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109 results for “low resolution”
High-resolution images from a low-cost imaging device for hyphae in soil
<p>This dataset contains high-resolution images produced by a low-cost imaging device for hyphae in soil called <em>Hyphascope</em>. Using a digital microscope camera (DMC; 600× magnification),<em> </em>the device takes detailed images (0.83 × 0.62 mm imaged area) of a soil profile from evenly spaced camera positions within a user-defined volume. Repeated imaging of a soil profile with <em>Hyphascope</em> enables researchers to observe and quantify changes in the amount, distribution, and morphology of hyphae.</p> <p>Individual images were combined using the <em>Grid/Collection stitching</em> plugin of the <em>Fiji</em> distribution of <em>imageJ</em> (Preibisch et al. 2009). All images are supplied in the JPG format to limit their file size. For more details on the assembly and application of <em>Hyphascope</em>, see <a href="https://doi.org/10.17504/protocols.io.bp2l6xo3zlqe/v1">this protocol on protocols.io</a>. For information on the development, limitations, and expected outcomes of the protocol, see <a href="https://doi.org/10.1371/journal.pone.0318083">this article</a> published in PLOS ONE. </p> <p> </p> <div> <h2>Image set 1: 10 × 10 mm soil profile area at 20 - 30 mm soil depth</h2> <p>Imaged at 0.65 μm px<sup>-1</sup> (39200 dpi)* in a <em>Quercus serrata</em> grove on 2023/05/25 during a period of high hyphal density in the soil.</p> <h3>Individual images (18 rows × 14 images each)</h3> <ul> <li> <p><em>set1_foc00000.zip</em> (focus depth 0 mm)</p> </li> </ul> <h3>Combined images</h3> <ul> <li> <p><em>set1_foc00000_combined.jpg</em> (focus depth 0 mm)</p> </li> </ul> <h2>Image set 2: 5 × 5 mm soil profile area at 100 - 105 mm soil depth</h2> <p>Imaged at 0.52 μm px<sup>-1</sup> (49000 dpi) in a <em>Quercus serrata</em> grove on 2023/09/25.</p> <h3>Individual images (9 rows × 7 images each)</h3> <ul> <li> <p><em>set1_foc00000.zip</em> (focus depth 0 mm)<em><br></em></p> </li> <li> <p><em>set1_foc00025zip</em> (focus depth 0.025 mm)</p> </li> <li><em>set1_foc00050.zip</em> (focus depth 0.05 mm)</li> </ul> <h3>Combined images</h3> <ul> <li> <p><em>set1_foc00000_combined.jpg</em> (focus depth 0 mm)</p> </li> <li> <p><em>set1_foc00025_combined.jpg</em> (focus depth 0.025 mm)</p> </li> <li><em>set1_foc00050_combined.jpg</em> (focus depth 0.05 mm)</li> </ul> <h2>Image set 3: 5 × 5 mm soil profile area at soil surface level</h2> <p>Imaged at 0.52 μm px<sup>-1</sup> (49000 dpi) in a <em>Quercus serrata</em> grove on 2023/10/14 during a rain event.</p> <h3>Individual images (9 rows × 7 images each)</h3> <ul> <li> <p><em>set2_foc00000.zip</em> (focus depth 0 mm)<em><br></em></p> </li> <li> <p><em>set2_foc00025zip</em> (focus depth 0.025 mm)</p> </li> <li><em>set2_foc00050.zip</em> (focus depth 0.05 mm)</li> </ul> <h3>Combined images</h3> <ul> <li> <p><em>set2_foc00000_combined.jpg</em> (focus depth 0 mm)</p> </li> <li> <p><em>set2_foc00025_combined.jpg</em> (focus depth 0.025 mm)</p> </li> <li><em>set2_foc00050_combined.jpg</em> (focus depth 0.05 mm)</li> </ul> <p> </p> <p> </p> <p><em>*Units of imaging resolution: </em></p> <ol> <li><em>pixel width (μm px-1), i.e. the horizontal or vertical distance on the imaged surface covered by a single pixel; </em></li> <li><em>dots per inch (dpi), i.e. the number of pixels along a horizontal or vertical distance of 25.4 mm on the imaged surface.</em></li> </ol> </div>
The potential of low-cost UAVs and open-source photogrammetry software for high-resolution monitoring of alpine glaciers: A case study from the Kanderfirn (Swiss Alps)
<p>This dataset contains high-resolution orthophotos (5 x 5 cm) and digital surface models (25 x 25 cm) of the Kandernfirn Glacier located in the Swiss Alps. Aerial images were aquired with a self-developed fixed-wing Unmanned Aerial Vehicle during ten surveys on five different days in 2017 and 2018. The open-source photogrammetry software OpenDroneMap (version 0.4.1) was used for image processing.</p> <p>The orthophotos and digital surface models were validated through dGNSS point measurements of ground control points. Please refer to the corresponding paper for information on the horizontal and vertical accuracy of the files.</p>
A multi-resolution, multi-epoch low Radio Frequency Survey of the Kepler K2 Mission Campaign 1 Field
<p>Data abstract:</p> <p>Contained within are the MWA images used as input data for this study. The production and analysis of these images are described in the linked paper. The final catalogues and light curves are available from VizieR (http://vizier.cfa.harvard.edu/viz-bin/VizieR?-source=J/AJ/152/82).</p> <p>Paper abstract:</p> <p>We present the first dedicated radio continuum survey of a Kepler K2 mission field, Field 1, covering the North Galactic Cap. The survey is wide field, contemporaneous, multi-epoch, and multi-resolution in nature and was conducted at low radio frequencies between 140 and 200 MHz. The multi-epoch and ultra wide field (but relatively low resolution) part of the survey was provided by 15 nights of observation using the Murchison Widefield Array (MWA) over a period of approximately a month, contemporaneous with K2 observations of the field. The multi-resolution aspect of the survey was provided by the low resolution (4‧) MWA imaging, complemented by non-contemporaneous but much higher resolution (20″) observations using the Giant Metrewave Radio Telescope (GMRT). The survey is, therefore, sensitive to the details of radio structures across a wide range of angular scales. Consistent with other recent low radio frequency surveys, no significant radio transients or variables were detected in the survey. The resulting source catalogs consist of 1085 and 1468 detections in the two MWA observation bands (centered at 154 and 185 MHz, respectively) and 7445 detections in the GMRT observation band (centered at 148 MHz), over 314 square degrees. The survey is presented as a significant resource for multi-wavelength investigations of the more than 21,000 target objects in the K2 field. We briefly examine our survey data against K2 target lists for dwarf star types (stellar types M and L) that have been known to produce radio flares.</p>
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>
Design files for a low-cost high-resolution imaging device for hyphae in soil
<p>This dataset contains the stereolithography (STL) files for the 3D-printed and cut parts of a low-cost high-resolution imaging device for hyphae in soil called <em>Hyphascope</em>. The design of <em>Hyphascope</em> was adopted from the 3D printer i3 MK3S+ by Prusa Research, with a digital microscope camera (DMC; 600× magnification) replacing the filament extruder. Repeated imaging of a soil profile with the imaging device enables researchers to observe and quantify changes in the amount, distribution, and morphology of hyphae.</p> <p>The parts were created and modified using <a href="https://www.freecad.org">FreeCAD</a> (version 0.20). STL files for the original parts are distributed under the Creative Commons Attribution 4.0 International License, STL files for the remixed parts under the GNU General Public License v2.0. For a detailed description on how to prepare and assemble the parts see <a href="https://doi.org/10.17504/protocols.io.bp2l6xo3zlqe/v1">this protocol on protocols.io</a>. For information on the development, limitations, and expected outcomes of the protocol, see <a href="https://doi.org/10.1371/journal.pone.0318083">this article</a> published in PLOS ONE.</p> <p> </p> <div> <h2>STL files of 3D-printed parts</h2> <h3>Original parts</h3> </div> <div> <div> <ul> <li><em>dmc-attachment.stl</em></li> <li> <div><em>dmc-attachment-gear.stl</em></div> </li> <li><em>dmc-attachment-gear-wider.stl</em> (optional part)<em><br></em></li> <li><em>dmc-holder-back.stl</em></li> <li><em>dmc-holder-front.stl</em></li> <li><em>f-axis-motor-gear.stl</em></li> <li><em>f-axis-spring-end.stl</em></li> <li><em>f-axis-tighteners.stl</em></li> <li><em>frame-foot-inserts.stl</em></li> <li> <div><em>frame-foot-left.stl</em></div> </li> <li> <div><em>frame-foot-right.stl</em></div> </li> <li> <div><em>frame-hat.stl</em></div> </li> <li> <div><em>frame-hat-insert.stl</em></div> </li> </ul> </div> <h3>Remixed parts originally designed by Prusa Research</h3> <p><em>The five parts below are <strong>remi</strong></em><strong><em>xed from <a href="https://www.printables.com/model/57217-i3-mk3s-printable-parts">i3 MK3S+ printable parts</a> </em></strong><em>and </em><strong><em>re-distributed under the <a href="http://www.gnu.org/licenses/old-licenses/gpl-2.0.html">GNU General Public License v2.0</a></em></strong><em>.</em></p> </div> <ul> <li> <div><em>dmc-carriage-back.stl</em> (Remix of <em>x-carriage-back.stl</em>; the design was largely modified to fit the DMC including changes to the shape and screw hole placement; the inserts for the linear bearings have the most resemblence to the original part.)</div> </li> <li><em>dmc-carriage-front.stl </em>(Remix of <em>x-carriage.stl</em>; the design was largely modified to fit the DMC including changes to the shape and screw hole placement; the inserts for the linear bearings have the most resemblence to the original part.)</li> <li><em>x-end-idler-mod.stl</em> (Remix of <em>x-end-idler.stl</em>; the height was increased by 20 mm.)</li> <li><em>x-end-motor-mod.stl</em> (Remix of <em>x-end-motor.stl</em>; the height was increased by 20 mm and the counterbores of the three motor screws were moved to the opposite side.)</li> <li><em>z-axis-top-mod.stl </em>(Remix of <em>z-axis-top.stl</em>; 14.8 mm-long spacers were added.)</li> </ul> <h3>Parts designed by Prusa Research</h3> <ul> <li> <div><em>z-axis-bottom.stl</em> (available from <a href="https://www.printables.com/model/57217-i3-mk3s-printable-parts" target="_blank" rel="noopener">Printables</a>)</div> </li> <li> <div><em>z-screw-cover.stl</em> (available from <a href="https://www.printables.com/model/57217-i3-mk3s-printable-parts" target="_blank" rel="noopener">Printables</a>)</div> </li> </ul> <p> </p> <div> <h2>STL files of cut parts</h2> <h3>Original parts</h3> </div> <ul> <li><em>box-bottom.stl</em></li> <li><em>box-hook.stl</em></li> <li> <div><em>box-lid.stl</em></div> </li> <li> <div><em>box-lid-frame.stl</em></div> </li> <li><em>box-lid-valve-base.stl</em></li> <li><em>box-wall.stl</em></li> <li><em>box-wall-cables.stl</em></li> <li> <div><em>frame.stl</em></div> </li> </ul> <p> </p>
Remote Sensing VQA - Low Resolution (RSVQA LR)
<p>Remote sensing images contain a wealth of information which can be useful for a wide range of tasks including land cover classification, object counting or detection. However, most of the available methodologies are task-specific, thus inhibiting generic and easy access to the information contained in remote sensing data. As a consequence, accurate remote sensing product generation still requires expert knowledge. With RSVQA, we propose a system to extract information from remote sensing data that is accessible to every user: we use questions formulated in natural language and use them to interact with the images. With the system, images can be queried to obtain high level information specific to the image content or relational dependencies between objects visible in the images. Using an automatic method, we built two datasets (using low and high resolution data) of image/question/answer triplets. The information required to build the questions and answers is queried from OpenStreetMap (OSM). The datasets can be used to train (when using supervised methods) and evaluate models to solve the RSVQA task.</p> <p>This page concerns the low resolution dataset.</p>
Characterisation and calibration of low-cost PM sensors at high temporal resolution to reference grade performances - dataset
<p>This repository contains the data used for the analysis of the paper "Characterisation and calibration of PM sensors at high temporal resolution to reference grade performances" submitted to Heliyon and available as a pre-print:</p> <p>Bulot, Florentin M. J. and Ossont, Steven J. and Morris, Andrew and Basford, Philip J. and Easton, Natasha H. C. and Mitchell, Hazel L. and Foster, Gavin L. and Cox, Simon J. and Loxham, Matthew, Characterisation and Calibration of Low-Cost Pm Sensors at High Temporal Resolution to Reference-Grade Performance. Available at SSRN: <a href="https://ssrn.com/abstract=4360707">https://ssrn.com/abstract=4360707</a> or <a href="http://dx.doi.org/10.2139/ssrn.4360707">http://dx.doi.org/10.2139/ssrn.4360707</a></p> <p> </p> <p>The code used to conduct the data analysis is available at <a href="https://doi.org/10.5281/zenodo.7261417">https://doi.org/10.5281/zenodo.7261417</a></p> <p> </p> <p>.</p> <p> </p> <p>The files are available in .csv and in .rds (for R) formats. For details about the measurement equipment used<br> during this study, please refer to the methods section of the paper.</p> <p>Description of the files.</p> <p>202007_to_202107_nocs - contains the data from the low-cost sensors</p> <p>It contains the following headers:<br> - "sensor" - sensor id<br> - "site" - name of the air quality monitor hosting the sensor<br> - "median_PM1" - PM1 mass concentration (ug/m3)<br> - "median_PM10" - PM10 mass concentration (ug/m3)<br> - "median_PM25" - PM25 mass concentration (ug/m3)<br> - "median_PM4" - PM4 mass concentration (ug/m3) (only available for SPS30)<br> - "median_n05" - particle number concentration (SPS30) of particles between 0.3um and 0.5um<br> - "median_n1" - particle number concentration (SPS30) of particles between 0.3um and 1um<br> - "median_n10" - particle number concentration (SPS30) of particles between 0.3um and 10um<br> - "median_n25" - particle number concentration (SPS30) of particles between 0.3um and 2.5um<br> - "median_n4" - particle number concentration (SPS30) of particles between 0.3um and 4um<br> - "median_gr03um" - particle number concentration (PMS5003) of particles >0.3um<br> - "median_gr05um" - particle number concentration (PMS5003) of particles >0.5um<br> - "median_gr100um" - particle number concentration (PMS5003) of particles >10um<br> - "median_gr10um" - particle number concentration (PMS5003) of particles >1um<br> - "median_gr25um" - particle number concentration (PMS5003) of particles >2.5um<br> - "median_gr50um" - particle number concentration (PMS5003) of particles >5um<br> - "median_pm100_cf1" - PM10 mass concentration with cf1 calibration for PMS5003<br> - "median_pm10_cf1" - PM1 mass concentration with cf1 calibration for PMS5003<br> - "median_pm25_cf1" - PM25 mass concentration with cf1 calibration for PMS5003<br> - "date" - date, format "yyyy-mm-dd HH:MM:SS GMT" </p> <p> </p> <p>df_pm_2min - contains the PM mass concentration data from the Fidas 200S.</p> <p>It contains the following headers:<br> - "PM2.5" - PM2.5 mass concentration (ug/m3) Fidas 200S<br> - "PM10" - PM10 mass concentration (ug/m3) Fidas 200S<br> - "PMtot" - PM total mass concentration (ug/m3) Fidas 200S<br> - "PM1" - PM1 mass concentration (ug/m3) Fidas 200S<br> - "date" - date, format "yyyy-mm-dd HH:MM:SS GMT" </p> <p> </p> <p>df_weather_2min - contains the weather data from the Fidas 200S</p> <p>It contains the following headers:<br> - "rh" - relative humidity (%)<br> - "dew_point_temperature" - dew point temperature (Celsius)<br> - "air_pressure" - Air pressure (hPa)<br> - "temperature" - temperature (Celsius)<br> - "date" - date, format "yyyy-mm-dd HH:MM:SS GMT"</p>
Planting Date Maps (Low Resolution) and County Data for the US Corn Belt
<p>Aggregated dataset on planting dates for maize and soybean crops in the US Corn Belt from 2000-2020. This dataset was generated from Landsat satellite data and covers 12 states. The methodology is described in <a href="https://doi.org/10.1016/j.rse.2023.113551">Deines et al. 2023 </a>in Remote Sensing of Environment (open access).</p> <p>Note: Version 2 is identical to Version 1, but includes a file missing from v1 (soybeans_progressByCounty_2000-2020_DeinesEtAl_vRSE_formatted.csv)</p> <p><strong>Preferred citation:</strong></p> <p>Deines, J.M., A. Swatantran, D. Ye, B. Myers, S. Archontoulis, & D.B. Lobell. 2023. Field-scale dynamics of planting dates in the US Corn Belt from 2000 to 2020. <em><strong>Remote Sensing of Environment. </strong></em><a href="https://doi.org/10.1016/j.rse.2023.113551">https://doi.org/10.1016/j.rse.2023.113551</a></p> <p><strong>Contents</strong></p> <ol> <li>Gridded map datasets of planting dates aggregated to 10 km resolution. Map datasets are contained in zip files by crop type. Each zip file contains 21 annual geotiff rasters covering the full study region. <ol> <li>Maize_plantingDates_2000-2020_10000m_DeinesEtAl_vRSE.zip</li> <li>Soybeans_plantingDates_2000-2020_10000m_DeinesEtAl_vRSE.zip</li> </ol> </li> <li>Tabular county planting date statistics derived from the high-resolution (30 m) planting date maps. This data is presented in .csv format and includes the day-of-year when 10, 25, 50, 75, and 90 percent of crop area was planted for each year. There is one record for each county-year; data files are separated by crop type. <ol> <li>maize_countyStats_doyPercentPlanted_2000-2020_DeinesEtAl_vRSE.csv</li> <li>soybeans_countyStats_doyPercentPlanted_2000-2020_DeinesEtAl_vRSE.csv</li> </ol> </li> <li>Tabular planting progress by county. These .csv's present the area planted for each crop by county for each day. They can be considered the "raw" version of item #2 (county stats) and can therefore be used to derive other planting progress percentiles other than those provided in item #2. <ol> <li>maize_progressByCounty_2000-2020_DeinesEtAl_vRSE_formatted.csv</li> <li>soybeans_progressByCounty_2000-2020_DeinesEtAl_vRSE_formatted.csv</li> </ol> </li> </ol> <p><strong>Metadata</strong></p> <p>1. Planting date maps</p> <ul> <li>Value: day-of-year of planting</li> <li>No Data value = -999 (grid cells outside of the study area or lacking maize/soybeans that year)</li> <li>Map projection: EPSG:5070, CONUS Albers Equal Area</li> <li>Maps for 2008-2018 use the USDA's Cropland Data Layers to identify maize pixels; maps for 1999-2007 use the Corn-Soy Data Layer produced by <a href="https://www.nature.com/articles/s41597-020-00646-4">Wang et al. 2000</a> (data + manuscript are open access).</li> </ul> <p>2. Tabular datasets</p> <ul> <li>GEOID = 5 digit county FIPS, or the unique identifier assigned to each county by the US TIGER county dataset</li> <li>county_code = 3 digit county identifier. Represents places 3-5 in the 5 digit code.</li> <li>state_code = 2 digit state identifier. Represents places 1-2 in the 5 digit code.</li> <li>DOY = day-of-year</li> <li>area_daily_m2 = area of that crop planted that day in that county, in square meters</li> <li>doy_X = day-of-year when X% of area was planted that year</li> </ul> <p><br> </p>
Open data for the article "Low-resistivity, high-resolution W-C electrical contacts fabricated by direct-write focused electron beam induced deposition"
<p>Open data for the article "Low-resistivity, high-resolution W-C electrical contacts fabricated by direct-write focused electron beam induced deposition", which will be published in Open Research Europe</p>
Flat-Field Super-Resolution Localization Microscopy with a Low-Cost Refractive Beam-Shaping Element
<p>Raw data for the article "Flat-Field Super-Resolution Localization Microscopy with a Low-Cost Refractive Beam-Shaping Element". Each set of three files is a set of dSTORM images, taken using either top-hat illumination or a Gaussian illumination. For Sample 0, the top-hat illumination was performed first. For Sample 1, the Gaussian illumination was performed first.</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>
Sensitivity experiments with EC-Earth3-Veg at low resolution
<p>piControl and abrupt4xCO2 experiments with the T159 configuration of EC-Earth3-Veg. Each file contains the annual global mean from one experiment, created with ecm. The details of each experiment are listed in the included README. </p>
RELLISUR: A Real Low-Light Image Super-Resolution Dataset
<p>The RELLISUR dataset contains real low-light low-resolution images paired with normal-light high-resolution reference image counterparts. This dataset aims to fill the gap between low-light image enhancement and low-resolution image enhancement (Super-Resolution (SR)) which is currently only being addressed separately in the literature, even though the visibility of real-world images is often limited by both low-light and low-resolution. The dataset contains 12750 paired images of different resolutions and degrees of low-light illumination, to facilitate learning of deep-learning based models that can perform a direct mapping from degraded images with low visibility to high-quality detail rich images of high resolution. The associated paper can be found here: <a title="https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/file/7ef605fc8dba5425d6965fbd4c8fbe1f-Paper-round2.pdf" href="https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/file/7ef605fc8dba5425d6965fbd4c8fbe1f-Paper-round2.pdf" target="_blank" rel="noopener">https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/file/7ef605fc8dba5425d6965fbd4c8fbe1f-Paper-round2.pdf</a></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>
INDIAN Steps Entry Sculpture (low resolution)
Entry to Indian Steps Museum located along the Susquehanna River, York County, Pennsylvania, USA Sculptor: Thomas J Carroll, dated 1912 Source: Objaverse 1.0 / Sketchfab
Drinking Fountain Uxbridge Low Resolution
A drinking fountain in Uxbridge Memorial ground opposite, the southern end of Windsor Street. Date: 1901 http://www.mdfcta.co.uk/details-f/f263.html 15000 triangles. 2k texture. 204 photos taken in July 2020 with a Sony a6000 and processed in Reality Capture. This model is the low resolution version of this high res scan: https://skfb.ly/6TXPV Source: Objaverse 1.0 / Sketchfab
Mahintale, Sri Lanka. Ambasthala Dagaba [low resolution version].
<p>Mahintale, Sri Lanka. Ambasthala Dagaba [low resolution version], as documented in 1999.</p>
Sringeri Sharadamba Low Resolution
Sringeri Sharadamba Low resolution 3D Model for printing, Saraswathi devi, godess of knowledge, jagadamba Source: Objaverse 1.0 / Sketchfab
Low resolution local L-sigma calibration dataset
<p>Measurements of the H-alpha luminosity and intrinsic velocity dispersion of 347 H II regions across 9 spiral galaxies. These properties were measured from integral field unit (IFU) spectroscopy taken by the Mulit-Object Spectroscopic Explorer (MUSE) on the Very Large Telescope (VLT). The L-sigma relation calibrated from these data is valid for low spectral (R ~ 3000) and spatial (~500 pc) resolution observations of giant H II regions. </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>
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.