Using a Jupyter Notebook to perform a reproducible scientific analysis over semantic web sources

Research output: Contribution to journalConference article

Abstract

In recent years there has been a reproducibility crisis in science. Computational notebooks, such as Jupyter, have been touted as one solution to this problem. However, when executing analyses over live SPARQL endpoints, we get different answers depending upon when the analysis in the notebook was executed. In this paper, we identify some of the issues discovered in trying to develop a reproducible analysis over a collection of biomedical data sources and suggest some best practice to overcome these issues.
Original languageEnglish
Pages (from-to)12-24
Number of pages13
JournalCEUR Workshop Proceedings
Volume2184
Publication statusPublished - 28 Aug 2018

Fingerprint Dive into the research topics of 'Using a Jupyter Notebook to perform a reproducible scientific analysis over semantic web sources'. Together they form a unique fingerprint.

Cite this