BEXIS 2 is a data life cycle management platform designed for collaborative research projects. It focuses on active data generated during a project's lifetime, primarily tabular data. BEXIS 2 aims to facilitate data integration and reuse through various features for end users and administrators. It also leverages semantic web techniques like ontologies to help address issues stemming from data heterogeneity and improve data discovery. The talk discusses BEXIS 2's key capabilities and how semantics can support better integration and linking of research data with related publications and analyses.

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. 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. Structure of the talk
• What is research data management about?
• What is BEXIS2?
• Semantic Web Techniques in BEXIS2
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4. WHY RESEARCH DATA
MANAGEMENT?
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5. Our motivation: Biodiversity Research
Photo: Andre Linder
„Reasons for and ecosystem consequences
of tropical biodiversity?“
Up to 300 tree species per ha…

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. Field Inventories
Model Output
Collection Data
Satellite
Data
Phylogenies
Distributions Experiments
Functional Traits
Ecosystem Data

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. 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. 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. WHAT IS BEXIS2?
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12. What is BEXIS2
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a Data Life Cycle Management Platform

13. Intro
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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. End‐User Features
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15. Admin Features
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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
• …
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17. Heterogenity
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18. Heterogenity
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For example: 18,200 different variables in 856 datasets
 mapped into ~80 Data Variables

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
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Units
Data Types

20. Data Structure Design & Re‐use
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21. WHY SEMANTICS?
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22. Why semantics?
• Heterogeneity on the schema level
• Ambiguous or hard to interpret column names
• Heterogeneity on the instance level
• ….
• Hampers:
– Discovery
– Integration
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23. TOWARDS SEMANTICS IN BEXIS2
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24. ADOnIS: AquaDiva Ontology‐based
Information System
JOYCE
AquaDiva
Ontology
2
4

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. Soil
Nitrogen

27. Integrated search across data and publications

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
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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
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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