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Figure 1. A typicla use of Google Maps
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Figure 2. Variuos Output Options.
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Figure 3. A number of data selection options exit, including graphical display.
Inspiration for the Project
The inspiration for this project has been the intractable problem facing researchers which is the lack of science data sharing. Estimated time-costs in collecting science data are more than $2 billion per year, but only about 1% of this data is shared. Of course, there are many reasons for this lack of science data sharing, which led the industry to formulate a set of principles that aims to make digital assets more shareable and reusable by both humans and machines, promoting better scientific discovery and collaboration. These principles are Findable, Accessible, Interoperable, and Reusable, i.e. FAIR compliant; and subsequently open was also considered, i.e. FAIR/O.
Project History.
As part of my doctorate dissertation, I studied this issue and defined a business model that seeks to include motivation for researchers to participate in a data sharing platform which is free for them to use, while also allowing them the opportunity to monetize their published data each month and on an equitable basis. Furthermore, I also concluded that the platform should be free to both the public and other researchers to access published data sets. Launching any platform has barriers, and the most formidable is the ’chicken or the egg’; i.e. which comes first the content contributors or the content consumers. The African continent was made a priority as it has traditionally been overlooked for research funding, and local University students were sought to undertake data collection and ingest, and this has resulted in approximately 5,000 data sets ingested and published concerning countries worldwide and primarily concerning community health. Subsequently research projects were sought where complimentary licenses to the platform were issued to enable the researchers to securely store their data and selectively publish and this has resulted in a growing number of research teams participating.
Use of Google Maps Platform
The key to this science data sharing platform is in enabling the community to quickly find the data sets that they seek. To this objective various menu options are provided where the results of their search are displayed in graphical form applying Google Maps (Figure 1). Google Earth navigation is intuitive, allowing the use of the mouse to zoom in and select data sets.
*Figure 1. A typical use of Google Maps. *
Subsequent selection of a published data set allows the user to visualize the data set, and potentially download it various formats, (Figure 2, and 3).
Figure 2. Various output options
Figure 3. A number of data selection options exit, including graphical display.
Learnings and Key Differentiators
A key learning is the ease of visualization and presentation of a range of complex science data sets that Google Earth can facilitate. This readily satisfies the ‘findable’ component of the FAIR/O principles which arguably has been the most difficult to address and which has been a significant historical barrier. Behind the scenes the programming interface is relatively easy to interact with Google Earth, which does much of the work and is also quite intuitive. The platform is scaling easily, and the types of data sets stored are growing organically as new data contributors join the platform. The utility of the platform is becoming more valuable now that a critical mass of data has been published. With increased platform traffic, our next stage will be to solicit platform sponsorship and advertisers where the platform revenue each month with be equitably shared with science data publishers based on the relative popularity of their data sets by the public’s interaction with it as calculated by the platform’s algorithm.
Summary
The ecodb.org platform relies heavily on Google Earth to present large quantities of research data sets in a manner that is intuitive and makes this data relatively easy to find. This goes a long way to satisfying the principles of FAIR/O, and seeks to overcome what has been an intractable problem which is the paucity of data sharing within the science community. The platform is highly scalable. Further, the platform allows researchers to publish their data sets whilst also allowing them to monetize this data over the long term, a feature that is highly valued in underdeveloped countries where research funding is sparse.
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