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722 results for “use case”
10 eADAGE models used for the PAO1 KEGG pathways case study in PathCORE
<p>ensemble Analysis using Denoising Autoencoders for Gene Expression<strong> </strong>(<strong>eADAGE</strong>) is an unsupervised feature construction algorithm developed by Tan et al. that uses an ensemble of neural networks (an ensemble of ADAGE models) to capture biological signatures embedded in the expression compendium. By initializing eADAGE with different random seeds, Tan et al. produced 10 eADAGE models that each extracted k=300 features from the compendium of genome-scale <em>P. aeruginosa</em> data.</p> <p>eADAGE is described in Tan et al.'s "System-wide automatic extraction of functional signatures in <em>Pseudomonas aeruginosa</em> with eADAGE" (https://doi.org/10.1101/078659). The code to construct these 10 models is available in this repository: https://bitbucket.org/greenelab/eadage (see eADAGE_construction.sh). </p>
Unfair Inequality in Education: A Benchmark for AI-Fairness Research (Aequitas WP7 Use Case S2)
<h1>Unfair Inequality in Education: A Benchmark for AI-Fairness Research</h1> <p>This dataset proposes a novel benchmark specifically designed for AI fairness research in education. It can be used for challenging tasks aimed at improving students' performance and reducing dropout rates which are also discussed in the paper to emphasize significant research directions. By prioritizing fairness, this benchmark aims to foster the development of bias-free AI solutions, promoting equal educational access and outcomes for all students.</p> <h2>Structure</h2> <p><code>benchmark</code> contains:</p> <ul> <li>the proposed dataset (<code>dataset.csv</code>), </li> <li>the mask for dealing with missing values (<code>missing_mask.csv</code>), and</li> <li> <div> <div>the meta-columns providing grouping criteria and sample weights for each student (<code>meta_cols.csv</code>).</div> </div> </li> </ul> <p><code>raw_data</code> includes:</p> <ul> <li>the original dataset (<code>original.csv</code>), and</li> <li>the intermediate stages of the pre-processing and validation pipelines (<code>split</code>, <code>pre_processed</code>, and <code>validation</code>).</li> </ul> <p><code>res</code> contains the documentation, including:</p> <ul> <li>the transformation mapping each column of the original dataset to the proposed one, along with the missingness category and original text (<code>meta_data_mapping.csv</code>),</li> <li>the value type and domains of each column of the proposed datasets (<code>meta_data_stats.json</code>), and</li> <li> <div> <div>the statistical indices of the validation pipeline (<code>bias_preservation_results.json</code>).</div> </div> </li> </ul> <p><code>src</code> contains the source code for running the pre-processing and corresponding analysis:</p> <ul> <li><code>pre_processing</code> and <code>stats</code>contain the code for the two corresponding tasks, and</li> <li><code>pre_processing.py</code> and <code>split.py</code> are two entry points.</li> </ul> <p>Finally, <code>Dockerfile</code> and <code>requirements.txt</code> set up the environment for running the applications across multiple platforms and with Python, respectively.</p>
Demonstration of semantic and inter-input constraints on software in OWL 2 and SPARQL for fulfilling the M1 Machine FAIR Use Case
<p>This video demonstrates using hypothetical examples how to (1) find a valid dataset for input into a software using OWL 2 classification inference, (2) validly combine two software using OWL 2 subsumption inference to infer that the output of software 1 is valid input to software 2, and (3) combine OWL 2 inference with a SPARQL query to find two datasets that satisfy a software's inter-input constraints.</p>
Supplementary data and code to article "Single-Well Microseismic Focal Mechanism Inversions Using Different Source Models: A Case Study in the Ordos Basin, China"
<p>Supplementary data and code to article "Single-Well Microseismic Focal Mechanism Inversions Using Different Source Models: A Case Study in the Ordos Basin, China".</p><p>Transformations among the parameters of the moment tensor model refer to the code package from Tape and Tape (https://github.com/carltape/mtbeach/; https://github.com/carltape/surfacevel2strain; Tape and Tape, 2009, 2012, 2013, 2015).</p><p>Tape, C., P. Muse, M. Simons, D. Dong, and F. Webb (2009). Multiscale estimation of GPS velocity fields, Geophys. J. Int. 179, no.2, 945-971, doi: 10.1111/j.1365-246X.2009.04337.x.</p><p>Tape, W., and C. Tape (2012). A geometric setting for moment tensors, Geophys. J. Int. 190, no. 1, 476–498, doi: 10.1111/j.1365-246X.2012.05491.x.</p><p>Tape, W., and C. Tape (2013). The classical model for moment tensors, Geophys. J. Int. 195, no. 3, 1701–1720, doi: 10.1093/gji/ggt302.</p><p>Tape, W., and C. Tape (2015). A uniform parametrization of moment tensors, Geophys. J. Int. 202, no. 3, 2074–2081, doi: 10.1093/gji/ggv262.</p>
Fig. 1. Merodon aureus Fabricius, 1805 in An assessment of new character in hoverfly species delimitation using linear and geometric morphometrics - genus Merodon Meigen, 1803 (Diptera: Syrphidae) as a case study
Fig. 1. Merodon aureus Fabricius, 1805, ♂, right wing with the character used in linear morphometric: a = intersection of R4+5 with r-m vein; b = intersection of R4+5 vein with a line drawn in the middle between a and c; c = the intersection of R4+5 with M1 vein; D = the angle formed by the lines that connect a, b and c.
Fig. 4 in An assessment of new character in hoverfly species delimitation using linear and geometric morphometrics - genus Merodon Meigen, 1803 (Diptera: Syrphidae) as a case study
Fig. 4. Results of the geometric morphometric wing shape analysis of species of the Merodon aureus complex. A. Scatter plot of individual scores showing R4+5 vein shape variability. B. Scatter plot of individual scores showing wing shape variability from Vujić et al. (2020c). C. Scatter plot of individual scores showing semilandmark R4+5 vein shape and landmark wing shape variability D. Superimposed outline drawings showing R4+5 vein shape differences among investigated species.
Fig. 5 in An assessment of new character in hoverfly species delimitation using linear and geometric morphometrics - genus Merodon Meigen, 1803 (Diptera: Syrphidae) as a case study
Fig. 5. Results of the geometric morphometric wing shape analysis of males of the Merodon natans group. A. Scatter plot of individual scores showing the R4+5 vein shape variability. B. Scatter plot of individual scores showing the wing shape variability from Vujić et al. (2021c). C. Scatter plot of individual scores showing the semilandmark R4+5 vein shape and landmark wing shape variability D. Superimposed outline drawings showing R4+5 vein shape differences among males of the investigated species.
Fig. 3. Box plot showing a in An assessment of new character in hoverfly species delimitation using linear and geometric morphometrics - genus Merodon Meigen, 1803 (Diptera: Syrphidae) as a case study
Fig. 3. Box plot showing a comparison of the angle at the intersection of the R4+5 vein and the middle
Fig. 2. Merodon aureus Fabricius, 1805 in An assessment of new character in hoverfly species delimitation using linear and geometric morphometrics - genus Merodon Meigen, 1803 (Diptera: Syrphidae) as a case study
Fig. 2. Merodon aureus Fabricius, 1805, ♂, right wing with the location of 20 semilandmarks selected for geometric morphometric analysis.
Fig. 3. Box plot showing a in An assessment of new character in hoverfly species delimitation using linear and geometric morphometrics - genus Merodon Meigen, 1803 (Diptera: Syrphidae) as a case study
Fig. 3. Box plot showing a comparison of the angle at the intersection of the R4+5 vein and the middle line for all species used in the analysis.
Fig. 7 in An assessment of new character in hoverfly species delimitation using linear and geometric morphometrics - genus Merodon Meigen, 1803 (Diptera: Syrphidae) as a case study
Fig. 7. Results of the geometric morphometric wing shape analysis of males of the Merodon clavipes and pruni groups. A–B. Scatter plot of individual scores showing the R4+5 vein shape variability. C–D. Scatter plot of individual scores showing the wing shape variability from Vujić et al. (in prep.). E–F. Scatter plot of individual scores showing the semilandmark R4+5 vein shape and landmark wing shape variability. G–H. Superimposed outline drawings showing the R4+5 vein shape differences between the males of the investigated species.
Fig. 6 in An assessment of new character in hoverfly species delimitation using linear and geometric morphometrics - genus Merodon Meigen, 1803 (Diptera: Syrphidae) as a case study
Fig. 6. Results of the geometric morphometric wing shape analysis of females of the Merodon natans group. A. Scatter plot of individual scores showing the R4+5 vein shape variability. B. Scatter plot of individual scores showing wing the shape variability from Vujić et al. (2021c). C. Scatter plot of individual scores showing the semilandmark R4+5 vein shape and landmark wing shape variability D. Superimposed outline drawings showing the R4+5 vein shape differences among females of the investigated species.
txtools use cases omic references and processed data
<p><strong>Dataset</strong></p> <p>This dataset entry is meant to be downloaded programmatically while rendering the txtools_useCases.Rmd notebooks, to facilitate their replication using the provided genomic references. Processed data is also provided to show ready-to-use examples of data processed by txtools.</p> <p><strong>Abstract</strong></p> <p>We present txtools, an R package that enables the processing, analysis, and visualization of RNA-seq data at the nucleotide-level resolution, seamlessly integrating alignments to the genome with transcriptomic representation. txtools’ main inputs are BAM files and a transcriptome annotation, and the main output is a table, capturing mismatches, deletions, and the number of reads beginning and ending at each nucleotide in the transcriptomic space. txtools further facilitates downstream visualization and analyses. We showcase, using examples from the epitranscriptomic field, how a few calls to txtools functions can yield insightful and ready-to-publish results. txtools is of broad utility also in the context of structural mapping and RNA:protein interaction mapping. By providing a simple and intuitive framework, we believe that txtools will be a useful and convenient tool and pave the path for future discovery. txtools is available for installation from its GitHub repository at <a href="https://github.com/AngelCampos/txtools">https://github.com/AngelCampos/txtools</a>. </p>
Input data for use cases of TransportTools
<ul> <li>Use case I: Disclosing rare transient tunnels and their usage by water molecules in 15 simulations of DhaA dehalogenase <ul> <li>tunnel data</li> <li>water traces data</li> <li>configuration file for TransportTools</li> </ul> </li> <li> <p>Use case II: Understanding the effect of mutations by contrasting simulations of three different variants of LinB dehalogenase</p> <ul> <li>tunnel data</li> <li>water traces data</li> <li>molecular dynamics simulations with relevant water molecules only</li> <li>configuration file for TransportTools</li> </ul> </li> <li> <p>Use Case III: Inferring selectivity of transport pathways in LinB86 dehalogenase for a substrate molecule from almost 600 simulations</p> <ul> <li>tunnel data</li> <li>substrate traces data</li> <li>configuration file for TransportTools</li> </ul> </li> </ul>
LEXIS Turbomachinary use case: T106 profile test case
<p>The test case for T106 profile in a linear cascade. Cascade rigs are a way to test in a simple way various turbomachinery blades. Blades are cylindrical, which means equal 2D sections from hub to tip wall, and repeated tangentially according to the specified pitch. In this case, end walls are linear, not cylindrical and not tapered. The dataset contains an</p> <ul> <li>excel file with <ul> <li>description of the airfoil (x, y coordinates in [mm])</li> <li>geometric pitch /chord data, aspect ratio (parallel end walls)</li> <li>entry boundary conditions <ul> <li>total pressure</li> <li>total temperature</li> <li>flow angle</li> </ul> </li> <li>outlet boundary condition <ul> <li>static pressure</li> </ul> </li> <li>Reynold number (120 000)</li> <li>Velocity at the exit (Mach = 0,59</li> </ul> </li> <li>OpenFOAM input files for <ul> <li>serial run (Allrun)</li> <li>parallel run (Allrun_parallel)</li> </ul> </li> <li>PDF file with the description of the case and obtained results</li> </ul>
ams-icdd-usecases: Use cases for employing ICDD containers for infrastructure asset management
<p>This repository provides two use cases for infrastructure asset management using information containers according to the <a href="https://www.iso.org/standard/74389.html">Information Container for linked Document Delivery standard (ISO 25197)</a>. Use Case 1 demonstrates the data preparation and result collection of bridge visual inspection with requirement- and delivery container. Use Case 2 demonstrates the pavement maintenance plan based on the existing condition data. Therefore, an additional connection to a relational database in the information container is provided in Use Case 2, which is registered within the container using an extension <a href="https://icdd.vm.rub.de/ontology/icdd/ExtendedDocument/">EXDOC:Extension for document types for the ISO 21597 ICDD Part 1 Container ontology</a>.</p> <p><strong>Full Changelog</strong>: <a href="https://github.com/RUB-Informatik-im-Bauwesen/ams-icdd-usecases/commits/v0.1">https://github.com/RUB-Informatik-im-Bauwesen/ams-icdd-usecases/commits/v0.1</a></p>
SMW-NCZARR-Use-Case-2
<p>Input Data for Use case 2 of the Shallow Water Model to experiment with Zarr backend of the NetCDF library via its Fortran Interface.</p>
JACUSA2 - use cases: 1 - 3
<p><strong>Supplementary data</strong> for use cases presented in:</p> <p>RNA modification mapping with JACUSA2<br> Michael Piechotta, Qi Wang, Janine Altmüller, Christoph Dieterich<br> <a href="https://doi.org/10.1101/2021.07.02.450888">doi: https://doi.org/10.1101/2021.07.02.450888</a></p> <p> </p> <p><strong>Use case 1: m6A mapping by MazF</strong></p> <ul> <li>MazF_vs_cond2_FTO_rtarrest_plain_result.out.gz (JACUSA2 output)</li> <li>Cutoff01_MazF_vs_cond2_FTO_rtarrest_plain_result.out.gz (filtered by pvalue <=0.1)</li> <li>MazF (BAM files): <ul> <li>SRR8450805_STARmapping_uniq_rmdup.bam</li> <li>SRR8450807_STARmapping_uniq_rmdup.bam</li> <li>SRR8450809_STARmapping_uniq_rmdup.bam</li> </ul> </li> <li>FTO: (BAM files): <ul> <li>SRR8450806_STARmapping_uniq_rmdup.bam</li> <li>SRR8450808_STARmapping_uniq_rmdup.bam</li> <li>SRR8450810_STARmapping_uniq_rmdup.bam</li> </ul> </li> </ul> <p><strong>Use case 2: </strong><strong>DART-seq</strong></p> <ul> <li>APOBEC1YTH_APOBEC1YTHmut_call2_result.out.gz (JACUSA2 output)</li> <li>APOBEC1YTH (BAM files): <ul> <li>SRR9940470_STARmapping_uniq_rmdup.bam</li> <li>SRR9940471_STARmapping_uniq_rmdup.bam</li> <li>SRR9940472_STARmapping_uniq_rmdup.bam</li> </ul> </li> <li>APOBEC1YTHmut (BAM files): <ul> <li>SRR9940474_STARmapping_uniq_rmdup.bam</li> <li>SRR9940475_STARmapping_uniq_rmdup.bam</li> <li>SRR9940476_STARmapping_uniq_rmdup.bam</li> </ul> </li> </ul> <p><strong>Use case 3: Nanopore</strong></p> <ul> <li>WT_vs_KO_call2_result.out.gz & WT100_vs_WT0_call2_result.out.gz (JACUSA2 output)</li> <li>WT (BAM files): <ul> <li>HEK293T-WT-rep2.bam</li> <li>HEK293T-WT-rep3.bam</li> <li>HEK293T-WT-100-rep1.bam</li> <li>HEK293T-WT-100-rep2.bam</li> <li>HEK293T-WT-100-rep3.bam</li> </ul> </li> <li>KO (BAM files): <ul> <li>HEK293T-KO-rep2.bam</li> <li>HEK293T-KO-rep3.bam</li> <li>HEK293T-WT-0-rep1.bam</li> <li>HEK293T-WT-0-rep2.bam</li> </ul> </li> </ul> <p>[1] Zhang, Z., Chen, L.-Q., Zhao, Y.-L., Yang, C.-G., Roundtree, I.A., Zhang, Z., Ren, J., Xie, W., He, C., Luo, G.-Z.: Single-base mapping of m6a by an antibody-independent method. Science advances 5, 0250 (2019). doi:10.1126/sciadv.aax0250</p> <p>[2] Garcia-Campos, M.A., Edelheit, S., Toth, U., Safra, M., Shachar, R., Viukov, S., Winkler, R., Nir, R., Lasman, L., Brandis, A., Hanna, J.H., Rossmanith, W., Schwartz, S.: Deciphering the ”m6a code” via antibody-independent quantitative profiling. Cell 178, 731–74716 (2019). doi:10.1016/j.cell.2019.06.013</p> <p>[3] Meyer, K.D.: Dart-seq: an antibody-free method for global m6a detection. Nature methods 16, 1275–1280 (2019). doi:10.1038/s41592-019- 0570-0</p> <p>[4] Pratanwanich, P.N., Yao, F., Chen, Y., Koh, C.W.Q., Hendra, C., Poon, P., Goh, Y.T., Yap, P.M.L., Yuan, C.J., Chng, 6 made available under aCC-BY-NC-ND 4.0 International license. (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is bioRxiv preprint doi: https://doi.org/10.1101/2021.07.02.450888; this version posted July 4, 2021. The copyright holder for this preprint W.J., Ng, S., Thiery, A., Goh, W.S.S., G¨oke, J.: Detection of differential rna modifications from direct rna sequencing of human cell lines. bioRxiv (2020). doi:10.1101/2020.06.18.160010. https://www.biorxiv.org/content/early/2020/06/20/2020.06.18.160010.full.pdf</p>
Genetic structure in patchy populations of a candidate foundation plant: a case study of Leymus chinensis using genetic and clonal diversity
<p><strong>PREMISE</strong>: The distribution of genetic diversity on the landscape has critical ecological and evolutionary implications. This may be especially the case on a local scale for foundation plant species since they create and define ecological communities, contributing disproportionately to ecosystem function.</p> <p><strong>METHODS</strong>: We examined the distribution of genetic diversity and clones, which we defined first as unique multilocus genotypes (MLG), and then by grouping similar MLGs into multilocus lineages (MLL). We used 186 markers from inter-simple sequence repeats (ISSR) across 358 ramets from 13 patches of the foundation grass <em>Leymus chinensis</em>. We examined the relationship between genetic and clonal diversities, their variation with patch-size, and the effect of the number of markers used to evaluate genetic diversity and structure in this species.</p> <p><strong>RESULTS</strong>: Every ramet had a unique MLG. Almost all patches consisted of individuals belonging to a single MLL. We confirmed this with a clustering algorithm to group related genotypes. The predominance of a single lineage within each patch could be the result of the accumulation of somatic mutations, limited dispersal, some sexual reproduction with partners mainly restricted to the same patch, or a combination of all three.</p> <p><strong>CONCLUSIONS</strong>: We found strong genetic structure among patches of <em>L. chinensis</em>. Consistent with previous work on the species, the clustering of similar genotypes within patches suggests that clonal reproduction combined with somatic mutation, limited dispersal, and some degree of sexual reproduction among neighbors causes individuals within a patch to be more closely related than among patches.</p>
EAMv1 simulation output from CondiDiag1.0 use case examples
<p>This archive contains data files from three EAMv1 simulations that demonstrate the usage of an online diagnostic tool called CondiDiag1.0. The three simulations are described in Section 6 in <a href="https://gmd.copernicus.org/preprints/gmd-2021-331/">Wan et al. (2022, GMDD)</a>. The model source code, run scripts, and post-processing scripts are available on Zenodo under the DOI <a href="https://doi.org/10.5281/zenodo.5530188">10.5281/zenodo.5530188</a>. EAMv1's master DOI is 10.11578/E3SM/dc.20180418.36, see <a href="https://www.osti.gov/doecode/biblio/10475">https://www.osti.gov/doecode/biblio/10475</a>.</p>
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.