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DTSTART;TZID=UTC:20200702T120000
DTEND;TZID=UTC:20200702T130000
DTSTAMP:20260404T140345
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SUMMARY:How computational modelling can force theory building in psychological science by Dr Olivia Guest
DESCRIPTION:About the speaker \n\nOlivia Guest is a computational cognitive modeller at RISE in Cyprus and University College London (UCL) in the UK. She emigrated to the UK to pursue a BSc in Computer Science at the University of York (2009)\, with an eye towards research at the intersection of AI and Psychology. She followed her undergraduate work with an MSc in Cognitive and Decision Sciences at UCL (2010) and a PhD in Psychology at Birkbeck (2014) where she focused on modelling semantic cognition using deep and shallow neural networks. Since then she has continued research in the area of categorisation\, and conceptual representation broadly\, using various modelling techniques — such as deep and shallow neural networks — with postdoctoral positions at the University of Oxford (2014–2016) and UCL (2016–2019). She was exposed to open communities in her teen years\, e.g. open-source and open licensing\, and believes modellers can have a doubly important role to play in guiding and enacting useful changes in open cognitive science: firstly\, from experience with the open-source community and secondly\, from experience navigating interdisciplinary settings. She is an editor-in-chief at ReScience C and a topic editor at the Journal of Open Source Software. She is committed to equity\, diversity\, and inclusion in (open) science\, e.g.\, promoting access to technical skills training. In addition\, Christina Bergmann and Olivia try to maintain a list of underrepresented cognitive computational scientists at compcog.science.\nYou can find more information about Olivia by clicking here. \nAbout the talk \nPsychology endeavours to develop theories of human capacities and behaviours based on a variety of methodologies and dependent measures. We argue that one of the most divisive factors in our field is whether researchers choose to employ computational modelling of theories (over and above data) during the scientific inference process. Modelling is undervalued\, yet holds promise for advancing psychological science. The inherent demands of computational modelling guide us towards better science by forcing us to conceptually analyze\, specify\, and formalise intuitions which otherwise remain unexamined — what we dub “open theory”. Constraining our inference process through modelling enables us to build explanatory and predictive theories. Herein\, we present scientific inference in psychology as a path function\, where each step shapes the next. Computational modelling can constrain these steps\, thus advancing scientific inference over and above stewardship of experimental practice (e.g. preregistration). If psychology continues to eschew computational modelling\, we predict more replicability “crises” and persistent failure at coherent theory-building. This is because without formal modelling we lack open and transparent theorising. We also explain how to formalise\, specify\, and implement a computational model\, emphasizing that the advantages of modelling can be achieved by anyone with benefit to all.\nYou can read more about those issues here.
URL:https://riotscience.co.uk/tribe-events/how-computational-modelling-can-force-theory-building-in-psychological-science-by-dr-olivia-guest/
LOCATION:MS Teams
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DTSTART;TZID=UTC:20200709T120000
DTEND;TZID=UTC:20200709T130000
DTSTAMP:20260404T140345
CREATED:20200622T134524Z
LAST-MODIFIED:20200709T162543Z
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SUMMARY:Why preclinical research should embrace Open Science by Dr Emily Sena
DESCRIPTION:About the speaker \nEmily is a Stroke Association Kirby Laing Foundation Senior Non-Clinical Lecturer at the University of Edinburgh. She is specialised in the validity of preclinical research. Her interests are in the use of meta-research approaches (research on research) to drive improvements in the validity\, transparency and reproducibility of primary research using animal models of human diseases. Her work has informed laboratory practice guidelines\, editorial policy and clinical trials design. Emily is the inaugural Editor-in-Chief of BMJ Open Science and convenor of CAMARADES. \nAbout the talk \nLaboratory experiments of the life sciences are often conducted with the aim of improving human health. The translation of findings observed in preclinical animal studies to humans in a clinical setting has proven difficult in many areas. Discrepancies between the results of preclinical animal studies and human clinical trials\, and limited reproducibility between laboratories\, has in part been attributed to compromised internal and external validity of animal experiments\, and the presence of publication bias. Meta-research approaches\, including\, systematic review and meta-analysis of preclinical studies have proven to be useful tools in quantitatively estimating the impact of study quality and informing the design of clinical trials. The use and implementation of open science approaches may improve the quality and transparency of preclinical research but it is important the effect of these tools are assessed. \nIf preclinical studies are used to inform future research decisions in the life sciences their design\, conduct and reporting must be rigorous and their results disseminated in an unbiased and timely manner. Improving our approach to preclinical practice and evidence based clinical trial design may improve translation from bench to bedside.
URL:https://riotscience.co.uk/tribe-events/why-preclinical-research-should-embrace-open-science-by-dr-emily-sena/
LOCATION:MS Teams
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BEGIN:VEVENT
DTSTART;TZID=UTC:20200716T120000
DTEND;TZID=UTC:20200716T130000
DTSTAMP:20260404T140345
CREATED:20200617T143654Z
LAST-MODIFIED:20200804T075227Z
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SUMMARY:Beyond authorship: Introducing the Contributor Role Taxonomy (CRediT) by Dr Liz Allen
DESCRIPTION:About the speaker\n \nLiz Allen is Director of Strategic Initiatives at F1000Research. Prior to joining F1000Research in 2015\, she spent over a decade leading the Evaluation Team at Wellcome.  Liz is a Visiting Senior Research Fellow in the Policy Institute at King’s College London\, with a particular interest in science policy and research-related indicators. Liz is a Crossref Board Director\, co-founder of CRediT (Contributor Roles Taxonomy)\, and serves on the Advisory Board for the Software Sustainability Institute.  \nAbout the talk \nOriginal research articles with one author – particularly in the life sciences – are increasingly rare\, and the concept of ‘authorship’ in science has become outdated and often unhelpful. CRediT was developed through a collaboration of research funders\, publishers\, researchers and institutions with the aim was to provide greater granularity\, visibility and transparency to the myriad of contributions that researchers make to published scholarly research output today. \nThis talk explains the origins\, rationale and current use of the Contributor Role Taxonomy (CRediT) – leaving room for discussion of the challenges and opportunities for the future of this initiative.
URL:https://riotscience.co.uk/tribe-events/beyond-authorship-introducing-the-contributor-role-taxonomy-credit/
LOCATION:MS Teams
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BEGIN:VEVENT
DTSTART;TZID=UTC:20200723T110000
DTEND;TZID=UTC:20200723T120000
DTSTAMP:20260404T140345
CREATED:20200715T095243Z
LAST-MODIFIED:20200804T075201Z
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SUMMARY:Collaborative Assessment for Trustworthy Science: the repliCATS project by Prof Fiona Fidler
DESCRIPTION:About the speaker \nFiona has a degree in Psychology\, with a second major in Sociology\, and was awarded her PhD in History and Philosophy of Science at the University of Melbourne in 2006. She is now a Professor at the University of Melbourne\, based between the School of BioSciences and the School of Historical and Philosophical Studies. Fiona is also an Australian Research Council Future Fellow\, leading a range of meta-research projects across ecology and conservation science\, as well as projects in psychology\, and other social science fields. Her meta-research work is driven by an underlying interest in how scientists and other experts reason\, make and justify decisions\, and change their minds. She is co-lead (with Simine Vazire) of MetaMelb\, a recently launched meta-science/meta-research group at the University of Melbourne\, and she leads the repliCATS project\, for Collaborative Assessments for Trustworthy Science. \nAbout the talk \nThe repliCATS project evaluates published scientific research. As the acronym—Collaborative Assessments for Trustworthy Science—suggests\, repliCATS is a group activity\, centred around assessing the trustworthiness of research claims. Reviewers first make private individual assessments about a research claim—judging its comprehensibility\, the prior plausibility of underlying effect\, and its likely replicability. Reviewers then share their judgements and reasoning with group members\, providing both new information and the opportunity for feedback and calibration. The group interrogates differences in opinion and explores counterfactuals. After discussion\, there is a final opportunity for privately updating individual judgements. Importantly the repliCATS process is not consensus-driven – reviewers can disagree\, and their ratings and probability judgements are mathematically aggregated into a final assessment. At the moment\, the repliCATS platform exists primarily to predict replicability. Launched in January 2019 as part of the DARPA SCORE program\, over 18 months repliCATS elicited group assessments and captured associated reasoning and discussion\, for 3\,000 published social scientific research claims in 8 disciplines (Business\, Criminology\, Economics\, Education\, Political Science\, Psychology\, Public Administration\, and Sociology). The repliCATS team are now working to extend the platform beyond merely predicting replicability\, to deliver a more comprehensive peer review protocol. Suspected advantages of a repliCATS process over traditional peer review include: inbuilt training and calibration; feedback that is intrinsically rewarding; an inherently interactive process\, but one which does not implicitly rely on ‘consensus by fatigue’; and a process that actively encourages interrogation. This talk will present some preliminary findings\, and discuss the future of the platform.
URL:https://riotscience.co.uk/tribe-events/collaborative-assessment-for-trustworthy-science-the-replicats-project-by-prof-fiona-fidler/
LOCATION:MS Teams
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BEGIN:VEVENT
DTSTART;TZID=UTC:20200730T120000
DTEND;TZID=UTC:20200730T130000
DTSTAMP:20260404T140345
CREATED:20200724T111448Z
LAST-MODIFIED:20200804T075252Z
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SUMMARY:Synthetic data: A primer by Dr Dan Quintana
DESCRIPTION:About the speaker \nDaniel S. Quintana is a researcher in biological psychiatry at the University of Oslo. His research interests include social behaviour\, the oxytocin system\, heart rate variability\, and meta-analysis. He currently leads a project investigating the role of the oxytocin system in the development of severe mental illness and metabolic disorder\, which is supported by an Excellence Project for Young Researchers grant from the Novo Nordisk Foundation. Dan is also known for the Everything Hertz podcast\, which he co-hosts with James Heathers. Widely praised for his training materials to support the adoption of open research practices\, Dan is a strong advocate for disseminating scholarly outputs with the aid of social media\, which was the subject of his talk for the Rotterdam RIOTS Club in May. \nAbout the talk \nOpen research data provide considerable scientific\, societal\, and economic benefits. However\, disclosure risks can sometimes limit the sharing of open data\, especially in datasets that include sensitive details or information from individuals with rare disorders. This talk introduces the concept of synthetic datasets\, which is an emerging method originally developed to permit the sharing of confidential census data. Synthetic datasets mimic real datasets by preserving their statistical properties and the relationships between variables. Importantly\, this method also reduces disclosure risk to essentially nil as no record in the synthetic dataset represents a real individual. This practical guide with accompanying R script enables biobehavioural researchers to create synthetic datasets and assess their utility via the synthpop R package. By sharing synthetic datasets that mimic original datasets that could not otherwise be made open\, researchers can ensure the reproducibility of their results and facilitate data exploration while maintaining participant privacy.
URL:https://riotscience.co.uk/tribe-events/synthetic-data-a-primer-by-dr-dan-quintana/
LOCATION:MS Teams
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