用于眼电生理学研究的模式视网膜电图综合数据集。

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2024-09-18 DOI:10.1038/s41597-024-03857-1
Itziar Fernández, Rubén Cuadrado-Asensio, Yolanda Larriba, Cristina Rueda, Rosa M Coco-Martín
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引用次数: 0

摘要

视网膜模式图(PERG)是眼科电生理学的重要工具,可对视网膜中央功能进行客观评估。它能量化黄斑和视网膜神经节细胞的活动,有助于区分黄斑和视神经病变。在本研究中,我们展示了 IOBA-PERG 数据集,这是一个可在 PhysioNet 存储库中访问的 1354 个瞬时 PERG 反应的广泛集合。这些记录是在巴利亚多利德大学应用眼生物学研究所(IOBA)进行的,时间跨度长达近二十年,从 2003 年到 2022 年。数据集包括 336 条记录,确保每只眼睛至少有一个 PERG 信号。数据集周到地包含了人口统计学和临床数据,包括年龄、性别、视力测量和专家诊断等信息。这个全面的数据集填补了眼电生理资料库的空白,加强了眼科研究。研究人员可以探索广泛的眼部相关状况和疾病,从而提高诊断准确性、创新治疗策略、方法论进步,并加深对眼部电生理学的理解。
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A comprehensive dataset of pattern electroretinograms for ocular electrophysiology research.

The Pattern Electroretinogram (PERG) is an essential tool in ophthalmic electrophysiology, providing an objective assessment of the central retinal function. It quantifies the activity of cells in the macula and the ganglion cells of the retina, assisting in the differentiation of macular and optic nerve conditions. In this study, we present the IOBA-PERG dataset, an extensive collection of 1354 transient PERG responses accessible on the PhysioNet repository. These recordings were conducted at the Institute of Applied Ophthalmobiology (IOBA) at University of Valladolid, over an extended period spanning nearly two decades, from 2003 to 2022. The dataset includes 336 records, ensuring at least one PERG signal per eye. The dataset thoughtfully includes demographic and clinical data, comprising information such as age, gender, visual acuity measurements, and expert diagnoses. This comprehensive dataset fills a gap in ocular electrophysiological repositories, enhancing ophthalmology research. Researchers can explore a broad range of eye-related conditions and diseases, leading to enhanced diagnostic accuracy, innovative treatment strategies, methodological advancements, and a deeper understanding of ocular electrophysiology.

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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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