Sign Up for a free trial: https://rampedup.io/sign-up-%2F-log-in - 7 Days and 50 Credits to test our quality and accuracy.

These are the fields available within the RampedUp Global dataset.

CONTACT DATA: Personal Email Address - We manage over 115 million personal email addresses Professional Email - We manage over 200 million professional email addresses Home Address - We manage over 20 million home addresses Mobile Phones - 65 million direct lines to decision makers Social Profiles - Individual Facebook, Twitter, and LinkedIn Local Address - We manage 65M locations for local office mailers, event-based marketing or face-to-face sales calls.

JOB DATA: Job Title - Standardized titles for ease of use and selection Company Name - The Contact's current employer Job Function - The Company Department associated with the job role Title Level - The Level in the Company associated with the job role Job Start Date - Identify people new to their role as a potential buyer

EMPLOYER DATA: Websites - Company Website, Root Domain, or Full Domain Addresses - Standardized Address, City, Region, Postal Code, and Country Phone - E164 phone with country code Social Profiles - LinkedIn, CrunchBase, Facebook, and Twitter

FIRMOGRAPHIC DATA: Industry - 420 classifications for categorizing the company’s main field of business Sector - 20 classifications for categorizing company industries 4 Digit SIC Code - 239 classifications and their definitions 6 Digit NAICS - 452 classifications and their definitions Revenue - Estimated revenue and bands from 1M to over 1B Employee Size - Exact employee count and bands Email Open Scores - Aggregated data at the domain level showing relationships between email opens and corporate domains. IP Address -Company level IP Addresses associated to Domains from a DNS lookup

CONSUMER DATA: Education - Alma Mater, Degree, Graduation Date Skills - Accumulated Skills associated with work experience
Interests - Known interests of contact Connections - Number of social connections. Followers - Number of social followers

Download our data dictionary: https://rampedup.io/our-data

Success.ai’s Ecommerce Merchant Data and B2B Contact Data for Global E-commerce Professionals provides a comprehensive and highly accurate database from over 170 million verified profiles. Specifically tailored for the e-commerce sector, this dataset features work emails, direct phone numbers, and enriched professional profiles to connect businesses with the leaders and decision-makers shaping the global e-commerce landscape. Continuously updated with advanced AI validation, this resource is ideal for enhancing marketing campaigns, sales initiatives, recruitment efforts, and market research.

Key Features of Success.ai's Global E-commerce Professional Contact Data

1. Global Data Coverage Gain access to an extensive database spanning key e-commerce markets worldwide. With verified profiles from 170M+ professionals, Success.ai ensures you can connect with global influencers, decision-makers, and strategists across diverse regions and industries.

2. AI-Driven Accuracy Harness the power of AI validation for 99% accuracy rates across emails and phone numbers. Our continuously updated dataset ensures that you reach the right professionals with reliable and actionable contact data.

3. Tailored for E-commerce Professionals Our data includes profiles of experts in online retail, supply chain logistics, payment systems, digital marketing, and e-commerce technology, making it a perfect fit for targeting niche segments within the e-commerce industry.

4. Customizable Data Delivery Choose from API integrations, custom flat files, or direct database access to seamlessly integrate this dataset into your existing systems, empowering your team with flexibility and efficiency.

5. Compliance-Ready Data Success.ai ensures all data is collected and processed in alignment with GDPR, CCPA, and other international compliance standards, so you can leverage this resource with confidence and ethical assurance.

Why Choose Success.ai for Global E-commerce Contact Data?

• Best Price Guarantee We offer a highly competitive pricing model that ensures the best value for high-quality, actionable data.

• Strategic Applications Success.ai’s B2B Contact Data supports a variety of business functions:

E-commerce Marketing Campaigns: Use verified contact information to launch targeted campaigns that reach decision-makers in the e-commerce sector. Sales and Outreach: Enhance your sales strategy with direct access to key players in global e-commerce. Talent Acquisition: Identify and engage with e-commerce professionals for roles in marketing, logistics, technology, and operations. Market Insights: Leverage enriched demographic and firmographic data to conduct in-depth market research and refine your strategies. Business Networking: Build connections with professionals and companies driving innovation in the global e-commerce ecosystem.

• Technology-Enhanced Solutions Our data delivery is optimized for seamless integration into your systems, including:

Enrichment API: Real-time updates to maintain the accuracy and relevance of your contact database. Lead Generation API: Maximize outreach efforts with access to key contact information, enabling up to 860,000 API calls per day.

• Data Highlights 170M+ Verified Global Profiles 50M Direct Phone Numbers 700M Total Professional Profiles Worldwide 70M Verified Company Profiles

• Use Cases

1. Enhanced Marketing: Empower your e-commerce marketing strategies with precise email and phone contact details.
2. Sales Growth: Equip your sales team to connect with top-level executives and decision-makers.
3. Recruitment Excellence: Source global e-commerce talent efficiently with verified professional profiles.
4. Customer Understanding: Deepen insights into customer demographics for improved personalization.
5. Partnership Building: Identify potential collaborators and strengthen relationships with influential industry players.

Success.ai is the ultimate choice for global e-commerce data solutions, delivering unmatched volume, accuracy, and flexibility:

• AI-Validated Data: Ensures a 99% accuracy rate to drive success in your campaigns. Extensive Reach: Access professionals and companies across key regions in the e-commerce sector.
• Seamless Integration: Choose the data delivery method that works best for your business needs.
• Compliance Assurance: Leverage ethically sourced data in adherence to global privacy regulations.

Transform your e-commerce strategies today with Success.ai. Gain access to reliable, verified contact data for global e-commerce professionals and unlock unparalleled opportunities for growth and innovation.

No one beats us on price. Period.

McGRAW’s US B2B Data: Accurate, Reliable, and Market-Ready

Our B2B database delivers over 80 million verified contacts with 95%+ accuracy. Supported by in-house call centers, social media validation, and market research teams, we ensure that every record is fresh, reliable, and optimized for B2B outreach, lead generation, and advanced market insights.

Our B2B database is one of the most accurate and extensive datasets available, covering over 91 million business executives with a 95%+ accuracy guarantee. Designed for businesses that require the highest quality data, this database provides detailed, validated, and continuously updated information on decision-makers and industry influencers worldwide.

The B2B Database is meticulously curated to meet the needs of businesses seeking precise and actionable data. Our datasets are not only extensive but also rigorously validated and updated to ensure the highest level of accuracy and reliability.

Key Data Attributes:

• Personal Identifiers: First name, last name
• Professional Details: Title, direct dial numbers
• Business Information: Company name, address, phone number, fax number, website
• Company Metrics: Employee size, sales volume
• Technology Insights: Information on hardware and software usage across organizations
• Social Media Connections: LinkedIn, Facebook, and direct dial contacts
• Corporate Insights: Detailed company profiles

Unlike many providers that rely solely on third-party vendor files, McGRAW takes a hands-on approach to data validation. Our dedicated nearshore and offshore call centers engage directly with data before each delivery to ensure every record meets our high standards of accuracy and relevance.

In addition, our teams of social media validators, market researchers, and digital marketing specialists continuously refine and update records to maintain data freshness. Each dataset undergoes multiple verification checks using internal validation processes and third-party tools such as Fresh Address, BriteVerify, and Impressionwise to guarantee the highest data quality.

Additional Data Solutions and Services

• Data Enhancement: Email and LinkedIn appends, contact discovery across global roles and functions

• Business Verification: Real-time validation through call centers, social media, and market research

• Technology Insights: Detailed IT infrastructure reports, spending trends, and executive insights

• Healthcare Database: Access to over 80 million healthcare professionals and industry leaders

• Global Reach: US and international GDPR-compliant datasets, complete with email, postal, and phone contacts

• Email Broadcast Services: Full-service campaign execution, from testing to live deployment, with tracking of key engagement metrics such as opens and clicks

Many B2B data providers rely on vendor-contributed files without conducting the rigorous validation necessary to ensure accuracy. This often results in outdated and unreliable data that fails to meet the demands of a fast-moving business environment.

McGRAW takes a different approach. By owning and operating dedicated call centers, we directly verify and validate our data before delivery, ensuring that every record is up-to-date and ready to drive business success.

Through continuous validation, social media verification, and real-time updates, McGRAW provides a high-quality, dependable database for businesses that prioritize data integrity and performance. Our Global Business Executives database is the ideal solution for companies that need accurate, relevant, and market-ready data to fuel their strategies.

911 Public Safety Answering Point (PSAP) service area boundaries in New Mexico According to the National Emergency Number Association (NENA), a Public Safety Answering Point (PSAP) is a facility equipped and staffed to receive 9-1-1 calls. The service area is the geographic area within which a 911 call placed using a landline is answered at the associated PSAP. This dataset only includes primary PSAPs. Secondary PSAPs, backup PSAPs, and wireless PSAPs have been excluded from this dataset. Primary PSAPs receive calls directly, whereas secondary PSAPs receive calls that have been transferred by a primary PSAP. Backup PSAPs provide service in cases where another PSAP is inoperable. Most military bases have their own emergency telephone systems. To connect to such system from within a military base it may be necessary to dial a number other than 9 1 1. Due to the sensitive nature of military installations, TGS did not actively research these systems. If civilian authorities in surrounding areas volunteered information about these systems or if adding a military PSAP was necessary to fill a hole in civilian provided data, TGS included it in this dataset. Otherwise military installations are depicted as being covered by one or more adjoining civilian emergency telephone systems. In some cases areas are covered by more than one PSAP boundary. In these cases, any of the applicable PSAPs may take a 911 call. Where a specific call is routed may depend on how busy the applicable PSAPS are (i.e. load balancing), operational status (i.e. redundancy), or time of date / day of week. If an area does not have 911 service, TGS included that area in the dataset along with the address and phone number of their dispatch center. These are areas where someone must dial a 7 or 10 digit number to get emergency services. These records can be identified by a "Y" in the [NON911EMNO] field. This indicates that dialing 911 inside one of these areas does not connect one with emergency services. This dataset was constructed by gathering information about PSAPs from state level officials. In some cases this was geospatial information, in others it was tabular. This information was supplemented with a list of PSAPs from the Federal Communications Commission (FCC). Each PSAP was researched to verify its tabular information. In cases where the source data was not geospatial, each PSAP was researched to determine its service area in terms of existing boundaries (e.g. city and county boundaries). In some cases existing boundaries had to be modified to reflect coverage areas (e.g. "entire county north of Country Road 30"). However, there may be cases where minor deviations from existing boundaries are not reflected in this dataset, such as the case where a particular PSAPs coverage area includes an entire county, and the homes and businesses along a road which is partly in another county. Text fields in this dataset have been set to all upper case to facilitate consistent database engine search results. All diacritics (e.g., the German umlaut or the Spanish tilde) have been replaced with their closest equivalent English character to facilitate use with database systems that may not support diacritics.

Dataset of 700,000 restaurants in the United States complete with detailed contact and geolocation data. The database includes multiple data points such as restaurant name, address, phone number, website, email, opening hours, latitude, longitude, and cuisine.

Abstract

Access to up-to-date socio-economic data is a widespread challenge in Vanuatu and other Pacific Island Countries. To increase data availability and promote evidence-based policymaking, the Pacific Observatory provides innovative solutions and data sources to complement existing survey data and analysis. One of these data sources is a series of High Frequency Phone Surveys (HFPS), which began in 2020 to monitor the socio-economic impacts of the COVID-19 Pandemic, and since 2023 has grown into a series of continuous surveys for socio-economic monitoring. See https://www.worldbank.org/en/country/pacificislands/brief/the-pacific-observatory for further details.

For Vanuatu, data for December 2023 – January 2025 was collected with each month having approximately 1000 households in the sample and is representative of urban and rural areas but is not representative at the province level. This dataset contains combined monthly survey data for all months of the continuous HFPS in Vanuatu. There is one date file for household level data with a unique household ID. And a separate file for individual level data within each household data, that can be matched to the household file using the household ID, and which also has a unique individual ID within the household data which can be used to track individuals over time within households, where the data is panel data.

Geographic coverage

National, urban and rural. Six provinces were covered by this survey: Sanma, Shefa, Torba, Penama, Malampa and Tafea.

Analysis unit

Household and individuals.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Vanuatu High Frequency Phone Survey (HFPS) sample is drawn from the list of customer phone numbers (MSIDNS) provided by Digicel Vanuatu, one of the country’s two main mobile providers. Digicel’s customer base spans all regions of Vanuatu. For the initial data collection, Digicel filtered their MSIDNS database to ensure a representative distribution across regions. Recognizing the challenge of reaching low-income respondents, Digicel also included low-income areas and customers with a low-income profile (defined by monthly spending between 50 and 150 VT), as well as those with only incoming calls or using the IOU service without repayment. These filtered lists were then randomized, and enumerators began calling the numbers.

This approach was used to complete the first round of 1,000 interviews. The respondents from this first round formed a panel to be surveyed monthly. Each month, phone numbers from the panel are contacted until all have been interviewed, at which point new phone numbers (fresh MSIDNS from Digicel’s database) are used to replace those that have been exhausted. These new respondents are then added to the panel for future surveys.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The questionnaire was developed in both English and Bislama. Sections of the Questionnaire:

-Interview Information -Household Roster (separate modules for new households and returning households) -Labor (separate modules for new households and returning households) -Food Security
-Household Income -Agriculture
-Social Protection
-Access to Services -Assets -Perceptions -Follow-up

Cleaning operations

At the end of data collection, the raw dataset was cleaned by the survey firm and the World Bank team. Data cleaning mainly included formatting, relabeling, and excluding survey monitoring variables (e.g., interview start and end times). Data was edited using the software STATA.

The data are presented in two datasets: a household dataset and an individual dataset. The total number of observations is 13,779 in the household dataset and 77,501 in the individual dataset. The individual dataset contains information on individual demographics and labor market outcomes of all household members aged 15 and above, and the household data set contains information about household demographics, education, food security, household income, agriculture activities, social protection, access to services, and durable asset ownership. The household identifier (hhid) is available in both the household dataset and the individual dataset. The individual identifier (hhid_mem) can be found in the individual dataset.

Response rate

In November 2024, a total of 7,874 calls were made. Of these, 2,251 calls were successfully connected, and 1,000 respondents completed the survey. By February 2024, the sample was fully comprised of returning respondents, with a re-contact rate of 99.9 percent.

Comprehensive SpamDB dataset containing detailed information about spam messages, scams, and suspicious texts sent from phone number 0553183291. The database includes verified user reports, sender details, message receipt dates, full message content, and phone number verification status. This data is collected to protect the public from digital fraud, unwanted spam messages, and phishing attempts. Each report undergoes review and verification to ensure data quality and reliability. The database helps users identify suspicious phone numbers, block unwanted numbers, and defend against phone and SMS fraud. The information is continuously updated and based on community contributions.

Pokémon TCG Pocket Card Dataset

This dataset contains detailed information about all cards available in the Pokémon Trading Card Game Pocket mobile app. The data has been carefully curated and cleaned to provide Pokémon enthusiasts and developers with accurate and comprehensive card information.

Dataset Contents

• 8+ Complete Sets: All major card sets including latest expansions
• 1000+ Cards: Every card with detailed metadata and classifications
• Clean Format: CSV format optimized for analysis, machine learning, and research

Key Features

🃏 Complete Card Data

• Card names and numbers with proper formatting
• Complete set and pack organization structure
• Release dates for all sets and expansions
• Total card counts per set for completion tracking

💎 Rarity Classifications

• 7+ Rarity Types including:
  • Common, Uncommon, Rare
  • Ultra Rare, Secret Rare, Special Art Rare
  • Crown Rare and other premium classifications
• Includes shiny and special variant cards
• Standardized rarity naming conventions

Use Cases

📊 Data Analysis & Research

• Card rarity distribution analysis across sets
• Set completion and collection tracking

🤖 Machine Learning & AI

• Card classification models
• Recommendation systems for collectors
• Rarity prediction algorithms
• Collection optimization models

📈 Visualization & Dashboards

• Interactive card browsers
• Collection progress tracking
• Rarity distribution charts
• Set release timeline visualizations

Data Quality

• ✅ Manually Verified: All card information cross-checked for accuracy
• ✅ Standardized Format: Consistent naming and classification across all entries
• ✅ Complete Coverage: All available cards from the mobile game
• ✅ Clean Structure: Optimized for both human readability and machine processing

Technical Specifications

📋 File Format

• Format: CSV (Comma Separated Values)
• Encoding: UTF-8 with full international character support
• Delimiter: Comma (,)
• Headers: Included in first row

🗂️ Column Structure (9 columns)

If you find this dataset useful, consider giving it an upvote — it really helps others discover it too! 🔼😊

Happy analyzing! 🎯📊

Abstract

To facilitate comparisons with the Latin America and the Caribbean (LAC) High-Frequency Surveys collected in 2021, harmonized versions of the COVID-19 High Frequency Phone Surveys 2022 Brazil databases have been produced. The databases follow the same structure as those for the countries in the region (for example, see: COVID-19 LAC High Frequency Phone Surveys 2021 (Wave 1)).

The Brazil 2021 COVID-19 Phone Survey was conducted to provide information on how the pandemic had been affecting Brazilian households in 2021, collecting information along multiple dimensions relevant to the welfare of the population (e.g. changes in employment and income, coping mechanisms, access to health and education services, gender inequalities, and food insecurity). A total of 2,166 phone interviews were conducted across all Brazilian states between July 26 and October 1, 2021. The survey followed an Random Digit Dialing (RDD) sampling methodology using a dual sampling frame of cellphone and landline numbers. The sampling frame was stratified by type of phone and state. Results are nationally representative for households with a landline or at least one cell phone and of individuals of ages 18 years and above who have an active cell phone number or a landline at home.

Geographic coverage

National level.

Analysis unit

Households and individuals of 18 years of age and older.

Sampling procedure

The sample is based on a dual frame of cell phone and landline numbers that was generated through a Random Digit Dialing (RDD) process and consisted of all possible phone numbers under the national phone numbering plan. Numbers were screened through an automated process to identify active numbers and cross-checked with business registries to identify business numbers not eligible for the survey. This method ensures coverage of all landline and cellphone numbers active at the time of the survey. The sampling frame was stratified by type of phone and state. See Sampling Design and Weighting document for more detail.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

Available in Portuguese. The questionnaire followed closely the LAC HFPS Questionnaire of Phase II Wave I but had some critical variations.

This dataset provides comprehensive contact information extracted from websites in real-time. It includes emails, phone numbers, and social media profiles, and other contact methods found across website pages. The data is extracted through intelligent parsing of website content, meta information, and structured data. Users can leverage this dataset for lead generation, sales prospecting, business development, and contact database building. The API enables efficient extraction of contact details from any website, helping businesses streamline their outreach and contact discovery processes. The dataset is delivered in a JSON format via REST API.

This database offers addresses, phone numbers, administrator names and state registration or licensure status for Minnesota health care providers. Federal certification classifications are also included. Provider types in the directory are boarding care homes, home health agencies, home care providers, hospices, hospitals, housing with services, nursing homes and supervised living facilities and other non-long term care providers. Providers can be identified by type, county, city or name. This page provides a link to download current data from the MDH database. The link works best in Internet Explorer and Firefox. This data is provided in tabular format. There is no assoicated geographic dataset; results require geocoding to be mapped. A link to the file with the field names and definitions is also provided below.

The O*NET Database contains hundreds of standardized and occupation-specific descriptors on almost 1,000 occupations covering the entire U.S. economy. The database, which is available to the public at no cost, is continually updated by a multi-method data collection program. Sources of data include: job incumbents, occupational experts, occupational analysts, employer job postings, and customer/professional association input.

Data content areas include:

• Worker Characteristics (e.g., Abilities, Interests, Work Styles)
• Worker Requirements (e.g., Education, Knowledge, Skills)
• Experience Requirements (e.g., On-the-Job Training, Work Experience)
• Occupational Requirements (e.g., Detailed Work Activities, Work Context)
• Occupation-Specific Information (e.g., Job Titles, Tasks, Technology Skills)

Bats play crucial ecological roles and provide valuable ecosystem services, yet many populations face serious threats from various ecological disturbances. The North American Bat Monitoring Program (NABat) aims to assess status and trends of bat populations while developing innovative and community-driven conservation solutions using its unique data and technology infrastructure. To support scalability and transparency in the NABat acoustic data pipeline, we developed a fully-automated machine-learning algorithm. This dataset includes audio files of bat echolocation calls that were considered to develop V1.0 of the NABat machine-learning algorithm, however the test set (i.e., holdout dataset) has been excluded from this release. These recordings were collected by various bat monitoring partners across North America using ultrasonic acoustic recorders for stationary acoustic and mobile acoustic surveys. For more information on how these surveys may be conducted, see Chapters 4 and 5 of “A Plan for the North American Bat Monitoring Program” (https://doi.org/10.2737/SRS-GTR-208). These data were then post-processed by bat monitoring partners to remove noise files (or those that do not contain recognizable bat calls) and apply a species label to each file. There is undoubtedly variation in the steps that monitoring partners take to apply a species label, but the steps documented in “A Guide to Processing Bat Acoustic Data for the North American Bat Monitoring Program” (https://doi.org/10.3133/ofr20181068) include first processing with an automated classifier and then manually reviewing to confirm or downgrade the suggested species label. Once a manual ID label was applied, audio files of bat acoustic recordings were submitted to the NABat database in Waveform Audio File format. From these available files in the NABat database, we considered files from 35 classes (34 species and a noise class). Files for 4 species were excluded due to low sample size (Corynorhinus rafinesquii, N=3; Eumops floridanus, N =3; Lasiurus xanthinus, N = 4; Nyctinomops femorosaccus, N =11). From this pool, files were randomly selected until files for each species/grid cell combination were exhausted or the number of recordings reach 1250. The dataset was then randomly split into training, validation, and test sets (i.e., holdout dataset). This data release includes all files considered for training and validation, including files that had been excluded from model development and testing due to low sample size for a given species or because the threshold for species/grid cell combinations had been met. The test set (i.e., holdout dataset) is not included. Audio files are grouped by species, as indicated by the four-letter species code in the name of each folder. Definitions for each four-letter code, including Family, Genus, Species, and Common name, are also included as a dataset in this release.

This dataset includes buildings occupied by the headquarters of cabinet level state government executive departments, legislative offices buildings outside of the capitol building, offices and court rooms associated with the highest level of the judicial branch of the state government, and large multi-agency state office buildings. Because the research to create this data was primarily keyed off of the headquarters of cabinet level state government agencies, some state office buildings that don't house a headquarters for such an agency may have been excluded. Intentionally excluded from this dataset are government run institutions (e.g. schools, colleges, prisons, and libraries). Also excluded are state capitol buildings. State owned or leased buildings whose primary purpose is not to house state offices have also been intentionally excluded from this dataset. Examples of these include "Salt Domes", "Park Shelters", and "Highway Garages". All entities that have been verified to have no building name, have had their [NAME] attribute set to "NO NAME". If the record in the original source data had no building name and TGS was unable to verify the building name, the [NAME] attribute was set to "UNKNOWN". All phone numbers in this dataset have been verified by TGS to be the main phone for the building. If the building was verified not to have a main phone number, the [AREA] and [PHONE] fields have been left blank. All entities located on military bases have been removed from this dataset. The text fields in this dataset have been set to all upper case to facilitate consistent database engine search results. All diacritics (e.g., the German umlaut or the Spanish tilde) have been replaced with their closest equivalent English character to facilitate use with database systems that may not support diacritics. The currentness of this dataset is indicated by the [CONTDATE] attribute. Based upon this attribute, the oldest record dates from 2007/12/03 and the newest record dates from 2007/12/06.

The databases of computational and experimental data from the first Aeroelastic Prediction Workshop are located here. The databases file names tell their contents by configuration, angle of attack, Mach number and Reynolds number where necessary. The experimental data sets are in files with _X in the name. Files without an _X are computational results. These are the files updated to include data received as of Sept 11, 2013. (JH)

This dataset is about book subjects. It has 2 rows and is filtered where the books is Database management on the Sinclair QL. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

The Google Maps dataset is ideal for getting extensive information on businesses anywhere in the world. Easily filter by location, business type, and other factors to get the exact data you need. The Google Maps dataset includes all major data points: timestamp, name, category, address, description, open website, phone number, open_hours, open_hours_updated, reviews_count, rating, main_image, reviews, url, lat, lon, place_id, country, and more.

Database description:

The written and spoken digits database is not a new database but a constructed database from existing ones, in order to provide a ready-to-use database for multimodal fusion.

The written digits database is the original MNIST handwritten digits database [1] with no additional processing. It consists of 70000 images (60000 for training and 10000 for test) of 28 x 28 = 784 dimensions.

The spoken digits database was extracted from Google Speech Commands [2], an audio dataset of spoken words that was proposed to train and evaluate keyword spotting systems. It consists of 105829 utterances of 35 words, amongst which 38908 utterances of the ten digits (34801 for training and 4107 for test). A pre-processing was done via the extraction of the Mel Frequency Cepstral Coefficients (MFCC) with a framing window size of 50 ms and frame shift size of 25 ms. Since the speech samples are approximately 1 s long, we end up with 39 time slots. For each one, we extract 12 MFCC coefficients with an additional energy coefficient. Thus, we have a final vector of 39 x 13 = 507 dimensions. Standardization and normalization were applied on the MFCC features.

To construct the multimodal digits dataset, we associated written and spoken digits of the same class respecting the initial partitioning in [1] and [2] for the training and test subsets. Since we have less samples for the spoken digits, we duplicated some random samples to match the number of written digits and have a multimodal digits database of 70000 samples (60000 for training and 10000 for test).

The dataset is provided in six files as described below. Therefore, if a shuffle is performed on the training or test subsets, it must be performed in unison with the same order for the written digits, spoken digits and labels.

Files:

• data_wr_train.npy: 60000 samples of 784-dimentional written digits for training;
• data_sp_train.npy: 60000 samples of 507-dimentional spoken digits for training;
• labels_train.npy: 60000 labels for the training subset;
• data_wr_test.npy: 10000 samples of 784-dimentional written digits for test;
• data_sp_test.npy: 10000 samples of 507-dimentional spoken digits for test;
• labels_test.npy: 10000 labels for the test subset.

References:

1. LeCun, Y. & Cortes, C. (1998), “MNIST handwritten digit database”.
2. Warden, P. (2018), “Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition”.

# Motion Sensor-Based Mobile Device Fingerprinting (MSMDF)

This database was collected by Carlos **Sulbaran Fandino** under the supervision of **Anne Josiane Kouam** and **Konrad Rieck**.

The database is divided in 2 directories: *raw data*, and *fingerprints*. Along with it the *figures* directory provides different visualizations of the different fingerprinting-datasets.

## Raw data
This repository contains the sensor data collected for our experiments. The repository is divided in 4 sub-repositories:
 1. **Example recordings:** This directory contains 1 recording representing a full data collection session and 2 motivational recordings representing two devices placed side by side on a desk. 
 2. **Original recordings:** This repository contains *340 two-minutes* recordings each named *"Device ID - Recording Instance"*. Each recording instance contains a *Metadata.csv* file (device name, platform, device id) together with 4 csv files corresponding to the different sensors: *Accelerometer.csv* , *Gravity.csv* , *Gyroscope.csv* , *Orientation.csv*. 
 3. **Separated by setting:** This repository contains 6 sub-repositories each corresponding to a different data collection setting (environmental condition). The sensor data in this directory is the result of the pre-processing stage of our MSMDF evaluation, therefore additional data streams have been added to each csv file and the *Orientation.csv* file is not included. 
 4. **Protected data - reduced:** This repository contains a reduced version of *separated by setting*, for each of the parameters used to evaluate the countermeasures. To produce a full version use: [CountermeasureApplier.py](https://github.com/carlossulba/MSMDF-Study/blob/main/Code/CountermeasureApplier.py) 


Each recording corresponds to a data collection session where the user: **1.** Holds its phone in hand for 10 seconds, **2.** Places it on a desk for 10 seconds, **3.** Holds it again in hand for 10 seconds but with inaudible audio stimulation, **4.** Again places the phone on a desk but with inaudible audio stimulation, **5.** Holds the phone on hand with extended arm while taking 10 steps in a straight line, and finally **6.** Repeats step five.


## Fingerprints
This repository contains a fingerprint-dataset for each fingerprint design. For each fingerprint design parameter you can find a respective sub-directory.

The following directories contain the fingerprint-datasets for each design parameter: 
 1. Sensor selection
 2. DC conditions
 3. Data stream set
 4. Feature set
 5. Window length (s)
 6. Sampling rate (Hz)
 7. Default
 8. Min FPs per device 

Inside them you will find 1 pickle file (.pkl) for each fingerprint design.

The following directories contain the fingerprint-dataset for each countermeasure parameter:
 1. Countermeasure strength
 2. Countermeasure resampling frequency

These were extracted after applying an anonimization step to the raw sensor data before extracting the fingerprints with the default fingerprint design.



## Using a fingerprint-dataset
The following data-structure describes the fingerprint-datasets. You can use them for training your own models or evaluating their distribution in the space.
```python
{
  'fingerprints': dict
  'config': FingerprintConfig
}
```

For opening a fingerprint-dataset you can use the following example code: [open fingerprint.py]()

The following data-structure describes the *fingerprints* dictionary.
```python
{
  'Setting 1': {
    'Device-01': [
      Fingerprint_01,
      Fingerprint_02
      ],
    'Device-02': [
      Fingerprint_01,
      Fingerprint_02,
      ],
  },
  'Setting 2': {
    'Device-01': [
      Fingerprint_01,
      Fingerprint_02,
      ],
  }
}
```

The following data-structure describes the *FingerprintConfig*.
```python
{
  "data_location": string,
  "fingerprint_length": int,
  "sampling_rate": int,
  "enabled_settings": list,
  "enabled_sensors": list,
  "enabled_streams": list,
  "enabled_features": list,
  "min_recordings": int,
  "repositioning": bool,
  "spectral_brightness_threshold": int,
  "spectral_rolloff_threshold": float,
  "frame_duration": float
}
```

# License
This database is open-source and available under the [GNU General Public License (GPL)](https://www.gnu.org/licenses/gpl-3.0.en.html). By using this database, you agree to the following conditions:
- Use responsibly and ethically.
- Cite this repository in your work or research.
- Ensure that any derivative works or modifications are open-sourced under the same license.

This dataset was created to simulate a market basket dataset, providing insights into customer purchasing behavior and store operations. The dataset facilitates market basket analysis, customer segmentation, and other retail analytics tasks. Here's more information about the context and inspiration behind this dataset:

Context:

Retail businesses, from supermarkets to convenience stores, are constantly seeking ways to better understand their customers and improve their operations. Market basket analysis, a technique used in retail analytics, explores customer purchase patterns to uncover associations between products, identify trends, and optimize pricing and promotions. Customer segmentation allows businesses to tailor their offerings to specific groups, enhancing the customer experience.

Inspiration:

The inspiration for this dataset comes from the need for accessible and customizable market basket datasets. While real-world retail data is sensitive and often restricted, synthetic datasets offer a safe and versatile alternative. Researchers, data scientists, and analysts can use this dataset to develop and test algorithms, models, and analytical tools.

Dataset Information:

The columns provide information about the transactions, customers, products, and purchasing behavior, making the dataset suitable for various analyses, including market basket analysis and customer segmentation. Here's a brief explanation of each column in the Dataset:

• Transaction_ID: A unique identifier for each transaction, represented as a 10-digit number. This column is used to uniquely identify each purchase.
• Date: The date and time when the transaction occurred. It records the timestamp of each purchase.
• Customer_Name: The name of the customer who made the purchase. It provides information about the customer's identity.
• Product: A list of products purchased in the transaction. It includes the names of the products bought.
• Total_Items: The total number of items purchased in the transaction. It represents the quantity of products bought.
• Total_Cost: The total cost of the purchase, in currency. It represents the financial value of the transaction.
• Payment_Method: The method used for payment in the transaction, such as credit card, debit card, cash, or mobile payment.
• City: The city where the purchase took place. It indicates the location of the transaction.
• Store_Type: The type of store where the purchase was made, such as a supermarket, convenience store, department store, etc.
• Discount_Applied: A binary indicator (True/False) representing whether a discount was applied to the transaction.
• Customer_Category: A category representing the customer's background or age group.
• Season: The season in which the purchase occurred, such as spring, summer, fall, or winter.
• Promotion: The type of promotion applied to the transaction, such as "None," "BOGO (Buy One Get One)," or "Discount on Selected Items."

Use Cases:

• Market Basket Analysis: Discover associations between products and uncover buying patterns.
• Customer Segmentation: Group customers based on purchasing behavior.
• Pricing Optimization: Optimize pricing strategies and identify opportunities for discounts and promotions.
• Retail Analytics: Analyze store performance and customer trends.

Note: This dataset is entirely synthetic and was generated using the Python Faker library, which means it doesn't contain real customer data. It's designed for educational and research purposes.