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286 results for “mocking”
Inscriptions from Bodhgayā, unpublished mock-up. British Museum 1897,0528,0.16.
<p>Alexander Cunningham, Inscriptions from Bodhgayā, unpublished mock-up. Probably prepared in 1891. British Museum 1897,0528,16.</p>
Mock Image Cube
<p>A mock image cube made from the ALMA logo, for testing purposes with radio interferometers. Total flux is comparable to a bright protoplanetary disk in 12CO J=2-1.</p>
Raw X-Ray CT data of CFC-Cu ITER monoblock mock-up
<p>Raw X-Ray CT data for CFC-Cu ITER monoblock mock-up. The monoblock was manufactured at Politecnico di Torino, Italy (Dr Valentina Casalegno) and X-ray tomography scanning was performed at the Manchester X-ray Imaging Facility, University of Manchester, UK (Dr Llion Evans).</p> <p>This data was used for the publication Evans, Ll.M. et al. "Transient Thermal Finite Element Analysis of CFC-Cu ITER Monoblock Using X-ray Tomography Data", Fusion Engineering and Design 2015. DOI: 10.1016/j.fusengdes.2015.04.048</p>
Amazon product reviews (mock dataset)
<p>About</p> <p>This is a mock dataset with Amazon product reviews. Classes are structured: 6 "level 1" classes, 64 "level 2" classes, and 510 "level 3" classes.</p> <p><br> 3 files are shared:</p> <ul> <li>train_40k.csv - training 40k Amazon product reviews</li> <li>valid_10k.csv - 10k reviews left for validation</li> <li>unlabeled_150k.csv - raw 150k Amazon product reviews, these can be used for language model finetuning.</li> </ul> <p>Level 1 classes are: health personal care, toys games, beauty, pet supplies, baby products, and grocery gourmet food.</p> <p>Dataset originally from <a href="https://www.kaggle.com/datasets/kashnitsky/hierarchical-text-classification">https://www.kaggle.com/datasets/kashnitsky/hierarchical-text-classification</a></p>
Plankton Temperature Measurements - Data Management - University of Tennessee - Mock Data
<p><strong>Comparison of conochilus unicornis (CONI) and conochilus hippocrepus (CHIP) depth and colony size over time at three different locations. </strong></p> <p>This contains colony size, depth, and density measurements from Name Pond and the data on which these data were gathered wereTHESE DATES.</p> <p><a href="https://zenodo.org/api/files/984109f0-dde8-46b7-bf8c-5556ac8b76b2/SEH_PlanktonNamePond_v7.27.2012.csv">SEH_PlanktonNamePond_v7.27.2012.csv </a></p> <p>This contains time, temperature, colony size, depth, and density measurements from Name Pond and the data on which these data were gathered wereTHESE DATES.</p> <p><a href="https://zenodo.org/api/files/984109f0-dde8-46b7-bf8c-5556ac8b76b2/SEH_PlanktonTempB_v.7.27.2012.csv">SEH_PlanktonTempA_v.7.27.2012.csv </a></p> <p>This contains time, temperature, colony size, depth, and density measurements from site A and the data on which these data were gathered wereTHESE DATES.</p> <p><a href="https://zenodo.org/api/files/984109f0-dde8-46b7-bf8c-5556ac8b76b2/SEH_PlanktonTempB_v.7.27.2012.csv">SEH_PlanktonTempB_v.7.27.2012.csv </a></p> <p>This contains time, temperature, colony size, depth, and density measurements from Site B and the data on which these data were gathered wereTHESE DATES.</p> <p><strong>Metadata</strong></p> <p>Date: Day that samples were collected</p> <p>Time_Day_Night: Gives time that the sample was gathered in the day</p> <p>Temp_C: Temperature of the water containing the plankton.</p> <p>CONI: Conochilus unicornis - species of plankton</p> <p>CHIP: Conochilus hippocrepis - plankton</p> <p>XXXX_ColonySize_mm: Average diameter (mm) of 5 randomly chose plankton colonies in the sample </p> <p>Temp: TemperatureYSI probe taken once at each depth</p> <p>ChlorophyllA_Units: Chlorophyll A values</p>
16S RC-PCR control samples and mock standards
<p>16S RC-PCR datasets of control samples and mock standards.<br> Produced during the 16S RC-PCR study.</p> <p>Datasets are produced using 2x150 Illumina MiniSeq.<br> A short description of each sample is given in the METADATA_zenodo file.</p>
Inscriptions from Bodhgayā, unpublished mock-up. British Museum 1897,0528,0.17.
<p>Alexander Cunningham, Inscriptions from Bodhgayā, unpublished mock-up, probably prepared in 1891. British Museum 1897,0528,0.17.</p>
Mock Ceramics Dataset
<p>This is an adaptation of SQLite and CSV files from the Portal Project Teaching Database, and of 'portal_data_joined.csv' created by Marco Chiapello. The data has been modified with mock values to represent data about ceramics, for use in a Data Carpentry course to teach R to Archaeologists. The data is not real and may not make sense!</p> <p><strong>Portal Project Teaching Database</strong></p> <p>Ernest, Morgan; Brown, James; Valone, Thomas; White, Ethan P. (2018): Portal Project Teaching Database. figshare. Dataset. https://doi.org/10.6084/m9.figshare.1314459.v10</p> <p><strong>portal_data_joined.csv</strong></p> <p>Chiapello, Marco (2020): Datasets. figshare. Dataset. https://doi.org/10.6084/m9.figshare.8336996.v1</p> <p> </p>
Inscriptions from Bodhgayā, unpublished mock-up. British Museum 1897,0528,0.26
<p>Inscriptions from Bodh Gaya, unpublished mock-up for <em>Mahâbodhi, or the great Buddhist temple under the Bodhi tree at Buddha-Gaya</em>. Probably prepared in 1891. British Museum 1897, 0528, 0.26</p>
Inscriptions from Bodhgayā, unpublished mock-up. British Museum 1897,0528,0.29
<p>Inscriptions from Bodhgayā, part of an unpublished mock-up or <em>Mahâbodhi, or the great Buddhist temple under the Bodhi tree at Buddha-Gaya</em>. Probably prepared in 1891. British Museum 1897, 0528, 0.29</p>
Gamma rays from dark matter spikes in EAGLE simulations - IMBH mock catalogue
<p>DArk Matter SPIkes (DAMSPI) is a fully Python-based software for the analysis of dark matter spikes around Intermediate Mass Black Holes (IMBHs) in the Milky Way. It allows to extract an IMBH catalogue and their corresponding dark matter spike parameters from the EAGLE simulations in order to probe a potential gamma-ray signal from dark matter self-annihilation. </p> <p>The dataset contains the IMBH catalogue including, among others, the coordinates, mass, formation redshift and spike parameters for each individual IMBH. Each column of the catalogue is described in detail in J. Aschersleben et al. (2024). We also provide separate files for which we calculated the gamma-ray fluxes for different dark matter masses and annihilation cross sections. Lastly, we provide a catalogue of our selection of Milky Way like galaxies within EAGLE. The columns of these files are also described in J. Aschersleben et al. (2024).</p> <p>The source code to extract this dataset is publicy available here:</p> <div> <div> <pre><a href="https://doi.org/10.5281/zenodo.11488472">https://doi.org/10.5281/zenodo.11488472</a></pre> </div> </div> <h2>Description of the data files</h2> <p>The imbh_catalogue/imbh/ directory contains the following files:</p> <ol> <li>catalogue_nfw.h5</li> <li>catalogue_cored_gamma_0p3.h5</li> <li>catalogue_cored_gamma_0p9.h5</li> <li>catalogue_cored_gamma_free.h5</li> </ol> <p>They contain the IMBH catalogues, including the coordinates and dark matter spike parameters, calculated assuming the 1.) NFW profile, 2.) cored profile with a fixed core index of 0.0, 3.) cored profile with a fixed core index of 0.4 and 4.) cored profile with the core index as a free fitting parameter.</p> <p>The imbh_catalogue/flux/<channel>/<energy_threshold>/ directory contains the gamma-ray fluxes of the IMBHs for a given annihilation channel, energy threshold, and dark matter mass. E.g. the imbh_catalogue/flux/b_channel/e_th_0.1GeV/m_dm_10.0GeV.h5 file contains the IMBH fluxes assuming the b-channel, an energy threshold of 0.1 GeV and a dark matter mass of 10 GeV. The IMBH fluxes are calculated for a variety of velocity weighted annihilation cross sections. </p> <p>The imbh_catalogue/galaxy/ directory contains the mw_galaxies_catalogue_nfw.h5 file which contains our selection of Milky Way-like galaxies within EAGLE.</p> <p>The HDF files can be opened in Python with:</p> <pre><code>import pandas as pd file_path = "<path_to_file>.h5" df = pd.read_hdf(file_path, key="table") # Printing the first few rows of the DataFrame print(df.head())</code><code> </code></pre>
Mock Neutron raytracing openPMD data
<p>Here a mock dataset in h5 format is uploaded, which complies with the openPMD format with the neutron raytracing extension which is under construction. The extension can be found here: <a href="https://github.com/PaNOSC-ViNYL/openPMD-standard/blob/domex_neutron_raytracing/EXT_NRAYTRACING.md">https://github.com/PaNOSC-ViNYL/openPMD-standard/blob/domex_neutron_raytracing/EXT_NRAYTRACING.md</a></p> <p>The dataset is created manually, and serves as an example of a file that complies with the current version of the openPMD extension for neutron raytracing.The dataset includes particle states of 5000 neutrons after a neutron guide, which are generated with reasonable correlations between velocity, weight and time. The positions at the guide exit are random within a 3 by 8 cm^2 area.</p> <p>The neutron state includes:</p> <ul> <li>position (x,y,z)</li> <li>positionOffset (x,y,z)</li> <li>velocity(x,y,z)</li> <li>polarization (x,y,z)</li> <li>weights</li> <li>time</li> </ul> <p>The python script used to generate this data is included.</p> <p>This dataset is part of the Deliverable D5.1 in Workpackage 5 (Virtual Neutron and X-ray Laboratory) of the Photon and Neutron Open Science Cloud (PaNOSC).</p> <p>This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 823852.</p>
JASMINE mini-Mock survey dataset
<p>Japan Astrometry Satellite Mission for INfrared Exploration (JASMINE) is a satellite mission that measures the precise positions and motions of stars in the Galactic nucleus region and inspects a periodic subtle dimming caused by exoplanets orbiting around M-type dwarf stars.</p> <p>We have generated small-scale JASMINE Galactic Centre survey data, JASMINE mini-Mock Survey (JmMS) data, for testing and validating JASMINE astrometric data analysis, especially for correction of optical distortion. For preliminary software validation, we design a simplified small-scale mock survey for “plate analysis”, where stellar motions and the variation in image distortion are treated as negligible within a dataset.</p> <p>To this end, we select a 1′×1′-target region around the Galactic center. We assume that JASMINE observes 4 fields per one orbit. Two of these fields overlap half of their field-of-view in the Galactic longitude direction, and another two fields overlap by half of their field-of-view in the Galactic latitude direction. The fields of these observations are randomly selected, but at least one of these 4 fields in one orbit covers the selected target region. In this mini-mock survey data, we consider 100 orbits. These data would be valuable for us to validate whether our astrometric analysis code can achieve the expected astrometric accuracy by correcting the image distortion using the assumption that the stellar position in the sky and the higher-order image distortion do not change during the observational time, ∼ 50 min, of one orbit. We summarise how to generate the mock data from the mini JASMINE Galactic Centre survey.</p> <p>The JASMINE mini-survey dataset contains the following files:</p> <ol> <li><code>readme.pdf</code>: A document explaining how the dataset is generated.</li> <li><code>jasmine_validation_augumented_source_catalog.fits.gz</code>: A FITS binary table of the source catalog that contains the ground truth astrometric parameters.</li> <li><code>jasmine_validation_reference_catalog.fits.gz</code>: A FITS binary table of the reference catalog that contains the astrometric parameters resampled from the source catalog.</li> <li><code>jasmine_validation_survey_strategy.fits.gz</code>: A FITS binary table of the survey strategy that describes the observation schedules with the telescope pointings and position angles.</li> <li><code>jasmine_validation_observation-vanilla_e4.0_20240530.tar.gz</code>: An archive of the JmMS dataset in case the telescope has no image distortion and the focal length does not change throughout the mission. The measurement errors are set to ~4.0 mas for all the sources.</li> <li><code>jasmine_validation_observation-distortion_e4.0_20240602.tar.gz</code>: An archive of the JmMS dataset in case the telescope suffers from image distortion and the focal length changes with every plate. The measurement errors are set to ~4.0 mas for all the sources.</li> </ol>
Figure 1 in Molecular identification of mimetic Mock Viper, Psammodynastes pulverulentus (Boie, 1827) (Reptilia: Squamata: Lamprophiidae) from Northeast India
Figure 1. (A) Map showing the distribution of P. pulverulentus in South and Southeast Asian countries. Green circle indicates the collection locality of P. pulverulentus from Mizoram state in northeast India. (B) Live photographs of P. pulverulentus collected from northeast India andthe processed hemipenis showing its sulcal (left) and asulcal (right) surfaces. (C) Bayesian phylogeny based on partial mtCytb gene inferred the phylogenetic relationship of P. pulverulentus with all extant Ophidian families and subfamilies. Numbers at internal branches indicate posterior probabilities support. (D) TCS networks showed distinct haplotype of P. pulverulentus collected from northeast India, compared with other collection localities (China and Myanmar). The estimated haplotypes are shown in different colors as represent by collection sites marked in the phylogeny.
Testing nauyaca: mock catalog and results
<p>Dataset associated with the paper "Nauyaca: a new tool to determine planetary masses and orbital elements through transit timing analysis". It includes the mock catalog of planetary systems and the results of the optimization and MCMC routines.</p>
Shotgun metagenomic sequencing dataset of a synthetic mock community containing 20 genomes spiked-in at even and staggered concentrations.
<p>Shotgun metagenomics (SM) sequencing is a popular method used in microbial ecology to obtain insights on microbial community structure and function potential in a given biological system without the need to cultivate microorganisms. The dataset described in this article describes technical triplicates of shotgun metagenomic sequence libraries generated from two purified and titrated mixes of 20 distinct reference bacterial genomes for which key characteristics such as genome size, sequence and spiked-in concentrations are known. In one of the genomic DNA mix, each genome is spiked-in at similar concentrations (representing an even microbial community) and in the other, genomes are spiked-in at different concentrations with some genomes highly abundant and other in low quantity, mimicking an uneven microbial community DNA extract. In order to be interpretable, SM sequencing data needs to be properly analyzed by complex analytical bioinformatic pipelines. Environments investigated with this method can range from simple to very complex. Typically, microbial communities contain microbes that are ubiquitous and some others much rarer. Analysis of rare microbes in a complex microbial community are challenging to perform as their sequencing signals get submerged by the microbial genomes that are more abundant. In this context, it is critical to have access to sequencing data of simple mock communities of mixes of well characterized genomes in order to develop and validate bioinformatic methods that aim to accurately analyze microbial communities.</p>
Spatially resolved mock observations of stellar kinematics: full radiative transfer treatment of the simulated galaxies of the Auriga project
<p>We present realistic mock spectroscopic observations for state-of-the-art hydrodynamical simulations, using high spectral resolution stellar population models (FSPS) and full radiative transfer treatment with SKIRT. The dataset contains IFS-like spectroscopy of the stellar continuum for 10 (of the 30) galaxies in the Auriga Project at z=0. One galaxy at z=0.8 (Au 6) and another at z=3 (Au 29).</p>
"I made the recording because Iam an amateur recording engineer and also work for a radio station. At the time, Iwas researching for a religious programme, for the radio and by pure chance and good luck, Iwas in the centre of York at the time the street preacher was there. Iam building up a personal library of 'ambient sounds' to use on various radio shows as 'sound effects'. The recording was taken outside St Helen's Church in St Helen's Square, in the centre of York. There was a fairly large crowd walking about, shopping. It was a Saturday. Some people were standing and listening to the man, some were mocking him, others didn't even notice. It was a sunny day, with a slight wind. St Helen's square is a large 'meeting place' for people with seats, flowers and usually musicians. I live in the centre of York and hear a lot of very interesting sounds there, everything from busking musicians, to many foreign languages, church bells, animals and much more. Ireally liked the recording of the preacher as it is quite clear that he passionately believes what he is saying. He was unaware that Iwas recording him. Iwish Ihad captured his whole sermon. He, and other members of his church visit the centre of York quite often, and preach there. Idon't know the name of his church." [Jools/vedas]19 in Collecting Sounds. Online Sharing of Field Recordings as Cultural Practice
"I made the recording because Iam an amateur recording engineer and also work for a radio station. At the time, Iwas researching for a religious programme, for the radio and by pure chance and good luck, Iwas in the centre of York at the time the street preacher was there. Iam building up a personal library of 'ambient sounds' to use on various radio shows as 'sound effects'. The recording was taken outside St Helen's Church in St Helen's Square, in the centre of York. There was a fairly large crowd walking about, shopping. It was a Saturday. Some people were standing and listening to the man, some were mocking him, others didn't even notice. It was a sunny day, with a slight wind. St Helen's square is a large 'meeting place' for people with seats, flowers and usually musicians. I live in the centre of York and hear a lot of very interesting sounds there, everything from busking musicians, to many foreign languages, church bells, animals and much more. Ireally liked the recording of the preacher as it is quite clear that he passionately believes what he is saying. He was unaware that Iwas recording him. Iwish Ihad captured his whole sermon. He, and other members of his church visit the centre of York quite often, and preach there. Idon't know the name of his church." [Jools/vedas]19
Genotyping by sequencing for estimating relative abundances of diatom taxa in mock communities
Open the record for dataset details and reuse information.
Clouds and Radiation in a mock-Walker Circulation model output
<p>Data used in the creation of figures for Silvers and Robinson, Clouds and Radiation in a mock-Walker Circulation. 2020, JAMES. </p> <p>These experiments are described in detail in the paper. They use the AM4.0 physics package on a doubly periodic domain with a warm patch of SST that is fixed in time at the center of the domain. Grid-Spacings of 1km, 2km, 25km, and 100km are explored on different domain sizes. The data set focuses on analysis of the atmospheric circulation and cloud response to this SST warm region.</p> <p>For questions please contact Levi Silvers at levi.silvers@stonybrook.edu, thomas.robinson@noaa.gov</p> <p>Directory contents are described below:</p> <p>#=========================================================================================<br> # data for the P100, P100L, P25, P25L, E25 experiments are in the following directories <br> #=========================================================================================<br> # P100<br> c8x40L33_am4p0_100km_wlkr_ent0p9<br> c8x40L33_am4p0_100km_wlkr_ent0p9_lwoff<br> # P100L<br> c8x160L33_am4p0_100km_wlkr_ent0p9<br> c8x160L33_am4p0_100km_wlkr_ent0p9_lwoff<br> # P25<br> c8x160L33_am4p0_25km_wlkr_ent0p9<br> c8x160L33_am4p0_25km_wlkr_ent0p9_lwoff<br> # P25L<br> c8x640L33_am4p0_25km_wlkr_ent0p9<br> c8x640L33_am4p0_25km_wlkr_ent0p9_lwoff<br> # E25<br> c8x160L33_am4p0_25km_wlkr_ent0p9_noconv<br> c8x160L33_am4p0_25km_wlkr_ent0p9_noconv_lwoff</p> <p># within the directories for each of these experiments: P100, P100L, P25, P25L, E25<br> # there are two files: 1979th1983_daily.nc and 1980th1983.atmos_month_tmn.nc</p> <p># within each of these files are the following variables: <br> 1979th1983_daily.nc<br> 'prec_conv'<br> 'prec_ls'<br> 'precip'</p> <p>1980th1983.atmos_month_tmn.nc<br> 'ps'<br> 'temp'<br> 'ucomp'<br> 'w'<br> 'sphum'<br> 'cld_amt'<br> 'z_full'<br> 'precip'<br> 'prec_conv'<br> 'prec_ls'<br> 'rh'<br> 'tot_liq_amt'<br> 'tot_ice_amt'<br> 'tdt_lw'<br> 'evap'<br> 'shflx'</p> <p><br> #=========================================================================================<br> # data for the E1 and E2 experiments are in the following directories <br> #=========================================================================================<br> # E1 <br> c10x4000L33_am4p0_1km_wlkr_4K<br> c10x4000L33_am4p0_1km_wlkr_4K_lwoff<br> # E2 <br> c50x2000L33_am4p0_2km_wlkr_4K<br> c50x2000L33_am4p0_2km_wlkr_4K_lwoff</p> <p># within each of these directories are two files: 1979_6mn.atmos_month.nc and 1979.6mn.atmos_daily_selvars.nc<br> # these files contain the following variables: </p> <p># 1979_6mn.atmos_month.nc<br> tdt_lw<br> t_surf<br> evap<br> shflx<br> tot_ice_amt<br> tot_liq_amt<br> rh<br> precip<br> z_full<br> cld_amt<br> sphum<br> ucomp<br> temp <br> w<br> ps<br> shflx</p> <p># 1979.6mn.atmos_daily_selvars.nc<br> t_surf<br> ps<br> precip</p> <p>Acknowledgment: Aparna Radhakrishnan supported the provision of this Zenodo DOI. <br> </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.