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1 to 10 of 67 Results
Replication Data for: Predicting the binding of small molecules to proteins through invariant representation of the molecular structure
Apr 29, 2024 - Guido Tiana Dataverse
Tiana, Guido, 2024, "Replication Data for: Predicting the binding of small molecules to proteins through invariant representation of the molecular structure", https://doi.org/10.13130/RD_UNIMI/5879ZG, UNIMI Dataverse, V1
Network parameters and datasets to reproduce the results of the article "Predicting the binding of small molecules to proteins through invariant representation of the molecular structure" by R. Beccaria, A. Lazzeri and G. Tiana. The scripts and the instructions can be obtained fr...
Linear Coupling Model
Feb 15, 2024 - Quantum Rates
Martinazzo, Rocco, 2024, "Linear Coupling Model", https://doi.org/10.13130/RD_UNIMI/BFQ87D, UNIMI Dataverse, V1
Results from quantum rate calculations of dissipative tunneling rates for a linear coupling model.
NonLinear Coupling Model A
Feb 15, 2024 - Quantum Rates
Martinazzo, Rocco, 2024, "NonLinear Coupling Model A", https://doi.org/10.13130/RD_UNIMI/WCZXRK, UNIMI Dataverse, V1
Dissipative tunneling rates and correlation function details for a non-linear coupling model (Model A)
NonLinear Coupling Model B
Feb 15, 2024 - Quantum Rates
Martinazzo, Rocco, 2024, "NonLinear Coupling Model B", https://doi.org/10.13130/RD_UNIMI/WHRHCT, UNIMI Dataverse, V1
Dissipative tunneing rates and correlation function details for a non-linear coupling model (Model B)
Quantum Rates(Università degli Studi di Milano)
Quantum Rates logo
Feb 15, 2024CDTG - Martinazzo Dataverse
FISH-AI Standard Operating Procedures
Feb 8, 2024 - Standard Operating Procedures
Pavlovic, Radmila; Ribeiro, Ricardo; Szabó, Anna; Bitan, Amir; Kortner, Trond; Rebecca, Heavyside, 2020, "FISH-AI Standard Operating Procedures", https://doi.org/10.13130/RD_UNIMI/MW6R2N, UNIMI Dataverse, V9
This dataset contains SOPs that have been developed and shared among FISH-AI partners
Heavy Metals-transforming strains characterization
Feb 7, 2024 - Heavy Metal Bio-recovery and Valorization
Zecchin, Sarah, 2024, "Heavy Metals-transforming strains characterization", https://doi.org/10.13130/RD_UNIMI/TD9T4C, UNIMI Dataverse, V1
This dataset includes data related to the characterization of bacterial strains able to transform heavy metals.
Heavy Metal Bio-recovery and Valorization
Feb 7, 2024Dipartimento di Scienze per gli Alimenti, la Nutrizione e l'Ambiente
Facing the necessity to improve environmental and economic sustainability of industrial processes, Circular Economy strategies are essential in partnership with scientific research. At global and local levels, development goals are declined in the respect of needs for civil socie...
Guido Tiana Dataverse(Università degli Studi di Milano)
Nov 10, 2023Dipartimento di Fisica
Dataverse for publication(Università degli Studi di Milano)
Sep 25, 2023Alberto Vertova dataverse
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