2.2 Open science and metadata education to address data
needs
To address the data-related challenges outlined above, we support the
ongoing shift towards open science within the Canadian hydrology
community. Open science is a movement to make scientific publications,
data, and software publicly accessible. The movement already has a
strong following. For example, funding agencies in Europe are mandating
open access publications (Schiltz et al., 2018), publishing datasets in
data journals is becoming increasingly popular (Carlson and Oda, 2018)
and “negative” results are being discussed and published more often
(van Emmerink et al., 2018). Open science is also popular among the
global hydrological community where a survey of 336 hydrologists showed
that 97% of participants felt all data should be shared, though no
consensus was formed on exactly how to share data and acknowledge the
person or group who collected them (Blume et al., 2017).
In a Canadian context, we suggest the hydrology community could benefit
from enhanced use of data sharing platforms (or developing
Canada-focused communities on existing platforms) to help combat the
fragmented state of many datasets. The use of communal databases or
online repositories (e.g. Zenodo) that allow for responsible and
consistent storage of datasets and models would ensure data are visible
and accessible, contain sufficient metadata, and are properly
quality-controlled. The adoption of such communal databases could reduce
research redundancy, facilitate integrated research efforts and
comparative studies, and lead to more broadly applicable findings and
higher impact publications from the Canadian hydrologic community.
Beyond simply making data accessible, including appropriate metadata is
essential to effective data-sharing. Since ECRs are often producing and
archiving datasets, we would benefit from more integration of data
management practices into graduate training curriculum. Furthermore,
data stewardship efforts could be enhanced by including standardized
procedures and templates within individual research groups, which has
been shown to increase model sharing (Weiler and Beven, 2015). These
templates could include naming conventions, file formats, metadata
structure, and collection techniques during fieldwork. Templates could
be shared with incoming ECRs, enhancing learning, promoting
institutional memory and allowing ECRs to focus on new findings.
Considering the short residence time of some ECR positions, longer-term
members of the research team such as laboratory managers, field
technicians, and professional research associates, could play a key role
in developing and maintaining standardized datasets. Data management and
protocol development require a time investment, but we argue this
initial cost is rewarded by facilitating data sharing and the subsequent
advance in scientific understanding.