This is a multiple regression analysis workflow designed to predict algal bloom risk in the Baltic Sea based on oceanographic and nutrient data. The workflow combines data preprocessing, statistical modeling, and spatial visualization to assess water quality at bathing sites.

These files go with a short transcriptomics (RNA-Seq) tutorial that I am preparing for an undergraduate level tutorial. The data analysis will be on a Galaxy server. I'll update the description with a link to the tutorial text when its ready. These data are a subset of those published by O’Connell R, Thon M et al. 2012. Lifestyle transitions in plant pathogenic Colletotrichum fungi defined by genome and transcriptome analyses. Nature Genetics. 44:1060–1065.

With NGS technologies, life sciences face a raw data deluge. Classical analysis processes of such data often begin with an assembly step, needing large amounts of computing resources, and potentially removing or modifying parts of the biological information contained in the data. Our approach proposes to directly focus on biological questions, by considering raw unassembled NGS data, through a suite of six command-line tools. Dedicated to whole genome assembly-free treatments, the Colibread tools suite uses optimized algorithms for various analyses of NGS datasets, such as variant calling or read set comparisons. Based on the use of de Bruijn graph and bloom filter, such analyses can be performed in few hours, using small amounts of memory. Applications on real data demonstrate the good accuracy of these tools compared to classical approaches. To facilitate data analysis and tools dissemination, we developed Galaxy tools and tool shed repositories. With the Colibread Galaxy tools suite, we give the possibility to a broad range of life scientists to analyze raw NGS data. More importantly, our approach allows to keep the maximum of biological information from data and use very low memory footprint.

Data from https://trace.ncbi.nlm.nih.gov/Traces/sra/sra.cgi?exp=SRX105188&cmd=search&m=downloads&s=seq and ftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCA/000/002/985/GCA_000002985.3_WBcel235

For Galaxy Training https://rna.usegalaxy.eu/workflows/run?id=a108b575b16e6cb9

This is a training dataset for use in Galaxy materials science tutorials. These files can be used to demonstrate the AIRSS (Ab-Initio Random Structure Searching) method for finding muon stopping sites, using the UEP (Unperturbed Electrostatic Potential) technique for the optimisation stage of that method.

The files included are:

Si.cell: structure file containing atom locations
Si.den_fmt: electron density data, generated with CASTEP
Si.castep: CASTEP log file for the electron density calculation
Si-muairss-uep.yaml: configuration file for the AIRSS / UEP workflow

This dataset provides the inputs needed for the Galaxy Pathway Analysis workflow training tutorial (https://galaxy-synbiocad.org).

This workflow asseses the performance of predicted pathways by computing 4 criteria (target product flux, thermodynamic feasibility, pathway length, and enzyme availability). A score inform the user about the best candidate pathways to produce a compound of interest. The generated output is a collection of scored and ranked heterologous pathways.

The content of the dataset is as follows:

• A set of pathways provided in the SBML format (Systems Biology Markup Language) to be ranked, modeling heterologous pathways such as those outputted by the RetroSynthesis workflow (https://galaxy-synbiocad.org).

• The GEM (Genome-scale metabolic models) which is a formalized representation of the metabolism of the host organism (the model is E. coli iML1515), provided in the SBML format.

Restart files for CLM-FATES version 2.0.1 for CLM-FATES EMERALD version 2.0.1.

CTSM_FATES-EMERALD_on_inputdata_version2.0.0_ALP1.tar_(restart_info):
- ALP1_refcase.datm.r.2300-01-01-00000.nc
- ALP1_refcase.datm.rs1.2300-01-01-00000.bin
- ALP1_refcase.cpl.r.2300-01-01-00000.nc
- ALP1_refcase.clm2.r.2300-01-01-00000.nc

This dataset is being used in the Galaxy Training tutorial on CLM-FATES.

This work has been done in in collaboration with Galaxy Europe and EOSC-Life:
- Within the 1st EOSC-Life Training Open Call, two out of four proposals have been awarded to the European Galaxy team to develop climate science e-learning material and mentoring and training opportunities for our communities.

CLM-FATES documentation can be found here.

A dataset of public corporate filings (such as annual reports, quarterly reports, and ad-hoc disclosures) for Galaxy Payroll Group Ltd (GLXG), provided by FinancialReports.eu.

A dataset of public corporate filings (such as annual reports, quarterly reports, and ad-hoc disclosures) for Galaxy Cosmos Mezz PLC (GCMEZZ), provided by FinancialReports.eu.

Europe Black Granite market size is USD 206742.66 million in 2024 and will expand at a compound annual growth rate (CAGR) of 1.0% from 2024 to 2031.

View Insolve europe ou c o galaxy global import data USA including customs records, shipments, HS codes, suppliers, buyer details & company profile at Seair Exim.

ERA5-Land monthly averaged data January 2019

Dataset has been retrieved on the Copernicus Climate data Store (https://cds.climate.copernicus.eu/#!/home) and is meant to be used for teaching purposes only. This dataset is used in the Galaxy training on "Visualize Climate data with Panoply in Galaxy".

See https://training.galaxyproject.org/ (topic: climate) for more information.

Product type: Monthly averaged reanalysis

Variable:

10m u-component of wind, 10m v-component of wind, 2m temperature, Leaf area index, high vegetation, Leaf area index, low vegetation, Snow cover, Snow depth

Year:

2019

Month:

January

Time:

00:00

Format:

NetCDF (experimental)

We provide measurements of the probability distribution function of specific black hole accretion rates within a sample of galaxies of a given stellar mass and redshift, (p(\log \lambda_{sBHAR} | M_*,z)). Measurements are provided for all galaxies, star-forming galaxies and quiescent galaxies. We also provide estimates of the AGN duty cycle, (f(\lambda_{sBHAR} >0.01)) i.e. the fraction of galaxies with an AGN above a given limit in specific accretion rate, based on the probability distribution functions. Full details are provided in Aird et al. (2018, MNRAS, 474, 1225); please cite this publication if you use these measurements.

View Insolv europe ou c o galaxy global import data USA including customs records, shipments, HS codes, suppliers, buyer details & company profile at Seair Exim.

Phylogenetic information inferred from the study of homologous genes helps us to understand the evolution of genes and gene families, including the identification of ancestral gene duplication events as well as regions under positive or purifying selection within lineages. Gene family and orthogroup characterisation enables the identification of syntenic blocks, which can then be visualised with various tools. Unfortunately, currently available tools display only an overview of syntenic regions as a whole, limited to the gene level, and none provide further details about structural changes within genes, such as the conservation of ancestral exon boundaries amongst multiple genomes. We present Aequatus, a standalone web-based tool that provides an in-depth view of gene structure across gene families, with various options to render and filter visualisations. It relies on pre-calculated alignment and gene feature information typically held in, but not limited to, the Ensembl Compara and Core databases. We also offer Aequatus.js, a reusable JavaScript module that fulfils the visualisation aspects of Aequatus, available within the Galaxy web platform as a visualisation plugin, which can be used to visualise gene trees generated by the GeneSeqToFamily workflow. Aequatus is an open-source tool freely available to download under the MIT license at https://github.com/TGAC/Aequatus A demo server is available at http://aequatus.earlham.ac.uk/ A publicly available instance of the GeneSeqToFamily workflow to generate gene tree information and visualise it using Aequatus is available on the Galaxy EU server at https://usegalaxy.eu

Spectroscopic and photometric data for galaxy COSMOS-11142, studied in Belli et al. (2024).

• The JWST/NIRSpec spectroscopy is stored as a FITS table which includes wavelength (in angstrom), calibrated flux, uncertainty, and best-fit model (in erg/(s cm2 A)).
• The JWST and HST photometry is stored as a FITS table which includes the name of each filter, the effective wavelength (in angstrom), the observed flux and its uncertainty (in microJy).

This repository contains datasets required for the online training "Data analysis and interpretation for clinical genomics" available at https://sigu-training.github.io/clinical_genomics/.

Tools used in the training are available at the European Galaxy instance running at https://usegalaxy.eu, which also includes a copy of this repository in the Shared Data Libraries. BAM files in this dataset are based on the hg38 reference genome.

This is part of a 4 dataset submission. Refer to this dataset for details.

Sample of 1732 galaxies that were used in Ebrová, Bílek, & Eliášek (2025): “Photometric stellar masses for galaxies in DESI Legacy Imaging Surveys” to derive formula for computing stellar masses from Legacy Surveys data.

The sample was processed by photomass_ls.py

Contains:

• stellar mass and basic galaxy parameters from the Spitzer Survey of Stellar Structure in Galaxies (S4G)
• Galactic extinction
• parameters of the GALFIT fit by a 2-D single-component Sersic profile
• stellar mass computed by Eq(1) of Ebrová, Bílek, & Eliášek (2025): 0.673M_g - 1.108M_r + 0.996

Acknowledgement: Funded by the European Union under the Marie Skłodowska-Curie grant agreement No. 101067618 (GalaxyMergers).
Disclaimer: Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Research Executive Agency (REA). Neither the European Union nor REA can be held responsible for them.

Discover the booming European smart watch market! Explore a €6.11 billion market projected to grow at a 10.84% CAGR through 2033, driven by fitness tracking, health monitoring, and innovative features. Learn about key players, segments, and market trends in this in-depth analysis. Recent developments include: October 2023: Samsung continued its lineup of successful smartwatches during its Unpacked event and revealed the Galaxy Watch 6 Classic and Watch 6, much earlier than their predecessors. Watch 6 40mm/44mm specs: 1.3"/1.5" Sapphire Crystal displays, 1.4 GHz Exynos W930, 2GB/16GB memory, 300mAh/425mAh battery, Watch 6 water resistance, 5ATM + IP68. The Galaxy Watch 6 takes over the smart timepiece duties from last year's Galaxy Watch 5 but comes with a 20% bigger screen in a package of similar size, meaning a much slimmer bezel as well. In fact, the bezel of the Watch 6 has been slimmed down by 30% for a more elegant look., September 2023: Apple introduced the Apple Watch Series 9, bringing new features to the world’s best-selling watch and performing a decisive environmental milestone. Apple Watch Series 9 is more advanced than ever with the new S9 SiP, which improves performance and abilities; a magical new double tap gesture; a brighter display; faster on-device Siri, Precision Finding for iPhone; now with the ability to access and log health data, and better. Apple Watch Series 9 runs watchOS 10, which supplies redesigned apps, the new Smart Stack, new watch faces, new hiking and cycling features, and tools to support mental health.. Key drivers for this market are: The Aging Population in European Countries with Increased Risk of Chronic Conditions, Growing Adoption of Connected Wearables in European Countries. Potential restraints include: The Aging Population in European Countries with Increased Risk of Chronic Conditions, Growing Adoption of Connected Wearables in European Countries. Notable trends are: Growing Adoption of Connected Wearables in European Countries is Expected to Drive the Studied Market.

This is a training dataset for use in Galaxy materials science tutorials. These files can be compared to the output of simulations by MuSpinSim for dissipation of muon spins.

The files included are:

dissipation_theory.dat: theoretical values formatted as a MuSpinSim output

experiment.dat: mock experimental values formatted as a MuSpinSim output