Data search has received more attention recently partly due to the open research data movement and the availability of the Google Database Search system. Issues of data discovery services have been explored by service providers. Yet we have a limited understanding of user search behavior in the process of data discovery. In this talk, I will present research findings from a quantitative and qualitative analysis of search logs in a data discovery portal, with particular emphasis on the query reformulation process. Implications for user-centric data discovery services, methodological reflections on user modeling, and enhancement of user experience in search processes from interdisciplinary perspectives will be discussed.

Ying-Hsang Liu is an associate professor at the Department of Design and Communication, University of Southern Denmark. He holds a Ph.D. in information science from the School of Communication and Information at Rutgers University. His research program lies at the intersections of information retrieval, knowledge organization, and human information behavior, with a particular interest in user concept formation within search contexts. He has published more than forty journal and conference papers and served on the editorial boards of Online Information Review, and Information Processing & Management. His most recent research project concerns the design and user evaluation of conversational agents for pilots.

Dr. Hsin-liang (Oliver) Chen joined the Missouri University of Science and Technology in July 2019. Prior to arriving at Missouri S&T, he was Associate Dean at the University of Massachusetts Boston Library. He also taught at the University of Wisconsin-Milwaukee, the University of Texas at Austin, the University of Missouri, Indiana University Purdue University Indianapolis and Long Island University. Dr. Chen’s research interests focus on the application of information and communication technologies (ICTs) to assist users in accessing and using information in different environments. Dr. Chen received his Ph.D. in Library and Information Science from the University of Pittsburgh in 1999, MA in Educational Communication and Technology from New York University in 1995, and BA in Library Science from Fu-Jen Catholic University in Taiwan in 1985.

(TBA)

(TBA)

The web provides access to millions of datasets. This data can have additional impact when it is used beyond the context for which it was originally created. There is plenty of advice on how to make a dataset easier to reuse, including technical standards, legal frameworks, and guidelines. While some of these are more widely used than others, overall we have very little empirical insight into what makes a dataset more reusable than others, and which of the existing enabling tools and principles, if any, make a difference. This talk will give an overview of our work on dataset reuse, looking both at a data consumer and a data producer perspective. This can facilitate discussions around the development of empirical approaches to study data reuse as well as the planning, resourcing and impact assessment of data-publishing projects.

Laura Koesten is a senior research fellow at King’s College London in the Department of Informatics in the Distributed Artificial Intelligence Group. She researches Human Data Interaction, aiming to understand data-centric work practices, data reuse, sensemaking and collaboration with data amongst different user groups. She has a background in Human Factors and a PhD in Computer Science from the University of Southampton and the Open Data Institute in London, UK, as a Maria Curie Skłodowska fellow. Her thesis is titled “A User Centred Perspective on Structured Data Discovery” and she co-chaired the past two DATA:SEARCH workshops at the Web Conference 2018 and SIGIR 2018. She is part of the EPSRC project DataStories, aiming to understand what makes data more findable, reusable and engaging; as well as the H2020 project TheyBuyForYou focusing on the accessibility and availability of procurement data.