jkh1

Most of the time in bio-related fields, we need high-throughput computing not high-performance computing.

cell/molecular biology

In my field, trying to reproduce results or conclusions from papers happens on a regular basis especially when the outcome matters for projects in the lab. However, whatever the outcome, it can't be published because either it confirms the previous results and so isn't new or it doesn't and no journal wants to publish negative results. The reproducibility attempts are generally discussed at conferences in the corridors between sessions or at the bar in the evening. This is part of how a scientific consensus is formed in a community.

Running locally is sometimes necessary, e.g. you don't want to send sensitive data to any random third party server.

The problem is that ISO standards are not open.

What seems to help in the life sciences is the existence of public repositories. These could be replaced by portals that collect info on data hosted elsewhere. But the main advantages are that they provide clear, well known places to start looking and they curate, standardize and organise the metada to make it searchable.

In the life sciences there are dedicated structured repositories. These are searchable by keywords and often crossreference each other. They are the goto places for finding data.

In biology we now routinely produce datasets in the multiple terrabytes range. It can easily be n = 3 x 10 TB such as for example imaging 3 fly embryos by light sheet microscopy.

Can I put my 25 TB microscopy image data set on GitHub? Will they host it for free indefinitely? In the life sciences, there are dedicated public repositories (databases) where the data is hosted (free to the researcher), catalogued, standardized and curated to some extent. These repositories are searchable and often crossreference each other. So you can find a data set even if you didn't know about it before. Putting data all over the internet in dumps like Zenodo, Dryad and the like is just not very useful. Advertizing your work is probably good for your career but this is not what makes your data and work useful to others. It's how easy you make it for others to understand, access and combine your data with their own data. This means providing data and metadata using open community standards (there are already a bunch of these in life sciences even if there are gaps in coverage).