Ties between the FAIR Guiding Principles for scientific data management, stewardship, publishing, and GIS
An article in Nature magazine about the FAIR guiding principles for scientific data management by Mark Wilkinson, Michael Dumontier, IJsbrand Jan Aalbersberg, and about 25 other authors I believe has thoughtful implications for us as GIS practitioners: How should we manage and serve our GIS data? What should be included in that data? The FAIR guiding principles-–Findable, Accessible, Interoperable, Reusable–-are good ones to keep in mind when setting up sites such as open data portals, ArcGIS Hub sites, and other tools and means to make data available.
These four FAIR principles should serve to guide data producers and publishers as they overcome challenges in serving data. As time advances, the power and ease of use of ArcGIS Hub and other tools diminish the technical challenges. But the organizational challenges, such as long term support, funding, staffing, gathering stakeholders, and establishing a vision, remain.
The article also seeks to identify the value gained by contemporary, formal scholarly digital publishing. The authors state that the FAIR principles apply not only to ‘data’ in the conventional sense, but also to the algorithms, tools, and workflows that led to that data. Interesting. The authors make the claim that all scholarly digital research objects–-from data to analytical pipelines-—benefit from application of these principles, since all components of the research process must be available to ensure transparency, reproducibility, and reusability.
All of this makes me re-question some key elements of GIS workflows: What should we include when we serve data? Is it only the vectors or rasters and metadata? Should we also consider including not just the raw data, but our methods as well? Including the methods will make our research more replicable and, potentially, more widely adopted, and potentially beneficial. But I'm a realist--I have been publishing for 30 years. I know publishing original research is critically important to many professions. I have also seen paragraphs I've written that have been used in others' articles and not attributed to me. Should I be stressed that these paragraphs were copied and I didn't get the credit, or honored that they were deemed valuable by someone else? What are the implications if someone copies our methods and claims them as their own? Or should we care so much about ownership nowadays in the face of the very serious community-to-global problems that we face? So many questions! But, I think, worthy ones to ask.
Of note is the FAIR webinar series (https://www.ands.org.au/working-with-data/the-fair-data-principles/fair-webinar-series) that, while dated, offer additional information as recordings.
One of my favorite points the authors make is that “Good data management is not a goal in itself, but rather is the key conduit leading to knowledge discovery and innovation, and to subsequent data and knowledge integration and reuse by the community after the data publication process.” The authors touch on a problem I have encountered in my own GIS work–-that research results are usually published without providing access to data. Certainly this is understandable when human subjects and other sensitive data are involved, but even then, couldn’t some steps be taken so individual identities are removed? The authors state that “Partially in response to this, science funders, publishers and governmental agencies are beginning to require data management and stewardship plans for data generated in publicly funded experiments.” If this were to happen across all disciplines and at many scales, the entire global society would benefit. Imagine the variety and volume of data we could access to address societal issues and problems if this goal of the authors were realized: “Beyond proper collection, annotation, and archival, data stewardship includes the notion of ‘long-term care’ of valuable digital assets, with the goal that they should be discovered and re-used for downstream investigations, either alone, or in combination with newly generated data.”
It’s clear to me that the current publishing and scholarly process is increasingly out of date with what society needs from research, particularly if we are going to solve problems in energy, water, human health, climate, economic inequality, biodiversity, agriculture, and other areas. A research article is valuable, but the data, the methods, the recommendations, are also increasingly needed. I salute the authors for nudging the community forward in thinking outside the box.
The authors seek to define what good data management actually is, and acknowledge that it is generally left as a decision for the data or repository owner. Therefore, their goal in this article and in the webinar series was to bring “some clarity around the goals and desiderata of good data management and stewardship, and defining simple guideposts to inform those who publish and/or preserve scholarly data, would be of great utility.” The authors recognize that this isn’t an easy task, because it involves numerous, diverse stakeholders with different interests, and it is intertwined with publishing, credit, data providers, service providers, academics, and others. But it is worth pursuing.