D2.1 Redefining What Data Is and the Terms We Use to Speak of It
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6 the Pursuit of Laws
UNIT 112 WHAT IS SOCIAL RESEARCH? 6 THE PURSUIT OF LAWS Another area of dispute in the social sciences has been between advocates of what are often termed nomothetic and idiographic approaches: between those who believe that social research is primarily concerned with discovering universal laws and those who regard its task as the production of knowledge about particu- lar situations (We came across this distinction in Section 4 when discussing the views of Max Weber.) The nomothetic approach is sometimes taken to be essential to scientific research. The goal here is often laws of the kind characteristic of physics whlch state invariant relationships among variables, for example Boyle's law claims that the volume of a gas in an enclosed space is inversely proportional to the pressure on it, given constant temperature. What are proposed are causal or functional relationships that hold universally, in all places and times where the conditions speckied in the law apply The social science discipline where the influence of the nomothetic approach has been strongest is psychology As we noted earlier, the foundations of modern experimental psychology were laid in the late nineteenth century in Germany A number of researchers, many of them trained in the natural sciences, sought to develop a scientific approach to understanding human perception and cognition. In setting up a form of psychology modelled on the natural sciences they came into direct conflict with the (at that time) much more prestigious discipline of his- tory which also regarded itself as concerned with human psychology, albeit the psychology of unique individuals - of the great figures of history. -
AAAI/SSS 2008 Program - SSKI Detailed Program (Stanford University, History Building)
AAAI/SSS 2008 Program - SSKI Detailed Program (Stanford University, History Building). Register at Cummings Hall. Wednesday, March 26 8:00 AM – 9:00 AM: Registration 8:45 AM – 9:00 AM: Semantic Scientific Knowledge Integration: Welcome Deborah L. McGuinness, Peter Fox, Boyan Brodaric 9:00 AM – 10:40 AM: Session I – Integration Foundations (moderator: Peter Fox) 9:00 AM – 9:30 AM: (Oral 17) Grounding the Foundations of Ontology Mapping on the Neglected Interoperability Ambition Hamid Haidarian Shahri, James Hendler, Donald Perlis 9:30 AM – 10:00 AM: (Oral 6) Ontological bridge building – using ontologies to merge spatial datasets Catherine Dolbear, Glen Hart 10:00 AM – 10:05 AM: (Poster 4 Intro) Integrating declarative knowledge: Issues, Algorithms and Future Work Doo Soon Kim, Bruce Porter 10:05 AM – 10:10 AM: (Poster 9 Intro) Improving Semantic Integration By Learning Semantic Interpretation Rules Michael Glass, Bruce Porter 10:10 AM – 10:15 AM: (Poster 16 Intro) Integrating Relational Databases with Semantic Web Ontologies: Reasoning and Query Answering using Views Linhua Zhou, Yuxin Mao. Huajun Chen 10:15 AM – 10:40 AM: Panel on Integration Foundations 10:40 AM – 11:00 AM: Break 11:00 AM – 12:30 PM: Session II - Upper Ontologies (Moderator: Deborah McGuinness) 11:00 AM – 11:30 AM: (Oral 27) DOLCE ROCKS: Integrating Geoscience Ontologies with DOLCE Boyan Brodaric, Florian Probst 11:30 AM – 12:00 PM: (Oral 23) Towards an Ontology for Data-driven Discovery of New Materials Kwok Cheung, John Drennan, Jane Hunter 12:00 PM – 12:30 PM: Panel on Use of Upper Ontologies for Integration 12:30 PM – 2:00 PM: Lunch 2:00 PM – 3:30 PM: Session III – Workflows and Service Composition (Moderator: Boyan Brodaric) 2:00 PM – 2:30 PM: Keynote – Scientific Workflow Design for Mere Mortals Bertram Ludaescher 2:30 PM – 2:35 PM: (Poster 11 Intro) Process-based Planning for Earth Science Data Acquisition Robert Morris, Jennifer Dungan, Petr Votava, Lina Khatib 2:35 PM – 2:40 PM: (Poster 12 Intro) Machine Learning for Earth Observation Flight Planning Optimization Elif Kurklu, Robert A. -
The 8Th ACM Web Science Conference 2016
This is a repository copy of The 8th ACM Web Science Conference 2016. White Rose Research Online URL for this paper: http://eprints.whiterose.ac.uk/103102/ Version: Accepted Version Article: Jäschke, R. (2016) The 8th ACM Web Science Conference 2016. ACM SIGWEB Newsletter (Summer). pp. 1-7. ISSN 1931-1745 https://doi.org/10.1145/2956573.2956574 Reuse Unless indicated otherwise, fulltext items are protected by copyright with all rights reserved. The copyright exception in section 29 of the Copyright, Designs and Patents Act 1988 allows the making of a single copy solely for the purpose of non-commercial research or private study within the limits of fair dealing. The publisher or other rights-holder may allow further reproduction and re-use of this version - refer to the White Rose Research Online record for this item. Where records identify the publisher as the copyright holder, users can verify any specific terms of use on the publisher’s website. Takedown If you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing [email protected] including the URL of the record and the reason for the withdrawal request. [email protected] https://eprints.whiterose.ac.uk/ pdfauthor The 8th ACM Web Science Conference 2016 Robert Jaschke¨ L3S Research Center Hannover, Germany This article provides an overview of this year’s ACM Web Science Conference (WebSci’16). It was located in Hannover, Germany, and organized by L3S Research Center and the Web Science Trust. WebSci’16 attracted more than 160 researchers from very different disciplines – ranging from computer science to anthropology. -
Centennial Bibliography on the History of American Sociology
University of Nebraska - Lincoln DigitalCommons@University of Nebraska - Lincoln Sociology Department, Faculty Publications Sociology, Department of 2005 Centennial Bibliography On The iH story Of American Sociology Michael R. Hill [email protected] Follow this and additional works at: http://digitalcommons.unl.edu/sociologyfacpub Part of the Family, Life Course, and Society Commons, and the Social Psychology and Interaction Commons Hill, Michael R., "Centennial Bibliography On The iH story Of American Sociology" (2005). Sociology Department, Faculty Publications. 348. http://digitalcommons.unl.edu/sociologyfacpub/348 This Article is brought to you for free and open access by the Sociology, Department of at DigitalCommons@University of Nebraska - Lincoln. It has been accepted for inclusion in Sociology Department, Faculty Publications by an authorized administrator of DigitalCommons@University of Nebraska - Lincoln. Hill, Michael R., (Compiler). 2005. Centennial Bibliography of the History of American Sociology. Washington, DC: American Sociological Association. CENTENNIAL BIBLIOGRAPHY ON THE HISTORY OF AMERICAN SOCIOLOGY Compiled by MICHAEL R. HILL Editor, Sociological Origins In consultation with the Centennial Bibliography Committee of the American Sociological Association Section on the History of Sociology: Brian P. Conway, Michael R. Hill (co-chair), Susan Hoecker-Drysdale (ex-officio), Jack Nusan Porter (co-chair), Pamela A. Roby, Kathleen Slobin, and Roberta Spalter-Roth. © 2005 American Sociological Association Washington, DC TABLE OF CONTENTS Note: Each part is separately paginated, with the number of pages in each part as indicated below in square brackets. The total page count for the entire file is 224 pages. To navigate within the document, please use navigation arrows and the Bookmark feature provided by Adobe Acrobat Reader.® Users may search this document by utilizing the “Find” command (typically located under the “Edit” tab on the Adobe Acrobat toolbar). -
The Role of Virtual Observatories and Data Frameworks in an Era of Big Data
The Role of Virtual Observatories and Data Frameworks in an Era of Big Data NIST bIG dATA June 14, 2012, Gaithersburg, MD Peter Fox (RPI and WHOI) [email protected] , @taswegian, http://tw.rpi.edu/web/person/PeterFox Tetherless World Constellation http://tw.rpi.edu and AOP&E Virtual Observatories Working premise Scientists – actually ANYONE - should be able to access a global, distributed knowledge base of scientific data that: • appears to be integrated • appears to be locally available Data intensive – volume, complexity, mode, discipline, scale, heterogeneity2 .. Technical advances From: C. Borgman, 2008, NSF Cyberlearning Report Prior to 2005, we built systems • Rough definitions – Systems have very well-define entry and exit points. A user tends to know when they are using one. Options for extensions are limited and usually require engineering – Frameworks have many entry and use points. A user often does not know when they are using one. Extension points are part of the design – Modern platforms are built on frameworks Tetherless World Constellation 4 Early days of VxOs ? VO 2 VO3 VO1 DB DB2 DB3 n DB1 … … … … 5 The Astronomy approach; data-types as a service Limited interoperability VOTable VO App VO App2 3 VO App1 Simple Image Access Protocol Simple Spectrum VO layer Access Protocol Simple Lightweight semantics Time Limited meaning, DB Access DB2 DB3 hard coded n DB1 … … … … Protocol Limited extensibility 6 VO Standards • Creation – largely technical activity • Adoption – largely cultural activity Means of conduct of research* • Induction • Deduction Theory Theory Tentative hyp. Hypothesis Pattern Observation Confirmation Observation Fundamentally though We’ve built capabilities in VOs to support induction or deduction and sometimes both, but does this really enable the breadth of science discoveries we seek in the ERA of bIG dATA? Edges? In-betweens? Discipline mashups? Accidental? … For real discovery – we need abduction! - a method of logical inference introduced by C. -
The Rise and Domestication of Historical Sociology
The Rise and Domestication of" Historical Sociology Craig Calhoun Historical sociology is not really new, though it has enjoyed a certain vogue in the last twenty years. In fact, historical research and scholarship (including comparative history) was central to the work of many of the founders and forerunners of sociology-most notably Max Weber but also in varying degrees Karl Marx, Emile Durkheim, and Alexis de Tocqueville among others. It was practiced with distinction more recently by sociologists as disparate as George Homans, Robert Merton, Robert Bellah, Seymour Martin Lipset, Charles Tilly, J. A. Banks, Shmuel Eisenstadt, Reinhard Bendix, Barrington Moore, and Neil Smelser. Why then, should historical sociology have seemed both new and controversial in the 1970s and early 1980s? The answer lies less in the work of historical sociologists themselves than in the orthodoxies of mainstream, especially American, sociology of the time. Historical sociologists picked one battle for themselves: they mounted an attack on modernization theory, challenging its unilinear developmental ten- dencies, its problematic histori<:al generalizations and the dominance (at least in much of sociology) of culture and psycllology over political economy. In this attack, the new generation of historical sociologists challenged the most influential of their immediate forebears (and sometimes helped to create the illusion that historical sociology was the novel invention of the younger gener- ation). The other major battle was thrust upon historical sociologists when many leaders of the dominant quantitative, scientistic branch of the discipline dismissed their work as dangerously "idiographic," excessively political, and in any case somehow not quite 'real' sociology. Historical sociology has borne the marks of both battles, and in some sense, like an army always getting ready to fight the last war, it remains unnecessarily preoccupied with them. -
Data Life Cycle: Introduction, Definitions and Considerations
Data Life Cycle: Introduction, Definitions and Considerations EUDAT, Sept. 25, 2014 Prof. Peter Fox ([email protected], @taswegian, #twcrpi) Tetherless World Constellation Chair, Earth and Environmental Science/ Computer Science/ Cognitive Science/ IT and Web Science) Rensselaer Polytechnic Institute, Troy, NY USA Life Cycle 2 Definitions • Data (management) life-cycle broad elements - – Acquisition: Process of recording or generating a concrete artefact from the concept (see transduction) – Curation: The activity of managing the use of data from its point of creation to ensure it is available for discovery and re-use in the future – Preservation: Process of retaining usability of data in some source form for intended and unintended use • Stewardship: Process of maintaining integrity for acquisition, curation, preservation • BUT… 3 Definitions ctd. • (Data) Management: Process of arranging for discovery, access and use of data, information and all related elements. Also oversees or effects control of processes for acquisition, curation, preservation and stewardship. Involves fiscal and intellectual responsibility. 4 5 Digital Curation Centre 6 MIT DDI Alliance Life Cycle 7 Data-Information-Knowledge Ecosystem Producers Consumers Experience Data Information Knowledge Creation Presentation Integration Gathering Organization Conversation Context 8 Data Life Cycle embedded in Research Life Cycle • Information Life Cycle • Knowledge Life Cycle Type of knowledge created • Tacit (created and stored informally): – Human memory – Localize, e.g. -
The Earth System Grid: a Visualisation Solution
The Earth System Grid: A Visualisation Solution Gary Strand Introduction Acknowledgments •PI’s •ESG Development Team –Veronika Nefedova (ANL) –Ian Foster (ANL) –Ann Chervenak (ISI/USC) –Don Middleton (NCAR) –Carl Kesselman (ISI/USC) –Dean Williams (LLNL) –David Bernholdt (ORNL) –Kasidit Chanchio (ORNL) –Line Pouchard (ORNL) –Alex Sim (LBNL) –Arie Shoshani (LBNL) –Bob Drach (LLNL) –Dave Brown (NCAR) –Gary Strand (NCAR) –Jose Garcia (NCAR) –Luca Cinquini (NCAR) –Peter Fox (NCAR) Current Practices oScientist (or others) wants a visualisation oVisualisation person gets appropriate data after verifying with data manager as to the name, location, total size, etc. oData moved to “local” machine that has visualisation tools oVisualization created on “local” machine oHopefully, someone remembers to archive the visualisation Simple Vis Example Problems in the process oWhat if the data cannot be found (e.g. we have 1.2 million files, 73 TB of data), or the data manager is unavailable? oWhat if there isn’t enough disk space or sufficient other resources? oWhat if a better visualisation tool is located elsewhere? oWhat if the visualisation should be shared? oWhat if the visualisation is lost? o ESG is part of the answers to these questions What is ESG? ANL: Computational grids, LBNL: Climate storage & grid-based applications facility LLNL: Model diagnostics & inter-comparison USC/ISI: Computational grids, & grid-based applications NCAR: Climate change LANL: Next generation ORNL: Climate storage & predication and scenarios coupled models & computing -
Lecture Notes in Computer Science 6378 Commenced Publication in 1973 Founding and Former Series Editors: Gerhard Goos, Juris Hartmanis, and Jan Van Leeuwen
Lecture Notes in Computer Science 6378 Commenced Publication in 1973 Founding and Former Series Editors: Gerhard Goos, Juris Hartmanis, and Jan van Leeuwen Editorial Board David Hutchison Lancaster University, UK Takeo Kanade Carnegie Mellon University, Pittsburgh, PA, USA Josef Kittler University of Surrey, Guildford, UK Jon M. Kleinberg Cornell University, Ithaca, NY, USA Alfred Kobsa University of California, Irvine, CA, USA Friedemann Mattern ETH Zurich, Switzerland John C. Mitchell Stanford University, CA, USA Moni Naor Weizmann Institute of Science, Rehovot, Israel Oscar Nierstrasz University of Bern, Switzerland C. Pandu Rangan Indian Institute of Technology, Madras, India Bernhard Steffen TU Dortmund University, Germany Madhu Sudan Microsoft Research, Cambridge, MA, USA Demetri Terzopoulos University of California, Los Angeles, CA, USA Doug Tygar University of California, Berkeley, CA, USA Gerhard Weikum Max Planck Institute for Informatics, Saarbruecken, Germany Deborah L. McGuinness James R. Michaelis Luc Moreau (Eds.) Provenance and Annotation of Data and Processes Third International Provenance andAnnotation Workshop IPAW 2010, Troy, NY, USA, June 15-16, 2010 Revised Selected Papers 13 Volume Editors Deborah L. McGuinness Tetherless World Constellation Rensselaer Polytechnic Institute 110 8th Street, Troy, NY 12180, USA E-mail: [email protected] James R. Michaelis Tetherless World Constellation Rensselaer Polytechnic Institute 110 8th Street, Troy, NY 12180, USA E-mail: [email protected] Luc Moreau University of Southampton School of Electronics and Computer Science Southampton SO17 1BJ, United Kingdom E-mail: [email protected] Library of Congress Control Number: 2010940987 CR Subject Classification (1998): H.3-4, D.4.6, I.2, H.5, K.6, K.4, C.2 LNCS Sublibrary: SL 3 – Information Systems and Application, incl. -
A Neuroimaging Data Model to Share Brain Mapping Statistical Results
bioRxiv preprint doi: https://doi.org/10.1101/041798; this version posted June 14, 2016. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. NIDM-Results: a Neuroimaging Data Model to share brain mapping statistical results Camille Maumet1, Tibor Auer2, Alexander Bowring1, Gang Chen15, Samir Das3, Guillaume Flandin4, Satrajit Ghosh5, Tristan Glatard3,6, Krzysztof J. Gorgolewski7, Karl G. Helmer8, Mark Jenkinson9, David Keator10, B. Nolan Nichols11, Jean-Baptiste Poline12, Richard Reynolds16, Vanessa Sochat7, Jessica Turner13 and Thomas E. Nichols1,14 1 Warwick Manufacturing Group, University of Warwick, Coventry, United Kingdom. 2 MRC Cognition and Brain Sciences Unit, Cambridge, United Kingdom. 3 McGill Centre for Integrative Neuroscience, Ludmer Centre, Montreal Neurological Institute, Montreal, Quebec, Canada. 4 Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, London, United Kingdom. 5 McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA. 6 Université de Lyon, CREATIS; CNRS UMR5220; Inserm U1044; INSA-Lyon; Université Claude Bernard Lyon 1, France. 7 Department of Psychology, Stanford University, Stanford, CA, USA. 8 Martinos Center for Biomedical Imaging, Massachusetts General Hospital; Dept. of Radiology, Boston, MA, USA. 9 University of Oxford, UK. 10 Dept. of Psychiatry and Human Behavior, Dept. of Computer Science, Dept. of Neurology, University of California, Irvine, CA, USA. 11 Center for Health Sciences, SRI International, Menlo Park, CA, USA. 12 Helen Wills Neuroscience Institute, H. Wheeler Jr. Brain Imaging Center, University of California, Berkeley, CA, USA. -
An Overview of the Anthropological Theories
International Journal of Humanities and Social Science Vol. 4, No. 10(1); August 2014 An Overview of the Anthropological Theories Nurazzura Mohamad Diah Head Department of Sociology & Anthropology International Islamic University Malaysia Dewan Mahboob Hossain Associate Professor Department of Accounting & Information Systems University of Dhaka Bangladesh Sohela Mustari PhD Student Department of Sociology & Anthropology International Islamic University Malaysia Noor Syafika Ramli Master in Anthropology Universiti Kebangsaan Malaysia Abstract Theories are treated as the lifeblood of the disciplines like sociology and anthropology. As a newer discipline that has grown approximately over the last two hundred years, anthropology has proposed different important theories on man and culture. This article presents with an overview of the theories of anthropology developed over the last two hundred years. This can be considered as a general summarized reading of the important anthropological theories like evolutionism, diffusionism, historical particularism, functionalism, culture and personality, structuralism, neo-evolutionism, cultural ecology, cultural materialism, postmodernist and feminist explanations. This article concludes that though each of these theories was criticized by the subsequent theorists, all these theories contributed a lot in the development of anthropology as a discipline. Keywords: anthropology, theory, anthropological theory 1.0. Introduction In the academic arena, anthropology is considered as a relatively new discipline as its major development mainly happened in the nineteenth and the twentieth centuries. Though, in France and Germany, this discipline got a momentum in the seventeenth and the eighteenth centuries in different names (like ethnology, Volkskunde, Volkerkunde etc.), in English, the word ‘anthropology’ first appeared in the year of 1805 (McGee and Warms, 2012; 6). -
Web Approach for Ontology-Based Classification, Integration, and Interdisciplinary Usage of Geoscience Metadata
Data Science Journal, Volume 11, 18 October 2012 WEB APPROACH FOR ONTOLOGY-BASED CLASSIFICATION, INTEGRATION, AND INTERDISCIPLINARY USAGE OF GEOSCIENCE METADATA B Ritschel1*, V Mende1*, L Gericke2*, R Kornmesser2*, and S Pfeiffer3 1*Data Center, GFZ German Research Centre for Geosciences, Potsdam, German 14473 Potsdam, Germany 2*Hasso-Plattner-Institut für Softwaresystemtechnik, Potsdam, Germany 2-3, 14482 Potsdam, Germany 3Hochschule Neubrandenburg, Neubrandenburg, Germany, Postfach 11 01 21, 17041 Neubrandenburg, Germany ABSTRACT The Semantic Web is a W3C approach that integrates the different sources of semantics within documents and services using ontology-based techniques. The main objective of this approach in the geoscience domain is the improvement of understanding, integration, and usage of Earth and space science related web content in terms of data, information, and knowledge for machines and people. The modeling and representation of semantic attributes and relations within and among documents can be realized by human readable concept maps and machine readable OWL documents. The objectives for the usage of the Semantic Web approach in the GFZ data center ISDC project are the design of an extended classification of metadata documents for product types related to instruments, platforms, and projects as well as the integration of different types of metadata related to data product providers, users, and data centers. Sources of content and semantics for the description of Earth and space science product types and related classes are standardized metadata documents (e.g., DIF documents), publications, grey literature, and Web pages. Other sources are information provided by users, such as tagging data and social navigation information. The integration of controlled vocabularies as well as folksonomies plays an important role in the design of well formed ontologies.