This dataset is step number 1 which is to check to make sure that there are correct coding sheets and that the right ontologies have been mapped to the columns in the dataset.

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 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 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 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 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.

“Integration of methylation and expression using MultiDataSet”. This file is a tutorial of how to develop a function to implement a new method using MultiDataSet capabilities. (HTML 78 kb)

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

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.

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

Quick Step QGIS MaxEnt Tutorial

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
• snv.mat.rds, denoised.mat.rds: raw and denoised SNV matrix

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

BackgroundTracer studies evaluate the effectiveness of university training by assessing how graduates perform in the job market. This study focused on Bachelor of Science in Nursing (BScN) graduates, aiming to describe their training experiences, application of acquired competencies, and overall stakeholder perceptions to inform BScN curriculum improvement.MethodsA convergent parallel mixed-method design was used to collect quantitative and qualitative data concurrently from 2016–2020 BScN graduates (February-May 2023). Graduates and other stakeholders (including educators, employers, and policymakers) in this study were selected from private, public, and faith-based hospitals and universities, colleges, and the Ministry of Health. Quantitative data were gathered via online structured questionnaires adapted and modified from the American International Health Alliance and the Technical Vocational Education and Training tools. Qualitative data were collected through interviews and focus groups with graduates, employers, educators, and policymakers. Quantitative data were analyzed using descriptive statistics, while qualitative data underwent thematic analysis. Integration occurred during interpretation to provide a comprehensive understanding of graduates’ experiences, competency application, and stakeholder perceptions of the BScN program.ResultsAmong the 61 graduates who completed the online survey, 37 (60.7%) were female. Most (48; 78.7%) worked as nurses, while 6 (9.8%) were tutors and 3 (4.9%) worked as tutorial assistants. Demonstration was rated the most useful teaching and learning method by 52 (85.2%) respondents, and 47 (81%) rated practical exams as a useful assessment method. These were also supported by graduates and stakeholders who shared their perspectives with regard to the benefits and impact of the BScN program and training quality. Additionally, 54 graduates (94.7%) found the program very useful in preparing them for their professional roles, which aligned with their views on the connection between acquired competencies and job performance. Both graduates and educators highlighted challenges encountered during training and in professional practice. Policymakers and graduates also offered recommendations for improving the program.Additionally, 54 graduates (94.7%) found the program very useful in preparing them for their professional roles, which aligned with their views on the connection between acquired competencies and job performance. Both graduates and educators highlighted challenges encountered during training and in professional practice. Policymakers and graduates also offered recommendations for improving the program.ConclusionThe findings demonstrate that the BScN program is widely regarded by graduates and stakeholders as effective in preparing students for professional practice, particularly through practical teaching methods such as demonstrations and practical exams. While the program’s impact on competency development and job performance was strongly affirmed, the study also revealed notable challenges during training and practice. These insights support the ongoing review and enhancement of the BScN curriculum.

Sociodemographic characteristics of participants involved in qualitative interviews (N = 37).

Distribution of graduates, response, and response rate.