We provide professional consulting for science researchers on:
- Database installation and data import (loading existing databases)
- Database design and implementation (building new databases with web site front ends)
- Database testing and performance optimization
- Database server capacity planning including defining memory, processor performance, network speed, and security requirements
- Database server deployment and operation
- Data workflows, analysis, and visualization
- Data management
“The act of processing the data, getting from the collecting stage to the
interpretation stage, can be time consuming and tedious. Shortening
the process with the Cornell Center for Advanced Computing lets me use my time more efficiently.”
Department of Statistical Science Professor
- ALFA Pulsar Studies – designed and operate large-scale archiving system for pulsar data;
provided consulting on data architecture and workflow (see Analyzing fast radio bursts case study)
- Cardiothoracic Surgery Project – developed HIPAA-protected surgical procedure database for regulatory outcome reporting and research into overall treatment outcomes (Weill Cornell Medicine)
- Maize Disease Resistance Project – ported plant breeding data from Access to SQL Server; developing a web front end for queries.
Our consulting staff can also operate as part of your research team providing computational science research support on a short-term or long-term basis, e.g., developing, maintaining, and upgrading web-database interfaces, etc.
if you are interested in database or data-related consulting.
For rates, visit how to start a project.
Visit services available to learn more about related services, e.g., high-performance computing services (including web hosting for research applications), storage services, and consulting.
“The collection, analysis and curation of data are critical components of research. Our expertise and experience in working with researchers on the handling of scientific data to enable scientific workflows includes: designing and managing large, sophisticated databases which reflect the semantics of the data; automated collection, organization and curation of scientific datasets; integration of data management into scientific pipelines; cooperating with researchers to enhance funding proposals, including writing data management plans; and distribution of data and data products to research consortia, the scientific community and the public.”
Adam Brazier, CAC Consultant