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1,637 results for “residency”
Water residence time and temperature drive the dynamics of dissolved organic matter in Alpine lakes in the Tibetan Plateau
<p>This dataset contains data from an investigation into the drivers on the spatial distribution of dissolved organic matter in alpine lakes in the Tibetan Plateau described in the paper: "Du Y., Chen F., Xiao K., Song C., He H., Zhang Q., Zhou Y., Jang K.-S., Zhang Y., Xing P., Liu Z., Zhang Y. and Lu Y. 2021. Water Residence Time and Temperature Drive the Dynamics of Dissolved Organic Matter in Alpine Lakes in the Tibetan Plateau, Global Biogeochemical Cycles 35(11), e2020GB006908". </p> <p>The primary objectives of the study were to: (i) evaluate the spatial variability of the amount, source, and composition of DOM from alpine lakes on the Tibetan Plateau, and (ii) determine the primary environmental controls and mechanisms responsible for the spatial variability.</p> <p>At first, we collected 35 water samples from 25 lakes distributed in the northwestern (33.1–33.6 °N, 78.9–80.4°E), central (30.5–31.9°N, 88.3–89.4°E) and southeastern (28.6–29.1 °N, 90.4–90.8°E) regions of the plateau. We characterized water chemistry and DOM composition in these lakes by measuring dissolved organic carbon (DOC) and DOM optical properties (i.e., absorbance and fluorescence spectroscopy). DOM compositions of three lakes of different water residence times (WRTs) were further analyzed using ultrahigh-resolution molecular techniques (i.e., electrospray ionization-assisted Fourier transform-ion cyclotron resonance mass spectrometry, ESI FT-ICR MS).</p> <p>Secondly, we collected the indices of climatic characteristics (i.e., mean annual temperature, mean annual precipitation, and mean annual irradiation period) and lake hydrology (i.e., catchment area, lake area, mean depth and water residence time) of the sampling lakes.</p> <p>Thirdly, we performed statistical analysis to determine the primary environmental control and predictors of the spatial variability in lacustrine DOM on the Tibetan Plateau. The statistical analyses included non-parametric Kruskal-Wallis with Dunn post hoc test, spearman's bivariate correlations, redundancy analysis, and linear regression models.</p> <p>Main results of this work are that (1) DOM in Tibetan alpine lakes is mediated more by in-lake production and transformations than by catchment inputs; (2) water residence time (WRT) of lakes and mean annual temperature (MAT) accounted for 30–59% of the spatial variance of the abundance of chromophoric DOM (CDOM) and fluorescent DOM (FDOM); (3) Alpine lakes on the Tibetan Plateau would play a more active and prominent role in regional and global carbon cycles in the face of climate change.</p>
Radiological Assessment of Peritoneal Carcinomatosis: A Primer for Resident.
<p>I uploaded the images of the manuscript "Radiological Assessment of Peritoneal Carcinomatosis: A Primer for Resident" submitted and accepted in European Review for Medical and Pharmacological Sciences.</p>
Water residence time and Damköhler number for DOC cycling in global watersheds
<p>The relative capacity for watersheds to eliminate or export reactive constituents has important implications on aquatic ecosystem ecology and biogeochemistry. Removal efficiency depends on factors that affect either the reactivity or advection of a constituent within river networks. In this dataset, we characterized instream water residence time and Damköhler number (Da) for dissolved organic carbon (DOC) uptake in global watersheds.</p>
Responses of a resident group to an outsider in the blue-breasted quail: a paradigm for studying social resettlement of dispersers
<p>Dispersal is an individual life history trait that can influence the ecological and evolutionary dynamics of both the source and recipient populations. Current studies of animal dispersal have paid little attention to how the responses of residents in a recipient population affect the social resettlement of dispersers into a new habitat. We addressed this question in the blue-breasted quail Synoicus chinensis by designing an outsider introduction experiment to simulate a scenario of interaction between residents and dispersers. In the experiment, we introduced an unfamiliar quail into a group of three differently ranked residents and then examined their behavioral responses to the arrival of the outsider. We found that all residents made negative responses by pecking at the outsider to maintain their pecking order, in which high-ranked residents displayed significantly greater intensity than those of lower ranks. This result highlighted that adverse behavioral responses of residents would prevent outsiders from obtaining hierarchical dominance in the recipient group. Moreover, the residents' sex ratio, their relative ages to the outsiders, and whether outsiders counter-pecked at the residents all influenced the probability of outsiders prevailing against the residents. Those outsiders that displayed counter-peck courage were more likely to gain higher dominance and hence resettle into the recipient group successfully. Our findings suggest that resident groups may impose a selection among dispersers via adverse behavioral responses. Therefore, social factors that can influence the resettlement step of dispersers in a new habitat should be accounted for in future studies of animal dispersal.</p>
American Beaver: GPS and VHF tag data from resident and translocated beavers on the Price and San Rafael Rivers, Utah
<p>Wildlife translocations can dramatically alter animal movement behavior. Thus, identifying common movement patterns post-translocation can aid in setting expectations and anticipating animal behavior in subsequent efforts. American and Eurasian beavers (Castor canadensis; C. fiber) are frequently translocated for reintroduction efforts, to mitigate human-wildlife conflict, and for use as an ecosystem restoration tool. However, little is known about movement behavior of translocated beavers post-release, especially in desert rivers where resources are patchy and dynamic. We identified space-use patterns to develop an expectation framework of beaver movement behavior for future beaver-assisted restoration efforts. We captured, tagged, translocated, and monitored 41 nuisance American beavers in desert river restoration sites on the Price and San Rafael Rivers, Utah, USA, and compared their space use to 16 resident beavers. We tracked beavers 2-7 times per week from May through October in 2019 and 2020 via GPS locations and radio-telemetry, and from May 2019 through March 2021 via passive integrated antennae installed in the rivers. Resident adult beavers were detected at a mean maximum distance of 0.86 ± 0.21 river kilometers (km; ±1 SE), while resident subadult (11.00 ± 4.24 km), translocated adult (19.69 ± 3.76 km), and translocated subadult (21.09 ± 5.54 km) beavers were detected at substantially greater maximum distances. Based on coarse-scale movement models, translocated and resident subadult beavers moved substantially farther from release sites and faster than resident adult beavers up to six months post-release. In contrast, based on fine-scale, short-term movement models over 5-minute intervals, we observed similar median distance traveled between resident adult and translocated beavers. Our findings suggest day-to-day activities such as foraging and resting were largely unaltered by translocation, but translocated beavers exhibited coarse-scale movement behavior most similar to dispersal by resident subadults. Coarse-scale movement rates decreased with time since release, suggesting that translocated beavers adjusted to the novel environment over time and eventually settled into a home range similar to resident adult beavers. This is the first study comparing resident and translocated beaver movement behavior in the same system. Understanding translocated beaver movement behavior in response to a novel desert system can help future beaver-assisted restoration efforts to identify appropriate release sites and strategies.</p>
Data from: Resident-disperser differences and genetic variability affect communities in microcosms
<p>Dispersal is a key process mediating ecological and evolutionary dynamics. Its effects on the dynamics of spatially-structured systems, population genetics, or species range distribution can depend on phenotypic differences between dispersing and non-dispersing individuals. However, scaling up the importance of resident-disperser differences to communities and ecosystems has rarely been considered, in spite of intraspecific phenotypic variability being an important factor mediating community structure and productivity. Here, we used the ciliate <em>Tetrahymena thermophila</em>, in which phenotypic traits are known to differ between residents and dispersers, to test (i) whether these resident-disperser differences affect biomass and composition in competitive communities composed of four other Tetrahymena species, and (ii) whether these effects are genotype-dependent. We found that dispersers led to lower community biomass compared to residents. This effect was highly consistent across the twenty <em>T. thermophila</em> genotypes used, despite intraspecific variability in resident-disperser phenotypic differences. We also found a significant genotypic effect on biomass production, showing that intraspecific variability has consequences for communities. Our study suggests that individual dispersal strategy can scale up to community productivity in a predictable way, opening new perspectives to the functioning of spatially structured ecosystems.</p>
Projeto Elétrico - Resid. Sérgio e Adriana
Projeto de Instalações Elétricas e Infraestrutura de Cabeamento Estruturado - Residência srs. Sérgio e Adriana - Jardim Inconfidência - Uberlândia/MG Source: Objaverse 1.0 / Sketchfab
Deep microbiome-based characterization of the alterations in resident bacterial communities of pasteurized bovine milk contaminated with Salmonella Typhimurium over time
Open the record for dataset details and reuse information.
Data for: Geolocation and immersion loggers reveal year-round residency and consequent nutrient deposition rates of adult red-footed boobies in the Chagos Archipelago, tropical Indian Ocean
<p>Bio-logging has revealed much about high-latitude seabird migratory strategies, but migratory behaviour in tropical species may differ, with implications for understanding nutrient deposition. Here we use combined light-level and saltwater immersion loggers to study the year-round movement behaviour of adult red-footed boobies (<em>Sula sula rubripes</em>) from the Chagos Archipelago, tropical Indian Ocean to assess migratory movements and estimate nutrient deposition rates based on the number of days they spent ashore. Light levels suggest that red-footed boobies are resident in the Chagos Archipelago year-round, although there are large latitudinal errors this close to the equator. Immersion data also indicate residency with tracked birds returning to land every one or two days. Spending an average of 79.86 ± 2.80 days and 280.84 ± 2.64 nights per year on land allows us to estimate that the 21,670 pairs of red-footed boobies deposit 37.34 ± 0.56 tonnes year<sup>-1</sup> of guano-derived nitrogen throughout the archipelago. Our findings have implications for tropical seabird conservation and phylogenetics, as well as for assessing the impact of seabird nutrients on coral reef ecosystems.</p>
Acoustic phenology of tropical resident birds differs between native forest species and parkland colonizer species
<p>Most birds are characterized by a seasonal phenology closely adapted to local climatic conditions, even in tropical habitats where climatic seasonality is slight. In order to better understand the phenologies of resident tropical birds, and how phenology may differ among species at the same site, we used ~70,000 hours of audio recordings collected continuously for two years at four recording stations in Singapore and nine custom-made machine learning classifiers to determine the vocal phenology of a panel of nine resident bird species. We detected distinct seasonality in vocal activity in some species but not others. Native forest species sang seasonally. In contrast, species which have had breeding populations in Singapore only for the last few decades exhibited seemingly aseasonal or unpredictable song activity throughout the year. Urbanization and habitat modification over the last 100 years have altered the composition of species in Singapore, which appears to have influenced phenological dynamics in the avian community. It is unclear what is driving the differences in phenology between these two groups of species, but it may be due to either differences in seasonal availability of preferred foods, or newly established populations may require decades to adjust to local environmental conditions. Our results highlight the ways that anthropogenic habitat modification may disrupt phenological cycles in tropical regions in addition to altering the species community.</p>
TGF-β neutralization attenuates tumor residency of activated T cells to enhance systemic immunity in mice
<p>Deep TCR sequencing was performed using the TCR Profiling Kit from MiLaboratories (Mouse α/β TCR RNA; Kit MiLaboratories; TMMR-001). Deep TCR sequencing was analyzed using the MiXCR software from MiLaboratories per manufacturer's recommendations. The files correspond to the TCR-beta sequences.<br>The files are named as follows:</p> <p> </p> <table> <tbody> <tr> <td>file_name</td> <td>cell type sequenced</td> <td>tissue of origin</td> <td>treatment</td> <td>mouse_id</td> </tr> <tr> <td>21BA1dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>bintrafusp alpha</td> <td>1</td> </tr> <tr> <td>23BA2dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>bintrafusp alpha</td> <td>2</td> </tr> <tr> <td>25BA3dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>bintrafusp alpha</td> <td>3</td> </tr> <tr> <td>27BA4dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>bintrafusp alpha</td> <td>4</td> </tr> <tr> <td>29BA5dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>bintrafusp alpha</td> <td>5</td> </tr> <tr> <td>22BA1SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>bintrafusp alpha</td> <td>1</td> </tr> <tr> <td>24BA2SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>bintrafusp alpha</td> <td>2</td> </tr> <tr> <td>26BA3SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>bintrafusp alpha</td> <td>3</td> </tr> <tr> <td>28BA4SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>bintrafusp alpha</td> <td>4</td> </tr> <tr> <td>30BA5SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>bintrafusp alpha</td> <td>5</td> </tr> <tr> <td>31CON1dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>control</td> <td>6</td> </tr> <tr> <td>33CON2dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>control</td> <td>7</td> </tr> <tr> <td>35CON3dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>control</td> <td>8</td> </tr> <tr> <td>37CON4dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>control</td> <td>9</td> </tr> <tr> <td>39CON5dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>control</td> <td>10</td> </tr> <tr> <td>32CON1SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>control</td> <td>6</td> </tr> <tr> <td>34CON2SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>control</td> <td>7</td> </tr> <tr> <td>36CON3SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>control</td> <td>8</td> </tr> <tr> <td>38CON4SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>control</td> <td>9</td> </tr> <tr> <td>40CON5SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>control</td> <td>10</td> </tr> <tr> <td>1aPDL11dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>anti-PDL1 antibody</td> <td>11</td> </tr> <tr> <td>3aPDL12dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>anti-PDL1 antibody</td> <td>12</td> </tr> <tr> <td>5aPDL13dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>anti-PDL1 antibody</td> <td>13</td> </tr> <tr> <td>7aPDL14dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>anti-PDL1 antibody</td> <td>14</td> </tr> <tr> <td>9aPDL15dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>anti-PDL1 antibody</td> <td>15</td> </tr> <tr> <td>2aPDL11SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>anti-PDL1 antibody</td> <td>11</td> </tr> <tr> <td>4aPDL12SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>anti-PDL1 antibody</td> <td>12</td> </tr> <tr> <td>6aPDL13SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>anti-PDL1 antibody</td> <td>13</td> </tr> <tr> <td>8aPDL14SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>anti-PDL1 antibody</td> <td>14</td> </tr> <tr> <td>10aPDL15SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>anti-PDL1 antibody</td> <td>15</td> </tr> <tr> <td>11aTGFB1dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>anti-TGF-beta antibody</td> <td>16</td> </tr> <tr> <td>13aTGFB2dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>anti-TGF-beta antibody</td> <td>17</td> </tr> <tr> <td>15aTGFB3dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>anti-TGF-beta antibody</td> <td>18</td> </tr> <tr> <td>17aTGFB4dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>anti-TGF-beta antibody</td> <td>19</td> </tr> <tr> <td>19aTGFB5dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>anti-TGF-beta antibody</td> <td>20</td> </tr> <tr> <td>12aTGFB1SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>anti-TGF-beta antibody</td> <td>16</td> </tr> <tr> <td>14aTGFB2SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>anti-TGF-beta antibody</td> <td>17</td> </tr> <tr> <td>16aTGFB3SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>anti-TGF-beta antibody</td> <td>18</td> </tr> <tr> <td>18aTGFB4SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>anti-TGF-beta antibody</td> <td>19</td> </tr> <tr> <td>20aTGFB5SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>anti-TGF-beta antibody</td> <td>20</td> </tr> </tbody> </table>
Figure 2 in Newly registered tracks of Raccoon dogs (Nyctereutes procyonoides) indicate the presence of resident population in the region of Bolata dere (NE Bulgaria)
Figure 2. Tracks of a Raccoon dog at the shore of Bolata dere; Scale bar – 10 cm.
Facility ownership and mortality among older adults residing in care homes
<p>Dataset, STATA do-file, and R script to replicate the findings of the following article:</p> <p>Damián J, Pastor-Barriuso R, García-López FJ, Ruigómez A, Martínez-Martín P, de Pedro-Cuesta J. Facility ownership and mortality among older adults residing in care homes. PLoS One 2019</p>
Fig. 1 in Do euglossine females reside in a single nest? Notes on Euglossa cordata (Hymenoptera: Apidae: Euglossini)
Fig. 1. Boxes of medium densitY fiberboard used in the studY.
Table 1 in Revised taxonomy of eastern North Pacific killer whales (Orcinus orca): Bigg's and resident ecotypes deserve species status
<p><b>Table 1.</b> Measures of differentiation, divergence and diagnosability based on nuclear microsatellite and SNP data. All frequency-based metrics (FST, F’ST, G’ST) were significantly different from zero at p <0.05.</p><table><tbody><tr><th>divergence metric</th><th></th><th>reference</th></tr></tbody><tbody><tr><th>dA (CR)</th><td>0.007 46a</td><td>[114]</td></tr><tr><th>dA (mitogenome)</th><td>0.003 91b</td><td>[114]</td></tr><tr><th>FST (microsatellite)</th><td>0.21–0.23</td><td>[56,85]</td></tr><tr><th>F’ST (microsatellite)</th><td>0.47</td><td>[56]</td></tr><tr><th>G’ST (microsatellite)</th><td>0.28</td><td>[56]</td></tr><tr><th>FST (SNP)</th><td>0.28</td><td>[15]</td></tr><tr><th>F(RADseq, neutral)c ST</th><td>0.27</td><td>[58]</td></tr><tr><th>F(RADseq, selected)c ST</th><td>0.67</td><td>[58]</td></tr><tr><th>FST (genomes)</th><td>0.32</td><td>[9] (electronic supplementary material, table S2)</td></tr><tr><th>diagnosability (CR)</th><td>100%</td><td>[114]</td></tr><tr><th>diagnosability (mitogenome)</th><td>100%</td><td>[114]</td></tr><tr><th>fixed differences (mitogenome)</th><td>57</td><td>[7]</td></tr><tr><th>fixed differences (3281 nuclear SNPs)</th><td>2–7</td><td>[58]</td></tr><tr><th>fixed differences (6 435 100 nuclear SNPs)</th><td>6361d</td><td>[113]</td></tr></tbody></table><p>a 95% confidence interval:0.00727–0.00772. b 95% confidence interval: 0.00388–0.00393. c Alaska transients (Bigg’s) versus Alaska residents. d Based on analysis of raw SNP data from Kardos et al. [113].</p>
TABLE 1 in Nesting evidence, density and vocalisations in a resident population of Savannah Sparrow Passerculus sandwichensis wetmorei in Guatemala
<p>TABLE 1 Mean (± SD) and range of signal characteristics of songs of breeding Savannah Sparrows <i>Passerculus sandwichensis wetmorei</i> in PRM Todos Santos Cuchumatán, dpto. Huehuetenango, Guatemala, in June 2016, <i>n</i> = 37 songs of four males.</p><table><thead><tr><th><b>Song section (see Fig. 5)</b></th><th><b>Duration (seconds)</b></th><th><b>Peak frequency (kHz)</b></th></tr></thead><tbody><tr><th>Entire song (<i>n</i> = 37)</th><td>2.5 ± 0.3 (2.1–3.1)</td><td></td></tr><tr><th>Introduction:</th><td></td><td></td></tr><tr><th><i>Chip</i> note (<i>n</i> = 97)</th><td>0.06 ± 0.01 (0.04–0.12)</td><td>7.906 ± 202 (6.938 –8.250)</td></tr><tr><th>Middle section:</th><td></td><td></td></tr><tr><th>Descending whistle (DW1) (<i>n</i> = 37)</th><td>0.10 ± 0.01 (0.08–0.11)</td><td>6.927 ± 132 (6.750 –7.125)</td></tr><tr><th>Trill (n = 37)</th><td>0.05 ± 0.01 (0.03–0.06)</td><td>6.471 ± 1.268 (4.125 –7.500)</td></tr><tr><th>Double <i>ch</i> note (<i>n</i> = 37)</th><td>0.08 ± 0.004 (0.07–0.10)</td><td>5.063 ± 378 (3.188 –5.625)</td></tr><tr><th>Dominant section:</th><td></td><td></td></tr><tr><th>Buzz (<i>n</i> = 37)</th><td>0.60 ± 0.06 (0.5–0.8)</td><td>6.456 ± 646 (4.688 –6.938)</td></tr><tr><th>Terminal section:</th><td></td><td></td></tr><tr><th>Descending whistle (DW2) (<i>n</i> = 37)</th><td>0.08 ± 0.005 (0.07–0.09)</td><td>7.566 ± 222 (7.313 –8.063)</td></tr><tr><th>Trill-whistle (n = 37)</th><td>0.29 ± 0.07 (0.16 – 0.39)</td><td>4.074 ± 355 (3.375 –4.313)</td></tr></tbody></table>
Resources for studying the aesthetic and diversity values of plants and pets in shaping biodiversity loss belief among urban residents
<p><span>Considering the issues of data transparency and the cost of reproduction, all data and code snippets of the study titled "From beauty to belief: The aesthetic and diversity values of plants and pets in shaping biodiversity loss belief among urban residents" are deposited here.</span></p>
Morphological consequences of climate change for resident birds in intact Amazonian rainforest
<p>First, this dataset contains morphological measurements (body mass, wing length) of birds from the Biological Dynamics of Forest Fragments Project (BDFFP), located in central Amazonia. Birds were captured using mist-nets between 1979 and 2019 in the understory of primary forest spanning ~40 km. Second, we have included climate data (precipitation, temperature) associated with this study area derived from the global EU Copernicus ERA5 climate reanalysis (<a href="https://cds.climate.copernicus.eu/">https://cds.climate.copernicus.eu</a>).</p>
Dataset (VII) related to publication: Decisive Role of Water and Protein Dynamics in Residence Time of p38a MAP Kinase Inhibitors
<p>MD simulation data of compound <strong>1</strong> in MSM <strong>2-<em>S</em><sub>3</sub></strong> conformations of the related to the publication Pantsar et al.: <em>Decisive Role of Water and Protein Dynamics in Residence Time of p38a MAP Kinase Inhibitors.</em></p> <p>Individual .zip files contain raw-desmond trajectories (-out.cms files and trj-files).</p> <p>All datasets related to this publication:</p> <p><a href="https://doi.org/10.5281/zenodo.4568113">https://doi.org/10.5281/zenodo.4568113</a>(compound <strong>1</strong>; dataset: I)</p> <p><a href="https://doi.org/10.5281/zenodo.4572444">https://doi.org/10.5281/zenodo.4572444</a> (compound <strong>1</strong>; dataset: II)</p> <p><a href="https://doi.org/10.5281/zenodo.4561797">https://doi.org/10.5281/zenodo.4561797</a>(compound <strong>2</strong>; dataset: III)</p> <p><a href="https://doi.org/10.5281/zenodo.4563896">https://doi.org/10.5281/zenodo.4563896</a> (compound <strong>2</strong>; dataset: IV)</p> <p><a href="https://doi.org/10.5281/zenodo.5563359">https://doi.org/10.5281/zenodo.5563359</a> (<strong>SB203580</strong>; dataset: V)</p> <p><a href="https://doi.org/10.5281/zenodo.5563655">https://doi.org/10.5281/zenodo.5563655</a> (<strong>SB203580</strong>; dataset: VI)</p> <p><a href="https://doi.org/10.5281/zenodo.5564118%20">https://doi.org/10.5281/zenodo.5564118 </a>(compound <strong>1</strong> simulated in compound <strong>2</strong> metastable state <strong>2-<em>S</em><sub>3</sub></strong>; dataset: VII)</p> <p><a href="https://doi.org/10.5281/zenodo.5564208%20">https://doi.org/10.5281/zenodo.5564208 </a>(compound <strong>1</strong> simulated in compound <strong>2</strong> metastable state <strong>2-<em>S</em><sub>3</sub></strong>; dataset: VIII)</p> <p><a href="https://doi.org/10.5281/zenodo.5564586">https://doi.org/10.5281/zenodo.5564586</a> (well-tempered metadynamics simulations of compounds <strong>1</strong> and <strong>2</strong>; dataset: IX)</p> <p><a href="https://doi.org/10.5281/zenodo.5570882">https://doi.org/10.5281/zenodo.5570882</a> (well-tempered metadynamics simulations of compounds <strong>1</strong> and <strong>2</strong>; dataset: X)</p> <p><a href="https://doi.org/10.5281/zenodo.5571352">https://doi.org/10.5281/zenodo.5571352</a> (well-tempered metadynamics simulations of compounds <strong>1</strong> and <strong>2</strong>; dataset: XI)</p> <p>The datasets include original Desmond raw-trajectories (datasets I–VIII), PDB-coordinates for the energy minimized metastable state derived structures (datasets II, IV and VI) and raw-trajectories of the well-tempered metadynamics simulations (dataset IX–XI).</p>
Dataset (VIII) related to publication: Decisive Role of Water and Protein Dynamics in Residence Time of p38a MAP Kinase Inhibitors
<p>MD simulation data of compound <strong>1</strong> in MSM <strong>2-<em>S</em><sub>3</sub></strong> conformations of the related to the publication Pantsar et al.: <em>Decisive Role of Water and Protein Dynamics in Residence Time of p38a MAP Kinase Inhibitors.</em></p> <p>Individual .zip files contain raw-desmond trajectories (-out.cms files and trj-files).</p> <p>All datasets related to this publication:</p> <p><a href="https://doi.org/10.5281/zenodo.4568113">https://doi.org/10.5281/zenodo.4568113</a>(compound <strong>1</strong>; dataset: I)</p> <p><a href="https://doi.org/10.5281/zenodo.4572444">https://doi.org/10.5281/zenodo.4572444</a> (compound <strong>1</strong>; dataset: II)</p> <p><a href="https://doi.org/10.5281/zenodo.4561797">https://doi.org/10.5281/zenodo.4561797</a>(compound <strong>2</strong>; dataset: III)</p> <p><a href="https://doi.org/10.5281/zenodo.4563896">https://doi.org/10.5281/zenodo.4563896</a> (compound <strong>2</strong>; dataset: IV)</p> <p><a href="https://doi.org/10.5281/zenodo.5563359">https://doi.org/10.5281/zenodo.5563359</a> (<strong>SB203580</strong>; dataset: V)</p> <p><a href="https://doi.org/10.5281/zenodo.5563655">https://doi.org/10.5281/zenodo.5563655</a> (<strong>SB203580</strong>; dataset: VI)</p> <p><a href="https://doi.org/10.5281/zenodo.5564118%20">https://doi.org/10.5281/zenodo.5564118 </a>(compound <strong>1</strong> simulated in compound <strong>2</strong> metastable state <strong>2-<em>S</em><sub>3</sub></strong>; dataset: VII)</p> <p><a href="https://doi.org/10.5281/zenodo.5564208%20">https://doi.org/10.5281/zenodo.5564208 </a>(compound <strong>1</strong> simulated in compound <strong>2</strong> metastable state <strong>2-<em>S</em><sub>3</sub></strong>; dataset: VIII)</p> <p><a href="https://doi.org/10.5281/zenodo.5564586">https://doi.org/10.5281/zenodo.5564586</a> (well-tempered metadynamics simulations of compounds <strong>1</strong> and <strong>2</strong>; dataset: IX)</p> <p><a href="https://doi.org/10.5281/zenodo.5570882">https://doi.org/10.5281/zenodo.5570882</a> (well-tempered metadynamics simulations of compounds <strong>1</strong> and <strong>2</strong>; dataset: X)</p> <p><a href="https://doi.org/10.5281/zenodo.5571352">https://doi.org/10.5281/zenodo.5571352</a> (well-tempered metadynamics simulations of compounds <strong>1</strong> and <strong>2</strong>; dataset: XI)</p> <p>The datasets include original Desmond raw-trajectories (datasets I–VIII), PDB-coordinates for the energy minimized metastable state derived structures (datasets II, IV and VI) and raw-trajectories of the well-tempered metadynamics simulations (dataset IX–XI).</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.