EAGER: Using Search Engines to Track Impact of Unsung Heroes of Big Data Revolution, Data Creators

在复杂数据的洪流中，有效的数据交换机制对科学（和社会）越来越重要。数据生产的速度及其复杂性意味着大量数据往往没有得到数据生产者的充分分析，由于其快速传播，广泛的社区正在参与其分析。然而，这种模式存在忽视原始数据创建者输入的风险，因为注意力转移到数据集成商和分析师身上。目前的信息传播和科学学分分配模式基于同行评审的出版物和以引用形式正式承认第三方贡献，偏向于参与知识创造和传播后期阶段的知名科学家和研究中心。我们建议使用无偏见的互联网搜索来识别研究文献中对数据集和资源的不可信使用，允许数据创建者和参与数据创建早期阶段的研究人员为其工作申请信贷。使用机器学习和文本挖掘技术，PI试图从通用搜索引擎的嘈杂结果中提取相关信息，并开发易于使用的界面，供公众使用这些资源，以补充官方的文献计量学资源。接受和引用偏见对研究人员的职业生涯有重大影响，而这些研究人员通常也是研究力量更加多样化的地方。首先，同行评审中被拒绝的可能性明显偏向于不太有名的科学家和研究密集度较低的机构。这些偏见不太可能影响数据的创建，因为数据库通常接受未经同行审查的数据，数据的价值可以通过其使用来衡量。同样的偏见也会影响引用的数量，人们倾向于引用更著名的、更成熟的科学家，或者引用经常被邀请的评论，只有领先的科学家才会被邀请撰写评论。因此，出版物和引文都严重偏向于已经被认可的科学家，这些科学家在个人和工作机构方面都代表了较少的多样性人群，而不是一般的科学家。这种偏见影响了年轻科学家在最好的研究机构的紧密合作网络之外工作的职业生涯和获得资助的能力，并创造了一个经典的富人越来越富，穷人越来越穷的循环。新的基于互联网的信息交流模式已经对研究人员产生了深远的影响？让他们的成果在公众视野中曝光的能力该建议旨在进一步减轻出版和引用偏见的方法，不是直接解决这个问题，而是通过开发更开放的方式来评估对科学的贡献。

Efficient mechanisms of data exchange are increasingly central to science (and society) in the midst of the deluge of complex data. The pace of data production and its complexity mean that a large amount of data is often not adequately analyzed by the data producers, and thanks to its rapid dissemination, the broad community is participating in its analysis. This model, however, carries a risk of neglecting the input of the original data creators, as attention shifts to data integrators and analyzers. The current paradigm of information dissemination and assigning credit in science, based on peer-reviewed publications and formal acknowledgment of third-party contributions in the form of citations, is biased toward high-profile, well-known scientists and research centers who participate in the latter stages of knowledge creation and dissemination. We propose to use unbiased internet searches to identify uncredited use of datasets and resources in research literature, allowing data creators and researchers participating in early stages of data creation to claim credit for their work. Using machine-learning and text-mining techniques, the PI seeks to extract relevant information from noisy results of general-purpose search engines and develop easy-to-use interfaces for public use of such resources to supplement official bibliometric resources.Acceptance and citation biases have a significant impact on careers of researchers outside the central foci of funding and publications, which are also typically places with more-diverse research forces. First, the probability of rejection in peer review is significantly biased against less-famous scientists and those at less-research-intensive institutions. These biases are less likely to affect data creation as databases typically accept data without peer review and the value of data can be measured by its use. The same biases affect number of citations, where people tend to cite more-famous, established scientists, or cite reviews that are often invited and only leading scientists would have been invited to write the review. As a result, both publications and citations are heavily biased toward already-recognized scientists that represent less-diverse populations, both in personal terms and in terms of the institutions where they work, as compared to the general population of scientists. Such biases affect careers and ability to obtaining grant funding for young scientist operating outside of the tight collaboration networks at best research institutions and creates a classical rich getting richer and poor getting poorer loop. The new internet-based information exchange paradigm has already had a profound effect on researchers? ability to getting their results in prominent, public view. This proposal aims at approaches that would further alleviate publication and citation bias, not by addressing it directly, but by developing more openways of evaluating contributions to science.