Resource Themes

Resource themes are collections of related resources developed and provided through PSDI and our partners - grouped by their contributing group or community and their Physical Sciences research theme. In some cases these collections of resources are targeted at a particular community.

You can narrow down the list of resource themes displayed on this page by selecting from the filters on the left. Click on a resource theme of interest to find out more about it, access the resource landing page and to access the individual resources that belong to it.

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Showing 25 of 28 resources

Data Sources for PSDI Cross Data Search

This resource theme includes data sources searchable via the PSDI Cross Data Search service. Users can search for substances (e.g., molecules or crystal structures) or publication references, depending on the selected data sources. It incorporates data from PSDI, partners, OPTIMADE providers, and other relevant external sources, offering a comprehensive discovery tool for the research community.

PSDI Data Conversion

Your one-stop shop for file format conversion, PSDI's Data Conversion Service enables users to seamlessly convert between different file formats and assess the quality of proposed conversions. Several options are available, depending on whether you want to upload your data to PSDI or run the conversion locally or as part of your own applications. These options include: the PSDI Hosted Web Application, a standalone Command Line application that can be installed and run locally, and a Python library for integration into your own pipelines.

PSDI Data Revival

The majority of laboratories and research groups have stacks of paper notebooks filled with valuable data that is difficult and time-consuming to access and use. Data Revival is an AI-driven service that can seamlessly convert your handwritten lab book pages into machine-readable data, unlocking the potential of your chemistry data. Operating with a high degree of accuracy, Data Revival creates relational links across your chemical data and enables users to run semantic searches across potentially millions of data points, including complex chemical structures. PSDI offers users the chance to experience Data Revival's capabilities firsthand.

PSDI Knowledge Base Topics

The PSDI Knowledge Base offers guidance and training resources for physical scientists covering various themes related to working with scientific research data, and using the technologies and resources provided by PSDI. The main collections of topics provided are described in this resource theme.

PSDI Organic Toolkit

The PSDI Organic Toolkit is a suite of web apps to support organic chemistry researchers in transparent and high-quality data-reporting. We provide services for visualisation of reaction scope and the variability of numeric reaction outcomes.

PSDI Subscribed Resources

This resource theme brings together trusted proprietary data sources and services developed by external providers, which PSDI subscribes to in order to make them available to its users. These include resources developed by the Cambridge Crystallographic Data Centre (CCDC), FIZ Karlsruhe and DECHEMA. Resources from the CCDC comprise the Cambridge Structural Database (CSD) and associated software which can be accessed through the PSDI Cross Data Search, the CCDC web interface, and via remote desktop. FIZ Karlsruhe develop and maintain the Inorganic Crystal Structure Database (ICSD), which is available in its own search web interface. DECHEMA develop and maintain DETHERM (Thermophysical Property Data) which is available via its own search web interface. Access to these resources requires PSDI login credentials, and certain datasets or tools may be restricted to specific PSDI user groups.

AI Ready Datasets

AI ready datasets give physical sciences researchers data they can trust and reuse. By standardising formats, capturing essential metadata, and ensuring experimental and simulation outputs are findable and interoperable, they remove the hidden labour of data cleaning and preparation. This lets researchers focus on analysis and discovery, and it enables AI tools to work reliably across instruments, facilities, and disciplines. This resource theme brings together datasets designed to support AI and machine learning workflows, ranging from general purpose collections that researchers can shape for their own models, to task-specific datasets that already include annotations and predefined training, validation, and test splits. Every record includes one or more datafiles accompanied by a Croissant format metadata description (https://mlcommons.org/working-groups/data/croissant/), ensuring that structure, provenance, and context are captured in a machine readable way.

BioSim (Biomolecular Simulations) Data Resources

BioSimDR (Biomolecular Simulation Data Resources) is a collection of tools and data services designed to transform and store biomolecular simulations by capturing and archiving the full provenance of each simulation. Our tools can be used to meticulously record every step - from experimental structures to force field configurations and computational parameters - ensuring research is reproducible, transparent, and efficient. Our data services allow for biomolecular simulations to be stored, shared, and discovered in one place. This approach helps scientists to avoid redundant efforts, improves the reliability of findings, and supports AI-driven drug discovery and molecular modelling.

Case Studies: Discovering Value in Legacy Data

This resource presents data exemplars and detailed case study reports from our investigations into unlocking the potential of legacy data. It highlights our approaches to meaningfully revive outdated datasets, enhancing their quality, and maximising their value. These efforts aim to provide practical guidance on methods for resurrecting data, uncovering value in hidden data sources, and accelerating scientific progress.

Collaborative Computational Project for NMR Crystallography (CCP-NC)

The Collaborative Computational Project for NMR Crystallography (CCP-NC) supports a multidisciplinary community by developing and integrating software tools that advance NMR crystallography. By combining experimental NMR with computational methods, CCP-NC enables researchers to gain atomic-scale insights into structure, disorder, and dynamics in the solid state. A key resource is its repository for first-principles NMR calculations, which standardises data using the .magres file format. The project also provides specialised software, such as MagresView, for data processing and visualisation, along with tools for electronic structure calculations and ab initio NMR parameter prediction. Additionally, CCP-NC offers training opportunities and access to high-performance computing (HPC) through the Young supercomputer, further enhancing research capabilities. Through these resources, CCP-NC empowers researchers to conduct cutting-edge NMR crystallography, improving efficiency, fostering collaboration, and expanding professional networks. By facilitating the development and adoption of advanced computational methods, the project contributes to the broader scientific community, driving innovation in materials research.

CrystaLLM-pi (AI-driven Prediction of Crystal Structure from Composition and Experimental Data)

CrystaLLM-pi is an open-source, transformer-based tool for generating crystalline materials directly in CIF format using large language model techniques. It enables rapid exploration of crystal structure space and supports both unconditional generation and property-conditioned design, allowing users to target materials with specific characteristics such as band gap, density, photovoltaic efficiency, or XRD patterns. Designed for materials scientists, it provides a fast, data-driven complement to traditional simulation methods, helping to generate candidate structures for further study or interpret experimental data. CrystaLLM-pi is available as source code, a prebuilt container for local deployment, and a web application for interactive use.

Data Collections for Chemical Modelling

This resource theme offers access to carefully curated data collections from diverse sources, as well as data exemplars developed through case studies. These exemplars explore innovative methods to enhance data capture, improve quality, facilitate sharing, and revive legacy data in meaningful ways. Our goal is to create, store, and manage robust data collections in the physical sciences, ensuring they are easily accessible to researchers. By simplifying data discovery and reuse, we promote broader utilisation and accelerate scientific advancements.

Data to Knowledge

Machine learning has transformed the field of materials modelling in the last few years. Given access to high-quality data from computations and/or experiments, machine learning can be used to develop expert systems powered by large language models (such as ChatGPT). These expert systems can be to train surrogate models that can predict properties of structures, eliminating the need for simulations, or speed up simulations by using machine learnt interatomic potentials (MLIPs). The Data to Knowledge resource theme is dedicated to making the creation of these models possible by providing data infrastructure and workflows enabling the generation and exploitation of these machine learnt models. The Data to Knowledge collections comprise curated datasets designed for use in machine learning or generated through machine learning. An example is the Machine Learning Interatomic Potentials (MLIPs) data collection, which includes MLIPs XYZ files used for training, the trained model itself, and, where possible, related data such as AIIDA provenance records. Making these datasets available enables those without the resources to compute data themselves to utilise them for machine learning and modelling. Training is a central focus of this resource theme. We provide two types of training: general training and tool-specific training. Our general training is provided as self-paced learning online.

Galaxy Workflow Management System

Post-processing experimental and simulation data from large-scale experiments conducted at national facilities requires integrating diverse software tools from various domains into cohesive workflows. Although these workflows can be highly complex, the software tools and data associated with them can be effectively managed using the open-source, web-based Galaxy Workflow Management System. Designed for FAIR data analysis, Galaxy enables users to run code in interactive environments, share and publish results, workflows, and visualisations, and ensure reproducibility by capturing and packaging the data, metadata, and provenance models essential for replicating and understanding their analyses. Use cases are provided for muon science and catalysis experiments, with plans to expand to other large-scale experiments.

High-Accuracy Excited-State Reference Benchmark Dataset for Organic Semiconductors

The BenchmarkSet1500 resource theme provides a dataset of multireference excited states for 1500 small organic semiconductors, alongside a Python-based workflow used to generate the associated high-level excited-state calculations. It is designed for researchers in organic electronics and data-driven chemistry who require reliable and reproducible excited-state data, as well as those developing machine learning models or screening pipelines. By combining standardised computational workflows with multi-level electronic structure methods (TD-DFT, CASSCF, NEVPT2), the resource theme enables reproducible data generation and delivers an AI-ready dataset suitable for structure-property analysis, direct quantum chemistry method comparison, and molecular design.

Machine Learning for Next-Generation Transmission Electron Microscopy (TEM)

TEMPOS (Transmission Electron Microscopy Particle Outline Segmentation) is used to generate synthetic datasets and train segmentation models via transfer learning, which are subsequently applied to segment experimental images to identify particle size, shape and morphology, across a range of substrates. Both the experimental data and their synthetic training counterparts, along with the resulting segmentation outputs, are archived and made available through 'TEM-ParticlesDB', an open-source repository of Transmission Electron Microscopy (TEM) images.

Open-source Repositories for Flow Battery Data

This resource theme pertains to data and metadata collected during flow battery reproducibility studies that have been conducted by over 30 institutions across the world. A community data collection contains extensive metadata associated with cell assembly and electrochemical testing, whilst a live data dashboard is available for visualising the raw and processed electrochemical data collected by the participants.

ORKiM: An Open Repository for Kinetic Mechanisms of Reacting Systems

ORKiM is an open-access database designed for the storage, dissemination, and retrieval of kinetic mechanisms describing reacting processes. The mechanisms hosted within ORKiM serve as foundational building blocks for the development of kinetic models, which are essential tools in chemical engineering for describing, analysing, and predicting the behaviour of reacting systems. Each record within the database corresponds to a specific chemical reaction mechanism and includes the set of reactions (e.g. elementary reaction steps, lumped reactions) that define the reaction network, together with the associated kinetic parameters. These parameters may be reported in the form of frequency factors and activation energies, or directly as rate coefficients and kinetic expressions. By enabling the dissemination and long-term preservation of kinetic mechanisms, ORKiM aims to improve reproducibility, transparency, and collaboration within the reaction engineering community. The repository is intended to support both experimental and computational researchers working on kinetic modelling, catalysis, combustion, pyrolysis, atmospheric chemistry, and related fields. Researchers developing and publishing kinetic models are strongly encouraged to contribute their mechanisms to ORKiM in order to benefit the wider scientific community and promote the reuse and validation of kinetic data.

PrISMa: Process-Informed design of tailor-made Sorbent Materials for energy efficient carbon capture

The PrISMa project (https://prisma.hw.ac.uk/) aims to accelerate the transition of the energy and industrial sectors to a low-carbon economy by developing a comprehensive technology platform that enables tailored, cost-effective carbon capture solutions for diverse CO2 sources and utilization/destination scenarios. This platform offers an integrated computational and experimental screening environment for identifying economically viable Metal-Organic Frameworks (MOFs) for carbon capture applications through a suite of advanced tools and data resources. The platform connects material properties to process engineering, system-level techno-economic analyses, and life-cycle assessment simulations across multiple CO2 point sources and carbon sink options. The overall PrISMa system include a range of client-side simulation tools (for laptop/desktop and HPC) and works alongside a bespoke web service for centralised data storage, providing an online viewing platform and related API services for client-side integration. The PrISMa Platform encompasses several interconnected projects, including the MOFevaluator Project. Various open datasets, downloadable tools and online services are listed against these projects, which enable users to explore, visualize, and access comprehensive data resources.

Project M: Calcium Carbonate Diffraction Datasets With Fit and Parameter Data

Project M (https://www.diamond.ac.uk/ProjectM/) was a citizen science initiative run with UK secondary schools that generated an open collection of more than 650 calcium carbonate diffraction datasets, crystallised under a wide range of additives and conditions, with all associated fits and parameters included. This dataset forms part of the PSDI 'AI for Science' sub project ('Provision of 'AI ready' data: prototyping data pipelines and repositories', grant APP84520, award UKRI2697, opportunity OPP1033: EPSRC AI for Science) and has been selected and prepared as an example of AI ready data suitable for training AI tools and systems.

PSDI Digital Lab Notebook Resources

This resource theme provides a range of services and guidance to help users explore and appreciate the importance of digital notebooks in documenting their experiments and research. The guidance introduces the benefits of both generic digital notebooks and Electronic Laboratory Notebooks (ELNs), and the different considerations for implementation in laboratories. The resource theme includes case studies and exemplars. It also links to ELNFinder which is an external service for which PSDI is an active collaborator. This service helps users find the most suitable ELN based on a comprehensive set of criteria.

PSDI Skills4Scientists

Today's researchers must possess skills that extend beyond the specialised knowledge gained through their degrees and professional roles. The Skills4Scientists guidance resources are designed to enhance researchers' capabilities in areas such as computing, research data management, and other transferable research skills, supporting them in all aspects of their work. These resources include interactive learning tutorials, practical guidance topics, and best practices. Additionally, we are collaborating with the community to continuously grow and improve the guidance offered through PSDI.

SimpNMR (Software for Ab Initio-assisted Analysis of pNMR Data) and SimpNMR_DB (Curated Database)

SimpNMR is a Python package that simplifies analysis of the NMR spectra (e.g. 1H, 13C) of paramagnetic metal complexes in solution by incorporating outputs of ab initio calculations. It allows for prediction of the 1D spectra, assignment of experimental peaks, and fitting of the magnetic susceptibility tensor and the correlation times to experimental pNMR data. With variable temperature experiments, SimpNMR can also extract complete g-tensor in the molecular frame and in certain cases even D-tensor from solution pNMR data. SimpNMR_DB is a curated database that stores input data for SimpNMR analysis.

Thematic Portal (Catalysis Data Infrastructure Example)

The Physical Sciences Data Infrastructure (PSDI) is set to become the largest UK data infrastructure for the physical sciences, supporting diverse research communities in accessing and sharing data resources. Community portals are designed to provide customized access points, enabling communities to showcase the research outputs generated using PSDI's resources and services. The database, services, code, and deployed portal demonstrate the benefits of community portals, highlighting their adaptability and ease of customization and deployment for diverse research communities.

TopoStats and AFMDB: Resources for Atomic Force Microscopy (AFM) Image Analysis

This resource theme contains AFM (Atomic Force Microscopy) resources that bring together tools and data collections to support the processing, analysis, and sharing of AFM datasets. The TopoStats tool enables the batch processing of AFM datafiles to obtain rich, quantitative statistics and is developed with the FAIR principles for research software in mind. The accompanying Community Data Collection, AFMDB, provides a platform for the sharing of AFM datafiles, along with processed images, to encourage open data practices and aid the development of deep learning tools for enhanced AFM analysis.

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