The recording from our third PSDI webinar is now available on our Youtube Channel! https://youtu.be/hKMhO1_xUtE
In this webinar Abraham Nieva de la Hidalga presented about producing publish ready data from processing and analysis processes within Catalysis and the activities that are being carried out in Pathfinder 1.
If you weren’t able to attend the webinar live, then check this recording out. The slides and linked document are available on zenodo: https://doi.org/10.5281/zenodo.10148638
Abstract: In this seminar we will demonstrate two techniques for processing and analysing data that generate the required metadata to create FAIR digital objects. These objects can then be published as supporting information for the results obtained. This approach requires minimum intervention from the researcher performing the processing and analysis tasks. Consequently, these methods are ideal for improving the practices of publishing data, facilitate reproducibility of results, and support greater reuse of published data.
The two proposed techniques are based on the use of the X-Ray Larch Python Library. The first technique uses Jupyter notebooks and MLProvLab. This approach is suitable for small scale spectra analysis, this is processing and analysis of a small number of XAS readings being studied. The second technique leverages Galaxy tools and workflows. This approach is suitable for large scale spectra analysis, which encompasses processing and analysis of large numbers of XAS readings, such as those resulting from in situ and operando experiments.
Both techniques produce the metadata required for reproducing the results, including data used, parameters set at each stage, sequence of operations and mapping between inputs and outputs. We will discuss the benefits of these type of tools such as, less work in documenting supporting data by producing publishing ready data objects, comparison of results when varying parameters and exploratory testing of different parameter combinations.
At the end of this seminar, you will be able to practice with your actual data using the resources presented (Jupyter notebooks). Additionally, we invite the community to provide ideas for improvements of the tools and for supplying ideas for further development.
