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5,155 results for “resolution”
Monthly averaged lightning and trace gases data extracted from EMAC simulations (2007, T42L90MA resolution)
<pre>About Dataset Monthly averaged lightning and trace gases data extracted from EMAC simulations (2007, T42L90MA resolution) Authors: Francisco J. Pérez-Invernon, Francisco J. Gordillo-Vázquez, Heidi Huntrieser, Patrick Jöckel and Eric J. Bucsela Description of the data CTR simulations: CTR_*.nc files LNOfs simulation: LNOfs_*.nc files *tr_*.nc: Monthly averaged trace gases *lnox*.nc: Monthly averaged lightning data<br>*ECHAM5*.nc: Monthly averaged dynamical variables<br>*grid_def*.nc: Monthly averaged grid variables<br>*tropop*.nc: Monthly averaged tropospheric variables </pre> <pre>File format: netcdf</pre> <p> </p>
Theoretical Resolution of the Birch and Swinnerton-Dyer Conjecture: A Comprehensive Approach: Stability Approach and Numerical Investigation of Elliptic Curves
<p>Abstract</p> <p> </p> <p>This paper introduces a comprehensive theoretical analysis of the Birch-Swinnerton-Dyer Conjecture, a longstanding and profound challenge in modern number theory. Our approach is built upon a novel method of stable numerical integration and Fourier transformation, specifically tailored to analyze elliptic curves under extreme mathematical and computational conditions. The method developed aims to ensure the stability of numerical computations, even when dealing with highly complex parameters and challenging computational environments. By maintaining computational stability, this approach allows for precise analysis of elliptic curves, revealing deeper insights into their properties without compromising the accuracy of the results.</p> <p> </p> <p>The significance of this method lies in its ability to provide reliable convergence results, which are crucial for understanding the deeper mathematical structure of elliptic curves. These structures are intimately tied to the Birch-Swinnerton-Dyer Conjecture, making this work a potentially important contribution to the ongoing efforts to address this conjecture.</p> <p> </p> <p>f(x) = e^{-tx^2} \quad \sqrt{\infty} \int_{0}^{\infty} t dt \quad (\text{Stable numerical integration})</p> <p> </p> <p>F{g(t)} = \int_{-\infty}^{\infty} g(t)e^{-i\omega t} dt \quad (\text{Stability of Fourier transformation}) ]</p> <p> </p> <p>y^2 = x^3 + ax + b \quad (\text{Elliptic curve and its numerical stability})</p> <p> </p> <p>The results presented in this theoretical study provide a significant step forward in the analysis of elliptic curves and their relation to the Birch-Swinnerton-Dyer Conjecture. While the analysis offers substantial progress, the findings remain theoretical and are subject to further review and validation by the broader mathematical community.</p> <p> </p> <p>In this paper, we will showcase the methods and frameworks used to achieve stable numerical results under extreme conditions, alongside a discussion of the potential implications for future research. These methods open new possibilities for tackling the Birch-Swinnerton-Dyer Conjecture and offer a foundation for further exploration into the deeper properties of elliptic curves. While detailed calculations and specific results are beyond the scope of this abstract, we present an overview of the approach taken and the rigorous mathematical tools employed. These include advanced techniques in numerical integration and Fourier analysis, both of which have been crucial in ensuring the accuracy and stability of the elliptic curve analysis.</p> <p> </p> <p>This theoretical work marks a significant contribution to the ongoing dialogue surrounding the Birch-Swinnerton-Dyer Conjecture. However, the results are preliminary, and further evaluation and peer review will be necessary to determine their full impact on the field. The proposed method offers a solid foundation for future investigations and potential advancements in the theory of elliptic curves.</p> <p> </p> <p> </p> <p>---</p> <p> </p> <p> </p>
Dataset of High-Resolution Micro-CT Imaging of Tumor Invasion and Metastasis in a Murine Esophageal Cancer PDX Model
<p>This dataset features high-resolution micro-CT imaging data capturing the progression of tumor invasion and metastasis in an orthotopic patient-derived xenograft (PDX) model of esophageal cancer. Using contrast-enhanced micro-CT, we visualized detailed patterns of tumor invasion, including budding, multicellular streaming, and expansive growth, across multiple abdominal organs such as the stomach, pancreas, liver, and spleen. The dataset includes two specimens, highlighting both the primary tumor site and extensive metastases throughout the abdominal cavity. Our imaging preserved the native tissue architecture, providing a unique three-dimensional view of tumor-host interactions. This collection offers valuable insights for researchers studying the dynamics of esophageal cancer invasion and metastasis. Detailed descriptions of the micro-CT scanning parameters, image analysis, and sample preparation are provided within the dataset archive.</p>
The High-resolution 3D QP Model of the China Seismic Experiment Site
<p><span>The CSES-Q1.0 is the highest resolution 3D <em>Q</em><sub>P</sub> model in the CSES to date.The first column of the file represents longitude, the second column represents latitude, the third column represents depth, and the fourth column represents QP values.</span></p>
DiCARN-DNase: Enhancing Cell-to-Cell Hi-C Resolution Using Dilated Cascading ResNet with Self-Attention and DNase-seq Chromatin Accessibility Data
<p>This repository contains the training, validation, and testing data for the DiCARN-DNase project. The GitHub repository is available via https://github.com/OluwadareLab/DiCARN_DNase</p>
ASM-SS: The First Quasi-Global High Spatial Resolution Coastal Storm Surge Dataset Reconstructed from Tide Gauge Records
<p>The ASM-SS dataset is a high spatial resolution (every 10 km per node along the coastline), long-term (over 80 years from 1940 to 2020), quasi-global (within 45°S-45°N), hourly data-driven storm surge dataset. Each NetCDF file includes five parameters: longitude, latitude, nodes, time, and surge level. Longitude and latitude are the location information of nodes in degree; the unit of time is accumulated hours since 1900-01-01 00:00:00; surge levels are given in meters. Users can use longitude, latitude, and time as keywords to select surge levels at nodes of interest within a target period. </p>
ENTROPY DR1. High-resolution near-UV/optical spectra of 2MASS J11151597+1937266
<h1>ENTROPY Data Release 1 (v2.0)</h1> <h2>Summary</h2> <p>The files in this data release consist of the flux-calibrated, stacked 1D spectum of the isolated, free-floating accreting planetary-mass object 2MASS J11151597+1937266 (2M1115). The data results from the high-resolution (R~50,000) observations by the Ultraviolet and Visual Echelle Spectrograph (UVES) at ESO's Very Large Telescope (VLT) in Chile, between 10-11 June 2023, taken as part of the ESO programme 0111.C-0166(A). This is the underlying data used for the analysis in the publication <a href="https://doi.org/10.1051/0004-6361/202450881" target="_blank" rel="noopener">Viswanath et al. (2024)</a> </p> <h2>Details of Observations</h2> <p>The observations of 2M1115 with UVES were carried out between 10-11 June 2023 (MJD 60105, 60106) as part of the ExoplaNeT accretion mOnitoring sPectroscopic surveY (ENTROPY) in #Dichroic 1 mode with both the blue (390 nm) and red (580 nm) arms using a 0.8 arcsec wide slit without AO or chopping.</p> <p>A total of four frames at 740 s exposure (NDIT=1) each were obtained across the two nights, giving a total integration time of 0.82 hr at an average seeing of 1.43 arcsec and an average airmass 1.416. The total wavelength range covered by the spectrum is 320–680 nm.</p> <h2>Target</h2> <p>Identifier: 2MASS J11151597+1937266</p> <p>ICRS RA (ep=2016.0): 168.816234 deg</p> <p>ICRA DEC (ep=2016.0): +19.623939 deg</p> <p>Distance: 45.21 +- 2.20 pc</p> <p>Age: 5-45 Myr</p> <p>Mass: 6(+8, -4) Mjup</p> <p>Radius: 1.5 +- 0.1 Rjup</p> <p>Effective Temperature: 1816 +- 63 K</p> <h2>Data reduction</h2> <p>Each of the 4 raw data frames from the original set of exposures from the 2 nights were bias subtracted and flat-field corrected using the calibration files from ESO. Inter-order background was also subtracted. Cosmic rays were accounted for by using a horizontal median filtering and masking out pixels higher than 10 times the local variance. The flux was extracted order by order using standard aperture photometry, which included the subtraction of sky background and telluric lines.</p> <p>The flux was calibrated based on the observations of a standard star taken contemporaneously with the same observing setup as the target, and was corrected for the relative slit loss (~4%) from seeing. Wavelength calibration was performed using the arc lamp spectrum and a Th–Ar line list. The wavelengths listed in the spectrum are in air. A barycentric velocity correction was applied to the wavelength calibration to transform the spectral reference system to that of the source. The stacked 1D spectrum available in this data release is obtained by taking the weighted average of the 4 individual spectra, with the weights determined from the respective photon noise.</p> <h2>Description of files</h2> <p>The data release in this second version consists of:</p> <p>a) 1 merged spectrum UVES-2023.06.10-11.2M1115.merged-spectra.txt covering the entire UVES range of these observations (320–680 nm). The flux for the overlapping wavelengths in adjacent orders of each arm were averaged to get a single flux value per wavelength. This allows for easy access of the entire spectral range of all arms together. The ASCII file has 4 columns:</p> <p>Column1 : Wavelength, in ångstrøm</p> <p>Column2 : Flux, in units ergs/s/cm2/ångstrøm</p> <p>Column3 : Flux_stddev, the weighted standard deviation of the flux across the 4 individual spectra, in units of ergs/s/cm2/ångstrøm (This is the recommended error bar)</p> <p>Column4 : Flux_phot_err, Average of the photometric uncertainties in flux across the 4 spectra, in units of ergs/s/cm2/ångstrøm</p> <p>b) The compressed file UVES-2023.06.10-11.2M1115.non-merged-spectra.zip contains one folder per each of the three arms (Blue, RedL, RedU). The folders include ASCII files containing the spectrum for individual orders of each arm, provided for the purpose of preserving the signal-to-noise ratio (S/N) of all orders. Some important emission lines like H𝛽 occur in both adjacent orders but at different signal strengths. Merging the orders by averaging the flux as in file (a) will suppress the S/N of such emission lines.</p> <p>The order numbers of the respective arms containing detected emission lines from 2M1115 can be found in Tables 2 and G.1 in <a href="https://doi.org/10.1051/0004-6361/202450881" target="_blank" rel="noopener">Viswanath et al. (2024)</a>. For the following Balmer lines detected in <a href="https://doi.org/10.1051/0004-6361/202450881">Viswanath et al. (2024)</a>, the corresponding rest wavelengths appear in two consecutive orders as indicated below:</p> <table> <tbody> <tr> <td><strong>Line</strong></td> <td><strong>Arm</strong></td> <td><strong>Orders</strong></td> <td><strong>Detection in Viswanath et al. (2024)</strong></td> </tr> <tr> <td>H𝛽</td> <td>RedL</td> <td>2, 3</td> <td>Both at > 3σ S/N </td> </tr> <tr> <td>Hγ</td> <td>Blue</td> <td>34, 35</td> <td>Both at > 3σ S/N </td> </tr> <tr> <td>Hδ</td> <td>Blue</td> <td>28, 29</td> <td>Only in 28</td> </tr> <tr> <td>H7</td> <td>Blue</td> <td>24, 25 </td> <td>Both, but only ~2σ S/N in 24</td> </tr> <tr> <td>H9</td> <td>Blue</td> <td>20, 21</td> <td>At only ~2σ S/N in both</td> </tr> </tbody> </table> <p> </p> <p>Each ASCII file has 4 columns with same description as in file (a). To plot the spectrum of all the orders of all the arms, the following python code can be used:</p> <p><code>import numpy as np</code><br><code>from glob import glob</code><br><code>from natsort import natsorted</code><br><code>arms = ['BLUE', 'REDL', 'REDU']</code><br><code>plt.figure()</code><br><code>for arm in arms:</code><br><code> files = natsorted(glob(f'UVES-2023.06.10-11.2M1115.non-merged-spectra/{arm}/*.txt'))</code><br><code> print(files)</code><br><code> for i in range(len(files)):</code><br><code> data = np.loadtxt(files[i])</code><br><code> plt.plot(data[:, 0], data[:, 1])</code><br><code>plt.show()</code></p> <pre> </pre> <p>c) 2M1115-Photometry.txt, an ASCII file containing the existing photometry for the target</p> <h2> </h2> <h2>Changes from previous version V1.0</h2> <p>The merged spectrum UVES-2023.06.10-11.2M1115.merged-spectra.txt in this version has been modified to preserve the original spectral resolution in the overlapping wavelegth regions. In V1.0, the corresponding regions had double the resolution due to an error in the interpolation method used.</p> <p>The non-merged spectra of the individual orders have been replaced in this version as a compressed zip folder (instead of .npz files) containing ASCII files for spectra of each individual order to facilitate easier data handling.</p> <p>The photometry file has been updated from that of last version with more information, including two additional filter bands (<em>Gaia</em> Gbp and Grp) as well as effective filter widths of all provided filter bands.</p>
High-resolution Industrial Production Energy (HIPE)
<p>The High-resolution Industrial Production Energy (HIPE) data set contains smart meter readings of ten machines and the main terminal of a power-electronics production plant over three months.</p> <p><a href="https://doi.org/10.1145/3208903.3210278" target="_blank" rel="noopener">The accompanying publication (published by ACM)</a> describes the data set and outlines open challenges and use cases with industrial energy data. This includes a dis­cussion of cha­rac­ter­is­tics of in­dus­tri­al machines and of differences to residential appliances.</p> <p>Links:</p> <ul> <li>Publication: <a href="https://doi.org/10.1145/3208903.3210278" target="_blank" rel="noopener">https://doi.org/10.1145/3208903.3210278</a></li> <li>Description: <a href="https://www.energystatusdata.kit.edu/hipe.php">https://www.energystatusdata.kit.edu/hipe.php</a></li> </ul>
Six years of high-resolution monitoring data of 40 borehole heat exchangers
<p>This dataset provides six years of monitoring data of the ground heat exchanger that supplies heat and cold to the E.ON Energy Research Center located in Aachen, Germany. The data belong to the following publication:</p> <blockquote> <p>Heim, E., Stoffel, P., Müller, D., & Klitzsch, N. (2024). <em>Six years of high-resolution monitoring data of 40 borehole heat exchangers</em>. Scientific Data, 11(1), 1334. <a href="https://doi.org/10.1038/s41597-024-04241-9" rel="nofollow">https://doi.org/10.1038/s41597-024-04241-9</a></p> </blockquote> <p>The ground heat exchanger consists of 40 double-U-loop borehole heat exchangers (BHE), that are arranged in three subfields. Each subfield is connected to an underground vault, in which sensors for the inlet fluid temperature, the outlet fluid temperature and the volume flow of each BHE are placed. The sensors record data in 30-second intervals. Coherent data is provided from July 1, 2018 to June 30, 2024 in two time resolutions and processing levels:</p> <ul> <li><strong>Raw data</strong>: The raw data in its initial resolution, aligned to coherent 30-second timestamps.</li> <li><strong>Prepared data</strong>: The raw data was resampled to 5 minute intervals using the weighted mean, with the volume flow as weight. Moreover, data periods that are not representative of the thermal exchange process in the BHE were masked (e.g., no-flow periods).</li> </ul> <p>Both raw and prepared data are provided as .csv files for each month individually. Temperature measurements are given in °C, the volume flow rate in l/min. </p> <p>In addition, a <strong>supporting file</strong> indicating the BHE locations is provided. It also contains the length of the horizontal connecting pipes going from the underground vaults (where the sensors are placed) to the BHE heads. </p> <p><strong>Code </strong>explaining how to open and work with the data is provided in a separate github repository:<strong> </strong><a href="https://github.com/elimh/ERC_BHEfield_Data_Code">https://github.com/elimh/ERC_BHEfield_Data_Code</a></p>
High resolution reanalysis for the northern Adriatic Sea (CADEAU project - CMEMS Demonstration 32-DEM-L5)
<p><span><span>This dataset includes the output of the high resolution reanalysis carried out in the framework of the CADEAU project (CMEMS Demonstration 32-DEM-L5). </span></span></p> <p><span><span>The reanalysis covers the northern Adriatic Sea for the 2006-2017 time period; the simulation is characterized by a horizontal resolution of 1/128° and 27 non-equally spaced vertical layers.</span></span></p> <p><span><span>The dataset includes the following physical and biogeochemical variables (averaged every 5 days): (1) salinity (S), temperature (T), chlorophyll (Chla), nitrate (N3n), phosphate (N1p).</span></span></p>
The species richness-productivity relationship varies among regions and productivity estimates, but not with spatial resolution
<p>The relationship between species richness and productivity (SRPR) has been a long-studied and hotly debated topic in ecology. Different studies have reported different results with variable shapes (i.e. unimodal, linear) and directions (i.e. positive, negative) of SRPRs depending on spatial grain (i.e. size of sampling unit for species richness), productivity estimates, and study extent. In this study, we quantified the effect of multiple estimates of productivity (aboveground, belowground and total biomass, and various measures of soil fertility) on species richness across three spatial grains (0.04 m<sup>2</sup>, 1 m<sup>2</sup>, and 25 m<sup>2</sup>) across temperate grasslands from two regions in Central Europe. We analyzed SRPR in each of the two regional datasets separately, as well as the two datasets pooled together. Our results have revealed that differences caused by spatial grain were unexpectedly small, and the direction of the SRPR was consistent within each productivity estimate, but differed between regions. Productivity estimates (across all spatial scales) had different, sometimes contrasting effects on SRPR (together with predictive power) within a region, and this pattern was more pronounced when compared between regions. The combination of different datasets led to very different results than when these were analyzed separately. We did not find any evidence for a unimodal response. This study points to the necessity of careful assessing when combining datasets from different regions, even if the plant communities belong to the same vegetation type. The dataset combination may blur the role of different drivers, which likely determine the shape and strength of SRPR. We suggest that data and study comparability may be enhanced by consistently using the same productivity estimates, which would allow for more robust interpretation of possible ecological drivers underlying the SRPR.</p>
A transcriptome for the early-branching fern Botrychium lunaria enables fine-grained resolution of population structure
<p>File S1: Alignment of Botrychium CRY2cA sequences used to infer the genus-level phylogeny.</p> <p>File S2: Peptide sequences used to infer the orthogroups named according species names or identifiers (see Table S1).</p> <p>File S3: Alignments of the orthogroup sequences subset used to infer the phylogenomy.</p> <p>File S4: Output files from modeltest-ng named by orthogroup names.</p> <p>File S5: Output files from raxml-ng named by orthogroup names.</p>
Supplemental High Resolution IPWM Run for "Ion heating in the polar cap under northwards IMF Bz": SDOP_April2016
<p>Full high resolution output from the Ionosphere/Polar Wind Model (IPWM) for results published in "Ion heating in the polar cap under northwards IMF Bz", by L. J. Lamarche, R. H. Varney, and A. S. Reimer. This is a simulation of the April 2016 event, with IPWM driven by SuperDARN convection maps and Ovation Prime precipitation (SDOP_April2016). Scripts to visualize these model output and produce the figures in the article are available in a separate repository <a href="http://doi.org/10.5281/zenodo.4453390">10.5281/zenodo.4453390</a>.</p> <p>If you are configuring the Resen bucket available in <a href="http://doi.org/10.5281/zenodo.4453390">10.5281/zenodo.4453390</a>, mount this repository to /home/jovyan/mount/SDOP_April2016_output.</p>
Supplemental High Resolution IPWM Run for "Ion heating in the polar cap under northwards IMF Bz": SDOP_May2014
<p>Full high resolution output from the Ionosphere/Polar Wind Model (IPWM) for results published in "Ion heating in the polar cap under northwards IMF Bz", by L. J. Lamarche, R. H. Varney, and A. S. Reimer. This is a simulation of the May 2014 event, with IPWM driven by SuperDARN convection maps and Ovation Prime precipitation (SDOP_May2014). Scripts to visualize these model output and produce the figures in the article are available in a separate repository <a href="http://doi.org/10.5281/zenodo.4453390">10.5281/zenodo.4453390</a>.</p> <p>If you are configuring the Resen bucket available in <a href="http://doi.org/10.5281/zenodo.4453390">10.5281/zenodo.4453390</a>, mount this repository to /home/jovyan/mount/SDOP_May2014_output.</p>
Energy consumption of 15 electric vehicles (one day resolution)
<p><strong>Energy consumption of 15 electric vehicles (one day resolution)</strong></p> <p>Sérgio Ramos, João Soares, Zahra Foroozandeh, Inês Tavares, Zita Vale</p> <p><strong>Paper title: TODO</strong></p> <p>Type: EV consumption</p> <p>Duration: One year</p> <p>Resolution: One day</p> <p>Application: Paper submitted on</p> <p>Sheets description:</p> <ul> <li>EV 1-15: Contains the information of the energy consumption and initial State of Charge of each EV (kWh).</li> </ul>
Energy consumption and PV generation data of 15 prosumers (15 minute resolution)
<p><strong>Energy consumption and PV generation data of 15 prosumers (15 minute resolution)</strong></p> <p>Sérgio Ramos, João Soares, Zahra Foroozandeh, Inês Tavares, Zita Vale</p> <p><strong>Paper title: (All papers)</strong></p> <p>Type: Energy consumption and PV generation data</p> <p>Duration: Year 2019 (15 minute – 35 040 periods)</p> <p>Resolution: 15 minutes</p> <p>Application: Paper submitted on</p> <p>Sheets description:</p> <ul> <li>Total PV production: Contains the generation of the PV panels;</li> <li>Common services: Contains information of the energy consumption of the common services of the building;</li> <li>Consumer 1-15: Contains the information of the energy consumption of each consumer.</li> </ul>
High-resolution mapping of the period landscape reveals polymorphism in cell cycle frequency tuning
<p>Biological oscillators adapt to environmental changes with widely tunable frequencies, a property theoretical studies attributed to positive feedbacks. However, no experiments have tested this theory. Here, we created synthetic cells to independently tune the frequency and feedback strength of a cell-cycle oscillator, enabling continuous mapping of period landscape in response to network perturbations. We found that although inhibiting positive feedback of cyclin-dependent kinase (Cdk1) reduces the tunability, the reduction is not as significant as theoretically predicted, and the Cdk1-counteracting phosphatase, PP2A, provides additional machinery to ensure frequency regulation. Additionally, cells exhibit polymorphic responses to PP2A inhibition, showing a monomodal distribution of oscillatory cells at low or high PP2A inhibition or a bimodal distribution at both low and high inhibitions. We explained the polymorphism by a model of two interlinked bistable switches of Cdk1 and PP2A where cell-cycle oscillations exhibit two modes in the presence or absence of PP2A bistability.</p>
A high-resolution finite element method (FEM) human head model for non-invasive brain stimulation
<p>High-resolution finite element method (FEM) model of a human head for non-invasive brain stimulation modeling using SimNIBS or other compatible software. The original head model (Ernie) was downloaded from the tutorial dataset of <a href="http://simnibs.org">www.simnibs.org</a> and further refined in grey matter and white matter regions.</p> <p>This supplementary dataset is released as part of the NeMo-TMS toolbox (<a href="https://github.com/OpitzLab/NeMo-TMS">https://github.com/OpitzLab/NeMo-TMS</a>). Please refer to the corresponding article for more information:</p> <p>Shirinpour, S., Hananeia, N., Rosado, J., Galanis, C., Vlachos, A., Jedlicka, P., Queisser, G., & Opitz, A. (2020). Multi-scale Modeling Toolbox for Single Neuron and Subcellular Activity under (repetitive) Transcranial Magnetic Stimulation. <em>BioRxiv</em>, 2020.09.23.310219. <a href="https://doi.org/10.1101/2020.09.23.310219">https://doi.org/10.1101/2020.09.23.310219</a></p>
High spatial resolution thickness changes of Pyrenean glaciers from 2011 to 2020
<p>Pyrenean glaciers are the largest in southern Europe. Because their very survival is threatened by climate change in the coming decades, it is urgently necessary to monitor their evolution. This dataset presents high spatial resolution observation of Pyrenean glaciers during the period 2011–2020. In 2011, the glacier surface was retrieved with an airborne lidar and in 2020 data were obtained with three different UAVs (fixed-wing or quad copters). Comparing these observations, glacier surface changes were computed for 17 out of the 24 remaining glaciers. Using high-resolution optical satellite imagery and field observations, glacier outline changes between 2011 and 2020 were also obtained. On average the glacierized area has shrunk by 23.2% and thickness has decreased on average by 6.3 m.</p>
Global daily Aerosol Optical Depth measurements from Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA's Aqua and Terra satellites
<p>This repository contains input MODIS AOD data prepared for “Subways and Urban Air Polution” by Gendron-Carrier, Gonzalez-Navarro, Polloni and Turner (American Economic Journal: Applied Economics, <a href="https://doi.org/10.1257/app.20180168">https://doi.org/10.1257/app.20180168</a>). The main replication archive is available at <a href="https://doi.org/10.3886/E126401V1">https://doi.org/10.3886/E126401V1</a> .</p> <p>Description of input MODIS AOD data</p> <p>The Moderate Resolution Imaging Spectroradiometers aboard the Terra and Aqua earth-observing satellites provide daily measures of the aerosol optical depth of the atmosphere at a 3km spatial resolution everywhere in the world. Data is available in ‘granules’ which describe five minutes of satellite time. These granules are available, more or less continuously, from February 24, 2000 for the Terra satellite and from July 4, 2002 for Aqua. During September of 2018, we downloaded all available granules for Terra and Aqua until August 31, 2018 and subsequently consolidated them into daily rasters describing global AOD. In August 2020, we processed additional Terra data. This archive therefore contains daily rasters for Aqua (from 2002-07-04 to 2018-08-31) and Terra (from 2000-02-24 to 2020-07-31). We note that February 2005 data are missing for the Aqua satellite.</p> <p> </p> <p>We use source products MOD04_3K (<a href="https://doi.org/10.5067/MODIS/MOD04_L2.006">https://doi.org/10.5067/MODIS/MOD04_L2.006</a>) and MYD04_3K (<a href="https://doi.org/10.5067/MODIS/MYD04_L2.006">https://doi.org/10.5067/MODIS/MYD04_L2.006</a>). The product files are stored in Hierarchical Data Format (HDF) and we use the "Optical Depth Land And Ocean" layer, which is stored as a Scientific Data Set (SDS) within the HDF file, as our measure of aerosol optical depth. The "Optical Depth Land And Ocean" dataset contains only the AOD retrievals of high quality. We convert all HDF formatted granules to GIS compatible formats using the HDF-EOS To GeoTIFF Conversion Tool (HEG) provided by NASA’s Earth Observing System Program. We consolidate GeoTIFF granules into a global raster for each day using ArcGIS. First, we keep only AOD values that do contain information. The missing value is -9999 in AOD retrievals. Second, we create a raster catalog with all the granules for a given day and calculate the average AOD value using the Raster Catalog to Raster Dataset tool. The code used to accomplish this is included for reference purposes in “dofiles/old_work” of the main replication archive at <a href="https://doi.org/10.3886/E126401V1">https://doi.org/10.3886/E126401V1</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.