Filtern
Dokumenttyp
- Vortrag (3) (entfernen)
Sprache
- Englisch (3)
Referierte Publikation
- nein (3) (entfernen)
Schlagworte
- Data management (3) (entfernen)
Organisationseinheit der BAM
Eingeladener Vortrag
- nein (3)
Introduction
A good laboratory organization can help address the reproducibility crisis in science, and easily multiply the scientific output of a laboratory, while greatly elevating the quality of the measurements. We have demonstrated this for small- and wide-angle X-ray scattering in the MOUSE project (Methodology Optimization for Ultrafine Structure Exploration). In the MOUSE, we have combined: a) a comprehensive laboratory workflow with b) a heavily modified, highly automated X-ray scattering instrument. This combination allows us to collect fully traceable scattering data, with a well-documented data flow (akin to what is found at the more automated beamlines). With two full-time researchers, the lab collects and interprets thousands of datasets, on hundreds of samples for dozens of projects per year, supporting many users along the entire process from sample selection and preparation, to the analysis of the resulting data.
While these numbers do not light a candle to those achieved by our hardworking compatriots at the synchrotron beamlines, the laboratory approach does allow us to continually modify and fine-tune the integral methodology. So for the last three years, we have incorporated e.g. FAIR principles, traceability, automated processing, data curation strategies, as well as a host of good scattering practices into the MOUSE system. We have concomitantly expanded our purview as specialists to include an increased responsibility for the entire scattering aspect of the resultant publications. This ensures full exploitation of the data quality, whilst avoiding common pitfalls.
Talk scope
This talk will present the MOUSE project as implemented to date, and will introduce foreseeable upgrades and changes. These upgrades include better pre-experiment sample scattering predictions to filter projects on the basis of their suitability, exploitation of the measurement database for detecting long-term changes and automated flagging of datasets, extending the measurement range through an Ultra-SAXS module, and enhancing MC fitting with sample scattering simulations for better matching of odd-shaped scatterers.
In typical material oriented research projects numerous types and sources of data need to get connected to form the final outcome. The data-structures have to be described in a manner semantically concise enough so that all participants share the meaning and at the same time flexible enough to allow connecting findings and facts that have not been connected before.
The present contribution describes the approach that is being taken in a public funded research project with two cooperating research institutes. The aim of the data-management solution is to describe and interlink three main entity types: material entities like a test specimen that is under observation, immaterial entities like a fatigue life model and processes that generate and transform the immaterial and material entities.
The shared understanding of what is described by the data is achieved by referring to existing linked data resources. In addition a common thesaurus that is subject to ongoing discussion is used for concepts and terms that need precision beyond publicly available concept descriptions. The data associated with the entities and processes and entities is rather small and well structured. The entities and processes covered by the vocabularies are typical to laboratories performing fatigue testing and microstructure analysis. Through the interlinking of the entities and processes it becomes possible to intuitively understand the knowledge that is accumulating in the database. For example it is easy to trace the history of a creep test specimen starting from the chemical composition that has been analyzed for the heat, continuing with the heat treatment of the material, going into the creep test itself with the associated creep data and finally finding the transmission electron microscopy images that have been taken from the crept sample.
The presented approach is prototypical way to tackle the sustainable management of structured open research data. Open questions remain for example with the management of attributions, the acceptance of an increased effort in data creation and the access control to specific regions of the data. On the other hand the approach sketches what added value can be created, if the community adopts and contributes to the linked data principles.
A brief introduction is given into our data collection and organization procedure, and why we have settled on the HDF5-based NeXus format for describing experimental data.
The links between NeXus and the SciCat data catalog is also provided, showing how the NeXus metadata is automatically added as searchable metadata in the catalog.