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UID:pretalx-euroscipy-2024-ZVBAKK@pretalx.com
DTSTART;TZID=CET:20240826T160000
DTEND;TZID=CET:20240826T173000
DESCRIPTION:Scientists are producing more and more images with telescopes\,
  microscopes\, MRI scanners\, etc. They need automatable tools to measure 
 what they've imaged and help them turn these images into knowledge. This t
 utorial covers the fundamentals of algorithmic image analysis\, starting w
 ith how to think of images as NumPy arrays\, moving on to basic image filt
 ering\, and finishing with a complete workflow: segmenting a 3D image into
  regions and making measurements on those regions.
DTSTAMP:20260311T010049Z
LOCATION:Room 6
SUMMARY:Image analysis in Python with scikit-image - Deleted User\, Mariann
 e Corvellec\, Stéfan van der Walt
URL:https://pretalx.com/euroscipy-2024/talk/ZVBAKK/
END:VEVENT
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UID:pretalx-euroscipy-2024-BXNEY8@pretalx.com
DTSTART;TZID=CET:20240828T132000
DTEND;TZID=CET:20240828T135000
DESCRIPTION:The conversation about reproducibility is usually focused on ho
 w to make research workflows (more) reproducible. Here\, we consider it fr
 om the opposite perspective\, and ask: How feasible is it\, in practice\, 
 to reproduce research which is meant to be reproducible? Is it even done o
 r attempted? We provide a detailed account of such an attempt\, trying to 
 reproduce some segmentation results for 3D microscopy images of a developi
 ng mouse embryo. The original research is a monumental work of bioimaging 
 and analysis at the single-cell level\, published in *Cell* in 2018\, alon
 gside with all the necessary research artifacts. Did we succeed in this at
 tempt? As we share the joys and pains of this journey\, many questions ari
 se: How do reviewers assess the reproducibility claims exactly? Incentiviz
 ing reproducible research is still an open problem\, since it is so much m
 ore costly (in time) to produce. And how can we incentivize those who test
  reproducibility? Not only is it costly to set up computational environmen
 ts and execute data-intensive scientific workflows\, but it may not appear
  as rewarding at first thought. In addition\, there is a human factor: It 
 is thorny to show authors that their publication does not hold up to their
  reproducibility claims.
DTSTAMP:20260311T010049Z
LOCATION:Room 6
SUMMARY:The joys and pains of reproducing research: An experiment in bioima
 ging data analysis - Marianne Corvellec
URL:https://pretalx.com/euroscipy-2024/talk/BXNEY8/
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