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143 results for “toys”
Slice-by-Slice X-ray Tomography dataset of Dog Toy
<p>This submission contains a dataset used in the paper</p> <p>"Ajinkya Kadu, Felix Lucka, and K. Joost Batenburg. "Single-shot Tomography of Discrete Dynamic Objects." <em>arXiv preprint <a href="https://arxiv.org/abs/2311.05269">arXiv:2311.05269</a></em> (2023)."</p> <p>The data collection has been acquired using a highly flexible, programmable and custom-built X-ray CT scanner, the FleX-ray scanner, developed by <a href="https://info.tescan.com/micro-ct">TESCAN-XRE NV,</a> located in the FleX-ray Lab at the <a href="https://www.cwi.nl/en/">Centrum Wiskunde & Informatica (CWI)</a> in Amsterdam, Netherlands. It consists of a cone-beam microfocus X-ray point source (limited to 90 kV and 90 W) that projects polychromatic X-rays onto a 14-bit CMOS (complementary metal-oxide semiconductor) flat panel detector with CsI(Tl) scintillator (Dexella 1512NDT). To create a 2D dataset, a fan-beam geometry was mimicked by only reading out the central row of the detector, which results in 956 detector pixel with an effective length of 149.6 <strong>μ</strong>m each. Between source and detector there is a rotation stage, upon which the sample was mounted. The sample that we imaged was a dog toy in a shape of a bone made of a rubber. The X-ray tube voltage was 90kV and a copper filter was used to block the low-energy part of the spectrum to limit beam-hardening artifacts. The source-to-detector distance was 487.9 mm, while the source-to-origin of the sample was 374.5 mm in a fan-beam geometry. We acquired 673 z-slices with 0.25 mm distance between slices. Further information about the technical details of X-ray CT can be found in the <a href="https://arxiv.org/abs/2311.05269">above paper</a> and in </p> <p>Maximilian B. Kiss, Sophia B. Coban, K. Joost Batenburg, Tristan van Leeuwen, and Felix Lucka “2DeteCT - A large 2D expandable, trainable, experimental Computed Tomography dataset for machine learning", <a href="https://doi.org/10.1038/s41597-023-02484-6"><em>Sci Data</em> <strong>10</strong>, 576 (2023)</a> or <a href="https://arxiv.org/abs/2306.05907">arXiv:2306.05907 (2023)</a></p> <p>The upload consists of two files, namely:</p> <ol> <li>GrayBone90kV4Filter.zip: contains the raw measurement data.</li> <li>GrayBone90kV4FilterPreprocessed.mat: contains preprocessed data to be used in the MATLAB script provided to do pseudo-dynamic tomography. It also contains reference reconstruction obtained via Filtered Back Projection (FBP) algorithm. </li> </ol> <p>In the Github repository <a href="https://github.com/ajinkyakadu/DynamicXRayCT">https://github.com/ajinkyakadu/DynamicXRayCT</a>, we provide the scripts to read and process the raw data. The Github repository also contains all the scripts to reconstruct the dynamic solution using advanced algorithms. Furthermore, the raw data formats are described in great details in <a href="https://www.nature.com/articles/s41597-023-02484-6">Kiss et al 2023</a> paper referenced above. </p>
S91| CECTOYS | Chemicals of Emerging Concern (CECs) in plastic toys
<p>This is the collection associated with list S91 CECTOYS, List of Chemicals of Emerging Concern (CECs) found in plastic toys on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p> </p> <p>A compiled list of 126 Chemicals of Emerging Concern (CECs) found in plastic toys described in Aurisano et. al DOI: 10.1016/j.envint.2020.106194. The list is categorized into four (as seen in CECTOYS_notes.txt) based on being included in regulatory lists of concern as well as reported hazard index (HI) and child cancer risk (CCR) based criteria.</p> <p>Structural identifiers and mapping to DTXSID provided by ECI.</p> <p> </p>
Datasets of fitted toy Monte Carlo samples for "Gaussian Process-based calculation of look-elsewhere trials factor"
<p>For the paper "Gaussian Process-based calculation of look-elsewhere trials factor", we generated and fitted a number of toy Monte Carlo samples which can be used to reproduce figures from the paper or do tests of hypotheses inspired by the method suggested in the article.</p> <p>Together with the hdf5 files containing the samples, we also provide a Jupyter notebook which was used to produce the figures.</p>
Antihypertensive drug metabolite screening toy dataset for MS/MS application
<p><strong>Objectives :</strong></p> <p>Detect and visualize antihypertensive drug metabolites in untargeted metabolomics experiments</p> <p><strong>Cohort :</strong></p> <p>6/26 patients on antihypertensive therapy</p> <p><strong>Mass spectrometer :</strong></p> <p>Thermo Q-Exactive coupled to pHILIC chromatography using data dependent analysis (DDA) MS/MS gas-phase experiments</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>
Sacurine toy dataset - 6 samples / 3 QC / 3 Blanks
<p><strong>Objective: </strong></p> <p>Influence of age, body mass index, and gender on the urine metabolome</p> <p><strong>Cohort: </strong></p> <p>183 employees from CEA</p> <p><strong>LC-HRMS: </strong></p> <p>LTQ-Orbitrap (negative ionization mode)</p>
Toy Qualitative Data Project (Interview Transcripts)
<p><strong>Please be advised that this project is intended solely for instructional purposes and should not be used for actual research. This dataset is intended to complement the instructional material and provide a hands-on learning experience for the workshop: <a href="https://rcurty.github.io/qualdata-training">Handling and Sharing Qualitative Data Responsibly and Effectively</a>.</strong></p> <p>This hypothetical research project is designed to demonstrate key concepts related to human subject qualitative data management and thematic analysis coding. It includes interview transcripts generated with ChatGPT 4.0 Mini for a fictional graduate student in Communication named Sarah, whose main research question is: <em><strong>How do content creators/digital influencers view their role in shaping their followers' consumer behavior, and what ethical dilemmas do they face when promoting products?</strong></em></p> <div> <p>Given the novelty of this research topic and the limited academic literature available, Sarah hopes that the insights gained from this small-scale qualitative exploratory study will help identify key variables for a larger survey study with a representative sample of content creators/digital influencers across the U.S.</p> <p>Sarah has previous experience with quantitative methods but is very new to qualitative research and could use our help for better handling the data. Having already conducted six short structured interviews with subjects from top revenue niches (i.e., Home Decor and DYI, Travel & Adventure, Fashion & Style, Health & Wellness, Finance & Investment, Beauty & Skincare) and planning to conduct a dozen more, Sarah is eager to begin engaging with the data she has collected so far and deciding how to best organize and interpret it. We’ll be walking her through this process, providing the necessary guidance and support for effective and responsible data management.</p> <p>Interviews were conducted over Zoom and audio recorded with participants' consent. The interview included four main questions, which were consistent across all interviews:</p> <p><em>Q1. Please tell me a little about your work as a content creator/digital influencer how it started, and how you have established yourself in your current niche.</em></p> <p><em>Q2. In what ways do you believe content creators/digital influencers shape consumer behavior? Could you share any examples?</em></p> <p><em>Q3. What strategies would you say content creators/digital influencers typically use to increase sales of sponsored products and services? Which ones have you used? What worked and what did not work for you? Why?</em></p> <p><em>Q4. In your view, what are the essential ethical responsibilities that content creators and digital influencers should uphold? Can you share any personal experiences that illustrate these responsibilities in action?</em></p> <p>Each interview generated approximately 15 minutes of audio recording, which Sarah manually transcribed. Sarah decided to keep the transcription true to the recordings and seek assistance to mitigate any risk of identification. </p> </div>
Paired-end sequencing data toy example (measles)
<p>700,000 paired-end reads essentially Morbillivirus hominis to be used for testing purpose in the Sequana project</p>
Dataset: Dow Jones U.S. Toys Total Stock (RILYM) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Toy 'tilt' data
<p>Toy data shown in the toy 'tilt' app.</p>
Sacurine toy dataset - 2 samples / 2 blanks
<p><strong>Objective: </strong></p> <p>Influence of age, body mass index, and gender on the urine metabolome</p> <p><strong>Cohort: </strong></p> <p>183 employees from CEA</p> <p><strong>LC-HRMS: </strong></p> <p>LTQ-Orbitrap (negative ionization mode)</p>
AdaptSearch - toy dataset - Trinity 4 samples
<p><a href="https://github.com/abims-sbr/adaptsearch"><strong>AdaptSearch</strong></a> is dedicated to the search of selection within branches and sites of phylogenetic trees built from groups of orthologous genes. The first tools proceeds to data filtering and building of genes groups, while the second part proceeds to phylogeny building (using RAxML) and selection detection (codeML).</p> <p>This dataset can be used as toy example to test the pipeline</p>
Coralshift Master of Research Project Toy Datasets
<p><a href="https://pro.arcgis.com/en/pro-app/latest/help/data/multidimensional/what-is-netcdf-data.htm#:~:text=NetCDF%20(network%20Common%20Data%20Form,view%20from%20the%20netCDF%20file.">.netcdf</a> files for demonstrating methods and visualising data reported in the "Coralshift" Master of Research project. Datasets simplified to reduce computational demand and/or limited to <a href="http://pid.geoscience.gov.au/dataset/ga/115066">Great Barrier Reef A</a>.</p> <p><strong>GBR_A_Couce_comparison.nc </strong>– time-parameterised statistics for comparison to Couce et al. (<a href="https://doi.org/10.1111/j.1365-2699.2012.02706.x">2013</a>, <a href="https://doi.org/10.1111/gcb.16690">2023</a>).</p> <p><strong>GBR_A_all_variables_timeseries.nc </strong>– annual means of all variables used in the accompanying report.</p>
UFO Files for the Toy ABC Model
<p>The UFO model correspond to the toy ABC model that which consists of three scalar particles which interact with each other only when all three are present at the vertex.</p> <p>Prepared using LanHEP, this UFO model can be used with MadGraph5.</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.