• Image of Ashley Moreno

    Why should industry care about the FAIR Data Principles?

    Formally published in Nature Scientific Data in 2016, the FAIR Data Principles provide a framework for scientific data management and stewardship. “FAIR” is an acronym for the Findability, Accessibility, Interoperability, and Reusability of data—for both humans and machines. In this Q&A-style blog, Carmen Nitsche (CCDC US general manager who is also active in several InChI and IUPAC data standards initiatives) answers common questions about how the FAIR Data Principles can help solve real-world challenges.

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  • Image of Jeff Lengyel

    Metal-organic frameworks (MOFs) Talk and Networking Event recording, presentation, and more!

    On 14 October, we hosted Dr Peyman Z. Moghadam from The University of Sheffield at our MOFs networking event. He presented his talk, High-throughput Computational Screening for MOF Materials Discovery. He spoke on how the analysis of MOFs data can support and guide the development of novel MOFs to suit specific applications like energy storage, catalysis, and CO2 sequestering. Here, you'll find materials from the event, including a recording of his presentation.

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  • Image of Ilaria Gimondi

    Introducing our CSD Champions

    It is my pleasure to introduce you to our new CSD Champions. Our CSD Champions are a network of CCDC collaborators and CSD experts who will help us create a stronger connection between communities around the CCDC, so we can help to advance structural science together.

    Who are our CSD Champions? What do they do? What is the goal of the initiative? Read further to find out more.

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  • Image of Sophie Bryant

    Our approach to the FAIR Data Principles

    The FAIR Data Principles stand for findability, accessibility, interoperability, and reusability of data for both humans and machines. Here we highlight a few ways CCDC supports the FAIR Data Principles.

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  • Image of Ioana Sovago

    Understanding solid form stability with Hydrogen Bond Statistics

    We’re pleased to present a new structural analysis tool in our latest release: Hydrogen Bond Statistics. Used in combination with the other solid form risk assessment tools in CSD-Materials, this allows you to understand how different characteristics influence stability in your solid-state material. Here we explain how Hydrogen Bond Statistics works.

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  • Image of Francesca Stanzione

    Pharmacophore data mining for drug discovery: updates to CSD-CrossMiner for the 2021.2 CSD Release

    Our software CSD-CrossMiner matches molecules to targets through pharmacophore-based data mining. Here we explain how it’s improved in our 2021.2 CSD release.

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  • Image of Ioana Sovago

    CSD in Action: achieving the largest possible mobility of organic semiconductors

    Here we highlight a paper by researchers at the University of Liverpool and Università di Siena who used the Cambridge Structural Database (CSD) to identify a set of promising compounds for use in semiconductors and a new tool for discovering materials with electronic properties. This is part of our series highlighting examples of​​ the Cambridge Crystallographic Data Centre (CCDC) tools in action by scientists around the world.

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  • Image of Peter Wood

    Remembering Jack Dunitz—crystallographer and pioneering scientist

    The CCDC team were deeply saddened to hear that Jack Dunitz has passed away at the age of 98. Here we reflect on Jack’s life and work with memories from colleagues and friends.

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  • Image of Paul McKeown

    CSD Data update September 2021

    We are pleased to announce the September 2021 data update of the Cambridge Structural Database (CSD) is now available!  This data update brings you 16,688 new organic and metal-organic experimentally determined structures (17,283 new entries) and increases the total size of the CSD to over 1,129,000 structures (1,152,000 entries).

     

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  • Image of Ioana Sovago

    Why do aromatic interactions matter?

    In our 2020.1 CSD Release, we launched the Aromatics Analyser – our first feature in Mercury based on a Neural Network that leverages deep learning. The tool allows you to quantitatively assess aromatic ring interactions and their likely contribution to the stability of a crystal structure. In this blog, let’s explore why it’s so important to understand aromatic interactions using ibuprofen and benzoic acids as examples.

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