The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly. While it was estimated at ***** zettabytes in 2025, the forecast for 2029 stands at ***** zettabytes. Thus, global data generation will triple between 2025 and 2029. Data creation has been expanding continuously over the past decade. In 2020, the growth was higher than previously expected, caused by the increased demand due to the coronavirus (COVID-19) pandemic, as more people worked and learned from home and used home entertainment options more often.

While official science has given its answer to the question on the origin of the Coronavirus (animal to human transmission), alternative theories on human creation of the virus – purposely or inadvertently – have flourished. Those alternative theories can be easily located among the family of conspiracy theories, as they always assume some secretive activity of some groups acting on their self-interest and against the good of the many. The article assesses the prevalence of these beliefs during the COVID-19 pandemic in Italy, studies its development during the pandemic, and investigates its potential determinants. In particular, it analyses the relationship between beliefs in alternative theories on the origin of the virus and political orientation, by arguing that the association cannot be attributed to (politically) motivated reasoning, as the issue has not been highly politicized in the Italian context. Alternatively, the article suggests that the main factor driving beliefs in alternative accounts on the origins of the virus is institutional trust. Political orientation moderates its effects, depending on specific conditions (e.g. cue taking, position of the supported party either in government or opposition), and eventually reinforcing scepticism towards epistemic authorities for those with low trust in institutions. Data come from the ResPOnsE COVID-19 survey, carried out with daily samples from April to July 2020 (N > 15.000) to monitor the development of the Italian public opinion during the Coronavirus pandemic.

Data from various sources are updated in the Statistical Information System of the City of Cologne. The annual statistical yearbook publishes these in tabular, graphic and cartographic form at the level of the city districts and districts. Furthermore, definitions and calculation bases are explained. Small-scale statistics at the level of the 86 districts can be obtained from the Cologne district information become. All levels of the local area structure are presented in this publication explained.

This statistical data catalogue supplements the range of small-scale data. Selected structural data can be called up here in compact tabular form at the level of the 570 statistical districts or the 86 districts. The two overviews provide information about which data is available and from which source it originates. The data itself is provided annually.

Notes:

• Data sources are indicated in the summary tables. When using the data, the data license Germany - attribution - version 2.0 must be observed.
• Some values ​​cannot be given to protect statistical confidentiality. For the data sets of the Federal Employment Agency, these are values ​​from 1 to < 10, for all further data records values ​​from 1 to < 5. This is marked in the data by a * .
• The differentiation of population figures by gender is currently made according to female and male residents. The case numbers of those who define themselves as non-binary/diverse are so low at a small-scale level that they cannot be reported for reasons of statistical confidentiality.
• The determination of residents with a migration background is carried out by combination various characteristics from the resident registration procedure. The data are to be interpreted as estimates. The statistical yearbook of the city of Cologne provides further details.
• The information on households comes from the household generation process. This is a statistical procedure in which residents within an address are assigned to a household as far as possible by querying certain criteria. If the procedure does not identify any connections, the allocation to single-person households takes place. The statistical yearbook of the city of Cologne provides further details.
• The data set pupils* at general schools (spatial location by place of residence) is available from 2013.
• The number of the statistical quarter or district is a spatial location and can be linked to the geodata (see related resource below).

Facebook received 73,390 user data requests from federal agencies and courts in the United States during the second half of 2023. The social network produced some user data in 88.84 percent of requests from U.S. federal authorities. The United States accounts for the largest share of Facebook user data requests worldwide.

Introduction:

In this case study the skills that I acquired from Google Data Analytics Professional Certificate Course is demonstrated. These skills will be used to complete the imagined task which was given by Netflix. The analysis process of this task will be consisted of following steps. Ask, Prepare, Process, Analyze, Share and Act.

Scenario:

The Netflix Chief Content Officer, Bela Bajaria, believes that companies success depends on to provide the customers what they want. Bajaria stated that the goal of this task is to find most wanted contents of the movies which will be added to the portfolio. Most of the movie contracts are signed before they come to the theaters, and it is hard to know if the customers really want to watch that movie and if the movie will be successful. There for my team wants to understand what type of content a movies success depends on. From these insights my team will design an investment strategy to choose the most popular movies that are expected to be in theaters in the near future. But first, Netflix executives must approve our recommendations. To be able to do that we must provide satisfying data insights along with professional data visualizations.

About the Company:

At Netflix, we want to entertain the world. Whatever your taste, and no matter where you live, we give you access to best-in-class TV series, documentaries, feature films and games. Our members control what they want to watch, when they want it, in one simple subscription. We’re streaming in more than 30 languages and 190 countries, because great stories can come from anywhere and be loved everywhere. We are the world’s biggest fans of entertainment, and we’re always looking to help you find your next favorite story.

As a company Netflix knows that it is important to acquire or produce movies that people want to watch.

There for Bajaria has set a clear goal: Define an investment strategy that will allow Netflix to provide customers the movies what they want to watch which will maximize the Sales.

Ask:

Business Task: To find out what kind of movie customers wants to watch and if the content type really has a correlation with the movie success. Stakeholders:

Bela Bajaria: She joined Netflix in 2016 to oversee unscripted and scripted series. Bajaria also responsible from the content selection and strategy for different regions.

Netflix content analytics team: A team of data analysts who are responsible for collecting, analyzing, and reporting data that helps guide Netflix content strategy.

Netflix executive team: The notoriously detail-oriented executive team will decide whether to approve the recommended content program.

Prepare:

I start my preparation procedure by downloading every piece of data I'll need for the study. Top 1000 Highest-Grossing Movies of All Time.csv will be used. Additionally, 15 Lowest-Grossing Movies of All Time.csv was found during the data research and this dataset will be analyst as well. The data has been made available by IMDB and shared this two following URL addresses: https://www.imdb.com/list/ls098063263/ and https://www.imdb.com/list/ls069238222/ .

Process:

Data Cleaning:

SQL: To begin the data cleaning process, I opened both csv file in SQL and conducted following operations:

• Checked for and removed any duplicates. • Checked if there any null values. • Removed the columns that are not necessary. • Trim the Description column to have only gross profit in it. (This cleaning procedure only used for 1000 Highest-Grossing Movies of All Time.csv dataset.)

• Renamed the Description column as Gross_Profit. (This cleaning procedure only used for 1000 Highest-Grossing Movies of All Time.csv dataset.)

Follwing SQL codes were used during the data cleaning:

SQL CODE used for Highest Grossing Movies DATASET

SELECT Position, SUBSTR(Description,34,12) as Gross_Profit, Title, IMDb_Rating, Runtime_mins_, Year, Genres, Num_Votes, Release_Date FROM even-electron-400301.Highest_Gross_Movies.1

SQL CODE used for Lowest Grossing Movies DATASET

SELECT Position, Title, IMDb_Rating, Runtime_mins_, Year, Genres, Num_Votes, Release_Date FROM even-electron-400301.Lowest_Grossing_Movies.2 Order By Position

Analyze:

As a starter, I want to reemphasize the business task once again. Is content has a big impact on a movie’s success?

To answer this question, there were a few information that I projected that I could pull of and use it during my analysis.

• Average gross profit • Number of Genres • Total Gross Profit of the most popular genres • The distribution of the Gross income on Genres

I used Microsoft Excel for the bullet points above. The operations to achieve the values above are as follows:

• Average function for Average Gross profit in 1000 Highest-Grossing Movies of All Time. • Created a pivot table to work on Genres and Gross_Pr...

Total number of housing completions (seasonally adjusted)

How the figure is calculated:

Total housing completions are reported by local authority and private building control organisations after the end of each quarter. A completion is counted when a dwelling is ready for habitation. The figures are seasonally adjusted to allow comparisons with previous quarters.

Why is this indicator in the business plan?

Increasing the supply of housing is a key part of DCLG policy. The house building figures are the most frequent and timely indicator of housing delivery.

How often is it updated?

Quarterly

Where does the data come from?

P2 quarterly house building returns by local authority building control departments; monthly information from the National House Building Council (NHBC) on the volume of building control inspections; and a quarterly survey of private building control companies. Published figures are at https://www.gov.uk/government/organisations/department-for-communities-and-local-government/series/house-building-statistics.

What area does the headline figure cover?

England

Are further breakdowns of the data available?

Yes, can be split by local authority area and by tenure

What does a change in this indicator show?

An increase in this indicator is good and shows more new houses are being completed.

Time Lag

Figures are published within two months of the end of the reporting period.

Next available update

May 2015.

Type of Data

National Statistics.

Robustness and data limitations

The P2 figures from local authorities and figures from private building control companies include imputation for a small number of missing returns.

Seasonal factors for the house building time series are re-calculated annually back to 2000. This is usually done in the second quarter of the calendar year. Therefore the seasonally adjusted house building figures throughout the whole period change slightly at that time but are not marked as 'revised'.

Links to Further Information

https://www.gov.uk/government/organisations/department-for-communities-and-local-government/series/house-building-statistics

Contact Details

CorporatePerformance@communities.gsi.gov.uk

Total number of housing starts (seasonally adjusted)

How the figure is calculated:

Total housing starts are reported by local authority and private building control organisations after the end of each quarter. A start is counted from the point at which foundation work begins. The figures are seasonally adjusted to allow comparisons with previous quarters.

Why is this indicator in the business plan?

Increasing the supply of housing is a key part of DCLG policy. The house building figures are the most frequent and timely indicator of housing delivery.

How often is it updated?

Quarterly

Where does the data come from?

P2 quarterly house building returns by local authority building control departments; monthly information from the National House Building Council (NHBC) on the volume of building control inspections; and a quarterly survey of private building control companies. Published figures are at https://www.gov.uk/government/organisations/department-for-communities-and-local-government/series/house-building-statistics.

What area does the headline figure cover?

England

Are further breakdowns of the data available?

Yes, can be split by local authority area and by tenure

What does a change in this indicator show?

An increase in this indicator is good and shows more new houses are being started.

Time Lag

Figures are published within two months of the end of the reporting period.

Next available update

May 2015.

Type of Data

National Statistics.

Robustness and data limitations

The P2 figures from local authorities and figures from private building control companies include imputation for a small number of missing returns.

Seasonal factors for the house building time series are re-calculated annually back to 2000. This is usually done in the second quarter of the calendar year. Therefore the seasonally adjusted house building figures throughout the whole period change slightly at that time but are not marked as 'revised'.

Links to Further Information

https://www.gov.uk/government/organisations/department-for-communities-and-local-government/series/house-building-statistics

Contact Details

CorporatePerformance@communities.gsi.gov.uk

This submission includes publicly available data extracted in its original form. Please reference the Related Publication listed here for source and citation information If you have questions about the underlying data stored here, please contact SAMHSA at SAMHSAInfo@samhsa.hhs.gov Details: The Mental Health Client-Level Data (MH-CLD) and the Mental Health Treatment Episode Data Set (MH-TEDS) systems provide information on demographic and socioeconomic characteristics, clinical attributes (including mental health diagnoses and substance use), and National Outcome Measures (NOMs).Where do the data come from?: Providers and facilities funded or operated by SMHAs report their data to their SMHA. SMHAs are responsible for reporting MH-CLD/MH-TEDS data to SAMHSA.Data limitations: The data limitations include the following:MH-CLD represents clients receiving publicly funded mental health and support services.The scope of providers and facilities reporting data varies across states.The mental health diagnoses in the dataset may not represent all diagnoses for individuals who are served. Some individuals have no valid mental health diagnosis reported. If the missing diagnosis is not randomly distributed across providers and/or facilities, estimated prevalence rates of mental health diseases may be biased.National outcome measures are reported by states based on state definitions as per block grant statute.Confidentiality protection: Several measures are taken to protect the confidentiality of all records.Disclosure analysis is used to identify records that have unique combinations of key (mostly) demographic variables that could link a record to an individual.The original location of these records in MH-CLD is changed. If this is not sufficient to satisfy confidentiality standards, further recoding is done on other key variables. The recoding of variables including location is kept minimal, leaving nearly all the data intact. The analysis of the public-use file should not be affected."Quote from https://www.samhsa.gov/data/data-we-collect/mh-cld-mental-health-client-level-data/about.

This map shows the access to mental health providers in every county and state in the United States according to the 2024 County Health Rankings & Roadmaps data for counties, states, and the nation. It translates the numbers to explain how many additional mental health providers are needed in each county and state. According to the data, in the United States overall there are 319 people per mental health provider in the U.S. The maps clearly illustrate that access to mental health providers varies widely across the country.The data comes from this County Health Rankings 2024 layer. An updated layer is usually published each year, which allows comparisons from year to year. This map contains layers for 2024 and also for 2022 as a comparison. County Health Rankings & Roadmaps (CHR&R), a program of the University of Wisconsin Population Health Institute with support provided by the Robert Wood Johnson Foundation, draws attention to why there are differences in health within and across communities by measuring the health of nearly all counties in the nation. This map's layers contain 2024 CHR&R data for nation, state, and county levels. The CHR&R Annual Data Release is compiled using county-level measures from a variety of national and state data sources. CHR&R provides a snapshot of the health of nearly every county in the nation. A wide range of factors influence how long and how well we live, including: opportunities for education, income, safe housing and the right to shape policies and practices that impact our lives and futures. Health Outcomes tell us how long people live on average within a community, and how people experience physical and mental health in a community. Health Factors represent the things we can improve to support longer and healthier lives. They are indicators of the future health of our communities. Some example measures are:Life ExpectancyAccess to Exercise OpportunitiesUninsuredFlu VaccinationsChildren in PovertySchool Funding AdequacySevere Housing Cost BurdenBroadband AccessTo see a full list of variables, definitions and descriptions, explore the Fields information by clicking the Data tab here in the Item Details of this layer. For full documentation, visit the Measures page on the CHR&R website. Notable changes in the 2024 CHR&R Annual Data Release:Measures of birth and death now provide more detailed race categories including a separate category for ‘Native Hawaiian or Other Pacific Islander’ and a ‘Two or more races’ category where possible. Find more information on the CHR&R website.Ranks are no longer calculated nor included in the dataset. CHR&R introduced a new graphic to the County Health Snapshots on their website that shows how a county fares relative to other counties in a state and nation. Data Processing:County Health Rankings data and metadata were prepared and formatted for Living Atlas use by the CHR&R team. 2021 U.S. boundaries are used in this dataset for a total of 3,143 counties. Analytic data files can be downloaded from the CHR&R website.

This presentation provides a comparative overview of three small area data resources: SAAD, SARTRE and SABAL. For each product, the following topics are discussed: What data exists? Where does it come from? What is its coverage (years, geography?), What is its content? What are the product's strengths & weaknesses?

Login Data Set for Risk-Based Authentication

Synthesized login feature data of >33M login attempts and >3.3M users on a large-scale online service in Norway. Original data collected between February 2020 and February 2021.

This data sets aims to foster research and development for Risk-Based Authentication (RBA) systems. The data was synthesized from the real-world login behavior of more than 3.3M users at a large-scale single sign-on (SSO) online service in Norway.

The users used this SSO to access sensitive data provided by the online service, e.g., a cloud storage and billing information. We used this data set to study how the Freeman et al. (2016) RBA model behaves on a large-scale online service in the real world (see Publication). The synthesized data set can reproduce these results made on the original data set (see Study Reproduction). Beyond that, you can use this data set to evaluate and improve RBA algorithms under real-world conditions.

WARNING: The feature values are plausible, but still totally artificial. Therefore, you should NOT use this data set in productive systems, e.g., intrusion detection systems.

Overview

The data set contains the following features related to each login attempt on the SSO:

Feature	Data Type	Description	Range or Example
IP Address	String	IP address belonging to the login attempt	0.0.0.0 - 255.255.255.255
Country	String	Country derived from the IP address	US
Region	String	Region derived from the IP address	New York
City	String	City derived from the IP address	Rochester
ASN	Integer	Autonomous system number derived from the IP address	0 - 600000
User Agent String	String	User agent string submitted by the client	Mozilla/5.0 (Windows NT 10.0; Win64; ...
OS Name and Version	String	Operating system name and version derived from the user agent string	Windows 10
Browser Name and Version	String	Browser name and version derived from the user agent string	Chrome 70.0.3538
Device Type	String	Device type derived from the user agent string	(mobile, desktop, tablet, bot, unknown)1
User ID	Integer	Idenfication number related to the affected user account	[Random pseudonym]
Login Timestamp	Integer	Timestamp related to the login attempt	[64 Bit timestamp]
Round-Trip Time (RTT) [ms]	Integer	Server-side measured latency between client and server	1 - 8600000
Login Successful	Boolean	True: Login was successful, False: Login failed	(true, false)
Is Attack IP	Boolean	IP address was found in known attacker data set	(true, false)
Is Account Takeover	Boolean	Login attempt was identified as account takeover by incident response team of the online service	(true, false)

Data Creation

As the data set targets RBA systems, especially the Freeman et al. (2016) model, the statistical feature probabilities between all users, globally and locally, are identical for the categorical data. All the other data was randomly generated while maintaining logical relations and timely order between the features.

The timestamps, however, are not identical and contain randomness. The feature values related to IP address and user agent string were randomly generated by publicly available data, so they were very likely not present in the real data set. The RTTs resemble real values but were randomly assigned among users per geolocation. Therefore, the RTT entries were probably in other positions in the original data set.

• The country was randomly assigned per unique feature value. Based on that, we randomly assigned an ASN related to the country, and generated the IP addresses for this ASN. The cities and regions were derived from the generated IP addresses for privacy reasons and do not reflect the real logical relations from the original data set.

• The device types are identical to the real data set. Based on that, we randomly assigned the OS, and based on the OS the browser information. From this information, we randomly generated the user agent string. Therefore, all the logical relations regarding the user agent are identical as in the real data set.

• The RTT was randomly drawn from the login success status and synthesized geolocation data. We did this to ensure that the RTTs are realistic ones.

Regarding the Data Values

Due to unresolvable conflicts during the data creation, we had to assign some unrealistic IP addresses and ASNs that are not present in the real world. Nevertheless, these do not have any effects on the risk scores generated by the Freeman et al. (2016) model.

You can recognize them by the following values:

• ASNs with values >= 500.000

• IP addresses in the range 10.0.0.0 - 10.255.255.255 (10.0.0.0/8 CIDR range)

Study Reproduction

Based on our evaluation, this data set can reproduce our study results regarding the RBA behavior of an RBA model using the IP address (IP address, country, and ASN) and user agent string (Full string, OS name and version, browser name and version, device type) as features.

The calculated RTT significances for countries and regions inside Norway are not identical using this data set, but have similar tendencies. The same is true for the Median RTTs per country. This is due to the fact that the available number of entries per country, region, and city changed with the data creation procedure. However, the RTTs still reflect the real-world distributions of different geolocations by city.

See RESULTS.md for more details.

Ethics

By using the SSO service, the users agreed in the data collection and evaluation for research purposes. For study reproduction and fostering RBA research, we agreed with the data owner to create a synthesized data set that does not allow re-identification of customers.

The synthesized data set does not contain any sensitive data values, as the IP addresses, browser identifiers, login timestamps, and RTTs were randomly generated and assigned.

Publication

You can find more details on our conducted study in the following journal article:

Pump Up Password Security! Evaluating and Enhancing Risk-Based Authentication on a Real-World Large-Scale Online Service (2022)
Stephan Wiefling, Paul René Jørgensen, Sigurd Thunem, and Luigi Lo Iacono.
ACM Transactions on Privacy and Security

Bibtex

@article{Wiefling_Pump_2022,
 author = {Wiefling, Stephan and Jørgensen, Paul René and Thunem, Sigurd and Lo Iacono, Luigi},
 title = {Pump {Up} {Password} {Security}! {Evaluating} and {Enhancing} {Risk}-{Based} {Authentication} on a {Real}-{World} {Large}-{Scale} {Online} {Service}},
 journal = {{ACM} {Transactions} on {Privacy} and {Security}},
 doi = {10.1145/3546069},
 publisher = {ACM},
 year  = {2022}
}

License

This data set and the contents of this repository are licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. See the LICENSE file for details. If the data set is used within a publication, the following journal article has to be cited as the source of the data set:

Stephan Wiefling, Paul René Jørgensen, Sigurd Thunem, and Luigi Lo Iacono: Pump Up Password Security! Evaluating and Enhancing Risk-Based Authentication on a Real-World Large-Scale Online Service. In: ACM Transactions on Privacy and Security (2022). doi: 10.1145/3546069

1. Few (invalid) user agents strings from the original data set could not be parsed, so their device type is empty. Perhaps this parse error is useful information for your studies, so we kept these 1526 entries.↩︎

OpenAQ has collected 231,965,688 air quality measurements from 8,469 locations in 65 countries. Data are aggregated from 105 government level and research-grade sources.

https://medium.com/@openaq/where-does-openaq-data-come-from-a5cf9f3a5c85

Disclaimers: - Some records contain encoding issues on specific characters; those issues are present in the raw API data and were not corrected. - Some dates are set in the future: those issues also come from the original data and were not corrected.

This data about nola.gov provides a window into how people are interacting with the the City of New Orleans online. The data comes from a unified Google Analytics account for New Orleans. We do not track individuals and we anonymize the IP addresses of all visitors.

APISCRAPY's AI & ML training data is meticulously curated and labelled to ensure the best quality. Our training data comes from a variety of areas, including healthcare and banking, as well as e-commerce and natural language processing.

Archived as of 11/14/2025: The datasets will no longer receive updates but the historical data will continue to be available for download. This dataset is the underlying data for the Public Safety portion of the Equity Data Portal displaying Indiana's total arrests by demographics. This data is from the Criminal History Records Information System (CHRIS), which comes from three main sources. Arrest data comes from the Live Scan system, which is used for finger printing and capturing other pertinent information at the time of the arrest. Criminal disposition data are maintained by prosecutors in the ProsLink system, and by courts in the Odyssey system. Arrest county is determined by the location of the booking agency. If the booking agency is missing, then the arresting agency is used. The % of IN Population will not equal 100% because we are excluding non-represented racial category "Two or More Races," which accounts for ~1.7% of Indiana's population. Because some arrests are not included in the individual race categories shown here, total counts and percentages from the individual race categories add up to less than the totals for “All” races. This dashboard uses 2010 Census data.

Jordan whatsapp number list helps businesses reach more people easily. You can use the numbers right away since they are ready and organized. Thus, you can quickly find the right ones. You sort the numbers by location, age, gender, and more. This helps you find the best audience for your business. You check the numbers often to make sure they are correct. You won’t waste time on bad data. These whatsapp data help you grow your business. You can contact people who have an interest in your services. On our site, List to Data, you can easily locate important phone numbers. Jordan whatsapp phone number data provides valuable information. Trusted sources collect the data, so you know it’s reliable. You can check where the data comes from, which builds trust. The data updates regularly, so you always get the newest information when you need it. With List to Data, you can effortlessly search for important phone numbers. Jordan whatsapp phone number data stays open 24/7, so you access the numbers whenever you want. If you need help, support is available at all times. This makes it easier for businesses to find the right data. Overall, this whatsapp data helps businesses expand and connect with more customers.

TechCorner Mobile Purchase & Engagement Data (2024-2025)

Context

TechCorner Mobile Sales & Customer Insights is a real-world dataset capturing 10 months of mobile phone sales transactions from a retail shop in Bangladesh. This dataset was designed to analyze customer location, buying behavior, and the impact of Facebook marketing efforts.

The primary goal was to identify whether customers are from the local area (Rangamati Sadar, Inside Rangamati) or completely outside Rangamati. Since TechCorner operates a Facebook page, the dataset also includes insights into whether Facebook marketing is effectively reaching potential buyers.

Additionally, the dataset helps in determining: ✔ How many customers are new vs. returning buyers ✔ If customers are followers of the shop’s Facebook page ✔ Whether a customer was recommended by an existing buyer

This dataset is valuable for:

Retail sales analysis to understand product demand fluctuations.

Marketing impact measurement (Facebook engagement vs. actual purchase behavior).

Customer segmentation (local vs. non-local buyers, social media influence, word-of-mouth impact).

Sales trend analysis based on preferred phone models and price ranges.

With a realistic, non-uniform distribution of daily sales and some intentional missing values, this dataset reflects actual retail business conditions rather than artificially smooth AI-generated data.

Marketing & Customer Queries

Does he/she Come from Facebook Page? → Whether the customer came from a Facebook page (Yes/No). Used to analyze Facebook marketing reach.

Does he/she Followed Our Page? → Whether the customer is already a follower of the shop’s Facebook page (Yes/No). Helps measure brand loyalty and organic engagement.

Did he/she buy any mobile before? → Whether the customer is a repeat buyer (Yes/No). Determines the percentage of returning customers.

Did he/she hear of our shop before? → Whether the customer knew about the shop before purchasing (Yes/No). Identifies the impact of referrals or previous marketing efforts.

Was this customer recommended by an old customer? → Whether an existing customer referred them to the shop (Yes/No). Helps evaluate the effectiveness of word-of-mouth marketing.

Acknowledgements

This dataset is derived from real-world mobile sales transactions recorded at TechCorner, a retail shop in Bangladesh. It accurately reflects customer purchasing behavior, pricing trends, and the effectiveness of Facebook marketing in driving sales. Special appreciation to TechCorner for providing comprehensive insights into daily sales patterns, customer demographics, and market dynamics.

This dataset can be used for:

📊 Predictive modeling of sales trends based on customer demographics and marketing channels. 📈 Marketing effectiveness analysis (impact of Facebook promotions vs. organic sales). 🔍 Clustering customers based on purchasing habits (new vs. returning buyers, Facebook users vs. walk-ins). 📌 Understanding demand for different smartphone brands in a local retail market. 🚀 Analyzing how word-of-mouth recommendations influence new customer acquisition.

💡 Can you build a model to predict if a customer is likely to return? 💬 How effective is Facebook in driving actual sales compared to walk-ins? 🔍 Can we cluster customers based on behavior and brand preferences?

Mineral predominance data were a derivative product from the Corescan© reflectance data. Corescan Hyperspectral Core Imager Mark III (HCI-III) system data were acquired for hand samples, and subsequent billets made from the hand samples, collected during the U.S. Geological Survey (USGS) 2014, 2015, and 2016 field seasons in the Nabesna area of the eastern Alaska Range. This area contains exposed porphyry deposits and hand samples were collected throughout the region in support of the HyMap imaging spectrometer survey (https://doi.org/10.5066/F7DN435W) (Kokaly and others, 2017a). The HCI-III system consists of three different components. The first is an imaging spectrometer which collects reflectance data with a spatial resolution of approximately 500 nanometers (nm) for 514 spectral channels covering the 450-2,500 nm wavelength range of the electromagnetic spectrum (Martini and others, 2017). The second is a spectrally calibrated RGB camera that collects high resolution imagery of the samples with a 50 micrometer (μm) pixel size. The third component is a three-dimensional (3D) laser profiler that measures sample texture, surface features and shape with a vertical resolution of 20 μm (Martini and others, 2017). A total of 63 hand samples and four billets were analyzed using the HCI-III system in three scans. The imaging spectrometer raw data was collected with an average bandpass of approximately 6 nm across the Short Wave Infrared (SWIR) but smoothing functions applied by Corescan during the conversion of raw data to reflectance result in a relative bandpass of approximately 13 nm in the data delivered to the USGS. Wavelength evaluations of the imaging spectrometer data revealed that the supplied wavelength values should be shifted and, thus, adjustments were made to the wavelength positions (Kokaly and others, 2017c). The wavelength and bandpass evaluation results are provided in the 'Calibration' section of this data release and were used to adjust the Corescan reflectance data. The calibrated Corescan data were combined into a reflectance data cube mosaic and are provided in the 'HyperspectralCalibrated' section. Calibrated reflectance data from Corescan were processed using the Material Identification and Characterization Algorithm (MICA), a module of the USGS PRISM (Processing Routines in IDL for Spectroscopic Measurements) software (Kokaly, 2011). MICA identifies the spectrally predominant mineral(s) in each pixel of imaging spectrometer data by comparing continuum-removed spectral features in the pixel’s reflectance spectrum to continuum-removed absorption features in reference spectra of minerals and other materials. For each pixel, the reference spectrum with the highest fit value identifies the predominant mineral class.

This data release contains numerous comma-separated text files with data summarizing observations in the within and adjacent to the Woodbury Fire, which burned from 8 June to 15 July 2019. In particular, this monitoring data was focused on debris flows in burned and unburned areas. Rainfall data (Wdby_Rainfall.zip) are contained in csv files called Wdby_Rainfall for 3 rain gages named: B2, B6, and Reavis. This is time-series data where the total rainfall is recorded at each timestamp. The location of each rain gage is listed as a latitude/longitude in each file. Data from absolute (i.e. not vented) pressure transducers (Wdby_Pressure.zip), which can be used to constrain the time of passage of a flood or debris flow, are available in csv files called Wdby_Pressure for four drainages (B1, B6, Reavis 1, and Reavis 2). This is time-series data where the measured pressure in kilopascals is recorded at each timestamp. The location of each pressure transducer is listed as a latitude/longitude in each file. Infiltration data are located in the csv file called WoodburyInfiltration.csv. The location of the measurement is listed as a latitude/longitude. Three measurement values are reported at each location: Saturated Hydraulic Conductivity (Ks) [mm/hr], Sorptivity (S) [mm/h^(1/2)], and pressure head (hf) [m]. The date of each measurement and soil burn severity class are also reported at each location, as well as a table explaining the burn-severity numerical class conversion. Particle size analyses using laser diffraction (WoodburyLaserDiffractionSummary.zip) are located in the files called WoodburyLaserDiffractionSummary for the fine fraction (< 2 mm) of hillslope and debris flow Deposits. The diameter of each particle size class is listed in the first column. All subsequent columns begin with the sample name. The value in each row is the percentage of the grain sizes in the size class. Location data for each of these samples is listed in the accompanying data table titled: WoodburyParticleSizeSummary.csv. The particle size data are summarized in the csv files (WoodburyParticleSizeSummary.zip) called WoodburyParticleSizeSummary by debris flow deposits and hillslope samples. These files group the raw data into more useable information. The sample name (Lab ID) is used to identify the Laser Diffraction data. The data columns (Lat) and (Lon) show the latitude and longitude of the sample locations. The total fraction of all the grain sizes, determined by sieving, are listed in three classes (Fraction < 16 mm, Fraction < 4 mm, Fraction < 2 mm). The fine fractions (< 2 mm) are also summarized in the columns (%Sand, %Silt, %Clay), as determined by laser diffraction. The data are identfied as in the burn area using entries of Yes, whereas unburned areas are shown as No, indicating no burn. The median particle size (D50) is listed if the sample collected in the field was representative of the deposit. In some cases, large cobbles and boulders had to be removed from the sample because were much too large to be included in sample bags that were brought back to the lab for analysis. The last column label (Description) contains notes about each sample. Pebble count data (WoodburyPebbleCountsSummary.zip) are available in csv files called WoodburyPebbleCountsSummary for six drainages (U10 Fan, U10 Channel, U22 Channel, B1 Channel, B7 Fan, and U42 Fan). Here U represents unburned, and B represents burned. The data name indicates whether the data come from a deposit located in a channel or a fan. In each file the particle is numbered (Num) and the B-axis measurement of the particle is reported in centimeters. The location of each pebble count is listed as a latitude/longitude in each file. Channel width measurements for 23 channels are saved in unique shapefiles within the file called Channel_Width_Transects.zip. These width measurements were made using Digital Globe imagery from 19 October 2019. The study basins used for the entire study can be found in the shapefile: Woodbury_StudyBasins.shp. The attribute table along with many morphometric and fire related statistics for each basin is also available in the file Woodbury_StudyBasins_Table.csv. A description of each column name in the table is available in the file Woodbury_StudyBasins_Table_descriptions.csv. Debris flow volumes were available in eleven drainage basins. The volume data is contained in the file Wdby_FlowVolume.csv in a column named (Volume). The volume units are cubic meters. The other column is the Basin ID, which can be found in the shapefile: Woodbury_StudyBasins.shp.

Israel Agriculture Account: Income Derived data was reported at 12,051.101 ILS mn in 2016. This records an increase from the previous number of 11,344.656 ILS mn for 2015. Israel Agriculture Account: Income Derived data is updated yearly, averaging 10,117.819 ILS mn from Dec 2002 (Median) to 2016, with 15 observations. The data reached an all-time high of 12,643.800 ILS mn in 2009 and a record low of 5,798.600 ILS mn in 2003. Israel Agriculture Account: Income Derived data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Global Database’s Israel – Table IL.B011: Agriculture Statistics.