Abstract
The explosion in volumes and types of data has led to substantial challenges in data management. These challenges are often faced by front-line researchers who are already dealing with rapidly changing technologies and have limited time to devote to data management. There are good high-level guidelines for managing and processing scientific data. However, there is a lack of simple, practical tools to implement these guidelines. This is particularly problematic in a highly distributed research environment where needs differ substantially from group to group and centralised solutions are difficult to implement and storage technologies change rapidly. To meet these challenges we have developed dtool, a command line tool for managing data. The tool packages data and metadata into a unified whole, which we call a dataset. The dataset provides consistency checking and the ability to access metadata for both the whole dataset and individual files. The tool can store these datasets on several different storage systems, including a traditional file system, object store (S3 and Azure) and iRODS. It includes an application programming interface that can be used to incorporate it into existing pipelines and workflows. The tool has provided substantial process, cost, and peace-of-mind benefits to our data management practices and we want to share these benefits. The tool is open source and available freely online at http://dtool.readthedocs.io.
Highlights
Science is an empirical discipline and requires careful data management
Advances in our ability to capture and store data have resulted in a ‘‘big data explosion’’. This is true in biology and has resulted in data management becoming one of the big challenges faced by the biological sciences (Howe et al, 2008; Stephens et al, 2015; Cook et al, 2018)
It consists of a command line tool and an application programming interface (API) for packaging and interacting with data
Summary
Science is an empirical discipline and requires careful data management. Advances in our ability to capture and store data have resulted in a ‘‘big data explosion’’. This is true in biology and has resulted in data management becoming one of the big challenges faced by the biological sciences (Howe et al, 2008; Stephens et al, 2015; Cook et al, 2018). Funders and the research community care about data being trusted, shared and reusable (Vision, 2010; Wilkinson et al, 2016; Waard, Cousijn & Aalbersberg, 2018, Leek, 2018). At the ground level individual researchers need to think about how to structure their data into files, how these data files are to be organised and how to associate metadata with these data files (Hart et al, 2016; Wickham, 2014; Leek, 2018)
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