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Provenance for Reproducible Data Science

Schreiber, Andreas (2017) Provenance for Reproducible Data Science. PyData Seattle 2017, 5.-7. Jul. 2017, Redmond, USA.

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Official URL: https://pydata.org/seattle2017/schedule/presentation/62/

Abstract

In science, results that are not reproducible by peer scientists are valueless and of no significance. Among other practices, recording the provenance of data facilitates to reproduce results in data science and users can be confident in quality of the data. The talk shows how to record and to analyse provenance using the provenance model PROV for Python data analytics processes.

Item URL in elib:https://elib.dlr.de/113546/
Document Type:Conference or Workshop Item (Speech)
Additional Information:Video: https://www.youtube.com/watch?v=79c3wCVNsnc
Title:Provenance for Reproducible Data Science
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Schreiber, AndreasAndreas.Schreiber (at) dlr.dehttps://orcid.org/0000-0001-5750-5649
Date:6 July 2017
Refereed publication:No
Open Access:Yes
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Status:Published
Keywords:data science, provenance, reproducibility, python, pydata
Event Title:PyData Seattle 2017
Event Location:Redmond, USA
Event Type:international Conference
Event Dates:5.-7. Jul. 2017
Organizer:NumFOCUS
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Space Technology
DLR - Research area:Raumfahrt
DLR - Program:R SY - Technik für Raumfahrtsysteme
DLR - Research theme (Project):R - Vorhaben SISTEC
Location: Köln-Porz
Institutes and Institutions:Institut of Simulation and Software Technology
Institut of Simulation and Software Technology > Distributed Systems and Component Software
Deposited By: Schreiber, Andreas
Deposited On:21 Aug 2017 10:33
Last Modified:31 Jul 2019 20:11

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