EAGER: DBfN: Data Bridge for Neuroscience: A novel way of discovery for Neuroscience Data

当成千上万的科学家正在创建数百万个数据集来描述越来越多样化的神经科学现象时，一个独立的科学家找到与特定研究方向相关的所有数据的机会每天都在减少。与此同时，存储数据的数量和多样性的快速增长意味着这些数据集的潜力相应增加，以支持重要的新合作研究和发现。应对这些挑战需要专门创建的工具，以帮助科学家寻找相关的数据集和合作者。由单个科学家和团体收集和生成的大量相对较小的数据集形成了一个独特的大数据类别，称为“科学长尾”数据，利用它们隐藏的力量对于推进科学至关重要。该项目将参与一系列规划活动，用于神经科学的新型数据发现系统的应用，该系统基于一个名为DataBridge的平台，该平台由研究人员及其合作者在NSF大数据计划的资助下开发。DataBridge应用“签名”和“相似性”算法，在语义上将大量不同的数据集连接到一个“社会计量”网络中。以前的工作集中在社会科学的数据。这项工作将研究这些技术的扩展到神经科学领域。如果这些技术可以被证明与神经科学数据一起使用，该项目将不仅在神经科学研究界产生广泛的影响，而且可能在其他科学界也产生广泛的影响。神经科学正处于一个转折点，越来越多的数据正在通过公共存储库进行汇总和共享。在大数据时代，神经科学社区面临的主要挑战是难以在这些存储库中发现相关数据集。有效发现和识别相关数据的复杂性形成了科学数据长尾的最后一公里问题。解决这一问题可以通过重用和重新利用来增加数据的价值，并通过提供对其各个主题领域数据的更多访问，使NSF大脑计划受益匪浅。该项目将启动基于DataBridge平台的神经科学新型数据发现系统的应用研究，该平台是项目团队在NSF大数据计划的资助下开发的。DataBridge应用“签名”和“相似性”算法，在语义上将大量不同的数据集连接到一个“社会计量”网络中。DataBridge for Neuroscience平台将尝试利用神经科学研究人员开发的复杂分析算法，以提取关键签名，并从大量所谓的“长尾”神经科学数据中找到数据关联。DataBridge通过模式分析、特征提取和其他相关标准提供了一个定义复杂搜索标准的场所，为科学数据提供了一个高度可定制的搜索引擎。 该项目将对DataBridge在神经科学数据上的适用性进行初步的可行性研究，目标有两个：1。实现一个试点DataBridge系统的神经科学和语义桥接神经科学数据集的一个小集合的概念证明，和2。举办一个研讨会，发展神经科学界的用户联盟，以便为神经科学建立一个可持续的基于数据桥的基础设施。这项基于社区的活动将利用NSF大数据中心和辐射计划创建的社区基础设施。

At a time when thousands of scientists are creating millions of datasets describing an increasingly diverse mix of neuroscience phenomena, the chances of an individual unaided scientist finding all of the data relevant to a particular line of investigation are shrinking every day. At the same time, this rapid increase in the amount and diversity of stored data implies a corresponding increase in the potential of these datasets to empower important new collaborative research and discovery. Meeting these challenges requires tools specifically created to assist scientists in their search for relevant datasets and collaborators. Massive number of relatively small datasets gathered and generated by individual scientists and groups, form a distinct class of Big Data called the "long tail of science" data and harnessing their hidden power is crucial for advancing science. This project will engage in a set of planning activities for the application of a novel data discovery system for Neuroscience, based upon a platform called, DataBridge, which has been developed by the investigators and their collaborators under a grant from the NSF Big Data program. DataBridge applies "signature" and "similarity" algorithms to semantically bridge large numbers of diverse datasets into a "sociometric" network. Prior work has focused on data from the Social Sciences. This work will study the extension of those techniques to the Neuroscience domain. If the techniques can be demonstrated to work with neuroscience data, the project would have broad impact not only in the neuroscience research community but, potentially, in other science communities as well.Neuroscience is at an inflection point where more and more data are being aggregated and shared through common repositories. The main challenge facing the neuroscience community in the Big Data era is the difficulty of discovering relevant datasets across these repositories. The complication of effective discovery and identification of relevant data forms the last mile problem for long tail of science data. Solving this problem can increase the value of the data through reuse and repurposing and can immensely benefit the NSF Brain Initiative by providing increased access to data in its various thematic areas. This project will initiate studies on the application of a novel data discovery system for Neuroscience based upon a platform called DataBridge, which the project team has developed under a grant from the NSF Big Data program. DataBridge applies "signature" and "similarity" algorithms to semantically bridge large numbers of diverse datasets into a "sociometric" network. The DataBridge for Neuroscience platform will attempt to harness complex analytics algorithms developed by neuroscience researchers in order to extract key signatures and find data associations from large volumes, and diverse collections, of so-called "long-tail" neuroscience data. By providing a venue for defining complicated search criteria through pattern analysis, feature extraction and other relevance criteria, DataBridge provides a highly customizable search engine for scientific data. This project will conduct a preliminary, feasibility study on the applicability of DataBridge on Neuroscience data with two goals: 1. Implement a pilot DataBridge system for Neuroscience and demonstrate a proof of concept for semantically bridging a small collection of neuroscience datasets, and 2. Conduct a workshop to develop a coalition of users from the neuroscience community in order to build a sustainable DataBridge-based infrastructure for neuroscience. This community-based activity will leverage the community infrastructure created by the NSF Big Data Hubs and Spokes program.