This dataset is obtained from our blockchain performance test using Hyperledger Caliper on Hyperledger Fabric 2.5. There are eight fields in it. Set Send Rates: The transaction arrival rate we set (from [10-200], step length 5) Send Rates: The transaction arrival rate in real-world situations Block Size: The block size we set (from [10-400], step length 5) Throughput: Throughput (TPS) Avg Latency: Average latency (seconds) Min Latency: Minimum latency (seconds) Max Latency: Maximum latency (seconds) CPU Usage (%): The CPU usage for related operations of blockchain system

For a fixed transaction arrival rate, we set different block sizes to test its blockchain performance. For each data, we used 1000 transactions and finally took the average value to get the least error and the most accurate performance test results.

Our dataset will be helpful for researchers to find the relationship (pattern) between block size and blockchain performance in Hyperledger Fabric with real-time and multi-machine environments. Obviously, most blockchain users do not change the block size (they tend to use the default block size) and they are not aware of the impact of different block sizes on the blockchain performance.

With this dataset, researchers can use methods such as deep learning to uncover the relationship between block size and blockchain performance to find the optimal block size (other parameters that may affect blockchain performance) in different scenarios.

Note that We have submitted a corresponding paper to IEEE Transactions on Cloud Computing, and if this paper can be accepted and this dataset is helpful for your research, we are grateful and hope you can cite this paper. We will update this page in the future.

[1] J. Wang, X. Zhang*, L. Liu, X. Yang, T. Zhou, C. Miao, R. Zhu, and Z. Jin, "BPO-CBS: A Data-Driven Blockchain Performance Optimization Framework for Cloud Blockchain Services," IEEE Transactions on Cloud Computing, Under review.

This will also motivate our future work, and we will continue to contribute more valuable datasets.

ukcp09-Gridded datasets based on surface observations have been generated for a range of climatic variables. The primary purpose of this data resource is to encourage and facilitate research into climate change impacts and adaptation. This data set includes monthly ukcp09-Gridded datasets at 5 x 5 km resolution. A grid for each month covering the whole of the UK, downloadable in 10-year blocks. The datasets have been created with financial support from the Department for Environment, Food and Rural Affairs (Defra) and they are being promoted by the UK Climate Impacts Programme (UKCIP) as part of the UK Climate Projections (UKCP09). http://ukclimateprojections.defra.gov.uk/content/view/12/689/. To view this data you will have to register on the Met Office website, here: http://www.metoffice.gov.uk/research/climate/climate-monitoring/UKCP09/register

The Arbuckle-Simpson aquifer covers an area of about 800 square miles in the Arbuckle Mountains and Arbuckle Plains of South-Central Oklahoma. The aquifer is in the Central Lowland Physiographic Province and is composed of the Simpson and Arbuckle Groups of Ordovician and Cambrian age. The aquifer is as thick as 9,000 feet in some areas. The aquifer provides relatively small, but important, amounts of water depended on for public supply, agricultural, and industrial use (HA 730-E). This product provides source data for the Arbuckle-Simpson aquifer framework, including: Georeferenced images: 1. i_46ARBSMP_bot.tif: Digitized figure of depth contour lines below land surface representing the base of fresh water in the Arbuckle-Simpson aquifer. The base of fresh water is considered to be the bottom of the Arbuckle-Simpson aquifer. The original figure is from the "Reconnaissance of the water resources of the Ardmore and Sherman Quadrangles, southern Oklahoma" report, map HA-3, page 2, prepared by the Oklahoma Geological Survey in cooperation with the U.S. Geological Survey (HA3_P2). Extent shapefiles: 1. p_46ABKSMP.shp: Polygon shapefile containing the areal extent of the Arbuckle-Simpson aquifer (Arbuckle-Simpson_AqExtent). The extent file contains no aquifer subunits. Contour line shapefiles: 1. c_46ABKSMP_bot.shp: Contour line dataset containing depth values, in feet below land surface, across the bottom of the Arbuckle-Simpson aquifer. This dataset is a digitized version of the map published in HA3_P2. This dataset was used to create the rd_46ABKSMP_bot.tif raster dataset. This map generalized depth values into zoned areas with associated ranges of depth. The edge of each zone was treated as the minimum value of the assigned range, thus creating the depth contour lines. This interpretation was favorable as it allowed for the creation of the resulting raster. This map was used because more detailed point or contour data for the area is unavailable. Altitude raster files: 1. ra_46ABKSMP_top.tif: Altitude raster dataset of the top of the Arbuckle-Simpson aquifer. The altitude values are in meters reference to North American Vertical Datum of 1988 (NAVD88). The top of the aquifer is assumed to be at land surface (NED, 100-meter) based on available data. This raster was interpolated from the Digital Elevation Model (DEM) dataset (NED, 100-meter). 2. ra_46ABKSMP_bot.tif: Altitude raster dataset of the bottom of the Arbuckle-Simpson aquifer. The altitude values are in meters referenced to NAVD88. Depth raster files: 1. rd_46ABKSMP_top.tif: Depth raster dataset of the top of the Arbuckle-Simpson aquifer. The depth values are in meters below land surface (NED, 100-meter). The top of the aquifer is assumed to be at land surface (NED, 100-meter) based on available data. 2. rd_46ABKSMP_bot.tif: Depth raster dataset of the bottom of the Arbuckle-Simpson aquifer. The depth values are in meters below land surface (NED, 100-meter). This raster was interpolated from the contour line dataset c_46ABKSMP_bot.shp.

Materials exhibiting higher mobility than conventional organic semiconducting materials, such as fullerenes and fused thiophenes, are in high demand for applications in printed electronics. To discover new molecules that might show improved charge mobility, the adaptive design of experiments (DoE) to design molecules with low reorganization energy was performed by combining density functional theory (DFT) methods and machine learning techniques. DFT-calculated values of 165 molecules were used as an initial training dataset for a Gaussian process regression (GPR) model, and five rounds of molecular designs applying the GPR model and validation via DFT calculations were executed. As a result, new molecules whose reorganization energy is smaller than the lowest value in the initial training dataset were successfully discovered.

This synthetic dataset contains 4,362 rows and five columns, including both numerical and categorical data. It is designed for data cleaning, imputation, and analysis tasks, featuring structured missing values at varying percentages (63%, 4%, 47%, 31%, and 9%).

The dataset includes:
- Category (Categorical): Product category (A, B, C, D)
- Price (Numerical): Randomized product prices
- Rating (Numerical): Ratings between 1 to 5
- Stock (Categorical): Availability status (In Stock, Out of Stock)
- Discount (Numerical): Discount percentage

This dataset is ideal for practicing missing data handling, exploratory data analysis (EDA), and machine learning preprocessing.

Overview

• Introduction
• Object Classes
  • Center
  • Max
  • Meter
  • Min
  • Pointer Tip

Introduction

This dataset is designed for detecting various components of analog meters. It includes five classes: center, max, meter, min, and pointer_tip. Each class represents a distinct part of the meter, essential for measurements and gauge readings.

Object Classes

Center

Description

The center is the pivot point around which the needle rotates. It is typically located at the center of the gauge face.

Instructions

Annotate the central pivot area of the meter. It is usually circular and located in the middle of the gauge face. Do not include any surrounding hardware or additional meter elements outside of the central pivot.

Max

Description

The max class represents the maximum value marker on the meter's scale, often found at the far end of the gauge.

Instructions

Identify and label the topmost marker on the scale, indicating the highest value measurable by the meter. This area is usually at the end of the measurement arc. Avoid including any numbers or labels present on the scale.

Meter

Description

The meter class comprises the entire gauge, including the face, scale, and surroundings.

Instructions

Draw a bounding box around the whole gauge, capturing the glass, face, and casing. Ensure that the edges are tight against the outermost part of the meter.

Min

Description

The min class depicts the minimum value marker on the meter's scale, typically at the opposite end of the max marker.

Instructions

Locate the bottommost marker on the scale, indicating the lowest value of the meter. This marker is generally found at the beginning of the measurement arc. Do not include any surrounding labels or numbers.

Pointer Tip

Description

The pointer tip is the end part of the needle, indicating the current measurement on the scale.

Instructions

Tag the end of the needle that points to the scale. Focus solely on the tip of the pointing needle without covering the base or middle sections. Ensure the annotation is strictly on the tip to prevent overlap with other gauge components.

This dataset is obtained from our blockchain performance test using Hyperledger Caliper on Hyperledger Fabric 2.3. There are five fields in it. Set Send Rates: the transaction send rates we set (from [10-200], step length 5) Send Rates: the transaction send rates in real-world situations Block Size: the block size we set (from [10-800], step length 10) Avg Latency: average latency (seconds) Throughput: throughput (TPS)

For a fixed transaction delivery rate, we set different block sizes to test its blockchain performance. For each data, we conducted 10 experiments and finally took the average value to get the least error and the most accurate performance test results.

Our dataset will be helpful for researchers to find the relationship (pattern) between block size and blockchain performance in Hyperledger Fabric. Obviously, most blockchain users do not change the block size (they tend to use the default block size) and they are not aware of the impact of different block sizes on the blockchain performance.

With this dataset, researchers can use methods such as deep learning to uncover the relationship between block size and blockchain performance to find the optimal block size in different scenarios.

Note that if this dataset is helpful for your research, we are grateful and hope you can cite these papers as follows.

[1] J. Wang, C. Zhu, C. Miao, R. Zhu, X. Zhang, Y. Tang, H. Huang, and C. Gao, "BPR: Blockchain-Enabled Efficient and Secure Parking Reservation Framework With Block Size Dynamic Adjustment Method," IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 3, pp. 3555-3570, March 2023, doi: 10.1109/TITS.2022.3222960.

This will also motivate our future work, and we will continue to contribute more valuable datasets.

About this dataset

Context

The dataset tabulates the population of Lake View by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Lake View. The dataset can be utilized to understand the population distribution of Lake View by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Lake View. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Lake View.

Key observations

Largest age group (population): Male # 50-54 years (62) | Female # 50-54 years (75). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Lake View population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Lake View is shown in the following column.
• Population (Female): The female population in the Lake View is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Lake View for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Lake View Population by Gender. You can refer the same here

The Property Valuation Data Listing offered by BatchData delivers an extensive and detailed dataset designed to provide unparalleled insight into real estate market trends, property values, and investment opportunities. This dataset includes over 9 critical data points that offer a comprehensive view of property valuations across various geographic regions and market conditions. Below is an in-depth description of the data points and their implications for users in the real estate industry.

The Property Valuation Data Listing by BatchData is categorized into four primary sections, each offering detailed insights into different aspects of property valuation. Here’s an in-depth look at each category:

1. Current Valuation AVM Value as of Specific Date: The Automated Valuation Model (AVM) estimate of the property’s current market value, calculated as of a specified date. This value reflects the most recent assessment based on available data. Use Case: Provides an up-to-date valuation, essential for making current investment decisions, setting sale prices, or conducting market analysis. Valuation Confidence Score: A measure indicating the confidence level of the AVM value. This score reflects the reliability of the valuation based on data quality, volume, and model accuracy. Use Case: Helps users gauge the reliability of the valuation estimate. Higher confidence scores suggest more reliable values, while lower scores may indicate uncertainty or data limitations.

2. Valuation Range Price Range Minimum: The lowest estimated market value for the property within the given range. This figure represents the lower bound of the valuation spectrum. Use Case: Useful for understanding the potential minimum value of the property, helping in scenarios like setting a reserve price in auctions or evaluating downside risk. Price Range Maximum: The highest estimated market value for the property within the given range. This figure represents the upper bound of the valuation spectrum. Use Case: Provides insight into the potential maximum value, aiding in price setting, investment analysis, and comparative market assessments. AVM Value Standard Deviation: A statistical measure of the variability or dispersion of the AVM value estimates. It indicates how much the estimated values deviate from the average AVM value. Use Case: Assists in understanding the variability of the valuation and assessing the stability of the estimated value. A higher standard deviation suggests more variability and potential uncertainty.

3. LTV (Loan to Value Ratio) Current Loan to Value Ratio: The ratio of the outstanding loan balance to the current market value of the property, expressed as a percentage. This ratio helps assess the risk associated with the loan relative to the property’s value. Use Case: Crucial for lenders and investors to evaluate the financial risk of a property. A higher LTV ratio indicates higher risk, as the property value is lower compared to the loan amount.

4. Valuation Equity Calculated Total Equity: based upon estimate amortized balances for all open liens and AVM value Use Case: Provides insight into the net worth of the property for the owner. Useful for evaluating the financial health of the property, planning for refinancing, or understanding the owner’s potential gain or loss in case of sale.

This structured breakdown of data points offers a comprehensive view of property valuations, allowing users to make well-informed decisions based on current market conditions, valuation accuracy, financial risk, and equity potential.

This information can be particularly useful for: - Automated Valuation Models (AVMs) - Fuel Risk Management Solutions - Property Valuation Tools - ARV, rental data, building condition and more - Listing/offer Price Determination

About this dataset

Context

The dataset tabulates the population of Prairie View by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Prairie View. The dataset can be utilized to understand the population distribution of Prairie View by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Prairie View. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Prairie View.

Key observations

Largest age group (population): Male # 20-24 years (1,407) | Female # 20-24 years (2,161). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Prairie View population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Prairie View is shown in the following column.
• Population (Female): The female population in the Prairie View is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Prairie View for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Prairie View Population by Gender. You can refer the same here

This is the second batch of WiFi RSS RTT datasets with LOS conditions we published. Please see https://doi.org/10.5281/zenodo.11558192 for the first release.

We provide three real-world datasets for indoor positioning model selection purpose. We divided the area of interest was divided into discrete grids and labelled them with correct ground truth coordinates and the LoS APs from the grid. The dataset contains WiFi RTT and RSS signal measures and is well separated so that training points and testing points will not overlap. Please find the datasets in the 'data' folder. The datasets contain both WiFi RSS and RTT signal measures with groud truth coordinates label and LOS condition label.

Lecture theatre: This is a entirely LOS scenario with 5 APs. 60 scans of WiFi RTT and RSS signal measures were collected at each reference point (RP).

Corridor: This is a entirely NLOS scenario with 4 APs. 60 scans of WiFi RTT and RSS signal measures were collected at each reference point (RP).

Office: This is a mixed LOS-NLOS scenario with 5 APs. At least one AP was NLOS for each RP. 60 scans of WiFi RTT and RSS signal measures were collected at each reference point (RP).

Collection methodology

The APs utilised were Google WiFi Router AC-1304, the smartphone used to collect the data was Google Pixel 3 with Android 9.

The ground truth coordinates were collected using fixed tile size on the floor and manual post-it note markers.

Only RTT-enabled APs were included in the dataset.

The features of the dataset

The features of the lecture theatre dataset are as follows:

Testbed area: 15 × 14.5 m2 Grid size: 0.6 × 0.6 m2Number of AP: 5 Number of reference points: 120 Samples per reference point: 60 Number of all data samples: 7,200 Number of training samples: 5,400 Number of testing samples: 1,800 Signal measure: WiFi RTT, WiFi RSS Note: Entirely LOS

The features of the corricor dataset are as follows:

Testbed area: 35 × 6 m2 Grid size: 0.6 × 0.6 m2Number of AP: 4 Number of reference points: 114 Samples per reference point: 60 Number of all data samples: 6,840 Number of training samples: 5,130 Number of testing samples: 1,710 Signal measure: WiFi RTT, WiFi RSS Note: Miexed LOS-NLOS. At least one AP was NLOS for each RP.

The features of the office dataset are as follows:

Testbed area: 18 × 5.5 m2 Grid size: 0.6 × 0.6 m2Number of AP: 5 Number of reference points: 108 Samples per reference point: 60 Number of all data samples: 6,480 Number of training samples: 4,860 Number of testing samples: 1,620 Signal measure: WiFi RTT, WiFi RSS Note: Entirely NLOS

Dataset explanation

The columns of the dataset are as follows:

Column 'X': the X coordinates of the sample. Column 'Y': the Y coordinates of the sample. Column 'AP1 RTT(mm)', 'AP2 RTT(mm)', ..., 'AP5 RTT(mm)': the RTT measure from corresponding AP at a reference point. Column 'AP1 RSS(dBm)', 'AP2 RSS(dBm)', ..., 'AP5 RSS(dBm)': the RSS measure from corresponding AP at a reference point. Column 'LOS APs': indicating which AP has a LOS to this reference point.

Please note:

The RSS value -200 dBm indicates that the AP is too far away from the current reference point and no signals could be heard from it.

The RTT value 100,000 mm indicates that no signal is received from the specific AP.

Citation request

When using this dataset, please cite the following three items:

Feng, X., Nguyen, K. A., & Zhiyuan, L. (2024). WiFi RSS & RTT dataset with different LOS conditions for indoor positioning [Data set]. Zenodo. https://doi.org/10.5281/zenodo.11558792

@article{feng2024wifi, title={A WiFi RSS-RTT indoor positioning system using dynamic model switching algorithm}, author={Feng, Xu and Nguyen, Khuong An and Luo, Zhiyuan}, journal={IEEE Journal of Indoor and Seamless Positioning and Navigation}, year={2024}, publisher={IEEE} }@inproceedings{feng2023dynamic, title={A dynamic model switching algorithm for WiFi fingerprinting indoor positioning}, author={Feng, Xu and Nguyen, Khuong An and Luo, Zhiyuan}, booktitle={2023 13th International Conference on Indoor Positioning and Indoor Navigation (IPIN)}, pages={1--6}, year={2023}, organization={IEEE} }

[Updated 28/01/25 to fix an issue in the ‘Lower’ values, which were not fully representing the range of uncertainty. ‘Median’ and ‘Higher’ values remain unchanged. The size of the change varies by grid cell and fixed period/global warming levels but the average difference between the 'lower' values before and after this update is 0.37°C.]What does the data show? This dataset shows the change in winter minimum temperature for a range of global warming levels, including the recent past (2001-2020), compared to the 1981-2000 baseline period. Here, winter is defined as December-January-February.The dataset uses projections of daily minimum air temperature from UKCP18 which are averaged to give values for the 1981-2000 baseline, the recent past (2001-2020) and global warming levels. The warming levels available are 1.5°C, 2.0°C, 2.5°C, 3.0°C and 4.0°C above the pre-industrial (1850-1900) period. The recent past value and global warming level values are stated as a change (in °C) relative to the 1981-2000 value. This enables users to compare winter minimum temperature trends for the different periods. In addition to the change values, values for the 1981-2000 baseline (corresponding to 0.51°C warming) and recent past (2001-2020, corresponding to 0.87°C warming) are also provided. This is summarised in the table below.PeriodDescription1981-2000 baselineAverage temperature (°C) for the period2001-2020 (recent past)Average temperature (°C) for the period2001-2020 (recent past) changeTemperature change (°C) relative to 1981-20001.5°C global warming level changeTemperature change (°C) relative to 1981-20002°C global warming level changeTemperature change (°C) relative to 1981-20002.5°C global warming level changeTemperature change (°C) relative to 1981-20003°C global warming level changeTemperature change (°C) relative to 1981-20004°C global warming level changeTemperature change (°C) relative to 1981-2000What is a global warming level?The Winter Minimum Temperature Change is calculated from the UKCP18 regional climate projections using the high emissions scenario (RCP 8.5) where greenhouse gas emissions continue to grow. Instead of considering future climate change during specific time periods (e.g. decades) for this scenario, the dataset is calculated at various levels of global warming relative to the pre-industrial (1850-1900) period. The world has already warmed by around 1.1°C (between 1850–1900 and 2011–2020), whilst this dataset allows for the exploration of greater levels of warming. The global warming levels available in this dataset are 1.5°C, 2°C, 2.5°C, 3°C and 4°C. The data at each warming level was calculated using a 21 year period. These 21 year periods are calculated by taking 10 years either side of the first year at which the global warming level is reached. This time will be different for different model ensemble members. To calculate the value for the Winter Minimum Temperature Change, an average is taken across the 21 year period.We cannot provide a precise likelihood for particular emission scenarios being followed in the real world future. However, we do note that RCP8.5 corresponds to emissions considerably above those expected with current international policy agreements. The results are also expressed for several global warming levels because we do not yet know which level will be reached in the real climate as it will depend on future greenhouse emission choices and the sensitivity of the climate system, which is uncertain. Estimates based on the assumption of current international agreements on greenhouse gas emissions suggest a median warming level in the region of 2.4-2.8°C, but it could either be higher or lower than this level.What are the naming conventions and how do I explore the data?These data contain a field for each warming level and the 1981-2000 baseline. They are named 'tasmin winter change' (change in air 'temperature at surface'), the warming level or baseline, and 'upper' 'median' or 'lower' as per the description below. e.g. ‘tasmin winter change 2.0 median' is the median value for winter for the 2.0°C warming level. Decimal points are included in field aliases but not in field names, e.g. 'tasmin winter change 2.0 median' is named ‘tasmin_winter_change_20_median'. To understand how to explore the data, refer to the New Users ESRI Storymap. Please note, if viewing in ArcGIS Map Viewer, the map will default to ‘tasmin winter change 2.0°C median’ values.What do the 'median', 'upper', and 'lower' values mean?Climate models are numerical representations of the climate system. To capture uncertainty in projections for the future, an ensemble, or group, of climate models are run. Each ensemble member has slightly different starting conditions or model set-ups. Considering all of the model outcomes gives users a range of plausible conditions which could occur in the future.For this dataset, the model projections consist of 12 separate ensemble members. To select which ensemble members to use, the Winter Minimum Temperature Change was calculated for each ensemble member and they were then ranked in order from lowest to highest for each location.The ‘lower’ fields are the second lowest ranked ensemble member. The ‘higher’ fields are the second highest ranked ensemble member. The ‘median’ field is the central value of the ensemble.This gives a median value, and a spread of the ensemble members indicating the range of possible outcomes in the projections. This spread of outputs can be used to infer the uncertainty in the projections. The larger the difference between the lower and higher fields, the greater the uncertainty.‘Lower’, ‘median’ and ‘upper’ are also given for the baseline period as these values also come from the model that was used to produce the projections. This allows a fair comparison between the model projections and recent past. Useful linksFor further information on the UK Climate Projections (UKCP).Further information on understanding climate data within the Met Office Climate Data Portal.

Visibility viewsheds incorporate influences of distance from observer, object size and limits of human visual acuity to define the degree of visibility as a probability between 1 - 0. Average visibility viewsheds represent the average visibility value across all visibility viewsheds, thus representing a middle scenario relative to maximum and minimum visibility viewsheds. Average Visibility viewsheds can be used as a potential resource conflict screening tools as it relates to the Great Plains Wind Energy Programmatic Environmental Impact Statement. Data includes binary and composite viewsheds, and average, maximum, minimum, and composite visibility viewsheds for the NPS unit. Viewsheds have been derived using a 30m National Elevation Dataset (NED) digital elevation model. Additonal viewshed parameters: Observer Height (offset A) was set at 2 meters. A vertical development object height (offset B) was set at 110 meters, representing an average wind tower and associated blade height. A binary viewshed (1 visible, 0 not visible) was created for the defined NPS Unit specific Key Observation Points (KOP). A composite viewshed is the visibility of multiple viewsheds combined into one. A visible value in a composite viewshed implies that across all the combined binary viewsheds (one per key observation pointacross the nps unit in this case), at a minimum at least one of the sample points is visible. On a cell by cell basis throughout the study area of interest the numbers of visible sample points are recorded in the composite viewshed. Composite viewsheds are a quick way to synthesize multiple viewsheds into one layer, thus giving an efficient and cursory overview of potential visual resource effects. To summarize visibility viewsheds across numerous viewsheds, (e.g. multiple viewsheds per high priority segment) three visibility scenario summary viewsheds have been derived: 1) A maximum visibility scenario is evaluated using a "Products" visibility viewshed, which represents the probability that all sample points are visible. Maximum visibility viewsheds are derived by multiplying probability values per visibility viewshed. 2) A minimum visibility scenario is assessed using a "Fuzzy sum" visibility viewshed. Minimum visibility viewsheds represent the probability that one sample point is visible, and is derived by calculating the fuzzy sum value across the probability values per visibility viewsheds. 3) Lastly an average visibility scenario is created from an "Average" visibility calculation. Average visibility viewsheds represent the average visibility value across all visibility viewsheds, thus representing a middle scenario relative to the aforementioned maximum and minimum visibility viewsheds. Equations for the maximum, average and minimum visibility viewsheds are defined below: Maximum Visibility: Products Visibility =(p1*p2*pn...), Average Visibility: Average Visibility =((p1*p2*pn)/n), and Minimum Visibility: Fuzzy Sum Visibility =(1-((1-p1 )*(1-p2 )*(1-pn )* ...). Moving beyond a simplistic binary viewshed approach, visibility viewsheds define the degree of visibility as a probability between 1 - 0. Visibility viewsheds incorporate the influences of distance from observer, object size (solar energy towers, troughs, panels, etc.) and limits of human visual acuity to derive a fuzzy membership value. A fuzzy membership value is a probability of visibility ranging between 1 - 0, where a value of one implies that the object would be easily visible under most conditions and for most viewers, while a lower value represents reduced visibility. Visibility viewshed calculation is performed using the modified fuzzy viewshed equations (Ogburn D.E. 2006). Visibility viewsheds have been defined using: a foreground distance (b1) of 1 km, a visual arc threshold value of 1 minute (limit of 20/20 vision) which is used in the object width multiplier calculation, and an object width value of 10 meters.

Introduction

UK Power Network maintains the 132kV voltage level network and below. An important part of the distribution network is the stepping down of voltage as it is moved towards the household; this is achieved using transformers. Transformers have a maximum rating for the utilisation of these assets based upon protection, overcurrent, switch gear, etc. This dataset contains the Primary Substation Transformers, that typically step-down voltage from 33kV to 11kV (occasionally from 132kV to 11kV). These transformers can be viewed on the single line diagrams in our Long-Term Development Statements (LTDS) and the underlying data is then found in the LTDS tables.Care is taken to protect the private affairs of companies connected to the 11kV network, resulting in the redaction of certain transformers. Where redacted, we provide monthly statistics to continue to add value where possible. Where monthly statistics exist but half-hourly is absent, this data has been redacted.This dataset provides monthly statistics data across these named transformers from 2021 through to the previous month across our license areas. The data are aligned with the same naming convention as the LTDS for improved interoperability.To find half-hourly current and power flow data for a transformer, use the ‘tx_id’ that can be cross referenced in the Primary Transformers Half Hourly Dataset.If you want to download all this data, it is perhaps more convenient from our public sharepoint: Open Data Portal Library - Primary Transformers - All Documents (sharepoint.com)This dataset is part of a larger endeavour to share more operational data on UK Power Networks assets. Please visit our Network Operational Data Dashboard for more operational datasets.Methodological ApproachThe dataset is not derived, it is the measurements from our network stored in our historian.The measurement devices are taken from current transformers attached to the cable at the circuit breaker, and power is derived combining this with the data from voltage transformers physically attached to the busbar. The historian stores datasets based on a report-by-exception process, such that a certain deviation from the present value must be reached before logging a point measurement to the historian. We extract the data following a 30-min time weighted averaging method to get half-hourly values. Where there are no measurements logged in the period, the data provided is blank; due to the report-by-exception process, it may be appropriate to forward fill this data for shorter gaps.We developed a data redactions process to protect the privacy or companies according to the Utilities Act 2000 section 105.1.b, which requires UK Power Networks to not disclose information relating to the affairs of a business. For this reason, where the demand of a private customer is derivable from our data and that data is not already public information (e.g., data provided via Elexon on the Balancing Mechanism), we redact the half-hourly time series, and provide only the monthly averages. This redaction process considers the correlation of all the data, of only corresponding periods where the customer is active, the first order difference of all the data, and the first order difference of only corresponding periods where the customer is active. Should any of these four tests have a high linear correlation, the data is deemed redacted. This process is not simply applied to only the circuit of the customer, but of the surrounding circuits that would also reveal the signal of that customer.The directionality of the data is not consistent within this dataset. Where directionality was ascertainable, we arrange the power data in the direction of the LTDS "from node" to the LTDS "to node". Measurements of current do not indicate directionality and are instead positive regardless of direction. In some circumstances, the polarity can be negative, and depends on the data commissioner's decision on what the operators in the control room might find most helpful in ensuring reliable and secure network operation. Quality Control StatementThe data is provided "as is". In the design and delivery process adopted by the DSO, customer feedback and guidance is considered at each phase of the project. One of the earliest steers was that raw data was preferable. This means that we do not perform prior quality control screening to our raw network data. The result of this decision is that network rearrangements and other periods of non-intact running of the network are present throughout the dataset, which has the potential to misconstrue the true utilisation of the network, which is determined regulatorily by considering only by in-tact running arrangements. Therefore, taking the maximum or minimum of these transformers are not a reliable method of correctly ascertaining the true utilisation. This does have the intended added benefit of giving a realistic view of how the network was operated. The critical feedback was that our customers have a desire to understand what would have been the impact to them under real operational conditions. As such, this dataset offers unique insight into that.

Assurance StatementCreating this dataset involved a lot of human data imputation. At UK Power Networks, we have differing software to run the network operationally (ADMS) and to plan and study the network (PowerFactory). The measurement devices are intended to primarily inform the network operators of the real time condition of the network, and importantly, the network drawings visible in the LTDS are a planning approach, which differs to the operational. To compile this dataset, we made the union between the two modes of operating manually. A team of data scientists, data engineers, and power system engineers manually identified the LTDS transformer from the single line diagram, identified the line name from LTDS Table 2a/b, then identified the same transformer in ADMS to identify the measurement data tags. This was then manually inputted to a spreadsheet. Any influential customers to that circuit were noted using ADMS and the single line diagrams. From there, a python code is used to perform the triage and compilation of the datasets. There is potential for human error during the manual data processing. These issues can include missing transformers, incorrectly labelled transformers, incorrectly identified measurement data tags, incorrectly interpreted directionality. Whilst care has been taken to minimise the risk of these issues, they may persist in the provided dataset. Any uncertain behaviour observed by using this data should be reported to allow us to correct as fast as possible.

Additional informationDefinitions of key terms related to this dataset can be found in the Open Data Portal Glossary.Download dataset information: Metadata (JSON)We would be grateful if you find this dataset useful to submit a “reuse” case study to tell us what you did and how you used it. This enables us to drive our direction and gain better understanding for how we improve our data offering in the future. Click here for more information: Open Data Portal Reuses — UK Power NetworksTo view this data please register and login.

Dataset Card for Omni-MATH

Recent advancements in AI, particularly in large language models (LLMs), have led to significant breakthroughs in mathematical reasoning capabilities. However, existing benchmarks like GSM8K or MATH are now being solved with high accuracy (e.g., OpenAI o1 achieves 94.8% on MATH dataset), indicating their inadequacy for truly challenging these models. To mitigate this limitation, we propose a comprehensive and challenging benchmark specifically designed… See the full description on the dataset page: https://huggingface.co/datasets/KbsdJames/Omni-MATH.

The Data were provided by Dennis Gallagher, MSFC. The Retarding Ion Mass Spectrometer, RIMS, consisted of a Retarding Potential Analyzer for Energy Analysis in Series with a Magnetic Ion-Mass Spectrometer for Mass Analysis. Multiple Sensor Heads permitted the Determination of the Thermal Plasma Flow Characteristics. This Instrument was designed to operate in two basic Commandable Modes: a High-Altitude mode in which the Density, Temperature, and Bulk Flow Characteristics of principally H+, He+, and O+ Ions were measured, and a Low-Altitude Mode that concentrated on the Composition in the 1-u to 32-u Range. This Investigation provided Information on: (1) The Densities of H+, He+, and O+ Ions in the Ionosphere, Plasmasphere, Plasma Trough, and Polar Cap including the Density Distribution along the Magnetic Vector in the Vicinity of the Satellite Apogee (2) The Temperature of H+, He+, and O+ Ions in the Ionosphere, Plasmasphere, Plasma Trough, and Polar Cap in the Energy Range from 0 eV to 45 eV (3) The Bulk Flow Velocities of H+, He+, and O+ in the Plasmapause, Plasma Trough and Polar Cap (4) The changing Character of the Cold Plasma Density, Temperature, and Bulk Flow in Regions of Interaction with Hot Plasma such as at the Boundary between the Plasmasphere and the Ring Current (5) The detailed Composition of Ionospheric Plasma in the Range from 1-u to 32-u He++ and O++ were also measured. The Instrument consisted of three Detector Heads. One looked out in the Radial Direction, and the other two looked out along the Plus and Minus Spin Axis Directions. Each Detector had a 55° half-cone Acceptance Angle. The Detector Heads had a gridded, weakly collimating Aperture where the Retarding Analysis was performed, followed by a Parallel Plate Ceramic Magnetic Mass Analyzer with two separate Exit Slits corresponding to Ion Masses in the Ratio 1:4. Ions exiting from these Slits were detected with Electron Multipliers. In the Apogee Mode, the Thermal Particle Fluxes were measured while the Potential on a Set of Retarding Grids was stepped through a Sequence of Settings. In the Perigee Mode, the Retarding Grids were grounded and the Detector utilized a Continuous Acceleration Potential Sweep that focused the Mass Ranges from 1 to 8, and 4-u to 32-u. The Time Resolution was 16 ms. Additional Details can be found in C.R. Chappell et al., Space Sci. Instrum., 5(4), 477, 1981. The Criterion for selecting Data Points to be appropriate for fitting include that the Aperature Bias equal to zero must have at least ten or more Non-Zero Points in the RPA Curve. If the Spacecraft Altitude was less that 1.3 Re, the High Voltage Monitor must be turned on, the Maximum Counter Rate Value must at least 5.0, and there must be at least four Points starting from the End of RPA Curve. Find three consecutive Points of increasing Value, reset the End of the RPA Curve to here and make certain that the last Point is one Sigma above the Noise Level (the Points excluded in previous Step). if not, then drop a Point and check the new last Point, continue until the Criteria is met. One must have at least three Points left starting at new End of the selected RPA Curve Stop first Point greater that 80% of the Maximum of Spin Curve. If not found, stop at the last Point that is less than the Maximum. There must be at least three Points left. Change the Curve from the Count Rate Curve to the l**2 Curve. If Number of Points are five or less, then do a Linear Least Squares Fit (LINFIT) to the Data. If the Linear Correlation Coefficient (LCC) greater than 0.8 then the Points will be used, if not, the data set is discarded. If the Number of Points are greater then five, then do a LINFIT to the bottom five and a LINFIT to the top five Points. If there are six or more Points, then apply LINFIT to the middle Set of five Points, saving the LCC and Slope for each Case. If all three LCCs are less than 0.800, then discard the Data Set. Through a Series of Tests, find the Set of five Points with the best LCC Slope Combination. Once the Set of five Points has been selected, add the Rest of the Points one at a time and recompute the LLC with LINFIT, if the LCC gets worse discard the added Point otherwise keep it, do this until all Points are checked. Once this Procedure is completed, the final Set of Points have been determined.

US Minimum Wage by State from 1968 to 2020

The Basics

• What is this? In the United States, states and the federal government set minimum hourly pay ("minimum wage") that workers can receive to ensure that citizens experience a minimum quality of life. This dataset provides the minimum wage data set by each state and the federal government from 1968 to 2020.

• Why did you put this together? While looking online for a clean dataset for minimum wage data by state, I was having trouble finding one. I decided to create one myself and provide it to the community.

• Who do we thank for this data? The United States Department of Labor compiles a table of this data on their website. I took the time to clean it up and provide it here for you. :) The GitHub repository (with R Code for the cleaning process) can be found here!

Content

This is a cleaned dataset of US state and federal minimum wages from 1968 to 2020 (including 2020 equivalency values). The data was scraped from the United States Department of Labor's table of minimum wage by state.

Description of Data

The values in the dataset are as follows: - Year: The year of the data. All minimum wage values are as of January 1 except 1968 and 1969, which are as of February 1. - State: The state or territory of the data. - State.Minimum.Wage: The actual State's minimum wage on January 1 of Year. - State.Minimum.Wage.2020.Dollars: The State.Minimum.Wage in 2020 dollars. - Federal.Minimum.Wage: The federal minimum wage on January 1 of Year. - Federal.Minimum.Wage.2020.Dollars: The Federal.Minimum.Wage in 2020 dollars. - Effective.Minimum.Wage: The minimum wage that is enforced in State on January 1 of Year. Because the federal minimum wage takes effect if the State's minimum wage is lower than the federal minimum wage, this is the higher of the two. - Effective.Minimum.Wage.2020.Dollars: The Effective.Minimum.Wage in 2020 dollars. - CPI.Average: The average value of the Consumer Price Index in Year. When I pulled the data from the Bureau of Labor Statistics, I selected the dataset with "all items in U.S. city average, all urban consumers, not seasonally adjusted". - Department.Of.Labor.Uncleaned.Data: The unclean, scraped value from the Department of Labor's website. - Department.Of.Labor.Cleaned.Low.Value: The State's lowest enforced minimum wage on January 1 of Year. If there is only one minimum wage, this and the value for Department.Of.Labor.Cleaned.High.Value are identical. (Some states enforce different minimum wage laws depending on the size of the business. In states where this is the case, generally, smaller businesses have slightly lower minimum wage requirements.) - Department.Of.Labor.Cleaned.Low.Value.2020.Dollars: The Department.Of.Labor.Cleaned.Low.Value in 2020 dollars. - Department.Of.Labor.Cleaned.High.Value: The State's higher enforced minimum wage on January 1 of Year. If there is only one minimum wage, this and the value for Department.Of.Labor.Cleaned.Low.Value are identical. - Department.Of.Labor.Cleaned.High.Value.2020.Dollars: The Department.Of.Labor.Cleaned.High.Value in 2020 dollars. - Footnote: The footnote provided on the Department of Labor's website. See more below.

Data Footnotes

As laws differ significantly from territory to territory, especially relating to whom is protected by minimum wage laws, the following footnotes are located throughout the data in Footnote to add more context to the minimum wage. The original footnotes can be found here.

Introduction

UK Power Network maintains the 132kV voltage level network and below. An important part of the distribution network is distributing this electricity across our regions through circuits. Electricity enters our network through Super Grid Transformers at substations shared with National Grid we call Grid Supply Points. It is then sent at across our 132 kV Circuits towards our grid substations and primary substations. From there, electricity is distributed along the 33 kV circuits to bring it closer to the home. These circuits can be viewed on the single line diagrams in our Long-Term Development Statements (LTDS) and the underlying data is then found in the LTDS tables.

This dataset provides half-hourly current and power flow data across these named circuits from 2021 through to the previous month in our South Eastern Power Networks (SPN) licence area. The data are aligned with the same naming convention as the LTDS for improved interoperability.

Care is taken to protect the private affairs of companies connected to the 33 kV network, resulting in the redaction of certain circuits. Where redacted, we provide monthly statistics to continue to add value where possible. Where monthly statistics exist but half-hourly is absent, this data has been redacted.

To find which circuit you are looking for, use the ‘ltds_line_name’ that can be cross referenced in the 33kV Circuits Monthly Data, which describes by month what circuits were triaged, if they could be made public, and what the monthly statistics are of that site.

If you want to download all this data, it is perhaps more convenient from our public sharepoint: Sharepoint

This dataset is part of a larger endeavour to share more operational data on UK Power Networks assets. Please visit our Network Operational Data Dashboard for more operational datasets.

Methodological Approach The dataset is not derived, it is the measurements from our network stored in our historian. The measurement devices are taken from current transformers attached to the cable at the circuit breaker, and power is derived combining this with the data from voltage transformers physically attached to the busbar. The historian stores datasets based on a report-by-exception process, such that a certain deviation from the present value must be reached before logging a point measurement to the historian. We extract the data following a 30-min time weighted averaging method to get half-hourly values. Where there are no measurements logged in the period, the data provided is blank; due to the report-by-exception process, it may be appropriate to forward fill this data for shorter gaps. We developed a data redactions process to protect the privacy or companies according to the Utilities Act 2000 section 105.1.b, which requires UK Power Networks to not disclose information relating to the affairs of a business. For this reason, where the demand of a private customer is derivable from our data and that data is not already public information (e.g., data provided via Elexon on the Balancing Mechanism), we redact the half-hourly time series, and provide only the monthly averages. This redaction process considers the correlation of all the data, of only corresponding periods where the customer is active, the first order difference of all the data, and the first order difference of only corresponding periods where the customer is active. Should any of these four tests have a high linear correlation, the data is deemed redacted. This process is not simply applied to only the circuit of the customer, but of the surrounding circuits that would also reveal the signal of that customer. The directionality of the data is not consistent within this dataset. Where directionality was ascertainable, we arrange the power data in the direction of the LTDS "from node" to the LTDS "to node". Measurements of current do not indicate directionality and are instead positive regardless of direction. In some circumstances, the polarity can be negative, and depends on the data commissioner's decision on what the operators in the control room might find most helpful in ensuring reliable and secure network operation. Quality Control Statement The data is provided "as is".
In the design and delivery process adopted by the DSO, customer feedback and guidance is considered at each phase of the project. One of the earliest steers was that raw data was preferable. This means that we do not perform prior quality control screening to our raw network data. The result of this decision is that network rearrangements and other periods of non-intact running of the network are present throughout the dataset, which has the potential to misconstrue the true utilisation of the network, which is determined regulatorily by considering only by in-tact running arrangements. Therefore, taking the maximum or minimum of these measurements are not a reliable method of correctly ascertaining the true utilisation. This does have the intended added benefit of giving a realistic view of how the network was operated. The critical feedback was that our customers have a desire to understand what would have been the impact to them under real operational conditions. As such, this dataset offers unique insight into that. Assurance StatementCreating this dataset involved a lot of human data imputation. At UK Power Networks, we have differing software to run the network operationally (ADMS) and to plan and study the network (PowerFactory). The measurement devices are intended to primarily inform the network operators of the real time condition of the network, and importantly, the network drawings visible in the LTDS are a planning approach, which differs to the operational. To compile this dataset, we made the union between the two modes of operating manually. A team of data scientists, data engineers, and power system engineers manually identified the LTDS circuit from the single line diagram, identified the line name from LTDS Table 2a/b, then identified the same circuit in ADMS to identify the measurement data tags. This was then manually inputted to a spreadsheet. Any influential customers to that circuit were noted using ADMS and the single line diagrams. From there, a python code is used to perform the triage and compilation of the datasets. There is potential for human error during the manual data processing. These issues can include missing circuits, incorrectly labelled circuits, incorrectly identified measurement data tags, incorrectly interpreted directionality. Whilst care has been taken to minimise the risk of these issues, they may persist in the provided dataset. Any uncertain behaviour observed by using this data should be reported to allow us to correct as fast as possible. Additional Information Definitions of key terms related to this dataset can be found in the Open Data Portal Glossary. Download dataset information: Metadata (JSON)We would be grateful if you find this dataset useful to submit a “reuse” case study to tell us what you did and how you used it. This enables us to drive our direction and gain better understanding for how we improve our data offering in the future. Click here for more information: Open Data Portal Reuses — UK Power Networks To view this data please register and login.

[update 28/03/24 - This description previously stated that the the field “2001-2020 (recent past) change” was a percentage change. This field is actually the difference, in units of mm/day. The table below has been updated to reflect this.][Updated 28/01/25 to fix an issue in the ‘Lower’ values, which were not fully representing the range of uncertainty. ‘Median’ and ‘Higher’ values remain unchanged. The size of the change varies by grid cell but for the fixed periods which are expressed in mm, the average difference between the 'lower' values before and after this update is 0.04mm. For the fixed periods and global warming levels which are expressed as percentage changes, the average difference between the 'lower' values before and after this update is 4.65%.]What does the data show?

This dataset shows the change in summer precipitation rate for a range of global warming levels, including the recent past (2001-2020), compared to the 1981-2000 baseline period. Here, summer is defined as June-July-August. Note, as the values in this dataset are averaged over a season they do not represent possible extreme conditions.

The dataset uses projections of daily precipitation from UKCP18 which are averaged over the summer period to give values for the 1981-2000 baseline, the recent past (2001-2020) and global warming levels. The warming levels available are 1.5°C, 2.0°C, 2.5°C, 3.0°C and 4.0°C above the pre-industrial (1850-1900) period. The recent past value and global warming level values are stated as a percentage change (%) relative to the 1981-2000 value. This enables users to compare summer precipitation trends for the different periods. In addition to the change values, values for the 1981-2000 baseline (corresponding to 0.51°C warming) and recent past (2001-2020, corresponding to 0.87°C warming) are also provided. This is summarised in the table below.

      Period
      Description


      1981-2000 baseline
      Average value for the period (mm/day)


      2001-2020 (recent past)
      Average value for the period (mm/day)


      2001-2020 (recent past) change
      Change (mm/day) relative to 1981-2000


      1.5°C global warming level change
      Percentage change (%) relative to 1981-2000


      2°C global warming level change
      Percentage change (%) relative to 1981-2000


      2.5°C global warming level change
      Percentage change (%) relative to 1981-2000


      3°C global warming level change
      Percentage change (%) relative to 1981-2000


      4°C global warming level change
      Percentage change (%) relative to 1981-2000

What is a global warming level?

The Summer Precipitation Change is calculated from the UKCP18 regional climate projections using the high emissions scenario (RCP 8.5) where greenhouse gas emissions continue to grow. Instead of considering future climate change during specific time periods (e.g. decades) for this scenario, the dataset is calculated at various levels of global warming relative to the pre-industrial (1850-1900) period. The world has already warmed by around 1.1°C (between 1850–1900 and 2011–2020), whilst this dataset allows for the exploration of greater levels of warming.

The global warming levels available in this dataset are 1.5°C, 2°C, 2.5°C, 3°C and 4°C. The data at each warming level was calculated using a 21 year period. These 21 year periods are calculated by taking 10 years either side of the first year at which the global warming level is reached. This time will be different for different model ensemble members. To calculate the value for the Summer Precipitation Change, an average is taken across the 21 year period.

We cannot provide a precise likelihood for particular emission scenarios being followed in the real world future. However, we do note that RCP8.5 corresponds to emissions considerably above those expected with current international policy agreements. The results are also expressed for several global warming levels because we do not yet know which level will be reached in the real climate as it will depend on future greenhouse emission choices and the sensitivity of the climate system, which is uncertain. Estimates based on the assumption of current international agreements on greenhouse gas emissions suggest a median warming level in the region of 2.4-2.8°C, but it could either be higher or lower than this level.

What are the naming conventions and how do I explore the data?

These data contain a field for each warming level and the 1981-2000 baseline. They are named 'pr summer change', the warming level or baseline, and 'upper' 'median' or 'lower' as per the description below. e.g. 'pr summer change 2.0 median' is the median value for summer for the 2.0°C warming level. Decimal points are included in field aliases but not in field names, e.g. 'pr summer change 2.0 median' is named 'pr_summer_change_20_median'.

To understand how to explore the data, refer to the New Users ESRI Storymap.

Please note, if viewing in ArcGIS Map Viewer, the map will default to ‘pr summer change 2.0°C median’ values.

What do the 'median', 'upper', and 'lower' values mean?

Climate models are numerical representations of the climate system. To capture uncertainty in projections for the future, an ensemble, or group, of climate models are run. Each ensemble member has slightly different starting conditions or model set-ups. Considering all of the model outcomes gives users a range of plausible conditions which could occur in the future.

For this dataset, the model projections consist of 12 separate ensemble members. To select which ensemble members to use, the Summer Precipitation Change was calculated for each ensemble member and they were then ranked in order from lowest to highest for each location.

 The ‘lower’ fields are the second lowest ranked ensemble member. 
 The ‘higher’ fields are the second highest ranked ensemble member. 
 The ‘median’ field is the central value of the ensemble.

This gives a median value, and a spread of the ensemble members indicating the range of possible outcomes in the projections. This spread of outputs can be used to infer the uncertainty in the projections. The larger the difference between the lower and higher fields, the greater the uncertainty.

‘Lower’, ‘median’ and ‘upper’ are also given for the baseline period as these values also come from the model that was used to produce the projections. This allows a fair comparison between the model projections and recent past.

Useful links

 For further information on the UK Climate Projections (UKCP).
 Further information on understanding climate data within the Met Office Climate Data Portal.

Introduction

UK Power Network maintains the 132kV voltage level network and below. An important part of the distribution network is the stepping down of voltage as it is moved towards the household; this is achieved using transformers. Transformers have a maximum rating for the utilisation of these assets based upon protection, overcurrent, switch gear, etc. This dataset contains the Primary Substation Transformers, that typically step-down voltage from 33kVto 11kV (occasionally from 132kV to 11kV). These transformers can be viewed on the single line diagrams in our Long-Term Development Statements (LTDS) and the underlying data is then found in the LTDS tables. This dataset provides half-hourly current and power flow data across these named transformers, in our South Eastern region, from 2021 through to the previous month across our license areas. The data are aligned with the same naming convention as the LTDS for improved interoperability.Care is taken to protect the private affairs of companies connected to the 11kV network, resulting in the redaction of certain transformers. Where redacted, we provide monthly statistics to continue to add value where possible. Where monthly statistics exist but half-hourly is absent, this data has been redacted. To find which transformer you are looking for, use the ‘tx_id’ that can be cross referenced in the Primary Transformers Monthly Dataset, which describes by month what transformers were triaged, if they could be made public, and what the monthly statistics are of that site. If you want to download all this data, it is perhaps more convenient from our public sharepoint: Open Data Portal Library - Primary Transformers - All Documents (sharepoint.com)This dataset is part of a larger endeavour to share more operational data on UK Power Networks assets. Please visit our Network Operational Data Dashboard for more operational datasets.

Methodological Approach The dataset is not derived, it is the measurements from our network stored in our historian.The measurement devices are taken from current transformers attached to the cable at the circuit breaker, and power is derived combining this with the data from voltage transformers physically attached to the busbar. The historian stores datasets based on a report-by-exception process, such that a certain deviation from the present value must be reached before logging a point measurement to the historian. We extract the data following a 30-min time weighted averaging method to get half-hourly values. Where there are no measurements logged in the period, the data provided is blank; due to the report-by-exception process, it may be appropriate to forward fill this data for shorter gaps.We developed a data redactions process to protect the privacy or companies according to the Utilities Act 2000 section 105.1.b, which requires UK Power Networks to not disclose information relating to the affairs of a business. For this reason, where the demand of a private customer is derivable from our data and that data is not already public information (e.g., data provided via Elexon on the Balancing Mechanism), we redact the half-hourly time series, and provide only the monthly averages. Where the primary transformer has 5 or fewer customers, we redact the dataset.The directionality of the data is not consistent within this dataset. Where directionality was ascertainable, we arrange the power data in the direction of the LTDS "from node" to the LTDS "to node". Measurements of current do not indicate directionality and are instead positive regardless of direction. In some circumstances, the polarity can be negative, and depends on the data commissioner's decision on what the operators in the control room might find most helpful in ensuring reliable and secure network operation.

Quality Control Statement The data is provided "as is". In the design and delivery process adopted by the DSO, customer feedback and guidance is considered at each phase of the project. One of the earliest steers was that raw data was preferable. This means that we do not perform prior quality control screening to our raw network data. The result of this decision is that network rearrangements and other periods of non-intact running of the network are present throughout the dataset, which has the potential to misconstrue the true utilisation of the network, which is determined regulatorily by considering only by in-tact running arrangements. Therefore, taking the maximum or minimum of these transformers are not a reliable method of correctly ascertaining the true utilisation. This does have the intended added benefit of giving a realistic view of how the network was operated. The critical feedback was that our customers have a desire to understand what would have been the impact to them under real operational conditions. As such, this dataset offers unique insight into that.

Assurance Statement Creating this dataset involved a lot of human data imputation. At UK Power Networks, we have differing software to run the network operationally (ADMS) and to plan and study the network (PowerFactory). The measurement devices are intended to primarily inform the network operators of the real time condition of the network, and importantly, the network drawings visible in the LTDS are a planning approach, which differs to the operational. To compile this dataset, we made the union between the two modes of operating manually. A team of data scientists, data engineers, and power system engineers manually identified the LTDS transformer from the single line diagram, identified the line name from LTDS Table 2a/b, then identified the same transformer in ADMS to identify the measurement data tags. This was then manually inputted to a spreadsheet. Any influential customers to that circuit were noted using ADMS and the single line diagrams. From there, a python code is used to perform the triage and compilation of the datasets. There is potential for human error during the manual data processing. These issues can include missing transformers, incorrectly labelled transformers, incorrectly identified measurement data tags, incorrectly interpreted directionality. Whilst care has been taken to minimise the risk of these issues, they may persist in the provided dataset. Any uncertain behaviour observed by using this data should be reported to allow us to correct as fast as possible.

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

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