Murray Glenn A, Crocker David P
Department of Chemical Engineering, Colorado School of Mines, Golden, CO, USA.
J Lab Autom. 2011 Oct;16(5):327-34. doi: 10.1016/j.jala.2010.04.005. Epub 2010 Jul 21.
The rise of new experimental techniques, such as high-throughput combinatorial methods, and the availability of large data sets by means of the Internet have greatly increased the amount of data that must be managed by relatively small projects. Scientific data management systems developed for large projects are often not available, suitable, nor affordable for projects with lesser resources. Increasing numbers of open-source frameworks have made available numerous options for smaller facilities to build for themselves effective and robust data management solutions. We will present considerations of these options and a case study.
新实验技术的兴起，如高通量组合方法，以及通过互联网可获取的大量数据集，极大地增加了相对较小的项目必须管理的数据量。为大型项目开发的科学数据管理系统通常不适用于资源较少的项目，也不适合其使用，而且成本过高。越来越多的开源框架为较小的机构提供了众多选择，使其能够自行构建有效且强大的数据管理解决方案。我们将介绍对这些选项的考量以及一个案例研究。
