Upload Image Files to IDR

We will upload image files to the Image Data Resource and add URL and metadata information to the Broad Bioimage Benchmark Collection.
We will use this issue to outline the required steps.
From IDR:

study file describing the overall study and the screens that were performed e.g. cell health
library file(s) describing the plate layout of each screen e.g. cell health
processed data file(s) containing summary results and/or a ‘hit' list for each screen

All files should be in tab-delimited text format.
Templates are provided but can be modified to suit your experiment.
Add or remove columns from the templates as necessary.
@gwaygenomics Did you have a processed data file for cell health?

A conclusion from our internal discussion: Let's also include the LKCP dataset when submitting to IDR.

The first step is to reach out to IDR to see if they would be interested in hosting these data. I plan on doing this today.
Becki will be taking notes on the submission process, on the wiki. I will use this issue to jot down specific metadata information that we'll likely need to track for IDR.
A couple immediate answers to track:
How many files?

Could you send us a draft of the publication or is it submitted to an pre-print archive such as BioRxiv? Do you have a time line for publication?
What is the image file format?
Do you have feature level data, ROIs or tracking data available for this dataset?
Could this dataset can be integrated with other datasets e.g. through genes (orthologs) or phenotypes?

Initial inquiry sent on March 16, 2021 with ipLINCS project tag and subject: "[IDR] LINCS Cell Painting - a 45TB benchmark dataset of drug perturbations"

On March 23, 2021, we received word from the IDR staff that they will not accept our data without first a manuscript draft.
I believe the current plan is to introduce this dataset with the LINCS profiling complementarity paper.

[ ]  @gwaygenomics Please note accepted datasets will be published under the Creative Commons Attribution 4.0 International license (CC BY 4.0, https://creativecommons.org/licenses/by/4.0/).  If this is not suitable please discuss your preferred licence with us before submitting any data.

This is less permissive than the license that we will use in the s3://cellpainting-gallery (CC0 https://github.com/awslabs/open-data-registry/blob/899c7a0e44e331dfc9c844a2a28261406ad73eb7/datasets/cellpainting-gallery.yml#L29) but that's ok I think. Do you see any issues @gwaygenomics ?

Sounds good to me 👍 - as long as people are free to use, I'm good

It's fine, we are happy to go with a more permissive license than CC BY 4.0, so CC0 is good for us.  Thanks

Hi all! Sorry to not have pinged sooner, but how are we doing with this upload?
We received favorable reviews, but the journal will not publish without public data. Thanks! (hope all is well!)

Josh said:

The next step is for you to send us a link to the Docker you already have for doing these conversions, and then @Erin Weisbart will create a tool use the template https://github.com/CellProfiler/Distributed-Something to do the conversion, and then actually do the conversion.

I gave everything a trial run earlier this week sans upload. (Scripts below) Download went well enough. With default parameters, conversion took 4 hours. If the download/upload are not part of the blackbox, then I can create this as an official image like “openmicroscopy/bioformats2raw”. If we want more logic within, then we’ll need to discuss naming.
head -n 100 download.sh
aws --no-sign-request --region us-east-1 s3 ls --summarize --human-readable --recursive s3://cellpainting-gallery/lincs/broad/images/2016_04_01_a549_48hr_batch1/images/SQ00014812__2016-05-23T20_44_31-Measurement1/Images/ 2>&1 | tail -n 2

exec time \
    conda run -n aws \
    aws --no-sign-request --region us-east-1 s3 sync \
    s3://cellpainting-gallery/lincs/broad/images/2016_04_01_a549_48hr_batch1/images/SQ00014812__2016-05-23T20_44_31-Measurement1/Images/ \
    SQ00014812__2016-05-23T20_44_31-Measurement1/Images/ | tee "$(date "+%F_%T").log"

head -n 100 run.sh
time sudo docker run \
    -u $(id -u) -v $PWD:/src --rm josh-bf2raw \
    --debug=INFO \
    /src/SQ00014812__2016-05-23T20_44_31-Measurement1/Images/Index.idx.xml \
    /src/SQ00014812__2016-05-23T20_44_31-Measurement1.ome.zarr

head -n 100 docker/Dockerfile
# See https://github.com/mamba-org/micromamba-docker

FROM mambaorg/micromamba

COPY --chown=$MAMBA_USER:$MAMBA_USER env.yaml /tmp/env.yaml

RUN micromamba install -y -f /tmp/env.yaml && \

    micromamba clean --all --yes

ENTRYPOINT ["/usr/local/bin/_entrypoint.sh", "bioformats2raw"]

du -sh SQ00014812__2016-05-23T20_44_31-Measurement1*

151G     SQ00014812__2016-05-23T20_44_31-Measurement1
202G     SQ00014812__2016-05-23T20_44_31-Measurement1.ome.zarr
ome_zarr info SQ00014812__2016-05-23T20_44_31-Measurement1.ome.zarr/

/data/josh/cellpainting/SQ00014812__2016-05-23T20_44_31-Measurement1.ome.zarr [zgroup]

- metadata
   - Plate

- data
   - (1, 5, 1, 2160, 3240)

ome_zarr info SQ00014812__2016-05-23T20_44_31-Measurement1.ome.zarr/0/0/0

/data/josh/cellpainting/SQ00014812__2016-05-23T20_44_31-Measurement1.ome.zarr/0/0/0 [zgroup]

- metadata
   - Multiscales

- data

   - (1, 5, 1, 2160, 2160)
   - (1, 5, 1, 1080, 1080)
   - (1, 5, 1, 540, 540)
   - (1, 5, 1, 270, 270)
   - (1, 5, 1, 135, 135)

Over to @ErinWeisbart and @bethac07

@joshmoore -

If the download/upload are not part of the blackbox, then I can create this as an official image like “openmicroscopy/bioformats2raw”. If we want more logic within, then we’ll need to discuss naming

I think it makes sense to leave the download and upload out .

@bethac07 : Perfect. Thanks.

151G     SQ00014812__2016-05-23T20_44_31-Measurement1
202G     SQ00014812__2016-05-23T20_44_31-Measurement1.ome.zarr

@joshmoore – do we expect this 33% increase in storage?
From David Logan, I had learned this:

With zlib compression switch on when running bioformat2raw causes a small (~5%) but acceptable increase in storage space for cell painting-style HCS images.

I wonder if the zlib compression switch was off in your conversion?

@joshmoore – do we expect this 33% increase in storage?

On average the TIFFs are 9 MB and 5 make up the equivalent of one OME Image. Looking at the pyramid of an OME-Zarr:
(base) [jamoore@pilot-zarr1-dev cellpainting]$ du -sh SQ00014812__2016-05-23T20_44_31-Measurement1.ome.zarr/4/0/6/*
45M	SQ00014812__2016-05-23T20_44_31-Measurement1.ome.zarr/4/0/6/0
12M	SQ00014812__2016-05-23T20_44_31-Measurement1.ome.zarr/4/0/6/1
2.8M	SQ00014812__2016-05-23T20_44_31-Measurement1.ome.zarr/4/0/6/2
720K	SQ00014812__2016-05-23T20_44_31-Measurement1.ome.zarr/4/0/6/3
184K	SQ00014812__2016-05-23T20_44_31-Measurement1.ome.zarr/4/0/6/4

the full resolution matches 5*9MB. So the extra space should come solely from the extra four levels of the pyramid (1080x1080, 540x540, 270x270 and 135x135):
>>> 45 * 0.34
15.3
>>> 12 + 2.8 + .72 + .184
15.704

A different compression might help, but configuring the pyramid levels will definitely make a difference:
  "compressor" : {
    "clevel" : 5,
    "blocksize" : 0,
    "shuffle" : 1,
    "cname" : "lz4",
    "id" : "blosc"
  },

Thanks for the explainer @joshmoore!

A different compression might help, but configuring the pyramid levels will definitely make a difference:

Is this a decision that IDR will make (to keep it standard across all datasets) or do we need to / get to decide? If we need to decide, we might need some help on understanding what we'd trade if we went with fewer levels (better storage-wise but worse interactivity-wise?)

At least from the OMERO perspective, the individual fields of view would not classify under the category of large images (aka larger than 3K x 3K) where the server would mandate  pyramidal levels. This means all data access operations would only happen using tiled access to the top-level resolution .
Said otherwise, the intermediate resolutions generated are not critical for OMERO/IDR and we can likely make compromises in order to keep the data volumes largely equivalent between both representation. I  pre-computing some intermediate resolution levels in the NGFF representation is valuable. In particular the lowest resolution typically correspond to the thumbnail representation of a field of view. OMERO  currently recomputes these levels internally but with growing usage of NGFF, I could certainly imagine it could make natively use of these resolutions if they exist.
Maybe rather than using scale factor of 2 which will create a 1/4+1/16+1/64+1/256 i.e. a 33% increase, we could use a scale factor of 4 which would bring us to 1/16+1/256 i.e. a 6.7 % increase for the conversion?

I've talked to Beth and I think I'm up to speed on my part of this project, at least as up-to-speed as I can get without starting to get my hands dirty. (Sorry for missing out on joining the meeting, but I'm on the West Coast which makes it quite a challenge to reasonably schedule meetings with folks across the pond).
It sounds like I should go ahead with a scale factor of 4?
@joshmoore It doesn't look like scale factor is being passed to the docker. Is this easily configurable?

Hi @ErinWeisbart. If a US-timezone call is necessary in the next few weeks, let me know. The docker has the ENTRYPOINT set to the equivalent of the bioformats2raw executable so all arguments should be passed directly including -h to see all available options.

Were you planning on creating an official openmicroscopy/bioformats2raw docker?

@joshmoore would you recommend that @ErinWeisbart creates a docker herself using this?:
# See https://github.com/mamba-org/micromamba-docker

FROM mambaorg/micromamba

COPY --chown=$MAMBA_USER:$MAMBA_USER env.yaml /tmp/env.yaml

RUN micromamba install -y -f /tmp/env.yaml && \

    micromamba clean --all --yes

ENTRYPOINT ["/usr/local/bin/_entrypoint.sh", "bioformats2raw"]

@shntnu : I've failed to find an automated mechanism that will keep the conda-based docker above up-to-date with the latest tag of glencoesoftware/bioformat2raw. Instead, I've built that repo directly and pushed it to openmicroscopy/bioformats2raw:0.4.0 (link). Note: there's no automation there either but adding it will be straight-forward if we choose to stick with this strategy.

@joshmoore I believe @ErinWeisbart will be following up on this once she is back from vacation.
Meanwhile, is it possible to get an IDR identifier while we are working through this pilot? Very soon, we will be submitting our revision for the paper associated with this dataset, and they require an identifier for us to be able to submit.

@shntnu: re: @ErinWeisbart :+1:. I'll be here. :wink:
For the IDR identifier, I'd gently push you back to the standard IDR channels.

For the IDR identifier, I'd gently push you back to the standard IDR channels.

Of course, will do

Frances said

Your IDR accession number is idr0125.  To cite your submission in a manuscript, please include your IDR accession number and the URL to the IDR homepage (https://idr.openmicroscopy.org/).  For example, “Data was deposited to the Image Data Resource (https://idr.openmicroscopy.org/) under accession number idr0125.”  Please note, this accession number won’t be active until your submission is publicly available in IDR.

🎉
cc @gwaygenomics

ome_zarr info SQ00014812__2016-05-23T20_44_31-Measurement1.ome.zarr/
doesn't return anything. This is what concerns me.

Agreed, that is concerning. Can you paste the contents of the top-level .zattrs file? Perhaps also run ome_zarr with -vvv: ome_zarr -vvv info ....

Debug First Run
nano /images_zarr/SQ00014812__2016-05-23T20_44_31-Measurement1.ome.zarr/.zattrs
{
  "bioformats2raw.layout" : 3
}

ome_zarr -vvv info images_zarr/SQ00014812__2016-05-23T20_44_31-Measurement1.ome.zarr
DEBUG:ome_zarr.format:Created nested FSStore(/home/ubuntu/ebs_tmp/images_zarr/SQ00014812__2016-05-23T20_44_31-Measurement1.ome.zarr, r, {'dimension_separator': '/', 'normalize_keys': False})
DEBUG:ome_zarr.format:0.4 matches None?
DEBUG:ome_zarr.format:0.3 matches None?
DEBUG:ome_zarr.format:0.2 matches None?
DEBUG:ome_zarr.format:V01:None v. 0.1
DEBUG:ome_zarr.reader:ignoring /home/ubuntu/ebs_tmp/images_zarr/SQ00014812__2016-05-23T20_44_31-Measurement1.ome.zarr [zgroup]

Best guess is this run got interrupted without me realizing it
New Run
I ran it again (same EC2 instance, on a different EBS volume, though that shouldn't matter), this time specifying --resolutions 4 and it seems like it works, so I suggest we skip trying to figure out what went wrong with the first run.
du -sh PLATE.ome.zarr/
202G
ome_zarr info PLATE.ome.zarr/
WARNING:ome_zarr.io:version mismatch: detected:FormatV02, requested:FormatV04
WARNING:ome_zarr.io:version mismatch: detected:FormatV04, requested:FormatV02
/home/ubuntu/ebs_tmp2/images_zarr/PLATE.ome.zarr [zgroup]
 - metadata
   - Plate
 - data
   - (1, 5, 1, 4320, 6480)

ome_zarr info PLATE.ome.zarr/0/0/0
WARNING:ome_zarr.io:version mismatch: detected:FormatV02, requested:FormatV04
WARNING:ome_zarr.io:version mismatch: detected:FormatV04, requested:FormatV02
/home/ubuntu/ebs_tmp2/images_zarr/PLATE.ome.zarr/0/0/0 [zgroup]
 - metadata
   - Multiscales
 - data
   - (1, 5, 1, 2160, 2160)
   - (1, 5, 1, 1080, 1080)
   - (1, 5, 1, 540, 540)
   - (1, 5, 1, 270, 270)

ome_zarr -vvv info PLATE.ome.zarr

click for output

```python
DEBUG:ome_zarr.format:Created nested FSStore(PLATE.ome.zarr/, r, {'dimension_separator': '/', 'normalize_keys': False})
DEBUG:ome_zarr.format:0.4 matches None?
DEBUG:ome_zarr.format:0.3 matches None?
DEBUG:ome_zarr.format:0.2 matches None?
DEBUG:ome_zarr.format:V01:None v. 0.1
DEBUG:ome_zarr.reader:treating /home/ubuntu/ebs_tmp2/images_zarr/PLATE.ome.zarr [zgroup] as Plate
INFO:ome_zarr.reader:root_attr: bioformats2raw.layout
DEBUG:ome_zarr.reader:3
INFO:ome_zarr.reader:root_attr: plate
DEBUG:ome_zarr.reader:{'columns': [{'name': '0'}, {'name': '1'}, {'name': '2'}, {'name': '3'}, {'name': '4'}, {'name': '5'}, {'name': '6'}, {'name': '7'}, {'name': '8'}, {'name': '9'}, {'name': '10'}, {'name': '11'}, {'name': '12'}, {'name': '13'}, {'name': '14'}, {'name': '15'}, {'name': '16'}, {'name': '17'}, {'name': '18'}, {'name': '19'}, {'name': '20'}, {'name': '21'}, {'name': '22'}, {'name': '23'}], 'name': 'SQ00014812', 'wells': [{'path': '0/0', 'row_index': 0, 'column_index': 0}, {'path': '0/1', 'row_index': 0, 'column_index': 1}, {'path': '0/2', 'row_index': 0, 'column_index': 2}, {'path': '0/3', 'row_index': 0, 'column_index': 3}, {'path': '0/4', 'row_index': 0, 'column_index': 4}, {'path': '0/5', 'row_index': 0, 'column_index': 5}, {'path': '0/6', 'row_index': 0, 'column_index': 6}, {'path': '0/7', 'row_index': 0, 'column_index': 7}, {'path': '0/8', 'row_index': 0, 'column_index': 8}, {'path': '0/9', 'row_index': 0, 'column_index': 9}, {'path': '0/10', 'row_index': 0, 'column_index': 10}, {'path': '0/11', 'row_index': 0, 'column_index': 11}, {'path': '0/12', 'row_index': 0, 'column_index': 12}, {'path': '0/13', 'row_index': 0, 'column_index': 13}, {'path': '0/14', 'row_index': 0, 'column_index': 14}, {'path': '0/15', 'row_index': 0, 'column_index': 15}, {'path': '0/16', 'row_index': 0, 'column_index': 16}, {'path': '0/17', 'row_index': 0, 'column_index': 17}, {'path': '0/18', 'row_index': 0, 'column_index': 18}, {'path': '0/19', 'row_index': 0, 'column_index': 19}, {'path': '0/20', 'row_index': 0, 'column_index': 20}, {'path': '0/21', 'row_index': 0, 'column_index': 21}, {'path': '0/22', 'row_index': 0, 'column_index': 22}, {'path': '0/23', 'row_index': 0, 'column_index': 23}, {'path': '1/0', 'row_index': 1, 'column_index': 0}, {'path': '1/1', 'row_index': 1, 'column_index': 1}, {'path': '1/2', 'row_index': 1, 'column_index': 2}, {'path': '1/3', 'row_index': 1, 'column_index': 3}, {'path': '1/4', 'row_index': 1, 'column_index': 4}, {'path': '1/5', 'row_index': 1, 'column_index': 5}, {'path': '1/6', 'row_index': 1, 'column_index': 6}, {'path': '1/7', 'row_index': 1, 'column_index': 7}, {'path': '1/8', 'row_index': 1, 'column_index': 8}, {'path': '1/9', 'row_index': 1, 'column_index': 9}, {'path': '1/10', 'row_index': 1, 'column_index': 10}, {'path': '1/11', 'row_index': 1, 'column_index': 11}, {'path': '1/12', 'row_index': 1, 'column_index': 12}, {'path': '1/13', 'row_index': 1, 'column_index': 13}, {'path': '1/14', 'row_index': 1, 'column_index': 14}, {'path': '1/15', 'row_index': 1, 'column_index': 15}, {'path': '1/16', 'row_index': 1, 'column_index': 16}, {'path': '1/17', 'row_index': 1, 'column_index': 17}, {'path': '1/18', 'row_index': 1, 'column_index': 18}, {'path': '1/19', 'row_index': 1, 'column_index': 19}, {'path': '1/20', 'row_index': 1, 'column_index': 20}, {'path': '1/21', 'row_index': 1, 'column_index': 21}, {'path': '1/22', 'row_index': 1, 'column_index': 22}, {'path': '1/23', 'row_index': 1, 'column_index': 23}, {'path': '2/0', 'row_index': 2, 'column_index': 0}, {'path': '2/1', 'row_index': 2, 'column_index': 1}, {'path': '2/2', 'row_index': 2, 'column_index': 2}, {'path': '2/3', 'row_index': 2, 'column_index': 3}, {'path': '2/4', 'row_index': 2, 'column_index': 4}, {'path': '2/5', 'row_index': 2, 'column_index': 5}, {'path': '2/6', 'row_index': 2, 'column_index': 6}, {'path': '2/7', 'row_index': 2, 'column_index': 7}, {'path': '2/8', 'row_index': 2, 'column_index': 8}, {'path': '2/9', 'row_index': 2, 'column_index': 9}, {'path': '2/10', 'row_index': 2, 'column_index': 10}, {'path': '2/11', 'row_index': 2, 'column_index': 11}, {'path': '2/12', 'row_index': 2, 'column_index': 12}, {'path': '2/13', 'row_index': 2, 'column_index': 13}, {'path': '2/14', 'row_index': 2, 'column_index': 14}, {'path': '2/15', 'row_index': 2, 'column_index': 15}, {'path': '2/16', 'row_index': 2, 'column_index': 16}, {'path': '2/17', 'row_index': 2, 'column_index': 17}, {'path': '2/18', 'row_index': 2, 'column_index': 18}, {'path': '2/19', 'row_index': 2, 'column_index': 19}, {'path': '2/20', 'row_index': 2, 'column_index': 20}, {'path': '2/21', 'row_index': 2, 'column_index': 21}, {'path': '2/22', 'row_index': 2, 'column_index': 22}, {'path': '2/23', 'row_index': 2, 'column_index': 23}, {'path': '3/0', 'row_index': 3, 'column_index': 0}, {'path': '3/1', 'row_index': 3, 'column_index': 1}, {'path': '3/2', 'row_index': 3, 'column_index': 2}, {'path': '3/3', 'row_index': 3, 'column_index': 3}, {'path': '3/4', 'row_index': 3, 'column_index': 4}, {'path': '3/5', 'row_index': 3, 'column_index': 5}, {'path': '3/6', 'row_index': 3, 'column_index': 6}, {'path': '3/7', 'row_index': 3, 'column_index': 7}, {'path': '3/8', 'row_index': 3, 'column_index': 8}, {'path': '3/9', 'row_index': 3, 'column_index': 9}, {'path': '3/10', 'row_index': 3, 'column_index': 10}, {'path': '3/11', 'row_index': 3, 'column_index': 11}, {'path': '3/12', 'row_index': 3, 'column_index': 12}, {'path': '3/13', 'row_index': 3, 'column_index': 13}, {'path': '3/14', 'row_index': 3, 'column_index': 14}, {'path': '3/15', 'row_index': 3, 'column_index': 15}, {'path': '3/16', 'row_index': 3, 'column_index': 16}, {'path': '3/17', 'row_index': 3, 'column_index': 17}, {'path': '3/18', 'row_index': 3, 'column_index': 18}, {'path': '3/19', 'row_index': 3, 'column_index': 19}, {'path': '3/20', 'row_index': 3, 'column_index': 20}, {'path': '3/21', 'row_index': 3, 'column_index': 21}, {'path': '3/22', 'row_index': 3, 'column_index': 22}, {'path': '3/23', 'row_index': 3, 'column_index': 23}, {'path': '4/0', 'row_index': 4, 'column_index': 0}, {'path': '4/1', 'row_index': 4, 'column_index': 1}, {'path': '4/2', 'row_index': 4, 'column_index': 2}, {'path': '4/3', 'row_index': 4, 'column_index': 3}, {'path': '4/4', 'row_index': 4, 'column_index': 4}, {'path': '4/5', 'row_index': 4, 'column_index': 5}, {'path': '4/6', 'row_index': 4, 'column_index': 6}, {'path': '4/7', 'row_index': 4, 'column_index': 7}, {'path': '4/8', 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DEBUG:ome_zarr.io:open(ZarrLocation(/home/ubuntu/ebs_tmp2/images_zarr/PLATE.ome.zarr/0/0))
DEBUG:ome_zarr.format:Created nested FSStore(/home/ubuntu/ebs_tmp2/images_zarr/PLATE.ome.zarr/0/0, r, {'dimension_separator': '/', 'normalize_keys': False})
DEBUG:ome_zarr.format:0.4 matches None?
DEBUG:ome_zarr.format:0.3 matches None?
DEBUG:ome_zarr.format:0.2 matches None?
DEBUG:ome_zarr.format:V01:None v. 0.1
DEBUG:ome_zarr.reader:treating /home/ubuntu/ebs_tmp2/images_zarr/PLATE.ome.zarr/0/0 [zgroup] as Well
INFO:ome_zarr.reader:root_attr: well
DEBUG:ome_zarr.reader:{'images': [{'path': '0', 'acquisition': 0}, {'path': '1', 'acquisition': 0}, {'path': '2', 'acquisition': 0}, {'path': '3', 'acquisition': 0}, {'path': '4', 'acquisition': 0}, {'path': '5', 'acquisition': 0}, {'path': '6', 'acquisition': 0}, {'path': '7', 'acquisition': 0}, {'path': '8', 'acquisition': 0}]}
INFO:ome_zarr.reader:well_data: {'images': [{'path': '0', 'acquisition': 0}, {'path': '1', 'acquisition': 0}, {'path': '2', 'acquisition': 0}, {'path': '3', 'acquisition': 0}, {'path': '4', 'acquisition': 0}, {'path': '5', 'acquisition': 0}, {'path': '6', 'acquisition': 0}, {'path': '7', 'acquisition': 0}, {'path': '8', 'acquisition': 0}]}
DEBUG:ome_zarr.io:open(ZarrLocation(/home/ubuntu/ebs_tmp2/images_zarr/PLATE.ome.zarr/0/0/0))
DEBUG:ome_zarr.format:Created nested FSStore(/home/ubuntu/ebs_tmp2/images_zarr/PLATE.ome.zarr/0/0/0, r, {'dimension_separator': '/', 'normalize_keys': False})
DEBUG:ome_zarr.format:0.4 matches 0.2?
DEBUG:ome_zarr.format:0.3 matches 0.2?
DEBUG:ome_zarr.format:0.2 matches 0.2?
WARNING:ome_zarr.io:version mismatch: detected:FormatV02, requested:FormatV04
DEBUG:ome_zarr.format:Created nested FSStore(/home/ubuntu/ebs_tmp2/images_zarr/PLATE.ome.zarr/0/0/0, r, {'dimension_separator': '/', 'normalize_keys': False})
DEBUG:ome_zarr.reader:treating /home/ubuntu/ebs_tmp2/images_zarr/PLATE.ome.zarr/0/0/0 [zgroup] as Multiscales
INFO:ome_zarr.reader:root_attr: multiscales
DEBUG:ome_zarr.reader:[{'metadata': {'method': 'loci.common.image.SimpleImageScaler', 'version': 'Bio-Formats 6.8.0'}, 'datasets': [{'path': '0'}, {'path': '1'}, {'path': '2'}, {'path': '3'}], 'version': '0.2'}]
INFO:ome_zarr.reader:datasets [{'path': '0'}, {'path': '1'}, {'path': '2'}, {'path': '3'}]
INFO:ome_zarr.reader:resolution: 0
INFO:ome_zarr.reader: - shape None = (1, 5, 1, 2160, 2160)
INFO:ome_zarr.reader: - chunks =  ['1', '1', '1', '1024 (+ 112)', '1024 (+ 112)']
INFO:ome_zarr.reader: - dtype = >u2
INFO:ome_zarr.reader:resolution: 1
INFO:ome_zarr.reader: - shape None = (1, 5, 1, 1080, 1080)
INFO:ome_zarr.reader: - chunks =  ['1', '1', '1', '1024 (+ 56)', '1024 (+ 56)']
INFO:ome_zarr.reader: - dtype = >u2
INFO:ome_zarr.reader:resolution: 2
INFO:ome_zarr.reader: - shape None = (1, 5, 1, 540, 540)
INFO:ome_zarr.reader: - chunks =  ['1', '1', '1', '540', '540']
INFO:ome_zarr.reader: - dtype = >u2
INFO:ome_zarr.reader:resolution: 3
INFO:ome_zarr.reader: - shape None = (1, 5, 1, 270, 270)
INFO:ome_zarr.reader: - chunks =  ['1', '1', '1', '270', '270']
INFO:ome_zarr.reader: - dtype = >u2
DEBUG:ome_zarr.io:open(ZarrLocation(/home/ubuntu/ebs_tmp2/images_zarr/PLATE.ome.zarr/0/0/0/labels))
DEBUG:ome_zarr.format:Created nested FSStore(/home/ubuntu/ebs_tmp2/images_zarr/PLATE.ome.zarr/0/0/0/labels, r, {'dimension_separator': '/', 'normalize_keys': False})
WARNING:ome_zarr.io:version mismatch: detected:FormatV04, requested:FormatV02
DEBUG:ome_zarr.format:Created nested FSStore(/home/ubuntu/ebs_tmp2/images_zarr/PLATE.ome.zarr/0/0/0/labels, r, {'dimension_separator': '/', 'normalize_keys': False})
DEBUG:ome_zarr.reader:creating lazy_reader. row:0 col:0
DEBUG:ome_zarr.reader:creating lazy_reader. row:0 col:1
DEBUG:ome_zarr.reader:creating lazy_reader. row:0 col:2
DEBUG:ome_zarr.reader:creating lazy_reader. row:1 col:0
DEBUG:ome_zarr.reader:creating lazy_reader. row:1 col:1
DEBUG:ome_zarr.reader:creating lazy_reader. row:1 col:2
DEBUG:ome_zarr.reader:creating lazy_reader. row:2 col:0
DEBUG:ome_zarr.reader:creating lazy_reader. row:2 col:1
DEBUG:ome_zarr.reader:creating lazy_reader. row:2 col:2
DEBUG:ome_zarr.reader:img_pyramid_shapes: [(1, 5, 1, 2160, 2160), (1, 5, 1, 1080, 1080), (1, 5, 1, 540, 540), (1, 5, 1, 270, 270)]
DEBUG:ome_zarr.reader:target_level: 3
DEBUG:ome_zarr.reader:get_stitched_grid() level: 3, tile_shape: (1, 5, 1, 270, 270)
DEBUG:ome_zarr.reader:treating /home/ubuntu/ebs_tmp2/images_zarr/PLATE.ome.zarr [zgroup] as ome-zarr
DEBUG:ome_zarr.reader:returning /home/ubuntu/ebs_tmp2/images_zarr/PLATE.ome.zarr [zgroup]
/home/ubuntu/ebs_tmp2/images_zarr/PLATE.ome.zarr [zgroup]
 - metadata
   - Plate
 - data
   - (1, 5, 1, 4320, 6480)
DEBUG:ome_zarr.utils:[dask.array]
```

@joshmoore
Do I need to worry about the version mismatch warnings?
Are there any other checks I should perform to confirm that this second .ome.zarr is as expected before processing the rest of the plates?
@sbesson said:

Maybe rather than using scale factor of 2 which will create a 1/4+1/16+1/64+1/256 i.e. a 33% increase, we could use a scale factor of 4 which would bring us to 1/16+1/256 i.e. a 6.7 % increase for the conversion?

Do I achieve this by setting --resolutions 2? Or...?

Do I need to worry about the version mismatch warnings?

No.  And I've fixed it in the mainline: https://github.com/ome/ome-zarr-py/pull/190

Are there any other checks I should perform to confirm that this second .ome.zarr is as expected before processing the rest of the plates?

I don't have any reason to believe that something would vary but the fact that you did have an issue makes me wary. What's the cost of the conversion? (i.e. how many plates are we talking about?) If significant at this point, I'd say it's worth having a few eyes on your converted data set and try with a few tools from the set: {raw2ometiff, napari, vizarr, ZarrReader, OMERO}.

Maybe rather than using scale factor of 2 which will create a 1/4+1/16+1/64+1/256 i.e. a 33% increase, we could use a scale factor of 4 which would bring us to 1/16+1/256 i.e. a 6.7 % increase for the conversion?

Do I achieve this by setting --resolutions 2? Or...?

Thanks for reminding me as I forgot to update on this front after a discussion with @melissalinkert. At the moment, the scaling factor is  not configurable in the bioformats2raw utility so there is no built-in way to achieve the proposal above.
To reduce the size of the secondary data,  the two options I can think of are:

either run the conversion tool as above and then write some post-processing tool that would delete the resolution levels 1 and 3 and update .zattrs accordingly for each field of view
or completely skip the sub-resolution generation using the --target-min-size option introduced in https://github.com/glencoesoftware/bioformats2raw/pull/114/ and available in bioformats2raw 0.4.0

@joshmoore
This dataset is ~100 plates/20 TB, so it's probably worth an extra check. I'll confirm that it opens with napari.
In thinking about it some more, I'm wondering if the culprit is just my first job getting interrupted before finishing without me realizing it. I noticed that the first (failed) .ome.zarr was 194G while the second was 202G (same as your test). I've repeated the creation a third time on the same instance and it looks fine.
@sbesson Thanks for the extra info.
I don't really have any sense of the pros/cons of:

making the .ome.zarr using default parameters (i.e. accepting the 33% increase)

@shntnu, how problematic is the size increase?

writing and running a post-processing tool to delete levels 1 and 3 from .ome.zarr made with default parameters

who would be responsible for writing this? what kind of timeline? do we have budget/time to write and/or run it?

skipping sub-resolution generation

would these meet the goal of playing nicely with OMERO/IDR?

(As a side note, https://forum.image.sc/t/fair-metadata-standards-for-cell-painting/65942 may be someone struggling with similar issues)

skipping sub-resolution generation
would these meet the goal of playing nicely with OMERO/IDR?

Since the actual size images are individual "normally" sized images (2.2k/side), I would hope so, but good to have an expert answer this

@joshmoore @sbesson Can you confirm that if I run our conversion skipping sub-resolution generation it would still meet the goal of playing nicely with OMERO/IDR?
And I would achieve this by including --target-min-size 2160?

@ErinWeisbart I think so under the same line of argument as https://github.com/broadinstitute/lincs-cell-painting/issues/54#issuecomment-1112063588. I will work on a minimal workflow and report findings here.

I was lagging on this thread but I like the idea of skipping sub-resolution generation here, if possible.

I will work on a minimal workflow and report findings here.

Thanks @sbesson!

@shntnu @ErinWeisbart as promised last week, here's a quick summary of my investigation.
As a preamble, all the work below consumes the recently released OME-NGFF 0.4 specification as well as development versions of bioformats2raw for the data generation and ZarrReader for the data reading and the integration into OMERO.

a minimal sample plate (16 wells, 1 field of view) with 5-channel images of XY dimension 2160x2160 defined using the Bio-Formats test images was convert using bioformats2raw into OME-NGFF using
bioformats2raw "test&plates=1&plateRows=16&plateCols=24&sizeX=2160&sizeY=2160.fake" --target-min-size 2160 /uod/idr-scratch/seb-test/cellpainting.zarr

the command above generated a Zarr dataset with 5K files where

the generated OME-NGFF fileset was imported into our CI OMERO deployments which include the ZarrReader under development

accessing the data from the Web UI worked without noticeable issue

Quick summary

on the sub-resolution question, this minimal example should demonstrate that given the image dimensions, a single resolution level (like the original images) does not prevent the data loading/access
this minimal workflow did not attempt to test import HCS at scale i.e. using a realistic number of wells/fields of views. This might higlight additional bottlenecks that will need to be addressed separately but this should not modify the statement above about the resolution requirements
most of the examples discussed above are based on the released bioformats2raw 0.4.0 which generates OME-NGFF datasets according to the 0.2 specification. The recently released 0.4 specification includes several improvements to the HCS specification and clarifies the semantics of several of these concepts. Having discussed this with the OME Formats team, we would strongly recommend to generate HCS according to this specification to simplify usability and support.
in terms of generation tooling, the Glencoe Software team just made available a first release candidate that writes OME-NGFF according to the 0.4 specification. We will internally carry out some more testing before cutting a final public release that can be used for the generation. If you would like to give this pre-release a try using some representative plates for this submission, happy to provide guidance and receive feedback.

Thanks for the extensive investigation here @sbesson

If you would like to give this pre-release a try using some representative plates for this submission, happy to provide guidance and receive feedback.

It sounds like this is indeed the thing we need to do next because that's the only way to skip the creation of sub-resolution images (and keep up with the specs guidelines); over to @ErinWeisbart

One question I have whether to proceed with 0.4.0 (and the 0.2 spec) or wait for 0.5.0 (with the 0.4 spec) to be further along is - what are the relevant timelines for a) when the Broad team needs these zarr files finished vs b) when is a stable-ish version of 0.5 expected to be ready? I'm guessing Erin doesn't mind waiting to proceed until there is a 0.5.0 bioformats2raw Docker, and it should be pretty straightforward to update her workflow as I understand it to handle that, but if we don't expect 0.5 to be available for ie a month or two and we need these made by 2-3 weeks from now, we might make a different decision.

I haven't been following this thread closely enough to know what the differences are between 0.4.0 and 0.5.0 so I can't really comment on the tradeoff, but...

what are the relevant timelines for a) when the Broad team needs these zarr files finished

Assuming that this is a critical step before idr0125 can be released, our timeline is on the order of months (maybe ~2). We're about to resubmit to the journal, and our response will need to be re-reviewed by the 3 reviewers.

b) when is a stable-ish version of 0.5 expected to be ready?

@sbesson I noticed you're tagged in the 4 issues in the 0.5.0 roadmap https://github.com/orgs/glencoesoftware/projects/11/views/1 – so I was wondering if you might be able to guess the timeline?

An 0.5.0rc1 tag was pushed earlier today, but latest won't be updated until the final release.

Great! Thanks @joshmoore !

@sbesson I noticed you're tagged in the 4 issues in the 0.5.0 roadmap https://github.com/orgs/glencoesoftware/projects/11/views/1 – so I was wondering if you might be able to guess the timeline?

Obviously not making the final call but my hope is that a full release should be expected in the next days to weeks.

@sbesson As I'm working on Distributed-BioFormats2Raw, I just wanted to confirm that there are no major API changes between 0.4 and 0.5 that would mean I couldn't just point to a new 0.5 docker using the infrastructure I've built around 0.4? (I'm guessing based on the command you showed above this is true but I'd like to confirm my understanding.) Thanks.

Assuming you are primarily wrapping the command-line utility, looking at https://github.com/glencoesoftware/bioformats2raw/compare/v0.4.0...v0.5.0rc1, I think you are correct there are no major API changes at this level, only some additional options.

@joshmoore With the 0.4.0 docker the command I used was sh /opt/bioformats2raw/bin/bioformats2raw {index_path} {zarr_path} but that command doesn't work with the 0.5.0rc1 docker.
Looking at the comparison between 0.4.0 and 0.5.0rc1 it doesn't look like the ENTRYPOINT ["/opt/bioformats2raw/bin/bioformats2raw"] changed. However, if I ls /opt in the 0.5.0rc1 docker there is only a conda folder but no bioformats2raw folder. (ls /opt in the 0.4.0 docker gives bioformats2raw	gradle	java).
What am I missing? Thanks again for your help.

With the 0.4.0 docker the command I used was sh /opt/bioformats2raw/bin/bioformats2raw {index_path} {zarr_path} but that command doesn't work with the 0.5.0rc1 docker.

Hi @ErinWeisbart. Apologies. I forgot that there was an upstream docker PR that might change things for you. Can you explain where you are using sh /opt/bioformats2raw/bin/bioformats2raw from? Does the ENTRYPOINT itself not work for you?

Looking at the comparison between 0.4.0 and 0.5.0rc1 it doesn't look like the ENTRYPOINT ["/opt/bioformats2raw/bin/bioformats2raw"] changed.

Hmmm. I see this change:
$ docker inspect -f '{{.Config.Entrypoint}}' openmicroscopy/bioformats2raw:0.4.0
[/opt/bioformats2raw/bin/bioformats2raw]

$ docker inspect -f '{{.Config.Entrypoint}}' openmicroscopy/bioformats2raw:0.5.0rc1
[/usr/local/bin/_entrypoint.sh bioformats2raw]

with the latest version coming from https://github.com/ome/bioformats2raw-docker/blob/main/Dockerfile

Sorry about that. The ENTRYPOINT does work when I'm testing just your docker but we don't use it directly in our distributed workflow because we make another docker that FROMs your docker.
We've now updated the point where we call Bioformats2Raw in our docker to reflect your new ENTRYPOINT.
(I was accidentally looking at the glencoesoftware/bioformats2raw Dockerfile not the ome/bioformats2raw-docker Dockerfile, but I see the change when I look at the right place :)

@ErinWeisbart
When you get to it, please create the NGFFs at s3://cellpainting-gallery/cpg0004-lincs instead of s3://cellpainting-gallery/lincs
We will eventually move all of s3://cellpainting-gallery/lincs to s3://cellpainting-gallery/cpg0004-lincs

We will eventually move all of s3://cellpainting-gallery/lincs to s3://cellpainting-gallery/cpg0004-lincs

I have done this
I am now deleting lincs
aws s3 rm --profile jump-cp-role --recursive --quiet s3://cellpainting-gallery/lincs/

cc @ErinWeisbart

😱

The .ome.zarr will open with napari-ome-zarr though it takes a long time and prints this warning many times before loading:
 c:\users\administrator\appdata\local\programs\python\python38\lib\site-packages\napari\_vispy\layers\image.py:231: UserWarning: data shape (34560, 51840) exceeds GL_MAX_TEXTURE_SIZE 8192 in at least one axis and will be downsampled. Rendering is currently in 2D mode.!

Coming back quickly to this warning, do you remembers which napari command was used? napari will fetch the data for the first image across all wells, stitch them into a grid using the plate layout. I think the message is expected as per the decision to keep only the full resolution of each well, it is not possible to retrieve a low-resolution version of each well so the stitched plate representation need to be downsampled for visualization purposes.

@sbesson Thanks for the explanation. That makes sense.
I get the same warning using napari {PLATE}.ome.zarr or just opening napari with napari and drag-and-drop the .ome.zarr folder into the view window.

Conversion is almost done. 130/136 plates present in cellpainting-gallery converted without error and are available at s3://cellpainting-gallery/cpg0004-lincs/broad/images/2016_04_01_a549_48hr_batch1/images_zarr/
Note that I had these four plates on a batch list (I don't remember where I pulled the list from), but they didn't have images in the gallery so they were not converted to .ome.zarr's.
SQ00015225__2016-10-29T16_09_17-Measurement1
SQ00015226__2016-10-29T17_50_20-Measurement1
SQ00015227__2016-10-29T19_31_37-Measurement1
SQ00015228__2016-10-29T21_13_50-Measurement1
These six plates had errors in their logs.
They all created .ome.zarr files of the appropriate size (all were 150.5 to 150.7 GB, 166675 total objects).
SQ00015168__2016-04-21T05_12_27-Measurement1
SQ00015216__2016-05-04T17_08_56-Measurement1
SQ00015162__2016-04-15T08_49_32-Measurement1
SQ00015116__2016-04-13T16_31_49-Measurement1
SQ00015121__2016-04-14T00_54_07-Measurement1
SQ00015098__2016-06-08T18_43_42-Measurement1
Details/Debug:
Note that if a task fails our script is set to retry but it all logs in the same place, so I am only copying down the first Exception/s can find.
SQ00015168__2016-04-21T05_12_27-Measurement1
Does not open in napari.
ome_zarr info 15168.ome.zarr doesn't return anything.
ome_zarr info 15168.ome.zarr\A\1\0 as expected.
nano 15168.ome.zarr\.zattrs returns only
{
  "bioformats2raw.layout" : 3
}

CloudWatch log:
Exception in thread "main" picocli.CommandLine$ExecutionException: Error while calling command (com.glencoesoftware.bioformats2raw.Converter@54c5a2ff): java.io.FileNotFoundException: /home/ubuntu/local/SQ00015168__2016-04-21T05_12_27-Measurement1/Images/Index.idx.xml (Unknown error 4094)

I can also see an out of space error shortly thereafter. The behavior of this plate is consistent with previous tests where I didn't have a large enough hard drive. However, it's not clear to me why that would happen as it started at 151.5GB and 17283 objects (whereas the plate next to it SQ00015167 started at 150.7GB and 17283 objects) and the EBS volume I used was 500 GB.
Caused by: java.io.IOException: No space left on device

The other five plates have similar behavior in CloudWatch logs. The two I dug into seemed to be fully functional:
SQ00015216__2016-05-04T17_08_56-Measurement1
Opens in napari. ome_zarr info 15216.ome.zarr, ome_zarr info 15216.ome.zarr\A\1\0 and nano 15216.ome.zarr\.zattrs as expected.
Cloudwatch:
java.nio.file.FileSystemException: /home/ubuntu/local/SQ00015216__2016-05-04T17_08_56-Measurement1.ome.zarr/J/22/6/0/0/4/0/1/2: Structure needs cleaning

SQ00015162__2016-04-15T08_49_32-Measurement1
Cloudwatch:
java.nio.file.FileSystemException: /home/ubuntu/local/SQ00015162__2016-04-15T08_49_32-Measurement1.ome.zarr/C/8/4: Structure needs cleaning

SQ00015116__2016-04-13T16_31_49-Measurement1
Cloudwatch:
Caused by: java.lang.RuntimeException: java.nio.file.FileSystemException: /home/ubuntu/local/SQ00015116__2016-04-13T16_31_49-Measurement1.ome.zarr/E/5/4: Structure needs cleaning

SQ00015121__2016-04-14T00_54_07-Measurement1
Cloudwatch:
java.nio.file.FileSystemException: /home/ubuntu/local/SQ00015121__2016-04-14T00_54_07-Measurement1.ome.zarr/B/23/8/0/0/1/0/0/2: Structure needs cleaning

SQ00015098__2016-06-08T18_43_42-Measurement1
Opens in napari. ome_zarr info 15216.ome.zarr, ome_zarr info 15216.ome.zarr\A\1\0 and nano 15216.ome.zarr\.zattrs as expected.
Cloudwatch:
java.nio.file.FileSystemException: /home/ubuntu/local/SQ00015098__2016-06-08T18_43_42-Measurement1.ome.zarr/C/3/7/0/0/0/0/2: Structure needs cleaning

So exciting!

Note that I had these four plates on a batch list (I don't remember where I pulled the list from), but they didn't have images in the gallery so they were not converted to .ome.zarr's.

I confirm that you can ignore those plates - they still appear in some lists, but those plates don't actually exist (see my notes in https://github.com/broadinstitute/lincs-cell-painting/issues/54#issuecomment-942670917 if you are curious)

SQ00015168__2016-04-21T05_12_27-Measurement1

I double-checked if the Index.idx.xml existed, just in case that was the problem; it does. So it's something else 🤷
https://cellpainting-gallery.s3.amazonaws.com/cpg0004-lincs/broad/images/2016_04_01_a549_48hr_batch1/images/SQ00015168__2016-04-21T05_12_27-Measurement1/Images/Index.idx.xml

The other five plates have similar behavior in CloudWatch logs. The two I dug into seemed to be fully functional:

This one seems to be a system-level error
https://unix.stackexchange.com/questions/654310/ext4-ubuntu-18-04-file-system-error-strcuture-needs-cleaning
I am hoping others might be able to comment on this.
An easy tack is to just run it again and see if that does it (no need to delete the existing files IIUC, unless the convertor creates temp files)

I re-converted SQ00015168__2016-04-21T05_12_27-Measurement1 using a 600GB drive instead of a 500GB drive and it is successfully converted. (I don't actually know if it's the re-run itself that fixed it or the fact that the re-run used a larger drive).
Regardless, I believe all the plates have been made into .ome.zarrs and are ready for IDR.
I would image part of the IDR workflow is to make sure that the plates all load in IDR, but we should pay extra attention to confirming that the 5 plates that were throwing the structure cleaning error do in fact play nicely with IDR.

Super!
Over to y'all @sbesson @joshmoore

From where can we pull the files?

Thanks, pulling the data now from s3://cellpainting-gallery/cpg0004-lincs/broad/images/2016_04_01_a549_48hr_batch1/images_zarr/ . Gonna try to import it into one of our test systems, will keep you posted.

Super!
 That is, the files will live on S3 as they are and will simply be accessed by IDR in some manner, right?
Of course it’s perfectly ok to copy it over, it’s just that I wanted to make sure that we’re able to test the key aspect of this setup, which is to have the files stored on and accessed from S3.

Hi @shntnu. For the remote access we will need to deploy new infrastructure, but in the meantime we can test a local copy.

Apologies for the delay here - @dominikl and I have been off recently - Now I'm back and trying to catch up...
Dom said he downloaded the 6 plates mentioned above and tried to import them:
2022-07-27 14:35:44,947 3432581 [ main] ERROR ome.formats.importer.cli.ErrorHandler - FILE_EXCEPTION: /nfs/bioimage/drop/idr0125-way-cellpainting/s3-20220725/SQ00015098__2016-06-08T18_43_42-Measurement1.ome.zarr/A/1/0/0/0/0/0/1/2
java.lang.ClassCastException: class java.lang.String cannot be cast to class java.lang.Integer (java.lang.String and java.lang.Integer are in module java.base of loader 'bootstrap')
at loci.formats.in.ZarrReader.parsePlate(ZarrReader.java:539)
at loci.formats.in.ZarrReader.initFile(ZarrReader.java:287)

In response to @shntnu "I wonder if there's some way of checking the consistency of NGFF files", there is a tool for validating the JSON against a schema: E.g. https://ome.github.io/ome-ngff-validator/?source=https://uk1s3.embassy.ebi.ac.uk/idr/zarr/v0.4/idr0056B/7361.zarr
As you can see, the URL to the plate is in the URL source=..., which is also the URL that you can open the same plate in napari,
E.g. $ napari --plugin napari-ome-zarr -vvv https://uk1s3.embassy.ebi.ac.uk/idr/zarr/v0.4/idr0056B/7361.zarr
Is you data publicly available in the same way?
Thanks,
Will.

Thanks for the update @will-moore
Our data is indeed publicly available.
Regarding the error: The validator seems to approve the wells, but it errors in the same way as you've reported.
https://ome.github.io/ome-ngff-validator/?source=https://cellpainting-gallery.s3.amazonaws.com/cpg0004-lincs/broad/images/2016_04_01_a549_48hr_batch1/images_zarr/SQ00015098__2016-06-08T18_43_42-Measurement1.ome.zarr/

Unfortunately, I have zero knowledge on what to do next, and I suspect the same for @ErinWeisbart, so hopefully, you / folks at your end know what to do next :D?

Thanks @will-moore. Did you and/or @dominikl try any of the rest of the batch that converted without any errors?
Did you see them same behavior for all 6 plates? i.e. Since the initial error I re-converted SQ00015168__2016-04-21T05_12_27-Measurement1 and it didn't error on re-conversion so I would hope/expect it works now.

Thanks all for the progress. Coming back  on the issue of the invalid plates

Regarding the error: The validator seems to approve the wells, but it errors in the same way as you've reported.
https://ome.github.io/ome-ngff-validator/?source=https://cellpainting-gallery.s3.amazonaws.com/cpg0004-lincs/broad/images/2016_04_01_a549_48hr_batch1/images_zarr/SQ00015098__2016-06-08T18_43_42-Measurement1.ome.zarr/

The root of the issue is that the type of plate.acquisitions.id field was  ambiguous in the original OME-NGFF 0.4 specification. We worked to clarify the attribute type expectation in the specification and add a JSON schema  used by the validator (https://github.com/glencoesoftware/bioformats2raw/pull/152) and  updated the converter to comply with this requirement (https://github.com/glencoesoftware/bioformats2raw/pull/152).
Looking at the timestamps, I realise this change was merged and released a few days after our conversation above and only made it into bioformats2raw 0.5.0rc2 while you used bioformats2raw 0.5.0rc1.
This is very unfortunate timing but on the flip side, the only thing that needs to be fixed are the top-level .zattrs defining the plate metadata where the id needs to be converted into an integer with a diff similar to:
    "acquisitions" : [ {
-      "id" : "0"
+      "id" : 0
    } ]

A full reconversion would fix this issue in a brute force approach but this feels completely unnecessary. I would instead propose to try and patch these top-level .zattrs files individually so that they pass the online validation and we  can continue the validation. Is that doable from a permissions perspective on your side? Assuming yes, do you have a preferred workflow and/or constraints for e.g. executing an update script against a series of S3 locations?

I checked the .zattrs for all 136 plates and only 63 of them contained the "plate":{... metadata:
63 plates list
SQ00014813__2016-05-23T19_03_28-Measurement1.ome.zarr
SQ00014815__2016-05-23T15_46_08-Measurement1.ome.zarr
SQ00014819__2016-05-23T08_51_51-Measurement1.ome.zarr
SQ00015043__2016-05-25T16_25_21-Measurement1.ome.zarr
SQ00015047__2016-05-20T00_41_45-Measurement1.ome.zarr
SQ00015051__2016-05-24T05_00_04-Measurement1.ome.zarr
SQ00015052__2016-05-24T03_21_49-Measurement1.ome.zarr
SQ00015053__2016-05-24T01_42_05-Measurement1.ome.zarr
SQ00015054__2016-05-24T00_02_16-Measurement1.ome.zarr
SQ00015055__2016-05-23T22_23_05-Measurement1.ome.zarr
SQ00015058__2016-05-20T08_50_58-Measurement1.ome.zarr
SQ00015098__2016-06-08T18_43_42-Measurement1.ome.zarr
SQ00015101__2016-05-16T20_23_02-Measurement1.ome.zarr
SQ00015105__2016-05-06T00_10_33-Measurement1.ome.zarr
SQ00015106__2016-05-05T22_30_28-Measurement1.ome.zarr
SQ00015107__2016-05-05T09_50_51-Measurement1.ome.zarr
SQ00015108__2016-05-05T08_10_09-Measurement1.ome.zarr
SQ00015109__2016-05-05T06_29_59-Measurement1.ome.zarr
SQ00015111__2016-05-05T03_10_03-Measurement1.ome.zarr
SQ00015112__2016-05-05T01_29_08-Measurement1.ome.zarr
SQ00015120__2016-04-13T23_14_32-Measurement1.ome.zarr
SQ00015121__2016-04-14T00_54_07-Measurement1.ome.zarr
SQ00015124__2016-04-14T07_35_09-Measurement1.ome.zarr
SQ00015125__2016-04-13T14_53_08-Measurement2.ome.zarr
SQ00015131__2016-03-25T17_23_46-Measurement1.ome.zarr
SQ00015132__2016-04-24T07_20_38-Measurement1.ome.zarr
SQ00015133__2016-04-24T08_59_07-Measurement1.ome.zarr
SQ00015134__2016-06-12T00_30_52-Measurement1.ome.zarr
SQ00015146__2016-05-19T10_37_52-Measurement1.ome.zarr
SQ00015147__2016-05-17T07_49_53-Measurement1.ome.zarr
SQ00015149__2016-05-16T23_39_00-Measurement1.ome.zarr
SQ00015152__2016-06-09T03_29_42-Measurement1.ome.zarr
SQ00015156__2016-04-14T19_29_39-Measurement1.ome.zarr
SQ00015157__2016-04-14T22_48_58-Measurement1.ome.zarr
SQ00015164__2016-04-20T22_38_54-Measurement1.ome.zarr
SQ00015165__2016-04-21T00_16_52-Measurement1.ome.zarr
SQ00015166__2016-04-21T01_55_54-Measurement1.ome.zarr
SQ00015167__2016-04-21T03_34_00-Measurement1.ome.zarr
SQ00015168__2016-04-21T05_12_27-Measurement1.ome.zarr
SQ00015170__2016-04-21T08_29_08-Measurement1.ome.zarr
SQ00015172__2016-04-21T18_01_12-Measurement1.ome.zarr
SQ00015173__2016-04-15T07_09_23-Measurement1.ome.zarr
SQ00015194__2016-03-25T12_39_04-Measurement1.ome.zarr
SQ00015195__2016-04-24T21_41_05-Measurement2.ome.zarr
SQ00015197__2016-04-24T13_30_35-Measurement1.ome.zarr
SQ00015199__2016-04-23T15_43_06-Measurement1.ome.zarr
SQ00015201__2016-04-25T02_38_35-Measurement2.ome.zarr
SQ00015202__2016-04-23T22_17_34-Measurement1.ome.zarr
SQ00015203__2016-04-25T00_59_49-Measurement2.ome.zarr
SQ00015206__2016-04-24T04_00_27-Measurement1.ome.zarr
SQ00015207__2016-04-24T05_39_26-Measurement1.ome.zarr
SQ00015208__2016-04-22T16_36_31-Measurement1.ome.zarr
SQ00015209__2016-04-22T18_16_23-Measurement1.ome.zarr
SQ00015214__2016-05-04T23_48_48-Measurement1.ome.zarr
SQ00015215__2016-05-04T18_49_26-Measurement1.ome.zarr
SQ00015216__2016-05-04T17_08_56-Measurement1.ome.zarr
SQ00015217__2016-05-04T22_08_57-Measurement1.ome.zarr
SQ00015220__2016-05-04T20_29_20-Measurement1.ome.zarr
SQ00015221__2016-05-04T06_58_09-Measurement1.ome.zarr
SQ00015224__2016-05-06T01_49_03-Measurement1.ome.zarr
SQ00015230__2016-05-12T18_00_11-Measurement1.ome.zarr
SQ00015231__2016-05-12T09_02_26-Measurement1.ome.zarr
SQ00015233__2016-05-12T05_47_03-Measurement1.ome.zarr

It seems that this metadata didn't get exported for the other plates.
But even for plates without the plates metadata, the Wells and Images seem to have exported OK, e.g. https://ome.github.io/ome-ngff-validator/?source=https://cellpainting-gallery.s3.amazonaws.com/cpg0004-lincs/broad/images/2016_04_01_a549_48hr_batch1/images_zarr/SQ00014812__2016-05-23T20_44_31-Measurement1.ome.zarr/P/24/
and they look good in vizarr: https://hms-dbmi.github.io/vizarr/?source=https://cellpainting-gallery.s3.amazonaws.com/cpg0004-lincs/broad/images/2016_04_01_a549_48hr_batch1/images_zarr/SQ00014812__2016-05-23T20_44_31-Measurement1.ome.zarr/P/24/
It seems that the .zattrs for every plate is identical apart from the name, so as above, it would be nice to fix this issue without repeating a full export, simply by duplicating the .zattrs for plates where it's missing, updating the name accordingly?

@sbesson
Thanks for diagnosing the issue!
@will-moore our messages crossed; I paste mine in here for completeness

I just noticed that the .zattrs are different across different plates (n=63 are 31518 bytes, and n=75 are just 33 bytes)
S3LOC=s3://cellpainting-gallery/cpg0004-lincs/broad/images/2016_04_01_a549_48hr_batch1/images_zarr

parallel "echo -n {}:; aws s3 ls ${S3LOC}/{}/.zattr" ::: `aws s3 ls ${S3LOC}/|tr -s " "|cut -d" " -f3|tr -d "/"` > ~/Desktop/lincs.txt

lincs.txt
The ones with 33 bytes look like this
{
  "bioformats2raw.layout" : 3
}

It seems that the .zattrs for every plate is identical apart from the name, so as above, it would be nice to fix this issue without repeating a full export, simply by duplicating the .zattrs for plates where it's missing, updating the name accordingly?

This sounds like a reasoable fix
I compared two at random, and indeed name is the only key that differs
diff SQ00015214__2016-05-04T23_48_48-Measurement1.ome.zarr.zattrs SQ00015217__2016-05-04T22_08_57-Measurement1.ome.zarr.zattrs
53c53
<     "name" : "SQ00015214",
---
>     "name" : "SQ00015217",

But I wonder if this is an indication of some bigger problem with the conversion process? If you're not worried about that, then I can proceed to make this fix; LMK @sbesson @will-moore

Thanks for the deep dive @will-moore and @sbesson.
I have full write permissions in the bucket so I can easily make the necessary changes.
Can you confirm that I'm understanding correctly that I should take any one of the complete .ome.zarr.zattrs files, confirm that "acquisitions" : [ {"id" : 0}] (int not string), and then use it to replace all of the .ome.zarr.zattrs files in the batch, changing "plate":{"name" : "SQ00014813"} to match?

But I wonder if this is an indication of some bigger problem with the conversion process? If you're not worried about that, then ~I~ @ErinWeisbart can proceed to make this fix; LMK @sbesson @will-moore

Specifically it will be great if you can confirm that bioformats2raw 0.5.0rc2 would have indeed created the the metadata for the 75 files for which bioformats2raw 0.5.0rc1 did not @sbesson

But I wonder if this is an indication of some bigger problem with the conversion process?

I have exactly the same concern @shntnu. The writing of the plate metadata is effectively one of the final steps in the conversion process. Similarly to Will's comment above, my minimal tests seem to indicate the metadata  is present in randomly chosen well samples on a plate. So in the less disruptive scenario, only the top-level plate metadata failed to write.
Did you happen to store logs of your distributed conversion for each worker? If yes, that might give us hints on where the conversion stopped/failed. If not, I assume our best way forward is to use the  IDR validation process to determine whether fixing the top-level metadata is sufficient to address the problem.

Specifically it will be great if you can confirm that bioformats2raw 0.5.0rc2 would have indeed created the metadata for the 75 files for which bioformats2raw 0.5.0rc1 did not @sbesson

I would expect the plate metadata to be present in the top-level keys of all converted datasets independently of the version of bioformats2raw.

Can you confirm that I'm understanding correctly that I should take any one of the complete .ome.zarr.zattrs files, confirm that "acquisitions" : [ {"id" : 0}] (int not string), and then use it to replace all of the .ome.zarr.zattrs files in the batch, changing "plate":{"name" : "SQ00014813"} to match?

If all your plates have the same HCS layout i.e. same number of rows/columns and same number of populated wells, the plate.name value might be the only metadata field that would vary between plates. If this assumption holds, that read like a sensible workflow that would allow us to test whether we can simply fix the plate metadata. Should we try with one of the 75 faulty plates ?

Logs coming from the Bioformats2Raw docker for passing plate (SQ00014813):
OpenJDK 64-Bit Server VM warning: You have loaded library /tmp/opencv_openpnp12767929091814070502/nu/pattern/opencv/linux/x86_64/libopencv_java342.so which might have disabled stack guard. The VM will try to fix the stack guard now.
It's highly recommended that you fix the library with 'execstack -c <libfile>', or link it with '-z noexecstack'.`
2022-07-05 20:22:13,158 [main] WARN  o.x.m.e.h.AcquisitionModeEnumHandler - Unknown AcquisitionMode value 'NonConfocal' will be stored as "Other"

(Lots of copies of this warning, then)
WARNING: An illegal reflective access operation has occurred
WARNING: Illegal reflective access by com.esotericsoftware.kryo.util.UnsafeUtil (file:/opt/conda/share/bioformats2raw-0.5.0rc1-0/lib/kryo-4.0.2.jar) to constructor java.nio.DirectByteBuffer(long,int,java.lang.Object)
WARNING: Please consider reporting this to the maintainers of com.esotericsoftware.kryo.util.UnsafeUtil
WARNING: Use --illegal-access=warn to enable warnings of further illegal reflective access operations
WARNING: All illegal access operations will be denied in a future release

Logs coming from the Bioformats2Raw docker for failing plate (SQ00014812):
Logs look the same as above, however, after successful completion and upload it looks like the job was accidentally pulled again and this time had the following errors and then overwrote the first successful copy:
2022-07-06 03:23:22,017 [pool-1-thread-4] ERROR c.g.bioformats2raw.Converter - Failure processing chunk; resolution=0 plane=1 xx=0 yy=1024 zz=0 width=1024 height=1024 depth=1
java.nio.file.FileSystemException: /home/ubuntu/local/SQ00014812__2016-05-23T20_44_31-Measurement1.ome.zarr/C/21/4/0/0/1/0/1: Structure needs cleaning
	at java.base/sun.nio.fs.UnixException.translateToIOException(UnixException.java:100)
	at java.base/sun.nio.fs.UnixException.rethrowAsIOException(UnixException.java:111)
	at java.base/sun.nio.fs.UnixException.rethrowAsIOException(UnixException.java:116)
	at java.base/sun.nio.fs.UnixFileSystemProvider.createDirectory(UnixFileSystemProvider.java:389)
	at java.base/java.nio.file.Files.createDirectory(Files.java:689)
	at java.base/java.nio.file.Files.createAndCheckIsDirectory(Files.java:796)
	at java.base/java.nio.file.Files.createDirectories(Files.java:782)
	at com.bc.zarr.storage.FileSystemStore$1.close(FileSystemStore.java:85)
	at com.bc.zarr.chunk.ChunkReaderWriterImpl_Short.write(ChunkReaderWriterImpl_Short.java:84)
	at com.bc.zarr.ZarrArray.write(ZarrArray.java:235)
	at com.glencoesoftware.bioformats2raw.Converter.writeBytes(Converter.java:907)
	at com.glencoesoftware.bioformats2raw.Converter.processChunk(Converter.java:1138)
	at com.glencoesoftware.bioformats2raw.Converter.lambda$saveResolutions$4(Converter.java:1334)
	at java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1128)
	at java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:628)
	at java.base/java.lang.Thread.run(Thread.java:834)

(repeats a number of times with different pool-n-thread-n and file)
Logs coming from the Bioformats2Raw docker for failing plate (SQ00015140):
Similar logs as passing plate but upload logs a lot of warnings
warning: Skipping file /home/ubuntu/local/SQ00015140__2016-06-11T14_43_11-Measurement1.ome.zarr/G/18/8. File does not exist
full error list
== ERR
warning: Skipping file /home/ubuntu/local/SQ00015140__2016-06-11T14_43_11-Measurement1.ome.zarr/G/18/8. File does not exist.
warning: Skipping file /home/ubuntu/local/SQ00015140__2016-06-11T14_43_11-Measurement1.ome.zarr/G/18/7. File does not exist.
warning: Skipping file /home/ubuntu/local/SQ00015140__2016-06-11T14_43_11-Measurement1.ome.zarr/G/18/6. File does not exist.
warning: Skipping file /home/ubuntu/local/SQ00015140__2016-06-11T14_43_11-Measurement1.ome.zarr/G/18/5/0/0/4. File does not exist.
warning: Skipping file /home/ubuntu/local/SQ00015140__2016-06-11T14_43_11-Measurement1.ome.zarr/G/18/5/0/0/0/0/1. File does not exist.
warning: Skipping file /home/ubuntu/local/SQ00015140__2016-06-11T14_43_11-Measurement1.ome.zarr/G/18/5/0/0/0/0/2. File does not exist.
warning: Skipping file /home/ubuntu/local/SQ00015140__2016-06-11T14_43_11-Measurement1.ome.zarr/G/18/5/0/0/0/0/0/2. File does not exist.
warning: Skipping file /home/ubuntu/local/SQ00015140__2016-06-11T14_43_11-Measurement1.ome.zarr/G/18/5/0/0/1/0/1. File does not exist.
warning: Skipping file /home/ubuntu/local/SQ00015140__2016-06-11T14_43_11-Measurement1.ome.zarr/G/18/5/0/0/1/0/2. File does not exist.
warning: Skipping file /home/ubuntu/local/SQ00015140__2016-06-11T14_43_11-Measurement1.ome.zarr/G/18/5/0/0/1/0/0/2. File does not exist.
warning: Skipping file /home/ubuntu/local/SQ00015140__2016-06-11T14_43_11-Measurement1.ome.zarr/G/18/5/0/0/2/0/1. File does not exist.
warning: Skipping file /home/ubuntu/local/SQ00015140__2016-06-11T14_43_11-Measurement1.ome.zarr/G/18/5/0/0/2/0/2. File does not exist.
warning: Skipping file /home/ubuntu/local/SQ00015140__2016-06-11T14_43_11-Measurement1.ome.zarr/G/18/5/0/0/2/0/0/1. File does not exist.
warning: Skipping file /home/ubuntu/local/SQ00015140__2016-06-11T14_43_11-Measurement1.ome.zarr/G/18/5/0/0/2/0/0/2. File does not exist.
warning: Skipping file /home/ubuntu/local/SQ00015140__2016-06-11T14_43_11-Measurement1.ome.zarr/G/18/5/0/0/3/0/1. File does not exist.
warning: Skipping file /home/ubuntu/local/SQ00015140__2016-06-11T14_43_11-Measurement1.ome.zarr/G/18/5/0/0/3/0/2. File does not exist.
warning: Skipping file /home/ubuntu/local/SQ00015140__2016-06-11T14_43_11-Measurement1.ome.zarr/G/18/5/0/0/3/0/0/1. File does not exist.
warning: Skipping file /home/ubuntu/local/SQ00015140__2016-06-11T14_43_11-Measurement1.ome.zarr/G/18/5/0/0/3/0/0/2. File does not exist.
warning: Skipping file /home/ubuntu/local/SQ00015140__2016-06-11T14_43_11-Measurement1.ome.zarr/G/19/1. File does not exist.
warning: Skipping file /home/ubuntu/local/SQ00015140__2016-06-11T14_43_11-Measurement1.ome.zarr/G/19/0/0/0/3. File does not exist.
warning: Skipping file /home/ubuntu/local/SQ00015140__2016-06-11T14_43_11-Measurement1.ome.zarr/G/19/0/0/0/1. File does not exist.
warning: Skipping file /home/ubuntu/local/SQ00015140__2016-06-11T14_43_11-Measurement1.ome.zarr/G/19/0/0/0/0. File does not exist.
warning: Skipping file /home/ubuntu/local/SQ00015140__2016-06-11T14_43_11-Measurement1.ome.zarr/G/19/0/0/0/2. File does not exist.
warning: Skipping file /home/ubuntu/local/SQ00015140__2016-06-11T14_43_11-Measurement1.ome.zarr/G/19/0/0/0/4. File does not exist.
warning: Skipping file /home/ubuntu/local/SQ00015140__2016-06-11T14_43_11-Measurement1.ome.zarr/G/20/0/0/0/0. File does not exist.
warning: Skipping file /home/ubuntu/local/SQ00015140__2016-06-11T14_43_11-Measurement1.ome.zarr/G/21/5. File does not exist.
warning: Skipping file /home/ubuntu/local/SQ00015140__2016-06-11T14_43_11-Measurement1.ome.zarr/G/21/8. File does not exist.
warning: Skipping file /home/ubuntu/local/SQ00015140__2016-06-11T14_43_11-Measurement1.ome.zarr/G/21/7. File does not exist.
warning: Skipping file /home/ubuntu/local/SQ00015140__2016-06-11T14_43_11-Measurement1.ome.zarr/G/21/6. File does not exist.
warning: Skipping file /home/ubuntu/local/SQ00015140__2016-06-11T14_43_11-Measurement1.ome.zarr/G/21/4. File does not exist.
warning: Skipping file /home/ubuntu/local/SQ00015140__2016-06-11T14_43_11-Measurement1.ome.zarr/G/21/0/0/0/0. File does not exist.
warning: Skipping file /home/ubuntu/local/SQ00015140__2016-06-11T14_43_11-Measurement1.ome.zarr/G/21/3/0/0/3/0/2/2. File does not exist.
warning: Skipping file /home/ubuntu/local/SQ00015140__2016-06-11T14_43_11-Measurement1.ome.zarr/G/21/3/0/0/4/0/2/2. File does not exist.
warning: Skipping file /home/ubuntu/local/SQ00015140__2016-06-11T14_43_11-Measurement1.ome.zarr/G/22/0/0/0/3. File does not exist.
warning: Skipping file /home/ubuntu/local/SQ00015140__2016-06-11T14_43_11-Measurement1.ome.zarr/G/22/0/0/0/1. File does not exist.
warning: Skipping file /home/ubuntu/local/SQ00015140__2016-06-11T14_43_11-Measurement1.ome.zarr/G/22/0/0/0/0. File does not exist.
warning: Skipping file /home/ubuntu/local/SQ00015140__2016-06-11T14_43_11-Measurement1.ome.zarr/G/22/0/0/0/2. File does not exist.
warning: Skipping file /home/ubuntu/local/SQ00015140__2016-06-11T14_43_11-Measurement1.ome.zarr/G/22/0/0/0/4. File does not exist.
warning: Skipping file /home/ubuntu/local/SQ00015140__2016-06-11T14_43_11-Measurement1.ome.zarr/G/23/0/0/0/1. File does not exist.
warning: Skipping file /home/ubuntu/local/SQ00015140__2016-06-11T14_43_11-Measurement1.ome.zarr/G/24/5. File does not exist.
warning: Skipping file /home/ubuntu/local/SQ00015140__2016-06-11T14_43_11-Measurement1.ome.zarr/G/24/3. File does not exist.
warning: Skipping file /home/ubuntu/local/SQ00015140__2016-06-11T14_43_11-Measurement1.ome.zarr/G/24/8. File does not exist.
warning: Skipping file /home/ubuntu/local/SQ00015140__2016-06-11T14_43_11-Measurement1.ome.zarr/G/24/7. File does not exist.
warning: Skipping file /home/ubuntu/local/SQ00015140__2016-06-11T14_43_11-Measurement1.ome.zarr/G/24/6. File does not exist.
warning: Skipping file /home/ubuntu/local/SQ00015140__2016-06-11T14_43_11-Measurement1.ome.zarr/G/24/4. File does not exist.
warning: Skipping file /home/ubuntu/local/SQ00015140__2016-06-11T14_43_11-Measurement1.ome.zarr/G/24/0/0/0/0. File does not exist.
warning: Skipping file /home/ubuntu/local/SQ00015140__2016-06-11T14_43_11-Measurement1.ome.zarr/G/24/2/0/0/1/0/2/2. File does not exist.
warning: Skipping file /home/ubuntu/local/SQ00015140__2016-06-11T14_43_11-Measurement1.ome.zarr/G/24/2/0/0/2/0/2/2. File does not exist.
warning: Skipping file /home/ubuntu/local/SQ00015140__2016-06-11T14_43_11-Measurement1.ome.zarr/G/24/2/0/0/3/0/2/2. File does not exist.
warning: Skipping file /home/ubuntu/local/SQ00015140__2016-06-11T14_43_11-Measurement1.ome.zarr/G/24/2/0/0/4/0/2/2. File does not exist.
warning: Skipping file /home/ubuntu/local/SQ00015140__2016-06-11T14_43_11-Measurement1.ome.zarr/H/10/5. File does not exist.
warning: Skipping file /home/ubuntu/local/SQ00015140__2016-06-11T14_43_11-Measurement1.ome.zarr/H/10/3. File does not exist.
warning: Skipping file /home/ubuntu/local/SQ00015140__2016-06-11T14_43_11-Measurement1.ome.zarr/H/10/8. File does not exist.
warning: Skipping file /home/ubuntu/local/SQ00015140__2016-06-11T14_43_11-Measurement1.ome.zarr/H/10/7. File does not exist.
warning: Skipping file /home/ubuntu/local/SQ00015140__2016-06-11T14_43_11-Measurement1.ome.zarr/H/10/2. File does not exist.
warning: Skipping file /home/ubuntu/local/SQ00015140__2016-06-11T14_43_11-Measurement1.ome.zarr/H/10/6. File does not exist.
warning: Skipping file /home/ubuntu/local/SQ00015140__2016-06-11T14_43_11-Measurement1.ome.zarr/H/10/4. File does not exist.
warning: Skipping file /home/ubuntu/local/SQ00015140__2016-06-11T14_43_11-Measurement1.ome.zarr/H/11/2. File does not exist.
warning: Skipping file /home/ubuntu/local/SQ00015140__2016-06-11T14_43_11-Measurement1.ome.zarr/H/12/2. File does not exist.
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