Do you work with Zarr data? The `pizzarr` package has just been published on CRAN! This is an R implementation for creating, reading, and writing chunked Zarr arrays, developed by David Blodgett and Mark Keller. It supports both Zarr V2 & V3 and efficient slicing of large N-dimensional arrays, making it perfect for handling massive datasets.
GitHub repository: https://github.com/zarr-developers/pizzarr
the gallery of real-world online Zarr stores {zaro} now tests against (via gha)
zaro needs your feedback, this is a #Zarr engine in R using #Arrow for the heavy lifting (IKR!, main player is arrow::S3FileSystem) #RStats
https://github.com/hypertidy/zaro
there are a lot of weirdo tricks stores hide behind, we need intel to find them, build a short set of knowhow to get them all
I added to "band_data" a '_CRS' attribute as dict with 'url', 'wkt' & 'projjson' infos (EPSG 3857), saved the whole thing as 
file and hoped 
can open it in 
However seems to have trouble finding the coordinate reference and assumes the origin is 0,0 (Extent 0.0, -8192.0 : 4096.0, 0.0).
Now, my question: where do I store the "_CRS" dict correctly? Do I also need to add a GeoTransform infos? If yes, where?
Sorry, that I misuse this platform for my silly question:
I have a 
dataset
Dimensions: (time: 1, band: 4, y: 8192, x: 4096)
Coordinates:
* time datetime64[ns] 8B 2023-06-15
* band <U5 80B 'Red' 'Green' 'Blue' 'Alpha'
* y float32 33kB 7.064e+06 7.064e+06
* x float32 16kB 1.194e+06 1.194e+06
Data variables:
band_data (time, band, y, x) uint8 134MB dask.array<chunksize=(1, 4, 1024, 1024), meta=np.ndarray>
spatial_ref int64 8B ...
As part of the @NFDI4DS conference, @haesleinhuepf from @nfdi4bioimage gave a presentation on 'How LLMs impact BioImage Data Science', which is now available on #Zenodo. 🍁
The presentation slides provide information on using #LLMs for bioimage data science, based on various use cases such as image segmentation, feature extraction and UMAP creation, as well as opening a #Zarr file. The focus is on code generation for bioimage analysis.
👉 Click here for the slides: https://doi.org/10.5281/zenodo.17669681
📢Blosc2 3.10.2 Released! 📢
Building on the functions introduced in version 3.9, we have extended lazy evaluation to general expressions with ALL blosc2 functions in version 3.10.
Blosc2's compute engine ingests many array formats, as well as blosc2 arrays, with impressive results - see the graphic below!
Details on the comparisons in our blog: https://www.blosc.org/posts/tensordot-pure-persistent/
Blosc2's compute engine also powers Cat2Cloud (visit the demo site here https://cat2.cloud/demo/)!
Krzysztof Dyba
Michael Sumner
Philipp Gärtner
NFDI4BIOIMAGE