Data discovery practices currently tend to be studied from the perspective of researchers or the perspective of support specialists. This separation is problematic, as it becomes easy for support specialists to build infrastructures and services based on perceptions of researchers' practices, rather than the practices themselves. This paper brings together and analyze both perspectives to support the building of effective infrastructures and services for data discovery. This is a meta-synthesis of work the authors have conducted over the last six years investigating the data discovery practices of researchers and support specialists, like data librarians. We bring together data collected from in-depth interview studies with 6 support specialists in the field of social science in Germany, with 21 social scientists in Singapore, an interview with 10 researchers and 3 support specialists from multiple disciplines, a global survey with 1630 researchers and 47 support specialists from multiple disciplines, an observational study with 12 researchers from the field of social science and a use case analysis of 25 support specialists from multiple disciplines. We found that while there are many similarities in what researchers and support specialists want and think about data discovery, there are some differences we have identified, most notably the interconnection of data discovery with web search, literature search and social networks. We conclude by proposing recommendations for how different types of support work can address these points of difference to better support researchers' data discovery practices.
翻译:目前,人们往往从研究人员或支助专家的角度研究数据发现做法。这种区分很成问题,因为支助专家很容易根据对研究人员做法的看法而不是实践本身来建立基础设施和服务。本文件汇集并分析了两种观点,以支持建立有效的基础设施和数据发现服务。这是作者在过去六年中调查研究人员和支助专家(如数据管理员)的数据发现做法的工作的元综合。我们把深入访谈研究收集的数据与德国社会科学领域6名支助专家(包括新加坡21名社会科学家)汇集在一起,与来自多个学科的10名研究人员和3名支助专家进行面谈,与1630名研究人员和47名支助专家进行全球调查,与来自多个学科的47名支助专家进行观察研究,与来自社会科学领域的12名研究人员进行观察研究,并使用来自多个学科的25名支助专家进行个案研究。我们发现,虽然研究人员和支助专家对数据发现的需求有许多相似之处,但对数据发现有不同之处,但我们发现了一些差异,最明显的是数据发现数据发现与网络搜索、文献搜索和社会网络之间的相互联系。我们最后建议研究人员如何更好地发现这些差异。我们建议,以不同的方式找出不同种类的数据发现。