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1. Large-Eddy Simulation Results for Half Ice / Half Water Surfaces

Author(s):
Fogarty, Joseph
Abstract:
Large-eddy simulations were employed over half-ice and half-water surfaces, with varying surface temperatures, wind speeds, directions, as to test if the atmospheric interaction with the heterogeneous surface can be predicted via a heterogeneity Richardson number. This dataset was used to determine that surface heat fluxes over ice, water, and the aggregate surface seem to be captured reasonably well by the wind direction and the heterogeneity Richardson number, but the mean wind and turbulent kinetic energy (TKE) profiles were not, suggesting that not only the difference in stability between the two surface, but also the individual stabilities over each surface influence the dynamics.
Type:
Dataset
Issue Date:
24 May 2023

2. Large-Eddy Simulation and Statistical Metric Results for Patterned Sea Ice Surfaces

Author(s):
Fogarty, Joseph
Abstract:
Large-eddy simulations were employed over five different sea ice patterns, with a constant ice fraction, to test if the overlying atmospheric boundary layer (ABL) dynamics and thermodynamics differs. The results of these simulations were used to determine that there were differences in vertical heat flux, momentum flux, and horizontal wind speed, and that more surface information is needed to predict the ABL over the sea ice surface. To see what other surface information is needed, twenty-two landscape metrics were calculated over forty-four different maps at differing resolutions, using the FRAGSTATs program. The results of that analysis are available in a .csv file in this dataset.
Type:
Dataset
Issue Date:
August 2023

3. 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

4. QCL Dataset, 10 Layer Structure, Tolerance [-2, +3] A, Electric Field [0,10,150] kV/cm

Author(s):
Hernandez, Andres Correa; Gmachl, Claire F.
Abstract:
This dataset contains 1800 quantum cascade (QC) structures generated by randomly modifying an initial 10-layer design in the tolerance range of -2 to +3 Angstroms at an applied electric field range of 0 to 150 kV/cm (in 10 kV/cm increments). One structure at one electric field is one design, thus there are 27000 unique designs, represented as a row in the dataset. The layer thicknesses (in angstroms) and the electric field are inputs which get evaluated using a Schrödinger solver, ErwinJr2, to identify the laser transition Figure of Merit (fom*), among other reported outputs.
Type:
Dataset
Issue Date:
December 2023

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

7. Achromonodin-1 NMR spectra

Author(s):
Link, A. James; Carson, Drew V.; So, Larry; Cheung-Lee, Wai Ling
Abstract:
This entry encompasses the raw NMR spectra used to determine the structure of the lasso peptide achromonodin-1. Within one file are included the five following spectra: COSY, TOCSY, NOESY (150 ms mixing time), NOESY (700 ms mixing time), and C,H HSQC. The file requires Mestrenova software to read. These spectra were used to develop the 3D structure models of achromonodin-1 that are deposited at the protein data bank (PDB) as entry 8SVB.
Type:
Dataset
Issue Date:
June 2023

8. Drosophila melanogaster Long Timescale Data 2022

Author(s):
McKenze-Smith, Grace C.; Wolf, Scott W.
Abstract:
This dataset contains lifetime recordings and associated postural data and ethograms for 47 male Drosophila melanogaster.
Type:
Dataset and MovingImage
Issue Date:
September 2023

9. 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

10. Data from "A Neural Network Water Model Based on the MB-pol Many-Body Potential"

Author(s):
Muniz, Maria Carolina
Abstract:
This dataset contains input files, training data and other files related to the machine learning models developed during the work by Muniz et al. In this work, we construct machine learning models based on the MB-pol many-body model. We find that the training set should include cluster configurations as well as liquid phase configurations in order to accurately represent both liquid and VLE properties. The results attest for the ability of machine learning models to accurately represent many-body potentials and provide an efficient avenue for water simulations.
Type:
Dataset
Issue Date:
27 September 2023