Statistical Analysis Software Market size was valued at USD 7,963.44 Million in 2023 and is projected to reach USD 13,023.63 Million by 2030, growing at a CAGR of 7.28% during the forecast period 2024-2030.

Global Statistical Analysis Software Market Drivers

The market drivers for the Statistical Analysis Software Market can be influenced by various factors. These may include:

Growing Data Complexity and Volume: The demand for sophisticated statistical analysis tools has been fueled by the exponential rise in data volume and complexity across a range of industries. Robust software solutions are necessary for organizations to evaluate and extract significant insights from huge datasets. Growing Adoption of Data-Driven Decision-Making: Businesses are adopting a data-driven approach to decision-making at a faster rate. Utilizing statistical analysis tools, companies can extract meaningful insights from data to improve operational effectiveness and strategic planning. Developments in Analytics and Machine Learning: As these fields continue to progress, statistical analysis software is now capable of more. These tools' increasing popularity can be attributed to features like sophisticated modeling and predictive analytics. A greater emphasis is being placed on business intelligence: Analytics and business intelligence are now essential components of corporate strategy. In order to provide business intelligence tools for studying trends, patterns, and performance measures, statistical analysis software is essential. Increasing Need in Life Sciences and Healthcare: Large volumes of data are produced by the life sciences and healthcare sectors, necessitating complex statistical analysis. The need for data-driven insights in clinical trials, medical research, and healthcare administration is driving the market for statistical analysis software. Growth of Retail and E-Commerce: The retail and e-commerce industries use statistical analytic tools for inventory optimization, demand forecasting, and customer behavior analysis. The need for analytics tools is fueled in part by the expansion of online retail and data-driven marketing techniques. Government Regulations and Initiatives: Statistical analysis is frequently required for regulatory reporting and compliance with government initiatives, particularly in the healthcare and finance sectors. In these regulated industries, statistical analysis software uptake is driven by this. Big Data Analytics's Emergence: As big data analytics has grown in popularity, there has been a demand for advanced tools that can handle and analyze enormous datasets effectively. Software for statistical analysis is essential for deriving valuable conclusions from large amounts of data. Demand for Real-Time Analytics: In order to make deft judgments fast, there is a growing need for real-time analytics. Many different businesses have a significant demand for statistical analysis software that provides real-time data processing and analysis capabilities. Growing Awareness and Education: As more people become aware of the advantages of using statistical analysis in decision-making, its use has expanded across a range of academic and research institutions. The market for statistical analysis software is influenced by the academic sector. Trends in Remote Work: As more people around the world work from home, they are depending more on digital tools and analytics to collaborate and make decisions. Software for statistical analysis makes it possible for distant teams to efficiently examine data and exchange findings.

Nonemployer Statistics is an annual series that provides statistics on U.S. businesses with no paid employees or payroll, are subject to federal income taxes, and have receipts of $1,000 or more ($1 or more for the Construction sector). This program is authorized by the United States Code, Titles 13 and 26. Also, the collection provides data for approximately 450 North American Industry Classification System (NAICS) industries at the national, state, county, metropolitan statistical area, and combined statistical area geography levels. The majority of NAICS industries are included with some exceptions as follows: crop and animal production; investment funds, trusts, and other financial vehicles; management of companies and enterprises; and public administration. Data are also presented by Legal Form of Organization (LFO) (U.S. and state only) as filed with the Internal Revenue Service (IRS). Most nonemployers are self-employed individuals operating unincorporated businesses (known as sole proprietorships), which may or may not be the owner's principal source of income. Nonemployers Statistics features nonemployers in several arts-related industries and occupations, including the following: Arts, entertainment, and recreation (NAICS Code 71) Performing arts companies Spectator sports Promoters of performing arts, sports, and similar events Independent artists, writers, and performers Museums, historical sites, and similar institutions Amusement parks and arcades Professional, scientific, and technical services (NAICS Code 54) Architectural services Landscape architectural services Photographic services Retail trade (NAICS Code 44-45) Sporting goods, hobby, and musical instrument stores Sewing, needlework, and piece goods stores Book stores Art dealers Nonemployer Statistics data originate from statistical information obtained through business income tax records that the Internal Revenue Service (IRS) provides to the Census Bureau. The data are processed through various automated and analytical review to eliminate employers from the tabulation, correct and complete data items, remove anomalies, and validate geography coding and industry classification. Prior to publication, the noise infusion method is applied to protect individual businesses from disclosure. Noise infusion was first applied to Nonemployer Statistics in 2005. Prior to 2005, data were suppressed using the complementary cell suppression method. For more information on the coverage and methods used in Nonemployer Statistics, refer to NES Methodology. The majority of all business establishments in the United States are nonemployers, yet these firms average less than 4 percent of all sales and receipts nationally. Due to their small economic impact, these firms are excluded from most other Census Bureau business statistics (the primary exception being the Survey of Business Owners). The Nonemployers Statistics series is the primary resource available to study the scope and activities of nonemployers at a detailed geographic level. For complementary statistics on the firms that do have paid employees, refer to the County Business Patterns. Additional sources of data on small businesses include the Economic Census, and the Statistics of U.S. Businesses. The annual Nonemployer Statistics data are available approximately 18 months after each reference year. Data for years since 2002 are published via comma-delimited format (csv) for spreadsheet or database use, and in the American FactFinder (AFF). For help accessing the data, please refer to the Data User Guide.

Central to spatial analysis of crime is the assumption that the offender has visited the locations analyzed. We show that even if this is incorrect, meaningful patterns can be identified. Using 390 locations from the Sherlock Holmes stories, we show that geographic profiling (GP) ranks Conan Doyle in the top 13 percent of 2,678 historical figures in London, despite the fact at the time of writing he had not visited all of the sites. Restricting the analysis to thirty authors contemporary with the Sherlock Holmes stories, Conan Doyle ranks first, with a hit score of 2.8 percent (above Holmes’s address at 221b Baker Street). Finally, we show that GP prioritizes sites strongly associated with Conan Doyle (example.g., his home) compared to those more tangentially associated with him, even when sites are close together. Our analysis, although mostly for amusement, underlines the ability of GP to extract useful information from complex data.

Invited talk given by Tim Evans (Imperial College London) at the EPSRC Workshop on "Scaling in Social Systems” held at the Saïd Business School, Oxford on 1st December 2011. Abstract:

The pattern of innovation seen through citations of academic papers has long fascinated academics. It has been known for at least fifty years that the data shows various long tailed distributions. In this talk I will look at some of the features of the data and show how to extract some simple universal patterns. I will discuss some of the implications of the results and some of the further questions it raises. •What is a citation? •What does an individual citation mean? •Is the data perfect? •Why citation count? •If not citation count, what else? •What does this data say about me? •Why h-index? •What is a self-citation? •How else can I use this data? •How will things change?

Tim S. Evans – Mini Biography Tim studied the mixture of quantum field theory and statistical physics in his PhD at Imperial College London. He was supervised by Prof. Ray Rivers who also supervised another speaker, Prof. Luis Bettencourt. Tim then spent time as a researcher at the University of Alberta in Edmonton Canada, before returning to research positions back here at Imperial, latterly as a Royal Society University Research Fellow. He was appointed to a lectureship at Imperial in 1997. Around 2003 he expanded his work on statistical physics to cover at problems in complexity, with a particular interest in network methods. This has included participation in an EU collaboration with social scientists on innovation, ―ISCOM, run in part by Prof. Geoff West (another speaker today). This fuelled his interest in social science applications and started an on going collaboration with an archaeologist.

Objectives: To examine trends in strong opioid prescribing in a primary care population in Wales and identify if factors such as age, deprivation and recorded diagnosis of depression or anxiety may have influenced any changes noted.

Design: Trend, cross-sectional and longitudinal analyses of routine data from the Primary Care General Practice database and accessed via the Secure Anonymised Information Linkage (SAIL) databank. Setting: A total of 345 Primary Care practices in Wales.

Participants: Anonymised records of 1,223,503 people aged 18 or over, receiving at least one opioid pre- scription between 1 January 2005 and 31 December 2015 were analysed. People with a cancer diagnosis (10.1%) were excluded from the detailed analysis.

Results: During the study period, 26,180,200 opioid prescriptions were issued to 1,223,503 individuals (55.9% female, 89.9% non-cancer diagnoses). The greatest increase in annual prescribing was in the 18–24 age group (10,470%), from 0.08 to 8.3 prescriptions/1000 population, although the 85+ age group had the highest prescribing rates across the study period (from 149.9 to 288.5 prescriptions/1000 popu- lation). The number of people with recorded diagnoses of depression or anxiety and prescribed strong opioids increased from 1.2 to 5.1 people/1000 population (328%). The increase was 366.9% in areas of highest deprivation compared to 310.3 in the least. Areas of greatest deprivation had more than twice the rate of strong opioid prescribing than the least deprived areas of Wales.

Conclusion: The study highlights a large increase in strong opioid prescribing for non-cancer pain, in Wales between 2005 and 2015. Population groups of interest include the youngest and oldest adult age groups and people with depression or anxiety particularly if living in the most deprived communities. Based on this evidence, development of a Welsh national guidance on safe and rational prescribing of opioids in chronic pain would be advisable to prevent further escalation of these medicines.

Methods Data extracted from the Secure Anonymised Information Linkage databank (SAIL). SQL code used to extract annualised totals for each subset of data.

Excel and SPSS25 used to analyse data using descriptive statistical methods.

Excel used to produce trend graphs and totals.

The Goodreads Book Reviews dataset encapsulates a wealth of reviews and various attributes concerning the books listed on the Goodreads platform. A distinguishing feature of this dataset is its capture of multiple tiers of user interaction, ranging from adding a book to a "shelf", to rating and reading it. This dataset is a treasure trove for those interested in understanding user behavior, book recommendations, sentiment analysis, and the interplay between various attributes of books and user interactions.

Basic Statistics: - Items: 1,561,465 - Users: 808,749 - Interactions: 225,394,930

Metadata: - Reviews: The text of the reviews provided by users. - Add-to-shelf, Read, Review Actions: Various interactions users have with the books. - Book Attributes: Attributes describing the books including title, and ISBN. - Graph of Similar Books: A graph depicting similarity relations between books.

Example (interaction data): json { "user_id": "8842281e1d1347389f2ab93d60773d4d", "book_id": "130580", "review_id": "330f9c153c8d3347eb914c06b89c94da", "isRead": true, "rating": 4, "date_added": "Mon Aug 01 13:41:57 -0700 2011", "date_updated": "Mon Aug 01 13:42:41 -0700 2011", "read_at": "Fri Jan 01 00:00:00 -0800 1988", "started_at": "" }

Use Cases: - Book Recommendations: Creating personalized book recommendations based on user interactions and preferences. - Sentiment Analysis: Analyzing sentiment in reviews and understanding how different book attributes influence sentiment. - User Behavior Analysis: Understanding user interaction patterns with books and deriving insights to enhance user engagement. - Natural Language Processing: Training models to process and analyze user-generated text in reviews. - Similarity Analysis: Analyzing the graph of similar books to understand book similarities and clustering.

Citation: Please cite the following if you use the data: Item recommendation on monotonic behavior chains Mengting Wan, Julian McAuley RecSys, 2018 [PDF](https://cseweb.ucsd.edu/~jmcauley/pdfs/recsys18e.pdf)

Code Samples: A curated set of code samples is provided in the dataset's Github repository, aiding in seamless interaction with the datasets. These include: - Downloading datasets without GUI: Facilitating dataset download in a non-GUI environment. - Displaying Sample Records: Showcasing sample records to get a glimpse of the dataset structure. - Calculating Basic Statistics: Computing basic statistics to understand the dataset's distribution and characteristics. - Exploring the Interaction Data: Delving into interaction data to grasp user-book interaction patterns. - Exploring the Review Data: Analyzing review data to extract valuable insights from user reviews.

Additional Dataset: - Complete book reviews (~15m multilingual reviews about ~2m books and 465k users): This dataset comprises a comprehensive collection of reviews, showcasing a multilingual facet with reviews about around 2 million books from 465,000 users.

Datasets:

Meta-Data of Books:

• Detailed Book Graph (goodreads_books.json.gz): A comprehensive graph detailing around 2.3 million books, acting as a rich source of book attributes and metadata.
  • Download Link
• Detailed Information of Authors (goodreads_book_authors.json.gz):
  • An extensive dataset containing detailed information about book authors, essential for understanding author-centric trends and insights.
  • Download Link
• Detailed Information of Works (goodreads_book_works.json.gz):
  • This dataset provides abstract information about a book disregarding any particular editions, facilitating a high-level understanding of each work.
  • Download Link
• Detailed Information of Book Series (goodreads_book_series.json.gz):
  • A dataset encompassing detailed information about book series, aiding in understanding series-related trends and insights. Note that the series id included here cannot be used for URL hack.
  • Download Link
• Extracted Fuzzy Book Genres (goodreads_book_genres_initial.json....

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Big Data In Manufacturing Market Size 2025-2029

The big data in manufacturing market size is forecast to increase by USD 21.44 billion at a CAGR of 26.4% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing adoption of Industry 4.0 and the emergence of artificial intelligence (AI) and machine learning (ML) technologies. The integration of these advanced technologies is enabling manufacturers to collect, process, and analyze vast amounts of data in real-time, leading to improved operational efficiency, enhanced product quality, and increased competitiveness. Cost optimization is achieved through root cause analysis and preventive maintenance, and AI algorithms and deep learning are employed for capacity planning and predictive modeling.
To capitalize on the opportunities presented by the market and navigate these challenges effectively, manufacturers must invest in building strong data analytics capabilities and collaborating with technology partners and industry experts. By leveraging these resources, they can transform raw data into actionable insights, optimize their operations, and stay ahead of the competition. The sheer volume, velocity, and variety of data being generated require sophisticated tools and expertise to extract meaningful insights. Additionally, ensuring data security and privacy, particularly in the context of increasing digitalization, is a critical concern.

What will be the Size of the Big Data In Manufacturing Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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In the dynamic manufacturing market, Business Intelligence (BI) plays a pivotal role in driving operational efficiency and competitiveness. Blockchain technology and industrial automation are key trends, enhancing transparency and security in supply chain operations. Real-time monitoring systems, Data Integration Tools, and Data Analytics Dashboards enable manufacturers to gain insights from vast amounts of data. Lifecycle analysis, Smart Manufacturing, and Cloud-based Data Analytics facilitate predictive maintenance and optimize production.
PLC programming, Edge AI, KPI tracking, and Automated Reporting facilitate data-driven decision making. Manufacturing Simulation Software and Circular Economy principles foster innovation and sustainability. The market is transforming towards Digital Transformation, incorporating Predictive Maintenance Software and Digital Thread for enhanced visibility and agility. SCADA systems, Carbon Footprint, and Digital Thread promote sustainable manufacturing practices. AI-powered Quality Control, Performance Measurement, and Sensor Networks ensure product excellence.

How is this Big Data In Manufacturing Industry segmented?

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

Type

  Services
  Solutions


Deployment

  On-premises
  Cloud-based
  Hybrid


Application

  Operational analytics
  Production management
  Customer analytics
  Supply chain management
  Others


Geography

  North America

    US
    Canada
    Mexico


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    Japan
    South Korea


  Rest of World (ROW)

By Type Insights

The services segment is estimated to witness significant growth during the forecast period. In the realm of manufacturing, the rise of data from sensors, machines, and operations presents a significant opportunity for analytics and insights. Big data services play a pivotal role in this landscape, empowering manufacturers to optimize resource allocation, minimize operational inefficiencies, and discover cost-saving opportunities. Real-time analytics enable predictive maintenance, reducing unplanned downtime and repair costs. Data visualization tools offer human-machine interfaces (HMIs) for seamless interaction, while machine learning and predictive modeling uncover hidden patterns and trends. Data security is paramount, with robust access control, encryption, and disaster recovery solutions ensuring data integrity. Supply chain management and demand forecasting are streamlined through data integration and real-time analytics.

Quality control is enhanced with digital twins and anomaly detection, minimizing defects and rework. Capacity planning and production monitoring are optimized through time series analysis and neural networks. IoT sensors and data acquisition systems feed data warehouses and data lakes, fueling statistical analysis and regression modeling. Energy efficiency is improved through data-driven insights, while inventory management

Overview

This dataset offers valuable insights into yearly domestic water consumption across various Lower Super Output Areas (LSOAs) or Data Zones, accompanied by the count of water meters within each area. It is instrumental for analysing residential water use patterns, facilitating water conservation efforts, and guiding infrastructure development and policy making at a localised level.

Key Definitions

Aggregation

The process of summarising or grouping data to obtain a single or reduced set of information, often for analysis or reporting purposes.

AMR Meter

Automatic meter reading (AMR) is the technology of automatically collecting consumption, diagnostic, and status data from a water meter remotely and periodically.

Dataset

Structured and organised collection of related elements, often stored digitally, used for analysis and interpretation in various fields.

Data Zone

Data zones are the key geography for the dissemination of small area statistics in Scotland

Dumb Meter

A dumb meter or analogue meter is read manually. It does not have any external connectivity.

Granularity

Data granularity is a measure of the level of detail in a data structure. In time-series data, for example, the granularity of measurement might be based on intervals of years, months, weeks, days, or hours

ID

Abbreviation for Identification that refers to any means of verifying the unique identifier assigned to each asset for the purposes of tracking, management, and maintenance.

LSOA

Lower Layer Super Output Areas (LSOA) are a geographic hierarchy designed to improve the reporting of small area statistics in England and Wales.

Open Data Triage

The process carried out by a Data Custodian to determine if there is any evidence of sensitivities associated with Data Assets, their associated Metadata and Software Scripts used to process Data Assets if they are used as Open Data.

Schema

Structure for organising and handling data within a dataset, defining the attributes, their data types, and the relationships between different entities. It acts as a framework that ensures data integrity and consistency by specifying permissible data types and constraints for each attribute.

Smart Meter

A smart meter is an electronic device that records information and communicates it to the consumer and the supplier. It differs from automatic meter reading (AMR) in that it enables two-way communication between the meter and the supplier.

Units

Standard measurements used to quantify and compare different physical quantities.

Water Meter

Water metering is the practice of measuring water use. Water meters measure the volume of water used by residential and commercial building units that are supplied with water by a public water supply system.

Data History

Data Origin

Domestic consumption data is recorded using water meters. The consumption recorded is then sent back to water companies. This dataset is extracted from the water companies.

Data Triage Considerations

This section discusses the careful handling of data to maintain anonymity and addresses the challenges associated with data updates, such as identifying household changes or meter replacements.

Identification of Critical Infrastructure

This aspect is not applicable for the dataset, as the focus is on domestic water consumption and does not contain any information that reveals critical infrastructure details.

Commercial Risks and Anonymisation

Individual Identification Risks

There is a potential risk of identifying individuals or households if the consumption data is updated irregularly (e.g., every 6 months) and an out-of-cycle update occurs (e.g., after 2 months), which could signal a change in occupancy or ownership. Such patterns need careful handling to avoid accidental exposure of sensitive information.

Meter and Property Association

Challenges arise in maintaining historical data integrity when meters are replaced but the property remains the same. Ensuring continuity in the data without revealing personal information is crucial.

Interpretation of Null Consumption

Instances of null consumption could be misunderstood as a lack of water use, whereas they might simply indicate missing data. Distinguishing between these scenarios is vital to prevent misleading conclusions.

Meter Re-reads

The dataset must account for instances where meters are read multiple times for accuracy.

Joint Supplies & Multiple Meters per Household

Special consideration is required for households with multiple meters as well as multiple households that share a meter as this could complicate data aggregation.

Schema Consistency with the Energy Industry:

In formulating the schema for the domestic water consumption dataset, careful consideration was given to the potential risks to individual privacy. This evaluation included examining the frequency of data updates, the handling of property and meter associations, interpretations of null consumption, meter re-reads, joint suppliers, and the presence of multiple meters within a single household as described above.

After a thorough assessment of these factors and their implications for individual privacy, it was decided to align the dataset's schema with the standards established within the energy industry. This decision was influenced by the energy sector's experience and established practices in managing similar risks associated with smart meters. This ensures a high level of data integrity and privacy protection.

Schema

The dataset schema is aligned with those used in the energy industry, which has encountered similar challenges with smart meters. However, it is important to note that the energy industry has a much higher density of meter distribution, especially smart meters.

Aggregation to Mitigate Risks

The dataset employs an elevated level of data aggregation to minimise the risk of individual identification. This approach is crucial in maintaining the utility of the dataset while ensuring individual privacy. The aggregation level is carefully chosen to remove identifiable risks without excluding valuable data, thus balancing data utility with privacy concerns.

Data Freshness

Users should be aware that this dataset reflects historical consumption patterns and does not represent real-time data.

Publish Frequency

Annually

Data Triage Review Frequency

An annual review is conducted to ensure the dataset's relevance and accuracy, with adjustments made based on specific requests or evolving data trends.

Data Specifications

For the domestic water consumption dataset, the data specifications are designed to ensure comprehensiveness and relevance, while maintaining clarity and focus. The specifications for this dataset include:

·
Each dataset encompasses recordings of domestic water consumption as measured and reported by the data publisher. It excludes commercial consumption.

· Where it is necessary to estimate consumption, this is calculated based on actual meter readings.

· Meters of all types (smart, dumb, AMR) are included in this dataset.

·
The dataset is updated and published annually.

·
Historical data may be made available to facilitate trend analysis and comparative studies, although it is not mandatory for each dataset release.

Context

Users are cautioned against using the dataset for immediate operational decisions regarding water supply management. The data should be interpreted considering potential seasonal and weather-related influences on water consumption patterns.

The geographical data provided does not pinpoint locations of water meters within an LSOA.

The dataset aims to cover a broad spectrum of households, from single-meter homes to those with multiple meters, to accurately reflect the diversity of water use within an LSOA.

Supplementary Information

1. Below is a curated selection of links for additional reading, which provide a deeper understanding of this dataset.

2. Ofwat guidance on water meters

3. https://www.ofwat.gov.uk/wp-content/uploads/2015/11/prs_lft_101117meters.pdf

Global green bean market is expected to show steady growth over the next seven years, with a forecasted increase in volume and value. Key market trends, consumption patterns, production statistics, import and export data, and price fluctuations are analyzed in detail.

This publication provides the most timely picture available of people using NHS funded secondary mental health, learning disabilities and autism services in England. These are experimental statistics which are undergoing development and evaluation. This information will be of use to people needing access to information quickly for operational decision making and other purposes. More detailed information on the quality and completeness of these statistics is made available later in our Mental Health Bulletin: Annual Report publication series. • COVID-19 and the production of statistics Due to the coronavirus illness (COVID-19) disruption, it would seem that this is now starting to affect the quality and coverage of some of our statistics, such as an increase in non-submissions for some datasets. We are also starting to see some different patterns in the submitted data. For example, fewer patients are being referred to hospital and more appointments being carried out via phone/telemedicine/email. Therefore, data should be interpreted with care over the COVID-19 period. • Early release of statistics To support the ongoing COVID-19 work, September 2020 monthly statistics were made available early and presented on our supplementary information pages. https://digital.nhs.uk/data-and-information/supplementary-information/2020/provisional-september-2020-mental-health-statistics • Changing existing measures The move to MHSDS version 4.1 from April 2020 has brought with it changes to the dataset; the construction of a number of measures have been changed as a result. Improvements in the methodology of reporting delay of discharge has also resulted in a change in the construction of the measure from the April 2020 publication onwards. From August 2020 onwards, the methodology for calculating restrictive interventions (MHS76 and MHS77) in the reporting month has been updated to include all restraints that span several months. Previously the measure only includes restraints that started or ended in the month and did not include those spanning more than 2 months. This change predominately impacts segregation. Full details of these changes are available in the associated Metadata file. • New measures A number of new measures have been included from the July 2020 publication onwards: • MHS81 Number of Detentions • MHS82 Number of Short Term Orders • MHS83 Number of uses of Section 136 • MHS84 Number of Community Treatment Orders Full details of these are available in the associated Metadata file. •Unpublished measures For August 2020, 72 hour follow-up measures (MHS78, MHS79 and MHS80) have not been released at the time of publication, while the underlying methodology is investigated. These will be made available as soon as the investigation is complete. NHS Digital apologises for any inconvenience caused. • CCG and STP changes A number of changes to NHS organisations were made operationally effective from 1 April 2020. These changes included: 74 former Clinical Commissioning Groups (CCGs) merging to form 18 new CCGs; alterations to commissioning hubs; provider mergers; and the incorporation of Sustainability and Transformation Partnerships (STPs) into the NHS commissioning hierarchy. The Organisation Data Service (ODS) is responsible for publishing organisation and practitioner codes, along with related national policies and standards. A series of ODS data amendments are required to support the introduction of these changes. This would normally result in a number of organisations becoming ‘legally’ closed including the 74 former CCGs. However, to minimise any burden to the NHS during the COVID-19 pandemic and remove any non-critical activity, these organisations remain open within ODS data. ODS aim to both legally and operationally close predecessor organisations involved in April 2020 Reconfiguration on 30 September 2020. Activity may be recorded against either former or current organisations, depending on data providers and processors ability to transition to the new organisation codes at this time. The same activity will not be recorded against both former and current organisations. There is no impact on the statistics presented here as CCG is derived in all cases within this publication.

Abstract

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 50% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE CENTRAL AGENCY FOR PUBLIC MOBILIZATION AND STATISTICS (CAPMAS)

The Household Income, Expenditure and Consumption Survey (HIECS) is of great importance among other household surveys conducted by statistical agencies in various countries around the world. This survey provides a large amount of data to rely on in measuring the living standards of households and individuals, as well as establishing databases that serve in measuring poverty, designing social assistance programs, and providing necessary weights to compile consumer price indices, considered to be an important indicator to assess inflation.

The First Survey that covered all the country governorates was carried out in 1958/1959 followed by a long series of similar surveys. The current survey, HIECS 2015, is the twelfth in this long series. Starting 2008/2009, Household Income, Expenditure and Consumption Surveys were conducted each two years instead of five years. this would enable better tracking of the rapid changes in the level of the living standards of the Egyptian households.

CAPMAS started in 2010/2011 to follow a panel sample of around 40% of the total household sample size. The current survey is the second one to follow a panel sample. This procedure will provide the necessary data to extract accurate indicators on the status of the society. The CAPMAS also is pleased to disseminate the results of this survey to policy makers, researchers and scholarly to help in policy making and conducting development related researches and studies

The survey main objectives are:

• To identify expenditure levels and patterns of population as well as socio- economic and demographic differentials.

• To measure average household and per-capita expenditure for various expenditure items along with socio-economic correlates.

• To Measure the change in living standards and expenditure patterns and behavior for the individuals and households in the panel sample, previously surveyed in 2008/2009, for the first time during 12 months representing the survey period.

• To define percentage distribution of expenditure for various items used in compiling consumer price indices which is considered important indicator for measuring inflation.

• To estimate the quantities, values of commodities and services consumed by households during the survey period to determine the levels of consumption and estimate the current demand which is important to predict future demands.

• To define average household and per-capita income from different sources.

• To provide data necessary to measure standard of living for households and individuals. Poverty analysis and setting up a basis for social welfare assistance are highly dependent on the results of this survey.

• To provide essential data to measure elasticity which reflects the percentage change in expenditure for various commodity and service groups against the percentage change in total expenditure for the purpose of predicting the levels of expenditure and consumption for different commodity and service items in urban and rural areas.

• To provide data essential for comparing change in expenditure against change in income to measure income elasticity of expenditure.

• To study the relationships between demographic, geographical, housing characteristics of households and their income.

• To provide data necessary for national accounts especially in compiling inputs and outputs tables.

• To identify consumers behavior changes among socio-economic groups in urban and rural areas.

• To identify per capita food consumption and its main components of calories, proteins and fats according to its nutrition components and the levels of expenditure in both urban and rural areas.

• To identify the value of expenditure for food according to its sources, either from household production or not, in addition to household expenditure for non-food commodities and services.

• To identify distribution of households according to the possession of some appliances and equipments such as (cars, satellites, mobiles ,…etc) in urban and rural areas that enables measuring household wealth index.

• To identify the percentage distribution of income earners according to some background variables such as housing conditions, size of household and characteristics of head of household.

• To provide a time series of the most important data related to dominant standard of living from economic and social perspective. This will enable conducting comparisons based on the results of these time series. In addition to, the possibility of performing geographical comparisons.

The raw survey data provided by the Statistical Agency were cleaned and harmonized by the Economic Research Forum, in the context of a major project that started in 2009. During which extensive efforts have been exerted to acquire, clean, harmonize, preserve and disseminate micro data of existing household surveys in several Arab countries.

Geographic coverage

Covering a sample of urban and rural areas in all the governorates.

Analysis unit

1- Household/family. 2- Individual/person.

Universe

The survey covered a national sample of households and all individuals permanently residing in surveyed households.

Kind of data

Sample survey data [ssd]

Sampling procedure

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 50% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE CENTRAL AGENCY FOR PUBLIC MOBILIZATION AND STATISTICS (CAPMAS)

The sample of HIECS 2015 is a self-weighted two-stage stratified cluster sample. The main elements of the sampling design are described in the following.

1- Sample Size The sample size is around 25 thousand households. It was distributed between urban and rural with the percentages of 45% and 55%, respectively.

2- Cluster size The cluster size is 10 households in most governorates. It reached 20 households in Port-Said, Suez, Ismailiya, Damietta, Aswan and Frontier governorates, since the sample size in those governorates is smaller compared to others.

3- Sample allocation in different governorates 45% of the survey sample was allocated to urban areas (11260 households) and the other 55% was allocated to rural areas (13740 households). The sample was distributed on urban/rural areas in different governorates proportionally with the household size A sample size of a minimum of 1000 households was allocated to each governorate to ensure accuracy of poverty indicators. Therefore, the sample size was increased in Port-Said, Suez, Ismailiya, kafr el-Sheikh, Damietta, Bani Suef, Fayoum, Qena, Luxor and Aswan, by compensation from other governorates where the sample size exceeds a 1000 households. All Frontier governorates were considered as one governorate.

4- Core Sample The core sample is the master sample of any household sample required to be pulled for the purpose of studying the properties of individuals and families. It is a large sample and distributed on urban and rural areas of all governorates. It is a representative sample for the individual characteristics of the Egyptian society. This sample was implemented in January 2010 and its size reached more than 1 million household selected from 5024 enumeration areas distributed on all governorates (urban/rural) proportionally with the sample size (the enumeration area size is around 200 households). The core sample is the sampling frame from which the samples for the surveys conducted by CAPMAS are pulled, such as the Labor Force Surveys, Income, Expenditure And Consumption Survey, Household Urban Migration Survey, ...etc, in addition to other samples that may be required for outsources.

A more detailed description of the different sampling stages and allocation of sample across governorates is provided in the Methodology document available among external resources in Arabic.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three different questionnaires have been designed as following:

1- Expenditure and Consumption Questionnaire. 2- Assisting questionnaire. 3- Income Questionnaire.

In designing the questionnaires of expenditure, consumption and income, we were taking into our consideration the following: - Using the recent concepts and definitions of International Labor Organization approved in the International Convention of Labor Statisticians held in Geneva, 2003. - Using the recent Classification of Individual Consumption According to Purpose (COICOP). - Using more than one approach of expenditure measurement to serve many purposes of the survey.

A brief description of each questionnaire is given next:

----> 1- Expenditure and Consumption Questionnaire This questionnaire comprises 14 tables in addition to identification and geographic data of household on the cover page. The questionnaire is divided into two main sections.

Section one: Household schedule and other information, it includes: - Demographic characteristics and basic data for all household individuals consisting of 25 questions for every person. - Members of household who are currently working abroad. - The household ration card. - The main outlets that provide food and beverage. - Domestic and foreign tourism. - The housing conditions including 16 questions. - Household ownership of means of transportation, communication and domestic appliances. - Date of purchase, status at purchase, purchase value and

Overview    This dataset offers valuable insights into yearly domestic water consumption across various Lower Super Output Areas (LSOAs) or Data Zones, accompanied by the count of water meters within each area. It is instrumental for analysing residential water use patterns, facilitating water conservation efforts, and guiding infrastructure development and policy making at a localised level. Key Definitions    Aggregation   The process of summarising or grouping data to obtain a single or reduced set of information, often for analysis or reporting purposes.     AMR Meter Automatic meter reading (AMR) is the technology of automatically collecting consumption, diagnostic, and status data from a water meter remotely and periodically. Dataset   Structured and organised collection of related elements, often stored digitally, used for analysis and interpretation in various fields.  Data Zone Data zones are the key geography for the dissemination of small area statistics in Scotland Dumb Meter A dumb meter or analogue meter is read manually. It does not have any external connectivity. Granularity   Data granularity is a measure of the level of detail in a data structure. In time-series data, for example, the granularity of measurement might be based on intervals of years, months, weeks, days, or hours   ID   Abbreviation for Identification that refers to any means of verifying the unique identifier assigned to each asset for the purposes of tracking, management, and maintenance.    LSOA Lower Layer Super Output Areas (LSOA) are a geographic hierarchy designed to improve the reporting of small area statistics in England and Wales. Open Data Triage   The process carried out by a Data Custodian to determine if there is any evidence of sensitivities associated with Data Assets, their associated Metadata and Software Scripts used to process Data Assets if they are used as Open Data.    Schema   Structure for organising and handling data within a dataset, defining the attributes, their data types, and the relationships between different entities. It acts as a framework that ensures data integrity and consistency by specifying permissible data types and constraints for each attribute.    Smart Meter A smart meter is an electronic device that records information and communicates it to the consumer and the supplier. It differs from automatic meter reading (AMR) in that it enables two-way communication between the meter and the supplier. Units   Standard measurements used to quantify and compare different physical quantities.  Water Meter Water metering is the practice of measuring water use. Water meters measure the volume of water used by residential and commercial building units that are supplied with water by a public water supply system. Data History    Data Origin    Domestic consumption data is recorded using water meters. The consumption recorded is then sent back to water companies. This dataset is extracted from the water companies. Data Triage Considerations    This section discusses the careful handling of data to maintain anonymity and addresses the challenges associated with data updates, such as identifying household changes or meter replacements. Identification of Critical Infrastructure  This aspect is not applicable for the dataset, as the focus is on domestic water consumption and does not contain any information that reveals critical infrastructure details. Commercial Risks and Anonymisation Individual Identification Risks There is a potential risk of identifying individuals or households if the consumption data is updated irregularly (e.g., every 6 months) and an out-of-cycle update occurs (e.g., after 2 months), which could signal a change in occupancy or ownership. Such patterns need careful handling to avoid accidental exposure of sensitive information. Meter and Property Association Challenges arise in maintaining historical data integrity when meters are replaced but the property remains the same. Ensuring continuity in the data without revealing personal information is crucial. Interpretation of Null Consumption Instances of null consumption could be misunderstood as a lack of water use, whereas they might simply indicate missing data. Distinguishing between these scenarios is vital to prevent misleading conclusions. Meter Re-reads The dataset must account for instances where meters are read multiple times for accuracy. Joint Supplies & Multiple Meters per Household Special consideration is required for households with multiple meters as well as multiple households that share a meter as this could complicate data aggregation. Schema Consistency with the Energy Industry: In formulating the schema for the domestic water consumption dataset, careful consideration was given to the potential risks to individual privacy. This evaluation included examining the frequency of data updates, the handling of property and meter associations, interpretations of null consumption, meter re-reads, joint suppliers, and the presence of multiple meters within a single household as described above. After a thorough assessment of these factors and their implications for individual privacy, it was decided to align the dataset's schema with the standards established within the energy industry. This decision was influenced by the energy sector's experience and established practices in managing similar risks associated with smart meters. This ensures a high level of data integrity and privacy protection. Schema The dataset schema is aligned with those used in the energy industry, which has encountered similar challenges with smart meters. However, it is important to note that the energy industry has a much higher density of meter distribution, especially smart meters. Aggregation to Mitigate Risks The dataset employs an elevated level of data aggregation to minimise the risk of individual identification. This approach is crucial in maintaining the utility of the dataset while ensuring individual privacy. The aggregation level is carefully chosen to remove identifiable risks without excluding valuable data, thus balancing data utility with privacy concerns. Data Freshness  Users should be aware that this dataset reflects historical consumption patterns and does not represent real-time data. Publish Frequency  Annually Data Triage Review Frequency    An annual review is conducted to ensure the dataset's relevance and accuracy, with adjustments made based on specific requests or evolving data trends. Data Specifications   For the domestic water consumption dataset, the data specifications are designed to ensure comprehensiveness and relevance, while maintaining clarity and focus. The specifications for this dataset include: Each dataset encompasses recordings of domestic water consumption as measured and reported by the data publisher. It excludes commercial consumption. Where it is necessary to estimate consumption, this is calculated based on actual meter readings. Meters of all types (smart, dumb, AMR) are included in this dataset. The dataset is updated and published annually. Historical data may be made available to facilitate trend analysis and comparative studies, although it is not mandatory for each dataset release. Context   Users are cautioned against using the dataset for immediate operational decisions regarding water supply management. The data should be interpreted considering potential seasonal and weather-related influences on water consumption patterns. The geographical data provided does not pinpoint locations of water meters within an LSOA. The dataset aims to cover a broad spectrum of households, from single-meter homes to those with multiple meters, to accurately reflect the diversity of water use within an LSOA.

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Data Analytics Market Size 2025-2029

The data analytics market size is forecast to increase by USD 288.7 billion, at a CAGR of 14.7% between 2024 and 2029.

The market is driven by the extensive use of modern technology in company operations, enabling businesses to extract valuable insights from their data. The prevalence of the Internet and the increased use of linked and integrated technologies have facilitated the collection and analysis of vast amounts of data from various sources. This trend is expected to continue as companies seek to gain a competitive edge by making data-driven decisions. However, the integration of data from different sources poses significant challenges. Ensuring data accuracy, consistency, and security is crucial as companies deal with large volumes of data from various internal and external sources. Additionally, the complexity of data analytics tools and the need for specialized skills can hinder adoption, particularly for smaller organizations with limited resources. Companies must address these challenges by investing in robust data management systems, implementing rigorous data validation processes, and providing training and development opportunities for their employees. By doing so, they can effectively harness the power of data analytics to drive growth and improve operational efficiency.

What will be the Size of the Data Analytics Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free SampleIn the dynamic and ever-evolving the market, entities such as explainable AI, time series analysis, data integration, data lakes, algorithm selection, feature engineering, marketing analytics, computer vision, data visualization, financial modeling, real-time analytics, data mining tools, and KPI dashboards continue to unfold and intertwine, shaping the industry's landscape. The application of these technologies spans various sectors, from risk management and fraud detection to conversion rate optimization and social media analytics. ETL processes, data warehousing, statistical software, data wrangling, and data storytelling are integral components of the data analytics ecosystem, enabling organizations to extract insights from their data. Cloud computing, deep learning, and data visualization tools further enhance the capabilities of data analytics platforms, allowing for advanced data-driven decision making and real-time analysis. Marketing analytics, clustering algorithms, and customer segmentation are essential for businesses seeking to optimize their marketing strategies and gain a competitive edge. Regression analysis, data visualization tools, and machine learning algorithms are instrumental in uncovering hidden patterns and trends, while predictive modeling and causal inference help organizations anticipate future outcomes and make informed decisions. Data governance, data quality, and bias detection are crucial aspects of the data analytics process, ensuring the accuracy, security, and ethical use of data. Supply chain analytics, healthcare analytics, and financial modeling are just a few examples of the diverse applications of data analytics, demonstrating the industry's far-reaching impact. Data pipelines, data mining, and model monitoring are essential for maintaining the continuous flow of data and ensuring the accuracy and reliability of analytics models. The integration of various data analytics tools and techniques continues to evolve, as the industry adapts to the ever-changing needs of businesses and consumers alike.

How is this Data Analytics Industry segmented?

The data analytics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. ComponentServicesSoftwareHardwareDeploymentCloudOn-premisesTypePrescriptive AnalyticsPredictive AnalyticsCustomer AnalyticsDescriptive AnalyticsOthersApplicationSupply Chain ManagementEnterprise Resource PlanningDatabase ManagementHuman Resource ManagementOthersGeographyNorth AmericaUSCanadaEuropeFranceGermanyUKMiddle East and AfricaUAEAPACChinaIndiaJapanSouth KoreaSouth AmericaBrazilRest of World (ROW)

By Component Insights

The services segment is estimated to witness significant growth during the forecast period.The market is experiencing significant growth as businesses increasingly rely on advanced technologies to gain insights from their data. Natural language processing is a key component of this trend, enabling more sophisticated analysis of unstructured data. Fraud detection and data security solutions are also in high demand, as companies seek to protect against threats and maintain customer trust. Data analytics platforms, including cloud-based offeri

The Semiconductor Metal Etching Equipment market plays a crucial role in the production of integrated circuits and semiconductor devices, which form the backbone of modern electronics. This equipment is designed to remove specific layers of material to create intricate patterns on silicon wafers, essential for produ

Description of the Dataset and Research Context

This dataset was generated for a systematic review that investigated the positive and negative impacts of social media use on learning in higher education. The research hypothesized that the educational use of social media platforms can produce both beneficial and adverse effects on student engagement, academic performance, and cognitive development, depending on the platform type, pedagogical goals, and disciplinary context.

Data Collection Process

Data were gathered from peer-reviewed empirical studies published between 2011 and 2025. A systematic search was conducted in four databases: PubMed, Scopus, Web of Science, and ERIC. Eligible studies included those using qualitative, quantitative, or mixed-method approaches, focusing on social media use in higher education contexts. Only studies published in English or Spanish were included. The selection process followed PRISMA 2020 guidelines and was managed using the Rayyan platform. Calibration between two independent reviewers was carried out, and inter-rater agreement was measured using Cohen’s Kappa.

A standardized Excel spreadsheet was used to extract and structure the data, which included bibliographic details, study characteristics, country, academic field, education level, social media platforms used, educational purposes, and reported outcomes (positive or negative). Both qualitative and quantitative data were collected.

Key Findings

The data revealed that Instagram, WhatsApp, and YouTube were the most frequently used platforms. Positive outcomes often included increased student engagement, collaborative learning, and knowledge sharing. However, negative outcomes such as distraction, reduced academic focus, and information overload were also recurrent. Studies represented 38 countries, with Latin America, Europe, and Asia being the most represented regions.

A mixed-methods synthesis was performed. Quantitative patterns were analyzed using descriptive statistics in RStudio (version 2025.05.0), while qualitative data were inductively coded and grouped into thematic categories related to educational outcomes and social media use patterns.

Interpretation and Use

This dataset provides structured empirical evidence on how social media impacts university-level learning environments. It can be used by researchers conducting further meta-analyses, education policymakers exploring digital integration, and educators aiming to make informed decisions about platform use. All data were independently verified by two reviewers. The full dataset and codebook are included in the repository to support reproducibility and secondary analysis.

Canada Re-Export Val: HS: IS: Flat rolled pdt, iron/non-alloy steel, not in coil, hot rolled >=600mm width, with patterns in relief data was reported at 28.030 CAD th in Aug 2020. This records an increase from the previous number of 0.000 CAD th for Jul 2020. Canada Re-Export Val: HS: IS: Flat rolled pdt, iron/non-alloy steel, not in coil, hot rolled >=600mm width, with patterns in relief data is updated monthly, averaging 2.897 CAD th from Feb 2010 (Median) to Aug 2020, with 78 observations. The data reached an all-time high of 91.785 CAD th in Aug 2014 and a record low of 0.000 CAD th in Jul 2020. Canada Re-Export Val: HS: IS: Flat rolled pdt, iron/non-alloy steel, not in coil, hot rolled >=600mm width, with patterns in relief data remains active status in CEIC and is reported by Statistics Canada. The data is categorized under Global Database’s Canada – Table CA.WA012: Re-Exports Value: by Harmonized System 6 Digits (Discontinued).

The datatsets are available in dta format. They contain information about the number of workers in South Korea in terms of their old residence, current residence, and current workplace location. Its observation unit is the districts of South Korea. Please refer to "codebook.xlsx" for the list of variables. It is constructed based on the individual records of the Population Census of South Korea (three waves: 2005, 2010, and 2015). These records were remotely accessed via RAS at Microdata Integrated Service, Statistics Korea. For security and confidentiality reasons, records cannot be moved out of the remote server. Instead, Statistics Korea approved the export of these data sets ("census20yy.dta" for yy = 05, 10, 15), which capture the migration and commuting patterns and DOES NOT contain any information that identifies specific individuals or groups. An excel file (codebook) is provided. These datasets may not be reused or redistributed without permission. Researchers interested in using the datasets for any purpose or anyone with questions about the datasets may contact Wookun Kim at wookunkim@smu.edu. Researchers interested in accessing the raw Census data via the Remote Access Service (RAS) at MDIS must follow the following steps: (1) register online with Statistics Korea and create an account (this step requires resident registration number and contact information in Korea); (2) submit an application with a detailed description of the proposed project, its purpose, its data requirements (e.g., “인구주택총조사 2015” in this case), its empirical methods, and its social contribution via the MDIS website. It may take months from the initial submission of an application to accessing data via RAS. This website (https://mdis.kostat.go.kr/eng/pageLink.do?link=mdisService) provides detailed access information. For additional information about MDIS, researchers may submit an inquiry directly to MDIS at mdis@stat.or.kr.

Abstract

The Household Income, Expenditure and Consumption Survey (HIECS) is of great importance among other household surveys conducted by statistical agencies in various countries around the world. This survey provides a large amount of data to rely on in measuring the living standards of households and individuals, as well as establishing databases that serve in measuring poverty, designing social assistance programs, and providing necessary weights to compile consumer price indices, considered to be an important indicator to assess inflation. The first survey that covered all the country governorates was carried out in 1958/1959 followed by a long series of similar surveys. The current survey, HIECS 2012/2013, is the eleventh in this long series. Starting 2008/2009, Household Income, Expenditure and Consumption Surveys were conducted each two years instead of five years. This would enable better tracking of the rapid changes in the level of the living standards of the Egyptian households. CAPMAS started in 2010/2011 to follow a panel sample of around 40% of the total household sample size. The current survey is the second one to follow a panel sample. This procedure will provide the necessary data to extract accurate indicators on the status of the society. The CAPMAS also is pleased to disseminate the results of this survey to policy makers, researchers and scholarly to help in policy making and conducting development related researches and studies The survey main objectives are: - To identify expenditure levels and patterns of population as well as socio- economic and demographic differentials. - To measure average household and per-capita expenditure for various expenditure items along with socio-economic correlates. - To Measure the change in living standards and expenditure patterns and behavior for the individuals and households in the panel sample, previously surveyed in 2008/2009, for the first time during 12 months representing the survey period. - To define percentage distribution of expenditure for various items used in compiling consumer price indices which is considered important indicator for measuring inflation. - To estimate the quantities, values of commodities and services consumed by households during the survey period to determine the levels of consumption and estimate the current demand which is important to predict future demands. - To define average household and per-capita income from different sources. - To provide data necessary to measure standard of living for households and individuals. Poverty analysis and setting up a basis for social welfare assistance are highly dependent on the results of this survey. - To provide essential data to measure elasticity which reflects the percentage change in expenditure for various commodity and service groups against the percentage change in total expenditure for the purpose of predicting the levels of expenditure and consumption for different commodity and service items in urban and rural areas. - To provide data essential for comparing change in expenditure against change in income to measure income elasticity of expenditure. - To study the relationships between demographic, geographical, housing characteristics of households and their income. - To provide data necessary for national accounts especially in compiling inputs and outputs tables. - To identify consumers behavior changes among socio-economic groups in urban and rural areas. - To identify per capita food consumption and its main components of calories, proteins and fats according to its nutrition components and the levels of expenditure in both urban and rural areas. - To identify the value of expenditure for food according to its sources, either from household production or not, in addition to household expenditure for non-food commodities and services. - To identify distribution of households according to the possession of some appliances and equipments such as (cars, satellites, mobiles ,…etc) in urban and rural areas that enables measuring household wealth index. - To identify the percentage distribution of income earners according to some background variables such as housing conditions, size of household and characteristics of head of household. - To provide a time series of the most important data related to dominant standard of living from economic and social perspective. This will enable conducting comparisons based on the results of these time series. In addition to, the possibility of performing geographical comparisons.

Compared to previous surveys, the current survey experienced certain peculiarities, among which : 1) The total sample of the current survey (24.9 thousand households) is divided into two sections: a -A new sample of 16.1 thousand households. This sample was used to study the geographic differences between urban governorates, urban and rural areas, and frontier governorates as well as other discrepancies related to households characteristics and household size, head of the household's education status, etc.
b -A panel sample of 2008/2009 survey data of around 8.8 thousand households were selected to accurately study the changes that may have occurred in the households' living standards over the period between the two surveys and over time in the future since CAPMAS will continue to collect panel data for HIECS in the coming years.

2) Some additional questions that showed to be important based on previous surveys results, were added to the survey questionnaire, such as: a - The extent of health services provided to monitor the level of services available in the Egyptian society. By collecting information on the in-kind transfers, the household received during the year; in order to monitor the assistance the household received from different sources government, association,..etc. b - Identifying the main outlet of fabrics, clothes and footwear to determine the level of living standards of the household.

3) Quality control procedures especially for fieldwork are increased, to ensure data accuracy and avoid any errors in suitable time, as well as taking all the necessary measures to guarantee that mistakes are not repeated, with the application of the principle of reward and punishment.

Geographic coverage

National coverage, covering a sample of urban and rural areas in all the governorates.

Analysis unit

• Household/family
• Individual/person

Universe

The survey covered a national sample of households and all individuals permanently residing in surveyed households.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample of HIECS 2012/2013 is a self-weighted two-stage stratified cluster sample, of around 24.9 households. The main elements of the sampling design are described in the following:

Sample Size The sample has been proportionally distributed on the governorate level between urban and rural areas, in order to make the sample representative even for small governorates. Thus, a sample of about 24863 households has been considered, and was distributed between urban and rural with the percentages of 45.4 % and 54.6, respectively. This sample is divided into two parts: a) A new sample of 16094 households selected from main enumeration areas. b) A panel sample of 8769 households (selected from HIECS 2010/2011 and the preceding survey in 2008/2009).

Cluster Size The cluster size in the previous survey has been decreased compared to older surveys since large cluster sizes previously used were found to be too large to yield accepted design effect estimates (DEFT). As a result, it has been decided to use a cluster size of only 8 households (In HIECS 2011/2012 a cluster size of 16 households was used). While the cluster size for the panel sample was 4 households.

Core Sample The core sample is the master sample of any household sample required to be pulled for the purpose of studying the properties of individuals and families. It is a large sample and distributed on urban and rural areas of all governorates. It is a representative sample for the individual characteristics of the Egyptian society. This sample was implemented in January 2012 and its size reached more than 1 million household (1004800 household) selected from 5024 enumeration areas distributed on all governorates (urban/rural) proportionally with the sample size (the enumeration area size is around 200 households). The core sample is the sampling frame from which the samples for the surveys conducted by CAPMAS are pulled, such as the Labor Force Surveys, Income, Expenditure And Consumption Survey, Household Urban Migration Survey, ...etc, in addition to other samples that may be required for outsources.

New Households Sample: 1000 sample areas were selected across all governorates (urban/rural) using a proportional technique with the sample size. The number required for each governorate (urban/rural) was selected from the enumeration areas of the core sample using a systematic sampling technique.A more detailed description of the different sampling stages and allocation of sample across governorates is provided in the Methodology document available among external resources in Arabic.

Sampling deviation

Given the sample design, these weights will vary to some extent for the over-sampled governorates compared with the others. It is also important to calculate measures of sampling variability for key survey estimates.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three different questionnaires have been designed as following: 1) Expenditure and Consumption Questionnaire. 2) Diary Questionnaire (Assisting questionnaire).

Technical Integration: Due to the minimal documentation, the specific integration details with CKAN are unclear. However, it is reasonable to assume that the extension utilizes CKAN's plugin architecture to hook into the search functionality. This likely involves implementing a plugin that intercepts search queries before they are executed and saves them to a storage mechanism (e.g., a database table, a file). The extension would then need to provide an API or other method for retrieving the saved search queries for autocomplete suggestions and statistics generation. Benefits & Impact: The Search History extension has the potential to significantly improve data discoverability within a CKAN instance. By providing autocomplete suggestions, it can help users find the datasets they are looking for more quickly and easily. The search statistics generated by the extension can provide valuable insights into user needs and data usage patterns, which can be used to improve the content and organization of the CKAN instance. This can lead to increased data utilization and more effective decision-making.

La Liga Players Performance Dataset

This dataset provides a comprehensive overview of player performance in the La Liga capturing a wide array of metrics related to gameplay, scoring, passing, and defensive actions. With records detailing individual player statistics across different teams, this dataset is a valuable resource for analysts, data scientists, and fans who are interested in diving into player performance data from one of the world’s top soccer leagues.

Each entry represents a single player's profile, featuring data on expected goals (xG), expected assists (xAG), touches, dribbles, tackles, and more. This dataset is ideal for analyzing various aspects of player contribution, both offensively and defensively, and understanding their impact on team performance.

Dataset Columns

Player: Name of the player Team: Team the player belongs to '#' : Player's jersey number Nation: Nationality of the player Position: Primary playing position on the field Age: Age of the player Minutes: Total minutes played Goals: Number of goals scored Assists: Number of assists Penalty Shoot on Goal: Penalty shots taken on goal Penalty Shoot: Total penalty shots attempted Total Shoot: Total shots attempted Shoot on Target: Shots successfully on target Yellow Cards: Number of yellow cards received Red Cards: Number of red cards received Touches: Total ball touches Dribbles: Total dribbles attempted Tackles: Total tackles made Blocks: Total blocks Expected Goals (xG): Expected goals, calculated based on shooting positions and likelihood of scoring Non-Penalty xG (npxG): Expected goals excluding penalties Expected Assists (xAG): Expected assists, based on actions leading to an expected goal (xG) Shot-Creating Actions: Actions leading to a shot attempt Goal-Creating Actions: Actions leading to a goal Passes Completed: Successful passes completed Passes Attempted: Total passes attempted Pass Completion %: Pass completion rate, expressed as a percentage (some entries have missing values here) Progressive Passes: Passes advancing the ball significantly toward the opponent’s goal Carries: Total ball carries Progressive Carries: Carries advancing the ball significantly toward the opponent’s goal Dribble Attempts: Total dribbles attempted Successful Dribbles: Total successful dribbles Date: Date of record collection or game date

Potential Use Cases

Data Visualization: Explore relationships between various performance metrics to identify patterns.

Player Comparisons: Compare individual players based on goals, assists, xG, xAG, and other metrics.

Team Analysis: Evaluate contributions of players within the same team to gain insights into team dynamics.

Predictive Modeling: Use the dataset to build models for predicting game outcomes, goals, or assists based on player performance metrics.