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Identifying the affective dimension of data mining practice: an exploratory study
Jo Bates, Jess Elmore
University of Sheffield, United Kingdom
The paper aims to illuminate how feeling, emotion and affect influence the practice of data mining. While data mining is sometimes presented as an objective, neutral technique by which to rationally understand and predict phenomena, we observe an important affective dimension in how people understand, engage in and respond to data mining practices. We report the findings of a small exploratory pilot study conducted in 2016 in which we used ethnographic meth-ods to observe the culture of a collaborative project between data scientists and a small digital marketing company. The project aimed to explore potential uses of data mining techniques in the process of telesales lead generation. Thematic analysis of collected data indicates that even in the case of a small scale project, the practice of mining data is deeply influenced by an underlying affective dimension. While these affective dynamics rarely surfaced explicitly in discussions between team members, it is clear from our interview data that feelings and emotions had a significant impact on how participants experienced and engaged with the practice of data mining. Our findings point to the necessity for a much deeper understand-ing of, and reflexivity in relation to, the affective dimension of data mining prac-tice and how it emerges in the cultures and practices of data science projects. We argue that a deeper awareness of, and openness about, this affective dimension could benefit practitioners’ understanding of their own practice and motivations in decision making, and thus has the potential to improve data science practice.
Data Retrieval = Text Retrieval?
Maryam Bugaje, Gobinda Chowdhury
Northumbria University, United Kingdom
Due to the comparatively more recent emergence of data retrieval systems than text-based search engines, the former have still yet to achieve similar maturity in terms of standards and techniques. Most of the existing solutions for data retrieval are more or less makeshift adaptations of text retrieval systems rather than purpose-built solutions specially designed to cater to the particular peculiarities, subtleties, and unique requirements of research datasets. In this paper we probe into the key differences between text and data retrieval that bear practical relevance to the retrieval question; these differences we demonstrate by evaluating some representative examples of research data repositories as well as presenting findings from previous studies.
Mining Open Government Data Used in Scientific Research
An Yan, Nic Weber
University of Washington, United States of America
In the following paper, we describe results from mining citations, mentions, and links to open government data (OGD) in peer-reviewed literature. We inductively develop a method for categorizing how OGD are used by different research communities, and provide descriptive statistics about the publication years, publication outlets, and OGD sources. Our results demonstrate that, 1. The use of OGD in research is steadily increasing from 2009 to 2016; 2. Researchers use OGD from 96 different open government data portals, with data.gov.uk and data.gov being the most frequent sources; and, 3. Contrary to previous findings, we provide evidence suggesting that OGD from developing nations, notably India and Kenya, are being frequently used to fuel scientific discoveries. The findings of this paper contribute to ongoing research agendas aimed at tracking the impact of open government data initiatives, and provides an initial description of how open government data are valuable to diverse scientific research communities.