Search

Start Over Source dataspace

1. MLP neural network trained on the QCL [-2, +3] Å dataset

Author(s):
Correa Hernandez, Andres; Gmachl, Claire F
Abstract:
This item contains two files. A multi-layer perceptron (MLP) neural network is built using the MATLAB Deep Network Designer (.m file). It imports a quantum cascade laser (QCL) dataset and splits it into 70% training, 15% validation, and 15% testing subsets. The network consists of an input layer, three hidden layers (each having a normalization and activation layer), and a regression output layer. All of the layers are fully connected, and the root-mean-square error (RMSE) is used to evaluate the accuracy of the network. An algorithm is trained on the [-2, +3] QCL dataset using 50 neurons, ReLU activation function, solver Adam, 0.001 learning rate, over 150 epochs, and is saved to be used in the prediction of figure of merit values for QCL designs (.mat file).
Type:
Software
Issue Date:
16 February 2024

2. Quantum cascade laser transition code

Author(s):
Correa Hernandez, Andres; Gmachl, Claire F
Abstract:
A code to identify the laser transition for a quantum cascade laser design based on the figure of merit. Variables such as the number of layers, and layer thicknesses, as well the applied electric field, materials composition, number of period repetitions, and layer tolerance ranges to generate random designs are specified. A folder containing a .csv file with all electronic state-pair transitions collected, a .png file of the bandstructure and the laser transition chosen (in red), for all electric field iterations, and a summary .csv file of all these laser transitions for a structure at each electric field is generated by the code. To use, first install ErwinJr2 on your computer. Then locate the "ErwinJr2" folder and copy these 6 files into that directory, overwriting the previous five files (Material.py, QCLayers.py, QCPlotter.py, QuantumTab.py, rFittings.py). Lastly, run the "acej-qcl-layer_10-lwrandom-v23.py" script using Python. The "summary-fomstar-3lu-eVmiddle-19.csv" file is generated after running the laser transition code, with all of the data collected for one structure at many electric fields. Running the script various times will generate random structures with the same electric field range. Joining these "summary" .csv files makes a QCL dataset.
Type:
Software
Issue Date:
16 February 2024

3. Data for 'Co-benefits of Transport Demand Reductions from Compact Urban Development in Chinese Cities'

Author(s):
Fu, Xiangwen; Cheng, Jing; Peng, Liqun; Zhou, Mi; Tong, Dan; Mauzerall, Denise L.
Abstract:
This dataset is created for the paper titled 'Co-benefits of Transport Demand Reductions from Compact Urban Development in Chinese Cities' and published on Nature Sustainability. We construct 6 scenarios of compact urban development, alternative energy vehicle deployment, and power decarbonization to explore the co-benefits of transport demand reductions via compact urban development for carbon emissions, energy use, air quality, and human health in China in 2050. This dataset provides the following gridded information for the scenarios: (1) monthly mean surface PM2.5 concentrations from the WRF-Chem model; (2) annual PM2.5-related premature deaths calculated by the GEMM model; (3) 2015 population in China; (4) mask for provinces in China; (5) longitude and latitude of each grid center.
Type:
Dataset
Issue Date:
January 2024

5. Data and code for 'Hydrological cycle amplification reshapes warming-driven oxygen loss in Atlantic Ocean'

Author(s):
Hogikyan, Allison
Abstract:
Physical and biogeochemical variables from the NOAA-GFDL Earth System Model 2M experiments (pre-processed), previously published observation-based datasets, and code to reproduce figures from these datasets, used for the study 'Hydrological cycle amplification reshapes warming-driven oxygen loss in Atlantic Ocean'.
Type:
Dataset and Software
Issue Date:
June 2023

6. Supporting GFDL data for Southern Ocean Freshwater release model experiments Initiative (SOFIA)

Author(s):
Griffies, Stephen M; Beadling, Rebecca L; Krasting, John P; Hurlin, William J
Abstract:
This output was produced in coordination with the Southern Ocean Freshwater release model experiments Initiative (SOFIA) and is the Tier 1 experiment where freshwater is delivered in a spatially and temporally uniform pattern at the surface of the ocean at sea surface temperature in a 1-degree latitude band extending from Antarctica’s coastline. The total additional freshwater flux imposed as a monthly freshwater flux entering the ocean is 0.1 Sv. Users are referred to the methods section of Beadling et al. (2022) for additional details on the meltwater implementation in CM4 and ESM4. The datasets in this collection contain model output from the coupled global climate model, CM4, and Earth System Model, ESM4, both developed at the Geophysical Fluid Dynamics Laboratory (GFDL) of the National Oceanic and Atmospheric Administration (NOAA). The ocean_monthly_z and ocean_annual_z output are provided as z depth levels in meters as opposed to the models native hybrid vertical ocean coordinate which consists of z* (quasi-geopotential) coordinates in the upper ocean through the mixed layer, transitioning to isopycnal (referenced to 2000 dbar) in the ocean interior. Please see README for further details.
Type:
Dataset
Issue Date:
18 April 2023

7. CLEVR-Matrices

Author(s):
Mondal, Shanka Subhra; Webb, Taylor; Cohen, Jonathan
Abstract:
A dataset of Raven’s Progressive Matrices (RPM)-like problems using realistically rendered 3D shapes, based on source code from CLEVR (a popular visual-question-answering dataset) (Johnson, J., Hariharan, B., Van Der Maaten, L., Fei-Fei, L., Lawrence Zitnick, C., & Girshick, R. (2017). Clevr: A diagnostic dataset for compositional language and elementary visual reasoning. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2901-2910)).
Type:
Dataset
Issue Date:
1 March 2023