DSE strives to be open in the work that we do. Open science is an umbrella term to refer to transparency, accessibility, collaborative development, and sharing of knowledge1, but the breadth and ambiguity in the definition often results in an unclear understanding of how ‘open’ is used in practice. Rather than trying to define what openness is, below we attempt to define our open work within the context of our values, which often align with open practices, including those articulated by FAIR2 and CARE3 principles.
We understand that some communities are unfairly excluded from open practices, and in some cases can be harmed. However, in other cases, open practices also work to counter other forms of inequity and cannibalization of shared resources. Each project is unique with different stakeholders and data sources, therefore we strive to evaluate our decisions in the context of each project. Many decisions are made along the data life cycle from collecting the data to interpreting the results and many choices in the process of handling data come with trade-offs. The design choices we make are based on the collaborators, community, and stakeholders (people and environment) that are affected by the work we do.
Overall, we make decisions on who we work with, how we work, and what we show to the world based on our values outlined below.
We value accessibility
We acknowledge there are inequalities in access to knowledge based on inequalities including socioeconomic factors. The mission of DSE is to use data and data handling tools to understand and act upon pressing environmental challenges, as such we focus on accessibility in the form of open software / tools, open data, and open knowledge to handle the data and understand the results. We strive to pursue work that allows open licensing whenever possible. DSE aims to build upon open infrastructure, including databases, data formats, open source software, so that others can build upon the work we do.
We value transparency
We aim to make the process and products of our work transparent so others can reproduce our work and so that people and communities that were involved or affected by the project can understand and question the decisions that have been made. While it may not be possible or practical to document every single decision that has been made in our work, we are committed to document whatever information we can to help shed light on why we chose the direction that we did. Transparency also allows for extensibility, so others can build off of the work we do, just as we acknowledge and build off the work of others. We choose these values to encourage a community surrounding open data practices and empowerment for all to collaborate.
We value data privacy and data sovereignty
In many cases data should be open and freely accessible to everyone, but in some cases we may choose to withhold information that causes harm. When working with environmental data, harm can take the form of data that exposes sensitive information on the location of endangered species, personal data, geographic area, and/or work that causes cultural harm. We may work with data where laws and rules for a geographic area might affect the way we treat data and will examine and strive to adhere to data sovereignty considerations of the nations and communities we collaborate with. In addition, we aim to reduce extractive behavior of those with power from those with less power. It is our goal to listen, support, and empower those with data to make their own positive impact on environmental challenges.
This document was created from discussions within the DSE team. An ongoing aspect of our work is the refinement of our process for instilling these values into actions. See our
We considered and learned from the work of others. This is a section of recommended readings to learn about the resources that inspire us.
Vicente-Saez, R. & Martinez-Fuentes, C. Open Science now: A systematic literature review for an integrated definition. J. Bus. Res. 88, 428–436 (2018).
Wilkinson, M. D. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3, 160018 (2016).
Carroll, S. R. et al. The CARE Principles for Indigenous Data Governance. 19, 43 (2020).