NeoRef
NeoRef is an archive for any digital material including articles, research notes, books, genetic sequences, concepts, and for derived data including indexing, reviews, claims. Areas of emphasis include optimization of the author self contribution process (user interface, selection and assignment of index terms from controlled vocabularies), automatic metadata extraction from existing materials, optimization of user interfaces for searching from very large content sets (federated universal open archives), and extending metadata to include "claims" to capture higher level knowledge representations about linkages between content items (in particular extending the ScholOnto work to the domain of bioinformatics). We also study the technical and policy issues involved with freely available archives of all scholarly literature that allow for the storage, searching and retrieval of information without the need for the traditional framework of publishers and review systems. NeoRef Scholarly Communications , NeoRef general presentation

Digital Libraries
Dr. Hemminger helped found The Center for Research and Development of Digital Libraries (CRADLE) in 2002, and currently serves as the organizer of the group. This project brings together many active digital library researchers and projects at UNC and worldwide. He is also interested in Electronic Theses and Disserations. He has developed a ETD for SILS scholarly literature (part of NeoRef), and leads the campus group charged with investigating and implementing ETDs for UNC. ETDs

User Interface Design
Development of computer workstations as the display interfaces for the practice of medicine, and in particular, radiology. Previous work includes design principles for radiology workstation design, and specific designs and implementations (chest CT, mammography, 3D). Current interests include (1) investigating what choices of mental models and roam and zoom techniques are most effective when using images larger than the display resolution, and (2) effective CHI techniques for dynamic searching of very large sets of content items (ie. universe of scholarly publications).

Bioinformatics
Ultrastructure: Working with Mike Giddings and Jeff Long to study the application of Ultrastructure, a novel notational system to represent knowledge in a flexible way using relational databases, to bioinformaticss to represent and integrate current and future information information from all genome sciences domains.

Mouse Database: working with Terry Magnuson, Jay Vivian and Yijing Chen to design and implement a database for their mouse mutation project. The challenge is to track the clone (created from frozen library, mutagenized with ENU) associated with phenotype changes.

Proteomics Database: Project with Marshall Pope and Christoph Borchers of the Proteomics Core Facility to develop standardized interchange of proteomics experimental data.

Plant Comparative Genetics: Project with Todd Vision of Biology to investigate database design for storage and query when doing comparative genome studies. The long range intent to have comparative measure of the tree of life.

Virseum
I have proposed a methodology for digitally capturing both the content and entire exhibits of museums, so that they can be visited in a virtual reality setting. The virtual reality representation is unique in that the environments and objects are captured and displayed in way that is both visually compelling (photographic quality), and spatial accurate (millimenter measurements are possible). This work is in conjunction with 3rdTech, who developed the DeltaSphere digitizer. Virseum

Medical Informatics
Digital Imaging and Communications (DICOM) standards work. Dr. Hemminger chairs Working Group 11 (Display), and is an active member and author in Working Groups 15 (Digital Xray, Mammo, Computer Aided Detection), Working Group 17 (3D), AAPM Task group 18 (Electronic Display). For a listing of existing DICOM international standards and new standards under development see the DICOM website at NEMA.

Databases
Integating disparate databases. Handling very large databases. Primary emphasis is on biological databases (genetics, protoemics, metabolomics).

Datamining
Knowledge discovery via data mining of large and integrated databases. Application of statistical pattern recognition and feature analysis techniques to the analysis of genomic and proteomic databases.