Mendeley Data Showcase

Supplementary Fig 1. Workflow illustrating tumors distribution according to dermoscopic, confocal and histologic diagnoses. Supplementary Fig 2. A) Dermoscopy showing brown pseudonetwork with defined borders, interpreted as solar lentigo. B-C) Reflectance confocal microscopy (RCM) revealing LM features, specifically: B) RCM at epidermal layer showing perifollicular and interfollicular dendritic cells. C) RCM at dermal-epidermal layer showing medusa-head like structure. Supplementary Fig 3. Double negative LM case: A) Dermoscopy showing multiple grey dots. B) RCM at epidermal layer showing honeycombed pattern with focal mottled pigmentation. C) RCM at dermal-epidermal layer showing abundant melanophages. Supplementary Fig 4. Double negative BCC case: A) Dermoscopy showing brown-red background. B) RCM at epidermal layer showing honeycombed pattern with focal mottled pigmentation. C) RCM at dermal-epidermal layer showing aspecific structures.

Research data related to the ​​School of Medicine and Surgery of the University of Milano - Bicocca

Live-cell holotomographic imaging of OHP-induced pathological changes in DRG neurons. These videos show primary adult mouse DRG neuronal cultures exposed to OHP at 7.5 or 25 µM. Refractive index-based imaging captures neurite fragmentation and degeneration, morphological features consistent with necroptosis, and autophagic vacuoles. Images were acquired using a 5 × 5 grid scan, with each grid measuring 90 × 90 µm, every 15 min for 28 h at 7 fps, from 24 to 52 h after OHP incubation.

These datasets contain survey responses collected to explore the underlying factors influencing consumer adoption of energy-efficient green products—specifically, LED Televisions and LED Bulbs. The data was gathered using structured questionnaires distributed to users of LED products. All measurement items assessing consumer perceptions and behavioral intentions were evaluated using a 5-point Likert scale, ranging from 1 (Strongly Disagree) to 5 (Strongly Agree). The repository includes two separate datasets: #LED TV Dataset: Comprises 408 valid responses evaluating the adoption of LED Televisions. #LED Bulb Dataset: Comprises 430 valid responses evaluating the adoption of LED Bulbs. The datasets capture variables across several established constructs related to technology adoption and consumer behavior, including: Performance Expectancy (PE), Effort Expectancy (EE), Role of Salesperson (RS), Facilitating Conditions (FC), Social Influence (SI), Hedonic Motivation (HM), Price Value (PV), Environmental Concern (EC), Personal Innovativeness (PI), Consumer Citizenship Behavior (CCB), and Behavioral Intention to Purchase (BIP).

In the associated paper, we revisit a recently published method of data reduction for Boolean matrix factorization and demonstrate that its row-selection criterion is flawed: reversing the selection logic, replacing it by simple random sampling, or by a criterion based solely on the number of 1s in a row, all yield comparable or even better results. We further propose a new row-sampling method, based on a theoretically justified score reflecting guaranteed coverage combined with randomized tie-breaking, and show experimentally that it outperforms both the examined method and the baseline approaches, especially on data with low row redundancy. This data package contains supplementary coverage and runtime tables together with folders of PDF charts that complement the findings presented in the paper.

This dataset contains processed bacterial 16S rRNA gene and fungal ITS sequencing results from reconstructed mine soil samples in the Tunlan mining area, Shanxi, China. The data include bacterial and fungal phylum-level relative abundance tables and alpha diversity indices for four soil reconstruction treatments at two soil depths.

Supplementary methods, figures, and tables

Table S1 Apatite U-Pb data for granitic samples from Peninsular Malaysia. Table S2 Apatite (U–Th)/He data for granitic samples from Peninsular Malaysia. Table S3 Zircon (U–Th)/He data for granitic samples from Peninsular Malaysia.

This dataset and its complementary software tools form the empirical validation framework of the Jach'a Qh'anax Model Ecosystem. This project explores bio-digital convergence and the application of sustainable Artificial Intelligence (AI) within the bioeconomy of the Andean-Amazonian and Mesoamerican regions, specifically covering Bolivia, Mexico, Guatemala, Honduras, and El Salvador. The primary objective of the ecosystem is to mitigate technical information asymmetries and climate risks faced by traditional rural agricultural producers through decentralized climate sensors and mobile tools, while actively integrating indigenous and ancestral knowledge. The collected data evaluates technological adoption, algorithmic predictive accuracy, the substitution of traditional chemical inputs for bioeconomic practices, and the direct impact on household income stability and food security across 200 multi-country participants during the 2026 agricultural cycle.