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This dataset comprises approximately 1 million high-confidence biomedical triples focused on pancreatic cancer, constructed from a curated set of 23 relevant biomedical entities (KRAS, TP53, gemcitabine) and 11 common relation types ( mutated_in, treats, interacts_with). Each triple is embedded in a synthetic, natural language sentence mimicking scientific phrasing and is paired with a simulated attention score ranging from 0.75 to 1.00, reflecting transformer-based model confidence. Heuristic boosting was applied to biologically plausible combinations, resulting in an average attention score near 0.88. This structured resource is ideal for training, validating, or benchmarking biomedical NLP models and knowledge extraction systems within the context of pancreatic cancer.

Sarcalumenin-like protein; Leishmania infantum (strain JPCM5)

We present the Materials Learning Algorithms (MALA) package, a scalable machine learning framework designed to accelerate density functional theory (DFT) calculations suitable for large-scale atomistic simulations. Using local descriptors of the atomic environment, MALA models efficiently predict key electronic observables, including local density of states, electronic density, density of states, and total energy. The package integrates data sampling, model training and scalable inference into a unified library, while ensuring compatibility with standard DFT and molecular dynamics codes. We demonstrate MALA's capabilities with examples including boron clusters, aluminum across its solid-liquid phase boundary, and predicting the electronic structure of a stacking fault in a large beryllium slab. Scaling analyses reveal MALA's computational efficiency and identify bottlenecks for future optimization. With its ability to model electronic structures at scales far beyond standard DFT, MALA is well suited for modeling complex material systems, making it a versatile tool for advanced materials research.

Dataset for structural condition assessment of residential buildings in Botswana

experimental data including the effects of sevoflurane, pentobarbital, and hypothermia

Data and codes for study on laminar architecture of OFC

This is an R code for the research on ESG & financial penalties by Václav Brož and Stephanie Miller.

Transcriptomics data are intended to prompt research on the relevant mechanism and to use that mechanism as a basis for further experiments. Through the transcriptomics of the TLR2/MAPK/NF-κB pathway, how Ustiloxins leads to kidney injury through the TLR2/MAPK/NF-κB pathway is explained step by step.

High-resolution stalagmite (LS12) δ13C and δ18O records during 146-136.6 ka BP from Luoshui Cave, central China.