Digital economy has spatial effect on carbon emission efficiency, which can improve the carbon emission efficiency of neighboring cities. Finally, based on the above results, suggestions are proposed from three aspects: promoting important industries and key areas for deep cultivation of carbon emission in digital economy, emphasizing regional balance in the development of digital economy, and strengthening regional cooperation in the development of digital economy, in order to continue to play a positive role in improving carbon emission efficiency through digital economy.

As a new type of economic format, digital economy has three major characteristics: technical, innovative, energy-saving and environmentally friendly. Acting on various sectors of the national economy, it is beneficial for improving carbon emission efficiency and is of great significance for achieving China’s two major goals of carbon peak and carbon neutrality. Firstly, theoretical analysis of the impact mechanism of digital economy on carbon emission efficiency, proposing research hypotheses on the direct effect, mediating effect, and spatial effect of digital economy on carbon emission efficiency. Secondly, based on panel data from 279 cities in China from 2011 to 2020, the econometric models are constructed to empirically analyze the direct, mediating, and spatial effects of digital economy on carbon emission efficiency. The results show that: 1) Digital economy can improve carbon emission efficiency; 2) The impact of digital economy on carbon emission efficiency has a “U”-shaped relationship, which is consistent with the "Environmental Kuznets Curve" hypothesis; 3) The impacts of digital economy on carbon emission efficiency exist in urban heterogeneity, specifically manifested as regional heterogeneity and urban scale heterogeneity; 4) Technological innovation is an important mediator for improving carbon emission efficiency in digital economy, and promoting technological innovation in digital economy can improve carbon emission efficiency; 5) Digital economy has spatial effect on carbon emission efficiency, which can improve the carbon emission efficiency of neighboring cities. Finally, based on the above results, suggestions are proposed from three aspects: promoting important industries and key areas for deep cultivation of carbon emission in digital economy, emphasizing regional balance in the development of digital economy, and strengthening regional cooperation in the development of digital economy, in order to continue to play a positive role in improving carbon emission efficiency through digital economy.

As a new type of economic format, digital economy has three major characteristics: technical, innovative, energy-saving and environmentally friendly. Acting on various sectors of the national economy, it is beneficial for improving carbon emission efficiency and is of great significance for achieving China’s two major goals of carbon peak and carbon neutrality. Firstly, theoretical analysis of the impact mechanism of digital economy on carbon emission efficiency, proposing research hypotheses on the direct effect, mediating effect, and spatial effect of digital economy on carbon emission efficiency. Secondly, based on panel data from 279 cities in China from 2011 to 2020, the econometric models are constructed to empirically analyze the direct, mediating, and spatial effects of digital economy on carbon emission efficiency. The results show that: 1) Digital economy can improve carbon emission efficiency; 2) The impact of digital economy on carbon emission efficiency has a “U”-shaped relationship, which is consistent with the "Environmental Kuznets Curve" hypothesis; 3) The impacts of digital economy on carbon emission efficiency exist in urban heterogeneity, specifically manifested as regional heterogeneity and urban scale heterogeneity; 4) Technological innovation is an important mediator for improving carbon emission efficiency in digital economy, and promoting technological innovation in digital economy can improve carbon emission efficiency; 5) Digital economy has spatial effect on carbon emission efficiency, which can improve the carbon emission efficiency of neighboring cities. Finally, based on the above results, suggestions are proposed from three aspects: promoting important industries and key areas for deep cultivation of carbon emission in digital economy, emphasizing regional balance in the development of digital economy, and strengthening regional cooperation in the development of digital economy, in order to continue to play a positive role in improving carbon emission efficiency through digital economy.

The tabular and visual dataset focuses on South African basic education and provides insights into the distribution of schools and basic population statistics across the country. This tabular and visual data are stratified across different quintiles for each provincial and district boundary. The quintile system is used by the South African government to classify schools based on their level of socio-economic disadvantage, with quintile 1 being the most disadvantaged and quintile 5 being the least disadvantaged. The data was joined by extracting information from the debarment of basic education with StatsSA population census data. Thereafter, all tabular data and geo located data were transformed to maps using GIS software and the Python integrated development environment. The dataset includes information on the number of schools and students in each quintile, as well as the population density in each area. The data is displayed through a combination of charts, maps and tables, allowing for easy analysis and interpretation of the information.

General Mills reported $758.1M in EBITDA for its fiscal quarter ending in May of 2025. Data for General Mills | GIS - Ebitda including historical, tables and charts were last updated by Trading Economics this last August in 2025.

Evaluation index system for the level of digital economy development.

General Mills reported $9.2B in Equity Capital and Reserves for its fiscal quarter ending in May of 2025. Data for General Mills | GIS - Equity Capital And Reserves including historical, tables and charts were last updated by Trading Economics this last August in 2025.

General Mills reported 564.55M in Outstanding Shares in April of 2024. Data for General Mills | GIS - Outstanding Shares including historical, tables and charts were last updated by Trading Economics this last August in 2025.