The Landscape of Data Reuse in Interactive Information Retrieval: Motivations, Sources, and Evaluation of Reusability (2411.15430v1)

Abstract: Sharing and reusing research data can effectively reduce redundant efforts in data collection and curation, especially for small labs and research teams conducting human-centered system research, and enhance the replicability of evaluation experiments. Building a sustainable data reuse process and culture relies on frameworks that encompass policies, standards, roles, and responsibilities, all of which must address the diverse needs of data providers, curators, and reusers. To advance the knowledge and accumulate empirical understandings on data reuse, this study investigated the data reuse practices of experienced researchers from the area of Interactive Information Retrieval (IIR) studies, where data reuse has been strongly advocated but still remains a challenge. To enhance the knowledge on data reuse behavior and reusability assessment strategies within IIR community, we conducted 21 semi-structured in-depth interviews with IIR researchers from varying demographic backgrounds, institutions, and stages of careers on their motivations, experiences, and concerns over data reuse. We uncovered the reasons, strategies of reusability assessments, and challenges faced by data reusers within the field of IIR as they attempt to reuse researcher data in their studies. The empirical finding improves our understanding of researchers' motivations for reusing data, their approaches to discovering reusable research data, as well as their concerns and criteria for assessing data reusability, and also enriches the on-going discussions on evaluating user-generated data and research resources and promoting community-level data reuse culture and standards.

• The paper demonstrates that data reuse enhances research reliability and comparability by saving time and resources.
• The paper employs semi-structured interviews with 21 IIR researchers to uncover how academic networks and publications facilitate the discovery of reusable data.
• The paper identifies challenges like loss of context and non-standardized documentation, urging community frameworks to improve data sharing practices.

Data Reuse in Interactive Information Retrieval: Motivations, Challenges, and Future Directions

This research paper addresses the intricacies and challenges associated with data reuse in the domain of Interactive Information Retrieval (IIR). By investigating data reuse practices, it aims to provide an empirical understanding of the factors influencing data reuse behavior and reusability assessment strategies among IIR researchers. Such insights are essential as the IIR community moves towards a data-driven approach coupled with methodological diversity, necessitating a deeper understanding of data sharing and reuse.

The study employs a qualitative approach through semi-structured interviews with 21 IIR researchers from various demographic backgrounds and career stages. These interviews reveal researchers' motivations, methods of discovering reusable data, assessment strategies, and concerns regarding data reuse. By detailing these findings, the study contributes to ongoing discussions on enhancing data reuse culture and infrastructure within the research community.

Key Findings

1. Motivations for Data Reuse:
   • The primary motivations for data reuse among IIR researchers include exploration of new insights, ground-truthing, and enhancing study reliability. Reusing data saves considerable time and resources, which supports productivity by focusing efforts on research questions rather than data collection.
   • Particularly for system-oriented researchers in the IIR domain, data reuse enables comparability with prior studies, offering evidence of the broader applicability of their findings.
2. Discovery and Access of Reusable Data:
   • Most researchers discover reusable data through academic publications or personal networks rather than actively searching for it. Academic advisors, colleagues, and workshops play pivotal roles in data discovery.
   • Data access is often facilitated through personal connections which ensures a greater level of trust and understanding regarding the shared data, reducing potential risks related to data misuse or misinterpretation.
3. Assessment of Data Reusability:
   • Researchers evaluate reusability based on understandability, trustworthiness, and the data's previous usages. Understandability emphasizes the need to comprehend the structure and variables within datasets.
   • Trustworthiness often relies on the academic reputation of data collectors and the dataset's inclusion in peer-reviewed publications.
4. Concerns and Challenges:
   • A major concern is the loss of context-specific information during data sharing, which affects the validity and potential reusability of datasets. This is exacerbated by non-standardized documentation, leading to reliance on personal assessment for understanding datasets.
   • Researchers are also wary of the perceived value of studies utilizing reused data, fearing it may devalue their contributions due to perceived lack of innovation.

Implications and Future Prospects

The paper's findings underscore the need for community-level frameworks to enhance data sharing practices in the IIR domain. Establishing standardized methods for documentation and data sharing can mitigate many concerns regarding data reusability and facilitate broader access across varying research orientations. Moreover, promoting familiarity with available data repositories and search engines can improve data discovery, encouraging more active data reuse practices.

From a theoretical perspective, the study highlights the significance of interdisciplinary collaboration and the impact of research community norms on data reuse behavior. Advancements in AI and data-driven research methodologies will necessitate further exploration of these dynamics, emphasizing the integration of robust data management practices.

Overall, promoting a culture of data reuse is crucial for scientific advancement within IIR and beyond. This paper provides a valuable contribution by articulating the practical challenges and potential solutions, steering future research efforts towards improved data sharing and reuse infrastructure.