Comments (12)
For one, you probably need a separate repository. How much data is it?
from ase_ani.
Hello,
We are currently in the process of publishing the data descriptor and data set. We will be submitting before the weekend. The data should be available shortly after. We will add a link on this repo's readme when we have it to share. Thanks!
from ase_ani.
Hey, @proteneer and @ghutchis : this is multigiabyte data set. We will provide a simple python package to read and slice data.
from ase_ani.
@isayev - I was guessing. But GitHub (even with LFS) may not be the ideal place to store it.
from ase_ani.
from ase_ani.
For a permanent solution I suggest storing the dataset somewhere that allows for a DOI. Not sure if you can get this using Google drive. Maybe datadryad.org? I guess it also depends on how much data we are talking about, and if you are willing to spend money on hosting at all.
from ase_ani.
What type of information is in the training set? Atomic coordinates, types, and predicted QM energies? Bond orders? Topologies? SMILES?
We could also consider hosting it ourselves as well as mirror.
from ase_ani.
@andersx yup, we will host it with DOI!
@proteneer this data is xyz file like. We have 3D array containing cartesian coordinates for each conformer of the molecule, vector of atom species and vector of energies. We don't use bond orders or topologies.
from ase_ani.
I see - I presume you're tossing out formal charges as well then? If you're also throwing away bond orders/topologies it might be a little difficult to do reconstruction/debugging but we can live with xyz-like for now. You can probably get away with 16bits of precision + compression to reduce the file sizes (internally we use something like the gromacs XTC format + gzip with 16bits to drastically reduce sizes).
from ase_ani.
from ase_ani.
Okay - works for us.
from ase_ani.
Closing - thanks guys!
from ase_ani.
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from ase_ani.