Introduction: In causal inference, the correct formulation of the scientific question of interest is a crucial step. The purpose of this study was to apply causal inference principles to external control analysis using observational data and illustrate the process to define the estimand attributes.Methods: This study compared long-term survival outcomes of a pooled set of three previously reported randomized phase 3 trials studying patients with metastatic non-small cell lung cancer receiving front-line chemotherapy and similar patients treated with front-line chemotherapy as part of routine clinical care. Causal inference frameworks were applied to define the estimand aligned with the research question and select the estimator to estimate the estimand of interest.Results: The estimand attributes of the ideal trial were defined using the estimand framework. The target trial framework was used to address specific issues in defining the estimand attributes using observational data from a nationwide electronic health record-derived de-identified database. The two frameworks combined allow to clearly define the estimand and the aligned estimator while accounting for key baseline confounders, index date, and receipt of subsequent therapies. The hazard ratio estimate (point estimate with 95% confidence interval) comparing the randomized clinical trial pooled control arm with the external control was close to 1, which is indicative of similar survival between the two arms.Discussion: The proposed combined framework provides clarity on the causal contrast of interest and the estimator to adopt, and thus facilitates design and interpretation of the analyses.

The aim of this study was to determine the efficacy of pixel value analysis using images generated by the Digora™ and Visualix™ systems for the early detection of external root resorption (ERR). Thirty extracted human lower incisors were radiographed using the Digora and Visualix systems; then, ERR was induced by immersing the teeth in 6 mol L-1 of hydrochloric acid for different periods of time (10, 30 and 60 minutes). ERR was confirmed by calcium quantification with an atomic absorption spectrophotometer. One digital image was acquired per time period at 70 kVp, 7 mA, 2.2 mm filtration, focus-film distance of 30 cm, and with exposure times of 0.09 s in the Digora system and 0.05 s in Visualix system. The region of interest was defined using ImageJ software. Statistical analysis was performed using ANOVA and Pearson’s correlation (p < 0.05). There was no statistically significant difference between the time for ERR induction and the pixel values with either system. A positive correlation between the time of ERR induction and the calcium concentration was observed (r = 0.8892; p < 0.001). In conclusion, independent of the site of ERR induction and the digital system, pixel value analysis was not effective for ERR detection.

Explore the dynamic External Controller-based Disk Storage market analysis, driven by cloud computing & big data. Discover market size, CAGR, key trends, and regional growth from 2019-2033.

During multiparameter chemical optimization, molecular capabilities increase as hits evolve into leads and development candidates. Like retrosynthetic analysis, where target molecules are transformed into structurally simplified starting materials, we introduce retro-optimization analysis, transforming sophisticated development candidates into less capable leads and hits. To retrospectively understand the logic of optimization in discovery campaigns, we enumerated a matched molecular pair network and compared the actual route of optimization taken to alternative theoretical routes of optimization. We noted differences in the network and properties of the lead molecule compared to those of alternatives. We identified substructures of the evolving ligand, named “optimizons,” and tracked their emphasis and discovery. While we initially defined and illustrated these methods for a single project, our expansion to three additional discovery projects and three external data sets proved consistent. We retrospectively define the logic of optimization at the project, molecular, and submolecular levels to prospectively guide current and future optimization campaigns.

External Blu-ray Drive Market Outlook

According to our latest research, the global external Blu-ray drive market size in 2024 stands at USD 1.12 billion, with a robust CAGR of 6.8% projected from 2025 to 2033. This growth trajectory indicates that by 2033, the market is forecasted to reach approximately USD 2.18 billion. The market’s expansion is primarily driven by the increasing demand for high-capacity optical storage solutions in both personal and commercial applications, alongside the continued popularity of high-definition content and the need for reliable backup solutions.

One of the most significant growth factors for the external Blu-ray drive market is the surging demand for high-definition media consumption and storage. As 4K and even 8K video content becomes increasingly mainstream, consumers and businesses alike require robust storage solutions capable of handling large file sizes with ease. External Blu-ray drives, with their ability to store up to 100GB per disc, offer an efficient and cost-effective means of archiving high-resolution video, photography, and critical data. Additionally, the proliferation of gaming, video editing, and virtual reality content has further fueled the need for high-capacity, reliable storage, positioning external Blu-ray drives as an essential peripheral in the digital ecosystem.

Another critical driver is the ongoing shift towards data security and offline backup solutions. With the rise in cyber threats and concerns regarding cloud storage privacy, both individuals and enterprises are increasingly turning to physical media for secure data archiving. External Blu-ray drives provide a tangible, offline alternative to cloud backups, reducing the risk of data breaches and ensuring long-term data integrity. This trend is particularly pronounced in sectors such as healthcare, legal, and finance, where regulatory compliance mandates secure and retrievable data storage. The ability to easily transport and store sensitive information further cements the role of external Blu-ray drives as a preferred choice for secure backup solutions.

Technological advancements in connectivity and compatibility have also played a pivotal role in market growth. Modern external Blu-ray drives now feature interfaces such as USB 3.0 and USB-C, enabling faster data transfer rates and seamless integration with a wide range of devices, including ultrabooks, tablets, and all-in-one desktops. This evolution has expanded the addressable market, making Blu-ray drives accessible to users across various platforms and operating systems. Moreover, the increasing affordability of Blu-ray media and drives, coupled with enhanced durability and lifespan compared to traditional DVDs, has contributed to sustained market demand across both developed and emerging economies.

From a regional perspective, Asia Pacific has emerged as a dominant force in the external Blu-ray drive market, accounting for the largest share in 2024. This leadership is attributed to the region’s thriving consumer electronics industry, rapid digitalization, and growing middle-class population with increasing disposable incomes. North America and Europe also represent significant markets, driven by high adoption rates of advanced media technologies and a strong emphasis on data security. Meanwhile, the Middle East & Africa and Latin America are witnessing steady growth, supported by expanding IT infrastructure and rising awareness of digital storage solutions. Collectively, these regional dynamics underscore the global appeal and resilience of the external Blu-ray drive market.

Product Type Analysis

The external Blu-ray drive market is segmented by product type into portable external Blu-ray drives and desktop external Blu-ray drives, each catering to distinct user needs and preferences. Portable external Blu-ray drives have gained remarkable traction among consumers who prioritize mobility and convenience. These compact, lightweight devices are ideal for users who need to access or back up data on t

Discover the latest market trends and projections for external controller-based disk storage. This comprehensive analysis explores key drivers, restraints, and competitive dynamics, featuring insights from leading vendors like EMC, IBM, and NetApp. Learn about the projected market size and CAGR through 2033.

Acoustic Doppler current profiler (ADCP) discharge measurement data were collected and analyzed for use in developing an operational uncertainty analysis tool known as QUant (Moore and others, 2016). These ADCP measurements were originally collected in the United States, Canada, and New Zealand as a part of research conducted to validate ADCP discharge measurements made with Teledyne RD Instruments RiverRay and SonTek M9 ADCPs (Boldt and Oberg, 2015). The data were chosen in order to represent a variety of geographic and streamflow conditions, such as mean depth and mean velocity. Due to current limitations in the QUant software, only measurements collected using Teledyne RD Instruments Rio Grande and StreamPro ADCPs were used. All measurements were collected and processed with WinRiver II (Teledyne RD Instruments, 2016). An appropriate method for estimation of flow near the water surface and the streambed was obtained by means of the extrap software (Mueller, 2013). The extrapolation method and parameters obtained with extrap were entered into WinRiver II and reprocessed before use in QUant. Due to the complexity of an ADCP data file and the various algorithms applied to compute the streamflow from ADCP data, these data are most useful in their original raw data format which can be opened and processed in either WinRiver II, which is available without cost at: http://www.teledynemarine.com/rdi/support#. Each measurement consists of: (1) .mmt file; an xml configuration file used by WinRiver II for instrument setup, specific measurement data entry, and filenames of the raw transect data files (.pd0). (2) .pd0 files; the raw binary data collected by WinRiver II. The format for these files is defined in Teledyne RD Instruments (2016). (3) .txt files; raw ASCII data from external sensors such as GPS receivers. These data are not used in WinRiver II nor for the present analyses. (4) *_extrap.txt file; a file that summarizes the method and parameters selected for estimation of near-surface and near-bed discharges. (5) WinRiver.pdf files; a file that provides a summary of the discharge measurement in pdf format. References Boldt, J. A., and Oberg, K. A., 2016, Validation of streamflow measurements made with M9 and RiverRay Acoustic Doppler current profilers: Journal of Hydraulic Engineering, v. 142, no. 2. [Also available at https://doi.org/10.1061/(asce)hy.1943-7900.0001087.] Moore, S.A., Jamieson, E. C., Rainville, F., Rennie, C.D.,and& Mueller, D.S., 2017, Monte Carlo approach for uncertainty analysis of Acoustic Doppler current profiler discharge measurement by moving boat: Journal of Hydraulic Engineering: v. 143 no. 3. [Also available at https://doi.org/10.1061/(asce)hy.1943-7900.0001249.] Mueller, D.S., 2013, extrap: Software to assist the selection of extrapolation methods for moving-boat ADCP streamflow measurements: Computers & Geosciences, v. 54, p. 211–218. [Also available at https://doi.org/10.1016/j.cageo.2013.02.001.] Teledyne RD Instruments, Inc., 2016, WinRiver II Software User’s Guide, P/N 957-6231-00, San Diego, CA, 310 p.

The External Controller-Based (ECB) Disk Storage market is experiencing robust growth, driven by the increasing demand for high-performance, scalable, and reliable data storage solutions across various industries. The market, estimated at $25 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 8% from 2025 to 2033, reaching an estimated market size of $45 billion by 2033. This growth is fueled by several key factors, including the proliferation of big data analytics, the rise of cloud computing, and the increasing adoption of virtualization technologies. Furthermore, the growing need for disaster recovery and business continuity solutions is further bolstering market expansion. Major players like Dell EMC, IBM, NetApp, and Hewlett Packard Enterprise are actively investing in research and development to enhance their product offerings and cater to evolving customer requirements. The market is segmented based on storage capacity, deployment type (on-premises, cloud), and industry vertical. The market's growth trajectory is anticipated to be influenced by technological advancements such as NVMe over Fabrics and the increasing adoption of software-defined storage solutions. However, factors like the emergence of alternative storage technologies, such as solid-state drives (SSDs) and object storage, pose challenges to the ECB market's sustained expansion. The competitive landscape is characterized by intense competition amongst established vendors and the emergence of new players offering innovative storage solutions. Geographic expansion, particularly in developing economies, presents significant opportunities for market growth. Strategic partnerships, mergers, and acquisitions are expected to shape the market dynamics in the coming years, resulting in a constantly evolving competitive landscape.

This dataset, provides realistic signal metrics for 3G, 4G, 5G, and LTE network analysis using DragonOS, Spike, and SDR devices. The dataset aims to provide a representative sample of signal measurements for various network types and locations in Bihar, India. The dataset also replicates the hardware setup involving the Spike software, DragonOS running on the Valve Steam Deck gaming system, BB60C spectrum analyzer powered by an external USB3 hub connected to the Steam Deck's USB C port, srsRan running on a separate laptop for creating the base station using the bladeRFxA9 device.

Features: The dataset includes the following features:

1. Timestamp: The timestamps represent the time at which the signal metrics were recorded, with a 10-minute interval between each timestamp.

2. Latitude and Longitude: The latitude and longitude coordinates indicate the location of the measurement in Bihar. The dataset covers 20 specified localities in Bihar, including Kankarbagh, Rajendra Nagar, Boring Road, Ashok Rajpath, Danapur, Anandpuri, Bailey Road, Gardanibagh, Patliputra Colony, Phulwari Sharif, Exhibition Road, Pataliputra, Fraser Road, Kidwaipuri, Gandhi Maidan, S.K. Puri, Anisabad, Boring Canal Road, Bankipore, and Kumhrar.

3. Signal Strength (dBm): The signal strength represents the received signal power in decibels (dBm) for different network types (3G, 4G, 5G, and LTE).

4. Signal Quality (%): The signal quality represents the percentage of signal strength relative to the maximum possible signal strength. It is calculated based on the signal strength values and is applicable for 3G, 4G, 5G, and LTE networks. Unfortunately, Signal Quality percentage yielded some error so it's 0.0 in all.

5. Data Throughput (Mbps): The data throughput represents the network's capacity to transmit data, measured in megabits per second (Mbps). Different network types have varying data throughput values.

6. Latency (ms): Latency refers to the time delay between the transmission and reception of data packets, measured in milliseconds (ms). Different network types have different latency values, generated using a random uniform distribution within appropriate ranges.

7. Network Type: The network type indicates the technology used for data transmission, such as 3G, 4G, 5G, or LTE.

8. BB60C Measurement (dBm): The BB60C measurement represents the signal strength measured using the BB60C spectrum analyzer device. The values are generated based on the signal strength values with added random uniform noise specific to 4G, 5G, and LTE networks.

9. srsRAN Measurement (dBm): The srsRAN measurement represents the signal strength measured using the srsRAN software-defined radio device.

10. BladeRFxA9 Measurement (dBm): The BladeRFxA9 measurement represents the signal strength measured using the BladeRFxA9 software-defined radio device.

The dataset is generated with a total of 1926 time periods and covers 20 localities in Bihar. It can be used for various purposes, including network optimization, coverage analysis, and performance evaluation.

Hardware Setup: The dataset replicates the hardware setup using the following components:

• Valve Steam Deck gaming system running DragonOS Focal
• BB60C spectrum analyzer powered by an external USB3 hub
• srsRan software-defined radio (SDR) device
• BladeRFxA9 software-defined radio (SDR) device

The BB60C spectrum analyzer is connected to the Steam Deck's USB C port via an external USB3 hub. The srsRan and BladeRFxA9 devices are connected to a separate laptop, which is running the srsenb software to create the base station.

Additionally, the Spike LTE Analysis tools are utilized to decode the LTE information in real-time. The dataset demonstrates how the Spike software, DragonOS, and SDR devices can be integrated to perform LTE analysis, and the results can be combined with a working GPS for mapping purposes within the Spike software.

Atlast, We'd like to extend credits to our volunteers in these localities who helped in logging the data after replicating the setup.

Let us know what you build out of this dataset. It's a subset of data that's being analysed for bio weapon usage in the Bihar area which's controlled via wireless signals to report to international delegates for expedited action against these.

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.

Output in the Construction Industry is published monthly by the Office for National Statistics (ONS), containing estimates of construction output. In the months before the quarter month, current and constant price non seasonally adjusted data by sector are published. On the quarter months, additional constant price seasonally adjusted index data and value data by sector are published. On the months after the quarter, additional current priced data by type of work and region are published.

http://www.ons.gov.uk/ons/publications/all-releases.html?definition=tcm%3A77-21530">New Orders in the Construction Industry is published quarterly by the Office for National Statistics (ONS). The publication includes estimates of construction new orders (current price and constant price seasonally adjusted) broken down by sector and, in current prices, by region and by type of work.

For more information about Output and New Orders in the Construction Industry please contact the ONS construction statistics team.

Before 2008, data on Output and New Orders were published by BIS. However, in May 2008, the responsibility for collection and publication of the statistics was http://www.bis.gov.uk/analysis/statistics/construction-statistics/output-and-new-orders/transfer-of-construction-statistics-to-ons">passed from BIS to the ONS. For methodology documents regarding the http://www.berr.gov.uk/files/file20903.pdf">2007 Output and http://www.berr.gov.uk/files/file21036.pdf">2007 New Orders publications, please follow the links (note that some elements of the methodology may have changed following the transfer of construction statistics).

2.5-Inch Drive Enclosure Market Outlook

As per our latest research, the global 2.5-inch drive enclosure market size reached USD 1.28 billion in 2024, reflecting robust demand across multiple sectors. The market is poised for significant expansion, projected to grow at a CAGR of 7.2% from 2025 to 2033. By the end of the forecast period, the market is anticipated to attain a value of USD 2.40 billion. This impressive growth trajectory is primarily driven by the escalating adoption of high-capacity storage solutions in both consumer and enterprise settings, alongside technological advancements in data transfer interfaces and enclosure materials.

The surge in digital content creation, cloud computing, and data backup requirements has been a pivotal growth factor for the 2.5-inch drive enclosure market. Increasing reliance on portable and external storage devices among professionals, gamers, and businesses is fueling demand for reliable, high-speed, and durable drive enclosures. The proliferation of high-definition media, large-scale data analytics, and virtualization in IT and media sectors necessitates efficient storage expansion solutions, positioning 2.5-inch drive enclosures as a preferred choice. Additionally, the rise of remote work and BYOD (Bring Your Own Device) policies has accelerated the need for secure and easily transportable storage, further boosting market growth.

Technological innovations in interface connectivity, such as the widespread adoption of USB 3.0, USB-C, and Thunderbolt, are significantly enhancing the performance and versatility of 2.5-inch drive enclosures. These advancements are enabling faster data transfer rates, improved device compatibility, and better power management, making these enclosures suitable for demanding applications in creative industries, engineering, and enterprise IT. Furthermore, the integration of advanced security features and ruggedized designs is expanding their utility in industrial and field environments, where data integrity and device durability are paramount. The growing trend toward modular and tool-less enclosures is also simplifying user experience and driving adoption among both personal and professional users.

Another crucial driver for the 2.5-inch drive enclosure market is the increasing affordability and accessibility of solid-state drives (SSDs) and high-capacity hard disk drives (HDDs). As the cost per gigabyte continues to fall, more consumers and organizations are upgrading their storage infrastructure, often repurposing existing drives with external enclosures for backup and data migration purposes. Environmental concerns and sustainability initiatives are encouraging the reuse and recycling of drives, which further propels the demand for versatile and reliable enclosures. The market is also witnessing growing penetration in emerging economies, fueled by expanding internet access, digitalization initiatives, and the rise of e-commerce platforms that facilitate easy purchase and delivery of these products.

From a regional perspective, Asia Pacific is emerging as the fastest-growing market for 2.5-inch drive enclosures, driven by rapid industrialization, urbanization, and the proliferation of consumer electronics. North America and Europe continue to maintain substantial market shares due to mature IT infrastructure, high consumer awareness, and a strong presence of technology companies. Latin America and the Middle East & Africa are witnessing steady growth, underpinned by increasing investments in digital transformation and expanding small and medium enterprise (SME) sectors. Regional dynamics are also influenced by local manufacturing capabilities, regulatory frameworks, and evolving consumer preferences, making the competitive landscape highly dynamic and regionally nuanced.

Product Type Analysis

The product type segment of the 2.5-inch drive enclosure market comprises HDD enclosures, SSD enclosures, and hybrid enclosures. HDD enclosures continue to dominate the mark

Additional file 1. Numerical example of Bayesian updating of a P-BLUP evaluation. This example shows that Bayesian updating of an external evaluation with the current data yields identical results to those from a joint analysis of the data from the external and the current evaluations. In this example, the posterior mean vectors and covariance matrices that are needed for the Bayesian updating analysis are obtained directly from the solutions and the inverse of the coefficient matrix for the MME of the external analysis, rather than from MCMC samples. The PDF file shows the Julia script and the results from running that script. The Jupyter Notebook containing the Julia script will require a Jupyter Notebook application to run or modify the script.

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 STEP target population is the urban population aged 15 to 64 included. Lao PDR sampled both urban and rural areas (with road) of the country. Areas are classified as rural or 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 STEP target population is the population aged 15 to 64 included, living in urban areas, as defined by each country's statistical office. 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

Laos' Target Population Description The target population comprises all non-institutionalized persons 15 to 64 years of age (inclusive) living in urban and rural areas of the country at the time of data collection. This includes all residents except foreign diplomats and non-nationals working for international organizations. There will be no exclusions for the target population. The survey tool is designed for Lao language only (the sole national language). IRL will make every effort to interview non-Lao speaking persons through the use of local translators. In most such cases, the household and individual modules will be carried out with the assistance of a translator when available. However, Modules 6 and 9 will not be administered to those respondents who do not speak or read Lao as per STEP technical standards.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Laos sample design is a 3 stage stratified sample design. The stratification variable is urban-rural indicator.

First Stage Sample The primary sample unit (PSU) is a village. The sampling objective was to conduct interviews in 134 urban villages and 54 rural villages. The villages were selected with probability proportional to size (PPS), where the measure of size was the number of households in a village.

Second Stage Sample The second stage sample unit (SSU) is a household. In the second stage, the number of households selected in each selected PSU was proportional to the size of the selected PSUs. The households were selected from a list of households in each selected PSU by systematic equal probability sampling. At the same time, a reserve sample of the same number of households as the target sample in each PSU was selected for use when needed to ensure that the target sample size is achieved.

Third Stage Sample The third 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.

Mode of data collection

Face-to-face [f2f]

Research instrument

The STEP survey instruments include: - a Background Questionnaire developed by the WB STEP team - 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 Lao PDR survey firm during the process and reviewed the adaptation and translation to Lao using a back translation.

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

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 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 “Guidelines for STEP Data Entry Programs” document, 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 95% was achieved in the Lao PDR STEP Survey.

Sampling error estimates

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

1. About the Dataset:

Description: Dive into the world of college placements with this dataset designed to unravel the factors influencing student placement outcomes. The dataset comprises crucial parameters such as IQ scores, CGPA (Cumulative Grade Point Average), and placement status. Aspiring data scientists, researchers, and enthusiasts can leverage this dataset to uncover patterns and insights that contribute to a deeper understanding of successful college placements.

2. Projects Ideas:

Project Idea 1: Predictive Modeling for College Placements Utilize machine learning algorithms to build a predictive model that forecasts a student's likelihood of placement based on their IQ scores and CGPA. Evaluate and compare the effectiveness of different algorithms to enhance prediction accuracy.

Project Idea 2: Feature Importance Analysis Conduct a feature importance analysis to identify the key factors that significantly influence placement outcomes. Gain insights into whether IQ, CGPA, or a combination of both plays a more dominant role in determining success.

Project Idea 3: Clustering Analysis of Placement Trends Apply clustering techniques to group students based on their placement outcomes. Explore whether distinct clusters emerge, shedding light on common characteristics or trends among students who secure placements.

Project Idea 4: Correlation Analysis with External Factors Investigate the correlation between the provided data (IQ, CGPA, placement) and external factors such as internship experience, extracurricular activities, or industry demand. Assess how these external factors may complement or influence placement success.

Project Idea 5: Visualization of Placement Dynamics Over Time Create dynamic visualizations to illustrate how placement trends evolve over time. Analyze trends, patterns, and fluctuations in placement rates to identify potential cyclical or seasonal influences on student placements.

3. Columns Explanation:

• IQ:

  • Definition: Intelligence Quotient, a measure of a person's intellectual abilities.
  • Data Type: Numeric
  • Range: Typically, IQ scores range from 70 to 130, with 100 being the average.

• CGPA:

  • Definition: Cumulative Grade Point Average, a measure of a student's overall academic performance.
  • Data Type: Numeric
  • Range: Typically, CGPA is on a scale of 0 to 4, with 4 being the highest possible score.

• Placement:

  • Definition: Binary variable indicating whether a student secured a placement (1) or not (0).
  • Data Type: Categorical (Binary)
  • Values: 1 (Placement secured) or 0 (No placement).

These columns collectively provide a comprehensive snapshot of a student's intellectual abilities, academic performance, and their success in securing a placement. Analyzing this dataset can offer valuable insights into the dynamics of college placements and inform strategies for optimizing student outcomes.

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 STEP target population is the urban population aged 15 to 64 included. Sri Lanka sampled both urban and rural areas. Areas are classified as rural or 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 Sri Lanka STEP survey comprised all non-institutionalized persons 15 to 64 years of age (inclusive) living in private dwellings in urban and rural areas of Sri Lanka at the time of data collection. Exclusions The target population excludes: - Foreign diplomats and non-nationals working for international organizations; - People in institutions such as hospitals or prisons; - Collective dwellings or group quarters; - Persons living outside the country at the time of data collection, e.g., students at foreign universities; - Persons who are unable to complete the STEP assessment due to a physical or mental condition, e.g., visual impairment or paralysis.

The sample frame for the selection of first stage sample units was the Census 2011/12

Kind of data

Sample survey data [ssd]

Sampling procedure

The Sri Lanka sample size was 2,989 households. The sample design is a 5 stage stratified sample design. The stratification variable is Urban-Rural indicator.

First Stage Sample The primary sample unit (PSU) is a Grama Niladari (GN) division. The sampling objective was to conduct interviews in 200 GNs, consisting of 80 urban GNs and 120 rural GNs. Because there was some concern that it might not be possible to conduct any interviews in some initially selected GNs (e.g. due to war, conflict, or inaccessibility, for some other reason), the sampling strategy also called for the selection of 60 extra GNs (i.e., 24 urban GNs and 36 rural GNs) to be held in reserve for such eventualities. Hence, a total of 260 GNs were selected, consisting of 200 'initial' GNs and 60 'reserve' GNs. Two GNS from the initial sample of GNs were not accessible and reserve sampled GNs were used instead. Thus a total of 202 GNs were activated for data collection, and interviews were conducted in 200 GNs. The sample frame for the selection of first stage sample units was the list of GNs from the Census 2011/12. Note: The sample of first stage sample units was selected by the Sri Lanka Department of Census & Statistics (DCS) and provided to the World Bank. The DCS selected the GNs with probability proportional to size (PPS), where the measure of size was the number of dwellings in a GN.

Second Stage Sample The second stage sample unit (SSU) is a GN segment, i.e., GN BLOCK. One GN Block was selected from each activated PSU (i.e., GN). According to the Sri Lanka survey firm, each sampled GN was divided into a number of segments, i.e., GN Blocks, with approximately the same number of households, and one GN Block was selected from each sampled GN.

Third Stage Sample The third stage sample unit is a dwelling. The sampling objective was to obtain interviews at 15 dwellings within each selected SSU.

Fourth Stage Sample The fourth stage sample unit is a household. The sampling objective was to select one household within each selected third stage dwelling.

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

Please refer to the Sri Lanka STEP Survey Weighting Procedures Summary for additional information on sampling.

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 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

The response rate for Sri Lanka (urban and rural) was 63%. (See STEP Methodology Note Table 4).

Sampling error estimates

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

Vehicle Data Compression Market Outlook

As per our latest research, the global vehicle data compression market size in 2024 is valued at USD 1.47 billion. The market is experiencing robust expansion, driven by the exponential growth in automotive data generation and the increasing integration of connected vehicle technologies. The market is projected to grow at a CAGR of 22.8% from 2025 to 2033, reaching a forecasted value of USD 12.2 billion by 2033. Key growth factors include the rising adoption of advanced driver-assistance systems (ADAS), the proliferation of telematics, and the demand for real-time data analytics in both passenger and commercial vehicles.

The rapid digital transformation within the automotive sector is a primary driver for the vehicle data compression market. Modern vehicles are now equipped with a multitude of sensors, cameras, and communication modules that continuously generate massive volumes of data. This surge in data generation necessitates effective data compression solutions to ensure efficient transmission, storage, and processing. Data compression not only reduces bandwidth and storage requirements but also enhances the speed and accuracy of data-driven applications such as predictive maintenance and fleet management. As automakers and fleet operators strive for operational efficiency and cost reduction, the demand for robust vehicle data compression technologies continues to escalate.

Another significant growth factor is the integration of advanced driver-assistance systems (ADAS) and autonomous driving technologies. These systems rely heavily on real-time data from various sources, including LiDAR, radar, and high-definition cameras. The sheer volume of data generated by these components can overwhelm traditional storage and communication infrastructures. Data compression technologies play a crucial role in mitigating these challenges by enabling faster and more reliable data transfer between vehicle subsystems and external cloud platforms. This, in turn, supports the development and deployment of safer, smarter, and more connected vehicles, further fueling market growth.

Additionally, the proliferation of electric vehicles (EVs) and the increasing focus on vehicle-to-everything (V2X) communication are contributing to the expansion of the vehicle data compression market. EVs, with their sophisticated battery management systems and connectivity features, generate unique data streams that require efficient compression for monitoring and optimization. Similarly, V2X communication, which facilitates interaction between vehicles and infrastructure, demands real-time data exchange with minimal latency. Data compression technologies are essential for meeting these requirements, ensuring seamless connectivity and enhanced user experiences across diverse automotive applications.

From a regional perspective, Asia Pacific is emerging as a dominant force in the global vehicle data compression market, owing to its rapidly expanding automotive industry, increasing adoption of connected vehicles, and government initiatives promoting smart mobility solutions. North America and Europe also hold significant market shares, driven by technological advancements, stringent regulatory frameworks, and the presence of leading automotive OEMs and technology providers. Meanwhile, Latin America and the Middle East & Africa are gradually embracing vehicle data compression technologies, supported by growing investments in automotive infrastructure and digitalization efforts.

Component Analysis

The vehicle data compression market is segmented by component into software, hardware, and services. Software solutions constitute the largest share of the market, as they provide the core algorithms and platforms required for compressing, decompressing, and managing vehicle data streams. Automotive OEMs and fleet operators are increasingly investing in advanced software solutions to support real-time analytics, over-the-air updates, and secure d