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BEXIS 2 - Semantic Web Techniques in Research Data Management

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LSWT2019 Talk by Prof. Dr. Birgitta König-Ries, Uni Jena

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BEXIS 2 - Semantic Web Techniques in Research Data Management

  1. 1. BEXIS 2 Semantic Web Techniques in Research Data  Management Birgitta König‐Ries Friedrich‐Schiller‐University Jena (Germany) Heinz Nixdorf Chair for Distributed Information Systems Fusion.cs.uni‐jena.de Bexis2.uni‐jena.de
  2. 2. BEXIS 2 Towards Semantic Web Techniques in  Research Data Management Birgitta König‐Ries Friedrich‐Schiller‐University Jena (Germany) Heinz Nixdorf Chair for Distributed Information Systems Fusion.cs.uni‐jena.de Bexis2.uni‐jena.de
  3. 3. Structure of the talk • What is research data management about? • What is BEXIS2? • Semantic Web Techniques in BEXIS2 birgitta.koenig‐ries@uni‐jena.de 3
  4. 4. WHY RESEARCH DATA MANAGEMENT? birgitta.koenig‐ries@uni‐jena.de 4
  5. 5. Our motivation: Biodiversity Research Photo: Andre Linder „Reasons for and ecosystem consequences of tropical biodiversity?“ Up to 300 tree species per ha…
  6. 6. Our motivation: Biodiversity Research Photo: Andre Linder „Reasons for and ecosystem consequences  of tropical biodiversity?“ Up to 300 tree species per ha… Foto https://www.flickr.com/photos/naturalbornstupid/4157426019/ …not just bananas
  7. 7. Field Inventories Model Output Collection Data Satellite Data Phylogenies Distributions Experiments Functional Traits Ecosystem Data
  8. 8. Field Inventories Model Output Collection Data Satellite Data Phylogenies Distributions Experiments Functional Traits Ecosystem Data All of this data needs to be integrated to answer the question
  9. 9. Field Inventories Model Output Collection Data Satellite Data Phylogenies Distributions Experiments Functional Traits Ecosystem Data If that data and its metadata is not high  quality, there is no chance that this will work
  10. 10. Field Inventories Model Output Collection Data Satellite Data Phylogenies Distributions Experiments Functional Traits Ecosystem Data If this is not a  consideration from the start, there is little chance that data and metadata will be of sufficient quality
  11. 11. WHAT IS BEXIS2? birgitta.koenig‐ries@uni‐jena.de 11
  12. 12. What is BEXIS2 12birgitta.koenig‐ries@uni‐jena.de a Data Life Cycle Management Platform
  13. 13. Intro birgitta.koenig‐ries@uni‐jena.de 13 BEXIS 2 is:  • designed for collaborative projects • focus on active data (i.e. project life‐time) • focus on tabular data, but not limited to it • focus on data integration and re‐use • generic, scalable, modular, free and open source
  14. 14. End‐User Features 14birgitta.koenig‐ries@uni‐jena.de
  15. 15. Admin Features 15birgitta.koenig‐ries@uni‐jena.de
  16. 16. Modules & Extensions • Party & Relations Management (e.g. people,  projects, organisations) • Notification Framework (e.g. request a dataset)  • Resource Management (e.g. instruments,  equipment, facilities) • Semantic Annotation tool • … 16birgitta.koenig‐ries@uni‐jena.de
  17. 17. Heterogenity birgitta.koenig‐ries@uni‐jena.de 17
  18. 18. Heterogenity birgitta.koenig‐ries@uni‐jena.de 18 For example: 18,200 different variables in 856 datasets  mapped into ~80 Data Variables
  19. 19. Example: Tabular data headers 19 Air Temp. Temperature Unit: Celsius Data Type: Float Rec. Time Air Temp. Soil Temp. Hu. 1 22 18 46 2 23 17 45 3 21 16 30 5 18 15 25 6 14 11 25 Soil Sampling Soil Temp. Ratio Unit: None Humidity Timestamp Unit: Time Data Type: DateTime Rec. Time Variable  Templates Variables Data Structure Dataset 1 22 18 46 2 23 17 45 3 21 16 30 5 18 15 25 6 14 11 25 birgitta.koenig‐ries@uni‐jena.de Units Data Types
  20. 20. Data Structure Design & Re‐use birgitta.koenig‐ries@uni‐jena.de 20
  21. 21. WHY SEMANTICS? birgitta.koenig‐ries@uni‐jena.de 21
  22. 22. Why semantics? • Heterogeneity on the schema level • Ambiguous or hard to interpret column names • Heterogeneity on the instance level • …. • Hampers: – Discovery – Integration birgitta.koenig‐ries@uni‐jena.de 22
  23. 23. TOWARDS SEMANTICS IN BEXIS2 birgitta.koenig‐ries@uni‐jena.de 23
  24. 24. ADOnIS: AquaDiva Ontology‐based  Information System JOYCE AquaDiva Ontology 2 4
  25. 25. Ontology Modularization, Selection  and Customization Erik Faessler, Friederike Klan, Alsayed Algergawy, Birgitta König‐Ries, Udo Hahn: Selecting and Tailoring Ontologies with JOYCE. EKAW (Satellite Events) 2016 Ontology Repository Modulari- zation Filter / Scorer Set Combi- nation Alignment/ Validation • 0.5 • 0.3 • 0.3 • 0.2 • 0.6 • 0.4 X X• 0.5 • 0.3 2 5 Key Terms
  26. 26. Soil Nitrogen
  27. 27. Integrated search across data and publications
  28. 28. Conclusion • Life‐cycle support for research data management essential for good science • Semantic web techniques can support data integration and discovery • Long term goals: – Seamless linking of data and publications – Seamless integration of data management and analysis including provenance management – Automatic hypotheses generation birgitta.koenig‐ries@uni‐jena.de 28
  29. 29. Thanks • to DFG for funding • to the BEXIS2 users for providing feedback and requirements • to everyone involved in the development of BEXIS and BEXIS2  including Payam Adineh, Masoud Allahyari, Arefeh Bahrami, Javad  Chamanara, Florian Gaffron, Jitendra Gaikwad, Roman Gerlach,  Thorsten Hindermann, Martin Hohmuth, Nafiseh Navabpour, Jens  Nieschulze, Michael Owonibi, Andreas Ostrowski, Eleonora Petzold,  David Schöne, Sirko Schindler, Markus Steinberg, Sven Thiel,  Franziska Zander and many others • to the AquaDiva Infra1 team: Udo Hahn, Erik Fäßler, Bernd Kampe,  Alsayed Algergawy, Hamdi Hamed and Friederike Klan • to Christian Wirth for the iDiv slides birgitta.koenig‐ries@uni‐jena.de 29
  30. 30. 30 Vielen Dank für Ihre  Aufmerksamkeit! Contact for BEXIS2 related questions: roman.gerlach@uni‐jena.de http://bexis2.uni‐jena.de/ birgitta.koenig‐ries@uni‐jena.de

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