Guidance

This topic collection provides an introduction to metadata and standards, and why they are important for research data. This topic collection also provides information about how PSDI uses metadata to manage and describe our resources, including the terminology used in PSDI; an overview of the PSDI metadata profiles available, vocabulary descriptions, and guidelines for using PSDI metadata.

This collection of topics provides guidance about selected resources provided by PSDI and how to use them.

This collection of topics provides an introduction to research data management, including a discussion of different research data types, how to identify your own research data, and creating a data inventory. Guidance is provided on the different stages of the research data lifecycle and the benefits of data management planning, and the role of the Data Management Plan (DMP) together with an example DMP for physical scientists. This section also includes topics on best practices for managing data when working with others, and where you can get additional help with managing your research data.

This collection of topics provides guidance on good practices for sharing your research data. This section introduces the FAIR principles and their importance, and provides step by step guidance on what is needed to make your research data and related outputs such as code and methods findable, accessible, interoperable, and reusable, whether or not you share them publicly. Guidance is given on choosing and writing licenses for your data, creating a data package to share or archive, and considerations for choosing a repository.

Training is at the core of this resource theme. The Chemistry and Materials Machine Learning (CaMML) school is run by PSDI in collaboration with a range of communities. Training is targeted towards PhD students, in particular those in the Materials and Molecular Simulations field, who have experience of coding but are not highly experienced with machine learning. The aim of this in-person training is to introduce attendees to the latest methods of machine learning for the atomistic simulation of materials.

Tutorials with pre-loaded user environments for using aiida-gromacs to produce provenance workflows for biomolecular simulations ( "Lysozyme MD simulation with aiida-gromacs" and "GPCR coarse-grained simulation setup with aiida-gromacs").

This topic collection offers practical guidance on selecting and implementing electronic notebooks for research. It covers the benefits and challenges of adopting digital notebooks and advice on choosing the right notebook for your project. Additionally guidance is provided on considerations for moving from paper to digital and steps needed to successfully deploy an ELN solution.

This course provides an introduction to the subject of ethics and research integrity. This includes details of the ethical review process, how ethics needs to be applied to data that is used for Machine Learning and Artificial Intelligence. The course also provides guidance on Responsible Research and Innovation.

Collection of tutorials developed and maintained by the worldwide Galaxy community. Includes tutorial for galaxy users, developers and administrators.

Hands on tutorial for finding muon stopping site with pymuon-suite using Galaxy. Will answer the questions: What are stopping sites and why is finding them important? What computational techniques can be used to find stopping sites? How can Galaxy help me with my analysis?

This course covers the fundamentals of literature and referencing in the physical sciences. Participants will learn to identify various scholarly sources, understand the purposes of citations, and appreciate their importance in the scientific process.This course covers the fundamentals of literature and referencing in the physical sciences. Participants will learn to identify various scholarly sources, understand the purposes of citations, and appreciate their importance in the scientific process.

Training is at the core of this resource theme. These tools help users to get started using our tools.

This case study explores the challenges of losing valuable raw data when it is embedded within PDFs as part of supplementary information (ESIs). To address this, data was extracted from the supplementary information of the paper titled "Towards the Prediction of Antimicrobial Efficacy for Hydrogen Bonded, Self-Associating Amphiphiles" (http://doi.org/10.1002/cmdc.202000533) and converted into JSON format. The process was carefully documented to showcase how such data can be transformed into a more machine-readable format, improving accessibility and usability for further analysis.

The Mini-Tutorials for XAFS RO-Crate Workflows in Galaxy provide a brief introduction to working with RO-Crates in Galaxy. The development of the tutorials originated as quickstart guidance for running and inspecting workflows used for the reproduction of X-ray absorption fine structure (XAFS) analyses. These self-paced learning resources cover three aspects of the RO-Crate lifecycle in the Galaxy platform: Importing (and running) a workflow, Creation (Exporting) an RO-Crate and Importing (and inspecting) an RO-Crate.

Documentation of the underlying MuDirac muon software tool that has been implemented as a Galaxy tool in our Galaxy instance. The MuDirac simulation software integrates the Dirac equation for muonic atoms and compute their X-Ray transition energies.

Documentation of the underlying MuSpinSim muon software tool that has been implemented as a Galaxy tool in our Galaxy instance. Muspinsim models spin dynamics calculations for muon science experiments.

This course is designed to teach participants the basics of programming using Python. For new programmers, the course will teach you the fundamentals of programming using Python as an example. Whilst those who are more familiar with programming concepts can use the course to learn Python syntax and its unique features.

This course builds on the basics of programming covered in the Python I course. For beginners, it expands on the foundational concepts taught in first course. For more experienced programmers, further examples and guidance are added to help you master Python syntax and its unique features.

This course provides an introduction to Research Data Management. Participants will explore the data lifecycle, identify its various stages, understand the importance of collaborative working environments, and learn how to identify and manage different types of data within their projects.

The preservation, accessibility, and reuse of scientific data are increasingly vital in contemporary research. Historical datasets, often termed legacy data, provide significant opportunities for new discoveries, validation of past results, and insights through modern analytical methods. Despite their potential value, legacy datasets frequently suffer from poor documentation, outdated formats, and technological obsolescence, complicating their reuse. This report addresses these challenges through the reconstruction of databases originating from Second Harmonic Generation (SHG) experiments, conducted as part of a doctoral research project completed in 2011 at the University of Southampton.

This course is designed to help participants understand the principles of version control and how it can be used effectively in research projects. Participants will explore common version control methods, learn the associated terminology and find out how software like GitHub implements these methods.

Documentation for the underlying Larch open-source library that has been implemented as a Galaxy tool in our Galaxy instance. Larch is used for processing and analysing X-ray absorption and fluorescence spectroscopy data, and X-ray fluorescence and diffraction image data obtained from synchrotron beamlines. Larch provides a comprehensive set of analysis tools for X-ray absorption fine-structure spectroscopy (XAFS), including both X-ray absorption near-edge spectroscopy (XANES) and extended X-ray absorption fine-structure spectroscopy (EXAFS). Larch also provides visualization and analysis tools for X-ray fluorescence (XRF) spectra and XRF and X-ray diffraction (XRD) images as collected at scanning X-ray microprobe beamlines.