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/.

Financial overview and grant giving statistics of Metro Ideas Project

This dataset contains information about projects and their results funded by the European Union under the Horizon Europe framework programme for research and innovation from 2021 to 2027.

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

• HORIZON projects – which includes participating organisations, legal basis information, topic information, project URLs and classification with the European Science Vocabulary (EuroSciVoc)
• HORIZON project deliverables (meta-data and links to deliverables)
• HORIZON project publications (meta-data and links to publications)
• HORIZON report summaries (periodic or final publishable summaries)

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.

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.

According to a survey conducted in the U.S. in 2019, most projects initiated by healthcare organizations, such as hospitals and medical centers, related to IT or customer service. Approximately 70 percent of healthcare organizations had conducted IT projects while 60 percent initiated some optimization in the area of Customer service.

The Capital Projects Database reports information at the project level on discrete capital investments from the Capital Commitment Plan.Each row is uniquely identified by its Financial Management Service (FMS) ID, and contains data pertaining to the sponsoring and managing agency.

To explore the data, please visit Capital Planning Explorer

For additional information, please visit A Guide to The Capital Budget

Healthcare Cost and Utilization Project (HCUP) Fast Stats provides easy access to the latest HCUP-based statistics for health care information topics. HCUP Fast Stats uses visual statistical displays in stand-alone graphs, trend figures, or simple tables to convey complex information at a glance. Fast Stats is updated regularly for timely, topic-specific national and State-level statistics. Fast Stats topics and graphics on hospital stays and emergency department visits, including information at the national, and state levels, trends over time, and selected priority topics such as: State Trends in Hospital User by Payer National Hospital Utilization and Costs Hurricane Impact on Hospital Use Opioids & Neonatal Abstinence Syndrome Severe Maternal Morbidity

Scientific Data Management (SDM) Program shares and manages scientific and scientific program information systems in ways that support the mission and business of the NWFSC. We strive to bring quality information, in the right form, to the right people at the right time to support necessary decisions and generate ideas. Multi-FMC annual project planning (for budget, people, and operational costs) and data set tracking (for data entry to feed InPort/NCEI/Data.gov) TEST CASE TWO.

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

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.

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.

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.

Data Package: Exploring the opportunities and challenges of implementing open research strategies within development institutions

DOI for this package: https://doi.org/10.5281/zenodo.844394

Project Description: https://doi.org/10.3897/rio.2.e8880

Data Management Plan: https://doi.org/10.3897/rio.3.e14672

Other Related Documents and Reports: https://riojournal.com/collection/18/

Funder: International Development Research Centre/Centre de rechereches pour le développement international, https://doi.org/10.13039/501100000193

Abstract

========

This is the Data Package for the project "Exploring the opportunities and challenges of implementing open research strategies within development institutions" the proposal for which was published as https://doi.org/10.3897/rio.2.e8880. The research project conducted open data pilot case studies with seven IDRC grantees to develop and implement open data management and sharing plans. The results of the case studies served to refine guidelines for the implementation of development research funders’ open research data policies.

Contents

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The Data Package contains all the public data generated by the project. The package was curated and metadata generated, including an HTML Catalog using the Calcyte Tool (https://codeine.research.uts.edu.au/eresearch/calcyte) developed at University of Technology Sydney.

The project had two major phases:

1. A review, based on desk work and interviews with data management experts

2. Case studies, based on implementing open data practices within seven IDRC funded research projects

Review

The review, published at https://riojournal.com/article/14673/ was supported by desk work and interviews. The materials related to the interviews can be found in the directory:

• Policy and Implementation Review Interviews

Case Studies

Seven IDRC-funded projects were contributed to the pilot project.

The materials generated by the case studies and used to support the final report (to be published with the collection at https://riojournal.com/collection/18/) are found in the following directories.

• Introductory_Data_Workshop_Materials

• Introductory_Workshop_Presentations

• Data Management Planning

• SciDataCon Presentations

• Final_Project_Workshop_Materials

• Final_Project_Workshop_Presentations

The files are encoded with a three letter code that identifies the relevant contributing project in each case. The contributing projects were:

• Crowd Sourcing Data to fight Social Crimes: Harassmap, Egypt (HMP)

• The Brazilian Virtual Herbarium: CRIA, Brazil(BVH)

• Strengthening the Economic Committee of the National Assembly in Vietnam: Centre for Analysis and Forecasting, Vietnam (ECV)

• The Impact of Copyright User Rights: Derechos Digitales, Columbia (DED)

• Establishing a clearinghouse for tobacco economic data in Africa: DataFirst, South Africa (TED)

• Les problèmes négligés des systèmes de santé en Afrique : une incitation aux réformes: LASDEL, Niger (NDF)

• Indigenous Knowledge in Climate Change: Natural Justice, South Africa (IKC)

More details will be found in the Case Studies and in the Final Report (forthcoming at https://riojournal.com/collection/18/)

References

==========

• Neylon C, Chan L (2016) Exploring the opportunities and challenges of implementing open research strategies within development institutions. Research Ideas and Outcomes 2: e8880. https://doi.org/10.3897/rio.2.e8880

• Neylon C (2017) Data Management Plan: IDRC Data Sharing Pilot Project. Research Ideas and Outcomes 3: e14672. https://doi.org/10.3897/rio.3.e14672

• Neylon C, Chan L (2016-17) Exploring the opportunities and challenges of implementing open research strategies within development institutions: A project of the International Development Research Center, Research Ideas and Outcomes Collection, https://riojournal.com/collection/18/

description: Quivira National Wildlife Refuge was established in 1955, and a detailed vegetation map was not available for management purposes. With the present development of a biological program and Comprehensive Conservation Plan (CCP), a baseline vegetation map of the refuge was identified as a necessity. Development of the vegetation map and associated report was a multi-step process. Aerial photography (NAIP, 2008) was used with eCognition to create polygons of different plant communities based on the likeness of surrounding pixels in the area. Prior to ground-truthing, the following activities were accomplished: training on vegetation mapping using GIS (previous experience and National Conservation Training Center course), creation of an vegetation association and alliance dichotomous key, development of a refuge plant key and identification skills, and preparation of maps for ground truthing. Once out in the field dominant plants were identified for appropriate vegetation alliance and association classification, plant specimens were collected for the refuge herbarium as necessary and additional observations and photos were gathered for the report. Over the course of the project, classification data was entered into a GIS and polygons were appropriately modified to create the final map. At Quivira, results found a total of 42 alliances and 43 associations.The most dominant plants throughout the refuge in 2008 based on canopy cover were saltgrass, plum, little bluestem and cottonwood. The number of alliances and associations found on the refuge show high species diversity.; abstract: Quivira National Wildlife Refuge was established in 1955, and a detailed vegetation map was not available for management purposes. With the present development of a biological program and Comprehensive Conservation Plan (CCP), a baseline vegetation map of the refuge was identified as a necessity. Development of the vegetation map and associated report was a multi-step process. Aerial photography (NAIP, 2008) was used with eCognition to create polygons of different plant communities based on the likeness of surrounding pixels in the area. Prior to ground-truthing, the following activities were accomplished: training on vegetation mapping using GIS (previous experience and National Conservation Training Center course), creation of an vegetation association and alliance dichotomous key, development of a refuge plant key and identification skills, and preparation of maps for ground truthing. Once out in the field dominant plants were identified for appropriate vegetation alliance and association classification, plant specimens were collected for the refuge herbarium as necessary and additional observations and photos were gathered for the report. Over the course of the project, classification data was entered into a GIS and polygons were appropriately modified to create the final map. At Quivira, results found a total of 42 alliances and 43 associations.The most dominant plants throughout the refuge in 2008 based on canopy cover were saltgrass, plum, little bluestem and cottonwood. The number of alliances and associations found on the refuge show high species diversity.

This statistic shows information on some of the fastest project launches on crowdfunding website Kickstarter as of November 2016, based on the amount of time several million dollar projects took to surpass the 1 million dollar funding mark. On Black Friday 2016, board game Kingdom Death: Monster 1.5, a follow-up to the 2012 game Kingdom Death, generated 1 million U.S. dollars in pledges within 19 minutes. On February 23, 2015, smartwatch Pebble Time surpassed the 1 million U.S. dollar mark within 49 minutes. The Veronica Mars movie project had been the fastest Kickstarter movie project to accumulate 1 million U.S. dollars, taking only 4 hours and 24 minutes to do so. The fastest gaming project to reach 1 million U.S. dollars in funding was Shenmue 3 as of June 2015. Successful Kickstarter campaigns - additional information
As of 2015, Kickstarter is one of the largest crowdfunding platforms in the world, having reportedly received more than 1.6 billion U.S. dollars in pledges from 8.9 million individuals since its 2009 launch. According to industry experts, global crowdfunding campaigns have raised a total of 16.2 billion U.S. dollars in 2014, a 167 percent growth from the previous year’s 6.1 billion. In 2015, the industry is expected to reach 34.4 billion U.S. dollars in pledges from all over the world. However, crowdfunding, financing of a project through small donations from a great number of individuals, is not in itself a new idea and has been used in history before. A notable example is the completion of the Statue of Liberty, which was backed by more than 120,000 contributors, most of whom gave less than a dollar, following a campaign initiated by famed journalist Joseph Pulitzer.

While other websites, such as GoFundMe, allow people to raise money for anything from graduations to medical bills, trips or charity, Kickstarter focuses on mainly creative projects, from craft ideas to music albums or technological innovations. As of June 2015, the most popular category of projects featured on the platform is games, with some 360 million U.S. dollars pledged, followed by technology, design, film & video, and music. As of April 2015, some 38 percent of the campaigns posted on Kickstarter have reached or even exceeded their funding goal. The most successful Kickstarter campaign of all time is the one supporting Pebble Time, a smartwatch developed by Pebble Technology Corporation, which had an initial fundraising target of 100 thousand U.S. dollars, but received pledges worth over 10 million U.S. dollars from almost 70 thousand backers. It is also the campaign fastest to reach pledges worth 1 million U.S. dollars, in a record 30 minutes. The popular smartwatch went into production and was released in 2013, selling its one millionth unit in December 2014.

The Older Persons and Informal Caregivers Survey - Minimum DataSet (TOPICS-MDS) is a public data repository which contains information on the physical and mental health and well-being of older persons and informal caregivers and their care use across the Netherlands. The database was developed at the start of The National Care for the Elderly Programme (‘Nationaal Programma Ouderenzorg’ - NPO) on behalf of the Organisation of Health Research and Development (ZonMw - The Netherlands), in part to ensure uniform collection of outcome measures, thus promoting comparability between studies.

53 Different research projects have contributed data to this initiative, resulting in a pooled dataset with cross-sectional and (partly) longitudinal data of >43,000 older persons and >9,000 informal caregivers. Out of these numbers, a number of 7,600 concerns care receiver - caregiver dyads of whom information on both the care receiver and caregiver is available.

Since September 2014, TOPICS-MDS data are also collected within the ZonMw funded ‘Memorabel’ programme. In Memorabel round 1 through 4, 11 different research projects have collected TOPICS-MDS data, which has resulted in a pooled dataset with cross-sectional and (partly) longitudinal data of 1,400 older persons with dementia and about 950 informal caregivers. Out of these numbers, a number of 919 concerns care receiver - caregiver dyads of whom information on both the care receiver and caregiver is available.

More TOPICS-MDS data from Memorabel are expected to become available in 2022 and onwards.

More information on TOPICS-MDS can be found on https://topics-mds.eu .

53 Different research projects have contributed data to this initiative, resulting in a pooled dataset with cross-sectional and (partly) longitudinal data of >43,000 older persons and >9,000 informal caregivers. Out of these numbers, a number of 7,600 concerns care receiver - caregiver dyads of whom information on both the care receiver and caregiver is available.

Since september 2014, TOPICS-MDS data are also collected within the ZonMw funded ‘Memorabel’ programme. These data will become available in 2020 and onwards.

More information on TOPICS-MDS can be found on https://topics-mds.eu .

MS971: Data sharing agreement

Definitions and concepts in the context of the main paper.

List of tested and analyzed data sharing tools (non-exhaustive)

Despite organizations’ documented tendency to repeat research collaborations with prior partners, scholarly understanding on the implications of recurring interactions for the content of the collaboration has been fairly limited. This paper investigates whether and under what conditions organizations use repeated research partnerships to explore new topics, as opposed to deepening their expertise in a single one (exploitation). The empirical analysis is based on the Spanish region of Valencia and its publicly funded R&D network. Employing lexical similarity to compare the topic and content of project abstracts, we find that strong ties are not always associated with the exploitation of the same topic. Yet, exploration is more likely when at least one of the partners mobilizes a network of distinct contacts and can access novel knowledge.