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Community Data Collections is a collaborative, subject-focused research data repository within the Physical Sciences Data Infrastructure (PSDI). It provides openly accessible datasets, currently serving the BioSim and Data to Knowledge communities, to facilitate the publication, sharing, discovery, and reuse of valuable data. The repository adheres to FAIR principles by implementing a comprehensive metadata schema that incorporates both standard properties and custom fields tailored to scientific needs. Metadata and full datasets are freely available to all users, and plans are in place to expand Community Data Collections by including additional relevant scientific communities in the future.

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.

The PSDI Cross Data Search service enables users to search for data across data sources provided by PSDI and other relevant data sources from the wider community using our online interface. The service provides a number of different ways to perform a search, for example searching substances by chemical formula, name, or number of elements, with the option to refine searches using a periodic table interface. Specialised searches can also be performed for molecules using molecular structures, crystalline forms, or specific symmetry or periodicity characteristics. There is also an advanced search option, enabling custom queries to be constructed for users who know which fields in the data sources they want to search. The search results provide both a summary of the data and links to the source of the data. Although the majority of data sources are completely open, authentication may be required for some data sources.

A range of interactive, self-paced learning modules provided by PSDI.

The PSDI Knowledge Base serves as a comprehensive resource for physical scientists and research support staff, offering guidance on a variety of themes related to research data, PSDI technologies and related resources. It also provides centralized access to a variety of training and learning, including self-paced courses, tutorials, webinar recordings, and in-person sessions such as workshops and summer schools. Additionally, the Knowledge Base offers technical support resources, including FAQs to address common challenges, guidance on how to contact us, and how to raise a support request if you can't find an answer to your question.

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.

abcd, Data Management for Machine Learning Interatomic Potentials (MLIP), offers a suite of tools that allow users to programmatically import data from a repository and utilize it on their local infrastructure. This enables exploration of data in ways that would be too resource-intensive for a general service. Designed specifically for machine-learned interatomic potentials, abcd supports both OpenSearch and MongoDB as backend options, giving users flexibility in their setup. The tool is developed in collaboration with the University of Cambridge.

AiiDA-MLIP is a plugin for AiiDA, a versatile workflow management system designed for simulations in materials and molecular sciences. Beyond managing workflows, AiiDA distinguishes itself from traditional tools by providing detailed data provenance, ensuring transparency and reproducibility in simulations. AiiDA-MLIP integrates the Janus core into the AiiDA ecosystem, enabling seamless utilization of machine-learned interatomic potentials (MLIPs) within this robust framework.

The Amber plugin for AiiDA aims to enable the capture and sharing of the full provenance of data when parameterising and running molecular dynamics simulations.

Topology, trajectory, and AiiDA data provenance files from molecular dynamics simulations of biomolecules are stored in a repository specifically designed for their preservation and accessibility.

The CSD is the world's largest curated database of real-world, small molecule organic and metal-organic crystal structures. All data are reviewed and annotated by expert scientists to enable easy retrieval and subsequent reuse in a range of contexts. The CSD can be used to guide molecular and materials design by comparing features of new molecules and materials against what has been experimentally observed to identify areas of risk or strategies for optimisation. The CSD can be searched via the PSDI Cross Data Search service allowing results to be combined with other data sources to generate novel insights to guide the design of new molecules and materials.

The data of the community portal is accessible programmatically through custom web services (API calls). Each entity can respond to call with JSON data which can be used to create custom views or feed third party applications. For example, the UK catalysis hub uses a web service that delivers filtered bibliographic lists of publications, is used to populate the publications pages on its website and to generate custom reports. We can assist in developing tailored services to meet specific community requirements.

The database model for the CDI underpins the knowledge graph of a research community, linking research products (publications, data) and entities (researchers, institutions). It enables data discovery, showcases collaborations, and provides insights into research output. While centred on Published Works, Researchers, Institutions, Research Themes, and Data Objects, it can be extended to support domain-specific data (e.g., chemical entities, materials, analysis methods) tailored to the needs of different communities.

The deployed Thematic Portal for the UK Catalysis Hub (CDI) offers summary reports on publications and research impact within this important scientific domain. It provides insights into publications, authors, institutions, top researchers, countries, and themes, while tracking changes over time to identify research trends. This use case illustrates how thematic portals can showcase research productivity, emphasize emerging trends, and simplify access to supporting data.

The Chemical Availability Search helps researchers find and compare chemicals from various suppliers for their projects. It saves time and resources by identifying readily available compounds, budget-friendly options, or alternatives. Users can search by drawing a molecule or entering a compound name through the PSDI Cross Data Search service. The tool provides details for over 250,000 chemicals from UK suppliers, including product name, supplier, country, quantity, price, unit price, catalogue number, and a link to the supplier's website.

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").

MagresView provides an online interface for the Collaborative Computational Project for NMR Crystallography (CCP-NC). This visualisation tool enables crystal structure and NMR tensor visualisation from .magres files. SSNMR parameters like magnetic shielding and electric field gradient can be displayed as scaled ellipsoids super-imposed on the crystal structure for visualisation.

The Collaborative Computational Project for NMR Crystallography (CCP-NC) database of calculated solid-state NMR data from DFT codes in MAGRES format.

Web front end to access the Collaborative Computational Project for NMR Crystallography (CCP-NC) MAGRES database. The database can be used to search for MAGRES files by DOI, Magnetic shielding, Electric Field Gradient Vzz, Chemical name, Form, Unit cell formula, Molecular formula, MRD reference number, External database reference and/or License. Users who have logged into the database using the ORCID ID can also deposit their own MAGRES files.

A tool to visualize predicted NMR parameters in an intuitive, graphically accessible way and convert these parameters into input files for the most common NMR simulation programs. SSNMR parameters like magnetic shielding and electric field gradient can be visually represented as scaled ellipsoids super-imposed on the crystal structure for visualisation.

This dataset is derived from the National Institute of Standards and Technology (NIST) publication, "Critical Micelle Concentrations of Aqueous Surfactant Systems" (http://doi.org/10.6028/NBS.NSRDS.36). The data was extracted using the AI-driven Data Revival service (http://www.data-revival.com/), then manually enhanced by adding SMILES strings, and finally reprocessed by Data Revival to be fully converted into a machine-readable format for improved accessibility and interoperability.

Open-access collection of crystal structures of organic, inorganic, metal-organic compounds and minerals, excluding biopolymers