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Dataset results
21 results for “low-resolution”
Simplified Object Detection for Manufacturing: Introducing a Low-Resolution Dataset
<p>This dataset was published with the dataset descriptor "Simplified Object Detection for Manufacturing: Introducing a Low-Resolution Dataset".</p> <p>ACKNOWLEDGEMENTS</p> <p>The project ”ZUKIPRO” is funded as part of the ”Future Centers” program by the Federal<br>Ministry of Labour and Social Affairs and the European Union through the European Social<br>Fund Plus (ESF Plus).Roles and Contributions.</p>
DiffModeler: Large Macromolecular Structure Modeling in Low-Resolution Cryo-EM Maps Using Diffusion Model
<p>Here, we store the modeled structures generated by DiffModeler for its 4 benchmark datasets: CryoREAD dataset(0-5A resolution, protein-DNA/RNA complex), ModelAngelo dataset(0-5A resolution, most protein complexes, a few protein-RNA complex), intermediate resolution dataset (5-10A resolution, protein complex), low resolution dataset (10-20A resolution, protein complex). For all protein-DNA/RNA complex, the map will be modeled by CryoREAD+DiffModeler.</p> <p>For each dataset, we keep the modeled structures by DiffModeler, named as [EMD-ID]_DiffModeler.cif; and their corressponding native structures from RCSB are saved as [EMD-ID]_[PDB_ID]_native.cif.</p> <p>For CryoREAD dataset, it includes 61 targets. For ModelAngelo dataset, it includes 28 targets. For intermediate resolution dataset , it includes 71 targets. For low resolution dataset, it incldues 6 targets.</p> <p>For intermediate resolution dataset, many maps were run with inaccurate AF2 predicted single-chain structures. We also benchmarked DiffModeler's performance by using native single-chain structures as input. They are saved under "dataset_5_10A_nativechain" folder.</p> <p>Additionally, we have stored the traced backbone map of the intermediate resolution dataset in the "dataset_5_10A_diffusion_traced_backbone_map" folder. The traced maps are saved as [EMD-ID]_diffusion.mrc. The intermediate reverse diffusion maps of the intermediate resolution dataset are saved in the "dataset_5_10A_reverse_diffusion_maps" folder. Each sub-folder is named according to the corresponding map's [EMD-ID] and contains three intermediate reverse diffusion maps: 20percentile_reverse_diffusion.mrc, 50percentile_reverse_diffusion.mrc, and 80percentile_reverse_diffusion.mrc. A higher percentile indicates a map closer to the end of the reverse diffusion steps.</p> <p>If you used DiffModeler, please cite: "Wang, Xiao, Han Zhu, Genki Terashi, Manav Taluja, and Daisuke Kihara. "DiffModeler: Large Macromolecular Structure Modeling in Low-Resolution Cryo-EM Maps Using Diffusion Model." bioRxiv (2024): 2024-01.".</p> <p>If you used CryoREAD, please cite: "Xiao Wang, Genki Terashi & Daisuke Kihara. De novo structure modeling for nucleic acids in cryo-EM maps using deep learning. Nature Methods, 2023."</p>
Low-resolution infrared thermal dataset and potential privacy-preserving applications
<p>A low-resolution infrared thermal dataset of people and thermal objects, such as a working laptop, in indoor environments. The dataset was collected by a far infrared thermal camera (32 by 24 pixels), which can capture the position and shape information of thermal objects without privacy issues that enable trustworthy computer vision applications. The dataset consists of 1770 thermal images with high-quality annotation collected from an indoor room with around 15 degrees.</p>
Stellar chromospheric activity database of solar-like stars based on the Ca II H and K lines of LAMOST Low-Resolution Spectroscopic Survey: the bolometric and photospheric calibration
<p>This database contains the stellar chromospheric activity parameters of solar-like stars based on the Ca II H and K lines from the spectra of the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) Low-Resolution Spectroscopic Survey (LRS). We extract 1,122,495 LRS spectra of solar-like stars from the LAMOST Data Release 8 v2.0 and provide the chromospheric activity parameters $S_{\rm tri}$, $S_{\rm MWO}$, $R_{\rm HK}$ and $R'_{\rm HK}$, as well as their uncertainties in the database. $R_{\rm HK}$ and $R'_{\rm HK}$ are derived from the method in the classic literature (denoted with classic) and the method based on the PHOENIX model (denoted with PHOENIX). These stellar chromospheric activity indexes and their uncertainties, along with the relevant spectroscopic parameters from the LAMOST catalogs, are saved in a CSV-format file (name: CaIIHK_Activity_Indexes_LAMOST_DR8_LRS.csv).</p> <p> </p> <p>Columns in the catalog of the database:</p> <p>obsid - Unique observation identifier of LAMOST LRS spectrum</p> <p>obsdate-Spectral observation date</p> <p>fitsname - FITS file name of LAMOST LRS spectrum</p> <p>snrg - Signal-to-noise ratio in the $g$ band ($\mathrm{S/N}_g$) of LAMOST LRS spectrum</p> <p>snrr - Signal-to-noise ratio in the $r$ band ($\mathrm{S/N}_r$) of LAMOST LRS spectrum</p> <p>teff - Effective temperature ($T_\mathrm{eff}$) derived from the LASP (unit: K)</p> <p>teff_err - Uncertainty of $T_\mathrm{eff}$ derived from the LASP (unit: K)</p> <p>logg - Surface gravity ($\log\,g$) derived from the LASP (unit: dex)</p> <p>logg_err - Uncertainty of $\log\,g$ derived from the LASP (unit: dex)</p> <p>feh - Metallicity ([Fe/H]) derived from the LASP (unit: dex)</p> <p>feh_err - Uncertainty of [Fe/H] derived from the LASP (unit: dex)</p> <p>rv - Radial velocity ($V_r$) derived from the LASP (unit: km/s)</p> <p>rv_err - Uncertainty of $V_r$ derived from the LASP (unit: km/s)</p> <p>ra_obs - Right ascension (RA) of fiber pointing (unit: degree)</p> <p>dec_obs - Declination (DEC) of fiber pointing (unit: degree) </p> <p>gaia_source_id - Source identifier in Gaia DR3 catalog</p> <p>gaia_g_mean_mag - $G$ magnitude in Gaia DR3 catalog</p> <p>S_tri* - $S_{\rm tri}$ index of LAMOST LRS </p> <p>S_tri_err* - Uncertainty of $S_{\rm tri}$ </p> <p>S_MWO* - $S_{\rm MWO}$ </p> <p>S_MWO_err* - Uncertainty of $S_{\rm MWO}$ </p> <p>log_R_HK_classic* - $\log\,R_{\rm HK,classic}$ </p> <p>log_R_HK_classic_err* - Uncertainty of $\log\,R_{\rm HK,classic}$ </p> <p>log_R_p_HK_classic* - $\log\,R'_{\rm HK,classic}$ </p> <p>log_R_p_HK_classic_err* - Uncertainty of $\log\,R'_{\rm HK,classic}$ </p> <p>log_R_HK_PHOENIX* - $\log\,R_{\rm HK,PHOENIX}$ </p> <p>log_R_HK_PHOENIX_err* - Uncertainty of $\log\,R_{\rm HK,PHOENIX}$ </p> <p>log_R_p_HK_PHOENIX* - $\log\,R'_{\rm HK,PHOENIX}$ </p> <p>log_R_p_HK_PHOENIX_err* - Uncertainty of $\log\,R'_{\rm HK,PHOENIX}$ </p> <p>(Note: The columns denoted by an asterisk represent the parameters provided by this database, and the remaining columns are collected from the data release of LAMOST DR8 V2.0. If the values are not available, they will be filled with '-9999' in this database.)</p>
Data for "Measuring mean radiant temperature for indoor comfort assessment using low-resolution optical sensors"
<p>Data for "Measuring mean radiant temperature for indoor comfort assessment using low-resolution optical sensors".</p>
Stellar chromospheric activity spectral database of solar-type stars based on the LAMOST Low-Resolution Spectroscopic Survey(disused)
<p>A stellar chromospheric activity spectral database of solar-type stars is constructed based on the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) Low-Resolution Spectroscopic Survey (LRS). The database contains 1,330,654 high-quality LRS spectra of solar-type stars with <em>T</em><sub>eff</sub> ranging from 4800 K to 6800 K. We measure the mean fluxes at line cores of the Ca II H&K lines using a 1 Å rectangular bandpass as well as a 1.09 Å full width at half maximum (FWHM) triangular bandpass, and the mean fluxes of two 20 Å pseudo-continuum bands on the two sides of the lines. Three chromospheric activity indexes, <em>S</em><sub>rec</sub> using the rectangular bandpass, and <em>S</em><sub>tri</sub> and <em>S</em><sub>MWL</sub> using the triangular bandpass, are evaluated based on the measured fluxes. The uncertainties of all the obtained parameters are estimated. We also produce spectrum diagrams of Ca II H&K lines for all the spectra in the database. This database with more than one million high-quality LAMOST LRS spectra and basal chromospheric activity parameters can be further used for investigating activity characteristics of solar-type stars and solar-stellar connection.</p> <p> </p> <p>The entity of the database is composed of (1) a catalog of spectral sample and activity parameters, and (2) a library of spectrum diagrams.</p> <p>(1) Catalog of Spectral Sample and Activity Parameters<br> CaIIHK_Sindex_LAMOST_DR7_LRS.csv</p> <p>(2) Library of Spectrum Diagrams<br> spectrum_diagrams_000-049.zip (46 subfolders)<br> spectrum_diagrams_050-099.zip (40 subfolders)<br> spectrum_diagrams_100-149.zip (47 subfolders)<br> spectrum_diagrams_150-199.zip (49 subfolders)<br> spectrum_diagrams_200-249.zip (47 subfolders)<br> spectrum_diagrams_250-299.zip (45 subfolders)<br> spectrum_diagrams_300-349.zip (41 subfolders)<br> spectrum_diagrams_350-399.zip (39 subfolders)<br> spectrum_diagrams_400-449.zip (45 subfolders)<br> spectrum_diagrams_450-499.zip (35 subfolders)<br> spectrum_diagrams_500-549.zip (27 subfolders)<br> spectrum_diagrams_550-599.zip (38 subfolders)<br> spectrum_diagrams_600-649.zip (32 subfolders)<br> spectrum_diagrams_650-699.zip (26 subfolders)<br> spectrum_diagrams_700-749.zip (28 subfolders)</p> <p> </p>
Stellar chromospheric activity spectral database of solar-type stars based on the LAMOST Low-Resolution Spectroscopic Survey (disused)
<p>A stellar chromospheric activity spectral database of solar-type stars is constructed based on the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) Low-Resolution Spectroscopic Survey (LRS). The database contains 1,330,654 high-quality LRS spectra of solar-type stars with effective temperature ranging from 4800 K to 6800 K. We measure the mean fluxes at line cores of the Ca II H&K lines using a 1 Å rectangular bandpass as well as a 1.09 Å full width at half maximum (FWHM) triangular bandpass, and the mean fluxes of two 20 Å pseudo-continuum bands on the two sides of the lines. Three chromospheric activity indexes, <em>S</em><sub>rec</sub> based on the 1 Å rectangular bandpass, and <em>S</em><sub>tri</sub> and <em>S</em><sub>MWL</sub> based on the 1.09 Å FWHM triangular bandpass, are evaluated from the measured fluxes. The uncertainties of all the obtained parameters are estimated. We also produce spectrum diagrams of Ca II H&K lines for all the spectra in the database. This database with more than one million high-quality LAMOST LRS spectra and basal chromospheric activity parameters can be further used for investigating activity characteristics of solar-type stars and solar-stellar connection.</p> <p> </p> <p>The entity of the database is composed of (1) a catalog of spectral sample and activity parameters, and (2) a library of spectrum diagrams.</p> <p>(1) Catalog of Spectral Sample and Activity Parameters<br> CaIIHK_Sindex_LAMOST_DR7_LRS.csv</p> <p>(2) Library of Spectrum Diagrams<br> spectrum_diagrams_000-049.zip (46 subfolders)<br> spectrum_diagrams_050-099.zip (40 subfolders)<br> spectrum_diagrams_100-149.zip (47 subfolders)<br> spectrum_diagrams_150-199.zip (49 subfolders)<br> spectrum_diagrams_200-249.zip (47 subfolders)<br> spectrum_diagrams_250-299.zip (45 subfolders)<br> spectrum_diagrams_300-349.zip (41 subfolders)<br> spectrum_diagrams_350-399.zip (39 subfolders)<br> spectrum_diagrams_400-449.zip (45 subfolders)<br> spectrum_diagrams_450-499.zip (35 subfolders)<br> spectrum_diagrams_500-549.zip (27 subfolders)<br> spectrum_diagrams_550-599.zip (38 subfolders)<br> spectrum_diagrams_600-649.zip (32 subfolders)<br> spectrum_diagrams_650-699.zip (26 subfolders)<br> spectrum_diagrams_700-749.zip (28 subfolders)</p>
Stellar chromospheric activity spectral database of solar-type stars based on the LAMOST Low-Resolution Spectroscopic Survey(disused)
<p>A stellar chromospheric activity spectral database of solar-type stars is constructed based on the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) Low-Resolution Spectroscopic Survey (LRS). The database contains 1,330,654 high-quality LRS spectra of solar-type stars with effective temperature ranging from 4800 K to 6800 K. We measure the mean fluxes at line cores of the Ca II H&K lines using a 1 Å rectangular bandpass as well as a 1.09 Å full width at half maximum (FWHM) triangular bandpass, and the mean fluxes of two 20 Å pseudo-continuum bands on the two sides of the lines. Three chromospheric activity indexes, <em>S</em><sub>rec</sub> based on the 1 Å rectangular bandpass, and <em>S</em><sub>tri</sub> and <em>S</em><sub>MWL</sub> based on the 1.09 Å FWHM triangular bandpass, are evaluated from the measured fluxes. The uncertainties of all the obtained parameters are estimated. We also produce spectrum diagrams of Ca II H&K lines for all the spectra in the database. This database with more than one million high-quality LAMOST LRS spectra and basal chromospheric activity parameters can be further used for investigating activity characteristics of solar-type stars and solar-stellar connection.</p> <p> </p> <p>The entity of the database is composed of (1) a catalog of spectral sample and activity parameters, and (2) a library of spectrum diagrams.</p> <p>(1) Catalog of Spectral Sample and Activity Parameters<br> CaIIHK_Sindex_LAMOST_DR7_LRS.csv</p> <p>(2) Library of Spectrum Diagrams<br> spectrum_diagrams_000-049.zip (46 subfolders)<br> spectrum_diagrams_050-099.zip (40 subfolders)<br> spectrum_diagrams_100-149.zip (47 subfolders)<br> spectrum_diagrams_150-199.zip (49 subfolders)<br> spectrum_diagrams_200-249.zip (47 subfolders)<br> spectrum_diagrams_250-299.zip (45 subfolders)<br> spectrum_diagrams_300-349.zip (41 subfolders)<br> spectrum_diagrams_350-399.zip (39 subfolders)<br> spectrum_diagrams_400-449.zip (45 subfolders)<br> spectrum_diagrams_450-499.zip (35 subfolders)<br> spectrum_diagrams_500-549.zip (27 subfolders)<br> spectrum_diagrams_550-599.zip (38 subfolders)<br> spectrum_diagrams_600-649.zip (32 subfolders)<br> spectrum_diagrams_650-699.zip (26 subfolders)<br> spectrum_diagrams_700-749.zip (28 subfolders)</p>
Project files provided as supporting information to the manuscript "Information-theoretical measures identify accurate low-resolution representations of protein configurational space"
<p>The dataset contains the following compressed folder:</p> <p>-Notebooks.zip:</p> <p>This folder contains:<br> -python_script:<br> -RESREL.py: script performing the clusterization and computing the relevance resolution curves<br> -random_curves.py: script generating the random value and computing the corresponding RES-REV curves_s<br> -Cluster_distance_matrix.py: script returning the distance among clusters for a given partition.<br> -python_notebook:<br> -Exploratory_analysis.ipynb: Analysis performed on the 12-protein_dataset<br> -DMAPS_ANTI.ipynb: Diffusion Map for the Antibody<br> -DMAPS_COV_1ake.ipynb: Diffusion Map + Inter-Intra state decomposition of covariance for 1ake</p> <p>Packages required for the usage of these python scripts/notebooks:<br> -numpy<br> -pandas<br> -matplotlib<br> -seaborn<br> -multiprocessing<br> -scipy</p> <p> </p> <p>========<br> RAW DATA<br> ========</p> <p>The raw data produced and employed in this study are available on a Google Drive folder at the following address:</p> <p>https://drive.google.com/drive/folders/1PasAUCgpR5-gdzUVEdyusgZIayQN0Le9</p> <p>In this folder, together with the compressed Notebooks.zip folder, one can fin the compressed folder Data.zip, within which the following data are present:</p> <p>-12-protein_dataset:<br> -md.mdp: the .mdp file used in the MD simulations<br> -PROTEIN_PDB_CODE:<br> -Hk_{sel}.npy & Hs_{sel}.npy: the Rel & Res curves, sel=[all, CA, CB]<br> -RMSD_{sel}.npy: the RMSD matrix, sel=[all, CA, CB]<br> -npt.gro:protein+water+ions structure @TEO the equilibration (NVT+NPT)<br> -MSR_df.csv: a dataset containing the following columns<br> 'area' : area behind the Relevance-Resolution curve;<br> 'selection': the atomic selection (['all', 'CA', 'CB']) used to compute the RMSD matrix used for the clusterization (and consequently the Relevance-Resolution curves)<br> 'method': the linkage measure used in the clustering procedure, an integer in [0,6];<br> 'method_name': the linkage measure used in the clustering procedure, a string in ['average','ward','complete','single','centroid','median','weighted'];<br> 'rmsd_mean': the mean value of the rmsd vector along the trajectory computed wrt the first frame;<br> 'rmsd_var': the variance of the rmsd vector along the trajectory computed wrt the first frame;<br> 'rgy_mean': the mean value of the radius of gyration along the trajectory;<br> 'rgy_var': the variance of the radius of gyration along the trajectory;<br> 'rmsf_mean': the mean value of the rmsf;<br> 'rmsf_var': the variance of the rmsf;<br> 'RMSD_M_mean': the mean value of the RMSD matrix.<br> 'RMSD_M_var': the variance of the RMSD matrix.<br> -Random:<br> -curves.npy= 100K Relevance-Resolution Random curves for M=40001<br> -curves_s.npy= 100K Relevance-Resolution Random curves for M=15000<br> -validation_dataset:<br> -antibody:<br> -Hk_CB.npy & Hs_CB.npy: the Rel & Res curves<br> -RMSD_CB.npy: the RMSD matrix<br> -DIFF_{M}.npy: the eigenvalue/vector of the 10-D diffusion space<br> -Label_{method}.npy: the label vector for n_clusters<br> -1ake:<br> -Hk_{sel}.npy & Hs_{sel}.npy: the Rel & Res curves<br> -RMSD_{sel}.npy: the RMSD matrix<br> -DIFF_{M}.npy: the eigenvalue/vector of the 10-D diffusion space<br> -Label_{method}.npy: the label vector for n_clusters<br> -intra_{m}.npy: the intra-cluster covariance matrix<br> -inter_cov_{m}.npy: the inter-cluster correlation matrix</p> <p> </p> <p>NOTE<br> =====</p> <p>The matrices of the cluster distances for adenylate kinase and antibody have been computed through the script Cluster_distance_matrix.py.</p> <p>These matrices have not been included in the dataset because of their large size; the raw data are however available upon request.<br> </p>
Revisiting the impacts of Stochastic Multicloud model on the MJO using low-resolution ECHAM6.3 atmosphere model
<p>There are the corresponding source codes and input data used to run the numerical experiments. Analysis scripts and model results are also included.</p>
Stellar chromospheric activity database of solar-like stars based on the LAMOST Low-Resolution Spectroscopic Survey
<p>A stellar chromospheric activity database of solar-like stars is constructed based on the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) Low-Resolution Spectroscopic Survey (LRS). The database contains spectral bandpass fluxes and indexes of Ca II H and K lines derived from 1,330,654 high-quality LRS spectra of solar-like stars. We measure the mean fluxes at line cores of the Ca II H and K lines using a 1 Å rectangular bandpass and a 1.09 Å FWHM triangular bandpass, as well as the mean fluxes of two 20 Å wide pseudocontinuum bands on the two sides of the lines. Three activity indexes of Ca II H and K lines, <em>S</em><sub>rec</sub> based on the 1 Å rectangular bandpass and <em>S</em><sub>tri</sub> and <em>S<sub>L</sub></em> based on the 1.09 Å FWHM triangular bandpass, are evaluated from the measured fluxes to quantitatively indicate the chromospheric activity level. The uncertainties of all the obtained parameters are estimated. We also produce spectrum diagrams of Ca II H and K lines for all the spectra in the database. This database, with more than one million high-quality LAMOST LRS spectra of Ca II H and K lines and basal chromospheric activity parameters, can be further used for investigating activity characteristics of solar-like stars and solar-stellar connection.</p> <p> </p> <p>The entity of the database is composed of (1) a catalog of spectral sample and activity parameters, and (2) a library of spectrum diagrams of Ca II H and K lines.</p> <p>(1) Catalog of Spectral Sample and Activity Parameters<br> CaIIHK_Sindex_LAMOST_DR7_LRS.csv<br> (see Table3 in the paper 2022_ApJS_263_12 for description of the columns)</p> <p>(2) Library of Spectrum Diagrams of Ca II H and K lines<br> spectrum_diagrams_000-049.zip (46 subfolders)<br> spectrum_diagrams_050-099.zip (40 subfolders)<br> spectrum_diagrams_100-149.zip (47 subfolders)<br> spectrum_diagrams_150-174.zip (25 subfolders)<br> spectrum_diagrams_175-199.zip (24 subfolders)<br> spectrum_diagrams_200-224.zip (24 subfolders)<br> spectrum_diagrams_225-249.zip (23 subfolders)<br> spectrum_diagrams_250-259.zip (10 subfolders)<br> spectrum_diagrams_260-274.zip (12 subfolders)<br> spectrum_diagrams_275-299.zip (23 subfolders)<br> spectrum_diagrams_300-349.zip (41 subfolders)<br> spectrum_diagrams_350-374.zip (20 subfolders)<br> spectrum_diagrams_375-399.zip (19 subfolders)<br> spectrum_diagrams_400-424.zip (23 subfolders)<br> spectrum_diagrams_425-449.zip (22 subfolders)<br> spectrum_diagrams_450-499.zip (35 subfolders)<br> spectrum_diagrams_500-549.zip (27 subfolders)<br> spectrum_diagrams_550-599.zip (38 subfolders)<br> spectrum_diagrams_600-649.zip (32 subfolders)<br> spectrum_diagrams_650-699.zip (26 subfolders)<br> spectrum_diagrams_700-749.zip (28 subfolders)</p>
Multi-Center Trial of High-resolution Transrectal Ultrasound Versus Standard Low-resolution Transrectal Ultrasound for the Identification of Clinically Significant Prostate Cancer
ClinicalTrials.gov study NCT02079025. IPD Sharing: Not stated. Countries: 2. Publications: 1.
DynaBench: A benchmark dataset for learning dynamical systems from low-resolution data (minimal)
<p>This is a minimal version of the DynaBench dataset, containing the first 5% of the data. The full dataset is available at <a href="https://professor-x.de/dynabench">https://professor-x.de/dynabench</a></p><p><strong>Abstract:</strong></p><p>Previous work on learning physical systems from data has focused on high-resolution grid-structured measurements. However, real-world knowledge of such systems (e.g. weather data) relies on sparsely scattered measuring stations. In this paper, we introduce a novel simulated benchmark dataset, DynaBench, for learning dynamical systems directly from sparsely scattered data without prior knowledge of the equations. The dataset focuses on predicting the evolution of a dynamical system from low-resolution, unstructured measurements. We simulate six different partial differential equations covering a variety of physical systems commonly used in the literature and evaluate several machine learning models, including traditional graph neural networks and point cloud processing models, with the task of predicting the evolution of the system. The proposed benchmark dataset is expected to advance the state of art as an out-of-the-box easy-to-use tool for evaluating models in a setting where only unstructured low-resolution observations are available. The benchmark is available at <a href="https://professor-x.de/dynabench">https://professor-x.de/dynabench</a>.</p><p><strong>Technical Info</strong></p><p>The dataset is split into 42 parts (6 equations x 7 combinations of resolution/structure). Each part can be downloaded separately and contains 7000 simulations of the given equation at the given resolution and structure. The simulations are grouped into chunks of 500 simulations saved in the hdf5 file format. Each chunk contains the variable "data", where the values of the simulated system are stored, as well as the variable "points", where the coordinates at which the system has been observed are stored. For more details visit the DynaBench website at <a href="https://professor-x.de/dynabench/">https://professor-x.de/dynabench/</a>. The dataset is best used as part of the dynabench python package available at <a href="https://pypi.org/project/dynabench/">https://pypi.org/project/dynabench/</a>.</p>
Humility Brings Prosperity gate - low-resolution
A low-res version of the Humility Brings Prosperity gate, now installed in Gallery 200 at Mia. The gate comes from Shanxi province, which, because of its dry climate, contains the largest concentration of surviving historic wooden structures in China. An inscription on the main beam dates the gate to 1858, during the Qing dynasty (1644–1912). [More info](https://collections.artsmia.org/art/127331/humility-brings-prosperity-gate-china) *This imposing gate once formed the entrance to the main courtyard of an urban middle-class family compound. The four-character panel over the doorway reads, Humility Brings Prosperity (Lüqian Jianfeng 履謙漸豐). The gate demonstrates the hallmarks of traditional Chinese architecture: a post-and-lintel structure that supports a graceful gable roof and intricate rafters, as well as brackets that extend the eaves and provide decoration. Like other works of traditional Chinese architecture, no glue or nails were used, and the gate was assembled using only joinery and wooden pins.* Source: Objaverse 1.0 / Sketchfab
Trained tesseract networks for low-resolution optical character recognition
<p>Tesseract eng.traineddata files for low-resolution optical character recognition (English).</p>
The high-resolution Global Aviation emissions Inventory based on ADS-B (GAIA) for 2019 - 2021: Low-resolution gridded outputs for 2019 - 2021
<p><strong>The high-resolution Global Aviation emissions Inventory based on ADS-B (GAIA) for 2019 – 2021: Low-resolution gridded outputs for 2019 - 2021</strong></p> <p><strong>Roger Teoh, Zebediah Engberg, Marc Shapiro, Lynnette Dray and Marc E.J. Stettler</strong></p> <p>These files contain the low-resolution monthly sum of the global flight distance flown, fuel consumption, and various pollutants from 2019 to 2021. The data is provided in a 4D grid with spatiotemporal resolution of 0.5° (longitude) x 0.5° (latitude), at altitude intervals of 1000 feet, and at a monthly temporal resolution.</p> <p>The global air traffic activity in GAIA was derived from Spire Aviation data. Non-commercial use and research purposes only.</p> <p>For further details, see README.txt</p> <p><strong>Change log</strong></p> <p>- 6-June-2023: The missing file "2021-08-monthly.nc" has been added and can be found in "Zenodo.zip"</p>
TIROS-4 Low-Resolution Omnidirectional Radiometer Level 1 Temperature Data V001 (TIROS4L1ORT) at GES DISC
The TIROS-4 Low-Resolution Omnidirectional Radiometer Level 1 Temperature Data product contains the black and white sensor temperature values in degrees Celsius. The experiment consisted of two sets of bolometers in the form of hollow aluminum hemispheres, mounted on opposite sides of the spacecraft, and whose optical axes were parallel to the spin axis. The bolometers were thermally isolated from but in close proximity to reflecting mirrors so that the hemispheres behaved like isolated spheres in space. The experiment was designed to measure the amount of solar energy absorbed, reflected, and emitted by the earth and its atmosphere in order to calculate the Earth's radiation budget. The data were originally written on IBM 7094 machines, and these have been recovered from magnetic tapes, referred to as the Omnidirectional Radiometer Temperature (ORT) tapes. The data are archived in their text format.The TIROS-4 satellite was successfully launched on February 8, 1962. The Low-Resolution Omnidirectional Radiometer experiment returned data for about five months. A previous instrument flew on TIROS-3 and a follow-on instrument was flown on TIROS-7, while a similar instrument flew on Explorer-7.The Principal Investigator for these data was Verner E. Suomi from the University of Wisconsin. This product was previously available from the NSSDC with the identifier ESAD-00252 (old id 62-002A-01A).
TIROS-3 Low-Resolution Omnidirectional Radiometer Level 1 Temperature Data V001 (TIROS3L1ORT) at GES DISC
The TIROS-3 Low-Resolution Omnidirectional Radiometer Level 1 Temperature Data product contains the black and white sensor temperature values in degrees Celsius. The experiment consisted of two sets of bolometers in the form of hollow aluminum hemispheres, mounted on opposite sides of the spacecraft, and whose optical axes were parallel to the spin axis. The bolometers were thermally isolated from but in close proximity to reflecting mirrors so that the hemispheres behaved like isolated spheres in space. The experiment was designed to measure the amount of solar energy absorbed, reflected, and emitted by the earth and its atmosphere in order to calculate the Earth's radiation budget. The data were originally written on IBM 7094 machines, and these have been recovered from magnetic tapes, referred to as the Omnidirectional Radiometer Temperature (ORT) tapes. The data are archived in their original text format.The TIROS-3 satellite was successfully launched on July 12, 1961. The Low-Resolution Omnidirectional Radiometer experiment returned data for about three months. Two follow-on instruments were flown on TIROS-4 and -7, while a similar instrument flew on Explorer-7.The Principal Investigator for these data was Verner E. Suomi from the University of Wisconsin. This product was previously available from the NSSDC with the identifier ESAD-00187 (old id 61-017A-01A).
TIROS-4 Low-Resolution Omnidirectional Radiometer Level 1 Radiance Data V001 (TIROS4L1ORR) at GES DISC
The TIROS-4 Low-Resolution Omnidirectional Radiometer Level 1 Radiance Data product contains the longwave radiation values in Langleys/min derived from the black and white sensors. The experiment consisted of two sets of bolometers in the form of hollow aluminum hemispheres, mounted on opposite sides of the spacecraft, and whose optical axes were parallel to the spin axis. The bolometers were thermally isolated from but in close proximity to reflecting mirrors so that the hemispheres behaved like isolated spheres in space. The experiment was designed to measure the amount of solar energy absorbed, reflected, and emitted by the earth and its atmosphere in order to calculate the Earth's radiation budget. The data were originally written on IBM 7094 machines, and these have been recovered from magnetic tapes, referred to as the Omnidirectional Radiometer Radiance (ORR) tapes. The data are archived in their text format.The TIROS-4 satellite was successfully launched on February 8, 1962. The Low-Resolution Omnidirectional Radiometer experiment returned data for about five months. A previous instrument flew on TIROS-3 and a follow-on instrument was flown on TIROS-7, while a similar instrument flew on Explorer-7.The Principal Investigator for these data was Verner E. Suomi from the University of Wisconsin. This product was previously available from the NSSDC with the identifier ESAD-00152 (old id 62-002A-01B).
Low-resolution Global Dataset of BaP based on IAP-AACM model (1° × 1°)
<p>This dataset contains the annual- and monthly-averaged BaP simulations in the atmosphere based on the IAP-AACM with a resolution of 1° × 1°.</p>
ScienceDex guides
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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.