Activity log
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to: ["Moodle course", "e-learning"]
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to: ["extract key length scales"]
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to: Course image taken by Andrew Brookes.
Course created by **Amanda Martinez Rodriguez**
We will learn with Pabitra Biswas what a [superconductor](https://en.wikipedia.org/wiki/Superconductivity) is, how we measure [magnetic fields](https://en.wikipedia.org/wiki/London_penetration_depth), extract key length scales ([vortex lattice](http://hyperphysics.phy-astr.gsu.edu/hbase/Solids/lattice.html)), see how the measurement works, what kind of data you get, and gain an overview of how to understand the new physics.
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to: [" analyse sas test data", " - polydispersity - resolution - interparticle interaction"]
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to: Teacher: **Wojciech Potrzebowski**
This course will demonstrate how to approach small-angle scattering (SAS) data analysis.
In **Section 1** you will familiarise yourself with the basic use of SasView.
In **Section 2** , you will **analyse SAS test data** with SasView software and see the effects of:
- Polydispersity
- Resolution
- Interparticle interaction.
**Section 3 and 4** are optional and will lead you through simultaneous fitting and \[P(r) \] analysis.
Software for this session: **SasView 5.0.5.**
General introduction to SasView: [https://youtu.be/Sbzf\_wdlPnQ](https://youtu.be/Sbzf_wdlPnQ)
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to: ["sans\r\ncase study", "sans wiki simulation project", "mcstas simulation tool"]
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to: This course is split into two sections:
- Theoretical background to SANS
- Case study of nanodisks using the LOKI instrument
**Useful links:**
[SasView course](https://pan-learning.org/moodle/course/view.php?id=19)
[Wiki page on SANS
](https://pan-learning.org/wiki/index.php/Small_angle_neutron_scattering,_SANS)[Wiki problems (exercises with solutions) on SANS](https://pan-learning.org/wiki/index.php/Page_of_all_exercises#Exercises_in_Small_angle_neutron_scattering)
[Wiki simulation project (based on SANS-2 at PSI)
](https://pan-learning.org/wiki/index.php/Simulation_project_SANS-2:_A_small_angle_neutron_scattering_instrument)[McStas Simulation Tool:](https://sim.e-neutrons.esss.dk/instrument-menu/intro-ns) Use the same login details as for pan-learning.org. There are currently two instruments: SANSsimple and SANSsimpleSpheres
Course photo courtesy of CERIC-EIRC
pan-training
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Introduction to small angle scattering at 2025-02-01 11:30:59 UTC.
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to: ["id=19\r\nsans simulation tool", "sanssimplespheres\r\npan-learning wiki pagehttps", "_a_small_angle_neutron_scattering_instrument\r\nill youtube videowww", "uwe keiderling - berlin neutron scattering schoolmartin müller - hercules 2016frank gabel - hercules 2016marianne liebi - hercules 2016", "paolo mariani - sisn 2015francesco spinozzi - sisn 2015leonardo chiappisi - bilbao neutron scattering school 2019adel len - cets 2019g", "pépy - cets 2019wim bouwman - erice 2017", "markus strobl - erice 2017"]
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to: **Link to SasView moodle course on PaN-learning.org**
[https://pan-learning.org/moodle/course/view.php?id=19](https://pan-learning.org/moodle/course/view.php?id=19)
**SANS simulation tool with McStas**
[https://sim.e-neutrons.esss.dk/instrument-menu/intro-ns
](https://sim.e-neutrons.esss.dk/instrument-menu/intro-ns)Use the same login details as for pan-learning.org. [
](https://sim.e-neutrons.esss.dk/instrument-menu/intro-ns)There are currently two instruments: SANSsimple and SANSsimpleSpheres
**PaN-learning wiki page**
[https://pan-learning.org/wiki/index.php/Small\_angle\_neutron\_scattering,\_SANS
](https://pan-learning.org/wiki/index.php/Small_angle_neutron_scattering,_SANS)[https://pan-learning.org/wiki/index.php/Page\_of\_all\_exercises#Exercises\_in\_Small\_angle\_neutron\_scattering](https://pan-learning.org/wiki/index.php/Page_of_all_exercises#Exercises_in_Small_angle_neutron_scattering)
[https://pan-learning.org/wiki/index.php/Simulation\_project\_SANS-2:\_A\_small\_angle\_neutron\_scattering\_instrument](https://pan-learning.org/wiki/index.php/Simulation_project_SANS-2:_A_small_angle_neutron_scattering_instrument)
**ILL youtube video**
[www.youtube.com/watch?v=doovK7DBuEM](https://pan-learning.org/moodle/www.youtube.com/watch?v=doovK7DBuEM)
There are also a set of animations for part of a SANS instrument, one of which is
[www.youtube.com/watch?v=xxYj9VLlBKI](https://pan-learning.org/moodle/www.youtube.com/watch?v=xxYj9VLlBKI)
**Slides from lectures by:**
Uwe Keiderling - Berlin neutron scattering school
Martin Müller - Hercules 2016
Frank Gabel - Hercules 2016
Marianne Liebi - Hercules 2016 (SAXS)
Paolo Mariani - SISN 2015
Francesco Spinozzi - SISN 2015
Leonardo Chiappisi - Bilbao neutron scattering school 2019
Adel Len - CETS 2019
G. Pépy - CETS 2019
Wim Bouwman - Erice 2017 (SESANS)
Markus Strobl - Erice 2017 (SEMSANS)
The following sections were pulled from existing courses on PaN-learning.org
pan-training
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Including Jupyter Notebooks in your Course at 2025-02-01 11:30:59 UTC.
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to: ["analyse experimental data", "virtually explore large scale facility instruments", "- teach students data modelling", "include common scientific python modules"]
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to: Course image by [GustavoAckles](https://pixabay.com/users/gustavoackles-6863234/?utm_source=link-attribution&utm_medium=referral&utm_campaign=image&utm_content=4869815) from [Pixabay](https://pixabay.com/?utm_source=link-attribution&utm_medium=referral&utm_campaign=image&utm_content=4869815).
As a teacher, you can link a JupyterHub instance to your course that runs on our e-learning servers.
The JupyterHub instance runs as a docker container that is launched when a student clicks on the link.
The container will clone your chosen github repository where you can edit and store Jupyter notebooks.
Students will be able to open these Jupyter notebooks and execute them remotely.
Crucially students do not require Jupyter to be installed on their computers or have prior knowledge of python.
Examples where this might be useful:
- Show students how to **reduce** and **analyse** experimental data themselves using **python**.
- Introduce students to **python modules** or **software** used in data reduction and analysis.
- Allow students to **virtually explore** large scale facility instruments. For example by using **McStas** (neutron instrument simulator).
- Teach students **data modelling** and **simulation** techniques that use **python**.
**Requirements:**
What the student will interact with must be in the form of a **Jupyter notebook**.
Your notebooks must be on a public **github repository**.
Note that the **entire repository** you provide will be **cloned** not just selected notebooks. However it does not have to be the master branch.
Our containers already include common scientific python modules such as numpy, matplotlib and ipython.
In addition we have containers with the following software installed:
1. McStas, McXtrace, SCIPP, SasVIew
2. Crispy
3. SimEx
If you require a container with other software to be installed it is possible to have a custom container created.
Please email admin@pan-learning.org for help.
**Useful Links:**
- **[Jupyter notebooks](https://jupyter.org/)**
- [**What is a docker container**?](https://www.docker.com/resources/what-container)(and the **[wiki page](https://en.wikipedia.org/wiki/Docker_(software))**)
- [**Docker and Jupyterhub**](https://hub.docker.com/r/jupyterhub/jupyterhub/#help-and-resources)
If you wish to include a link to a Jupyterhub container for your course.
Please fill the form: Information to set up your container
Once you have filled in the form you will receive an email to let you know the JupyterHub link is being set up and any further instructions that are required.
Please note it may take a few days to be up and running.
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to: ["recommend open broadcasting software", "video editing software"]
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to: We recommend Open Broadcasting Software (OBS) as a great way to record yourself giving a presentation or lecture.
It has more flexibility and results in a higher quality video than simply recording your screen on zoom/skype.
It is easy to install and much easier to use than any video editing software.
Use the following four resources to help you:
1. **Introduction to OBS** and how it can be used.
2. Tips for good **video layout** (this is useful regardless of whether you chose to use OBS).
3. Links to **OBS download instructions** for Mac, Windows & Linux.
4. How you could **include live annotations** or a blackboard style presentation with OBS
Photos by [Jeremy Yap](https://unsplash.com/@jeremyyappy) and [Science in HD](https://unsplash.com/@scienceinhd) on [Unsplash](https://unsplash.com/s/photos/blackboard?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText)
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pan-training
pan-training
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Introduction to X-ray Spectroscopy Calculations at 2025-02-01 11:30:56 UTC.
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pan-training
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Introduction to X-ray fluorescence (XRF) at 2025-02-01 11:30:56 UTC.
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to: ["gain knowledge basic knowledge", "cultural heritage\r\nshort test", "real raw data\r\n\r\nkey resources"]
Someone
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to: Introduction to X-ray fluorescence (XRF) is a short introductory course intended for students of physics, chemistry and material science that want to get acquainted with basic principles of XRF and some of its use cases. It has been designed by [Alessandra Gianoncelli](http://www.elettra.eu/PEOPLE/index.php?n=AlessandraGianoncelli.HomePage) (Elettra Sincrotrone, Italy) and [Aljoša Hafner](https://www.ceric-eric.eu/about-us/staff#hafner-aljosa---computational-physicist) (CERIC-ERIC, Italy).
Course duration is approximately 45 min.
The main goal of the course is to gain knowledge basic knowledge of synchrotron XRF and its applications. The following topics are covered:
- Basic theoretical background and principles
- Instrumentation and experimental setup (source, samples, detection)
- Scientific examples from biomedical applications and cultural heritage
- Short test of knowledge gained on real RAW data
Key resources:
- [https://en.wikipedia.org/wiki/X-ray\_fluorescence](https://en.wikipedia.org/wiki/X-ray_fluorescence)
- [https://www.amptek.com/resources/xrf](https://www.amptek.com/resources/xrf)
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to: ["python based mcstas script", " data analysis library pandas", "mcstas script\r\nextras\r\nscipp"]
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to: THIS IS THE PREVIOUS IKON PYTHON COURSE FROM MARCH 2021 - The new one can be found **[here](https://pan-learning.org/moodle/course/view.php?id=89)**
AS OF 16/09/2021 THERE IS AN ISSUE WITH THE LINK - THIS WILL BE FIXED SOON
This course has something for all levels of python and is split into 7 sections.
The first four contain a set of jupyter notebooks that are designed to be worked through individually. You are encouraged to pick and choose which sections you are interested in.
The other three contain notebooks to help you learn how to use python based McStas script and SCIPP along with an extra module with an example of Covid data anlysis.
1. **Jupyter basics: ** Jupyter notebook introduction.
2. **Python basics: ** Basic language principles, Sequence data types, Control structures, Working with functions, Using modules, Input and output, Python 2 vs 3, Python classes.
3. **Using external Libraries: ** Scientific libraries numpy, Plotting with matplotlib, Ipywidgets, Fitting scipy, Data analysis library pandas, Testing.
4. **Molecular visualization: ** Visualization tutorial, Atomistic simulation environment.
5. **McStas script**
6. **Extras**
7. **SCIPP**
The notebooks can accessed and run via the [Jupyter hub](https://jhubworkshop.esss.dk/hub/home) link. A new window will open, which will ask you to **Start My Server**.
You can then chose which environment you would like to use (Basic Jupyter with McStas or Jupyter with SCIPP). Note that both include all the notebooks.
To access the notebooks please navigate to **python-course-ikon** \> **notebooks**.
This course was created by the Data Reduction, Analysis and Modelling Group of the ESS
This version of the course was specifically created for IKON20 in March 2021.
Course image by [Chris Ried](https://unsplash.com/@cdr6934?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText) on [Unsplash](https://unsplash.com/s/photos/python?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText)
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to: Course created by **Sanghamitra Mukhopadhyay**.
This module is divided into 10 sections. First nine will teach you about QENS instruments and theory. A case study is presented in the last section.
Estimated Completion Time: **1 Hour**
pan-training
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Materials science in the virtual neutron facility at 2025-02-01 11:30:53 UTC.
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to: This short course allows you to investigate the properties of a rehargeable battery by neutron experiments at a virtual neutron faclity. Specifically you will investigate the various component of the battery by imaging, diffraction and quasi-elastic neutron scattering through a learning game.
The game was developed as a collaboration between [Labster](https://www.labster.com) and the group of Linda Udby at Niels Bohr Institite, University of Copenhagen.
The development of the game was made possible by a grant in the Education 2016 project of University of Copenhagen.
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to: Course photo by [Fletcher Pride](https://unsplash.com/@fletcher_pride?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText) on [Unsplash](https://unsplash.com/s/photos/quiz?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText)
This [quiz](https://pan-learning.org/moodle/mod/quiz/view.php?id=838) has 5 questions on the basics of neutron science.
pan-training
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Introduction to Muon Spin Spectroscopy at 2025-02-01 11:30:52 UTC.
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to: ["hifi muon instrument courtesy", "muon spin spectroscopy", "‘cosmic ray muons", "muon spin relaxation", "‘muon data analysis", "cosmic ray muons module", "muon data analysis modules", "learn basic data analysis"]
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to: Course image of [HiFi muon instrument](https://www.isis.stfc.ac.uk/Pages/Hifi.aspx) courtesy of [ISIS](https://www.isis.stfc.ac.uk/Pages/home.aspx) and [STFC](https://stfc.ukri.org/), UK taken by Stephen Kill.
Course created by **Peter Baker.**
Welcome to the course on muon spin spectroscopy. This course is made up of ten modules: ‘Cosmic Ray Muons’, ‘Different Types of μSR’, ‘Analysis Methods of μSR’, 'Studying Diffusion with Muon Spin Relaxation', ‘Muon Data Analysis using Mantid Workbench’, ‘Muon Data Analysis Using MantidPlot’, ‘Muesr’, ‘Using Maximum Entropy Analysis in Mantid’, ‘Analysing ALC Data in Mantid’ and ‘Muon Data Analysis Using WiMDA’.
Within the Cosmic Ray Muons module you will learn the fundamentals of muons and their applications within a simple experiment. The Different Types of μSR module covers the principles and applications of the six variations of μSR, and Analysis Methods of μSR goes through the various ways the data from these experiments can be analysed. Within the Muon Data Analysis modules, you will learn basic data analysis of real experimental data using either the MantidPlot or WiMDA software.
pan-training
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Small Angle Scattering Data Analysis using SasView at 2025-02-01 11:30:51 UTC.
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to: Teachers: Paul Butler & Wojciech Potrzebowski
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to: ["improve online material", "making online learning material", "conducting massive open online courses", "providing high quality feedback"]
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to: This set of manuals is intended to help developers/teachers on the e-neutrons portal make and improve online material that can be used for learning about neutron scattering.
In sum, they represent both some of the experiences we have had with making online learning material for the e-neutrons portal and the choices we have made regarding how to make an e-learning portal. Many of these choices differ from choices made when conducting massive open online courses (MOOCs).
Our design choices are based on educational research, and we pay a lot of attention to what the goals for the portal and for each learning activity are. In particular, we have used research literature on digital learning, on assessment for learning, and on intended learning outcomes. We seek to align our online activities and the way we assess them with intended learning outcomes. Since the goal for the portal is not to provide users with a formal set of papers on how well they have performed, we have chosen not to provide students with traditional summative assessment such as grading or other performance evaluators. Rather, we have chosen to focus on providing high quality feedback to facilitate that students engage as much as possible with the learning material.
The e-neutrons portal itself is an object of ongoing e-learning. Thus, we actively use student interactions with the portal to investigate more broadly how this type of e-learning works.