1. Research Data Management towards Data Integration
Roman Gerlach, Birgitta König-Ries, Javad Chamanara, David Blaa, Sven Thiel, Martin Hohmuth, Nafiseh Navabpour
Friedrich-Schiller-University, Jena (Germany)
Endowed Chair for Distributed Information Systems
(Research Data Management Helpdesk)

2. Intro BEXIS 2 is: Data Management Platform (i.e. software)
designed for large research projects with central data management (incl. data manager)
focus on active data (i.e. project live time)
focus on tabular data, but not limited to
focus on data integration and re-use
generic, scalable, modular, free and open source

3. BExIS++ Project (DFG) BEXIS 2 SOFTWARE SUSTAINABILITY DEVELOPMENT
OUTREACH
SUPPORT
TRAINING

4. BExIS Community BEXIS 2 BExIS++ BExIS AquaDiva iDiv TerraSensE GFBio
UFZ Halle
BExIS++
Biodiversity
Exploratories
Kilimanjaro
GRK 1086
Jena
Experiment
EFForTS
MPI-BGC
Research
Database
BEFmate
GRK 1666
BExIS

5. What do we do to facilitate data integration and re-use?

6. No Data in Black Boxes

7. Let‘s take a look inside!
Carl Zeiss Jena Biotar 2.0/58mm f. Exakta (

8. Heterogenity

9. Heterogenity For example: 18,200 different variables in 856 datasets
Download of templates
 mapped into ~80 Data Attributes

10. Example: Tabular data headers
Data Type: DateTime
Unit: None
Data Structure
Unit: Time
Unit: Celecius
Data Type: Float
Data Attributes
Soil Sampling
Timestamp
Temperature
Ratio
Rec. Time
Air Temp.
Soil Temp.
Humidity
Variables
Sharing Data attributes among variables
Sharing units and data types among data attributes
Good for automatic data conversion, cross data set search, and data integration 1 22 18 46 2 23 17 45 3 21 16 30 5 15 25 6 14 11
Rec. Time
Air Temp.
Soil Temp.
Hu. 1 22 18 46 2 23 17 45 3 21 16 30 5 15 25 6 14 11
Dataset

11. Data structure creation
Providing support at dataset design time

12. Data Package Red classes come from other packages

13. Views Subset of a dataset obtained by selection or projection Purpose
Further processing, sharing or sampling
Security /Digital rights management
Spanning view
View across multiple dataset using the same Data Structure
Only data structure? How about same attributes? Does not apply!

14. Metadata level

15. Metadata level Import/export of multiple schemas/standards
mapping between different schemas
User-friendly tools to create metadata
re-use (e.g. enter once, copy, import)
guidance (e.g. terminologies, autocomplete)
custom structure (standard compliant)

16. System level Interaction with external systems
Persistent Identifier Providers
Authentication Providers (e.g. LDAP)
Annotation Providers (GFBio terminology services)
Geographic Information Systems

17. Web API Data Access Sample REST API calls: Data
/api/data/6?header=id,name
/api/data/6?filter=(Grade>50 AND Grade <90)
/api/data/6?header=id,name&filter=(Grade>50)
Sample REST API calls: Metadata

18. Conclusion
Facilitating data integration is one of the big challenges in data life cycle management
Data integration starts with data design
System should provide support (e.g. data structure design)

19. Further Reading
A conceptual model for data management in the field of ecology, Javad Chamanara, Birgitta König-Ries, Journal of Ecological Informatics, volume 24, November 2014, Pages 261–272, doi: /j.ecoinf
An Extensible Conceptual Model for Tabular Scientific Datasets, Javad Chamanara, Michael Owonibi, Alsayed Algergawy, Roman Gerlach, The International Symposium on Challenges for Designing and Using Datasets (DATASETS 2015), June , 2015, Brussels, Belgium,
BEXIS 2 Tech Talk Series:
Conceptual Model:

20. Thanks! Questions? Contact: roman.gerlach@uni-jena.de