Comments (3)
Thanks for reaching out!
That is correct: at the moment, the Croissant spec does not support TDMS files: one cannot refer to TDMS concepts (properties, channel groups and channels) to define Croissant FileSet
s or RecordSets
.
Support for TDMS files could be added in a next version of the Croissant format spec.
In the meantime, we suggest to create a Croissant dataset that specifies the dataset level information and the resources, while omitting RecordSets that would need data coming from TDMS files. I would use mime type application/x-tdms
for the encodingFormat
of your corresponding FileObject
definitions. For example:
{
"name": "my-tdms-dataset",
"license": "...",
"description": "...",
"distribution": [
{
"@type": "cr:FileObject",
"@id": "data",
"name": "data",
"contentUrl": "data/my_tdms_file.tdms",
"encodingFormat": "application/x-tdms",
"sha256": "..."
},
...
]
}
This would allow tools that can work with only such metadata to already support your dataset (eg: index dataset, download raw data), while providing a signal for the Croissant contributors on the importance of supporting TDMS format, as well as an example of dataset using TDMS to test implementations when TDMS support is added to the spec and various tools implementations.
Please let us know if there are problems with defining such an incomplete croissant definition and we will look into this.
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Thank you very much @pierrot0 . If I'm understanding it correctly, I need to manually input these meta data in the JSON file, which is to be noted as the Croissant metadata file, right? So far, I found the easiest way to obtain a Croissant metadata file is to upoload my dataset onto Hugging Face's repository and use the Croissant Editor hosted by Hugging Face to produce the metadata. So, actually anyone can just manually input these information and save it as a JSON file, which will function as a Croissant metadata?
I know I might have asked too many questions. Many thanks in advacne :)
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The procedure you are describing might work indeed.
You don't need to manually input the meta data in the JSON file though, you can, but don't have to. As you describe it yourself, you might be able to use the croissant editor hosted on HuggingFace to create/edit the Croissant config.
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Related Issues (20)
- Joins as described by Croissant Format Specification are not supported by mlcroissant python library.
- Add containedIn fileProperty to the Extract class
- [NeurIPS] How do i create a Croissant File using this library HOT 2
- [NeurIPS] Fileld has no source HOT 8
- Feature Request: support for CoNLL format HOT 2
- [NeurIPS] How do i create a Croissant File using this library for my dataset? HOT 1
- [NeurIPS] Compatibility with HuggingFace Image dataset HOT 1
- [NeurIPS] Data type extension (Video and multichannel time series ) HOT 3
- [NeurIPS] URL to dataset metedata
- Example for audio dataset HOT 2
- [NeurIPS] How to "ingest" multiple datasets made of .xz files as data/samples and space-separated .txt files as ground truth
- Please improve documentation
- Intro example not working HOT 3
- [Neurips]: any support for large h5 files? tried various encodings but no luck. HOT 1
- JSON LD parsing fails when multiple strings given to FileSet.includes HOT 1
- [NeurIPS] Join fails with reference if read from fileObject HOT 2
- Feature request: loading a record set as a pandas dataframe HOT 3
- Expose the underlying pd.DataFrames instead of an iterator
- Feature request: Add a mapping from FieldId to RecordSetId to Metadata.
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