Advanced omics technologies and facilities generate a wealth of valuable data
daily; however, the data often lacks the essential metadata required for
researchers to find and search them effectively. The lack of metadata poses a
significant challenge in the utilization of these datasets. Machine
learning-based metadata extraction techniques have emerged as a potentially
viable approach to automatically annotating scientific datasets with the
metadata necessary for enabling effective search. Text labeling, usually
performed manually, plays a crucial role in validating machine-extracted
metadata. However, manual labeling is time-consuming; thus, there is an need to
develop automated text labeling techniques in order to accelerate the process
of scientific innovation. This need is particularly urgent in fields such as
environmental genomics and microbiome science, which have historically received
less attention in terms of metadata curation and creation of gold-standard text
mining datasets.
In this paper, we present two novel automated text labeling approaches for
the validation of ML-generated metadata for unlabeled texts, with specific
applications in environmental genomics. Our techniques show the potential of
two new ways to leverage existing information about the unlabeled texts and the
scientific domain. The first technique exploits relationships between different
types of data sources related to the same research study, such as publications
and proposals. The second technique takes advantage of domain-specific
controlled vocabularies or ontologies. In this paper, we detail applying these
approaches for ML-generated metadata validation. Our results show that the
proposed label assignment approaches can generate both generic and
highly-specific text labels for the unlabeled texts, with up to 44% of the
labels matching with those suggested by a ML keyword extraction algorithm.

本文提出了两种新颖的自动文本标注方法用于验证机器学习生成的未标记文本的元数据，特别适用于环境基因组学领域。我们的技术展示了利用未标记文本和科学领域的现有信息的两种新方法的潜力。结果表明，所提出的标签分配方法可以为未标记文本生成通用和高度特定的文本标签，其中有多达 44% 的标签与机器学习关键词提取算法建议的标签匹配。

ML 基础关键短语提取与验证的科学文本自动标注

Automated Annotation of Scientific Texts for ML-based Keyphrase  Extraction and Validation

The importance of improving the FAIRness (findability, accessibility,
interoperability, reusability) of research data is undeniable, especially in
the face of large, complex datasets currently being produced by omics
technologies. Facilitating the integration of a dataset with other types of
data increases the likelihood of reuse, and the potential of answering novel
research questions. Ontologies are a useful tool for semantically tagging
datasets as adding relevant metadata increases the understanding of how data
was produced and increases its interoperability. Ontologies provide concepts
for a particular domain as well as the relationships between concepts. By
tagging data with ontology terms, data becomes both human and machine
interpretable, allowing for increased reuse and interoperability. However, the
task of identifying ontologies relevant to a particular research domain or
technology is challenging, especially within the diverse realm of fundamental
plant research. In this review, we outline the ontologies most relevant to the
fundamental plant sciences and how they can be used to annotate data related to
plant-specific experiments within metadata frameworks, such as
Investigation-Study-Assay (ISA). We also outline repositories and platforms
most useful for identifying applicable ontologies or finding ontology terms.

改善研究数据的 FAIR 性（可找性、可访性、互操作性、可重复性）的重要性不容忽视，特别是在当前由组学技术产生的大规模复杂数据集面前。本篇综述介绍了与基础植物科学相关的本体论术语，并说明了如何使用这些术语来注释与植物特定实验相关的数据，在元数据框架（如 ISA）中标记植物科学相关数据。同时，还介绍了用于确定适用本体论或查找本体论术语的资源库和平台。