Improvements to the 2D backend calculations
Added array size thresholds for handing off to serialized fill methods
Normalized version of the Stanford Helicopter Dataset (http://heli.stanford.edu/dataset/), used in the paper Scalable Identification of Partially Observed Systems with Certainty-Equivalent EM.
Tagging an initial release for DOI.
Associated with article: https://ui.adsabs.harvard.edu/abs/2016ApJ...819....3M/abstract
Contributors:Christopher A. Esterhuyse, Hans-Dieter A. Hiep
This document serves as the documentation and specification of the Reowolf project, aiming to provide connectors as a generalization of BSD-sockets for multi-party communication over the Internet. The document and source code repository are a work in progress, and is extended further as the project develops.
A copy of the source code repository is included. Future development can also be followed on GitHub. See also https://github.com/sirkibsirkib/Reowolf/tree/v0.1.0
Contributors:Mauricio Verano Merino
Set of Rascal DSLs that can be used in Jupyter notebooks using Bacatá
Contributors:Tyler K. Chafin
FGTpartitioner is a software for the rapid partitioning of full-genome sequence alignments into 'recombination-free' segments using minimal analytical assumptions. In doing so, FGTpartitioner provides a means for simple and rapid block delimitation in genome-wide datasets as a pretext for phylogenomic analysis, thus increasing accessibility of phylogenomic methods to researchers studying non-model species. Source code: https://github.com/tkchafin/FGTpartitioner
Contributors:David LeBauer, Nick Heyek, Rachel Shekar, Katrin Leinweber, JD Maloney, TinoDornbusch, The Gitter Badger
Coordination of Data Products and Standards for TERRA reference data
Contributors:Gong, Shengxia, Wieczorek, Mark
Supplementary data to the article
Gong, S. and Wieczorek, M. (2020) Is the lunar magnetic field correlated
with gravity or topography? Journal of Geophysical Research: Planets.
This archive contains the Bouguer gravity model used in the analyses of the
above cited manuscript, as well as the data files to reproduce Figures 2-3 and
Figures S1-S3. For the correlation results, the bandwidth and angular radius of
the window were 26 and 10 degrees, respectively, which yields a concentration
factor that is better than 99%.
This file contains the spherical harmonic coefficients of the Bouguer
gravity model up to degree and order 900. The two values in the first
row correspond to the reference radius of the model in km and the
constant GM in km^3/s^-2. To generate this model, all known gravitational
contributions from the crust were removed from the free-air gravity,
including surface relief, lateral variations in crustal density, and
crustal thickness variations. The crustal thickness model is from
Wieczorek et al. (2013), which has an average thickness of 34 km, a
constant crustal porosity of 12%, and a mantle density of 3200 kg/m^3.
Data used to generate the lower panel of Figure 2. This file contains the
95% confidence limits of the average correlation from the Monte Carlo
simulations which were performed every 30 degrees in both longitude and
latitude. The first two columns correspond to the latitude and longitude,
and the third to fifth columns correspond to the 95% confidence limits by
using topography, total free-air gravity, and total Bouger gravity,
Data used to generate Figure 3. Correlation results between total magnetic
field and topography, total free-air gravity, and total Bouguer gravity at
the surface. The first two columns correspond to the latitude and longitude,
and the third to fifth columns correspond to the ratio between the average
correlation and its 95% confidence limits by using topography, total
free-air gravity, and total Bouger gravity, respectively. If the value is
equal to or greater than 1, this indicates that the total magnetic field is
positively correlated with the testing field (topography, total free-air
gravity, or total Bouguer gravity); If the value is equal to or less than
-1, this indicates that the total magnetic field is negatively correlated
with the testing field.
Data used to generate Figure S1. Correlation results calculated at the
surface by removing the first 4*lwin degrees.
Data used to generate Figure S2. Correlation results calculated at 30 km
Data used to generate Figure S3. Correlation results calculated at the
surface by using the 99% confidence limits.
Contributors:Martin Losch, Andrew T. T. McRae
optimisation package for MITgcm based on m1qn3 with proper reverse control