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650 results for “Workflow”
PROMs Comparing Digital & Conventional Workflows
ClinicalTrials.gov study NCT04986761. IPD Sharing: NO. Countries: 1. Publications: 1.
Data from: Expanding the described metabolome of the marine cyanobacterium Moorea producens JHB through orthogonal natural products workflows
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iValiD-TB: A fully characterized Mycobacterium tuberculosis dataset for antimicrobial resistance bioinformatics workflow validations
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Data from: Specimens at the center: an informatics workflow and toolkit for specimen-level analysis of public DNA database data
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Development of a cost-effective, multifunctional SNP panel and analysis workflow for wolf monitoring in Finland
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Data from: A data-driven geospatial workflow to map species distributions for conservation assessments
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Data from: A from-benchtop-to-desktop workflow for validating HTS data and for taxonomic identification in diet metabarcoding studies
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Are non-animal systemic safety assessments protective? A toolbox and workflow
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Data from: From population genomics to conservation and management: a workflow for targeted analysis of markers identified using genome-wide approaches in Atlantic salmon Salmo salar
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Workflow of the system for CRISPR Outcome and Risk Evaluation (SCORE)
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An ArcGIS Pro workflow to extract vegetation indices from aerial imagery of small‐plot turfgrass research
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Overview of XCT data processing workflow for ammonium nitrate prills quantitative analysis
<p>This video presents the data processing workflow that was developped to perform the quantitative structureal and morphological analysis of ammonium nitrate prills by X-ray computed tomography.. </p>
Test data for running snakePipes : DNA-mapping workflow
<p><strong>Test files for running snakePipes workflows</strong></p> <p><strong>snakePipes</strong> are pipelines built using snakemake and python for the analysis of epigenomic datasets. Please refer to <a href="https://snakepipes.readthedocs.io/en/latest/">this link</a> for further information on snakePipes.</p> <p>This folder contains test files that can be used to run DNA-mapping workflow under snakePipes. To test the workflow, follow the following steps : </p> <ul> <li>Download or prepare genome fasta, indices and annotations for human (<strong>hg38</strong>) genome.</li> <li>Download and install snakePipes via `conda create -n snakePipes -c mpi-ie -c bioconda -c conda-forge snakePipes`</li> <li>Update <a href="https://snakepipes.readthedocs.io/en/latest/content/running_snakePipes.html#genome-configuration-file">Genome configuration file</a> with path to indices and annotations.</li> <li>Move to this repository and run the example <strong>command.sh</strong></li> </ul>
MePPi: A complete and flexible workflow for metaproteomics data analyses
<p>Data for an examplary metaproteomics data analysis with the <a href="https://github.com/compomics/meta-proteome-analyzer">MetaProteomeAnalyzer</a> (MPA) and <a href="https://gitlab.com/s.fuchs/prophane/">Prophane</a> software tools. Data is from the PRIDE dataset <a href="https://www.ebi.ac.uk/pride/archive/projects/PXD010550/">PXD010550</a>.</p> <p>Files include:</p> <ul> <li>protein databases (FASTA) : <ol> <li>UniProt Swiss-Prot: <a href="https://zenodo.org/record/3727600/files/UniprotSwP-2020_03.fasta">UniprotSwP-2020_03.fasta</a></li> <li>Metagenome (+ Swiss-Prot): <a href="https://zenodo.org/record/3727600/files/MG_BG__UPSP-sp_2020_03.fasta">MG_BG__UPSP.fasta</a></li> </ol> </li> <li>MS Datasets (MGF): <ol> <li>FASP digest: <a href="https://zenodo.org/record/3727600/files/FASP_BGP_A.mgf">FASP_BGP_A.mgf</a></li> <li>In-gel digest: <a href="https://zenodo.org/record/3727600/files/InGel_BGP_A.mgf">InGel_BGP_A.mgf</a></li> </ol> </li> <li>Example results for a single experiment analysis (Sample A, based on: MS data: FASP digest, FASTA: UniProt Swiss-Prot): <ul> <li>MPA results: <a href="https://zenodo.org/record/3727600/files/mpa_result-sample_a-fdr_0.05-single_exp.csv">mpa_result-sample_a-fdr_0.05-single_exp.csv</a></li> <li>Prophane results: <a href="https://zenodo.org/record/3727600/files/prophane_result-sample_a.zip">prophane_result-sample_a.zip</a></li> </ul> </li> <li>Example results for a multi-experiment analysis (Sample B, based on: MS data: FASP + in-gel digest, FASTA: Metagenome): <ul> <li>MPA results: <a href="https://zenodo.org/record/3727600/files/mpa_result-sample_b-fdr_0.01-multi_exp.csv">mpa_result-sample_b-fdr_0.01-multi_exp.csv</a></li> <li>Prophane results: <a href="https://zenodo.org/record/3727600/files/prophane_result-sample_b.zip">prophane_result-sample_b.zip</a></li> </ul> </li> <li><a href="https://zenodo.org/record/3727600/files/mpa_ressources_incl_swissprot_03-2020.zip">MPA data dump</a> including preprocessed UniProt Swiss-Prot FASTA (optionally used by <a href="https://anaconda.org/bioconda/mpa-server">conda mpa-server package</a>)</li> </ul>
Analytical workflow for "Grad-seq shines light on unrecognized RNA and protein complexes in the model bacterium Escherichia coli", Hör et al. 2020
<p>Analytical workflow including scripts, data and Singularity image for "Grad-seq shines light on unrecognized RNA and protein complexes in the model bacterium Escherichia coli", Hör et al. 2020, (<a href="https://doi.org/10.1101/2020.06.29.177014">https://doi.org/10.1101/2020.06.29.177014</a>)</p>
Figure 1d from: Owen D, Livermore L, Groom Q, Hardisty A, Leegwater T, van Walsum M, Wijkamp N, Spasić I (2020) Towards a scientific workflow featuring Natural Language Processing for the digitisation of natural history collections. Research Ideas and Outcomes 6: e55789. https://doi.org/10.3897/rio.6.e55789
Figure 1d A range of sample specimens that demonstrate the wide taxonomic range of specimens encountered in collections. They also demonstrate the diversity of label types, which include handwritten, typed, and printed labels. Note the presence of various barcodes, rulers, and a colour chart in addition to labels describing the origin of the specimen and its identity. - Fossilised animal skin (Natural History Museum 2009)
Figure 1b from: Owen D, Livermore L, Groom Q, Hardisty A, Leegwater T, van Walsum M, Wijkamp N, Spasić I (2020) Towards a scientific workflow featuring Natural Language Processing for the digitisation of natural history collections. Research Ideas and Outcomes 6: e55789. https://doi.org/10.3897/rio.6.e55789
Figure 1b A range of sample specimens that demonstrate the wide taxonomic range of specimens encountered in collections. They also demonstrate the diversity of label types, which include handwritten, typed, and printed labels. Note the presence of various barcodes, rulers, and a colour chart in addition to labels describing the origin of the specimen and its identity. - Pinned insect specimen (Natural History Museum 2018)
Figure 1c from: Owen D, Livermore L, Groom Q, Hardisty A, Leegwater T, van Walsum M, Wijkamp N, Spasić I (2020) Towards a scientific workflow featuring Natural Language Processing for the digitisation of natural history collections. Research Ideas and Outcomes 6: e55789. https://doi.org/10.3897/rio.6.e55789
Figure 1c A range of sample specimens that demonstrate the wide taxonomic range of specimens encountered in collections. They also demonstrate the diversity of label types, which include handwritten, typed, and printed labels. Note the presence of various barcodes, rulers, and a colour chart in addition to labels describing the origin of the specimen and its identity. - Microscope slide (Natural History Museum 2017)
Figure 1a from: Owen D, Livermore L, Groom Q, Hardisty A, Leegwater T, van Walsum M, Wijkamp N, Spasić I (2020) Towards a scientific workflow featuring Natural Language Processing for the digitisation of natural history collections. Research Ideas and Outcomes 6: e55789. https://doi.org/10.3897/rio.6.e55789
Figure 1a A range of sample specimens that demonstrate the wide taxonomic range of specimens encountered in collections. They also demonstrate the diversity of label types, which include handwritten, typed, and printed labels. Note the presence of various barcodes, rulers, and a colour chart in addition to labels describing the origin of the specimen and its identity. - Herbarium specimen (Natural History Museum 2007a)
Figure 11 from: Owen D, Livermore L, Groom Q, Hardisty A, Leegwater T, van Walsum M, Wijkamp N, Spasić I (2020) Towards a scientific workflow featuring Natural Language Processing for the digitisation of natural history collections. Research Ideas and Outcomes 6: e55789. https://doi.org/10.3897/rio.6.e55789
Figure 11 The distribution of languages across the specimen and herbaria. EN=English, FR=French, LA=Latin, ET=Estonian, DE=German, NL=Dutch, PT=Portuguese, ES=Spanish, SV=Swedish, RU=Russian, FI=Finnish, IT=Italian, ZZ=Unknown. The codes for the contributing herbaria are listed in Table 11 (from Dillen et al. 2019).
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.