Abstract

Access to up-to-date socio-economic data is a widespread challenge in Tonga 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 as a way 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 Tonga, after two rounds of data collection from in 2022, monthly HFPS data collection commenced in April 2023 and continued until November 2024 (but with some gaps in the months of collection). The survey collected socio-economic data on topics including employment, income, food security, health, food prices, assets and well-being. Each month of collection has approximately 415 households in the sample and is representative of urban and rural areas. This dataset contains combined monthly survey data for all months of the continuous HFPS in Tonga.

Geographic coverage

National urban and rural areas (5 islands): Tongatapu, Vava'u, Ha'apai, Eua, Ongo Niua

Analysis unit

Individual and household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Tonga High Frequency Phone Survey (HFPS) monthly sample was generated in three ways. The first method is Random Digit Dialing (RDD) process covering all cell telephone numbers active at the time of the sample selection. The RDD methodology generates virtually all possible telephone numbers in the country under the national telephone numbering plan and then draws a random sample of numbers. This method guarantees full coverage of the population with a phone.

First, a large first-phase sample of cell phone numbers was selected and screened through an automated process to identify the active numbers. Then, a smaller second-phase sample was selected from the active residential numbers identified in the first-phase sample and was delivered to the data collection team to be called by the interviewers. When a cell phone was called, the call answerer was interviewed as long as he or she was 18 years of age or above and knowledgeable about the household activities.

It was initially planned to stratify the sample by island group based on the phone number prefixes. However, this was not feasible given the high internal migration across islands and the atypical assignment of phone number prefixes across islands in Tonga. The raw sample is overrepresenting urban areas and the population of Tongatapu.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The questionnaire was developed in both English and Tongan and can be found in this documentation in Excel format. Sections of the Questionnaire are provided below: 1. Interview information and Basic information 2. Household roster 3. Labor 4. Food security and food prices 5. Household income 6. Agriculture 7. Social protection 8. Access to services 9. Assets 10. Education 11. 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.

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.

Synthetic data used to demonstrate the effectiveness of the MKAD algorithm with respect to detecting anomalies in both the continuous numerical data and binary discrete data.

‘○’ reflects the implementation of truly unobserved data. ‘×’ indicates the implementation of error-prone data without measurement error correction. ‘−’ represents no value. A pair (x, y) in the column (π11, π00) is the implementation of the corrected control chart with parameter values (π11, π00) = (x, y) in (10) accommodated.

Synthetic data used to demonstrate the effectiveness of the MKAD algorithm with respect to detecting anomalies in both the continuous numerical data and binary discrete data.

Synthetic data used to demonstrate the effectiveness of the MKAD algorithm with respect to detecting anomalies in both the continuous numerical data and binary discrete data.

This dataset contains a collection of 100 randomly generated data points representing the relationship between the number of hours a student spends studying and their corresponding performance, measured as a score. The data has been generated to simulate a real-world scenario where study hours are assumed to influence academic outcomes, making it an excellent resource for linear regression analysis and other machine learning tasks.

Each row in the dataset consists of:

Hours: The number of hours a student dedicates to studying, ranging between 0 and 10 hours. Scores: The student's performance score, represented as a percentage, ranging from 0 to 100. Use Cases: This dataset is particularly useful for:

Linear Regression: Exploring how study hours influence student performance, fitting a regression line to predict scores based on study time. Data Science & Machine Learning: Practicing regression analysis, training models, and applying other predictive algorithms. Educational Research: Simulating data-driven insights into student behavior and performance metrics. Features: 100 rows of data. Continuous numerical variables suitable for regression tasks. Generated for educational purposes, making it ideal for students, teachers, and beginners in machine learning and data science. Potential Applications: Build a linear regression model to predict student scores. Investigate the correlation between study time and performance. Apply data visualization techniques to better understand the data. Use the dataset to experiment with model evaluation metrics like Mean Squared Error (MSE) and R-squared.

Implicit Processing of Numerical Order: Evidence from a Continuous Interocular Flash Suppression Study

Dana Sury and Orly Rubinsten

Abstract

Access to up-to-date socio-economic data is a widespread challenge in Papua New Guinea 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 as a way 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 PNG, after five rounds of data collection from 2020-2022, in April 2023 a monthly HFPS data collection commenced and continued for 18 months (ending September 2024) –on topics including employment, income, food security, health, food prices, assets and well-being. This followed an initial pilot of the data collection from January 2023-March 2023. Data for April 2023-September 2023 were a repeated cross section, while October 2023 established the first month of a panel, which is ongoing as of March 2025. For each month, approximately 550-1000 households were interviewed. The sample 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 PNG. There is one date file for household level data with a unique household ID, and separate files for individual level data within each household data, and household food price data, that can be matched to the household file using the household ID. A unique individual ID within the household data which can be used to track individuals over time within households.

Geographic coverage

Urban and rural areas of Papua New Guinea

Analysis unit

Household, Individual

Kind of data

Sample survey data [ssd]

Sampling procedure

The initial sample was drawn through Random Digit Dialing (RDD) with geographic stratification from a large random sample of Digicel’s subscribers. As an objective of the survey was to measure changes in household economic wellbeing over time, the HFPS sought to contact a consistent number of households across each province month to month. This was initially a repeated cross section from April 2023-Dec 2023. The resulting overall sample has a probability-based weighted design, with a proportionate stratification to achieve a proper geographical representation. More information on sampling for the cross-sectional monthly sample can be found in previous documentation for the PNG HFPS data.

A monthly panel was established in October 2023, that is ongoing as of March 2025. In each subsequent round of data collection after October 2024, the survey firm would first attempt to contact all households from the previous month, and then attempt to contact households from earlier months that had dropped out. After previous numbers were exhausted, RDD with geographic stratification was used for replacement households.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

he questionnaire, which can be found in the External Resources of this documentation, is in English with a Pidgin translation.

The survey instrument for Q1 2025 consists of the following modules: -1. Basic Household information, -2. Household Roster, -3. Labor, -4a Food security, -4b Food prices -5. Household income, -6. Agriculture, -8. Access to services, -9. Assets -10. Wellbeing and shocks -10a. WASH

Cleaning operations

The raw data were cleaned by the World Bank team using STATA. This included formatting and correcting errors identified through the survey’s monitoring and quality control process. The data are presented in two datasets: a household dataset and an 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, food prices, 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 (id_member) can be found in the individual dataset.

The Supermarket Sales Dataset captures detailed transaction data from a supermarket, including product categories, unit prices, quantities, and gross income. It also records customer demographics like gender, payment method, and membership type. The dataset is ideal for analyzing sales trends, customer behavior, and revenue performance. It offers valuable insights for optimizing promotions and product management strategies.

Invoice ID: A unique identifier for each transaction or purchase. This is typically a string or alphanumeric code.

Branch: Indicates the specific branch of the supermarket where the transaction took place. This could be categorical (e.g., "A", "B", "C").

City:The city where the supermarket branch is located. This is also categorical and helps in geographic analysis.

Customer Type: Defines the type of customer (e.g., "Member" or "Normal"). This could be useful for segmentation and understanding purchasing behavior.

Gender: The gender of the customer (e.g., "Male" or "Female"). This can be useful for demographic analysis.

Product Line: Specifies the category of products purchased (e.g., "Groceries", "Clothing", "Electronics"). This helps in product performance analysis.

Unit Price: The price of a single unit of the product. This is a continuous numerical value.

Quantity: The number of units purchased in a single transaction. This is also a continuous numerical value.

Tax 5%:The amount of tax applied to the purchase, calculated as 5% of the total before tax. This is a continuous numerical value.

Total: The total amount paid by the customer, including tax. This is a continuous numerical value.

Date:The date of the transaction, which can help in time series analysis. Format is typically in YYYY-MM-DD.

Time:The time of the transaction, which can be used to analyze peak shopping hours.

Payment:The method of payment used (e.g., "Cash", "Credit Card", "Debit Card"). This is categorical and provides insights into payment preferences.

COGS: Cost of Goods Sold, representing the total cost of producing the goods sold. This is a continuous numerical value.

Gross Margin Percentage: The percentage difference between sales and the cost of goods sold, indicating the profitability of sales.

Gross Income:The income remaining after deducting the cost of goods sold from total sales. This is a continuous numerical value.

Rating:The customer’s rating of the product or service, usually on a scale (e.g., 1 to 5). This can help in understanding customer satisfaction.

Background Pairs of related individuals are widely used in linkage analysis. Most of the tests for linkage analysis are based on statistics associated with identity by descent (IBD) data. The current biotechnology provides data on very densely packed loci, and therefore, it may provide almost continuous IBD data for pairs of closely related individuals. Therefore, the distribution theory for statistics on continuous IBD data is of interest. In particular, distributional results which allow the evaluation of p-values for relevant tests are of importance. Results A technology is provided for numerical evaluation, with any given accuracy, of the cumulative probabilities of some statistics on continuous genome data for pairs of closely related individuals. In the case of a pair of full-sibs, the following statistics are considered: (i) the proportion of genome with 2 (at least 1) haplotypes shared identical-by-descent (IBD) on a chromosomal segment, (ii) the number of distinct pieces (subsegments) of a chromosomal segment, on each of which exactly 2 (at least 1) haplotypes are shared IBD. The natural counterparts of these statistics for the other relationships are also considered. Relevant Maple codes are provided for a rapid evaluation of the cumulative probabilities of such statistics. The genomic continuum model, with Haldane's model for the crossover process, is assumed. Conclusions A technology, together with relevant software codes for its automated implementation, are provided for exact evaluation of the distributions of relevant statistics associated with continuous genome data on closely related individuals.

This dataset simulates resource usage, access patterns, and device-related features for a college English resource-sharing system. It aims to classify the sharing efficiency of resources based on their attributes and usage metrics. The dataset includes both categorical and numerical features, with a target variable labeled as sharing_efficiency with three classes: High, Medium, and Low.

Features The dataset consists of the following attributes:

resource_type: Type of resource being shared. Possible values: e-book, multimedia, quiz, interactive. (Categorical)

resource_size_mb: Size of the resource in megabytes (MB). (Numerical - Continuous)

access_frequency: Number of times the resource is accessed within a specific time frame. (Numerical - Integer)

device_type: Type of device used to access the resource. Possible values: Mobile, Sensor, Computer. (Categorical)

device_availability_hours: Number of hours the device is available for accessing resources daily. (Numerical - Integer)

data_transmission_latency: Latency in data transmission measured in milliseconds. (Numerical - Continuous)

resource_age_days: Age of the resource since its creation, in days. (Numerical - Integer)

update_frequency: Frequency at which the resource is updated. Possible values: Daily, Weekly, Monthly. (Categorical)

normalized_data_quality: Normalized score (0.4 to 1.0) representing the quality of the resource data. (Numerical - Continuous)

user_feedback_score: Average user feedback score on a scale of 1 to 5. (Numerical - Continuous)

Target Variable sharing_efficiency: The efficiency of resource sharing, categorized into three levels: High: Resources with high access frequency and high data quality. Medium: Resources with moderate access frequency and moderate data quality. Low: Resources with low access frequency or poor data quality. (Categorical)

When calling the National Geographic Information Institute continuous digital topographic map road centerline data with RestAPI, data is returned in json/xml format. API is provided as OGC international standard.

Abstract

Access to up-to-date socio-economic data is a widespread challenge in Solomon Islands 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 as a way 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 Solmon Islands, after five rounds of data collection from 2020-2020, in April 2023 a monthly HFPS data collection commenced and continued for 18 months (ending September 2024) –on topics including employment, income, food security, health, food prices, assets and well-being. Fieldwork took place in two non-consecutive weeks of each month. Data for April 2023-December 2023 were a repeated cross section, while January 2024 established the first month of a panel, the was continued to September 2024. Each month has approximately 550 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 Solomon Islands. 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

Urban and rural areas of Solomon Islands.

Analysis unit

Household, individual.

Kind of data

Sample survey data [ssd]

Sampling procedure

The initial sample was drawn through Random Digit Dialing (RDD) with geographic stratification. As an objective of the survey was to measure changes in household economic wellbeing over time, the HFPS sought to contact a consistent number of households across each province month to month. This was initially a repeated cross section from April 2023-Dec 2023. The initial sample was drawn from information provided by a major phone service provider in Solomon Islands, covering all the provinces in the country. It had a probability-based weighted design, with a proportionate stratification to achieve geographical representation. The geographical distribution compared to the 2019 Census is listed below for the first month of the HFPS monthly survey:

Choiseul : Census: 4.3%, HFPS: 5.2% Western : Census: 14.4%, HFPS: 13.7% Isabel : Census: 4.8%, HFPS: 4.7% Central : Census: 3.6%, HFPS: 5.2% Ren Bell : Census: 0.6%, HFPS: 1.4% Guadalcanal: Census: 19.8%, HFPS: 21.1% Malaita : Census: 23.1%, HFPS: 18.7% Makira : Census: 5.6%, HFPS: 5.6% Temotu: Census: 3.0%, HFPS: 3% Honiara: Census: 20.7%, HFPS: 21.3%

Source: Census of Population and Housing 2019

Note: The values in the HFPS column represent the proportion of survey participants residing in each province, based on the raw HFPS data from April.

In April 2023, the geographic distribution of World Bank HFPS participants was generally similar to that of the census data at the province level, though within provinces, areas with less mobile phone connectivity are likely to be underrepresented. One indication of this is that urban areas constituted 38.2 percent of the survey sample, which is a slight overrepresentation, compared to 32.5 percent in the Census 2019.

A monthly panel was established in January 2024, that is ongoing as of March 2025. In each subsequent month after January 2024, the survey firm would first attempt to contact all households from the previous month and then attempt to contact households from earlier months that had dropped out. After previous numbers were exhausted, RDD with geographic stratification was used for replacement households. Across all months of the survey a total of, 9,926 interviews were completed.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The questionnaire, which can be found in the External Resources of this documentation, is available in English, with Solomons Pijin translation. There were few changes to the questionnaire across the survey months, but some sections were only introduced in 2024, namely energy access questions and questions to inform the baseline data of the Solomon Islands Government Integrated Economic Development and Climate Resilience (IEDCR) project.

Cleaning operations

The raw data were cleaned by the World Bank team using STATA. This included formatting and correcting errors identified through the survey’s monitoring and quality control process. The data are presented in two datasets: a household dataset and an individual dataset. The total number of observations is 9,926 in the household dataset and 62,054 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, food prices, 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 (id_member) can be found in the individual dataset.

Numerical data represented in the figures which are graphs. This dataset is associated with the following publication: Spencer, M., M. Miller, J. Richwine, K. Duckworth, L. Racz, M. Grimaila, M. Magnuson , S. Willison , and R. Phillips. Pulsed and Continuous UV LED Reactor for Water Treatment. Aqua - Journal of Water Supply Research and Technology, International Water Supply Association (London, England). Blackwell Publishing, Malden, MA, USA, 1-75, (2016).

The emergence of SARS-CoV-2 variants during the COVID-19 pandemic caused frequent global outbreaks that confounded public health efforts across many jurisdictions, highlighting the need for better understanding and prediction of viral evolution. Predictive models have been shown to support disease prevention efforts, such as with the seasonal influenza vaccine, but they require abundant data. For emerging viruses of concern, such models should ideally function with relatively sparse data typically encountered at the early stages of a viral outbreak. Conventional discrete approaches have proven difficult to develop due to the spurious and reversible nature of amino acid mutations and the overwhelming number of possible protein sequences adding computational complexity. We hypothesized that these challenges could be addressed by encoding discrete protein sequences into continuous numbers, effectively reducing the data size while enhancing the resolution of evolutionarily relevant differences. To this end, we developed a viral protein evolution prediction model (VPRE), which reduces amino acid sequences into continuous numbers by using an artificial neural network called a variational autoencoder (VAE) and models their most statistically likely evolutionary trajectories over time using Gaussian process (GP) regression. To demonstrate VPRE, we used a small amount of early SARS-CoV-2 spike protein sequences. We show that the VAE can be trained on a synthetic dataset based on this data. To recapitulate evolution along a phylogenetic path, we used only 104 spike protein sequences and trained the GP regression with the numerical variables to project evolution up to 5 months into the future. Our predictions contained novel variants and the most frequent prediction mapped primarily to a sequence that differed by only a single amino acid from the most reported spike protein within the prediction timeframe. Novel variants in the spike receptor binding domain (RBD) were capable of binding human angiotensin-converting enzyme 2 (ACE2) in silico, with comparable or better binding than previously resolved RBD-ACE2 complexes. Together, these results indicate the utility and tractability of combining deep learning and regression to model viral protein evolution with relatively sparse datasets, toward developing more effective medical interventions.

****Attribute information:****

Martial Status: Categorical (Married, Single). Gender: Categorical (Male, Female). Income: Numerical (Continuous). Children: Numerical (Discrete). Education: Categorical (Bachelors, Partial College, High School, Partial High School, Graduate Degree). Occupation: Categorical (Skilled Manual, Clerical, Professional, Management, Manual). Home Owner: Categorical (Yes, No). Cars: Numerical (Discrete). Commute Distance: Categorical (0-1 Miles, 1-2 Miles, 2-5 Miles, 5-10 Miles, More than 10 Miles). Region: Categorical (Europe, Pacific, North America). Age: Numerical (Continuous). Age Bracket: Categorical (Middle Age, Old, Adolescent).

Target Variable: Purchased Bike: Categorical (Yes, No).

Austria Number of Instruments: VSE: Equity: Standard Market: Continuous data was reported at 4.000 Unit in Mar 2025. This records an increase from the previous number of 3.000 Unit for Feb 2025. Austria Number of Instruments: VSE: Equity: Standard Market: Continuous data is updated monthly, averaging 4.000 Unit from Jan 2002 (Median) to Mar 2025, with 279 observations. The data reached an all-time high of 16.000 Unit in Jan 2002 and a record low of 1.000 Unit in Aug 2018. Austria Number of Instruments: VSE: Equity: Standard Market: Continuous data remains active status in CEIC and is reported by CEE Stock Exchange Group. The data is categorized under Global Database’s Austria – Table AT.Z004: Vienna Stock Exchange: Number of Instruments.

This dataset contains corrected particle number concentration data measured during the year-long MOSAiC expedition from October 2019 to September 2020. Some periods of the measurements were affected by repeated step changes in the particle number concentrations. This was caused by a valve, which was positioned in the line behind (downstream) the CPC and switched between a total air inlet and an interstitial inlet. During odd hours (i.e. 1 am, 3 am etc.) the valve was open to the interstitial inlet, during even hours to the total inlet. During the affected periods (4.9% of all data points), the measured particle number concentrations behind the interstitial inlet are lower compared to the previous and the following hour behind the total inlet. To correct for these step changes in concentration, we calculated two correction factors, i.e. at the beginning and at the end of the affected period. The two correction factors are derived by dividing the median particle number concentrations of 3 minutes before (after) the start (end) of the affected period by the median particle number concentration of 3 minutes after (before) the start (end) of the affected period. Each data point of the affected period was then corrected by multiplying its particle number concentration by the linearly interpolated correction factor at the corresponding timestamp. The corrected data set may still contain minor artifacts related to the step change correction, i.e. small deviations of the number concentration in the order of < 10 %. The counting uncertainty of a CPC is nominally 10 %. Depending on how the dataset is used this might be relevant or not. The dataset has a time resolution of 10 sec. More detailed information about the measurement setup can be found in Beck et al. (2022). The data columns include time, latitude, longitude, particle number concentration and a correction flag (0 = uncorrected, 1 = corrected).

At BatchData, we specialize in providing advanced data solutions designed to enhance your business operations. Our Contact Enrichment data offers crucial insights that can transform how you engage with property owners. By uncovering detailed owner information, including contact details (email address data + phone number data) and reachability metrics, you can more effectively target your outreach efforts, tailor your offers, and improve your overall engagement strategies.

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Contact Enrichment Data with access to 12+ data points including: - Owner: Total properties owned, average price, years built, and acquisition dates. - Contact Information: One or multiple 10-digit phone numbers and email addresses. - Reachability: DNC status, known litigators, reachability score, and carrier information.

Common Use Cases: - Investors, Agents & Brokers: Connect with homeowners who might be interested in selling and craft personalized offers. - Home Improvement Services: Target homeowners for services such as remodeling, landscaping, roofing, and painting. - Insurance Companies: Offer insurance policies to homeowners with high-value properties or recent purchases.