Experimental Philosophy of Science and Philosophical Differences Across the Sciences
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Science of Team Science and Collaborative Research
Stephen M. Fiore, Ph.D. University of Central Florida Cognitive Sciences, Department of Philosophy and Institute for Simulation & Training Fiore, S. M. (2015). The Science of Team Science and Collaborative Research. Invited Colloquium, University of Cincinnati, Office of Research Advanced Seminar Series. October 19th, Cincinnati, OH. This work by Stephen M. Fiore, PhD is licensed under a Creative Commons Attribution-NonCommercial- NoDerivs 3.0 Unported License 2012. Not for commercial use. Approved for redistribution. Attribution required. ¡ Part 1. Laying Founda1on for a Science of Team Science ¡ Part 2. Developing the Science of Team Science ¡ Part 3. Applying Team Theory to Scienfic Collaboraon § 3.1. Of Teams and Tasks § 3.2. Leading Science Teams § 3.3. Educang and Training Science Teams § 3.4. Interpersonal Skills in Science Teams ¡ Part 4. Resources on the Science of Team Science ISSUE - Dealing with Scholarly Structure ¡ Disciplines are distinguished partly for historical reasons and reasons of administrative convenience (such as the organization of teaching and of appointments)... But all this classification and distinction is a comparatively unimportant and superficial affair. We are not students of some subject matter but students of problems. And problems may cut across the borders of any subject matter or discipline (Popper, 1963). ISSUE - Dealing with University Structure ¡ What is critical to realize is that “the way in which our universities have divided up the sciences does not reflect the way in which nature has divided up its problems” (Salzinger, 2003, p. 3) To achieve success in scientific collaboration we must surmount these challenges. Popper, K. (1963). Conjectures and Refutations: The Growth of Scientific Knowledge. -
A Comprehensive Framework to Reinforce Evidence Synthesis Features in Cloud-Based Systematic Review Tools
applied sciences Article A Comprehensive Framework to Reinforce Evidence Synthesis Features in Cloud-Based Systematic Review Tools Tatiana Person 1,* , Iván Ruiz-Rube 1 , José Miguel Mota 1 , Manuel Jesús Cobo 1 , Alexey Tselykh 2 and Juan Manuel Dodero 1 1 Department of Informatics Engineering, University of Cadiz, 11519 Puerto Real, Spain; [email protected] (I.R.-R.); [email protected] (J.M.M.); [email protected] (M.J.C.); [email protected] (J.M.D.) 2 Department of Information and Analytical Security Systems, Institute of Computer Technologies and Information Security, Southern Federal University, 347922 Taganrog, Russia; [email protected] * Correspondence: [email protected] Abstract: Systematic reviews are powerful methods used to determine the state-of-the-art in a given field from existing studies and literature. They are critical but time-consuming in research and decision making for various disciplines. When conducting a review, a large volume of data is usually generated from relevant studies. Computer-based tools are often used to manage such data and to support the systematic review process. This paper describes a comprehensive analysis to gather the required features of a systematic review tool, in order to support the complete evidence synthesis process. We propose a framework, elaborated by consulting experts in different knowledge areas, to evaluate significant features and thus reinforce existing tool capabilities. The framework will be used to enhance the currently available functionality of CloudSERA, a cloud-based systematic review Citation: Person, T.; Ruiz-Rube, I.; Mota, J.M.; Cobo, M.J.; Tselykh, A.; tool focused on Computer Science, to implement evidence-based systematic review processes in Dodero, J.M. -
Peer Review of Team Science Research
Peer Review of Team Science Research J. Britt Holbrook School of Public Policy Georgia Institute of Technology 1. Overview This paper explores how peer review mechanisms and processes currently affect team science and how they could be designed to offer better support for team science. This immediately raises the question of how to define teams.1 While recognizing that this question remains open, this paper addresses the issue of the peer review of team science research in terms of the peer review of interdisciplinary and transdisciplinary research. Although the paper touches on other uses of peer review, for instance, in promotion and tenure decisions and in program evaluation, the main 1 Some researchers who assert the increasing dominance of teams in knowledge production may have a minimalist definition of what constitutes a team. For instance, Wuchty, Jones, and Uzzi (2007) define a team as more than a single author on a journal article; Jones (2011) suggests that a team is constituted by more than one inventor listed on a patent. Such minimalist definitions of teams need not entail any connection with notions of interdisciplinarity, since the multiple authors or inventors that come from the same discipline would still constitute a team. Some scholars working on team collaboration suggest that factors other than differences in disciplinary background – factors such as the size of the team or physical distance between collaborators – are much more important determinants of collaboration success (Walsh and Maloney 2007). Others working on the science of teams argue that all interdisciplinary research is team research, regardless of whether all teams are interdisciplinary; research on the science of teams can therefore inform research on interdisciplinarity, as well as research on the science of team science (Fiore 2008). -
PDF Download Starting with Science Strategies for Introducing Young Children to Inquiry 1St Edition Ebook
STARTING WITH SCIENCE STRATEGIES FOR INTRODUCING YOUNG CHILDREN TO INQUIRY 1ST EDITION PDF, EPUB, EBOOK Marcia Talhelm Edson | 9781571108074 | | | | | Starting with Science Strategies for Introducing Young Children to Inquiry 1st edition PDF Book The presentation of the material is as good as the material utilizing star trek analogies, ancient wisdom and literature and so much more. Using Multivariate Statistics. Michael Gramling examines the impact of policy on practice in early childhood education. Part of a series on. Schauble and colleagues , for example, found that fifth grade students designed better experiments after instruction about the purpose of experimentation. For example, some suggest that learning about NoS enables children to understand the tentative and developmental NoS and science as a human activity, which makes science more interesting for children to learn Abd-El-Khalick a ; Driver et al. Research on teaching and learning of nature of science. The authors begin with theory in a cultural context as a foundation. What makes professional development effective? Frequently, the term NoS is utilised when considering matters about science. This book is a documentary account of a young intern who worked in the Reggio system in Italy and how she brought this pedagogy home to her school in St. Taking Science to School answers such questions as:. The content of the inquiries in science in the professional development programme was based on the different strands of the primary science curriculum, namely Living Things, Energy and Forces, Materials and Environmental Awareness and Care DES Exit interview. Begin to address the necessity of understanding other usually peer positions before they can discuss or comment on those positions. -
Survey Experiments
IU Workshop in Methods – 2019 Survey Experiments Testing Causality in Diverse Samples Trenton D. Mize Department of Sociology & Advanced Methodologies (AMAP) Purdue University Survey Experiments Page 1 Survey Experiments Page 2 Contents INTRODUCTION ............................................................................................................................................................................ 8 Overview .............................................................................................................................................................................. 8 What is a survey experiment? .................................................................................................................................... 9 What is an experiment?.............................................................................................................................................. 10 Independent and dependent variables ................................................................................................................. 11 Experimental Conditions ............................................................................................................................................. 12 WHY CONDUCT A SURVEY EXPERIMENT? ........................................................................................................................... 13 Internal, external, and construct validity .......................................................................................................... -
Abstracts 2018
SciTS 2018 ABSTRACTS THEMATIC PAPER SESSIONS/PANELS POSTER SESSIONS Table of Contents ABSTRACTS THEMATIC PAPER SESSIONS/PANELS May 22, 2018 Tuesday Morning Session (11:00 - 12:00) | p. 3 Tuesday Afternoon Session 1 (1:30 - 2:50) | p. 11 Tuesday Afternoon Session 2 (3:15 - 4:35) | p. 19 Tuesday Afternoon Session 3 (5:00 - 6:00) | p. 31 May 23, 2018 Wednesday Afternoon Session 1 (1:30 - 2:50) | p. 41 Wednesday Afternoon Session 2 (3:30 - 4:50) | p. 54 POSTER SESSION Monday, May 21, 2018 (6:30 - 7:30) | p. 63 TUESDAY, MAY 22 – MORNING SESSION (11:00 – 12:00) FUTURE DIRECTIONS Paper: Applying Artificial Intelligence, Neural Networks, and Machine Learning to SciTS 2017 saw prominent technology companies, including Google, Microsoft, and IBM, make tools and technologies based on artificial intelligence, neural networks, and machine learning publicly available. These tools have included conversational agents (CAs) and chatbots, as well as tools for text and video-based content and emotion analysis. These tools will have a variety of implications for the way that we conduct and study team science, including, but not limited to, the technological readiness of teams, the ways that teams and team scientists conduct research, and the composition of teams. This talk will present an accessible introduction to and overview of artificial intelligence (AI), neural networks, and machine learning technologies that can (and could in the future) be applied to the science of team science. As CAs are one of the most popular types of these technologies, I will explore technologies including various tools Author: for conversational assistant/chatbot technologies from IBM Watson and Stephanie Vasko Dialogflow (formerly API.AI), along with applications of these technologies (Michigan State to team science and community engagement (Vasko, 2017 presentation). -
Lab Experiments Are a Major Source of Knowledge in the Social Sciences
IZA DP No. 4540 Lab Experiments Are a Major Source of Knowledge in the Social Sciences Armin Falk James J. Heckman October 2009 DISCUSSION PAPER SERIES Forschungsinstitut zur Zukunft der Arbeit Institute for the Study of Labor Lab Experiments Are a Major Source of Knowledge in the Social Sciences Armin Falk University of Bonn, CEPR, CESifo and IZA James J. Heckman University of Chicago, University College Dublin, Yale University, American Bar Foundation and IZA Discussion Paper No. 4540 October 2009 IZA P.O. Box 7240 53072 Bonn Germany Phone: +49-228-3894-0 Fax: +49-228-3894-180 E-mail: [email protected] Any opinions expressed here are those of the author(s) and not those of IZA. Research published in this series may include views on policy, but the institute itself takes no institutional policy positions. The Institute for the Study of Labor (IZA) in Bonn is a local and virtual international research center and a place of communication between science, politics and business. IZA is an independent nonprofit organization supported by Deutsche Post Foundation. The center is associated with the University of Bonn and offers a stimulating research environment through its international network, workshops and conferences, data service, project support, research visits and doctoral program. IZA engages in (i) original and internationally competitive research in all fields of labor economics, (ii) development of policy concepts, and (iii) dissemination of research results and concepts to the interested public. IZA Discussion Papers often represent preliminary work and are circulated to encourage discussion. Citation of such a paper should account for its provisional character. -
Mapping a Research Agenda for the Science of Team Science
Research Evaluation, 20(2), June 2011, pages 145–158 DOI: 10.3152/095820211X12941371876580; http://www.ingentaconnect.com/content/beech/rev Mapping a research agenda for the science of team science Holly J Falk-Krzesinski, Noshir Contractor, Stephen M Fiore, Kara L Hall, Cathleen Kane, Joann Keyton, Julie Thompson Klein, Bonnie Spring, Daniel Stokols and William Trochim An increase in cross-disciplinary, collaborative team science initiatives over the last few decades has spurred interest by multiple stakeholder groups in empirical research on scientific teams, giving rise to an emergent field referred to as the science of team science (SciTS). This study employed a collaborative team science concept-mapping evaluation methodology to develop a comprehensive research agenda for the SciTS field. Its integrative mixed-methods approach combined group process with statistical analysis to derive a conceptual framework that identifies research areas of team science and their relative importance to the emerging SciTS field. The findings from this concept-mapping project constitute a lever for moving SciTS forward at theoretical, empirical, and translational levels. URING THE PAST DECADES, expanding way that their efforts are coordinated and integrated investments in team science have resulted in (Fiore, 2008; NAS, 2004). Although it is possible for Dgreater interest in research across scientific team science to be unidisciplinary, team science disciplines and knowledge domains to address com- most often connotes cross-disciplinarity (multi-, plex environmental, social, and health problems. inter-, and trans-disciplinarity), a composite term for These developments have been propelled by re- team science programs and projects that differ in the searchers’ increasing commitment and scientific degree to which they interact and integrate across capacity to address complex societal problems (Disis disciplinary, professional, and institutional bounda- and Slattery, 2010; Wuchty et al, 2007). -
Combining Epistemic Tools in Systems Biology Sara Green
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Philsci-Archive Final version to be published in Studies of History and Philosophy of Biological and Biomedical Sciences Published online: http://www.sciencedirect.com/science/article/pii/S1369848613000265 When one model is not enough: Combining epistemic tools in systems biology Sara Green Centre for Science Studies, Department of Physics and Astronomy, Ny Munkegade 120, Bld. 1520, Aarhus University, Denmark [email protected] Abstract In recent years, the philosophical focus of the modeling literature has shifted from descriptions of general properties of models to an interest in different model functions. It has been argued that the diversity of models and their correspondingly different epistemic goals are important for developing intelligible scientific theories (Levins, 2006; Leonelli, 2007). However, more knowledge is needed on how a combination of different epistemic means can generate and stabilize new entities in science. This paper will draw on Rheinberger’s practice-oriented account of knowledge production. The conceptual repertoire of Rheinberger’s historical epistemology offers important insights for an analysis of the modelling practice. I illustrate this with a case study on network modeling in systems biology where engineering approaches are applied to the study of biological systems. I shall argue that the use of multiple means of representations is an essential part of the dynamic of knowledge generation. It is because of – rather than in spite of – the diversity of constraints of different models that the interlocking use of different epistemic means creates a potential for knowledge production. -
The Science of Team Science: an Emerging Context for Research in Human Information Behavior and Knowledge Management
The Science of Team Science: An Emerging Context for Research in Human Information Behavior and Knowledge Management Patricia Katopol Assistant Professor School of Library and Information Science University of Iowa [email protected] 319.335.5714 Introduction This abstract describes a framework for examining the information behavior of teams engaged in scientific research and the use of this framework to develop knowledge management systems for the emerging discipline of the Science of Team Science (SciTS). Presented as a new area of study in 2006 (National Cancer Institute), the Science of Team Science promotes “team-based research through empirical examination of the processes by which scientific teams organize, communicate, and conduct research. The field is concerned with understanding and managing circumstances that facilitate or hinder the effectiveness of large-scale collaborative research, training, and translational initiatives.” (Northwestern University, Clinical and Translational Sciences Institute website, 2010). Translational initiatives bring information developed by researchers to practitioners (National Institutes of Health, 2009). For example, translational medical teams bring the information discovered in research to doctors who apply the information in clinical trials. The growing importance of team, rather than individual research is related to the need for expensive laboratories and equipment as well as the multiple research perspectives and skills required for any given project. The shift to team-based research also is evidenced by the increasing number of patents obtained by teams and team-authored peer-reviewed articles (Wuchty, Jones, Uzzi, 2007). Another push for collaborative work comes from the United States government’s preference for funding such work, so that researchers are strongly encouraged to form collaborations in order to obtain funding (Fiore, 2008). -
Experimental Philosophy and Feminist Epistemology: Conflicts and Complements
City University of New York (CUNY) CUNY Academic Works All Dissertations, Theses, and Capstone Projects Dissertations, Theses, and Capstone Projects 9-2018 Experimental Philosophy and Feminist Epistemology: Conflicts and Complements Amanda Huminski The Graduate Center, City University of New York How does access to this work benefit ou?y Let us know! More information about this work at: https://academicworks.cuny.edu/gc_etds/2826 Discover additional works at: https://academicworks.cuny.edu This work is made publicly available by the City University of New York (CUNY). Contact: [email protected] EXPERIMENTAL PHILOSOPHY AND FEMINIST EPISTEMOLOGY: CONFLICTS AND COMPLEMENTS by AMANDA HUMINSKI A dissertation submitted to the Graduate Faculty in Philosophy in partial fulfillment of the requirements for the degree of Doctor of Philosophy, The City University of New York 2018 © 2018 AMANDA HUMINSKI All Rights Reserved ii Experimental Philosophy and Feminist Epistemology: Conflicts and Complements By Amanda Huminski This manuscript has been read and accepted for the Graduate Faculty in Philosophy in satisfaction of the dissertation requirement for the degree of Doctor of Philosophy. _______________________________ ________________________________________________ Date Linda Martín Alcoff Chair of Examining Committee _______________________________ ________________________________________________ Date Nickolas Pappas Executive Officer Supervisory Committee: Jesse Prinz Miranda Fricker THE CITY UNIVERSITY OF NEW YORK iii ABSTRACT Experimental Philosophy and Feminist Epistemology: Conflicts and Complements by Amanda Huminski Advisor: Jesse Prinz The recent turn toward experimental philosophy, particularly in ethics and epistemology, might appear to be supported by feminist epistemology, insofar as experimental philosophy signifies a break from the tradition of primarily white, middle-class men attempting to draw universal claims from within the limits of their own experience and research. -
Enhancing Effectiveness of Team Science (PDF)
THE NATIONAL ACADEMIES PRESS This PDF is available at http://www.nap.edu/19007 SHARE Enhancing the Effectiveness of Team Science DETAILS 280 pages | 6 x 9 | PAPERBACK ISBN 978-0-309-31682-8 | DOI: 10.17226/19007 AUTHORS BUY THIS BOOK Nancy J. Cooke and Margaret L. Hilton, Editors; Committee on the Science of Team Science; Board on Behavioral, Cognitive, and Sensory Sciences; Division of Behavioral and Social Sciences and FIND RELATED TITLES Education; National Research Council Visit the National Academies Press at NAP.edu and login or register to get: – Access to free PDF downloads of thousands of scientific reports – 10% off the price of print titles – Email or social media notifications of new titles related to your interests – Special offers and discounts Distribution, posting, or copying of this PDF is strictly prohibited without written permission of the National Academies Press. (Request Permission) Unless otherwise indicated, all materials in this PDF are copyrighted by the National Academy of Sciences. Copyright © National Academy of Sciences. All rights reserved. Enhancing the Effectiveness of Team Science ENHANCING THE EFFECTIVENESS OF TEAM SCIENCE Nancy J. Cooke and Margaret L. Hilton, Editors Committee on the Science of Team Science Board on Behavioral, Cognitive, and Sensory Sciences Division of Behavioral and Social Sciences and Education Copyright © National Academy of Sciences. All rights reserved. Enhancing the Effectiveness of Team Science THE NATIONAL ACADEMIES PRESS 500 Fifth Street, NW Washington, DC 20001 NOTICE: The project that is the subject of this report was approved by the Governing Board of the National Research Council, whose members are drawn from the councils of the National Academy of Sciences, the National Academy of Engineering, and the Institute of Medicine.