User Experience Analysis in Dataset Search Platforms

Core Concepts

Enhancing user experience in dataset search platforms through improved design and functionality.

Abstract

Investigates User Experience (UX) issues in dataset search platform interfaces, focusing on Google Dataset Search and data.europa.eu.
Evaluation method combines user tasks, think-aloud methods, and questionnaires.
Findings lead to the development of 10 new design prototypes to improve usability.
Recommendations include improvements in initial interaction, search process, dataset exploration, filtering and sorting, dataset actions, and assistance and feedback.
INTRODUCTION

Data-driven sectors rely on diverse datasets but face challenges due to data silos.
Previous studies highlight the importance of metadata in dataset searches.
This research aims to explore user experience in navigating dataset search platforms.
RELATED WORKS

Information seeking behavior models may not fully apply to dataset retrieval.
Different approaches exist for dataset searches like 'basic' and 'user-organized'.
Online dataset accessibility is crucial for usability and findability.
APPROACH
Explore Platform Features

Evaluates features of Google Dataset Search and data.europa.eu for task creation.
Explore User Experience Aspects

Identifies six facets of User Experience: Initial Interaction, Search Process, Dataset Exploration, Filters and Sorting, Dataset Actions, Feedback and Help.
Design User Task

"The Pandemic Puzzle" task designed for understanding user interactions with datasets on COVID-19.
Use of User Study Methods

Concurrent Think-Aloud method used for real-time insights into participant thinking.
Implement Questionnaires

Demographic questionnaires provide insights into participant backgrounds.
Participant Recruitment

Diverse pool of participants recruited from academia and industry.
RESULTS - Google Dataset Search & data.europa.eu
Initial Interaction - Google Dataset Search:
P1: Participants found search features easily accessible but language switch button less noticeable.
N3: Detailed information appeared compressed leading to reading fatigue.
Search Process - Google Dataset Search:
P7: Participants utilized system suggestions effectively.
N16: Participants spent time downloading datasets with missing values.
Dataset Exploration - Google Dataset Search:
P13-P16: Participants focused on titles, update times, descriptions.
N29: Participants initially cared about ranking but later ignored it.
Usage of Filters & Sorting - Google Dataset Search:
P23: Participants understood filters without help.
N50: Some participants ignored filters altogether.
Dataset Actions - Google Dataset Search:
P25: Participants easily found save/share/cite buttons.
N56: Some preferred using browser's share function over platform's options.
Feedback & Help - Google Dataset Search:
P27: Participants found help documentation user-friendly but noted errors in user guide page.
New Prototypes for Dataset Search Platform:
Recommendations include improvements in initial interaction, search process, dataset exploration, filters/sorting, actions, feedback/help.

Stats

This section does not contain key metrics or important figures.

Quotes

This section does not contain any striking quotes supporting the author's key logics.

Key Insights Distilled From

User Experience in Dataset Search Platform Interfaces

by Yiha... at arxiv.org 03-26-2024

https://arxiv.org/pdf/2403.15861.pdf

Deeper Inquiries

How can the proposed design prototypes be implemented practically?

The proposed design prototypes can be implemented practically by following a structured approach. Firstly, the changes should be prioritized based on their impact on improving user experience. This involves collaborating with designers and developers to create wireframes and mockups of the new features. The implementation process should involve iterative testing with real users to gather feedback and make necessary adjustments. Additionally, ensuring that the color schemes are accessible to all users, including those with color vision deficiencies, is crucial. Finally, thorough documentation of the design decisions and rationale behind each change will aid in future updates and maintenance.

What are the potential challenges in integrating these improvements into existing dataset search platforms?

Integrating these improvements into existing dataset search platforms may face several challenges. One challenge could be technical constraints within the current platform architecture that limit certain design changes or require significant redevelopment efforts. Another challenge could arise from resistance to change from stakeholders who may prefer maintaining the status quo rather than implementing new features. Ensuring seamless integration across different devices and screen sizes while maintaining consistency in user experience poses another challenge. Moreover, balancing aesthetic enhancements with functional improvements without overwhelming users or compromising usability is essential.

How might advancements in AI impact the future of UX design in dataset search platforms?

Advancements in AI have significant implications for UX design in dataset search platforms. AI-powered algorithms can enhance personalized recommendations based on user behavior patterns, leading to more relevant search results tailored to individual preferences. Natural Language Processing (NLP) capabilities can improve query understanding and provide more accurate results through semantic analysis of user queries. Machine learning models can optimize metadata tagging processes, improving data organization and retrieval efficiency for users.
Furthermore, AI-driven chatbots or virtual assistants can offer proactive assistance during searches, guiding users through complex queries or suggesting alternative keywords for better results.