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Country & City of Origin

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Validation Status: 100% Human-Checked

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Abstract

This research was conducted in Lithuania from June 19 to July 31, 2002, as part of the second round of the Business Environment and Enterprise Performance Survey. The objective of the survey is to obtain feedback from enterprises on the state of the private sector as well as to help in building a panel of enterprise data that will make it possible to track changes in the business environment over time, thus allowing, for example, impact assessments of reforms. Through face-to-face interviews with firms in the manufacturing and services sectors, the survey assesses the constraints to private sector growth and creates statistically significant business environment indicators that are comparable across countries.

The survey topics include company's characteristics, information about sales and suppliers, competition, infrastructure services, judiciary and law enforcement, security, government policies and regulations, bribery, sources of financing, overall business environment, performance and investment activities, and workforce composition.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study is the establishment.

Universe

The manufacturing and services sectors are the primary business sectors of interest.

Kind of data

Sample survey data [ssd]

Sampling procedure

The information below is taken from "The Business Environment and Enterprise Performance Survey - 2002. A brief report on observations, experiences and methodology from the survey" prepared by MEMRB Custom Research Worldwide (now part of Synovate), a research company that implemented BEEPS II instrument.

The general targeted distributional criteria of the sample in BEEPS II countries were to be as follows:

1) Coverage of countries: The BEEPS II instrument was to be administered to approximately 6,500 enterprises in 28 transition economies: 16 from CEE (Albania, Bosnia and Herzegovina, Bulgaria, Croatia, Czech Republic, Estonia, FR Yugoslavia, FYROM, Hungary, Latvia, Lithuania, Poland, Romania, Slovak Republic, Slovenia and Turkey) and 12 from the CIS (Armenia, Azerbaijan, Belarus, Georgia, Kazakhstan, Kyrgyzstan, Moldova, Russia, Tajikistan, Turkmenistan, Ukraine and Uzbekistan).

2) In each country, the sector composition of the total sample in terms of manufacturing versus services (including commerce) was to be determined by the relative contribution of GDP, subject to a 15% minimum for each category. Firms that operated in sectors subject to government price regulations and prudential supervision, such as banking, electric power, rail transport, and water and wastewater were excluded.

Eligible enterprise activities were as follows (ISIC sections): - Mining and quarrying (Section C: 10-14), Construction (Section F: 45), Manufacturing (Section D: 15-37) - Transportation, storage and communications (Section I: 60-64), Wholesale, retail, repairs (Section G: 50-52), Real estate, business services (Section K: 70-74), Hotels and restaurants (Section H: 55), Other community, social and personal activities (Section O: selected groups).

3) Size: At least 10% of the sample was to be in the small and 10% in the large size categories. A small firm was defined as an establishment with 2-49 employees, medium - with 50-249 workers, and large - with 250 - 9,999 employees. Companies with only one employee or more than 10,000 employees were excluded.

4) Ownership: At least 10% of the firms were to have foreign control (more than 50% shareholding) and 10% of companies - state control.

5) Exporters: At least 10% of the firms were to be exporters. A firm should be regarded as an exporter if it exported 20% or more of its total sales.

6) Location: At least 10% of firms were to be in the category "small city/countryside" (population under 50,000).

7) Year of establishment: Enterprises which were established later than 2000 should be excluded.

The sample structure for BEEPS II was designed to be as representative (self-weighted) as possible to the population of firms within the industry and service sectors subject to the various minimum quotas for the total sample. This approach ensured that there was sufficient weight in the tails of the distribution of firms by the various relevant controlled parameters (sector, size, location and ownership).

As pertinent data on the actual population or data which would have allowed the estimation of the population of foreign-owned and exporting enterprises were not available, it was not feasible to build these two parameters into the design of the sample guidelines from the onset. The primary parameters used for the design of the sample were: - Total population of enterprises; - Ownership: private and state; - Size of enterprise: Small, medium and large; - Geographic location: Capital, over 1 million, 1 million-250,000, 250-50,000 and under 50,000; - Sub-sectors (e.g. mining, construction, wholesale, etc).

For certain parameters where statistical information was not available, enterprise populations and distributions were estimated from other accessible demographic (e.g. human population concentrations in rural and urban areas) and socio-economic (e.g. employment levels) data.

Sampling deviation

The survey was discontinued in Turkmenistan due to concerns about Turkmen government interference with implementation of the study.

Mode of data collection

Face-to-face [f2f]

Research instrument

The current survey instruments are available: - Screener and Main Questionnaires.

The survey topics include company's characteristics, information about sales and suppliers, competition, infrastructure services, judiciary and law enforcement, security, government policies and regulations, bribery, sources of financing, overall business environment, performance and investment activities, and workforce composition.

Cleaning operations

Data entry and first checking and validation of the results were undertaken locally. Final checking and validation of the results were made at MEMRB Custom Research Worldwide headquarters.

Response rate

Overall, in all BEEPS II countries, the implementing agency contacted 18,052 enterprises and achieved an interview completion rate of 36.93%.

Respondents who either refused outright (i.e. not interested) or were unavailable to be interviewed (i.e. on holiday, etc) accounted for 38.34% of all contacts. Enterprises which were contacted but were non-eligible (i.e. business activity, year of establishment, etc) or quotas were already met (i.e. size, ownership etc) or to which “blind calls” were made to meet quotas (i.e. foreign ownership, exporters, etc) accounted for 24.73% of the total number of enterprises contacted.

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Snapshot img

Big Data As A Service Market Size 2025-2029

The big data as a service market size is forecast to increase by USD 75.71 billion, at a CAGR of 20.5% between 2024 and 2029.

The Big Data as a Service (BDaaS) market is experiencing significant growth, driven by the increasing volume of data being generated daily. This trend is further fueled by the rising popularity of big data in emerging technologies, such as blockchain, which requires massive amounts of data for optimal functionality. However, this market is not without challenges. Data privacy and security risks pose a significant obstacle, as the handling of large volumes of data increases the potential for breaches and cyberattacks. Edge computing solutions and on-premise data centers facilitate real-time data processing and analysis, while alerting systems and data validation rules maintain data quality.
Companies must navigate these challenges to effectively capitalize on the opportunities presented by the BDaaS market. By implementing robust data security measures and adhering to data privacy regulations, organizations can mitigate risks and build trust with their customers, ensuring long-term success in this dynamic market.

What will be the Size of the Big Data As A Service Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free Sample

The market continues to evolve, offering a range of solutions that address various data management needs across industries. Hadoop ecosystem services play a crucial role in handling large volumes of data, while ETL process optimization ensures data quality metrics are met. Data transformation services and data pipeline automation streamline data workflows, enabling businesses to derive valuable insights from their data. Nosql database solutions and custom data solutions cater to unique data requirements, with Spark cluster management optimizing performance. Data security protocols, metadata management tools, and data encryption methods protect sensitive information. Cloud data storage, predictive modeling APIs, and real-time data ingestion facilitate agile data processing.
Data anonymization techniques and data governance frameworks ensure compliance with regulations. Machine learning algorithms, access control mechanisms, and data processing pipelines drive automation and efficiency. API integration services, scalable data infrastructure, and distributed computing platforms enable seamless data integration and processing. Data lineage tracking, high-velocity data streams, data visualization dashboards, and data lake formation provide actionable insights for informed decision-making.
For instance, a leading retailer leveraged data warehousing services and predictive modeling APIs to analyze customer buying patterns, resulting in a 15% increase in sales. This success story highlights the potential of big data solutions to drive business growth and innovation.

How is this Big Data As A Service Industry segmented?

The big data as a service industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Type

  Data Analytics-as-a-service (DAaaS)
  Hadoop-as-a-service (HaaS)
  Data-as-a-service (DaaS)


Deployment

  Public cloud
  Hybrid cloud
  Private cloud


End-user

  Large enterprises
  SMEs


Geography

  North America

    US
    Canada
    Mexico


  Europe

    France
    Germany
    Russia
    UK


  APAC

    China
    India
    Japan


  Rest of World (ROW)

By Type Insights

The Data analytics-as-a-service (DAaas) segment is estimated to witness significant growth during the forecast period. The data analytics-as-a-service (DAaaS) segment experiences significant growth within the market. Currently, over 30% of businesses adopt cloud-based data analytics solutions, reflecting the increasing demand for flexible, cost-effective alternatives to traditional on-premises infrastructure. Furthermore, industry experts anticipate that the DAaaS market will expand by approximately 25% in the upcoming years. This market segment offers organizations of all sizes the opportunity to access advanced analytical tools without the need for substantial capital investment and operational overhead. DAaaS solutions encompass the entire data analytics process, from data ingestion and preparation to advanced modeling and visualization, on a subscription or pay-per-use basis. Data integration tools, data cataloging systems, self-service data discovery, and data version control enhance data accessibility and usability.

The continuous evolution of this market is driven by the increasing volume, variety, and velocity of data, as well as the growing recognition of the business value that can be derived from data insights. Organizations across var

The global data visualization development services market size, valued at USD 6.4 billion in 2022, is anticipated to reach USD 32.2 billion by 2033, expanding at a robust CAGR of 20.7% during the forecast period (2023-2033). The market is driven by the increasing adoption of data visualization tools across various industry verticals to analyze large volumes of data, make informed decisions, and improve operational efficiency. Additionally, the growing demand for data-driven insights and the rise of big data analytics are fueling the market growth. Key market trends include the increasing adoption of cloud-based data visualization services, the integration of artificial intelligence and machine learning techniques, the growing demand for custom data visualization solutions, and the emergence of augmented reality and virtual reality (AR/VR) technologies in data visualization. The market is dominated by North America, followed by Europe and Asia Pacific. However, the Asia Pacific region is expected to witness the fastest growth during the forecast period due to the rising adoption of data visualization services in emerging economies like China and India.

The global online custom printing services market is projected to reach a value of USD XXX million by 2033, expanding at a CAGR of XX% during the forecast period (2025-2033). The market has witnessed significant growth in recent years due to the increasing demand for personalized products, coupled with the growing popularity of e-commerce. The rising adoption of digital printing technologies has further fueled the market's expansion, enabling businesses to customize their prints efficiently and cost-effectively. The market is segmented based on application, including commercial and personal printing, and by type, encompassing business cards, postcards, flyers, stickers and labels, shopping bags, clothing, and others. The commercial printing segment holds a prominent share of the market, driven by businesses' demand for branded materials and marketing collateral. The personal printing segment is also growing rapidly, as consumers seek personalized gifts, photo books, and home décor items. Key players in the market include VistaPrint, MOO, Canvaspop, Zazzle, and Snapfish. North America and Europe are major regional markets, owing to the presence of established printing companies and a high demand for customized products. However, emerging markets in Asia Pacific and Latin America offer significant growth potential due to rising consumer spending and the expansion of the e-commerce industry.

Abstract

This research was conducted in Bulgaria from June 19 to July 31, 2002, as part of the second round of the Business Environment and Enterprise Performance Survey. The objective of the survey is to obtain feedback from enterprises on the state of the private sector as well as to help in building a panel of enterprise data that will make it possible to track changes in the business environment over time, thus allowing, for example, impact assessments of reforms. Through face-to-face interviews with firms in the manufacturing and services sectors, the survey assesses the constraints to private sector growth and creates statistically significant business environment indicators that are comparable across countries.

The survey topics include company's characteristics, information about sales and suppliers, competition, infrastructure services, judiciary and law enforcement, security, government policies and regulations, bribery, sources of financing, overall business environment, performance and investment activities, and workforce composition.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study is the establishment.

Universe

The manufacturing and services sectors are the primary business sectors of interest.

Kind of data

Sample survey data [ssd]

Sampling procedure

The information below is taken from "The Business Environment and Enterprise Performance Survey - 2002. A brief report on observations, experiences and methodology from the survey" prepared by MEMRB Custom Research Worldwide (now part of Synovate), a research company that implemented BEEPS II instrument.

The general targeted distributional criteria of the sample in BEEPS II countries were to be as follows:

1) Coverage of countries: The BEEPS II instrument was to be administered to approximately 6,500 enterprises in 28 transition economies: 16 from CEE (Albania, Bosnia and Herzegovina, Bulgaria, Croatia, Czech Republic, Estonia, FR Yugoslavia, FYROM, Hungary, Latvia, Lithuania, Poland, Romania, Slovak Republic, Slovenia and Turkey) and 12 from the CIS (Armenia, Azerbaijan, Belarus, Georgia, Kazakhstan, Kyrgyzstan, Moldova, Russia, Tajikistan, Turkmenistan, Ukraine and Uzbekistan).

2) In each country, the sector composition of the total sample in terms of manufacturing versus services (including commerce) was to be determined by the relative contribution of GDP, subject to a 15% minimum for each category. Firms that operated in sectors subject to government price regulations and prudential supervision, such as banking, electric power, rail transport, and water and wastewater were excluded.

Eligible enterprise activities were as follows (ISIC sections): - Mining and quarrying (Section C: 10-14), Construction (Section F: 45), Manufacturing (Section D: 15-37) - Transportation, storage and communications (Section I: 60-64), Wholesale, retail, repairs (Section G: 50-52), Real estate, business services (Section K: 70-74), Hotels and restaurants (Section H: 55), Other community, social and personal activities (Section O: selected groups).

3) Size: At least 10% of the sample was to be in the small and 10% in the large size categories. A small firm was defined as an establishment with 2-49 employees, medium - with 50-249 workers, and large - with 250 - 9,999 employees. Companies with only one employee or more than 10,000 employees were excluded.

4) Ownership: At least 10% of the firms were to have foreign control (more than 50% shareholding) and 10% of companies - state control.

5) Exporters: At least 10% of the firms were to be exporters. A firm should be regarded as an exporter if it exported 20% or more of its total sales.

6) Location: At least 10% of firms were to be in the category "small city/countryside" (population under 50,000).

7) Year of establishment: Enterprises which were established later than 2000 should be excluded.

The sample structure for BEEPS II was designed to be as representative (self-weighted) as possible to the population of firms within the industry and service sectors subject to the various minimum quotas for the total sample. This approach ensured that there was sufficient weight in the tails of the distribution of firms by the various relevant controlled parameters (sector, size, location and ownership).

As pertinent data on the actual population or data which would have allowed the estimation of the population of foreign-owned and exporting enterprises were not available, it was not feasible to build these two parameters into the design of the sample guidelines from the onset. The primary parameters used for the design of the sample were: - Total population of enterprises; - Ownership: private and state; - Size of enterprise: Small, medium and large; - Geographic location: Capital, over 1 million, 1million-250,000, 250-50,000 and under 50,000; - Sub-sectors (e.g. mining, construction, wholesale, etc).

For certain parameters where statistical information was not available, enterprise populations and distributions were estimated from other accessible demographic (e.g. human population concentrations in rural and urban areas) and socio-economic (e.g. employment levels) data.

Sampling deviation

The survey was discontinued in Turkmenistan due to concerns about Turkmen government interference with implementation of the study.

Mode of data collection

Face-to-face [f2f]

Research instrument

The current survey instruments are available: - Screener and Main Questionnaires.

The survey topics include company's characteristics, information about sales and suppliers, competition, infrastructure services, judiciary and law enforcement, security, government policies and regulations, bribery, sources of financing, overall business environment, performance and investment activities, and workforce composition.

Cleaning operations

Data entry and first checking and validation of the results were undertaken locally. Final checking and validation of the results were made at MEMRB Custom Research Worldwide headquarters.

Response rate

Overall, in all BEEPS II countries, the implementing agency contacted 18,052 enterprises and achieved an interview completion rate of 36.93%.

Respondents who either refused outright (i.e. not interested) or were unavailable to be interviewed (i.e. on holiday, etc) accounted for 38.34% of all contacts. Enterprises which were contacted but were non-eligible (i.e. business activity, year of establishment, etc) or quotas were already met (i.e. size, ownership etc) or to which “blind calls” were made to meet quotas (i.e. foreign ownership, exporters, etc) accounted for 24.73% of the total number of enterprises contacted.

Salutary Data is a boutique, B2B contact and company data provider that's committed to delivering high quality data for sales intelligence, lead generation, marketing, recruiting / HR, identity resolution, and ML / AI. Our database currently consists of 148MM+ highly curated B2B Contacts ( US only), along with over 4MM+ companies, and is updated regularly to ensure we have the most up-to-date information.

We can enrich your in-house data ( CRM Enrichment, Lead Enrichment, etc.) and provide you with a custom dataset ( such as a lead list) tailored to your target audience specifications and data use-case. We also support large-scale data licensing to software providers and agencies that intend to redistribute our data to their customers and end-users.

What makes Salutary unique? - We offer our clients a truly unique, one-stop aggregation of the best-of-breed quality data sources. Our supplier network consists of numerous, established high quality suppliers that are rigorously vetted. - We leverage third party verification vendors to ensure phone numbers and emails are accurate and connect to the right person. Additionally, we deploy automated and manual verification techniques to ensure we have the latest job information for contacts. - We're reasonably priced and easy to work with.

Products: API Suite Web UI Full and Custom Data Feeds

Services: Data Enrichment - We assess the fill rate gaps and profile your customer file for the purpose of appending fields, updating information, and/or rendering net new “look alike” prospects for your campaigns. ABM Match & Append - Send us your domain or other company related files, and we’ll match your Account Based Marketing targets and provide you with B2B contacts to campaign. Optionally throw in your suppression file to avoid any redundant records. Verification (“Cleaning/Hygiene”) Services - Address the 2% per month aging issue on contact records! We will identify duplicate records, contacts no longer at the company, rid your email hard bounces, and update/replace titles or phones. This is right up our alley and levers our existing internal and external processes and systems.

Salutary Data is a boutique, B2B contact and company data provider that's committed to delivering high quality data for sales intelligence, lead generation, marketing, recruiting / HR, identity resolution, and ML / AI. Our database currently consists of 148MM+ highly curated B2B Contacts ( US only), along with over 4M+ companies, and is updated regularly to ensure we have the most up-to-date information.

We can enrich your in-house data ( CRM Enrichment, Lead Enrichment, etc.) and provide you with a custom dataset ( such as a lead list) tailored to your target audience specifications and data use-case. We also support large-scale data licensing to software providers and agencies that intend to redistribute our data to their customers and end-users.

What makes Salutary unique? - We offer our clients a truly unique, one-stop aggregation of the best-of-breed quality data sources. Our supplier network consists of numerous, established high quality suppliers that are rigorously vetted. - We leverage third party verification vendors to ensure phone numbers and emails are accurate and connect to the right person. Additionally, we deploy automated and manual verification techniques to ensure we have the latest job information for contacts. - We're reasonably priced and easy to work with.

Products: API Suite Web UI Full and Custom Data Feeds

Services: Data Enrichment - We assess the fill rate gaps and profile your customer file for the purpose of appending fields, updating information, and/or rendering net new “look alike” prospects for your campaigns. ABM Match & Append - Send us your domain or other company related files, and we’ll match your Account Based Marketing targets and provide you with B2B contacts to campaign. Optionally throw in your suppression file to avoid any redundant records. Verification (“Cleaning/Hygiene”) Services - Address the 2% per month aging issue on contact records! We will identify duplicate records, contacts no longer at the company, rid your email hard bounces, and update/replace titles or phones. This is right up our alley and levers our existing internal and external processes and systems.

About this dataset

Context

The dataset tabulates the population of Agency by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Agency. The dataset can be utilized to understand the population distribution of Agency by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Agency. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Agency.

Key observations

Largest age group (population): Male # 50-54 years (21) | Female # 15-19 years (29). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

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

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Agency population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Agency is shown in the following column.
• Population (Female): The female population in the Agency is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Agency for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Agency Population by Gender. You can refer the same here

This data set contains hydrographic and velocity measurements of high spatial resolution which were collected during 13 shipboard surveys between 2004 and 2018. The surveys included seven transects across the northern slope of Iceland. The hydrographic data on all of the cruises were obtained using a Sea-Bird 911+ conductivity-temperature-depth (CTD) instrument. The CTD was mounted on a rosette with Niskin bottles to collect water samples, which were used to calibrate the conductivity sensor. The resulting accuracy of the CTD measurements is 0.3 db for pressure, 0.001 °C for temperature, and 0.002 for salinity. Velocities were measured using acoustic Doppler current profiler (ADCP) instruments. On three of the cruises (RRS James Clark Ross 2004 JR105, R/V Knorr 2008 KN194, NRV Alliance 2018 ALL0118), a vessel-mounted ADCP (VMADCP) was used, while an upward- and downward-facing lowered ADCP (LADCP) system mounted on the rosette was utilized on the remaining surveys. The VMADCP data on the R/V Knorr 2008 (KN194) and NRV Alliance 2018 (ALL0118) cruises were acquired using the University of Hawaii Data Acquisition System (UHDAS) and the VMDAS collection software (Teledyne RDInstruments), respectively. Subsequently, these data were processed using the Common Ocean Data Access System (CODAS; Firing and Hummon 2010). On the RRS James Clark Ross 2004 cruise (JR105), VMADCP data were collected and processed using a custom data acquisition system unique to the ship (Pstar system). The LADCP data were processed using the LADCP Processing Software Package from the Lamont-Doherty Earth Observatory (Thurnherr 2010, 2018). Following the processing, the barotropic tides were removed from all of the velocity data sets by applying an updated version of the regional tidal model of Egbert and Erofeeva (2002), which has a resolution of 1/60°. This data set also includes data from a current meter mooring deployed from 23 August 2005 to 10 August 2006, situated 19 km north of the shelf break at the Hornbanki transect. The instrument was an Aanderaa RCM-7, sampling hourly, which was placed at 365 m depth on the 620 m isobath.

Abstract

This research was conducted in Ukraine from June 19 to July 31, 2002, as part of the second round of the Business Environment and Enterprise Performance Survey. The objective of the survey is to obtain feedback from enterprises on the state of the private sector as well as to help in building a panel of enterprise data that will make it possible to track changes in the business environment over time, thus allowing, for example, impact assessments of reforms. Through face-to-face interviews with firms in the manufacturing and services sectors, the survey assesses the constraints to private sector growth and creates statistically significant business environment indicators that are comparable across countries.

The survey topics include company's characteristics, information about sales and suppliers, competition, infrastructure services, judiciary and law enforcement, security, government policies and regulations, bribery, sources of financing, overall business environment, performance and investment activities, and workforce composition.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study is the establishment.

Universe

The manufacturing and services sectors are the primary business sectors of interest.

Kind of data

Sample survey data [ssd]

Sampling procedure

The information below is taken from "The Business Environment and Enterprise Performance Survey - 2002. A brief report on observations, experiences and methodology from the survey" prepared by MEMRB Custom Research Worldwide (now part of Synovate), a research company that implemented BEEPS II instrument.

The general targeted distributional criteria of the sample in BEEPS II countries were to be as follows:

1) Coverage of countries: The BEEPS II instrument was to be administered to approximately 6,500 enterprises in 28 transition economies: 16 from CEE (Albania, Bosnia and Herzegovina, Bulgaria, Croatia, Czech Republic, Estonia, FR Yugoslavia, FYROM, Hungary, Latvia, Lithuania, Poland, Romania, Slovak Republic, Slovenia and Turkey) and 12 from the CIS (Armenia, Azerbaijan, Belarus, Georgia, Kazakhstan, Kyrgyzstan, Moldova, Russia, Tajikistan, Turkmenistan, Ukraine and Uzbekistan).

2) In each country, the sector composition of the total sample in terms of manufacturing versus services (including commerce) was to be determined by the relative contribution of GDP, subject to a 15% minimum for each category. Firms that operated in sectors subject to government price regulations and prudential supervision, such as banking, electric power, rail transport, and water and wastewater were excluded.

Eligible enterprise activities were as follows (ISIC sections): - Mining and quarrying (Section C: 10-14), Construction (Section F: 45), Manufacturing (Section D: 15-37) - Transportation, storage and communications (Section I: 60-64), Wholesale, retail, repairs (Section G: 50-52), Real estate, business services (Section K: 70-74), Hotels and restaurants (Section H: 55), Other community, social and personal activities (Section O: selected groups).

3) Size: At least 10% of the sample was to be in the small and 10% in the large size categories. A small firm was defined as an establishment with 2-49 employees, medium - with 50-249 workers, and large - with 250 - 9,999 employees. Companies with only one employee or more than 10,000 employees were excluded.

4) Ownership: At least 10% of the firms were to have foreign control (more than 50% shareholding) and 10% of companies - state control.

5) Exporters: At least 10% of the firms were to be exporters. A firm should be regarded as an exporter if it exported 20% or more of its total sales.

6) Location: At least 10% of firms were to be in the category "small city/countryside" (population under 50,000).

7) Year of establishment: Enterprises which were established later than 2000 should be excluded.

The sample structure for BEEPS II was designed to be as representative (self-weighted) as possible to the population of firms within the industry and service sectors subject to the various minimum quotas for the total sample. This approach ensured that there was sufficient weight in the tails of the distribution of firms by the various relevant controlled parameters (sector, size, location and ownership).

As pertinent data on the actual population or data which would have allowed the estimation of the population of foreign-owned and exporting enterprises were not available, it was not feasible to build these two parameters into the design of the sample guidelines from the onset. The primary parameters used for the design of the sample were: - Total population of enterprises; - Ownership: private and state; - Size of enterprise: Small, medium and large; - Geographic location: Capital, over 1 million, 1 million-250,000, 250-50,000 and under 50,000; - Sub-sectors (e.g. mining, construction, wholesale, etc).

For certain parameters where statistical information was not available, enterprise populations and distributions were estimated from other accessible demographic (e.g. human population concentrations in rural and urban areas) and socio-economic (e.g. employment levels) data.

Sampling deviation

The survey was discontinued in Turkmenistan due to concerns about Turkmen government interference with implementation of the study.

Mode of data collection

Face-to-face [f2f]

Research instrument

The current survey instruments are available: - Screener and Main Questionnaires.

The survey topics include company's characteristics, information about sales and suppliers, competition, infrastructure services, judiciary and law enforcement, security, government policies and regulations, bribery, sources of financing, overall business environment, performance and investment activities, and workforce composition.

Cleaning operations

Data entry and first checking and validation of the results were undertaken locally. Final checking and validation of the results were made at MEMRB Custom Research Worldwide headquarters.

Response rate

Overall, in all BEEPS II countries, the implementing agency contacted 18,052 enterprises and achieved an interview completion rate of 36.93%.

Respondents who either refused outright (i.e. not interested) or were unavailable to be interviewed (i.e. on holiday, etc) accounted for 38.34% of all contacts. Enterprises which were contacted but were non-eligible (i.e. business activity, year of establishment, etc) or quotas were already met (i.e. size, ownership etc) or to which “blind calls” were made to meet quotas (i.e. foreign ownership, exporters, etc) accounted for 24.73% of the total number of enterprises contacted.

Overview

The SUMR-D CART2 turbine data are recorded by the CART2 wind turbine's supervisory control and data acquisition (SCADA) system for the Advanced Research Projects Agency–Energy (ARPA-E) SUMR-D project located at the National Renewable Energy Laboratory (NREL) Flatirons Campus. For the project, the CART2 wind turbine was outfitted with a highly flexible rotor specifically designed and constructed for the project. More details about the project can be found here: https://sumrwind.com/. The data include power, loads, and meteorological information from the turbine during startup, operation, and shutdown, and when it was parked and idle.

Data Details

Additional files are attached:
sumr_d_5-Min_Database.mat - a database file in MATLAB format of this dataset, which can be used to search for desired data files; sumr_d_5-Min_Database.xlsx - a database file in Microsoft Excel format of this dataset, which can be used to search for desired data files; loadcartU.m - this script loads in a CART data file and puts it in your workspace as a Matlab matrix (you can call this script from your own Matlab scripts to do your own analysis); charts.mat - this is a dependency file needed for the other scripts (it allows you to make custom preselections for cartPlotU.m); cartLoadHdrU.m - this script loads in the header file information for the data file (the header is embedded in each data file at the beginning); cartPlotU.m - this is a graphic user interface (GUI) that allows you to interactively look at different channels (to use it, run the script in Matlab, and load in the data file(s) of interest; from there, you can select different channels and plot things against each other; note that this script has issues with later versions of MATLAB; the preferred version to use is R2011b).

Data Quality

Wind turbine blade loading data were calibrated using blade gravity calibrations prior to data collection and throughout the data collection period. Blade loading was also checked for data quality following data collection as strain gauge measurements drifted throughout the data collection. These drifts in the strain gauge measurements were removed in post processing.

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Introduction

Welcome to the Native American Multi-Year Facial Image Dataset, thoughtfully curated to support the development of advanced facial recognition systems, biometric identification models, KYC verification tools, and other computer vision applications. This dataset is ideal for training AI models to recognize individuals over time, track facial changes, and enhance age progression capabilities.

Facial Image Data

This dataset includes over 5,000+ high-quality facial images, organized into individual participant sets, each containing:

Diversity & Representation

Image Quality & Capture Conditions

To ensure model generalization and practical usability, images in this dataset reflect real-world diversity:

Metadata

Each participant’s dataset is accompanied by rich metadata to support advanced model training and analysis, including:

Use Cases & Applications

This dataset is highly valuable for a wide range of AI and computer vision applications:

Secure & Ethical Collection

Dataset Updates & Customization

To keep pace with evolving AI needs, this dataset is regularly updated and customizable. Custom data collection options include:

The Algorithm Design Services market is experiencing robust growth, driven by the increasing demand for data-driven decision-making across diverse sectors. The market's expansion is fueled by the proliferation of big data, the rise of artificial intelligence (AI) and machine learning (ML), and the need for optimized solutions across technology, finance, healthcare, and other industries. The global market, currently estimated at $15 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 18% from 2025 to 2033, reaching approximately $50 billion by 2033. This significant growth is attributed to several key drivers, including the increasing adoption of cloud computing, the development of sophisticated algorithms for improved efficiency and automation, and the growing need for custom-built algorithms tailored to specific business needs. While data security and privacy concerns present challenges, the market is also seeing increased adoption of domain-specific algorithms tailored to industry-specific challenges, further driving its expansion. The segment encompassing general-purpose algorithms currently holds a larger market share, but the domain-specific algorithms segment is experiencing faster growth fueled by industry-specific needs. North America and Europe currently dominate the market, with significant growth potential in the Asia-Pacific region driven by technological advancements and increasing digitalization in countries like China and India. Key players such as Teksun, AVN Innovations, Cybiant, SANEI HYTECHS, Communere, Antecanis, StreamHPC, and Cornerstone are actively shaping the market landscape through innovation and strategic partnerships. Competition is expected to intensify as more companies enter the market, leading to further innovation and a broader range of algorithm design services. The ongoing development of advanced technologies like quantum computing and edge AI is expected to influence future market growth, creating new opportunities and challenges for service providers. The focus will likely shift toward developing algorithms that are more efficient, scalable, and adaptable to changing data environments. Furthermore, the rising demand for explainable AI (XAI) will influence the design and development of algorithms, ensuring transparency and trust in their applications.

The global custom antibody services market is anticipated to exhibit robust growth in the coming years, with a projected CAGR of XX% from 2025 to 2033. The market size, valued at XXX million in 2019, is expected to reach a staggering valuation by the end of the forecast period. The increasing prevalence of chronic diseases, the rising demand for personalized medicine, and advancements in antibody engineering techniques are primarily driving the market growth. Key trends shaping the custom antibody services market include the growing focus on targeted therapies, increased adoption of immunoassays for disease diagnosis, and the emergence of miniaturized antibody production platforms. The market is highly competitive, with established players such as ThermoFisher, GenScript, and Abcam dominating the landscape. However, emerging biotech companies are also gaining traction by offering innovative and cost-effective antibody production solutions. Regional variations in adoption rates and regulatory frameworks impact the market dynamics across North America, Europe, Asia-Pacific, and the Middle East & Africa.

Introduction

Welcome to the Caucasian Multi-Year Facial Image Dataset, thoughtfully curated to support the development of advanced facial recognition systems, biometric identification models, KYC verification tools, and other computer vision applications. This dataset is ideal for training AI models to recognize individuals over time, track facial changes, and enhance age progression capabilities.

Facial Image Data

This dataset includes over 5,000+ high-quality facial images, organized into individual participant sets, each containing:

Diversity & Representation

Image Quality & Capture Conditions

To ensure model generalization and practical usability, images in this dataset reflect real-world diversity:

Metadata

Each participant’s dataset is accompanied by rich metadata to support advanced model training and analysis, including:

Use Cases & Applications

This dataset is highly valuable for a wide range of AI and computer vision applications:

Secure & Ethical Collection

Dataset Updates & Customization

To keep pace with evolving AI needs, this dataset is regularly updated and customizable. Custom data collection options include:

About this dataset

Context

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

Content

The dataset will have the following datasets when applicable

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

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

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Interested in deeper insights and visual analysis?

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

Abstract

This research was conducted in Azerbaijan from June 19 to July 31, 2002, as part of the second round of the Business Environment and Enterprise Performance Survey. The objective of the survey is to obtain feedback from enterprises on the state of the private sector as well as to help in building a panel of enterprise data that will make it possible to track changes in the business environment over time, thus allowing, for example, impact assessments of reforms. Through face-to-face interviews with firms in the manufacturing and services sectors, the survey assesses the constraints to private sector growth and creates statistically significant business environment indicators that are comparable across countries.

The survey topics include company's characteristics, information about sales and suppliers, competition, infrastructure services, judiciary and law enforcement, security, government policies and regulations, bribery, sources of financing, overall business environment, performance and investment activities, and workforce composition.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study is the establishment.

Universe

The manufacturing and services sectors are the primary business sectors of interest.

Kind of data

Sample survey data [ssd]

Sampling procedure

The information below is taken from "The Business Environment and Enterprise Performance Survey - 2002. A brief report on observations, experiences and methodology from the survey" prepared by MEMRB Custom Research Worldwide (now part of Synovate), a research company that implemented BEEPS II instrument.

The general targeted distributional criteria of the sample in BEEPS II countries were to be as follows:

1) Coverage of countries: The BEEPS II instrument was to be administered to approximately 6,500 enterprises in 28 transition economies: 16 from CEE (Albania, Bosnia and Herzegovina, Bulgaria, Croatia, Czech Republic, Estonia, FR Yugoslavia, FYROM, Hungary, Latvia, Lithuania, Poland, Romania, Slovak Republic, Slovenia and Turkey) and 12 from the CIS (Armenia, Azerbaijan, Belarus, Georgia, Kazakhstan, Kyrgyzstan, Moldova, Russia, Tajikistan, Turkmenistan, Ukraine and Uzbekistan).

2) In each country, the sector composition of the total sample in terms of manufacturing versus services (including commerce) was to be determined by the relative contribution of GDP, subject to a 15% minimum for each category. Firms that operated in sectors subject to government price regulations and prudential supervision, such as banking, electric power, rail transport, and water and wastewater were excluded.

Eligible enterprise activities were as follows (ISIC sections): - Mining and quarrying (Section C: 10-14), Construction (Section F: 45), Manufacturing (Section D: 15-37) - Transportation, storage and communications (Section I: 60-64), Wholesale, retail, repairs (Section G: 50-52), Real estate, business services (Section K: 70-74), Hotels and restaurants (Section H: 55), Other community, social and personal activities (Section O: selected groups).

3) Size: At least 10% of the sample was to be in the small and 10% in the large size categories. A small firm was defined as an establishment with 2-49 employees, medium - with 50-249 workers, and large - with 250 - 9,999 employees. Companies with only one employee or more than 10,000 employees were excluded.

4) Ownership: At least 10% of the firms were to have foreign control (more than 50% shareholding) and 10% of companies - state control.

5) Exporters: At least 10% of the firms were to be exporters. A firm should be regarded as an exporter if it exported 20% or more of its total sales.

6) Location: At least 10% of firms were to be in the category "small city/countryside" (population under 50,000).

7) Year of establishment: Enterprises which were established later than 2000 should be excluded.

The sample structure for BEEPS II was designed to be as representative (self-weighted) as possible to the population of firms within the industry and service sectors subject to the various minimum quotas for the total sample. This approach ensured that there was sufficient weight in the tails of the distribution of firms by the various relevant controlled parameters (sector, size, location and ownership).

As pertinent data on the actual population or data which would have allowed the estimation of the population of foreign-owned and exporting enterprises were not available, it was not feasible to build these two parameters into the design of the sample guidelines from the onset. The primary parameters used for the design of the sample were: - Total population of enterprises; - Ownership: private and state; - Size of enterprise: Small, medium and large; - Geographic location: Capital, over 1 million, 1million-250,000, 250-50,000 and under 50,000; - Sub-sectors (e.g. mining, construction, wholesale, etc).

For certain parameters where statistical information was not available, enterprise populations and distributions were estimated from other accessible demographic (e.g. human population concentrations in rural and urban areas) and socio-economic (e.g. employment levels) data.

Sampling deviation

The survey was discontinued in Turkmenistan due to concerns about Turkmen government interference with implementation of the study.

Mode of data collection

Face-to-face [f2f]

Research instrument

The current survey instruments are available: - Screener and Main Questionnaires.

The survey topics include company's characteristics, information about sales and suppliers, competition, infrastructure services, judiciary and law enforcement, security, government policies and regulations, bribery, sources of financing, overall business environment, performance and investment activities, and workforce composition.

Cleaning operations

Data entry and first checking and validation of the results were undertaken locally. Final checking and validation of the results were made at MEMRB Custom Research Worldwide headquarters.

Response rate

Overall, in all BEEPS II countries, the implementing agency contacted 18,052 enterprises and achieved an interview completion rate of 36.93%.

Respondents who either refused outright (i.e. not interested) or were unavailable to be interviewed (i.e. on holiday, etc) accounted for 38.34% of all contacts. Enterprises which were contacted but were non-eligible (i.e. business activity, year of establishment, etc) or quotas were already met (i.e. size, ownership etc) or to which “blind calls” were made to meet quotas (i.e. foreign ownership, exporters, etc) accounted for 24.73% of the total number of enterprises contacted.