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.

This dataset is a list of research topics for the Fair Trade Commission's commissioned research project in the year 2016.

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 ---

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

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.

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.

This is a repository for a UKRI Economic and Social Research Council (ESRC) funded project to understand the software used to analyse social sciences data. Any software produced has been made available under a BSD 2-Clause license and any data and other non-software derivative is made available under a CC-BY 4.0 International License. Note that the software that analysed the survey is provided for illustrative purposes - it will not work on the decoupled anonymised data set. Exceptions to this are: Data from the UKRI ESRC is mostly made available under a CC BY-NC-SA 4.0 Licence. Data from Gateway to Research is made available under an Open Government Licence (Version 3.0). Contents Survey data & analysis: esrc_data-survey-analysis-data.zip Other data: esrc_data-other-data.zip Transcripts: esrc_data-transcripts.zip Data Management Plan: esrc_data-dmp.zip Survey data & analysis The survey ran from 3rd February 2022 to 6th March 2023 during which 168 responses were received. Of these responses, three were removed because they were supplied by people from outside the UK without a clear indication of involvement with the UK or associated infrastructure. A fourth response was removed as both came from the same person which leaves us with 164 responses in the data. The survey responses, Question (Q) Q1-Q16, have been decoupled from the demographic data, Q17-Q23. Questions Q24-Q28 are for follow-up and have been removed from the data. The institutions (Q17) and funding sources (Q18) have been provided in a separate file as this could be used to identify respondents. Q17, Q18 and Q19-Q23 have all been independently shuffled. The data has been made available as Comma Separated Values (CSV) with the question number as the header of each column and the encoded responses in the column below. To see what the question and the responses correspond to you will have to consult the survey-results-key.csv which decodes the question and responses accordingly. A pdf copy of the survey questions is available on GitHub. The survey data has been decoupled into: survey-results-key.csv - maps a question number and the responses to the actual question values. q1-16-survey-results.csv- the non-demographic component of the survey responses (Q1-Q16). q19-23-demographics.csv - the demographic part of the survey (Q19-Q21, Q23). q17-institutions.csv - the institution/location of the respondent (Q17). q18-funding.csv - funding sources within the last 5 years (Q18). Please note the code that has been used to do the analysis will not run with the decoupled survey data. Other data files included CleanedLocations.csv - normalised version of the institutions that the survey respondents volunteered. DTPs.csv - information on the UKRI Doctoral Training Partnerships (DTPs) scaped from the UKRI DTP contacts web page in October 2021. projectsearch-1646403729132.csv.gz - data snapshot from the UKRI Gateway to Research released on the 24th February 2022 made available under an Open Government Licence. locations.csv - latitude and longitude for the institutions in the cleaned locations. subjects.csv - research classifications for the ESRC projects for the 24th February data snapshot. topics.csv - topic classification for the ESRC projects for the 24th February data snapshot. Interview transcripts The interview transcripts have been anonymised and converted to markdown so that it's easier to process in general. List of interview transcripts: 1269794877.md 1578450175.md 1792505583.md 2964377624.md 3270614512.md 40983347262.md 4288358080.md 4561769548.md 4938919540.md 5037840428.md 5766299900.md 5996360861.md 6422621713.md 6776362537.md 7183719943.md 7227322280.md 7336263536.md 75909371872.md 7869268779.md 8031500357.md 9253010492.md Data Management Plan The study's Data Management Plan is provided in PDF format and shows the different data sets used throughout the duration of the study and where they have been deposited, as well as how long the SSI will keep these records.

This dataset contains all projects funded by the European Union under the fourth framework programme for research and technological development (FP4) from 1994 to 1998.

The file 'FP4 Projects' contains the public grant information for each project, including the following information: Record Control Number (RCN), project ID (grant agreement number), project acronym, project status, funding programme, topic, project title, project start date, project end date, project objective, project total cost, EC max contribution (commitment), call ID, funding scheme (type of action), coordinator, coordinator country, participants (ordered in a semi-colon separated list), participant countries (ordered in a semi-colon separated list).

The participating organisations are listed in the file 'FP4 Organisations' which includes: project Record Control Number (RCN), project ID, project acronym, organisation role, organisation ID, organisation name, organisation short name, organisation type, participation ended (true/false), EC contribution, organisation country.

The dataset has been updated to match the structure of more recent datasets - some fields may not be populated.

Reference data (countries, funding schemes/types of action, subjects (SIC codes)) can be found in this dataset: https://data.europa.eu/euodp/en/data/dataset/cordisref-data

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.

This dataset relates to research on the connections between archives professionals and research data management. It consists of a single Excel spreadsheet with four sheets, containing an analysis of emails sent to two email discussions lists: Archives-NRA (Archivists, conservators and records managers) and Research-Dataman. The coded dataset and a list of codes used for each mailing list is provided.The two datasets were downloaded from the JiscMail Email Discussion list archives on 27 July 2018. The Archives-NRA dataset was compiled by conducting a free text search for "research data" on the mailing list's archives, and the metadata for every search result was downloaded and coded (144 metadata records in total). The resulting coded dataset demonstrates how frequently archivists and records professionals discuss research data on the Archives-NRA list, the topics which are discussed, and an increase in these discussions over time. The Research-Dataman dataset was compiled by conducting a free text search for "archivist" on the mailing list's archives, and the metadata for every search result was downloaded and coded (197 emails total). The resulting coded dataset demonstrates how frequently data management professionals seek the advice of archivists or advertise vacancies for archivists, and how often archivists email this mailing list. The names and email addresses of the mailing list participants have been redacted for privacy reasons but the original full-text emails can be accessed by members of the respective mailing lists using the URLs provided in the dataset.

The bulk of research on citizen science participants is project-centric, based on an assumption that volunteers experience a single project. Contrary to this assumption, survey responses (n=3,894) and digital trace data (n=3,649) from volunteers, who collectively engaged in 1,126 unique projects, revealed that multi-project participation was the norm. Only 23% of volunteers were singletons (who participated in only one project), and multi-project participants split evenly between disciplines specialists (39%) and discipline spanners (38% joined projects with different disciplinary topics), and unevenly between mode specialists (67%) and mode spanners (33% participated in online and offline projects). Public engagement was narrow: multi-project participants were eight times more likely to be white, and five times more likely to hold advanced degrees, than the general population. We propose a volunteer-centric framework that explores how the dynamic accumulation of experiences in a project ecosystem can support broad learning objectives and inclusive citizen science. Methods The purpose of this project was to collect data about volunteers who do citizen science projects, particilarly the number and type of projects that these participants do, and what demographic communities these volunteers represent. There were four data sources: digital trace data from the website "SciStarter.org," a survey distributed to SciStarter volunteers, a survey distributed to volunteers with the project "The Christmas Bird Count" and volunteers with the project "Candid Critters." We used this data to create a list of citizen science projects, which we categorized according to disciplinary topic (ecology, astronomy, etc.) and participation mode (online or offline). We then categorized each volunteer in our data source according to how many projects they did, and whether the project(s) they did were from multiple disciplinary topics and modes. Finally, we used regression to assess what demographics and other factors predicted joining multiple projects, joining projects from multiple disciplines, and joining projects from multiple modes.

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.

Ministry of the Interior National Immigration Agency List of Self-Research Topics for the 100th to 111th Year

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

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.

For information on economic census geographies, including changes for 2012, see the economic census Help Center..Includes only establishments of firms with payroll. See Table Notes for more information. Data based on the 2012 Economic Census. For information on confidentiality protection, sampling error, nonsampling error, and definitions, see Methodology..Table NameManagement of Companies and Enterprises: Subject Series: Misc Subjects: Summary Statistics for Research and Development Acquisition for Selected Industries for the U.S.: 2012ReleaseScheduleThe data in this file are scheduled for release in June 2016.Key TableInformationSee Methodology. for information on data limitations.UniverseThe universe of this file is selected establishments of firms with payroll in business at any time during 2012 and classified in Management of Companies and Enterprises (Sector 55).GeographyCoverageThe data are shown at the United States level only.IndustryCoverageThe data are shown for 551114 2012 NAICS code only.Data ItemsandOtherIdentifyingRecordsThis file contains data on:.Establishments.Annual payroll.Employment of establishments reporting research and development as percent of total employment.Each record includes a RESEARCHACQ code which represents establishments with revenue from research and development..FTP DownloadDownload the entire table athttps://www2.census.gov/econ2012/EC/sector55/EC1255SXSB7.zipContactInformationU.S. Census Bureau, Economy Wide Statistics Division. Data User Outreach and Education Staff. Washington, DC 20233-6900. Tel: (800) 242-2184. Tel: (301) 763-5154. Email: ewd.outreach@census.gov. . .Symbols:D - Withheld to avoid disclosing data for individual companies; data are included in higher level totalsN - Not available or not comparableFor a complete list of all economic programs symbols, see the Symbols Glossary.Source: U.S. Census Bureau, 2012 Economic Census.Note: The data in this file are based on the 2012 Economic Census. To maintain confidentiality, the U.S. Census Bureau suppresses data to protect the identity of any business or individual. The census results in this file contain sampling and nonsampling error. Data users who create their own estimates using data from this file should cite the U.S. Census Bureau as the source of the original data only. For the full technical documentation, see Methodology link in above headnote.

Analysis of ‘CORDIS - EU research projects under Horizon 2020 (2014-2020)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/cordish2020projects on 08 January 2022.

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

This dataset contains projects and related organisations funded by the European Union under the Horizon 2020 framework programme for research and innovation from 2014 to 2020.

The file 'H2020 Projects' contains the public grant information for each project, including the following information: Record Control Number (RCN), project ID (grant agreement number), project acronym, project status, funding programme, topic, project title, project start date, project end date, project objective, project total cost, EC max contribution (commitment), call ID, funding scheme (type of action), coordinator, coordinator country, participants (ordered in a semi-colon separated list), participant countries (ordered in a semi-colon separated list).

In the individual XML files of the projects you'll find, in addition, their classification with the newly introduced EuroSciVoc taxonomy (fields of science) as well as links to related editorial articles such as news or Results in Brief.

The participating organisations are listed in the file 'H2020 Organisations' which includes: project Record Control Number (RCN), project ID, project acronym, organisation role, organisation ID, organisation name, organisation short name, organisation type, participation ended (true/false), EC contribution, organisation country.

The periodic or final report summaries (or publishable summaries) from the projects have been included since September 2018.

The lists of publications and deliverables from the projects have been included since May 2019.

Reference data (programmes topics, 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

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

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

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.

The Excel file is organized into the following sheets:

• SM_Sheet1 - global bibliometric indicators in the sport sciences
• SM_Sheet2 - main indicators at country for two different period
• SM_Sheet3 - list of 4159 research topics for world science research
• SM_Sheet4 - a detailed description of main research topics in sport sciences
• SM_Sheet5 - the number of journals included in the Journal Citation Reports (JCR) in the sport science category and the topic distribution according to the Gini Index are as follows