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This is a set of use examples for the HDF5Translator framework. This framework lets you translate measurement files into a different (e.g. NeXus-compatible) structure, with some optional checks and conversions on the way. For an in-depth look at what it does, there is a blog post here.
The use examples provided herein are each accompanied by the measurement data necessary to test and replicate the conversion. The README.md files in each example show the steps necessary to do the conversion for each.
We encourage those who have used or adapted one or more of these exampes to create their own conversion, to get in touch with us so we may add your example to the set.
Trinamic TMCL IOC is a Python package designed for controlling stepper motors connected to a Trinamic board using the TMCL language (all boards supported by PyTrinamic should now work, has been tested on the TMCM 6110 and the TMCM 6214). Since it is implementing the TMCL protocol, it should be easy to adapt to other Trinamic motor controller boards. This package assumes the motor controller is connected over a machine network via a network-to-serial converter, but the underlying PyTrinamic package allows for other connections too.
This allows the control of attached motors via the EPICS Channel-Access virtual communications bus. If EPICS is not desired, plain Pythonic control via motion_control should also be possible. An example for this will be provided in the example.ipynb Jupyter notebook.
This package leverages Caproto for EPICS IOCs and a modified PyTrinamic library for the motor board control, and interfaces between the two via an internal set of dataclasses. Configuration for the motors and boards are loaded from YAML files (see tests/testdata/example_config.yaml).
The modifications to PyTrinamic involved extending their library with a socket interface. This was a minor modification that should eventually find its way into the official package (a pull request has been submitted).
This presentation highlights ongoing scientific misconduct as found in academic literature. This includes data- and image manipulation, and paper mills. Starting with an expose of examples, it delves deeper into the causes and metrics driving this phenomenon. Finally a range of possible tools is presented, that the young researcher can use to prevent themselves from sliding into the dark scientific methods.
By automatically recording as much information as possible in automated laboratory setups, reproducibility and traceability of experiments are vastly improved. This presentation shows what such an approach means for the quality of experiments in an X-ray scattering laboratory and an automated synthesis set-up.
A round-robin study has been carried out to estimate the impact of the human element in small-angle scattering data analysis. Four corrected datasets were provided to participants ready for analysis. All datasets were measured on samples containing spherical scatterers, with two datasets in dilute dispersions and two from powders. Most of the 46 participants correctly identified the number of populations in the dilute dispersions, with half of the population mean entries within 1.5% and half of the population width entries within 40%. Due to the added complexity of the structure factor, far fewer people submitted answers on the powder datasets. For those that did, half of the entries for the means and widths were within 44 and 86%, respectively. This round-robin experiment highlights several causes for the discrepancies, for which solutions are proposed.
In our (dramatically understaffed) X-ray scattering laboratory, developing a systematic, holistic methodology1 let us provide scattering and diffraction information for more than 2100 samples for 200+ projects led by 120+ collaborators over the last five years. Combined with universal, automat-ed data correction pipelines, as well as our analysis and simulation software, this led to more than 40 papers2 in the last 5 years with just over 2 full-time staff members.
While this approach greatly improved the consistency of the results, the consistency of the samples and sample series provided by the users was less reliable nor necessarily reproducible. To address this issue, we built an EPICS-controlled, modular synthesis platform to add to our laboratory. To date, this has prepared over 1200 additional (Metal-Organic Framework) samples for us to meas-ure, analyse and catalogue. By virtue of the automation, the synthesis of these samples is automat-ically documented in excruciating detail, preparing them for upload and exploitation in large-scale materials databases alongside the morphological results obtained from the automated X-ray scat-tering analysis.
Having developed these proof-of-concepts, we find that the consistency of results are greatly im-proved by virtue of their reproducibility, hopefully adding to the reliability of the scientific findings as well. Additionally, the nature of the experiments has changed greatly, with much more emphasis on preparation and careful planning. This talk will discuss the advantages and disadvantages of this highly integrated approach and will touch upon upcoming developments.
In our (dramatically understaffed) X-ray scattering laboratory, developing a systematic, holistic methodology let us provide scattering and diffraction information for more than 2100 samples for 200+ projects led by 120+ collaborators. Combined with automated data correction pipelines, and our analysis and simulation software, this led to more than 40 papers in the last 5 years with just over 2 full-time staff members.
This year, our new, modular synthesis platform has made more than 1000 additional samples for us to analyse and catalogue. By virtue of the automation, the synthesis of these samples is automatically documented in excruciating detail, preparing them for upload and exploitation in large-scale materials databases. Having developed these proof-of-concepts, we find that materials research itself is changed dramatically by automating dull tasks in a laboratory.
This talk is intended to spark ideas and collaborations by providing an overview of: 1) the current improvements in our scattering laboratory methodology, 2) introducing our open, modular robotic platform that is used for systematic sample preparation, and 3) demonstrating the data structure of the synthesis logs and measurements. Finally, the remaining bottlenecks and points of attention across all three are highlighted.
In this talk, the importance of metadata is underscored by real-world examples.
Metadata is essential to alleviating the reproducibility crises in science. This imples that a wide range of metadata must be collected, with a heavy emphasis on the automated collection of such metadata. This must subsequently be organized in an intelligible, archival structure, when possible with units and uncertainties.
Such metadata can aid in improving the usage efficiency of instrumentation, as is demonstrated on the MOUSE instrument. This metadata can now be used to connect the various aspects of the holistic experimental procedure to gain better insights on the materials structure.
A second example shows the extraction and organization of such metadata from an automated materials development platform, collected during the synthesis of 1200 samples. These metadata from the synthesis can then be linked to the results from the analysis of these samples, to find direct correlations between the synthesis parameters and the final structure of the materials.
An isotopic effect of normal (H2O) vs. heavy water (D2O) is well known to fundamentally affect structure and chemical properties of proteins, for instance. Here we correlate results from small angle X-ray and neutron scattering (SAXS, SANS) with high-resolution scanning transmission electron microscopy to track the evolution of CdS nanoparticle size and crystallinity from aqeuous solution in presence of the organic ligand ethylenediaminetetraacetate (EDTA) at room temperature in both H2O and D2O. We provide evidence via SANS experiments that exchanging H2O by D2O impacts nanoparticle formation by changing the equilibria and dynamics of EDTA clusters in solution as investigated by nuclear magnetic resonance. The colloidal stability of the CdS nanoparticles, covered by a layer of [Cd(EDTA)]2- complexes, is significantly reduced in D2O despite the strong stabilizing effect of EDTA in suspensions of normal water. Hence, conclusions about nanoparticle formation mechanisms from D2O solutions can bare limited transferability to reactions in normal water due to isotopic effects, which thus need to be discussed for contrast match experiments.