This dataset is one of eight datasets produced by this study. Four of the datasets predict the probability of detecting atrazine and(or) desethyl-atrazine (a breakdown product of atrazine) in ground water in Colorado; the other four predict the probability of detecting elevated concentrations of nitrate in ground water in Colorado. The four datasets that predict the probability of atrazine and (or) desethyl-atrazine (atrazine/DEA) are differentiated by whether or not they incorporated atrazine use and whether or not they incorporated hydrogeomorphic regions. The four datasets that predict the probability of elevated concentrations of nitrate are differentiated by whether or not they incorporated fertilizer use and whether or not they incorporated hydrogeomorphic regions. Each of the eight datasets has its own unique strengths and weaknesses. The user is cautioned to read Rupert (2003, Probability of detecting atrazine/desethyl-atrazine and elevated concentrations of nitrate in ground water in Colorado: U.S. Geological Survey Water-Resources Investigations Report 02-4269, 35 p., https://water.usgs.gov/pubs/wri/wri02-4269/) to determine if he(she) is using the most appropriate dataset for his(her) particular needs. This dataset specifically predicts the probability of detecting elevated concentrations of nitrate in ground water in Colorado with hydrogeomorphic regions and fertilizer use not included. The following text was extracted from Rupert (2003). Draft Federal regulations may require that each State develop a State Pesticide Management Plan for the herbicides atrazine, alachlor, metolachlor, and simazine. Maps were developed that the State of Colorado could use to predict the probability of detecting atrazine/DEA in ground water in Colorado. These maps can be incorporated into the State Pesticide Management Plan and can help provide a sound hydrogeologic basis for atrazine management in Colorado. Maps showing the probability of detecting elevated nitrite plus nitrate as nitrogen (nitrate) concentrations in ground water in Colorado also were developed because nitrate is a contaminant of concern in many areas of Colorado. Maps showing the probability of detecting atrazine/DEA at or greater than concentrations of 0.1 microgram per liter and nitrate concentrations in ground water greater than 5 milligrams per liter were developed as follows: (1) Ground-water quality data were overlaid with anthropogenic and hydrogeologic data by using a geographic information system (GIS) to produce a dataset in which each well had corresponding data on atrazine use, fertilizer use, geology, hydrogeomorphic regions, land cover, precipitation, soils, and well construction. These data then were downloaded to a statistical software package for analysis by logistic regression. (2) Relations were observed between ground-water quality and the percentage of land-cover categories within circular regions (buffers) around wells. Several buffer sizes were evaluated; the buffer size that provided the strongest relation was selected for use in the logistic regression models. (3) Relations between concentrations of atrazine/DEA and nitrate in ground water and atrazine use, fertilizer use, geology, hydrogeomorphic regions, land cover, precipitation, soils, and well-construction data were evaluated, and several preliminary multivariate models with various combinations of independent variables were constructed. (4) The multivariate models that best predicted the presence of atrazine/DEA and elevated concentrations of nitrate in ground water were selected. (5) The accuracy of the multivariate models was confirmed by validating the models with an independent set of ground-water quality data. (6) The multivariate models were entered into a geographic information system and the probability GRIDS were constructed.

About this dataset

Context

The dataset tabulates the Show Low median household income by race. The dataset can be utilized to understand the racial distribution of Show Low income.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Show Low, AZ median household income breakdown by race betwen 2011 and 2021
• Median Household Income by Racial Categories in Show Low, AZ (2021, in 2022 inflation-adjusted dollars)

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/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Show Low median household income by race. You can refer the same here

About this dataset

Context

The dataset tabulates the population of Show Low by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Show Low across both sexes and to determine which sex constitutes the majority.

Key observations

There is a slight majority of female population, with 51.59% of total population being female. Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

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. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Show Low is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Show Low total population. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Show Low Population by Gender. You can refer the same here

This dataset contains the geographic data used to create maps for the San Diego County Regional Equity Indicators Report led by the Office of Equity and Racial Justice (OERJ). The full report can be found here: https://data.sandiegocounty.gov/stories/s/7its-kgpt

Demographic data from the report can be found here: https://data.sandiegocounty.gov/dataset/Equity-Report-Data-Demographics/q9ix-kfws

Filter by the Indicator column to select data for a particular indicator map.

Export notes: Dataset may not automatically open correctly in Excel due to geospatial data. To export the data for geospatial analysis, select Shapefile or GEOJSON as the file type. To view the data in Excel, export as a CSV but do not open the file. Then, open a blank Excel workbook, go to the Data tab, select “From Text/CSV,” and follow the prompts to import the CSV file into Excel. Alternatively, use the exploration options in "View Data" to hide the geographic column prior to exporting the data.

USER NOTES: 4/7/2025 - The maps and data have been removed for the Health Professional Shortage Areas indicator due to inconsistencies with the data source leading to some missing health professional shortage areas. We are working to fix this issue, including exploring possible alternative data sources.

5/21/2025 - The following changes were made to the 2023 report data (Equity Report Year = 2023). Self-Sufficiency Wage - a typo in the indicator name was fixed (changed sufficienct to sufficient) and the percent for one PUMA corrected from 56.9 to 59.9 (PUMA = San Diego County (Northwest)--Oceanside City & Camp Pendleton). Notes were made consistent for all rows where geography = ZCTA. A note was added to all rows where geography = PUMA. Voter registration - label "92054, 92051" was renamed to be in numerical order and is now "92051, 92054". Removed data from the percentile column because the categories are not true percentiles. Employment - Data was corrected to show the percent of the labor force that are employed (ages 16 and older). Previously, the data was the percent of the population 16 years and older that are in the labor force. 3- and 4-Year-Olds Enrolled in School - percents are now rounded to one decimal place. Poverty - the last two categories/percentiles changed because the 80th percentile cutoff was corrected by 0.01 and one ZCTA was reassigned to a different percentile as a result. Low Birthweight - the 33th percentile label was corrected to be written as the 33rd percentile. Life Expectancy - Corrected the category and percentile assignment for SRA CENTRAL SAN DIEGO. Parks and Community Spaces - corrected the category assignment for six SRAs.

5/21/2025 - Data was uploaded for Equity Report Year 2025. The following changes were made relative to the 2023 report year. Adverse Childhood Experiences - added geographic data for 2025 report. No calculation of bins nor corresponding percentiles due to small number of geographic areas. Low Birthweight - no calculation of bins nor corresponding percentiles due to small number of geographic areas.

Prepared by: Office of Evaluation, Performance, and Analytics and the Office of Equity and Racial Justice, County of San Diego, in collaboration with the San Diego Regional Policy & Innovation Center (https://www.sdrpic.org).

Context

Since i have started research in the field of data science, i have noticed there are lot of data sets available for NLP, medicine, images and other subjects but i could not find any single adequate data for the domain of software testing. The data sets which are hardly available are extracted from some piece of code or some historical data that too not available publicly to analyze. The domain of software testing and data science, especially machine learning has a lot of potential. While conducting research on testcase prioritization especially in initial stages of software test cycle the way companies set the priorities in software industry there is no black box data set available in that format. This was the reason that i wanted such data set to exist. So i collected the necessary attributes , arrange them against their values and make one.

Content

This data was gathered in [Jan, 2021], from a local industry's MIS, developed by a software team worked on company's whole software package including their management system. The dataset is in .csv format, there are 1314 rows and 8 columns in this data set. The detail of these eight attributes are as under: B_Req --> Business Requirement R_Prioirty --> Requirement Priority of particular business requirement and explained in .txt file. Weight --> I have assigned a weightage against "R_Priority(Requirement Priority)" its criteria is explained in Testing_MIS.txt file. FP --> Function point of each testing task, which in our case are test cases against each requirement under covers a particular FP Complexity --> Complexity of a particular function point or related modules(the description of assigning complexity is listed below in this section)* Time --> Estimated max time assigned to each Function Point of particular testing task by QA team lead. Cost --> Calculated cost for each function point using complexity and time with function point estimation technique to calculates cost using the formula listed below: cost = “Cost = (Complexity * Time) * average amount set per task or per Function Point note: In this case it is set as 7$ per man hour. The criteria for complexity is listed in .txt file attached with this version. Prioirty --> Is the assigned testcases priority against each Function Point by the testing team.

Acknowledgements

I would like to thank the persons from QA departments of different software companies. Especially team of the the company who provided me this estimation data and traceability matrix to extract data and compile these in to a dataset. I get a great help from the websites like www.softwaretestinghelp.com, www.coderus.com and many other sources which helps me to understand all the testing process and in which phases priorities are assigned usually.

Inspiration

My inspiration to collect this data is the shortage of dataset showing the priority of testcases with their requirements and estimated metrics to analyze the data while doing research in automation of testcase priority using machine learning. --> The dataset can be used to analyze and apply classification or any machine learning algorithm to prioritize testcases. --> Can be used reduce , select or automate testing based on priority, or cost and time or complexity and requirements. --> Can be used to build recommendation system problem related to software testing which helps software testing team to ease their task based on estimation and recommendation.

This data set shows all fatal crashes and their investigative outcomes from PPD's Accident Investigation Unit (AID) from 1/1/19 to the present. The whole dataset gets refreshed nightly. This means the dataset will show new records the day after the source data has updated. For those conducting analysis, this dataset by PPD and OTIS' crash data should not be compared, or should be used together cautiously. The same crash may show as in different locations between the two datasets since PPD data represent the location of where crashes are initially reported whereas OTIS' crash data involves further investigation to confirm initial reports. If you want to analyze the location of crashes in Philadelphia, use OTIS' dataset. If you want to understand the investigative outcomes of crashes, use the PPD dataset.

About this dataset

Context

The dataset tabulates the Tuscaloosa population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Tuscaloosa across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2022, the population of Tuscaloosa was 110,602, a 1.39% increase year-by-year from 2021. Previously, in 2021, Tuscaloosa population was 109,082, an increase of 4.67% compared to a population of 104,214 in 2020. Over the last 20 plus years, between 2000 and 2022, population of Tuscaloosa increased by 31,687. In this period, the peak population was 110,602 in the year 2022. The numbers suggest that the population has not reached its peak yet and is showing a trend of further growth. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2022

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2022)
• Population: The population for the specific year for the Tuscaloosa is shown in this column.
• Year on Year Change: This column displays the change in Tuscaloosa population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Tuscaloosa Population by Year. You can refer the same here

A. SUMMARY

This dataset reports the number of new residential units made available for occupancy in San Francisco since January 2018. Each row in this dataset shows the change in the number of new units associated with a building permit application. Each row also includes the date those units were approved for occupancy, the type of document approving them, and their address.

Values in the column [Number of Units Certified] can be added together to produce a count of new units approved for occupancy since January 2018.

These records provide a preliminary count of new residential units. The San Francisco Planning Department issues a Housing Inventory Report each year that provides a more complete account of new residential units, and those results may vary slightly from records in this dataset. The Housing Inventory Report is an in-depth annual research project requiring extensive work to validate information about projects. By comparison, this dataset is meant to provide more timely updates about housing production based on available administrative data. The Department of Building Inspection and Planning Department will reconcile these records with future Housing Inventory Reports.

B. METHODOLOGY

At the end of each month, DBI staff manually calculate how many new units are available for occupancy for each building permit application and enters that information into this dataset. These records reflect counts for all types of residential units, including authorized accessory dwelling units. These records do not reflect units demolished or removed from the city’s available housing stock.

Multiple records may be associated with the same building permit application number, which means that new certifications or amendments were issued. Only changes to the net number of units associated with that permit application are recorded in subsequent records.

For example, Building Permit Application Number [201601010001] located at [123 1st Avenue] was issued an [Initial TCO] Temporary Certificate of Occupancy on [January 1, 2018] approving 10 units for occupancy. Then, an [Amended TCO] was issued on [June 1, 2018] approving [5] additional units for occupancy, for a total of 15 new units associated with that Building Permit Application Number. The building will appear as twice in the dataset, each row representing when new units were approved.

If additional or amended certifications are issued for a building permit application, but they do not change the number of units associated with that building permit application, those certifications are not recorded in this dataset. For example, if all new units associated with a project are certified for occupancy under an Initial TCO, then the Certificate of Final Completion (CFC) would not appear in the dataset because the CFC would not add new units to the housing stock. See data definitions for more details.

C. UPDATE FREQUENCY

This dataset is updated monthly.

D. DOCUMENT TYPES

Several documents issued near or at project completion can certify units for occupation. They are: Initial Temporary Certificate of Occupancy (TCO), Amended TCO, and Certificate of Final Completion (CFC).

• Initial TCO is a document that allows for occupancy of a unit before final project completion is certified, conditional on if the unit can be occupied safely. The TCO is meant to be temporary and has an expiration date. This field represents the number of units certified for occupancy when the TCO is issued. • Amended TCO is a document that is issued when the conditions of the project are changed before final project completion is certified. These records show additional new units that have become habitable since the issuance of the Initial TCO. • Certificate of Final Completion (CFC) is a document that is issued when all work is completed according to approved plans, and the building is ready for complete occupancy. These records show additional new units that were not accounted for in the Initial or Amended TCOs.

This dataset displays potential future sea levels. The purpose of this dataset is to provide coastal managers and scientists with a preliminary look at sea level rise and coastal flooding impacts. The viewer is a screening-level tool that uses nationally consistent data sets and analyses. The data and maps in this tool illustrate the scale of potential flooding, not the exact location, and do not account for erosion, subsidence, or future construction. Water levels are shown as they would appear during the highest high tides (excludes wind driven tides). The data, maps, and information provided should be used only as a screening-level tool for management decisions. This dataset was created as part of the National Oceanic and Atmospheric Administration Office for Coastal Management's efforts to create an online mapping viewer depicting potential sea level rise and its associated impacts on the nation's coastal areas. The purpose of the mapping viewer is to provide coastal managers and scientists with a preliminary look at sea level rise and coastal flooding impacts. The viewer is a screening-level tool that uses nationally consistent data sets and analyses. Data and maps provided can be used at several scales to help gauge trends and prioritize actions for different scenarios. The purpose of this dataset is to show potential sea level rise inundation of 7ft above current Mean Higher High Water (MHHW) for the area. Tiles have been cached down to Level ID 15 (1:18,055). This dataset illustrates the scale of potential flooding, not the exact location, and does not account for erosion, subsidence, or future construction. Inundation is shown as it would appear during the highest high tides (excludes wind driven tides) with the sea level rise amount. The dataset should be used only as a screening-level tool for management decisions. As with all remotely sensed data, all features should be verified with a site visit. The dataset is provided "as is," without warranty to its performance, merchantable state, or fitness for any particular purpose. The entire risk associated with the results and performance of this dataset is assumed by the user. This dataset should be used strictly as a planning reference and not for navigation, permitting, or other legal purposes. For more information visit the Sea Level Rise Impacts Viewer (https://coast.noaa.gov/slr).View Dataset on the Gateway

This paper explores a unique dataset of all the SET ratings provided by students of one university in Poland at the end of the winter semester of the 2020/2021 academic year. The SET questionnaire used by this university is presented in Appendix 1. The dataset is unique for several reasons. It covers all SET surveys filled by students in all fields and levels of study offered by the university. In the period analysed, the university was entirely in the online regime amid the Covid-19 pandemic. While the expected learning outcomes formally have not been changed, the online mode of study could have affected the grading policy and could have implications for some of the studied SET biases. This Covid-19 effect is captured by econometric models and discussed in the paper. The average SET scores were matched with the characteristics of the teacher for degree, seniority, gender, and SET scores in the past six semesters; the course characteristics for time of day, day of the week, course type, course breadth, class duration, and class size; the attributes of the SET survey responses as the percentage of students providing SET feedback; and the grades of the course for the mean, standard deviation, and percentage failed. Data on course grades are also available for the previous six semesters. This rich dataset allows many of the biases reported in the literature to be tested for and new hypotheses to be formulated, as presented in the introduction section. The unit of observation or the single row in the data set is identified by three parameters: teacher unique id (j), course unique id (k) and the question number in the SET questionnaire (n ϵ {1, 2, 3, 4, 5, 6, 7, 8, 9} ). It means that for each pair (j,k), we have nine rows, one for each SET survey question, or sometimes less when students did not answer one of the SET questions at all. For example, the dependent variable SET_score_avg(j,k,n) for the triplet (j=Calculus, k=John Smith, n=2) is calculated as the average of all Likert-scale answers to question nr 2 in the SET survey distributed to all students that took the Calculus course taught by John Smith. The data set has 8,015 such observations or rows. The full list of variables or columns in the data set included in the analysis is presented in the attached filesection. Their description refers to the triplet (teacher id = j, course id = k, question number = n). When the last value of the triplet (n) is dropped, it means that the variable takes the same values for all n ϵ {1, 2, 3, 4, 5, 6, 7, 8, 9}.Two attachments:- word file with variables description- Rdata file with the data set (for R language).Appendix 1. Appendix 1. The SET questionnaire was used for this paper. Evaluation survey of the teaching staff of [university name] Please, complete the following evaluation form, which aims to assess the lecturer’s performance. Only one answer should be indicated for each question. The answers are coded in the following way: 5- I strongly agree; 4- I agree; 3- Neutral; 2- I don’t agree; 1- I strongly don’t agree. Questions 1 2 3 4 5 I learnt a lot during the course. ○ ○ ○ ○ ○ I think that the knowledge acquired during the course is very useful. ○ ○ ○ ○ ○ The professor used activities to make the class more engaging. ○ ○ ○ ○ ○ If it was possible, I would enroll for the course conducted by this lecturer again. ○ ○ ○ ○ ○ The classes started on time. ○ ○ ○ ○ ○ The lecturer always used time efficiently. ○ ○ ○ ○ ○ The lecturer delivered the class content in an understandable and efficient way. ○ ○ ○ ○ ○ The lecturer was available when we had doubts. ○ ○ ○ ○ ○ The lecturer treated all students equally regardless of their race, background and ethnicity. ○ ○

About this dataset

Context

The dataset tabulates the population of Redondo Beach by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Redondo Beach. The dataset can be utilized to understand the population distribution of Redondo Beach by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Redondo Beach. 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 Redondo Beach.

Key observations

Largest age group (population): Male # 40-44 years (3,140) | Female # 30-34 years (3,053). 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 Redondo Beach population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Redondo Beach is shown in the following column.
• Population (Female): The female population in the Redondo Beach 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 Redondo Beach 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 Redondo Beach Population by Gender. You can refer the same here

The NOAA Regional Surface Data - 1989 (NCDC) Data Set contains hourly surface meteorological data for the FIFE area. Though the measurements presented in this data set were not taken precisely at the FIFE study area, it is hypothesized that they present a representative horizontal cross-section of meteorological variables and sky conditions in and around the site. It is also realized that many of the variables presented in this data set are somewhat subjective and dependent on the skill (and biases) of the observer, such as estimates of cloud amount and height. This data may be used as input data and/or verification data for numerical simulation models.

INFORMATION ABOUT 7/12/2019 CHANGES TO THIS DATASET: http://bit.ly/2XV3ERS -- This dataset includes information about currently-valid building permits issued by the City of Chicago from 2006 to the present. Building permits are issued subject to payment of applicable fees. If building or zoning permit fees show as unpaid, the permit is not valid. (A permit is valid if only “other fees” are shown as unpaid.) This dataset does not include permits which have been issued and voided or revoked. This dataset also does not include permits for mechanical amusement riding devices and carnivals issued by the Department of Buildings.

Property Index Numbers (PINs) and geographic information (ward, community area and census tract) are provided for most permit types issued in 2008 or later.

For more information on building permits, see https://www.chicago.gov/city/en/depts/bldgs/provdrs/permits.html.

For an application related to building permits and inspections, see https://webapps1.chicago.gov/buildingrecords.

This project focuses on analyzing human body movements during common exercises by capturing and processing angles of key body joints. We utilized video data to extract frame-by-frame angles of the following body parts during various exercises such as push-ups, jumping jacks, pull-ups, squats, and Russian twists. For pose estimation, MediaPipe was used to detect body landmarks, while YOLOv6 was employed for object detection to enhance accuracy.

Methodology

• Video Collection: Videos were recorded for each exercise (push-ups, jumping jacks, pull-ups, squats, Russian twists), ensuring proper form and variety in movement.
• Frame-by-Frame Analysis: Each video was processed frame by frame, and landmarks were detected using MediaPipe's Pose Estimation. We calculated the angles of key joints by using the positional data of landmarks across different frames.
• Object Detection with YOLOv6: YOLOv6 was used to identify specific objects and enhance the robustness of the pose estimation by detecting outliers or incorrect poses during exercises, thereby improving the accuracy of the analysis.

Applications This dataset can be used for multiple applications: - Form Correction: By comparing these angles with standard benchmarks, feedback can be provided to improve exercise form. - Performance Tracking: Over time, users can monitor their improvement by analyzing the changes in their joint angles during exercises. - Pose Classification: Machine learning models can be trained to classify correct vs. incorrect form, enabling the development of smart fitness assistants. - Real-time Feedback Systems: Using pose estimation in conjunction with live video, real-time systems can be developed to guide users during workouts.

Exercises Analyzed The following exercises were captured and analyzed for this dataset:

• Push-ups: Key focus on shoulder, elbow, and hip angles.
• Jumping Jacks: Full-body motion tracked via shoulder, elbow, hip, knee, and ankle angles.
• Pull-ups: Primarily focused on shoulder and elbow joint movements.
• Squats: Analyzed hip, knee, and ankle angles for depth and posture analysis.
• Russian Twists: Core movement tracked via shoulder and hip angles to assess rotational motion.

Potential Analysis - Time-Series Analysis: The data can be treated as a time-series, allowing for the identification of trends in joint movement over the duration of an exercise. - Pose Optimization: Optimization models can be used to suggest improvements in form based on angle analysis. - Machine Learning Integration: The dataset can serve as input for machine learning algorithms to automate form correction and workout optimization.

This dataset contains FEMA applicant-level data for the Individuals and Households Program (IHP). All PII information has been removed. The location is represented by county, city, and zip code. This dataset contains Individual Assistance (IA) applications from DR1439 (declared in 2002) to those declared over 30 days ago. The full data set is refreshed on an annual basis and refreshed weekly to update disasters declared in the last 18 months. This dataset includes all major disasters and includes only valid registrants (applied in a declared county, within the registration period, having damage due to the incident and damage within the incident period). Information about individual data elements and descriptions are listed in the metadata information within the dataset.rnValid registrants may be eligible for IA assistance, which is intended to meet basic needs and supplement disaster recovery efforts. IA assistance is not intended to return disaster-damaged property to its pre-disaster condition. Disaster damage to secondary or vacation homes does not qualify for IHP assistance.rnData comes from FEMA's National Emergency Management Information System (NEMIS) with raw, unedited, self-reported content and subject to a small percentage of human error.rnAny financial information is derived from NEMIS and not FEMA's official financial systems. Due to differences in reporting periods, status of obligations and application of business rules, this financial information may differ slightly from official publication on public websites such as usaspending.gov. This dataset is not intended to be used for any official federal reporting. rnCitation: The Agency’s preferred citation for datasets (API usage or file downloads) can be found on the OpenFEMA Terms and Conditions page, Citing Data section: https://www.fema.gov/about/openfema/terms-conditions.rnDue to the size of this file, tools other than a spreadsheet may be required to analyze, visualize, and manipulate the data. MS Excel will not be able to process files this large without data loss. It is recommended that a database (e.g., MS Access, MySQL, PostgreSQL, etc.) be used to store and manipulate data. Other programming tools such as R, Apache Spark, and Python can also be used to analyze and visualize data. Further, basic Linux/Unix tools can be used to manipulate, search, and modify large files.rnIf you have media inquiries about this dataset, please email the FEMA News Desk at FEMA-News-Desk@fema.dhs.gov or call (202) 646-3272. For inquiries about FEMA's data and Open Government program, please email the OpenFEMA team at OpenFEMA@fema.dhs.gov.rnThis dataset is scheduled to be superceded by Valid Registrations Version 2 by early CY 2024.

A cells polygon feature class was created by the U. S. Geological Survey (USGS) to illustrate the degree of exploration, type of production, and distribution of production in the State of Indiana. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or the type of production of the wells located within the cell is unknown or dry. Cells were developed as a graphic solution to overcome the problem of displaying proprietary well data. No proprietary data are displayed or included in the cell maps. The data are current as of 2006.

In Rhineland-Palatinate there are always mass movements. The Landslide Database Rhineland-Palatinate is a joint project of the State Office for Geology and Mining Rhineland-Palatinate (LGB) and the Forschungsstelle Rutschungen e.V. at Johannes Gutenberg University Mainz (FSR). Originally, the database was created at the then State Geological Office and continued at the Research Centre for Landslides. Since 2009, the two cooperation partners have been working together on a complete reprocessing. The database includes landslides, rockfalls, rockfalls, earthfalls and daybreaks in Rhineland-Palatinate. In total, there were 2,291 claims (as at: 01.06.2012), which were mainly recorded and archived by the two project partners LGB and FSR in the field. Further data come from various diploma theses and dissertations, which were supervised by the aforementioned institutions. The oldest documented case of damage occurred in 1655. Most events cover the period from 1950 to the present day. The MS Access database based on Oracle, which is specially programmed in the LGB, contains 33 different data fields, which include information on the location, geology, causes and safeguards of the mass movement. The data sets are mainly recorded by means of specially created lists of terms in order to ensure uniform documentation. The online presentation, which was created on the basis of the landslide database, shows in which areas of Rhineland-Palatinate mass movements have occurred so far. It is aimed at municipalities, engineering offices, planners, appraisers, architects and interested citizens who use this information, among other things, for the planning and preliminary exploration of construction projects. The aim is to provide clues about the range of mass movements. Possible problematic areas can thus be identified in good time, examined accordingly and an adapted approach taken. The Mapserver application represents systematically arranged tiles with an extension of 1 x 1 km, the color variation of which is due to the number of mass movements within the tile. It is expressly pointed out that for data protection reasons, a pinpoint, parcel-sharp location representation of the mass movements is dispensed with. A concrete reference to the location and the associated inference to individual plots of land are thus excluded. Furthermore, it is pointed out that the representation of a mass movement within a tile does not mean that this danger is present throughout the entire tile. Likewise, the lack of information on mass movements does not mean that they can be completely excluded there. In addition, the classification says nothing about the current activity. Planned construction projects within a tile affected by mass movements do not necessarily have to pose problems, so it is also pointed out in this context that the hazards shown here do not replace spot and object-related investigations or on-site assessments. A rockfall is a fall event in which the ground or rock material falls mostly free-falling, jumping or rolling. Demolition is often carried out along divisions. In contrast to rockfall, a rockfall includes cubatures from 10 m3 volume.

This data set contains 10000054 ratings and 95580 tags applied to 10681 movies by 71567 users of the online movie recommender service MovieLens.

Users were selected at random for inclusion. All users selected had rated at least 20 movies. Unlike previous MovieLens data sets, no demographic information is included. Each user is represented by an id, and no other information is provided.

The data are contained in three files, movies.dat, ratings.dat, and tags.dat. Also included are scripts for generating subsets of the data to support the five-fold cross-validation of rating predictions. More details about the contents and use of all these files follow.

This and other GroupLens data sets are publicly available for download at GroupLens Data Sets.

State, 2016 –2020; County, 2020. The report includes both state and county level water fluoridation data generated from the Water Fluoridation Reporting System (WFRS). State level statistics include data from the biennial report originally published at https://www.cdc.gov/fluoridation/statistics/reference_stats.htm. State and county data include percentage of people, number of people, and number of water systems receiving fluoridated water. County level data is not displayed for all states. Participation in sharing county level data is voluntary and state programs determine if data will be shown.

A. SUMMARY This dataset contains data from financial statements of campaign committees that file with the San Francisco Ethics Commission and (1) contribute to or (2) receive funds from a San Francisco committee which was Primarily Formed for a local election, or (3) filed a Late Reporting Period statement with the SFEC. Financial statements are included for a committee if they meet any of the three criteria for each election included in the search parameters and are not primarily formed for the election. The search period for financial statements begins two years before an election and runs through the next semi-annual filing deadline. The dataset currently filters by the elections of 2024-03-05 and 2024-11-05. B. HOW THE DATASET IS CREATED During an election period an automated script runs nightly to examine filings by Primarily Formed San Francisco committees. If a primarily formed committee reports accepting money from or giving money to a second committee, that second committee's ID number is added to a filter list. If a committee electronically files a late reporting period form with the San Francisco Ethics Commission, the committee's ID number is also included in the filter list. The filter list is used in a second step that looks for filings by committees that file with the San Francisco Ethics Commission or the California Secretary of State. This dataset shows the output of the second step for committees that file with the San Francisco Ethics Commission. The data comes from a nightly search of the Ethics Commission campaign database. A second dataset includes committees that file with the Secretary of State. C. UPDATE PROCESS This dataset is rewritten nightly and is based on data derived from campaign filings. The update script runs automatically on a timer during the 90 days before an election. Refer to the "Data Last Updated" date in the section "About This Dataset" on the landing page to see when the script last ran successfully. D. HOW TO USE THIS DATASET Transactions from all FPPC Form 460 schedules are presented together, refer to the Form Type to differentiate. Transactions from FPPC Form 461 and Form 465 filings are presented together, refer to the Form Type to differentiate. Transactions with a Form Type of D, E, F, G, H, F461P5, F465P3, F496, or F497P2 represent expenditures, or money spent by the committee. Transactions with Form Type A, B1, C, I, F496P3, and F497P1 represent receipts, or money taken in by the committee. Refer to the instructions for Forms 460, 496, and 497 for more details. Transactions on Form 460 Schedules D, F, G, and H are also reported on Schedule E. When doing summary statistics use care not to double count expenditures. Transactions from FPPC Form 496 and Form 497 filings are presented in this dataset. Transactions that were reported on these forms are also reported on the Form 460 at the next filing deadline. If a 460 filing deadline has passed and the committee has filed a campaign statement, transactions on 496/497 filings from the late reporting period should be disregarded. This dataset only shows transactions from the most recent filing version. Committee amendments overwrite filings which come before in sequence. Campaign Committees are required to file statements according to a schedule set out by the C