Duplicate Article/Plagiarism Discovery

描述（由申请人提供）：由于发现的机会很少，预算不断减少，但持续的出版压力，重复出版和剽窃的不道德行为是显着的。由于没有强大的方法来识别编辑和审稿人现有的和潜在的重复科学文章，这意味着到目前为止，这可以不受检查。eTBLAST是一种可在网络上使用的文本相似性搜索工具，已被用于证明我们可以通过系统地比较每个Medline摘要（或综述中的摘要）与所有其他Medline记录来检测高灵敏度和特异性的推定重复/剽窃文章。我们假设，严格识别这种行为的提供者，对重复文章进行详尽的标记，以及为编辑，审稿人，授予官员等定制的搜索工具的可用性，以在他们被接受出版之前检测潜在的问题手稿，将是一个实质性的威慑，最终提高所有科学报告的质量。我们将通过以下具体目标来解决这个问题：1）优化统计预测因子，阈值，签名和算法，以最大限度地提高我们在Medline中检测推定重复和抄袭文章的效率。2)系统地检查每条Medline记录，以开发一个已手动审查/验证的可疑文章的公共数据库，以分配重复的可能性。3)对趋势、比率和任何统计相关分布进行分析，以了解并解决此行为的根本原因。4)创建一个安全的资源，对所有期刊/审稿人开放，使他们能够在接受之前估计新奇和与以前出版物的可能重叠。

DESCRIPTION (provided by applicant): With little chance for discovery and decreasing budgets, yet sustained pressure to publish, the unethical practices of duplicate publications and plagiarism are significant. With no robust method to identify existing and potential duplicate scientific articles by editors and reviewers means that this can go unchecked, until now. eTBLAST, a text similarity search tool available to all on the web, has been used to demonstrate that we can detect with high sensitivity and specificity putative duplicate/plagiarized articles by systematically comparing each Medline abstract (or abstract in review) to all other Medline records. We hypothesize that rigorous identification of purveyors of this behavior, the exhaustive tagging of duplicate articles and the availability of a search tool customized for use by editors, reviewers, granting officials, etc. to detect potential problem manuscripts before they are accepted for publication will be a substantial deterrent, ultimately improving the quality of reported science for all. We will address this through the following specific aims: 1) Refine statistical predictors, thresholds, signatures and algorithms to maximize the efficiency by which we can detect putative duplicate and plagiarized articles within Medline. 2) Systematically check every Medline record against every other to develop a public database of questionable articles that have been reviewed/verified manually to assign a probability of duplication. 3) Perform an analysis of trends, rates and any statistically relevant distributions to understand and address root causes for this behavior. 4) Create a secure resource that is available and open to all journals/reviewers, thus enabling them to estimate novelty and probable overlap with previous publications prior to acceptance.