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These are four datasets that were made available to the participants of the Small-angle Scattering data analysis round robin. The intent was to find out how comparable results from different researchers are, who analyse exactly the same processed, corrected dataset.
In this repository, there are:
1) a PDF document with more details for the study,
2) the datasets for people to try and fit,
3) an Excel spreadsheet to document the results.
Datasets 1 and 2 were modified from: Deumer, Jerome, & Gollwitzer, Christian. (2022). npSize_SAXS_data_PTB (Version 5) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5886834
Datasets 3 and 4 were collected in-house on the MOUSE instrument.
How much do we, the small-angle scatterers, influence the results of an investigation? What uncertainty do we add by our human diversity in thoughts and approaches, and is this significant compared to the uncertainty from the instrumental measurement factors?
After our previous Round Robin on data collection, we know that many laboratories can collect reasonably consistent small-angle scattering data on easy samples1. To investigate the next, human component, we compiled four existing datasets from globular (roughly spherical) scatterers, each exhibiting a common complication, and asked the participants to apply their usual methods and toolset to the quantification of the results https://lookingatnothing.com/index.php/archives/3274).
Accompanying the datasets was a modicum of accompanying information to help with the interpretation of the data, similar to what we normally receive from our collaborators. More than 30 participants reported back with volume fractions, mean sizes and size distribution widths of the particle populations in the samples, as well as information on their self-assessed level of experience and years in the field.
While the Round Robin is still underway (until the 25th of April, 2022), the initial results already show significant spread in the results. Some of these are due to the variety in interpretation of the meaning of the requested parameters, as well as simple human errors, both of which are easy to correct for. Nevertheless, even after correcting for these differences in understanding, a significant spread remains. This highlights an urgent challenge to our community: how can we better help ourselves and our colleagues obtain more reliable results, how could we take the human factor out of the equation, so to speak?
In this talk, we will introduce the four datasets, their origins and challenges. Hot off the press, we will summarize the anonymized, quantified results of the Data Analysis Round Robin. (Incidentally, we will also see if a correlation exists between experience and proximity of the result to the median). Lastly, potential avenues for improving our field will be offered based on the findings, ranging from low-effort yet somehow controversial improvements, to high-effort foundational considerations.
Dataset and Jupyter worksheet interpreting the (results from) small- and wide-angle scattering data from a series of boehmite/epoxy nanocomposites. Accompanies the publication "Competition of nanoparticle-induced mobilization and immobilization effects on segmental dynamics of an epoxy-based nanocomposite", by Paulina Szymoniak, Brian R. Pauw, Xintong Qu, and Andreas Schönhals.
Datasets are in three-column ascii (processed and azimuthally averaged data) from a Xenocs NanoInXider SW instrument. Monte-Carlo analyses were performed using McSAS 1.3.1, other analyses are in the Python 3.7 worksheet. Graphics and result tables are output by the worksheet.
A brief introduction to the efforts we have done in our lab towards AI/ML analysis of SAXS data. For this, we need to extend the data with an extensive, structured hierarchy of metadata and associated data. A practical look into the information stored in our files, and the organization of the files in a data catalog is presented.
The SPONGE
(2020)
Recorded at the Better with Scattering workshop 2020, this talk highlights the complete set of data correction steps that we do for the MAUS, and how they can be used elsewhere too. This links well with the talk in this series by Dr. Tim Snow, and also highlights the details of the background subtraction that needs to be done.
X-ray generation
(2020)
After a colleague sent me a more useful measurement from a laboratory instrument than what I could get from the beamline, I knew it was time to reassess my life's choices. Over the course of several subsequent post-doc and permanent positions around lab instruments, I managed to refine a flexible, comprehensive methodology for data collection, correction and analysis which can be applied to many X-ray scattering investigations at the lab and at the synchrotron. With the help of friends at round places, this methodology was implemented and put into production, and has been delivering high-quality data since then. Now, we have almost all possible data corrections (for X-ray scattering) implemented, and are improving the hardware to deliver higher-quality metadata to enable the corrections to be performed to a higher accuracy.
Simultaneously, we have set up a mini-large facility at BAM with the MAUS, the Multi-scale Analyzer for Ultrafine Structures. The MAUS combines the freedom of a laboratory instrument, with the spectrum of users of a beamline: besides measuring our own samples, and performing our own machine and methodology developments, we have opened this instrument for collaboration with fellow scientists from within BAM and from external institutes and universities. Here, we provide a comprehensive support for these collaborations, guiding the user from concept to sample selection, to interpretation and analysis. In 2019, we have supported over 30 different projects this way, leading to seven co-authored publications involving the MAUS in that year alone. As the MAUS uses the latest iteration of our comprehensive measurement methodology, the data quality is unmatched by any other lab instrument, and fully traceable to boot.
The freedom of the laboratory allows for more proof-of-principle experimentation than what is possible at the synchrotron. Therefore, the MAUS provides a good first (and sometimes final) step towards many experimental materials science investigations, nicely complementing the capabilities of the synchrotron. If and when more flux is needed, the step to the synchrotron is now smaller than ever, in particular with the same method