Government funding for academic research will remain limited, and competition for grants will remain high. Broad adjustments will be needed
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The New Normal in Funding University Science | Issues in Science and Technology - 1 views
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systemic problems that arise from the R&D funding system and incentive structure that the federal government put in place after World War II
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unding rates in many National Institutes of Health (NIH) and National Science Foundation (NSF) programs are now at historical lows, declining from more than 30% before 2001 to 20% or even less in 2011
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even the most prominent scientists will find it difficult to maintain funding for their laboratories, and young scientists seeking their first grant may become so overwhelmed that individuals of great promise will be driven from the field
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The growth of the scientific enterprise on university campuses during the past 60 years is not sustainable and has now reached a tipping point at which old models no longer work
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ederal funding agencies must work with universities to ensure that new models of funding do not stymie the progress of science in the United States
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The deeper sources of the problem lie in the incentive structure of the modern research university, the aspirations of scientists trained by those universities, and the aspirations of less research-intensive universities and colleges across the nation
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if a university wants to attract a significant amount of sponsored research money, it needs doctoral programs in the relevant fields and faculty members who are dedicated to both winning grants and training students
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Even though not all doctorate recipients become university faculty, the size of the science and engineering faculty at U.S. universities has grown substantially
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These strategies make sense for any individual university, but will fail collectively unless federal funding for R&D grows robustly enough to keep up with demand.
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At the very time that universities were enjoying rapidly growing budgets, and creating modes of operation that assumed such largess was the new normal, Price warned that it would all soon come to a halt
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the human and financial resources invested in science had been increasing much faster than the populations and economies of those regions
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growth in the scientific enterprise would have to slow down at some point, growing no more than the population or the economy.
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studies sounded an alarm about the potential decline in U.S. global leadership in science and technology and the grave implications of that decline for economic growth and national security
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Although we are not opposed to increasing federal funding for research, we are not optimistic that it will happen at anywhere near the rate the Academies seek, nor do we think it will have a large impact on funding rates
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universities should not expect any radical increases in domestic R&D budgets, and most likely not in defense R&D budgets either, unless the discretionary budgets themselves grow rapidly. Those budgets are under pressure from political groups that want to shrink government spending and from the growth of spending in mandatory programs
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The basic point is that the growth of the economy will drive increases in federal R&D spending, and any attempt to provide rapid or sustained increases beyond that growth will require taking money from other programs.
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The demand for research money cannot grow faster than the economy forever and the growth curve for research money flattened out long ago.
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The goal cannot be to convince the government to invest a higher proportion of its discretionary spending in research
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Getting more is not in the cards, and some observers think the scientific community will be lucky to keep what it has
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The potential to take advantage of the infrastructure and talent on university campuses may be a win-win situation for businesses and institutions of higher education.
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Why should universities and colleges continue to support scientific research, knowing that the financial benefits are diminishing?
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faculty members are committed to their scholarship and will press on with their research programs even when external dollars are scarce
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it is critical to have active research laboratories, not only in elite public and private research institutions, but in non-flagship public universities, a diverse set of private universities, and four-year colleges
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How then do increasingly beleaguered institutions of higher education support the research efforts of the faculty, given the reality that federal grants are going to be few and far between for the majority of faculty members? What are the practical steps institutions can take?
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change the current model of providing large startup packages when a faculty member is hired and then leaving it up to the faculty member to obtain funding for the remainder of his or her career
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universities invest less in new faculty members and spread their internal research dollars across faculty members at all stages of their careers, from early to late.
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national conversation about changes in startup packages and by careful consultations with prospective faculty hires about long-term support of their research efforts
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Many prospective hires may find smaller startup packages palatable, if they can be convinced that the smaller packages are coupled with an institutional commitment to ongoing research support and more reasonable expectations about winning grants.
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Smaller startup packages mean that in many situations, new faculty members will not be able to establish a functioning stand-alone laboratory. Thus, space and equipment will need to be shared to a greater extent than has been true in the past.
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construction of open laboratory spaces and the strategic development of well-equipped research centers capable of efficiently servicing the needs of an array of researchers
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Collaborative proposals and the assembly of research teams that focus on more complex problems can arise relatively naturally as interactions among researchers are facilitated by proximity and the absence of walls between laboratories.
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The more likely trajectory of a junior faculty member will evolve from contributing team member to increasing leadership responsibilities to team leader
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nternal evaluations of contributions and potential will become more important in tenure and promotion decisions.
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relationships with foundations, donors, state agencies, and private business will become increasingly important in the funding game
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Further complicating university collaborations with business is that past examples of such partnerships have not always been easy or free of controversy.
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some faculty members worried about firms dictating the research priorities of the university, pulling graduate students into proprietary research (which could limit what they could publish), and generally tugging the relevant faculty in multiple directions.
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University faculty and businesspeople often do not understand each other’s cultures, needs, and constraints, and such gaps can lead to more mundane problems in university/industry relations, not least of which are organizational demands and institutional cultures
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n addition to funding for research, universities can receive indirect benefits from such relationships. High-profile partnerships with businesses will underline the important role that universities can play in the economic development of a region.
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Universities have to see firms as more than just deep pockets, and firms need to see universities as more than sources of cheap skilled labor.
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We do not believe that research proposed and supervised by individual principal investigators will disappear anytime soon. It is a research model that has proven to be remarkably successful and enduring
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However, we believe that the most vibrant scientific communities on university and college campuses, and the ones most likely to thrive in the new reality of funding for the sciences, will be those that encourage the formation of research teams and are nimble with regard to funding sources, even as they leave room for traditional avenues of funding and research.
Sensat creates digital twins for public infrastructure to improve productivity, raises ... - 1 views
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Citizen Weather Observer Program - 0 views
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"The Citizen Weather Observer Program (CWOP) is a public-private partnership with three goals: 1) to collect weather data contributed by citizens; 2) to make these data available for weather services and homeland security; and 3) to provide feedback to the data contributors so they have the tools to check and improve their data quality. In fact, the web address, wxqa.com, stands for weather quality assurance. "
About Public Lab | publiclaboratory.org - 0 views
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GitHub Has Big Dreams for Open-Source Software, and More - NYTimes.com - 0 views
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GitHub has no managers among its 140 employees, for example. “Everyone has management interests,” he said. “People can work on things that are interesting to them. Companies should exist to optimize happiness, not money. Profits follow.” He does, however, retain his own title and decides things like salaries.
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Another member of GitHub has posted a talk that stresses how companies flourish when people want to work on certain things, not because they are told to.
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Asana bases work on a series of to-do lists that people assign one another. Inside Asana there are no formal titles, though like GitHub there are bosses at the top who make final decisions.
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Mr. Preston-Werner thinks the way open source requires a high degree of trust and collaboration among relative equals (plus a few high-level managers who define the scope of a job and make final decisions) can be extended more broadly, even into government.
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GitHub’s popularity has also made it an important way for companies to recruit engineers, because some of the best people in the business are showing their work or dissecting the work of others inside some of the public pull requests.
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For all the happiness and sharing, real money is involved here. In July GitHub received $100 million from the venture capital firm Andreessen Horowitz. This early in most software companies’ lives, $20 million would be a fortune.
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“For now this is about code, but we can make the burden of decision-making into an opportunity,” he said. “It would be useful if you could capture the process of decision-making, and see who suggested the decisions that created a law or a bill.”
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As complex as an open-source project may be, it is also based on a single, well-defined outcome, and an engineering task that is generally free of concepts like fairness and justice, about which people can debate endlessly.
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Google once prided itself on few managers and fast action, but has found that getting big can also involve lots more meetings.
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Still, these fast-rising successes may be on to something more than simply universalizing the means of their own good fortune. An early guru of the Information Age, Peter Drucker, wrote often in the latter part of his career of the need for managers to define tasks, and for workers to seek fulfillment before profits.
Michel Bauwens's Public Library | Diigo - 0 views
The Public Laboratory | publiclaboratory.org - 1 views
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Comparative study technology incubators in Quebec and abroad - 6. Evaluation ... - 0 views
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Votorola - 1 views
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Votorola is social software in support of non-party primary elections and public rule making. We develop the tools to enable a radically free democracy based on unrestricted voting, drafting and discussion. Our alpha prototypes cover everything from voter registration in electoral districts to consensus making, and we lead the field in design, theory and inventions.
New Book Released! | Sustainable Economies Law Center - 2 views
A Return to Being Human - Tibbs - 3 views
www.scribd.com/...A-Return-to-Being-Human-Tibbs
return being human book knowledge publication economy ecosystem model theory
![](/images/link.gif)
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What is an ontology and why we need it - 1 views
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an ontology is a formal explicit description of concepts in a domain of discourse (classes (sometimes called concepts)), properties of each concept describing various features and attributes of the concept (slots (sometimes called roles or properties)), and restrictions on slots (facets (sometimes called role restrictions)). An ontology together with a set of individual instances of classes constitutes a knowledge base. In reality, there is a fine line where the ontology ends and the knowledge base begins.
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Here we discuss general issues to consider and offer one possible process for developing an ontology. We describe an iterative approach to ontology development: we start with a rough first pass at the ontology. We then revise and refine the evolving ontology and fill in the details. Along the way, we discuss the modeling decisions that a designer needs to make, as well as the pros, cons, and implications of different solutions.
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In practical terms, developing an ontology includes: � defining classes in the ontology, � arranging the classes in a taxonomic (subclass–superclass) hierarchy, � defining slots and describing allowed values for these slots, � filling in the values for slots for instances.
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We can then create a knowledge base by defining individual instances of these classes filling in specific slot value information and additional slot restrictions.
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There is no one correct way to model a domain— there are always viable alternatives. The best solution almost always depends on the application that you have in mind and the extensions that you anticipate. 2) Ontology development is necessarily an iterative process. 3) Concepts in the ontology should be close to objects (physical or logical) and relationships in your domain of interest. These are most likely to be nouns (objects) or verbs (relationships) in sentences that describe your domain.
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We suggest starting the development of an ontology by defining its domain and scope. That is, answer several basic questions: � What is the domain that the ontology will cover? � For what we are going to use the ontology? � For what types of questions the information in the ontology should provide answers? � Who will use and maintain the ontology?
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If the people who will maintain the ontology describe the domain in a language that is different from the language of the ontology users, we may need to provide the mapping between the languages.
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One of the ways to determine the scope of the ontology is to sketch a list of questions that a knowledge base based on the ontology should be able to answer, competency questions
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Innovation Canada: A Call to Action - Review of Federal Support to Research and Develop... - 1 views
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Canada has a solid foundation on which to build success as a leader in the knowledge economy of tomorrow
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innovation is the ultimate source of the long-term competitiveness of businesses and the quality of life of Canadians
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We heard that the government should be more focussed on helping innovative firms to grow and, particularly, on serving the needs of small and medium-sized enterprises (SMEs)
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innovation support is too narrowly focussed on R&D – more support is needed for other activities along the continuum from ideas to commercially useful innovation
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whole-of-government program delivery vehicle – the Industrial Research and Innovation Council (IRIC)
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includes non-labour costs, such as materials and capital equipment, the calculation of which can be highly complex
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the base for the tax credit should be labour-related costs, and the tax credit rate should be adjusted upward
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facilitating access by such firms to an increased supply of risk capital at both the start-up and later stages of their growth.
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encouragement of innovation in the Canadian economy should become a stated objective of procurement policies and programs.
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Innovation Advisory Committee (IAC) – a body with a whole-of-government focus that would oversee the realization of our proposed action plan, as well as serve as a permanent mechanism to promote the refinement and improvement of the government's business innovation programs going forward.
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focus resources where market forces are unlikely to operate effectively or efficiently and, in that context, address the full range of business innovation activities, including research, development, commercialization and collaboration with other key actors in the innovation ecosystem
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the closer the activity being supported is to market, and therefore the more likely it is that the recipient firm will capture most of the benefit for itself.
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Still stack with the old paradigm of the "knowledge economy" http://en.wikipedia.org/wiki/Knowledge_economy My opinion is that we're moving into a know-how economy.
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The Implications of Crypto Assets Part 3: Distributed Autonomous Corporations - 0 views
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Namecoins are mined in the same manner as bitcoins
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The idea of distributed autonomous corporations already exists, so now we just have to wait for the programmers and entrepreneurs to create the applications that build on the original thought.
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little to no profit incentive for the developers and supporters of the projects. You can only go so far with donations
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When you can create an encrypted email DAC that focuses on privacy and providing a quality product that can rival Gmail, it becomes much easier for the general public to care about computer security.
Public Lab: a community for DIY environmental investigation - 0 views
On the Commons - 0 views
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POWER-CURVE SOCIETY: The Future of Innovation, Opportunity and Social Equity in the Eme... - 1 views
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how technological innovation is restructuring productivity and the social and economic impact resulting from these changes
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concern about the technological displacement of jobs, stagnant middle class income, and wealth disparities in an emerging "winner-take-all" economy
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personal data ecosystems that could potentially unlock a revolutionary wave of individual economic empowerment
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As the technology boom of the 1990s increased productivity, many assumed that the rising water level of the economy was raising all those middle class boats. But a different phenomenon has also occurred. The wealthy have gained substantially over the past two decades while the middle class has remained stagnant in real income, and the poor are simply poorer.
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America is turning into a power-curve society: one where there are a relative few at the top and a gradually declining curve with a long tail of relatively poorer people.
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For the first time since the end of World War II, the middle class is apparently doing worse, not better, than previous generations.
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as businesses struggle to come to terms with this revolution, a new set of structural innovations is washing over businesses, organizations and government, forcing near-constant adaptation and change. It is no exaggeration to say that the explosion of innovative technologies and their dense interconnections is inventing a new kind of economy.
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the new technologies are clearly driving economic growth and higher productivity, the distribution of these benefits is skewed in worrisome ways.
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the networked economy seems to be producing a “power-curve” distribution, sometimes known as a “winner-take-all” economy
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major component of this new economy, Big Data, and the coming personal data revolution fomenting beneath it that seeks to put individuals, and not companies or governments, at the forefront. Companies in the power-curve economy rely heavily on big databases of personal information to improve their marketing, product design, and corporate strategies. The unanswered question is whether the multiplying reservoirs of personal data will be used to benefit individuals as consumers and citizens, or whether large Internet companies will control and monetize Big Data for their private gain.
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A special concern is whether information and communications technologies are actually eliminating more jobs than they are creating—and in what countries and occupations.
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Is it polarizing income and wealth distributions? How is it changing the nature of work and traditional organizations and altering family and personal life?
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many observers fear a wave of social and political disruption if a society’s basic commitments to fairness, individual opportunity and democratic values cannot be honored
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what role government should play in balancing these sometimes-conflicting priorities. How might educational policies, research and development, and immigration policies need to be altered?
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Conventional economics says that progress comes from new infusions of capital, whether financial, physical or human. But those are not necessarily the things that drive innovation
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economists have developed a number of proxy metrics for innovation, such as research and development expenditures.
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Atkinson believes that economists both underestimate and overestimate the scale and scope of innovation.
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Calculating the magnitude of innovation is also difficult because many innovations now require less capital than they did previously.
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believes that technological innovation follows the path of an “S-curve,” with a gradual increase accelerating to a rapid, steep increase, before it levels out at a higher level. One implication of this pattern, he said, is that “you maximize the ability to improve technology as it becomes more diffused.” This helps explain why it can take several decades to unlock the full productive potential of an innovation.
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innovation keeps getting harder. It was pretty easy to invent stuff in your garage back in 1895. But the technical and scientific challenges today are huge.”
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costs of innovation have plummeted, making it far easier and cheaper for more people to launch their own startup businesses and pursue their unconventional ideas
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Atkinson conceded such cost-efficiencies, but wonders if “the real question is that problems are getting more complicated more quickly than the solutions that might enable them.
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we may need to parse the different stages of innovation: “The cost of innovation generally hasn’t dropped,” he argued. “What has become less expensive is the replication and diffusion of innovation.”
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A lot of barriers to innovation can be found in the lack of financing, organizational support systems, regulation and public policies.
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there is a serious mismatch between the pace of innovation unleashed by Moore’s Law and our institutional and social capacity to adapt.
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This raises the question of whether old institutions can adapt—or whether innovation will therefore arise through other channels entirely. “Existing institutions are often run by followers of conventional wisdom,”
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The best way to identify new sources of innovation, as Arizona State University President Michael Crow has advised, is to “go to the edge and ignore the center.”
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Paradoxically, one of the most potent barriers to innovation is the accelerating pace of innovation itself.
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Part of the problem, he continued, is that our economy is based on “push-based models” in which we try to build systems for scalable efficiencies, which in turn demands predictability.
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The real challenge is how to achieve radical institutional innovations that prepare us to live in periods of constant two- or three-year cycles of change. We have to be able to pick up new ideas all the time.”
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The App Economy consists of a core company that creates and maintains a platform (such as Blackberry, Facebook or the iPhone), which in turn spawns an ecosystem of big and small companies that produce apps and/or mobile devices for that platform
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tied this success back to the open, innovative infrastructure and competition in the U.S. for mobile devices
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small businesses are becoming more comfortable using such systems to improve their marketing and lower their costs; and, vast new pools of personal data are becoming extremely useful in sharpening business strategies and marketing.
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Another great boost to innovation in some business sectors is the ability to forge ahead without advance permission or regulation,
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“In bio-fabs, for example, it’s not the cost of innovation that is high, it’s the cost of regulation,”
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“In Europe and China, the law holds that unless something is explicitly permitted, it is prohibited. But in the U.S., where common law rather than Continental law prevails, it’s the opposite