Chapter 1: Introduction to Scientific Thinking
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Science Serving the Nation, Impact of Basic Research
Science Serving the Nation The Impact of Basic Research Basic Energy Sciences Science Serving the Nation The Impact of Basic Research Basic Energy Sciences BASIC ENERGY SCIENCES ENERGY ENVIRONMENT SECURITY MATERIALS SCIENCES ENGINEERING CHEMICAL GEOSCIENCES BIOSCIENCES RESEARCH UNDERSTAND PREDICT CONTROL MATTER ENERGY This document highlights the breadth of the scientific programs and the impact of this research in commerce and in advancing scientific frontiers. To look in more detail at our research portfolio and see how we manage our programs, consult our BES 2011 Summary Report, available at http://science.energy.gov/~/media/bes/pdf/reports/files/bes2011sr_rpt.pdf. Additional information about the impact of BES research can be found on our website at http://science.energy.gov/bes/. New highlights showcasing the benefits of BES programs are frequently posted to the web as they arise. Our user facility brochure (http://science.energy.gov/~/media/bes/suf/pdf/BES_Facilities.pdf) contains additional information on all of the BES national scientific user facilities. Energy technologies, environmental tech- nologies, and national security all depend on the progress of basic science research. By understanding, predicting, and ulti- mately controlling matter and energy at the level of electrons, atoms, and molecules, scientists have the capacity to transform the technologies we use in everyday life. From Fundamental Science to Energy Technology Basic Energy Sciences (BES) supports the science that is the foundation for new technologies essential to the U.S. Department of Energy (DOE) missions in energy, environment, and national security. We are one of the Nation’s leading sponsors of fundamental research across broad areas of materials sciences and engineering, chemical sciences, geosciences, and biosciences. -
Measuring Science: Is There “Basic Research” Without Statistics? Benoît Godin 3465, Rue Durocher Montréal (QUE) Canada
Measuring Science: Is There “Basic Research” Without Statistics? Benoît Godin 3465, rue Durocher Montréal (QUE) Canada H2X 2C6 Project on the History and Sociology of S&T Statistics Paper No. 3 2000 Previous papers in the series: 1. B. Godin, Outlines for a History of Science Measurement. 2. B. Godin, The Measure of Science and the Construction of a Statistical Territory: The Case of the National Capital Region (NCR). 1 Measuring Science : Is There “Basic Research” Without Statistics? Measuring Science: Is There “Basic Research” Without Statistics? Fundamental research is a central category of science policy and science measurement. Of all the concepts defined in the first edition of the Frascati manual, the OECD (Organization for Economic and Co-operation Development) methodological guide for official surveys on R&D, the first dealt with fundamental research. While a definition of research itself did not appear until the second edition in 1970, fundamental research was defined explicitly as follows: Work undertaken primarily for the advancement of scientific knowledge, without a specific practical application in view. 1 In the last edition of the manual (1994), the definition is substantially the same as the one in 1963, although the term “basic” is now used instead of fundamental: Basic research is experimental or theoretical work undertaken primarily to acquire new knowledge of the underlying foundation of phenomena and observable facts, without any particular application or use in view. 2 Between 1963 and 1994, therefore, all five editions of the manual carry essentially the same definition without any significant changes: basic research is research concerned with knowledge as contrasted with applied research, which is concerned with the application of knowledge. -
Concepts and Definitions for Identifying R&D
Frascati Manual 2015 Guidelines for Collecting and Reporting Data on Research and Experimental Development © OECD 2015 Chapter 2 Concepts and definitions for identifying R&D This chapter provides the definition of research and experimental development (R&D) and of its components, basic research, applied research and experimental development. These definitions are essentially unchanged from those in previous editions of the manual. Where there are differences, they reflect changes in culture and the use of language. To provide guidance on what is and what is not an R&D activity, five criteria are provided which require the activity to be novel, creative, uncertain in it outcome, systematic and transferable and/or reproducible. Since the last edition, the treatment of R&D expenditure in the System of National Accounts (SNA) has changed from an expense to a capital investment. As a result, the language of this manual, and of the SNA, is closer and there are additional requirements for measurements of financial flows. While the manual has always applied to all scientific disciplines, there is more emphasis on the social sciences, humanities and the arts, in addition to the natural sciences and engineering. Measuring R&D activities through surveys, administrative data, or interviews raises questions about boundaries and what is and what is not included and this chapter provides examples to help answer those questions. The manual is used to interpret R&D data as part of policy development and evaluation, but the focus of this chapter is on definitions for measurement purposes. 43 I-2. CONCEPTS AND DEFINITIONS FOR IDENTIFYING R&D 2.1. -
PSY 4960/5960 Science Vs. Pseudoscience Human Life
PSY 4960/5960 Science vs. Pseudoscience •What is science? Human Life Expectancy Why has the average human lifespan doubled over the past 200 years? 1 Quick Quiz • True or false? • Most people use only about 10% of their brain capacity • Drinking coffee is a good way to sober up after heavy drinking • Hypnosis can help us to recall things we’ve forgotten • If you’re unsure of your answer while taking a test, it’s best to stick with your initial answer Common Sense? •Look before you leap. •He who hesitates is lost. •Birds of a feather flock together. •Opposites attract. •Absence makes the heart grow fonder. •Out of sight, out of mind. • •Better safe than sorry. •Nothing ventured, nothing gained. •Two heads are better than one. •Too many cooks spoil the bthbroth. •The bigger the better. •Good things come in small packages. •Actions speak louder than words. •The pen is mightier than the sword. •Clothes make the man. •Don’t judge a book by its cover. •The more the merrier. •Two’s company, three’s a crowd. •You’re never too old to learn. •You can’t teach an old dog new tricks. Lilienfeld et al. (2007) Operational Definition • Science is – “A set of methods designed to describe and interpret observed or inferred phenomena, past or pp,resent, and aimed at building a testable body of knowledge open to rejection or confirmation.” – A toolbox of skills designed to prevent us from fooling ourselves – Learning to minimize your thinking errors – Self‐correcting Shermer (2002) 2 What Makes a Good Scientist? • Communalism –a willingness to share data • Disinterestedness –trying not to be influenced by personal or financial investments • A tiny voice saying “I might be wrong” • “Utter honesty –a kind of leaning over Merton (1942) backwards” Sagan (1995) Feynman (1988) Quick Quiz Write down the names of as many living scientists as you can, including their fields. -
Science for Energy Technology: Strengthening the Link Between Basic Research and Industry
ďŽƵƚƚŚĞĞƉĂƌƚŵĞŶƚŽĨŶĞƌŐLJ͛ƐĂƐŝĐŶĞƌŐLJ^ĐŝĞŶĐĞƐWƌŽŐƌĂŵ ĂƐŝĐŶĞƌŐLJ^ĐŝĞŶĐĞƐ;^ͿƐƵƉƉŽƌƚƐĨƵŶĚĂŵĞŶƚĂůƌĞƐĞĂƌĐŚƚŽƵŶĚĞƌƐƚĂŶĚ͕ƉƌĞĚŝĐƚ͕ĂŶĚƵůƟŵĂƚĞůLJĐŽŶƚƌŽů ŵĂƩĞƌĂŶĚĞŶĞƌŐLJĂƚƚŚĞĞůĞĐƚƌŽŶŝĐ͕ĂƚŽŵŝĐ͕ĂŶĚŵŽůĞĐƵůĂƌůĞǀĞůƐ͘dŚŝƐƌĞƐĞĂƌĐŚƉƌŽǀŝĚĞƐƚŚĞĨŽƵŶĚĂƟŽŶƐ ĨŽƌŶĞǁĞŶĞƌŐLJƚĞĐŚŶŽůŽŐŝĞƐĂŶĚƐƵƉƉŽƌƚƐKŵŝƐƐŝŽŶƐŝŶĞŶĞƌŐLJ͕ĞŶǀŝƌŽŶŵĞŶƚ͕ĂŶĚŶĂƟŽŶĂůƐĞĐƵƌŝƚLJ͘dŚĞ ^ƉƌŽŐƌĂŵĂůƐŽƉůĂŶƐ͕ĐŽŶƐƚƌƵĐƚƐ͕ĂŶĚŽƉĞƌĂƚĞƐŵĂũŽƌƐĐŝĞŶƟĮĐƵƐĞƌĨĂĐŝůŝƟĞƐƚŽƐĞƌǀĞƌĞƐĞĂƌĐŚĞƌƐĨƌŽŵ ƵŶŝǀĞƌƐŝƟĞƐ͕ŶĂƟŽŶĂůůĂďŽƌĂƚŽƌŝĞƐ͕ĂŶĚƉƌŝǀĂƚĞŝŶƐƟƚƵƟŽŶƐ͘ ďŽƵƚƚŚĞ͞ĂƐŝĐZĞƐĞĂƌĐŚEĞĞĚƐ͟ZĞƉŽƌƚ^ĞƌŝĞƐ KǀĞƌƚŚĞƉĂƐƚĞŝŐŚƚLJĞĂƌƐ͕ƚŚĞĂƐŝĐŶĞƌŐLJ^ĐŝĞŶĐĞƐĚǀŝƐŽƌLJŽŵŵŝƩĞĞ;^ͿĂŶĚ^ŚĂǀĞĞŶŐĂŐĞĚ ƚŚŽƵƐĂŶĚƐŽĨƐĐŝĞŶƟƐƚƐĨƌŽŵĂĐĂĚĞŵŝĂ͕ŶĂƟŽŶĂůůĂďŽƌĂƚŽƌŝĞƐ͕ĂŶĚŝŶĚƵƐƚƌLJĨƌŽŵĂƌŽƵŶĚƚŚĞǁŽƌůĚƚŽƐƚƵĚLJ ƚŚĞĐƵƌƌĞŶƚƐƚĂƚƵƐ͕ůŝŵŝƟŶŐĨĂĐƚŽƌƐ͕ĂŶĚƐƉĞĐŝĮĐĨƵŶĚĂŵĞŶƚĂůƐĐŝĞŶƟĮĐďŽƩůĞŶĞĐŬƐďůŽĐŬŝŶŐƚŚĞǁŝĚĞƐƉƌĞĂĚ ŝŵƉůĞŵĞŶƚĂƟŽŶŽĨĂůƚĞƌŶĂƚĞĞŶĞƌŐLJƚĞĐŚŶŽůŽŐŝĞƐ͘dŚĞƌĞƉŽƌƚƐĨƌŽŵƚŚĞĨŽƵŶĚĂƟŽŶĂůĂƐŝĐZĞƐĞĂƌĐŚEĞĞĚƐƚŽ ƐƐƵƌĞĂ^ĞĐƵƌĞŶĞƌŐLJ&ƵƚƵƌĞǁŽƌŬƐŚŽƉ͕ƚŚĞĨŽůůŽǁŝŶŐƚĞŶ͞ĂƐŝĐZĞƐĞĂƌĐŚEĞĞĚƐ͟ǁŽƌŬƐŚŽƉƐ͕ƚŚĞƉĂŶĞůŽŶ 'ƌĂŶĚŚĂůůĞŶŐĞƐĐŝĞŶĐĞ͕ĂŶĚƚŚĞƐƵŵŵĂƌLJƌĞƉŽƌƚEĞǁ^ĐŝĞŶĐĞĨŽƌĂ^ĞĐƵƌĞĂŶĚ^ƵƐƚĂŝŶĂďůĞŶĞƌŐLJ&ƵƚƵƌĞ ĚĞƚĂŝůƚŚĞŬĞLJďĂƐŝĐƌĞƐĞĂƌĐŚŶĞĞĚĞĚƚŽĐƌĞĂƚĞƐƵƐƚĂŝŶĂďůĞ͕ůŽǁĐĂƌďŽŶĞŶĞƌŐLJƚĞĐŚŶŽůŽŐŝĞƐŽĨƚŚĞĨƵƚƵƌĞ͘dŚĞƐĞ ƌĞƉŽƌƚƐŚĂǀĞďĞĐŽŵĞƐƚĂŶĚĂƌĚƌĞĨĞƌĞŶĐĞƐŝŶƚŚĞƐĐŝĞŶƟĮĐĐŽŵŵƵŶŝƚLJĂŶĚŚĂǀĞŚĞůƉĞĚƐŚĂƉĞƚŚĞƐƚƌĂƚĞŐŝĐ ĚŝƌĞĐƟŽŶƐŽĨƚŚĞ^ͲĨƵŶĚĞĚƉƌŽŐƌĂŵƐ͘;ŚƩƉ͗ͬͬǁǁǁ͘ƐĐ͘ĚŽĞ͘ŐŽǀͬďĞƐͬƌĞƉŽƌƚƐͬůŝƐƚ͘ŚƚŵůͿ ϭ ^ĐŝĞŶĐĞĨŽƌŶĞƌŐLJdĞĐŚŶŽůŽŐLJ͗^ƚƌĞŶŐƚŚĞŶŝŶŐƚŚĞ>ŝŶŬďĞƚǁĞĞŶĂƐŝĐZĞƐĞĂƌĐŚĂŶĚ/ŶĚƵƐƚƌLJ Ϯ EĞǁ^ĐŝĞŶĐĞĨŽƌĂ^ĞĐƵƌĞĂŶĚ^ƵƐƚĂŝŶĂďůĞŶĞƌŐLJ&ƵƚƵƌĞ ϯ ŝƌĞĐƟŶŐDĂƩĞƌĂŶĚŶĞƌŐLJ͗&ŝǀĞŚĂůůĞŶŐĞƐĨŽƌ^ĐŝĞŶĐĞĂŶĚƚŚĞ/ŵĂŐŝŶĂƟŽŶ ϰ ĂƐŝĐZĞƐĞĂƌĐŚEĞĞĚƐĨŽƌDĂƚĞƌŝĂůƐƵŶĚĞƌdžƚƌĞŵĞŶǀŝƌŽŶŵĞŶƚƐ ϱ ĂƐŝĐZĞƐĞĂƌĐŚEĞĞĚƐ͗ĂƚĂůLJƐŝƐĨŽƌŶĞƌŐLJ -
Chapter 5 Measurement Operational Definitions Numbers and Precision
5 - 1 Chapter 5 Measurement Operational Definitions Numbers and Precision Scales of Measurement Nominal Scale Ordinal Scale Interval Scale Ratio Scale Validity of Measurement Content Validity Face Validity Concurrent Validity Predictive Validity Construct Validity Thinking Critically About Everyday Information Reliability of Measurement Test–Retest Reliability Alternate Form Reliability Split-Half Reliability Factors That Affect Reliability Case Analysis General Summary Detailed Summary Key Terms Review Questions/Exercises 5 - 2 Operational Definitions An essential component of an operational definition is measurement. A simple and accurate definition of measurement is the assignment of numbers to a variable in which we are interested. These numbers will provide the raw material for our statistical analysis. Measurement is so common and taken for granted that we seldom ask why we measure things or worry about the different forms that measurement may take. It is often not sufficient to describe a runner as “fast,” a basketball player as “tall,” a wrestler as “strong,” or a baseball hitter as “good.” If coaches recruited potential team members on the basis of these imprecise words, they would have difficulty holding down a job. Coaches want to know how fast the runner runs the 100-yard dash or the mile. They want to know exactly how tall the basketball player is, the strength of the wrestler, the batting average of the hitter. Measurement is a way of refining our ordinary observations so that we can assign numerical values to our observations. It allows us to go beyond simply describing the presence or absence of an event or thing to specifying how much, how long, or how intense it is. -
Developing an Operational Definition of Intellectual Disability for the Purpose of National Health Surveillance
Developing an Operational Definition of Intellectual Disability for the Purpose of National Health Surveillance Prepared by: Alexandra Bonardi OTR/L, MHA Emily Lauer, MPH In cooperation with: Steven Staugaitis PhD Julie Bershadsky PhD Sarah Taub MS Courtney Noblett MPA November 2011 A 2010 Research Topic of Interest (RTOI): Health Surveillance of Adults with Intellectual Disability, awarded by the Association of University Centers on Disabilities (AUCD) and funded through a cooperative agreement with the Centers for Disease Control and Prevention (CDC) National Center on Birth Defects and Developmental Disabilities (NCBDDD) ACKNOWLEDGEMENTS The RTOI Project team would like to thank all participants at the April 13th, Operational Definition of ID Summit for their clear and compelling arguments, their engaged and respectful sharing of opinion and expertise, and for their dedication to enhancing surveillance as a means to improve the health of people with an intellectual disability. A complete list of the Summit Participants and their affiliations are included in Appendix A The RTOI Project Advisory Group provided valuable input into the planning and interpretation of the outcomes from the summit: Robert Baldor, Mary Blauvelt, Val Bradley, Mike Fox, Matt Janicki, Christine Linehan, Chas Moseley, Deirdra Murphy, Susan Parish, Ismaila Ramon, Steven Staugaitis. Others who offered advice, especially Max Barrows and Karen Topper of Self Advocates Becoming Empowered (SABE) and those self advocates, family members, and researchers who committed -
Operational Definition Refinement: A
From: AAAI-92 Proceedings. Copyright ©1992, AAAI (www.aaai.org). All rights reserved. Operational definition refinement: a Jan M. iytkow Jieming Zhu obert Zembowicz Department of Computer Science Wichita State University, Wichita, KS 67208 Abstract that high quality data are obtained from a user or a simulation. In true science, however, the requests for Operational definitions link scientific attributes to ex- data are based on empirical knowledge. For instance, perimental situations, prescribing for the experimenter measurements must be made at concrete times. If a the actions and measurements needed to measure or measurement is made too early, before a reaction is control attribute values. While very important in real completed or before a measuring device stabilizes, the science, operational procedures have been neglected in results will be systematically wrong or the error may machine discovery. We argue that in the preparatory be large. If the measurements are made too late, the stage of the empirical discovery process each opera- interference of a disturbing phenomenon may cause an- tional definition must be adjusted to the experimental other systematic error. The exact timing of “too early task at hand. This is done in the interest of error re- and too late” may vary. A reaction may be completed duction and repeatability of measurements. Both small at different times for varying amounts of chemicals. error and high repeatability are instrumental in theory Similarly, the time after which a measuring device will formation. We demonstrate that operational proce- stabilize may depend on the mass added and other cir- dure refinement is a discovery process that resembles cumstances. -
Introduction to Philosophy of Science
INTRODUCTION TO PHILOSOPHY OF SCIENCE The aim of philosophy of science is to understand what scientists did and how they did it, where history of science shows that they performed basic research very well. Therefore to achieve this aim, philosophers look back to the great achievements in the evolution of modern science that started with the Copernicus with greater emphasis given to more recent accomplishments. The earliest philosophy of science in the last two hundred years is Romanticism, which started as a humanities discipline and was later adapted to science as a humanities specialty. The Romantics view the aim of science as interpretative understanding, which is a mentalistic ontology acquired by introspection. They call language containing this ontology “theory”. The most successful science sharing in the humanities aim is economics, but since the development of econometrics that enables forecasting and policy, the humanities aim is mixed with the natural science aim of prediction and control. Often, however, econometricians have found that successful forecasting by econometric models must be purchased at the price of rejecting equation specifications based on the interpretative understanding supplied by neoclassical macroeconomic and microeconomic theory. In this context the term “economic theory” means precisely such neoclassical equation specifications. Aside from economics Romanticism has little relevance to the great accomplishments in the history of science, because its concept of the aim of science has severed it from the benefits of the examination of the history of science. The Romantic philosophy of social science is still resolutely practiced in immature sciences such as sociology, where mentalistic description prevails, where quantification and prediction are seldom attempted, and where implementation in social policy is seldom effective and often counterproductive. -
Program Schedule
PROGRAM SCHEDULE FRIDAY, MAY 28, 2010 12:15 – 1:45 pm A01 A REPORT OF THE FIRST RSA CAREER RETREAT Co-Chairs: Cheryl Geisler, Simon Fraser University; Michael Halloran, Rensselaer Polytechnic Institute; Krista Ratcliffe, Marquette University Participants: Jen Bacon, West Chester University; Suzanne Bordelon, San Diego State University; Elizabeth Britt, Northeastern University; Leah Ceccarelli, University of Washington; Martha Cheng, Rollins College; Janice Chernekoff, Kutztown University; Arabella Lyon, SUNY-Buffalo; Carole Clark Papper, Hofstra University; Barbara Schneider, University of Toledo; Christine Tully, University of Wisconsin, Madison; Liz Wright, Rivier College A02 BARACK OBAMA: THE RHETORIC OF A POSTMODERN PRESIDENT Chair: Connie Johnson, University of Texas at Austin A Tale of Two Cities: Obama at Howard and at South New Hampshire Sheena Howard, Howard University Cultural Memory and Productive Forgetting in Obama’s A More Perfect Union Speech G. Mitchell Reyes, Lewis and Clark College A More Perfect Union: A Critical Analysis of Obama’s "Race" Speech Matt Morris, University of Texas at Austin Barack Obama’s Heroic Whiteness: Invoking the Founding Fathers to Win the Presidency Connie Johnson, University of Texas at Austin Respondent : Barry Brummett, University of Texas at Austin A03 RHETORIC OF SCIENCE Chair: Virginia Anderson, Indiana University Southeast Raising Moral Questions through Science: Silent Spring’s Use of Narrative, Time, and Space Jessica Prody, University of Minnesota Evolving Concordance: An Aristotelian -
Foundations of Scientific Research
2012 FOUNDATIONS OF SCIENTIFIC RESEARCH N. M. Glazunov National Aviation University 25.11.2012 CONTENTS Preface………………………………………………….…………………….….…3 Introduction……………………………………………….…..........................……4 1. General notions about scientific research (SR)……………….……….....……..6 1.1. Scientific method……………………………….………..……..……9 1.2. Basic research…………………………………………...……….…10 1.3. Information supply of scientific research……………..….………..12 2. Ontologies and upper ontologies……………………………….…..…….…….16 2.1. Concepts of Foundations of Research Activities 2.2. Ontology components 2.3. Ontology for the visualization of a lecture 3. Ontologies of object domains………………………………..………………..19 3.1. Elements of the ontology of spaces and symmetries 3.1.1. Concepts of electrodynamics and classical gauge theory 4. Examples of Research Activity………………….……………………………….21 4.1. Scientific activity in arithmetics, informatics and discrete mathematics 4.2. Algebra of logic and functions of the algebra of logic 4.3. Function of the algebra of logic 5. Some Notions of the Theory of Finite and Discrete Sets…………………………25 6. Algebraic Operations and Algebraic Structures……………………….………….26 7. Elements of the Theory of Graphs and Nets…………………………… 42 8. Scientific activity on the example “Information and its investigation”……….55 9. Scientific research in Artificial Intelligence……………………………………..59 10. Compilers and compilation…………………….......................................……69 11. Objective, Concepts and History of Computer security…….………………..93 12. Methodological and categorical apparatus of scientific research……………114 13. Methodology and methods of scientific research…………………………….116 13.1. Methods of theoretical level of research 13.1.1. Induction 13.1.2. Deduction 13.2. Methods of empirical level of research 14. Scientific idea and significance of scientific research………………………..119 15. Forms of scientific knowledge organization and principles of SR………….121 1 15.1. Forms of scientific knowledge 15.2. -
In Defence of Curiosity Driven Basic Scientific Research
FEATURE ARTICLE In Defence of Curiosity Driven Basic Scientific Research Felix Bast curiosity, wondered what causes UST take a look at products ray. Faraday’s and Thompson’s them to fluoresce, which ultimately that we take for granted – seminal discoveries opened up led to the development of the Green the mobile phone, computer, the whole vista for the subsequent J Fluorescent Protein (GFP). Most of internet, electricity, GPS, radar, radio, development of electronic gadgets. these products were the resultants of television, air conditioner, X-rays, Albert Einstein’s theory of decades-long collaborative research. aircraft, lasers, electric engines; even relativity is used in today’s GPS The promises of overnight success vaccines, antibiotic drugs, advanced devices. The internet and the World deliverables by applied utility-driven genome-editing tool CRISPR/CAS9 Wide Web was the result of a curiosity- research is nothing but mere myth and Velcro. The list is endless. A vast driven idea to support a network of like those purported panaceas of majority of the products including all particle physicists. Alexander Fleming pseudosciences. tech gadgets comprising the modern did not develop penicillin in some Science in its purest basic form consumerist lifestyle were developed target-oriented applied research; he is indeed driven by human being’s by the Western or Japanese scientists. serendipitously discovered a clear innate desire and curiosity to know halo around fungal contaminations in But without an application- the wonders of nature. This scientific discarded petri-dishes. oriented, agenda-driven research curiosity is akin to a child’s curiosity. those commercial blockbuster products Nature continues to pique A child might ask questions like would never have been developed, curiosity and inspire engineers to ‘why we yawn’, ‘why plants don’t right? The so-called pure or basic mimic it (the discipline is called get cancer’ and so on.