In 2020, according to respondents surveyed, data masters typically leverage a variety of external data sources to enhance their insights. The most popular external data sources for data masters being publicly available competitor data, open data, and proprietary datasets from data aggregators, with 98, 97, and 92 percent, respectively.

Dataset

This dataset was created by Andy Chamberlain

Contents

This dataset is comprised of a collection of example DMPs from a wide array of fields; obtained from a number of different sources outlined below. Data included/extracted from the examples include the discipline and field of study, author, institutional affiliation and funding information, location, date created, title, research and data-type, description of project, link to the DMP, and where possible external links to related publications or grant pages. This CSV document serves as the content for a McMaster Data Management Plan (DMP) Database as part of the Research Data Management (RDM) Services website, located at https://u.mcmaster.ca/dmps. Other universities and organizations are encouraged to link to the DMP Database or use this dataset as the content for their own DMP Database. This dataset will be updated regularly to include new additions and will be versioned as such. We are gathering submissions at https://u.mcmaster.ca/submit-a-dmp to continue to expand the collection.

Agency-level reports that external federal oversight entities such as the White House, Congress, OMB, the Office of Personnel Management (OPM), and General Services Administration (GSA), require under authorities such as (but not limited to) OMB Circular A-123, the Federal Managers Financial Integrity Act (FMFIA), the Chief Financial Officers Act (CFOA), the Paperwork Reduction Act (PRA), Joint Committee on Printing requirements, and the FAIR Act. Examples include:rn- Agency Financial Report (AFR)rn- Statement of Assurance (per FMFIA), or equivalent rn- information collection clearancesrn- report on financial management systems’ compliance with requirements (per FMFIA), or equivalentrn- report on internal controls for corporations covered by the Government Corporation Control Act (per CFOA) rn- EEOC reports rn- Analysis and Action Plans and other reports required by EEOC’s MD 715rn- No FEAR Act reportsrn- service organization auditor report, or equivalentrn- improper payments report rn- premium class travel report rn- report on property provided to non-federal recipients, schools, and nonprofit educational institutionsrn- feeder reports to the Status of Telework in the Federal Government Report to Congressrn- feeder reports to GSA fleet reports

There are many advantages to individual participant data meta-analysis for combining data from multiple studies. These advantages include greater power to detect effects, increased sample heterogeneity, and the ability to perform more sophisticated analyses than meta-analyses that rely on published results. However, a fundamental challenge is that it is unlikely that variables of interest are measured the same way in all of the studies to be combined. We propose that this situation can be viewed as a missing data problem in which some outcomes are entirely missing within some trials, and use multiple imputation to fill in missing measurements. We apply our method to 5 longitudinal adolescent depression trials where 4 studies used one depression measure and the fifth study used a different depression measure. None of the 5 studies contained both depression measures. We describe a multiple imputation approach for filling in missing depression measures that makes use of external calibration studies in which both depression measures were used. We discuss some practical issues in developing the imputation model including taking into account treatment group and study. We present diagnostics for checking the fit of the imputation model and investigating whether external information is appropriately incorporated into the imputed values.

Abstract

The intention is to collect data for the calendar year 2009 (or the nearest year for which each business keeps its accounts. The survey is considered a one-off survey, although for accurate NAs, such a survey should be conducted at least every five years to enable regular updating of the ratios, etc., needed to adjust the ongoing indicator data (mainly VAGST) to NA concepts. The questionnaire will be drafted by FSD, largely following the previous BAS, updated to current accounting terminology where necessary. The questionnaire will be pilot tested, using some accountants who are likely to complete a number of the forms on behalf of their business clients, and a small sample of businesses. Consultations will also include Ministry of Finance, Ministry of Commerce, Industry and Labour, Central Bank of Samoa (CBS), Samoa Tourism Authority, Chamber of Commerce, and other business associations (hotels, retail, etc.).

The questionnaire will collect a number of items of information about the business ownership, locations at which it operates and each establishment for which detailed data can be provided (in the case of complex businesses), contact information, and other general information needed to clearly identify each unique business. The main body of the questionnaire will collect data on income and expenses, to enable value added to be derived accurately. The questionnaire will also collect data on capital formation, and will contain supplementary pages for relevant industries to collect volume of production data for selected commodities and to collect information to enable an estimate of value added generated by key tourism activities.

The principal user of the data will be FSD which will incorporate the survey data into benchmarks for the NA, mainly on the current published production measure of GDP. The information on capital formation and other relevant data will also be incorporated into the experimental estimates of expenditure on GDP. The supplementary data on volumes of production will be used by FSD to redevelop the industrial production index which has recently been transferred under the SBS from the CBS. The general information about the business ownership, etc., will be used to update the Business Register.

Outputs will be produced in a number of formats, including a printed report containing descriptive information of the survey design, data tables, and analysis of the results. The report will also be made available on the SBS website in “.pdf” format, and the tables will be available on the SBS website in excel tables. Data by region may also be produced, although at a higher level of aggregation than the national data. All data will be fully confidentialised, to protect the anonymity of all respondents. Consideration may also be made to provide, for selected analytical users, confidentialised unit record files (CURFs).

A high level of accuracy is needed because the principal purpose of the survey is to develop revised benchmarks for the NA. The initial plan was that the survey will be conducted as a stratified sample survey, with full enumeration of large establishments and a sample of the remainder.

Geographic coverage

National Coverage

Analysis unit

The main statistical unit to be used for the survey is the establishment. For simple businesses that undertake a single activity at a single location there is a one-to-one relationship between the establishment and the enterprise. For large and complex enterprises, however, it is desirable to separate each activity of an enterprise into establishments to provide the most detailed information possible for industrial analysis. The business register will need to be developed in such a way that records the links between establishments and their parent enterprises. The business register will be created from administrative records and may not have enough information to recognize all establishments of complex enterprises. Large businesses will be contacted prior to the survey post-out to determine if they have separate establishments. If so, the extended structure of the enterprise will be recorded on the business register and a questionnaire will be sent to the enterprise to be completed for each establishment.

SBS has decided to follow the New Zealand simplified version of its statistical units model for the 2009 BAS. Future surveys may consider location units and enterprise groups if they are found to be useful for statistical collections.

It should be noted that while establishment data may enable the derivation of detailed benchmark accounts, it may be necessary to aggregate up to enterprise level data for the benchmarks if the ongoing data used to extrapolate the benchmark forward (mainly VAGST) are only available at the enterprise level.

Universe

The BAS's covered all employing units, and excluded small non-employing units such as the market sellers. The surveys also excluded central government agencies engaged in public administration (ministries, public education and health, etc.). It only covers businesses that pay the VAGST. (Threshold SAT$75,000 and upwards).

Kind of data

Sample survey data [ssd]

Sampling procedure

-Total Sample Size was 1240 -Out of the 1240, 902 successfully completed the questionnaire. -The other remaining 338 either never responded or were omitted (some businesses were ommitted from the sample as they do not meet the requirement to be surveyed) -Selection was all employing units paying VAGST (Threshold SAT $75,000 upwards)

WILL CONFIRM LATER!!

OSO LE MEA E LE FAASA...AEA :-)

Mode of data collection

Mail Questionnaire [mail]

Research instrument

1. General instructions, authority for the survey, etc;
2. Business demography information on ownership, contact details, structure, etc.;
3. Employment;
4. Income;
5. Expenses;
6. Inventories;
7. Profit or loss and reconciliation to business accounts' profit and loss;
8. Fixed assets - purchases, disposals, book values
9. Thank you and signature of respondent.

Supplementary Pages Additional pages have been prepared to collect data for a limited range of industries. 1.Production data. To rebase and redevelop the Industrial Production Index (IPI), it is intended to collect volume of production information from a selection of large manufacturing businesses. The selection of businesses and products is critical to the usefulness of the IPI. The products must be homogeneous, and be of enough importance to the economy to justify collecting the data. Significance criteria should be established for the selection of products to include in the IPI, and the 2009 BAS provides an opportunity to collect benchmark data for a range of products known to be significant (based on information in the existing IPI, CPI weights, export data, etc.) as well as open questions for respondents to provide information on other significant products. 2.Tourism. There is a strong demand for estimates of tourism value added. To estimate tourism value added using the international standard Tourism Satellite Account methodology requires the use of an input-output table, which is beyond the capacity of SBS at present. However, some indicative estimates of the main parts of the economy influenced by tourism can be derived if the necessary data are collected. Tourism is a demand concept, based on defining tourists (the international standard includes both international and domestic tourists), what products are characteristically purchased by tourists, and which industries supply those products. Some questions targeted at those industries that have significant involvement with tourists (hotels, restaurants, transport and tour operators, vehicle hire, etc.), on how much of their income is sourced from tourism would provide valuable indicators of the size of the direct impact of tourism.

Cleaning operations

Partial imputation was done at the time of receipt of questionnaires, after follow-up procedures to obtain fully completed questionnaires have been followed. Imputation followed a process, i.e., apply ratios from responding units in the imputation cell to the partial data that was supplied. Procedures were established during the editing stage (a) to preserve the integrity of the questionnaires as supplied by respondents, and (b) to record all changes made to the questionnaires during editing. If SBS staff writes on the form, for example, this should only be done in red pen, to distinguish the alterations from the original information.

Additional edit checks were developed, including checking against external data at enterprise/establishment level. External data to be checked against include VAGST and SNPF for turnover and purchases, and salaries and wages and employment data respectively. Editing and imputation processes were undertaken by FSD using Excel.

Sampling error estimates

NOT APPLICABLE!!

Snapshot img

Big Data as a Service Market Size 2024-2028

The big data as a service market size is forecast to increase by USD 41.20 billion at a CAGR of 28.45% between 2023 and 2028.

The market is experiencing significant growth due to the increasing volume of data and the rising demand for advanced data insights. Machine learning algorithms and artificial intelligence are driving product quality and innovation in this sector. Hybrid cloud solutions are gaining popularity, offering the benefits of both private and public cloud platforms for optimal data storage and scalability. Industry standards for data privacy and security are increasingly important, as large amounts of data pose unique risks. The BDaaS market is expected to continue its expansion, providing valuable data insights to businesses across various industries.

What will be the Big Data as a Service Market Size During the Forecast Period?

Request Free Sample

Big Data as a Service (BDaaS) has emerged as a game-changer in the business world, enabling organizations to harness the power of big data without the need for extensive infrastructure and expertise. This service model offers various components such as data management, analytics, and visualization tools, enabling businesses to derive valuable insights from their data. BDaaS encompasses several key components that drive market growth. These include Business Intelligence (BI), Data Science, Data Quality, and Data Security. BI provides organizations with the ability to analyze data and gain insights to make informed decisions.



Data Science, on the other hand, focuses on extracting meaningful patterns and trends from large datasets using advanced algorithms. Data Quality is a critical component of BDaaS, ensuring that the data being analyzed is accurate, complete, and consistent. Data Security is another essential aspect, safeguarding sensitive data from cybersecurity threats and data breaches. Moreover, BDaaS offers various data pipelines, enabling seamless data integration and data lifecycle management. Network Analysis, Real-time Analytics, and Predictive Analytics are other essential components, providing businesses with actionable insights in real-time and enabling them to anticipate future trends. Data Mining, Machine Learning Algorithms, and Data Visualization Tools are other essential components of BDaaS.

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.

Type

  Data analytics-as-a-Service
  Hadoop-as-a-service
  Data-as-a-service


Deployment

  Public cloud
  Hybrid cloud
  Private cloud


Geography

  North America

    Canada
    US


  APAC

    China


  Europe

    Germany
    UK


  South America



  Middle East and Africa

By Type Insights

The data analytics-as-a-service segment is estimated to witness significant growth during the forecast period.

Big Data as a Service (BDaaS) is a significant market segment, highlighted by the availability of Hadoop-as-a-Service solutions. These offerings enable businesses to access essential datasets on-demand without the burden of expensive infrastructure. DAaaS solutions facilitate real-time data analysis, empowering organizations to make informed decisions. The DAaaS landscape is expanding rapidly as companies acknowledge its value in enhancing internal data. Integrating DAaaS with big data systems amplifies analytics capabilities, creating a vibrant market landscape. Organizations can leverage diverse datasets to gain a competitive edge, driving the growth of the global BDaaS market. In the context of digital transformation, cloud computing, IoT, and 5G technologies, BDaaS solutions offer optimal resource utilization.

However, regulatory scrutiny poses challenges, necessitating stringent data security measures. Retail and other industries stand to benefit significantly from BDaaS, particularly with distributed computing solutions. DAaaS adoption is a strategic investment for businesses seeking to capitalize on the power of external data for valuable insights.

Get a glance at the market report of share of various segments Request Free Sample

The Data analytics-as-a-Service segment was valued at USD 2.59 billion in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 35% to the growth of the global market during the forecast period.

Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

For more insights on the market share of various regions Request Free Sample

Big Data as a Service Market analysis, North America is experiencing signif

Abstract

The STEP (Skills Toward Employment and Productivity) Measurement program is the first ever initiative to generate internationally comparable data on skills available in developing countries. The program implements standardized surveys to gather information on the supply and distribution of skills and the demand for skills in labor market of low-income countries.

The uniquely-designed Household Survey includes modules that measure the cognitive skills (reading, writing and numeracy), socio-emotional skills (personality, behavior and preferences) and job-specific skills (subset of transversal skills with direct job relevance) of a representative sample of adults aged 15 to 64 living in urban areas, whether they work or not. The cognitive skills module also incorporates a direct assessment of reading literacy based on the Survey of Adults Skills instruments. Modules also gather information about family, health and language.

Geographic coverage

13 major metropolitan areas: Bogota, Medellin, Cali, Baranquilla, Bucaramanga, Cucuta, Cartagena, Pasto, Ibague, Pereira, Manizales, Monteira, and Villavicencio.

Analysis unit

The units of analysis are the individual respondents and households. A household roster is undertaken at the start of the survey and the individual respondent is randomly selected among all household members aged 15 to 64 included. The random selection process was designed by the STEP team and compliance with the procedure is carefully monitored during fieldwork.

Universe

The target population for the Colombia STEP survey is all non-institutionalized persons 15 to 64 years old (inclusive) living in private dwellings in urban areas of the country at the time of data collection. This includes all residents except foreign diplomats and non-nationals working for international organizations.

The following groups are excluded from the sample: - residents of institutions (prisons, hospitals, etc.) - residents of senior homes and hospices - residents of other group dwellings such as college dormitories, halfway homes, workers' quarters, etc. - persons living outside the country at the time of data collection.

Kind of data

Sample survey data [ssd]

Sampling procedure

Stratified 7-stage sample design was used in Colombia. The stratification variable is city-size category.

First Stage Sample The primary sample unit (PSU) is a metropolitan area. A sample of 9 metropolitan areas was selected from the 13 metropolitan areas on the sample frame. The metropolitan areas were grouped according to city-size; the five largest metropolitan areas are included in Stratum 1 and the remaining 8 metropolitan areas are included in Stratum 2. The five metropolitan areas in Stratum 1 were selected with certainty; in Stratum 2, four metropolitan areas were selected with probability proportional to size (PPS), where the measure of size was the number of persons aged 15 to 64 in a metropolitan area.

Second Stage Sample The second stage sample unit is a Section. At the second stage of sample selection, a PPS sample of 267 Sections was selected from the sampled metropolitan areas; the measure of size was the number of persons aged 15 to 64 in a Section. The sample of 267 Sections consisted of 243 initial Sections and 24 reserve Sections to be used in the event of complete non-response at the Section level.

Third Stage Sample The third stage sample unit is a Block. Within each selected Section, a PPS sample of 4 blocks was selected; the measure of size was the number of persons aged 15 to 64 in a Block. Two sample Blocks were initially activated while the remaining two sample Blocks were reserved for use in cases where there was a refusal to cooperate at the Block level or cases where the block did not belong to the target population (e.g., parks, and commercial and industrial areas).

Fourth Stage Sample The fourth stage sample unit is a Block Segment. Regarding the Block segmentation strategy, the Colombia document 'FINAL SAMPLING PLAN (ARD-397)' states "According to the 2005 population and housing census conducted by DANE, the average number of dwellings per block in the 13 large cities or metropolitan areas was approximately 42 dwellings. Based on this finding, the defined protocol was to report those cases in which 80 or more dwellings were present in a given block in order to partition block using a random selection algorithm." At the fourth stage of sample selection, 1 Block Segment was selected in each selected Block using a simple random sample (SRS) method.

Fifth Stage Sample The fifth stage sample unit is a dwelling. At the fifth stage of sample selection, 5582 dwellings were selected from the sampled Blocks/Block Segments using a simple random sample (SRS) method. According to the Colombia document 'FINAL SAMPLING PLAN (ARD-397)', the selection of dwellings within a participant Block "was performed differentially amongst the different socioeconomic strata that the Colombian government uses for the generation of cross-subsidies for public utilities (in this case, the socioeconomic stratum used for the electricity bill was used). Given that it is known from previous survey implementations that refusal rates are highest amongst households of higher socioeconomic status, the number of dwellings to be selected increased with the socioeconomic stratum (1 being the poorest and 6 being the richest) that was most prevalent in a given block".

Sixth Stage Sample The sixth stage sample unit is a household. At the sixth stage of sample selection, one household was selected in each selected dwelling using an SRS method.

Seventh Stage Sample The seventh stage sample unit was an individual aged 15-64 (inclusive). The sampling objective was to select one individual with equal probability from each selected household.

Sampling methodologies are described for each country in two documents and are provided as external resources: (i) the National Survey Design Planning Report (NSDPR) (ii) the weighting documentation (available for all countries)

Mode of data collection

Face-to-face [f2f]

Research instrument

The STEP survey instruments include:

• The background questionnaire developed by the World Bank (WB) STEP team
• Reading Literacy Assessment developed by Educational Testing Services (ETS).

All countries adapted and translated both instruments following the STEP technical standards: two independent translators adapted and translated the STEP background questionnaire and Reading Literacy Assessment, while reconciliation was carried out by a third translator.

The survey instruments were piloted as part of the survey pre-test.

The background questionnaire covers such topics as respondents' demographic characteristics, dwelling characteristics, education and training, health, employment, job skill requirements, personality, behavior and preferences, language and family background.

The background questionnaire, the structure of the Reading Literacy Assessment and Reading Literacy Data Codebook are provided in the document "Colombia STEP Skills Measurement Survey Instruments", available in external resources.

Cleaning operations

STEP data management process:

1) Raw data is sent by the survey firm 2) The World Bank (WB) STEP team runs data checks on the background questionnaire data. Educational Testing Services (ETS) runs data checks on the Reading Literacy Assessment data. Comments and questions are sent back to the survey firm. 3) The survey firm reviews comments and questions. When a data entry error is identified, the survey firm corrects the data. 4) The WB STEP team and ETS check if the data files are clean. This might require additional iterations with the survey firm. 5) Once the data has been checked and cleaned, the WB STEP team computes the weights. Weights are computed by the STEP team to ensure consistency across sampling methodologies. 6) ETS scales the Reading Literacy Assessment data. 7) The WB STEP team merges the background questionnaire data with the Reading Literacy Assessment data and computes derived variables.

Detailed information on data processing in STEP surveys is provided in "STEP Guidelines for Data Processing", available in external resources. The template do-file used by the STEP team to check raw background questionnaire data is provided as an external resource, too.`

Response rate

An overall response rate of 48% was achieved in the Colombia STEP Survey.

Abstract

The STEP (Skills Toward Employment and Productivity) Measurement program is the first ever initiative to generate internationally comparable data on skills available in developing countries. The program implements standardized surveys to gather information on the supply and distribution of skills and the demand for skills in labor market of low-income countries.

The uniquely-designed Household Survey includes modules that measure the cognitive skills (reading, writing and numeracy), socio-emotional skills (personality, behavior and preferences) and job-specific skills (subset of transversal skills with direct job relevance) of a representative sample of adults aged 15 to 64 living in urban areas, whether they work or not. The cognitive skills module also incorporates a direct assessment of reading literacy based on the Survey of Adults Skills instruments. Modules also gather information about family, health and language.

Geographic coverage

The survey covered the following regions: Western, Central, Greater Accra, Volta, Eastern, Ashanti, Brong Ahafo, Northern, Upper East and Upper West.
- Areas are classified as urban based on each country's official definition.

Analysis unit

The units of analysis are the individual respondents and households. A household roster is undertaken at the start of the survey and the individual respondent is randomly selected among all household members aged 15 to 64 included. The random selection process was designed by the STEP team and compliance with the procedure is carefully monitored during fieldwork.

Universe

The target population for the Ghana STEP survey comprises all non-institutionalized persons 15 to 64 years of age (inclusive) living in private dwellings in urban areas of the country at the time of data collection. This includes all residents except foreign diplomats and non-nationals working for international organizations. Exclusions : Military barracks were excluded from the Ghana target population.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Ghana sample design is a four-stage sample design. There was no explicit stratification but the sample was implicitly stratified by Region. [Note: Implicit stratification was achieved by sorting the PSUs (i.e., EACode) by RegnCode and selecting a systematic sample of PSUs.]

First Stage Sample The primary sample unit (PSU) was a Census Enumeration Area (EA). Each PSU was uniquely defined by the sample frame variables Regncode, and EAcode. The sample frame was sorted by RegnCode to implicitly stratify the sample frame PSUs by region. The sampling objective was to select 250 PSUs, comprised of 200 Initial PSUs and 50 Reserve PSUs. Although 250 PSUs were selected, only 201 PSUs were activated. The PSUs were selected using a systematic probability proportional to size (PPS) sampling method, where the measure of size was the population size (i.e., EAPopn) in a PSU.

Second Stage Sample The second stage sample unit is a PSU partition. It was considered necessary to partition 'large' PSUs into smaller areas to facilitate the listing process. After the partitioning of the PSUs, the survey firm randomly selected one partition. The selected partition was fully listed for subsequent enumeration in accordance with the field procedures.

Third Stage Sample The third stage sample unit (SSU) is a household. The sampling objective was to obtain interviews at 15 households within each selected PSU. The households were selected in each PSU using a systematic random method.

Fourth Stage Sample The fourth stage sample unit was an individual aged 15-64 (inclusive). The sampling objective was to select one individual with equal probability from each selected household.

Sample Size The Ghana firm's sampling objective was to obtain interviews from 3000 individuals in the urban areas of the country. In order to provide sufficient sample to allow for a worst case scenario of a 50% response rate the number of sampled cases was doubled in each selected PSU. Although 50 extra PSUs were selected for use in case it was impossible to conduct any interviews in one or more initially selected PSUs only one reserve PSU was activated. Therefore, the Ghana firm conducted the STEP data collection in a total of 201 PSUs.

Sampling methodologies are described for each country in two documents: (i) The National Survey Design Planning Report (NSDPR) (ii) The weighting documentation

Mode of data collection

Face-to-face [f2f]

Research instrument

The STEP survey instruments include: (i) a Background Questionnaire developed by the WB STEP team (ii) a Reading Literacy Assessment developed by Educational Testing Services (ETS).

All countries adapted and translated both instruments following the STEP Technical Standards: 2 independent translators adapted and translated the Background Questionnaire and Reading Literacy Assessment, while reconciliation was carried out by a third translator. The WB STEP team and ETS collaborated closely with the survey firms during the process and reviewed the adaptation and translation (using a back translation). In the case of Ghana, no translation was necessary, but the adaptation process ensured that the English used in the Background Questionnaire and Reading Literacy Assessment closely reflected local use.

• The survey instruments were both piloted as part of the survey pretest.
• The adapted Background Questionnaires are provided in English as external resources. The Reading Literacy Assessment is protected by copyright and will not be published.

Cleaning operations

STEP Data Management Process 1. Raw data is sent by the survey firm 2. The WB STEP team runs data checks on the Background Questionnaire data. - ETS runs data checks on the Reading Literacy Assessment data. - Comments and questions are sent back to the survey firm. 3. The survey firm reviews comments and questions. When a data entry error is identified, the survey firm corrects the data. 4. The WB STEP team and ETS check the data files are clean. This might require additional iterations with the survey firm. 5. Once the data has been checked and cleaned, the WB STEP team computes the weights. Weights are computed by the STEP team to ensure consistency across sampling methodologies. 6. ETS scales the Reading Literacy Assessment data. 7. The WB STEP team merges the Background Questionnaire data with the Reading Literacy Assessment data and computes derived variables.

Detailed information data processing in STEP surveys is provided in the 'Guidelines for STEP Data Entry Programs' document provided as an external resource. The template do-file used by the STEP team to check the raw background questionnaire data is provided as an external resource.

Response rate

An overall response rate of 83.2% was achieved in the Ghana STEP Survey. Table 20 of the weighting documentation provides the detailed percentage distribution by final status code.

Sampling error estimates

A weighting documentation was prepared for each participating country and provides some information on sampling errors. The weighting documentation is provided as an external resource.

This is a test collection for passage and document retrieval, produced in the TREC 2023 Deep Learning track. The Deep Learning Track studies information retrieval in a large training data regime. This is the case where the number of training queries with at least one positive label is at least in the tens of thousands, if not hundreds of thousands or more. This corresponds to real-world scenarios such as training based on click logs and training based on labels from shallow pools (such as the pooling in the TREC Million Query Track or the evaluation of search engines based on early precision).Certain machine learning based methods, such as methods based on deep learning are known to require very large datasets for training. Lack of such large scale datasets has been a limitation for developing such methods for common information retrieval tasks, such as document ranking. The Deep Learning Track organized in the previous years aimed at providing large scale datasets to TREC, and create a focused research effort with a rigorous blind evaluation of ranker for the passage ranking and document ranking tasks.Similar to the previous years, one of the main goals of the track in 2022 is to study what methods work best when a large amount of training data is available. For example, do the same methods that work on small data also work on large data? How much do methods improve when given more training data? What external data and models can be brought in to bear in this scenario, and how useful is it to combine full supervision with other forms of supervision?The collection contains 12 million web pages, 138 million passages from those web pages, search queries, and relevance judgments for the queries.

A major challenge for rare disease clinical trials is the limited amount of available information for making robust statistical inference. While external data present information integration opportunities to enhance statistical inference, conventional data combining methods, for example, meta-analysis, usually do not adequately address study population differences. Matching methods, on the other hand, directly account for population characteristics but often lead to inefficient use of data by underutilizing unmatched data points. Aiming at a better bias-variance tradeoff, we propose an intuitive integrated inference framework to borrow information from all relevant data sources and make inference on the response of interest over a target population precisely characterized by the joint distribution of baseline covariates. The method is easily implemented and can be complemented by modern statistical learning or machine learning tools. Statistical inference is facilitated by the bootstrap. We argue that the integrated inference framework not only provides an intuitive and coherent perspective for a variety of clinical trial inference problems but also has broad application areas in clinical trial settings and beyond, as a quantitative data integration tool for making robust inference in a target population precise manner for policy and decision makers.

Mathematica files used to solve for the support of the optimal matching in the numeric examples presented in The Comparative Statics of Sorting in Figures 4 and 8-12. This paper does not involve analysis of external data.

This dataset captures data on Zika-related health services collected by external evaluations in the Dominican Republic using the following approaches : 1) external Evaluators re-assessed the same patient’s records that were originally reviewed by facility quality improvement teams ; 2) external evaluators selected a new systematic random sample of records; and 3) external evaluators tallied totals for the indicators of interest from facility registers

Snapshot img

Enterprise External OEM Storage Systems Market Size 2024-2028

The enterprise external OEM storage systems market size is forecast to increase by USD 4.94 billion at a CAGR of 2.79% between 2023 and 2028.

The market is experiencing significant growth due to the increasing demand for non-volatile memory express (NVMe)-enabled storage solutions. This technology offers faster data transfer rates and lower latency, making it an attractive option for businesses seeking to optimize their data processing capabilities. This trend is driven by the need for efficient data lifecycle management and scalable file systems in modern data centers. High-availability storage solutions are also gaining popularity, as businesses seek to minimize downtime and ensure business continuity. Furthermore, the adoption of software-defined data centers (SDDC) and storage orchestration technologies is fueling the market's growth Another trend driving market growth is the increasing focus on hybrid storage solutions, which combine the benefits of both traditional and cloud-based storage systems. However, the high initial cost of setting up enterprise external OEM storage systems remains a challenge for many organizations, particularly smaller businesses. Despite this, the market is expected to continue expanding as businesses increasingly prioritize data management and processing efficiency.

What will be the Size of the Enterprise External OEM Storage Systems Market During the Forecast Period?

Request Free Sample

The market is experiencing significant growth due to the increasing demand for advanced storage solutions that cater to the complex data management needs of businesses. Key trends shaping this market include the adoption of storage tiering for optimizing data access and reducing costs, cloud storage integration for enhanced flexibility and scalability, and disaster recovery solutions for business continuity. Additionally, storage management software, provisioning, and data archiving are crucial for effective data organization and retention. Data replication, scalability, and cold storage solutions are essential for managing large volumes of data and ensuring business continuity. Distributed storage, object storage gateways, and Disaster Recovery as a Service (DRaaS) are gaining popularity for their ability to provide high availability and disaster recovery capabilities.
Immutable storage, elastic block storage, and NVMe technology are driving innovation in data center storage, offering faster data access and improved performance. Storage encryption, compliance, multi-cloud storage solutions, and persistent storage are also critical considerations for businesses seeking to secure and manage their data effectively. Flash storage and data lifecycle management are essential for optimizing storage usage and reducing costs. Scalable file systems and high-availability storage solutions ensure that businesses can handle increasing data demands while maintaining performance and availability. Overall, the market is dynamic and evolving, with a focus on delivering advanced, flexible, and cost-effective storage solutions to meet the diverse needs of businesses.

How is this Enterprise External OEM Storage Systems Industry segmented and which is the largest segment?

The industry 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.

End-user

  SMEs
  Large Enterprises


Type

  SAN
  NAS
  DAS


Geography

  North America

    US


  Europe

    UK
    France


  APAC

    China
    Japan


  South America



  Middle East and Africa

By End-user Insights

The SMEs segment is estimated to witness significant growth during the forecast period.

Enterprise external OEM storage systems cater to the data storage and protection requirements of small enterprises, which typically have smaller IT infrastructures and budgets compared to larger organizations. These systems offer an effective solution for small businesses in sectors such as e-commerce, finance, healthcare, and manufacturing. They provide features like efficient data management, increased storage capacity, enhanced system performance, and reduced risk of data loss. Enterprise external OEM storage systems encompass various technologies including External Storage Arrays, Storage Virtualization, All-Flash Arrays, Hybrid Storage Systems, RAID, Data Deduplication, Data Compression, Storage Tiering, Cloud Storage Integration, Disaster Recovery, and Storage Management Software.

Get a glance at the market report of share of various segments Request Free Sample

The SMEs segment was valued at USD 18.68 billion in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

APAC is estimated to c

Understanding Society (the UK Household Longitudinal Study), which began in 2009, is conducted by the Institute for Social and Economic Research (ISER) at the University of Essex, and the survey research organisations Verian Group (formerly Kantar Public) and NatCen. It builds on and incorporates, the British Household Panel Survey (BHPS), which began in 1991.

The Understanding Society: UKMOD Input Data, 2010-2019: Special Licence Access dataset provides input data for the longitudinal version of the UKMOD microsimulation model. The longitudinal version of UKMOD runs on Understanding Society data and covers the period 2010-2019 (Waves 1-11). The UKHLS is a large panel survey with a sample of approximately 40,000 households in its first wave. It contains detailed income data and a wide range of demographic and labour market information. As such it is the primary survey of interest in the UK for those interested in longitudinal analysis.

UKMOD is a tax-benefit microsimulation model (MSM) for the UK and its constituent nations (England, Wales, Scotland, and Northern Ireland) that originated from EUROMOD. Since 2020 UK MOD has replaced, as a stand-alone model, the UK component of EUROMOD. UKMOD is freely accessible, released open-source, and thoroughly documented and validated using external data. The standard cross-sectional version of UKMOD uses the Family Resources Survey (FRS) as its input dataset.

A tax-benefit microsimulation model is a computer code that calculates disposable income for each micro-unit (such as individual or household) in a representative sample of the population, under a specific policy scenario. Based on micro-level information about the individual and household characteristics and on legislative rules, UKMOD simulates the amount of fiscal liabilities and benefit entitlements at the tax-benefit unit level.

Tax-benefit microsimulation models are used to answer "what if" questions about the effects of tax and benefit reforms on household incomes and the income distribution. Such analysis is regularly performed for Budget and other Government policy announcements, and is also highly relevant for the design of alternative reforms and new policy instruments taking account of the diverse economic circumstances of the UK population. The longitudinal version of the data allows researchers to address additional questions, for example about the evolution of outcomes of interest over time. It also allows UKMOD outputs to be linked with additional information not used by the tax and benefit model but present in the UKHLS data for example, about labour market activity, retirement, health and wellbeing and others.

SN 9147 can be used on its own with UKMOD and is sufficient to conduct typical UKMOD analysis. For applicants seeking to add additional Understanding Society variables not included in the UKMOD dataset SN 9147, accessing SN 6614 (Safeguarded / EUL) and linking it to dataset SN 9147 normally provides a sufficient level of detail. However, in some instances, the desired variables may only be available in the safeguarded / Special Licence equivalent, SN 6931. Full details of how to link the datasets are provided in the User Guide.

Abstract copyright UK Data Service and data collection copyright owner. The aims of this study were: to identify the management competencies and weaknesses of small and medium-sized enterprises (SMEs), focusing on the availability of internal expertise in relation to that available from external sources; to assess the significance for SMEs of private and public sector business service expertise (Business Link) for processes of strategic change and innovation or adaptation to external competitive pressures; to explore the processes by which the demand for external expertise is generated by SMEs, the types of work undertaken and the impact of such expertise on performance and competitiveness; to explore the regional implications of these decisions by examining these processes in different operating environments; to extend theoretical thinking about the position of SMEs in the wider process of production, and to develop the notion of the 'extended' firms in which the boundaries between externalised and internalised management expertise are increasingly blurred. Main Topics: The topics covered are: management strengths and weaknesses, education of owner/manager, use of external advisers (consultants), types of external advisers used, and most important factors to affect business over previous three years. Some of the data covers employment by firm concerned between 1992-1996. Simple random sample

Snapshot img

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?

Request Free Sample

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.

Get a glance at the market share of various segments Request Free Sample

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

Lebanon LB: External Debt: DOD: Stocks: Variable Rate data was reported at 1.001 USD bn in 2016. This records an increase from the previous number of 947.588 USD mn for 2015. Lebanon LB: External Debt: DOD: Stocks: Variable Rate data is updated yearly, averaging 80.354 USD mn from Dec 1970 (Median) to 2016, with 47 observations. The data reached an all-time high of 2.027 USD bn in 2005 and a record low of 0.000 USD mn in 1992. Lebanon LB: External Debt: DOD: Stocks: Variable Rate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Lebanon – Table LB.World Bank: External Debt: Debt Outstanding, Debt Ratio and Debt Service. Variable interest rate is long-term external debt with interest rates that float with movements in a key market rate; for example, the London interbank offered rate (LIBOR) or the U.S. prime rate. This item conveys information about the borrower's exposure to changes in international interest rates. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars.; ; World Bank, International Debt Statistics.; Sum;

This dataset provides sample premium information for individual ACA-compliant health insurance plans available to Iowans for 2025 based on age, rating area and metal level. These are premiums for individuals, not families.

Explore and drill into the data using the 2025 Sample Premium Explorer.

Please note that not every plan ID is available in every county. On or after November 1, 2024, please go to www.healthcare.gov to determine if your plan is available in the county you reside in.

Background: Reference to so-called real-world data is more often made in marketing authorization applications for medicines intended to diagnose, prevent or treat rare diseases compared to more common diseases. We provide granularity on the type and aim of any external data on efficacy aspects from both real-world data sources and external trial data as discussed in regulatory submissions of orphan designated medicinal products in the EU. By quantifying the contribution of external data according to various regulatory characteristics, we aimed at identifying specific opportunities for external data in the field of orphan conditions.Methods: Information on external data in regulatory documents covering 72 orphan designations was extracted. Our sample comprised public assessment reports for approved, refused, or withdrawn applications concluded from 2019–2021 at the European Medicines Agency. Products with an active orphan designation at the time of submission were scrutinized regarding the role of external data on efficacy aspects in the context of marketing authorization applications, or on the criterion of “significant benefit” for the confirmation of the orphan designation at the time of licensing. The reports allowed a broad distinction between clinical development, regulatory decision making, and intended post-approval data collection. We defined three categories of external data, administrative data, structured clinical data, and external trial data (from clinical trials not sponsored by the applicant), and noted whether external data concerned the therapeutic context of the disease or the product under review.Results: While reference to external data with respect to efficacy aspects was included in 63% of the approved medicinal products in the field of rare diseases, 37% of marketing authorization applications were exclusively based on the dedicated clinical development plan for the product under review. Purely administrative data did not play any role in our sample of reports, but clinical data collected in a structured manner (from routine care or clinical research) were often used to inform on the trial design. Two additional recurrent themes for the use of external data were the contextualization of results, especially to confirm the orphan designation at the time of licensing, and reassurance of a large difference in treatment effect size or consistency of effects observed in clinical trials and practice. External data on the product under review were restricted to either active substances already belonging to the standard of care even before authorization or to compassionate use schemes. Furthermore, external data were considered pivotal for marketing authorization only exceptionally and only for active substances already in use within the specific therapeutic indication. Applications for the rarest conditions and those without authorized treatment alternatives were especially prominent with respect to the use of external data from real-world data sources both in the pre- and post-approval setting.Conclusion: Specific opportunities for external data in the setting of marketing authorizations in the field of rare diseases were identified. Ongoing initiatives of fostering systematic data collection are promising steps for a more efficient medicinal product development in the field of rare diseases.