1. David De Roure WSRI Summer School RPI July 2009

2. 1.You will be able to answer the question “What is Web 2.0?” 2.You will have some ideas about how our co-constituted Web is co-evolving :-) On the way we will touch on Web of Services and on end-user programming Objectives

3. 1.Web 2.0 Design Patterns circa 2005 2.A case study: myexperiment.org 3.Reflection on the patterns Overview

4. What is Web 2.0?

5. Wikis Blogs User generated content Mashups Software that fosters communities User interaction and collaboration Adhocracy REST Collective intelligence Rich user interfaces Unspoken agreement on branding The read-write-web Marketing term User Generated Slide Content...

7. http://oreilly.com/web2/archive/what-is-web-20.html Web 2.0 Design patterns / memes

8. 1.The Long Tail Small sites make up the bulk of the internet's content; narrow niches make up the bulk of the internet's possible applications. Therefore: Leverage customer-self service and algorithmic data management to reach out to the entire web, to the edges and not just the center, to the long tail and not just the head.

10. 2.Data is the Next Intel Inside Applications are increasingly data-driven. Therefore: For competitive advantage, seek to own a unique, hard-to-recreate source of data.

11. 3.Users Add Value The key to competitive advantage in internet applications is the extent to which users add their own data to that which you provide. Therefore: Don't restrict your “architecture of participation” to software development. Involve your users both implicitly and explicitly in adding value to your application. “Second Life sells the land, the customers make it a reality”

12. 4.Network Effects by Default Only a small percentage of users will go to the trouble of adding value to your application. Therefore: Set inclusive defaults for aggregating user data as a side-effect of their use of the application.

13. 5.Some Rights Reserved Intellectual property protection limits re-use and prevents experimentation. Therefore: When benefits come from collective adoption, not private restriction, make sure that barriers to adoption are low. Follow existing standards, and use licenses with as few restrictions as possible. Design for "hackability" and "remixability."

14. 6.The Perpetual Beta When devices and programs are connected to the internet, applications are no longer software artifacts, they are ongoing services. Therefore: Don't package up new features into monolithic releases, but instead add them on a regular basis as part of the normal user experience. Engage your users as real-time testers, and instrument the service so that you know how people use the new features.

15. 7.Cooperate, Don't Control Web 2.0 applications are built of a network of cooperating data services. Therefore: Offer web services interfaces and content syndication, and re-use the data services of others. Support lightweight programming models that allow for loosely- coupled systems.

16. 8.Software Above the Level of a Single Device The PC is no longer the only access device for internet applications, and applications that are limited to a single device are less valuable than those that are connected. Therefore: Design your application from the get-go to integrate services across handheld devices, PCs, and internet servers.

17. A case study Overview The experiment that is What it is, how it’s used and how it’s built

18. The Web was invented by a physicist! The Web was co-constituted in a technology-rich environment with research users Researchers are often early adopters e.g. Internet, data on the Web Research collaborations vary in organisation, culture, governance, rights flow, reward structures, within and between communities Are researchers a good case study?

19. ?

20. His friends and colleagues Literature Images LogBook Software Presentations Data (files, spreadsheets) Compute resource Backup and Archive Thanks to Carole Goble Duncan’s Research Environment

21. “There are these great collaboration tools that 12-year-olds are using. It’s all back to front.” Robert Stevens

22. scientists Local Web Repositories Graduate Students Undergraduate Students Virtual Learning Environment Technical Reports Reprints Peer- Reviewed Journal & Conference Papers Preprints & Metadata Certified Experimental Results & Analyses experimentation Data, Metadata, Provenance, Scripts, Workflows, Services, Ontologies, Blogs,... Digital Libraries The social process of Science 1.0 2.0 Next Generation Researchers Thanks to Simon Coles

23. “A biologist would rather share their toothbrush than their gene name” Mike Ashburner and others Professor Genetics, University of Cambridge, UK “Data mining: my data’s mine and your data’s mine” Thanks to Carole Goble

24. Web 2 Open Repositories Researchers Social Network The experiment that is

25. mySpace for scientists!Facebook for scientists!Not Facebook for scientists!

26. “Facebook for Scientists”...but different to Facebook! A repository of research methods (an SGDL?) A community social network of people and things A Social Virtual Research Environment Open source (BSD) Ruby on Rails application with HTML, REST and SPARQL interfaces Project started March 2007 Closed beta July 2007 Open beta November 2007 myExperiment currently has 2200 registered users, 160 groups, 750 workflows, 220 files and 70 packs. Go to www.myexperiment.org to access publicly available content or create an account.www.myexperiment.org

27. http://usefulchem.wikispaces.com/page/code/EXPLAN001 http://www.microsoft.com/mscorp/tc/trident.mspx http://www.mygrid.org.uk/tools/taverna/ Sharing pieces of process

28. Workflows are the new rock and roll Machinery for coordinating the execution of (scientific) services and linking together (scientific) resources The era of Service Oriented Applications Repetitive and mundane boring stuff made easier E. Science laboris

29. Paul writes workflows for identifying biological pathways implicated in resistance to Trypanosomiasis in cattle Paul meets Jo. Jo is investigating Whipworm in mouse. Jo reuses one of Paul’s workflow without change. Jo identifies the biological pathways involved in sex dependence in the mouse model, believed to be involved in the ability of mice to expel the parasite. Previously a manual two year study by Jo had failed to do this. Reuse, Recycling, Repurposing

31. User Profiles Groups Friends Sharing Tags Workflows Developer interface Credits and Attributions Fine control over privacy Packs Federation Enactment myExperiment Features Distinctives

33. Control over sharing The most important aspect of myExperiment Designed by scientists The most important aspect of myExperiment Designed by scientists

34. Packs

35. ResultsLogs Results Metadata Paper Slides Workflow 16 Workflow 13 Common pathways QTL Paul’s Pack

38. Of the 661 workflows, 531 are publicly visible whereas 502 are publicly downloadable. 3% of the workflows with restricted access are entirely private to the contributor and for the remaining they elected to share with individual users and groups. 69 workflows (over 10%) have been shared, with the owner granting edit permissions to specific users and groups. In addition there are 52 instances where users have noted that a workflow is based on another workflow on the site. The most viewed workflow has 1566 views. There are 50 packs, ranging from tutorial examples to bundles of materials relating to specific experiments. C Scientists do share! Consumers > Curators > Producers

39. workflow results input Packs in Practice

41. 24/5/2007 | myExperiment | Slide 41 Co-operate, don’t control

42. Search Engine reviews ratings groups friendships tags Enactor files workflows ` HTML For Developers RDF Store SPARQL endpoint Managed REST API facebookiGoogleandroid XML API config mySQL profiles packs credits

43. Google Gadgets Bringing myExperiment to the iGoogle user

44. Taverna Plugin Bringing myExperiment to the Taverna user

45. Facebook

46. reviews ratings groups friendships tags files workflows RDF Store SPARQL endpoint mySQL profiles packs credits Transform Modularised myExperiment Ontology myExperiment data model (evolving!) SPARQL endpoint rdf.myexperiment.org DC, FOAF, SIOC (Semantically-Interlinked Online Communities)

47. Exporting packs

48. New Instances

50. 1.Fit in, Don’t Force Change 2.Jam today and more jam tomorrow 3.Just in Time and Just Enough 4.Act Local, think Global 5.Enable Users to Add Value 6.Design for Network Effects 1.Fit in, Don’t Force Change 2.Jam today and more jam tomorrow 3.Just in Time and Just Enough 4.Act Local, think Global 5.Enable Users to Add Value 6.Design for Network Effects Six Principles of Software Design to Empower Scientists 1.Keep your Friends Close 2.Embed 3.Keep Sight of the Bigger Picture 4.Favours will be in your Favour 5.Know your users 6.Expect and Anticipate Change 1.Keep your Friends Close 2.Embed 3.Keep Sight of the Bigger Picture 4.Favours will be in your Favour 5.Know your users 6.Expect and Anticipate Change De Roure, D. and Goble, C. "Software Design for Empowering Scientists," IEEE Software, vol. 26, no. 1, pp. 88-95, January/February 2009

51. Reflection on the design patterns The Long Tail Data is the Next Intel Inside Users Add Value Cooperate, Don't Control Are any obsolete? Do we need new ones? Overview

52. 1.The Long Tail Small sites make up the bulk of the internet's content; narrow niches make up the bulk of the internet's possible applications. Pushback: Crowd sourcing is ok for flickr, data collection by ornithologists and mechanical turk, but only the skilled can do research and indiscriminate sharing is harmful.

53. How do we move from heroic scientists doing heroic science with heroic infrastructure to everyday scientists doing science they couldn’t do before? humanists archaeologists geographers musicologists... researchers! research It’s the democratisation of e-Science!

54. No pedestrians You’re letting the oiks in!

55. You’re letting the muggles in! No muggles

56. 2.Data is the Next Intel Inside Applications are increasingly data-driven. Pushback: Scientists don’t work with just one type of data! Sometimes they are messy too.

57. Results Logs Results Metadata Paper Slides Feeds into produces Included in produces Published in produces Included in Published in Workflow 16 Workflow 13 Common pathways QTL Paul Fisher

58. David Shotton

59. Anatomy of a Research Object

60. We want research to be: 1.Replayable – go back and see what happened 2.Repeatable – run the experiment again 3.Reproducible – new expt to reproduce results 4.Reusable – use as part of new experiments 5.Repurposeable – reuse the pieces in new expt 6.Replicatable – for scale and automation 7.Reliable – systematic, unbiased and robust My Seven Rs

61. Communications of the ACM 51, 4 (Apr. 2008), 52-58 Scientific Discourse Relationships Ontology Specification Open Provenance Model

62. 3.Users Add Value The key to competitive advantage in internet applications is the extent to which users add their own data to that which you provide. Pushback: But now users want to specify the behaviour of applications: “End user programming” of web apps, workflows and mashups

63. Kepler Triana BPEL Ptolemy II Taverna Trident BioExtract

65. Gibson, A.; Gamble, M.; Wolstencroft, K.; Oinn, T.; Goble, C., "The Data Playground: An Intuitive Workflow Specification Environment," e-Science and Grid Computing, IEEE International Conference, pp.59-68, 10-13 Dec. 2007

66. 7.Cooperate, Don't Control Web 2.0 applications are built of a network of cooperating data services. Pushback: But Web 2 sites like Facebook don’t really contribute to the Web, while publication of RDF by myExperiment or the BBC does

67. Loose coupling in myExperiment Thanks to Francois Belleau

68. The Long Tail – But letting the muggles in! Web 2 means better peer review? Data is the Next Intel Inside – But people are messy, and what about Semantic Web? Is this Web 3.0? My “Web-Particle duality” Users Add Value – Behaviours too? Will Semantic Web help mashups? Linking the data, finding the services? Cooperate, Don't Control – But walled gardens don’t add to the Web. We are more Web 2 than Web 2 sites! User generated discussion slide

69. Web 2 is characterised by a set of memes (or patterns) which are really observations on society and the Web - they act as witness to the co-constitution myExperiment.org is a case study of Web 2 memes in a social site for researchers Watch for the evolution of end user programming and adoption of Semantic Web approaches Take Homes

70. Contact David De Roure dder@ecs.soton.ac.uk Carole Goble carole.goble@manchester.ac.uk Visit wiki.myexperiment.org

71. Sergejs Aleksejevs Jiten Bhagat Simon Coles Don Cruickshank Cat De Roure Paul Fisher Jeremy Frey Antoon Goderis Andrew Harrison Duncan Hull Yuwei Lin Danius Michaelides David Newman Cameron Neylon Stuart Owen Savas Parastatidis Meik Poschen Rob Procter Marco Roos Stian Soiland Ian Taylor Andrea Wiggins Alan Williams Katy Wolstencroft Mark Borkum Tom Eveleigh June Finch Matt Lee Kurt Mueller Alexander Voss David Withers

72. References De Roure, D., Goble, C. and Stevens, R. (2009) The Design and Realisation of the myExperiment Virtual Research Environment for Social Sharing of Workflows. Future Generation Computer Systems 25, pp. 561-567. doi:10.1016/j.future.2008.06.010 doi:10.1016/j.future.2008.06.010 De Roure, D. and Goble, C. (2009) "Software Design for Empowering Scientists," IEEE Software, vol. 26, no. 1, pp. 88-95, January/February 2009. doi:10.1109/MS.2009.22 doi:10.1109/MS.2009.22 Goble, C. And De Roure, D. (2008) Curating Scientific Web Services and Workflows. EDUCAUSE Review, vol. 43, no. 5 (September/October 2008) http://connect.educause.edu/Library/EDUCAUSE+Review/CuratingScientificWeb Serv/47226 http://connect.educause.edu/Library/EDUCAUSE+Review/CuratingScientificWeb Serv/47226 See http://wiki.myexperiment.org/index.php/Papers