Khan Arshad M, Hahn Joel D, Cheng Wei-Cheng, Watts Alan G, Burns Gully A P C
Neuroscience Research Institute, Department of Biological Sciences, 3641 Watt Way, Hedco Neurosciences Building, University of Southern California, Los Angeles, CA 90089-2520, USA.
Neuroinformatics. 2006;4(2):139-62. doi: 10.1385/NI:4:2:139.
Scientists continually relate information from the published literature to their current research. The challenge of this essential and time-consuming activity increases as the body of scientific literature continues to grow. In an attempt to lessen the challenge, we have developed an Electronic Laboratory Notebook (ELN) application. Our ELN functions as a component of another application we have developed, an open-source knowledge management system for the neuroscientific literature called NeuroScholar (http://www. neuroscholar. org/). Scanned notebook pages, images, and data files are entered into the ELN, where they can be annotated, organized, and linked to similarly annotated excerpts from the published literature within Neuroscholar. Associations between these knowledge constructs are created within a dynamic node-and-edge user interface. To produce an interactive, adaptable knowledge base. We demonstrate the ELN's utility by using it to organize data and literature related to our studies of the neuroendocrine hypothalamic paraventricular nucleus (PVH). We also discuss how the ELN could be applied to model other neuroendocrine systems; as an example we look at the role of PVH stressor-responsive neurons in the context of their involvement in the suppression of reproductive function. We present this application to the community as open-source software and invite contributions to its development.
科学家们不断地将已发表文献中的信息与他们当前的研究联系起来。随着科学文献数量的持续增长,这项重要且耗时的活动所面临的挑战也在增加。为了减轻这一挑战,我们开发了一个电子实验室笔记本(ELN)应用程序。我们的ELN作为我们开发的另一个应用程序的组件,即一个名为NeuroScholar(http://www.neuroscholar.org/)的用于神经科学文献的开源知识管理系统。扫描的笔记本页面、图像和数据文件被输入到ELN中,在那里它们可以被注释、组织,并与NeuroScholar中已发表文献的类似注释摘录相链接。这些知识结构之间的关联在一个动态的节点和边用户界面中创建,以生成一个交互式、适应性强的知识库。我们通过使用ELN来组织与我们对神经内分泌下丘脑室旁核(PVH)的研究相关的数据和文献,展示了ELN的实用性。我们还讨论了ELN如何应用于对其他神经内分泌系统进行建模;例如,我们研究了PVH应激反应神经元在其参与生殖功能抑制背景下的作用。我们将这个应用程序作为开源软件呈现给社区,并邀请大家为其开发做出贡献。