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Data · collection · 2016

MNRAS 2016

Listed in DataCite

Description

Scripts and data needed for reproducing the results of Stensbo-Smidt et al. (2016). To run the code, you will need Python 2.7 and the python libraries numpy , pandas and scikit-learn . In addition, the feature selection requires the speedynn package: github.com/gieseke/speedynn Put the script and speedynn package in the same folder and unzip the data here (keep it as a subfolder called 'data').

Running the experiments.py script will rerun the experiments of the paper.

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Provenance · 1 source records, 9 field assertions
SourceKeyLast seenRaw
DataCite10.6084/m9.figshare.c.32782677 d agoJSON v1
FieldAssertionExtractorEvidence
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concepts[field].fos:computer-and-information-sciencessource · DataCiteconnector:datacite@1.0.0
concepts[field].fos:physical-sciencessource · DataCiteconnector:datacite@1.0.0
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