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1,861 results for “banding”
Dendrometer band measurement data from two tidal forest plots at GCE 11 on the Altamaha River in Southeast Georgia from December 2014 to December 2020
We established two 0.1-ha plots in December 2013. In each plot, we identified and measured DBH (diameter at breast height) of every tree using standard diameter tapes. We also placed dendrometric bands on 40 trees (20 per plot) in December 2013. Bands were measured in December 2014 as baseline measurements and yearly thereafter.
Microplastic Abundance, Shape, and Color in Passerines Captured at Rushton Woods Preserve Bird Banding Station in Newtown Square, Pennsylvania, USA, April-September 2024
Fecal samples were collected from 5 species of passerine birds between April and September 2024 at the Rushton Woods Preserve Bird Banding Station. Samples were chemically digested and filtered for the purpose of extracting, quantifying, and describing microplastics.
Tree band growth data taken at BCEF sites (1989 -Present)
This file contains the yearly diameter of select trees within each of the forested LTER control plots. Diameter is calculated from adding the diameter increment based on circumference growth taken from dendrometer bands read each fall.
Raw band intensity values for ATG3 WT or Mutants in vitro LC3 lipidation
<p>Raw band intensity values used for the quantification of in vitro LC3 lipidation results (Fig4C) in Three-step docking by WIPI2, ATG16L1 and ATG3 delivers LC3 to the phagophore.</p>
Dataset of the paper "Control of electronic band profiles through depletion layer engineering in core-shell nanocrystals"
<p>This dataset provides the raw data of the paper "Control of electronic band profiles through depletion layer engineering in core-shell nanocrystals"</p>
Coherence and indistinguishability of highly pure single photons from non-resonantly and resonantly excited telecom C-band quantum dots
<p>ABSTRACT</p> <p>The role of resonant pumping schemes in improving the photon coherence is investigated on InAs/InGaAs/GaAs quantum dots (QDs) emitting in the telecom C-band. The linewidths of transitions of multiple exemplary quantum dots are determined under above-band pumping and resonance fluorescence (RF) via Fourier-transform spectroscopy and resonance scans, respectively. The average linewidth is reduced from (9.74 ± 3.3) GHz in the above-band excitation to (3.50 ± 0.39) GHz under RF underlining its superior coherence properties. Furthermore, the feasibility of coherent state preparation with a fidelity of (49.2 ± 5.8) % is demonstrated, constituting a first step toward on-demand generation of coherent, single, telecom C-band photons directly emitted by QDs. Finally, two-photon excitation of the biexciton is investigated as a resonant pumping scheme. A deconvoluted single-photon purity value of 𝑔(2)HBT(0)=0.072 ± 0.104 and a postselected degree of indistinguishability of 𝑉HOM=0.894 ± 0.109 are determined for the biexciton transition. This represents another step in demonstrating the necessary quantum optical properties for prospective applications.</p>
Experimental data for "Yu-Shiba-Rusinov bands in a self-assembled kagome lattice of magnetic molecules"
<p>Here, we provide all original data used in the manuscript "Yu-Shiba-Rusinov bands in a self-assembled kagome lattice of magnetic molecules"</p> <p>We acknowledge financial support by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) through projects 277101999 (CRC 183, project C03) and FR2726/10-1.</p>
Landsat bands (cloud free), tree cover (2000, 2010), bare-ground and surface water occurrence at 250 m based on GlobalForestWatch and USGS
<p>Landsat bands (cloud free) and tree cover (2000) based on Hansen et al. (2013), global surface water occurrence based on Pekel at al. (2016), and tree cover and bare-ground cover (2010) based the USGS land cover mapping projects (University of Maryland, Department of Geographical Sciences and USGS). All layers resampled to spatial resolution 1/480 d.d. (about 250 m) using gdalwarp with "average" resampling. Antarctica is not included. Original layers are available at 30 m resolution.</p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>lcv = theme: land cover,</li> <li>bareground = variable: occurrence of bareground,</li> <li>landsat.usgs = determination method: Landsat landcover at 30 m resolution project (https://landcover.usgs.gov/glc/),</li> <li>p = probability or fraction,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>s0..0cm = vertical reference: land surface,</li> <li>2010..2010 = time reference: year 2010,</li> <li>v1.0 = version number: 1.0,</li> </ul>
Experimental data for bulk valley transport and Berry curvature spreading at the edge of flat bands
<p>This dataset was used in our study of bulk valley transport and Berry curvature spreading at the edge of flat bands in twisted double bilayer graphene.</p>
RCSED - A Value-Added Reference Catalog of Spectral Energy Distributions of 800,299 Galaxies in 11 Ultraviolet, Optical, and Near-Infrared Bands: Morphologies, Colors, Ionized Gas and Stellar Populations Properties
<p>We present RCSED, the value-added Reference Catalog of Spectral Energy Distributions of galaxies, which contains homogenized spectrophotometric data for 800,299 low and intermediate redshift galaxies (0.007 < z < 0.6) selected from the Sloan Digital Sky Survey spectroscopic sample. Accessible from the Virtual Observatory (VO) and complemented with detailed information on galaxy properties obtained with the state-of-the-art data analysis, RCSED enables direct studies of galaxy formation and evolution during the last 5 Gyr. We provide tabulated color transformations for galaxies of different morphologies and luminosities and analytic expressions for the red sequence shape in different colors. RCSED comprises integrated k-corrected photometry in up-to 11 ultraviolet, optical, and near-infrared bands published by the GALEX, SDSS, and UKIDSS wide-field imaging surveys; results of the stellar population fitting of SDSS spectra including best-fitting templates, velocity dispersions, parameterized star formation histories, and stellar metallicities computed for instantaneous starburst and exponentially declining star formation models; parametric and non-parametric emission line fluxes and profiles; and gas phase metallicities. We link RCSED to the Galaxy Zoo morphological classification and galaxy bulge+disk decomposition results by Simard et al. We construct the color-magnitude, Faber-Jackson, mass-metallicity relations, compare them with the literature and discuss systematic errors of galaxy properties presented in our catalog. RCSED is accessible from the project web-site and via VO simple spectrum access and table access services using VO compliant applications. We describe several SQL query examples against the database. Finally, we briefly discuss existing and future scientific applications of RCSED and prospectives for the catalog extension to higher redshifts and different wavelengths.</p>
A Non-galvanic D-band MMIC-to-Waveguide Transition Using eWLB Packaging Technology-dataset
<p>This paper presents a novel D-band interconnect implemented in a low-cost embedded Wafer Ball Grid Array (eWLB) commercial process. The transition is realized through a patch slot antenna directly radiating to a standard waveguide opening. The interconnect achieves low insertion loss and good bandwidth. The measured minimum Insertion Loss (IL) is 2 dB and the average is 3 dB across a bandwidth of 22% covering the frequency range 110-138 GHz. In addition, the structure is easy to integrate as it does not require any special assembly nor any galvanic contacts. Adopting the low-cost eWLB process and standard waveguides makes the transition an attractive solution for interconnects beyond 100 GHz.</p>
GLAB-VOD: Global L-band AI-Based Vegetation Optical Depth Dataset Based on Machine Learning and Remote Sensing
<p>GLAB VOD is a Global L-band Ai-Based vegetation optical depth dataset with 18-day temporal and 25 km spatial resolution, covering 2002 to 2020. The dataset is created using a neural network with SMOS-SMAP-INRAE-BORDEAUX (SMOSMAP-IB) VOD product as a target (over 2015-2020) and brightness temperatures (TB) from the SMOS, AMSR-E, and AMSR-2 spaceborne missions alongside with a novel soil moisture dataset (CASM) as inputs. The GLAB-VOD dataset was created using a recently developed methodology previously used to create a long-term consistent soil moisture dataset CASM, adapted to the VOD retrievals. First, the TB and VOD signals were divided into fixed seasonal cycle and residuals, where the residual part of the signal contains sub-seasonal periodic signals, trends, extremes, and noise. Then, a multi-staged neural network training scheme was used to achieve internally consistent predictions by merging data from different sources without introducing biases or compromising data distribution. A side-product of this project is GLAB TB - a global long-term brightness temperature dataset that matches SMOS TB quality and spawns back to 2002. GLAB TB has daily temporal resolution and 25 km spatial resolution. </p>
Data for "Multi-band Spectropolarimetric Image Data of Lunar Maria, Pyroclastics, Fresh Craters, and Swirl Materials"
<p>This data repository contains the georeferenced spectropolarimetric data analyzed in the following article:</p> <p>Wöhler, C., Arnaut, M., Bhatt, M., 2024. Multi-band Spectropolarimetry of Lunar Maria, Pyroclastics, Fresh Craters, and Swirl Material. Astronomical Journal, accepted for publication.</p> <p>The data products are available in separate .zip archives in GEOTIFF and BSQ format. Each .zip archive contains data from eight different observations:</p> <table> <tbody> <tr> <td>Dataset</td> <td>Date</td> <td>UT time</td> <td>Phase angle</td> </tr> <tr> <td>20221114_WOP</td> <td>Nov 14th, 2022</td> <td>05:20</td> <td>64°</td> </tr> <tr> <td>20221216_WOP</td> <td>Dec 16th, 2022</td> <td>04:10</td> <td>88°</td> </tr> <tr> <td>20230225_AT</td> <td>Feb 25th, 2023</td> <td>19:20</td> <td>108°</td> </tr> <tr> <td>20230227_AT</td> <td>Feb 27th, 2023</td> <td>21:44</td> <td>85°</td> </tr> <tr> <td>20230228_AT</td> <td>Feb 28th, 2023</td> <td>19:33</td> <td>74°</td> </tr> <tr> <td>20230302_MV</td> <td>Mar 2nd, 2023</td> <td>19:03</td> <td>52°</td> </tr> <tr> <td>20230302_AT</td> <td>Mar 2nd, 2023</td> <td>19:10</td> <td>52°</td> </tr> <tr> <td>20230402_AT</td> <td>Apr 2nd, 2023</td> <td>20:03</td> <td>38°</td> </tr> </tbody> </table> <p> </p> <p>Data products of spectropolarimetric image analysis in GEOTIFF and BSQ format<br>=============================================================================</p> <p>Prefix 1: Date of data acquisition (YYYYMMDD)<br>Prefix 2: Area (WOP: Western Oceanus Procellarum; MV: Mare Vaporum; AT: Atlas)</p> <p>F : image intensity (5 bands) [DN]<br>P : degree of linear polarization (DoLP) (5 bands)<br>W : angle of linear polarization (AoLP) (5 bands) [degrees]<br>GS : relative grain size (5 bands)<br>logGS: logarithm of relative grain size (5 bands)<br>UE : across-band Umov exponent (1 band)<br>UEres: residual of across-band Umov exponent (1 band)<br>PCAP : scores on the first three principal components derived from the DoLP (3 bands) <br>PCAW : scores on the first three principal components derived from the AoLP (3 bands)<br>CIM : cluster index map (1 band)</p> <p><br>The longitude ranges of the maps are as follows:</p> <p>WOP: Longitude=[-70 -35], Latitude=[2 33]<br>MV : Longitude=[-13 12], Latitude=[-2 17]<br>AT : Longitude=[35 60], Latitude=[40 55]</p> <p>All maps are in simple cylindrical projection with a resolution of 30 pixels per degree.</p> <p>The CIM maps are provided in uint8 numerical format.<br>All other maps are provided in single-precision (32-bit) floating point numerical format.<br>The BSQ files are in binary format without header. Matlab example:<br>BSQ=multibandread('20230302_AT_P__Latitude_35_60__Latitude_35_60.bsq',[750 750 5],'single',0,'bsq','ieee-le');</p> <p> </p>
Landsat-based Spectral Indices for pan-EU 2000-2022 - Annual predictor P75 (2019): Reflectances bands, NDVI and NDWI
<h2><strong>Data information</strong></h2> <p>This dataset provides the P75 (percentile 75) values of corresponding predictors for the year 2019. The P25 values are calculated from the bimonthly values of six corresponding predictors throughout the year. The predictors in this subset include seven reflectance bands: red, green, blue, NIR, SWIR1, SWIR2, and thermal; as well as two spectral indices: NDVI and NDWI.</p> <h2><strong>As a part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>
Landsat-based Spectral Indices for pan-EU 2000-2022 - Annual predictor P75 (2020): Reflectances bands, NDVI and NDWI
<h2><strong>Data information</strong></h2> <p>This dataset provides the P75 (percentile 75) values of corresponding predictors for the year 2020. The P25 values are calculated from the bimonthly values of six corresponding predictors throughout the year. The predictors in this subset include seven reflectance bands: red, green, blue, NIR, SWIR1, SWIR2, and thermal; as well as two spectral indices: NDVI and NDWI.</p> <h2><strong>As a part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>
Landsat-based Spectral Indices for pan-EU 2000-2022 - Annual predictor P75 (2011): Reflectances bands, NDVI and NDWI
<h2><strong>Data information</strong></h2> <p>This dataset provides the P75 (percentile 75) values of corresponding predictors for the year 2011. The P25 values are calculated from the bimonthly values of six corresponding predictors throughout the year. The predictors in this subset include seven reflectance bands: red, green, blue, NIR, SWIR1, SWIR2, and thermal; as well as two spectral indices: NDVI and NDWI.</p> <h2><strong>As a part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>
Landsat-based Spectral Indices for pan-EU 2000-2022 - Annual predictor P75 (2008): Reflectances bands, NDVI and NDWI
<h2><strong>Data information</strong></h2> <p>This dataset provides the P75 (percentile 75) values of corresponding predictors for the year 2008. The P25 values are calculated from the bimonthly values of six corresponding predictors throughout the year. The predictors in this subset include seven reflectance bands: red, green, blue, NIR, SWIR1, SWIR2, and thermal; as well as two spectral indices: NDVI and NDWI.</p> <h2><strong>As a part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>
Landsat-based Spectral Indices for pan-EU 2000-2022 - Annual predictor P75 (2002): Reflectances bands, NDVI and NDWI
<h2><strong>Data information</strong></h2> <p>This dataset provides the P75 (percentile 75) values of corresponding predictors for the year 2002. The P25 values are calculated from the bimonthly values of six corresponding predictors throughout the year. The predictors in this subset include seven reflectance bands: red, green, blue, NIR, SWIR1, SWIR2, and thermal; as well as two spectral indices: NDVI and NDWI.</p> <h2><strong>As a part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>
Landsat-based Spectral Indices for pan-EU 2000-2022 - Annual predictor P75 (2005): Reflectances bands, NDVI and NDWI
<h2><strong>Data information</strong></h2> <p>This dataset provides the P75 (percentile 75) values of corresponding predictors for the year 2005. The P25 values are calculated from the bimonthly values of six corresponding predictors throughout the year. The predictors in this subset include seven reflectance bands: red, green, blue, NIR, SWIR1, SWIR2, and thermal; as well as two spectral indices: NDVI and NDWI.</p> <h2><strong>As a part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>
Landsat-based Spectral Indices for pan-EU 2000-2022 - Annual predictor P75 (2000): Reflectances bands, NDVI and NDWI
<h2><strong>Data information</strong></h2> <p>This dataset provides the P75 (percentile 75) values of corresponding predictors for the year 2000. The P25 values are calculated from the bimonthly values of six corresponding predictors throughout the year. The predictors in this subset include seven reflectance bands: red, green, blue, NIR, SWIR1, SWIR2, and thermal; as well as two spectral indices: NDVI and NDWI.</p> <h2><strong>As a part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</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.