  In 2015, the United Nations put forward 17 Sustainable Development Goals
(SDGs) to be achieved by 2030, where data has been promoted as a focus to
innovating sustainable development and as a means to measuring progress towards
achieving the SDGs. In this study, we propose a systematic approach towards
discovering data types and sources that can be used for SDG research. The
proposed method integrates a systematic mapping approach using manual
qualitative coding over a corpus of SDG-related research literature followed by
an automated process that applies rules to perform data entity extraction
computationally. This approach is exemplified by an analysis of literature
relating to SDG 7, the results of which are also presented in this paper. The
paper concludes with a discussion of the approach and suggests future work to
extend the method with more advance NLP and machine learning techniques.
