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2,532 results for “at present”
EEG (128 ch) on presentation of cardinal and oblique grids
<p><strong>Subjects</strong></p> <p>The study involved 20 subjects (9 women and 11 men) without signs of pathology, without the absence of complaints and severe neurological symptoms (found by questioning). The average age of volunteers is 22.5±4 years. In all cases, the subjects were informed about the methodology, which was approved by the ethics committee of the Institute of Higher Nervous Activity and Neurophysiology of RAS (protocol No. 5 dated 02.12.2020). Recordings were made in 2020 and 2021.</p> <p><strong>Registration</strong></p> <p>Registration was carried out on a 128-channel Geodesic-400 electroencephalograph during the task. The central electrode Cz was used as a reference electrode. For each subject, the positions of the electrodes were recorded using the Geodesic GPS (Geodesic Photogrammetry System) photographic system. MRI was used to calculate the surfaces of the head, skull, and cerebral cortex.</p> <p><strong>Visual stimulation</strong></p> <p>During the study, the subjects sat in a comfortable chair in a darkened room at a distance of 57 cm from the screen. Illumination at eye level was 3 lux. After recording the background EEG signal for two minutes at rest with eyes closed, the volunteers began to perform the task. Visual stimuli appeared on the screen in the form of a grid with different inclinations: vertical (90°), horizontal (0°), tilt to the left (135°), tilt to the right (45°) The slope types are grouped into two groups: cardinal (90° and 0°) and oblique (45° and 135°). The size of the stimulus on the screen was 4.7 cm, which at a distance of 57 cm from the screen was 4.71 angular degrees. The duration of each stimulus was 100 ms. The interstimulus interval was changed randomly from 3 to 4 seconds. Each stimulus was presented 34 times, also in random order. The total duration of the experiment was about 9 min. The subject, upon presentation of the stimulus, had to recognize the slope of the lines and choose one of the answer options: cardinal or inclined lines by pressing the appropriate key.</p> <p><strong>File codes</strong></p> <table> <tbody> <tr> <td> </td> <td><strong>Code</strong></td> <td><strong>Age</strong></td> <td><strong>Sex</strong></td> <td><strong>MRI</strong></td> </tr> <tr> <td>1</td> <td>E01V3</td> <td>65</td> <td>m</td> <td>*</td> </tr> <tr> <td>2</td> <td>E02</td> <td>18</td> <td>m</td> <td>E02MRI</td> </tr> <tr> <td>3</td> <td>E04</td> <td>22</td> <td>m</td> <td>E04MRI</td> </tr> <tr> <td>4</td> <td>E05</td> <td>18</td> <td>f</td> <td>E05MRI</td> </tr> <tr> <td>5</td> <td>E06V7</td> <td>22</td> <td>f</td> <td>*</td> </tr> <tr> <td>6</td> <td>E07V6</td> <td>18</td> <td>f</td> <td>*</td> </tr> <tr> <td>7</td> <td>E08</td> <td>34</td> <td>m</td> <td>E08MRI</td> </tr> <tr> <td>8</td> <td>E09</td> <td>21</td> <td>f</td> <td>E09MRI</td> </tr> <tr> <td>9</td> <td>E10</td> <td>23</td> <td>f</td> <td>E10MRI</td> </tr> <tr> <td>10</td> <td>E11</td> <td>18</td> <td>m</td> <td>E11MRI</td> </tr> <tr> <td>11</td> <td>E12</td> <td>22</td> <td>m</td> <td>E12MRI</td> </tr> <tr> <td>12</td> <td>E13</td> <td>23</td> <td>m</td> <td>E13MRI</td> </tr> <tr> <td>13</td> <td>E14</td> <td>29</td> <td>m</td> <td>E14MRI</td> </tr> <tr> <td>14</td> <td>E15V1</td> <td>23</td> <td>m</td> <td>*</td> </tr> <tr> <td>15</td> <td>E16</td> <td>21</td> <td>f</td> <td>E16MRI</td> </tr> <tr> <td>16</td> <td>E17V4</td> <td>27</td> <td>m</td> <td>*</td> </tr> <tr> <td>17</td> <td>E18</td> <td>21</td> <td>f</td> <td>E18MRI</td> </tr> <tr> <td>18</td> <td>E19V2</td> <td>23</td> <td>f</td> <td>*</td> </tr> <tr> <td>19</td> <td>E20</td> <td>21</td> <td>f</td> <td>E20MRI</td> </tr> <tr> <td>20</td> <td>E21</td> <td>23</td> <td>f</td> <td>E21MRI</td> </tr> </tbody> </table> <p>* The subject was recorded MEG in 2022. See his data including MRI in another dataset: https://doi.org/10.5281/zenodo.7458233</p> <p><strong>Acknowledgments</strong></p> <p>The reported study was funded by RFBR, project number 20-015-00475.</p> <p> </p>
List of non-naturalized plant species present in France extracted from Pl@ntNet data (exotic ornamental and cultivated plants in particular).
<p>This dataset contains the list of plant species that have been observed on the French territory using the <a href="https://plantnet.org/">Pl@ntNet </a>application and that are NOT know as being either native or naturalized according to Kew's Plants of the World Online repository (<a href="https://powo.science.kew.org/">POWO</a>). Such species are typically exotic species managed by humans in anthropized environments such as guardens, houses or cultivated areas. This includes commercialized plants for various usage such as ornemental plants, eatable plants, phytotherapy, etc. The list contains 5,589 species, each associated with its scientific name and the number of valid Pl@ntNet observations of that species geo-localized in the metropolitan French territory. </p>
NIR-MFCO dataset: Near-infrared-based false-color images of post-consumer plastics at different material flow compositions and material flow presentations
<p>Determining mass-based material flow compositions (MFCOs) is crucial for assessing and optimizing the recycling of post-consumer plastics. Currently, MFCOs in plastic recycling are mostly determined through manual sorting analysis, but the use of inline near-infrared (NIR) sensors holds potential to automate the characterization process, paving the way for novel sensor-based material flow characterization (SBMC) applications. The NIR-MFCO dataset aims to expedite SBMC research by providing NIR-based false-color images of plastic material flows with their corresponding MFCOs. The false-color images were created through the pixel-based classification of binary material mixtures using a hyperspectral imaging camera (EVK HELIOS NIR G2-320; 990 nm – 1678 nm wavelength range) and the on-chip classification algorithm (CLASS 32). The resulting NIR-MFCO dataset includes <em>n</em> = 880 false-color images from three test series: (T1) high-density polyethylene (HDPE) and polyethylene terephthalate (PET) flakes, (T2a) post-consumer HDPE packaging and PET bottles, and (T2b) post-consumer HDPE packaging and beverage cartons for <em>n</em> = 11 different HDPE shares (0% - 50%) at four different material flow presentations (singled, monolayer, bulk height H1, bulk height H2). The dataset can be used, e.g., to train machine learning algorithms, evaluate the accuracy of inline SBMC applications, and deepen the understanding of segregation effects of anthropogenic material flows, thus further advancing SBMC research and enhancing post-consumer plastic recycling.</p>
Audio and Slides from TEI2022 Presentations.
<p>For TEI2022 conference sessions (not workshops) the slides and audio were recorded using our Newcastle University ReCap (Panopto) System. However, despite arranging for them to be so, Sessions 1A and 1B are only partly recorded and the Opening Keynote was kindly re-recorded later. In many sessions if the presenter strayed too far (really, any distance at all) from the presenting podium (where the microphone was), it immediately cut out recording the audio. Some sessions recorded fine, others not, for which we can only apologise. You may find skipping ahead in a presentation returns to a bit where the presenter was closer to the microphone. Any Closed Captions have been automatically generated and have not been corrected (and as such are mostly rubbish). The recordings are copyright of the presenters and licensed with a Creative Comomns Attribution License.</p> <p>The files here in Zenodo are stored as a backup. They are the downloaded versions of the following recordings which are probably better to use if still there since they give greater control, etc. </p> <ul> <li>Opening Keynote: <a href="https://campus.recap.ncl.ac.uk/Panopto/Pages/Viewer.aspx?id=5b5f0893-04a5-4d9e-ab61-af1f00f7f24d">https://campus.recap.ncl.ac.uk/Panopto/Pages/Viewer.aspx?id=5b5f0893-04a5-4d9e-ab61-af1f00f7f24d</a></li> <li>Session 1A: <a href="https://campus.recap.ncl.ac.uk/Panopto/Pages/Viewer.aspx?id=aba3720f-2bd6-4a1c-9716-af1f0125f723">https://campus.recap.ncl.ac.uk/Panopto/Pages/Viewer.aspx?id=aba3720f-2bd6-4a1c-9716-af1f0125f723</a></li> <li>Session 1B: <a href="https://campus.recap.ncl.ac.uk/Panopto/Pages/Viewer.aspx?id=79564977-bada-43f6-875f-af100093c855">https://campus.recap.ncl.ac.uk/Panopto/Pages/Viewer.aspx?id=79564977-bada-43f6-875f-af100093c855</a></li> <li>Session 2A: <a href="https://campus.recap.ncl.ac.uk/Panopto/Pages/Viewer.aspx?id=f00844c4-166d-4d16-9d57-af1000989b0a">https://campus.recap.ncl.ac.uk/Panopto/Pages/Viewer.aspx?id=f00844c4-166d-4d16-9d57-af1000989b0a</a></li> <li>Session 2B: <a href="https://campus.recap.ncl.ac.uk/Panopto/Pages/Viewer.aspx?id=52a60bc3-b34c-4d0e-b912-af100095168e">https://campus.recap.ncl.ac.uk/Panopto/Pages/Viewer.aspx?id=52a60bc3-b34c-4d0e-b912-af100095168e</a></li> <li>Session 3A: <a href="https://campus.recap.ncl.ac.uk/Panopto/Pages/Viewer.aspx?id=8455d767-99ef-40d9-80ec-af100098fe7f">https://campus.recap.ncl.ac.uk/Panopto/Pages/Viewer.aspx?id=8455d767-99ef-40d9-80ec-af100098fe7f</a></li> <li>Session 3B: <a href="https://campus.recap.ncl.ac.uk/Panopto/Pages/Viewer.aspx?id=ca4d2ad2-9143-418d-b6d2-af100095474b">https://campus.recap.ncl.ac.uk/Panopto/Pages/Viewer.aspx?id=ca4d2ad2-9143-418d-b6d2-af100095474b</a></li> <li>Session 4A: <a href="https://campus.recap.ncl.ac.uk/Panopto/Pages/Viewer.aspx?id=5ffa9be3-a17d-412f-817d-af100098c840">https://campus.recap.ncl.ac.uk/Panopto/Pages/Viewer.aspx?id=5ffa9be3-a17d-412f-817d-af100098c840</a></li> <li>Session 4B: <a href="https://campus.recap.ncl.ac.uk/Panopto/Pages/Viewer.aspx?id=3fe86162-57f3-4e6f-8c69-af10009a03ab">https://campus.recap.ncl.ac.uk/Panopto/Pages/Viewer.aspx?id=3fe86162-57f3-4e6f-8c69-af10009a03ab</a></li> <li>Session 5A: <a href="https://campus.recap.ncl.ac.uk/Panopto/Pages/Viewer.aspx?id=c0dd6c31-5ca9-4869-b75f-af10009a3410">https://campus.recap.ncl.ac.uk/Panopto/Pages/Viewer.aspx?id=c0dd6c31-5ca9-4869-b75f-af10009a3410</a></li> <li>Session 5B: <a href="https://campus.recap.ncl.ac.uk/Panopto/Pages/Viewer.aspx?id=966bc842-1f7e-4cda-a24f-af10009a6845">https://campus.recap.ncl.ac.uk/Panopto/Pages/Viewer.aspx?id=966bc842-1f7e-4cda-a24f-af10009a6845</a></li> <li>Session 6A: <a href="https://campus.recap.ncl.ac.uk/Panopto/Pages/Viewer.aspx?id=74228091-5c8a-4d62-82a6-af10009c6ea7">https://campus.recap.ncl.ac.uk/Panopto/Pages/Viewer.aspx?id=74228091-5c8a-4d62-82a6-af10009c6ea7</a></li> <li>AGM: <a href="https://campus.recap.ncl.ac.uk/Panopto/Pages/Viewer.aspx?id=b383fc3a-3bb7-472b-994f-af10009cefae">https://campus.recap.ncl.ac.uk/Panopto/Pages/Viewer.aspx?id=b383fc3a-3bb7-472b-994f-af10009cefae</a></li> <li>Session 7A: <a href="https://campus.recap.ncl.ac.uk/Panopto/Pages/Viewer.aspx?id=67ee0984-8507-4196-934d-af1000a06568">https://campus.recap.ncl.ac.uk/Panopto/Pages/Viewer.aspx?id=67ee0984-8507-4196-934d-af1000a06568</a></li> <li>Session 7B: <a href="https://campus.recap.ncl.ac.uk/Panopto/Pages/Viewer.aspx?id=35e3495b-713b-49d5-b3e4-af1000a08f58">https://campus.recap.ncl.ac.uk/Panopto/Pages/Viewer.aspx?id=35e3495b-713b-49d5-b3e4-af1000a08f58</a></li> <li>Session 8A: <a href="https://campus.recap.ncl.ac.uk/Panopto/Pages/Viewer.aspx?id=118ecd3a-6563-4a0b-9492-af1000a207b8">https://campus.recap.ncl.ac.uk/Panopto/Pages/Viewer.aspx?id=118ecd3a-6563-4a0b-9492-af1000a207b8</a></li> <li>Session 8B: <a href="https://campus.recap.ncl.ac.uk/Panopto/Pages/Viewer.aspx?id=be7284bb-6fa5-4eab-b544-af1000a29f93">https://campus.recap.ncl.ac.uk/Panopto/Pages/Viewer.aspx?id=be7284bb-6fa5-4eab-b544-af1000a29f93</a></li> <li>Closing Keynote: <a href="https://campus.recap.ncl.ac.uk/Panopto/Pages/Viewer.aspx?id=de1c42e1-d4e9-4491-8846-af1000a2e4cb">https://campus.recap.ncl.ac.uk/Panopto/Pages/Viewer.aspx?id=de1c42e1-d4e9-4491-8846-af1000a2e4cb</a></li> </ul>
Past, present and future rainfall erosivity in Central Europe
<p>Past, present and future rainfall erosivity in central Europe calculated from convection-permitting climate simulations in COSMO-CLM using emission scenario RCP 8.5. A description of the dataset and methodology is given in the article "Past, present and future rainfall erosivity in central Europe based on convection-permitting climate simulations" by Magdalena Uber et al. (2024) in Hydrology and Earth System Sciences (https://doi.org/10.5194/hess-28-87-2024).</p> <p>This work was funded by the German Federal Ministry for Digital and Transport Network of Experts.</p> <p> </p> <p> </p>
Regional distribution of annual sea-to-air OCS fluxes for the present day atmosphere with 500 ppt OCS and the two OCS geoengineering scenarios with 4.8 ppb and 35.5 ppb.
<p>This dataset was prepared for a publication by von Hobe et al. (2023):</p> <p><strong>Comment on “An approach to sulfate geoengineering with surface emissions of carbonyl sulfide” by Quaglia et al. (2022)</strong></p> <p>In that publication, the data are displayed in Figure 3.</p> <p>Average annual sea-to-air OCS fluxes on a 2.8 ° latitude x 2.8 ° longitude grid for the present day atmosphere and for the two OCS emission scenarios considered by Quaglia et al. (2022) were obtained from a 2003 - 2019 simulation, using a model described in Lennartz et al. (2021).</p> <p>- - - - - - - - - - -</p> <p>File format:</p> <p> netCDF</p> <p>Index Variables:</p> <p> latitude</p> <p> longitude</p> <p>Parameters:</p> <p> ocsem500: mean annual sea-to-air OCS flux calculated for an atmospheric OCS mole fraction of 500 ppt</p> <p> ocsem4800: mean annual sea-to-air OCS flux calculated for an atmospheric OCS mole fraction of 4.8 ppb</p> <p> ocsem35500: mean annual sea-to-air OCS flux calculated for an atmospheric OCS mole fraction of 35.5 ppb</p> <p>- - - - - - - - - - -</p> <p><strong>References:</strong></p> <p>Lennartz, S. T., Gauss, M., von Hobe, M., and Marandino, C. A.: Monthly resolved modelled oceanic emissions of carbonyl<br> sulphide and carbon disulphide for the period 2000–2019, Earth Syst. Sci. Data, 13, 2095-2110, 10.5194/essd-13-2095-2021, 2021.</p> <p>Quaglia, I., Visioni, D., Pitari, G., and Kravitz, B.: An approach to sulfate geoengineering with surface emissions of carbonyl<br> sulfide, Atmos. Chem. Phys., 22, 5757-5773, 10.5194/acp-22-5757-2022, 2022.</p> <p>von Hobe, M., Brühl, C., Lennartz, S. T., Whelan, M. E., and Kaushik, A.: Comment on “An approach to sulfate geoengineering with surface emissions of carbonyl sulfide” by Quaglia et al. (2022) , EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-268, 2023.</p>
Simple Biosphere model version 4.2 (SiB4) simulations for the present day atmosphere with 500 ppt OCS and the two OCS geoengineering scenarios with 4.8 ppb and 35.5 ppb OCS.
<p>This dataset was prepared for a publication by von Hobe et al. (2023):</p> <p><strong>Comment on “An approach to sulfate geoengineering with surface emissions of carbonyl sulfide” by Quaglia et al. (2022)</strong></p> <p>In that publication, the data are displayed in Figures 1 and 2.</p> <p>Simple Biosphere model version 4.2 (SiB4, Haynes et al., 2019; Sellers et al., 1986) was used to calculate (i) the average increase in evapotranspiration anticipated under an elevated OCS scenario for the years 2000-2021 on a 0.5 ° latitude x 0.5 ° longitude grid and (ii) OCS uptake by plants and soils, per month, at baseline (500 ppt) and elevated (4.8 and 35.5 ppb) OCS levels averaged over the years 2000-2021.</p> <p>- - - - - - - - - - -</p> <p><em>File 1: vonHobe_et_al_2023_CarbonylSulfideGeoengineeringScenarios_DeltaEvapotranspiration_GloballyGridded_SiB4.nc</em></p> <p>File Format:</p> <p> netCDF</p> <p>Index Variables:</p> <p> latitude</p> <p> longitude</p> <p>Parameters:</p> <p> percent_diff_et: relative increase in % of evapotranspiration in a scenario where 20% of terrestrial plants exhibit a 50% increase in stomatal conductance under high OCS</p> <p>- - - - - -</p> <p><em>File 2: vonHobe_et_al_2023_CarbonylSulfideGeoengineeringScenarios_BiosphereUptake_MonthlyIntegrated_SiB4.csv</em></p> <p>File Format:</p> <p> comma delimited text file (.csv)</p> <p>Index Variable:</p> <p> time: monthly, format m/dd/yy</p> <p>Parameters:</p> <p> ocs_veg_base: simulated monthly OCS uptake by terrestrial vegetation at an atmospheric OCS mole fraction of 500 ppt</p> <p> ocs_soil_base: simulated monthly OCS uptake by soils at an atmospheric OCS mole fraction of 500 ppt</p> <p> ocs_veg_4.8ppb: simulated monthly OCS uptake by terrestrial vegetation at an atmospheric OCS mole fraction of 4.8 ppb</p> <p> ocs_soil_4.8ppb: simulated monthly OCS uptake by soils at an atmospheric OCS mole fraction of 4.8 ppb</p> <p> ocs_veg_35.5ppb: simulated monthly OCS uptake by terrestrial vegetation at an atmospheric OCS mole fraction of 35.5 ppb</p> <p> ocs_soil_35.5ppb: simulated monthly OCS uptake by soils at an atmospheric OCS mole fraction of 35.5 ppb</p> <p>- - - - - - - - - - -</p> <p><strong>References:</strong></p> <p>Haynes, K. D., Baker, I. T., Denning, A. S., Stöckli, R., Schaefer, K., Lokupitiya, E. Y., and Haynes, J. M.: Representing<br> Grasslands Using Dynamic Prognostic Phenology Based on Biological Growth Stages: 1. Implementation in the Simple<br> Biosphere Model (SiB4), Journal of Advances in Modeling Earth Systems, 11, 4423-4439, 10.1029/2018ms001540, 2019.</p> <p>Quaglia, I., Visioni, D., Pitari, G., and Kravitz, B.: An approach to sulfate geoengineering with surface emissions of carbonyl sulfide, Atmos. Chem. Phys., 22, 5757-5773, 10.5194/acp-22-5757-2022, 2022.</p> <p>Sellers, P. J., Mintz, Y., Sud, Y. C., and Salcher, A.: A Simple Biosphere Model (SiB) for Use within General Circulation Models, Journal of the Atmospheric Sciences, 43, 505-531, 1986.</p> <p>von Hobe, M., Brühl, C., Lennartz, S. T., Whelan, M. E., and Kaushik, A.: Comment on “An approach to sulfate geoengineering with surface emissions of carbonyl sulfide” by Quaglia et al. (2022) ,</p>
Animated video presenting the advancements from AI4DI Supply Chain for Food and Beverage industries
<p>This animated video presents the dvancements from AI4DI Supply Chain for Food and Beverage industries.</p>
Supplementary data for Long-term trends in functional diversity of exploited marine fish in the Azores archipelago: past and present
<p>Supplementary infromation for the manuscript entitled "<strong>Long-term trends in functional diversity of exploited marine fish in the Azores’ archipelago: past and present</strong>"</p>
The data presented in Figure 6, the 120 mare domes hosting no RMDSs shown in Fig. 5, and all the RMDSs (more than 6400 in number) identified in Mare Tranquillitatis
<p>The datasets presented here are provided as supporting information for our work, the detail of which is as below:</p> <p>Feng Zhang, James W. Head, Lionel Wilson, Yibo Meng, Christian Wӧhler, Dijun Guo, Shengli Niu, Roberto Bugiolacchi, Le Qiao, Yanan Dang, Yang Liu, Yongliao Zou</p> <p><strong>Insights into Lunar Basaltic Volcanism from Mare Domes Superposed by Ring-Moat Dome Structures (RMDSs) in Mare Tranquillitatis</strong></p> <p>Journal of Geophysical Research: Planets, 129, e2023JE007969. https://doi.org/10.1029/2023JE007969.</p> <p>A detailed description of these datasets is provided below:</p> <p>(1) 527_RMDSs_in_mare_plains_Fig6.csv</p> <p>Measured morphometric properties (e.g., diameter, height, slope, etc.) of the 527 RMDSs (Fig. 6), which are located in the lunar maira surrounding the 200 mare domes in the Tranquillitatis basin on the Moon as shown in the paper Fig. 4 (green solid circles and stars).</p> <p>(2) Supporting_Table_S1_Dome_Without_RMDS_120_Fig5.xlsx</p> <p>Locations and morphometric parameters of the 120 mare domes without being superposed by RMDSs: their locations can be found in the paper Fig. 4 (green stars) and their morphometric parameters (e.g., diameter, height, slope, volume, etc.) can refer to Figure 5 for statistics. The newly identified 38 mare domes hosting no RMDSs are labeled in red color, and they were not reported in previous studies.</p> <p>(3) 1027_RMDSs_on_domes_Fig6.csv</p> <p>Measured morphometric properties (e.g., diameter, height, slope, etc.) of the 1027 RMDSs (Fig. 6), which are located on the 80 mare domes in the Tranquillitatis basin on the Moon as shown in the paper Fig. 4 (green solid circles).</p> <p>(4) RMDSs in Tranquillitatis-Shp-style.zip</p> <p>All the RMDSs of ~6400 in number are located in Mare Tranquillitatis and have been identified up to the submission of the paper.</p>
"Presenting Euroqcharm project outcomes to foster global database synchronization" Zoom Webinar video recording
<p>Video recording of the "Presenting Euroqcharm project outcomes to foster global database synchronization" Zoom Webinar</p>
Global hourly t2m storyline data in the 2015-2019 (2017-2019) period in the present (2 and 4 K warmer climates)
<p>Global hourly t2m storyline data (in the 2015-2019 (2017-2019) period in the present (2 and 4 K warmer climates) are provided in three .nc files (one file for each climate, with 2038-2040 and 2093-2095 in the model representing 2017-2019 for the 2 and 4 K warmer climates respectively). For the joint 2017-2019 period, ensemble member one used in Sánchez-Benítez et al. 2022 (see more details about the methodology used there) is provided. Meanwhile, the 2015-2016 present-climate data come from a nudged simulation which is identical to the present-climate nudged simulation described in Sánchez-Benítez et al. 2022, except that the simulation is branched off already in 1979 instead of 2017.</p>
Full-coverage high-resolution (Daily, 1-km) PM2.5 dataset in China (2000-present)
<p>We have estimated full-coverage, daily 1-km PM2.5 data from 2000 to 2022 in China using a random forest-based hindcast modeling method. <strong>Our modeling method focused on improving pre-2013 PM2.5 estimates because for those years no available PM2.5 measurements can be directly used for constructing the model and evaluating the model performance. </strong>In our proposed method, observed predictor information before 2013 was incoporated into the modeling for the first time. Multiple sources were used as inputs, including MAIAC AOD, meteorological data from CMA, reanalysis data from ERA-5, and other land-related data. The daily average data during 2000-2022 are released here and free for non-commercial use. <em><strong>If you want use our dataset, please cite the following publication. </strong></em></p> <p>The estimates in 2021-2022 are separately predicted using the same modeling method developed in the publication below and samples in the corresponding predictive year (sample-based 10-fold cross validation R2 [RMSE] values are 0.91 [8.84 ug/m3] for 2021 and 0.93 [7.42 ug/m3] for 2022, respectively. </p> <p> </p> <p><strong>-He, Q., Ye, T., Wang, W., Luo, M., Song, Y., & Zhang, M. (2023). Spatiotemporally continuous estimates of daily 1-km PM2. 5 concentrations and their long-term exposure in China from 2000 to 2020. <em>Journal of Environmental Management</em>, <em>342</em>, 118145.[<a href="https://doi.org/10.1016/j.jenvman.2023.118145">url</a>]</strong></p> <p><strong>-He, Q., Wang, W., Song, Y., Zhang, M., & Huang, B. (2023). Spatiotemporal high-resolution imputation modeling of aerosol optical depth for investigating its full-coverage variation in China from 2003 to 2020. <em>Atmospheric Research</em>, <em>281</em>, 106481.[<a href="https://doi.org/10.1016/j.atmosres.2022.106481">url</a>]</strong></p> <p>Full-coverage daily estimates spanning the years 2015 to Jun 2021 are archived here. These records, organized by month, are available for download in CSV format. For Jul-Dec 2021, please go to <a href="https://zenodo.org/record/8084388">10.5281/zenodo.8084388</a>.</p> <p>If you want more data (e.g.daily estimates before 2015), have any question, or further collaborate with us, please contact us via qqhe@whut.edu.cn.</p> <p>If you want to use <strong>monthly</strong> estimates from 2000 to 2022, please go to <a href="https://zenodo.org/record/8084388">10.5281/zenodo.8084388</a>.</p> <p><strong>We also estimate other atmospheric data:</strong></p> <p>For full-coverage, 1-km, AOD data in China, please go to <a href="https://dataverse.harvard.edu/dataverse/atmospheric_data_by_WHUT">harvard dataverse</a>. This dataset was imputed based on MODIS MAIAC 1-km AOD retrievals.</p> <p> </p> <p> </p>
Data presented in "Fitting cumulus cloud size distributions from idealized cloud resolving model simulations"
<p>Updated repository containing the data produced and analzed in the manuscript entitled "Fitting cumulus cloud size distributions from idealized cloud resolving model simulations" by J. Savre and G. Craig, submitted to the Journal of Advances in Modeling Earth Systems.</p> <p>New uploads: time series file (T_S), vertical profiles (profiles_tot.nc) and horizontal slices at 2km (slice_z_2000_bis.nc). Uploaded files concern the LBA case only.</p>
There and back to the present: a model-based framework to estimate phylogenetically constrained alpha diversity gradients
<p>The imprint left by niche evolution on the variation of biological diversity across spatial and environmental gradients is still debated among ecologists. Furthermore, understanding to what extent dispersal limitation may reinforce or blur such an imprint is still a gap in our ecological knowledge. In this article we introduce a simulation approach coupled to Approximate Bayesian Computation (ABC) that parameterizes both the adaptation rate of species' niche positions over the evolution of a monophyletic lineage and the intensity of dispersal limitation associated with the variation of species alpha diversity among assemblages distributed across spatial and environmental gradients. The analytical tool was implemented in the R package <em>mcfly</em>. We evaluated the statistical performance of the analytical framework using simulated datasets, which confirmed the suitability of the analysis to estimate the adaptation rate parameter but showed to be less precise in relation to the dispersal limitation parameter. Also, we found that increased dispersal limitation levels improved the parameterization of the adaptation rate of species' niche positions in simulated datasets. Further, we evaluated the role played by niche evolution and dispersal limitation on species alpha diversity variation of Phyllostomidae bats across the Neotropics. The framework proposed here sheds light on the links between niche evolution, dispersal limitation and gradients of biological diversity, and thereby improved our understanding of evolutionary imprints on current biological diversity patterns.</p>
CULTIVATE Project Presentation Video
<p>A short video explaining the Horizon Europe Innovation Action, CULTIVATE, and how it supports innovative and resilient food-sharing initiatives in urban and peri-urban areas across Europe. Visit the project website to learn more: <a href="https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqbGJMbzZ0czBRWlNjNWw1WVU3ay1Xd2h4VTlzZ3xBQ3Jtc0tuQjRtMEM1LU96c2FEcDcxLWVKTjFsRXVzMGZ1cWZIRjBSNXhtZ1QtcGkyQk1TWFo2MUJOekExQ0UxaGlsOHV6NDdsYXdLeTZ4X2hZa19OV3NHWURaVG9keVY5SFNkcXM3MFRjRG92NzJMd1RTWURnUQ&q=https%3A%2F%2Fcultivate-project.eu%2F&v=rTogV9sPhJs">https://cultivate-project.eu/</a></p> <p>Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or REA. Neither the European Union nor the granting authority can be held responsible for them.</p>
FIGURE 5 in Identification of past and present gobies: distinguishing Gobius and Pomatoschistus (Teleostei: Gobioidei) species using characters of otoliths, meristics and body morphometry
FIGURE 5 Otoliths (mesial view) of Gobius bucchichi (a–c: Selce, 3l, 1l, 4l), G. cruentatus (d–f: Selce, 2l, 3l, 8l), G. niger (g–i: Pilsey Island, 2l, 6l, 5l) and G. roulei (j–l: Selce, 2l, 1l, 3l). Numbers following the localities refer to the fish specimen from which the otolith was extracted; l, left otolith. SL denotes the standard length (in mm) of the corresponding fish specimen. Scale bars: 0.5 mm. All figured otoliths are kept in the Bavarian State Collection (collection number SNSB-BSPG 2020 LIV).
FIGURE 6 in Identification of past and present gobies: distinguishing Gobius and Pomatoschistus (Teleostei: Gobioidei) species using characters of otoliths, meristics and body morphometry
FIGURE 6 Otoliths (mesial view) of Pomatoschistus knerii (a–d: Krk, 1r, 2r, each mirrored, 9l, 4r mirrored), P. marmoratus (e–h: Selce, 1r, 2r, 3r, each mirrored, 4l), P. microps (i–l: Stralsund, 8l, 5l, 6l, 14l), P. minutus (m: Stralsund, 1l), P. montenegrensis (n–p: Skadar lake, 8l, 9l, 6l), P. pictus (m: Norway, 2r mirrored), and P. quagga (r–t: Krk, 2l, 8r mirrored, 4l). Numbers following the localities refer to the fish specimen from which the otolith was extracted; l, left otolith; r, right otolith, mirrored for better comparison. SL denotes the standard length (in mm) of the corresponding fish specimen. Scale bars: 0.5 mm. All figured otoliths are kept in the Bavarian State Collection (collection number SNSB-BSPG 2020 LIV). Downloaded from Brill.com10/07/2022 07:35:45PM via free access
FIGURE 4 in Identification of past and present gobies: distinguishing Gobius and Pomatoschistus (Teleostei: Gobioidei) species using characters of otoliths, meristics and body morphometry
FIGURE 4 Otoliths (mesial view) of Gobius cobitis (a–c: Montenegro, 7l, 4l, 1l), G. geniporus (d: Montenegro, 3l; e, f: Selce, 2l, 'medium'), G. incognitus (g–i: Pelješac Peninsula, J1914l, J1910r mirrored, J1906l), G. paganellus (j–l: Galicia, 1l, 6l, 8l) and G. vittatus (m, Selce, 2l; n, o, Krk, Krk, 1l; 2l; p: Selce 2l). Numbers following the localities refer to the fish specimen from which the otolith was extracted; l, left otolith; r, right otolith, mirrored for better comparison. SL denotes the standard length (in mm) of the corresponding fish specimen. Scale bars: 0.5 mm. All figured otoliths are kept in the Bavarian State Collection (collection number SNSB-BSPG 2020 LIV).
FIGURE 3 in Identification of past and present gobies: distinguishing Gobius and Pomatoschistus (Teleostei: Gobioidei) species using characters of otoliths, meristics and body morphometry
FIGURE 3 Otoliths (mesial view) of the species of the Gobius auratus complex, i.e. G. auratus (a: Selce, 6l; b: Selce, 5l; c: Krk, 2r mirrored), G. couchi (d–f: Krk, 2l, 3r mirrored, 1l), G. fallax (g, h: Unije Island, 2r mirrored, 1l), G. gasteveni (i, j: Galicia, 1l, 1r mirrored) and G. kolombatovici (k, l: Krk, 1l, 2l). Numbers following the localities refer to the fish specimen from which the otolith was extracted; l, left otolith; r, right otolith, mirrored for better comparison. SL denotes the standard length (in mm) of the corresponding fish specimen. Scale bars: 0.5 mm. All figured otoliths are kept in the Bavarian State Collection (collection number SNSB-BSPG 2020 LIV).
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.