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PRODID:-//Columba Systems Ltd//NONSGML CPNG/SpringViewer/ICal Output/3.3-
 M3//EN
VERSION:2.0
CALSCALE:GREGORIAN
METHOD:PUBLISH
BEGIN:VEVENT
DTSTAMP:20191003T110406Z
DTSTART:20191014T130000Z
DTEND:20191014T140000Z
SUMMARY:Seminar: ML to Augment Scholarly Infrastructure
UID:{http://www.columbasystems.com/customers/uom/gpp/eventid/}nqe-k1aldw3
 t-yajcqa
DESCRIPTION:Join us for the next Computer Science Seminar with speaker Pa
 co Nathan\n\nML to Augment Scholarly Infrastructure\nThis talk is about 
 Rich Context and the associated new features for data governance and col
 laboration which are going into JupyterLab. Rich Context is a research e
 ffort within the Coleridge Initiative at NYU Wagner\, developing a knowl
 edge graph built from metadata about the use of curated datasets and rel
 ated research. As a foundation\, the Administrative Data Research Facili
 ty (ADRF) is a platform used across 15 government agencies in the US for
  social science research with sensitive data. ADRF promotes evidence-bas
 ed policymaking and provides support for data stewardship practices amon
 g the agencies.? Rich Context\, in turn\, is used to represent several k
 inds of entities involved: datasets\, data providers\, researchers\, res
 earch publications\, subject headings\, etc. Machine learning applicatio
 ns leverage this graph to perform entity linking\, e.g.\, identifying da
 taset attribution within open access research publications.\nCollaborati
 on between Coleridge Initiative and Project Jupyter has led to a new fea
 ture set for JupyterLab that supports Rich Context and other scholarly i
 nfrastructure. Developed as extensions to JupyterLab\, these new data go
 vernance features support: dataset registry\, metadata exchange\, usage 
 telemetry\, and comments/annotations about datasets which offer feedback
  for data stewards. For example\, a researcher working with a new datase
 t can use new features in Jupyter to explore the metadata describing tha
 t dataset.\nOther approaches explored by the Rich Context project includ
 e:\n-	a machine learning leaderboard competition on GitHub for the ML ap
 ps that leverage the Rich Context knowledge graph (AllenAI won first rou
 nd\, LARC @ Singapore Mgmt U is currently leading)\n-	semi-supervised le
 arning: inferred metadata from the ML competition gets presented back to
  authors through scholarly infrastructure platforms such as RePEc for th
 eir feedback.\nAn immediate goal is to provide recommendations for resea
 rchers working with well-known datasets\, e.g.\, those curated by govern
 ment agencies. A long-term goal is to collect workflow configurations as
 sociated with those datasets\, then provide meta-learning services (Auto
 ML)\, e.g.\, suggested configurations to help optimize research workflow
 s and support reproducible research.\nOverall\, Coleridge Initiative is 
 collaborating with Bundesbank\, USDA\, Digital Science\, SAGE Pub\, Rese
 archGate\, RePEc\, and GESIS on this work\, which is funded by Schmidt F
 utures\, Sloan\, and Overdeck.\n\nhttps://derwen.ai/paco\n\n\n
STATUS:TENTATIVE
TRANSP:TRANSPARENT
CLASS:PUBLIC
LOCATION:Atlas Suite\, Kilburn Building\, Manchester
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