A collection of flax accessions from Russian Federal Research Center for Bast Fiber Crops was characterised to evaluate its phenotypic diversity. 406 samples representing different morphotypes were selected for thorough quantitative assessment of various agronomic traits. We measured height, length of technical part of the stem, technical part weight, inflorescence length, number of bolls and seeds per plant, 1000 seed weight, the diameter of the stem, the number of internodes and finally, distance between internodes. The fiber quality was estimated by calculating stem slenderness, stem taperingness and elementary fiber length. The dataset was produced in a framework of a project focused on characterization of diversity of flax genotypes and phenotypes, as well as on identification of genomic regions associated with various traits, it is hosted on Figshare.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8220315 | PMC |
| http://dx.doi.org/10.1016/j.dib.2021.107224 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Bioinformatics analysis of miRNAs germacrone protection on diabetic nephropathy.
Sci Rep
December 2024
Department of Nephrology, the First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, Zhejiang, 310000, People's Republic of China.
Diabetes nephropathy (DN) is a prevalent and severe microvascular diabetic complication. Despite the recent developments in germacrone-based therapies for DN, the underlying mechanisms of germacrone in DN remain poorly understood. This study used comprehensive bioinformatics analysis to identify critical microRNAs (miRNAs) and the potential underlying pathways related to germacrone activities.
View Article and Find Full Text PDFUnraveling the Causal Relationship Between Blood Metabolites and Acne: A Metabolomic Mendelian Randomization Study.
J Cosmet Dermatol
January 2025
Department of Dermatology, Chinese People's Liberation Army Western Theater Command General Hospital, Chengdu, China.
Background: Acne is a common skin disorder that may be linked to metabolic dysfunction. However, the causal impact of blood metabolites on acne has not been thoroughly investigated.
Methods: We performed a metabolome-wide Mendelian randomization (MR) analysis on 486 blood metabolites and acne using a genome-wide association dataset.
Coupling coordination relationship between health resource allocation and regional economic development: an empirical study based on five provinces in eastern China.
Front Public Health
December 2024
School of Public Health, Shandong Second Medical University, Weifang, China.
Background: Improving system coordination is a pivotal strategy and a critical pathway for social governance. Chinese society is currently facing a significant challenge in aligning the allocation of health resources with economic development. Evaluating the level of coordinated development within the system can provide valuable insights to support the construction of a more coordinated China and foster high-quality development.
View Article and Find Full Text PDFPhotovoltaic module dataset for automated fault detection and analysis in large photovoltaic systems using photovoltaic module fault detection.
Data Brief
December 2024
Department of Computer Systems Engineering, Faculty of Information and Communication Technology, Tshwane University of Technology, South Africa.
Solar energy has become the fastest growing renewable and alternative source of energy. However, there is little or no open-source datasets to advance research knowledge in photovoltaic related systems. The work presented in this article is a step towards deriving Photo-Voltaic Module Dataset (PVMD) of thermal images and ensuring they are publicly available.
View Article and Find Full Text PDFAalto Gear Fault datasets for deep-learning based diagnosis.
Data Brief
December 2024
Department of Mechanical Engineering, Aalto University, Espoo, Finland.
Accurate system health state prediction through deep learning requires extensive and varied data. However, real-world data scarcity poses a challenge for developing robust fault diagnosis models. This study introduces two extensive datasets, Aalto Shim Dataset and Aalto Gear Fault Dataset, collected under controlled laboratory conditions, aimed at advancing deep learning-based fault diagnosis.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!