Why are e-Infrastructures useful from a small business perspective?

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Slides of talk at seminar for the EuroRIs network (http://www.euroris-net.eu) of National Contact Points (NCPs) for EU funding programmes on Research Infrastructures.

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  • From data cultivation to data blossom , the Agricultural Data platform is an end-to-end modular solution that can transform data into meaningful services. The agricultural data are harvested from diverse sources and after they enrichment are published through a set of web services to external systems. The enrichment of data includes: improvement of data descriptions annotation of data with ontologies translation of data descriptions The enrichment of the data allows the development of high quality services for specific agricultural communities. Publishing is responsible for the exposure of agricultural data in a form that can be used a) for the development of data discovery services b) authoring services and c) analytics dashboards to track and study how the agricultural data are used.
  • All the services provided to the museums take advantage of the cloud. For instance the interactive installation does not need to have servers that hosts locally the collections and educational material that is used but it connects directly to the infrastructure that runs over the cloud
  • Check the cost of back up for a VM in the US cloud.
  • Check how AJAX is characterized as technology
  • Check how AJAX is characterized as technology
  • Why are e-Infrastructures useful from a small business perspective?

    1. 1. Nikos Manouselis Agro-Know Technologies nikosm@agroknow.gr Why are e-Infrastructures useful from a small business perspective?
    2. 2. intro
    3. 3. “The future belongs to the companies that turn data into products”
    4. 4. We help organizations and people to address societal and environmental challenges using solutions that are informed and enhanced by high-quality data We develop and put in real practice end-to-end, modular solutions that transform data into meaningful knowledge and services
    5. 5. Our values use open data to solve meaningful societal challenges create a data-powered ecosystem that may bootstrap agricultural & food innovation embrace all data sources, formats & types relevant to agricultural research & innovation promote open source and open data
    6. 6. Our vision To add value to the rich information available in the wide spectrum of agricultural and biodiversity sciences To make it universally accessible, useful and meaningful, through innovative tools, services and applications
    7. 7. Unorganized Content in local and remote sites Widgets Authoring services Data Discovery Services Analytics services Agro-Know Data Platform Ingestion Translation Publication Harvesting BlossomCultivation Organized and structured Content in local and remote DBs Educational Bibliographic Other Enrichment Aggregate data from diverse sources Works with different type of data Prepare data for meaningful services Educational Bibliographic data aggregation & sharing hub
    8. 8. • Value Generation Methods & Tools – Green Learning Network (GLN) Data Pool – Agricultural Bibliography Network (ABN) Data Pool • Data Sharing Tools – OER & educational pathways – digital libraries & repositories – digitized specimens & observations – learning management systems • Discovery Spaces – Landing pages, Micro-sites, Web portals, Apps • Innovation Methods & Tools – Creativity Accelerator, Training curricula, Open Data Incubator product families
    9. 9. why?
    10. 10. Resilience, flexibility and policies that favor R&D investment in staple food research and efficient input use will be the pillars on which future food security depends. - FAO Report (http://www.fao.org/docrep/014/i2280e/i2280e10.pdf) 10
    11. 11. 11 Key facts about agricultural trends Agriculture is about to experience a “growth shock” in order to cover the exponentially increasing food needs of the global population • All demographic and food demand projections suggest that, by 2050, the planet will face severe food crises due to our inability to meet agricultural demand – by 2050: • 9.3 billion global population, 34% higher than today • 70% of the world’s population will be urban, compared to 49% today • food production (net of food used for biofuels) must increase by 70% • According to these projections, and in order to achieve the forecasted food levels by 2050, a total investment of USD 83 billion per annum will be required • A large part of this investment will need to be focused on R&D
    12. 12. 12 Open Data in Agriculture One of the most promising routes to agriculture modernisation is the provision of Open Data to all interested parties • In an era of Big Data, one of the most promising routes to achieve R&D excellence in agriculture is Open Data, and in particular: – provisioning, – maintaining, – enriching with relevant metadata and – making openly available a vast amount of open agricultural data • The use and wide dissemination of these data sets is strongly advocated by a number of global and national policy makers such as: – The New Alliance for Food Security and Nutrition G-8 initiative – FAO of the UN – DEFRA & DFID in UK – USDA & USAID in the US
    13. 13. 13 There is a tremendous global business opportunity for companies that can leverage open agricultural data and expose such data into real- world agricultural applications
    14. 14. at the core
    15. 15. • publications, theses, reports, other grey literature • educational material and content, courseware • primary data, such as measurements & observations – structured, e.g. datasets as tables – digitized, e.g. images, videos • secondary data, such as processed elaborations – e.g. dendrograms, pie charts, models • provenance information, incl. authors, their organizations and projects • experimental protocols & methods • social data, tags, ratings, etc. • … research(+) content
    16. 16. • stats • gene banks • gis data • blogs, • journals • open archives • raw data • technologies • learning objects • ……….. educators’ view
    17. 17. • stats • gene banks • gis data • blogs, • journals • open archives • raw data • technologies • learning objects • ……….. researchers’ view
    18. 18. • stats • gene banks • gis data • blogs, • journals • open archives • raw data • technologies • learning objects • ……….. practioners’ view
    19. 19. • stats • gene banks • gis data • blogs, • journals • open archives • raw data • technologies • learning objects • ………..
    20. 20. • aim is: promoting data sharing and consumption related to any research activity aimed at improving productivity and quality of crops ICT for computing, connectivity, storage, instrumentation research data infrastructures
    21. 21. Publisher Date Catalog Subject ID Author Title we actually share metadata
    22. 22. …sometimes, data also included
    23. 23. metadata aggregations • concerns viewing merged collections of metadata records from different sources • useful: when access to specific supersets or subsets of networked collections –records actually stored at aggregator –or queries distributed at virtually aggregated collections 23
    24. 24. typically look like this 24 Ternier et al., 2010
    25. 25. metadata aggregation tools More than a harvester:  Validation Service  Repository Software  Registry Service  Harvester 25 Powered by
    26. 26. workflows with commonalities Harvesting Validating Transforming OAI target - XMLs IndexingStoring Automatic metadata generation De - duplication service XMLs Triplification
    27. 27. typical problem: computing
    28. 28. typical problem: hosting
    29. 29. to curate & preserve we need
    30. 30. even when machinery exists there are problems • hardware maintenance • technical support • interoperability limitations – no APIs for the dissemination of data across systems • hardware costs
    31. 31. the cloud approach Students Researchers Academics
    32. 32. what can be hosted on the cloud • Data storage & management tools – APIs for content dissemination in large networks • Processing & visualisation tools • Metadata aggregation infra • Search engines and apps for institutions or communities
    33. 33. what data providers need … only a browser and internet connection
    34. 34. examples
    35. 35. CASE 1: DATA MANAGEMENT TOOL OVER THE CLOUD
    36. 36. Educational Pathway Authoring Tool
    37. 37. Educational Pathway Authoring Tool
    38. 38. today
    39. 39. in the cloud
    40. 40. comparing costs for hosting data management tool at own site and cloud Cloud •cloud hosting = 20 euros/month •set up effort = 1hr •back up included •Total for 5 years = 1200 euros Hosting at institution •1 server+monitor+ups = 1200 euros •set up > 1 day effort or 100 euros •hardware maintenance effort = difficult to be defined but significant •Total for 5 years = 1300 +personnel for hardware maintenance+ costs of unexpected HW breakdowns e.g. supplier, hard disk Costs of software support could be the same for both cases Costs of software support could be the same for both cases After 5 years the HW should be renewed/upgraded After 5 years the HW should be renewed/upgraded
    41. 41. CASE 2: GRID-POWERED MEGA DATA POOLS
    42. 42. today
    43. 43. today
    44. 44. today
    45. 45. we create data silos
    46. 46. CASE 3: SETTING UP SEARCH SERVICE/PORTAL OVER THE CLOUD
    47. 47. today Metadata aggregator for educational content Search API Template customization html, css, Ajax, JS Aggregator Educational collection management tool Metadata aggregator for other data types Search API Data management tool Institution
    48. 48. specialise & replicate (a lot!) Metadata aggregator for educational content Specialised API Template customization html, css, Ajax, JS Cloud Educational collection management tool Metadata aggregator for other data types Specialised API Data management tool widget in Facebook page
    49. 49. exploitation
    50. 50. Our aim To create data-powered innovation ecosystems around organisations generating, managing & sharing digital collections+
    51. 51. Need: to cover a specific gap in a data-powered innovation ecosystem Open data providers (cultural institutions, public sector etc) Open data providers (cultural institutions, public sector etc) Creative start ups & industry Creative start ups & industry Innovative data- powered start ups Innovative data- powered start ups VCs / angel investors Incubators VCs / angel investors Incubators Open Data Incubator Open Data Incubator Data scientists, tech start ups, etc. Data scientists, tech start ups, etc. 54 missing component
    52. 52. • We work in focused efforts that will bring together and support three different groups of start-ups: – Start-ups that process agro data (data science powered) – Start-ups that build apps on agro data (agro data consumers, agro apps producers) – Start-ups that develop innovative agro/ food products (agro apps consumers) 55 We want to create a new generation of domain- focused SMEs
    53. 53. Open Agro Data Incubation programme Open Agro Data Hackathon Open Agro Data Hackathon Open Agro Data Boot camp Open Agro Data Boot camp Open Agro Data Meet Ups Open Agro Data Investor Days Open Agro Data Investor Days Open Agro Data Introductory Course Open Agro Data Introductory Course We believe that a community-powered comprehensive, end-to-end, modular approach can greatly facilitate the process of attracting, selecting and incubating data-powered start-ups in the knowledge domain of agriculture 56
    54. 54. OpenData Incubator Abstractandgeneric Applicabletoany knowledgedomain Attractivetomajor stakeholderssuchas Europeana OpenAgroData Incubator Areal-world,tangible proof-of-conceptfor theOpenDataIncubator Applicabletothe Agro-Biodiversity knowledgedomains Attractiveto sustainability incubators,investors, andstakeholders we believe that it can be generalised
    55. 55. summing up
    56. 56. thank you! nikosm@agroknow.gr http://www.agroknow.gr

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