University of Maryland Libraries joins CADRE as the 10th BTAA partner-institution

June 3, 2020
The Collaborative Archive & Data Research Environment (CADRE) is excited to announce today that the University of Maryland Libraries is joining the esteemed cohort of library partners supporting the CADRE project.

Maryland will join nine other Big Ten Academic Alliance (BTAA) libraries to support CADRE. These libraries also help shape CADRE through their researchers who use the platform: The CADRE team utilizes user stories, such as researchers’ requests for certain functionality or technical support, to inform CADRE’s design.

The BTAA, along with the Indiana University Network Science Institute, serve as major partners on CADRE, led by Indiana University Libraries.

All Maryland-affiliated researchers will now be able to access CADRE’s proprietary tier of service, which includes access to CADRE’s innovative features and to the Web of Science and Microsoft Academic Graph datasets. Even more, the university will join the founding community of academic libraries that are pooling resources to build the premier solution for standardized text- and data-mining of big bibliometric datasets.

"Improving our data services and curation for the University of Maryland community features heavily in our new strategic plan," said Adriene Lim, Dean of University Libraries. "In our initiatives aimed at improving the Libraries' ability to meet researchers' data needs, joining CADRE and leveraging the collective wisdom and work of BTAA colleagues and supporting existing technological solutions are logical and effective next steps."

Associate Dean of Libraries Daniel Mack agreed, stating, "CADRE will be a valuable tool to help us make informed decisions about subscriptions, partnerships, open access, and other collections issues."

The list of CADRE’s other BTAA library partners includes:

A shared infrastructure

Now, more than ever, it is critical for academic libraries to assess how to provide librarians and researchers with standardized text- and data-mining services for licensed and open big datasets at lower costs. CADRE was created to be an affordable, sustainable solution for libraries that cannot afford to purchase big bibliometric datasets, or build the infrastructure and provide the services necessary to host such data, on their own.

Not only does each new partner on the CADRE project contribute resources to grow its shared infrastructure, but each partner also lowers costs for everyone and strengthens CADRE’s commitment to building a collaborative, community-oriented platform.

“At its core, CADRE is a community effort to solve the expensive problem of making big data accessible to researchers. Without collaboration, there is no CADRE,” said CADRE Project Director Jamie Wittenberg. “We are thrilled to welcome the University of Maryland on board and look forward to working with Maryland researchers and librarians to advance our development and improve our service offerings.”

Please contact the CADRE team if you are interested in working with or partnering with CADRE.

You can also access CADRE’s free tier of service, which includes Microsoft Academic Graph, during the alpha phase here:


CADRE addresses a critical emergent issue faced by academic libraries: providing sustainable, affordable, and standardized text- and data-mining services for licensed big datasets, as well as open and non-consumptive datasets too large or unwieldy to work with in existing research library environments. By sharing costs across a large number of academic libraries, CADRE will create a cloud-based solution for making these data available to its member institutions--with appropriate security, stewardship, and storage--at a fraction of what it would cost them to do alone.

This project is led by Indiana University Libraries, in partnership with the Indiana University Network Science Institute and the Big Ten Academic Alliance. CADRE is funded with IMLS award LG-70-18-0202 and is additionally supported by a unique group of cross-industry partners, including: