Powered by OpenAIRE graph
Found an issue? Give us feedback
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Other literature type . 2023
License: CC BY
Data sources: ZENODO
ZENODO
Presentation . 2023
License: CC BY
Data sources: Datacite
ZENODO
Presentation . 2023
License: CC BY
Data sources: Datacite
versions View all 2 versions
addClaim

PSDI Webinar: Pathfinder 1 Data Capture in Catalysis - Slides

Authors: Nieva de la Hidalga, Abraham;

PSDI Webinar: Pathfinder 1 Data Capture in Catalysis - Slides

Abstract

In the Physical Sciences Data Infrastructure (PSDI) Our first round pathfinders are exploratory pieces of work looking at an application area where PSDI could develop tools to enhance the research infrastructure. This video presents a recording of the Pathfinder 1: Experimental Data Capture in Catalysis webinar presented by Abraham Nieva de la Hidalga which was run on 3rd October 2023. https://www.psdi.ac.uk/event/webinar-psdi-pf1/ 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. The recording of this webinar can be found on YouTube: https://youtu.be/hKMhO1_xUtE

Related Organizations
  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    0
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green