This data contains information on people who have subscribed to a certain kind of Cable TV service. The data contains just 300 observations and 7 variables.

The 7 variables are:
Age - the age of the TV subscriber
Gender - the gender of the TV subscriber
Income - the income of the TV subscriber
kids - the number of kids the TV subscriber has
ownHome - if the TV subscriber owns the home or not
subscribe - if they have subscribed to the TV services or not
segment - the segment of the TV subscriber's subscription

Source: Udemy course on Data Analytics

About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within Cable town. The dataset can be utilized to gain insights into gender-based income distribution within the Cable town population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Cable town, among individuals aged 15 years and older with income, there were 293 men and 367 women in the workforce. Among them, 141 men were engaged in full-time, year-round employment, while 117 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 34.04% fell within the income range of under $24,999, while 11.97% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 10.64% of men in full-time roles earned incomes exceeding $100,000, while 9.40% of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

Content

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

Income brackets:

• $1 to $2,499 or loss
• $2,500 to $4,999
• $5,000 to $7,499
• $7,500 to $9,999
• $10,000 to $12,499
• $12,500 to $14,999
• $15,000 to $17,499
• $17,500 to $19,999
• $20,000 to $22,499
• $22,500 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $54,999
• $55,000 to $64,999
• $65,000 to $74,999
• $75,000 to $99,999
• $100,000 or more

Variables / Data Columns

• Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
• Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
• Part-Time Males: The count of males employed part-time and earning within a specified income bracket
• Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
• Part-Time Females: The count of females employed part-time and earning within a specified income bracket

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

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 Cable town median household income by race. You can refer the same here

Update Update Date: Aug.31.2020 Update Content: Data combined into one xlsx file.

Description Health Indexing Method: Generally in compliance with the condition scoring methods described in CEATI Report No. T134700-50/118 Source: A TFO in West Canada Asset: 138-kV Class EPR Cable Asset Inspection Year: 2003,2008,2013,2018 Asset Quantity: 4682 (cable segments) Condition/Health Index Attributes: 1.Partial Discharge (Min-Max Normalized) 2.Loading Condition (Recorded Peak Loading in Amps) 3.Visual Condition 4.Neutral Corrosion (Min-Max Normalized)

Inspiration 1. How does the health of power assets change over time? 2. What is the optimal strategy to manage power assets?

Acknowledgements This dataset is provided through Utility Analytics Network, a group sharing data and promoting research and application of utility data analytics for North American utility companies.

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Cable town. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Cable town, the median income for all workers aged 15 years and older, regardless of work hours, was $35,982 for males and $28,920 for females.

These income figures indicate a substantial gender-based pay disparity, showcasing a gap of approximately 20% between the median incomes of males and females in Cable town. With women, regardless of work hours, earning 80 cents to each dollar earned by men, this income disparity reveals a concerning trend toward wage inequality that demands attention in thetown of Cable town.

- Full-time workers, aged 15 years and older: In Cable town, among full-time, year-round workers aged 15 years and older, males earned a median income of $58,438, while females earned $50,268, resulting in a 14% gender pay gap among full-time workers. This illustrates that women earn 86 cents for each dollar earned by men in full-time positions. While this gap shows a trend where women are inching closer to wage parity with men, it also exhibits a noticeable income difference for women working full-time in the town of Cable town.

Interestingly, when analyzing income across all roles, including non-full-time employment, the gender pay gap percentage was higher for women compared to men. It appears that full-time employment presents a more favorable income scenario for women compared to other employment patterns in Cable town.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

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 Cable town median household income by race. You can refer the same here

Abstract from http://www.emodnet-humanactivities.eu/search-results.php?dataname=Telecommunication+Cables+%28schematic+routes%29 The dataset on submarine telecom cables was created by Cogea in 2014 for the European Marine Observation and Data Network. The underlying data is property of Telegeography and is available online at https://github.com/telegeography/www.submarinecablemap.com. Compared with the previous version, this version of includes the gigabit per second values that come from the Cable System Database of the Packet Clearing House organization and are available online at https://prefix.pch.net/applications/cablesystem/. The database contains lines and points representing cables and related landing points. Cables are represented as stylised paths, as actual cable routes locations are not available in most cases. The dataset includes any cable that crosses the EU waters (Marine regions). Marine regions and subregions boudaries are defined in Article 4 of the Marine Strategy Framework Directive (MSFD) and available online at https://www.eea.europa.eu/data-and-maps/data/msfd-regions-and-subregions. Citation Title EMODnet Human Activities: Telecom cables (schematic routes) Publication date  2014-08-24 Revision date  2016-07-07 Creation date  2014-07-31

Context

A fictional telco company that provided home phone and Internet services to 7043 customers in California in Q3.

Data Description 7043 observations with 33 variables

CustomerID: A unique ID that identifies each customer.

Count: A value used in reporting/dashboarding to sum up the number of customers in a filtered set.

Country: The country of the customer’s primary residence.

State: The state of the customer’s primary residence.

City: The city of the customer’s primary residence.

Zip Code: The zip code of the customer’s primary residence.

Lat Long: The combined latitude and longitude of the customer’s primary residence.

Latitude: The latitude of the customer’s primary residence.

Longitude: The longitude of the customer’s primary residence.

Gender: The customer’s gender: Male, Female

Senior Citizen: Indicates if the customer is 65 or older: Yes, No

Partner: Indicate if the customer has a partner: Yes, No

Dependents: Indicates if the customer lives with any dependents: Yes, No. Dependents could be children, parents, grandparents, etc.

Tenure Months: Indicates the total amount of months that the customer has been with the company by the end of the quarter specified above.

Phone Service: Indicates if the customer subscribes to home phone service with the company: Yes, No

Multiple Lines: Indicates if the customer subscribes to multiple telephone lines with the company: Yes, No

Internet Service: Indicates if the customer subscribes to Internet service with the company: No, DSL, Fiber Optic, Cable.

Online Security: Indicates if the customer subscribes to an additional online security service provided by the company: Yes, No

Online Backup: Indicates if the customer subscribes to an additional online backup service provided by the company: Yes, No

Device Protection: Indicates if the customer subscribes to an additional device protection plan for their Internet equipment provided by the company: Yes, No

Tech Support: Indicates if the customer subscribes to an additional technical support plan from the company with reduced wait times: Yes, No

Streaming TV: Indicates if the customer uses their Internet service to stream television programing from a third party provider: Yes, No. The company does not charge an additional fee for this service.

Streaming Movies: Indicates if the customer uses their Internet service to stream movies from a third party provider: Yes, No. The company does not charge an additional fee for this service.

Contract: Indicates the customer’s current contract type: Month-to-Month, One Year, Two Year.

Paperless Billing: Indicates if the customer has chosen paperless billing: Yes, No

Payment Method: Indicates how the customer pays their bill: Bank Withdrawal, Credit Card, Mailed Check

Monthly Charge: Indicates the customer’s current total monthly charge for all their services from the company.

Total Charges: Indicates the customer’s total charges, calculated to the end of the quarter specified above.

Churn Label: Yes = the customer left the company this quarter. No = the customer remained with the company. Directly related to Churn Value.

Churn Value: 1 = the customer left the company this quarter. 0 = the customer remained with the company. Directly related to Churn Label.

Churn Score: A value from 0-100 that is calculated using the predictive tool IBM SPSS Modeler. The model incorporates multiple factors known to cause churn. The higher the score, the more likely the customer will churn.

CLTV: Customer Lifetime Value. A predicted CLTV is calculated using corporate formulas and existing data. The higher the value, the more valuable the customer. High value customers should be monitored for churn.

Churn Reason: A customer’s specific reason for leaving the company. Directly related to Churn Category.

Source This dataset is detailed in: https://community.ibm.com/community/user/businessanalytics/blogs/steven-macko/2019/07/11/telco-customer-churn-1113

Downloaded from: https://community.ibm.com/accelerators/?context=analytics&query=telco%20churn&type=Data&product=Cognos%20Analytics

There are several related datasets as documented in: https://community.ibm.com/community/user/businessanalytics/blogs/steven-macko/2018/09/12/base-samples-for-ibm-cognos-analytics

Abstract:

Soil spectroscopy has emerged as a solution to the limitations associated with traditional soil surveying and analysis methods, addressing the challenges of time and financial resources. Analyzing the soil's spectral reflectance enables to observe the soil composition and simultaneously evaluate several attributes because the matter, when exposed to electromagnetic energy, leaves a "spectral signature" that makes such evaluations possible. The Soil Spectral Library (SSL) consolidates soil spectral patterns from a specific location, facilitating accurate modeling and reducing time, cost, chemical products, and waste in surveying and mapping processes. Therefore, an open access SSL benefits society by providing a fine collection of free data for multiple applications for both research and commercial use.

BSSL Description and Usefulness

The Brazilian Soil Spectral Library (BSSL), available at https://bibliotecaespectral.wixsite.com/english, is a comprehensive repository of soil spectral data. Coordinated by JAM Demattê and managed by the GeoCiS research group, the BSSL was initiated in 1995 and published by Demattê and collaborators in 2019. This initiative stands out due to its coverage of diverse soil types, given Brazil's significance in the agricultural and environmental domains and its status as the fifth largest territory in the world (IBGE, 2023). In addition, a Middle Infrared (MIR) dataset has been published (Mendes et al., 2022), part of which is included in this repository. The database covers 16,084 sites and includes harmonized physicochemical and spectral (Vis-NIR-SWIR and MIR range) soil data from various sources at 0-20 cm depth. All soil samples have Vis-NIR-SWIR data, but not all have MIR data.

The BSSL provides open and free access to curated data for the scientific community and interested individuals. Unrestricted access to the BSSL supports researchers in validating their results by comparing measured data with predicted values. This initiative also facilitates the development of new models and the improvement of existing ones. Moreover, users can employ the library to test new models and extract information about previously unknown soil properties. With its extensive coverage of tropical soil classes, the BSSL is considered one of the most significant soil spectral libraries worldwide, with 42 institutions and 61 researchers participating. However, 47 collaborators from 29 institutions have authorized the data opening. Other researchers can also provide their data upon request through the coordinator of this initiative.

The data from the BSSL project can also help wet labs to improve their analytical capabilities, contributing to developing hybrid wet soil laboratory techniques and digital soil maps while informing decision-makers in formulating conservation and land use policies. The soil's capacity for different land uses promotes soil health and sustainability.

Coverage

The BSSL data covers all regions of Brazil, including 26 states and the Federal District. It is in a .xlsx format and has a total size of 305 Mb. The table is structured in sheets with rows for observations, and columns, representing various soil attributes in the surface layer, from 0 to 20 cm depth. The database includes environmental and physicochemical properties (20 columns and 16,084 rows), Vis-NIR-SWIR spectral bands (2151 columns and 16,084 rows), and MIR channels (681 columns and 1783 rows). An ID unique column can merge the sheet for each attribute or spectral range.

Accessing original data source

Using these data requires their reference in any situation under copyright infringement penalty. Three mechanisms are available for users to reach the original and complete data contributors:

a) Refer to sheet two for name and code-based searches;

b) Visit the website https://bibliotecaespectral.wixsite.com/english/lista-de-cedentes or locate the contributors' list by Brazilian state;

c) Visit the website of the Brazilian Soil Spectral Service – Braspecs http://www.besbbr.com.br/, an online platform for soil analysis that uses part of the current SSL (Demattê et al., 2022) - It was developed and managed by GeoCiS. There, owners from all over the country can be found.

Proceeding to data analysis

We registered and organized the samples at the ESALQ/USP Soil Laboratory. Some samples arrived without preliminary data analyses, so we analyzed them for soil organic matter (SOM), granulometry, cation exchange capacity (CEC), pH in water, and the presence of Ca, Mg, and Na, following the recommendations of Donagemma et al. (2011).

The GeoCiS research group performed spectral analyses following the procedures described by Bellinaso et al. (2010). Demattê et al. (2019) provide detailed methods for sampling, preparation, and soil analyses, including reflectance spectroscopy. Latitude and longitude data can be requested directly from the data owner. In summary, the following steps are involved in data acquisition.

a) We subjected the soil samples to a preliminary treatment, which involved drying them in an oven at 45°C for 48 hours, grinding them, and sieving them through a 2mm mesh;

b) We placed the samples in Petri dishes with a diameter of 9 cm and a height of 1.5 cm;

c) We homogenized and flattened the surface of the samples to reduce the shading caused by larger particles or foreign bodies, making them ready for spectral readings;

d) The spectral analyses took place in a darkened room to avoid interference from natural light. We used a computer to record the electromagnetic pulses through an optical fiber connected to the sensor, capturing the spectral response of the soil sample;

e) We obtained reflectance data in the Visible-Near Infrared-Shortwave Infrared (Vis-NIR-SWIR) range using a FieldSpec 3 spectroradiometer (Analytical Spectral Devices, ASD, Boulder, CO), which operates in the spectral range from 350 to 2500 nm;

f) The sensor had a spectral resolution of 3 nm from 350-700 nm and 10 nm from 700-2500 nm, automatically interpolated to 1 nm spectral resolution in the output data, resulting in 2151 channels (or bands); and

g) We positioned the lamps at 90° from each other and 35 cm away from the sample, with a zenith angle of 30°.

The sensor captured the light reflected through the fiber optic cable, which was positioned 8 cm from the sample's surface.

We used two 50W halogen lamps as the power source for the artificial light. It's important to note that we took three readings for each sample at different positions by rotating the Petri dish by 90°.

Each reading represents the average of 100 scans taken by the sensor. From these three readings, we calculated the final spectrum of the samples. Notably, the laboratory's equipment and procedures for soil sample spectral analyses followed the ASD's recommendations, particularly about sensor calibration using a white spectralon plate as a 100% reflectance standard.

For the analysis in the Middle Infrared (MIR) spectral region, we followed the procedures outlined by Mendes et al. (2022). We milled the soil fraction smaller than 2 mm, sieved it to 0.149 mm, and scanned it using a Fourier Transform Infrared (FT-IR) alpha spectroradiometer (Bruker Optics Corporation, Billerica, MA 01821, USA) equipped with a DRIFT accessory.

The spectroradiometer measured the diffuse reflectance using Fourier transformation in the spectral range from 4000 cm^-1 to 600 cm^-1, with a resolution of 2 cm^-1. We conducted these measurements in the Geotechnology Laboratory of the Department of Soil Science at Esalq-USP. We took the average of 32 successive readings to obtain a soil spectrum. Sensor calibration took place before each spectral acquisition of the sample set by standardizing it against the maximum reflectance of a gold plate.

Dataset characterization

The database, named BSSL_DB_Key_Soils, has five sheets containing the key soil attributes, Vis-NIR-SWIR and MIR datasets, descriptions of the contributors and the proximal sensing methods used for spectral soil analysis. The sheets can be linked by "ID_Unique" columns, which bring the corresponding rows according to the data type. Some cells are empty because collaborators have already provided data in this way. However, we have decided to keep them in the database because they have other soil key attributes. Every Column in the data sheets is described as follows:

Sheet 1. BSSL_Soil_Attributes_Dataset

Column 1. ID_unique: Sequential code assigned to every record;

Column 2. Owner code: Acronym assigned to each contributor who allowed access to their proprietary data;

Column 3. Vis_NIR_SWIR_availability: availability of spectral data in visible, near-infrared, and shortwave infrared ranges;

Column 4. MIR_availability: availability of spectral data in the middle infrared range;

Column 5. Sampling: type of soil sampling;

Column 6. Depth_cm: soil surface layer depth in centimeters;

Column 7. Region: Brazilian geographical region of samples' source;

Column 8. Municipality: Brazilian municipality of samples' source;

Column 9. State: Brazilian Federation Unit of samples'

Comprehensive demographic dataset for Cable, OH, US including population statistics, household income, housing units, education levels, employment data, and transportation with year-over-year changes.

BackgroundPrevious studies have found a correlation between varicose veins (VVs) and cognitive decline, and individuals with VVs have a higher prevalence of Alzheimer’s disease (AD). However, the associations between VVs and the core pathologies of AD have not yet been investigated. The research was designed to analyze the relationships between VVs and cerebrospinal fluid (CSF) biomarkers of AD pathologies.MethodsWe included 1,298 participants from the Chinese Alzheimer’s Biomarker and LifestylE (CABLE) database without dementia. Multiple linear regression (MLR) model was applied to assess the relationships between the VVs and CSF AD biomarkers. Then, we conducted subgroup analyses according to age, gender, education levels and apolipoprotein E genotype ε4 (APOE-ε4) carrier status. Additionally, mediation effects were assessed using causal mediation analyses with 10,000 bootstrapped iterations.ResultsIn total subjects, VVs had negative correlations with CSF Aβ42 (β = −0.157, p = 0.038) and CSF Aβ42/Aβ40 ratio (β = −0.272, p < 0.001), as well as positive correlations with CSF Aβ40 (β = 0.170, p = 0.024), CSF p-tau (β = 0.192, p = 0.008), CSF t-tau/Aβ42 ratio (β = 0.190, p = 0.011), and CSF p-tau/Aβ42 ratio (β = 0.248, p = 0.001), after adjusting for age, sex, education levels and APOE-ε4 carrier status. Subgroup analyses demonstrated that the relations between VVs and CSF AD biomarkers were more significant in female, mid-life adults (40–65 years), less-educated individuals and APOE-ε4 non-carriers. Moreover, CSF Aβ42/Aβ40 ratio might be a partial mediator of the association between VVs and p-tau pathology.ConclusionOur study found correlations between VVs and CSF AD biomarkers, suggesting that VVs may be a potential risk factor for the development of AD.

SA4 based data for Dwelling Internet Connection by Indigenous Status of Household, in Aboriginal and Torres Strait Islander People Profile (ATSIP), 2016 Census. Count of occupied private dwellings. Excludes 'Visitors only' and 'Other non-classifiable' households. Records whether any member of the household accesses the internet from the dwelling. This includes accessing the internet through a desktop/laptop computer, mobile or smart phone, tablet, music or video player, gaming console, smart TV or any other devices. It also includes accessing through any type of connection for example ADSL, fibre, cable, wireless, satellite and mobile broadband (3G/4G). A household with Aboriginal and/or Torres Strait Islander person(s) is any household that had at least one person of any age as a resident at the time of the Census who identified as being of Aboriginal and/or Torres Strait Islander origin. The data is by SA4 2016 boundaries. Periodicity: 5-Yearly. Note: There are small random adjustments made to all cell values to protect the confidentiality of data. These adjustments may cause the sum of rows or columns to differ by small amounts from table totals. For more information visit the data source: http://www.abs.gov.au/census.