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512 results for “strengthening”
Trait strengthening in mussels
<p><span><span><span><span><span><span><span><span><span><span><span>Plastic pollution is ubiquitous with increasing recognition of its direct effects on species' fitness. Little is known, however, about its more subtle effects, including the influence of plastic pollution on the morphological, functional and behavioural traits of organisms that are central to their ability to withstand disturbances. Among the least obvious but most pernicious forms of plastic-associated pollution are the chemicals that leach from microplastics. Here, we investigate how such leachates influence species' traits by assessing functional trait compensation across four species of intertidal mussels, through investigations of byssal thread production, movement and aggregation behaviour for mussels held in natural seawater or seawater contaminated by microplastic leachates. We found no evidence for compensation of functional traits, but for each species, microplastic leachates reinforced one trait while others remained unaffected. Two species (<i>Perna perna</i> and <i>Mytilus galloprovincialis</i>), were characterized by a resistance strategy to disturbance; they produced more byssal threads in microplastic leachate seawater than in control seawater, while motility and aggregation remained essentially unaffected. In contrast, the other two species (<i>M. edulis</i> and <i>Choromytilus meridionalis</i>), showed a resilience strategy to disturbance through reduced motility and aggregation in leachate seawater, while byssal thread production remained unaffected. These results suggest that the competitive abilities of intertidal mussels may be related to their sensitivity to microplastic leachates or other chemical disturbance. Importantly, the trait strengthening observed will affect the ability of these mussels to form spatially patterned beds, with implications for their quality as autogenic ecological engineers or foundation species. Thus, our findings have implications for the ability of mussel beds to tolerate disturbance, and hence for central ecosystem services, such as their ability to support biodiversity and enhance secondary and tertiary production. The results suggest that an inconspicuous aspect of plastic pollution has the potential to influence other communities and ecosystems in powerful ways.</span></span></span></span></span></span></span></span></span></span></span></p>
Data from: Landscape heterogeneity strengthens the relationship between β-diversity and ecosystem function
Consensus has emerged in the literature that increased biodiversity enhances the capacity of ecosystems to perform multiple functions. However, most biodiversity/ecosystem function studies focus on a single ecosystem, or on landscapes of homogenous ecosystems. Here we investigate how increased landscape-level environmental dissimilarity may affect the relationship between different metrics of diversity (α, β, or γ) and ecosystem function. We produced a suite of simulated landscapes, each of which contained four experimental outdoor aquatic mesocosms. Differences in temperature and nutrient conditions of the mesocosms allowed us to simulate landscapes containing a range of within-landscape environmental heterogeneities. We found that the variation in ecosystem functions was primarily controlled by environmental conditions, with diversity metrics accounting for a smaller (but significant) amount of variation in function. When landscapes were more homogeneous, α, β, and γ diversity was not associated with differences in primary production, and only γ was associated with changes in decomposition. In these homogeneous landscapes, differences in these two ecosystem functions were most strongly related to nutrient and temperature conditions in the ecosystems. However, as landscape-level environmental dissimilarity increased, the relationship between α, β, or γ and ecosystem functions strengthened, with β being a greater predictor of variation in decomposition at the highest levels of environmental dissimilarity than α or γ. We propose that when all ecosystems in a landscape have similar environmental conditions, species sorting is likely to generate a single community composition that is well suited to those environmental conditions, β is low, and the efficiency of diversity-ecosystem function couplings is similar across communities. Under this low β, the effect of abiotic conditions on ecosystem function will be most apparent. However, when environmental conditions vary among ecosystems, species sorting pressures is different among ecosystems, producing different communities among locations in a landscape. These conditions lead to stronger relationships between β and the magnitude of ecosystem functions. Our results illustrate that abiotic conditions and the homogeneity of communities influence ecosystem function expressed at the landscape-scale.
Data from: Nutrient scarcity strengthens soil fauna control over leaf litter decomposition in tropical rainforests
Soil fauna is a key control of the decomposition rate of leaf litter, yet its interactions with litter quality and the soil environment remain elusive. We conducted a litter decomposition experiment across different topographic levels within the landscape replicated in two rainforest sites providing natural gradients in soil fertility to test the hypothesis that low nutrient availability in litter and soil increases the strength of fauna control over litter decomposition. We crossed these data with a large dataset of 44 variables characterizing the biotic and abiotic microenvironment of each sampling point and found that microbe-driven Carbon (C) and Nitrogen (N) losses from leaf litter were 10.1 and 17.9 % lower, respectively, in the nutrient-poorest site but this among-site difference was equalized when meso- and macrofauna had access to the litterbags. Further, on average soil fauna enhanced the rate of litter decomposition by 22.6%, and this contribution consistently increased as nutrient availability in the microenvironment declined. Our results indicate that nutrient scarcity increases the importance of soil fauna on C and N cycling in tropical rainforests. Further, soil fauna is able to equalize differences in microbial decomposition potential thus buffering to a remarkable extent nutrient shortages at an ecosystem level.
Data from: Water stress strengthens mutualism among ants, trees, and scale insects
Abiotic environmental variables strongly affect the outcomes of species interactions. For example, mutualistic interactions between species are often stronger when resources are limited. The effect might be indirect: water stress on plants can lead to carbon stress, which could alter carbon-mediated plant mutualisms. In mutualistic ant–plant symbioses, plants host ant colonies that defend them against herbivores. Here we show that the partners' investments in a widespread ant–plant symbiosis increase with water stress across 26 sites along a Mesoamerican precipitation gradient. At lower precipitation levels, Cordia alliodora trees invest more carbon in Azteca ants via phloem-feeding scale insects that provide the ants with sugars, and the ants provide better defense of the carbon-producing leaves. Under water stress, the trees have smaller carbon pools. A model of the carbon trade-offs for the mutualistic partners shows that the observed strategies can arise from the carbon costs of rare but extreme events of herbivory in the rainy season. Thus, water limitation, together with the risk of herbivory, increases the strength of a carbon-based mutualism.
Dataset for the article entitled: "Revealing the strengthening contribution of stacking faults, dislocations and grain boundaries in severely deformed LPBF AlSi10Mg alloy"
<p>Dataset includes:</p> <p>EBSD data:</p> <p>AlSi10Mg_HT320.ang - Heat treated sample</p> <p>HT320E100.ang - Heat treated ECAP processed sample</p> <p>TKD data:</p> <p>HT320E100.ang - Heat treated ECAP processed sample</p> <p>XRD data:</p> <p>AlSi10Mg_HT320.ASC - Heat treated sample</p> <p>AlSi10Mg_HT320_ECAP100.ASC - Heat treated ECAP processed sample</p>
Supporting Data for "The Toba Eruption 74,000 Years ago Strengthened the Indian Winter Monsoon - Evidence from Coccolithophores" - AGU Paleoceanography and Paleoclimatology
<p>All data tables (Tables S1-S9) supporting the manuscript "The Toba Eruption 74,000 Years Ago Strengthened the Indian Winter Monsoon - Evidence from Coccolithophores". Files are uploaded as .csv or in Excel (.xlsx) formats, both compressed as a .zip file. See below for a brief description of the contents of each table found in both formats. Please refer to the manuscript for details of the referenced publications in the dataset.</p> <p> </p> <table> <tbody> <tr> <td>Table</td> <td>Table Description</td> </tr> <tr> <td>S1</td> <td>Ages used for calculating the error-weighted mean age of the YTT eruption (Crick et al., 2021; Du et al., 2019; Mark et al., 2017; Svennson et al., 2013).</td> </tr> <tr> <td>S2</td> <td>RAMPFIT parameters used to construct the modified age model in this study.</td> </tr> <tr> <td>S3</td> <td>Resampled L* reflectance data (Deplazes et al., 2013c) used for RAMPFIT modeling.</td> </tr> <tr> <td>S4</td> <td>Modified age model used in this study.</td> </tr> <tr> <td>S5</td> <td><em>Florisphaera profunda</em> and <em>Helicosphaera carteri</em> relative abundance, estimated primary productivity, and paleo-temperature estimates based on the <em>Gephyrocapsa </em>transfer function.</td> </tr> <tr> <td>S6</td> <td>Calculations of the duration of the enclosed interval and the lower limb using various age models.</td> </tr> <tr> <td>S7</td> <td>Summary of calculations of sedimentation rates and the estimated duration of the enclosed interval.</td> </tr> <tr> <td>S8</td> <td>Comparison of selected "warm" and "cold" periods between the dataset of Rogalla & Andruleit (2005) and this study.</td> </tr> <tr> <td>S9</td> <td>Original and adjusted age models of selected marine (Deplazes et al., 2013c) and terrestrial proxy records (Andersen et al., 2004; Du et al., 2019; Svensson et al., 2013) in this study.</td> </tr> </tbody> </table>
Strengthening the global environmental assessment seascape in support of ocean sustainability
<p>Ambitious evidence-based policies are urgently needed to redirect mankind’s trajectory towards ocean sustainability. While global environmental assessments (GEAs) synthesizing ocean knowledge are multiplying, it is crucial to ensure that their processes and outputs are conducive to social legitimacy, scientific credibility, and are relevant to decision makers’ needs. Building upon institutional and scientific literature, we consolidate a list of best practices for GEAs to achieve legitimacy, credibility, and salience. We then develop a standardized framework to score the level of implementation of these best practices and to assess the coverage of ocean knowledge in GEAs. Lastly, we apply this framework to review 12 influential reports at the ocean science-policy interface. We found that credibility best practices are well implemented but that all GEAs, and in particular ocean-focused assessments, have significant opportunities to strengthen the implementation of legitimacy and salience best practices. We identify that increasing stakeholder engagement and broadening the diversity of knowledge represented is needed to improve the legitimacy of GEAs. Additionally, we found that GEAs could increase their salience by featuring more actionable knowledge for decision-makers, including futures thinking, multi-scale intervention options, and evaluation of progress towards international targets. Finally, we highlight four recommendations to strengthen the GEA seascape: elevating co-production practices, bridging scales through multi-level approaches, increasing transparency in knowledge choices and gaps, and coordinating assessment processes.</p>
Supporting data for: "Weakening of the AMOC and Strengthening of Labrador Sea Deep Convection in Response to External Freshwater Forcing"
<p>This repository contains the key supporting data (in the netcdf format) for the following paper:<br>Wei, X., Zhang, R. Weakening of the AMOC and strengthening of Labrador Sea deep convection in response to external freshwater forcing. <em>Nat Commun</em> <strong>15</strong>, 10357 (2024). https://doi.org/10.1038/s41467-024-54756-3</p> <p>In this study, control and water hosing ensembles are conducted using a coupled climate model (GFDL CM4) with an eddy-permitting ocean component. The anomaly is defined as the difference between the water hosing and control ensembles (anomaly = water hosing - control). This paper investigates mechanisms of the AMOC weakening and its subsequent impact on the Labrador Sea open-ocean deep convection in response to external freshwater forcing.</p> <p><strong>Descriptions of data files in this repository:</strong></p> <p><strong>Main figures:</strong></p> <p>1. The anomalies of the OSNAP AMOC and the Labrador Sea March mixed layer depth (MLD), as shown in Fig. 1 in the paper. </p> <p>The anomalies of the maximum AMOC and the AMOC at a relatively dense level around sigma0=27.84 kg/m3 across the entire OSNAP section in density space:</p> <p>Anomaly_AMOC_max_OSNAP.nc</p> <p>Anomaly_AMOC_denser_OSNAP.nc</p> <p>The anomalies of the maximum AMOC across OSNAP West and OSANP East in density space:</p> <p>Anomaly_AMOC_max_OSNAP_WEST.nc</p> <p>Anomaly_AMOC_max_OSNAP_EAST.nc</p> <p>The March mixed layer depth (MLD) in the Labrador Sea:</p> <p>Anomaly_MLD_003_March_Labrador.nc</p> <p>The spatial map of March MLD climatology and anomaly:</p> <p>Control_MLD_003_March_spatial.nc</p> <p>Anomaly_MLD_003_March_spatial.nc</p> <p>2. The climatological mean (from the control and water hosing ensembles) and anomalies of the AMOC streamfunction across the OSNAP section, in density-space and depth-space, as shown in Fig. 2 in the paper.</p> <p>OSNAP West:</p> <p>Control_moc_sigma0_OSNAP_WEST.nc</p> <p>Control_moc_z_OSNAP_WEST.nc</p> <p>WaterHosing_moc_sigma0_OSNAP_WEST.nc</p> <p>WaterHosing_moc_z_OSNAP_WEST.nc</p> <p>Anomaly_moc_sigma0_OSNAP_WEST.nc</p> <p>Anomaly_moc_z_OSNAP_WEST.nc</p> <p>OSNAP East:</p> <p>Control_moc_sigma0_OSNAP_EAST.nc</p> <p>Control_moc_z_OSNAP_EAST.nc</p> <p>WaterHosing_moc_sigma0_OSNAP_EAST.nc</p> <p>WaterHosing_moc_z_OSNAP_EAST.nc</p> <p>Anomaly_moc_sigma0_OSNAP_EAST.nc</p> <p>Anomaly_moc_z_OSNAP_EAST.nc</p> <p>Entire OSNAP section:</p> <p>Control_moc_sigma0_OSNAP.nc</p> <p>Control_moc_z_OSNAP.nc</p> <p>WaterHosing_moc_sigma0_OSNAP.nc</p> <p>WaterHosing_moc_z_OSNAP.nc</p> <p>Anomaly_moc_sigma0_OSNAP.nc</p> <p>Anomaly_moc_z_OSNAP.nc</p> <p>3. The climatological mean (from the control ensemble) and anomalies of salinity, potential temperature, and potential density across the OSNAP section, as shown in Fig. 3 in the paper.</p> <p>Control_salinity_OSNAP.nc</p> <p>Control_temperature_OSNAP.nc</p> <p>Control_sigma0_OSNAP.nc</p> <p>Anomaly_salinity_OSNAP.nc</p> <p>Anomaly_temperature_OSNAP.nc</p> <p>Anomaly_sigma0_OSNAP.nc</p> <p>4. The sigma-z diagram of climatological mean (from the control and water hosing ensembles) and anomalies of the AMOC transport across OSNAP East, i.e. integrated volume transport across OSNAP East over each potential density bin and depth bin, as shown in Fig. 4 in the paper.</p> <p>Control_SigmaZ_OSNAP_EAST.nc</p> <p>WaterHosing_SigmaZ_OSNAP_EAST.nc</p> <p>Anomaly_SigmaZ_OSNAP_EAST.nc</p> <p>5.The deep ocean potential density anomalies along with its thermal and haline components over the west boundary and eastern regions of the OSNAP East subsection, and the AMOC anomalies at a relatively dense level around sigma0=27.84 kg/m3 across OSNAP East in density space, as shown in Fig. 5 in the paper.</p> <p>Anomaly_sigma_west.nc</p> <p>Anomaly_sigmaS_west.nc</p> <p>Anomaly_sigmaT_west.nc</p> <p>Anomaly_sigma_east.nc</p> <p>Anomaly_sigmaS_east.nc</p> <p>Anomaly_sigmaT_east.nc</p> <p>Anomaly_sigma_diff.nc</p> <p>Anomaly_sigmaS_diff.nc</p> <p>Anomaly_sigmaT_diff.nc</p> <p>Anomaly_AMOC_denser_OSANP_EAST.nc</p> <p>6.The transient salt-based FWF anomalies, dye-based FWF anomalies and their difference at the upper ocean (413m), as shown in Fig. 6 in the paper, and at the deep ocean (2250m), as shown in Fig. 7 in the paper.</p> <p>Anomaly_FWF_salt_413m_10yr.nc</p> <p>Anomaly_FWF_dye_413m_10yr.nc</p> <p>Anomaly_FWF_diff_413m_10yr.nc</p> <p>Anomaly_FWF_salt_413m_20yr.nc</p> <p>Anomaly_FWF_dye_413m_20yr.nc</p> <p>Anomaly_FWF_diff_413m_20yr.nc</p> <p>Anomaly_FWF_salt_413m_30yr.nc</p> <p>Anomaly_FWF_dye_413m_30yr.nc</p> <p>Anomaly_FWF_diff_413m_30yr.nc</p> <p>Anomaly_FWF_salt_413m_40yr.nc</p> <p>Anomaly_FWF_dye_413m_40yr.nc</p> <p>Anomaly_FWF_diff_413m_40yr.nc</p> <p>Anomaly_FWF_salt_413m_50yr.nc</p> <p>Anomaly_FWF_dye_413m_50yr.nc</p> <p>Anomaly_FWF_diff_413m_50yr.nc</p> <p>Anomaly_FWF_salt_2250m_10yr.nc</p> <p>Anomaly_FWF_dye_2250m_10yr.nc</p> <p>Anomaly_FWF_diff_2250m_10yr.nc</p> <p>Anomaly_FWF_salt_2250m_20yr.nc</p> <p>Anomaly_FWF_dye_2250m_20yr.nc</p> <p>Anomaly_FWF_diff_2250m_20yr.nc</p> <p>Anomaly_FWF_salt_2250m_30yr.nc</p> <p>Anomaly_FWF_dye_2250m_30yr.nc</p> <p>Anomaly_FWF_diff_2250m_30yr.nc</p> <p>Anomaly_FWF_salt_2250m_40yr.nc</p> <p>Anomaly_FWF_dye_2250m_40yr.nc</p> <p>Anomaly_FWF_diff_2250m_40yr.nc</p> <p>Anomaly_FWF_salt_2250m_50yr.nc</p> <p>Anomaly_FWF_dye_2250m_50yr.nc</p> <p>Anomaly_FWF_diff_2250m_50yr.nc</p> <p>7.Climatological mean (from the control ensemble) and anomalies of salinity, potential temperature, potential density and zonal velocity along the Iceland-Scotland Overflow pathway, as shown in Fig. 8 in the paper.</p> <p>Control_salinity_ISOW.nc</p> <p>Control_temperature_ISOW.nc</p> <p>Control_sigma_ISOW.nc</p> <p>Control_u_ISOW.nc</p> <p>Anomaly_salinity_ISOW.nc</p> <p>Anomaly_temperature_ISOW.nc</p> <p>Anomaly_sigma_ISOW.nc</p> <p>Anomaly_u_ISOW.nc</p> <p>8.The salt-based FWF anomalies, dye-based FWF anomalies and their difference across the Iceland-Scotland Overflow section, as shown in Fig. 9 in the paper.</p> <p>Anomaly_FWF_salt_ISOW.nc</p> <p>Anomaly_FWF_dye_ISOW.nc</p> <p>Anomaly_FWF_diff_ISOW.nc</p> <p><strong>Supplementary figures:</strong></p> <p>S1. The anomalies of the density-space AMOC streamfunction as shown in Supplementary Fig. 1 in the paper.</p> <p>SuppFig1.nc</p> <p>S2. The extra-tropical North Atlantic subsurface (413m) temperature anomalies as shown in Supplementary Fig. 2 in the paper.</p> <p>SuppFig2.nc</p> <p>S3. The climatological mean (from the control and water hosing ensembles) and anomalies of the AMOC streamfunction, surface forced water mass transformation (WMTS) and interior mixing forced water mass transformation (WMTM), as shown in Supplementary Fig. 3 in the paper.</p> <p>SuppFig3_streamfunction.nc</p> <p>SuppFig3_surfaceWMT.nc</p> <p>SuppFig3_interiorWMT.nc</p> <p>SuppFig3_mask_section.nc</p> <p>S4. The climatological mean of the velocity across the OSNAP section in the control ensemble, as shown in Supplementary Fig. 4 in the paper.</p> <p>SuppFig4.nc</p> <p>S5. The transient salinity anomalies, potential temperature anomalies and potential density anomalies across the OSNAP section, as shown in Supplementary Fig. 5 in the paper.</p> <p>SuppFig5_11_20yr.nc</p> <p>SuppFig5_21_30yr.nc</p> <p>SuppFig5_31_40yr.nc</p> <p>SuppFig5_41_50yr.nc</p> <p>S6. The transient salt-based FWF anomalies, dye-based FWF anomalies and their difference at 625m, as shown in Supplementary Fig. 6 in the paper.</p> <p>SuppFig6_10yr.nc</p> <p>SuppFig6_20yr.nc</p> <p>SuppFig6_30yr.nc</p> <p>SuppFig6_40yr.nc</p> <p>SuppFig6_50yr.nc</p> <p>S7. The salt-based FWF anomalies, dye-based FWF anomalies and their difference across the OSNAP section, as shown in Supplementary Fig. 7 in the paper.</p> <p>SuppFig7.nc</p> <p>S8. The anomalies of March Labrador Sea mixed layer depth (MLD), vertical potential density difference, surface and deep ocean potential density, as shown in Supplementary Fig. 8 in the paper.</p> <p>SuppFig8.nc</p> <p>S9. The climatological mean salinity difference between the ISOW-associated NEADW layer and the core Labrador Sea Water layer, and the inverse horizontal grid resolution of GFDL CM4 (this study) and CMIP6 models as shown in Supplementary Fig. 9 in the paper. The CMIP6 model data were downloaded from https://aims2.llnl.gov/search/cmip6/.</p> <p>The climatological mean salinity difference between the ISOW-associated NEADW layer and the core Labrador Sea Water layer in WOA18 as shown in Supplementary Fig. 9 in the paper. The WOA18 data were downloaded from the NOAA National Centers for Environmental Information (formerly the National Oceanographic Data) https://www.ncei.noaa.gov/products/world-ocean-atlas/.</p> <p>SuppFig9.nc</p> <p>S10. The supplementary Fig. 10 shares the same data of climatological mean of the AMOC streamfunction across the OSNAP section in density-space in the control ensemble with Figure 2 in the paper. The OSNAP observation data were downloaded from www.o-snap.org.</p> <p>Control_moc_sigma0_OSNAP_WEST.nc</p> <p>Control_moc_sigma0_OSNAP_EAST.nc</p> <p>Control_moc_sigma0_OSNAP.nc</p> <p><strong>Acknowledgments</strong></p> <p>We acknowledge the use of the following datasets and model code in this study: The World Ocean Atlas 2018 (WOA18) data were downloaded from the NOAA National Centers for Environmental Information (formerly the National Oceanographic Data) https://www.ncei.noaa.gov/products/world-ocean-atlas/. The Data from the OSNAP (Overturning in the Subpolar North Atlantic Program) array were downloaded from https://www.o-snap.org/. OSNAP data were collected and made freely available by the OSNAP project and all the national programs that contribute to it (www.o-snap.org). The CMIP6 (Coupled Model Intercomparison Project Phase 6) model data were downloaded from https://aims2.llnl.gov/search/cmip6/. The source code of the Geophysical Fluid Dynamics Laboratory (GFDL) coupled climate model version 4 (CM4) is publicly available at https://doi.org/10.5281/zenodo.3339397. The surface forced water mass transformation (WMTS) is calculated using the source code developed by Drake et al. 2024 at https://github.com/hdrake/xwmt. The relevant citations for the above datasets and model code are listed in Wei and Zhang 2024.</p>
Knowledge and perception of community on health-related activities of Inclusive Self Help Groups in three regions of Myanmar and ways for strengthening
<p>This data is gathered in 2019. </p>
Diversity-conditioned soil strengthens plant diversity-productivity relationship
<p><span>How biodiversity affects terrestrial productivity is important to the maintenance of ecosystem services under global change. </span><span>Although the crucial role of plant-soil feedbacks (PSF) in determining diversity-productivity relationship has been increasingly recognized in recent years, its legacy effects on subsequent diversity-productivity relationship are still unclear.</span> </p> <p><span>We conducted a classic PSF experiment to assess how plant diversity-conditioned soils influenced subsequent plant diversity-productivity relationships, where three plant diversity levels (1, 4, and 8 species) were planted in soils conditioned at three diversity levels (conditioned by 1, 4, and 8 species for 3 years). In addition, to test the role of soil microbial diversity in mediating the effects of soil conditioning diversity</span><span>,</span><span> the three plant diversity levels were planted with</span><span> low, moderate, and high soil biodiversity created by a soil inoculum dilution.</span></p> <p><span>The results showed that plant productivities were promoted by mixed-conditioned soils (4 and 8 species) compared to mono-conditioned soils (1 species). Productivity was </span><span>positively related to </span><span>planted diversity</span><span> in mixed-conditioned soils, while</span><span> showing no relation to planted diversity in </span><span>mono-conditioned soils. Productivity was promoted by soil </span><span>biodiversity </span><span>when 4 and 8 species were mixed planted, while it did not change when 1 species was planted. </span></p> <p><span><em><strong>Synthesis and applications</strong></em>. </span><span>Our results highlight that</span> <span>PSF is crucial to strengthening the positive effects of biodiversity on productivity, implying that </span><span>diversifying cropping systems</span><span> should be encouraged in agroecosystem management to </span><span>benefit from</span><span> positive PSF effects.</span><span> The importance of soil legacy for optimizing plant productivity is particularly important for conditioning soils</span> <span>with an intermediate number of plant species</span><span>.</span></p>
Wolbachia strengthens the match between pre-mating and early post-mating isolation in spider mites
Open the record for dataset details and reuse information.
Remagnetization of pre-fan sediments offshore Sumatra: alteration associated with seismogenic diagenetic strengthening
<p>This repository contains the rock magnetic and paleomagnetic data associated with the paper entitled "Remagnetization of pre-fan sediments offshore Sumatra: alteration associated with seismogenic diagenetic strengthening" by Yang et al. published in Journal of Geophysical Research: Solid Earth (Volume 129, Issue 8, August 2024, e2023JB028460, https://doi.org/10.1029/2023JB028460). </p>
Dataset and analyses for: Force-field perturbations and muscle vibration strengthen stability-related foot placement responses during steady-state gait in healthy adults
<p>We have collected kinematic data (heel and pelvis markers) from healthy adults during treadmill walking, whilst force-field perturbations and timed muscle vibrations were applied. We assessed the (after-)effects of these two interventions by evaluating foot placement control through outcome measures derived from a regression model which predicts foot placement based on the center-of-mass kinematic state. The data and analyses provided belong to the scientific publication: "Force-field perturbations and muscle vibration strengthen stability-related foot placement responses during steady-state gait in healthy adults". In the manuscript we place these analyses in the context of stability control and speculate on how these trainining interventions may improve stability control in patient populations.</p>
Monitoring of the UHPFRC strengthened Chillon viaduct under environmental and operational variability
<p>Datasets corresponding to 3 months of data of the monitoring of Chillon viaduct.</p> <p>Each file contains: </p> <ul> <li>the vibration data from 11 acelerometers, in vertical direction</li> <li>the sampling ratio</li> <li>Temperature </li> <li>Humidity</li> </ul> <p>The accelerometer positions are illustarted in the pdf "accelerometres Position.pdf". </p> <p>When employing this dataset, please cite:"Martín-Sanz, Henar, Konstantinos Tatsis, Vasilis K. Dertimanis, Luis David Avendaño-Valencia, Eugen Brühwiler, and Eleni Chatzi. "Monitoring of the UHPFRC strengthened Chillon viaduct under environmental and operational variability." <em>Structure and Infrastructure Engineering</em> (2019): 1-31."</p>
FIG. 1 in Warming Strengthens the Ecological Role of Intraspecific Variation in a Predator
FIG. 1. The temperature profiles of both fish sources used in the study. ''Cool-source'' fish were taken from the artesian well and ''warm-source'' fish were taken from Keough's Hot Ditch, both in Bishop, California, USA. Daily average air temperatures are plotted from the Bishop Airport weather station, Bishop, California (Station ID GHCND:USW00023157), measured over the same period. In these geothermal systems, air temperature has little influence on water temperature.
FIG. 2 in Warming Strengthens the Ecological Role of Intraspecific Variation in a Predator
FIG. 2. (A) The mean of each logged temperature for both the warmed (black) and unwarmed (gray) treatments. (B) The difference between treatment means at each time-point, with the hatched line showing the overall mean temperature increase in the warming treatment: 2.218C.
FIG. 6 in Warming Strengthens the Ecological Role of Intraspecific Variation in a Predator
FIG. 6. Trait differences between populations at the end of the experiment. Fish population growth (A) was the number of offspring present at the end of the experiment, and total fish biomass (B) was the dry mass of all fish within a mesocosm at the end of the experiment. Means and standard errors were calculated without removing outliers (Supplemental Appendix 3; see Data Accessibility). Raw data points each reflect a mesocosm (triangles are warm-source, and circles are cool-source).
FIG. 5 in Warming Strengthens the Ecological Role of Intraspecific Variation in a Predator
FIG. 5. The absolute value of effect sizes for all responses calculated over all sampling points, separately for (A) the fish introduction effect and (B) the intraspecific variation effect.
FIG. 4 in Warming Strengthens the Ecological Role of Intraspecific Variation in a Predator
FIG. 4. Response of net primary production (A) and phosphate concentration (B) to warming and fish treatments at the final sampling point of the experiment—four weeks after fish introduction. Y-axes are plotted on the log10 scale.
Dataset: Enhancing homogenous precipitation and strengthening effectiveness in AlCuMg alloy
<p>Set of data used for publication in Journal of Materials Research and Technology entitled 'Enhancing homogenous precipitation and strengthening effectiveness in AlCuMg alloy'</p> <p><strong>Research Funders</strong></p> <div> <div>National Science Centre Poland <div> </div> <span>Grant numbers: 2020/39/D/ST5/01375</span></div> </div>
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.