Year: 2022
Type: article
Abstract: Permafrost thaw is a major concern both in the context of climate change as part of global carbon feedback systems (Natali et al., 2021) and as geohazard threatening ways of life in northern regions (Calmels et al., 2014;Meredith et al., 2019) and infrastructure (Hjort et al., 2022).Many kinds of data are used to understand permafrost (Wang et al., 2018).Of these, ground temperature time series data are commonly used to understand and predict permafrost thaw, for example through the development and evaluation of numerical models (Melton et al., 2019).Unfortunately, cleaning, organizing, and managing ground temperature data continues to be an obstacle.A survey of 37 data-producing institutions worldwide suggests that more than 50% of environmental science datasets use 'in-house' text formats to store and distribute data (Bavay et al., 2020).Consequently, most researchers rely on ad-hoc scripts to manage data; new data sources commonly take a day or more to integrate into workflows.Members of the permafrost community describe a lack of capacity as one of the main barriers to better data management and more frequent data publication (Brown et al., 2020).
DOI: 10.21105/joss.04704
Author: Nicholas Brown
Institution: Carleton University
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4297962053