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Dataset results
671 results for “Dilatancy”
Randomized Evaluation of Maxillary Antrostomy Versus Ostial Dilation Efficacy Through Long-Term Follow-Up
ClinicalTrials.gov study NCT01525849. IPD Sharing: NO. Countries: 1. Publications: 3.
A Rollover Study of ARRY-371797 in Patients With LMNA-Related Dilated Cardiomyopathy
ClinicalTrials.gov study NCT02351856. IPD Sharing: YES. Countries: 1. Publications: 3.
DilaCheck Cervical Dilation Measurement Trial
ClinicalTrials.gov study NCT03440723. IPD Sharing: NO. Countries: 1. Publications: 1.
A Study of ARRY-371797 (PF-07265803) in Patients With Symptomatic Dilated Cardiomyopathy Due to a Lamin A/C Gene Mutation
ClinicalTrials.gov study NCT03439514. IPD Sharing: YES. Countries: 10. Publications: 4.
Evaluation of Pupil Dilation Speed With the MAP Dispenser
ClinicalTrials.gov study NCT04907474. IPD Sharing: NO. Countries: 1. Publications: 1.
XprESS Eustachian Tube Dilation Study
ClinicalTrials.gov study NCT02391584. IPD Sharing: NO. Countries: 1. Publications: 2.
Cervical Preparation Before Dilation and Evacuation
ClinicalTrials.gov study NCT01751087. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Optimization and Refinement of Technique in In-Office Sinus Dilation 2
ClinicalTrials.gov study NCT01107379. IPD Sharing: Not stated. Countries: 1. Publications: 1.
A Pivotal Study to Assess the Effectiveness of Nasal Dilator (Breathe Right Nasal Strips)
ClinicalTrials.gov study NCT03549130. IPD Sharing: Not stated. Countries: 1. Publications: 1.
A Pivotal Subjective Sleep Study of a Nasal Dilator (Breathe Right Tan)
ClinicalTrials.gov study NCT03549117. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Dogs’ looking times and pupil dilation response reveal expectations about contact causality
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In vivo x-ray diffraction and simultaneous EMG reveal the timecourse of myofilament lattice dilation and filament stretch
<p>Muscle function within an organism depends on the feedback between molecular and meter-scale processes. Although the motions of muscle's contractile machinery are well described in isolated preparations, only a handful of experiments have documented the kinematics of the lattice occurring when multi-scale interactions are fully intact. We used time-resolved X-ray diffraction to record the kinematics of the myofilament lattice within a normal operating context: the tethered flight of Manduca sexta. As the primary flight muscles of M. sexta are synchronous, we used these results to reveal the timing of in vivo cross-bridge recruitment, which occurred 24 ms (s.d. 26) following activation. In addition, the thick filaments stretched an average of 0.75% (s.d. 0.32) and thin filaments stretched 1.11% (s.d. 0.65). In contrast to other in vivo preparations, lattice spacing changed an average of 2.72% (s.d. 1.47). Lattice dilation of this magnitude significantly affects shortening velocity and force generation, and filament stretching tunes force generation. While the kinematics were consistent within individual trials, there was extensive variation between trials. Using a mechanism-free machine learning model we searched for patterns within and across trials. Although lattice kinematics were predictable within trials, the model could not create predictions across trials. This indicates that the variability we see across trials may be explained by latent variables occurring in this naturally functioning system. The diverse kinematic combinations we documented mirror muscle's adaptability and may facilitate its robust function in unpredictable conditions.<br> <br> </p>
Material parameter influence on solitary wave induced surface dilation: restart files, output files, videos and input scripts
<p>Restart files, output files, videos and input scripts for Material parameter influence on the expression of Solitary-Wave-Induced Surface Dilation (Frizzell and Hartzell, 2024). Input files can be find at the linked repository (https://github.com/efrizz-umd/SID_sensitivity). Restart files and output files were made with LIGGGHTS (https://www.cfdem.com/liggghtsr-open-source-discrete-element-method-particle-simulation-code) and videos were made with OVITO (https://www.ovito.org/).</p>
Pupil dilation during orienting of attention and conscious detection of visual targets in patients with left spatial neglect
<p>Right Brain-Damaged patients (RBD) with left spatial neglect (N+), are characterised by deficits in orienting and re-orienting attention to stimuli in the contralesional left side of space. In a recent ERPs study with visual stimuli (Lasaponara et al., 2018) we have pointed out that the pathological attentional bias of N+ is matched with exaggerated novelty reaction and contextual updating of targets in the right ipsilesional space and reduced novelty reaction and contextual updating of targets in the left contralesional space. To characterise further the attentional performance of N+, here we measured Pupil Dilation (PDil), which is a reliable marker of noradrenergic-locus coeruleus activity and response to unexpected events/rewards. Compared to Neutral and Valid targets, N+ patients displayed a pathological reduction of PDil in response to infrequent Invalid targets in the left side of space, while in Healthy Controls (HC) and RBD without neglect (N-) the same targets enhanced PDil with respect to Neutral and frequent Valid targets. Invalid targets in the right side of space enhanced PDil in all experimental groups. Interestingly, both N- and N+ showed a consistent number of target omissions both in the left and right side of space. With respect to seen targets, N- showed reduced PDil in response to unseen targets both in the left and right side of space. In contrast, N+ had reduced PDil in response to unseen targets in the left side of space though not in the right side, where seen and unseen targets evoked comparable levels of PDil. These results disclose, for the first time, the PDil correlates of spatial attention in left spatial neglect and suggest that the pathological attentional bias suffered by N+ might enhance the autonomic responses reflected in PDil to unseen ipsilesional stimuli. This enhancement can contribute to biasing contextual updating and predictive coding of stimuli in the ipsilesional space, thus worsening the pathological attentional bias of N+.</p>
Time-averaged simulations results for bi-phasic blood flow simulations in realistic microvascular networks for multi-capillary dilation scenarios mimicking pericyte ablation
<p>Documentation to reproduce in silico analyses related to the manuscript<br> <strong>Pericyte remodelling is deficient in the aged brain and contributes to impaired capillary flow and structure</strong></p> <p>by</p> <p>Andrée-Anne Berthiaume, Franca Schmid, Stefan Stamenkovic, Vanessa Coelho-Santos, Cara D. Nielson, Bruno Weber, Mark W. Majesky and Andy Y. Shih</p> <p>Published in<br> Nature Communications (doi: 10.1038/s41467-022-33464-w)</p> <p>All simulations are performed based on the in silico blood flow model with discrete red blood cell (RBC) tracking as described in Schmid et al., 2017, PLoS Comp Biol (doi: <a href="https://doi.org/10.1371/journal.pcbi.1005392">10.1371/journal.pcbi.1005392</a>). The bi-phasic blood flow simulations have been performed in two realistic microvascular networks from the somatosensory cortex of the mouse first published in Blinder et al., 2013, Nature Neuroscience (doi: 10.1038/nn.3426). </p> <p>For further information and instructions please contact Franca Schmid (franca.schmid@unibe.ch, orcid.org/0000-0002-0689-9366).</p> <p><br> <strong>Simulation results:</strong></p> <p>All time-averaged simulation results are saved as vascular graphs building on the python library igraph and stored as python pickle files (Python 2.7). For each simulation two files are available: <em>verticesDict.pkl</em> and <em>edgesDict.pkl</em>containing all vertex and edge specific data, respectively. A summary of the vertex and edge attributes is provided below. The folder <em>Baseline</em> contains the simulation results for microvascular network 1 (MVN1) and MVN2 for the reference simulation, i.e. without any dilation. Folder <em>Dilated</em> contains the simulation results mimicking the four pericyte ablation scenarios. Subfolders <em>dc_x.x</em> contain the simulation results for the different diameter changes. Note that, folder <em>dc_0.0</em> contains no new simulation results but is a dummy folder containing the information about the vessels to be dilated for the different dilation scenarios (namely edge attribute: <em>toDilate</em> and <em>base_capillary</em>). </p> <p> </p> <p><strong>Reproducing figure 8:</strong></p> <p>Panels a-c: created by illustrating the simulation results with the open source software Paraview (v5.7.0).<br> Panels d-f & h: can be generated by executing make_all_figures.py in Python 2.7 within the provided folder structure.<br> Panel g: can be generated by executing make_figure_8g.py after installation of the the vgm-framework (further information see below). </p> <p><br> Output: All created Figures are saved in the folder <em>Figures</em>. The associated source data is available in Excel format in the folder <em>SourceData</em>.</p> <p> </p> <p><strong>Edge attributes:</strong></p> <p>diameter: vessel diameter [µm]<br> mainAV: 1 if ascending venule main branch, 0 otherwise<br> connectivity: vertex tuple to define location of edge<br> flow: flow rate [µm<sup>3</sup>/ms]<br> mainDA: 1 if descending arteriole main branch, 0 otherwise<br> nkind: 0: pial artery, 1: pial vein, 2: descending arteriole, 3: ascending venule, 4: capillary<br> htt: tube hematocrit [-]<br> toDilate: 1 if vessel is dilated for the current dilation scenario, 0 otherwise<br> base_capillary: 1 if vessel is the base capillary of the current dilation scenario, 0 otherwise</p> <p> </p> <p><strong>Vertex attributes:</strong></p> <p>index: vertex index<br> pressure: pressure [mmHg]<br> nkind: 0: pial artery, 1: pial vein, 2: descending arteriole, 3: ascending venule, 4: capillary<br> coords: vertex coordinates x,y,z [µm]<br> pBC: pressure boundary conditions at inflow vertices [mmHg], None at internal nodes</p> <p> </p> <p><strong>Obtaining simulation results:</strong><br> General:</p> <ul> <li>Running bi-phasic blood flow simulations requires setting-up the vgm-framework available at: <a href="https://github.com/Franculino/vgm.git">https://github.com/Franculino/vgm.git</a> (v.1.0).</li> <li>vgm is written in Python 2.7 and builds on standard python libraries.</li> <li>vgm has been used on macOS, Ubuntu and Windows Systems.</li> <li>Installation time < 5min. Further details available within the vgm README.</li> <li>Runtime depends on the network size, the chosen blood flow model and the initial conditions (e.g. ~8hrs for a Restart simulation of MVN1 with the bi-phasic blood flow model, see Restarty.py).</li> <li>scripts/Test.py provides an example how a simulation can be initiated. A Demo case is provided (details see below).</li> <li>Output: sampledict_BackUp_xx.pkl</li> <li>The bi-phasic blood flow model can be applied on all kind of microvascular graphs.</li> </ul> <p>Specific for current application:</p> <ul> <li>Simulations are a restart on the statistical steady state of the baseline cases.</li> <li>All relevant pre-processing functions for the current study are available in scripts/find_stroke_locations.py. Further details are available from the definition of the different functions.</li> <li>The simulations are initiated with scripts/Restart.py.</li> <li>To obtain the time-averaged simulation results scripts/01_put_together_sampledicts.py and scripts/02_convergenceDiscrete.py need to be executed. This results in the file G_averaged.pkl that is used for further analyses.</li> </ul> <p>Demo:</p> <ul> <li>Contains a small hexagonal microvascular network to test the code.</li> <li>1) Run Test.py to start the simulation</li> <li>2) Run 01_put_together_sampledicts.py</li> <li>3) Run 02_convergenceDiscrete.py to obtain time-averaged results (<em>G_averaged.pkl</em>)</li> </ul>
Dataset for "Temperature dependence of Young's modulus, damping and dilatation during repeated thermal cycling of silica refractories for high-temperature thermal energy storage (TES) "
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FIGURE 109. Daviesia incrassata subsp. incrassata. A. Flowering branchlet with terete upper phyllodes. B. Lower phyllode from the same plant, showing vertical dilation. C. Flowering branchlet with vertically compressed phyllodes. D. Lower phyllode from the same plant, showing pronounced vertical dilation. E. Inflorescence. F. Pod. A, B from Crisp 6132 in A monograph of Daviesia (Mirbelieae, Faboideae, Fabaceae)
FIGURE 109. Daviesia incrassata subsp. incrassata. A. Flowering branchlet with terete upper phyllodes. B. Lower phyllode from the same plant, showing vertical dilation. C. Flowering branchlet with vertically compressed phyllodes. D. Lower phyllode from the same plant, showing pronounced vertical dilation. E. Inflorescence. F. Pod. A, B from Crisp 6132; C, D from Chapman (16)78; E from Crisp 1021; F from Crisp 5368. Drawn by A.L. Prowse. Adapted from Crisp (1995) with permission from CSIRO Publishing.
FIGURE. Conduplicate-erect (CE), conduplicate-patent (CP), conduplicate-spiral (CS), and simple-dilated (SD) stigma types of different genera in Bromelioideae. A. Aechmea (Pseudaechmea) filicaulis (CP, Leme 2268). B. Aechmea subg. Podaechmea: A. lueddemanniana (CS, Leme 001). C. Aechmea subg. Pothuava s.l: A. pectinata (CS, Leme 231). D. Aechmea subg. Platyaechmea s.l.: A. smithiorum (CS, Leme 1709). E. Aechmea subg. Chevaliera s.l.: A. tayoensis (CS, Leme 3240). F. Ananas nanus (CS, Leme 9381). G. Araeococcus flagellifolius (SD, Leme 9696); H. Billbergia subg. Billbergia: B. amoena var. robertiana (CS, Leme 246). I. Billbergia subg. Helicodea: B. porteana (CS, Leme 9584). J. Bromelia auriculata (CE, Leme 8108). K. Fernseea bocainensis (CS, Leme 1422); L. Hohenbergia catingae (CS, Leme 2330). M. Neoglaziovia variegata (CS, Leme 9153). N. Greigia stenolepis (CS, Leme 9738). O. Portea grandiflora (CS, Leme 4011). P. Pseudananas sagenarius (CS, Leme 5556). Q. Quesnelia arvensis (CS, Leme 2695). R. Wittmackia bicolor (CS, Leme 4228). S. Wittmackia (Wittmackiopsis) penduliflora (CS, Leme 3832). T. Wittmackia silvana (CS, Leme 7060). Bars = 1 mm. in Re-evaluation of the Amazonian Hylaeaicum (Bromeliaceae: Bromelioideae) based on neglected morphological traits and molecular evidence
FIGURE. Conduplicate-erect (CE), conduplicate-patent (CP), conduplicate-spiral (CS), and simple-dilated (SD) stigma types of different genera in Bromelioideae. A. Aechmea (Pseudaechmea) filicaulis (CP, Leme 2268). B. Aechmea subg. Podaechmea: A. lueddemanniana (CS, Leme 001). C. Aechmea subg. Pothuava s.l: A. pectinata (CS, Leme 231). D. Aechmea subg. Platyaechmea s.l.: A. smithiorum (CS, Leme 1709). E. Aechmea subg. Chevaliera s.l.: A. tayoensis (CS, Leme 3240). F. Ananas nanus (CS, Leme 9381). G. Araeococcus flagellifolius (SD, Leme 9696); H. Billbergia subg. Billbergia: B. amoena var. robertiana (CS, Leme 246). I. Billbergia subg. Helicodea: B. porteana (CS, Leme 9584). J. Bromelia auriculata (CE, Leme 8108). K. Fernseea bocainensis (CS, Leme 1422); L. Hohenbergia catingae (CS, Leme 2330). M. Neoglaziovia variegata (CS, Leme 9153). N. Greigia stenolepis (CS, Leme 9738). O. Portea grandiflora (CS, Leme 4011). P. Pseudananas sagenarius (CS, Leme 5556). Q. Quesnelia arvensis (CS, Leme 2695). R. Wittmackia bicolor (CS, Leme 4228). S. Wittmackia (Wittmackiopsis) penduliflora (CS, Leme 3832). T. Wittmackia silvana (CS, Leme 7060). Bars = 1 mm.
Shock Induced Dilation - Validation data
<p>Input files needed for running shock simulations in LIGGGHTS as well as the results of a single granular shock run. Full description is provided in Frizzell and Hartzell, 2023 - Simulation of Shock Induced Inertial Dilation at the Surface of an Unconfined Granular Assembly</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>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.