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Ever need to help a researcher share and archive their research data? Would you know how to advise them on managing their data so it can be easily shared and re-used? This workshop will cover best practices for collecting and organizing research data related to the goal of data preservation and sharing. We will focus on best practices and tips for collecting data, including file naming, documentation/metadata, quality control, and versioning, as well as access and control/security, backup and storage, and licensing. We will discuss the library’s role in data management, and the opportunities and challenges around supporting data sharing efforts. Through case studies we will explore a typical research data scenario and propose solutions and services by the library and institutional partners. Finally, we discuss methods to stay up to date with data management related topics.
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The global data collector market is experiencing robust growth, driven by increasing automation across diverse sectors and the escalating demand for real-time data analysis. This market, estimated at $15 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 7% from 2025 to 2033, reaching an estimated value of $25 billion by 2033. Key drivers include the expanding adoption of data analytics in precision agriculture, the rising prevalence of IoT devices generating massive datasets in industrial settings, and the growing need for advanced security systems relying on real-time data capture. The market is segmented by data collector type (portable and desktop) and application (agriculture, healthcare, security, industrial, communication, and others). The portable segment holds a significant market share due to its flexibility and ease of use in diverse field applications. North America and Europe currently dominate the market, but the Asia-Pacific region is poised for substantial growth fueled by increasing industrialization and technological advancements. However, factors such as high initial investment costs for advanced data collection systems and the need for skilled professionals to operate and interpret the data could act as market restraints. The competitive landscape features a mix of established technology giants like Microsoft and IBM alongside specialized data collector manufacturers like LUDECA, Inc., and PANalytical. These companies are actively engaged in research and development, focusing on improving data accuracy, speed, and integration capabilities. The increasing convergence of data collection with cloud computing and artificial intelligence is further shaping the market, creating opportunities for innovative solutions that enhance data analysis and decision-making across sectors. The market's future trajectory is closely tied to technological advancements in sensor technology, data storage, and communication networks, promising continued expansion and innovation throughout the forecast period.
Comprehensive dataset of 2 Garbage collection services in Мерсин, Turkey as of June, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.
Resources represent descriptions of collections and detail the Creators, dates, historical/biographical description, extent and formats available (boxes, folders, photographs, maps, volumes, digital objects, etc.). The data represents collections described at the collection level for improved accessibility.
The data can be used to research and/or request collections available at the Municipal Archives. They can also be accessed at https://a860-collectionguides.nyc.gov/https://www.datainsightsmarket.com/privacy-policyhttps://www.datainsightsmarket.com/privacy-policy
The B2B debt collection services market is experiencing robust growth, driven by a confluence of factors. Increasing business-to-business transactions, coupled with economic fluctuations and a rise in late payments, are fueling demand for efficient and effective debt recovery solutions. The market is witnessing a significant shift towards digitalization, with businesses increasingly adopting advanced technologies like AI-powered analytics, automation, and cloud-based platforms to streamline their debt collection processes. This technological advancement leads to improved efficiency, reduced operational costs, and enhanced recovery rates. Furthermore, the growing emphasis on regulatory compliance and data security is driving demand for sophisticated solutions that adhere to stringent industry standards. The competitive landscape is marked by both established players like Experian and TransUnion, and emerging technology-driven companies. These companies are constantly innovating to offer comprehensive solutions encompassing diverse functionalities, from automated debt placement to predictive analytics for risk assessment. This competitive environment fosters innovation and drives continuous improvement in the quality and efficacy of B2B debt collection services. The market is segmented by various factors including service type (e.g., first-party, third-party), deployment model (cloud, on-premise), and business size (SMEs, large enterprises). While the North American and European markets currently hold significant market share, growth is also anticipated in emerging economies driven by increased business activity and expanding digital infrastructure. However, challenges remain, including stringent data privacy regulations and the ethical considerations associated with aggressive debt collection practices. The market’s future trajectory will hinge on the continued adoption of innovative technologies, the ability of service providers to adapt to evolving regulatory frameworks, and the increasing demand for transparent and ethical debt recovery methods. Assuming a conservative CAGR of 10% and a 2025 market size of $5 billion (a reasonable estimate based on industry reports), the market is projected for significant expansion throughout the forecast period.
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IntelligentMonitor: Empowering DevOps Environments With Advanced Monitoring and Observability aims to improve monitoring and observability in complex, distributed DevOps environments by leveraging machine learning and data analytics. This repository contains a sample implementation of the IntelligentMonitor system proposed in the research paper, presented and published as part of the 11th International Conference on Information Technology (ICIT 2023).
If you use this dataset and code or any herein modified part of it in any publication, please cite these papers:
P. Thantharate, "IntelligentMonitor: Empowering DevOps Environments with Advanced Monitoring and Observability," 2023 International Conference on Information Technology (ICIT), Amman, Jordan, 2023, pp. 800-805, doi: 10.1109/ICIT58056.2023.10226123.
For any questions and research queries - please reach out via Email.
Abstract - In the dynamic field of software development, DevOps has become a critical tool for enhancing collaboration, streamlining processes, and accelerating delivery. However, monitoring and observability within DevOps environments pose significant challenges, often leading to delayed issue detection, inefficient troubleshooting, and compromised service quality. These issues stem from DevOps environments' complex and ever-changing nature, where traditional monitoring tools often fall short, creating blind spots that can conceal performance issues or system failures. This research addresses these challenges by proposing an innovative approach to improve monitoring and observability in DevOps environments. Our solution, Intelligent-Monitor, leverages realtime data collection, intelligent analytics, and automated anomaly detection powered by advanced technologies such as machine learning and artificial intelligence. The experimental results demonstrate that IntelligentMonitor effectively manages data overload, reduces alert fatigue, and improves system visibility, thereby enhancing performance and reliability. For instance, the average CPU usage across all components showed a decrease of 9.10%, indicating improved CPU efficiency. Similarly, memory utilization and network traffic showed an average increase of 7.33% and 0.49%, respectively, suggesting more efficient use of resources. By providing deep insights into system performance and facilitating rapid issue resolution, this research contributes to the DevOps community by offering a comprehensive solution to one of its most pressing challenges. This fosters more efficient, reliable, and resilient software development and delivery processes.
Components The key components that would need to be implemented are:
Implementation Details The core of the implementation would involve the following: - Setting up the data collection pipelines. - Building and training anomaly detection ML models on historical data. - Developing a real-time data processing pipeline. - Creating an alerting framework that ties into the ML models. - Building visualizations and dashboards.
The code would need to handle scaled-out, distributed execution for production environments.
Proper code documentation, logging, and testing would be added throughout the implementation.
Usage Examples Usage examples could include:
References The implementation would follow the details provided in the original research paper: P. Thantharate, "IntelligentMonitor: Empowering DevOps Environments with Advanced Monitoring and Observability," 2023 International Conference on Information Technology (ICIT), Amman, Jordan, 2023, pp. 800-805, doi: 10.1109/ICIT58056.2023.10226123.
Any additional external libraries or sources used would be properly cited.
Tags - DevOps, Software Development, Collaboration, Streamlini...
Established to gain a better understanding of the current situation with regard to NHS wheelchair services in England and to support commissioners and providers to improve services.
Access to up-to-date socio-economic data is a widespread challenge in Tonga and other Pacific Island Countries. To increase data availability and promote evidence-based policymaking, the Pacific Observatory provides innovative solutions and data sources to complement existing survey data and analysis. One of these data sources is a series of High Frequency Phone Surveys (HFPS), which began in 2020 as a way to monitor the socio-economic impacts of the COVID-19 Pandemic, and since 2023 has grown into a series of continuous surveys for socio-economic monitoring. See https://www.worldbank.org/en/country/pacificislands/brief/the-pacific-observatory for further details. For Tonga, after two rounds of data collection from in 2022, monthly HFPS data collection commenced in April 2023 and continued until November 2024 (but with some gaps in the months of collection). The survey collected socio-economic data on topics including employment, income, food security, health, food prices, assets and well-being. Each month of collection has approximately 415 households in the sample and is representative of urban and rural areas. This dataset contains combined monthly survey data for all months of the continuous HFPS in Tonga.
Cleaned, labelled and anonymized version of the master file provided by the World Bank.
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The global market for data capturing tools is experiencing robust growth, driven by the increasing need for businesses to leverage vast amounts of unstructured data for informed decision-making. The market, estimated at $5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $15 billion by 2033. This expansion is fueled by several key factors, including the rising adoption of cloud-based solutions, the proliferation of big data analytics, and the increasing demand for automation in data processing. Businesses across diverse sectors, from finance and healthcare to e-commerce and manufacturing, are actively seeking efficient and accurate data capture tools to streamline operations, improve customer experiences, and gain a competitive edge in the market. Key trends include the integration of artificial intelligence (AI) and machine learning (ML) for enhanced accuracy and automation, the growing preference for user-friendly interfaces, and the increasing focus on data security and compliance. While some restraints exist, such as the high initial investment costs associated with advanced solutions and the need for skilled personnel, the overall market outlook remains positive due to the continued digital transformation efforts undertaken by companies worldwide. The competitive landscape is marked by a mix of established players and emerging startups. Companies like Softomotive, Octopus Data, and Hubdoc offer comprehensive solutions catering to various business needs. Others, such as Diggernaut and Salestools.io, focus on niche markets. The market is characterized by ongoing innovation, with companies continuously developing new features and functionalities to meet evolving customer demands. This includes advancements in optical character recognition (OCR), natural language processing (NLP), and robotic process automation (RPA) technologies, all of which contribute to the increasingly sophisticated capabilities of data capturing tools. The regional distribution is expected to show strong growth across North America and Europe, driven by high technological adoption and a strong emphasis on data-driven decision-making. Asia-Pacific is also projected to witness significant growth in the coming years, fueled by increasing digitalization efforts across various industries.
The national wheelchair data collection was introduced to establish a better understanding of the current situation of NHS wheelchair services in England and to support commissioners and providers to improve services. Wheelchairs provide a significant gateway to independence, well-being and quality of life for thousands of adults and children and the collection will enable benchmarking and the use of transparent data to drive improvements.
The collection is a quarterly Clinical Commissioning Group (CCG) level collection that captures aggregate information on the number of registered users of NHS funded wheelchair services, time from referral to equipment delivery or modification. Data is also collected on expenditure on wheelchair services.
Official statistics are produced impartially and free from any political influence.
Comprehensive dataset of 96 Garbage collection services in Kanagawa, Japan as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.
Introducing Job Posting Datasets: Uncover labor market insights!
Elevate your recruitment strategies, forecast future labor industry trends, and unearth investment opportunities with Job Posting Datasets.
Job Posting Datasets Source:
Indeed: Access datasets from Indeed, a leading employment website known for its comprehensive job listings.
Glassdoor: Receive ready-to-use employee reviews, salary ranges, and job openings from Glassdoor.
StackShare: Access StackShare datasets to make data-driven technology decisions.
Job Posting Datasets provide meticulously acquired and parsed data, freeing you to focus on analysis. You'll receive clean, structured, ready-to-use job posting data, including job titles, company names, seniority levels, industries, locations, salaries, and employment types.
Choose your preferred dataset delivery options for convenience:
Receive datasets in various formats, including CSV, JSON, and more. Opt for storage solutions such as AWS S3, Google Cloud Storage, and more. Customize data delivery frequencies, whether one-time or per your agreed schedule.
Why Choose Oxylabs Job Posting Datasets:
Fresh and accurate data: Access clean and structured job posting datasets collected by our seasoned web scraping professionals, enabling you to dive into analysis.
Time and resource savings: Focus on data analysis and your core business objectives while we efficiently handle the data extraction process cost-effectively.
Customized solutions: Tailor our approach to your business needs, ensuring your goals are met.
Legal compliance: Partner with a trusted leader in ethical data collection. Oxylabs is a founding member of the Ethical Web Data Collection Initiative, aligning with GDPR and CCPA best practices.
Pricing Options:
Standard Datasets: choose from various ready-to-use datasets with standardized data schemas, priced from $1,000/month.
Custom Datasets: Tailor datasets from any public web domain to your unique business needs. Contact our sales team for custom pricing.
Experience a seamless journey with Oxylabs:
Effortlessly access fresh job posting data with Oxylabs Job Posting Datasets.
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In 2023, the global Big Data and Business Analytics market size is estimated to be valued at approximately $274 billion, and with a projected compound annual growth rate (CAGR) of 12.4%, it is anticipated to reach around $693 billion by 2032. This significant growth is driven by the escalating demand for data-driven decision-making processes across various industries, which leverage insights derived from vast data sets to enhance business efficiency, optimize operations, and drive innovation. The increasing adoption of Internet of Things (IoT) devices, coupled with the exponential growth of data generated daily, further propels the need for advanced analytics solutions to harness and interpret this information effectively.
A critical growth factor in the Big Data and Business Analytics market is the increasing reliance on data to gain a competitive edge. Organizations are now more than ever looking to uncover hidden patterns, correlations, and insights from the data they collect to make informed decisions. This trend is especially prominent in industries such as retail, where understanding consumer behavior can lead to personalized marketing strategies, and in healthcare, where data analytics can improve patient outcomes through precision medicine. Moreover, the integration of big data analytics with artificial intelligence and machine learning technologies is enabling more accurate predictions and real-time decision-making, further enhancing the value proposition of these analytics solutions.
Another key driver of market growth is the continuous technological advancements and innovations in data analytics tools and platforms. Companies are increasingly investing in advanced analytics capabilities, such as predictive analytics, prescriptive analytics, and real-time analytics, to gain deeper insights into their operations and market environments. The development of user-friendly and self-service analytics tools is also democratizing data access within organizations, empowering employees at all levels to leverage data in their daily decision-making processes. This democratization of data analytics is reducing the reliance on specialized data scientists, thereby accelerating the adoption of big data analytics across various business functions.
The increasing emphasis on regulatory compliance and data privacy is also driving growth in the Big Data and Business Analytics market. Strict regulations, such as the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the United States, require organizations to manage and analyze data responsibly. This is prompting businesses to invest in robust analytics solutions that not only help them comply with these regulations but also ensure data integrity and security. Additionally, as data breaches and cybersecurity threats continue to rise, organizations are turning to analytics solutions to identify potential vulnerabilities and mitigate risks effectively.
Regionally, North America remains a dominant player in the Big Data and Business Analytics market, benefiting from the presence of major technology companies and a high rate of digital adoption. The Asia Pacific region, however, is emerging as a significant growth area, driven by rapid industrialization, urbanization, and increasing investments in digital transformation initiatives. Europe also showcases a robust market, fueled by stringent data protection regulations and a strong focus on innovation. Meanwhile, the markets in Latin America and the Middle East & Africa are gradually gaining momentum as organizations in these regions are increasingly recognizing the value of data analytics in enhancing business outcomes and driving economic growth.
The Big Data and Business Analytics market is segmented by components into software, services, and hardware, each playing a crucial role in the ecosystem. Software components, which include data management and analytics tools, are at the forefront, offering solutions that facilitate the collection, analysis, and visualization of large data sets. The software segment is driven by a demand for scalable solutions that can handle the increasing volume, velocity, and variety of data. As organizations strive to become more data-centric, there is a growing need for advanced analytics software that can provide actionable insights from complex data sets, leading to enhanced decision-making capabilities.
In the services segment, businesses are increasingly seeking consultation, implementation, and support services to effective
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The global third-party commercial debt collection services market is experiencing robust growth, driven by a rising volume of outstanding commercial debts, increasing business insolvencies, and the growing adoption of technology-driven collection solutions. The market's expansion is fueled by the need for businesses to efficiently recover overdue payments without diverting significant internal resources. The increasing complexity of commercial transactions, coupled with fluctuating economic conditions, necessitates the expertise of specialized debt collection agencies. These agencies leverage advanced analytics, automation, and legal expertise to maximize recovery rates and minimize the time and cost associated with debt recovery. While regulatory scrutiny and consumer protection laws pose challenges, the market is adapting with robust compliance programs and ethical collection practices. The market is segmented by service type (e.g., first-party collection, third-party collection, legal assistance), industry (e.g., healthcare, finance, telecom), and geography. Key players are continuously investing in technological advancements like AI-powered predictive modeling and automated communication systems to enhance efficiency and effectiveness. This competitive landscape drives innovation and fosters a dynamic market. The projected market size in 2025 is estimated to be $15 billion, based on reasonable assumptions about industry growth and available information. Considering a conservative CAGR of 7% (a commonly observed rate for similar service sectors), the market is poised for substantial expansion throughout the forecast period (2025-2033). While precise regional breakdowns are not provided, it is reasonable to assume a significant market share distribution across North America, Europe, and Asia-Pacific, reflecting the concentration of major economies and commercial activity. However, emerging markets are also presenting significant growth opportunities. The continued adoption of digital technologies, coupled with a rising demand for outsourced collection services, will continue to shape the market landscape in the coming years. The market is expected to witness consolidation among existing players, as larger firms acquire smaller agencies to increase market share and leverage technological advantages.
Comprehensive dataset of 5 Garbage collection services in Mordovia Republic, Russia as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.
This survey was conducted in Slovak Republic between January 2013 and March 2014 as part of the fifth round of the Business Environment and Enterprise Performance Survey (BEEPS V), a joint initiative of the World Bank Group and the European Bank for Reconstruction and Development. 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.
Data from 276 establishments was analyzed. Stratified random sampling was used to select the surveyed businesses.
The survey topics include firm characteristics, information about sales and suppliers, competition, infrastructure services, judiciary and law enforcement collaboration, security, government policies, laws and regulations, financing, overall business environment, bribery, capacity utilization, performance and investment activities, and workforce composition.
In 2011, the innovation module was added to the standard set of Enterprise Surveys questionnaires to examine in detail how introduction of new products and practices influence firms' performance and management.
National
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.
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.
Sample survey data [ssd]
The sample was selected using stratified random sampling. 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 one manufacturing industry, and two service industries (retail, and other services).
Size stratification was defined following the standardized definition for the rollout: small (5 to 19 employees), medium (20 to 99 employees), and large (more than 99 employees). For stratification purposes, the number of employees was defined on the basis of reported permanent full-time workers. This seems to be an appropriate definition of the labor force since seasonal/casual/part-time employment is not common practice, apart from the construction and agriculture sectors which are not included in the survey.
Regional stratification was defined in four regions (city and the surrounding business area) throughout Slovak Republic.
The database "Albertina Company Monitor" was used as the frame for the selection of a sample with the aim of obtaining interviews at 270 establishments with five or more employees.
Given the impact that non-eligible units included in the sample universe may have on the results, adjustments may be needed when computing the appropriate weights for individual observations. The percentage of confirmed non-eligible units as a proportion of the total number of sampled establishments contacted for the survey was 1.9 % (31 out of 1,613 establishments).
In the dataset, the variables a2 (sampling region), a6a (sampling establishment's size), and a4a (sampling sector) contain the establishment's classification into the strata chosen for each country using information from the sample frame. Variable a4a is coded using ISIC Rev 3.1 codes for the chosen industries for stratification. These codes include most manufacturing industries (15 to 37), retail (52), and (45, 50, 51, 55, 60-64, 72) for other services.
Face-to-face [f2f]
Three different versions of the questionnaire were used. The basic questionnaire, the Core Module, includes all common questions asked to all establishments from all sectors. The second expanded variation, the Manufacturing Questionnaire, is built upon the Core Module and adds some specific questions relevant to manufacturing sectors. The third expanded variation, the Retail Questionnaire, is also built upon the Core Module and adds to the core specific questions.
The innovation module was added to the standard set of Enterprise Surveys questionnaires to examine how introduction of new products and practices influence firms' performance and management.
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.
Survey non-response must be differentiated from item non-response. The former refers to refusals to participate in the survey altogether, while 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 as a different option from don’t know. 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 number of contacted establishments per realized interview was 0.14. This number is the result of two factors: explicit refusals to participate in the survey, as reflected by the rate of rejection (which includes rejections of the screener and the main survey) and the quality of the sample frame, as represented by the presence of ineligible units. The number of rejections per contact was 0.56.
The Bangladesh Demographic and Health Survey (BDHS) 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 BDHS is intended to serve as a source of population and health data for policymakers and the research community. In general, the objectives of the BDHS are to: - assess the overall demographic situation in Bangladesh, - assist in the evaluation of the population and health programs in Bangladesh, and - advance survey methodology.
More specifically, the objective of the BDHS is to provide up-to-date information on fertility and childhood mortality levels; nuptiality; fertility preferences; awareness, approval, and use of family planning methods; breastfeeding practices; nutrition levels; and maternal and child health. This information is intended to assist policymakers and administrators in evaluating and designing programs and strategies for improving health and family planning services in the country.
National
Sample survey data
Bangladesh is divided into six administrative divisions, 64 districts (zillas), and 490 thanas. In rural areas, thanas are divided into unions and then mauzas, a land administrative unit. Urban areas are divided into wards and then mahallas. The 1996-97 BDHS employed a nationally-representative, two-stage sample that was selected from the Integrated Multi-Purpose Master Sample (IMPS) maintained by the Bangladesh Bureau of Statistics. Each division was stratified into three groups: 1 ) statistical metropolitan areas (SMAs), 2) municipalities (other urban areas), and 3) rural areas. 3 In the rural areas, the primary sampling unit was the mauza, while in urban areas, it was the mahalla. Because the primary sampling units in the IMPS were selected with probability proportional to size from the 1991 Census frame, the units for the BDHS were sub-selected from the IMPS with equal probability so as to retain the overall probability proportional to size. A total of 316 primary sampling units were utilized for the BDHS (30 in SMAs, 42 in municipalities, and 244 in rural areas). In order to highlight changes in survey indicators over time, the 1996-97 BDHS utilized the same sample points (though not necessarily the same households) that were selected for the 1993-94 BDHS, except for 12 additional sample points in the new division of Sylhet. Fieldwork in three sample points was not possible (one in Dhaka Cantonment and two in the Chittagong Hill Tracts), so a total of 313 points were covered.
Since one objective of the BDHS is to provide separate estimates for each division as well as for urban and rural areas separately, it was necessary to increase the sampling rate for Barisal and Sylhet Divisions and for municipalities relative to the other divisions, SMAs and rural areas. Thus, the BDHS sample is not self-weighting and weighting factors have been applied to the data in this report.
Mitra and Associates conducted a household listing operation in all the sample points from 15 September to 15 December 1996. A systematic sample of 9,099 households was then selected from these lists. Every second household was selected for the men's survey, meaning that, in addition to interviewing all ever-married women age 10-49, interviewers also interviewed all currently married men age 15-59. It was expected that the sample would yield interviews with approximately 10,000 ever-married women age 10-49 and 3,000 currently married men age 15-59.
Note: See detailed in APPENDIX A of the survey report.
Face-to-face
Four types of questionnaires were used for the BDHS: a Household Questionnaire, a Women's Questionnaire, a Men' s Questionnaire and a Community Questionnaire. The contents of these questionnaires were based on the DHS Model A Questionnaire, which is designed for use in countries with relatively high levels of contraceptive use. These model questionnaires were adapted for use in Bangladesh during a series of meetings with a small Technical Task Force that consisted of representatives from NIPORT, Mitra and Associates, USAID/Bangladesh, the International Centre for Diarrhoeal Disease Research, Bangladesh (ICDDR,B), Population Council/Dhaka, and Macro International Inc (see Appendix D for a list of members). Draft questionnaires were then circulated to other interested groups and were reviewed by the BDHS Technical Review Committee (see Appendix D for list of members). The questionnaires were developed in English and then translated into and printed in Bangla (see Appendix E for final version in English).
The Household Questionnaire was used to list all the usual members and visitors in the selected households. Some basic information was collected on the characteristics of each person listed, including his/her age, sex, education, and relationship to the head of the household. The main purpose of the Household Questionnaire was to identify women and men who were eligible for the individual interview. In addition, information was collected about the dwelling itself, such as the source of water, type of toilet facilities, materials used to construct the house, and ownership of various consumer goods.
The Women's Questionnaire was used to collect information from ever-married women age 10-49. These women were asked questions on the following topics: - Background characteristics (age, education, religion, etc.), - Reproductive history, - Knowledge and use of family planning methods, - Antenatal and delivery care, - Breastfeeding and weaning practices, - Vaccinations and health of children under age five, - Marriage, - Fertility preferences, - Husband's background and respondent's work, - Knowledge of AIDS, - Height and weight of children under age five and their mothers.
The Men's Questionnaire was used to interview currently married men age 15-59. It was similar to that for women except that it omitted the sections on reproductive history, antenatal and delivery care, breastfeeding, vaccinations, and height and weight. The Community Questionnaire was completed for each sample point and included questions about the existence in the community of income-generating activities and other development organizations and the availability of health and family planning services.
A total of 9,099 households were selected for the sample, of which 8,682 were successfully interviewed. The shortfall is primarily due to dwellings that were vacant or in which the inhabitants had left for an extended period at the time they were visited by the interviewing teams. Of the 8,762 households occupied, 99 percent were successfully interviewed. In these households, 9,335 women were identified as eligible for the individual interview (i.e., ever-married and age 10-49) and interviews were completed for 9,127 or 98 percent of them. In the half of the households that were selected for inclusion in the men's survey, 3,611 eligible ever-married men age 15-59 were identified, of whom 3,346 or 93 percent were interviewed.
The principal reason for non-response among eligible women and men was the failure to find them at home despite repeated visits to the household. The refusal rate was low.
Note: See summarized response rates by residence (urban/rural) in Table 1.1 of the survey report.
The estimates from a sample survey are affected by two types of errors: (1) non-sampling errors, and (2) sampling errors. Non-sampling errors are the results 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 BDHS to minimize this type of error, non-sampling 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 BDHS 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 BDHS sample is the result of a two-stage stratified design, and, consequently, it was necessary to use more complex formulae. The computer software used to calculate sampling errors for the BDHS is the ISSA Sampling Error Module. This module used the Taylor
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Integrated Urgent Care (IUC) describes a range of services including NHS 111 and Out of Hours services, which aim to ensure a seamless patient experience with minimum handoffs and access to a clinician where required.
Access to up-to-date socio-economic data is a widespread challenge in Papua New Guinea and other Pacific Island Countries. To increase data availability and promote evidence-based policymaking, the Pacific Observatory provides innovative solutions and data sources to complement existing survey data and analysis. One of these data sources is a series of High Frequency Phone Surveys (HFPS), which began in 2020 as a way to monitor the socio-economic impacts of the COVID-19 Pandemic, and since 2023 has grown into a series of continuous surveys for socio-economic monitoring. See https://www.worldbank.org/en/country/pacificislands/brief/the-pacific-observatory for further details.
For PNG, after five rounds of data collection from 2020-2022, in April 2023 a monthly HFPS data collection commenced and continued for 18 months (ending September 2024) –on topics including employment, income, food security, health, food prices, assets and well-being. This followed an initial pilot of the data collection from January 2023-March 2023. Data for April 2023-September 2023 were a repeated cross section, while October 2023 established the first month of a panel, which is ongoing as of March 2025. For each month, approximately 550-1000 households were interviewed. The sample is representative of urban and rural areas but is not representative at the province level. This dataset contains combined monthly survey data for all months of the continuous HFPS in PNG. There is one date file for household level data with a unique household ID, and separate files for individual level data within each household data, and household food price data, that can be matched to the household file using the household ID. A unique individual ID within the household data which can be used to track individuals over time within households.
Urban and rural areas of Papua New Guinea
Household, Individual
Sample survey data [ssd]
The initial sample was drawn through Random Digit Dialing (RDD) with geographic stratification from a large random sample of Digicel’s subscribers. As an objective of the survey was to measure changes in household economic wellbeing over time, the HFPS sought to contact a consistent number of households across each province month to month. This was initially a repeated cross section from April 2023-Dec 2023. The resulting overall sample has a probability-based weighted design, with a proportionate stratification to achieve a proper geographical representation. More information on sampling for the cross-sectional monthly sample can be found in previous documentation for the PNG HFPS data.
A monthly panel was established in October 2023, that is ongoing as of March 2025. In each subsequent round of data collection after October 2024, the survey firm would first attempt to contact all households from the previous month, and then attempt to contact households from earlier months that had dropped out. After previous numbers were exhausted, RDD with geographic stratification was used for replacement households.
Computer Assisted Telephone Interview [cati]
he questionnaire, which can be found in the External Resources of this documentation, is in English with a Pidgin translation.
The survey instrument for Q1 2025 consists of the following modules: -1. Basic Household information, -2. Household Roster, -3. Labor, -4a Food security, -4b Food prices -5. Household income, -6. Agriculture, -8. Access to services, -9. Assets -10. Wellbeing and shocks -10a. WASH
The raw data were cleaned by the World Bank team using STATA. This included formatting and correcting errors identified through the survey’s monitoring and quality control process. The data are presented in two datasets: a household dataset and an individual dataset. The individual dataset contains information on individual demographics and labor market outcomes of all household members aged 15 and above, and the household data set contains information about household demographics, education, food security, food prices, household income, agriculture activities, social protection, access to services, and durable asset ownership. The household identifier (hhid) is available in both the household dataset and the individual dataset. The individual identifier (id_member) can be found in the individual dataset.
Integrated Urgent Care (IUC) describes a range of services including NHS 111 and Out of Hours services, which aim to ensure a seamless patient experience with minimum handoffs and access to a clinician where required. The Integrated Urgent Care Aggregate Data Collection (IUC ADC) provides a detailed breakdown of IUC service demand, performance and activity. The IUC ADC is published as Experimental Statistics from June 2019 (April 2019 data) to May 2021 (March 2021 data). This collection becomes the official source of integrated urgent care statistics, replacing the NHS 111 minimum dataset, and used to monitor the IUC ADC KPIs, from June 2021 (April 2021 data). Official statistics are produced impartially and free from any political influence.
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