The D4Science Infrastructure to Support Academic Courses
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The document discusses the d4science e-infrastructure, designed to support educational courses in data science, particularly focusing on space data. It highlights the need for operational data platforms that facilitate hands-on experience for trainees and reduce the manual effort required for training preparation. The initiative aims to promote collaboration and ease of access to diverse scientific resources and tools, fostering an effective learning environment for both trainers and trainees.
The D4Science Infrastructure to Support Academic Courses
- 1. 2016 Conference on Big Data from Space BiDS’16 Tenerife, Spain March 16, 2016 Nadia Nardi, Engineering The D4Science Infrastructure to Support Academic Courses
- 2. • A perspective of the educational path • Do (data) scientists, students have access to explore an operational data platform and gain hands-on experience on real scientific challenges? • Is there a workspace that exists where to create and consume academic courses in an optimal manner? • What would an optimal workspace be like? www.eng.it2 our 20 mins
- 3. www.eng.it3 perspective of educational path • Education + Research & Innovation = priorities of EU Investments to boost jobs and growth • To be able to promote and exploit Data, such as Space Data, a clever workspace, a training environment, is needed to support ONE trainees (data scientists) and TWO trainers
- 4. www.eng.it4 problem ? • Trainees need the possibility to work on an operational data platform • Experiments and academic works shouldn’t just be done on simulated environments or sample data sets • Trainees need access to a data platform that supports manipulation and management of data (of any nature)
- 5. www.eng.it5 problem ? • There is lack of technological support - preparing courses and workshops require manual work, which is repeated each time a new course is held because of the need to reset and reconfigure the training environment for the next course • Available data processing services and models come under heterogeneous programming languages, which usually require installing complex software on users’ computers • Executing models on users’ data usually requires a long phase of data preparation and powerful hardware (not always available in the labs) • Sharing data, parameters and results of experiments is difficult and collaboration between trainers and trainees is limited to the duration of the course
- 6. www.eng.it6 d4science.org • The D4Science (hybrid data) e- Infrastructure has been around since 2004 to support Science and educational challenges • Designed to integrate resources from other e- Infrastructures and commercial vendors, offering unified access to integrated resources and services 99.7% service availability place connecting +2000 scientists in 44countries integrating +55 heterogeneous data providers providing access to > 1 Billion quality records in repositories worldwide Executing +13000 models/algorithms a month osts +40VRE’s
- 7. www.eng.it7 d4science.org • Supports the operation of a large set of diverse Initiatives, Communities of Practice, and Projects by offering Virtual Research Environments (VREs) and Services • Aims to offer trainees remote computational facilities to execute experiments and provide facilities to visualize datasets and perform analysis • Serves: biological, ecological, environmental, mining, statistical communities worldwide place
- 9. www.eng.it9 facilities ? • to manage distributed resources efficiently (monitoring, accounting, alerting, failure-recovery, scale-in/out) • for end-users according to the Science 2.0 paradigm (web portal, workspace, messaging system, social networking) • for data management (data and metadata harmonization, metadata generation, data discovery and access) • to visualize large datasets (GIS maps, graphs, tables) • to perform data analysis (Artificial Intelligence algorithms; forecasting and signal processing methods) • to perform computations on large datasets (cloud computing and high- throughput computing)
- 10. www.eng.it10 bluebridge-vres.eu Fishery and Marine Sciences Community •Simplifies the sharing and re-use of knowledge, facilitating cross-fertilization and collaboration among their actors - supports activities contributing to the H2020 Blue Growth Societal challenge with a strong focus on sustainable growth. •Trainees- have tools to easily discover and access a rich set of structured and high quality data, can exploit a large variety of always up-to-date models to first analyse, compare and share resulting datasets, and then to discuss analytical processes. •Trainers- lower effort to prepare the environment for the courses, boosting training programmes, giving them a new volume and a new thematic and geographic reach.
- 11. www.eng.it11 D4Science serves different scientific and industrial contexts • Expliots the e-Infrastructure to support the practical uptake of scientific knowledge (in workshops and academic courses), leveraging on the offered capacity • Specific VREs for education offer services enabling collaboration and integrated access to digital research resources, cross-disciplinary and cross-community tools, data and services
- 12. www.eng.it12 bluebridge-vres.eu Some services... •Stock Assessment Dashboard: efficient assessment of fishery stocks and the production of related knowledge and indicators. •Global Record of Stocks and Fisheries Knowledge base: access to evidence based information on the status of marine stocks and fisheries. •Aquaculture Atlas Generation: produce aquaculture products (maps of human activity and natural zones) contributing to an aquaculture atlas compliant with NASO standards. •Protected Area Impact Maps Production: production of maps of vegetation types and human impacts on them to enable ecosystem degradation analysis.
- 13. www.eng.it13 bluebridge-vres.eu Some services... •Performance Evaluation in Aquaculture Dashboard: support performance estimation, benchmarking, decision making and strategic investment analysis in aquaculture. •Strategic Investment Analysis Dashboard: support identification of strategic locations of interest for investment meeting multifactor selection criteria.
- 14. www.eng.it14 join us user group community you can manage the communities and offer them a set of virtual environments can create your own virtual environment and add the applications you need securely preserve, access (from anywhere), and confidentially share your data and exploit one or more of the existing applications
- 15. home sweet home www.eng.it15 • Education + Research & Innovation = priorities of EU Investments to boost jobs and growth.. Right? • Education must be giving the right importance and effort to continuously improve the experience for both trainer and trainee. • D4Science supports academic courses by taking a comprehensive approach to what it means to boost education and facilitate knowledge bridging between research and innovation… and scientific and training requirements • 12 years later D4Science is still here and through BlueBRIDGE is looking also for new opportunities to promote interdisciplinary collaboration
- 16. Thanks for your attention www.eng.it16 [email protected]
- 17. time to talk www.eng.it17 Data Science technologies impact how research is conducted - how data is used and shared….. How can e-Infrastructures reflect the needs of Data Scientist to study/work at best? What are the training needs specific to this community?
Editor's Notes
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- #9: The D4Science e-Infrastructure is logically composed of seven areas: Enabling Layer, Spatial Data Infrastructure, OLAP Infrastructure, Storage Infrastructure, Computing Infrastructure, Analytical Infrastructure, and Registries. Each of the areas exploits several heterogeneous technologies integrating more than 500 software components. All of them are made interoperable through the exploitation of gCube Mediators while common registries make resources and data easy to discover. The publication of resources and data profiles is performed by Mediators that, in a transparent manner to the federated resources, perform harmonization and publication realizing a unified view of the infrastructure resources.
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- #11: Scientific training environments: to reduce trainers’ effort in preparing platforms for practical experimentation in courses organised by universities, institutions and companies that require multidisciplinary data access, curation and analytics, e.g. Ecosystem modelling, Vessel Monitoring Systems, Fisheries Management, Biodiversity Conservation & Geospatial Data Management
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