Digital Public Library of America recently launched a new application to run a core part of its infrastructure, the DPLA API. This software upgrade improves security, performance, reliability, and privacy for our users. It also allows developers to adapt to new services and maintain the code over time. The DPLA tech team transitioned to the […]
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Curatorial work with large archival collections is challenging. In order to find artifacts related to a chosen topic, curators have to sift through thousands – sometimes millions – of materials and determine which are truly relevant. Machine learning can help streamline curatorial workflows by analyzing complex datasets and making some of their underlying patterns legible.
How can artificial intelligence and machine learning help organize, describe, and provide access to the growing volume of materials in digital libraries and archives? This was the central question of a workshop series hosted by the National Archives and Records Administration (NARA) and the University Libraries at Virginia Tech (VT).
We just released a new Analytics Dashboard, a web application that provides our Member Hubs with detailed data about how their materials are being used, and the quality of their metadata records. In this post, we wanted to share the tools and services powering this new product behind the scenes.
The Digital Public Library of America (DPLA) has released a new, custom-built Analytics Dashboard for our Member Hubs. Through this easy-to-use web-based dashboard, our Members now have one-stop access to up-to-date information about how their rich collections of cultural heritage materials are being used.
The DPLA is launching an open-source tool for fast, large-scale data harvests from OAI repositories. The tool uses a Spark distributed processing engine to speed up and scale up the harvesting operation, and to perform complex analysis of the harvested data. It is helping us improve our internal workflows and provide better service to our hubs. The Spark OAI Harvester is freely available and we hope that others working with interoperable cultural heritage or science data will find uses for it in their own projects.
Following several studies about the use and usability of the DPLA website, we’ve just completed a set of small but significant changes. We believe these changes will create a more pleasant, intuitive experience on our website, connecting people more easily with the cultural heritage materials our partners provide.
Join us for a two-day hackathon during DPLAfest 2015 (Indianapolis, April 17-18) to collaborate with members of the DPLA community and build something awesome with our API. A hackathon is a concentrated period of time for creative people to come together and make something new. In their excellent hackathon planning guide, DPLA community reps Chad Nelson and Nabil Kashyap described a hackathon as “an alternative space–outside of day-to-day assignments, project management procedures, and decision-making processes–to think differently about a problem, a tool, a dataset, or even an institution.”