1. The Humanities in a Global e-Infrastructure A Shopping-List Gregory Crane, Perseus Project, Tufts Brian Fuchs, Internet Centre, Imperial College Dolores Iorizzo,Internet Centre, Imperial College

2. Why e-infrastructure? Data-driven Science Global not regional models Interactive and Intelligent Digital Libraries Collaboration in Arts, Humanities and Sciences Pre-Enlightenment Europe - Mash-up technologies Epistemic Networks

3. Why services? Synergy Build super-corpora Economy of scale Leverage existing services Possibilities of scale Analyze a document against the entire corpus of 19 c. newspapers in English

4. Global Grid Efforts in the Humanities Cyberinfrastructure ACLS/NSF DELOS DARIAH DRIVER D-GRID (Text Grid) OAI-ORE

5. The Big Picture Leverage power of global grid networks in the science for the Humanities - EGEE Interrogation of large digital resources Metadata standards that work across the sciences, arts and humanities Semantic interoperability Web 2.0 (3.0) and GRID Web Services

6. Services for the Humanities Catalogue services Named entity services Customisation services User-contribution services

7. Catalogue Services So far... FRBR Document-granularity in collections, allowing us to.... identify and organize all editions, translations, commentaries, indices, and other documents related to a single text.

8. Catalogue Services Related Documents Document chunk

9. Catalogue Services: What we need... Intra-document citation Ability to cite text-chunks Citation management Understand standard citation schemes E.g. Bekker and Stephanus Version analysis Compare versions of an historical text over its lifespan e.g. Lucretius Canonical text service Provide a benchmark text for each author

10. Named Entity Services Automatically Extracted Named Entities Intra-document chunk

11. Named Entity Services--Data Sources Language models Does “est” = “he is” or “he eats”? Language models should give probabilities. Print gazetteers Historical gazetteers Dictionaries Digitized: the Perseus LSJ Digital: Wordnet Training sets for machine learning... Documents with labelled features.

12. Customization / Personalisation Personal Vocabulary Profile

13. Customization--Data sources Personal profiles How often have I accessed features XYZ? What importance do I assign features XYZ? Voting / Recommendation readers who looked up words X, Y, and Z, also were interested in words M, N. and O.

14. Structured User Contributions Resolution Is this Berlin, New Hampshire or Berlin, DE? Correction “est” means “is” here, not “eat” Annotation and Labelling This paragraph discusses gravity.

15. Research Assessment: Classicists rewarded for digital projects A community of tenured faculty Chris Blackwell, Furman Gregory Crane, Tufts Helma Dik, Chicago Bruce Robertson, Mount Allison Jeff Rydberg-Cox, Missouri Charlotte Roueché, King’s College Ross Scaife, Kentucky Mark Schiefsky - Harvard Neel Smith, Holy Cross

16. A Humanities e-infrastructure: the benefits Cross-semination in IR, authority services, personalization Basis for web 2.0 applications Better, more participatory interfaces for students Open academic and educational markets to wider audiences Link Cultural Heritage material to science, industry and tourism.