Context The data has created to perform the different operations related to Statistical Measures of Central Tendency.
Source The Data has Created Manually. Inspiration You can use this csv data to practice different operations related to Measures of Central Tendency.

This dataset contains the data to calculate the spatial distribution of the dissipation as well as the absorption efficiencies of both Gold and Silicon designs, as presented in the article "Time-domain topology optimization of power dissipation in dispersive dielectric and plasmonic nanostructures". This includes the electric field distribution in 3D for multiple wavelengths (netCDF), the final density (netCDF), the design (STL) and material and simulation parameters (JSON) used in the optimization. The evaluation of this data can be performed using the code published on https://github.com/JoGed/dissipation-calculation

Distance Calculation

## Overview

Distance Calculation is a dataset for object detection tasks - it contains Vehicles annotations for 4,056 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [Public Domain license](https://creativecommons.org/licenses/Public Domain).

This technical report documents the acquisition of source data, and calculation of land cover summary statistics datasets for ten National Park Service National Capital Region park units and three custom areas of analysis: Catoctin Mountain Park (CATO), Chesapeake & Ohio Canal National Historical Park (CHOH), George Washington Memorial Parkway (GWMP), Harpers Ferry National Historical Park (HAFE), Manassas National Battlefield Park (MANA), Monocacy National Battlefield (MONO), National Capital Parks - East (NACE), Prince William Forest Park (PRWI), Rock Creek Park (ROCR), Wolf Trap National Park for the Performing Arts (WOTR), and the three custom areas of analysis - National Capital Parks - East: Oxon Cove Park, Oxon Hill Farm, Piscataway Park (NCRN_NACE_OXHI_PISC), National Capital Parks - East: Greenbelt Park and Baltimore-Washington Parkway (NCRN_NACE_GREE_BAWA), and National Capital Parks - East: DC and Suitland Parkway (NCRN_NACE_DC_SUIT). The source data and land cover calculations are available for use within the National Park Service (NPS) Inventory and Monitoring Program. Land cover summary statistics datasets can be calculated for all geographic regions within the extent of the NPS; this report includes statistics calculated for the conterminous United States. The land cover summary statistics datasets are calculated from multiple sources, including Multi-Resolution Land Characteristics Consortium products in the National Land Cover Database (NLCD) and United States Geological Survey’s (USGS) Earth Resources Observation and Science (EROS) Center products in the Land Change Monitoring, Assessment, and Projection (LCMAP) raster dataset. These summary statistics calculate land cover at up to three classification scales: Level 1, modified Anderson Level 2, and Natural versus Converted land cover. The output land cover summary statistics datasets produced here for the ten National Capital Region park units and three custom areas of analysis utilize the most recent versions of the source datasets (NLCD and LCMAP). These land cover summary statistics datasets are used in the NPS Inventory and Monitoring Program, including the NPS Environmental Settings Monitoring Protocol and may be used by networks and parks for additional efforts.

Load and view a real-world dataset in RStudio

• Calculate “Measure of Frequency” metrics

• Calculate “Measure of Central Tendency” metrics

• Calculate “Measure of Dispersion” metrics

• Use R’s in-built functions for additional data quality metrics

• Create a custom R function to calculate descriptive statistics on any given dataset

"We believe that by accounting for the inherent uncertainty in the system during each measurement, the relationship between cause and effect can be assessed more accurately, potentially reducing the duration of research."

Short description

This dataset was created as part of a research project investigating the efficiency and learning mechanisms of a Bayesian adaptive search algorithm supported by the Imprecision Entropy Indicator (IEI) as a novel method. It includes detailed statistical results, posterior probability values, and the weighted averages of IEI across multiple simulations aimed at target localization within a defined spatial environment. Control experiments, including random search, random walk, and genetic algorithm-based approaches, were also performed to benchmark the system's performance and validate its reliability.

The task involved locating a target area centered at (100; 100) within a radius of 10 units (Research_area.png), inside a circular search space with a radius of 100 units. The search process continued until 1,000 successful target hits were achieved.

To benchmark the algorithm's performance and validate its reliability, control experiments were conducted using alternative search strategies, including random search, random walk, and genetic algorithm-based approaches. These control datasets serve as baselines, enabling comprehensive comparisons of efficiency, randomness, and convergence behavior across search methods, thereby demonstrating the effectiveness of our novel approach.

Uploaded files

The first dataset contains the average IEI values, generated by randomly simulating 300 x 1 hits for 10 bins per quadrant (4 quadrants in total) using the Python programming language, and calculating the corresponding IEI values. This resulted in a total of 4 x 10 x 300 x 1 = 12,000 data points. The summary of the IEI values by quadrant and bin is provided in the file results_1_300.csv. The calculation of IEI values for averages is based on likelihood, using an absolute difference-based approach for the likelihood probability computation. IEI_Likelihood_Based_Data.zip

The weighted IEI average values for likelihood calculation (Bayes formula) are provided in the file Weighted_IEI_Average_08_01_2025.xlsx

This dataset contains the results of a simulated target search experiment using Bayesian posterior updates and Imprecision Entropy Indicators (IEI). Each row represents a hit during the search process, including metrics such as Shannon entropy (H), Gini index (G), average distance, angular deviation, and calculated IEI values. The dataset also includes bin-specific posterior probability updates and likelihood calculations for each iteration. The simulation explores adaptive learning and posterior penalization strategies to optimize the search efficiency. Our Bayesian adaptive searching system source code (search algorithm, 1000 target searches): IEI_Self_Learning_08_01_2025.pyThis dataset contains the results of 1,000 iterations of a successful target search simulation. The simulation runs until the target is successfully located for each iteration. The dataset includes further three main outputs: a) Results files (results{iteration_number}.csv): Details of each hit during the search process, including entropy measures, Gini index, average distance and angle, Imprecision Entropy Indicators (IEI), coordinates, and the bin number of the hit. b) Posterior updates (Pbin_all_steps_{iter_number}.csv): Tracks the posterior probability updates for all bins during the search process acrosations multiple steps. c) Likelihoodanalysis(likelihood_analysis_{iteration_number}.csv): Contains the calculated likelihood values for each bin at every step, based on the difference between the measured IEI and pre-defined IE bin averages. IEI_Self_Learning_08_01_2025.py

Based on the mentioned Python source code (see point 3, Bayesian adaptive searching method with IEI values), we performed 1,000 successful target searches, and the outputs were saved in the:Self_learning_model_test_output.zip file.

Bayesian Search (IEI) from different quadrant. This dataset contains the results of Bayesian adaptive target search simulations, including various outputs that represent the performance and analysis of the search algorithm. The dataset includes: a) Heatmaps (Heatmap_I_Quadrant, Heatmap_II_Quadrant, Heatmap_III_Quadrant, Heatmap_IV_Quadrant): These heatmaps represent the search results and the paths taken from each quadrant during the simulations. They indicate how frequently the system selected each bin during the search process. b) Posterior Distributions (All_posteriors, Probability_distribution_posteriors_values, CDF_posteriors_values): Generated based on posterior values, these files track the posterior probability updates, including cumulative distribution functions (CDF) and probability distributions. c) Macro Summary (summary_csv_macro): This file aggregates metrics and key statistics from the simulation. It summarizes the results from the individual results.csv files. d) Heatmap Searching Method Documentation (Bayesian_Heatmap_Searching_Method_05_12_2024): This document visualizes the search algorithm's path, showing how frequently each bin was selected during the 1,000 successful target searches. e) One-Way ANOVA Analysis (Anova_analyze_dataset, One_way_Anova_analysis_results): This includes the database and SPSS calculations used to examine whether the starting quadrant influences the number of search steps required. The analysis was conducted at a 5% significance level, followed by a Games-Howell post hoc test [43] to identify which target-surrounding quadrants differed significantly in terms of the number of search steps. Results were saved in the Self_learning_model_test_results.zip

This dataset contains randomly generated sequences of bin selections (1-40) from a control search algorithm (random search) used to benchmark the performance of Bayesian-based methods. The process iteratively generates random numbers until a stopping condition is met (reaching target bins 1, 11, 21, or 31). This dataset serves as a baseline for analyzing the efficiency, randomness, and convergence of non-adaptive search strategies. The dataset includes the following: a) The Python source code of the random search algorithm. b) A file (summary_random_search.csv) containing the results of 1000 successful target hits. c) A heatmap visualizing the frequency of search steps for each bin, providing insight into the distribution of steps across the bins. Random_search.zip

This dataset contains the results of a random walk search algorithm, designed as a control mechanism to benchmark adaptive search strategies (Bayesian-based methods). The random walk operates within a defined space of 40 bins, where each bin has a set of neighboring bins. The search begins from a randomly chosen starting bin and proceeds iteratively, moving to a randomly selected neighboring bin, until one of the stopping conditions is met (bins 1, 11, 21, or 31). The dataset provides detailed records of 1,000 random walk iterations, with the following key components: a) Individual Iteration Results: Each iteration's search path is saved in a separate CSV file (random_walk_results_.csv), listing the sequence of steps taken and the corresponding bin at each step. b) Summary File: A combined summary of all iterations is available in random_walk_results_summary.csv, which aggregates the step-by-step data for all 1,000 random walks. c) Heatmap Visualization: A heatmap file is included to illustrate the frequency distribution of steps across bins, highlighting the relative visit frequencies of each bin during the random walks. d) Python Source Code: The Python script used to generate the random walk dataset is provided, allowing reproducibility and customization for further experiments. Random_walk.zip

This dataset contains the results of a genetic search algorithm implemented as a control method to benchmark adaptive Bayesian-based search strategies. The algorithm operates in a 40-bin search space with predefined target bins (1, 11, 21, 31) and evolves solutions through random initialization, selection, crossover, and mutation over 1000 successful runs. Dataset Components: a) Run Results: Individual run data is stored in separate files (genetic_algorithm_run_.csv), detailing: Generation: The generation number. Fitness: The fitness score of the solution. Steps: The path length in bins. Solution: The sequence of bins visited. b) Summary File: summary.csv consolidates the best solutions from all runs, including their fitness scores, path lengths, and sequences. c) All Steps File: summary_all_steps.csv records all bins visited during the runs for distribution analysis. d) A heatmap was also generated for the genetic search algorithm, illustrating the frequency of bins chosen during the search process as a representation of the search pathways.Genetic_search_algorithm.zip

Technical Information

The dataset files have been compressed into a standard ZIP archive using Total Commander (version 9.50). The ZIP format ensures compatibility across various operating systems and tools.

The XLSX files were created using Microsoft Excel Standard 2019 (Version 1808, Build 10416.20027)

The Python program was developed using Visual Studio Code (Version 1.96.2, user setup), with the following environment details: Commit fabd6a6b30b49f79a7aba0f2ad9df9b399473380f, built on 2024-12-19. The Electron version is 32.6, and the runtime environment includes Chromium 128.0.6263.186, Node.js 20.18.1, and V8 12.8.374.38-electron.0. The operating system is Windows NT x64 10.0.19045.

The statistical analysis included in this dataset was partially conducted using IBM SPSS Statistics, Version 29.0.1.0

The CSV files in this dataset were created following European standards, using a semicolon (;) as the delimiter instead of a comma, encoded in UTF-8 to ensure compatibility with a wide

This report documents the acquisition of source data, and calculation of land cover summary statistics datasets for four National Park Service Greater Yellowstone Network park units and six custom areas of analysis: Bighorn Canyon National Recreation Area, Grand Teton National Park, John D. Rockefeller Jr. Memorial Parkway, Yellowstone National Park, and the six custom areas of analysis. The source data and land cover calculations are available for use within the National Park Service (NPS) Inventory and Monitoring Program. Land cover summary statistics datasets can be calculated for all geographic regions within the extent of the NPS; this report includes statistics calculated for the conterminous United States. The land cover summary statistics datasets are calculated from multiple sources, including Multi-Resolution Land Characteristics Consortium products in the National Land Cover Database (NLCD) and the United States Geological Survey’s (USGS) Earth Resources Observation and Science (EROS) Center products in the Land Change Monitoring, Assessment, and Projection (LCMAP) raster dataset. These summary statistics calculate land cover at up to three classification scales: Level 1, modified Anderson Level 2, and Natural versus Converted land cover. The output land cover summary statistics datasets produced here for the four Greater Yellowstone Network park units and six custom areas of analysis utilize the most recent versions of the source datasets (NLCD and LCMAP). These land cover summary statistics datasets are used in the NPS Inventory and Monitoring Program, including the NPS Environmental Settings Monitoring Protocol and may be used by networks and parks for additional efforts.

This technical report documents the acquisition of source data, and calculation of land cover summary statistics datasets for Antietam National Battlefield. The source data and land cover calculations are available for use within the National Park Service (NPS) Inventory and Monitoring Program. Land cover summary statistics datasets can be calculated for all geographic regions within the extent of the NPS; this report includes statistics calculated for the conterminous United States. The land cover summary statistics datasets are calculated from multiple sources, including Multi-Resolution Land Characteristics Consortium products in the National Land Cover Database (NLCD) and United States Geological Survey’s (USGS) Earth Resources Observation and Science (EROS) Center products in the Land Change Monitoring, Assessment, and Projection (LCMAP) raster dataset. These summary statistics calculate land cover at up to three classification scales: Level 1, modified Anderson Level 2, and Natural versus Converted land cover. The output land cover summary statistics datasets produced here for Antietam National Battlefield utilize the most recent versions of the source datasets (NLCD and LCMAP). These land cover summary statistics datasets are used in the NPS Inventory and Monitoring Program, including the NPS Environmental Settings Monitoring Protocol and may be used by networks and parks for additional efforts.

This dataset is about books. It has 1 row and is filtered where the book is Exercises in arithmetic by calculating machine. It features 7 columns including author, publication date, language, and book publisher.

This dataset contains input files and numerical data supporting the results of the paper:

"Unified Mechanism of Body-Centered-Cubic Phase Stability in Compressed Solids"

The archive is organized into two main directories:

• Main_text/: Contains input and output files used to generate Figure 2, which shows the total and partial density of states (DOS) of bcc Ti at various pressures, calculated using WIEN2k. A description is provided in Main_text/readme.txt.

• SM_data/: Contains structure search results for As, Sb, and Ti under high pressures using Quantum ESPRESSO, as well as phonon calculation inputs for Ti used in the Supplemental Material figures. A full description of file contents and organization is provided in SM_data/Supplemental_Material_Description.txt.

Output log files (.out) are excluded to reduce the total size, but can be provided upon reasonable request.

This dataset is intended to ensure reproducibility of all relevant figures and results presented in the manuscript and Supplemental Material.

Description

This dataset was used in our manuscript titled “Persistent homology-based descriptor for machine-learning potential of amorphous structures” (arXiv:2206.13727 [cs.LG] https://arxiv.org/abs/2206.13727).

Methods to generate the dataset

The amorphous carbon dataset was generated using ab initio calculations with VASP software. We utilized the LDA exchange-correlation functional and the PAW potential for carbon. Melt-quench simulations were performed to create amorphous and liquid-state structures. A simple cubic lattice of 216 carbon atoms was chosen as the initial state. Simulations were conducted at densities of 1.5, 1.7, 2.0, 2.2, 2.4, 2.6, 2.8, 3.0, 3.2, 3.4, and 3.5 g/cm3 to produce a variety of structures. The NVT ensemble was employed for all melt-quench simulations, and the density was adjusted by modifying the size of the simulation cell. A time step of 1 fs was used for the simulations. For all densities, only the Γ points were sampled in the k-space. To increase structural diversity, six independent simulations were performed.

In the melt-quench simulations, the temperature was raised from 300 K to 9000 K over 2 ps to melt carbon. Equilibrium molecular dynamics (MD) was conducted at 9000 K for 3 ps to create a liquid state, followed by a decrease in temperature to 5000 K over 2 ps, with the system equilibrating at that temperature for 2 ps. Finally, the temperature was lowered from 5000 K to 300 K over 2 ps to generate an amorphous structure.

During the melt-quench simulation, 30 snapshots were taken from the equilibrium MD trajectory at 9000 K, 100 from the cooling process between 9000 and 5000 K, 25 from the equilibrium MD trajectory at 5000 K, and 100 from the cooling process between 5000 and 300 K. This yielded a total of 16,830 data points.

Data for diamond structures containing 216 atoms at densities of 2.4, 2.6, 2.8, 3.0, 3.2, 3.4, and 3.5 g/cm3 were also prepared. Further data on the diamond structure were obtained from 80 snapshots taken from the 2 ps equilibrium MD trajectory at 300 K, resulting in 560 data points.

To validate predictions for larger structures, we generated data for 512-atom systems using the same procedure as for the 216-atom systems. A single simulation was conducted for each density. The number of data points was 2,805 for amorphous and liquid states.

Contents of each folder

・216atom_amorphous: Contains six xyz files generated from the trajectory of the melt-quench simulation.

・216atom_crystal: Contains a single xyz file with data of diamond structures containing 216 atoms at densities of 2.4, 2.6, 2.8, 3.0, 3.2, 3.4, and 3.5 g/cm3.

・512atom_amorphous: Contains a single xyz file with data of 512-atom systems.

・dataset_train_test_split: The training and test data used in the manuscript, constructed from splitting the entire 216atom_amorphous dataset.

Dataset Card for Dataset Name

  Dataset Summary

This dataset card aims to be a base template for new datasets. It has been generated using this raw template.

  Supported Tasks and Leaderboards

[More Information Needed]

  Languages

[More Information Needed]

  Dataset Structure





  Data Instances

[More Information Needed]

  Data Fields

[More Information Needed]

  Data Splits

[More Information Needed]

  Dataset Creation… See the full description on the dataset page: https://huggingface.co/datasets/Jornt/calculations.

Excel Age-Range creator for Office for National Statistics (ONS) Mid year population estimates (MYE) covering each year between 1999 and 2014

https://londondatastore-upload.s3.amazonaws.com/mye-custom-tool.JPG" alt="" />

These files take into account the revised estimates for 2002-2010 released in April 2013 down to Local Authority level and the post 2011 estimates based on the Census results. Scotland and Northern Ireland data has not been revised, so Great Britain and United Kingdom totals comprise the original data for these plus revised England and Wales figures.

This Excel based tool enables users to query the single year of age raw data so that any age range can easily be calculated without having to carry out often complex, and time consuming formulas that could also be open to human error. Simply select the lower and upper age range for both males and females and the spreadsheet will return the total population for the range. Please adhere to the terms and conditions of supply contained within the file.

Tip: You can copy and paste the rows you are interested in to another worksheet by using the filters at the top of the columns and then select all by pressing Ctrl+A. Then simply copy and paste the cells to a new location.

ONS Mid year population estimates

Open Excel tool (London Boroughs, Regions and National, 1999-2014)

Also available is a custom-age tool for all geographies in the UK. Open the tool for all UK geographies (local authority and above) for: 2010, 2011, 2012, 2013, and 2014.

This full MYE dataset by single year of age (SYA) age and gender is available as a Datastore package here.

Ward Level Population estimates

Excel single year of age population tool for 2002 to 2013 for all wards in London.

New 2014 Ward boundary estimates

This data is only for wards in the three London boroughs that changed their ward boundaries in May 2014. The estimates in this spreadsheet have been calculated by the GLA by taking the proportion of a the old ward that falls within the new ward based on the proportion of population living in each area at the 2011 Census. Therefore, these estimates are purely indicative and are not official statistics and not endorsed by ONS.

Algebra equation solving performance using hands-on equations for students with learning disabilities and their peers (grades 6-8)

Dataset DOI: 10.5061/dryad.sn02v6xh8

File list

• Algebra_Equation_Solving_Performance_Using_Hands-On_Equations_Manipulatives_(Grades_6-8).csv
• LD_Study_SAV_File.sav
• Borenson_LD_Study_Output_File.pdf

File descriptions

Algebra_Equation_Solving_Performance_Using_Hands-On_Equations_Manipulatives_(Grades_6-8).csv– the data in unformatted form.

LD_Study_SAV_File.sav– the data for direct use in SPSS.

Borenson_LD_Study_Output_File.pdf- results from statistical analysis (see Output file description under Usage Notes for further information).

Usage Notes

Datafile Description

• Variables/Columns:
  • Nr: Anonymized unique identifier for each student.
  • pretest: Score (0-6 points) on the initial algebra assessment administered before instruction.
  • posttestM: Score (0-6 points) on the...

Golf Ball Distance Calculation

## Overview

Golf Ball Distance Calculation is a dataset for object detection tasks - it contains Golf Balls annotations for 318 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

A. SUMMARY This is a dataset containing points representing the midpoint of each block in the City of San Francisco. This dataset is used for geomasking (e.g. removing an exact address and replacing it with a nearby anonymized point) and other anonymization tasks. B. HOW THE DATASET IS CREATED This dataset was created by calculating the midpoint of each the street segment in the city (see Streets - Active and Retired) then returning the latitude and longitude of that midpoint. A street segment is a line between two intersections. Street midpoints are calculated for each side of the block. C. UPDATE PROCESS This dataset is updated weekly. D. HOW TO USE THIS DATASET Use this dataset for geomasking and other anonymization workflows. E. RELATED DATASETS - Streets - Active and Retired

Code: calculation of PRSL for both ‘coral’ and ‘range’ type vertical uncertainties, as well as U-series and radiocarbon ages [pdf of Matlab code file]Supplement to Hibbert et al., 2018 (Scientific Data)

On October 20, 2022, CDC began retrieving aggregate case and death data from jurisdictional and state partners weekly instead of daily. This dataset contains archived community transmission and related data elements by county as originally displayed on the COVID Data Tracker. Although these data will continue to be publicly available, this dataset has not been updated since October 20, 2022. An archived dataset containing weekly community transmission data by county as originally posted can also be found here: Weekly COVID-19 County Level of Community Transmission as Originally Posted | Data | Centers for Disease Control and Prevention (cdc.gov).

Related data CDC has been providing the public with two versions of COVID-19 county-level community transmission level data: this dataset with the daily values as originally posted on the COVID Data Tracker, and an historical dataset with daily data as well as the updates and corrections from state and local health departments. Similar to this dataset, the original historical dataset is archived on 10/20/2022. It will continue to be publicly available but will no longer be updated. A new dataset containing historical community transmission data by county is now published weekly and can be found at: Weekly COVID-19 County Level of Community Transmission Historical Changes | Data | Centers for Disease Control and Prevention (cdc.gov).

This public use dataset has 7 data elements reflecting community transmission levels for all available counties and jurisdictions. It contains reported daily transmission levels at the county level with the same values used to display transmission maps on the COVID Data Tracker. Each day, the dataset is appended to contain the most recent day's data. Transmission level is set to low, moderate, substantial, or high using the calculation rules below.

Methods for calculating county level of community transmission indicator The County Level of Community Transmission indicator uses two metrics: (1) total new COVID-19 cases per 100,000 persons in the last 7 days and (2) percentage of positive SARS-CoV-2 diagnostic nucleic acid amplification tests (NAAT) in the last 7 days. For each of these metrics, CDC classifies transmission values as low, moderate, substantial, or high (below and here). If the values for each of these two metrics differ (e.g., one indicates moderate and the other low), then the higher of the two should be used for decision-making.

CDC core metrics of and thresholds for community transmission levels of SARS-CoV-2

Total New Case Rate Metric: "New cases per 100,000 persons in the past 7 days" is calculated by adding the number of new cases in the county (or other administrative level) in the last 7 days divided by the population in the county (or other administrative level) and multiplying by 100,000. "New cases per 100,000 persons in the past 7 days" is considered to have a transmission level of Low (0-9.99); Moderate (10.00-49.99); Substantial (50.00-99.99); and High (greater than or equal to 100.00).

Test Percent Positivity Metric: "Percentage of positive NAAT in the past 7 days" is calculated by dividing the number of positive tests in the county (or other administrative level) during the last 7 days by the total number of tests conducted over the last 7 days. "Percentage of positive NAAT in the past 7 days" is considered to have a transmission level of Low (less than 5.00); Moderate (5.00-7.99); Substantial (8.00-9.99); and High (greater than or equal to 10.00).

If the two metrics suggest different transmission levels, the higher level is selected.

The reported transmission categories include:

Low Transmission Threshold: Counties with fewer than 10 total cases per 100,000 population in the past 7 days, and a NAAT percent test positivity in the past 7 days below 5%;

Moderate Transmission Threshold: Counties with 10-49 total cases per 100,000 population in the past 7 days or a NAAT test percent positivity in the past 7 days of 5.0-7.99%;

Substantial Transmission Threshold: Counties with 50-99 total cases per 100,000 population in the past 7 days or a NAAT test percent positivity in the past 7 days of 8.0-9.99%;

High Transmission Threshold: Counties with 100 or more total cases per 100,000 population in the past 7 days or a NAAT test percent positivity in the past 7 days of 10.0% or greater.

Blank: total new cases in the past 7 days are not reported (county data known to be unavailable) and the percentage of positive NAATs tests during the past 7 days (blank) are not reported.

Data Suppression To prevent the release of data that could be used to identify people, data cells are suppressed for low frequency. When the case counts used to calculate the total new case rate metric ("cases_per_100K_7_day_count_change") is greater than zero and less than 10, this metric is set to "suppressed" to protect individual privacy. If the case count is 0, the total new case rate metric is still displayed.

The data in this dataset are considered provisional by CDC and are subject to change until the data are reconciled and verified with the state and territorial data providers. This dataset is created using CDC’s Policy on Public Health Research and Nonresearch Data Management and Access.

Summary:

Estimated stand-off distance between ADS-B equipped aircraft and obstacles. Obstacle information was sourced from the FAA Digital Obstacle File and the FHWA National Bridge Inventory. Aircraft tracks were sourced from processed data curated from the OpenSky Network. Results are presented as histograms organized by aircraft type and distance away from runways.

Description:

For many aviation safety studies, aircraft behavior is represented using encounter models, which are statistical models of how aircraft behave during close encounters. They are used to provide a realistic representation of the range of encounter flight dynamics where an aircraft collision avoidance system would be likely to alert. These models currently and have historically have been limited to interactions between aircraft; they have not represented the specific interactions between obstacles and aircraft equipped transponders. In response, we calculated the standoff distance between obstacles and ADS-B equipped manned aircraft.

For robustness, this assessment considered two different datasets of manned aircraft tracks and two datasets of obstacles. For robustness, MIT LL calculated the standoff distance using two different datasets of aircraft tracks and two datasets of obstacles. This approach aligned with the foundational research used to support the ASTM F3442/F3442M-20 well clear criteria of 2000 feet laterally and 250 feet AGL vertically.

The two datasets of processed tracks of ADS-B equipped aircraft curated from the OpenSky Network. It is likely that rotorcraft were underrepresented in these datasets. There were also no considerations for aircraft equipped only with Mode C or not equipped with any transponders. The first dataset was used to train the v1.3 uncorrelated encounter models and referred to as the “Monday” dataset. The second dataset is referred to as the “aerodrome” dataset and was used to train the v2.0 and v3.x terminal encounter model. The Monday dataset consisted of 104 Mondays across North America. The other dataset was based on observations at least 8 nautical miles within Class B, C, D aerodromes in the United States for the first 14 days of each month from January 2019 through February 2020. Prior to any processing, the datasets required 714 and 847 Gigabytes of storage. For more details on these datasets, please refer to "Correlated Bayesian Model of Aircraft Encounters in the Terminal Area Given a Straight Takeoff or Landing" and “Benchmarking the Processing of Aircraft Tracks with Triples Mode and Self-Scheduling.”

Two different datasets of obstacles were also considered. First was point obstacles defined by the FAA digital obstacle file (DOF) and consisted of point obstacle structures of antenna, lighthouse, meteorological tower (met), monument, sign, silo, spire (steeple), stack (chimney; industrial smokestack), transmission line tower (t-l tower), tank (water; fuel), tramway, utility pole (telephone pole, or pole of similar height, supporting wires), windmill (wind turbine), and windsock. Each obstacle was represented by a cylinder with the height reported by the DOF and a radius based on the report horizontal accuracy. We did not consider the actual width and height of the structure itself. Additionally, we only considered obstacles at least 50 feet tall and marked as verified in the DOF.

The other obstacle dataset, termed as “bridges,” was based on the identified bridges in the FAA DOF and additional information provided by the National Bridge Inventory. Due to the potential size and extent of bridges, it would not be appropriate to model them as point obstacles; however, the FAA DOF only provides a point location and no information about the size of the bridge. In response, we correlated the FAA DOF with the National Bridge Inventory, which provides information about the length of many bridges. Instead of sizing the simulated bridge based on horizontal accuracy, like with the point obstacles, the bridges were represented as circles with a radius of the longest, nearest bridge from the NBI. A circle representation was required because neither the FAA DOF or NBI provided sufficient information about orientation to represent bridges as rectangular cuboid. Similar to the point obstacles, the height of the obstacle was based on the height reported by the FAA DOF. Accordingly, the analysis using the bridge dataset should be viewed as risk averse and conservative. It is possible that a manned aircraft was hundreds of feet away from an obstacle in actuality but the estimated standoff distance could be significantly less. Additionally, all obstacles are represented with a fixed height, the potentially flat and low level entrances of the bridge are assumed to have the same height as the tall bridge towers. The attached figure illustrates an example simulated bridge.

It would had been extremely computational inefficient to calculate the standoff distance for all possible track points. Instead, we define an encounter between an aircraft and obstacle as when an aircraft flying 3069 feet AGL or less comes within 3000 feet laterally of any obstacle in a 60 second time interval. If the criteria were satisfied, then for that 60 second track segment we calculate the standoff distance to all nearby obstacles. Vertical separation was based on the MSL altitude of the track and the maximum MSL height of an obstacle.

For each combination of aircraft track and obstacle datasets, the results were organized seven different ways. Filtering criteria were based on aircraft type and distance away from runways. Runway data was sourced from the FAA runways of the United States, Puerto Rico, and Virgin Islands open dataset. Aircraft type was identified as part of the em-processing-opensky workflow.

• All: No filter, all observations that satisfied encounter conditions
• nearRunway: Aircraft within or at 2 nautical miles of a runway
• awayRunway: Observations more than 2 nautical miles from a runway
• glider: Observations when aircraft type is a glider
• fwme: Observations when aircraft type is a fixed-wing multi-engine
• fwse: Observations when aircraft type is a fixed-wing single engine
• rotorcraft: Observations when aircraft type is a rotorcraft

License

This dataset is licensed under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International(CC BY-NC-ND 4.0).

This license requires that reusers give credit to the creator. It allows reusers to copy and distribute the material in any medium or format in unadapted form and for noncommercial purposes only. Only noncommercial use of your work is permitted. Noncommercial means not primarily intended for or directed towards commercial advantage or monetary compensation. Exceptions are given for the not for profit standards organizations of ASTM International and RTCA.

MIT is releasing this dataset in good faith to promote open and transparent research of the low altitude airspace. Given the limitations of the dataset and a need for more research, a more restrictive license was warranted. Namely it is based only on only observations of ADS-B equipped aircraft, which not all aircraft in the airspace are required to employ; and observations were source from a crowdsourced network whose surveillance coverage has not been robustly characterized.

As more research is conducted and the low altitude airspace is further characterized or regulated, it is expected that a future version of this dataset may have a more permissive license.

Distribution Statement

DISTRIBUTION STATEMENT A. Approved for public release. Distribution is unlimited.

© 2021 Massachusetts Institute of Technology.

Delivered to the U.S. Government with Unlimited Rights, as defined in DFARS Part 252.227-7013 or 7014 (Feb 2014). Notwithstanding any copyright notice, U.S. Government rights in this work are defined by DFARS 252.227-7013 or DFARS 252.227-7014 as detailed above. Use of this work other than as specifically authorized by the U.S. Government may violate any copyrights that exist in this work.

This material is based upon work supported by the Federal Aviation Administration under Air Force Contract No. FA8702-15-D-0001. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Federal Aviation Administration.

This document is derived from work done for the FAA (and possibly others); it is not the direct product of work done for the FAA. The information provided herein may include content supplied by third parties. Although the data and information contained herein has been produced or processed from sources believed to be reliable, the Federal Aviation Administration makes no warranty, expressed or implied, regarding the accuracy, adequacy, completeness, legality, reliability or usefulness of any information, conclusions or recommendations provided herein. Distribution of the information contained herein does not constitute an endorsement or warranty of the data or information provided herein by the Federal Aviation Administration or the U.S. Department of Transportation. Neither the Federal Aviation Administration nor the U.S. Department of

This dataset is about book subjects. It has 2 rows and is filtered where the books is Electrotechnical systems : calculation and analysis with Mathematical and PSpice. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.