CorText Manager around the world

@misc{Vanderfeesten2020,
title = {Text Analyses of Survey Data on "Mapping Research Output to the Sustainable Development Goals(SDGs)"},
author = {Maurice Vanderfeesten and Eike Spielberg and Linda Hasse},
url = {https://doi.org/10.5281/zenodo.3832090},
doi = {10.5281/zenodo.3832090},
year = {2020},
date = {2020-05-01},
abstract = {This package contains data on five text analysis types (term extraction, contract analysis, topic modeling, network mapping), based on the survey data where researchers selected research output that are related to the 17 Sustainable Development Goals (SDGs). This is used as input to improve the current SDG classification model v4.0 to v5.0

Sustainable Development Goals are the 17 global challenges set by the United Nations. Within each of the goals specific targets and indicators are mentioned to monitor the progress of reaching those goals by 2030. In an effort to capture how research is contributing to move the needle on those challenges, we earlier have made an initial classification model than enables to quickly identify what research output is related to what SDG. (This Aurora SDG dashboard is the initial outcome as proof of practice.)

The initiative started from the Aurora Universities Network in 2017, in the working group "Societal Impact and Relevance of Research", to investigate and to make visible 1. what research is done that are relevant to topics or challenges that live in society (for the proof of practice this has been scoped down to the SDGs), and 2. what the effect or impact is of implementing those research outcomes to those societal challenges (this also have been scoped down to research output being cited in policy documents from national and local governments an NGO's).},
note = {Sustainable Development Goals SDG Classification model Search Queries SCOPUS Text indexingControlled vocabulary},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}

@misc{cortext2015training,
title = {Training Book CorTexT},
author = {Cortext},
year = {2015},
date = {2015-01-01},
note = {RISIS short course type A, 6 -7 October 2015},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}

@misc{turenne2015open,
title = {Open data platform for knowledge access in plant health domain: VESPA Mining},
author = {Nicolas Turenne and Mathieu Andro and Roselyne Corbière and Tien T Phan},
url = {https://hal.archives-ouvertes.fr/hal-01208763},
year = {2015},
date = {2015-01-01},
journal = {arXiv preprint},
abstract = {Important data are locked in ancient literature. It would be uneconomic to produce these data again and today or to extract them without the help of text mining technologies. Vespa is a text mining project whose aim is to extract data on pest and crops interactions, to model and predict attacks on crops, and to reduce the use of pesticides. A few attempts proposed an agricultural information access. Another originality of our work is to parse documents with a dependency of the document architecture. },
keywords = {},
pubstate = {published},
tppubtype = {misc}
}