数据集检索会话的特征：来自现实生活中数字图书馆的经验
Characteristics of Dataset Retrieval Sessions: Experiences from a Real-life Digital Library
摘要：
二次分析或重新使用现有的调查数据是社会科学家的常见做法。在数字图书馆中搜索相关数据集对这个社区来说是一种陌生的行为。数据集检索，特别是在社会科学中，结合了额外的材料，如密码本，问卷调查，原始数据文件和更多。我们的假设是，由于数据集的多样性，文档检索模型通常不能有效地检索数据集。增强这些类型的搜索的一种方法是结合用户的交互上下文，以便个性化数据集检索会话。作为实现这一长期目标的第一步，我们研究的特点，从现实生活中的数字图书馆的社会科学，包括：研究数据和出版物的数据集检索会话。先前的研究报告了一种通过查询长度在文档搜索和数据集搜索之间辨别查询的方法。在本文中，我们认为索赔，并报告我们的调查结果的查询，无论是针对数据集或文档的不确定性。除其他外，我们报告我们的调查结果的数据集检索会话查询特性，交互序列和主题漂移内65,000个独特的会话。
英文摘要：
Secondary analysis or the reuse of existing survey data is a common practice among social scientists. Searching for relevant datasets in Digital Libraries is a somehow unfamiliar behaviour for this community. Dataset retrieval, especially in the social sciences, incorporates additional material such as codebooks, questionnaires, raw data files and more. Our assumption is that due to the diverse nature of datasets, document retrieval models often do not work as efficiently for retrieving datasets. One way of enhancing these types of searches is to incorporate the users' interaction context in order to personalise dataset retrieval sessions. As a first step towards this long term goal, we study characteristics of dataset retrieval sessions from a real-life Digital Library for the social sciences that incorporates both: research data and publications. Previous studies reported a way of discerning queries between document search and dataset search by query length. In this paper, we argue the claim and report our findings of an indistinguishability of queries, whether aiming for a dataset or a document. Amongst others, we report our findings of dataset retrieval sessions with respect to query characteristics, interaction sequences and topical drift within 65,000 unique sessions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
