To stimulate and encourage creativity and innovation within the NASA Centers. The activities are envisioned to fall within the scope of NASA Space Technology or technology addressing a significant National need.

This document includes data from International Development Innovation Network (IDIN) program monitoring and evaluation surveys from 2014-2017. IDIN was a program led by the Massachusetts Institute of Technology’s D-Lab, implemented by a global consortium of academic, institutional, and innovation center partners, and supported by USAID’s Higher Education Solutions Network in the U.S. Global Development Lab. Together with IDIN Network members and partners the D-Lab team worked to support innovators and entrepreneurs around the globe to design, develop, and disseminate technologies to improve the lives of people living in poverty. The program consisted of five components: design workshops and summits, innovation project funding, local innovation centers, research, and MIT student engagement.

Gain insights from global innovation programs in health and wellness, focusing on decision-making, market trends, and strategic initiatives. Read More

Innovation and business strategy, government support programs critical for innovation activities, by North American Industry Classification System (NAICS), government programs and enterprise size for Canada and selected provinces from 2007 to today.

Workforce Innovation and Opportunities Act Measures This dataset contains Workforce Innovation and Opportunities Act (WIOA) measures covering Title I Adult, Title I Dislocated Workers and Title I Youth programs. This program replaces the Workforce Investment Act, (WIA), program. The Workforce Investment Act (WIA) performance goals and outcomes reported through Program Year 2015 before transitioning to WIOA performance outcomes commencing Program Year 2016.

The CMS Innovation Center Innovation Advisors dataset provides information on individuals chosen by CMS as participants in the Innovation Advisors Program. The data includes the name of the initiative, as well as participants names, geographic location including city and state, and geographic reach of the practice.

Each individual NASA Center has full discretion on the use of the funds and the Center Chief Technologists coordinates a competitive process at their Center for the selection of projects. Kennedy Space Center (KSC) considers CIF very important because it encourages employees to develop innovative concepts that, if successful, can solve future mission needs. As in prior years, KSC put a strong emphasis on low Technology Readiness Level (TRL) technologies for CIF in support of Space Technology Area Roadmaps that align with KSC core capabilities.

KSC has emphasized partnerships for several years and has benefited in many ways from the other NASA centers and government agencies, academia and industry collaborations. In keeping with this emphasis, a key selection criterion for KSC CIF relates to collaborations and partnerships (with leveraged funding). 

Survey of innovation and business strategy, use of government support programs for innovation, by North American Industry Classification System (NAICS), enterprise size, and level of government for Canada and regions from 2007/2009 to today.

Percentage of enterprises for which a specific type of government program was the most critical for the business's innovation activities, by North American Industry Classification System (NAICS) code and enterprise size, based on a three-year observation period. Government programs include tax incentive or tax credit programs, grants and contributions programs, training and hiring programs, procurement, and other government programs.

Survey of innovation and business strategy, government support programs most critical for innovation activities, by North American Industry Classification System (NAICS) and enterprise size for Canada and regions from 2007/2009 to today.

As part of this national strategy, the Ministry of Education, Culture, Sports, Science and Technology (MEXT) had launched a 5-year (FY2007 - 2011) initiative called the Innovative Program of Climate Change Projection for the 21st Century (KAKUSHIN Program), using the Earth Simulator (ES) to address emerging research challenges, such as those derived from the outcomes of the MEXT's Kyosei Project (FY2002 - 2006), that had made substantial contributions to the Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC). The KAKUSHIN Program was expected to further contribute to the Fifth Assessment Report (AR5).

The research items include the advancement and forecasting of global warming models, the quantification and reduction of model uncertainty, and the evaluation of the impacts of natural disasters based on forecast information. Much of the data submitted to CMIP5 from Japan were generated under this KAKUSHIN program using the global climate models and the Earth system models developed in Japan. This dataset is the result of using the Earth System Model MIROC-ESM-CHEM.

All CMIP5 data are collected, managed, and published by the Earth System Grid Federation (ESGF), and DIAS serves as an ESGF node. All public datasets, including this dataset, are available from ESGF. For information on how to use these datasets, including this dataset, see "CMIP5 Data - Getting Started" (URL is available in the online information below). Please note that an ESGF account is required to download the CMIP5 data.

Because the terms of use for CMIP5 data are different from CMIP6 in many respects, please check the following Terms of Use carefully: https://pcmdi.llnl.gov/mips/cmip5/terms-of-use.html Currently, all CMIP5 data, including this dataset, is classified as "unrestricted" within it.

"Revolutionizing clinical research: Exploring open data initiatives for innovation and impact" an open data webinar hosted by the Pediatric Sepsis Data CoLaboratory. Given how pervasive (and complex) Artificial Intelligence (AI) I is going to be, it is unlikely that a few organizations can truly regulate the technology. Its oversight will require everyone pitching in. That, in turn, requires everyone having some basic understanding of how AI is developed, and more importantly, the risks associated with its use. But how do we build capacity in a field that is moving at warped speed? There will be no individual experts in the field, only collective wisdom. Legacy education and knowledge systems are too inflexible and siloed to keep up with the science and understand the sociology of AI. Datathons are designed to operationalize the multi-disciplinary hive learning that addresses the challenges of AI education and training. In this webinar, speakers discuss data bias and AI and their experience with optimizing reuse of data to advance health research through Datathons. Presenters: 1. "What's the Fuss about Artificial Intelligence (AI)?" - Speaker: Leo Anthony Celi, Principal Research Scientist, Massachusetts Institute of Technology; Associate Professor of Medicine, Harvard Medical School; Editor-in-Chief, PLOS Digital Health. 2. "SATI-Q Program: 20 Years of History in Quality Benchmarking, Evolution and Future Perspectives" - Speaker: Ariel Leonardo Fernández, Software Developer, Quality Benchmarking Program (SATI-Q), Argentina Society of Intensive Care (SATI). Data Description: Presentation slides, webinar video - Full (55m), webinar video - Celi (21m), webinar video - Fernández (13m). NOTE for restricted files: If you are not yet a CoLab member, please complete our membership application survey to gain access to restricted files within 2 business days. Some files may remain restricted to CoLab members. These files are deemed more sensitive by the file owner and are meant to be shared on a case-by-case basis. Please contact the CoLab coordinator at sepsiscolab@bcchr.ca or visit our website.

From 2012-2017, the International Development Innovation Network (IDIN) was a program led by the Massachusetts Institute of Technology’s D-Lab; implemented by a global consortium of academic, institutional, and innovation center partners; and supported by USAID’s Higher Education Solutions Network in the U.S. Global Development Lab. Today, IDIN Network members and partners continue to support innovators and entrepreneurs around the globe to design, develop, and disseminate technologies to improve the lives of people living in poverty. The IDIN Network is made up of more than 1,000 dynamic innovators from around the world who all share a common experience: attending an International Development Design Summit to create technologies with communities in developing countries. After a design summit, Network members pursued innovative projects, some from a summit and some of their own creation. With access to funding, training, mentorship, and workshop space, these innovators’ prototypes became products designed to make a difference. What We Do The IDIN program supported local innovation to create impact in five ways: Design Summits – Hands-on design experiences co-creating low-cost, practical technologies for people living in poverty IDIN Network Resources – Funding, training, and educational opportunities to support our 1,000+ Network members around the world Innovation Centers – Maker spaces connecting innovators to resources and training to develop technologies that make a social impact Research – A team generating new knowledge on local innovation and the role it plays in sustainable and community development Student Participation – Classes, projects, research, field visits, and technology development to train the next generation of innovators

Funds are distributed to each NASA Center to support emerging technologies and creative initiatives that leverage Center talent and capabilities. NASA scientists and engineers will lead projects and partnerships among Centers and with other agencies, research laboratories, academia and private industry are encouraged.

This dataset provides location and program information for Rural Innovation Fund (RIF) grant allocation areas. Data for this service is derived from one of two sources: Rural Innovation Fund grantee applications describing proposed allocation areas using U.S. Census block geography, or block-level data identified and selected through the use of mapping tool available to applicants through HUDUSER.gov. Each Grantee was allowed up to three target area locations according to the RIF NOFA. Some Grantee locations serve the same target areas so the map layer does include some overlapping boundaries.

Abstract The aim of this paper is to propose a methodology for innovation management, which aims to focus on the needs of micro and small technology-based companies. The development of this methodology occurs in the context of the Brazilian National Project Financier (Financiadora de Estudo e Projetos [FINEP]) program for the structuring of Innovation Management Support Centers (Núcleos de Apoio à Gestão da Inovação [NAGI]), focusing on the elaboration of Innovation Management Plans and Projects in Brazilian companies. A qualitative research was developed in two main stages for the development of the present methodology. The first phase developed a literature review of the main elements for innovation management, especially in the context of micro and small enterprises. In the second phase, five NAGIs were selected from the 24 those approved by MCTIC (now MCTIC: Ministry of Science, Technology, Innovation and Communication) and FINEP, according to their trajectory up until the time of the research in January 2014. The proposed methodology - Route of Innovation – is based on the Resource Based View (RBV) within a procedural and playful perspective that relates the innovation process to navigating a route. The logic of the islands in the Route of Innovation is based on the stages of the innovation process. The purpose of this approach was to make the methodology easily understood and implemented by micro and small companies.

Thank you for your interest in the Investing in Innovation (i3) Program! You can use the tools below to look at general information about the applications, and you can also find specific applications that you would like to explore in more detail. The data is sourced from supplemental forms as reported by applicants. The data from these forms may not be a full or accurate representation of the information provided in the formal application.

The Innovation Center Model Summary Information dataset contains various data points related to CMS Innovation Center models, demonstrations, programs, and initiatives. This can includes name, start and end date, statutory or regulatory authority, keywords, stage of implementation, participants, beneficiaries and physicians impacted, and categories related to health care quality, cost, payment, and delivery.

BC Stats (with partners at the Ministry of Housing, Ministry of Social Development and Poverty Reduction (SDPR), and BC Housing) has developed aggregated summary statistics estimating the homeless population in B.C. These estimates were derived from three administrative service use datasets from the Data Innovation Program (DIP): shelter use from BC Housing, social assistance payments from SDPR, demographic information from the Health medical service plan (MSP) central demographics file. The analytic definition of homelessness includes individuals who received income assistance with no fixed address for at least three consecutive months or those who visited a shelter at any time throughout the year. Estimates have been aggregated into four tables: * Annual estimates of the homeless population by age and gender * Annual estimates of the homeless population by chronicity category (chronic vs non-chronic homelessness) * Annual estimates of the homeless population by census division * Monthly estimates of the homeless population by service use (income assistance with no fixed address, shelter use, or both) \ Estimates are available for 2019-2022. Full methodology details are available in the Homeless Cohort Development - Technical Documentation resource.

Data for this service is derived from one of two sources: Rural Innovation Fund grantee applications describing proposed allocation areas using U.S. Census block geography, or block-level data identified and selected through the use of mapping tool available to applicants through HUDUSER.gov. Each Grantee was allowed up to three target area locations according to the RIF NOFA. Some Grantee locations serve the same target areas so the map layer does include some overlapping boundaries.

To learn more about the Rural Innovation Fund (RIF) Program visit: https://www.hud.gov/hudprograms/rural-innovation, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Data Dictionary: DD_Rural Innovation Fund (RIF) Grantees