Mendeley Data Showcase

In Arabidopsis, B-BOX domain-containing proteins, BBX24 and BBX25, play a key role in inhibiting photomorphogenesis. However, their role in temperature-mediated regulation of growth remains unknown. Here, we show that BBX24/BBX25 are essential for warm temperature-mediated growth. Analysis of bbx24 bbx25 double mutants show strong insensitivity to warm temperature-mediated hypocotyl growth, which is due to the reduced expression of growth-related genes. However, the overexpression lines show enhanced hypocotyl growth. Warm temperature enhances the gene expression and protein stability of BBX24/25. Epistatic analysis suggests that BBX24 and BBX25 function in the same pathway as PHYTOCHROME INTERACTING FACTOR 4 and are essential for PIF4-mediated thermomorphogenic growth. BBX24 and BBX25 promote PIF4 protein stability, probably through physical interaction. Furthermore, EARLY FLOWERING 3, a key negative regulator of thermosensory growth, functions upstream and inhibits BBX24 and BBX25 gene expression, depicting an indirect pathway of regulation of PIF4 activity by ELF3. Thus, our study uncovered a novel mechanism through which plants integrate temperature cues and promote warm temperature-mediated growth.

This dataset contains transcriptomic data from the comparison of wild-type (WT) and JAMC^-/- GSC1 clones, as presented in Figure 3 of the associated study. Four independent biological samples were analyzed using 3′ RNA sequencing to assess differential gene expression between these two conditions. The experiment was conducted to explore the transcriptional changes associated with the knockout of the JAMC gene in glioblastoma stem-like cells.

This study investigates the gene expression changes in human glioblastoma cells subjected to endothelial cell condition media.

Adapter cone for a Foss CT 293 Cyclotec impeller abrasive ring grinder. This funnel is used for faster sample processing and replaces the proprietary glass jar provided with the machine.

Measured and predicted groundwater depth and soil salinity; Long-term dynamics of soil salinity

This dataset represents the largest empirical collection of user story ambiguities, encompassing 12,847 authentic user stories from eight companies spanning finance, healthcare, e-commerce, telecommunications, and manufacturing domains. The collection addresses a critical gap in requirements engineering research by providing systematically annotated real-world data for investigating ambiguity patterns in agile development environments. The dataset reveals significant organisational variation, with ambiguity rates ranging from 15.3% to 67.8% across companies, reflecting genuine differences in agile maturity and domain complexity. Seven distinct ambiguity types were identified, with semantic ambiguities being most prevalent (34.2%), followed by scope (28.7%) and actor ambiguities (19.4%). This distribution provides crucial insights into the most common sources of requirements confusion in practice. Structured across five interconnected sheets, the dataset includes comprehensive attributes covering team characteristics, project outcomes, and temporal progression data. Notably, the temporal analysis demonstrates a 23.4% average improvement in story quality over 12-month periods, providing empirical evidence of organisational learning effects in requirements practices. The collection serves multiple research purposes, from training machine learning models for automated ambiguity detection to validating requirements engineering frameworks across different organisational contexts. Strong statistical foundations underpin the dataset, with robust correlations between team experience (r=-0.73) and domain complexity (r=0.52) with ambiguity rates, supported by high inter-rater reliability (α=0.77). This resource enables researchers to conduct comparative studies, develop evidence-based tools, and advance our understanding of requirements quality in agile environments, making it an invaluable asset for the empirical software engineering community.

Supplementary Table 1 Chemical composition identification of Chaige Changyuan mixture

This data set contains the ShinyFMBN app and the FoodMicrobionet database v5.1.1 The ShinyFMBN app allows you to access FoodMicrobionet 5, a repository of data on food microbiome studies including data for both bacteria and fungi. To run the app you need to install R and R Studio. Data are available in R (.rds) format. The FoodMicrobionet database and related scripts are available on GitHub (https://github.com/ep142/FoodMicrobionet) This compressed folder contains folder shiny_FMBN_3_1: contains the app folder, the runShinyFMBN_3.R script (a R script to install all needed packages and run the app) and the app manual in .html format

UPDATE: Images from the third class, old leaves, are now included in the dataset. The dataset contains images of strawberry leaves. There are 1600 images in the dataset. The dataset can be used for multi-class classification problems as there are leaves from three classes in the dataset: 1 - healthy leaves 2- leaves with calcium deficiency 3- old leaves

The website offers a set of services that researchers need while writing their research papers or scientific articles. We will discuss them in detail. After accessing the tool’s website through the following link, you need to log in and create an account using your personal email address.