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A CWL pipeline for processing spatial transcriptomics data. The latest stable version of this pipeline is available on Github.

This toolbox implements various tools for working with simple, non-marked, one dimensional point processes. The highlight of this toolbox is the new goodness-of-fit test developed for parametric models.

A CWL pipeline for processing spatial transcriptomics data. The latest stable version of this pipeline is available on Github.

Python-based tool chain ("Pyafscgap.org") for working with the public bottom trawl surveys data from the NOAA AFSC GAP. This provides information about where certain species were seen and when under what conditions, information useful for research in ocean health. See webpage, project Github, and example notebook.

Python-based tool chain ("Pyafscgap.org") for working with the public bottom trawl surveys data from the NOAA AFSC GAP. This provides information about where certain species were seen and when under what conditions, information useful for research in ocean health. See webpage, project Github, and example notebook.

Python-based tool chain ("Pyafscgap.org") for working with the public bottom trawl surveys data from the NOAA AFSC GAP. This provides information about where certain species were seen and when under what conditions, information useful for research in ocean health. See webpage, project Github, and example notebook.

{"references": ["Hassell et al., (2020). cfdm: A Python reference implementation of the CF data model. Journal of Open Source Software, 5(54), 2717, https://doi.org/10.21105/joss.02717"]} A Python reference implementation of the CF data model.

Repository to automatically process Fluorescence In Situ Hybridization (FISH) images.

There are several research infrastructures or other data services running in Europe that cover a multitude of marine-related sciences, providing specific datasets coming from observations collected with different methods. These infrastructures constitute a diverse world, each looking at a piece of the big picture, sometimes hindering collaboration and data sharing. Blue-Cloud aims to overcome fragmentation and build a bridge between thematic science clusters - such as marine, climate, food and agriculture sciences - and EOSC, creating a data federation and providing a common access to a so-called thematic EOSC for marine data. By connecting leading marine data management infrastructures with horizontal e-infrastructures, the project aims to maximise the exploitation of data resources available from different sources. The Blue-Cloud framework consists of two major technical components: (1) a Blue-Cloud Data Discovery and Access service, already presented in a previous EOSC in practice story, to serve federated discovery and access to blue data infrastructures, and (2) a Blue-Cloud Virtual Research Environment (VRE) to provide computing platforms and analytical services facilitating the collaboration between researchers, which is detailed hereafter. The Blue-Cloud VRE is powered by the D4Science Infrastructure. [M. Assante et al. (2019) Enacting open science by D4Science. Future Gener. Comput. Syst. 101: 555-563 10.1016/j.future.2019.05.063 ] The full list of EOSC in practice stories is available here