html_url,issue_url,id,node_id,user,created_at,updated_at,author_association,body,reactions,performed_via_github_app,issue
https://github.com/pydata/xarray/issues/4118#issuecomment-1040778284,https://api.github.com/repos/pydata/xarray/issues/4118,1040778284,IC_kwDOAMm_X84-CQQs,167802,2022-02-15T20:48:51Z,2022-07-18T13:05:09Z,CONTRIBUTOR,"Thanks for launching this discussion @TomNicholas !
I'm a core dev of pytroll/satpy which handles earth observing satellite data. I got interested in DataTree because we have data from the same instruments available at mulitple resolution, hence not fitting into a single Dataset.
For use Option 1 is probably feeling better. Even when having data at multiple resolutions, it is still a limited number of resolutions and hence splitting them in groups is the natural way of going I would say.
We do not use the features you mention in Zarr or GRIB, as a majority of the satellite data we use is provided in netcdf nowadays.
Don't hesitate to ask if you want to know more or if something is unclear, we are really interested in these developments, so if we can help that way...","{""total_count"": 2, ""+1"": 2, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,628719058
https://github.com/pydata/xarray/issues/4118#issuecomment-1044853795,https://api.github.com/repos/pydata/xarray/issues/4118,1044853795,IC_kwDOAMm_X84-RzQj,23738400,2022-02-18T17:06:57Z,2022-02-18T17:06:57Z,CONTRIBUTOR,"I am not sure I completely understand option 2, but option 1 seems a better fit to what we are doing at ArviZ (so far we are managing quite well with the InferenceData mentioned above which is a collection of independent xarray datasets). In our case, well defined selection for multiple variables at the same time (i.e. at the dataset level) is very useful.
I was also wondering what changes (if any) would each option imply when using `apply_ufunc`","{""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,628719058
https://github.com/pydata/xarray/issues/4118#issuecomment-833535376,https://api.github.com/repos/pydata/xarray/issues/4118,833535376,MDEyOklzc3VlQ29tbWVudDgzMzUzNTM3Ng==,25432,2021-05-06T13:45:16Z,2021-05-06T13:45:16Z,CONTRIBUTOR,"For scientific imaging, i.e. biomicroscopy, medical imaging, where xarray compatibility [is being considered](https://github.com/ome/ngff/issues/48) in the [NGFF](https://ngff.openmicroscopy.org/0.1/), it would be helpful to avoid unnecessary divergence by ensuring the proposed hierarchical storage is compatible. This would mean:
1. Each scale / group can be independently treated as an `xarray.Dataset`.
2. They are organized in such a way that the collection of scales can be referenced [as it is now](https://github.com/zarr-developers/zarr-specs/issues/50), i.e. as a collection of paths,
```{
“multiscales”: [
{
“datasets” : [
{""path"": ""0""},
{""path"": ""1""},
{""path"": ""2""},
{""path"": ""3""},
{""path"": ""4""}
]
“version” : “0.1”
}
]
}
```","{""total_count"": 3, ""+1"": 3, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,628719058
https://github.com/pydata/xarray/issues/4118#issuecomment-808694777,https://api.github.com/repos/pydata/xarray/issues/4118,808694777,MDEyOklzc3VlQ29tbWVudDgwODY5NDc3Nw==,11289391,2021-03-27T08:55:26Z,2021-03-27T08:55:26Z,CONTRIBUTOR,"Whoa, that sounds awesome! Thanks for the heads up :) Definitely could be quite handy, looking forward to seeing how this develops. @rocco8773 this should be interesting for you as well :)","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,628719058
https://github.com/pydata/xarray/issues/4118#issuecomment-807892921,https://api.github.com/repos/pydata/xarray/issues/4118,807892921,MDEyOklzc3VlQ29tbWVudDgwNzg5MjkyMQ==,23738400,2021-03-26T02:39:24Z,2021-03-26T02:39:24Z,CONTRIBUTOR,"Here are some biomedical papers that are using ArviZ and therefore xarray even if most don't cite xarray and some don't cite ArviZ either. Topics are quite disperse: covid, psychology, biomolecules, oncology...
Some ArviZ recent biomedical citations
* Arroyuelo, A., Vila, J., & Martin, O. A. (2020). Exploring the quality of protein structural models from a Bayesian perspective. bioRxiv.
* Axen, S. D. (2020). Representing Ensembles of Molecules (Doctoral dissertation, UCSF).
* Brauner, J. M., Mindermann, S., Sharma, M., Johnston, D., Salvatier, J., Gavenčiak, T., ... & Kulveit, J. (2021). Inferring the effectiveness of government interventions against COVID-19. Science, 371(6531).
* Busch-Moreno, S., Tuomainen, J., & Vinson, D. (2020). Trait Anxiety Effects on Late Phase Threatening Speech Processing: Evidence from EEG. bioRxiv.
* Busch-Moreno, S., Tuomainen, J., & Vinson, D. (2021). Semantic and prosodic threat processing in trait anxiety: is repetitive thinking influencing responses?. Cognition and Emotion, 35(1), 50-70.
* Dehning, J., Zierenberg, J., Spitzner, F. P., Wibral, M., Neto, J. P., Wilczek, M., & Priesemann, V. (2020). Inferring change points in the spread of COVID-19 reveals the effectiveness of interventions. Science, 369(6500).
* Heilbron, E., Martìn, O., & Fumagalli, E. (2020). Efectos protectores de los alimentos andinos contra el daño producido por el alcohol a nivel del epitelio intestinal, una aproximación estadística. Ciencia, Docencia y Tecnología, 31(61 nov-mar).
* Legrand, N., Nikolova, N., Correa, C., Brændholt, M., Stuckert, A., Kildahl, N., ... & Allen, M. (2021). The heart rate discrimination task: a psychophysical method to estimate the accuracy and precision of interoceptive beliefs. bioRxiv.
* Wang, Y. (2020, September). Data Analysis of Psychological Measurement of Intelligent Internet-assisted Sports Training based on Bio-Sensors. In 2020 International Conference on Smart Electronics and Communication (ICOSEC) (pp. 474-477). IEEE.
* WASSERMAN, A., SHRAGER, J., & SHAPIRO, M. A Multilevel Bayesian Model for Precision Oncology.
* Weindel, G., Anders, R., Alario, F. X., & Burle, B. (2020). Assessing model-based inferences in decision making with single-trial response time decomposition. Journal of Experimental Psychology: General.
* Yamagata, Y. (2020). Simultaneous estimation of the effective reproducing number and the detection rate of COVID-19. arXiv e-prints, arXiv-2005.