12 Works

AI3SD Video: Skills4Scientists - Poster & Careers Symposium - Poster Compilation

András Vekassy, Aspen Fenzl, Erhan Gulsen, Hewan Zewdu, Jamie Longio, Maximilian Hoffman, Rhyan Barrett, Rubaiyat Khondaker, Anna Catton, Hongyang Dong, Kevin Calvache, Kaylee Patel, King Wong, Louis Greenhalgh, Rebecca Jane Clements, Thomas Allam, Sarah Scripps, Gavin Man, Samuel Munday, Michael Blakey, Graeme M. Day, Chris-Kriton Skylaris, Simon J. Coles, Stephen Gow & William Brocklesby
This video forms part of the Skills4Scientists Series which has been organised as a joint venture between the Artificial Intelligence for Scientific Discovery Network+ (AI3SD) and the Physical Sciences Data-Science Service (PSDS). This series ran over summer 2021 and aims to educate and improve scientists skills in a range of areas including research data management, python, version control, ethics, and career development. This series is primarily aimed at final year undergraduates / early stage PhD...

SDS Platform: Example Project Migrations 2022

SDS Admin
Example SDS Project Migrations: Beckett Digital Manuscript Project & Preserving the Endangered Languages of Austronesia.

SDS Platform: Example Project Migrations 2022

SDS Admin
Example SDS Project Migrations: Beckett Digital Manuscript Project & Preserving the Endangered Languages of Austronesia.

Growth of SDS at the University of Oxford

Catherine Conisbee
Recording of an ITS905 "SDS Update & Expansion Plans" presentation given on 9 August 2022 to University of Oxford colleagues.

AI3SD Video: Cross-architecture tuning of quantum devices faster than human experts

Natalia Ares
A concerning consequence of quantum device variability is that the tuning of each qubit in a quantum circuit constitutes a time-consuming non-trivial process that has to be independently performed for each device, requiring a deep understanding of the particular device to be tuned and "muscle memory". I will show a machine-learning based approach that can tune quantum devices completely automatically, regard less of the device architecture and being agnostic to the material realisation. Our algorithm...

Growth of SDS at the University of Oxford

Catherine Conisbee
Recording of an ITS905 "SDS Update & Expansion Plans" presentation given on 9 August 2022 to University of Oxford colleagues.

Introduction to the SDS Service & Platform 2022

SDS Admin
A presentation that provides a general overview of the SDS service and SDS Platform, recorded on 9 August 2022.

Introduction to the SDS Service & Platform 2022

SDS Admin
A presentation that provides a general overview of the SDS service and SDS Platform, recorded on 9 August 2022.

Additional file 6 of The effectiveness and acceptability of evidence synthesis summary formats for clinical guideline development groups: a mixed-methods systematic review

Melissa K. Sharp, Dayang Anis Binti Awang Baki, Joan Quigley, Barrie Tyner, Declan Devane, Kamal R. Mahtani, Susan M. Smith, Michelle O’Neill, Máirín Ryan & Barbara Clyne
Additional file 6: Figures 6, 7, and 8. Recommendations for Practice.

Additional file 1 of Malaria attributable fractions with changing transmission intensity: Bayesian latent class vs logistic models

Kennedy Mwai, Irene Nkumama, Amos Thairu, James Mburu, Dennis Odera, Rinter Kimathi, Lydia Nyamako, James Tuju, Samson Kinyanjui, Eustasius Musenge & Faith Osier
Additional file 1: Table S1. Parasites/µL cut off using Logistic regression. Fig S1. Distribution of predicted probabilities. Fig S2. Comparison of probabilities for Bayesian and Logistic, Junju 2008. Table S2. Anova test for Figure 2B. Fig S3. Comparison of probability of febrile and non-febrile. Fig S4. Predicted probabilities over age groups. Fig S5. Sensitivity and specificity of the different years. Fig S6. Posterior estimates of AF.

Additional file 6 of The effectiveness and acceptability of evidence synthesis summary formats for clinical guideline development groups: a mixed-methods systematic review

Melissa K. Sharp, Dayang Anis Binti Awang Baki, Joan Quigley, Barrie Tyner, Declan Devane, Kamal R. Mahtani, Susan M. Smith, Michelle O’Neill, Máirín Ryan & Barbara Clyne
Additional file 6: Figures 6, 7, and 8. Recommendations for Practice.

Additional file 1 of Malaria attributable fractions with changing transmission intensity: Bayesian latent class vs logistic models

Kennedy Mwai, Irene Nkumama, Amos Thairu, James Mburu, Dennis Odera, Rinter Kimathi, Lydia Nyamako, James Tuju, Samson Kinyanjui, Eustasius Musenge & Faith Osier
Additional file 1: Table S1. Parasites/µL cut off using Logistic regression. Fig S1. Distribution of predicted probabilities. Fig S2. Comparison of probabilities for Bayesian and Logistic, Junju 2008. Table S2. Anova test for Figure 2B. Fig S3. Comparison of probability of febrile and non-febrile. Fig S4. Predicted probabilities over age groups. Fig S5. Sensitivity and specificity of the different years. Fig S6. Posterior estimates of AF.

Registration Year

  • 2022
    11
  • 2021
    1

Resource Types

  • Audiovisual
    12

Affiliations

  • University of Oxford
    12
  • University Hospital Heidelberg
    2
  • Royal College of Surgeons in Ireland
    2
  • University of Southampton
    2
  • Pwani University
    2
  • Trinity College Dublin
    2
  • University College Cork
    2
  • National University of Ireland, Galway
    2
  • University of the Witwatersrand
    2
  • Cork University Hospital
    2