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CALSCALE:GREGORIAN
BEGIN:VEVENT
DTSTAMP:20260710T183004Z
UID:ecee5042-6f38-4df7-b2ea-74bb81587312
DTSTART:20230112T110000Z
DTEND:20230112T150000Z
DESCRIPTION:Researchers often need to extract\, manipulate and integrate da
 ta and/or metadata from different sources\, such as repositories\, databas
 es\, or flat files. Much research time is spent on trivial and not-so-triv
 ial details of data wrangling: to reformat data structures\; clean up erro
 rs\; remove duplicate data\; or map and integrate dataset fields. Software
  for data wrangling and analysis\, such as Pandas\, R or Frictionless\, is
  useful\, but researchers still regularly end up with hard-to-reuse script
 s\, often with manual steps.\n\nuniFAIR is a new Python library with a sys
 tematic and scalable approach to research data wrangling. With uniFAIR\, r
 esearchers can import (meta)data in almost any shape or form: nested JSON\
 ; tabular (relational) data\; binary streams\; or other data structures. D
 ata is continuously parsed and reshaped through a step-by-step process acc
 ording to a series of data model transformations. uniFAIR provides a catal
 ogue of generic task and subflow templates that the researcher can refine 
 and apply to carry out the transformations needed to wrangle data into the
  required shape.\n\nFor large datasets\, uniFAIR allows local test jobs on
  sample-sized data to be seamlessly scaled up to the full datasets and off
 loaded to external compute resources. Persistent access to the state of th
 e data is available at every step.
LOCATION:Georg Sverdrups hus\, 39 Moltke Moes vei
SUMMARY:Hands-on introduction to the Python package uniFAIR: a systematic a
 nd scalable approach to research data wrangling
URL;VALUE=URI:https://www.ub.uio.no/english/courses-events/events/all-libra
 ries/2023/digital-scholarship-days/unifair.html
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