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US Enterprise Data Management Market Size 2024-2028

The US enterprise data management market size is forecast to increase by USD 5.59 billion at a CAGR of 13.6% between 2023 and 2028.

The market, including Enterprise Data Management (EDM) software, is experiencing significant growth due to increasing demand for data integration and visual analytics. The BFSI industry's reliance on data warehousing and data security continues to drive market expansion. Technological advancements, such as artificial intelligence and machine learning are revolutionizing EDM solutions, offering enhanced capabilities for data processing and analysis. However, the high cost of implementing these advanced EDM solutions remains a challenge for some organizations. Additionally, data security concerns and the need for regulatory compliance are ongoing challenges that require continuous attention and investment. In the telecom sector, the trend towards digital transformation and the generation of vast amounts of data are fueling the demand for strong EDM solutions. Overall, the EDM software market is expected to continue its growth trajectory, driven by these market trends and challenges.

What will be the size of the US Enterprise Data Management Market during the forecast period?

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The Enterprise Data Management (EDM) market in the BFSI sector is experiencing significant growth due to the industry's expansion and strict regulations. With the increasing volume, velocity, and complexity of data, IT organizations in banks and other financial institutions are prioritizing EDM solutions to handle massive datasets and ensure information accuracy. These systems enable data synchronization, address validation, and single-source reporting, addressing data conflicts and silos that hinder effective business operations. EDM solutions are essential for both internal applications and external communication, allowing for leveraging analytics to gain a competitive edge. In the BFSI sector, where risk control is paramount, EDM plays a crucial role in managing and consuming datasets efficiently.
The market is characterized by a competitive environment, with IT investments focused on multiuser functionality and Big Data capabilities to meet the diverse needs of various business verticals, including manufacturing and services industries. Overall, EDM is a strategic imperative for businesses seeking to stay competitive and compliant in today's data-driven economy.

How is this market segmented and which is the largest segment?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Deployment

  On-premises
  Cloud


Ownership

  Large enterprise
  Small and medium enterprise


End-user

  Commercial banks
  Savings institutions


Geography

  US

By Deployment Insights

The on-premises segment is estimated to witness significant growth during the forecast period. The BFSI sector in the US is witnessing a significant expansion in the enterprise data management market, driven by strict regulations and the competitive environment. Large organizations, including commercial banks, insurance companies, and non-banking financial institutions, are prioritizing data management to ensure information accuracy and risk control. Enterprise Data Management (EDM) solutions are crucial for internal applications and external communication, enabling data synchronization and business operations. Leveraging analytics, IT organizations manage vast datasets and datasets' consumption, addressing data conflicts and ensuring data quality for reporting. EDM encompasses handling massive data through Business Analytics, ETL tools, data pipelines, and data warehouses, as well as data visualization tools.

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The on-premises segment was valued at USD 2.9 billion in 2018 and showed a gradual increase during the forecast period.

Market Dynamics

Our researchers analyzed the data with 2023 as the base year, along with the key drivers, trends, and challenges. A holistic analysis of drivers will help companies refine their marketing strategies to gain a competitive advantage.

What are the key market drivers leading to the rise in adoption of US Enterprise Data Management Market?

Growing demand for data integration and visual analytics is the key driver of the market. In the BFSI sector, strict regulations necessitate the effective management of large volumes of structured and unstructured data. The industry's expansion and competitive environment necessitate the need for advanced data management solutions. Enterprises are leveraging Enterprise Data Management (EDM) systems to address the challenges of data synchronization, internal

This dataset contains restricted areas (surface objects) of PPRT Risk industrialist of Lacq Mont (L_ZONE_REG_PPRT_20130259_064) Risk prevention plans (RPPs) are the key instrument of the state in risk prevention. Their objective is to control development in areas at risk. The development of a risk prevention plan generates a spatial dataset organised into several data sets. This game of data describes the restricted areas of the plan once approved. Regulations of RPPs generally distinguish between ‘construction ban areas’, known as ‘red areas’, where the hazard level is strong and the general rule is the prohibition on construction; the ‘prescribed areas’, referred to as ‘blue zones’ where the hazard level is and that the projects are subject to requirements adapted to the type of issue and the areas not directly exposed to risks but subject to prohibitions or requirements.

The health impairments of construction workers due to exposure to PM and TS are found to have profound long-term implications for the construction industry. Since the 2000s, researchers have been demanding a dataset covering PM emissions. This dataset can help construction supervisor to mitigate and control the PM emissions and health impacts on construction workers. Although recent literature attempted to develop a PM database for various construction activities, it did not accommodate concerns regarding the different materials involved in construction activities and the percentage of toxic substances (TSs) present in the PM. Moreover, the data available is in raw format which cannot be considered a universally accepted dataset

A Credit Card Dataset for Machine Learning!

Don't ask me where this data come from, the answer is I don't know!

Context

Credit score cards are a common risk control method in the financial industry. It uses personal information and data submitted by credit card applicants to predict the probability of future defaults and credit card borrowings. The bank is able to decide whether to issue a credit card to the applicant. Credit scores can objectively quantify the magnitude of risk.

Generally speaking, credit score cards are based on historical data. Once encountering large economic fluctuations. Past models may lose their original predictive power. Logistic model is a common method for credit scoring. Because Logistic is suitable for binary classification tasks and can calculate the coefficients of each feature. In order to facilitate understanding and operation, the score card will multiply the logistic regression coefficient by a certain value (such as 100) and round it.

At present, with the development of machine learning algorithms. More predictive methods such as Boosting, Random Forest, and Support Vector Machines have been introduced into credit card scoring. However, these methods often do not have good transparency. It may be difficult to provide customers and regulators with a reason for rejection or acceptance.

Task

Build a machine learning model to predict if an applicant is 'good' or 'bad' client, different from other tasks, the definition of 'good' or 'bad' is not given. You should use some techique, such as vintage analysis to construct you label. Also, unbalance data problem is a big problem in this task.

Content & Explanation

There're two tables could be merged by ID:

1. Temporal Coverage of Data: The data collection periods are 2010, 2030, and 2050.2. Spatial Coverage and Projection:Spatial Coverage: GlobalLongitude: -180° - 180°Latitude: -90° - 90°Projection: GCS_WGS_19843. Disciplinary Scope: The data pertains to the fields of Earth Sciences and Geography.4. Data Volume: The total data volume is approximately 31.5 MB.5. Data Type: Raster (GeoTIFF)6. Thumbnail (illustrating dataset content or observation process/scene): · 7. Field (Feature) Name Explanation:a. Name Explanation: IND: Industrial Value Addedb. Unit of Measurement: Unit: US Dollars (USD)8. Data Source Description:a. Remote Sensing Data:2010 Global Vegetation Index data (Enhanced Vegetation Index, EVI, from MODIS monthly average data) and 2010 Nighttime Light Remote Sensing data (DMSP/OLS)b. Meteorological Data:From the CMCC-CM model in the Fifth International Coupled Model Intercomparison Project (CMIP5) published by the United Nations Intergovernmental Panel on Climate Change (IPCC)c. Statistical Data:From the World Development Indicators dataset of the World Bank and various national statistical agenciesd. Gross Domestic Product Data:Sourced from the project "Study on the Harmful Processes of Population and Economic Systems under Global Change" under the National Key R&D Program "Mechanisms and Assessment of Risks in Population and Economic Systems under Global Change," led by Researcher Sun Fubao at the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciencese. Other Data:Rivers, roads, settlements, and DEM, sourced from the National Oceanic and Atmospheric Administration (NOAA), Global Risk Data Platform, and Natural Earth9. Data Processing Methods(1) Spatialization of Baseline Industrial Value Added: Using 2010 global EVI vegetation index data and nighttime light remote sensing data, we addressed the oversaturation issue in nighttime light data by constructing an adjusted nighttime light index to obtain the optimal global light data. The EANTIL model was developed using NTL, NTLn, and EVI data, with the following formula:Here, EANTLI represents the adjusted nighttime light index, NTL represents the original nighttime light intensity value, and NTLn represents the normalized nighttime light intensity value. Based on the optimal light index EANTLI and the industrial value-added data from the World Bank, we constructed a regression allocation model to derive industrial value added (I), generating the global 2010 industrial value-added data with the formula:Here, I represents the industrial value added for each grid cell, and Ii represents the industrial value added for each country, EANTLi derived from ArcGIS statistical analysis and the regression allocation model.(2) Spatial Boundaries for Future Industrial Value Added: Using the Logistic-CA-Markov simulation principle and global land use data from 2010 and 2015 (from the European Space Agency), we simulated national land use changes for 2030 and 2050 and extracted urban land data as the spatial boundaries for future industrial value added. To comprehensively characterize the influence of different factors on land use and considering the research scale, we selected elevation, slope, population, GDP, distance to rivers, and distance to roads as land use driving factors. Accuracy validation using global 2015 land use data showed an average accuracy of 91.89%.(3) Estimation of Future Industrial Value Added: Based on machine learning and using the random forest model, we constructed spatialization models for industrial value added under different climate change scenarios: Here, tem represents temperature, prep represents precipitation, GDP represents national economic output, L represents urban land, D represents slope, and P represents population. The random forest model was constructed using factors such as 2010 industrial value added, urban land distribution, elevation, slope, distances to rivers, roads, railways (considering transportation), and settlements (considering noise and environmental pollution from industrial buildings), along with temperature and precipitation as climate scenario data. Except for varying temperature and precipitation values across scenarios, other variables remained constant. The model comprised 100 decision trees, with each iteration randomly selecting 90% of the samples for model construction and using the remaining 10% as test data, achieving a training sample accuracy of 0.94 and a test sample accuracy of 0.81.By analyzing the proportion of industrial value added to GDP (average from 2000 to 2020, data from the World Bank) and projected GDP under future Shared Socioeconomic Pathways (SSPs), we derived future industrial value added for each country under different SSP scenarios. Using these projections, we constructed regression models to allocate future industrial value added proportionally, resulting in spatial distribution data for 2030 and 2050 under different SSP scenarios.10. Applications and Achievements of the Dataseta. Primary Application Areas: This dataset is mainly applied in environmental protection, ecological construction, pollution prevention and control, and the prevention and forecasting of natural disasters.b. Achievements in Application (Awards, Published Reports and Articles):Achievements: Developed a method for downscaling national-scale industrial value-added data by integrating DMSP/OLS nighttime light data, vegetation distribution, and other data. Published the global industrial value-added dataset.

The cohort consists of all individuals over 18 years with lymphoma identified from the regional cancer registry in southern Sweden during the years 1990 to 1998. In the first step the primary physician was contacted and after consent questionnaires were mailed to the patients. For each case, two controls matched for sex, age and place of residence and the same questions were also sent to them. The questionnaire addressed issues such as education, smoking, occupation and exposure to various substances. Participants were asked if they had had one of 25 specified occupations where you work with substances that may be hazardous, such as in the textile industry, the oil industry or in the rubber industry. The questionnaire also contained specific questions about the subjects had been exposed to any of 16 different subjects, eg nickel, asbestos, solvents, gasoline, other petroleum products, pesticides, radiation or radioactivity, and also the time of exposure. Further questions concerning what animals they had been exposed to both at home and outside the home.

Purpose:

The aim is to create a case control cohort to study risk factors of malignant lymphoma.

The dataset includes the controlgroup in the study.

Abstract copyright UK Data Service and data collection copyright owner. The Organisation for Economic Co-operation and Development (OECD) Insurance Statistics are presented in the following tables: Balance sheet and income The balance sheet and income dataset shows data for direct insurance and reinsurance by life, non-life and composite categories shown in US dollars or national currency. Data are available from 2008 onwards. Business written in the reporting country This dataset contains business written in the reporting country on a gross and net premium basis. It contains a breakdown by ownership between domestic companies, foreign-controlled companies and branches and agencies or foreign companies. It also comprises various type of premiums (gross premiums, premiums ceded, net written premium) as well as insurance type (life, non-life, composite) and facultative reinsurance may be included under (direct business or reinsurance accepted) according to practice in the reporting country. Data are expressed in national currency, USD or Euro (in millions) and presented from 1983 onwards. Commissions This dataset includes statistics related to commissions in the reporting country, containing a breakdown between domestic companies, foreign-controlled companies and branches and agencies of foreign companies. The commissions in the reporting country can then be compared by ownership (domestic undertakings, foreign controlled undertakings, branches and agencies of foreign undertakings) by insurance type (life, non-life, composite) and facultative reinsurance (direct business, reinsurance accepted). Data are expressed in national currency, USD or Euro (in millions) and presented from 1993 onwards. Gross claims payments This dataset contains data related to gross claims payments in the reporting country, containing a breakdown between domestic companies, foreign-controlled companies and branches and agencies of foreign companies. The core variable can be further analysed by type of insurance (life, non-life, composite). Data are expressed in national currency, USD or Euro (in millions) and starting from 1993 onwards. Gross operating expenses This dataset contains gross operating expenses in the reporting country, with a breakdown between domestic companies, foreign-controlled companies and branches and agencies of foreign companies. This table also compares the core variable by type of insurance (life, non-life, composite) and currency (euro, USD). Data are available starting from 1993. Insurance activity indicators This comparative table comprises statistics on the insurance industry indicators as this sector is a key component of the economy by virtue of the amount of premiums it collects, the scale of its investment and the essential social and economic role it plays on personal and business risk coverage. This dataset includes insurance activity indicators such as market share, density, penetration, life insurance share, premiums per employee, retention ratio, ratio of reinsurance accepted, market share of foreign companies and market share of branches/agencies. Data are presented from 1983 onwards with annual datapoints. Insurance business by domestic and foreign risks This subset of OECD Insurance Statistics presents statistics on the insurance industry with a focus on domestic and foreign business risk. The type of risk can be further analysed by type of premium (net written premium, gross premiums, premium ceded), ownership (domestic company, foreign controlled undertakings, branches and agencies of foreign undertakings) and type of insurance (life, non-life, composite). Data are expressed in different currency terms and are presented from 1983 onwards. Insurance business written abroad by branches This dataset includes statistics pertaining to the insurance business written abroad by branches. It also includes variables such as premium type (gross premium, premium ceded, net written premium), branches and agencies, subsidiaries, insurance type (life, non-life, composite), partner country, direct business and reinsurance accepted. Data are expressed in national currency, USD or Euro (in millions) and are presented from 1983 onwards. Insurance business written in the reporting country This dataset includes statistics on business written in the reporting country by premiums (gross premium, premium ceded, net written premium), by classes of non-life insurance (freight insurance, general liability insurance, treaty reinsurance). Business should include all business written in the reporting country, whether in respect of domestic or foreign (worldwide) risks. Data are presented from 1987 onwards. General Insurance Statistics This dataset provides information on number of insurance companies and employees within the sector. The number of insurance undertakings is then examined by ownership (domestic undertakings, foreign controlled undertakings, branches and agencies of foreign undertakings) and by insurance type (life, non-life, composite, reinsurance). Number of insurance employees is also available by employer type (insurance undertakings, intermediaries). Data is available starting from 1983. Destinations of investments by direct insurance or reinsurance companies This dataset includes statistics related to outstanding investment by direct insurance companies, classified by investment category (real estate, mortgage loans, shares, bonds, loans, other investment), companies nationality, destination (foreign or domestic), ownership, insurance type, insurer type (direct insurer, reinsurer). Data are expressed in different currencies and are available from 1988 onwards. These data were first provided by the UK Data Service in October 2014.

Background: The purpose of this study was to estimate the incidence and prevalence of hearing loss for noise-exposed U.S. workers by industry sector and 5-year time period, covering 30 years.

Methods: Audiograms for 1.8 million workers from 1981-2010 were examined. Incidence and prevalence were estimated by industry sector and time period. The adjusted risk of incident hearing loss within each time period and industry sector as compared with a reference time period was also estimated.

Results: The adjusted risk for incident hearing loss decreased over time when all industry sectors were combined. However, the risk remained high for workers in Healthcare and Social Assistance, and the prevalence was consistently high for Mining and Construction workers.

Conclusions: While progress has been made in reducing the risk of incident hearing loss within most industry sectors, additional efforts are needed within Mining, Construction and Healthcare and Social Assistance.

The PPRT is a document drawn up by the State which is intended to facilitate the control of urbanisation around high-risk industrial sites (also referred to as SEVESO high threshold). It also minimises the effects of accidents that may occur at these facilities and may have effects on public health, health and safety, directly or indirectly by pollution of the environment.

These plans define an area of exposure to risks taking into account the nature and intensity of the technological risks and the preventive measures implemented. The PPRT is a document drawn up by the State which is intended to facilitate the control of urbanisation around high-risk industrial sites (also referred to as SEVESO high threshold).
It also minimises the effects of accidents that may occur at these facilities and may have effects on public health, health and safety, directly or indirectly by pollution of the environment.

These plans define an area of exposure to risks taking into account the nature and intensity of the technological risks and the preventive measures implemented.

Time: 2007 Q1 to 2023 Q4Space: Panel data for 25 provinces in China (excluding Henan, Hebei, Gansu, Tibet, Qinghai, Ningxia, Taiwan, Hong Kong and Macao, with Hebei, Henan and Gansu excluded due to differences in the level of development between the control and experimental groups, and the other regions excluded due to missing data)Variable information:loc: locale identifiertime: time identifiertr: transformation risk of financial institutionspolicy: ETS pilot policypre: n quarters before the modeled policy implementation time pointaftn: n quarters after the modeled policy implementation time pointpolicy2021: National ETS policyfi : fixed asset investmentfep: volume of fossil energy productslfa: level of financial agglomerationis: level of industrial advancedizationndl: direct losses from natural disasterspgdp: GDP per capitaiip: investment in industrial pollution controlipm: profit margin of industry above scalepgi: green invention patent applicationspgu: green utility patent applicationsgc: green credit indicatorcpu: Climate Policy Uncertainty Indicatorcov: New Crown Epidemic “Category B A Control”.per: Pilot Reform of Vertical Management System for Monitoring, Supervision and Law Enforcement of Environmental Protection Agenciespet: Pilot project on trading of energy use rightspca: Pilot project on climate-resilient city buildingcl: dummy variable for geographical locationar: dummy variable for acid rain control areasd: dummy variable for sulfur dioxide control areaMissing data were filled in using interpolation.Translated with DeepL.com (free version)

Risk Prevention Plans (RPPs) are the key government instrument for risk prevention. Their objective is to control development in areas at risk. The development of a risk prevention plan generates a set of spatial data organised into several data sets. The same PPR may include spatial datasets containing: — main scopes of the RPP; — restricted areas of the plan once approved. RPP regulations generally distinguish between ‘construction ban areas’, so-called ‘red areas’, where the hazard level is high and where the general rule is the construction ban; ‘areas subject to requirements’, known as ‘blue zones’ where the hazard level is medium and projects are subject to requirements adapted to the type of issue and areas not directly exposed to risks but subject to prohibitions or prescriptions; — hazard areas represented on the map of hazards used for risk analysis by crossing with the stakes, specifying for each zone the level of the hazards to which it is exposed; — issues which are persons, property, activities and elements of cultural or environmental heritage threatened by a hazard and likely to be affected or damaged by it; — origins of risk, i.e. the entity of the real world which, through its presence, represents a potential risk. This entity may be characterised by a name, a reference to an external object or a geographical object that locates the actual entity causing the risk.

Each element in the same PPRN dataset is bound by the GASPAR format identifier “ddd[PREF|DDT|DDT|DREAL]aaaannnn” (AAAA and NNNN correspond to the reference year and the order number of the PPR procedure associated in GASPAR) to a single object in the PPRN document table described by the N_DOCUMENT_PPRN metadata sheet.

Risk Prevention Plans (RPPs) are the key government instrument for risk prevention. Their objective is to control development in areas at risk. The development of a risk prevention plan generates a set of spatial data organised into several data sets. The same PPR may include spatial datasets containing: — main scopes of the RPP; — restricted areas of the plan once approved. RPP regulations generally distinguish between ‘construction ban areas’, so-called ‘red areas’, where the hazard level is high and where the general rule is the construction ban; ‘areas subject to requirements’, known as ‘blue zones’ where the hazard level is medium and projects are subject to requirements adapted to the type of issue and areas not directly exposed to risks but subject to prohibitions or prescriptions; — hazard areas represented on the map of hazards used for risk analysis by crossing with the stakes, specifying for each zone the level of the hazards to which it is exposed; — issues which are persons, property, activities and elements of cultural or environmental heritage threatened by a hazard and likely to be affected or damaged by it; — origins of risk, i.e. the entity of the real world which, through its presence, represents a potential risk. This entity may be characterised by a name, a reference to an external object or a geographical object that locates the actual entity causing the risk.

Each element in the same PPRN dataset is bound by the GASPAR format identifier “ddd[PREF|DDT|DDT|DREAL]aaaannnn” (AAAA and NNNN correspond to the reference year and the order number of the PPR procedure associated in GASPAR) to a single object in the PPRN document table described by the N_DOCUMENT_PPRN metadata sheet.

Abstract copyright UK Data Service and data collection copyright owner.The European Working Conditions Survey (EWCS) is conducted by Eurofound (the European Foundation for the Improvement of Living and Working Conditions). Since its launch in 1990, the EWCS has provided an overview of working conditions in Europe. The main objectives of the survey are to:assess and quantify working conditions of both employees and the self-employed across Europe on a harmonised basis;analyse relationships between different aspects of working conditions;identify groups at risk and issues of concern as well as of progress;monitor trends by providing homogeneous indicators on these issues; andcontribute to European policy development in particular on quality of work and employment issues.Themes covered include employment status, working time duration and organisation, work organisation, learning and training, physical and psychosocial risk factors, health and safety, work-life balance, worker participation, earnings and financial security, as well as work and health.The EWCS paints a wide-ranging picture of Europe at work across countries, occupations, sectors and age groups. Its findings highlight actions for policy actors to help them address the challenges facing Europe today. The EWCS is generally conducted once every five years, although an extra wave was conducted in 2001 to cover the new acceding and candidate EU countries. The survey is based on a questionnaire which is administered face-to-face to a random sample of 'persons in employment' (i.e. employees and the self-employed), representative of the working population in each EU country. An integrated dataset is also available (see SN 7363) which combines data from the first five waves of the survey in one file. Before working with the EWCS data, users are recommended to read the latest supplementary supporting documentation on the Eurofound European Working Conditions Survey webpages. Further information about the series can be found there, including methodological information, technical reports and reports on translation, sampling implementation, sampling evaluation and weighting, coding, quality control, quality assurance and other publications. The sixth EWCS interviewed nearly 44,000 workers in 35 countries. Its findings provide detailed information on a broad range of issues, including exposure to physical and psychosocial risks, work organisation, work–life balance, and health and well-being, the reconciliation of work and private life and the sustainability of work. For the 4th edition (June 2017), an updated version of the data file was deposited. Improvements in the population figures have led to a revision to the post-stratification weights of the 6th EWCS dataset, affecting the weighting variables w4, w5_EU12, w5_EU15, w5_EU27 and w5_EU28. Although Eurofound considers that this revision should only result in marginal changes in the weighted data, users are recommended to re-run analyses using the new weights in order to assess whether the change has had any impact on the outcome. The Readme file has been updated accordingly. Main Topics: The 2015 questionnaire included information on: employment status, sectors and occupations, company size; physical environment; work intensity; working time and commuting; social environment; work-related health risks and well-being; cognitive and psychosocial factors; harassment and discrimination; skills, training and discretion; job prospects, job security and sustainability; work satisfaction; earnings; unpaid work; work-life balance. Demographic information was also collected. Standard Measures: The International Standard Classification of Occupations (ISCO), Nomenclature generale des Activites Economiques dans les Communautes Europeennes (NACE) and International Standard Classification of Education (ISCED) schedules were used. Multi-stage stratified random sample Face-to-face interview 2015 ACCIDENTS AT WORK AGE ALLERGIES ANXIETY ASSAULT AUTONOMY AT WORK Albania Austria BACK PAIN BONUS PAYMENTS BULLYING Belgium Bulgaria CARE OF DEPENDANTS CAREER DEVELOPMENT CHIEF INCOME EARNERS CHILD CARE CHILD DAY CARE CITIZENSHIP COMMUNICATION PROCESS COMMUTING COMPUTERS CONDITIONS OF EMPLO... CUSTOMERS Croatia Cyprus Czech Republic DECISION MAKING DISABILITY DISCRIMI... DISCRIMINATION AGAI... DOMESTIC RESPONSIBI... Denmark ECONOMIC ACTIVITY EDUCATIONAL BACKGROUND EDUCATIONAL COURSES EDUCATIONAL LEAVE EMOTIONAL STATES EMPLOYEES EMPLOYMENT EMPLOYMENT CONTRACTS EMPLOYMENT HISTORY EUROPEAN UNION EXPOSURE TO NOISE Estonia FAMILIES FAMILY LIFE FATIGUE PHYSIOLOGY FINANCIAL INCENTIVES FLEXIBLE WORKING TIME FREQUENCY OF PAY FRIENDS FULL TIME EMPLOYMENT FUMES Finland France GENDER Germany October 1990 Greece HARASSMENT HEADACHES HEALTH HEALTH STATUS HEARING IMPAIRMENTS HEART DISEASES HOLIDAY LEAVE HOME BASED WORK HOURS OF WORK HOUSEHOLDS HOUSING TENURE Hungary INDUSTRIAL INJURIES INDUSTRIAL NOISE INDUSTRIES INFORMATION SOURCES INTERNET Ireland Italy JOB CHANGING JOB SATISFACTION JOB SECURITY LABOUR LAW LEAVE LEGISLATION Labour and employment Latvia Lithuania Luxembourg MANAGEMENT OPERATIONS MANAGERS MANUAL WORKERS MATERNITY LEAVE MUSCULOSKELETAL DIS... Macedonia Malta Montenegro NATIONALITY DISCRIM... Netherlands Norway OCCUPATIONAL DISEASES OCCUPATIONAL LIFE OCCUPATIONAL SAFETY OCCUPATIONS PARENTAL LEAVE PART TIME EMPLOYMENT PASSIVE SMOKING PATERNITY LEAVE PAYMENTS PEER GROUP RELATION... PERSONAL PROTECTIVE... PERSONAL SAFETY PHYSICAL ACTIVITIES POLITICAL ATTITUDES POLITICAL PARTICIPA... PRIVATE SECTOR PROBLEM SOLVING PRODUCTION MANAGEMENT PROFIT SHARING PUBLIC HEALTH RISKS PUBLIC SECTOR Poland Portugal QUALITY CONTROL QUALITY OF LIFE RACIAL DISCRIMINATION RADIATION RELIGIOUS AFFILIATION RELIGIOUS ATTENDANCE RELIGIOUS DISCRIMIN... REPETITIVE WORK RESPIRATORY TRACT D... RESPONSIBILITY RISK Romania SELF EMPLOYED SEX DISCRIMINATION SEXUAL HARASSMENT SHARES SHIFT WORK SICK LEAVE SKIN DISEASES SLEEP DISORDERS SOCIAL CLASS SOCIAL LIFE SOCIAL PARTICIPATION STOMACH DISORDERS STRESS PSYCHOLOGICAL SUBSIDIARY EMPLOYMENT SUPERVISORY STATUS Serbia Slovakia Slovenia Social behaviour an... Social conditions a... Spain Sweden Switzerland TELEPHONES TELEWORK TEMPERATURE TRADE UNION MEMBERSHIP TRAINING Turkey UNSOCIAL WORKING HOURS United Kingdom VIBRATIONS VISION IMPAIRMENTS VOLUNTARY WORK WAGES WORK LIFE BALANCE WORKERS PARTICIPATION WORKING CONDITIONS WORKPLACE

Abstract copyright UK Data Service and data collection copyright owner.The European Working Conditions Survey (EWCS) is conducted by Eurofound (the European Foundation for the Improvement of Living and Working Conditions). Since its launch in 1990, the EWCS has provided an overview of working conditions in Europe. The main objectives of the survey are to:assess and quantify working conditions of both employees and the self-employed across Europe on a harmonised basis;analyse relationships between different aspects of working conditions;identify groups at risk and issues of concern as well as of progress;monitor trends by providing homogeneous indicators on these issues; andcontribute to European policy development in particular on quality of work and employment issues.Themes covered include employment status, working time duration and organisation, work organisation, learning and training, physical and psychosocial risk factors, health and safety, work-life balance, worker participation, earnings and financial security, as well as work and health.The EWCS paints a wide-ranging picture of Europe at work across countries, occupations, sectors and age groups. Its findings highlight actions for policy actors to help them address the challenges facing Europe today. The EWCS is generally conducted once every five years, although an extra wave was conducted in 2001 to cover the new acceding and candidate EU countries. The survey is based on a questionnaire which is administered face-to-face to a random sample of 'persons in employment' (i.e. employees and the self-employed), representative of the working population in each EU country. An integrated dataset is also available (see SN 7363) which combines data from the first five waves of the survey in one file. Before working with the EWCS data, users are recommended to read the latest supplementary supporting documentation on the Eurofound European Working Conditions Survey webpages. Further information about the series can be found there, including methodological information, technical reports and reports on translation, sampling implementation, sampling evaluation and weighting, coding, quality control, quality assurance and other publications. Main Topics: The questionnaire covers all aspects of working conditions, including working time; physical risk factors; violence, harassment and discrimination in the workplace; nature and organisation of work; impact of work on health; management and communication structures; work-life balance; income and payment systems. Standard Measures: The International Standard Classification of Occupations (ISCO) and the Nomenclature generale des Activites Economiques dans les Communautes Europeennes (NACE) schedules were used. Multi-stage stratified random sample Face-to-face interview 2005 ACCIDENTS AT WORK AGE ALLERGIES ANXIETY ASSAULT AUTONOMY AT WORK Austria BACK PAIN BONUS PAYMENTS BULLYING Belgium Bulgaria CARE OF DEPENDANTS CAREER DEVELOPMENT CHIEF INCOME EARNERS CHILD CARE CHILD DAY CARE CITIZENSHIP COMMUNICATION PROCESS COMMUTING COMPUTERS CONDITIONS OF EMPLO... CUSTOMERS Croatia Cyprus Czech Republic DECISION MAKING DISABILITY DISCRIMI... DISCRIMINATION AGAI... DOMESTIC RESPONSIBI... Denmark ECONOMIC ACTIVITY EDUCATIONAL BACKGROUND EDUCATIONAL COURSES EDUCATIONAL LEAVE EMOTIONAL STATES EMPLOYEES EMPLOYMENT EMPLOYMENT CONTRACTS EMPLOYMENT HISTORY EUROPEAN UNION EXPOSURE TO NOISE Estonia European Union Coun... FAMILIES FAMILY LIFE FATIGUE PHYSIOLOGY FINANCIAL INCENTIVES FLEXIBLE WORKING TIME FREQUENCY OF PAY FRIENDS FULL TIME EMPLOYMENT FUMES Finland France GENDER Germany October 1990 Greece HARASSMENT HEADACHES HEALTH HEARING IMPAIRMENTS HEART DISEASES HOLIDAY LEAVE HOME BASED WORK HOURS OF WORK HOUSEHOLDS HOUSING TENURE Hungary INDUSTRIAL INJURIES INDUSTRIAL NOISE INDUSTRIES INFORMATION SOURCES INTERNET Ireland Italy JOB CHANGING JOB SATISFACTION JOB SECURITY LABOUR LAW LEAVE LEGISLATION Labour and employment Latvia Lithuania Luxembourg MANAGEMENT OPERATIONS MANAGERS MANUAL WORKERS MATERNITY LEAVE MUSCULOSKELETAL DIS... Malta NATIONALITY DISCRIM... Netherlands Norway OCCUPATIONAL DISEASES OCCUPATIONAL LIFE OCCUPATIONAL SAFETY OCCUPATIONS PARENTAL LEAVE PART TIME EMPLOYMENT PASSIVE SMOKING PATERNITY LEAVE PAYMENTS PEER GROUP RELATION... PERSONAL PROTECTIVE... PHYSICAL ACTIVITIES POLITICAL ATTITUDES POLITICAL PARTICIPA... PRIVATE SECTOR PROBLEM SOLVING PRODUCTION MANAGEMENT PROFIT SHARING PUBLIC HEALTH RISKS PUBLIC SECTOR Poland Portugal QUALITY CONTROL QUALITY OF LIFE RACIAL DISCRIMINATION RADIATION RELIGIOUS AFFILIATION RELIGIOUS ATTENDANCE RELIGIOUS DISCRIMIN... REPETITIVE WORK RESPIRATORY TRACT D... RESPONSIBILITY RISK Romania SELF EMPLOYED SEX DISCRIMINATION SEXUAL HARASSMENT SHARES SHIFT WORK SICK LEAVE SKIN DISEASES SLEEP DISORDERS SOCIAL CLASS SOCIAL LIFE SOCIAL PARTICIPATION STOMACH DISORDERS STRESS PSYCHOLOGICAL SUBSIDIARY EMPLOYMENT SUPERVISORY STATUS Slovakia Slovenia Social behaviour an... Social conditions a... Spain Sweden Switzerland TELEPHONES TELEWORK TEMPERATURE TRADE UNION MEMBERSHIP TRAINING Turkey UNSOCIAL WORKING HOURS United Kingdom VIBRATIONS VISION IMPAIRMENTS VOLUNTARY WORK WAGES WORK LIFE BALANCE WORKERS PARTICIPATION WORKING CONDITIONS WORKPLACE

Abstract copyright UK Data Service and data collection copyright owner.The European Working Conditions Survey (EWCS) is conducted by Eurofound (the European Foundation for the Improvement of Living and Working Conditions). Since its launch in 1990, the EWCS has provided an overview of working conditions in Europe. The main objectives of the survey are to:assess and quantify working conditions of both employees and the self-employed across Europe on a harmonised basis;analyse relationships between different aspects of working conditions;identify groups at risk and issues of concern as well as of progress;monitor trends by providing homogeneous indicators on these issues; andcontribute to European policy development in particular on quality of work and employment issues.Themes covered include employment status, working time duration and organisation, work organisation, learning and training, physical and psychosocial risk factors, health and safety, work-life balance, worker participation, earnings and financial security, as well as work and health.The EWCS paints a wide-ranging picture of Europe at work across countries, occupations, sectors and age groups. Its findings highlight actions for policy actors to help them address the challenges facing Europe today. The EWCS is generally conducted once every five years, although an extra wave was conducted in 2001 to cover the new acceding and candidate EU countries. The survey is based on a questionnaire which is administered face-to-face to a random sample of 'persons in employment' (i.e. employees and the self-employed), representative of the working population in each EU country. An integrated dataset is also available (see SN 7363) which combines data from the first five waves of the survey in one file. Before working with the EWCS data, users are recommended to read the latest supplementary supporting documentation on the Eurofound European Working Conditions Survey webpages. Further information about the series can be found there, including methodological information, technical reports and reports on translation, sampling implementation, sampling evaluation and weighting, coding, quality control, quality assurance and other publications. Main Topics: The questionnaire covers all aspects of working conditions, including physical, organisational and social factors of work; time patterns and working hours; and work-related health problems. Standard Measures: The International Standard Classification of Occupations (ISCO) and the Nomenclature generale des Activites Economiques dans les Communautes Europeennes (NACE) schedules were used. Multi-stage stratified random sample Face-to-face interview 2000 ACCIDENTS AT WORK AGE ALLERGIES ANXIETY ASSAULT AUTONOMY AT WORK Austria BACK PAIN BONUS PAYMENTS BULLYING Belgium CARE OF DEPENDANTS CAREER DEVELOPMENT CHIEF INCOME EARNERS CHILD CARE CHILD DAY CARE CITIZENSHIP COMMUTING COMPUTERS CONDITIONS OF EMPLO... CUSTOMERS DECISION MAKING DISABILITY DISCRIMI... DISCRIMINATION AGAI... DOMESTIC RESPONSIBI... Denmark ECONOMIC ACTIVITY EDUCATIONAL BACKGROUND EDUCATIONAL COURSES EDUCATIONAL LEAVE EMOTIONAL STATES EMPLOYEES EMPLOYMENT EMPLOYMENT CONTRACTS EMPLOYMENT HISTORY EUROPEAN UNION EXPOSURE TO NOISE European Union Coun... FAMILIES FAMILY LIFE FATIGUE PHYSIOLOGY FINANCIAL INCENTIVES FLEXIBLE WORKING TIME FREQUENCY OF PAY FRIENDS FULL TIME EMPLOYMENT FUMES Finland France GENDER Germany October 1990 Greece HEADACHES HEALTH HEARING IMPAIRMENTS HEART DISEASES HOLIDAY LEAVE HOME BASED WORK HOURS OF WORK HOUSEHOLDS HOUSING TENURE INDUSTRIAL INJURIES INDUSTRIAL NOISE INDUSTRIES INFORMATION SOURCES INTERNET Ireland Italy JOB CHANGING JOB SATISFACTION JOB SECURITY LABOUR LAW LEAVE LEGISLATION Labour and employment Luxembourg MANAGERS MANUAL WORKERS MATERNITY LEAVE MUSCULOSKELETAL DIS... NATIONALITY DISCRIM... Netherlands Norway OCCUPATIONAL DISEASES OCCUPATIONAL LIFE OCCUPATIONAL SAFETY OCCUPATIONS PARENTAL LEAVE PART TIME EMPLOYMENT PASSIVE SMOKING PATERNITY LEAVE PEER GROUP RELATION... PERSONAL PROTECTIVE... PHYSICAL ACTIVITIES POLITICAL ATTITUDES POLITICAL PARTICIPA... PRIVATE SECTOR PROBLEM SOLVING PRODUCTION MANAGEMENT PROFIT SHARING PUBLIC HEALTH RISKS PUBLIC SECTOR Portugal QUALITY CONTROL RACIAL DISCRIMINATION RADIATION RELIGIOUS AFFILIATION RELIGIOUS ATTENDANCE RELIGIOUS DISCRIMIN... REPETITIVE WORK RESPIRATORY TRACT D... RESPONSIBILITY SELF EMPLOYED SEX DISCRIMINATION SEXUAL HARASSMENT SHARES SHIFT WORK SICK LEAVE SKIN DISEASES SLEEP DISORDERS SOCIAL CLASS SOCIAL LIFE SOCIAL PARTICIPATION STOMACH DISORDERS STRESS PSYCHOLOGICAL SUBSIDIARY EMPLOYMENT SUPERVISORY STATUS Social behaviour an... Social conditions a... Spain Sweden TELEPHONES TELEWORK TEMPERATURE TRADE UNION MEMBERSHIP TRAINING UNSOCIAL WORKING HOURS United Kingdom VIBRATIONS VISION IMPAIRMENTS VOLUNTARY WORK WAGES WORK LIFE BALANCE WORKERS PARTICIPATION WORKING CONDITIONS WORKPLACE

Abstract copyright UK Data Service and data collection copyright owner.The European Working Conditions Survey (EWCS) is conducted by Eurofound (the European Foundation for the Improvement of Living and Working Conditions). Since its launch in 1990, the EWCS has provided an overview of working conditions in Europe. The main objectives of the survey are to:assess and quantify working conditions of both employees and the self-employed across Europe on a harmonised basis;analyse relationships between different aspects of working conditions;identify groups at risk and issues of concern as well as of progress;monitor trends by providing homogeneous indicators on these issues; andcontribute to European policy development in particular on quality of work and employment issues.Themes covered include employment status, working time duration and organisation, work organisation, learning and training, physical and psychosocial risk factors, health and safety, work-life balance, worker participation, earnings and financial security, as well as work and health.The EWCS paints a wide-ranging picture of Europe at work across countries, occupations, sectors and age groups. Its findings highlight actions for policy actors to help them address the challenges facing Europe today. The EWCS is generally conducted once every five years, although an extra wave was conducted in 2001 to cover the new acceding and candidate EU countries. The survey is based on a questionnaire which is administered face-to-face to a random sample of 'persons in employment' (i.e. employees and the self-employed), representative of the working population in each EU country. An integrated dataset is also available (see SN 7363) which combines data from the first five waves of the survey in one file. Before working with the EWCS data, users are recommended to read the latest supplementary supporting documentation on the Eurofound European Working Conditions Survey webpages. Further information about the series can be found there, including methodological information, technical reports and reports on translation, sampling implementation, sampling evaluation and weighting, coding, quality control, quality assurance and other publications. Main Topics: The 2010 questionnaire covered several aspects of working conditions, including physical environment, workplace design, working hours, work organisation, well-being, and social/colleague relationships in the workplace. Demographic information was also collected. Standard Measures: The International Standard Classification of Occupations (ISCO), Nomenclature generale des Activites Economiques dans les Communautes Europeennes (NACE) and International Standard Classification of Education (ISCED) schedules were used. Multi-stage stratified random sample Face-to-face interview 2010 ACCIDENTS AT WORK AGE ALLERGIES ANXIETY ASSAULT AUTONOMY AT WORK Albania Austria BACK PAIN BONUS PAYMENTS BULLYING Belgium Bulgaria CARE OF DEPENDANTS CAREER DEVELOPMENT CHIEF INCOME EARNERS CHILD CARE CHILD DAY CARE CITIZENSHIP COMMUNICATION PROCESS COMMUTING COMPUTERS CONDITIONS OF EMPLO... CUSTOMERS Croatia Cyprus Czech Republic DECISION MAKING DISABILITY DISCRIMI... DISCRIMINATION AGAI... DOMESTIC RESPONSIBI... Denmark ECONOMIC ACTIVITY EDUCATIONAL BACKGROUND EDUCATIONAL COURSES EDUCATIONAL LEAVE EMOTIONAL STATES EMPLOYEES EMPLOYMENT EMPLOYMENT CONTRACTS EMPLOYMENT HISTORY EUROPEAN UNION EXPOSURE TO NOISE Estonia FAMILIES FAMILY LIFE FATIGUE PHYSIOLOGY FINANCIAL INCENTIVES FLEXIBLE WORKING TIME FREQUENCY OF PAY FRIENDS FULL TIME EMPLOYMENT FUMES Finland France GENDER Germany October 1990 Greece HARASSMENT HEADACHES HEALTH HEARING IMPAIRMENTS HEART DISEASES HOLIDAY LEAVE HOME BASED WORK HOURS OF WORK HOUSEHOLDS HOUSING TENURE Hungary INDUSTRIAL INJURIES INDUSTRIAL NOISE INDUSTRIES INFORMATION SOURCES INTERNET Ireland Italy JOB CHANGING JOB SATISFACTION JOB SECURITY LABOUR LAW LEAVE LEGISLATION Labour and employment Latvia Lithuania Luxembourg MANAGEMENT OPERATIONS MANAGERS MANUAL WORKERS MATERNITY LEAVE MUSCULOSKELETAL DIS... Macedonia Malta Montenegro NATIONALITY DISCRIM... Netherlands Norway OCCUPATIONAL DISEASES OCCUPATIONAL LIFE OCCUPATIONAL SAFETY OCCUPATIONS PARENTAL LEAVE PART TIME EMPLOYMENT PASSIVE SMOKING PATERNITY LEAVE PAYMENTS PEER GROUP RELATION... PERSONAL PROTECTIVE... PHYSICAL ACTIVITIES POLITICAL ATTITUDES POLITICAL PARTICIPA... PRIVATE SECTOR PROBLEM SOLVING PRODUCTION MANAGEMENT PROFIT SHARING PUBLIC HEALTH RISKS PUBLIC SECTOR Poland Portugal QUALITY CONTROL QUALITY OF LIFE RACIAL DISCRIMINATION RADIATION RELIGIOUS AFFILIATION RELIGIOUS ATTENDANCE RELIGIOUS DISCRIMIN... REPETITIVE WORK RESPIRATORY TRACT D... RESPONSIBILITY RISK Romania SELF EMPLOYED SEX DISCRIMINATION SEXUAL HARASSMENT SHARES SHIFT WORK SICK LEAVE SKIN DISEASES SLEEP DISORDERS SOCIAL CLASS SOCIAL LIFE SOCIAL PARTICIPATION STOMACH DISORDERS STRESS PSYCHOLOGICAL SUBSIDIARY EMPLOYMENT SUPERVISORY STATUS Slovakia Slovenia Social behaviour an... Social conditions a... Spain Sweden TELEPHONES TELEWORK TEMPERATURE TRADE UNION MEMBERSHIP TRAINING Turkey UNSOCIAL WORKING HOURS United Kingdom VIBRATIONS VISION IMPAIRMENTS VOLUNTARY WORK WAGES WORK LIFE BALANCE WORKERS PARTICIPATION WORKING CONDITIONS WORKPLACE

Hypothesis: higher AUDIT scores are associated with increased risk of injuries among commercial motorcycle drivers in Dar es Salaam. Our data shows a four fold increase in risk among risky drinkers compared with non-drinkers. Structured questionnaire was used to data from motorcyclists and recorded in RedCap. Data was then exported into Excel and entered into Stata

Background: Twenty-two million workers are exposed to hazardous noise in the United States. The purpose of this study is to estimate the prevalence of hearing loss among U.S. industries.

Methods: We examined 2000–2008 audiograms for male and female workers ages 18–65, who had higher occupational noise exposures than the general population. Prevalence and adjusted prevalence ratios (PRs) for hearing loss were estimated and compared across industries.

Results: In our sample, 18% of workers had hearing loss. When compared with the Couriers and Messengers industry sub-sector, workers employed in Mining (PR = 1.65, CI = 1.57–1.73), Wood Product Manufacturing (PR = 1.65, CL = 1.61– 1.70), Construction of Buildings (PR = 1.59, CI = 1.51–1.68), and Real Estate and Rental and Leasing (PR = 1.61, CL = 1.51–1.71) had higher risks for hearing loss.

Conclusions: Workers in the Mining, Manufacturing, and Construction industries need better engineering controls for noise and stronger hearing conservation strategies. More hearing loss research is also needed within traditional ‘‘low-risk’’ industries like Real Estate.

This dataset contains the study areas of the Facilities Classified for Environmental Protection (ICPE) subject to Meurthe-et-Moselle (PAC). The hazard studies of certain installations classified for environmental protection show that the distances of effects (calculated according to the effect thresholds set by the Order of 29 September 2005) of accidents likely to occur within these establishments may, where appropriate, extend beyond the ownership limits of the industries concerned. Future urbanisation around these sites should therefore be controlled in order to limit the risk exposure of riparian populations. The regulations provide for several types of functional approaches to the administrative status of industries that cause these effect distances: — for high threshold SEVESO establishments (classified below the AS threshold of the classification of installations), it is appropriate to prescribe Public Utility Servitudes (SUPs) that can be applied to third parties. — for older SEVESO establishments, the regulations provide for plans for the prevention of technological risks (PPRTs) which are also enforceable against third parties but which, unlike SUPs, take into account urban planning already in place. For other undertakings falling within the threshold of the simple authorisation or the low SEVESO threshold of the nomenclature of classified installations, the circular of 4 May 2007 provides for the town halls to be informed (PAC) of the extent of the hazard zones determined by the calculation of the effect distances indicated above. The circular of 4 May 2007 also provides for the town halls to be offered urban planning management within these areas proportionate to their level of danger.