Large-scale land abandonment and reconstruction activity has altered the ecosystem structure in the evacuation area for the Fukushima Daiichi power plant accident in 2011. Despite social concerns about changes in the avian assemblages that occurred after the accident, publicly accessible data are quite limited. We engaged in acoustic monitoring of birds using digital voice recorders from 2014 in and around the Fukushima evacuation zone. All monitoring sites were located within schoolyards (including those that had been converted to community centers) to examine the bird assemblages in the urban and rural landscapes that were heavily altered by land abandonment due to the nuclear plant accident. A digital voice recorder was installed at each monitoring site during May–July, and we recorded 20 minutes a day using timer-recording mode. We divided the audio data into 1-minute segments and identified species occurred in sampled segments by experts. These data represent the presence-absence records from 52 sites monitored in 2014, 57 sites monitored in 2015, 54 sites monitored in 2016, 57 sites monitored in 2017, 56 sites monitored in 2018, 52 sites monitored in 2019 and 50 sites monitored in 2020. We identified the species for 7,222 segments in total and 68 species occurred in 2014, 8,017 segments in total and 64 species occurred in 2015, 5,289 segments in total and 58 species occurred in 2016, 4,092 segments in total and 60 species occurred in 2017, 4,200 segments in total and 65 species occurred in 2018, 4,000 segments in total and 59 species occurred in 2019 and 3,900 segments in total and 56 species occurred in 2020. We are continuing to monitor and intend to update the dataset with new observations hereafter. Our dataset will help people to recognize the status and dynamics of avian assemblage inside the evacuation zone, and will contribute to promote open science in avian ecological studies.

The classification of point cloud datasets to identify distribution wires is useful for identifying vegetation encroachment around power lines. Such workflows are important for preventing fires and power outages and are typically manual, recurring, and labor-intensive. This model is designed to extract distribution wires at the street level. Its predictions for high-tension transmission wires are less consistent with changes in geography as compared to street-level distribution wires. In the case of high-tension transmission wires, a lower ‘recall’ value is observed as compared to the value observed for low-lying street wires and poles.Using the modelFollow the guide to use the model. The model can be used with ArcGIS Pro's Classify Point Cloud Using Trained Model tool. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.InputThe model accepts unclassified point clouds with point geometry (X, Y and Z values). Note: The model is not dependent on any additional attributes such as Intensity, Number of Returns, etc. This model is trained to work on unclassified point clouds that are in a projected coordinate system, in which the units of X, Y and Z are based on the metric system of measurement. If the dataset is in degrees or feet, it needs to be re-projected accordingly. The model was trained using a training dataset with the full set of points. Therefore, it is important to make the full set of points available to the neural network while predicting - allowing it to better discriminate points of 'class of interest' versus background points. It is recommended to use 'selective/target classification' and 'class preservation' functionalities during prediction to have better control over the classification and scenarios with false positives.The model was trained on airborne lidar datasets and is expected to perform best with similar datasets. Classification of terrestrial point cloud datasets may work but has not been validated. For such cases, this pre-trained model may be fine-tuned to save on cost, time, and compute resources while improving accuracy. Another example where fine-tuning this model can be useful is when the object of interest is tram wires, railway wires, etc. which are geometrically similar to electricity wires. When fine-tuning this model, the target training data characteristics such as class structure, maximum number of points per block and extra attributes should match those of the data originally used for training this model (see Training data section below).OutputThe model will classify the point cloud into the following classes with their meaning as defined by the American Society for Photogrammetry and Remote Sensing (ASPRS) described below: Classcode Class Description 0 Background Class 14 Distribution Wires 15 Distribution Tower/PolesApplicable geographiesThe model is expected to work within any geography. It's seen to produce favorable results as shown here in many regions. However, results can vary for datasets that are statistically dissimilar to training data.Model architectureThis model uses the RandLANet model architecture implemented in ArcGIS API for Python.Accuracy metricsThe table below summarizes the accuracy of the predictions on the validation dataset. - Precision Recall F1-score Background (0) 0.999679 0.999876 0.999778 Distribution Wires (14) 0.955085 0.936825 0.945867 Distribution Poles (15) 0.707983 0.553888 0.621527Training dataThis model is trained on manually classified training dataset provided to Esri by AAM group. The training data used has the following characteristics: X, Y, and Z linear unitmeter Z range-240.34 m to 731.17 m Number of Returns1 to 5 Intensity1 to 4095 Point spacing0.2 ± 0.1 Scan angle-42 to +35 Maximum points per block20000 Extra attributesNone Class structure[0, 14, 15]Sample resultsHere are a few results from the model.

For natural PPRs, the Environmental Code defines two categories of zones (L562-1): risk-exposed areas and areas that are not directly exposed to risks but where measures can be foreseen to avoid exacerbating the risk.

In the case of this RPP, there are 3 categories of zones: — R: red zone to preserve from any new urbanisation

— O: area Orange corresponding to non-urbanised areas where only certain particular constructions can be authorised. This zone is divided into 3 sub-zones: —O1: unurbanised area affected by low-level localised collapse —O2: non-urbanised area affected by a low level compaction and/or slippage —O3: non-urbanised area affected by low-level localised collapse and contingencies and/or low-level slippage

— B: blue zone corresponding to mine hazard zones in an urbanised area where constructive arrangements are made to guarantee the safety of property and people. This zone is divided into 3 sub-zones: —B1: urbanised area affected by low-level localised collapse —B2: urbanised area affected by a low level compaction and/or slippage —B3: urbanised area affected by localised low-level collapse and low-level settlement and/or slippage

The power outages in this layer are pulled directly from the utility public power outage maps and is automatically updated every 15 minutes. This dataset represents only the most recent power outages and does not contain any historical data. The following utility companies are included:Pacific Gas and Electric (PG&E)Southern California Edison (SCE)San Diego Gas and Electric (SDG&E)Sacramento Municipal Utility District (SMUD)Los Angeles Water & Power (LAWP)Layers included in this dataset:Power Outage Incidents - Point layer that shows data from all of the utilities and is best for showing a general location of the outage and driving any numbers in dashboards.Power Outage Areas - Polygon layer that shows rough power outage areas from PG&E only (They are the only company that feeds this out publicly). With in the PG&E territory this layer is useful to show the general area out of power. The accuracy is limited by how the areas are drawn, but is it good for a visual of the impacted area.Power Outages by County - This layer summaries the total impacted customers by county. This layer is good for showing where outages are on a statewide scale.If you have any questions about this dataset please email GIS@caloes.ca.gov

This data-set contains all data resources, either directly downloadable via this platform or as links to external databases, to execute the generic modeling tool as described in D5.4

• 0 generic model
  file: d5.4-generic-model_february-2020.pdf
  Report with methodology and source code for the generic model
• 1.1 COPERNICUS
  https://land.copernicus.eu/local/urban-atlas/urban-atlas-2012?tab=download.%20
  The European Urban Atlas provides reliable, inter-comparable, high-resolution land use and land cover data for 800 Functional Urban Area (FUA) for the 2012 reference year in EEA39 countries.
• 1.2a 1km grid shapefiles for NOx emissions in EU
  The shapefiles are: nox.dbf nox.prj nox.shp nox.shx
• 1.2b 1km grid shapefiles for PM10 emissions in EU
  The shapefiles are: pm10.dbf pm10.prj pm10.shp pm10.shx
• 2.1 Eurostat energy consumption data
  https://ec.europa.eu/eurostat/web/energy/data/database?p_p_id=NavTreeportletprod_WAR_NavTreeportletprod_INSTANCE_QAMy7Pe6HwI1&p_p_lifecycle=0&p_p_state=normal&p_p_mode=view&p_p_col_id=column-2&p_p_col_count=1
• 2.2 JODI gas consumption data
  https://www.jodidata.org/gas/database/data-downloads.aspx
  Complete data series for all products, flows and countries, from January 2009 to one month-old can be downloaded, for free, in a beta version of .csv format.
• 2.3 Meteo data UK
  https://www.metoffice.gov.uk/services/data/datapoint/datapoint-products
• 2.4 Meteo data NL
  https://www.knmi.nl/nederland-nu/klimatologie/daggegevens
• 2.5 hourly electricity load profiles
  https://data.open-power-system-data.org/time_series/
• 3.1 fleet data
  file: 200210_summary_fleet_2016.csv
  Summary of historical fleet for EU28+NO by age and vehicle type: Diesel, CNG, Petrol, Electric, PHEV, LPG
• 3.2 modal shares
  file: 200210_summary_shares.csv
  This data set holds the modal share of different modes, for EU cities at level of vehicle kilometers. Modes considered: bus car heavy duty truck >32t heavy duty truck 16-32t heavy duty truck 3_5-7_5t heavy duty truck 7_5-16t light duty truck metro/tram moped motorcycle passenger train slow mode van
• 3.3 emission factors
  file: 200212_summary_copert_ef.xlsx
  Emission factor for passenger cars (in g/km )from COPERT V, urban conditions. Data dimensions: 1. type: diesel, petrol, CNG, LPG, PHEV (in line with fleet data) 2. pollutant: NOx or PM 3. Peak/off-peak: (P/OP) 4. year of built: YOB
• 5.1 Crude death rates
  http://apps.who.int/healthinfo/statistics/mortality/causeofdeath_query/start.php
  the total number of deaths in a country per year per 1,000 people broken down by age cohort and sex
• 5.2 UN population data
  https://population.un.org/wpp/Download/Standard/Population/
  the number of inhabitants at country level broken down by age cohort and sex the time a person has lived broken down by age cohort and sex, and country

Power Outages by County

Between 1954 and 1996, more than 200 nuclear power projects were publicly announced in the USA. Barely half of these projects were completed and generated power commercially. Existing research has highlighted a number of potential explanations for the varying siting outcomes of these projects, including contentious political protest, socioeconomic, and political conditions within potential host communities, regulatory changes (‘ratcheting’), and cost overruns. However, questions remain about which of these factors, if any, had an impact on these outcomes. We created a new data set of 228 host communities where siting was attempted to illuminate the factors that led projects towards either completion or cancellation. We include county-level regulatory, reactor-specific, demographic, and political factors which may correlate with the outcomes of attempts to site nuclear reactors over this time period. Draft of forthcoming peer reviewed article in International Journal of Energy Research can be found at http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2423935. We include the Stata dataset, the codebook, and the .do file used to create the statistical analysis for the paper.

Over the past half a century, the world's electricity consumption has continuously grown, reaching approximately 27,000 terawatt-hours by 2023. Between 1980 and 2023, electricity consumption more than tripled, while the global population reached eight billion people. Growth in industrialization and electricity access across the globe have further boosted electricity demand. China's economic rise and growth in global power use Since 2000, China's GDP has recorded an astonishing 15-fold increase, turning it into the second-largest global economy, behind only the United States. To fuel the development of its billion-strong population and various manufacturing industries, China requires more energy than any other country. As a result, it has become the largest electricity consumer in the world. Electricity consumption per capita In terms of per capita electricity consumption, China and other BRIC countries are still vastly outpaced by developed economies with smaller population sizes. Iceland, with a population of less than half a million inhabitants, consumes by far the most electricity per person in the world. Norway, Qatar, Canada, and the United States also have among the highest consumption rates. Multiple contributing factors such as the existence of power-intensive industries, household sizes, living situations, appliance and efficiency standards, and access to alternative heating fuels determine the amount of electricity the average person requires in each country.

The Politbarometer has been conducted since 1977 on an almost monthly basis by the Forschungsgruppe Wahlen on behalf of the Second German Television (ZDF). Since 1990, this database has also been available for the new German states. The survey focuses on the opinions and attitudes of the voting-age population in the Federal Republic on current political issues, parties, politicians, and voting behavior. From 1990 to 1995 and from 1999 onward, the Politbarometer surveys were conducted separately both in the newly formed eastern and in the western German states (Politbarometer East and Politbarometer West). The separate monthly surveys of a year are integrated into a cumulative data set that includes all surveys of a year and all variables of the respective year. Starting in 2003, the Politbarometer short surveys, collected with varying frequency throughout the year, are integrated into the annual cumulation.
Assessment of personal economic situation and future development; judgement on current and future economic development of the country; fear for job security; interest in politics; memory of voting behavior in the Federal Parliament election 1976 (first vote and second vote); party preference (ballot procedure, rank order procedure); party inclination; party one cannot vote for; satisfaction with achievements of the government and opposition (scale); satisfaction with democracy; reasons for satisfaction or dissatisfaction with democracy; the right people in leading positions; necessity of founding new parties; expected election winner and expected effects on the SPD/FDP coalition; desire for the CSU to be on the ballot in the entire Federal Republic; knowledge of the name of the Federal President and preference for a president or king as leader of the Federal Republic; general judgement on the situation of pensioners in the Federal Republic and financial protection of retirement pensions; preference for increase in retirement insurance or a subsidy by the government in case of insufficient coverage; assumed reasons for the increase in costs in the public health system; attitude to a strike by doctors and introduction of a road toll for citizens of the GDR in the FRG; judgement on the reform of political boundaries with consolidation of municipalities into larger units; preference for provision of energy by nuclear power plants or by other power plants; assessment of the danger from nuclear power plants; attitude to construction of a nuclear power plant in the immediate vicinity and protest behavior in such a case; expected energy shortage without nuclear power plants; personal willingness to conserve power; attitude to European unification; advantageousness of membership of the Federal Republic in the EC; judgement on student demands for better study conditions and increased financial support; general attitude to Italy and the Italians; knowledge about the election success of the communist party in Italy and attitude to cooperation of the Christian-Democratic party with the communists; perceived differences between the communist party of Italy and the parties in the East Bloc; knowledge about the term Euro-Communism; assessment of equal opportunities in the Federal Republic; the death penalty as a means to reduce crime; judgement on the usefulness of citizen initiatives; judgement on the base treaty between the Federal Republic and the GDR; judgement on improvement or deterioration of the relation with the GDR and the side responsible for this; the Federal Government giving in too much in negotiations with the GDR; judgement on task fulfillment of the parties; most important causes as well as judgement on further development of unemployment, price stability, certainty of pensions, the fight against terrorism and youth unemployment; institution most able to solve these problems; personal thoughts about growing old and feelings of concern or pleasure; threat to the state from terrorism and necessity of special laws to fight against terrorism; personal threat from terrorism; willingness to accept increased controls as security measure against terrorism; judgement on the conduct of the Federal Government and authorities after Schleyer´s kidnapping and expected search outcome in this case; terrorism and unemployment as problems calling for a strong man to lead the nation; interest of politicians in what the people think; attitude to a single strong party that represents the interests of all classes; introducing the death penalty for certain crimes; interest in a strong leadership personality to govern the country with a strong hand; adequate differences between the political views and goals of the parties; National Socialism as a good idea; attitude to the increasing number of books published about Hitler and the Third Reich; political retrospect on the year; increase of unemployment and short time work as temporary or long-term phenomenon; union membership; typical occupational groups in one´s...

Introduction This dataset reports on UK Power Networks' use of paid flexibility services. UK Power Networks uses flexibility (demand/generation turn up/down) in London, the South East, and the East of England to manage electricity flows on the local electricity distribution network. Flexibility dispatches data can be used to understand historical volumes, prices paid, geographic locations, providers, and technologies used. Using the Analyse tab, users can visualize and explore the growth of flexibility dispatches. These transparent insights can inform current and prospective flexibility services providers on how often flexibility is dispatched and at what price, including local authorities, electricity suppliers, industrial/commercial energy users, and generation operators. The data can also be used by wider stakeholders such as market analysts, advisers, regulators, and policymakers. A wide variety of energy resources and low carbon technologies already provide flexibility services to UK Power Networks, including grid-scale batteries, electric vehicle charge points, solar farms, wind farms, and residential energy users. These are grouped using the industry standard technology categorizations as used for regulatory reporting. To find out more about how to participate in flexibility tenders and become a flexibility provider, visit our webpage: Flexibility - UKPN DSO (ukpowernetworks.co.uk). Flexibility dispatches are currently reported from 1 April 2023, with new dispatches added monthly. Each row includes the timing, location, product, capacity, technology, and provider for our growing volume of flexibility dispatches. The data is assessed for errors using algorithmic quality control as well as being evaluated manually by a flexibility engineer before publication. The dataset can be downloaded or incorporated into the user’s interface via API. Requested volumes may not match delivered volumes, depending on performance against the relevant baseline. You can find actual dispatch data in the yearly Procurement Statements and Reports at Tender Hub - UKPN DSO (ukpowernetworks.co.uk). This includes our annual Flexibility Statement (forecasts for the next regulatory year), Flexibility Report (outcomes from last regulatory year), and data appendices.

Methodological Approach

Dispatches are made by a control engineer in the DSO Operations team to manage local constraints. Dispatches of flexible units (FUs) may be made either by API or email, depending on the FU's technological capabilities and preference. This dataset reports on dispatches made under Secure, Dynamic, and Day-Ahead products. Flexibility provided through our Sustain product is not dispatched and hence is not included within this dataset. Requested volumes may not match delivered volumes, depending on performance against the relevant baseline. Historic data may be updated from time to time where data errors are identified.

Quality Control Statement Dispatches are passed through a quality control algorithm to flag anomalies and erroneous data. Quality control checks include:

Times are consistent with the contracted service window; Dispatches are matched to the correct flexibility zone; Dispatches are unique (no duplicates); Dispatches are issued at the contracted price and volume; Dispatches are matched with an active contract.

Assurance Statement The flexibility dispatch report is reviewed by a flexibility engineer and a member of the Data Science team to ensure the data is accurate before publication on the Open Data Portal. Any data errors in previous reports are corrected on an ongoing basis and updated monthly.

Other Download dataset information: Metadata (JSON) Definitions of key terms related to this dataset can be found in the Open Data Portal Glossary: Open Data Portal Glossary

Introduction Generation customers connected to UK Power Networks can be subjected to curtailment through our Distributed Energy Resource Management System (DERMS) if they accepted a curtailable-connection. During periods of network congestion, these DERS will have their access reduced to mitigate network constraint breaches. Their reduction is organised according to their connection application date in a last-in first-out (LIFO) arrangement. The Constraints Real Time Meter Readings dataset on the Open Data Portal (ODP) gives a near real time status of the constraints on our network that are used by DERMS to reduce access. This API accessible dataset can be used to see just how congested the network is, and for the specific DER operators themselves, they have access and visibility to the constraints of their specific site. The dataset contains a timestamp, the constraint identifier, the most recent current reading in amps, the trim and release limits (curtailment starts at the trim and ends at the release), whether the site is in breach, a description of the constraint, and (only if you have access) the name of the DER. The dataset updates as close to real time as is possible. Our scheduling is as follows:

At 15s past the minute mark, we scrape the network data and push it to the ODP server On the minute mark, the ODP runs an update to refresh the dataset The dataset refresh is completed between 5-15s past the minute mark Only after this refresh has completed can you get the latest values from the ODP

You can run this notebook to see the dataset in action: https://colab.research.google.com/drive/1Czx98U6zttlA3PC2OfI_0UzAbE48BvEq?usp=sharing

Methodological Approach

A Remote Terminal Unit (RTU) is installed at each curtailable-connection site providing live telemetry data into the DERMS. It measures communications status, generator output, and mode of operation. RTUs are also installed at constraint locations (physical parts of the network, e.g., transformers, cables which may become overloaded under certain conditions). These are identified through planning power load studies. These RTUs monitor current at the constraint and communications status. The DERMS design integrates network topology information. This maps constraints to associated curtailable connections under different network running conditions, including the sensitivity of the constraints to each curtailable connection. In general, a 1MW reduction in generation of a customer will cause <1MW reduction at the constraint. Each constraint is registered to a GSP. DERMS monitors constraints against the associated breach limit. When a constraint limit is breached, DERMS calculates the amount of access reduction required from curtailable connections linked to the constraint to alleviate the breach. This calculation factors in the real-time level of generation of each customer and the sensitivity of the constraint to each generator. Access reduction is issued to each curtailable-connection via the RTU until the constraint limit breach is mitigated. Multiple constraints can apply to a curtailable-connection and constraint breaches can occur simultaneously. Where multiple constraint breaches act upon a single curtailable-connection, we apportion the access reduction of that connection to the constraint breaches depending on the relative magnitude of the breaches. Where customer curtailment occurs without any associated constraint breach, we categorize the curtailment as non-constraint driven. Future developments will include the reason for non-constraint driven curtailment.

Quality Control Statement Quality Control Measures include:

Manual review and correction of data inconsistencies. Use of additional verification steps to ensure accuracy in the methodology.

Assurance Statement The DSO Data Science Team checked to ensure data accuracy and consistency.

Other Download dataset information: Metadata (JSON) Definitions of key terms related to this dataset can be found in the Open Data Portal Glossary: https://ukpowernetworks.opendatasoft.com/pages/glossary/

The following data is based on statistics collected by us from mines and quarry sites throughout Queensland.

Incident frequency rates data shows the quarterly frequency rates for serious accidents, high potential incidents, recordable injuries, lost-time injuries and disabling injuries for each major industry sector, for the period specified.

Mining industry worker numbers data shows the number of workers by mine and sector, for the period specified.

The lost-time injuries/diseases data covers occurrences that resulted in fatalities, permanent disability or time lost from work of 1 shift or more. Data is categorised by body part affected, hazards identified, injury type, major equipment, mechanism of injury, occurrence class and worksite location.

Known entrances to abandoned underground coal mines including hoisting shafts, air/escape shafts, and slopes (adits).

New nuclear power Plant at the Jaslovské Bohunice site. Report on the environmental impact assessment of the proposed activity Consultation report. In the Slovak Republic there are two nuclear power plants, Bohunice NPP and Mochovce NPP, consisting of a total of four VVER 440/V-213 pressurized water reactors, owned and operated by Slovenské Elektrárne. These four units produce about half of the electricity produced in the country. In order to secure this share in the future, the energy policy of the Slovak Republic envisages the construction of a new reactor unit at the Bohunice site.

This data set describes the approximate location of structures typically associated with Western Power's distribution network. Note this dataset also includes Western Power managed streetlight poles and support structures and as such may not necessarily indicate the presence of an overhead network. © Western Power 2017 WARNING: This data should be considered approximate only and is intended to give the user a reasonably good idea of location relative to their own area of interest. They MUST NOT be used as a substitute for the ‘Dial Before You Dig’ service, ‘feasibility study’ or ‘technical evaluation, or professional advice. Please refer to the Western Power website for further information and technical advice. Western Power’s Licence Conditions govern these Data and Resources. By accessing or using the Data and/or Resources, the user agrees to be bound by Western Power’s Licence Conditions available here

The total electric power consumption in Nigeria was forecast to remain on a similar level in 2029 as compared to 2024 with 0.04 million kilowatt hours. According to this forecast, the electric power consumption will stay nearly the same over the forecast period. Depicted is the estimated electric power consumption per capita in the country or region at hand. Both demand from private households as industrial consumption are included in the figures.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the total electric power consumption in countries like Ivory Coast and Senegal.