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363 results for “harmonics”
Raw data and scripts for "The Period-Modulated Harmonic Locked Loop (PM-HLL): A low-effort algorithm for rapid time-domain multi-periodicity estimation"
<p>This package contains all required scripts to generate the simulations and figures from the study "The Period-Modulated Harmonic Locked Loop (PM-HLL): A low-effort algorithm for rapid time-domain multi-periodicity estimation" by Volker Hohmann, published in Acta Acustica:</p> <p>The Period-Modulated Harmonic Locked Loop (PM-HLL): A low-effort algorithm for rapid time-domain multi-periodicity estimation<br>Volker Hohmann<br>Acta Acust. 5 56 (2021)<br>DOI: 10.1051/aacus/2021050</p> <p>When referring to this work, please cite the journal paper.</p> <p>Note that additive noise is generated at random, i.e., small differences in the estimation accuracy occur when repeating a simulation. For further details see the journal paper.</p> <p>Thank you for downloading the package. Your comments are very welcome!</p> <p>Method patented: DE Patent DE102021207339B3</p> <p>Author: Volker Hohmann, Carl von Ossietzky Universität Oldenburg, Germany</p>
Harmonized chronologies of a global late Quaternary pollen dataset (LegacyAge 1.0) in R
<p>We present a chronology framework named LegacyAge 1.0 containing harmonized chronologies for 2831 pollen records (downloaded from the Neotoma Paleoecology Database and the supplementary Asian datasets) together with their age control points and metadata in machine-readable data formats. Here, we provide the R-code for calculation and comparison chronologies with embedded manual, metadata for code runs, Bacon output graphs of each record, graphs comparison of original chronologies and LegacyAge 1.0, and a short shared-screen video of the R-code to show the usage on two example records separately.</p> <p>Supplement to: Li, C., Postl, A. K., Böhmer, T., Cao, X., Dolman, A. M., and Herzschuh, U.: Harmonized chronologies of a global late Quaternary pollen dataset (LegacyAge 1.0), Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2021-212, in review, 2021.</p>
Animation of Spin Up 3rd Eigenvector for 2-D Simple Harmonic Oscillator Potential (MP4)
<p>MP4 video animation of 3rd Eigenvector for a 2-D SHO potential. This animation accompanies the paper "Real Wave Quantum Mechanics: Solutions for 2-D Simple Harmonic Potential Well". The animation shows the repeated multiplication of the Eigenvector by the (complex) normalized eigenvalue. The magnitude of the FRs are unchanged but their phase drifts in the same direction as the FR spin.</p>
Animation of 13th Eigenstructure (Spin Down) for Simple Harmonic Oscillator Potential (MP4)
<p>MP4 video animation of 13th Eigenvector for a 2-D SHO potential. This animation accompanies the paper "Real Wave Quantum Mechanics: Solutions for 2-D Simple Harmonic Potential Well". The animation shows the repeated multiplication of the Eigenvector by the (complex) normalized eigenvalue. The magnitude of the FRs are unchanged but their phase drifts in the same direction as the FR spin.<br> </p>
Neural processing and perception of Schroeder‐phase harmonic tone complexes in the gerbil: Relating single‐unit neurophysiology to behavior
<p><span>Schroeder-phase harmonic tone complexes have been used in physiological and psychophysical studies in several species to gain insight into cochlear function. Each pitch period of the Schroeder stimulus contains a linear frequency sweep; the duty cycle, sweep velocity, and direction are controlled by parameters of the phase spectrum. Here, responses to a range of Schroeder-phase harmonic tone complexes were studied both behaviorally and in neural recordings from the auditory nerve and inferior colliculus of Mongolian gerbils. Gerbils were able to discriminate Schroeder-phase harmonic tone complexes based on sweep direction, duty cycle, and/or velocity for fundamental frequencies up to 200 Hz. Temporal representation in neural responses based on the van Rossum spike-distance metric, with time constants of either 1 ms or related to the stimulus' period, was compared to average discharge rates. Neural responses and behavioral performance were both expressed in terms of sensitivity, d', to allow direct comparisons. Our results suggest that in the auditory nerve, stimulus fine structure is represented by spike timing while envelope is represented by rate. In the inferior colliculus, both temporal fine structure and envelope appear to be represented best by rate. However, correlations between neural d' values and behavioral sensitivity for sweep direction were strongest for both temporal metrics, for both auditory nerve and inferior colliculus. Furthermore, the high sensitivity observed in the inferior colliculus neural rate-based discrimination suggests that these neurons integrate across multiple inputs arising from the auditory periphery.</span></p>
Dataset and data processing tools of EPL 139 (2022) 55002 — Harmonic calibration of quadrature phase interferometry
<p>Dataset for <em>EPL</em> <strong>139</strong> (2022) 55002 — Harmonic calibration of quadrature phase interferometry</p> <p><strong>Scripts: </strong>the main Matlab script to analyse all data is <strong>analyseall.m</strong>, and uses all other scripts and functions (<strong>*.m</strong>). See comments in this main script for details. The script <strong>DefineDatasets.m,</strong> and its output <strong>Dataset.mat</strong>, were run before to define the main parameters of the 20 datasets, and define the frequency range where to estimate the background noise to be subtracted from the spectra of the driven cantilever when computing the total harmonic distortion.</p> <p><strong>Data:</strong> the 100 data files (.mat) have the following name scheme: [driving-frequency]-[Amplitude]-(low frequency external phase)-001 to 005.mat. For example 1kHz-10nm-w1Hz150nm-001.mat correspond to a driving of the cantilever at 1kHz, with an oscillation amplitude close to 10nm, with a low frequency driving of the external optical phase at 1Hz and equivalent 150nm amplitude. 001 stand for the first of the five files corresponding to this measurement. Note that the low frequency driving is optional. The main driving frequency is either directly given in kHz, or corresponds to one of the 2 first resonance modes of the cantilever (mode1 at 13.4kHz, and mode2approx at 84.6 kHz). Each of these data files contains 5 variables: Cx and Cy are the 2 outputs of the quadrature phase interferometer (1s of acquisition, 2e6 samples), fs is the sampling frequency (2 MHz), Ix and Iy are the mean total intensities on the photodiodes corresponding to Cx and Cy (units: A, this information can be useful to estimate the expected shot noise floor on the measurement).</p>
Data from: Manipulation of Miniature and Microminiature Bodies on a Harmonically Oscillating Platform by Controlling Dry Friction
<p>Data from the paper "Manipulation of Miniature and Microminiature Bodies on a Harmonically Oscillating Platform by Controlling Dry Friction" <a href="https://doi.org/10.3390/mi12091087">https://doi.org/10.3390/mi12091087</a></p> <p>Currently used nonprehensile manipulation systems that are based on vibrational techniques employ temporal (vibrational) asymmetry, spatial asymmetry, or force asymmetry to provide and control a directional motion of a body. This paper presents a novel method of nonprehensile manipulation of miniature and microminiature bodies on a harmonically oscillating platform by creating a frictional asymmetry through dynamic dry friction control. To theoretically verify the feasibility of the method and to determine the control parameters that define the motion characteristics, a mathematical model was developed, and modeling was carried out. Experimental setups for miniature and microminiature bodies were developed for nonprehensile manipulation by dry friction control, and manipulation experiments were carried out to experimentally verify the feasibility of the proposed method and theoretical findings. By revealing how characteristic control parameters influence the direction and velocity, the modeling results theoretically verified the feasibility of the proposed method. The experimental investigation verified that the proposed method is technically feasible and can be applied in practice, as well as confirmed the theoretical findings that the velocity and direction of the body can be controlled by changing the parameters of the function for dynamic dry friction control. The presented research enriches the classical theories of manipulation methods on vibrating plates and platforms, as well as the presented results, are relevant for industries dealing with feeding, assembling, or manipulation of miniature and microminiature bodies.</p>
Audio Examples for "Directional Frequency Filtering of Recordings in Spherical Harmonics Domain for Innovative Noise Reduction Strategies"
<p>This is a collection of audio examples of the processing corresponding to the master thesis "Directional Frequency Filtering of Recordings in Spherical Harmonics Domain for Innovative Noise Reduction Strategies"<br> The examples (ambisonics and binaural) contain recordings of moving sources in an anechoic chamber, which were made within different scenes partly containing noise barriers. Furthermore the simulation of noise barriers was implemented using directional filtering within a plane wave decomposition of the signals in sh domain.</p>
Photoresponsive Nanocarriers Based on Lithium Niobate Nanoparticles for Harmonic Imaging and On-Demand Release of Anticancer Chemotherapeutics
<p>Data set associated with the following publication:</p> <p>Gheata, A., Gaulier, G., Campargue, G., Vuilleumier, J., Kaiser, S., Gautschi, I., Riporto, F., Beauquis, S., Staedler, D., Diviani, D., Bonacina, L., Gerber-Lemaire, S. Photoresponsive Nanocarriers Based on Lithium Niobate Nanoparticles for Harmonic Imaging and On-Demand Release of Anticancer Chemotherapeutics. <em>ACS Nanoscience Au</em> <strong>2022</strong>, <em>2</em>, 355-366.</p> <p>Raw data for characterization of molecules and nanoparticles: NMR, TEM, DLS.</p> <p>Raw data for cell assays, cell imaging, cytotoxicity experiments and EGFR quantification.</p> <p>Metadata file.</p> <p>Copy of labbooks.</p>
Data for Human sperm steer with second harmonics of the flagellar beat
<p>Files containing all data processed for the article “Human sperm steer with second harmonics of the flagellar beat” by Guglielmo Saggiorato, Luis Alvarez, Jan F. Jikeli, U. Benjamin Kaupp, Gerhard Gompper, and Jens Elgeti</p> <p><br> collected_data.zip<br> compressed containing one folder per experiment. Each folder features the raw data (original movie) and the corresponding flagellar parameters measured: trajectory and curvature. </p> <p>movie.tbz2<br> files to make the movie comparing original sperm recording and simulation</p> <p>steer_with_phase.bz2<br> xyz file of Supplementary Movie 2 where sperm steers with the second-harmonic phase</p> <p>analysis_scripts.zip contains<br> scripts used for data analysis: curvature.py, pma.py, and spectrogram.py </p> <p>compare_2nd_harmonic_to_average_curvature.tar.bz2<br> files comparing the contribution of the 2nd harmonic and average curvature</p> <p> </p>
Harmonized livestock number dataset for Europe
<p>This dataset comprises spatial and temporal data on ruminant livestock distributions on detailed spatial level. We collected statistics on cattle, sheep and goats distribution for 43 countries and territories in Europe: for 31 countries and territories we collected data on the level of local administrative units, for 8 countries on regional level (NUTS 3), and on national level (NUTS 0) for 4 countries. We provide data for over 73 thousand administrative units, making them more detailed than publicly provided by the European Statistical Office (which reports data for 325 adminstrative units). The data are available for the periods corresponding to 2000, 2010 and 2020.</p> <p><strong>Harmonizing to livestock units:</strong></p> <p>We harmonized the data to livestock units (LSU), making them easy to use and compare between different countries and regions. We used livestock coefficients provided by EUROSTAT (https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Glossary:Livestock_unit_(LSU) ) to harmonize numbers of different livestock types, with more detail on the processing provided in the accompanying data_sources.xlsx file.<br><br><strong>File description:</strong></p> <p>The data-set consists of the following files:</p> <ul> <li>a spreadsheet describing in detail the sources of data and how the data was processed (file <a href="../api/records/11058509/draft/files/data_sources.xlsx/content" target="_blank" rel="noopener noreferrer">data_sources.xlsx</a>)</li> <li>three geopackage files (archived as a zip file) for each year (2000, 2010, 2020) for harmonized ruminant livestock numbers for all 43 countries and territories (<a href="../api/records/11058509/draft/files/livestock2000.zip/content" target="_blank" rel="noopener noreferrer">livestock2000.zip</a>, <a href="../api/records/11058509/draft/files/livestock2010.zip/content" target="_blank" rel="noopener noreferrer">livestock2010.zip</a>, <a href="../api/records/11058509/draft/files/livestock2020.zip/content" target="_blank" rel="noopener noreferrer">livestock2020.zip</a> )</li> <li>three geopackage files (archived as a zip file) for each year (2000, 2010, 2020) for sheep and goats for Poland (as the numbers are provided on a different level for these two livestock type for Poland) (<a href="../api/records/11058509/draft/files/poland_sheep_goats2000.zip/content" target="_blank" rel="noopener noreferrer">poland_sheep_goats2000.zip</a> , <a href="../api/records/11058509/draft/files/poland_sheep_goats2010.zip/content" target="_blank" rel="noopener noreferrer">poland_sheep_goats2010.zip</a> , <a href="../api/records/11058509/draft/files/poland_sheep_goats2020.zip/content" target="_blank" rel="noopener noreferrer">poland_sheep_goats2020.zip</a> )</li> <li>a geopackage file (archived as a zip file) with (estimated) shares of cattle grazing for each country, in many cases for subnational units (<a href="../api/records/11058509/draft/files/grazing_share.zip/content" target="_blank" rel="noopener noreferrer">grazing_share.zip</a> )</li> </ul> <p><strong>Source information:</strong></p> <p>The raw data on livestock numbers are available from each country individually, and we provide the sources in the data_sources.xlsx file , to enable future updates.</p> <p> </p> <div> <p>This dataset has been created as part of LAMASUS Project under the scope of Deliverable 2.1 titled "The LUM Geodatabase and Area Estimates of Land Use Change to 2018 ". The full text of the deliverable can be accessed via: <a href="https://www.lamasus.eu/wp-content/uploads/LAMASUS_D2.1_LUMGeodatabase.pdf">https://www.lamasus.eu/wp-content/uploads/LAMASUS_D2.1_LUMGeodatabase.pdf.</a></p> </div>
Data set: A solid-state high harmonic generation spectrometer with cryogenic cooling
<p>Solid-state high harmonic generation (sHHG) spectroscopy is a promising technique for studying electronic structure, symmetry, and dynamics in condensed matter systems. Here, we report on the implementation of an advanced sHHG spectrometer based on a vacuum chamber and closed-cycle helium cryostat. Using an in situ temperature probe, it is demonstrated that the sample interaction region retains cryogenic temperature during the application of high-intensity femtosecond laser pulses that generate high harmonics. The presented implementation opens the door for temperature-dependent sHHG measurements down to a few Kelvin, which makes sHHG spectroscopy a new tool for studying phases of matter that emerge at low temperatures, which is particularly interesting for highly correlated materials.</p>
Harmonized metadata of Hexa-X-II enablers of Hexa-X-II D2.3 public deliverable
<p>Harmonized metadata of Hexa-X-II enablers of Hexa-X-II D2.3 public deliverable, used in the enabler selection methodology used in the chapter 7 of the deliverable - based on enebaler data collected from the contributors of Hexa-X-II project.</p>
Datased for a publication: "Harmonizing Microstructures and Enhancing Mechanical Resilience: Novel Powder Metallurgy Approach for Zn-Mg Alloys"
<p>These data are published as a part of the paper: Harmonizing Microstructures and Enhancing Mechanical Resilience: Novel Powder Metallurgy Approach for Zn-Mg Alloys. The structure and organization of the data are outlined in the readme file. </p> <p> </p> <p><strong>Dataset versions:</strong></p> <p><strong>V1:</strong> Original version of the data.</p>
Raw Data for "Synthesis and functionalisation of biodegradable Second Harmonic Generation nanoprobes for cell targeting"
<p>Raw data files used in the "Synthesis and functionalisation of biodegradable Second Harmonic Generation nanoprobes for cell targeting" manuscript.</p>
Data for: A harmonized database of European forest simulations under climate change
<p>This repository contains the database presented in the publication "A harmonized database of European forest simulations under climate change". It contains a collection of harmonized forest simulation model outputs from 17 different models covering 1.1 million individual simulation runs, over 136 million simulation years across over 13,599 unique locations in Europe.</p> <p>Detailed description can be found in the publication (<a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.dib.2024.110384" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.dib.2024.110384</a>). The file "forest_simulation_db_v1.7z" contains all raw simulation outputs and a metadata table of all simulations including information about locations and harmonized soil conditions for those locations. Simulation outputs with harmonized climate data are stored in one SQLite database per climate scenario.</p> <p>The code that was used to create the database, as well as to access and explore the data can be found here: https://github.com/magrueni/forest_simulation_database.git</p> <p>Note: Please be cautious with the use of the simulations with unique identifiers 1037-1047. There were some abrupt species compositions changes reported that suggest that in a small number of the original simulations there was an underlying issue in the compilation of the raw simulation data.</p> <p> </p> <p>--- Please use the updated version 1.1 ---</p> <p>Unfortunately we found an bug in the daily climate extraction process of the previous version, leading to inconsistencies in the harmonized climate data. We corrected the harmonized daily climate data for all scenarios. Additionally, the LAI values in the raw data of the simulations with the unique identifier 1016 were calculated wrongly and therefore corrected in this version. Please use the updated version for all analyses. We apologize for any inconveniences.</p> <p> </p> <p>--- Please use the updated version 1.1 ---</p>
Resonant Scattering of Sub-keV Electrons by Beam-driven Electron Cyclotron Harmonic Waves
<p>Data repository for ``Resonant Scattering of Sub-keV Electrons by Beam-driven Electron Cyclotron Harmonic Waves''. This repository contains raytracing results of beam-driven ECH waves and loss-cone-driven ECH waves calculated from HOTRAY ray-tracing program.</p>
Packing-Inspired Algorithms for Periodic Scheduling Problems with Harmonic Periods - instances
<p>Instances for periodic scheduling problem used in conference paper <a title="Paper Details, Citation and Download" href="https://www.scitepress.org/PublicationsDetail.aspx?ID=nP/EuJcR7dI=&t=1">Packing-Inspired Algorithms for Periodic Scheduling Problems with Harmonic Periods</a> <span></span> <a href="https://doi.org/10.5220/0012325800003639" target="_blank" rel="noopener">10.5220/0012325800003639</a></p> <p> </p>
Integrated Approach to Global Land Use and Land Cover Reference Data Harmonization
<h2><strong>INTRODUCTION</strong></h2> <p>This document outlines the creation of a global inventory of reference samples and Earth Observation (EO) / gridded datasets for the Global Pasture Watch (GPW) initiative. This inventory supports the training and validation of machine-learning models for GPW grassland mapping. This documentation outlines methodology, data sources, workflow, and results.</p> <p><strong>Keywords:</strong> Grassland, Land Use, Land Cover, Gridded Datasets, Harmonization</p> <p> </p> <h2><strong>OBJECTIVES</strong></h2> <ul> <li> <p>Create a global inventory of existing reference samples for land use and land cover (LULC);</p> </li> <li> <p>Compile global EO / gridded datasets that capture LULC classes and harmonize them to match the GPW classes;</p> </li> <li> <p>Develop automated scripts for data harmonization and integration.</p> </li> </ul> <p> </p> <h2><strong>DATA COLLECTION </strong></h2> <p>Datasets incorporated:</p> <table> <tbody> <tr> <td><strong>Datasets</strong></td> <td> <p><strong>Spatial distribution</strong></p> </td> <td><strong>Time period</strong></td> <td><strong>Number of individual samples</strong></td> </tr> <tr> <td>WorldCereal</td> <td>Global</td> <td>2016-2021</td> <td>38,267,911</td> </tr> <tr> <td>Global Land Cover Mapping and Estimation (GLanCE)</td> <td>Global</td> <td>1985-2021</td> <td>31,061,694</td> </tr> <tr> <td>EuroCrops</td> <td>Europe</td> <td>2015-2022</td> <td>14,742,648</td> </tr> <tr> <td>GeoWiki G-GLOPS training dataset</td> <td>Global</td> <td>2021</td> <td>11,394,623</td> </tr> <tr> <td>MapBiomas Brazil</td> <td>Brazil</td> <td>1985-2018</td> <td>3,234,370</td> </tr> <tr> <td>Land Use/Land Cover<br>Area Frame Survey (LUCAS)</td> <td>Europe</td> <td>2006-2018</td> <td>1,351,293</td> </tr> <tr> <td>Dynamic World</td> <td>Global</td> <td>2019-2020</td> <td>1,249,983</td> </tr> <tr> <td>Land Change Monitoring,<br>Assessment, and Projection (LCMap)</td> <td>U.S. (CONUS)</td> <td>1984-2018</td> <td>874,836</td> </tr> <tr> <td>GeoWiki 2012</td> <td>Global</td> <td>2011-2012</td> <td>151,942</td> </tr> <tr> <td>PREDICTS</td> <td>Global</td> <td>1984-2013</td> <td>16,627</td> </tr> <tr> <td>CropHarvest</td> <td>Global</td> <td>2018-2021</td> <td>9,714</td> </tr> </tbody> </table> <p><strong>Total:</strong> 102,355,642 samples</p> <p> </p> <h2><strong>WORKFLOW</strong></h2> <h3><strong>Harmonization Process</strong></h3> <p>We harmonized global reference samples and EO/gridded datasets to align with GPW classes, optimizing their integration into the GPW machine-learning workflow.</p> <p>We considered reference samples derived by visual interpretation with spatial support of at least 30 m (Landsat and Sentinel), that could represent LULC classes for a point or region.</p> <p>Each dataset was processed using automated Python scripts to download vector files and convert the original LULC classes into the following GPW classes:</p> <p> 0. Other land cover</p> <p> 1. Natural and Semi-natural grassland</p> <p> 2. Cultivated grassland</p> <p> 3. Crops and other related agricultural practices</p> <p>We empirically assigned a weight to each sample based on the original dataset's class description, reflecting the level of mixture within the class. The weights range from 1 (Low) to 3 (High), with higher weights indicating greater mixture. Samples with low mixture levels are more accurate and effective for differentiating typologies and for validation purposes.</p> <p>The harmonized dataset includes these columns:</p> <table> <tbody> <tr> <td><strong>Attribute Name</strong></td> <td><strong>Definition</strong></td> </tr> <tr> <td>dataset_name</td> <td>Original dataset name</td> </tr> <tr> <td>reference_year</td> <td>Reference year of samples from the original dataset</td> </tr> <tr> <td>original_lulc_class</td> <td>LULC class from the original dataset</td> </tr> <tr> <td>gpw_lulc_class</td> <td>Global Pasture Watch LULC class</td> </tr> <tr> <td>sample_weight</td> <td>Sample's weight based on the mixture level within the original LULC class</td> </tr> </tbody> </table> <p> </p> <h2><strong>ACKNOWLEDGMENTS</strong></h2> <p>The development of this global inventory of reference samples and EO/gridded datasets relied on valuable contributions from various sources. We would like to express our sincere gratitude to the creators and maintainers of all datasets used in this project.</p> <p> </p> <h2><strong>REFERENCES</strong></h2> <ul> <li> <p>Brown, C.F., Brumby, S.P., Guzder-Williams, B. et al. Dynamic World, Near real-time global 10 m land use land cover mapping. Sci Data 9, 251 (2022). https://doi.org/10.1038/s41597-022-01307-4Van Tricht, K. et al. Worldcereal: a dynamic open-source system for global-scale, seasonal, and reproducible crop and irrigation mapping. Earth Syst. Sci. Data 15, 5491–5515, 10.5194/essd-15-5491-2023 (2023)</p> </li> <li> <p>Buchhorn, M.; Smets, B.; Bertels, L.; De Roo, B.; Lesiv, M.; Tsendbazar, N.E., Linlin, L., Tarko, A. (2020): Copernicus Global Land Service: Land Cover 100m: Version 3 Globe 2015-2019: Product User Manual; Zenodo, Geneve, Switzerland, September 2020; doi: 10.5281/zenodo.3938963</p> </li> <li> <p>d’Andrimont, R. et al. Harmonised lucas in-situ land cover and use database for field surveys from 2006 to 2018 in the european union. Sci. data 7, 352, 10.1038/s41597-019-0340-y (2020)</p> </li> <li> <p>Fritz, S. et al. Geo-Wiki: An online platform for improving global land cover, Environmental Modelling & Software, 31, https://doi.org/10.1016/j.envsoft.2011.11.015 (2012)</p> </li> <li> <p>Fritz, S., See, L., Perger, C. et al. A global dataset of crowdsourced land cover and land use reference data. Sci Data 4, 170075 https://doi.org/10.1038/sdata.2017.75 (2017)</p> </li> <li> <p>Schneider, M., Schelte, T., Schmitz, F. & Körner, M. Eurocrops: The largest harmonized open crop dataset across the european union. Sci. Data 10, 612, 10.1038/s41597-023-02517-0 (2023)</p> </li> <li> <p>Souza, C. M. et al. Reconstructing Three Decades of Land Use and Land Cover Changes in Brazilian Biomes with Landsat Archive and Earth Engine. Remote. Sens. 12, 2735, 10.3390/rs12172735 (2020)</p> </li> <li> <p>Stanimirova, R. et al. A global land cover training dataset from 1984 to 2020. Sci. Data 10, 879 (2023) </p> </li> <li>Stehman, S. V., Pengra, B. W., Horton, J. A. & Wellington, D. F. Validation of the us geological survey’s land change monitoring, assessment and projection (lcmap) collection 1.0 annual land cover products 1985–2017. Remot Sensing environment 265, 112646, 10.1016/j.rse.2021.112646 (2021).</li> <li> <p>Tsendbazar, N. et al. Product validation report (d12-pvr) v 1.1 (2021).</p> </li> <li>Tseng, G., Zvonkov, I., Nakalembe, C. L., & Kerner, H. (2021). CropHarvest: A global dataset for crop-type classification. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track.</li> </ul>
Spherical harmonic models of the magnetic field of Mars from Cain et al. (2003)
<p>This archive contains spherical harmonic coefficients of the two magnetic field models of Mars that were published in Cain et al. (2003): FSU50 and FSU90. The two models here are identical to those found in the supplemental materials of that manuscript, with the exception that they have been reformatted for easy input into the pyshtools software.</p> <p>Cain, J. C., Ferguson, B. B., Mozzoni, D. (2003), An n = 90 internal potential function of the Martian crustal magnetic field, Journal of Geophysical Research: Planets, 108 (E2), doi:10.1029/2000JE001487.</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.