Tomato Drought Stress Response Data with Nanoparticle Treatment and Transcriptomics
by Svitlana Plokhovska·Updated 1mo ago
20.9 KB1files
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Description
20.9 KB document detailing physiological and transcriptomic data from a study on tomato plants under drought stress. The dataset, authored by Svitlana Plokhovska and last updated in May 2026, includes measurements of shoot length, fresh/dry weight, H2O2 content, MDA levels, and SOD activity, comparing control plants with those treated with silver nanoparticles coated with Pseudomonas N5.12 metabolites. Transcriptomic analysis reveals differentially expressed genes related to chloroplast and mitochondrial functions.
Use Cases
Modeling the relationship between nanoparticle treatment and plant growth metrics (shoot length, weight) under drought conditions.
Analyzing oxidative stress markers (H2O2, MDA) and antioxidant enzyme activities (SOD) as indicators of plant stress response.
Investigating gene expression profiles related to organelle function (chloroplasts, mitochondria) in response to drought and treatment.
Strengths
Includes specific quantitative results, such as a 50.1% improvement in dry weight and a 66.5% increase in SOD activity for treated plants.
Combines physiological measurements with transcriptomic analysis, providing a multi-faceted view of plant response.
Released under a permissive CC-BY-4.0 license, facilitating reuse and redistribution.
Limitations
Row count and column-level documentation are absent; field semantics must be inferred after download.
The dataset is very small (20.9 KB), indicating limited scope, likely containing summary results rather than raw experimental data.
Provenance
Source
figshare
Collection Method
Data appears to be derived from a controlled experiment where tomato plants were treated and exposed to drought stress, with physiological, biochemical, and transcriptomic parameters evaluated.
Time Range
null
Freshness
Last updated 2026-05-04 05:31:05; freshness should be verified.
Geography
null
Primary data file is a DOCX document, which may require conversion or manual extraction for computational analysis.