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DTSTART;TZID=Europe/Paris:20260423T140000
DTEND;TZID=Europe/Paris:20260423T150000
DTSTAMP:20260502T132042
CREATED:20260310T164302Z
LAST-MODIFIED:20260501T130001Z
UID:36874-1776952800-1776956400@www.psdi.ac.uk
SUMMARY:Webinar: BioSimDR - A Collection of Data Tools and Infrastructure for Biomolecular Simulation
DESCRIPTION:This webinar illustrates how BioSimDR transforms scattered biomolecular simulation data into interoperable\, provenance-rich resources for broader reuse. \nThe recording of this webinar is now available on YouTube \n			\n				\n				\n				\n				\n				Abstract\nBiomolecular simulations generate rich\, atomic-level insights into the dynamics of complex biological systems\, but sharing\, interpreting and reusing these datasets remains challenging. BioSimDR (BioSim Data Resources) is a PSDI-funded initiative that works with the CCPBioSim and HECBioSim communities to bring FAIR principles to biomolecular simulation data. \nIn this webinar\, we will outline common barriers to simulation reproducibility\, including inaccessible protocols\, missing metadata\, and incomplete records of simulation steps\, and we will introduce the BioSimDR tools designed to address these challenges. We will demonstrate BioSimDB\, a prototype data repository tailored for biomolecular simulations\, and new provenance-capture tools that allow researchers to automatically record every simulation step for easier sharing and reuse. \nAttendees will learn: \n\n\n\nHow provenance capture supports reproducible and reusable MD simulations\nHow BioSimDB enables standardised storage\, discovery\, and sharing of biomolecular simulation datasets\nHow the BioSimDR initiative collaborates with the community to build consensus-driven standards for FAIR simulation data\n\n\n\nThis session is intended for researchers generating\, analysing\, or reusing biomolecular simulations who want to improve transparency and reproducibility in their workflows. \n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Biography\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Dr Jas Kalayan from STFC is a scientific software engineer specialising in reproducible workflow development and data‑sharing solutions for molecular simulation. She has a strong research background in advanced molecular modelling\, including the development of machine‑learned interatomic potentials and entropy‑based methodologies. Her work has supported molecular dynamics studies focusing on protein–ligand binding\, hydration phenomena\, and free‑energy calculations\, with an overarching goal of improving transparency and reproducibility in computational biomolecular science. \n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Watch the recording\nYou can watch the recording of this webinar via our YouTube channel. Slides are available on Zenodo. \n\n\n\n\n\n\n\n The PSDI team looks forward to seeing you at the webinar\, if you have any questions you can always get in contact with us.
URL:https://www.psdi.ac.uk/event/webinar-biosimdr/
LOCATION:Online\, Virtual Event\, Online
CATEGORIES:Webinar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Atlantic/Azores:20260430T150000
DTEND;TZID=Atlantic/Azores:20260430T160000
DTSTAMP:20260502T132042
CREATED:20260401T100337Z
LAST-MODIFIED:20260420T103423Z
UID:36980-1777561200-1777564800@www.psdi.ac.uk
SUMMARY:Webinar: From Project to Platform: New Resources on PSDI - Session 1
DESCRIPTION:Registration link: https://us06web.zoom.us/webinar/register/WN_kaav0loGQp24L4VDOEPuuA \nPSDI is pleased to launch a new webinar series entitled “From Project to Platform: New Resources on PSDI”. This series aims to showcase the high-quality tools and resources developed through the funding call 2025\, introduce them to a broader community\, and foster engagement with relevant user groups.   \n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Presentation 1\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				“Universal” Hyper-Active Learning for Machine Learning Interatomic Potentials\n			\n				\n				\n				\n				\n				Challenge\n			\n				\n				\n				\n				\n				\n\n\nBuilding accurate machine‑learning models of atomic interactions requires carefully curated training datasets\, yet generating these datasets is often the hardest and most time‑consuming step.\n\n\n\n			\n				\n				\n				\n				\n				Approach\n			\n				\n				\n				\n				\n				\n\n\nWe introduce ase‑uhal\, a Python tool developed through a PSDI Pilot Project (Oct 2025–Mar 2026).\nIt automates and accelerates dataset generation\, steering atomistic simulations toward the most informative configurations and avoiding redundant calculations.\nThe tool is available via pip install ase-uhal and integrates seamlessly with the ASE ecosystem.\n\n\n\n			\n				\n				\n				\n				\n				Innovation\n			\n				\n				\n				\n				\n				\n\n\nA “universal” extension of the Hyperactive Learning (HAL) framework makes the method compatible with modern foundation models that can be fine‑tuned.\nA new batched workflow significantly increases throughput compared to existing methods.\nDemonstrated on an InGaP alloy system\, where models trained on diverse data outperform those trained on random sampling.\n\n\n\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				James Kermode is a Professor in the School of Engineering at the University of Warwick (UoW)\, where he directs the EPSRC Centre for Doctoral Training in Modelling of Heterogeneous Systems (HetSys CDT) and the Warwick Centre for Predictive Modelling (WCPM)\, both of which have strong synergies with PSDI activities across the full spectrum from theory and algorithm development through research software engineering to applications. \n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Presentation 2\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				MOFevaluator: A Cloud-Based Platform for Process-Informed Discovery of Metal–Organic Frameworks for Carbon Capture and Beyond\n			\n				\n				\n				\n				\n				Challenge\n			\n				\n				\n				\n				\n				\n\n\nMetal‑Organic Frameworks (MOFs) are promising for carbon capture and gas‑separation applications\, but moving from research to industrial‑scale decarbonization requires demonstrating economically viable production and deployment routes.\nIdentifying optimal MOFs requires understanding the full energy‑system context\, including CO₂ sources\, sinks\, costs\, and process constraints.\n\n\n\n			\n				\n				\n				\n				\n				Approach\n			\n				\n				\n				\n				\n				\n\n\nThe MOFevaluator project builds on the PrISMa platform\, which evaluates MOF performance based on:\n\nspecific CO₂ sources (power plants\, industry\, direct air capture)\npossible CO₂ sinks (geological storage\, mineralisation\, conversion\, etc.)\nregional constraints\n\n\nThis includes process modelling\, techno‑economic analysis\, and life‑cycle assessment\, ensuring that system‑scale requirements guide material discovery.\n\n\n\n			\n				\n				\n				\n				\n				Innovation\n			\n				\n				\n				\n				\n				\n\n\nMOFevaluator transforms the workflow from a local simulation tool into a cloud‑based platform with a fully searchable MOF materials database.\nResearchers can:\n\nvisualise data through an interactive web interface\nintegrate the database via API\nuse a streamlined environment to explore new opportunities for MOF discovery and application\n\n\nThe platform enables faster\, more scalable\, and system‑informed materials discovery.\n\n\n\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Peter McCallum is a Research Software Engineer at Heriot-Watt University\, specialising in the architectures and development of web-based research systems. Having spent a decade in industry working on low-carbon energy system as a mechanical engineer\, he has since led software development activities in academic settings\, across themes including fluid dynamics\, control engineering\, energy networks\, built-environment modelling\, and for the new MOFevaluator web-platform. His main ambition is to build tools that not only support research but also translate quickly to applied industrial settings\, through distributed computing\, web-based visuals\, and API connected data via the cloud. \n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Susana Garcia Trained as a Chemical Engineer\, Susana Garcia is a Full Professor in Chemical and Process Engineering and the Associate Director on CCUS at the Research Center for Carbon Solutions (RCCS) in Heriot-Watt University (Edinburgh). An internationally recognised expert on low carbon separation processes\, CCUS and DAC technologies\, leading AI-driven materials discovery for industrial decarbonisation projects. \n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Register for this webinar\nRegister for this webinar directly through zoom:https://us06web.zoom.us/webinar/register/WN_kaav0loGQp24L4VDOEPuuA \n\n\n\n\n\n\n\n The PSDI team looks forward to seeing you at the webinar\, if you have any questions you can always get in contact with us.
URL:https://www.psdi.ac.uk/event/new-resources-webinar-1/
LOCATION:Online\, Virtual Event\, Online
CATEGORIES:Webinar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Atlantic/Azores:20260507T140000
DTEND;TZID=Atlantic/Azores:20260507T150000
DTSTAMP:20260502T132042
CREATED:20260416T102949Z
LAST-MODIFIED:20260423T085945Z
UID:37017-1778162400-1778166000@www.psdi.ac.uk
SUMMARY:Webinar: From Project to Platform: New Resources on PSDI - Session 2
DESCRIPTION:Registration link: https://us06web.zoom.us/webinar/register/WN_uCSiENnvQpCUjLNERzvDFg \nPSDI is pleased to launch a new webinar series entitled “From Project to Platform: New Resources on PSDI”. This series aims to showcase the high-quality tools and resources developed through the funding call 2025\, introduce them to a broader community\, and foster engagement with relevant user groups.   \n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Abstract\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Challenge\n			\n				\n				\n				\n				\n				\n\n\nGrowing concerns of a reproducibility crisis in electrochemical devices\, particularly within the flow battery research community\nA lack of standardised experimental practices and few consistent reporting frameworks\nLimited reliability and comparability of reported results across laboratories\nInter‑lab differences are difficult to interpret\, slowing collective progress and best‑practice development\n\n\n\n			\n				\n				\n				\n				\n				Approach\n			\n				\n				\n				\n				\n				\n\n\nSince 2023\, multi‑institutional round‑robin studies co‑led by QUB and MIT\nSystematic investigation of repeatability\, replicability\, reproducibility in flow battery cell testing\nPhase 1 (complete)\n\nIdentical flow battery test cell kits distributed to 11 researchers from 7 institutions\nNominally identical electrochemical measurements performed\n\n\nPhase 2 (on-going)\n\nCommunity‑scale expansion to over 40 researchers from 35 institutions\nPhase 2a: reproducibility using participants’ own cells\nPhase 2b: large‑scale replicability study using updated standardised kits\n\n\n\n\n\n			\n				\n				\n				\n				\n				Innovation\n			\n				\n				\n				\n				\n				\n\n\nCombination of community‑scale participation\, shared nomenclature\, and affordable 3D-printed cells\nDevelopment of a PSDI‑supported data infrastructure for:\n\ncross‑institutional data collection\ninteractive visualisation\ncomparative analysis at scale\n\n\nEnables identification of systematic trends across laboratories\nEstablishes a pathway toward transparent\, comparable\, and reproducible testing standards for single‑cell flow batteries\n\n\n\n			\n				\n				\n				\n				\n				Phase 1: Replicability Study Timeline\n			\n				\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				Phase 2: Community‑Scale Participation\n			\n				\n				\n				\n				\n				\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Bio\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Josh J. Bailey is an Illuminate Fellow at Queen’s University Belfast\, working at the interface of physical experimentation and computational modelling to improve performance\, durability\, and sustainability of electrochemical devices. He co-leads international activities aiming to measure and improve reproducibility in flow battery testing\, whilst designing new materials and protocols. \n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Register for this webinar\nRegister for this webinar directly through zoom:https://us06web.zoom.us/webinar/register/WN_uCSiENnvQpCUjLNERzvDFg \n			\n				\n				\n				\n				\n				The PSDI team looks forward to seeing you at the webinar\, if you have any questions you can always get in contact with us.
URL:https://www.psdi.ac.uk/event/new-resources-webinar-2/
LOCATION:Online\, Virtual Event\, Online
CATEGORIES:Webinar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Atlantic/Azores:20260521T140000
DTEND;TZID=Atlantic/Azores:20260521T150000
DTSTAMP:20260502T132042
CREATED:20260417T151512Z
LAST-MODIFIED:20260421T122659Z
UID:37049-1779372000-1779375600@www.psdi.ac.uk
SUMMARY:Webinar: From Project to Platform: New Resources on PSDI - Session 3
DESCRIPTION:Registration link: https://us06web.zoom.us/webinar/register/WN_El8J-MXqTii2ij81QZOxEw \nPSDI is pleased to launch a new webinar series entitled “From Project to Platform: New Resources on PSDI”. This series aims to showcase the high-quality tools and resources developed through the funding call 2025\, introduce them to a broader community\, and foster engagement with relevant user groups.   \n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Presentation 1\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				When TD-DFT Fails: BenchmarkSet1500\, a Multireference Excited-State Dataset for Organic Semiconductor Discovery\n			\n				\n				\n				\n				\n				Challenge\n			\n				\n				\n				\n				\n				\n\n\nAccurate excited‑state prediction is critical for organic semiconductor design (e.g. OLEDs\, OPVs)\nWidely used single‑reference methods (e.g. TD‑DFT) often fail for: strong static correlation; double‑excitation character; inverted singlet–triplet gaps\nLack of reliable\, large‑scale multireference benchmark data limits: method development; validation of excited‑state models; data‑driven and ML‑based discovery\n\n\n\n			\n				\n				\n				\n				\n				Approach\n			\n				\n				\n				\n				\n				\n\n\nDevelopment of BenchmarkSet1500\n\na curated dataset of 1\,500 organic molecules\nexcited‑state properties computed using multireference electronic structure methods\n\n\nSystematic analysis of\n\nmolecular diversity\nstatistical distribution of excited‑state properties\n\n\nDerivation of practical guidelines\n\nselecting suitable levels of theory\nbased on molecular fragment type\n\n\nDemonstration through targeted molecular screening\n\ninverted singlet–triplet gaps\nthermally activated delayed fluorescence (TADF)\ndeviations from Kasha’s rule\n\n\n\n\n\n			\n				\n				\n				\n				\n				Innovation\n			\n				\n				\n				\n				\n				\n\n\nFirst large‑scale multireference benchmark dataset focused on organic excited states\nEnables quantitative assessment of TD‑DFT failure regimes\nProvides a foundation for systematic excited‑state photophysics exploration\nSupports method development\, benchmarking\, and validation beyond single‑reference models\nEstablishes a high‑quality data resource for future machine‑learning‑driven materials discovery\n\n\n\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Malin Zollner (University of Strathclyde) is a Research Assistant in Chemistry at the University of Strathclyde\, funded by PSDI. Her work focuses on developing data resources to support organic semiconductor discovery\, with applications in data-driven modelling and machine learning.She completed her MChem in Pure and Applied Chemistry at the University of Strathclyde in 2024\, where she began exploring the intersection of computational chemistry and materials discovery\, and has since developed a strong background in machine learning for chemical applications. \n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Presentation 2\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				SimpNMR – a Tool for Ab initio-assisted analysis of NMR data of paramagnetic metal complexes in solution\n			\n				\n				\n				\n				\n				Challenge\n			\n				\n				\n				\n				\n				\n\n\nNMR spectra of paramagnetic metal complexes in solution are notoriously difficult to interpret\, as unpaired electrons produce large chemical shifts\, broadened lineshapes\, and temperature-dependent behaviour that standard diamagnetic analysis tools cannot handle.\nExtracting meaningful electronic structure information (magnetic susceptibility tensors\, correlation times\, spin-Hamiltonian parameters) from pNMR data requires combining experimental spectra with ab initio calculations\, a workflow that today remains fragmented\, manual\, and inaccessible to many researchers.\n\n\n\n			\n				\n				\n				\n				\n				Approach\n			\n				\n				\n				\n				\n				\n\n\nSimpNMR is a Python package that streamlines pNMR analysis by directly incorporating outputs of ab initio calculations.\nIt provides an end-to-end workflow for paramagnetic complexes in solution\, including:\n\nprediction of 1D NMR spectra (e.g. 1H\, 13C)\nassignment of experimental peaks to molecular sites\nfitting of the magnetic susceptibility tensor and correlation times to experimental pNMR data\n\n\nWith variable-temperature experiments\, SimpNMR extracts spin-Hamiltonian parameters such as the g-tensor\, and in certain cases the D-tensor\, directly from solution pNMR data\, information typically accessible only from EPR or SQUID magnetometry.\n\n\n\n			\n				\n				\n				\n				\n				Innovation\n			\n				\n				\n				\n				\n				\n\n\nSimpNMR transforms pNMR analysis from a bespoke\, expert-only procedure into a reproducible\, scriptable Python workflow that bridges computational and experimental chemistry.\nSimpNMR_DB\, a curated companion database\, stores the input data required for SimpNMR analysis\, enabling:\n\nreuse and benchmarking of ab initio inputs across complexes\nreproducible\, shareable analysis pipelines\naccelerated discovery by lowering the barrier to quantitative pNMR interpretation\n\n\nTogether\, SimpNMR and SimpNMR_DB open up solution pNMR as a practical route to electronic-structure parameters of paramagnetic metal complexes.\n\n\n\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Dr Elizaveta A. Suturina (University of Bath) is a senior lecturer in Computational Chemistry at the University of Bath. Her research combines computational and experimental approaches to reveal key structural modifications that enhance magnetic properties in cobalt(II) complexes.She currently leads a project developing a Python toolkit for ab initio-assisted analysis of paramagnetic NMR of metal complexes\, which has inspired further research extending these approaches across different chemical systems. \n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Register for this webinar\nRegister for this webinar directly through zoom:https://us06web.zoom.us/webinar/register/WN_El8J-MXqTii2ij81QZOxEw \n			\n				\n				\n				\n				\n				The PSDI team looks forward to seeing you at the webinar\, if you have any questions you can always get in contact with us.
URL:https://www.psdi.ac.uk/event/new-resources-webinar-3/
LOCATION:Online\, Virtual Event\, Online
CATEGORIES:Webinar
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DTSTART;TZID=Europe/London:20260716T100000
DTEND;TZID=Europe/London:20260716T160000
DTSTAMP:20260502T132042
CREATED:20260430T113441Z
LAST-MODIFIED:20260430T114200Z
UID:37165-1784196000-1784217600@www.psdi.ac.uk
SUMMARY:Transitioning from FAIR to AI Ready Data in the Physical Sciences: A PSDI & AIchemy Workshop
DESCRIPTION:Event Details\n📅Date: Thursday\, 16 July\n🕘Time: 10:00 am – 4:00 pm\n📍Location: University of Southampton\, B100 Room 6009\, Highfield Campus\, Southampton\, SO17 1BJ\n			\n				\n				\n				\n				\n				About the Workshop\nIn recent years\, the physical sciences community has been generating increasingly large and complex datasets\, at a scale that is now beyond what can be fully explored or analysed by humans alone. As a result\, researchers are turning to AI and machine‑learning techniques\, which have matured significantly and offer powerful new ways to extract insight from data. However\, while the adoption of FAIR data principles has improved data sharing and reuse\, experience is showing that FAIR does not necessarily mean AI‑ready. Many datasets remain difficult to use effectively in AI and Machine Learning models.   \nThis interactive workshop has been co-created by the Physical Sciences Data Infrastructure (PSDI) and the AI in Chemistry Hub (AIchemy). It aims to bring together researchers\, data professional and infrastructure developers to facilitate knowledge exchange and explore what it truly means to be “AI Ready”. The workshop is comprised of invited presentations\, lightning talks from participants and interactive discussion sessions. The talks will share current practices\, highlighting successes and challenges\, and the discussion sessions will explore the practical approaches and tools for evaluating and improving AI readiness.   \nAudience\nThis in-person event is aimed at anyone interested in dataset standards\, curation\, and developing robust methods to assess the applicability and reliability of data for reuse. It will be particularly relevant for researchers and research software engineers working with data and AI/ML\, data stewards and research data managers\, infrastructure and platform developers\, and scientists interested in enabling future reuse of their datasets.  \nDraft Agenda  \n\n10:00-10:30: Coffee & Registration  \n\n\n10:30-10:35: Housekeeping & Welcome \n\n\n10:35-10:45: Introduction to PSDI \n\n\n10:45-10:55: Introduction to AIchemy \n\n\n10:55-11:10: Setting the Scene: From FAIR to AI-Ready \n\n\n11:10-11:25: Coffee Break & Networking  \n\n\n11:25-12:45: Invited Speakers (Matthew Partridge\, Aileen Day\, Nessa Carson) \n\n\n12:45-13:30: Networking Lunch \n\n\n13:30-14:00: Participant Lightning Talks \n\n\n14:00-14:15: Introduction to Discussion Sessions  \n\n\n14:15-14:45: Discussion Sessions Part 1 \n\n\n14:45-15:00: Coffee Break & Networking  \n\n\n15:00-15:30: Discussion Sessions Part 2 \n\n\n15:30-16:00: Feedback & Wrap Up  \n\nEvent Travel \nThe University of Southampton is accessible via various different transport links \n\nTravelling by Train: Southampton Airport Parkway is the closest station\, but Highfield Campus is also close to St Denys and Southampton Central Station\n\n\nTravelling by Bus: Highfield Campus is on the bus route for all Unilink Busses\n\n\nTravelling by Car: Highfield Campus has very limited parking\, if you require on-site parking for accessibility reasons then please note that in your registration\, or email psdi@soton.ac.uk at least one week in advance\, and we will try and accommodate this if possible (NB: we cannot guarantee this in the week leading up to the event). There is also a 100-space short stay carpark (with charges)\, but this is likely to be very busy.\n\n			\n				\n				\n				\n				\n				Registration Details\nPlease register for this event here\, please note spaces are limited. \n 
URL:https://www.psdi.ac.uk/event/ai-ready-data/
LOCATION:University of Southampton\, Highfield Campus\, Southampton\, Hampshire\, SO17 1BJ\, United Kingdom
CATEGORIES:Workshop
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