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An approach for managing specimen-sample-taxon-trait data from the 27 projects of the DFG priority program 1991 ‘Taxon-Omics’

An approach for managing specimen-sample-taxon-trait data from the 27 projects of the DFG priority program 1991 ‘Taxon-Omics’
管理来自 DFG 优先计划 1991“Taxon-Omics” 27 个项目的标本-样本-分类群-性状数据的方法
批准号:
410254097
负责人:
Professor Dr. Miguel Vences, since 10/2020
金额:
$0.0万
依托单位:
依托单位国家:
德国
项目类别:
Priority Programmes
财政年份:
2018
资助国家:
德国
项目状态:
已结题
起止时间:
2017-12-31 至 2023-12-31

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中文摘要
翻译
在过去的10年里,分类学经历了一个重大的转变,因为它朝着基于dna和数字化数据的方向发展,这些数据可以很容易地共享。这为合作开辟了新的途径,并有能力回答以前无法解决的问题。认识到这一点,德国研究基金会决定在DFG-SPP 1991的框架下资助27个关于“分类组学——发现和命名生物多样性的新方法”的单独项目。11个项目关注动物,8个关注植物,5个关注微生物,3个关注真菌。我们在此提交一份附加申请,要求一名博士后和一名兼职科学数据管理员实施一种机制,通过这种机制,27个项目中的每一个都可以立即开始在符合DFG GFBio数据基础设施的工作环境中存储与标本、样品、特征或分类相关的数据。我们的方法将依赖于数字对象标识符(doi)和具有图形用户界面(GUI)的标识符注册表,该注册表将建立在现有的解决方案上,用于管理样本和分类单元相关、分类组学和性状数据,目标是使数据在GBIF和GFBio平台上免费提供。作为次要目标,我们将鼓励pi提供合适的数据,用于使用机器学习方法测试高通量物种划界算法。这个项目将有力地促进spp1991项目之间的联网,并鼓励pi开始以一种有助于将来多用途重用的形式存档数据。
英文摘要
Over the past 10 years, taxonomy has undergone a major shift because of its move towards DNA-based and digitized data, which can readily be shared. This has opened new avenues for collaboration and the ability to answer questions that could not be tackled before. In recognition of this, the German Research Foundation decided to fund 27 individual projects under the umbrella of DFG-SPP 1991 on “Taxon-Omics – new approaches for discovering and naming biodiversity.” Eleven projects focus on animals, eight on plants, five on microorganisms, and three on fungi. We are here submitting an add-on request for a postdoc and a halftime scientific data curator to implement a mechanism by which each of 27 projects could immediately start depositing data linked to specimens, samples, traits, or taxonomies in a working environment compliant to the DFG’s GFBio data infrastructure. Our approach will rely on Digital Object Identifiers (DOIs) and an identifier registry with a graphic user interface (GUI) that will build on existing solutions for managing sample- and taxon-related, taxon-omics and trait data, with the goal of making data freely available on GBIF and GFBio platforms. As a secondary objective, we will encourage PIs to contribute suitable data for testing a high-throughput species delimitation algorithm using machine-learning approaches. This project will strongly contribute to an increased networking among SPP 1991 projects and encourage PIs to start archiving data in a form that will facilitate future multi-purpose reuse.
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