Earlier this week, the NSF-funded Science Gateways Community Institute hosted its annual Gateways 2019 conference.
The institute, in partnership with seven universities including Indiana University, provides resources and support for the development of science gateways. The Gateways 2019 conference gives gateway creators and users an opportunity to connect and share resources.
So what’s a science gateway, and what does it have to do with CADRE?
A science gateway gives access to shared scientific resources through simplified, user-friendly interfaces. While science gateways offer researchers accessible and affordable options to work with data and advance their research, constructing and maintaining these gateways is not easy.
Take CADRE’s GUI query builder. If an institution can afford to purchase big bibliometric datasets, it often lacks the resources to provide standardized access to the data. Researchers who aren’t proficient coders are then unable to utilize these large, unwieldy datasets.
At the same time, building a GUI query builder is a costly and time-consuming task. It requires buying, seeding, and maintaining enormous datasets; figuring out how to clean the data and fit it into a combination of database technology; refining the querying process to be more efficient; and continuously maintaining it all.
The final product, however, will offer researchers with no programming or data science experience an affordable way to effortlessly query millions of scientific publications and save results.
So why does CADRE go through all the trouble? The CADRE platform was created to help the IMLS-funded Shared BigData Gateway for Research Libraries achieve its mission of developing and maintaining a cloud-based, extendable cyberinfrastructure for sharing large academic library data resources.
We believe libraries’ ability to provision large datasets is the modern equivalent of the collection building and stewardship roles libraries have always been entrusted with. However, libraries are struggling to do this.
Creating and curating infrastructure to contain these large, unwieldy bilbiometric datasets and providing analysis, visualization, and machine-learning services to work with these data is costly and time-consuming, as we said before.
Our science gateway will also facilitate collaboration between researchers through reproducibility. When researchers can share research and results within a common infrastructure, it will become easier to compare, collaborate, and build on each other's work and advance research.