Share your research data

mRNA-sequencing raw data. Method: We collected algae after 72 h exposure to 10 mg/L AuNP and AuNS for RNA-seq to analyze mRNA expression. Chlamydomonas_reinhardtii (Version: CC-503 cw92 mt+). The differentially expressed transcripts and genes were selected with log2 (fold change) ≥ 1 or log2 (fold change) ≤ -1 and p value < 0.05 criteria with the R package edgeR(https://bioconductor.org/packages/edgeR). Results: RNA sequencing identified 9 upregulated and 38 downregulated differentially expressed genes (DEGs) in the 10 mg/L AuNP treated cells, impairing photosynthesis and energy storage via the photosystem II subunit S1 (PSBS1)/ early light-inducible protein (ELI3) pathway. In contrast, the AuNS group exhibits 246 upregulated and 145 downregulated DEGs, affecting membrane integrity and nitrogen metabolism through the nitrate reductase (NIT1)/ aminomethyl transferase (AMT1)/ protein kinase domain-containing protein (A0A2K3CRU5) pathway.

SOURCE DATA: Data from the manuscript of Springstein et al., "Repurposing of a DNA segregation machinery into a cytoskeletal system controlling cell shape”. - Alignments/ - Cyanobacteria_species.aln Concatenation of the multiple sequence alignments of the RNA polymerase β and β′ subunit protein sequences from all Cyanobacteria species in the dataset, used to reconstruct a species phylogeny of the Cyanobacteria phylum. - ParM_Bacteria.aln Multiple sequence alignment of representative ParM proteins identified across all bacterial phyla. - ParM_ParR_Cyanobacteria.aln Concatenation of the multiple sequence alignments of ParM and ParR protein sequences identified in Cyanobacteria. - Phylogenies/ - Cyanobacteria_species.treefile Species phylogeny of Cyanobacteria (in Newick format). - Cyanobacteria_species.pdf Species phylogeny of Cyanobacteria (in pdf format with annotations). - ParM_Bacteria.treefile Phylogeny of ParM proteins identified across all bacterial phyla (in Newick format). - ParM_Bacteria.pdf Phylogeny of ParM proteins identified across all bacterial phyla (in pdf format with annotations). - ParM_ParR_Cyanobacteria.treefiles Phylogeny of the concatenation of ParM and ParR proteins identified in Cyanobacteria (in Newick format). - ParM_ParR_Cyanobacteria.pdf Phylogeny of the concatenation of ParM and ParR proteins identified in Cyanobacteria (in pdf format with annotations). - Profiles/ - RNApolB.hmm HMM profile used for the identification of RNA polymerase β proteins in the Cyanobacteria genomes. - RNApolBp.hmm HMM profile used for the identification of RNA polymerase β′ proteins in the Cyanobacteria genomes. - ParM.hmm HMM profile used for the identification of ParM proteins in bacterial genomes. - ParR.hmm HMM profile used for the identification of ParR proteins in bacterial genomes.

The data come from NCBI BioProject PRJNA1088471, as originally analyzed in Overton et. al (2024). These data are short read sequences from wastewater in Toronto, Ontario. See Overton et. al (2024) for a detailed description of the genomic sequencing process. Data processing involved alignment of the short reads to the Wuhan-1 reference sequence (NC_045512) with `minimap2` v2.28, identifying the mutations relative to the reference, and recording the number of times a mutation was observed (counts) and the depth of coverage. The frequency is calculated as the counts divided by the coverage. The mutation pre-processing pipeline is available at \url{https://github.com/DASL-Lab/data-treatment-plant}, and heavily relies on the GromStole pipeline (\url{https://github.com/PoonLab/gromstole}). After being processed into counts and coverage, the data were filtered to only include mutations that are relevant to analysis. There were many mutations with either consistently low counts (possibly due to sequencing errors) or low coverage. We found all mutations that had both a frequency of at least 0.1 and a frequency below 0.9 (with a coverage at least 40) at at least two time points during the study in any location. This ensures that we have all of the mutations that were potentially part of a circulating lineage without relying on lineage definitions. Overton, Alyssa K., Jennifer J. Knapp, Opeyemi U. Lawal, et al. “Genomic Surveillance of a Canadian Airport Wastewater Samples Allows Early Detection of Emerging SARS-CoV-2 Lineages.” Preprint, April 9, 2024. https://doi.org/10.21203/rs.3.rs-4183960/v1.

The development of AI, at an above-benchmark pace, has become a worldwide worrying issue and a central narrative in the tertiary level learning institutions. Digital transformation, on the other hand, is updating the educational systems, which necessitates effective governance to steer these improvements. This paper provides an integrated overview of these three pillars – AI, digital transformation, and governance – and explores their interplay in reshaping the landscape of higher education. Drawing on key insights from the EUNIS 2024 conference and recent global studies, the paper examines AI’s transformative potential in optimizing learning and administration, the role of digital transformation in enabling scalable and innovative educational services, and the urgent need for governance frameworks to ensure ethical, equitable, and sustainable practices. The analysis highlights current gaps, such as the lagging development of regulatory frameworks amid rapid tech progress, and it sheds light on emerging needs, barriers, and innovations (including the use of AI and XR in teaching and learning, strategies for digital readiness, and models for ethical leadership). The argument provided by AI, digital transformation, and governance is of particular interest, which justifies the need for a Global Compact on AI in education that would foster international collaboration and standards. This all-encompassing tracking and mapping of trends, challenges, and opportunities provides higher education institutions a blueprint for strategic decisionmaking concerning technological integration and guides policymakers in reforming higher learning education.

The shared dataset compiles measured air-changes-per-hour values in a high-altitude university in Quito, presented as an alternative mitigation strategy to maintaining 2 m of social distancing in classroom environments. These data enable meaningful comparison with findings from other regions, thereby supporting informed decision-making aimed at reducing infection risk.

Supplementary Materials for Mironova et al, Serum proteomics of insulin resistance disorders distinguish MASLD from lipodystrophy and insulin receptor defects Journal of Clinical Endocrinology & Metabolism, 2025 https://doi.org/10.1210/clinem/dgaf516

Postgraduate students awareness of the gender pay gap

Functional imaging using genetically encoded indicators, such as GCaMP, has become a foundational tool for in vivo experiments and allows for the analysis of cellular dynamics, sensory processing, and cellular communication. However, large scale or complex functional imaging experiments pose analytical challenges. Many programs have worked to create pipelines to address these challenges, however, most platforms require proprietary software, impose operational restrictions, offer limited outputs, or require significant knowledge of various programming languages, which collectively can limit utility. To address this, we designed MCA (a Multicellular Analysis toolkit) to work with ImageJ, a widely used open-source software which has been the standard image analysis platform for the last 30 years. We developed MCA to be visually intuitive, utilizing ImageJ’s platform to generate new images based on completed tasks so users can visually see each step in the analysis pipeline. In addition, MCA implements a user-friendly GUI providing a simple interface which resembles other native ImageJ plugins. We incorporated functionality for rigid registration to correct motion artifacts, algorithms for cell body prediction, and methods for annotating cells and exporting data. For cell prediction, we trained a custom model in Cellpose 2.0 for segmentation of nuclei expressing pan-neuronal nuclear localized GCaMP in zebrafish. We validated the accuracy of MCA output to previously published zebrafish calcium imaging data which elicited visually evoked neuronal responses. To show the versatility of MCA, we also show that our software can be utilized for multiple sensory modalities, brain regions, and multiple model organisms including Drosophila and mouse. Together these data show that MCA is viable for extracting calcium dynamics in a user-friendly environment for multiple forms of functional imaging.

This repository provides the data and R scripts necessary to replicate the tables, figures, and empirical analyses reported in the article “Income, Place, and Perceptions of Fiscal Fairness: Evidence from Australian Federalism” by Tracy Fenwick and Thiago N. Silva, published in Territory, Politics, Governance.

The Combined Hibiscus and Tea Leaf Image Dataset is a comprehensive and well-balanced collection comprising 1,413 original high-quality leaf images captured under natural outdoor conditions across various regions of Bangladesh using a SONY α7 II DSLR camera and a OnePlus 7T smartphone. The dataset includes two major plant species—Hibiscus and Tea—and aims to facilitate research in plant disease detection, agricultural image analysis, and computer vision–based crop health monitoring. The Hibiscus subset consists of 1,165 images categorized into eight distinct classes: Healthy (473 images), Mild Edge Damage (226 images), Citruspot (150 images), Slightly Diseased (109 images), Early Mild Spotting (83 images), Wrinkled (56 images), Senescent (40 images), and Fungal Infected (28 images). The Tea subset contains 248 images divided into five disease categories: Algal Leaf Spot (54 images), Brown Blight (48 images), Grey Blight (53 images), Healthy (49 images), and Red Leaf Spot (44 images). These class distributions capture a diverse range of leaf conditions, disease severities, and environmental variations, providing a realistic foundation for machine learning and deep learning applications. To overcome class imbalance and enrich the dataset, extensive image augmentation was performed using both PIL and OpenCV techniques, including brightness and contrast adjustment, color enhancement, rotation, flipping, scaling, cropping, shifting, zooming, and Gaussian noise addition. Through this process, each class was expanded to 1,000 images, resulting in a total of 13,000 augmented images evenly distributed across the 13 classes. All images are stored in .JPG format and organized into separate folders per class, maintaining a consistent structure and naming convention. Overall, this dataset offers a rich and diverse resource for developing robust models for leaf disease classification, precision agriculture, and automated plant health monitoring, making it a valuable contribution to the fields of computer vision and agricultural research.