HT 2012, Period 1, II2202 Research Methodology and Scientific Writing

Announcements

Still under development - draft for 2012

II2202 Research Methodology and Scientific Writing (II2202 Forskningsmetodik och vetenskapligt skrivande 7,5 hp) is a 7.5 credits course. The course provides the theoretical insight and practical skills required to plan, implement, analyse and report a scientific experiment in the area of communication systems.

The main parts of the course are scientific methods of projects including research methodology, and ethics both theoretical and practical. It also includes scientific writing, reviewing, and presentation of texts.

Course Organisation

The course is given in period 1. It is divided into three parts, where lectures and labs provide support for handling:

• Research Methodology (40%), which is motivating, and preparing as well as performing an
• Evaluation (20%), giving insights in evaluations for scientific research, and a
• Scientific Report (40%), reporting the outcome of the evaluation.

These three parts are examined by a project proposal (or project plan), a method description and a report as well as a opposition report. The guidelines for the assignments can be fetched from Bilda. (Note that you can choose your prefered language when interacting with Bilda via the Personal->Preferances menu (Personlight->Inställningar). The choices are English and Swedish. Activate by clicking on Save (Spar) at the bottom of the page.)

Assignments

There are four different assignments during the course. Observe that the assignment must be handed in before deadline to be considered during the course. Please check the schedule for the deadlines!

Scientific report

The scientific report should have the layout of a scientific paper, a maximum of five A4 pages in length (atleast 1200 words). The paper should reflect the number of hours put into it. The report is assigned 2.5 credits, which means roughly 100 hours of work. Two people will work together on the paper and each of the parts of the report must indicate who is the responsible student. Therefore, make sure you explicitly indicate who has written which section of the paper.

The course is taught in English. All scheduled course activities are located in Kista.

Information is available on:

• Learning Outcomes
• Prerequisites
• Contents
• Schedule
• Lecture Plan and Lecture Material (OH slides)
• Literature and Course Material (Textbook, Reference books and other references)
• Examination Requirements and Registrations
• Staff Associated with the Course
• Registering for the Course
• Other on-line Course Material (More References)
• Announcements
• Previous versions of the course

Learning Outcomes

The aim of the course is to give the students the theoretical and practical skills to conduct, analyze and present in written an experimental task in the area of data commuincation and to give insight and understanding of research methodology.

Following this course a student should be able to:

• explain and apply techniques for scientific writing and research methodology to prepare the writing of a scientific report.
• perform investigation using methods, explain and take position on the results as well as summarize related work
• apply the knowledge in scientific writing and research methodology and use the knowledge to write a scientific report.

Prerequisites

Good knowledge of English and basic knowledge of data communication.

Contents

The course is divided into three parts where the parts are integrated in a final project. The three parts are:

• report writing
• research methodology
• experimental assignment

Topics

• To be added

Examination Requirements

• INLA - Assignment 1, 0.5 credits, grade scale: P, F
• INLB - Assignment 2, 1.5 credits, grade scale: P, F
• INLC - Report, 2.0 credits, grade scale: A, B, C, D, E, FX, F
• INLD - Opposition, 0.5 credits, grade scale: A, B, C, D, E, FX, F
• VSK1 - Scientific Writing, 3.0 credits, grade scale: A, B, C, D, E, FX, F

Requirements for final grade

The course is divided into three parts, and to receive a final grade "pass" all three parts must be approved:

• scientific writing (A-F): mandatory participation in lectures and submission of an approved written report
• research methodology (Pass/Fail): mandatory participation in lectures and approved home assignments
• experimental assignment (Pass/Fail): mandatory particpation

A higher grade than "pass" is determined by the quality of the written report. A higher grade requires high quality of the report's structure, research methodology, summary technique, handling of references, description of experiment, and analysis of experimental (measurement) data.

Code of Honor and Regulations

It is KTH policy that there is zero tolerance for cheating, plagiarism, etc. - for details see KTH startpage->Student->Student rights See also the KTH Ethics Policies

Staff Associated with the Course

• Dr. Anne Håkansson (lecturer, kursansvarig, föreläsare) (annehak@kth.se)
• Prof. Gerald Q. Maguire Jr. (maguire@kth.se)
• Prof. Mark T. Smith

Registering

Use the normal process for registering. For most students this means you should speak with your study advisor (studievägledare).

Literature

Main Text-Book

The course will mainly be based on the books:

• Peter Bock, Getting It Right: R&D Methods for Science and Engineering, Academic Press; 1 edition (September 13, 2001), 406 pages, ISBN-10: 0121088529, ISBN-13: 978-0121088521
• Justin Zobel, Writing for Computer Science, Springer; 2nd edition (April 27, 2004), paperback: 280 pages, ISBN-10: 1852338024, ISBN-13: 978-1852338022; web page for the book
• Wayne C. Booth, Gregory G. Colomb, and Joseph M. Williams, The Craft of Research, 2nd edition (Chicago Guides to Writing, Editing, and Publishing), University Of Chicago Press; 1 edition (March 2003), paperback: 336 pages, ISBN-10: 0226065685, ISBN-13: 978-0226065687

Additional Reference Books

• Angelika H. Hofmann, Scientific Writing and Communication: Papers, Proposals, and Presentations, Oxford University Press, USA (December 16, 2009), Paperback: 704 pages, ISBN-10: 0195390059, ISBN-13: 978-0195390056

The books are to be available via the KTH student bookstore - 5th floor of the Forum building. There will also be copies of all the textbooks on reserve in the library.

Lecture notes will be available on-line in PDF format. See the notes associated with each of the course topics.

Supplementary readings

• To be added

Lecture Plan and Lecture Material (OH slides)

Schedule

The schedule for the course is shown below (Note that in the following "xx" means "xx:00", not "xx:15".):

Note: The scheduled is subject to changes in times, dates, topics, and teachers.

Other on-line Course related Material

Books, articles, web pages, and other information

• Tom Tullis and Bill Albert, Measuring the User Experience: Collecting, Analyzing, and Presenting Usability Metrics, Morgan-Kaufmann, 2008, ISBN 978-0-12-373558-4
• Dennis Meredith, Explaining Research: How to Reach Key Audiences to Advance Your Work, Oxford University Press, 2010, ISBN 13: 978-0-19-973205-0, ISBN 10: 0-19-973205-1, paperback, 376 pages; book website and author's blog
• Nancy Baron, Escape from the Ivory Tower: A Guide to Making Your Science Matter, Island Press, 2010, 240 pages, ISBN 13: 978-1-59726-664-2; ISBN 10: 1-59726-664-7; book website
• Marion Ben-Jacob, Computer Ethics: Integrating Across the Curriculum, Jones and Bartlett Publishers, 2010, ISBN 9780763778095 (audio book/CD-ROM)
• Ken Kelley, Keke Lai, and Po-Ju Wu, Using R for Data Analysis: A Best Practice for Research, Chapter 34 in Jason W. Osborne (Editor), Best Advanced Practices in Quantitative Methods, Sage Publishing, Thousand Oaks, CA, USA, 2008, pages 535-572 - a very nice introduction to how to use R for data analysis with lots of examples (including loading data from a Excel spreadsheet using ODBC); the emphasis in this chapter is on the "Methods for the Behavioral, Educational, and Social Sciences" (MBESS) R package; there is web page for the whole book
• Joseph L. Schafer, Simple Linear Regression, Part I (Linear regression in R using lsfit.), Lecture notes for Stat 511, Lecture 10, Department of Statistics and The Methodology Center, The Pennsylvania State University, 17 February 2009 (note: This course uses the textbook: Michael Kutner, Christopher Nachtsheim, John Neter, and William Li, Applied Linear Statistical Models,McGraw-Hill/Irwin; 5th edition, 2004, 1396 pages ISBN-10: 007310874X and ISBN-13: 978-0073108742)
• Sharon L. Lohr, Sampling: Design and Analysis, second edition, Brooks/Cole, Cengage Learning, 2010, 608 Pages, ISBN-10: 0-495-10527-9, ISBN-13: 9780495105275; Table of contents for first edition
• Nicholas J. Horton and Ken Kleinman, SAS and R: Examples of tasks replicated in SAS and R, blog, for example their posting on their book: Nicholas J. Horton and Ken Kleinman, Using R for Data Management, Statistical Analysis, and Graphics, CRC Press, 28 July 2010, 297 pages, ISBN-10: 1439827559 and ISBN-13: 978-1439827550
• Phil Spector, Data Manipulation with R (Use R), Springerm 19 March 2008, 154 pages, ISBN-10: 0387747303 and ISBN-13: 978-0387747309
• Paul Webb, R and Quantitative Data Analysis, social research UPDATE, Department of Sociology, University of Surrey, Guildford, United Kingdom, ISSN: 1360-7898, Issue 55, Spring 2009
• Juan Miguel Marín Diazaraque of the Department of Statistics, Universidad Carlos III of Madrid has written a Tutorial for SAMPLING in R
• Michael E. Driscoll, How Google and Facebook are using R, Dataspora Blog: Big Data, open source analytics, and data visualization February 19, 2009 - panel discussion with four R users from industry: Bo Cowgill (Google), Itamar Rosenn (Facebook), David Smith (Revolution Computing), and Jim Porzak (The Generations Network and Co-Chair of Bay Area R Users Group)
• Making pie charts in R this is part of Robert I. Kabacoff's Quick-R website. For some of the arguments against using pie charts and some ways to overcome some of these objections, see "Pie Charts in ggplot2", July 29, 2010.
• Methodology tutorial - quantitative data analysis, web page, EduTech Wiki, Last modified: 4 August 2009
• Q-Q Plots, webpage, Murdoch University, School of Chemical and Mathematical Sciences, HTML last modified 22 July 2009 - give useful insights into how to interpret a Quantile-Quantile plot
• Probability plots webpage, Quantitative Decisions, page was created 24 January and last updated 18 February 2003.
• America Physical Society (APS) has a collection of material about Ethics for both faculty and students.
• (U.S.) National Research Council (NRC) document "A framework for K-12 Science Education", July 2011. The chair of this committee, Helen Quinn, in "The Back Page: A Framework for K-12 Science Education", APS News, 8 November 2011, page 8:
  "The eight pratices described in the framework are intended to better define what scientific inquiry and engineering design look like, and to ensure that students are asked to engage in the process. These are: 1. Asking questions (science) and defining a problem (engineering); 2. Developing and using models; 3. Planning and carrying out investigations; 4. Analyzing and interpreting data; 5. Using mathematics and computational thinking; 6. Constructing explainations (science) and designing solutions (engineering); 7. Engaging in argument from evidence; and 8. Obtaining, evaluating and communication information.
  The view of scientific and engineering practice here does beyond doing a lab or a hands-on activity. It also moves away from a single definition of "scientific method". It includes multiple interpretive and discourse practices that tie the investigation of phenomena to the process of developing new understanding about them. Notably, six of the eight practices are common for engineering and science. The two practices where science and engineering differ relate to the primary goals of each discipline (Constructing explainations and designing solutions) and the beginning stage of approaching such a goal (asking questions and defining a problem). Scientists can plan an important role in helping teachers and teacher educators understand these practices and find ways to implement them at the appropriate level in science classrooms."
• Dennis Roberts, The Overrated Importance of Statistics in Research, Educational Psychology, Penn State University, University Park, PA USA, 12 February 2001 - the author states "The conclusion I draw from all these years of helping students and even other faculty design and implement and interpret their research is that it is RARELY the formal statistical analysis part that gets in the way of VALID conclusions being extracted from the data. To put it simply: it is usually an error of design or implementation that messes things up, not how we handle the data. [suggestion: underline the phrase after the colon for emphasis. I think this is the key message] When you look at bad research, it typically IS bad because it failed to sample appropriately, or assign Subjects to groups appropriately, or failed to control the implementation of the treatments, or failed to use good criterion measures or finally, made claims in the discussion section that simply cannot be supported by the DATA THAT WERE COLLECTED. Thus, in my mind, screwing up the statistical part is rarely the culprit in research that is done poorly. Sometimes major goofs do happen at the analysis stage but, they pale in comparison to the other sources of potential miscues."
• How to Lie with Statistics by Darrell Huff (Author) and Irving Geis (Illustrator) was first published in 1954. It has been through many editions and was reissued by W. W. Norton & Company in 1993 with ISBN-10: 0393310728 and ISBN-13: 978-0393310726.
• A very interting article about Huff's book and its popularity is given in J. Michael. Steele, "Darrell Huff and Fifty Years of How to Lie with Statistics", Statistical Science, 20 (3), 2005, DOI: 10.1214/088342305000000205, pp. 205-209. The points that Steele raises about why the book has been so popular are (1) the title, (2) the illustrations and illustrator, (3) the style of writing, and (4) the content. Considering these points will help your writing have a greater effect, than it might otherwise.
• Jari Oksanen, Cluster Analysis: Tutorial with R, University of Oulu, Department of Biology, Oulu, Finland, Available at http://cc.oulu.fi/~jarioksa/opetus/metodi/sessio3.pdf, February 2012.
  Some additional sources regarding clustering:
  • Frank Höppner, Fuzzy Clustering
  • The R statistical program has a package for fuzzy clustering: fanny {cluster}
  • Jari Oksanen, Cluster Analysis: Tutorial with R, University of Oulu. Department of Biology, February 2, 2012
  • Yanchang Zhao, Examples on Clustering with R, August 25, 2011
  • Jan Jantzen, Tutorial On Fuzzy Clustering, Technical University of Denmark, 14-May-2005
  • Some reasons to use fuzzy clustering are given in B. Jayaram and F. Klawonn, Can Fuzzy Clustering Avoid Local Minima and Undesired Partitions, in Computational Intelligence in Intelligent Data Analysis, vol. 445, C. Moewes and A. Nürnberger, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013, pp. 31-44.
  • There are many clustering algorithms, see for example: Friedrich Leisch and Bettina Gruen, CRAN Task View: Cluster Analysis & Finite Mixture Models, version 2012-08-30; Thomas Girke, R & Bioconductor Manual, UC Riverside, Institute for Integrative Genome Biology, 1 October 2012; Data Mining Algorithms In R: Clustering: Fuzzy Clustering - Fuzzy C-means, Wikibooks, last modified 6 February 2012; ...

Surveys and analysis of surveys related references

To read the literature on surveys and important term to understand is: primary sampling unit (PSU)

• Thomas S. Lumley, Complex Surveys: A Guide to Analysis Using R, Wiley, March 2010, 296 pages, ISBN-10: 0470284307 and ISBN-13: ISBN: 978-0-470-28430-8
• Thomas S. Lumley, R survey package, web page, last modified 13 August 2010.
• Summary of Survey Analysis Software, Section on Survey Research Methods, American Statistical Association, last modified 9 September 2008
• David Eagle, R Survey Analysis: Weighting Data: Inverse-Probability Weights and Inverse-Variance Weighting, web page, last modified 17 September 2010
• Household Sample Surveys in Developing and Transition Countries, United Nations, Department of Economic and Social Affairs, Economic and Social Development, Statistics Division, Report number ST/ESA/STAT/SER.F/96/WWW, March 2005. See for example James R. Chromy and Savitri Abeyasekera, Chapter 19: Statistical analysis of survey data, 30 pages.
• Glenn D. Israel, Analyzing Survey Data, Cooperative Extension Service, Institute of Food and Agricultural Sciences (IFAS), University of Florida, Gainesville, FL, USA. See other papers by this author.
• The United Kingdom's Economic and Social Research Council (ESRC) sponsored a Research Methods Programme (2002-2007), which in turn sponsored P|E|A|S (Practical Exemplars and Survey Analysis) - this site gives examples (with different types of surveys and using different techniques), theory, and descriptions of software that can be used for surveys. This work in continuing as part of the U.K.'s ESRC National Centre for Research Methods
• Steven G. Heeringa, Brady T. West, and Patricia A. Berglund, Applied Survey Data Analysis, Chapman & Hall/CRC Statistics in the Social and Behavioral Science, April 5, 2010, 487 pages, ISBN-10: 1420080660 and ISBN-13: 978-1420080667, web site Applied Survey Data Analysis
• Christian Boudreau of the Dept. of Statistics & Actuarial Science, University of Waterloo, gave a workshop on Analysis of Survey Data to the Milwaukee Chapter of the ASA (MILWASA) of the American Statistical Association (ASA) on September 26, 2008
• The Morgan Centre for the Study of Relationships and Personal Life of the University of Manchester, Manchester, U.K. as part of the ESRC National Centre for Research Methods has made available a webpage of Resources for teachers and supervisors
• CRAN Task View: Official Statistics & Survey Methodology
• Phillip S. Kott, A Model-Based Look at Linear Regression with Survey Data, The American Statistician, American Statistical Association, Vol. 45, No. 2 (May, 1991), pp. 107-112
• Stefan Loehnert, "About Statistical Analysis of Qualitative Survey Data", International Journal of Quality, Statistics, and Reliability, vol. 2010, Article ID 849043, 12 pages, 2010. doi:10.1155/2010/849043
• David A. Binder and Georgia R. Roberts, Statistical inference in survey data analysis: where does the sample design fit in?, lecture slides, for the conference: Statistics Canada Research Data Centre Network Conference 2003: Transitions in Employment, Income and Wellbeing, McMaster University, Hamilton, Ontario, Canada, 24 September 2003
• R. L. Chambers, C. J. Skinner (Editors), Analysis of Survey Data, John Wiley & Sons, Ltd., 2003 Print ISBN: 9780471899877, Online ISBN: 9780470867204, Published Online: 30 May 2003, DOI: 10.1002/0470867205
• Techniques for describing relationships among variables: correspondence analysis (CA), multiple correspondence analysis (MCA), and joint correspondence analysis (JCA):
  • Michael Greenacre, Correspondence analysis of raw data, Ecology, 91(4), Ecological Society of America, 2010, pp. 958-963, DOI: 10.1890/09-0239.1
  • Oleg Nenadić and Michael Greenacre, Correspondence analysis in R, with two- and three-dimensional graphics: The ca package. Journal of Statistical Software, 20 (3), May 2007.
  • Oleg Nenadić and Michael Greenacre, Computation of Multiple Correspondence Analysis, with Code in R, in Multiple Correspondence Analysis and Related Methods (eds. Michael J. Greenacre and Jörg Blasius), Chapmann & Hall / CRC, Boca Raton, London, New York, 2007, ISBN: 978-1-58488-628-0, pp. 523-551.
  • Michael Greenacre and Rafael Pardo, Subset correspondence analysis: visualizing relationships among a selected set of response categories from a questionnaire survey. Sociological Methods and Research, 35, 2006, pp. 193-218.
  • Michael Greenacre and Oleg Nenadić, R Package ‘ca’, Simple, Multiple and Joint Correspondence Analysis, Version 0.33, February 14, 2012
  • Michael Greenacre and John Aitchison, Biplots of Compositional Data, Journal of the Royal Statistical Society: Series C (Applied Statistics), 51 (4), October 2002, pp. 375-392, DOI: 10.1111/1467-9876.00275
  • Michael Greenacre, Multivariate Statistics‘ Project, Website, Fundación BBVA, 22 June 2011
  • Eric J. Beh, "Simple Correspondence Analysis: A Bibliographic Review", International Statistical Review, vol. 72, no. 2, pp. 257-284, 2004, DOI:10.1111/j.1751-5823.2004.tb00236.x.
• Profile Analysis via Multidimensional Scaling (PAMS) - a technique based on CA, some examples of its used in education:
  • Cody Ding, "Exploratory Longitudinal Profile Analysis via Multidimensional Scaling", Practical Assessment, Research & Evaluation, vol. 8, 2003, Available at http://pareonline.net/getvn.asp?v=8&n=12.
  • Herbert W. Marsh, Oliver Ludtke, Benjamin Nagengast, Ulrich Trautwein, Alexandre J. S. Morin, Adel S. Abduljabbar, and Olaf Koller, "Classroom Climate and Contextual Effects: Conceptual and Methodological Issues in the Evaluation of Group-Level Effects", Educational Psychologist, vol. 47, no. 2, pp. 106-124, 2012, DOI:10.1080/00461520.2012.670488.
  • Mark L. Davison, Se-Kang Kim, and Shuai Ding, "Profile Analysis via Multidimensional Scaling (PAMS): Exploring the Predominant Profile Patterns in Data", presented at the Annual Meeting of the American Educational Research Association, Seattle, WA, USA, 2001, p. 34, Available at http://www.eric.ed.gov/ERICWebPortal/contentdelivery/servlet/ERICServlet?accno=ED453270.
  • Shane T. Harvey, David Bimler, Ian M. Evans, John Kirkland, and Pia Pechtel, "Mapping the Classroom Emotional Environment", Teaching and Teacher Education: An International Journal of Research and Studies, vol. 28, no. 4, pp. 628-640, May 2012, DOI:10.1016/j.tate.2012.01.005.
  • Se-Kang Kim, Craig L. Frisby, and Mark L. Davison, "Estimating Cognitive Profiles Using Profile Analysis via Multidimensional Scaling (PAMS)", Multivariate Behavioral Research, vol. 39, no. 4, pp. 595-624, October 2004, DOI:10.1207/s15327906mbr3904_2.

On-line survey tools

• SnapSurveys.com
• SurveyGizmo.com (They have a special free student account.)
• SurveyMokey.com
• SurveyShare.com (lots of templates)
• Zoomerang.com
• web-online-surveys.com
• freeonlinesurveys.com
• www.kwiksurveys.com/

Some tips on writing a questionnaire

• David O'Brien, Questionnaire Design A report for the course CS 6751, Human-Computer Interface, College of Computing, Georgia Institute of Technology, Atlanta, GA, USA, Winter 1997
• Bob Kaden, Guidelines for Writing an Effective Questionnaire, CustomerThink Corporation, 20 November 2007
• Steve Gould, 1.05 How to write a questionnaire, Part of Study Guides: Writing, Learner Development Unit, Birmingham City University, Birmingham, UK, Last updated: 20 February 2007
• Office of Educational Assessment, Tips for Writing Questionnaire Items, Catalyst Tools Workshops, Learning & Scholarly Technologies, University of Washington, Seattle, WA, USA, Last Updated: October 2006
• How To Write A Good Survey, InfoPoll: Software for Surveys & Polls, Softlogic Inc. Dartmouth, NS, Canada, September 19, 1998
• Wai-Ching Leung, How to design a questionnaire, StudentBMJ, BMJ Publishing Group Limited, U.K., volume 9, May 2001, pages 143-145
• Statistical Computing Seminars Introduction to Survey Data Analysis, University of California at Los Angles (UCLA): Academic Technology Services, Statistical Consulting Group, accessed on 2010.09.28 - the focus is on Stata, SUDAAN, WesVar, and SAS - but includes some useful definitions and examples
• Hossein Arsham, Questionnaire Design and Surveys Sampling, 9th Edition, University of Baltimore Baltimore, Maryland, USA.
• Willem E. Saris and Irmtraud N. Gallhofer, Design, Evaluation, and Analysis of Questionnaires for Survey Research, Wiley Series in Survey Methodology, Wiley-Interscience, 3 August 2007, 392 pages, ISBN-10: 0470114959 and ISBN-13: 978-0470114957.
• Katja Lozar Manfreda, Zenel Batagelj, and Vasja Vehovar, "Design of Web Survey Questionnaires: Three Basic Experiments", Journal of Computer-Mediated Communication (JCMC), 7(3), April 2002.
• Jezz Fox, Craig Murray, and Anna Warm, "Conducting research using web-based questionnaires: practical, methodological, and ethical considerations", Internation Journal on Social Research Metholdology, ISSN 1364-5579 print, ISSN 1464-5300 online, Taylor & Francis Ltd., 2003, Volume 6, number 2, pages 167-180, DOI: 10.1080/1364557021042883
• Stephen J. Sills and Chunyan Song, Innovations in Survey Research: An Application of Web-Based Surveys", Social Science Computer Review. SAGE Publications (UK and US), eISSN: 1552-8286, ISSN: 0894-4393, 2002, Volume 20, number 1, pages 22-30

Principal Components Analysis

• Steven M. Ho!and, Principal Components Analysis (PCA), Tutorial, May 2008

Sample size

• Martin Schmettow, Sample size in usability studies, Communications of the ACM, vol. 55, no. 4, p. 64-70, April 2012, DOI:10.1145/2133806.2133824 - This article is interesting because it suggests that for usability studies the number of test subjects is large than usually assumed.

Computer ethics

• Deborah G. Johnson, Computer ethics (2nd ed.), 1994, Prentice-Hall, Inc., Upper Saddle River, NJ, USA, ISBN 0-13-290339-3, 978-0-13-290339-4

Some useful tools

• Zotero extention to FireFox for helping you manage references, Center for History and New Media, George Mason University, Fairfax, Virginia, USA
• Zotero style IEEElike-with-access used in the example in lecture 6.
• BibTeX web page, last accessed 19 September 2010 - note that there is a BibTeX mode in emacs
• Bevan S. Weir, Step-by-step guide to using EndNote with LaTeX and BibTeX, web page, Last modified 25 March 2010
• James, How to use JabRef (BibTeX) with Microsoft Word 2003, web page, medicalnerds.com, "technology, stats and IT for medics", 25 March 2007
• JabRef - an open source reference manager written in Java
• ispell - use with the "-t" option for LaTeX files
• OpenOffice.org - open source word processing, with grammer checking via the LanguageTool extention or After the Deadline
• Using dynamic abbreviations (dabbrev) in Emacs - can greatly increase your typing speed
• Two GNU programs style and diction. For more information see Michael Stutz, " Explore powerful UNIX writer's tools: Using new, open source equivalents of the classic UNIX Writer's Workbench", a tutorial from IBM's developerWorks web site, 22 May 2007
• gnuplot - basic plotting in 2D and 3D with lots of output formats
• See also Micah Altman, The Impoverished Social Scientist's Guide to Free Statistical Software and Resources, web page, Last Updated: December 18, 2008
• Converting a PCAP file to LaTeX can be done with pcap2tex

Some sample data files - with .PCAP files from Wireshark

• Wireshark SampleCaptures has quite a variety of captured traffic
• the LBL Enterprise traces offer quite a bit of interesting data, such as lbl-internal.20050106-1827.port006.dump.anon a 159 MB with 3.9 million packets (in ~3600 seconds); of these there are more than 300K TCP packet headers
• See the LBL Enterprise trace README file for the difficulting of anonymizing traffic - in the paper: Ruoming Pang, Mark Allman, Vern Paxson, Jason Lee. The Devil and Packet Trace Anonymization. ACM Computer Communication Review, 36(1), January 2006.

Example reports

• Jeffrey P. Koplow, A Fundamentally New Approach to Air-cooled Heat Exchangers, Sandia National Laboratories, SANDIA REPORT, SAND2010-0258, January 2010

The Centre for Academic Writing (Språkverkstaden)

In the conjunction with your subsequent studies at KTH (i.e. after completing the course II2202) - for individual help in specific writing problems or oral presentation problems - see the services offered by The Centre for Academic Writing (Språkverkstaden).

Previous versions of the course

• 2011
• 2010
• 2009

Page History

© Copyright 2010, 2011, 2012 Anne Håkansson and G. Q. Maguire Jr. (maguire@kth.se)
All Rights Reserved.
Mon Nov 5 10:42:57 CET 2012