Field Data Capture Software Market Outlook

According to our latest research, the global Field Data Capture Software market size reached USD 2.41 billion in 2024, with a robust year-over-year growth trajectory. The market is expected to expand at a CAGR of 13.2% during the forecast period, reaching approximately USD 6.98 billion by 2033. This significant growth is propelled by increasing digital transformation initiatives across industries, the proliferation of mobile devices, and the growing need for real-time data collection and analytics in field operations. As organizations strive for operational efficiency, compliance, and enhanced decision-making, the adoption of field data capture software continues to accelerate worldwide.

One of the primary growth drivers for the Field Data Capture Software market is the rising emphasis on data-driven decision-making across sectors such as oil & gas, construction, agriculture, and healthcare. Organizations are increasingly recognizing the value of capturing accurate, real-time data from field operations to streamline workflows, reduce manual errors, and ensure compliance with regulatory requirements. The integration of advanced technologies such as IoT sensors, GPS, and cloud computing into field data capture solutions has significantly improved the quality, accessibility, and security of field data. This technological evolution is enabling businesses to optimize resource allocation, monitor assets remotely, and respond proactively to operational challenges, thereby fueling market growth.

Another critical factor contributing to the expansion of the Field Data Capture Software market is the widespread adoption of mobile devices and cloud-based platforms. As field teams become increasingly mobile, the need for seamless, user-friendly solutions that facilitate data entry, validation, and synchronization has become paramount. Cloud-based field data capture software offers scalability, flexibility, and centralized data management, empowering organizations to deploy solutions rapidly and support remote fieldwork. Furthermore, the ongoing shift toward paperless operations and the demand for sustainability have prompted enterprises to invest in digital tools that minimize paperwork, enhance traceability, and support environmental goals.

The market is also experiencing growth due to regulatory pressures and compliance requirements, particularly in highly regulated industries such as energy, utilities, and healthcare. Governments and industry bodies are mandating stricter reporting, documentation, and audit trails, compelling organizations to adopt robust field data capture solutions. These platforms not only help organizations maintain accurate records but also enable real-time monitoring and reporting, reducing the risk of non-compliance and associated penalties. The ability to customize workflows, automate data validation, and generate instant reports further enhances the appeal of field data capture software, driving its adoption across diverse end-user segments.

Regionally, North America holds the largest share of the Field Data Capture Software market, followed closely by Europe and Asia Pacific. The dominance of North America can be attributed to the early adoption of advanced technologies, significant investments in digital infrastructure, and the presence of leading software vendors. However, Asia Pacific is anticipated to witness the fastest growth during the forecast period, driven by rapid industrialization, expanding construction activities, and increasing awareness of digital solutions among small and medium enterprises. The region's dynamic economic landscape, coupled with government initiatives to promote digitalization, positions Asia Pacific as a key growth engine for the global market.

Component Analysis

The Component segment of the Field Data Capture Software market is bifurcated into software and services, each playing a pivotal role in the market’s overall growth and adoption. The software segment encompasses a wide range of solutions designed to facilitate on-site data collection, including mobile applications, web-based portals, and integrated platforms that support workflow automation, data validation, and real-time analytics. These solutions have evolved to include features such as offline data capture, customizable forms, and seamless integration with enterprise systems, enabling organizations to tailor their fiel

The global Match Data Collection market is poised for substantial growth, projected to reach an estimated market size of approximately USD 7,500 million by 2025, with a robust Compound Annual Growth Rate (CAGR) of around 15% anticipated through 2033. This expansion is primarily fueled by the insatiable demand for real-time, in-depth analytics across the sports and burgeoning esports industries. The proliferation of advanced sensor technologies, sophisticated video analysis platforms, and the increasing adoption of AI-powered insights are key drivers. Sports organizations, media outlets, and betting companies are heavily investing in high-fidelity data to enhance fan engagement, optimize athlete performance, and refine strategic decision-making. The growing complexity of sports, coupled with the rise of fantasy sports and sports betting, further amplifies the need for precise and comprehensive match data. Emerging markets, particularly in Asia Pacific and South America, are expected to contribute significantly to this growth as sports ecosystems mature and technological adoption accelerates. The competitive landscape of the Match Data Collection market is characterized by the presence of established players and emerging innovators, each vying to offer superior data accuracy, analytical capabilities, and technological solutions. Companies like Opta, Sportradar, and Stats Perform are at the forefront, leveraging their extensive experience and advanced infrastructure. However, newer entrants are continuously pushing the boundaries with novel approaches to data acquisition and analysis, particularly in areas like player biomechanics and predictive modeling. The market segmentation clearly indicates a strong preference for Sensor Data and Video Data, reflecting the industry's shift towards more granular and objective performance metrics. While the sports industry remains the dominant application, the rapid ascent of esports presents a significant growth avenue, demanding specialized data solutions that capture in-game dynamics and player interactions. Despite the immense potential, challenges such as data standardization, privacy concerns, and the high cost of sophisticated data collection infrastructure could pose moderate restraints to the market's unhindered progress. This report delves into the dynamic realm of Match Data Collection, a critical component fueling innovation and strategic decision-making across the sports and esports ecosystems. Spanning a comprehensive Study Period from 2019 to 2033, with a Base Year and Estimated Year of 2025, and a Forecast Period extending from 2025 to 2033, this analysis meticulously examines the market's trajectory through its Historical Period of 2019-2024. We will explore the intricate details of data collection, from cutting-edge Sensor Data to insightful Video Data and a range of other specialized data types. The report will also highlight the pivotal role of key players and significant industry developments, offering a deep dive into a market projected to reach substantial financial milestones in the millions.

Charging data are collected from one of three sources, each with varying levels of additional information. These sources, in approximate order from most to least additional information, are: • The electric vehicle supply equipment (charger) • Onboard the vehicle itself • From a utility submeter. Many chargers provide software that allows for the collection and reporting of charging session data. If unavailable, data may be recorded by the charging vehicle’s onboard systems. If neither of these options is available, data can be acquired from utility submeters that simply track the energy flowing to one or more chargers. Data collected directly from the electric vehicle supply equipment (EVSE) are typically the most accurate and highest frequency. However, it is not always possible to discern which exact vehicle is being charged during any one session. EVSE-side data can be identified where a single charger ID but a range of vehicle IDs are present (e.g., CH001, EV001-EV005). Data collected from the vehicle’s onboard systems usually does not provide information on which exact charger is being used. Vehicle-side data can be identified where a single Vehicle ID but a range of Charger IDs are present (e.g., EV001, CH001-CH005). Data collected from utility submeters provide no information on which specific vehicle is charging or which specific charger is in use. Submeter data can be identified where multiple Vehicle IDs and multiple Charger IDs are present, but only a single Fleet ID is present (e.g., EV001-EV005, CH001-CH005, Fleet01). The Charge Data Daily/Session Dictionaries contains definitions for each available parameter collected as part of an individual charging session, aggregated at either a daily or session level. The parameters available will vary between vehicles and chargers. The Charger Attributes table contains specific charger characteristics, coded to at least one anonymous Charger ID and linked to either a single or a range of Vehicle IDs. Vehicle ID can be used as a key between charging data and vehicle attribute tables. The Charger Attributes Data Dictionary contains definitions for each available parameter collected on the physical and operational characteristics of the charging hardware itself. The Vehicle Attributes Data Dictionary contains definitions for each available parameter associated with a vehicle’s physical and functional attributes and fleet context. The Vehicle Attributes table contains specific vehicle characteristics, coded to an anonymous Vehicle ID. This Vehicle ID can be used as a key between vehicle data and vehicle attribute tables, and in cases where charging data are supplied, links a vehicle with the charger(s) that supplied it power. The Charging Data tables contain the data from each charger’s operations, coded to at least one anonymous Charger ID and linked to either a single or a range of Vehicle IDs. Vehicle ID can be used as a key between charging data and vehicle attribute tables. Data is being uploaded quarterly through 2023 and subject to change until the conclusion of the project.

Sports Data Service Market Outlook

The global sports data service market size was valued at approximately $3.9 billion in 2023 and is projected to reach around $12.5 billion by 2032, reflecting a robust compound annual growth rate (CAGR) of 13.6% during the forecast period. This impressive growth is driven by an increasing reliance on data analytics, advancements in technology, and the rising importance of data-driven decision-making in sports.

One significant growth factor in the sports data service market is the escalating adoption of advanced analytics by professional sports teams and organizations. These entities are increasingly utilizing data analytics to gain insights into player performance, optimize team strategies, and enhance overall operational efficiency. The use of sophisticated software and hardware for collecting, analyzing, and interpreting large volumes of data has become crucial, helping teams to make informed decisions, improve training regimens, and minimize the risk of injuries. This trend is anticipated to continue driving market expansion.

Another noteworthy driver is the growing importance of fan engagement in the sports industry. Sports organizations are leveraging data services to understand fan behavior, preferences, and engagement patterns. This data is used to create personalized experiences, improve fan interaction, and boost revenue streams through targeted marketing and promotions. The use of data analytics in fan engagement not only enhances the fan experience but also builds brand loyalty and drives long-term growth for sports organizations.

The integration of wearable technology and health assessment tools is also contributing to the market's growth. Wearables and other health monitoring devices provide real-time data on players' physical condition, helping coaches and health professionals to monitor fitness levels, detect potential injuries, and tailor training programs accordingly. This data-centric approach to health assessment is becoming increasingly prevalent across various sports disciplines, further propelling the demand for sports data services.

The evolution of Sport Software has played a pivotal role in transforming the sports data service market. With the advent of sophisticated software solutions, sports organizations can now analyze vast amounts of data with unprecedented accuracy and speed. Sport Software encompasses a range of tools and platforms that enable teams to gather and process data from various sources, including player performance metrics, game statistics, and fan engagement data. This technological advancement has not only streamlined data collection and analysis but also empowered sports entities to make data-driven decisions that enhance competitive performance and operational efficiency. As the demand for real-time insights and predictive analytics grows, Sport Software continues to evolve, offering more advanced features and capabilities that cater to the dynamic needs of the sports industry.

From a regional perspective, North America holds a significant share of the sports data service market, owing to the presence of major sports leagues, technological advancements, and high investments in sports analytics. Europe and Asia Pacific are also emerging as key regions due to the growing popularity of sports, increased focus on player performance, and rising investments in sports infrastructure. The Middle East & Africa and Latin America are expected to exhibit steady growth, driven by the increasing adoption of sports data services and rising awareness of the benefits of data-driven decision-making in sports.

Component Analysis

The sports data service market can be segmented by component into software, hardware, and services. Each of these components plays a critical role in the collection, analysis, and application of sports data. Software solutions are the backbone of sports data analytics, providing the tools necessary for data collection, processing, and analysis. These software solutions can range from basic statistical analysis programs to advanced machine learning and artificial intelligence platforms. The demand for robust and scalable software solutions is rising as sports organizations seek to harness the full potential of their data.

Hardware components, including wearables, sensors, and other data collection devices, are also integral to the sports data service market. These devices capture a wide range of data poin

The United Nations began its World Crime Surveys in 1978. The first survey collected statistics on a small range of offenses and on the criminal justice process for the years 1970-1975. The second survey collected data on a wide range of offenses, offenders, and criminal justice process data for the years 1975-1980. Several factors make these two collections difficult to use in combination. Some 25 percent of those countries responding to the first survey did not respond to the second and, similarly, some 30 percent of those responding to the second survey did not respond to the first. In addition, many questions asked in the second survey were not asked in the first survey. This data collection represents the efforts of the investigators to combine, revise, and recheck the data of the first two surveys. The data are divided into two parts. Part 1 comprises all data on offenses and on some criminal justice personnel. Crime data are entered for 1970 through 1980. In most cases 1975 is entered twice, since both surveys collected data for this year. Part 2 includes data on offenders, prosecutions, convictions, and prisons. Data are entered for 1970 through 1980, for every even year.

Data Acquisition Hardware Market Outlook

The global data acquisition hardware market size is projected to grow significantly, reaching approximately USD 4.5 billion by 2032, up from USD 2.8 billion in 2023, with a compound annual growth rate (CAGR) of 5.3% over this period. This growth trajectory highlights the increasing demand for efficient data collection and interpretation solutions across various industries. Key growth factors contributing to this upward trend include technological advancements in data acquisition systems, the rising need for real-time data analytics, and the growing adoption of automation and monitoring systems across multiple sectors. The demand for enhanced data quality and integrity, alongside predictive maintenance applications, further bolsters this market's expansion.

The surge in demand for precise and reliable data collection systems is one of the primary growth drivers of the data acquisition hardware market. Industries such as automotive, aerospace, and healthcare are increasingly relying on sophisticated data acquisition systems to capture, store, and analyze critical data accurately. This trend is propelled by the necessity to improve operational efficiency, safety, and compliance with stringent regulatory standards. For instance, in the automotive sector, data acquisition hardware plays a pivotal role in vehicle testing and development, enabling manufacturers to analyze performance metrics and improve vehicle designs. Similarly, in healthcare, the integration of data acquisition systems into medical devices aids in patient monitoring, diagnosis, and treatment planning, thereby enhancing healthcare outcomes.

Technological advancements in data acquisition hardware have significantly contributed to market growth. Innovations such as multi-channel data loggers, wireless connectivity, and cloud-based data acquisition solutions have revolutionized the way data is collected and processed. These advancements have facilitated the seamless integration of data acquisition systems with other digital platforms, enabling real-time data monitoring and analysis. Additionally, the development of user-friendly interfaces and software applications has simplified the process of data collection and analysis, making it accessible to a broader range of users across various industries. The ongoing trend towards digital transformation and Industry 4.0 initiatives further underscores the importance of advanced data acquisition solutions, driving market growth.

The increasing adoption of automation and monitoring systems across diverse industries is another major factor driving the growth of the data acquisition hardware market. Automation technologies rely heavily on accurate and timely data to optimize processes, reduce downtime, and improve productivity. Data acquisition systems provide the essential framework for gathering and analyzing this data, enabling organizations to make informed decisions and implement predictive maintenance strategies. In sectors such as energy and power, environmental monitoring, and manufacturing, data acquisition hardware is instrumental in monitoring critical parameters and ensuring operational efficiency. The growing focus on sustainability and energy efficiency also fuels the demand for data acquisition solutions, as organizations seek to minimize environmental impact and comply with regulatory requirements.

Regionally, North America holds a significant share of the data acquisition hardware market, driven by the presence of major industry players and the rapid adoption of advanced technologies across various sectors. The region's strong focus on research and development, coupled with favorable government initiatives supporting technological innovation, further propels market growth. Meanwhile, the Asia Pacific region is expected to witness substantial growth during the forecast period, attributed to the increasing industrialization, expanding manufacturing sector, and rising investments in infrastructure development. Countries such as China, India, and Japan are at the forefront of this growth, as they embrace automation and digitalization to enhance productivity and competitiveness in the global market.

Product Type Analysis

The data acquisition hardware market encompasses a diverse range of product types, each serving distinct applications and industry needs. Among these, PCI-based data acquisition hardware remains a staple in the market, leveraging its high-speed data transfer capabilities and extensive compatibility with various computing systems. PCI, or Peripheral Component Interconnect, is particularly favored in environments that demand robus

Project Summary This dataset contains all qualitative and quantitative data collected in the first phase of the Pandemic Journaling Project (PJP). PJP is a combined journaling platform and interdisciplinary, mixed-methods research study developed by two anthropologists, with support from a team of colleagues and students across the social sciences, humanities, and health fields. PJP launched in Spring 2020 as the COVID-19 pandemic was emerging in the United States. PJP was created in order to “pre-design an archive” of COVID-19 narratives and experiences open to anyone around the world. The project is rooted in a commitment to democratizing knowledge production, in the spirit of “archival activism” and using methods of “grassroots collaborative ethnography” (Willen et al. 2022; Wurtz et al. 2022; Zhang et al 2020; see also Carney 2021). The motto on the PJP website encapsulates these commitments: “Usually, history is written only by the powerful. When the history of COVID-19 is written, let’s make sure that doesn’t happen.” (A version of this Project Summary with links to the PJP website and other relevant sites is included in the public documentation of the project at QDR.) In PJP’s first phase (PJP-1), the project provided a digital space where participants could create weekly journals of their COVID-19 experiences using a smartphone or computer. The platform was designed to be accessible to as wide a range of potential participants as possible. Anyone aged 15 or older, living anywhere in the world, could create journal entries using their choice of text, images, and/or audio recordings. The interface was accessible in English and Spanish, but participants could submit text and audio in any language. PJP-1 ran on a weekly basis from May 2020 to May 2022. Data Overview This Qualitative Data Repository (QDR) project contains all journal entries and closed-ended survey responses submitted during PJP-1, along with accompanying descriptive and explanatory materials. The dataset includes individual journal entries and accompanying quantitative survey responses from more than 1,800 participants in 55 countries. Of nearly 27,000 journal entries in total, over 2,700 included images and over 300 are audio files. All data were collected via the Qualtrics survey platform. PJP-1 was approved as a research study by the Institutional Review Board (IRB) at the University of Connecticut. Participants were introduced to the project in a variety of ways, including through the PJP website as well as professional networks, PJP’s social media accounts (on Facebook, Instagram, and Twitter) , and media coverage of the project. Participants provided a single piece of contact information — an email address or mobile phone number — which was used to distribute weekly invitations to participate. This contact information has been stripped from the dataset and will not be accessible to researchers. PJP uses a mixed-methods research approach and a dynamic cohort design. After enrolling in PJP-1 via the project’s website, participants received weekly invitations to contribute to their journals via their choice of email or SMS (text message). Each weekly invitation included a link to that week’s journaling prompts and accompanying survey questions. Participants could join at any point, and they could stop participating at any point as well. They also could stop participating and later restart. Retention was encouraged with a monthly raffle of three $100 gift cards. All individuals who had contributed that month were eligible. Regardless of when they joined, all participants received the project’s narrative prompts and accompanying survey questions in the same order. In Week 1, before contributing their first journal entries, participants were presented with a baseline survey that collected demographic information, including political leanings, as well as self-reported data about COVID-19 exposure and physical and mental health status. Some of these survey questions were repeated at periodic intervals in subsequent weeks, providing quantitative measures of change over time that can be analyzed in conjunction with participants' qualitative entries. Surveys employed validated questions where possible. The core of PJP-1 involved two weekly opportunities to create journal entries in the format of their choice (text, image, and/or audio). Each week, journalers received a link with an invitation to create one entry in response to a recurring narrative prompt (“How has the COVID-19 pandemic affected your life in the past week?”) and a second journal entry in response to their choice of two more tightly focused prompts. Typically the pair of prompts included one focusing on subjective experience (e.g., the impact of the pandemic on relationships, sense of social connectedness, or mental health) and another with an external focus (e.g., key sources of scientific information, trust in government, or COVID-19’s economic impact). Each week,...

Abstract

The JPFHS is part of the worldwide Demographic and Health Surveys Program, which is designed to collect data on fertility, family planning, and maternal and child health. The primary objective of the Jordan Population and Family Health Survey (JPFHS) is to provide reliable estimates of demographic parameters, such as fertility, mortality, family planning, fertility preferences, as well as maternal and child health and nutrition that can be used by program managers and policy makers to evaluate and improve existing programs. In addition, the JPFHS data will be useful to researchers and scholars interested in analyzing demographic trends in Jordan, as well as those conducting comparative, regional or crossnational studies.

The content of the 2002 JPFHS was significantly expanded from the 1997 survey to include additional questions on women’s status, reproductive health, and family planning. In addition, all women age 15-49 and children less than five years of age were tested for anemia.

Geographic coverage

National

Analysis unit

• Household
• Children under five years
• Women age 15-49
• Men

Kind of data

Sample survey data

Sampling procedure

The estimates from a sample survey are affected by two types of errors: 1) nonsampling errors and 2) sampling errors. Nonsampling errors are the result of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2002 JPFHS to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2002 JPFHS is only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

A sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95 percent of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2002 JPFHS sample is the result of a multistage stratified design and, consequently, it was necessary to use more complex formulas. The computer software used to calculate sampling errors for the 2002 JPFHS is the ISSA Sampling Error Module (ISSAS). This module used the Taylor linearization method of variance estimation for survey estimates that are means or proportions. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

Note: See detailed description of sample design in APPENDIX B of the survey report.

Mode of data collection

Face-to-face

Research instrument

The 2002 JPFHS used two questionnaires – namely, the Household Questionnaire and the Individual Questionnaire. Both questionnaires were developed in English and translated into Arabic. The Household Questionnaire was used to list all usual members of the sampled households and to obtain information on each member’s age, sex, educational attainment, relationship to the head of household, and marital status. In addition, questions were included on the socioeconomic characteristics of the household, such as source of water, sanitation facilities, and the availability of durable goods. The Household Questionnaire was also used to identify women who are eligible for the individual interview: ever-married women age 15-49. In addition, all women age 15-49 and children under five years living in the household were measured to determine nutritional status and tested for anemia.

The household and women’s questionnaires were based on the DHS Model “A” Questionnaire, which is designed for use in countries with high contraceptive prevalence. Additions and modifications to the model questionnaire were made in order to provide detailed information specific to Jordan, using experience gained from the 1990 and 1997 Jordan Population and Family Health Surveys. For each evermarried woman age 15 to 49, information on the following topics was collected:

1. Respondent’s background
2. Birth history
3. Knowledge and practice of family planning
4. Maternal care, breastfeeding, immunization, and health of children under five years of age
5. Marriage
6. Fertility preferences
7. Husband’s background and respondent’s employment
8. Knowledge of AIDS and STIs

In addition, information on births and pregnancies, contraceptive use and discontinuation, and marriage during the five years prior to the survey was collected using a monthly calendar.

Cleaning operations

Fieldwork and data processing activities overlapped. After a week of data collection, and after field editing of questionnaires for completeness and consistency, the questionnaires for each cluster were packaged together and sent to the central office in Amman where they were registered and stored. Special teams were formed to carry out office editing and coding of the open-ended questions.

Data entry and verification started after one week of office data processing. The process of data entry, including one hundred percent re-entry, editing and cleaning, was done by using PCs and the CSPro (Census and Survey Processing) computer package, developed specially for such surveys. The CSPro program allows data to be edited while being entered. Data processing operations were completed by the end of October 2002. A data processing specialist from ORC Macro made a trip to Jordan in October and November 2002 to follow up data editing and cleaning and to work on the tabulation of results for the survey preliminary report. The tabulations for the present final report were completed in December 2002.

Response rate

A total of 7,968 households were selected for the survey from the sampling frame; among those selected households, 7,907 households were found. Of those households, 7,825 (99 percent) were successfully interviewed. In those households, 6,151 eligible women were identified, and complete interviews were obtained with 6,006 of them (98 percent of all eligible women). The overall response rate was 97 percent.

Note: See summarized response rates by place of residence in Table 1.1 of the survey report.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: 1) nonsampling errors and 2) sampling errors. Nonsampling errors are the result of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2002 JPFHS to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2002 JPFHS is only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

A sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95 percent of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2002 JPFHS sample is the result of a multistage stratified design and, consequently, it was necessary to use more complex formulas. The computer software used to calculate sampling errors for the 2002 JPFHS is the ISSA Sampling Error Module (ISSAS). This module used the Taylor linearization method of variance estimation for survey estimates that are means or proportions. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

Note: See detailed

Data represent reports of capture of black carp by commercial fishers and biologists with information regarding size characteristics of collected individuals, dimensions of capture gears, and spatial and temporal distributions of captures.

The global advanced data collectors market is poised for substantial growth, with an estimated market size of XXX million units in 2025 and a projected CAGR of XX% between 2025 and 2033. This market is driven by the increasing demand for data collection and analysis in a wide range of industries, including communication equipment, automotive, consumer electronics, aerospace and defense, and healthcare. Moreover, the advancements in technology and the growing need for real-time data insights are further fueling market growth. Key market trends include the rising adoption of standalone data collectors for portability and convenience, the integration of AI and ML capabilities for enhanced data analysis, and the increasing demand for wireless data collection systems. The market is highly competitive, with leading players such as Keysight Technologies, Yokogawa Electric, Anritsu, and National Instruments holding significant market shares. North America is expected to remain the dominant regional market, followed by Europe and Asia Pacific. The Asia Pacific region is projected to exhibit the highest growth rate due to the rapidly expanding manufacturing and automotive sectors in countries such as China and India.

Abstract

The documented dataset covers Enterprise Survey (ES) panel data collected in Dominican Republic in 2010 and 2016, as part of Latin America and the Caribbean Enterprise Surveys rollout, an initiative of the World Bank. The objective of the Enterprise 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.

Enterprise Surveys target a sample consisting of longitudinal (panel) observations and new cross-sectional data. Panel firms are prioritized in the sample selection, comprising up to 50% of the sample. For all panel firms, regardless of the sample, current eligibility or operating status is determined and included in panel datasets.

Dominican Republic ES 2010 was conducted in March - September 2011, ES 2016 was carried out in August 2016 - April 2017. Stratified random sampling was used to select the surveyed businesses. Data was collected using face-to-face interviews.

Data from 719 establishments was analyzed: 257 businesses were from 2010 ES only, 256 - from 2016 only, and 206 firms were from 2010 and 2016.

The standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs and labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures. Over 90 percent of the questions objectively measure characteristics of a country’s business environment. The remaining questions assess the survey respondents’ opinions on what are the obstacles to firm growth and performance.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study is an establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

The whole population, or the universe, covered in the Enterprise Surveys is the non-agricultural private economy. It comprises: all manufacturing sectors according to the ISIC Revision 3.1 group classification (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this population definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, except sub-sector 72, IT, which was added to the population under study), and all public or utilities sectors. Companies with 100% government ownership are not eligible to participate in the Enterprise Surveys.

Kind of data

Sample survey data [ssd]

Sampling procedure

Three levels of stratification were used in this country: industry, establishment size and region.

Industry stratification was designed as follows: the universe was stratified as into manufacturing and services industries - Manufacturing (ISIC Rev. 3.1 codes 15 - 37), and Services (ISIC codes 45, 50-52, 55, 60-64, and 72).

Size stratification was defined as follows: small (5 to 19 employees), medium (20 to 99 employees), and large (100 or more employees).

In 2016, regional stratification was done across three regions: Santo Domingo, Santiago-Puerto Plata-Espaillat and the Rest of the country.

The sample frame consisted of listings of firms from three sources: for panel firms the list of 360 firms from the Dominican Republic 2010 ES was used and for fresh firms (i.e., firms not covered in 2010) a listing of firms obtained from El Directorio de Empresas y Establecimientos (DEE) 2015 and Oficina Nacional de Estadística (ONE), were used.

In 2010, regional stratification was defined in two locations: Santo Domingo and the rest of the country (constituted by urban centers around Santiago and Higuey). For the purposes of sampling, the rest of the country was treated as one area.

The sample frame for 2010 ES was provided by the Oficina Nacional de Estadistica (ONE), dated 2009.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

Data entry and quality controls are implemented by the contractor and data is delivered to the World Bank in batches (typically 10%, 50% and 100%). These data deliveries are checked for logical consistency, out of range values, skip patterns, and duplicate entries. Problems are flagged by the World Bank and corrected by the implementing contractor through data checks, callbacks, and revisiting establishments.

Response rate

Survey non-response must be differentiated from item non-response. The former refers to refusals to participate in the survey altogether whereas the latter refers to the refusals to answer some specific questions. Enterprise Surveys suffer from both problems and different strategies were used to address these issues.

Item non-response was addressed by two strategies: a- For sensitive questions that may generate negative reactions from the respondent, such as corruption or tax evasion, enumerators were instructed to collect "Refusal to respond" (-8) as a different option from "Don't know" (-9). b- Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary.

Survey non-response was addressed by maximizing efforts to contact establishments that were initially selected for interview. Attempts were made to contact the establishment for interview at different times/days of the week before a replacement establishment (with similar strata characteristics) was suggested for interview. Survey non-response did occur but substitutions were made in order to potentially achieve strata-specific goals.

The NSW BioNet Flora Survey Data Collection is maintained via the Flora Survey Module of the NSW BioNet-Atlas application. This collection is a central, authoritative database for systematic vegetation survey data in NSW. Among other applications, this plot data is used to construct and maintain the quantitative Plant Community Type classification & Vegetation Integrity Benchmarks held in the BioNet Vegetation Classification Data Collection. These plots are also used to construct and update State Vegetation Type Maps held in the BioNet Vegetation Map Data Collection. ACCESS: Full datasets (site and species) may be accessed via the BioNet-Atlas application http://www.BioNet.nsw.gov.au/. Survey site level data is available in a machine readable form via the BioNet OData Web Service https://data.bionet.nsw.gov.au/. That data service is delivered to SEED where it is rendered as a Web Map Service. Further detail is available from http://www.environment.nsw.gov.au/research/VISplot.htm.
This data collection includes over 100,000 survey plots, that are generally compatible with standard vegetation survey methodologies outlined in the NSW Native Vegetation Interim Type Standard http://www.environment.nsw.gov.au/resources/nativeveg/10060nvinttypestand.pdf. The Type Standard and application accommodate a range of data types from various surveys, including: 1. full floristic survey data associated with vegetation classification and mapping; 2. rapid survey sites associated with field validation and vegetation type mapping; and 3. land-use data associated with the Monitoring Evaluation and Reporting Program (MER) Vegetation Condition site assessment. Species records in the Flora Survey Data Collection are also queried through the species sightings searches in BioNet-Atlas. Data in BioNet is made available in accordance with OEH's Sensitive Species Data Policy http://www.environment.nsw.gov.au/policiesandguidelines/SensitiveSpeciesPolicy.htm. For species categorised as "sensitive", location information may be withheld depending on the species' status under the policy, and on the access rights of the user. Records in BioNet are not guaranteed to be free from error or omission.

This statistic shows the amount of data collected by smart buildings worldwide, from 2010 to 2020. In 2015, smart buildings collected *** zetabytes of data globally, through a range of sensors and smart and connected devices.

DWR has a long history of studying and characterizing California’s groundwater aquifers as a part of California’s Groundwater (Bulletin 118). California's Groundwater Basin Characterization Program provides the latest data and information about California’s groundwater basins to help local communities better understand their aquifer systems and support local and statewide groundwater management.

Under the Basin Characterization Program, new and existing data (AEM, lithology logs, geophysical logs, etc.) are integrated to create continuous maps and three-dimensional models. To support this effort, new data analysis tools have been developed to create texture models, hydrostratigraphic models, and aquifer flow parameters. Data collection efforts have been expanded to include advanced geologic, hydrogeologic, and geophysical data collection and data digitization and quality control efforts will continue. To continue to support data access and data equity, the Basin Characterization Program has developed new online, GIS-based, visualization tools to serve as a central hub for accessing and exploring groundwater related data in California.

Additional information can be found on the Basin Characterization Program webpage.

DWR's Evaluation of Groundwater Resources

Maps and Models

DWR is undertaking local, regional, and statewide investigations to evaluate California's groundwater resources and develop state-stewarded maps and models. New and existing data have been combined and integrated using the analysis tools described below to develop maps and models that describe grain size, the hydrostratigraphic properties, and hydrogeologic conceptual properties of California’s aquifers. These maps and models help groundwater managers understand how groundwater is stored and moves within the aquifer. The models will be state-stewarded, meaning that they will be regularly updated, as new data becomes available, to ensure that up-to-date information is used for groundwater management activities. The first iterations of the following maps and models will be published as they are developed:

• Texture Models
• Hydrostratigraphic Models
• Aquifer Recharge Potential Maps
• Extent of Important Aquifer Units
• Depth to Basement
• Depth to Freshwater

Click on the link below for each local, regional, or statewide investigation to find the following datasets.

Local Investigations

• Madera & North Kings: AEM data collection, lithology log digitization, texture model development, aquifer recharge potential mapping analysis.
• Pajaro: AEM and tTEM data collection
• Western San Joaquin Valley: AEM data collection, lithology and geophysical log data digitization, texture model development.
• Prospect Island: AEM data collection, FloaTEM data collection test.

Regional Investigations

• Sacramento Valley: AEM data collection, texture model development, aquifer recharge potential mapping analysis.
• Four County Area of San Joaquin Valley (Madera, Fresno, Kings, and Tulare)
• San Joaquin Valley

Statewide Investigations

• Statewide AEM Surveys: AEM data collection, geophysical and lithology log data digitization.

Data Collection & Compilation

As a part of the Basin Characterization Program, advanced geologic, hydrogeologic, and geophysical data will be collected to improve our understanding of groundwater basins. Data collected under Basin Characterization are collected at a local, regional, or statewide scale depending on the scope of the study. Advanced data collection methods include:

• Airborne electromagnetics (AEM)
• All-terrain vehicle towed electromagnetics (tTEM)
• Watercraft towed electromagnetics (FloaTEM)
• Geophysical borehole logging

Digitized Existing Lithology and Geophysical Logs

Lithology and geophysical logging data have been digitized to support the Statewide AEM Survey Project and will continue to be digitized to support Basin Characterization efforts. All digitized lithology logs with Well Completion Report IDs will be imported back into the OSWCR database. Digitized lithology and geophysical logging can be found under the following resource:

• Digitized Lithology and Geophysical Logs.

Analysis Tools and Process Documents

To develop the state-stewarded maps and models outlined above, new tools and process documents have been created to integrate and analyze a wide range of data, including geologic, geophysical, and hydrogeologic information. By combining and assessing various datasets, these tools help create a more complete picture of California's groundwater basins. All tools, along with guidance documents, are made publicly available for local groundwater managers to use to support development of maps and models at a local scale. All tools and guidance will be updated as revisions to tools and process documents are made.

Data Analysis Tools

• Data2Texture: Data2Texture is an advanced spatial data interpolation tool for estimating the distribution of sediment textures from airborne electromagnetic data and lithology logs to create a 3D texture model

• Data2HSM - Smart Interpretation: Data2HSM via Smart Interpretation (SI) is a semi-automatic Python tool for delineating continuous hydrogeologic surfaces from airborne electromagnetic data products.

• Data2HSM - Gaussian Mixture Model: The Data2HSM via Gaussian Mixture Model tool ingests the AEM data and groups the data into a user-specified number of clusters that are interpreted as stratigraphic units in the hydrostratigraphic model (HSM)

• Data2HSM - Geological Pseudolabel Deep Neural Network: The GeoPDNN (Geological Pseudolabel Deep Neural Network) is a semi-supervised machine learning tool that integrates lithologic well logs and AEM data into plausible stratigraphic surfaces.

• Texture2Par V2: Texture2Par V2 is a groundwater model pre-processor and parameterization utility developed to work with the IWFM and MODFLOW families of hydrologic simulation code.

Process Documents

• Aquifer Recharge Potential Mapping: The Aquifer Recharge Potential (ARP) Mapping Process Document provides a framework for identifying locations that have relatively higher potential for managed aquifer recharge.

Data Visualization

Data access equity is a priority for the Basin Characterization Program. To ensure data access equity, the Basin Characterization Program has developed applications and tools to allow data to be visualized without needing access to expensive data visualization software. This list below provides links and descriptions for the Basin Characterization's suite of data viewers.

SGMA Data Viewer: Basin Characterization tab: Provides maps, depth slices, and profiles of Basin Characterization maps, models, and datasets, including the following:

• Aquifer Recharge Potential Maps
• Subsurface Texture Model Depth Slices
• Statewide AEM Survey Texture Depth Slices
• Lithology Log Location Maps
• Geophysical Logs Location Maps
• Statewide AEM Survey Profile Images

3D AEM Data Viewer: Displays the Statewide AEM Survey electrical resistivity and coarse fraction data, along with lithology logs, in a three-dimensional space.

California's Groundwater Subsurface Viewer: Provides a map view and profile view of the Statewide AEM Survey electrical resistivity and coarse fraction data, along with lithology logs. The map view dynamically shows the exact location of AEM data displayed.

Basin Characterization Exchange

The Basin Characterization

Motivation: Home range is a common measure of animal space use as it provides ecological information that is useful for conservation applications. In macroecological studies, values are typically aggregated to species means to examine general patterns of animal space use. However, this ignores the environmental context in which the home range was estimated and does not account for intraspecific variation in home range size. In addition, the focus of macroecological studies on home ranges has been historically biased toward terrestrial mammals. The use of aggregated numbers and terrestrial focus limits our ability to examine home range patterns across different environments, variation in time and between different levels of organisation. Here we introduce HomeRange, a global database with 75,611 home-range values across 960 different mammal species, including terrestrial, as well as aquatic and aerial species. Main types of variable contained: The dataset contains mammal home-range estimates, species names, methodological information on data collection, home-range estimation method, period of data collection, study coordinates and name of location, as well as species traits derived from the studies, such as body mass, life stage, reproductive status and locomotor habit. Spatial location and grain: The collected data is distributed globally. Across studies, the spatial accuracy varies, with the coarsest resolution being 1 degree. Time period and grain: The data represent information published between 1939 and 2022. Across studies, the temporal accuracy varies, some studies report start and end dates specific to the day. For other studies, only the month or year is reported. Major taxa and level of measurement: Mammal species from 24 of the 27 different taxonomic orders. Home-range estimates range from individual-level values to population-level averages. Methods Mammalian home range papers were compiled via an extensive literature search. All home range values were extracted from the literature including individual, group and population-level home range values. Associated values were also compiled including species names, methodological information on data collection, home-range estimation method, period of data collection, study coordinates and name of location, as well as species traits derived from the studies, such as body mass, life stage, reproductive status and locomotor habit. Here we include the database, associated metadata and reference list of all sources from which home range data was extracted from. We also provide an R package, which can be installed from https://github.com/SHoeks/HomeRange. The HomeRange R package provides functions for downloading the latest version of the HomeRange database and loading it as a standard dataframe into R, plotting several statistics of the database and finally attaching species traits (e.g. species average body mass, trophic level) from the COMBINE (Soria et al. 2021) for statistical analysis.

Hudson River Estuary Shallow Water Surveys. Subbottom data was collected November 5 to December 15, 2009, in the estuary north from Saugerties to Troy. Data Collection and Processing: Subbottom Data - Fugro utilized the EdgeTech SB216 Chirp subbottom profiler system for seismic data collection. This system was operated using a swept frequency range of 2-16 KHz, maximizing subsurface resolutio...

Database file containing data collected during in the Balconies experiment by a long-range pulsed lidar, installed at 200 m.a.g.l. in the northern mast at the Østerild Test Centre, Denmark.Details of the Balconies experiment can be found in doi :10.1088/1742-6596/1037/5/052029The database consists of two tables:Table_raw: raw lidar scansTable_fil: filtered lidar scans using the methodology described in doi :10.5194/amt-13-6237-2020.Both tables contain the following columns,'name': TEXT containing the date of the measurement'scan': BIGINT scan number covering a complete azimuthal swept of 90 degrees. It starts at 0.'index': BIGINT, row index'start_id': BIGINT parameter of the windscanner file storing system.'stop_id': BIGINT parameter of the windscanner file storing system.'start_time': FLOAT start time of line-of-sight measurement in Mac epoch, seconds since 1904-01-01 00:00.'stop_time': FLOAT stop time of line-of-sight measurement in Mac epoch, seconds since 1904-01-01 00:00.'azim': FLOAT azimuth angle of the line-of-sight measurements in degrees.'elev': FLOAT elevation angle of the line-of-sight measurements in degrees.'range_gate_NN': BIGINT distance between lidar and center of range gate, in meters. NN identifier goes form 0 to 197.'ws_NN': FLOAT line-of-sight wind speed meters per second at the center of the range gate. Negative values indicate the wind is blowing towards the lidar. NN identifier goes form 0 to 197.'CNR_NN': FLOAT Carrier-to-noise ratio at the center of the range gate, in dB. NN identifier goes form 0 to 197.'Sb_NN' : FLOAT Spectral broadening at the center of the range gate, in meters per second. NN identifier goes form 0 to 197.This version 2 of the dataset fix a typo in the metadata file and the description in the previous version:version 1:- name: scan- description: scan number covering a complete azimuthal swept of 45 degrees. It starts at 0.version 2, corrected:- name: scan- description: scan number covering a complete azimuthal swept of 90 degrees. It starts at 0.

Abstract

The survey was conducted in Papua New Guinea from August 2015 to June 2016 as part of Enterprise Surveys project, an initiative of the World Bank. 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 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. Only registered businesses are surveyed in the Enterprise Survey.

Data from 65 establishments was analyzed. Stratified random sampling was used to select the surveyed businesses. The data was collected using face-to-face interviews.

The standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs and labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures. Over 90 percent of the questions objectively ascertain characteristics of a country’s business environment. The remaining questions assess the survey respondents’ opinions on what are the obstacles to firm growth and performance.

Geographic coverage

Lae, Port Moresby

Analysis unit

The primary sampling unit of the study is an establishment. The establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

The whole population, or the universe, covered in the Enterprise Surveys is the non-agricultural private economy. It comprises: all manufacturing sectors according to the ISIC Revision 3.1 group classification (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this population definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, except sub-sector 72, IT, which was added to the population under study), and all public or utilities sectors. Companies with 100% government ownership are not eligible to participate in the Enterprise Surveys.

Kind of data

Sample survey data [ssd]

Sampling procedure

Three levels of stratification were used in this country: industry, establishment size, and region.

Industry stratification was designed in the way that follows: the universe was stratified into Manufacturing industries (ISIC Rev. 3.1 codes 15 - 37), and Services industries (ISIC codes 45, 50-52, 55, 60-64, and 72).

Size stratification was defined as follows: small (5 to 19 employees), medium (20 to 99 employees), and large (100 or more employees).

Regional stratification was done across these regions: Lae and Port Moresby.

Mode of data collection

Face-to-face [f2f]

Research instrument

The following survey instruments are available: - Manufacturing Module Questionnaire - Services Module Questionnaire

Questionnaires have common questions (core module) and respectfully additional manufacturing and services specific questions.

The eligible manufacturing industries have been surveyed using the Manufacturing questionnaire (includes the core module, plus manufacturing specific questions). Retail firms have been interviewed using the Services questionnaire (includes the core module plus retail specific questions) and the residual eligible services have been covered using the Services questionnaire (includes the core module). Each variation of the questionnaire is identified by the index variable, a0.

The survey is fielded via manufacturing or services questionnaires in order not to ask questions that are irrelevant to specific types of firms, e.g. a question that relates to production and nonproduction workers should not be asked of a retail firm. In addition to questions that are asked across countries, all surveys are customized and contain country-specific questions. An example of customization would be including tourism-related questions that are asked in certain countries when tourism is an existing or potential sector of economic growth.

Cleaning operations

Data entry and quality controls are implemented by the contractor and data is delivered to the World Bank in batches (typically 10%, 50% and 100%). These data deliveries are checked for logical consistency, out of range values, skip patterns, and duplicate entries. Problems are flagged by the World Bank and corrected by the implementing contractor through data checks, callbacks, and revisiting establishments.

Response rate

Survey non-response must be differentiated from item non-response. The former refers to refusals to participate in the survey altogether whereas the latter refers to the refusals to answer some specific questions. Enterprise Surveys suffer from both problems and different strategies were used to address these issues.

Item non-response was addressed by two strategies: a- For sensitive questions that may generate negative reactions from the respondent, such as corruption or tax evasion, enumerators were instructed to collect "Refusal to respond" (-8) as a different option from "Don't know" (-9). b- Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary.

Survey non-response was addressed by maximizing efforts to contact establishments that were initially selected for interview. Attempts were made to contact the establishment for interview at different times/days of the week before a replacement establishment (with similar strata characteristics) was suggested for interview. Survey non-response did occur but substitutions were made in order to potentially achieve strata-specific goals.

Abstract

The survey was conducted in Honduras between July 2016 and September 2017 as part of Enterprise Surveys project, an initiative of the World Bank. Data from 332 establishments was analyzed.

The objective of the Enterprise Survey is to gain an understanding of what firms experience in the private sector. Through 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 standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs and labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures. Over 90 percent of the questions objectively ascertain characteristics of a country's business environment. The remaining questions assess the survey respondents' opinions on what are the obstacles to firm growth and performance.

Geographic coverage

National Coverage

Analysis unit

The primary sampling unit of the study is the establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

The whole population, or universe of the study, is the non-agricultural economy. It comprises: all manufacturing sectors according to the group classification of ISIC Revision 3.1: (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, except sub-sector 72, IT, which was added to the population under study), and all public or utilities-sectors.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for 2016 Honduras ES was selected using stratified random sampling.

Three levels of stratification were used in this country: industry, establishment size, and region.

Industry stratification was designed as follows: the universe was stratified into Manufacturing industries (ISIC Rev. 3.1 codes 15- 37), Retail industries (ISIC code 52) and Other Services (ISIC codes 45, 50, 51, 55, 60-64, and 72).

For the Honduras ES, size stratification was defined as follows: small (5 to 19 employees), medium (20 to 99 employees), and large (100 or more employees).

Regional stratification for the Honduras ES was done across three regions: Tegucigalpa, San Pedro Sula and Rest of the Country.

Given the stratified design, sample frames containing a complete and updated list of establishments as well as information on all stratification variables (number of employees, industry, and region) are required to draw the sample. Great efforts were made to obtain the best source for these listings.

The sample frame consisted of listings of firms from two sources: The panel firms list of 360 firms from the Honduras 2010 ES was used. For fresh firms (i.e., firms not covered in 2010), firm data from Servicio de Administración de Rentas, SAR was used.

Mode of data collection

Face-to-face [f2f]

Research instrument

The structure of the data base reflects the fact that 2 different versions of the survey instrument were used for all registered establishments. Questionnaires have common questions (core module) and respectfully additional manufacturing- and services-specific questions.

The eligible manufacturing industries have been surveyed using the Manufacturing questionnaire (includes the core module, plus manufacturing specific questions).

Retail firms have been interviewed using the Services questionnaire (includes the core module plus retail specific questions) and the residual eligible services have been covered using the Services questionnaire (includes the core module).

Each variation of the questionnaire is identified by the index variable, a0.

The last complete fiscal year is January to December 2015. For questions pertaining to monetary amounts, the unit is the Honduran Lempira.

Cleaning operations

Data entry and quality controls are implemented by the contractor and data is delivered to the World Bank in batches (typically 10%, 50% and 100%). These data deliveries are checked for logical consistency, out of range values, skip patterns, and duplicate entries. Problems are flagged by the World Bank and corrected by the implementing contractor through data checks, callbacks, and revisiting establishments.

Response rate

Survey non-response must be differentiated from item non-response. The former refers to refusals to participate in the survey altogether whereas the latter refers to the refusals to answer some specific questions. Enterprise Surveys suffer from both problems and different strategies were used to address these issues.

Item non-response was addressed by two strategies: a- For sensitive questions that may generate negative reactions from the respondent, such as corruption or tax evasion, enumerators were instructed to collect the refusal to respond (-8) as a different option from don’t know (-9). b- Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary. However, there were clear cases of low response. Please, note that for this specific question, refusals were not separately identified from “Don’t know” responses.

Survey non-response was addressed by maximizing efforts to contact establishments that were initially selected for interview. Attempts were made to contact the establishment for interview at different times/days of the week before a replacement establishment (with similar strata characteristics) was suggested for interview. Survey non-response did occur but substitutions were made in order to potentially achieve strata-specific goals; whenever this was done, strict rules were followed to ensure replacements were randomly selected within the same stratum. Further research is needed on survey non-response in the Enterprise Surveys regarding potential introduction of bias.

The share of rejections per contact was 0.21.

The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching *** zettabytes in 2024. Over the next five years up to 2028, global data creation is projected to grow to more than *** zettabytes. In 2020, the amount of data created and replicated reached a new high. The growth was higher than previously expected, caused by the increased demand due to the COVID-19 pandemic, as more people worked and learned from home and used home entertainment options more often. Storage capacity also growing Only a small percentage of this newly created data is kept though, as just * percent of the data produced and consumed in 2020 was saved and retained into 2021. In line with the strong growth of the data volume, the installed base of storage capacity is forecast to increase, growing at a compound annual growth rate of **** percent over the forecast period from 2020 to 2025. In 2020, the installed base of storage capacity reached *** zettabytes.