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ESS Data Management and Software Centre Summer School

  • Welcome

Python

  • Introduction to JupyterLab
  • Python basics
    • Basic language principles
    • Sequence data types
    • Control Structures
    • Working with functions
    • Intermediate topics
  • Using external libraries
    • Arrays with Numpy
    • Plotting with Matplotlib
    • Interactive widgets
    • Data analysis with Pandas
    • Unit testing

Proposals, DMPs and FAIR

  • The ESS Viewpoint

McStas

  • Simulation
  • Powder diffraction exercise
  • QENS exercise
  • SANS exercise

Reduction with scipp

  • Introduction to Scipp
  • Coordinate transformations
  • Powder diffraction data reduction
  • QENS data reduction
  • SANS data reduction

Analysis with EasyScience

  • Model-dependent analysis
  • Thinking about data probabilistically
  • The EasyScience framework
  • Fitting data with easyscience
  • Uniform priors in easyscience
  • Fitting Powder diffraction data
  • Fitting QENS data
  • Fitting SANS data
  • Markov chain Monte Carlo
  • Bayesian Model Selection
  • Bayesian analysis of powder diffraction data
  • Bayesian analysis of QENS data
  • Bayesian analysis of SANS data

Scicat

  • Dataset
  • Data Curation
  • FAIR Data
  • Data and Metadata
  • Data Catalog
  • SciCat
  • Python Libraries
  • Example
  • Data Curation Exercise

Appendices

  • Glossary
  • DMSC School Lecture Materials
  • Repository
  • Open issue

Index

B | C | H | L | M | N | O | P | T | W

B

  • Bayesian methods

C

  • credible intervals

H

  • homoscedastic

L

  • likelihood

M

  • Markov chain Monte Carlo
  • maximum likelihood estimation
  • model dataset

N

  • nested sampling
  • normal distribution

O

  • optimisation algorithm

P

  • parameters
  • posterior distribution
  • prior knowledge

T

  • thinning

W

  • walkers

By Data Management and Software Centre

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