Abstract

The Project for Statistics on Living standards and Development was a coutrywide World Bank 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 statistical information about the conditions under which South Africans live in order to provide policymakers with the data necessary for planning strategies. 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

National coverage

Analysis unit

• Households
• Individuals
• Community

Universe

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 on a similar basis as described above for the households in ESDs.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample size is 9,000 households

The sample design adopted for the study was a two-stage self-weightingdesign in which the first stage units were Census Enumerator Subdistricts (ESDs, or their equivalent) and the second stage were households.

The advantage of using such a design is that it provides a representative sample that need not be based on accurate census population distribution.in the case of South Africa, the sample will automatically include many poor people, without the need to go beyond this and oversample the poor. Proportionate sampling as in such a self-weighting sample design offers the simplest possible data files for further analysis, as weights do not have to be added. However, in the end this advantage could not be retained and weights had to be added.

The sampling frame was drawn up on the basis of small, clearly demarcated area units, each with a population estimate. The nature of the self-weighting procedure adopted ensured that this population estimate was not important for determining the final sample, however. For most of the country, census ESDs were used. Where some ESDs comprised relatively large populations as for instance in some black townships such as Soweto, aerial photographs were used to divide the areas into blocks of approximately equal population size. In other instances, particularly in some of the former homelands, the area units were not ESDs but villages or village groups.

In the sample design chosen, the area stage units (generally ESDs) were selected with probability proportional to size, based on the census population. Systematic sampling was used throughout that is, sampling at fixed interval in a list of ESDs, starting at a randomly selected starting point. Given that sampling was self-weighting, the impact of stratification was expected to be modest. The main objective was to ensure that the racial and geographic breakdown approximated the national population distribution. This was done by listing the area stage units (ESDs) by statistical region and then within the statistical region by urban or rural. Within these sub-statistical regions, the ESDs were then listed in order of percentage African. The sampling interval for the selection of the ESDs was obtained by dividing the 1991 census population of 38,120,853 by the 300 clusters to be selected. This yielded 105,800. Starting at a randomly selected point, every 105,800th person down the cluster list was selected. This ensured both geographic and racial diversity (ESDs were ordered by statistical sub-region and proportion of the population African). In three or four instances, the ESD chosen was judged inaccessible and replaced with a similar one.

In the second sampling stage the unit of analysis was the household. In each selected ESD a listing or enumeration of households was carried out by means of a field operation. From the households listed in an ESD a sample of households was selected by systematic sampling. Even though the ultimate enumeration unit was the household, in most cases "stands" were used as enumeration units. However, when a stand was chosen as the enumeration unit all households on that stand had to be interviewed.

Census population data, however, was available only for 1991. An assumption on population growth was thus made to obtain an approximation of the population size for 1993, the year of the survey. The sampling interval at the level of the household was determined in the following way: Based on the decision to have a take of 125 individuals on average per cluster (i.e. assuming 5 members per household to give an average cluster size of 25 households), the interval of households to be selected was determined as the census population divided by 118.1, i.e. allowing for population growth since the census. It was subsequently discovered that population growth was slightly over-estimated but this had little effect on the findings of the survey.

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 on a similar basis as described abovefor the households in ESDs.

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 demography, 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 referred to above, 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.

Cleaning operations

All the questionnaires were checked when received. Where information was incomplete or appeared contradictory, the questionnaire was sent back to the relevant survey organization. As soon as the data was available, it was captured using local development platform ADE. This was completed in February 1994. Following this, a series of exploratory programs were written to highlight inconsistencies and outlier. For example, all person level files were linked together to ensure that the same person code reported in different sections of the questionnaire corresponded to the same person. The error reports from these programs were compared to the questionnaires and the necessary alterations made. This was a lengthy process, as several files were checked more than once, and completed at the beginning of August 1994. In some cases questionnaires would contain missing values, or comments that the respondent did not know, or refused to answer a question.

These responses are coded in the data files with the following values: VALUE MEANING -1 : The data was not available on the questionnaire or form -2 : The field is not applicable -3 : Respondent refused to answer -4 : Respondent did not know answer to question

Data appraisal

The data collected in clusters 217 and 218 should be viewed as highly unreliable and therefore removed from the data set. The data currently available on the web site has been revised to remove the data from these clusters. Researchers who have downloaded the data in the past should revise their data sets. For information on the data in those clusters, contact SALDRU http://www.saldru.uct.ac.za/.

During the period between 2014 and 2020, the Indonesian Ministry of Tourism had a total of 44 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

Financial overview and grant giving statistics of Idea Project

Abstract copyright UK Data Service and data collection copyright owner.

The aims of this project, which ran from November 1978 to October 1981 were:
a) to survey the intuitions of chance and the concepts of probability possessed by English school pupils aged 11 to 16 years residing in the East Midlands region.
b) to establish patterns of development of probability concepts and to relate these to other mathematics concepts.
A special Probability Concepts Test was devised over a two year period, entailing six pilot versions before achieving the definitive form.
Main Topics:

Variables
Altogether about four thousand pupils were involved in the testing although ultimately only 2930 comprised the final 'Sample'. Pupils in East Midlands state comprehensive mixed schools aged 11 to 16 years (i.e. in normal school years 1 to 5) were tested on a class basis with:
i) Probability Concepts Test (about 1 hour)
ii) NFER AH2 Test of General Reasoning Ability (about 1 hour)
The mathematics teachers of the sample pupils were asked to supply details of each pupil's mathematical ability (M.A.) on a ten-point scale.

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.

Analysis of ‘Your Voice Your Choice Project Ideas’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/ec9400b9-59fd-4a96-8994-9867942296ea on 26 January 2022.

--- Dataset description provided by original source is as follows ---

A program of Seattle Department of Neighborhoods, this is a list of street or park improvement ideas submitted by community members as a part of Your Voice Your Choice Participatory Budgeting. Ideas were vetted by project development teams made up of community members who volunteered to evaluate each project. Seattle Parks and Recreation and Seattle Department of Transportation also reviewed the projects for feasibility. The results and evaluation, along with location are provided in the set. The list will be finalized and ready for the community to vote (by council district) beginning June 3.

--- Original source retains full ownership of the source dataset ---

Differential privacy (DP) formalizes privacy guarantees in a rigorous mathematical framework and is a state-of-the-art concept in data privacy research. The DP mechanisms ensure the privacy of each individual in a sensitive dataset while releasing useful information about the whole population in that dataset. Since its debut in 2006, significant advancements in DP theory, methodologies, and applications have been made; new research topics and questions have been proposed and studied. This dissertation aims to contribute to the advancement of DP concepts and methods in the robustness of DP mechanisms to privacy attacks, privacy amplification through subsampling, and DP guarantees of procedures with their intrinsic randomness. Specifically, this dissertation consists of three research projects on DP. The first project explores the protection potency of DP mechanisms against homogeneity attacks (HA) by providing analytical relations between measures of disclosure risk from HA and privacy loss parameters, which will assist practitioners in understanding the abstract concepts of DP by putting them in a concrete privacy attack model and offer a perspective for choosing privacy loss parameters. The second project proposes a class of subsampling methods ``MUltistage Sampling Technique (MUST)'' for privacy amplification. It provides the privacy composition analysis over repeated applications of MUST via the Fourier accountant algorithm. The utility experiments show that MUST demonstrates comparable utility and stability in privacy-preserving outputs compared to one-stage subsampling methods at similar privacy loss while improving the computational efficiency of algorithms requiring complex function calculations on distinct data points. MUST can be seamlessly integrated into stochastic optimization algorithms or procedures involving parallel or simultaneous subsampling when DP guarantees are necessary. The third project investigates the inherent DP guarantees in Bayesian posterior sampling. It provides a new privacy loss bound in releasing a single posterior sample with any prior given a bounded log ratio of the likelihood kernels based on two neighboring data sets. The new bound is tighter than the existing bounds and consistent with the likelihood principle. Experiments show that the privacy-preserving synthetic data released from Bayesian models leveraging the inherently private posterior samples are of improved utility compared to those generated by sanitizing the original information through explicit DP mechanisms.

This statistic demonstrates the shares of Russians feeling confident in different types of home improvement activities in the year 2014. While the majority stated feeling confident in wallpapering, accumulating to 80 percent, only roughly one third felt confidence at plumbing.

The texts of the loan conditions used as raw data for this article was supplied to us by the World Bank on a confidential basis. The replication data therefore only provides our aggregated data that reproduces all the results in the publication itself. To promote replicability and to promote future research on the World Bank and its lending practices, we have reproduced the confidential raw data to the fullest extent possible by relying only on publicly available information. The data set contains the project-specific conditions attached to 1242 World Bank loan and borrowing agreements. For each of these, the data set lists the project number, project year, borrower country ISO3C code, the date of the document, the abbreviated World Bank project name, the URL to the text or PDF document, and the texts of the loan-specific conditions. The latter were extracted through a combination of quantitative text analysis and reading of the actual loan agreement documents. This data covers all the observations used in the article, with the exception of 205 projects with their conditions that the World Bank has chosen to keep confidential. Nearly all of these are from the 1980s. We encourage future research using this data. If you undertake such work, please cite the article as source.

The automatic extraction of topics is a standard technique for summarizing text corpora from various domains (e.g., news articles, transport or logistic reports, scientific publications) that has several applications. Since, in many cases, topics are subject to continuous change there is the need to monitor the evolution of a set of topics of interest, as the corresponding corpora are updated. The evolution of scientific topics, in particular, is of great interest for researchers, policy makers, fund managers, and other professionals/engineers in the research and academic community. In this dataset, we provide a set of topics for scientific publications gathered from Crossref. The topics have been produced by performing a topic modeling analysis on two distinct sets of publications, each coming from a different time period. Acknowledgements: This research was partially funded by project ENIRISST under grant agreement No. MIS 5027930 (co-financed by Greece and the EU through the European Regional Development Fund).

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.

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.

Dataset

This dataset was created by Maxim Schmidt

Contents

Activity Project Area Timber Sale represents an area (polygon) within which one or more Timber Sale related activities are aggregated or organized. The data comes from the Forest Service's Natural Resource Manager (NRM) Forest Activity Tracking System (FACTS), which is the agency standard for managing information about activities related to fire/fuels, silviculture, and invasive species. FACTS is an activity tracking application for all levels of the Forest Service.These data are a central source for project area boundaries for use in national information requests and cross unit analysis and makes the project area boundaries and their basic attributes more easily available to field units. It also provides public access to the data during project planning and implementation. Please note that this dataset is not complete and forests continue to improve the quality of the data over time.Metadata and DownloadsThis record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService CSV Shapefile GeoJSON KML https://apps.fs.usda.gov/arcx/rest/services/EDW/EDW_ActivityProjectAreas_01/MapServer/2 https://www.fs.fed.us/forestmanagement/products/contracts.shtml For complete information, please visit https://data.gov.

This dataset contains information about projects and their results funded by the European Union under the Horizon 2020 framework programme for research and innovation from 2014 to 2020.

The dataset is composed of six (6) different sub-set (in different formats):

• H2020 projects – which includes participating organisations, legal basis information, topic information, project URLs and classification with the European Science Vocabulary (EuroSciVoc)
• H2020 project IPRs (Intellectual Property Rights) [N.B.: This dataset only includes patent data for awarded patents available in the database of the European Patent Office (EPO)]
• H2020 project deliverables (meta-data and links to deliverables included since May 2019)
• H2020 project publications (meta-data and links to publications included since May 2019)
• H2020 report summaries (periodic or final publishable summaries included since September 2018)
• Principal Investigators in Horizon 2020 ERC projects

Reference data (programmes, topics, topic keywords funding schemes (types of action), organisation types and countries) can be found in this dataset: https://data.europa.eu/euodp/en/data/dataset/cordisref-data

EuroSciVoc is available here: https://data.europa.eu/data/datasets/euroscivoc-the-european-science-vocabulary

CORDIS datasets are produced monthly. Therefore, inconsistencies may occur between what is presented on the CORDIS live website and the datasets.

Kickstarter, the popular crowdfunding platform, has seen a significant number of projects fall short of their funding goals. As of January 2025, 376,698 projects failed to reach their targets, with the majority (246,351) achieving only 1-20 percent of their funding objectives. This failure rate underscores the challenges creators face in securing financial backing for their ideas, despite Kickstarter's global reach and billions in pledged funds. Crowdfunding's growing impact Since its launch in 2009, Kickstarter has become a major player in the crowdfunding industry. The number of projects hosted on the platform exceeded 651,000 projects, with pledges surpassing 8.5 billion U.S. dollars. Notably, the most successful project to date, "Surpise! Four Secret Novels by Brandon Sanderson", raised an impressive 41 million U.S. dollars in 2022. These figures highlight the platform's potential for creators to secure substantial funding for their projects. Success rates vary by category While many projects struggle to meet their funding goals, success rates differ significantly across categories. As of January 2025, comics boasted the highest success rate at 67.65 percent, followed by dance at 61.11 percent and theater at 59.72 percent. These statistics suggest that certain creative fields may resonate more strongly with Kickstarter's backer community, potentially offering better odds for project success in these areas.

Participatory Budgeting is a democratic process in which community members directly decide how to spend part of a public budget. Council Members choose to join Participatory Budgeting New York City (PBNYC), giving at least $1 million from their budget for the whole community to participate in decision-making. It’s a yearlong process of public meetings, to ensure that people have the time and resources to make informed decisions. Community members discuss local needs and develop proposals to meet these needs. Through a public vote, residents then decide which proposals to fund.

More info can be found at http://council.nyc.gov/pb/

This project sought to address the changing role of ideas and intellectuals in the public discourses of contemporary 'knowledge societies'. To this end, the ethos and development of two ideas institutions were investigated - the think tank Demos and the LSE. The project sought to reconceptualise the general dynamics of societal ideas and the role of contemporary intellectuals by suggesting that ideas are increasingly 'vehicular' in character, that is, flexible, mobile, pragmatic, inclusive, and geared towards producing an image of their intellectual 'mediators' as agenda-setting agents in an increasingly media-orientated public sphere. Together with the project's analysis of two prominent 'vehicular ideas' - 'Third Way' and 'knowledge society' - the case studies suggest that this perspective is at least fruitful and challenging in understanding contemporary forms of 'ideas work'. Another research task was to assess whether think tanks and universities are converging in style and practice, and some evidence for this was found. However, the influence runs in both directions, with think tanks like Demos incorporating several typically academic norms. The study produced 33 interview transcripts, 18 research diaries, 14 working papers, 13 possible publications, and a journal special issue. Two successful public meetings were held with the hosts at the LSE and Demos.

Laos (FDI) Foreign Direct Investment: Number of Projects: Activities of Extraterritions data was reported at 0.000 Unit in 2016. This stayed constant from the previous number of 0.000 Unit for 2015. Laos (FDI) Foreign Direct Investment: Number of Projects: Activities of Extraterritions data is updated yearly, averaging 0.000 Unit from Dec 2015 (Median) to 2016, with 2 observations. Laos (FDI) Foreign Direct Investment: Number of Projects: Activities of Extraterritions data remains active status in CEIC and is reported by Lao Statistics Bureau. The data is categorized under Global Database’s Laos – Table LA.O003: Foreign Direct Investment: No of Projects Approved: By Industry.