1. David De Roure Eindhoven Edition

2. Due to the complexity of the software and the backend infrastructural requirements, e-Science projects usually involve large teams managed and developed by research laboratories, large universities or governments. e-Science is about global collaboration in key areas of science, and the next generation of infrastructure that will enable it.

3. How do we know when e-Science has succeeded? Not just accelerated but new A. When everyone is using the Grid B. When there are routine scientific advances that would not have happened otherwise

4. 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-Research

5. scientists Local Web Repositories Digital Libraries 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 Workflows Ontologies The social process of science

6. Between 19 th October and 23 rd November 2007 I attended six international meetings related to e-Science Grid 2007 Scientific and Scholarly Workflows e-Social Science 2007 W3C Open Grid Forum Microsoft e-Science This is what I found

7. Everyday researchers doing everyday research Not just a specialist few doing heroic science with heroic infrastructure Chemists are blogging the lab Everyone is mashing up Everday hardware – multicore machines and mobile devices 1

8. A data-centric perspective, like researchers Data is large, rich, complex and real-time There is new value in data, through new digital artefacts and through metadata e.g. context, provenance, workflows This isn’t “anti-computation” – design interaction around data 2

9. Collaborative and participatory The social process of science revisited in the digital age Collaborative tools – blogs and Wikis e-Science now focuses on publishing as well as consuming Scholarly lifecycle perspective 3

10. Benefitting from the scale of digital science activity to support science This is new and powerful! Community intelligence Review Usage informing recommendation e.g. OpenWetWare e.g. myExperiment 4

11. Increasingly open Preprints servers and institutional repositories Open journals Open access to data Science Commons Object Reuse & Exchange 5

12. Better not Perfect The technologies people are using are not perfect They are better They are easy to use They are chosen by scientists 6

13. Empowering researchers The success stories come from the researchers who have learned to use ICT Domain ICT experts are delivering the solutions Anything that takes away autonomy will be resisted 7

14. About pervasive computing e-Science is about the intersection of the digital and physical worlds Sensor networks Mobile handheld devices 8

15. 1.Everyday researchers doing everyday research 2.A data-centric perspective, like researchers 3.Collaborative and participatory 4.Benefitting from the scale of digital science activity to support science 5.Increasingly open 6.Better not Perfect 7.Empowering researchers 8.About pervasive computing Signs of the Times

16. e-Science is now enabling researchers to do some completely new stuff! As the individual pieces become easy to use, researchers can bring them together in new ways and ask new questions “The next level” Onward and Upward “Standing on the shoulders of giants” www.w3.org/2007/Talks/www2007-AnsweringScientificQuestions-Ruttenberg.pdf (Everyday researchers are giants too)

17. Note to Reader. The next slides are not intended to be anti-grid. Everyone working on Grid is doing great work.

18. Everyday researchers doing everyday research BUT heroic Grid infrastructure not being adopted A data-centric perspective, like researchers BUT Grid gives APIs to computation not data Collaborative and participatory BUT Grid has deeply rooted service provider mindset Better not Perfect BUT Grid aims to provide well-engineered perfect solution Giving autonomy to researchers BUT Grid has feel of institutional control (at this time) About pervasive computing BUT Grid is about portals, not the next generation of users The Grid Problem

19. e-Science Technology Creators & Integrators Applications Research EE Research Socio-economic & Commercial Innovation e-Science bespoke tailoring Mass Use by Researchers 5 years CS Research e-Science 10s of integrators 100s of embedded consultants 1000s of research users The Arrow Problem e-Science Pipeline Malcolm Atkinson NB This isn’t wrong!

20. Don’t think rollout of technologies... Think roll-in of researchers... Mass Use by Researchers Mass Use by Researchers Knowledge co-production vs Service Delivery!

21. Web ServicesRESTful APIscmd linessshhttp Web BrowserMobile phoneiPodCarEquipmentPDA P2P mashups workflows services applications Subject ICT experts Computer Scientists Software Companies Workflow tools Ruby on Rails ecosystem Scientists open source Software Engineers nesc OeRC

22. It’s about empowerment as well as provision People power – the new instrument of scale! Hence usability: – Simple/familiar interfaces for users – Simple/familiar interfaces for developers – No need for a summer school! Step into user space and look back Computer Scientists as facilitators and problem solvers(?) For a flourishing ecosystem...

23. Wikis Mashups REST APIs Google Maps Technologies: – AJAX, JSON, Ruby on Rails,... Social networking Web as a distributed application platform – Amazon S3 and EC2 But what about Web 2.0?!

24. Signs of the Times The Long Tail Data is the Next Intel Inside Users add value Network effects by default Some Rights Reserved The Perpetual Beta Cooperate, don’t Control Software above the level of the single device Web 2.0 patterns www.oreilly.com/pub/a/oreilly/tim/news/2005/09/30/what-is-web-20.html 1.Everyday researchers doing everyday research 2.A data-centric perspective, like researchers 3.Collaborative and participatory 4.Benefitting from the scale of digital science activity 5.Increasingly open 6.Better not Perfect 7.Empowering researchers 8.About pervasive computing

26. use Web 2.0 here? Grid

27. use Web 2.0 here? Grid

28. use Web 2.0 here Grid cloud HPC

29. A utility is a directly and immediately useable service with established functionality, performance and dependability, illustrating the emphasis on user needs and issues such as trust Services are knowledge- assisted (‘semantic’) to facilitate automation and advanced functionality, the knowledge aspect reinforced by the emphasis on delivering high level services to the user The architecture comprises services which may be instantiated and assembled dynamically, hence the structure, behaviour and location of software is changing at run-time Service-Oriented Knowledge Utility semanticgrid.org/NGG3

30. If you peel back the label and its says “Grid” or “OGSA” underneath… its not a cloud. If you need to send a 40 page requirements document to the vendor then… it is not cloud. If you can’t buy it on your personal credit card… it is not a cloud If they are trying to sell you hardware… its not a cloud. If there is no API… its not a cloud. If you need to rearchitect your systems for it… Its not a cloud. If it takes more than ten minutes to provision… its not a cloud. If you can’t deprovision in less than ten minutes… its not a cloud. If you know where the machines are… its not a cloud. If there is a consultant in the room… its not a cloud. If you need to specify the number of machines you want upfront… its not a cloud. If it only runs one operating system… its not a cloud. If you can’t connect to it from your own machine… its not a cloud. If you need to install software to use it… its not a cloud. If you own all the hardware… its not a cloud. James Governor

31. Multicore chips will offer so much performance that we need not cobble together heterogeneous resources but rather can deploy simple powerful systems Geoffrey Fox

32. Web 2.0 is not high performance – It improves the performance of science and people! Web 2.0 is not a properly engineered solution – Scientists want better, not perfect. And agility. Web 2.0 is not secure – People do lots of “secure” things on the Web Web 2.0 is a fad that will pass – It’s inevitable and it’s already happened! Web 2.0 works for teenagers but it won’t for scientists – See OpenWetWare Web 2.0 lets the oiks in and this is a bad thing – Now we can do peer review even better! Myths

33. N2N2 N N

34. One Middleware 2N N N

35. Middleware ? N N Polynomial involving N1, N2 and M

36. www.myexperiment.org

37. 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 Carole Goble

38. 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. Recycling, Reuse, Repurposing

39. 20072006200520042003 40 Taverna downloads per day taverna.sourceforge.net

40. Run on your laptop – no sysadmin required Access independent third party world-wide service providers of applications, tools and datasets – 850 databases, 166 web servers Nucleic Acids Research Jan 2006 My local applications, tools and datasets. In the Enterprise. In the laboratory. Easily incorporate new services without coding The Superclient

41. Kepler Triana BPEL Ptolemy II

42. myExperiment.org is… “Facebook for Scientists”...but different to Facebook! A community social network. A gateway to other publishing environments A federated repository A platform for launching workflows Publishing self-describing Encapsulated myExperiment Objects Mindful publication Started March 2007 Closed beta since July 2007 Open beta November 2007 myExperiment.org is...

44. Google Gadget

45. Ownership and Attribution

46. 24/5/2007 | myExperiment | Slide 46

47. ` users descriptions groups friendships tags Enactor blobs workflows HTML XML Snapshot map of resources with their relationships and versions

48. 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 Workflows Ontologies Digital Libraries The social process of science 2.0

49. e-Research is about doing new research Grid is just one part of the solution Users are not just consumers of infrastructure. Empower them. Web 2.0 is a set of design patterns Think Web 2.0 coupling Grid and other services Workflows make e-Science easier, and Web 2 makes workflows easier Take Homes 2.0

50. Contact David De Roure dder@ecs.soton.ac.uk Carole Goble carole.goble@manchester.ac.uk Thanks Malcolm Atkinson, Geoffrey Fox, Jeremy Frey, Savas Parastatides, The myGrid Family

51. Provenance Harvesting myExperiment metadata bus ORE RDF Store Encapsulated myExperiment Object (EMO) Metadata

52. ReM=Resource Map, A=aggregation, AR=Aggregated Resource http://www.openarchives.org/ore/0.1/datamodel-overview OAI-ORE Object Exchange and Reuse

53. Anatomy of an EMO EMO Metadata creator, modified, rights URIs into myExperiment(s) with types and comments workflow, data, description URIs to external resources, with alternates, types, comments, versions Optional annotations of URIs and their relationships

54. Linked Data

55. TAVERNA FUNCTIONAL LANGUAGE SHOCK! RESEARCH DAILY British Scientists revealed today that Taverna is in fact a functional language. In a police statement, Taverna creator Tom Oinn said “it’s a fair cop guv”... Advertisement New Improved Closurize and Concentrate TM Add Lambda Calculus to your Lambda Network! Satisfaction guaranteed in several different colours

56. Original workflow High-level design of quality filter Compilation to quality workflow Compilation to quality workflow Integration New quality filter Quality-aware workflow Declarative specification Declarative spec is formal (XML) Compilation is automated QW follows predictable pattern  integration also automated Declarative spec is formal (XML) Compilation is automated QW follows predictable pattern  integration also automated Quality Workflows Paolo Missier

57. Malcolm Atkinson