Link Domain Tools / Extras for Andrews-Dallas Public Library
The machine actionable data published in this Library Link domain lowers the cost and simplifies the development of a wide range of different applications designed to help libraries, staff and patrons alike. Here are just a few of the "extras" registered with this domain.
Bibframe resource types provide common control points to connect data in the Library and across the Web. Exposing these data via Data Filters and Identifier Services allows this data to be easily consumed by open source tools as Elasticsearch.
Linked Library Enrichment, powered by NoveList
Linked Library Enrichment uses NoveList’s curated and highly structured data (such as read-alike recommendations, appeal factors, and other reader information) to supercharge your linked data. Check out some of the benefits your library can expect from linked data.
We love lists. Many organizations maintain lists for any number of reasons. But when these organization share their lists via Web standards we can view these through the actionable data available in the Library. In this case, these lists become something even far more useful. Here are just a few examples of some of the Living Lists available in this Library Link domain.
The collections.library.link service provides an open and extensible collection of Lists and Mixes. You can make your own List or Mix to engage with your community, and also use Lists and Mixes created by other libraries in The Library.Link Network.
- Lists are a fixed, ordered set of library items that have been organized for a particular purpose. Any List can be used by any Library in The Library.Link Network.
- Mixes are more dynamic and blend library items that share concepts (including more specialized persons,topics, forms, places, etc.) together to create new collections. Any Mix can be used by any Library in The Library.Link Network.
The Library.Link Network helps amplify the collaborative nature of libraries and allows collections to easily be built by one library and then instantly used by another. Here, for example, is a self-improvement guide contributed from the Dallas Public Library viewed though the Andrews-Dallas Public Library. And here are all of the Collaborative Collections available for Andrews-Dallas Public Library created by others in the network.
For more information on how to use and create Lists and Mixes, visit the FAQ.
Created especially for people who love books, NoveList Lists provides hundreds of reading lists, such as “Top 10” and “Best of” and is based on NoveList's premier online database for reading recommendations which is available through libraries around the world.
The New York Times Best Seller list is widely considered the preeminent list of best-selling books in the United States. Published weekly in The New York Times Book Review, the best-seller list has been published in the Times since October 12, 1931. In recent years it has evolved into multiple lists in different categories, broken down by fiction and non-fiction, hardcover, paperback and electronic, and different genres.
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The Library.Link Network has published over 100+ TB of RDF machine readable data designed to help Libraries, Museums, Archives and Historical Societies all over the world tell their story and connect to where their users are at - on the Web.
If HTML and the Web made all the online documents look like one huge book, RDF ... will make all the data in the world look like one huge database.
RDF is at the core of W3C's Data Web. It is the standard specifically designed to provide a way to produce and consume data on the Web. Exposing library data in an open and actionable manner frees this data from the applications that have traditionally managed it, allowing for new ways to integrate this with other web content, visualize and explore, analyze, compare, and enable intelligent agents to carry out tasks on behalf of users in new and useful ways. And that is just the start.
The Library.Link FAQ is an evolving place to learn more about applications built using the Library's Linked Data.