同意代码:在不断扩大的开放科学生态系统中维护同意。

IF 2.7 4区医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Neuroinformatics Pub Date : 2023-01-01 DOI:10.1007/s12021-022-09577-4

Stephanie O M Dyke, Kathleen Connor, Victoria Nembaware, Nchangwi S Munung, Kathy Reinold, Giselle Kerry, Mamana Mbiyavanga, Lyndon Zass, Mauricio Moldes, Samir Das, John M Davis, Jordi Rambla De Argila, J Dylan Spalding, Alan C Evans, Nicola Mulder, Jason Karamchandani

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引用次数: 0

摘要

我们之前提出了一个记录基于同意的数据使用“类别”和“要求”的结构-同意代码-以支持最大限度地使用和整合基因组研究数据集，并减少允许重用共享数据的不确定性。在这里，我们将根据新的应用领域(如神经科学、生物银行、H3Africa)、政策发展(如研究结果的返回)和进一步的实际考虑，包括自动化同意管理方法的发展，讨论同意代码(v4)的澄清和后续更新。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Consent Codes: Maintaining Consent in an Ever-expanding Open Science Ecosystem.

查看原文本刊更多论文

Consent Codes: Maintaining Consent in an Ever-expanding Open Science Ecosystem.

We previously proposed a structure for recording consent-based data use 'categories' and 'requirements' - Consent Codes - with a view to supporting maximum use and integration of genomic research datasets, and reducing uncertainty about permissible re-use of shared data. Here we discuss clarifications and subsequent updates to the Consent Codes (v4) based on new areas of application (e.g., the neurosciences, biobanking, H3Africa), policy developments (e.g., return of research results), and further practical considerations, including developments in automated approaches to consent management.

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来源期刊

Neuroinformatics 医学-计算机：跨学科应用

CiteScore

6.00

自引率

6.70%

发文量

审稿时长

3 months

期刊介绍： Neuroinformatics publishes original articles and reviews with an emphasis on data structure and software tools related to analysis, modeling, integration, and sharing in all areas of neuroscience research. The editors particularly invite contributions on: (1) Theory and methodology, including discussions on ontologies, modeling approaches, database design, and meta-analyses; (2) Descriptions of developed databases and software tools, and of the methods for their distribution; (3) Relevant experimental results, such as reports accompanie by the release of massive data sets; (4) Computational simulations of models integrating and organizing complex data; and (5) Neuroengineering approaches, including hardware, robotics, and information theory studies.