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2,118 results for “Metal”
From Sintering to Particle Discrimination: New Opportunities in Metal–Organic Frameworks Scintillators
<p>Dataset from publication</p>
K-band FMCW Data for Corroded Steel vs. Non-Corroded Metal Targets
<p>This dataset represents the comparison of FMCW radar responses to lightly corroded steel sheet vs. other, non-corroded metals (and concrete).The associated powerpoint file provides key equipment parameters.</p>
Boosting Activity and Stability of Metal Single-Atom Catalysts via Regulation of Coordination Number and Local Composition
<p>Optimized geometry coordinates in DFT calculations for the publication "Boosting Activity and Stability of Metal Single-Atom Catalysts via Regulation of Coordination Number and Local Composition".</p> <p> </p>
Sonora Cholla: A Grid of Cloud-free, Solar Metallicity Models in Chemical Disequilibrium for the JWST Era
<p>Exoplanet and brown dwarf atmospheres commonly show signs of disequilibrium chemistry. In the James Webb Space Telescope era high resolution spectra of directly imaged exoplanets will allow the characterization of their atmospheres in more detail, and allow systematic tests for the presence of chemical species that deviate from thermochemical equilibrium in these atmospheres. Constraining the presence of disequilibrium chemistry in these atmospheres as a function of parameters such as their effective temperature and surface gravity will allow us to place better constrains in the physics governing these atmospheres.</p> <p>This database contains a grid of cloud-free, solar metallicity atmospheres for brown dwarfs and wide separation giant planets with key molecular species such as CH<sub>4</sub>, H<sub>2</sub>O, CO and NH<sub>3</sub> in disequilibrium. The grid covers atmospheres with T<sub>eff</sub>~[500 K,1300 K], log<em>g</em>~[3.0,5.5] (cgs) and an eddy diffusion parameter of log K<sub>zz</sub>=2, 4 and 7 (cgs). A description of the code and a study of the effect of different parameters within the grid on the temperature and composition profiles of our atmospheres is presented in the accompanying paper "The Sonora Substellar Atmosphere Models. II. Cholla: A Grid of Cloud-free, Solar Metallicity Models in Chemical Disequilibrium for the<em> JWST </em>Era" (accepted to ApJ, Oct. 2021)</p>
Influence of Alkali Metal Cations in the Formation of the Heterobimetallic Actinide tert-Butoxides [AnM3(OtBu)7] and [AnM2(OtBu)6] (AnIV = Th, U; MI = Li, Na, K, Rb, Cs)
<p>These are the raw data for the modulated structure pertaining to compound <strong>URb</strong>. The following is the abstract of the corresponding paper.</p> <p>Heterobimetallic <em>tert</em>-butoxides of alkali metal cations and tetravalent actinide centers exhibit two distinctive structural motifs, [AnM<sub>2</sub>(O<em><sup>t</sup></em>Bu)<sub>6</sub>] and [AnM<sub>3</sub>(O<em><sup>t</sup></em>Bu)<sub>7</sub>] (An<sup>IV</sup> = Th, U and M<sup>I</sup> = Li, Na, K, Rb, Cs), evidently govern by the size of the alkali metal ions. Both [AnM<sub>3</sub>(O<em><sup>t</sup></em>Bu)<sub>7</sub>] <strong>AnM3 </strong>(An<sup>IV</sup> = U, M<sup>I</sup> = Li; An<sup>IV</sup> = Th, M<sup>I</sup> = Li, Na) and<strong> </strong>[AnM<sub>2</sub>(O<em><sup>t</sup></em>Bu)<sub>6</sub>] <strong>AnM2</strong> (An<sup>IV</sup> = U, M<sup>I</sup> = Na – Cs; An<sup>IV</sup> = Th, M<sup>I</sup> = K - Cs) compounds are obtained in nearly quantitative yields by reacting the actinide and alkali metal silyl amides with excess of <em>tert</em>-butyl alcohol. The <strong>AnM3</strong> complexes form a cubane-type coordination motif, whereas the <strong>AnM2</strong> complexes display a geometry resembling two face-shared bipyramids. The sodium derivatives of thorium and uranium (<strong>ThNa3</strong> and <strong>UNa2</strong>) allow to determine the structural transition threshold as function of the ratio of the ionic radii, r<sub>i</sub>(An<sup>IV</sup>)/r<sub>i</sub>(M<sup>I</sup>). The <strong>AnM3</strong> complexes are formed for ratios above 0.92 and the <strong>AnM2</strong> type is formed for ratios below 0.87. All compounds are unambiguously characterized in both solution and the solid-state via NMR and IR spectroscopic studies and single crystal X-ray diffraction analyses, respectively.</p>
Influence of Alkali Metal Cations in the Formation of the Heterobimetallic Actinide tert-Butoxides [AnM3(OtBu)7] and [AnM2(OtBu)6] (AnIV = Th, U; MI = Li, Na, K, Rb, Cs)
<p>These are the raw data the compound <strong>ThCs2</strong>, refined as a merohedral twin. The following is the abstract of the corresponding paper.</p> <p>Heterobimetallic <em>tert</em>-butoxides of alkali metal cations and tetravalent actinide centers exhibit two distinctive structural motifs, [AnM<sub>2</sub>(O<em><sup>t</sup></em>Bu)<sub>6</sub>] and [AnM<sub>3</sub>(O<em><sup>t</sup></em>Bu)<sub>7</sub>] (An<sup>IV</sup> = Th, U and M<sup>I</sup> = Li, Na, K, Rb, Cs), evidently govern by the size of the alkali metal ions. Both [AnM<sub>3</sub>(O<em><sup>t</sup></em>Bu)<sub>7</sub>] <strong>AnM3 </strong>(An<sup>IV</sup> = U, M<sup>I</sup> = Li; An<sup>IV</sup> = Th, M<sup>I</sup> = Li, Na) and<strong> </strong>[AnM<sub>2</sub>(O<em><sup>t</sup></em>Bu)<sub>6</sub>] <strong>AnM2</strong> (An<sup>IV</sup> = U, M<sup>I</sup> = Na – Cs; An<sup>IV</sup> = Th, M<sup>I</sup> = K - Cs) compounds are obtained in nearly quantitative yields by reacting the actinide and alkali metal silyl amides with excess of <em>tert</em>-butyl alcohol. The <strong>AnM3</strong> complexes form a cubane-type coordination motif, whereas the <strong>AnM2</strong> complexes display a geometry resembling two face-shared bipyramids. The sodium derivatives of thorium and uranium (<strong>ThNa3</strong> and <strong>UNa2</strong>) allow to determine the structural transition threshold as function of the ratio of the ionic radii, r<sub>i</sub>(An<sup>IV</sup>)/r<sub>i</sub>(M<sup>I</sup>). The <strong>AnM3</strong> complexes are formed for ratios above 0.92 and the <strong>AnM2</strong> type is formed for ratios below 0.87. All compounds are unambiguously characterized in both solution and the solid-state via NMR and IR spectroscopic studies and single crystal X-ray diffraction analyses, respectively.</p>
Bone Metal 3D Segmentation
<p>3D CT data for bone with metallic implants , for Deep learning 3d semantic segmentation of bones with metallic implants and correct metallic artifacts in CT images by deep learning , Novel Approach ,Fully deep artifact correction by threshold masking and 3d segmentation, Work genuinely by Eng. <strong>Bashar Shami</strong>., <strong>biodevelopeng@gmail.com</strong></p>
IDMT-ISA-Metal-Balls Dataset
<p>The IDMT_ISA_METAL_BALLS dataset contains sound files of three differently coated types of metal balls running down a metal slide (K1, K2, K3). The different surfaces cause a hearable difference of the rolling sounds, which we consider as indicator regarding the condition of the coating. The dataset was created using a low-cost microphone as sensor. Industrial background noise was played via external loudspeakers during the recording process for simulating a realistic acoustic environment.</p> <p>We provide both an unbalanced split, which resulted from the original recording process, with a total size of 2267 recordings as well as a balanced split for reproducibility of experiments. Two additional test sets with more than 250 recordings each are provided where general settings like the angle of the slide and the position of microphone have been altered for testing the robustness of the system to changes in the setup.</p> <ul> <li>File duration: 18.87 minutes</li> <li># K1: 987</li> <li># K2: 1198</li> <li># K3: 647</li> <li># Total WAV Files: 2832</li> <li>Sampling rate: 44.1KHz</li> <li>Resolution: 32-bit</li> <li>Mono audio</li> </ul>
Assessing the antimicrobial Capacity of metal oxide nanomaterials using ZOI and MIC measurements by employing Machine learning tools.
<p>Assessing the antimicrobial Capacity of metal and metal oxide nanomaterials (IONPs, AgNPs, ZnONPs) using ZOI and MIC measurements by employing Machine learning tools.</p>
Spatial separation of spin currents in transition metal dichalcogenides
<p>Code and data from the manuscript "Spatial separation of spin currents in transition metal dichalcogenides"</p>
Dataset for "Lateral Charge Migration in 1D Semiconductor-Metal Hybrid Photocatalytic Systems"
<p>This is the dataset including absorption, photoluminescence quantum yield, and transient absorption spectroscopy.</p>
Understanding the diurnal cycle of midlatitude sporadic E. The role of metal atoms.
<p>We provide the Arecibo dataset and the FORTRAN program used to produce Figure 4, of the paper (MS# 2023JA031336) enitled "Understanding the diurnal variation of midlatitude sporadic E. The role of metal atoms- ", which was submitted for publication in the Journal of Geophysical Research - Space Physics, January 2023.Write to Christos Haldoupis</p> <p> </p>
Understanding the diurnal cycle of midlatitude sporadic E. The role of metal atoms
<p>We provide the Arecibo dataset and the FORTRAN program used to produce Figure 4, of the paper (MS# 2023JA031336) entitled "Understanding the diurnal variation of midlatitude sporadic E. The role of metal atoms- ", which was submitted for publication in the Journal of Geophysical Research - Space Physics.</p>
Understanding the diurnal cycle of midlatitude sporadic E. The role of metal atoms
<p>We provide the Arecibo dataset and the FORTRAN program used to produce Figure 4, of the paper (MS# 2023JA031336) entitled "Understanding the diurnal variation of midlatitude sporadic E. The role of metal atoms- ", which was submitted for publication in the Journal of Geophysical Research - Space Physics.</p>
Understanding the diurnal cycle of midlatitude sporadic E. The role of metal atoms.
<p>Dataset and FORTRAN program used to produce Figure 4, of the paper (MS# 2023JA031336) entitled "Understanding the diurnal variation of midlatitude sporadic E. The role of metal atoms- ", which was submitted for publication in the Journal of Geophysical Research - Space Physics. The data file was provided by Qihou Zhou of Miami University in Ohio and the program by co-author Chris Meek, University of Saskatchewan.</p>
Urban soil quality is being deteriorated even with low heavy metal levels: An arthropod-based multi-indices approach
<p><span>Urban-induced habitat conversion drastically changes soil life in a variety of ways. Soil sealing, human disturbance, habitat fragmentation, industrial and vehicular pollution are the main causes of urban soil degradation. Soil arthropods, as the most abundant and diverse group of soil fauna, are involved in many soil processes that are of great importance in maintaining soil health and multifunctionality. Nevertheless, soil quality is still mainly characterized by physical, chemical, and microbiological parameters.</span></p> <p><span>Here, we assessed and compared the biological soil quality in woody (REF: reference forest, REM: remnant forest) and non-woody (TURF: public turfgrass, and RUD: ruderal habitat) types of urban green spaces along a disturbance and management intensity gradient in the Budapest metropolitan area (Hungary), using community metrics and soil arthropod-based indicators. Vegetation cover and landscape characteristics of study sites were quantified through vegetation and urbanization indices, respectively. Basic soil properties, total and bioavailable concentrations of the main heavy metals (Cd, Co, Hg, Ni, Zn) were also measured. </span></p> <p><span>Acari, Collembola, and Hymenoptera (mainly Formicidae) were the most abundant groups. Litter-dweller taxa, particularly Protura, proved to be the most sensitive to urban disturbance. Representatives of Hemiptera, Diptera, Symphyla, and Pauropoda were common in low densities. Soil arthropod assemblages in RUD and TURF were more diverse taxonomically than in REM and REF sites. Although the integrated faunal indices showed no differences among soil habitat types, they provided different responses and, consequently, different information. Our findings demonstrated that the biological quality and arthropod community structure of soils were strongly impacted by soil C/N and heavy metal contamination. </span></p> <p><span>We found that low and moderate levels of pollution have adverse effects on edaphic fauna, suggesting biological degradation of soils, even below pollution limits. Nevertheless, more disturbed urban green spaces have been shown to play a significant role in maintaining belowground biodiversity, thereby soil functions.</span></p>
Data and Software for "Metal Pad-Enhanced Resistive Pulse Sensor Reveals Complex-Valued Braess Paradox"
<p>Data and software for the paper entitled "Metal Pad-Enhanced Resistive Pulse Sensor Reveals Complex-Valued Braess Paradox", by Alan Dong, Lydia Sohn, and Michael Lustig, for Physical Review E.</p>
Molybdenum isotope heterogeneity of metal sulfides from magmatic hydrothermal systems
<p>Exploration of the application of Mo isotope in the field of mineralogy with precise knowledge of the geochemical behaviors of Mo in the PCDs is of considerable concerns. This study puts critical constraints on the Mo isotope geochemistry in magmatic-hydrothermal systems based on analysis of Mo isotope of the metal sulfides from different mineralization stages (i.e., epidote-chlorite, chlorite-illite, and quartz-illite stages) in Pulang porphyry Cu deposit, Yunnan, Southwest China. We observed that pyrites, chalcopyrites and pyrrhotites with highly variableMo abundances and heavier δ<sup>98</sup>Mo (-0.34 ± 0.04‰ to 3.01 ± 0.03‰) in comparison to molybdenites (δ<sup>98</sup>Mo: -0.90 ± 0.04‰ to 0.08 ± 0.04‰), whole-rock ore-forming porphyries (δ<sup>98</sup>Mo: -0.18 ± 0.03‰ to -0.08 ± 0.03‰) and surrounding rocks (-0.41± 0.05‰ to -0.14± 0.03‰).The variability of δ<sup>98</sup>Mo signatures of these metal sulfides is independent of lithology of originally magma sources, which instead constrained by the evolutions of ore-forming fluids, changes of ore-forming temperature or metallogenic settings, as well as differential geochemical behaviors of Mo species.</p> <p>In the magmatic hydrothermal metallogenic system, there is a progressive partitioning of Mo into exsolved metallic minerals, with molybdenites preferentially partitioning lighter δ<sup>98</sup>Mo while pyrites, chalcopyrites and pyrrhotites are enriched in heavier δ<sup>98</sup>Mo. Additionally, these metallic sulfides have a preference for relatively heavier δ<sup>98</sup>Mo enrichment in the early metallogenic stage, thereby leaving residual metallogenic fluids with progressively homogenized lighter δ<sup>98</sup>Mo, and consequently relatively homogeneous but lighter isotopes of the metallic minerals in the later stages. The extensively maintained Mo isotope fractionation in PCDs is indicative of a maldistribution of ore-forming components in the sulfides, of which it is this disequilibrium effect that points to the enrichment of metal elements as well as the significant process of magmatic hydrothermal metallogenesis. Mo isotope systematics provide a robust geochemical proxy to interrogate PCDs genetic mechanisms and metal precipitation stages, allowing to consider it as a subservience indicator for the complex metal enrichment that can be extrapolated to other porphyry-type deposits.</p>
Rotational and Dilational Reconstruction in Transition Metal Dichalcogenide Moire Bilayers
<p>Data sets used in "Rotational and Dilational Reconstruction in Transition Metal Dichalcogenide Moire Bilayers" by Van Winkle and Craig et al.</p> <table> <tbody> <tr> <td>File name</td> <td>Appearance in Manuscript</td> <td>Material</td> <td>P/AP</td> <td>Twist Angle (°)</td> <td>Hetstrain (%)</td> <td>Scan Shape (pixels)</td> <td>Pixel Size (nm)</td> <td>Smoothing Sigma</td> </tr> <tr> <td>DS1</td> <td>Fig. 2m, 2n</td> <td>MoS2/MoS2</td> <td>P</td> <td>0.369</td> <td>0.451</td> <td>200x200</td> <td>0.5</td> <td>2</td> </tr> <tr> <td>DS2</td> <td>Fig. 2m, 2n</td> <td>MoS2/MoS2</td> <td>P</td> <td>0.523</td> <td>0.138</td> <td>200x200</td> <td>0.5</td> <td>2</td> </tr> <tr> <td>DS3</td> <td>Fig. 2m, 2n</td> <td>MoS2/MoS2</td> <td>P</td> <td>1.849</td> <td>0.546</td> <td>100x100</td> <td>0.5</td> <td>2</td> </tr> <tr> <td>DS4</td> <td>Fig. 2m, 2n</td> <td>MoS2/MoS2</td> <td>P</td> <td>1.814</td> <td>0.453</td> <td>100x100</td> <td>0.5</td> <td>2</td> </tr> <tr> <td>DS5</td> <td>Fig. 2m, 2n, 5a, 5d</td> <td>MoS2/MoS2</td> <td>P</td> <td>1.769</td> <td>0.472</td> <td>100x100</td> <td>0.5</td> <td>2</td> </tr> <tr> <td>DS6</td> <td>Fig. 2m, 2n</td> <td>MoS2/MoS2</td> <td>P</td> <td>1.721</td> <td>0.849</td> <td>100x100</td> <td>0.5</td> <td>2</td> </tr> <tr> <td>DS7</td> <td>Fig. 2a, 2d, 2g, 2m, 2n</td> <td>MoS2/MoS2</td> <td>P</td> <td>1.227</td> <td>0.304</td> <td>100x100</td> <td>0.5</td> <td>2</td> </tr> <tr> <td>DS8</td> <td>Fig. 2m, 2n, 5b, 5e</td> <td>MoS2/MoS2</td> <td>P</td> <td>1.644</td> <td>0.970</td> <td>100x100</td> <td>0.5</td> <td>2</td> </tr> <tr> <td>DS9</td> <td>Fig. 2m, 2n</td> <td>MoS2/MoS2</td> <td>P</td> <td>1.353</td> <td>0.597</td> <td>100x100</td> <td>0.5</td> <td>2</td> </tr> <tr> <td>DS10</td> <td>Fig. 2m, 2n</td> <td>MoS2/MoS2</td> <td>P</td> <td>1.597</td> <td>0.930</td> <td>100x100</td> <td>0.5</td> <td>2</td> </tr> <tr> <td>DS11</td> <td>Fig. 5c, 5f</td> <td>MoS2/MoS2</td> <td>P</td> <td>1.919</td> <td>1.320</td> <td>100x100</td> <td>0.5</td> <td>2</td> </tr> <tr> <td>DS12</td> <td>Fig. 2m, 2n</td> <td>MoS2/MoS2</td> <td>P</td> <td>1.193</td> <td>0.581</td> <td>100x100</td> <td>0.5</td> <td>2</td> </tr> <tr> <td>DS13</td> <td>Fig. 2m, 2n</td> <td>MoS2/MoS2</td> <td>P</td> <td>0.856</td> <td>0.619</td> <td>200x200</td> <td>0.5</td> <td>2</td> </tr> <tr> <td>DS14</td> <td>Fig. 1b, 1c, 2m, 2n</td> <td>MoS2/MoS2</td> <td>P</td> <td>0.818</td> <td>0.319</td> <td>200x200</td> <td>0.5</td> <td>2</td> </tr> <tr> <td>DS15</td> <td>Fig. 2k, 2l</td> <td>MoS2/MoS2</td> <td>AP</td> <td>2.562</td> <td>0.444</td> <td>100x100</td> <td>0.5</td> <td>2</td> </tr> <tr> <td>DS16</td> <td>Fig. 2k, 2l</td> <td>MoS2/MoS2</td> <td>AP</td> <td>2.121</td> <td>0.595</td> <td>100x100</td> <td>0.5</td> <td>2</td> </tr> <tr> <td>DS17</td> <td>Fig. 5h, 5k</td> <td>MoS2/MoS2</td> <td>AP</td> <td>1.533</td> <td>1.353</td> <td>100x100</td> <td>0.5</td> <td>2</td> </tr> <tr> <td>DS18</td> <td>Fig. 2k, 2l</td> <td>MoS2/MoS2</td> <td>AP</td> <td>2.697</td> <td>0.455</td> <td>100x100</td> <td>0.5</td> <td>2</td> </tr> <tr> <td>DS19</td> <td>Fig. 5i, 5l</td> <td>MoS2/MoS2</td> <td>AP</td> <td>1.459</td> <td>1.138</td> <td>100x100</td> <td>0.5</td> <td>2</td> </tr> <tr> <td>DS20</td> <td>Fig. 2k, 2l</td> <td>MoS2/MoS2</td> <td>AP</td> <td>0.514</td> <td>0.434</td> <td>200x200</td> <td>0.5</td> <td>2</td> </tr> <tr> <td>DS21</td> <td>Fig. 2k, 2l</td> <td>MoS2/MoS2</td> <td>AP</td> <td>0.652</td> <td>0.500</td> <td>200x200</td> <td>0.5</td> <td>2</td> </tr> <tr> <td>DS22</td> <td>Fig. 2c, 2f, 2i, 2k, 2l</td> <td>MoS2/MoS2</td> <td>AP</td> <td>1.688</td> <td>0.588</td> <td>100x100</td> <td>0.5</td> <td>2</td> </tr> <tr> <td>DS23</td> <td>Fig. 2k, 2l, 5g, 5j</td> <td>MoS2/MoS2</td> <td>AP</td> <td>1.361</td> <td>0.620</td> <td>100x100</td> <td>0.5</td> <td>2</td> </tr> <tr> <td>DS24</td> <td>Fig. 2k, 2l</td> <td>MoS2/MoS2</td> <td>AP</td> <td>1.226</td> <td>0.472</td> <td>100x100</td> <td>0.5</td> <td>2</td> </tr> <tr> <td>DS25</td> <td>Fig. 1d, 2b, 2e, 2h, 2k, 2l</td> <td>MoS2/MoS2</td> <td>AP</td> <td>0.771</td> <td>0.380</td> <td>100x100</td> <td>0.5</td> <td>2</td> </tr> <tr> <td>DS26</td> <td>Fig. 2k, 2l</td> <td>MoS2/MoS2</td> <td>AP</td> <td>1.483</td> <td>0.568</td> <td>100x100</td> <td>0.5</td> <td>2</td> </tr> <tr> <td>DS28</td> <td>Fig. 1b, 1c, 3c, 3f, 3j</td> <td>WSe2/MoS2</td> <td>P</td> <td>0.795</td> <td> </td> <td>100x100</td> <td>0.5</td> <td>2</td> </tr> <tr> <td>DS29</td> <td>Fig. 1d</td> <td>WSe2/MoS2</td> <td>AP</td> <td>0.070</td> <td> </td> <td>100x100</td> <td>0.5</td> <td>2</td> </tr> <tr> <td>DS30</td> <td>Fig. 4g, 4j</td> <td>WSe2/MoS2</td> <td>AP</td> <td>0.776</td> <td> </td> <td>100x100</td> <td>0.5</td> <td>2</td> </tr> <tr> <td>DS31 (and raw dm4 data)</td> <td>Fig. 4h, 4k</td> <td>WSe2/MoS2</td> <td>AP</td> <td>0.852</td> <td> </td> <td>100x100</td> <td>0.5</td> <td>2</td> </tr> <tr> <td>DS32</td> <td>Fig. 3b, 3e, 3i</td> <td>WSe2/MoS2</td> <td>AP</td> <td>1.072</td> <td> </td> <td>100x100</td> <td>0.5</td> <td>2</td> </tr> <tr> <td>DS33</td> <td>Fig. 4i, 4l</td> <td>WSe2/MoS2</td> <td>AP</td> <td>0.824</td> <td> </td> <td>100x100</td> <td>0.5</td> <td>2</td> </tr> <tr> <td>DS34</td> <td>Fig. 4a, 4d, 4g, 4j</td> <td>WSe2/MoS2</td> <td>AP</td> <td>0.097</td> <td> </td> <td>100x100</td> <td>0.5</td> <td>2</td> </tr> <tr> <td>DS35</td> <td>Fig. 4b, 4e, 4h, 4k</td> <td>WSe2/MoS2</td> <td>AP</td> <td>0.096</td> <td> </td> <td>100x100</td> <td>0.5</td> <td>2</td> </tr> <tr> <td>DS36</td> <td>Fig. 3a, 3d, 3h</td> <td>WSe2/MoS2</td> <td>AP</td> <td>0.127</td> <td> </td> <td>100x100</td> <td>0.5</td> <td>2</td> </tr> <tr> <td>DS37</td> <td>Fig. 4c, 4f, 4i, 4l</td> <td>WSe2/MoS2</td> <td>AP</td> <td>0.167</td> <td> </td> <td>100x100</td> <td>0.5</td> <td>2</td> </tr> </tbody> </table>
Gradients 1 and Gradients 2 trace metal concentration data
<p>Dissolved trace metal concentration data from the SCOPE Gradients 1 and Gradients 2 cruises.</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.