Abstract

The Project for Statistics on Living standards and Development was a countrywide World Bank sponsored Living Standards Measurement Survey. It covered approximately 9000 households, drawn from a representative sample of South African households. The fieldwork was undertaken during the nine months leading up to the country's first democratic elections at the end of April 1994. The purpose of the survey was to collect data on the conditions under which South Africans live in order to provide policymakers with the data necessary for development planning. This data would aid the implementation of goals such as those outlined in the Government of National Unity's Reconstruction and Development Programme.

Geographic coverage

The survey had national coverage

Analysis unit

Households and individuals

Universe

The survey covered all household members. Individuals in hospitals, old age homes, hotels and hostels of educational institutions were not included in the sample. Migrant labour hostels were included. In addition to those that turned up in the selected ESDs, a sample of three hostels was chosen from a national list provided by the Human Sciences Research Council and within each of these hostels a representative sample was drawn for the households in ESDs.

Kind of data

Sample survey data

Mode of data collection

Face-to-face [f2f]

Research instrument

The main instrument used in the survey was a comprehensive household questionnaire. This questionnaire covered a wide range of topics but was not intended to provide exhaustive coverage of any single subject. In other words, it was an integrated questionnaire aimed at capturing different aspects of living standards. The topics covered included demographics, household services, household expenditure, educational status and expenditure, remittances and marital maintenance, land access and use, employment and income, health status and expenditure and anthropometry (children under the age of six were weighed and their heights measured). This questionnaire was available to households in two languages, namely English and Afrikaans. In addition, interviewers had in their possession a translation in the dominant African language/s of the region.

In addition to the detailed household questionnaire, a community questionnaire was administered in each cluster of the sample. The purpose of this questionnaire was to elicit information on the facilities available to the community in each cluster. Questions related primarily to the provision of education, health and recreational facilities. Furthermore there was a detailed section for the prices of a range of commodities from two retail sources in or near the cluster: a formal source such as a supermarket and a less formal one such as the "corner cafe" or a "spaza". The purpose of this latter section was to obtain a measure of regional price variation both by region and by retail source. These prices were obtained by the interviewer. For the questions relating to the provision of facilities, respondents were "prominent" members of the community such as school principals, priests and chiefs.

A literacy assessment module (LAM) was administered to two respondents in each household, (a household member 13-18 years old and a one between 18 and 50) to assess literacy levels.

Data appraisal

The data collected in clusters 217 and 218 are highly unreliable and have therefore been removed from the dataset currently available on the portal. Researchers who have downloaded the data in the past should download version 2.0 of the dataset to ensure they have the corrected data. Version 2.0 of the dataset excludes two clusters from both the 1993 and 1998 samples. During follow-up field research for the KwaZulu-Natal Income Dynamics Study (KIDS) in May 2001 it was discovered that all 39 household interviews in clusters 217 and 218 had been fabricated in both 1993 and 1998. These households have been dropped in the updated release of the data. In addition, cluster 206 is now coded as urban as this was incorrectly coded as rural in the first release of the data. Note: Weights calculated by the World Bank and provided with the original data are NOT updated to reflect these changes.

We include the course syllabus used to teach quantitative research design and analysis methods to graduate Linguistics students using a blended teaching and learning approach. The blended course took place over two weeks and builds on a face to face course presented over two days in 2019. Students worked through the topics in preparation for a live interactive video session each Friday to go through the activities. Additional communication took place on Slack for two hours each week. A survey was conducted at the start and end of the course to ascertain participants' perceptions of the usefulness of the course. The links to online elements and the evaluations have been removed from the uploaded course guide.Participants who complete this workshop will be able to:- outline the steps and decisions involved in quantitative data analysis of linguistic data- explain common statistical terminology (sample, mean, standard deviation, correlation, nominal, ordinal and scale data)- perform common statistical tests using jamovi (e.g. t-test, correlation, anova, regression)- interpret and report common statistical tests- describe and choose from the various graphing options used to display data- use jamovi to perform common statistical tests and graph resultsEvaluationParticipants who complete the course will use these skills and knowledge to complete the following activities for evaluation:- analyse the data for a project and/or assignment (in part or in whole)- plan the results section of an Honours research project (where applicable)Feedback and suggestions can be directed to M Schaefer schaemn@unisa.ac.za

Financial overview and grant giving statistics of Metro Ideas Project

During the period between 2014 and 2020, the Indonesian Ministry of Tourism had a total of ** project procurement for its digital activities. Social media and influencer advertising were among the digital activities done by the Indonesian government. These types of advertising were seen as a tool to reach out to the millennials in the country who were predominantly active social media users.

This database includes simulated data showing the accuracy of estimated probability distributions of project durations when limited data are available for the project activities. The base project networks are taken from PSPLIB. Then, various stochastic project networks are synthesized by changing the variability and skewness of project activity durations. Number of variables: 20 Number of cases/rows: 114240 Variable List: • Experiment ID: The ID of the experiment • Experiment for network: The ID of the experiment for each of the synthesized networks • Network ID: ID of the synthesized network • #Activities: Number of activities in the network, including start and finish activities • Variability: Variance of the activities in the network (this value can be either high, low, medium or rand, where rand shows a random combination of low, high and medium variance in the network activities.) • Skewness: Skewness of the activities in the network (Skewness can be either right, left, None or rand, where rand shows a random combination of right, left, and none skewed in the network activities)
• Fitted distribution type: Distribution type used to fit on sampled data • Sample size: Number of sampled data used for the experiment resembling limited data condition • Benchmark 10th percentile: 10th percentile of project duration in the benchmark stochastic project network • Benchmark 50th percentile: 50th project duration in the benchmark stochastic project network • Benchmark 90th percentile: 90th project duration in the benchmark stochastic project network • Benchmark mean: Mean project duration in the benchmark stochastic project network • Benchmark variance: Variance project duration in the benchmark stochastic project network • Experiment 10th percentile: 10th percentile of project duration distribution for the experiment • Experiment 50th percentile: 50th percentile of project duration distribution for the experiment • Experiment 90th percentile: 90th percentile of project duration distribution for the experiment • Experiment mean: Mean of project duration distribution for the experiment • Experiment variance: Variance of project duration distribution for the experiment • K-S: Kolmogorov–Smirnov test comparing benchmark distribution and project duration • distribution of the experiment • P_value: the P-value based on the distance calculated in the K-S test

Given the wide diversity in applications of biological mass spectrometry, custom data analyses are often needed to fully interpret the results of an experiment. Such bioinformatics scripts necessarily include similar basic functionality to read mass spectral data from standard file formats, process it, and visualize it. Rather than having to reimplement this functionality, to facilitate this task, spectrum_utils is a Python package for mass spectrometry data processing and visualization. Its high-level functionality enables developers to quickly prototype ideas for computational mass spectrometry projects in only a few lines of code. Notably, the data processing functionality is highly optimized for computational efficiency to be able to deal with the large volumes of data that are generated during mass spectrometry experiments. The visualization functionality makes it possible to easily produce publication-quality figures as well as interactive spectrum plots for inclusion on web pages. spectrum_utils is available for Python 3.6+, includes extensive online documentation and examples, and can be easily installed using conda. It is freely available as open source under the Apache 2.0 license at https://github.com/bittremieux/spectrum_utils.

Project Idea Notes based on the developed SoE and NEMS

Purpose: This map contains project data for the Arches recreational hot spot study, PIN 16097, for the Arches Hotspot Preliminary Project Ideas App 2018 study and is embedded within that storymap. It illustrates proposed parking, cycling trail, and other recreational transportation projects.The data was completed in 2018 by Jones and DeMille Engineers. For questions on the data, please contact Adam Perschon at adam.p@jonesanddemille.com. It was transferred ownership from Paul Damron to Bracken on 6/23/23.Go Live Date: January 2018Project PIN: 16097ePM Project Name: Moab Area Recreational Hot Spot StudyOwner: Bracken Davis (bdavis1@utah.gov)Update Interval: One-time creation.Data Location: MoabHotspotStudy hosted feature layer.Associated Apps: Arches Hotspot Preliminary Project storymapUDOT Region 4 - Arches Hotspot Improvement Projects 2018 storymapUDOT Region 4 - Arches Hotspot Additional Study Information 2018 storymapExpected Life of Data:There is no foreseeable end date for this data.

A data set of cross-nationally comparable microdata samples for 15 Economic Commission for Europe (ECE) countries (Bulgaria, Canada, Czech Republic, Estonia, Finland, Hungary, Italy, Latvia, Lithuania, Romania, Russia, Switzerland, Turkey, UK, USA) based on the 1990 national population and housing censuses in countries of Europe and North America to study the social and economic conditions of older persons. These samples have been designed to allow research on a wide range of issues related to aging, as well as on other social phenomena. A common set of nomenclatures and classifications, derived on the basis of a study of census data comparability in Europe and North America, was adopted as a standard for recoding. This series was formerly called Dynamics of Population Aging in ECE Countries. The recommendations regarding the design and size of the samples drawn from the 1990 round of censuses envisaged: (1) drawing individual-based samples of about one million persons; (2) progressive oversampling with age in order to ensure sufficient representation of various categories of older people; and (3) retaining information on all persons co-residing in the sampled individual''''s dwelling unit. Estonia, Latvia and Lithuania provided the entire population over age 50, while Finland sampled it with progressive over-sampling. Canada, Italy, Russia, Turkey, UK, and the US provided samples that had not been drawn specially for this project, and cover the entire population without over-sampling. Given its wide user base, the US 1990 PUMS was not recoded. Instead, PAU offers mapping modules, which recode the PUMS variables into the project''''s classifications, nomenclatures, and coding schemes. Because of the high sampling density, these data cover various small groups of older people; contain as much geographic detail as possible under each country''''s confidentiality requirements; include more extensive information on housing conditions than many other data sources; and provide information for a number of countries whose data were not accessible until recently. Data Availability: Eight of the fifteen participating countries have signed the standard data release agreement making their data available through NACDA/ICPSR (see links below). Hungary and Switzerland require a clearance to be obtained from their national statistical offices for the use of microdata, however the documents signed between the PAU and these countries include clauses stipulating that, in general, all scholars interested in social research will be granted access. Russia requested that certain provisions for archiving the microdata samples be removed from its data release arrangement. The PAU has an agreement with several British scholars to facilitate access to the 1991 UK data through collaborative arrangements. Statistics Canada and the Italian Institute of statistics (ISTAT) provide access to data from Canada and Italy, respectively. * Dates of Study: 1989-1992 * Study Features: International, Minority Oversamples * Sample Size: Approx. 1 million/country Links: * Bulgaria (1992), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/02200 * Czech Republic (1991), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06857 * Estonia (1989), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06780 * Finland (1990), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06797 * Romania (1992), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06900 * Latvia (1989), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/02572 * Lithuania (1989), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/03952 * Turkey (1990), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/03292 * U.S. (1990), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06219

University capstone projects can offer science students a rich research experience that illustrates the process of doing scientific research, and can also help students better choose future academic and career pathways. While capstone projects are an effective component of students' learning in the sciences, they are resource and labor intensive for supervising faculty and are not always logistically feasible in understaffed and/or under-resourced departments and colleges. A good compromise is to incorporate a significant research component into upper division classes. This article documents a project I have incorporated into a plant ecology course that I teach every spring. This project gives students a taste of what practicing ecologists do in their professional lives. Students learn how to survey vegetation and environmental factors in the field, apply several statistical analysis techniques, formulate testable hypotheses relevant to a local plant community, analyze a large shared data set, and communicate their findings both in writing and in a public presentation. Over the weeks required for this project, students learn that doing science is quite different from how they typically learn about science. Most say that, while this project is one of the hardest they have completed in their time in university, they appreciate being treated like a fellow scientist rather than as "just a student." Additionally, students' findings often reveal complex and subtle interactions in the plant community sampled, providing further insight to and examples of emergent properties of biological communities.

Research Data Spring idea to project requirements document to consult in advance and during the sandpit workshop on 26-27 February 2015. For a list and description of ideas see links.

Launched on April 28, 2009, Kickstarter is a Public Benefit Corporation based in Brooklyn, New York. It is a global crowdfunding platform that helps to fund new creative projects and ideas through direct support from individuals (backers) from around the world who pledge money to bring these projects and ideas to life. Kickstarter supports many different kinds of projects. Everything from films, games, and music to art, design, and technology. Funding on Kickstarter is based on the all-or-nothing model. Backers who pledge their support towards a particular project won't be charged unless the funding goal has been reached. Successfully funded projects reward their backers with one-of-a-kind experiences, e.g., limited editions, or copies of the creative work being produced. This study includes three datasets: (1) Kickstarter Project (public-use file), (2) Backer Location file, and (3) Kickstarter Project (restricted-use file). The public-use Kickstarter Project dataset contains detailed information about all successful and unsuccessful Kickstarter projects (N=610,015) from 2009-2023, including the project category and subcategory, project location (city, state (for U.S.-based projects), and country), funding goal in original and U.S. currencies, amount pledged in dollars, and the number of backers for each project. The restricted file adds the project title, 150-character project description, and the URL for the project on the Kickstarter site. The Backer Location dataset includes information about backers' country and state and the total amount pledged for each geographic location.

Abstract

The Community Survey (CS) is a nationally representative, large-scale household survey which was conducted from February to March 2007. The Community Survey is designed to provide information on the trends and levels of demographic and socio-economic data, such as population size and distribution; the extent of poor households; access to facilities and services, and the levels of employment/unemployment at national, provincial and municipality level. The data can be used to assist government and the private sector in the planning, evaluation and monitoring of programmes and policies. The information collected can also be used to assess the impact of socio-economic policies and provide an indication as to how far the country has gone in its strides to eradicate poverty.

Censuses 1996 and 2001 are the only all-inclusive censuses that Statistics South Africa has thus far conducted under the new democratic dispensation. Demographic and socio-economic data were collected and the results have enabled government and all other users of this information to make informed decisions. When cabinet took a decision that Stats SA should not conduct a census in 2006, it created a gap in information or data between Census 2001 and the next Census scheduled to be carried out in 2011. A decision was therefore taken to carry out the Community Survey in 2007.

The main objectives of the survey were: · To provide estimates at lower geographical levels than existing household surveys; · To build human, management and logistical capacities for Census 2011; and · To provide inputs into the preparation of the mid-year population projections.

The wider project strategic theme is to provide relevant statistical information that meets user needs and aspirations. Some of the main topics that are covered by the survey include demography, migration, disability and social grants, educational levels, employment and economic activities.

Geographic coverage

The survey covered the whole of South Africa, including all nine provinces as well as the four settlement types - urban-formal, urban-informal, rural-formal (commercial farms) and rural-informal (tribal areas).

Analysis unit

Households

Universe

The Community Survey covered all de jure household members (usual residents) in South Africa. The survey excluded collective living quarters (institutions) and some households in EAs classified as recreational areas or institutions. However, an approximation of the out-of-scope population was made from the 2001 Census and added to the final estimates of the CS 2007 results.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Design

The sampling procedure that was adopted for the CS was a two-stage stratified random sampling process. Stage one involved the selection of enumeration areas, and stage tow was the selection of dwelling units.

Since the data are required for each local municipality, each municipality was considered as an explicit stratum. The stratification is done for those municipalities classified as category B municipalities (local municipalities) and category A municipalities (metropolitan areas) as proclaimed at the time of Census 2001. However, the newly proclaimed boundaries as well as any other higher level of geography such as province or district municipality, were considered as any other domain variable based on their link to the smallest geographic unit - the enumeration area.

The Frame

The Census 2001 enumeration areas were used because they give a full geographic coverage of the country without any overlap. Although changes in settlement type, growth or movement of people have occurred, the enumeration areas assisted in getting a spatial comparison over time. Out of 80 787 enumeration areas countrywide, 79 466 were considered in the frame. A total of 1 321 enumeration areas were excluded (919 covering institutions and 402 recreational areas).

On the second level, the listing exercise yielded the dwelling frame which facilitated the selection of dwellings to be visited. The dwelling unit is a structure or part of a structure or group of structures occupied or meant to be occupied by one or more households. Some of these structures may be vacant and/or under construction, but can be lived in at the time of the survey. A dwelling unit may also be within collective living quarters where applicable (examples of each are a house, a group of huts, a flat, hostels, etc.).

The Community Survey universe at the second-level frame is dependent on whether the different structures are classified as dwelling units (DUs) or not. Structures where people stay/live were listed and classified as dwelling units. However, there are special cases of collective living quarters that were also included in the CS frame. These are religious institutions such as convents or monasteries, and guesthouses where people stay for an extended period (more than a month). Student residences - based on how long people have stayed (more than a month) - and old-age homes not similar to hospitals (where people are living in a communal set-up) were treated the same as hostels, thereby listing either the bed or room. In addition, any other family staying in separate quarters within the premises of an institution (like wardens' quarters, military family quarters, teachers' quarters and medical staff quarters) were considered as part of the CS frame. The inclusion of such group quarters in the frame is based on the living circumstances within these structures. Members are independent of each other with the exception that they sleep under one roof.

The remaining group quarters were excluded from the CS frame because they are difficult to access and have no stable composition. Excluded dwelling types were prisons, hotels, hospitals, military barracks, etc. This is in addition to the exclusion on first level of the enumeration areas (EAs) classified as institutions (military bases) or recreational areas (national parks).

The Selection of Enumeration Areas (EAs)

The EAs within each municipality were ordered by geographic type and EA type. The selection was done by using systematic random sampling. The criteria used were as follows: In municipalities with fewer than 30 EAs, all EAs were automatically selected. In municipalities with 30 or more EAs, the sample selection used a fixed proportion of 19% of all sampled EAs. However, if the selected EAs in a municipality were less than 30 EAs, the sample in the municipality was increased to 30 EAs.

The Selection of Dwelling Units

The second level of the frame required a full re-listing of dwelling units. The listing exercise was undertaken before the selection of DUs. The adopted listing methodology ensured that the listing route was determined by the lister. Thisapproach facilitated the serpentine selection of dwelling units. The listing exercise provided a complete list of dwelling units in the selected EAs. Only those structures that were classified as dwelling units were considered for selection, whether vacant or occupied. This exercise yielded a total of 2 511 314 dwelling units.

The selection of the dwelling units was also based on a fixed proportion of 10% of the total listed dwellings in an EA. A constraint was imposed on small-size EAs where, if the listed dwelling units were less than 10 dwellings, the selection was increased to 10 dwelling units. All households within the selected dwelling units were covered. There was no replacement of refusals, vacant dwellings or non-contacts owing to their impact on the probability of selection.

Mode of data collection

Face-to-face [f2f]

Research instrument

Consultation on Questionnaire Design Ten stakeholder workshops were held across the country during August and September 2004. Approximately 367 stakeholders, predominantly from national, provincial and local government departments, as well as from research and educational institutions, attended. The workshops aimed to achieve two objectives, namely to better understand the type of information stakeholders need to meet their objectives, and to consider the proposed data items to be included in future household surveys. The output from this process was a set of data items relating to a specific, defined focus area and outcomes that culminated with the data collection instrument (see Annexure B for all the data items).

Questionnaire Design The design of the CS questionnaire was household-based and intended to collect information on 10 people. It was developed in line with the household-based survey questionnaires conducted by Stats SA. The questions were based on the data items generated out of the consultation process described above. Both the design and questionnaire layout were pre-tested in October 2005 and adjustments were made for the pilot in February 2006. Further adjustments were done after the pilot results had been finalised.

Cleaning operations

Editing The automated cleaning was implemented based on an editing rules specification defined with reference to the approved questionnaire. Most of the editing rules were categorised into structural edits looking into the relationship between different record type, the minimum processability rules that removed false positive readings or noise, the logical editing that determine the inconsistency between fields of the same statistical unit, and the inferential editing that search similarities across the domain. The edit specifications document for the structural, population, mortality and housing edits was developed by a team of Stats SA subject-matter specialists, demographers, and programmers. The process was successfully

Materials Project is an open database of computed materials properties aiming to accelerate materials science research. The resources in this OpenData dataset contain the raw, parsed, and build data products.

Basic statistics for ERC funding activities include data on granted projects and evaluated projects, which can be filtered by funding scheme, call year, domain.

Data can be exported in CSV or PDF formats. It reflects the current status of granting process.

Hong Kong Innovation Activities Statistics - Table 710-86082 : Number of ongoing and approved applied R&D projects receiving ITC funding support

Presentation by Bruce Haupt on May 14, 2013 to the NetSquared Houston organization. Includes additional project ideas (snapshots) and links to projects from other Cities.

The Convergent Aeronautics Solutions (CAS) Project uses short-duration activities to establish early-stage concept and technology feasibility for high-potential solutions. Internal teams propose ideas for overcoming key barriers associated with large-scale aeronautics problems associated with ARMD’s six strategic thrusts. The teams will conduct initial feasibility studies, perform experiments, try out new ideas, identify failures, and try again. At the end of the cycle, a review determines whether the developed solutions have met their goals, established initial feasibility, and identified potential for future aviation impact. During these reviews, the most promising capabilities will be considered for continued development further by other ARMD programs or by direct transfer to the aviation community. In the dynamic environment of new ideas, ARMD also gains significant value from the knowledge gained in activities that do not proceed.

In order to enable new capabilities in commercial aviation, the CAS Project’s focus is on merging traditional aeronautics disciplines with advancements driven by the non-aeronautics world.  The Project will draw on external collaborators to supplement in-house NASA expertise in technologies and disciplines that broadly support advancements in all ARMD strategic thrusts.

BackgroundPhysical activity reduces the risk of noncommunicable diseases and is therefore an essential component of a healthy lifestyle. Regular engagement in physical activity can produce immediate and long term health benefits. However, physical activity levels are not as high as might be expected. For example, according to the global World Health Organization (WHO) 2017 statistics, more than 80% of the world’s adolescents are insufficiently physically active. In response to this problem, physical activity programs have become popular, with step counts commonly used to measure program performance. Analysing step count data and the statistical modeling of this data is therefore important for evaluating individual and program performance. This study reviews the statistical methods that are used to model and evaluate physical activity programs, using step counts.MethodsAdhering to PRISMA guidelines, this review systematically searched for relevant journal articles which were published between January 2000 and August 2017 in any of three databases (PubMed, PsycINFO and Web of Science). Only the journal articles which used a statistical model in analysing step counts for a healthy sample of participants, enrolled in an intervention involving physical exercise or a physical activity program, were included in this study. In these programs the activities considered were natural elements of everyday life rather than special activity interventions.ResultsThis systematic review was able to identify 78 unique articles describing statistical models for analysing step counts obtained through physical activity programs. General linear models and generalized linear models were the most popular methods used followed by multilevel models, while structural equation modeling was only used for measuring the personal and psychological factors related to step counts. Surprisingly no use was made of time series analysis for analysing step count data. The review also suggested several strategies for the personalisation of physical activity programs.ConclusionsOverall, it appears that the physical activity levels of people involved in such programs vary across individuals depending on psychosocial, demographic, weather and climatic factors. Statistical models can provide a better understanding of the impact of these factors, allowing for the provision of more personalised physical activity programs, which are expected to produce better immediate and long-term outcomes for participants. It is hoped that this review will identify the statistical methods which are most suitable for this purpose.

The National Household Education Survey, 2005 After-School Programs and Activities (ASPA-NHES:2005), is a study that is part of the National Household Education Survey (NHES) program. ASPA-NHES:2005 (https://nces.ed.gov/nhes/) is a cross-sectional survey that collects data directly from households on educational issues. This study was conducted using address based sample, self-administered questionnaires of households. Households in 2005 were sampled. The study response rate was 67.5 percent. Key statistics produced from ASPA-NHES:2005 are participation in after-school programs and activities.