Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.
BEXIS 2
Semantic Web Techniques in Research Data 
Management
Birgitta König‐Ries
Friedrich‐Schiller‐University Jena (Germa...
BEXIS 2
Towards Semantic Web Techniques in 
Research Data Management
Birgitta König‐Ries
Friedrich‐Schiller‐University Jen...
Structure of the talk
• What is research data management about?
• What is BEXIS2?
• Semantic Web Techniques in BEXIS2
birg...
WHY RESEARCH DATA
MANAGEMENT?
birgitta.koenig‐ries@uni‐jena.de 4
Our motivation: Biodiversity Research
Photo: Andre Linder
„Reasons for and ecosystem consequences
of tropical biodiversity...
Our motivation: Biodiversity Research
Photo: Andre Linder
„Reasons for and ecosystem consequences 
of tropical biodiversit...
Field Inventories
Model Output
Collection Data
Satellite
Data
Phylogenies
Distributions Experiments
Functional Traits
Ecos...
Field Inventories
Model Output
Collection Data
Satellite
Data
Phylogenies
Distributions Experiments
Functional Traits
Ecos...
Field Inventories
Model Output
Collection Data
Satellite
Data
Phylogenies
Distributions Experiments
Functional Traits
Ecos...
Field Inventories
Model Output
Collection Data
Satellite
Data
Phylogenies
Distributions Experiments
Functional Traits
Ecos...
WHAT IS BEXIS2?
birgitta.koenig‐ries@uni‐jena.de 11
What is BEXIS2
12birgitta.koenig‐ries@uni‐jena.de
a Data Life Cycle Management Platform
Intro
birgitta.koenig‐ries@uni‐jena.de 13
BEXIS 2 is: 
• designed for collaborative projects
• focus on active data (i.e. ...
End‐User Features
14birgitta.koenig‐ries@uni‐jena.de
Admin Features
15birgitta.koenig‐ries@uni‐jena.de
Modules & Extensions
• Party & Relations Management (e.g. people, 
projects, organisations)
• Notification Framework (e.g....
Heterogenity
birgitta.koenig‐ries@uni‐jena.de 17
Heterogenity
birgitta.koenig‐ries@uni‐jena.de 18
For example: 18,200 different variables in 856 datasets
 mapped into ~80...
Example: Tabular data headers
19
Air Temp.
Temperature
Unit: Celsius
Data Type: Float
Rec. Time Air Temp. Soil Temp. Hu.
1...
Data Structure Design & Re‐use
birgitta.koenig‐ries@uni‐jena.de 20
WHY SEMANTICS?
birgitta.koenig‐ries@uni‐jena.de 21
Why semantics?
• Heterogeneity on the schema level
• Ambiguous or hard to interpret column names
• Heterogeneity on the in...
TOWARDS SEMANTICS IN BEXIS2
birgitta.koenig‐ries@uni‐jena.de 23
ADOnIS: AquaDiva Ontology‐based 
Information System
JOYCE
AquaDiva
Ontology
2
4
Ontology Modularization, Selection 
and Customization
Erik Faessler, Friederike Klan, Alsayed Algergawy, Birgitta König‐Ri...
Soil
Nitrogen
Integrated search across data and publications
Conclusion
• Life‐cycle support for research data
management essential for good science
• Semantic web techniques can supp...
Thanks
• to DFG for funding
• to the BEXIS2 users for providing feedback and requirements
• to everyone involved in the de...
30
Vielen Dank für Ihre 
Aufmerksamkeit!
Contact for BEXIS2 related questions:
roman.gerlach@uni‐jena.de
http://bexis2.uni...
Upcoming SlideShare
Loading in …5
×

of

BEXIS 2 - Semantic Web Techniques in Research Data Management Slide 1 BEXIS 2 - Semantic Web Techniques in Research Data Management Slide 2 BEXIS 2 - Semantic Web Techniques in Research Data Management Slide 3 BEXIS 2 - Semantic Web Techniques in Research Data Management Slide 4 BEXIS 2 - Semantic Web Techniques in Research Data Management Slide 5 BEXIS 2 - Semantic Web Techniques in Research Data Management Slide 6 BEXIS 2 - Semantic Web Techniques in Research Data Management Slide 7 BEXIS 2 - Semantic Web Techniques in Research Data Management Slide 8 BEXIS 2 - Semantic Web Techniques in Research Data Management Slide 9 BEXIS 2 - Semantic Web Techniques in Research Data Management Slide 10 BEXIS 2 - Semantic Web Techniques in Research Data Management Slide 11 BEXIS 2 - Semantic Web Techniques in Research Data Management Slide 12 BEXIS 2 - Semantic Web Techniques in Research Data Management Slide 13 BEXIS 2 - Semantic Web Techniques in Research Data Management Slide 14 BEXIS 2 - Semantic Web Techniques in Research Data Management Slide 15 BEXIS 2 - Semantic Web Techniques in Research Data Management Slide 16 BEXIS 2 - Semantic Web Techniques in Research Data Management Slide 17 BEXIS 2 - Semantic Web Techniques in Research Data Management Slide 18 BEXIS 2 - Semantic Web Techniques in Research Data Management Slide 19 BEXIS 2 - Semantic Web Techniques in Research Data Management Slide 20 BEXIS 2 - Semantic Web Techniques in Research Data Management Slide 21 BEXIS 2 - Semantic Web Techniques in Research Data Management Slide 22 BEXIS 2 - Semantic Web Techniques in Research Data Management Slide 23 BEXIS 2 - Semantic Web Techniques in Research Data Management Slide 24 BEXIS 2 - Semantic Web Techniques in Research Data Management Slide 25 BEXIS 2 - Semantic Web Techniques in Research Data Management Slide 26 BEXIS 2 - Semantic Web Techniques in Research Data Management Slide 27 BEXIS 2 - Semantic Web Techniques in Research Data Management Slide 28 BEXIS 2 - Semantic Web Techniques in Research Data Management Slide 29 BEXIS 2 - Semantic Web Techniques in Research Data Management Slide 30
Upcoming SlideShare
What to Upload to SlideShare
Next
Download to read offline and view in fullscreen.

1 Like

Share

Download to read offline

BEXIS 2 - Semantic Web Techniques in Research Data Management

Download to read offline

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

Related Books

Free with a 30 day trial from Scribd

See all

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
  • ShannonRodriguez19

    Nov. 28, 2021

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

Views

Total views

691

On Slideshare

0

From embeds

0

Number of embeds

0

Actions

Downloads

0

Shares

0

Comments

0

Likes

1

×