Ocean Sampling Day (OSD) is a simultaneous sampling campaign of the world’s oceans, which took place for the first time in 2014 (via the MicroB3 project) and again in 2018 (via the ASSEMBLE Plus project). The OSD samples, which are related in time, space, and collected environmental parameters, provide insights into microbial diversity and function, enriching our understanding of marine biodiversity and contributing to the blue economy through the identification of ocean-derived biotechnologies. We see OSD data as a reference data set for generations of experiments. The data for OSD2014 were published via PANGAEA. The sampling and event metadata, collected environmental values, documentation (including SOPs) and the ENA accession numbers for the raw sequences (18S, 16S, shotgun metagenomics) can be found there. There is also a (no-longer maintained) Github repository for Ocean Sampling Day 2014 and 2015 https://github.com/MicroB3-IS/osd-analysis. This includes Overview and documentation for the sequencing and analysis done (2014, 2015) Links to raw and (partially-)processed sequence data (2014, 2015) Links to metagenomic analysis results (2014) Links to the environmental data (2014) Scripts used to produce some of these datasets (2014, 2015) (A page on ODS2016 is also included in the Github repository, however very little was done and no data are provided.) The activities and data of OSD 2018 to OSD2021 were covered by ASSEMBLE Plus. The sampling, event, environmental, and ENA accession numbers, plus documentation, are published in the IMIS datasets catalogue for OSD2018 and OSD2019. The 2020-21 OSD events were interrupted by Covid-19, but these data will eventually also be published in IMIS. Additionally, one can find the same data, but in a wider range of formats, in the (new) OSD GitHub site (available here for the standard Github site https://github.com/ocean-sampling-day, and here for a webview of the site https://data.ocean-sampling-day.org/). Developed by: OSD 2014/5 were coordinated by the MicroB3 project, as was the MyOSD2016 public participation event. The OSD2018/19 events were coordinated by HCMR in Greece and run under ASSEMBLE Plus. Technology or platform: The descriptions, code, and links to data are provided on the above-mentioned GitHub sites.

IMNGS is a platform that uniformly and systematically screens for, retrieves, processes, and analyses all available prokaryotic 16S rRNA gene amplicon datasets from public repositories and uses them to build sample-specific sequence databases and OTU-based profiles. The retrieved information can be used to address questions of relevance in microbial ecology, for example with respect to the occurrence of specific microorganisms in different ecosystems or to perform targeted diversity studies. IMNGS also offers a complete pipeline for de novo analysis of a user's own raw amplicon sequencing data generated using the Illumina technology. Due to the personalised nature of many functions and the high computational cost related to the performed tasks, only registered users can submit jobs (registration can be done via the IMNGS home page; regsiter there as a Guest to learn more about what it can do). The IMNGS web interface can then be used to: search and select datasets/samples (runs), create and launch jobs, and inspect the outputs of the jobs. Developed by: Ilias Lagkouvardos, Technical university of Munich (TUM) Used data resources: SRA repository amplicon studies, user query 16S sequence, user raw amplicon sequences Used in tools: USEARCH Technology or platform: Django, Python, PostgreSQL

Rhea: R scripts for the analysis of microbial profiles The importance of 16S rRNA gene amplicon profiles for understanding the influence of microbes in a variety of environments coupled with the steep reduction in sequencing costs led to a surge of microbial sequencing projects. Among available pipeline options for high-throughput 16S rRNA gene analysis, the R programming language and software environment for statistical computing stands out for its power and increased flexibility, and the possibility to adhere to most recent best practices and to adjust to individual project needs. The Rhea pipeline is a set of R scripts that encode a series of well-documented choices for the downstream analysis of Operational Taxonomic Units (OTUs) tables, including normalisation steps, alpha- and beta-diversity analysis, taxonomic composition, statistical comparisons, and calculation of correlations. Rhea is primarily a straightforward starting point for beginners, but can also be used as a framework for advanced users who can modify and expand the tool. Rhea is composed of 6 steps that can be run independently or as a set. Normalization Alpha-Diversity Beta-Diversity Taxonomic-Binning Serial-Group-Comparisons Correlations