Towards a taxonomy for annotation of data science experiment repositories

Data scientists, like software engineers, use search engines, code repositories, tutorials, and question and answer sites for finding code snippets. The objective of this study is to understand what information can be extracted from data science experiment repositories for quicker availability of relevant information when data scientists search for information. In this paper, we investigated a set of notebooks to identify recurring data science techniques for efficient information retrieval and easy adaptation from online solutions to support their search during experimentation. From the manual annotation of 57 natural language processing notebooks, a taxonomy on 106 data science techniques was developed, grouped by data science workflow stages. The preliminary evaluation shows that our constructed taxonomy is relevant to retrieve information that data scientists are searching for. Future work will continue to investigate the creation of a context aware code snippet engine designed for data scientists.