 |
Helping Interdisciplinary Vocabulary Engineering (HIVE)
Helping Interdisciplinary Vocabulary Engineering (HIVE) is an automatic metadata/Linked Open Data project that dynamically integrates discipline-specific SKOS vocabularies. |
 |
NSF Institute for Data-Driven Dynamical Research
The goal of this institute is to bring together the experimentally validated best approaches and tools to design and study new materials and structures. |
 |
HSF-HDR: Accelerating the Discovery of Electronic Materials through Human-Computer Active Search
The goal of this project is to accelerate the discovery of materials with tailored electronic properties through human-computer active search. |
 |
NSF-HDR: Biology-Guided Neural Networks for Discovering Phenotypical Traits
|
 |
Developing the Data Set of Nineteenth-Century Knowledge
A project to study the structure and transformation of nineteenth-century knowledge via computational analysis of several editions of the Encyclopedia Britannica from 1788 to 1911. |
 |
A Licensing Model and Ecosystem for Data Sharing
“A Licensing Model and Ecosystem for Data Sharing” is a spokes project led by researchers at Massachusetts Institute of Technology (MIT), Brown University, and Drexel University, as part of the Northeast Big Data Innovation Hub. |
 |
LIS Education and Data Science for the National Digital Platform (LEADS-4-NDP)
As part of the Institute of Library and Museum Services (IMLS) Laura Bush 21st Century Librarian Program, LEADS-4-NDP will prepare next generation LIS faculty so they may meaningfully integrate data science and LIS education. |
 |
DataSAR
DataSAR is an open repository of data sharing agreements used among industry, academia, government, and nonprofits. The goal of DataSAR is to facilitate the process of sharing data, particularly for sensitive data types. |
 |
The MetaDataCAPT’L Initiative
The Metadata Capital Initiative (MetaDataCAPT’L) is exploring methods for quantifying metadata cost and value over time. Metadata capital targets metadata as an asset containing contextual knowledge about data content. |
|
|
Metadata Mixers |
| View Our Past Research |
