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Predictive text mining for global biodiversity indicators and models.

全球生物多样性指标和模型的预测文本挖掘。

基本信息

批准号：
2004077
负责人：
金额：
--
依托单位：
Imperial College London
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2018
资助国家：
英国
起止时间：
2018 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=studentship-2004077
关键词：
Predictive text mining global biodiversity

项目摘要

Biodiversity indicators and models are important for policy decisions, scientific understanding and public engagement. Although advances in remote-sensing are revolutionising the production of indicators of ecosystem structure and function, biodiversity indicators still rely on bottom-up aggregation and synthesis of data from many local surveys. Current ways of doing this are very time-consuming because finding and extracting them from the burgeoning literature is still a slow, manual process. The PREDICTS (based at the NHM) and LPI (based at the ZSL) databases are global in coverage and their associated biodiversity indicators are internationally recognsed. However, both datasets have key spatial and thematic gaps. These gaps undermine attempts to produce indicators that are representative of biodiversity, rather than reflecting the data's geographic, taxonomic or ecological biases.This project will use new developments in text mining and machine learning to greatly increase the rate of data flow into the above datasets, with the potential for preferentially targeting data concerning taxa and geographic regions which are currently under-represented. Subsequently, recalculating the associated indicators using the enhanced databases will provide a better overview of the state of the natural world and faciliate a comparison with the values obtained from the current, manually collated data. Given the broad ecological coverage of the two databases, further avenues of research will also be possible.

生物多样性指标和模型对于政策决定、科学理解和公众参与都很重要。虽然遥感技术的进步正在使生态系统结构和功能指标的编制发生革命性变化，但生物多样性指标仍然依赖于自下而上对许多地方调查数据的汇总和综合。目前这样做的方法是非常耗时的，因为从新兴的文献中找到和提取它们仍然是一个缓慢的手动过程。PREDICTS（基于NHM）和LPI（基于OHL）数据库覆盖全球，其相关的生物多样性指标得到国际认可。然而，这两个数据集都有关键的空间和专题差距。该项目将利用文本挖掘和机器学习方面的新发展，大大提高上述数据集的数据流量，并有可能优先针对目前代表性不足的分类群和地理区域的数据。随后，利用增强的数据库重新计算相关指标，将更好地概述自然界的状况，并便于与从目前人工整理的数据中获得的数值进行比较。鉴于这两个数据库的生态覆盖面很广，还可以开展进一步的研究。