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Mendeley Data Showcase

Important notice
After careful consideration, Elsevier has decided to discontinue Data Monitor. After 30 June 2025, this solution will no longer be available for use. We notified your institution during the sunset process but understand that as a user this announcement may come as a surprise. We understand that this decision may impact your workflows, and we sincerely apologize for any inconvenience this may cause.
Mendeley Data: While you will no longer be able to see federated search results from external repositories, as previously provided by Data Monitor, please be aware that Mendeley Data will continue to return search results from all datasets uploaded to the repository. Our users can expect additions to search functionality and enhancements to make the overall experience more user friendly, while all non-federated search features will remain the same. We are interested in exploring additional opportunities for federated search in the future.
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53051878 results
  • Pancreatic Cancer Biomedical Knowledge Graph
    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.
    • Dataset
  • LINF_190007600
    Sarcalumenin-like protein; Leishmania infantum (strain JPCM5)
    • Dataset
  • Materials Learning Algorithms (MALA): Scalable machine learning for electronic structure calculations in large-scale atomistic simulations
    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
  • Interaction between edge effects and urbanization: Response of soil microbial communities and carbon functional genes in urban remnant mountains
    • Dataset
  • Condition Assessment Dataset
    Dataset for structural condition assessment of residential buildings in Botswana
    • Dataset
  • dataset-EMGSH-hypothermia
    experimental data including the effects of sevoflurane, pentobarbital, and hypothermia
    • Dataset
  • 2025 -- Laminar OFC
    Data and codes for study on laminar architecture of OFC
    • Dataset
  • ESG penalties R code
    This is an R code for the research on ESG & financial penalties by Václav Brož and Stephanie Miller.
    • Dataset
  • Expression matrix of transcriptomics
    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.
    • Dataset
  • High-resolution stalagmite δ13C and δ18O records during 146-136.6 ka BP, central China.
    High-resolution stalagmite (LS12) δ13C and δ18O records during 146-136.6 ka BP from Luoshui Cave, central China.
    • Dataset
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