Duplicate Article/Plagiarism Discovery

描述(由申请者提供)：由于几乎没有发现的机会，预算减少，但持续的发表压力，重复发表和抄袭的不道德做法是严重的。由于没有可靠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，这一点可以不受检查。ETBLAST，一个网上所有人都可以使用的文本相似性搜索工具，已经被用来证明，我们可以通过系统地将每个Medline摘要(或审查中的摘要)与所有其他Medline记录进行比较，以高灵敏度和特异度检测假定的重复/抄袭文章。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，并提供定制的搜索工具，供编辑、审稿人、授权官员等使用，以便在接受发表之前发现潜在的问题手稿，将起到很大的威慑作用，最终提高所有人报告的科学的质量。我们将通过以下具体目标来解决这一问题：1)完善统计预测器、阈值、签名和算法，以最大限度地提高我们在Medline中检测假定重复和抄袭文章的效率。2)系统地核对每一条Medline记录，建立一个公共数据库，记录已人工审查/核实的可疑文章，以确定复制的可能性。3)对趋势、比率和任何统计相关的分布进行分析，以了解并解决此行为的根本原因。4)建立一个对所有期刊/审查员开放的安全资源，从而使他们能够在接受之前评估新颖性和可能与以前的出版物重叠的情况。

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.