论文标题
“数据”是什么意思？拟议的艺术和人文数据类型分类
What do we mean by "data"? A proposed classification of data types in the arts and humanities
论文作者
论文摘要
目的：本文介绍了我们在2021年末与博洛尼亚大学古典语言学和意大利研究系的19位研究人员进行的访谈。主要目的是阐明人文科学领域中“数据”一词的定义，就公平的数据管理实践以及研究人员对该术语的看法。方法论：我们邀请了一名研究人员参加部门内代表的每个官方纪律领域，并接受了所有参加研究的研究人员。然后将参与者分为5个主要研究领域：语言学和文学批评，语言和语言学，艺术史，计算机科学，档案研究。采访使用接地理论方法进行了转录和分析。调查结果：由于参与者收集的信息，已汇编了13种研究类型的列表。尽管仍然存在很多混乱，但“数据”一词并非特别有问题。研究当前的研究管理实践，方法论和团队合作似乎比以前报道的更重要。创意：我们的发现证实，公平框架内的“数据”应包括所有类型的输入和输出人文科学研究工作，包括出版物。此外，这项研究的参与者似乎已经准备好进行讨论，以使他们的研究数据公平：他们并没有觉得术语特别有问题，而他们依靠精确和公认的方法，以及与同事共享和合作。
Purpose: This article describes the interviews we conducted in late 2021 with 19 researchers at the Department of Classical Philology and Italian Studies at the University of Bologna. The main purpose was to shed light on the definition of the word "data" in the humanities domain, as far as FAIR data management practices are concerned, and on what researchers think of the term. Methodology: We invited one researcher for each of the official disciplinary areas represented within the department and all 19 accepted to participate in the study. Participants were then divided into 5 main research areas: philology and literary criticism, language and linguistics, history of art, computer science, archival studies. The interviews were transcribed and analysed using a grounded theory approach. Findings: A list of 13 research data types has been compiled thanks to the information collected from participants. The term "data" does not emerge as especially problematic, although a good deal of confusion remains. Looking at current research management practices, methodologies and teamwork appear more central than previously reported. Originality: Our findings confirm that "data" within the FAIR framework should include all types of input and outputs humanities research work with, including publications. Also, the participants to this study appear ready for a discussion around making their research data FAIR: they do not find the terminology particularly problematic, while they rely on precise and recognised methodologies, as well as on sharing and collaboration with colleagues.
