Experimental and calculated small molecule hydration free energies
-
Updated
Oct 14, 2022 - Python
Experimental and calculated small molecule hydration free energies
LabNotebook is a tool that allows you to flexibly monitor, record, save, and query all your machine learning experiments.
datalab is a place to store experimental data and the connections between them.
Experimental small molecule hydration free energy dataset
Experimental study and analysis on the effect of using a wide range of different supply voltage values on the reliability, latency, and retention characteristics of DDR3L DRAM SO-DIMMs
Structure learning for Bayesian networks using the CCDr algorithm.
Software for learning sparse Bayesian networks
Optimal numerical differentiation of noisy time series data in python.
A flexible and modular Structure Optimization suite for combining experimental data with energy simulations to create atomic structures.
Python library and command line tool performing the Transient Scanning Technique by Brouwer et al.
ADAPT is designed for the inverse parameter identification of constitutive material models using mathmatical optimisation. It is designed to work with finite element simulations but its modular implementation offers an interface to basically any simulation framework. The tool is able to use global data such as forces as well as local data such a…
Reinforcement Learning Course Project - IIT Bombay Fall 2018
CRAN Task View: Design of Experiments (DoE) & Analysis of Experimental Data
Rapid Data Import Environmnent
Concentration-Response Statistics
Code for exploring how we distribute our thoughts over time when we remember, using data from a naturalistic memory experiment.
A collection of behavioral data sets & some functions for extracting semantic associations and network structures from term-feature matrices.
Experimental Testing of a Brake-Reuss Beam.
Supplementary data for "A Bayesian Sequential Learning Framework to Parameterise Continuum Models of Melanoma Invasion into Human Skin" Bull Math Biol 2019
BEER determines an ECC code's parity-check matrix based on the uncorrectable errors it can cause. BEER targets Hamming codes that are used for DRAM on-die ECC but can be extended to apply to other linear block codes (e.g., BCH, Reed-Solomon). BEER is described in the 2020 MICRO paper by Patel et al.: https://arxiv.org/abs/2009.07985.
Add a description, image, and links to the experimental-data topic page so that developers can more easily learn about it.
To associate your repository with the experimental-data topic, visit your repo's landing page and select "manage topics."