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The prevalence and correlates of depression and anxiety symptoms among older adults in Shenzhen, China
<p><strong>Objectives: </strong>To investigate the prevalence of depression and anxiety symptoms among older adults in an urban district in China, as well as their associated factors.</p> <p><strong>Participants:</strong> A total of 5,372 community-dwelling older adults aged 65 years or older were initially recruited. Ultimately, 5,331 participants met the inclusion criteria and were included in this study.</p> <p><strong>Methods:</strong> Participants completed a sociodemographic questionnaire, along with assessments including the Patient Health Questionnaire-9, Generalized Anxiety Scale-7, UCLA Loneliness Simplification Scale, Insomnia Severity Index Scale, Community Dementia Brief Screening Scale, and the 8-item Dementia Screening Questionnaire. Statistical analyses included the Shapiro‒Wilk test, independent t-test, Wilcoxon rank test, c<sup>2</sup> test, and univariate and multivariate linear regression analysis.</p> <p><strong>Results:</strong> The prevalence of depression and anxiety symptoms among older adults in Shenzhen communities was 10.4% and 11.3%, respectively. In multivariate analysis, age (B=-0.01, <em>P</em><0.05), relatively poor health status in the past year (B=1.00, <em>P</em><0.01), poor health status in the past year (B=2.40, <em>P</em><0.01), ISI score (B=0.21, <em>P</em><0.01), AD8 score (B=0.22, <em>P</em><0.01), ULS score (B=0.24, <em>P</em><0.01) were significantly associated with the severity of depression symptom, Compared to their respective reference categories, relatively poor health status in the past year (B=0.50, <em>P</em><0.01), poor health status in the past year (B=1.32, <em>P</em><0.01), ISI score (B=0.23, <em>P</em><0.01), sleep duration (B=0.05, <em>P</em><0.01), AD8 score (B=0.21, <em>P</em><0.01), CSID score (B=0.13, <em>P</em><0.01), ULS score (B=0.22, <em>P</em><0.01) were significantly associated with the severity of anxiety symptom.</p> <p><strong>Conclusions: </strong>We observed a high prevalence of depression and anxiety symptoms among older adults in this study. The existing welfare system and infrastructure should remain and targeted mental health programs addressing the identified risk factors should be proposed.</p>
Short-range correlation of stress chains near solid-to-liquid transition in active monolayers
<p>Using a three-dimensional model of cell monolayers, we study the spatial organization of active stress chains as the monolayer transitions from a solid to a liquid state. The critical exponents that characterize this transition map the isotropic stress percolation onto the two-dimensional random percolation universality class suggesting short-range stress correlations near this transition. This mapping is achieved via two distinct, independent pathways: (i) cell-cell adhesion and (ii) active traction forces. We unify our findings by linking the nature of this transition to high-stress fluctuations, distinctly linked to each pathway. The results elevate the importance of the transmission of mechanical information in dense active matter and provide a new context for understanding the non-equilibrium statistical physics of phase transition in active systems.</p>
A Correlated-k Parameterization for O2 Photolysis in the Schumann-Runge Bands
<p>This dataset includes the correlated-k table (both 4- and 12- terms, named as 'k4_O2SR.dat' and 'k12_O2SR.dat', respectively) used in our 1-D photochemical model for parameterizing O2 absorption cross sections in the O2 Schumann-Runge (SR) bands (175–205 nm). Transmission data are saved in files 'Transmission_.csv'. Photolysis frequencies and rates for gases calculated from the 1-D photochemical model can be accessed in files 'PHOTO_.csv'. Matlab is used to plot figures, and the code is in 'OLD_CORRK.m'. '.zip' files should be decompressed for using 1-D output data. Those data are comparisons of different O2 photolysis parameterizations (AF82 band model, Old exponential sum, WACCM, Correlated-k) at the SR bands with/without scattering in the 1-D photochemical model. Specifically, in '1PAL.zip' and '0001PAL.zip', '00' refers to WACCM without scattering, '01' refers to WACCM with scattering, '10' refers to correlated-k without scattering, '11' refers to correlated-k with scattering. 'OLD_1PAL.zip' refers to the 1-D model with old exponential sum fits. 'AF_1PAL.zip' refers to the 1-D model with AF82 band model. All the files can be open with MATLAB code 'OLD_CORRK.m' and be used to plotting all the figures in the paper 'A Correlated-k Parameterization for O2 Photolysis in the Schumann-Runge Bands'.</p>
FIGURE 2 in Environmental correlates of the European common toad hybrid zone
FIGURE 2 Two-species 'global' distribution model for Bufo toads in western Europe. The colour scale runs from deep red for B. spinosus (Pb is zero) to deep blue for B. bufo (Pb at unity). The interrupted black line represents the center of the species' hybrid zone from molecular data, as in fig. 1. Outlined circular windows are those for which model fit is less than good (AUC <0.8, windows 1, 6–8 and 14–16).
FIGURE 3 in Environmental correlates of the European common toad hybrid zone
FIGURE 3 Two regions in the common–spined toad hybrid zone where the mutual species border appears to coincide with rivers. Coloured dots indicate toad populations with nuclear genetic species identifications as Bufo bufo (Q = Pb> 0.5, blue symbols) and B. spinosus (Q = Pb <0.5, red symbols). Open dots have Q-values in the 0.2–0.8 range. For numerical detail see supplementary table S1. Base map figure credits as in fig. 1. A) central France where the species border appears to coincide with the northern- most sections of the Loire (windows 5 and 6) and the upper stretches of the Cher (windows 7 and 8). B) southeastern France where the species border coincides with the Rhône and the lower Isère river at window 13. Note the paucity of data for the high Alps at windows 15 and 16 (see also Lescure and de Downloaded from Brill.com 12/12/2023 03:07:30PM Massary, 2012; Arntzen et al., via2017Open). Access. This is an open access article distributed under the terms of the CC-BY 4.0 License. https://creativecommons.org/licenses/by/4.0/
FIGURE 1 in Environmental correlates of the European common toad hybrid zone
FIGURE 1 Western Europe with France and adjacent countries in Mercator projection. Colours from green to brown indicate increasing altitudes. The Bufo bufo versus B. spinosus mutual range delineation is based upon molecular genetic data, in which the smooth interrupted line is derived by linear interpolation whereas the more angular line is based upon Dirichlet cells (for details see text). The small bodied common toad B. bufo occurs to the northeast and the large bodied spined toad B. spinosus to the southwest of the mutual range border. Environmental data were gathered for 17 overlapping and adjoining circular windows positioned over the mutual range border. Here shown are window 1 in the northwest of France, window 17 in the northwest of Italy and windows 5, 9 and 13 in between. The two boxed areas are highlighted in fig. 3. The base map was downloaded from MapsLand at https://www.mapsland.com, under a Creative Commons Attribution-ShareAlike 3.0 Licence. The animal drawings are by Bas Blankevoort, Naturalis Biodiversity Center.
FIGURE 4 Average values for eight environmental variables over 17 windows that follow the Bufo bufo – B in Environmental correlates of the European common toad hybrid zone
FIGURE 4 Average values for eight environmental variables over 17 windows that follow the Bufo bufo – B. spinosus hybrid zone from the Atlantic coast (window 1) to the Mediterranean (window 17). Variables shown are those selected by a logistic regression analysis, with 'species' as dependent variable and explanatory variables available for selection as in table 1. Units are as in table 1; see also Hijmans et al. (2005). Values for B. bufo and B. spinosus are shown by small and large dots, respectively. Grey areas indicate that values for B. bufo are lower than for B. spinosus. The graph at the top left provides AUC model fit values along with major topographical references. Rectangles indicate stretches of the species contact for which the environmental models have good fit (AUC> 0.8), with consistent results indicated by green shadings. For the other windows with less than good model fit, signals are likely to be absent or void, either from poor sampling (window 1), the presence of rivers (windows 5–9, 13–14), or a thin or absent species' contact (windows 16–17) (see fig. 3).
IODP Expedition 378 Stratigraphic Correlation
Affine and splice files created for stratigraphic correlation.
Figure data for article "Controlling the dynamics of atomic correlations via the coupling to a dissipative cavity"
<p>The files contain the data depicted in the figures of the article "Controlling the dynamics of atomic correlations via the coupling to a dissipative cavity", Phys. Rev. Lett. <strong>134</strong>, 073604 (2025)</p> <p>The format of the data and to which figure it corresponds is described in the file "read_me_metadata.txt".</p>
The structure of simple satellite variation in the human genome and its correlation with centromere ancestry (Supplemental Data)
<p>Accompanying <a href="https://github.com/is-the-biologist/1KGP_SATS" target="_blank" rel="noopener">Github</a></p> <p><strong>Supplemental File 1.</strong> BLAST results of k-mer concatemers against T2T-CHM13-v2.0.</p> <p><strong>Supplemental File 2.</strong> Annotations of centromeres, and telomeres of T2T-CHM13-v2.20. Table of abundance of k-mers in annotated regions as numpy file from BLAST hits. Abundance of k-mers across genome in 100kb bins from BLAST hits as .npz files accessible by example:</p> <p> import numpy as np<br> dense = np.load("filename.npz")<br> dense["chr1"]<br> <br><strong>Supplemental File 3</strong>. Table of pairwise R2 between simple satellites and table of pairwise interspersion OR between simple satellites. Folder containing QQ plots of negative binomial fit of satellite copy number distribution used to qualitatively asses model fit.</p> <p><strong>Supplemental File 4. </strong>Materials and results of cenGRM analysis. Boundaries used for centromeric regions of each cenGRM, cenGRMs in GCTA format, and tables with the results of cenGRM GCTA runs. Also provide pdfs of the dendrograms/heatmaps produced from UPGMA clustering of each cenGRM. </p> <p><strong>Supplemental File 5</strong> Non-human significant BLAST hits from BLAST-ing k-mer concatamers to non-human sequences.</p> <p><strong>Supplemental Table 1.</strong> Copy number normalized to 1x depth given GC bias of 126 most abundant satellites analyzed in paper in each individual. Additional columns represent metadata of the individual:</p> <ul> <li>instrument: sequencer instrument name used to sequence library.</li> <li>run: sequencer run of the library.</li> <li>flow: flowcell ID of the ibrary.</li> <li>pop: 1,000 Genomes Project population ID.</li> <li>superpop: 1,000 Genomes Project superpopulation ID.</li> <li>reads: average autosomal read depth of the library.</li> </ul> <p><strong>Supplemental Table 2. </strong>Copy number normalized to 1x depth given GC bias of the top 126 most abundant satellites analyzed in paper in each individual of the 1KGP, plus estimates of the same satellites in CHM13 short-read libraries subsampled from 18x-0.5x, 18x depth simulated library of the T2T-CHM13v2.0 assembly analyzed using k-Seek, and Tandem Repeat Finder results of the T2T-CHM13v2.0 asembly <a href="https://doi.org/10.1126/science.abk3112" target="_blank" rel="noopener">Hoyt 2022</a>.</p> <p><strong>Supplemental Table 3.</strong> Copy number normalized to 1x depth given GC bias of all tandem repeats with k-mer <= 20 (6,309) found collectively in the CHM13 short-read libraries subsampled from 18x-0.5x, 18x depth simulated library of the T2T-CHM13v2.0 assembly analyzed using k-Seek, and Tandem Repeat Finder results of the T2T-CHM13v2.0 asembly <a href="https://doi.org/10.1126/science.abk3112" target="_blank" rel="noopener">Hoyt 2022</a>.</p>
Data for JGR-Atmospheres Paper: Stratospheric Hydration Processes in Tropopause-Overshooting Convection Revealed by Tracer-Tracer Correlations from the DCOTSS Field Campaign
<p>Airborne 1-second data merger of observations from the NASA Dynamics and Chemistry of the Summer Stratosphere (DCOTSS) field campaign. This merger includes subjective feature identifications analyzed in the paper referenced in the title. </p>
Data from: Insights from a 31-year study demonstrate an inverse correlation between recreational activities and red deer fecundity, with body weight as a mediator
<p>Human activity is omnipresent in our landscapes. Animals can perceive risk from humans similar to predation risk, which could affect their fitness. We assessed the influence of the relative intensity of recreational activities on body weight and pregnancy rates of red deer (<em>Cervus elaphus</em>) between 1985 and 2015. We hypothesized that stress, as a result of recreational activities, affects pregnancy rates of red deer directly and indirectly via a reduction in body weight. Furthermore, we expected non-motorized recreational activities to have a larger negative effect on both body weight and fecundity, compared to motorized recreational activities. The intensity of recreational activities was recorded through visual observations. We obtained pregnancy data from female red deer that were shot during the regular hunting season. Additionally, age and body weight were determined through post-mortem examination. We used two generalized linear mixed models (GLMM) to test the effect of different types of recreation on 1) pregnancy rates and 2) body weight of red deer. Recreation had a direct negative correlation with the fecundity of red deer, with body weight as a mediator as expected. Besides, we found a negative effect of non-motorized recreation on fecundity and body weight and no significant effect of motorized recreation. Our results support the concept of humans as an important stressor affecting wild animal populations at a population level and plead to regulate recreational activities in protected areas that are sensitive. The fear humans induce in large-bodied herbivores and its consequences for fitness may have strong implications for animal populations.</p>
F I G U R E 4 in Environmental correlates of adaptive diversification in postglacial freshwater fishes
F I G U R E 4 Visual representation of trends in ecomorph number based on phosphorus concentration (μg L 1). Data from Landry et al. (2007) and Siwertsson et al. (2010). Graph plotted in R using the ggplot2 package (Wickham, 2016).
F I G U R E 1 in Environmental correlates of adaptive diversification in postglacial freshwater fishes
F I G U R E 1 Conceptual diagram of the different components examined in this paper and how they may relate to origins and maintenance of sympatric divergent ecomorphs. Created with BioRender.com.
F I G U R E 5 in Environmental correlates of adaptive diversification in postglacial freshwater fishes
F I G U R E 5 Visual representation of trends in ecomorph number based on the number of fish species present in a lake, other than the diversifying species pairs/groups. Data from Siwertsson et al. (2010), Vamosi (2003), and Öhlund et al. (2020). Graph plotted in R using the ggplot2 package (Wickham, 2016).
F I G U R E 3 in Environmental correlates of adaptive diversification in postglacial freshwater fishes
F I G U R E 3 Visual representation of trends in ecomorph number based on bathymetric traits. (a) Lake surface area (km2). Data from Bolnick and Lau (2008), Gordeeva et al. (2015), Lucek et al. (2016), Öhlund et al. (2020), Siwertsson et al. (2010), and Vamosi (2003). (b) Lake maximum depth (m). Data from Gordeeva et al. (2015), Landry et al. (2007), Öhlund et al. (2020), and Siwertsson et al. (2010). Box plots indicate median and interquartile range. Note that the y-axes in graphs consist of untransformed data but are plotted on logarithmic scales due to the large range in reported values for these variables. Graphs plotted in R using the ggplot2 package (Wickham, 2016).
FIG. 5. Correlation between the spatial distribution index and population density. A in Demographic and spatial structure at the stage of expansion in the populations of some alien land snails in Belgorod city (Central Russian Upland)
FIG. 5. Correlation between the spatial distribution index and population density. A. For Brephulopsis cylindrica and Xeropicta derbentina at 160 plots for three years. B. For Harmozica ravergiensis in nine sites×20 plots for two years. РИС. 5. Корреляция меЖду индексом пространственного распределения и плотностью популяции. А. Для Brephulopsis cylindrica и Xeropicta derbentina на 160 плоЩадках За три года. В. Для Harmozica ravergiensis на девяти участках по 20 плоЩадок За два года.
CoUDlabs_TA_OTHU-SuDS-NBS-RECON: Correlating plant health with soil moisture in NBS
<p>This repository contains the dataset ‘CoUDlabs_TA_OTHU-SuDS-NBS-RECON: Correlating plant health with soil moisture in NBS’ which is a result from the Transnational Access, within Co-UDlabs project, funded under the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101008626. </p> <p>These experiments introduce a new approach which uses low-cost multi-spectral camera (Plant-o-Meter) to estimate plant health (through various parameters) to link with soil moisture of the bioretention systems (biofilters and green roof) as a common element of nature-based solutions (NBS). Soil moisture is used as a proxy for polluted water retention and pollutant removal within the system. The aim of this work is to provide a proof of concept for indirect measurement of urban polluted water treatment through different types of NBS. The experiments have been done on real stormwater biofilters, under controlled conditions (artificial irrigation). Data from the experimental campaign presented here will help to understand fundamental correlations between plant health parameters and the change in soil moisture, giving us a tool for quick assessment of the need for maintenance of various NBS as a crucial parts of UD systems.</p> <p>The authors acknowledge financial support from the European Union under the Horizon 2020 program within a contract for Integrating Activities for Starting Communities (Ref. 101008626)</p> <p>>>>For detailed description of the dataset, please see <a href="https://zenodo.org/api/records/14191602/draft/files/README_NBS-RECON_dataset.txt/content" target="_blank" rel="noopener noreferrer">README_NBS-RECON_dataset.txt</a> and <a href="https://zenodo.org/api/records/14191602/draft/files/CoUDlabsDataStorageReport_NBS-RECON.docx/content" target="_blank" rel="noopener noreferrer">CoUDlabsDataStorageReport_NBS-RECON.docx</a></p>
LoLiPoP-IoT TU/e Dataset: Preferred Illuminance at Different Correlated Color Temperatures for Paper- and Tablet-based Reading Tasks in an Office Environment
<p>The dataset has the following <strong>576</strong> (=i.e., <strong>32</strong> Participants × <strong>3</strong> CCTs × <strong>2</strong> task media × <strong>3</strong> repetitions) preferred illuminance values (unit: lx).</p> <table> <tbody> <tr> <td> </td> <td><strong>Paper</strong></td> <td><strong>Paper</strong></td> <td><strong>Paper</strong></td> <td><strong>Paper</strong></td> <td><strong>Paper</strong></td> <td><strong>Paper</strong></td> <td><strong>Paper</strong></td> <td><strong>Paper</strong></td> <td><strong>Paper</strong></td> </tr> <tr> <td><strong>Participant ID</strong></td> <td><strong>CCT 1 (3000 K)</strong></td> <td><strong>CCT 1 (3000 K)</strong></td> <td><strong>CCT 1 (3000 K)</strong></td> <td><strong>CCT 2 (4000 K)</strong></td> <td><strong>CCT 2 (4000 K)</strong></td> <td><strong>CCT 2 (4000 K)</strong></td> <td><strong>CCT 3 (6000 K)</strong></td> <td><strong>CCT 3 (6000 K)</strong></td> <td><strong>CCT 3 (6000 K)</strong></td> </tr> <tr> <td><strong>1</strong></td> <td>1098.00</td> <td>1668.00</td> <td>452.70</td> <td>396.20</td> <td>812.70</td> <td>589.60</td> <td>1139.00</td> <td>603.40</td> <td>502.60</td> </tr> <tr> <td><strong>2</strong></td> <td>275.90</td> <td>322.20</td> <td>316.00</td> <td>455.60</td> <td>412.30</td> <td>387.40</td> <td>382.30</td> <td>346.10</td> <td>411.10</td> </tr> <tr> <td><strong>3</strong></td> <td>2872.00</td> <td>399.70</td> <td>304.60</td> <td>1814.00</td> <td>339.80</td> <td>574.70</td> <td>1750.00</td> <td>725.60</td> <td>442.20</td> </tr> <tr> <td><strong>4</strong></td> <td>843.20</td> <td>686.40</td> <td>1088.00</td> <td>755.40</td> <td>1001.00</td> <td>818.20</td> <td>817.10</td> <td>790.70</td> <td>810.40</td> </tr> <tr> <td><strong>5</strong></td> <td>541.00</td> <td>404.10</td> <td>561.10</td> <td>513.50</td> <td>380.20</td> <td>419.70</td> <td>335.20</td> <td>343.30</td> <td>459.10</td> </tr> <tr> <td><strong>6</strong></td> <td>308.30</td> <td>289.90</td> <td>237.20</td> <td>485.20</td> <td>437.00</td> <td>403.90</td> <td>385.90</td> <td>462.80</td> <td>478.00</td> </tr> <tr> <td><strong>7</strong></td> <td>1111.00</td> <td>1531.00</td> <td>1280.00</td> <td>1422.00</td> <td>1273.00</td> <td>1812.00</td> <td>1408.00</td> <td>1618.00</td> <td>1734.00</td> </tr> <tr> <td><strong>8</strong></td> <td>744.20</td> <td>545.00</td> <td>462.90</td> <td>605.50</td> <td>616.00</td> <td>575.10</td> <td>1020.00</td> <td>721.20</td> <td>661.10</td> </tr> <tr> <td><strong>9</strong></td> <td>422.50</td> <td>707.40</td> <td>363.40</td> <td>460.30</td> <td>514.00</td> <td>353.40</td> <td>433.00</td> <td>463.80</td> <td>481.60</td> </tr> <tr> <td><strong>10</strong></td> <td>833.00</td> <td>626.80</td> <td>595.60</td> <td>773.60</td> <td>727.40</td> <td>363.40</td> <td>746.90</td> <td>723.70</td> <td>214.30</td> </tr> <tr> <td><strong>11</strong></td> <td>379.20</td> <td>689.10</td> <td>703.60</td> <td>526.80</td> <td>711.30</td> <td>981.40</td> <td>431.40</td> <td>511.20</td> <td>873.10</td> </tr> <tr> <td><strong>12</strong></td> <td>683.30</td> <td>764.60</td> <td>1124.00</td> <td>995.10</td> <td>990.10</td> <td>1116.00</td> <td>901.10</td> <td>962.50</td> <td>1296.00</td> </tr> <tr> <td><strong>13</strong></td> <td>318.30</td> <td>406.10</td> <td>492.20</td> <td>424.80</td> <td>531.40</td> <td>549.80</td> <td>510.70</td> <td>471.70</td> <td>529.20</td> </tr> <tr> <td><strong>14</strong></td> <td>228.80</td> <td>200.40</td> <td>233.30</td> <td>326.00</td> <td>322.30</td> <td>342.00</td> <td>425.40</td> <td>337.70</td> <td>411.10</td> </tr> <tr> <td><strong>15</strong></td> <td>252.10</td> <td>296.60</td> <td>293.60</td> <td>323.90</td> <td>318.40</td> <td>351.70</td> <td>328.10</td> <td>321.50</td> <td>572.70</td> </tr> <tr> <td><strong>16</strong></td> <td>473.70</td> <td>682.90</td> <td>709.00</td> <td>468.60</td> <td>855.50</td> <td>896.50</td> <td>611.00</td> <td>1165.00</td> <td>846.30</td> </tr> <tr> <td><strong>17</strong></td> <td>388.20</td> <td>266.90</td> <td>300.00</td> <td>386.30</td> <td>404.30</td> <td>372.30</td> <td>383.60</td> <td>364.20</td> <td>370.60</td> </tr> <tr> <td><strong>18</strong></td> <td>626.90</td> <td>740.70</td> <td>891.80</td> <td>644.60</td> <td>815.60</td> <td>938.50</td> <td>541.50</td> <td>558.20</td> <td>780.10</td> </tr> <tr> <td><strong>19</strong></td> <td>1025.00</td> <td>558.70</td> <td>535.70</td> <td>1056.00</td> <td>747.30</td> <td>1608.00</td> <td>819.70</td> <td>1092.00</td> <td>1259.00</td> </tr> <tr> <td><strong>20</strong></td> <td>661.80</td> <td>533.20</td> <td>464.60</td> <td>1093.00</td> <td>861.80</td> <td>1070.00</td> <td>894.60</td> <td>738.40</td> <td>1459.00</td> </tr> <tr> <td><strong>21</strong></td> <td>466.60</td> <td>401.70</td> <td>455.00</td> <td>542.90</td> <td>550.00</td> <td>491.30</td> <td>482.00</td> <td>540.60</td> <td>431.10</td> </tr> <tr> <td><strong>22</strong></td> <td>1093.00</td> <td>1037.00</td> <td>1106.00</td> <td>612.30</td> <td>777.20</td> <td>1041.00</td> <td>353.00</td> <td>848.10</td> <td>478.20</td> </tr> <tr> <td><strong>23</strong></td> <td>688.80</td> <td>1034.00</td> <td>746.90</td> <td>752.60</td> <td>1301.00</td> <td>817.80</td> <td>717.70</td> <td>730.20</td> <td>893.70</td> </tr> <tr> <td><strong>24</strong></td> <td>468.00</td> <td>231.50</td> <td>150.20</td> <td>666.90</td> <td>304.20</td> <td>282.40</td> <td>486.20</td> <td>319.30</td> <td>276.60</td> </tr> <tr> <td><strong>25</strong></td> <td>646.80</td> <td>1064.00</td> <td>743.10</td> <td>1072.00</td> <td>996.10</td> <td>961.20</td> <td>623.60</td> <td>1203.00</td> <td>1026.00</td> </tr> <tr> <td><strong>26</strong></td> <td>307.90</td> <td>224.70</td> <td>255.40</td> <td>410.10</td> <td>331.50</td> <td>331.80</td> <td>259.00</td> <td>354.60</td> <td>311.10</td> </tr> <tr> <td><strong>27</strong></td> <td>609.00</td> <td>661.80</td> <td>526.00</td> <td>451.50</td> <td>496.20</td> <td>557.70</td> <td>421.30</td> <td>475.60</td> <td>594.70</td> </tr> <tr> <td><strong>28</strong></td> <td>456.40</td> <td>479.60</td> <td>412.40</td> <td>458.60</td> <td>314.70</td> <td>349.90</td> <td>445.40</td> <td>331.80</td> <td>445.80</td> </tr> <tr> <td><strong>29</strong></td> <td>546.40</td> <td>921.10</td> <td>872.40</td> <td>906.50</td> <td>1001.00</td> <td>873.70</td> <td>576.90</td> <td>897.10</td> <td>840.70</td> </tr> <tr> <td><strong>30</strong></td> <td>438.90</td> <td>398.30</td> <td>372.90</td> <td>559.30</td> <td>344.60</td> <td>475.30</td> <td>359.90</td> <td>314.90</td> <td>403.50</td> </tr> <tr> <td><strong>31</strong></td> <td>667.10</td> <td>554.10</td> <td>449.70</td> <td>737.10</td> <td>730.10</td> <td>589.60</td> <td>789.00</td> <td>474.60</td> <td>603.80</td> </tr> <tr> <td><strong>32</strong></td> <td>1234.00</td> <td>1198.00</td> <td>1007.00</td> <td>849.00</td> <td>1158.00</td> <td>770.00</td> <td>909.70</td> <td>989.80</td> <td>726.70</td> </tr> </tbody> </table> <p> </p> <p> </p> <table> <tbody> <tr> <td><strong>Participant ID</strong></td> <td><strong>Tablet</strong></td> <td><strong>Tablet</strong></td> <td><strong>Tablet</strong></td> <td><strong>Tablet</strong></td> <td><strong>Tablet</strong></td> <td><strong>Tablet</strong></td> <td><strong>Tablet</strong></td> <td><strong>Tablet</strong></td> <td><strong>Tablet</strong></td> </tr> <tr> <td> </td> <td><strong>CCT 1 (3000 K)</strong></td> <td><strong>CCT 1 (3000 K)</strong></td> <td><strong>CCT 1 (3000 K)</strong></td> <td><strong>CCT 2 (4000 K)</strong></td> <td><strong>CCT 2 (4000 K)</strong></td> <td><strong>CCT 2 (4000 K)</strong></td> <td><strong>CCT 3 (6000 K)</strong></td> <td><strong>CCT 3 (6000 K)</strong></td> <td><strong>CCT 3 (6000 K)</strong></td> </tr> <tr> <td><strong>1</strong></td> <td>154.80</td> <td>149.50</td> <td>98.75</td> <td>630.00</td> <td>288.90</td> <td>304.10</td> <td>181.80</td> <td>192.80</td> <td>199.60</td> </tr> <tr> <td><strong>2</strong></td> <td>179.40</td> <td>225.40</td> <td>241.00</td> <td>242.60</td> <td>316.80</td> <td>372.50</td> <td>206.40</td> <td>333.80</td> <td>275.20</td> </tr> <tr> <td><strong>3</strong></td> <td>681.70</td> <td>662.10</td> <td>703.70</td> <td>484.90</td> <td>662.70</td> <td>468.90</td> <td>2085.00</td> <td>537.40</td> <td>385.70</td> </tr> <tr> <td><strong>4</strong></td> <td>632.10</td> <td>1171.00</td> <td>998.30</td> <td>613.10</td> <td>1012.00</td> <td>818.40</td> <td>678.30</td> <td>693.70</td> <td>675.30</td> </tr> <tr> <td><strong>5</strong></td> <td>421.70</td> <td>349.30</td> <td>234.40</td> <td>392.50</td> <td>303.40</td> <td>280.20</td> <td>307.20</td> <td>428.70</td> <td>268.00</td> </tr> <tr> <td><strong>6</strong></td> <td>217.40</td> <td>206.00</td> <td>198.10</td> <td>324.40</td> <td>304.40</td> <td>343.70</td> <td>283.70</td> <td>285.70</td> <td>293.10</td> </tr> <tr> <td><strong>7</strong></td> <td>992.50</td> <td>1338.00</td> <td>1807.00</td> <td>1849.00</td> <td>1301.00</td> <td>1423.00</td> <td>1637.00</td> <td>1442.00</td> <td>1700.00</td> </tr> <tr> <td><strong>8</strong></td> <td>840.20</td> <td>1131.00</td> <td>778.60</td> <td>891.20</td> <td>1019.00</td> <td>651.80</td> <td>810.80</td> <td>1016.00</td> <td>866.20</td> </tr> <tr> <td><strong>9</strong></td> <td>391.40</td> <td>337.40</td> <td>355.70</td> <td>314.90</td> <td>510.80</td> <td>463.60</td> <td>209.80</td> <td>450.60</td> <td>435.30</td> </tr> <tr> <td><strong>10</strong></td> <td>493.40</td> <td>535.00</td> <td>691.80</td> <td>657.60</td> <td>740.50</td> <td>342.60</td> <td>1710.00</td> <td>781.00</td> <td>339.60</td> </tr> <tr> <td><strong>11</strong></td> <td>361.70</td> <td>277.90</td> <td>431.00</td> <td>342.80</td> <td>327.80</td> <td>337.60</td> <td>350.40</td> <td>237.20</td> <td>308.90</td> </tr> <tr> <td><strong>12</strong></td> <td>857.10</td> <td>1114.00</td> <td>1348.00</td> <td>1302.00</td> <td>1408.00</td> <td>1294.00</td> <td>1031.00</td> <td>1142.00</td> <td>1078.00</td> </tr> <tr> <td><strong>13</strong></td> <td>371.80</td> <td>358.20</td> <td>731.40</td> <td>320.10</td> <td>459.50</td> <td>360.00</td> <td>440.40</td> <td>507.10</td> <td>436.90</td> </tr> <tr> <td><strong>14</strong></td> <td>361.20</td> <td>267.00</td> <td>285.80</td> <td>414.80</td> <td>398.70</td> <td>365.90</td> <td>411.50</td> <td>381.50</td> <td>392.30</td> </tr> <tr> <td><strong>15</strong></td> <td>376.00</td> <td>297.80</td> <td>294.20</td> <td>326.20</td> <td>330.50</td> <td>351.70</td> <td>327.90</td> <td>397.00</td> <td>357.00</td> </tr> <tr> <td><strong>16</strong></td> <td>221.20</td> <td>557.40</td> <td>536.30</td> <td>885.40</td> <td>518.90</td> <td>688.50</td> <td>192.40</td> <td>703.50</td> <td>568.90</td> </tr> <tr> <td><strong>17</strong></td> <td>435.70</td> <td>479.40</td> <td>444.50</td> <td>391.00</td> <td>452.90</td> <td>426.60</td> <td>421.40</td> <td>390.60</td> <td>397.80</td> </tr> <tr> <td><strong>18</strong></td> <td>431.50</td> <td>728.60</td> <td>985.70</td> <td>319.40</td> <td>542.10</td> <td>890.40</td> <td>326.90</td> <td>696.70</td> <td>575.80</td> </tr> <tr> <td><strong>19</strong></td> <td>470.80</td> <td>586.10</td> <td>476.90</td> <td>667.10</td> <td>570.10</td> <td>614.20</td> <td>803.80</td> <td>638.80</td> <td>679.50</td> </tr> <tr> <td><strong>20</strong></td> <td>186.10</td> <td>386.50</td> <td>335.60</td> <td>457.40</td> <td>471.20</td> <td>370.90</td> <td>324.70</td> <td>292.60</td> <td>273.40</td> </tr> <tr> <td><strong>21</strong></td> <td>420.80</td> <td>494.30</td> <td>447.60</td> <td>500.80</td> <td>470.30</td> <td>613.00</td> <td>581.80</td> <td>411.40</td> <td>361.40</td> </tr> <tr> <td><strong>22</strong></td> <td>510.20</td> <td>221.60</td> <td>228.60</td> <td>300.80</td> <td>285.50</td> <td>282.30</td> <td>477.30</td> <td>326.50</td> <td>185.20</td> </tr> <tr> <td><strong>23</strong></td> <td>202.20</td> <td>294.20</td> <td>252.00</td> <td>261.60</td> <td>308.80</td> <td>259.90</td> <td>476.10</td> <td>157.80</td> <td>313.10</td> </tr> <tr> <td><strong>24</strong></td> <td>464.10</td> <td>477.30</td> <td>516.50</td> <td>629.00</td> <td>516.70</td> <td>568.30</td> <td>842.50</td> <td>507.90</td> <td>613.20</td> </tr> <tr> <td><strong>25</strong></td> <td>451.80</td> <td>288.20</td> <td>444.10</td> <td>317.20</td> <td>326.40</td> <td>324.20</td> <td>456.40</td> <td>299.80</td> <td>294.80</td> </tr> <tr> <td><strong>26</strong></td> <td>157.40</td> <td>104.90</td> <td>198.70</td> <td>343.70</td> <td>328.60</td> <td>316.30</td> <td>157.40</td> <td>210.90</td> <td>225.10</td> </tr> <tr> <td><strong>27</strong></td> <td>510.00</td> <td>488.90</td> <td>552.30</td> <td>467.70</td> <td>572.20</td> <td>511.20</td> <td>440.10</td> <td>587.80</td> <td>376.60</td> </tr> <tr> <td><strong>28</strong></td> <td>257.20</td> <td>195.70</td> <td>240.90</td> <td>275.90</td> <td>286.00</td> <td>269.70</td> <td>410.00</td> <td>219.40</td> <td>235.40</td> </tr> <tr> <td><strong>29</strong></td> <td>732.00</td> <td>614.40</td> <td>567.20</td> <td>1046.00</td> <td>664.60</td> <td>693.30</td> <td>711.40</td> <td>733.80</td> <td>645.80</td> </tr> <tr> <td><strong>30</strong></td> <td>280.90</td> <td>279.90</td> <td>381.70</td> <td>376.30</td> <td>305.10</td> <td>333.30</td> <td>199.20</td> <td>311.20</td> <td>456.50</td> </tr> <tr> <td><strong>31</strong></td> <td>501.40</td> <td>341.50</td> <td>365.80</td> <td>549.80</td> <td>442.70</td> <td>442.00</td> <td>697.00</td> <td>499.20</td> <td>449.20</td> </tr> <tr> <td><strong>32</strong></td> <td>1039.00</td> <td>1198.00</td> <td>1256.00</td> <td>993.80</td> <td>1129.00</td> <td>1166.00</td> <td>916.50</td> <td>858.70</td> <td>881.30</td> </tr> </tbody> </table>
Dataset of the publication: Probing Short-Range Correlations in the van der Waals Magnet CrSBr by Small-Angle Neutron Scattering
<p>Dataset of the publication: Probing Short-Range Correlations in the van der Waals Magnet CrSBr by Small-Angle Neutron Scattering</p> <p>DOI: 10.1002/smsc.202400244</p> <p>A. Rybakov, C. Boix-Constant, D. Alba Venero, H. S. J. van der Zant, S. Mañas-Valero, E. Coronado</p> <p>Small Science, 4, 8, 2400244 (2024)</p>
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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.