data_journalistic_data_collections.xlsx

This is a dataset published prior to a journal contribution.<br>Abstract:Surveys and crowdsourcing projects, data scraping, the use of sensors and other experiments are part of the repertoire of journalistic research methods these days. When journalists collect quantitative data, the question arises whether these datasets must meet scientific quality standards. The key question of this paper is what criteria can be used to measure the quality of journalistic data collections. Therefore, it is essential to ask where this phenomenon is to be located between journalism and science.To answer these questions, a two-stage empirical procedure was chosen, which is based on a classic Delphi design. In open, qualitative interviews, twelve experts have been questioned, whose views were then quantified in a written survey. The perspectives of four different groups of experts were included: data and science journalists were interviewed as well as methodology experts from science and media scientists.As a result, a first catalog of criteria for assessing the quality of journalistic data collections was drawn up. It was also found that these criteria often remain unfulfilled in reality, which is mainly due to a lack of resources and insufficient training of journalists. A possible solution might be to collaborate with scientists.本数据集系期刊投稿前公开发布的研究数据集。 【摘要】当前，调查与众包项目、数据爬取、传感器应用及各类实验手段，已成为新闻研究方法的常用范畴。当新闻从业者采集定量数据时，便会产生这类数据集是否需要符合科学质量标准的疑问。本文的核心问题为：可采用何种标准评估新闻类数据集的质量？因此，厘清该现象在新闻业与科学研究之间的定位，实属必要。 为解答上述问题，本研究采用了基于经典德尔菲法（Delphi）的两阶段实证流程：首先通过开放式定性访谈对12名专家进行调研，随后通过书面调查将专家观点予以量化。本次调研纳入四类专家群体的视角：数据新闻与科学新闻从业者、科研领域的方法学专家，以及媒介研究学者。 本研究最终拟定了首套用于评估新闻类数据集质量的标准清单。同时发现，实际场景中这些标准往往难以落实，其主要原因在于新闻从业者资源匮乏且培训不足。与科研人员开展合作，或为可行的解决路径。