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SCISIPBIO: A data-science approach to evaluating the likelihood of fraud and error in published studies

SCISIPBIO：一种评估已发表研究中欺诈和错误可能性的数据科学方法

基本信息

批准号：
1956338
负责人：
Luis Amaral
金额：
$ 35万
依托单位：
Northwestern University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-01 至 2022-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1956338&HistoricalAwards=false
关键词：
SCISIPBIO data science approach evaluating

项目摘要

Scientific literature servers several important roles. Within the sciences it can inform future research, can pave the way toward new discoveries, and guide the future plans of individual scientists and how they spend their own time and careers. Outside of the sciences, scientific literature too serves several roles, such as informing policies or guiding individual judicial decisions. For all of these reasons, maintaining the integrity of the scientific literature is of uttermost importance for scientists, the broad public, and ultimately the public’s perception of individual scientific fields. Yet, identifying non-trustworthy scientific literature even remains difficult for scientists and editors of scientific journals. This project seeks to identify suspicious scientific manuscripts before they are publicized, using a data-scientific approach in which we capture many distinct traits of scientific manuscripts and their content, as well as information about the authors. The outcome of the project will include a programmatic and web-based interface that allowed third parties such as policy makers and scientific journals to scan manuscripts for signs of scientific fraud and error. The project will focus on the biomedical sciences. The system beneath this interface includes 81 distinct databases that have been aggregated, annotated (e.g., with the chemical and biological properties of included genes), and linked through publication metadata (e.g., references, authorship, funding). These data will be matched with a database on fraudulent and erroneous publications (using retractionwatch and a manually curated database). Features of fraudulent and non-fraudulent publications will be conditioned on these databases, with additional features based on network-properties of genes and authors. The project will employ distinct machine learning approaches, such as Gradient Boosting and auto-learners, whose performance will be evaluated out-of-sample. Forth, to improve interpretability, and better understand scientific fraud and error, and possibly improve the robustness of models, the project will regularize and simplify the models to reduce their predictive capabilities to a small set of the information. Lastly, the project will create a REST-based interface that will allow the import from custom manuscripts. The proposed work is unique for conditioning manuscripts on highly distinct properties of manuscripts including content and world-leading training data. This will provide a data-driven tool for policy makers and scientific editors to identify suspicious manuscripts before they enter the published scientific record.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

科学文献有几个重要的作用。在科学中，它可以为未来的研究提供信息，为新的发现铺平道路，并指导科学家个人的未来计划以及他们如何度过自己的时间和职业生涯。在科学之外，科学文献也扮演着多种角色，例如为政策提供信息或指导个人司法决定。由于所有这些原因，维护科学文献的完整性对科学家、广大公众以及最终公众对各个科学领域的看法至关重要。然而，对于科学家和科学期刊的编辑来说，识别不可信的科学文献仍然很困难。该项目旨在使用数据科学方法在公开之前识别可疑的科学手稿，其中我们捕获了科学手稿及其内容的许多独特特征，以及有关作者的信息。该项目的成果将包括一个基于网络的程序界面，使决策者和科学期刊等第三方能够扫描手稿，以寻找科学欺诈和错误的迹象。该项目将侧重于生物医学科学。该界面下的系统包括81个不同的数据库，这些数据库已经被聚合、注释（例如，具有所包括基因的化学和生物学性质），并通过出版物元数据（例如，参考文献、作者、资金）。这些数据将与关于欺诈性和错误出版物的数据库进行匹配（使用RetractionWatch和人工管理的数据库）。欺诈性和非欺诈性出版物的特征将以这些数据库为条件，并具有基于基因和作者的网络属性的附加特征。该项目将采用不同的机器学习方法，如梯度提升和自动学习器，其性能将在样本外进行评估。第四，为了提高可解释性，更好地理解科学欺诈和错误，并可能提高模型的鲁棒性，该项目将规范和简化模型，将其预测能力降低到一小部分信息。最后，该项目将创建一个基于REST的接口，允许从自定义手稿导入。拟议的工作是独一无二的，可以根据手稿的高度不同属性来调节手稿，包括内容和世界领先的培训数据。这将为政策制定者和科学编辑提供一个数据驱动的工具，以便在可疑手稿进入已发表的科学记录之前识别它们。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

项目成果

期刊论文数量（5）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

The characteristics of early-stage research into human genes are substantially different from subsequent research.

DOI：
10.1371/journal.pbio.3001520
发表时间：
2022-01
期刊：
PLoS biology
影响因子：
9.8
作者：
Stoeger T;Nunes Amaral LA
通讯作者：
Nunes Amaral LA

A cautionary tale from the machine scientist

机器科学家的警示故事

DOI：
10.1038/s42256-022-00491-7
发表时间：
2022
期刊：
Nature Machine Intelligence
影响因子：
23.8
作者：
Amaral, Luís A.
通讯作者：
Amaral, Luís A.

Protection of the human gene research literature from contract cheating organizations known as research paper mills.

DOI：
10.1093/nar/gkac1139
发表时间：
2022-11-28
期刊：
NUCLEIC ACIDS RESEARCH
影响因子：
14.9
作者：
Byrne, Jennifer A.;Park, Yasunori;Richardson, Reese A. K.;Pathmendra, Pranujan;Sun, Mengyi;Stoeger, Thomas
通讯作者：
Stoeger, Thomas

COVID-19 research risks ignoring important host genes due to pre-established research patterns.

DOI：
10.7554/elife.61981
发表时间：
2020-11-24
期刊：
eLife
影响因子：
7.7
作者：
Stoeger T;Nunes Amaral LA
通讯作者：
Nunes Amaral LA

Identification of human gene research articles with wrongly identified nucleotide sequences.

DOI：
10.26508/lsa.202101203
发表时间：
2022-04
期刊：
Life science alliance
影响因子：
4.4
作者：
Park Y;West RA;Pathmendra P;Favier B;Stoeger T;Capes-Davis A;Cabanac G;Labbé C;Byrne JA
通讯作者：
Byrne JA