Mendeley Data Showcase

Dataset contains R code for analysis and visualistions as well as the data generated for the purpose of the paper. Continuous time method (CTM) analysis can be reproduced using files: - CTM_predator_harvesting.R - CTM_predator_harvesting_noprey.R - CTM_prey_harvesting.R - CTM_prey_harvesting_nopred.R Each file conducts analysis for harvesting specific species in two versions - when other species is present or not. User have to change the parameters of the model manually. Markov decision process (MDP) approach form both Nash equilibrium and optimal harvest can be reproduced using file: - MDP_predator_prey.R File contains all scripts used for the analysis and saves the results in Output folder. Scripts are run from command line in R. Other elments of data analysis such as trajectory analysis and hierarchical cluster analysis can be reproduced using file: - data_analysis.R File should be copied to the specific result folder in Output folder. Scripts are run from command line in R. Visualisations can be reproduced using file: - visualisations.R File should be copied to the specific result folder in Output folder. Scripts are run from command line in R.

Is derived from a structured mental health and depression survey and contains 1,998 cleaned responses. It includes 21 demographic, lifestyle, behavioral, and psychological features, with the primary objective of supporting multi-class depression classification tasks. Each record is labeled with one of twelve clinically motivated depression types, making the dataset particularly suitable for supervised learning, explainable AI studies, and mental health analytics. 🎯 Target Variable: Depression_Type The target variable is numerically encoded as follows: Code Depression Type (Academic Naming) 0 No clinically significant depression 1 Minimal / Mild depression 2 Moderate depression 3 Moderately-severe depression 4 Severe depression 5 Persistent depressive disorder (Dysthymia) 6 Seasonal affective pattern 7 Peripartum / Postpartum depression 8 Bipolar-related depressive episode 9 Situational / Reactive depression 10 Psychotic depression 11 Other specified depressive disorder These class names are aligned with commonly recognized categories in clinical and academic research and are suitable for use directly in scholarly publications. 🧩 Feature Variables and Encoding Scheme All categorical variables were numerically encoded to support statistical analysis and machine learning models. The encodings follow logical ordinal or nominal mappings, as outlined below: Gender: 0 = Male, 1 = Female Education_Level: 0 = Primary or below 1 = Secondary / High school 2 = Undergraduate 3 = Postgraduate or higher Employment_Status: 0 = Unemployed 1 = Student 2 = Employed 3 = Self-employed 4 = Other Symptoms: Encoded numerically to represent different symptom clusters (e.g., sleep disturbance, appetite loss, etc.) Low_Energy: 0 = No, 1 = Yes, 2 = Occasionally Low_SelfEsteem: 0 = No, 1 = Yes, 2 = Occasionally Search_Depression_Online: 0 = No, 1 = Yes Worsening_Depression: 0 = No, 1 = Yes Overeating Level: 0 = None 1–4 = Mild 5–8 = Moderate 9–12 = Severe (Grouped for interpretability) Eating Frequency (per day): 0 = ≤2 meals 1 = 3 meals 2 = 4–5 meals 3 = >5 meals SocialMedia_WhileEating: 0 = Never 1 = Rarely 2 = Often 3 = Always Self_Harm: 0 = No history 1 = History of self-harm Mental_Health_Support: 0 = No 1 = Yes Suicide_Attempts: 0 = None 1 = Once 2 = Twice 3 = Three or more All variables were checked, and no missing values remain after preprocessing. ⚙️ Data Preprocessing & Normalization Removed non-informative identifiers and irrelevant text fields Verified zero missing values across all 21 features Standardized continuous variables (e.g., Depression_Score, Sleep_Hours) using Z-score normalization Split data into 80% training and 20% testing Feature Selection Using the ANOVA F-test, the following top features were identified as most discriminative: Education_Level Employment_Status Symptoms Low_Energy Search_Depression_Online Worsening_Depression Eating Frequency SocialMedia_WhileEating Nervous_Level Mental_Health_Support

The collected research data include indicators of soil microbial communities involved in the C cycle and environmental indicators. The microbial indicators consist of species diversity, relative abundance, functional strength, and functional diversity, considering overall, rare, common, and abundant taxa separately. Based on our research hypotheses: i) soil C cycle multifunctionality will increase during the restoration process; ii) the shifts in microbial communities involved in soil C cycling will differ among abundant, common, and rare taxa across the restoration stages; and iii) the contributions of these microbial taxa to soil C cycle multifunctionality will also differ throughout the restoration chronosequence. We conducted a space-for-time sequential study across multiple restoration stages. For each stage, ten fixed plots were randomly set up, with dimensions of SB (shrubland) in both SG (secondary-growth forest) and OG (old-growth forest) systems. The results showed that the SCCM index increased significantly throughout restoration, with microbial functional traits exerting more influence than microbial diversity. Within the microbial community, functional reorganization involves a shift in functional dominance from rare to non-rare (common and abundant) taxa, indicating improved functional stability. Specifically, common taxa were the primary drivers of the enhanced SCCM, with increases in their functional diversity and strength playing a pivotal role. The concurrent recovery of plant diversity and soil properties facilitated these microbial shifts. In summary, natural restoration promotes SCCM mainly by augmenting microbial functional performance, particularly that of common taxa, a subgroup whose role in supporting soil ecological functions has historically been underestimated.

Dataset for trust metrics in DER

This dataset contains all raw and processed data supporting the findings of the research article titled "Harpagide from Scrophularia Alleviates Cerebral Ischemic Injury via AMPK-MCU Phosphorylation-Dependent Inhibition of Mitochondrial Calcium Overload“. The data provided herein enable the full replication of the statistical analyses, figures, and conclusions presented in the manuscript. The study investigates the neuroprotective mechanism of harpagide against cerebral ischemia/reperfusion injury, focusing on the AMPK-MCU mitochondrial calcium homeostasis axis. Data were generated from both in vitro oxygen-glucose deprivation (OGD) models using PC12 and SH-SY5Y cell lines and an in vivo mouse model of transient middle cerebral artery occlusion (MCAO). The dataset comprises the following main categories: In vitro Pharmacological and Biochemical Data (PC12 & SH-SY5Y cells): Cell Viability & Apoptosis: Raw flow cytometry data (Annexin V-FITC/PI staining) for apoptosis rates across experimental groups (Control, OGD, OGD+Harpagide, OGD+RR, OGD+Compound C). Cell Injury and Oxidative Stress Markers: Raw absorbance/fluorescence values for Lactate Dehydrogenase (LDH) release, Malondialdehyde (MDA) content, Superoxide Dismutase (SOD) activity, and intracellular ATP levels. Mitochondrial Function Parameters: Raw fluorescence intensity data for Reactive Oxygen Species (ROS) detection (DCFH-DA probe), mitochondrial membrane potential (JC-1 probe), and mitochondrial calcium concentration ([Ca2+]mito, Rhod-2 AM probe). Gene and Protein Expression:Raw Cycle Threshold (Ct) values from RT-qPCR for MCU, AIF, Endo G, Cyt C, Caspase-3， and AMPK mRNA /protein expression. In vivo Neuroprotective Efficacy Data (MCAO Mice): Neurological and Infarct Assessment: Individual animal scores from the Longa neurological deficit scale. Raw image files and subsequent calculated infarct volume percentages from Triphenyltetrazolium Chloride (TTC) staining. Cerebral Blood Flow (CBF): Raw perfusion unit data or derived relative CBF values from Laser Speckle Contrast Imaging (LSCI) for each mouse. Mitochondrial Ultrastructure: Representative transmission electron microscopy (TEM) images of cortical neuron mitochondria from different treatment groups. Tissue Protein Expression: Raw data from immunohistochemical (IHC) analysis quantifying the MCU-positive expression area in the temporal cortex. Acknowledgements and Citation This work was supported by grants from the Natural Science Key Foundation of Zhejiang, China (No. LZ25H310002), National Natural Science Foundation of China (No.82204837,No.82073824), Natural Science Foundation of Zhejiang, China (No. LQ23H290004), Jiaxing Key Laboratory of Brain Protection Basic and Clinical Research in Acute Ischemic cerebrovascular Disease. If you use this dataset in your research, please cite both the associated research article (when available) and this dataset using its persistent DOI provided by the repository

This dataset contains UV absorbance values measured between 290 and 320 nm for ethanolic extracts of five freshwater microalgae species. The absorbance data were generated to evaluate the in vitro sun protection factor (SPF) of the extracts using the Mansur spectrophotometric method. The dataset supports the assessment of the photoprotective potential of freshwater microalgae as natural sources of UV-protective compounds.

Data and processing related to the article entitled “Dynamics of assortments under different treatments of early thinning from above in Eucalyptus dunnii Maiden”

The attached csv files contain the results to the synthetic testbed for disaster variants: PO-B, PA-L, and PA-B. These are in reference to the paper: Optimizing Resilient Facility Locations under Natural Disaster Disruptions.

This dataset contains the original, uncropped western blot images supporting all figures in the manuscript titled ‘[Porcine IRF2 suppresses the cGAS-STING mediated antiviral IFN signaling by competing IRF3 for binding to the IFN promoter]’. For each figure panel, the corresponding raw files are provided.

Objective: Healthcare providers perform approximately 300 million major non-cardiac surgeries worldwide yearly and individualized risk assessments can significantly reduce perioperative complications. Telemedicine improves access to specialists, but its effectiveness in these evaluations is not fully understood. This study aims to investigate the clinical characteristics and management strategies of patients referred for telecardiology consultations specifically for preoperative cardiovascular risk assessment. Methods: A retrospective observational study at a Telemedicine Center in São Paulo, Brazil, reference for 380 primary health care facilities was performed from May 2021 to March 2024. We included adults over 18 years old. Thirty-five cardiologists conducted real-time teleconsultations alongside general practitioners, reviewing patient data, surgical type, clinical details, complementary exams, and consultation outcomes. Descriptive statistics were used for analysis. Results: A total of 3,476 patients (63% female, mean age 52.1 years) were evaluated. Among them, 3,307 (95.1%) were asymptomatic, 1,241 (35.70%) had hypertension, and 1,312 (48.4%) led sedentary lifestyles. The most frequently performed surgical procedure was intra-abdominal surgery, performed in 1,665 (47.9%) cases. Risk stratification categorized 3,161 (90.93%) patients as low risk, 90 (2.59%) as intermediate risk, and 36 (1.03%) as high risk. Ancillary testing was requested in 504 (14.50%) cases, with laboratory evaluations conducted in 304 (8.74%) and electrocardiograms in 234 (6.73%). Telecardiology interventions encompassed antihypertensive adjustments for 192 (5.52%) patients, statin prescriptions for 88 (2.53%), and guidance for postoperative monitoring in 98 (2.81%). Conclusion: This exploratory study demonstrates that most cardiologists successfully perform perioperative risk stratification via telemedicine during the initial consultation, targeting a predominantly low-risk population.