Foundational Practices of Research Data Management

Abstract

The importance of research data has grown as researchers across disciplines seek to ensure reproducibility, facilitate data reuse, and acknowledge data as a valuable scholarly commodity. Researchers are under increasing pressure to share their data for validation and reuse. Adopting good data management practices allows researchers to efficiently locate their data, understand it, and use it throughout all of the stages of a project and in the future. Additionally, good data management can streamline data analysis, visualization, and reporting, thus making publication less stressful and time-consuming. By implementing foundational practices of data management, researchers set themselves up for success by formalizing processes and reducing common errors in data handling, which can free up more time for research. This paper provides an introduction to best practices for managing all types of data.

Ten best practices to strengthen stewardship and sharing of water science data in Canada

“…Properly documenting datasets from the outset of a project will ensure their usability in the future, both by the original data collectors and by other researchers across disciplines. Briney et al (2020) of different versions of a dataset. Standardizing processes and procedures will help researchers capture and create consistent data and metadata and provide an easy reference throughout the project (Laudon & Taberman, 2016).…”

“…Properly documenting datasets from the outset of a project will ensure their usability in the future, both by the original data collectors and by other researchers across disciplines. Briney et al (2020) states that ‘much like preventative medicine, a little time spent managing data at the beginning of a research project can save a huge amount of time later in having to deal with a data disaster’. To help develop the picture of what the end dataset should look like, Wilson et al, 2017 suggests that researchers ‘ Create the data set you wish you had received’.…”

“…Data documentation enables other researchers (including your future self) to discover, understand, use, share, and manage data over time (Briney et al, 2020; Laudon & Taberman, 2016; Lowndes et al, 2019). Data documentation should include detailed information about the context under which the raw data were collected (the how, what, when, where and why), any steps used to process the raw data, the quality control processes used to ensure that data are complete and valid, information on data confidentiality, access and use conditions (where applicable), data formats, and identification and tracking of different versions of a dataset.…”

“…Metadata should also be documented in a standardized manner. Metadata schemas define a set of fields to describe data (Briney et al, 2020; Laudon & Taberman, 2016) and may prescribe controlled vocabularies and ontologies to standardize elements such as dates, names and places. Generally, it is best to adopt, adapt or extend existing metadata standards to conserve time and resources (Park & Tosaka, 2010).…”

“…Domain‐specific schemas, such as Climate and Forecast (CF) metadata, have a much richer structure but tend to be highly specialized and only understandable by researchers proficient in the types of files used, although CF is steadily being extended to provide metadata suitable for describing observational data (e.g., ground‐based hydrometeorological observations and satellite data (Lammert et al, 2019). Researchers are encouraged to choose a metadata schema at the outset of the research process because it prompts the collection of information that will support good data management practices, research reproducibility, and capture information about context or collection issues that may affect future interpretation and application of data (Briney et al, 2020).…”