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3,481 results for “data set”
Example Eiger data with 6 virtual data sets dereferencing images
<p>Standard Eiger data set from Diamond Light Source I04, consisting of 180° of rotation data from a cubic insulin crystal, with 6 virtual data sets each corresponding to 30° of data, to illustrate how multi-trigger Eiger data sets could be structured efficiently. </p> <p> </p> <p>Virtual data sets made with </p> <p> </p> <p><a href="https://github.com/graeme-winter/NXmxtools/blob/master/vdsmaker.py">https://github.com/graeme-winter/NXmxtools/blob/master/vdsmaker.py</a></p> <p> </p> <p>Key feature is that the underlying data type (UINT16) corresponds to the virtual data set type. </p> <p> </p> <p>This will require HDF5 1.10 series to read. </p> <p> </p> <p>Files:</p> <p>insu_d200_1.nxs - NXmx formatted data with internal VDS</p> <p>insu_d200_1_000001.h5 - real data 1/2</p> <p>insu_d200_1_000002.h5 - real data 2/2</p> <p>insu_d200_1_1.nxs - VDS subset of data 1/6</p> <p>...</p> <p>insu_d200_1_6.nxs - VDS subset of data 1/6</p> <p>insu_d200_1_master.h5 - DECTRIS style master file</p> <p>insu_d200_1_meta.h5 - metadata</p> <p>insu_d200_1_meta_pack.h5 - repacked metadata (not used)</p>
Data set for: Real-space Imaging of Confined Magnetic Skyrmion Tubes
<p>This repository contains the scripts and notebooks to reproduce the figures, simulations and numerical data shown in <strong>Real-space Imaging of Confined Magnetic Skyrmion Tubes</strong> by <em>M. T. Birch, D. Cortés-Ortuño, L. A. Turnbull, M. N. Wilson, F. Groß, N. Träger, A. Laurenson, N. Bukin, S. H. Moody, M. Weigand, G. Schütz, H. Popescu, R. Fan, P. Steadman, J. A. T. Verezhak, G. Balakrishnan, J. C. Loudon, A. C. Twitchett-Harrison, O. Hovorka, H. Fangohr, F. Ogrin, J. Gräfe and P. D. Hatton.</em></p> <p>Both simulation and experimental data analysis are performed using Python with the Matplotlib, Jupyter, Scipy, Numpy and h5py libraries.</p> <p>Jupyter notebooks are provided to process the experimental data and reproduce the STXM, X-Ray Holography and LTEM images, which are shown as Figures 2, 3, 4 and 5 in the paper.</p> <p>Simulation scripts are based on the finite difference micromagnetic code OOMMF with the extension to simulate DMI for materials with symmetry class <em>T</em>: [oommf-extension-dmi-t](https://github.com/joommf/oommf-extension-dmi-t)</p> <p>The analysis of OOMMF's output files, which are in the `OMF` format, are processed using the [OOMMFPy](https://github.com/davidcortesortuno/oommfpy) library, which can calculate the topological charge in a 2D slice.</p> <p>Three-dimensional visualisations of the magnetic states are performed using Paraview. In order to get VTK files for visualisation, convert the `OMF` files into `.vtk` using the `OOMMFPy` library.</p> <p> </p> <p>Latest version of this Data Set can be found at the Github repository:</p> <p><a href="https://github.com/davidcortesortuno/paper-2020_real-space_imaging_of_confined_magnetic_skyrmion_tubes">https://github.com/davidcortesortuno/paper-2020_real-space_imaging_of_confined_magnetic_skyrmion_tubes</a></p>
TIPP 2.0.0 taxonomic profiling of the CAMI 2 Mouse Gut Toy data set, samples 0-63
<strong>Software: </strong>TIPP<br><strong>SoftwareVersion: </strong>2.0.0<br><strong>DataURL: </strong> https://data.cami-challenge.org/participate<br><strong>SoftwareURL:</strong> https://github.com/smirarab/sepp<br><strong>DockerImage:</strong> stefanjanssen/docker_profiling_tools:tipp<br><strong>IsBiobox:</strong> True<br><strong>BioboxYAMLFile:</strong> https://zenodo.org/record/3629567/files/biobox.yaml?download=1<br><strong>ReferenceDatabase:</strong> 2015<br><strong>ShortReadsUsed:</strong> True<br><strong>LongReadsUsed:</strong> False<br><strong>CommandsUsed:</strong> docker run \<br>--volume="/path/to/19122017_mousegut_scaffolds_yaml:/bbx/mnt/yaml:ro" \<br>--volume="/path/to/19122017_mousegut_scaffolds:/bbx/mnt/input:ro" \<br>--volume="/path/to/output:/bbx/mnt/output:rw" \<br>--volume="/path/to/output/metadata:/bbx/metadata:rw" \<br>--volume="/path/to/output/cache:/cache:rw" \<br>stefanjanssen/docker_profiling_tools:tipp
Bracken 2.5 taxonomic profiling of the CAMI 2 Mouse Gut Toy data set, samples 0-63
<strong>Software: </strong>Bracken<br><strong>SoftwareVersion: </strong>2.5<br><strong>DataURL: </strong> https://data.cami-challenge.org/participate<br><strong>SoftwareURL:</strong> https://github.com/jenniferlu717/Bracken<br><strong>DockerImage:</strong> cami/bracken:2.5<br><strong>IsBiobox:</strong> True<br><strong>BioboxYAMLFile:</strong> https://zenodo.org/record/3629567/files/biobox.yaml?download=1<br><strong>ReferenceDatabase:</strong> Kraken standard db built May 2019<br><strong>ShortReadsUsed:</strong> True<br><strong>LongReadsUsed:</strong> False<br><strong>CommandsUsed:</strong> docker run \<br>--volume="/path/to/19122017_mousegut_scaffolds_yaml:/bbx/mnt/yaml:ro" \<br>--volume="/path/to/19122017_mousegut_scaffolds:/bbx/mnt/input:ro" \<br>--volume="/path/to/output:/bbx/mnt/output:rw" \<br>--volume="/path/to/output/metadata:/bbx/metadata:rw" \<br>--volume="/path/to/output/cache:/cache:rw" \<br>cami/bracken:2.5
CAT 4.6 taxonomic binning of the CAMI 2 Mouse Gut Toy data set, gold standard pooled assembly
<p>Taxonomic binning of the gold standard pooled assembly<br> <strong>Software: </strong>CAT<br> <strong>SoftwareVersion: </strong>4.6<br> <strong>DataURL: </strong> https://data.cami-challenge.org/participate<br> <strong>SoftwareURL:</strong> https://github.com/dutilh/CAT<br> <strong>ReferenceDatabase:</strong> prebuilt 2018-12-12<br> <strong>Taxonomy:</strong> NCBI 2018-12-12<br> <strong>ShortReadsUsed:</strong> False<br> <strong>LongReadsUsed:</strong> False<br> <strong>CommandUsed:</strong> CAT contigs -c anonymous_gsa_pooled.fasta -d CAT_prepare_20181212/2018-12-12_CAT_database/ -t CAT_prepare_20181212/2018-12-12_taxonomy/ --tmpdir tmp --nproc 16</p>
PhyloPythiaS+ 1.4 taxonomic binning of the CAMI 2 Mouse Gut Toy data set, gold standard pooled assembly
<p>Taxonomic binning of the gold standard pooled assembly<br> <strong>Software: </strong>PhyloPythiaS+<br> <strong>SoftwareVersion: </strong>1.4<br> <strong>DataURL: </strong> https://data.cami-challenge.org/participate<br> <strong>SoftwareURL:</strong> https://github.com/algbioi/ppsp<br> <strong>DockerImage:</strong> cami/ppsp:1.4<br> <strong>IsBiobox:</strong> False<br> <strong>ReferenceDatabase:</strong> RefSeq 93, SILVA 132<br> <strong>Taxonomy:</strong> NCBI 2018-02-26<br> <strong>ShortReadsUsed:</strong> False<br> <strong>LongReadsUsed:</strong> False<br> <strong>CommandUsed:</strong> run_ppsp.py --pipelineDir ppsp_pipepline --inputFastaFile anonymous_gsa_pooled.fasta --databaseFile ncbi_taxonomy --refSeq refseq93 --s16Database SILVA_132 --mgDatabase reference_NCBI201502/mg5</p>
MetaPalette 1.0.0 taxonomic profiling of the CAMI 2 Mouse Gut Toy data set, samples 0-63
<strong>Software: </strong>MetaPalette<br><strong>SoftwareVersion: </strong>1.0.0<br><strong>DataURL: </strong> https://data.cami-challenge.org/participate<br><strong>SoftwareURL:</strong> https://doi.org/10.5281/zenodo.1730624<br><strong>DockerImage:</strong> stefanjanssen/docker_profiling_tools:commonkmers<br><strong>IsBiobox:</strong> True<br><strong>BioboxYAMLFile:</strong> https://zenodo.org/record/3629567/files/biobox.yaml?download=1<br><strong>ReferenceDatabase:</strong> https://zenodo.org/record/1749272<br><strong>ShortReadsUsed:</strong> True<br><strong>LongReadsUsed:</strong> False<br><strong>CommandsUsed:</strong> docker run \<br>--volume="/path/to/19122017_mousegut_scaffolds_yaml:/bbx/mnt/yaml:ro" \<br>--volume="/path/to/19122017_mousegut_scaffolds:/bbx/mnt/input:ro" \<br>--volume="/path/to/output:/bbx/mnt/output:rw" \<br>--volume="/path/to/output/metadata:/bbx/metadata:rw" \<br>--volume="/path/to/output/cache:/cache:rw" \<br>--volume="/path/to/reference_database:/exchange/db:rw" \<br>stefanjanssen/docker_profiling_tools:commonkmers
MetaBAT 2.12.1 genome binning of the CAMI 2 Mouse Gut Toy data set, samples 0-63, gold standard pooled assembly
Genome binning of the gold standard pooled assembly <br><strong>Software: </strong>MetaBAT<br><strong>SoftwareVersion: </strong>2.12.1<br><strong>DataURL: </strong> https://data.cami-challenge.org/participate<br><strong>SoftwareURL:</strong> https://bitbucket.org/berkeleylab/metabat<br><strong>ShortReadsUsed:</strong> True<br><strong>LongReadsUsed:</strong> False<br><strong>CommandUsed:</strong> bowtie2-build anonymous_gsa_pooled.fasta anonymous_gsa_pooled.fasta<br>for i in {0..63}; do bowtie2 -q --threads 30 --fr -x anonymous_gsa_pooled.fasta --interleaved sample_${i}/anonymous_reads.fq -S anonymous_reads_sample_${i}.sam ; done<br>for i in {0..63}; do samtools view -b sample_${i}.sam -o anonymous_reads_sample_${i}.bam & done<br>for i in {0..63}; do samtools sort anonymous_reads_sample_${i}.bam -o anonymous_reads_sample_${i}.sorted.bam ; done<br>for i in {0..63}; do samtools index anonymous_reads_sample_${i}.sorted.bam ; done<br>runMetaBat.sh -l anonymous_gsa_pooled.fasta anonymous_reads_sample_*.sorted.bam
Kraken 2.0.8 beta taxonomic binning of the CAMI 2 Mouse Gut Toy data set, gold standard pooled assembly
<p>Taxonomic binning of the gold standard pooled assembly<br> <strong>Software: </strong>Kraken<br> <strong>SoftwareVersion: </strong>2.0.8 beta<br> <strong>DataURL: </strong> https://data.cami-challenge.org/participate<br> <strong>SoftwareURL:</strong> https://ccb.jhu.edu/software/kraken2/<br> <strong>ReferenceDatabase:</strong> built 2019-05-22<br> <strong>Taxonomy:</strong> NCBI 2019-05-22<br> <strong>ShortReadsUsed:</strong> False<br> <strong>LongReadsUsed:</strong> False<br> <strong>CommandUsed:</strong> kraken2-build --standard --db kraken2db_std --use-ftp<br> kraken2 --db kraken2db_std --threads 16 --output 19122017_mousegut_scaffolds.kraken --report 19122017_mousegut_scaffolds.kreport anonymous_gsa_pooled.fasta<br> cat 19122017_mousegut_scaffolds | awk '{print $2 "\t" $3}' > 19122017_mousegut_scaffolds.cami</p>
InterFlex RWTH_WP8 data set_
<p>Within the context of InterFlex project under GA 731289, an agent-based control power scheduling framework for interconnected Local Energy Communities (LECs) has been developed. The interconnection of differnt LECs is inspired by the InterFlex Simris demonstrator. For furhther information, please refer to the respective publications: http://publications.rwth-aachen.de/record/775099; http://publications.rwth-aachen.de/record/751892?ln=de; http://publications.rwth-aachen.de/record/759872/files/759872.pdf; https://cired-repository.org/handle/20.500.12455/640; https://www.cired-repository.org/handle/20.500.12455/480; http://publications.rwth-aachen.de/record/763488;</p>
GC-MS data set for Generation of a chromosome-scale genome assembly of the insect-repellant terpenoid-producing Lamiaceae species, Callicarpa americana
<p>RAW GC/MS data set for characterization of class II terpene synthases from <em>Callicarpa americana </em></p>
Data Set - Laboratory measurement of the wave–induced plastic particles motion: The influence of wave period, plastic size and plastic density
<p><strong>Data set - Laboratory measurement of the wave–induced plastic particles motion: The influence of wave period, plastic size and plastic density</strong></p> <p>This data set describes the wave flume experimental data on the wave-induced plastic particles motion induced by different wave conditions and different plastic particles density and size. A manuscript is currently under review describing the analysis of the data.</p> <p>The data set is divided in two parts:</p> <p>- <strong>Wave flume hydrodynamics</strong>. With measured water surface elevation at different locations within the wave flume. These data are stored in txt files with headings describing the type of measurement, i.e. wave paddle motion, water surface elevation at different sensors, synchronization signal for the video-cameras.</p> <p>- <strong>Lagrangian trajectories. </strong>hdf5 files with information of the particles position, velocity and time (with respect to the synchronization signal in the respective hydrodynamic file) for each experiment. Two tar.gx files have been uploaded with trajectories information:</p> <p> - TOPIOS_Trajectories_FloatingParticles.tar.xz, with information of floating plastic particles and,</p> <p> - TOPIOS_Trajectories_NonfloatingParticles.tar.xz, with information of non-floating plastc particles.</p> <p>An excel file with information of filenames, cross-shore locations of sensors, plastic particles and wave conditions is also uploaded (TOPIOS_Control_exp.xlsx).</p> <p>Any question regarding the data can be addressed at jose.alsina@upc.edu</p>
LRRo: A Lip Reading Data Set for the Under-resourced Romanian Language
<p>Two distinct collections are presented in this repository:</p> <p>(i) wild LRRo data is designed for an Internet in-the-wild, ad-hoc scenario, coming with more than 35 different speakers, 1.1k words, a vocabulary of 21 words, and more than 20 hours;</p> <p>(ii) lab LRRo data, addresses a lab controlled scenario for more accurate data, coming with 19 different speakers, 6.4k words, a vocabulary of 48 words, and more than 5 hours.</p>
The ECHAM/MESSy idealized (EMIL) model set-up: data of reference simulations
<p>This data set contains the data from simulations performed with the ECHAM/MESSy IdeaLized (EMIL) dry dynamical core model, as presented in the GMD(D) publication by Garny et al., available under doi https://doi.org/10.5194/gmd-2019-330. For details, please refer to the enclosed data description file.</p>
Data set | Water- and land-borne geophysical surveys before and after the sudden water-level decrease of two large karst lakes in southern Mexico (v1.1)
<p>This repository contains raw and processed data along with the Matlab scripts used to prepare the visualizations presented in the manuscript</p> <p>Bücker, M., Flores Orozco, A., Gallistl, J., Steiner, M., Aigner, L., Hoppenbrock, J., Glebe, R., Morales Barrera, W., Pita de la Paz, C., García García, E., Razo Pérez, J.A., Buckel, J., Hördt, A., Schwalb, A., and Perez, L. (2020). <strong><em>Water- and land-borne geophysical surveys before and after the sudden water-level decrease of two large karst lakes in southern Mexico</em></strong>. Submitted to Solid Earth.</p> <p>If you find this data useful in your own research, please mention this data set and/or the manuscript.</p>
Geophysical data set for San Ramon Fault master section
<p>This data set is a multivariable analysis carried out in the San Ramon Fault (SRF) along a master section perpendicular to the main fault scarp. These data include: (1) a ~ 1 km long Electrical resistivity tomography (ERT) with a dipolo-dipolo configuration, 48 channels every 20 meters (named as the master section). (2) Differential GPS data with the location of the ERT electrodes and gravity stations. (3) Data of the gravity stations, with a Garmin GPS data. (4) Seismic data of an active experiment of 24 channels every 5 m, with several hammer strikes, which are explained in a text-document inside each seismic file (TRV_seismic_data and SRF_seismic_data). (5) Time-series of the Nakamura stations located along the ERT master profile. (6) The voltage decay curve of a TEM-station in the western edge of the master section, which is ready for modeling.</p> <p> </p>
Data set related to the manuscript "Efficient prediction of Nucleus Independent Chemical Shifts for polycyclic aromatic hydrocarbons"
<p>Input/output files for Gaussian calculations, data sets for all plots shown in the manuscript "Efficient prediction of Nucleus Independent Chemical Shifts for polycyclic aromatic hydrocarbons", C code for the NICS calculations through the dipolar model and python code for the NICS calculations through the tight-binding model described in the manuscript.</p>
The Set Increment with Limited Views Encoding Ratio (SILVER) Method for Optimizing Radial Sampling of Dynamic MRI: Supporting Data
<p>This data was created to perform the first experiments with the SILVER method of optimizing radial MRI acquisition. The files are mainly .mat files containing the numerical data used to assess the SILVER method using MATLAB (Version R2018b). Instructions on how to use the data and how to reproduce the experiments are available on https://github.com/SophieSchau/SILVER</p>
Enhanced Molecular Spin-Photon Coupling at Superconducting Nanoconstrictions. Open data sets
<p>Includes data relevant for publication with DOI <a href="https://doi.org/10.1021/acsnano.0c03167">10.1021/acsnano.0c03167</a> plus a table with information on how the data were obtained and processed.</p>
Archäologische Chronologie und historische Interpretation: Die Merowingerzeit in Süddeutschland (Correspondence Analysis Data Set)
<p>This data set is a supplement to the book "Archäologische Chronologie und historische Interpretation: Die Merowingerzeit in Süddeutschland" (De Gruyter, 2016) and comprises the archaeological data and the results of the correspondence analysis of Merovingian-period graves from southern Germany and their chronological classification. The data sets for female and male burials can be downloaded as PDF, EXCEL and CSV files.</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.