Scientific data management system
As with many other laboratory informatics tools, the lines between a LIMS, ELN, and an SDMS are at times blurred. However, there are some essential qualities that an SDMS owns that distinguishes it from other informatics systems:
1. While a LIMS has traditionally been built to handle structured, mostly homogeneous data, a SDMS (and systems like it) is built to handle unstructured, mostly heterogeneous data.
2. A SDMS typically acts as a seamless "wrapper" for other data systems like LIMS and ELN in the laboratory, though sometimes the SDMS software is readily apparent.
3. A SDMS is designed primarily for data consolidation, knowledge management, and knowledge asset realization. 
An SDMS can be seen as one potential solution for handling unstructured data, which can make up nearly 75 percent of a research and development unit's data. This includes PDF files, images, instrument data, spreadsheets, and other forms of data rendered in many environments in the laboratory. Traditional SDMSs have focused on acting as a nearly invisible blanket or wrapper that integrate information from corporate offices (SOPs, safety documents, etc.) with data from lab devices and other data management tools, all to be indexed and searchable from a central database. An SDMS also must be focused on increasing research productivity without sacrificing data sharing and collaboration efforts.
- retrieve worklists from LIMS and convert them to sequence files
- interact real-time with simple and complex laboratory instruments
- analyze and create reports on laboratory instrument functions
- perform complex calculations and comparisons of two different sample groups
- monitor environmental conditions and react when base operating parameters are out of range
- act as an operational database that allows selective importation/exportation of ELN data
- manage workflows based on data imported into the SDMS
- validate other computer systems and software in the laboratory
See the SDMS vendor page for a list of SDMS vendors past and present.
- Elliott, Michael H. (15 September 2009). "Considerations for Management of Laboratory Data". Scientific Computing. http://www.scientificcomputing.com/considerations-for-management.aspx. Retrieved 04 May 2011.
- Wood, Simon (2007). "Comprehensive Laboratory Informatics: A Multilayer Approach", pp. 3.
- Deutsch, Scott (30 July 2008). "Tomorrow’s Successful Research Organizations Face a Critical Challenge". Scientific Computing. http://www.scientificcomputing.com/tomorrows-successful-research.aspx. Retrieved 04 May 2011.
- Valle, Mario. "Scientific Data Management". Swiss National Supercomputing Center. Archived from the original on 06 March 2012. http://web.archive.org/web/20120306015034/http://personal.cscs.ch/~mvalle/sdm/scientific-data-management.html. Retrieved 05 March 2013.
- Heyward, Joseph E. II (2009). "Selection of a Scientific Data Management System (SDMS) Based on User Requirements", pp. 1–5 (PDF).