Being relatively new to the field, electromechanical actuators in aerospace applications lack the knowledge base compared to ones accumulated for the other actuator types, especially when it comes to fault detection and characterization. Lack of health monitoring data from fielded systems and prohibitive costs of carrying out real flight tests push for the need of building system models and designing affordable but realistic experimental setups. This paper presents our approach to accomplish a comprehensive test environment equipped with fault injection and data collection capabilities. Efforts also include development of multiple models for EMA operations, both in nominal and fault conditions that can be used along with measurement data to generate effective diagnostic and prognostic estimates. A detailed description has been provided about how various failure modes are inserted in the test environment and corresponding data is collected to verify the physics based models under these failure modes that have been developed in parallel. A design of experiment study has been included to outline the details of experimental data collection. Furthermore, some ideas about how experimental results can be extended to real flight environments through actual flight tests and using real flight data have been presented. Finally, the roadmap leading from this effort towards developing successful prognostic algorithms for electromechanical actuators is discussed.*

Data DescriptionThe CADDI dataset is designed to support research in in-class activity recognition using IMU data from low-cost sensors. It provides multimodal data capturing 19 different activities performed by 12 participants in a classroom environment, utilizing both IMU sensors from a Samsung Galaxy Watch 5 and synchronized stereo camera images. This dataset enables the development and validation of activity recognition models using sensor fusion techniques.Data Generation ProceduresThe data collection process involved recording both continuous and instantaneous activities that typically occur in a classroom setting. The activities were captured using a custom setup, which included:A Samsung Galaxy Watch 5 to collect accelerometer, gyroscope, and rotation vector data at 100Hz.A ZED stereo camera capturing 1080p images at 25-30 fps.A synchronized computer acting as a data hub, receiving IMU data and storing images in real-time.A D-Link DSR-1000AC router for wireless communication between the smartwatch and the computer.Participants were instructed to arrange their workspace as they would in a real classroom, including a laptop, notebook, pens, and a backpack. Data collection was performed under realistic conditions, ensuring that activities were captured naturally.Temporal and Spatial ScopeThe dataset contains a total of 472.03 minutes of recorded data.The IMU sensors operate at 100Hz, while the stereo camera captures images at 25-30Hz.Data was collected from 12 participants, each performing all 19 activities multiple times.The geographical scope of data collection was Alicante, Spain, under controlled indoor conditions.Dataset ComponentsThe dataset is organized into JSON and PNG files, structured hierarchically:IMU Data: Stored in JSON files, containing:Samsung Linear Acceleration Sensor (X, Y, Z values, 100Hz)LSM6DSO Gyroscope (X, Y, Z values, 100Hz)Samsung Rotation Vector (X, Y, Z, W quaternion values, 100Hz)Samsung HR Sensor (heart rate, 1Hz)OPT3007 Light Sensor (ambient light levels, 5Hz)Stereo Camera Images: High-resolution 1920×1080 PNG files from left and right cameras.Synchronization: Each IMU data record and image is timestamped for precise alignment.Data StructureThe dataset is divided into continuous and instantaneous activities:Continuous Activities (e.g., typing, writing, drawing) were recorded for 210 seconds, with the central 200 seconds retained.Instantaneous Activities (e.g., raising a hand, drinking) were repeated 20 times per participant, with data captured only during execution.The dataset is structured as:/continuous/subject_id/activity_name/ /camera_a/ → Left camera images /camera_b/ → Right camera images /sensors/ → JSON files with IMU data

/instantaneous/subject_id/activity_name/repetition_id/ /camera_a/ /camera_b/ /sensors/ Data Quality & Missing DataThe smartwatch buffers 100 readings per second before sending them, ensuring minimal data loss.Synchronization latency between the smartwatch and the computer is negligible.Not all IMU samples have corresponding images due to different recording rates.Outliers and anomalies were handled by discarding incomplete sequences at the start and end of continuous activities.Error Ranges & LimitationsSensor data may contain noise due to minor hand movements.The heart rate sensor operates at 1Hz, limiting its temporal resolution.Camera exposure settings were automatically adjusted, which may introduce slight variations in lighting.File Formats & Software CompatibilityIMU data is stored in JSON format, readable with Python’s json library.Images are in PNG format, compatible with all standard image processing tools.Recommended libraries for data analysis:Python: numpy, pandas, scikit-learn, tensorflow, pytorchVisualization: matplotlib, seabornDeep Learning: Keras, PyTorchPotential ApplicationsDevelopment of activity recognition models in educational settings.Study of student engagement based on movement patterns.Investigation of sensor fusion techniques combining visual and IMU data.This dataset represents a unique contribution to activity recognition research, providing rich multimodal data for developing robust models in real-world educational environments.CitationIf you find this project helpful for your research, please cite our work using the following bibtex entry:@misc{marquezcarpintero2025caddiinclassactivitydetection, title={CADDI: An in-Class Activity Detection Dataset using IMU data from low-cost sensors}, author={Luis Marquez-Carpintero and Sergio Suescun-Ferrandiz and Monica Pina-Navarro and Miguel Cazorla and Francisco Gomez-Donoso}, year={2025}, eprint={2503.02853}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2503.02853}, }

These occurrence data were encoded from the inventory works carried out by 5 students at the end of their training at the University of Abomey-Calavi, in the context of the accomplishment of their thesis

This document from the Pinyon Jay Working Group presents recommendations and guidelines to prevent researchers or surveyors from inadvertently disturbing or negatively affecting Pinyon Jays during their work.

Collaboratory is a software product developed and maintained by HandsOn Connect Cloud Solutions. It is intended to help higher education institutions accurately and comprehensively track their relationships with the community through engagement and service activities. Institutions that use Collaboratory are given the option to opt-in to a data sharing initiative at the time of onboarding, which grants us permission to de-identify their data and make it publicly available for research purposes. HandsOn Connect is committed to making Collaboratory data accessible to scholars for research, toward the goal of advancing the field of community engagement and social impact.Collaboratory is not a survey, but is instead a dynamic software tool designed to facilitate comprehensive, longitudinal data collection on community engagement and public service activities conducted by faculty, staff, and students in higher education. We provide a standard questionnaire that was developed by Collaboratory’s co-founders (Janke, Medlin, and Holland) in the Institute for Community and Economic Engagement at UNC Greensboro, which continues to be closely monitored and adapted by staff at HandsOn Connect and academic colleagues. It includes descriptive characteristics (what, where, when, with whom, to what end) of activities and invites participants to periodically update their information in accordance with activity progress over time. Examples of individual questions include the focus areas addressed, populations served, on- and off-campus collaborators, connections to teaching and research, and location information, among others.The Collaboratory dataset contains data from 45 institutions beginning in March 2016 and continues to grow as more institutions adopt Collaboratory and continue to expand its use. The data represent over 6,200 published activities (and additional associated content) across our user base.Please cite this data as:Medlin, Kristin and Singh, Manmeet. Dataset on Higher Education Community Engagement and Public Service Activities, 2016-2023. Collaboratory [producer], 2021. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2023-07-07. https://doi.org/10.3886/E136322V1When you cite this data, please also include: Janke, E., Medlin, K., & Holland, B. (2021, November 9). To What End? Ten Years of Collaboratory. https://doi.org/10.31219/osf.io/a27nb

A global survey from Capgemini showed that retail companies were lagging behind consumer products enterprises in the use of data. The gap was significant in the automation of processes and in data collecting: only ** percent of retailers automated data collection, against ** percent of consumer goods companies. However, one in **** organizations in both categories reported to have implemented practices involving data engineering, machine learning, and DevOps.

The “All Activity Trend” shows the number of positive cases that were interviewed (bar graph) and the percentage of those interviewed who reported each select high to moderate exposure activity types (i.e. personal care, dining out, social-related activities, work, travel, gym/fitness, sports, and faith-related events) during their exposure period (trend lines) on a weekly basis.Note: Data subject to change on a daily basis. Data are restricted to positive cases with a completed contact tracing interview. Possible exposure data are collected during the contact tracing interview as self-reported activities occurring within the 2-week period before the date of symptom onset for symptomatic individuals or the date of test sample collection for asymptomatic individuals. Data collection methods were altered starting the week of Dec 11 for gym/fitness and sports, so should not be compared to previous values.* High to Moderate Exposure Activity Types are not exhaustive and include travel, personal care, faith events, work, dining out, social events, gym/fitness, and sports.Data is updated on a weekly basis.

The goal of our research is to identify strengths and weaknesses of high school level science fair and improvements that might enhance learning outcomes based on empirical assessment of student experiences. We use the web-based data collection program REDCap to implement anonymous and voluntary surveys about science fair experiences with two independent groups—high school students who recently competed in the Dallas Regional Science and Engineering Fair and post high school students (undergraduates, 1st year medical students, and 1st year biomedical graduate students) on STEM education tracks doing research at UT Southwestern Medical Center. Herein, we report quantitative and qualitative data showing student opinions about the value of science fair. Few students in any group thought that competitive science fair (C-SF) should be required. The most common reasons given for not requiring C-SF were no enjoyment and no interest in competing. On the other hand, student attitudes towards requiring non-competitive science fair (NC-SF) were nuanced and ranged as high as 91%, increasing with student maturation, science fair experience, and STEM track. The most common reasons given for requiring NC-SF were learning scientific thinking skills and research skills. Students opposed to requiring NC-SF most frequently mentioned no enjoyment and no interest in science. Several student comments critical of the fairness of science fair led us to determine possible differences in science fair experiences depending on whether or not students received help from scientists. Those who received help from scientists had an easier time getting their research idea, more access to articles in books and magazines, and less difficulty getting resources. We discuss the idea that two different types of science fairs—competitive science fair with a performance goal orientation and non-competitive science fair with a mastery goal orientation—might be required to promote the broad goal of educating all students about science and engineering.

Despite many innovative ideas generated in response to COVID-19, few studies have examined community preferences for these ideas. Our study aimed to determine university community members’ preferences for three novel ideas identified through a crowdsourcing open call at the University of North Carolina (UNC) for making campus safer in the pandemic, as compared to existing (i.e. pre-COVID-19) resources. An online survey was conducted from March 30, 2021 – May 6, 2021. Survey participants included UNC students, staff, faculty, and others. The online survey was distributed using UNC’s mass email listserv and research directory, departmental listservs, and student text groups. Collected data included participant demographics, COVID-19 prevention behaviors, preferences for finalist ideas vs. existing resources in three domains (graduate student supports, campus tours, and online learning), and interest in volunteering with finalist teams. In total 437 survey responses were received from 228 (52%) staff, 119 (27%) students, 78 (18%) faculty, and 12 (3%) others. Most participants were older than age 30 years (309; 71%), women (332, 78%), and white (363, 83.1%). Five participants (1%) were gender minorities, 66 (15%) identified as racial/ethnic minorities, and 46 (10%) had a disability. Most participants preferred the finalist idea for a virtual campus tour of UNC’s lesser-known history compared to the existing campus tour (52.2% vs. 16.0%). For graduate student supports, 41.4% of participants indicated no preference between the finalist idea and existing supports; for online learning resources, the existing resource was preferred compared to the finalist idea (41.6% vs. 30.4%). Most participants agreed that finalists’ ideas would have a positive impact on campus safety during COVID-19 (81.2%, 79.6%, and 79.2% for finalist ideas 1, 2 and 3 respectively). 61 (14.1%) participants indicated interest in volunteering with finalist teams. Together these findings contribute to the development and implementation of community-engaged crowdsourced campus safety interventions during COVID-19. Methods An online survey was distributed to members of the UNC Chapel Hill community using multiple digital strategies, including a mass informational email system (UNC’s Mass Mail system), circulation on 12 departmental listservs, UNC GroupMe text messages, and the Research For Me @ UNC database. Survey responses were collected via a Qualtrics survey form. Survey responses were collected online from March 30, 2021 to May 6, 2021. Survey participants completed electronic informed consent prior to answering the survey. All survey response data collected from participants were compiled using Microsoft Excel. Data collected include demographic information of participants, questions about COVID-19-related behaviors, and preferences for crowdsourced strategies for enhancing campus safety during the pandemic vs. existing comparable resources at UNC.

Writing activities are an integral part of the learning process. Writing means managing ideas systematically and expressing them explicitly. Writing can mean lowering or describing graphic symbols that describe a language understood by someone. For a researcher, compiling a research management is a very important step because this step greatly determines the success or failure of all research activities. Research activities are one of the activities that are scientific in nature. Before someone starts with research activities then he must make a written plan commonly referred to as the management of research data collection. In addition, scientific research management has a clear purpose, which can later be useful as a study material about a matter and most importantly as a reference in making decisions both for the benefit of the public or government or the private sector or company.

The dataset and accompanying R code provided in the R Markdown file linked to the following manuscript submitted for review: Altwegg et al. "Emerging topics and new directions in statistical ecology".Abstract of linked material:Ecological science relies on robust estimates of the abundance, diversity, and spatial distribution of individuals and species, but these quantities are notoriously difficult to observe directly. Data collected on these quantities not only reflect the ecological processes giving rise to them but also the observation process, which is often biased by factors such as uneven sampling effort or imperfect detection. Furthermore, collecting data according to standard sampling designs is often not possible. Statistical ecology as a research field specialises in developing statistical methods for analysing such complex ecological data. Here, we apply text analysis tools to the abstracts submitted to eight International Statistical Ecology Conferences between 2008 and 2022 to guide a review of recent topics in statistical ecology. Results show that estimating various aspects of demography (including survival, recruitment, abundance, density and movement) and spatial distribution remains a key area of research. The field has benefited from and embraced new data collection methods such as automated recorders and rapidly developing remote sensing techniques. How to integrate data from different sources is a central challenge that spans multiple areas of statistical ecology. The statistical ecology community strives to be inclusive. It also promotes robust data analysis strategies that underpin reproducible research and transparent conservation decisions. With the increasing pressure of human society on nature, we feel statistical ecology is becoming an ever more important research field. Files:Data_Altwegg_et_al_JSTP_2025.csvData_Altwegg_et_al_JSTP_2025.rmd

Connecting language classrooms with 21st-century skills could be the potential framework for enhancing EFL learners’ performance in writing classes. However, investigating whether project-based learning, as a new field within ELT with unique pedagogical affordances, can enhance learners’ writing skills still needs to be improved in the literature. Accordingly, this study aimed to investigate the impact of project-based learning on EFL learners’ writing performance. It sought to determine whether and to what extent project-based learning could enhance writing skills in an EFL context. The study employed a quasi-experimental design with an interrupted pre-test-post-test time series design with single group participants. Twenty-three third-year EFL undergraduate students enrolled in the Advanced Writing Skills I course were selected using a comprehensive sampling method. An essay writing test and interview were used to gather data. The participants of the study were given a series of three problem-solving essay writing tests before and after the intervention, which employed project-based essay writing instruction. In addition, to discover their attitudes toward the impacts of project-based learning and its applications on the ground, three randomly selected students were interviewed at the end of the intervention. The data collected through the tests were analyzed through a one-way repeated measure ANOVA; narration was also used to analyze the qualitative data gathered through interviews. Accordingly, the quantitative data suggested that project-based learning significantly enhances EFL learners’ writing performance. Moreover, interview data showed that students felt optimistic about the impact of project-based learning on their writing performance, idea generation, and cooperation among themselves. Therefore, project-based learning is suggested as another method in ELT writing classes because it enhances learners’ writing via idea generation, data collection, organization, cooperation, and general communication skills. As students work on worthwhile projects, its emphasis on real-world applicability and realistic activities can help them become better writers. Hence, teachers can reinforce the relationship between form and purpose by incorporating a variety of genres and collaborative writing to reflect real-world or professional situations.

Abstract

The objectives in this ex-post performance evaluation target how the education sub-activity was implemented, if and how it has been sustained, and its perceived outcomes. To meet these objectives, MCC and Social Impact, Inc. (SI), outlined four evaluation questions: 1. What are the current conditions of MCC investments made for the education sub-activity? How do the conditions of MCC investments compare to non-MCC-supported sites? 2. How did the implementation process and/or post-completion maintenance contribute to current conditions of MCC investments? 3. What other factors explain both perceived school-level outcomes and the current conditions of schools? 4. What are the perceived outcomes of the investments in school infrastructure?

To answer the evaluation questions, SI supplemented existing data with two distinct but related data collection activities: first, a school conditions survey to answer Evaluation Question 1, and second, cross-case studies to answer Evaluation Questions 2, 3, and 4.

Overall findings show that on average, MCC schools are in better condition than non-MCC schools, while schools in the Southern zone are in better condition, on average, compared to those in Afram zone and Northern zone.

Qualitative data shows that differences in implementation and maintenance practices had an effect on the current condition of schools. Lack of maintenance funding and community buy-in were identified as major barriers to maintenance. Respondents also highlighted misuse of school facilities by community members (across all zones and schools), harsh weather (primarily in Afram and Northern zones, but all school types), and environment (primarily in low scoring MCC schools) adversely affected school conditions. However, PTAs and SMCs in high scoring MCC and non-MCC schools were more proactive in addressing these factors than those at low-scoring MCC schools. The perception across all zones in all study schools was that improvements in infrastructure positively affected enrollment, attendance, completion and learning.

Geographic coverage

Data was collected from schools in the three zones where MCC interventions took place: Afram Basin, Northern Region and Southern Horticulture Zone.

Analysis unit

School

Universe

All the schools that had been considered for the MCC education intervention.

Kind of data

Sample survey data [ssd]

Sampling procedure

MCC schools: All 221 schools that received MCC funding were included in the study. Non-MCC schools: All 337 remaining schools that (1) had been considered for MCC funding but didn't receive it and (2) that MiDA could provide names for.

Sampling deviation

N/A

Research instrument

Quantitative questionnaire: School Conditions Survey The school conditions survey was a systematic examination of current school infrastructure conditions against international standards, GoG building guidelines, and the MiDA maintenance manual. The enumerators scored different aspects of school infrastructure, including the condition of school grounds, classroom blocks, equipment and furniture, and toilet facilities and polytanks. Ratings of condition were made on a three-point system-poor, average, and good-and each rating was followed up with a photograph of the object being rated.

Qualitative questionnaires: Key informant interviews (KIIs), focus group discussions (FGDs), and community score cards (CSCs) were conducted with parents, students, teachers, school leaders or headmasters, district education officers, individuals responsible for operations and management, construction consultants and implementers, MiDA and MCC staff, and a representative from the Ministry of Education. Questions were asked to understand the processes that may have led to the current conditions of school infrastructure, and perceptions of key stakeholders on the relationship between the investments made and school-level outcomes such as enrollment, attendance, completion, and learning.

Cleaning operations

Data cleaning was done for the school conditions survey. This included: - consistency checks and removing duplicate entries - coding and labeling variables - checks on ratings by enumerators - corrections made to 'Don't Know' ratings where a rating could be given from the photograph

Response rate

MCC schools: All 221 schools surveyed Non-MCC schools: 192 schools out of 337 could be surveyed. This is because many of the schools in the list provided by MiDA were duplicates (already included in the MCC funded list).

Sampling error estimates

N/A

The “Event Distancing Trend” indicates the number of positive cases interviewed per week who reported attending an event with five or more individuals and where social distancing was NOT maintained.Note: Data subject to change on a daily basis. Data are restricted to positive cases with a completed contact tracing interview. Possible exposure data are collected during the contact tracing interview as self-reported activities occurring within the 2-week period before the date of symptom onset for symptomatic individuals or the date of test sample collection for asymptomatic individuals. Data collection methods were altered starting the week of Dec 11 for gym/fitness and sports, so should not be compared to previous values.* High to Moderate Exposure Activity Types are not exhaustive and include travel, personal care, faith events, work, dining out, social events, gym/fitness, and sports.Data is updated on a weekly basis.

Abstract copyright UK Data Service and data collection copyright owner. The Online Time Use Survey (OTUS) was developed by the Office for National Statistics to help improve the measurement of unpaid household production and caring activities that are not captured within traditional economic measures, and to understand better time use from a well-being and quality of life perspective. The survey collects information from adults aged 18 years and over who are randomly sampled from the NatCen Opinion Panel, which is representative of the UK population. Data collected between March 2020 and March 2021 covers Great Britain and data collected from March 2022 onwards covers the United Kingdom. Participants were issued with two pre-allocated diary days (one on a weekday and one on a weekend day). They were asked to record their main activities (in 10-minute intervals) and up to five secondary activities (in five-minute intervals) in every 24 hours within an online diary tool. Respondents were able to select activities from a pre-defined list. They were also asked to rate how much they enjoyed different activities. In addition, respondents were asked to complete a demographic questionnaire which records personal and household characteristics.Latest edition informationFor the third edition (August 2024), data and documentation for Wave 8 (9 to 17 March 2024) were added to the study. Main Topics: The annual data files include the following variables:main activities (in 10-minute periods) up to five secondary activities (in five-minute periods)count of all 5-minute primary activities total in minutes of all primary activitiescount of all 5-minute secondary activitiestotal in minutes of all secondary activitiesenjoyment level (scale 1-7) for all primary activities (in 10-minute periods) enjoyment level (scale 1-7) for all secondary activities (in five-minute periods)basic demographics, including personal well-being rating variables.

The American Time Use Survey (ATUS) is the Nation's first federally administered, continuous survey on time use in the United States. This multi-year data collection contains information on the amount of time (in minutes) that people spent doing various activities on a given day, including the arts activities, in the years 2003 through 2021. Data collection for the ATUS began in January 2003. Sample cases for the survey are selected monthly, and interviews are conducted continuously throughout the year. In 2021, approximately 9,000 individuals were interviewed. Estimates are released annually. ATUS sample households are chosen from the households that completed their eighth (final) interview for the Current Population Survey (CPS), the nation's monthly household labor force survey. ATUS sample households are selected to ensure that estimates will be nationally representative. One individual age 15 or over is randomly chosen from each sampled household. This "designated person" is interviewed by telephone once about his or her activities on the day before the interview--the "diary day." The ATUS Activity Coding Lexicon is a 3-tiered classification system with 17 first-tier categories. Each of the first-tier categories has two additional levels of detail. Respondents' reported activities are assigned 6-digit activity codes based on this classification system. Additionally, the study provides demographic information--including sex, age, ethnicity, race, education, employment, and children in the household. IMPORTANT: The 2020 ATUS was greatly affected by the coronavirus (COVID-19) pandemic. Data collection was suspended in 2020 from mid-March to mid-May. ATUS data files for 2020 contain all ATUS data collected in 2020--both before and after data collection was suspended. For more information, please visit BLS's ATUS page. The weighting method changed in 2020 to account for the suspension of data collection in early 2020 due to the COVID-19 pandemic. Respondents from 2020 will have missing values for the replicate weights on this data file. The Pandemic Replicate weights file for 2019-20 contains 160 replicate final weights for each ATUS final weight created using the 2020 weighting method. Chapter 7 of the ATUS User's Guide provides more information about the 2020 weighting method.

411 adolescents born in 1978 or 1979 in two socioeconomically different cities are included in the study. Collection of the material has occurred three times for each youth, at 15, 17 and 20 years of age. 103 boys and 106 girls in Uppsala, and 90 boys and 112 girls in Trollhättan participated in the study.

In Part 1 of the longitudinal study, adolescents were examined at 15, 17 and 20.5 years of age. Lifestyle factors such as nutrition, physical activity, smoking and alcohol habits were studied to later be able to determine whether these are associated with health problems in adulthood such as cardiovascular disease, obesity, osteoporosis, diabetes and cancer. The idea is also to analyze whether differences in adolescents’ socio-economic background correlates with lifestyle factors, health and development of disease in adulthood.

Serum and in some cases whole blood from 15, 17 and 20.5 years of age are stored in Uppsala Biobank, in addition to measurement data and questionnaire responses.

There is an opportunity to continue with Part 2 of the study, since the adolescents are now more than 35 years old.

Purpose:

Prospective study of adolescent health, nutrition and physical activity and its importance for future morbidity.

Abstract

The presence of adequate and current statistical data in various economic sectors that are considered essential for development planning, socio-economic policy formulation and economic analysis is vital in promoting the economic development of a country. Based on this general objective, the Central Statistical Agency (CSA) has been conducting surveys of various economic activities, of which, the annual Large and Medium Scale Manufacturing Industries survey is one.

Manufacturing is defined here according to International Standard Industrial Classification (ISIC Revision-3.1) as “the physical or chemical transformation of materials or components into new products, whether the work is performed by power-driven machines or by hand, whether it is done in a factory or in the worker's home, and whether the products are sold at wholesale or retail. The assembly of the component parts of manufactured products is also considered as manufacturing activities.”

CSA has been publishing results of the survey of Manufacturing and Electricity Industries on annual basis since 1968 Ethiopian Calendar to provide users with reliable, comprehensive and timely statistical data on these sectors. In this respect, this survey, which is conducted on annual basis, is the principal source of industrial statistics on large and medium scale manufacturing industries in the country.

The main objectives of the annual survey of Large and Medium Scale Manufacturing and Electricity Industries are to: 1.Obtain basic statistical data that are essential for policy makers, planners and researchers by major industrial group. 2.Collect basic quantitative information on employment, volume of quantitative information on employment, volume of production and raw materials, structure and performance of the country's Large and Medium Scale Manufacturing and Electricity Industries. 3.Compile statistical data which will be an input to the System of National Accounts (SNA), on Large and Medium Scale Manufacturing and Electricity establishments as a whole and by major industrial group. 4.Obtain the number of proprietors engaged in these sectors and find out the major problems that create stumbling blocks for their activities.

Geographic coverage

National

Analysis unit

Establishment/ Enterprise

Universe

The universe of the large and medium scale manufacturing survey is confined to those establishments which engaged 10 persons and above and use power-driven machines and covers both public and private industries in all Regions of the country.

Kind of data

Census/enumeration data [cen]

Sampling procedure

Not applicable - the survey enumerated all manufacturing industries/ enterprises that qualified as large and medium manufacturing industry category.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questinnaire contains the following sections/ items:

Item 1.1. Adress of the establishments: This section has varibles that identify the questionnaire uniquely. The variables are; Killil, Zone, Wereda, Town, Higher, Kebele, House no, Year, ISIC, Establishmnet no, Eelephone no and P.O.Box codes or numbers.

Item 1.2. Address of Head Office if Separated From Factory: In this section information about factory head office is collected (if the factory is separated from the head office). The varibles used to collect the information are; Killil, Zone, Wereda, Town, Higher, Kebele, House no, Telephone no and P.O.Box.

Item 2. Basic Information About The Establishment: This section has questions related to basic information about the establishment.

Item 3.1. Number of Persons Engaged: This section has variables (questions) that used to collect establishment's employees number by employees occupation.

Item 3.2. Number of Persons Engaged by Educational Status: This section has varabils (questions) that used to collect establishment's employees number by their educational status.

Item 3.3. Number of Persons Engaged by Age Group: Contains variables that used to collect information about employees number by employees age group.

Item 3.4. Wages and Salaries and Other Employee Benefits Paid: This section has variables related to wages and other employees benefits by employee occupation.

Item 3.5. Number of Permanent Employees by Basic Salary Group: This section has variables related to salary groups by sex of employees

Item 4.1. Products and By-products: This section has questions related to product produced, produced quantity and sales.

Item 4.2. Service and Other Receipts: Contains questions related to income from different source other than selling the products.

Item 5. Value of Stocks: Contains questions that related to information about materials in the stock.

Item 6.1. Cost and Quantity of Raw Materials, Parts and Containers Used: This section has questions related to principal raw materials, raw material type, quantity, value and source (local or imported).

Item 6.2. Other Industrial Costs: This sections has questions related to other industrial costs including cost of energy and other expenses.

Item 6.3. Other Non-industrial Expenses: Contains questions related to non-industrial expenses like license fee, advertising, stationary, etc.

Item 6.4. Taxes Paid: This section has questions related to taxes like indirect tax and income tax.

Item 7. Fixed Assets and Investment: This section has questions related to fixed assets and investment on fixed assests and working capital.

Item 8.1. Annual Production at Full Capacity: This section has questions about quantity and value of products if the establishment uses its full capacity.

Item 8.2. Estimated Value and Quantity of Raw Materials Needed, at Full Capacity: This section has questions about the estimate of quantity and value of raw materials that needed to function at full capacity.

Item 8.3. The three major problems that prevented the establishment from operating at full capacity.

Item 8.4. The three major problems that are facing the establishment at present.

Cleaning operations

Editing, Coding and Verification: A number of quality control steps were taken to ensure the quality of data. The first step taken in this direction was, to revise the questionnaire, to make it easier for internal consistency checking or editing, both at field and office level. Furthermore, based on this revised questionnaire, revised instruction manual with field editing procedures were prepared in Amharic for both enumerators and supervisors (field editors). Using this manual, some editing and coding were carried out by field editors during the data collection stage.

After the majority of the completed questionnaires were brought back to head office, final editing, coding and verification were performed by editors, statistical technicians and statisticians. Finally, the edited and coded questionnaires were checked and verified by other senior professionals.

Data Entry, Cleaning and Tabulation: The data were entered and verified on personal computers using CSpro (Census and Survey Processing System) Software. Fifteen CSA data entry staff and one data cleaner participated in this activity for fifteen days with close supervision of the activities by two professionals. Then, the data entered were cleaned hundred percent using personal computers in combination with manual cleaning for some serious errors. Finally, the tabulation of the results was processed using the same software by one programmer with technical assistance from Industry, Trade and Services Statistics Department staff.

Abstract

The Volunteer Activities Survey (VAS) is a household-based survey conducted by Statistics South Africa (Stats SA). The VAS collects information on the volunteer activities of individuals aged 15 years and older in South Africa. The respondents were selected from households who took part in the second quarter Quarterly Labour Force Survey (QLFS). Volunteer activities covers unpaid non-compulsory work; that is, the time individuals give without pay to activities performed either through an organization or directly for others outside their own household.

Data on volunteering provides important information on skills application, social network development, social capital and quality of life outcomes. The main aim of the survey is to provide information on the scale of volunteer work and bring into view the sizeable part of the labour force that is invisible in existing labour statistics. The objectives of the VAS are:

• To collect reliable data about people who are involved in volunteer activities. • To identify organization-based and direct volunteering. • To give a profile of those engaged in volunteer activities. • To estimate the economic value of volunteer work.

Geographic coverage

National coverage

Analysis unit

Households and individuals

Universe

The target population of the survey consists of individuals aged 15 years and older who live in South Africa and who are members of households living in dwellings that have been selected to take part in the second quarter Quarterly Labour Force Survey (QLFS).

Kind of data

Sample survey data [ssd]

Sampling procedure

The Quarterly Labour Force Survey (QLFS) sample frame was used for data collection in the VAS. The sample for the QLFS is based on a stratified two-stage design with probability proportional to size (PPS) sampling of primary sampling units (PSUs) in the first stage, and sampling of dwelling units (DUs) with systematic sampling in the second stage. The frame was developed as a general-purpose household survey frame that can be used by all other household surveys irrespective of the sample size requirement of the survey. The sample is based on information collected by Statistics SA during the 2001 Population Census and is designed to be representative at the provincial level and within provinces at the metro/non-metro level. Within the metros, the sample is further distributed by geography type. The four geography types are: urban formal, urban informal, farms and tribal land.

Mode of data collection

Face-to-face [f2f]

Research instrument

The 2014 VAS questionnaire consists of the following sections: - Particulars of the dwelling - Households at selected dwelling unit - Response details - Main activities

Abstract copyright UK Data Service and data collection copyright owner. The purpose of this survey was to provide information on people's daily activities for use in planning broadcasting services. As a detailed study of time use in Britain, however, it may prove of use in social, environmental and leisure studies, marketing problems, and many aspects of local and national administration. Main Topics: Variables To collect the statistics, over 3,500 respondents from two random samples (one summer, one winter) of the population (excluding the under-fives) completed seven-day diaries covering each half-hour from 5.00 a.m. to 2.00 a.m. Each diary was coded, using a summary list of 40 activity categories, distinguishing between main and secondary activities. Multi-stage stratified random sample Diaries