Sample .mcool file could not be identified by cooler

In the [Tutorial Page](https://docs.higlass.io/tutorial.html), Sample data is recommend as
```
wget https://pkerp.s3.amazonaws.com/public/dixon2012-h1hesc-hindiii-allreps-filtered.1000kb.multires.cool -O /tmp/sample.mcool
```
However, the cool file you provided cannot be identified using cooler(version 0.9.3):
```
cooler info ./Higlass/dixon2012-h1hesc-hindiii-allreps-filtered.mcool
# KeyError: 'No cooler found at: ./Higlass/dixon2012-h1hesc-hindiii-allreps-filtered.mcool
```
I found this problem because my own mcool file doesn't recognize properly when I'm using higlass-python, it occurred:
```
[envs/higlass-python/lib/python3.13/site-packages/clodius/tiles/cooler.py:498](http://10.1.176.244:8890/envs/higlass-python/lib/python3.13/site-packages/clodius/tiles/cooler.py#line=497): RuntimeWarning: overflow encountered in scalar add
genome_length = int(sum(f["resolutions"][resolution]["chroms"]["length"]))
```
however, it works fine with the sample files you provided, which means that our higlass seems to be working properly, and the discrepancy appears in the mcool format file. The mcool files I used can be recognized by cooler as follows:
```
cooler info GSM4604282_546.iced.mcool::/resolutions/10000
{
"bin-size": 10000,
"bin-type": "fixed",
"creation-date": "2019-10-09T09:10:47.186556",
"format": "HDF5::Cooler",
"format-url": "https://github.com/mirnylab/cooler",
"format-version": 3,
"generated-by": "cooler-0.8.6.post0",
"genome-assembly": "hg38",
"nbins": 303114,
"nchroms": 23,
"nnz": 62252354,
"storage-mode": "symmetric-upper",
"sum": 107292104
}
```

It seems that higlass-python has been neglected for some time, and there is a lot of confusion regarding file versions. This includes issues such as compatibility with the most suitable version of Jupyter, the fact that the latest version no longer uses nbextension, and so on. It appears that your documentation is outdated.

Despite these issues, I would like to express gratitude for your dedications, which have made higlass a great tool.

Start with the Tutorial Page at docs.higlass.io/tutorial.html and verify the linked sample download using cooler info. Review the higlass-python and Jupyter integration guidance mentioned in the issue, then update the documentation so the sample format and relevant compatibility expectations are accurate and testable.

Written by the indexing model from the issue text.