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Comparing and Considering: Exhibit vs Palladio

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A brief overview presentation looking at Simile Exhibit and Palladio from CESTA as possible platforms to share academic research.

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Comparing and Considering: Exhibit vs Palladio

  1. 1. Palladio versus Exhibit Giving Digital Research Data Temporal, Spatial and Relational Dimensions
  2. 2. Objective(s) ‣ To introduce and compare two tools for presentation of scholarly research in temporal, spatial and relational visualisations; ‣ To engage in free and informal discussion about how these might be employed in your own research; ‣ Most of all: Inspire and Imagine.
  3. 3. Agenda ‣ Why Consider Either of These Tools? ‣ What is Exhibit? ‣ What is Palladio? ‣ The common denominators ‣ Strengths,Weaknesses & InterOperability ‣ Discussion
  4. 4. Why areYou Here?
  5. 5. Exhibit simile-widgets.org/exhibit/
  6. 6. Simile Exhibit ‣ Exhibit lets you easily create web pages with advanced text search and filtering functionalities, with interactive maps, timelines, and other visualisations; ‣ Beginners can deploy their dataset with minimal intervention and limited Javascript or HTML intervention; ‣ Experts can use their JSON, JS, HTML, CSS skills to create custom installations building on Exhibit platform; ‣ It can pull in media from any online source.
  7. 7. Exhibit ‣ What’s it based on? ‣ What does it do? ‣ How can I use it? ‣ What do I need to use it?
  8. 8. The SIMILE Stable of Tools http://simile-widgets.org
  9. 9. A Brief History ‣ This software was originally sponsored partially by The AndrewW. Mellon Foundation as part of the SIMILE project. ‣ Its original author is David François Huynh. ‣ Now it is being maintained and developed by members of an open-source community. ‣ Initially in the SIMILE (Semantic Interoperability of Metadata and Information in unLike Environments) Lab at MIT ‣ Google Summer of Code ‣ Library of Congress
  10. 10. Integrating Exhibit ‣ WordPress Plug-ins ‣ Deploy onYour Server ‣ Choose Between a Server-based and Browser-based Version
  11. 11. TwoVariants - How to Decide? ‣ Scripted ‣ Limited - Thousands of Records ‣ Requires access to two files no server ‣ How much data do you have? ‣ How much technical skill do you possess? ‣ Staged ‣ Scaleable - Millions of Records ‣ Requires Server & Config
  12. 12. Two Exhibit Tools ‣ DataPress (http://projects.csail.mit.edu/datapress/) ‣ DiDo (http://projects.csail.mit.edu/exhibit/Dido/) ‣ Caveat: Both are academic research projects. If you use, always best to acquire source code for your own sustainability.
  13. 13. Ingredients for Exhibit ‣ What DoYou need to Make the Magic Happen? ‣ A Text Editor - NotePad or TextWrangler ‣ AWeb Browser ‣ A Data Manipulation Tool - Excel, GoogleDocs? ‣ A Dataset ‣ An Open Mind ‣ A Few Hours ‣ Willingness to Play
  14. 14. Exhibit in a Nutshell Data json file Description html file Browsable/ Searchable/Visual Website 'the Exhibit'
  15. 15. Recipe Step 1 ‣ Prepare your data for use
  16. 16. Recipe Step 2 ‣ Edit HTML to Point to Datafile ‣ <html> ‣ <head> ‣ <title>MIT Nobel PrizeWinners</title> ‣ <link href="nobelists.js" type="application/json" rel="exhibit/data" /> ‣ <script src=http://static.simile.mit.edu/exhibit/api-2.0/exhibit-api.js type="text/javascript"></script> ‣ <style></style> ‣ </head> ‣ <body> ‣ <h1>MIT Nobel PrizeWinners</h1> ‣ <table width="100%”> ‣ <tr valign="top”> ‣ <td ex:role="viewPanel”><div ex:role="view"></div></td><td width="25%”>browsing controls here… </td></tr> ‣ </table> ‣ </body> ‣ </html>
  17. 17. Recipe Step 3 ‣ Stage and Inspect Result
  18. 18. Recipe Step 4 ‣ Add Embellishments
  19. 19. A Case Study / Caveat
  20. 20. What’s Cool? ‣ A broad community of Support; ‣ Mature code - well over a decade; ‣ Can Embed in your own web page; ‣ Can Embed in aWordPress Blog (Plug-In); ‣ Can choose from a variety ofVisualisations; ‣ Allows full interactivity and transparency to users; ‣ You can modify appearance as you gain familiarity; ‣ Data is stored on your own service in your own space; ‣ It’s OpenSource.
  21. 21. Why Exhibit ‣ Simple ‣ Javascipt - Approachable - Example Based ‣ Modular ‣ Standards Based ‣ Doesn’t Require Server Technology ‣ Browser Based ‣ Allows focus on content not on the technology
  22. 22. Palladio palladio.designhumanities.org Now for something entirely different
  23. 23. Palladio ‣ Palladio is a demonstration application to allow scholars to easily upload data for use with an intertwined set of visualizations for analysis of complex, multi-dimensional data. ‣ Beginners can deploy their dataset with minimal intervention and no code requirement; ‣ Experts can apply their deeper understanding of the dimensions of their data to construct elaborate visualisations for analysis and presentation.
  24. 24. Palladio ‣ What’s it based on? ‣ What does it do? ‣ How can I use it? ‣ What do I need to use it?
  25. 25. CESTA ‣ Center for Spatial and Textual Analysis ‣ Stanford
  26. 26. A Stable of Tools
  27. 27. At the Outset ‣ Palladio is an evolving tool
  28. 28. Ingredients ‣ A Data File; ‣ Your Browser.
  29. 29. Recipe Step 1 ‣ MassageYour Data
  30. 30. Recipe Step 2
  31. 31. Recipe Step 3
  32. 32. Recipe Step 4
  33. 33. Recipe Step 5
  34. 34. Recipe Step 6
  35. 35. Recipe Step 7
  36. 36. Recipe Step 8
  37. 37. Recipe Step 9 - The Most Important!!
  38. 38. A Case Study ‣ Maria Comsa, PhD candidate Stanford ‣ French society theater in the 18th century ‣ Data: the people, locations, performances, and texts from society theatre; ‣ Visualise, explore, and augment the data to answer research questions; ‣ Identify trends that would not be evident otherwise; ‣ eg. Study the social composition of the network of people to see how many were part of the elite (aristocracy). ‣ Full review: http://hdlab.stanford.edu/lab-notebook/palladio/ 2014/08/21/Comsa/
  39. 39. What’s Cool? ‣ Aesthetically stunning; ‣ Powerful tool and robust server; ‣ Evolving and growing; ‣ Combination of tools doesn’t exist elsewhere; ‣ Directed to an academic audience; ‣ Free to use; ‣ You can modify appearance as you gain familiarity; ‣ Data is stored on your own service in your own space; ‣ It’s OpenSource.
  40. 40. Why Palladio ‣ Wizard Driven; ‣ Powerful; ‣ Doesn’t Require Server Technology; ‣ Browser Based; ‣ Allows focus on content not on the technology!
  41. 41. Where Else to Look ‣ RAW - densitydesign
  42. 42. Summarising ‣ Flexibility ‣ Level of Development ‣ Cost ‣ Data Sharing ‣ Sustainability ‣ Ease of Use ‣ Extensibility ‣ Applicability
  43. 43. Upcoming Seminars ‣ 16 Feb - How to PutYour Data on the Map: Geospatial Data Manipulation andVisualisation for Digital Humanities ‣ 23 March - Using Google Tools for Digital Humanities Scholarship ‣ 20 April - Requirements Engineering for Humanities/ Social Science Scholars ‣ May - Digital Project Management
  44. 44. Thanks Shawn Day @iridium s.day@qub.ac.uk http://qubdh.co.uk

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