Additional file 2: Supplementary Table 1. 39 R tools for multi-omics data integration. Supplementary Table 2. Gene annotation of 'no-denoised' and 'denoised' candidate genes. Annotation information have been retrieved from PlantGenIE (https://plantgenie.org/) with Populus trichocarpa v3.1 as a reference. The column ‘common_before_and_after_denoising’ indicates whether the gene is shared between 'denoised' and 'no-denoised' data or not (TRUE/FALSE).

This data is used for the Seurat version of the batch correction and integration tutorial on the Galaxy Training Network. The input data was provided by Seurat in the 'Integrative Analysis in Seurat v5' tutorial. The input dataset provided here has been filtered to include only cells for which nFeature_RNA > 1000. The other datasets were produced on Galaxy. The original dataset was published as: Ding, J., Adiconis, X., Simmons, S.K. et al. Systematic comparison of single-cell and single-nucleus RNA-sequencing methods. Nat Biotechnol 38, 737–746 (2020). https://doi.org/10.1038/s41587-020-0465-8.

This dataset is step number 2 which is to bookmark your final dataset that will be used for integration and to click on "Workspace" in the tDAR tool bar. All numbered tutorial datasets will work for integration purposes. See notes for information on which variables are display and which ones are integration variables. Each number represents the steps of the integration process (corresponding to the steps in the tutorial).

This dataset contains the data processed to demonstrate the multi-source spatial data integration methodology proposed in the paper "Multisource spatial data integration for use cases applications".

It contains:

- the building footprint extracted from the IFC model of a newly designed building in WKT format, by using the GeoBIM_Tool (https://github.com/twut/GEOBIM_Tool);

- the extrusion of the footprint until the measured height measured with the same GeoBIM_Tool;

- a portion of the Rotterdam 3D city model generated with 3dfier and available at https://3d.bk.tudelft.nl/opendata/3dfier/, converted in CityJSON with the citygml-tools (https://www.cityjson.org/tutorials/conversion/), developed to convert data between CityGML and CityJSON.

Data and pre-trained MEFISTO model to run the vignettes and tutorials provided at https://biofam.github.io/MOFA2/MEFISTO.html.* Evodevo application: Input data is provided as evodevo.csv and evodevo.RData, the trained MEFISTO model is provided in evodevo_model.hdf5 * Longitudinal microbiome application: Input data is provided as microbiome_data.csv and microbiome_features_metadata.csv, the trained MEFISTO model is provided in microbiome_model.hdf5 * single cell multi-omics application: Input data is provided as scnmt_data.txt.gz and scnmt_sample_metadata.txt the trained MEFISTO model is provided in scnmt_mefisto_model.rds * spatial transcriptomics application: Input data is downloaded as described in the tutorial, the trained MEFISTO model is provided in ST_model.hdf5

Processed PBMC data for integration tutorial in https://github.com/rpmccordlab/SMILE.

Hydrological and meteorological information can help inform the conditions and risk factors related to the environment and their inhabitants. Due to the limitations of observation sampling, gridded data sets provide the modeled information for areas where data collection are infeasible using observations collected and known process relations. Although available, data users are faced with barriers to use, challenges like how to access, acquire, then analyze data for small watershed areas, when these datasets were produced for large, continental scale processes. In this tutorial, we introduce Observatory for Gridded Hydrometeorology (OGH) to resolve such hurdles in a use-case that incorporates NetCDF gridded data sets processes developed to interpret the findings and apply secondary modeling frameworks (landlab).

LEARNING OBJECTIVES - Familiarize with data management, metadata management, and analyses with gridded data - Inspecting and problem solving with Python libraries - Explore data architecture and processes - Learn about OGH Python Library - Discuss conceptual data engineering and science operations

Use-case operations: 1. Prepare computing environment 2. Get list of grid cells 3. NetCDF retrieval and clipping to a spatial extent 4. Extract NetCDF metadata and convert NetCDFs to 1D ASCII time-series files 5. Visualize the average monthly total precipitations 6. Apply summary values as modeling inputs 7. Visualize modeling outputs 8. Save results in a new HydroShare resource

For inquiries, issues, or contribute to the developments, please refer to https://github.com/freshwater-initiative/Observatory

`bcr_phylo_tutorial.zip` is used in the Reconstruction and analysis of B-cell lineage trees from single cell data using Immcantation tutorial.

`immcantation-BCR-Seurat-tutorial.zip` is used in the Integration of BCR and GEX data tutorial.

Have you ever wanted to create your own maps, or integrate and visualize spatial datasets to examine changes in trends between locations and over time? Follow along with these training tutorials on QGIS, an open source geographic information system (GIS) and learn key concepts, procedures and skills for performing common GIS tasks – such as creating maps, as well as joining, overlaying and visualizing spatial datasets. These tutorials are geared towards new GIS users. We’ll start with foundational concepts, and build towards more advanced topics throughout – demonstrating how with a few relatively easy steps you can get quite a lot out of GIS. You can then extend these skills to datasets of thematic relevance to you in addressing tasks faced in your day-to-day work.

Quick Step QGIS MaxEnt Tutorial

Both desktop and web-based solutions are included.This table comprises a list of potential software solutions for typical genomic data analysis tasks in molecular ecology (e.g. alignment, phylogenetics, data exploration, etc.).

In order to digitally plan a cable line using the QGIS plugin ‘Seilaplan’, maps with various background information are helpful. In this tutorial we show you how to obtain maps that are helpful for cable line planning, for example a national map of Switzerland at different scales, the NFI vegetation height model or the NFI forest mix rate. For this we explain what WMS datasets are and how to integrate them into QGIS. No download of large data is needed for this, only a good internet connection. Please note that the tutorial language is German! Link for the integration of WMS data: https://wms.geo.admin.ch/ Link to the description on the Swisstopo website: https://www.geo.admin.ch/en/geo-services/geo-services/portrayal-services-web-mapping/web-map-services-wms.html Link to the Seilaplan website: https://seilaplan.wsl.ch

Für die Verwendung des QGIS Plugins Seilaplan zur digitalen Seillinienplanung sind verschiedene Hintergrundkarten hilfreich. In diesem Tutorialvideo zeigen wir, was WMS Daten sind und wie man diese in QGIS einbinden kann. Dafür müssen die Daten nicht heruntergeladen werden. Es braucht lediglich eine gute Internetverbindung. Für die Seillinienplanung hilfreiche Karten sind bspw. die Landeskarte der Schweiz in verschiedenen Massstäben, das Vegetationshöhenmodell LFI oder der Waldmischungsgrad LFI. Link zur Einbindung der WMS Daten: https://wms.geo.admin.ch/ Link zur Beschreibung auf der Swisstopo Webseite: https://www.geo.admin.ch/de/geo-dienstleistungen/geodienste/darstellungsdienste-webmapping-webgis-anwendungen/web-map-services-wms.html Link zur Seilaplan-Website: https://seilaplan.wsl.ch

GWAS summary statistics imputation, integration with PrediXcan MASHR-M The file sample_data.tar contains all necessary files to perform imputation of GWAS summary statistics to the GTEx v8 QTL data set. It includes 1000 Genomes individuals' genotypes as reference panel. The .tar archive, upon uncompression, contains the following: data/ ├── eur_ld.bed.gz ├── gtex_v8_eur_filtered_maf0.01_monoallelic_variants.txt.gz ├── coordinate_map ├── gwas ├── liftover ├── models │ ├── eqtl │ │ └── mashr │ └── sqtl │ └── mashr └── reference_panel_1000G data/eur_ld.bed.gz contains definitions of approximately independent LD-regions in hg38 (Berisa-Pickrell regions, lifted over) data/gtex_v8_eur_filtered_maf0.01_monoallelic_variants.txt.gz is a snp annotation file, listing all GTEx v8 variants with MAF>0.01 in europeans. data/coordinate_map contains precomputed mapping tables that MetaXcan tools can use to convert GWAS' genomic coordinates in GWAS between genome assemblies. data/gwas contains a sample GWAS file for the purposes of a tutorial (data obtained from Nikpay et al (Nat Gen 2016) https://www.ncbi.nlm.nih.gov/pubmed/26343387 data/liftover contains Liftover chains to map coordinates between human genome assemblies (used by full harmonization tools) data/models contains PrediXcan MASHR-M models, and cross-tissue S-MultiXcan LD compilation, from eQTL and sQTL. data/reference_panel_1000G contains 1000G hg38 genotypes, in parquet format, to be used by imputation tools.

Data used for tutorial.

• fragments.tsv.gz(.tbi), singlecell.csv, filtered_peak_bc_matrix.h5: scATAC-seq pre-processing and cell annotation
• peak.mat.rds: corrected chromatin accessibility profile
• cancer.cnv.csv: copy number profile of cancer cells
• .assignment.txt, .clones.txt: cluster assignment, and genotype of each cluster by the CBM method (https://github.com/zhyu-lab/cbm)
• snv.mat.rds, denoised.mat.rds: raw and denoised SNV matrix

Abstract:

Background: Performance bugs can lead to severe issues regarding computation efficiency, power consumption, and user experience. Locating these bugs is a difficult task because developers have to judge for every costly operation whether runtime is consumed necessarily or unnecessarily. Objective: We wanted to investigate how developers, when locating performance bugs, navigate through the code, understand the program, and communicate the detected issues.

Method: We performed a qualitative user study observing twelve developers trying to fix documented performance bugs in two open source projects. The developers worked with a profiling and analysis tool that visually depicts runtime information in a list representation and embedded into the source code view.

Results: We identified typical navigation strategies developers used for pinpointing the bug, for instance, following method calls based on runtime consumption. The integration of visualization and code helped developers to understand the bug. Sketches visualizing data structures and algorithms turned out to be valuable for externalizing and communicating the comprehension process for complex bugs.

Conclusion: Fixing a performance bug is a code comprehension and navigation problem. Flexible navigation features based on executed methods and a close integration of source code and performance information support the process.

Dataset:

Tutorial: We provide the slides (PDF) and the video (MP4) we used in the tutorial phase of our study.

Locating Bugs: We also provide supplementary material for each research question. We provide the advices we prepared for each bug in case a team got stuck (PDF); the questions we asked after each bug fixing session can be found on the introduction slides (PDF).

RQ1: Navigating and Understanding

RQ1.1: How was information from the profiling tool or other parts of the IDE used to locate the performance bug? Cross-case analysis (in German) (XLSX+ODS)

RQ1.2: Is the in-situ visualization of the profiling data beneficial compared to a traditional list representation? Cross-case analysis (in German) (XLSX+ODS)

RQ1.3: What navigation strategies do developers pursue to locate a specific performance bug? Interaction logs (TXT), Navigation visualizations (SVG), Screen recordings for Bug 3 (MP4, without audio because of confidentiality)

RQ2: Understanding and Communicating

RQ2.1: How do developers communicate with each other when locating a performance bug? Coding (XLSX+ODS), Sketches (PDF), Screen recordings for Bug 3 (MP4, without audio because of confidentiality)

RQ2.2: Could sketches help to understand and communicate a performance bug? Coding (XLSX+ODS), Sketches (PDF), Cross-case analysis (in German) (XLSX+ODS), Sketching videos for Bug 3 (MP4, without audio because of confidentiality)

Questionnaire: The questionnaire that the participants filled out at the end of the study can be found here (PDF).

Breast cancer cells migrating collectively.

This dataset is used in a tutorial on using TrackMate and its cellpose integration to track such cells.

See here for details: https://imagej.net/plugins/trackmate/trackmate-cellpose