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289 results for “data reduction”
Supplementary data for "Dimensionality reduction distills complex evolutionary relationships in seasonal influenza and SARS-CoV-2"
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Data for publication: Encapsulation of an Au25 Nanocluster inside a Porphyrin Nanoring Enhances Singlet Oxygen Generation and Photo-Electrocatalytic CO2 Reduction (Angewandte Chemie 2024)
<p>Original data of the paper Encapsulation of an Au25 Nanocluster inside a Porphyrin Nanoring Enhances Singlet Oxygen Generation and Photo-Electrocatalytic CO2 Reduction (Angewandte Chemie 2024)</p>
Dimensional reduction of phenotypes from 53,000 mouse models reveals a diverse landscape of gene function - data bundle
<p>This bundle is an archive of data files, configuration files, and scripts related to the manuscript "Dimensional reduction of phenotypes from 53,000 mouse models reveals a diverse landscape of gene function".</p> <p> </p>
An ocean turbulence data reduction scheme for autonomous, vertically profiling floats: datasets
<p>See <a href="https://doi.org/10.5281/zenodo.5719505">https://doi.org/10.5281/zenodo.5719505</a> instead</p>
Supplementary data on car company reduction target analysis and GHG emission model
<p>This dataset contains supplementary information for the Master's thesis "<em>Greenhouse gas emission coverage and reduction potentials of scope 3 emissions in car manufacturing"</em> published on 16 November 2022 at Utrecht University. The data consists of two separately files, including the car company reduction target analysis and the GHG emission calculation model that were covered in the thesis report. The supplementary data can be used in combination with the final thesis report.</p>
DFT data for "The Role of Ion Solvation in Lithium Mediated Nitrogen Reduction"
<p>Density Functional Theory data used in the paper "The Role of Ion Solvation in Lithium Mediated Nitrogen Reduction".</p>
Data set for the journal article: The Spatial Distribution of Cobalt Phthalocyanine and Copper Nanocubes Controls the Selectivity towards C2 Products in Tandem Electrocatalytic CO2 Reduction
<p>Data set for the journal article:</p> <p>The coupling of CO-generating molecular catalysts with copper electrodes in tandem schemes is a promising strategy to boost the formation of multi-carbon products in the electrocatalytic reduction of CO<sub>2</sub>. While the spatial distribution of the two components is important, this aspect remains underexplored, especially for molecular-based tandem systems. Herein, we address this knowledge gap by studying tandem catalysts comprising Co-phthalocyanine (CoPc) and Cu nanocubes (Cu<sub>cub</sub>). In particular, we identify the importance of the relative spatial distributions of the two components on the performance of the tandem catalyst by preparing CoPc-Cu<sub>cub</sub>/C, wherein the CoPc and Cu<sub>cub</sub> share an interface, and CoPc-C/Cu<sub>cub</sub>, wherein the CoPc is loaded first on carbon black (C) before mixing with the Cu<sub>cub</sub>. The electrocatalytic measurements of these two catalysts show that the faradaic efficiency towards C<sub>2 </sub>products almost doubles for the CoPc-Cu<sub>cub</sub>/C, whereas it decreases by half for the CoPc-C/Cu<sub>cub</sub>, compared to the Cu<sub>cub</sub>/C. Our results highlight the importance of a direct contact between the CO-generating molecular catalyst and the Cu to promote C-C coupling, which hints at a surface transport mechanism of the CO intermediate between the two components of the tandem catalyst instead of a transfer via CO diffusion in the electrolyte followed by re-adsorption.</p>
Data from: Great tits do not compensate over time for a radio-tag-induced reduction in escape-flight performance
<p>The use of biologging and tracking devices is widespread in avian behavioural and ecological studies. Carrying these devices rarely has major behavioural or fitness effects in the wild, yet it may still impact animals in more subtle ways, such as during high power demanding escape manoeuvres. Here, we tested whether or not great tits (Parus major) carrying a backpack radio-tag changed their body-mass or flight behaviour over time to compensate for the detrimental effect of carrying a tag. We tested 18 great tits, randomly assigned to a control (untagged) or one of two different types of a radio-tag as used in previous studies in the wild (0.9 g or 1.2 g; ~5% or ~6–7% of body mass, respectively), and determined their upward escape-flight performance 1, 7, 14, and 28 days-after-tagging. In between experiments, birds were housed in large free-flight aviaries. For each escape-flight, we used high-speed 3D videography to determine flight paths, escape-flight-speed, wingbeat frequency and actuator-disk-loading (ratio between the bird weight and aerodynamic thrust production capacity). Tagged birds flew upwards with lower escape-flight speeds, caused by an increased actuator-disk-loading. During the 28-day period, all groups slightly increased their body mass and their in-flight wingbeat frequency. In addition, during this period all groups of birds increased their escape-flight speed, but tagged birds did so at a lower rate than untagged birds. This suggests that birds may increase their escape flight performance through skill-learning, however, tagged birds still remained slower than controls. Our findings suggest that tagging a songbird can have a prolonged effect on the performance of rapid flight manoeuvres. Given the absence of tag-effects on reproduction and survival in most songbird radio-tagging studies, tagged birds in the wild might adjust their risk-taking behaviour to avoid performing rapid flight manoeuvres.</p>
Experimental and Computational data related to research on ``Investigating the Electronic Structure of Prospective Water-splitting Oxide BaCe0.25Mn0.75O3−δ Before and After Thermal Reduction''
<p>Data files and gnuplot scripts for the figures included in the submission titled ``<strong>Investigating the Electronic Structure of Prospective Water-splitting Oxide BaCe<sub>0.25</sub>Mn<sub>0.75</sub>O<sub>3−δ </sub>Before and After Thermal Reduction''</strong></p>
Data source and projections of maintenance energy gaps for "Caloric reductions needed to achieve obesity goals by 2030 and 2040: A modeling study"
<p><strong>Variables in "data_ENSANUT_waves.xlsx"</strong></p> <table> <thead> <tr> <th scope="col">Name</th> <th scope="col">Variable</th> </tr> </thead> <tbody> <tr> <td><em>id</em></td> <td>Identifier for each individual in the data.</td> </tr> <tr> <td><em>est_var</em></td> <td>Strata for the estimation of variances, accounting for survey design.</td> </tr> <tr> <td><em>svy_weights</em></td> <td>Complex survey weight.</td> </tr> <tr> <td>code_upm</td> <td>Identifier of the primary sampling unit.</td> </tr> <tr> <td>sex</td> <td>Sex of the individual (``male'' or ``female'').</td> </tr> <tr> <td>age</td> <td>Age (yrs).</td> </tr> <tr> <td>body_weight</td> <td>Measured body weight (kg).</td> </tr> <tr> <td>height</td> <td>Measured height (cm).</td> </tr> <tr> <td>bmi</td> <td>Body mass index, estimated before the simulation process (kg/m<sup>2</sup>).</td> </tr> <tr> <td>SES</td> <td>Socioeconomic level, divided in tertiles. This variable was constructed using Principal Components Analysis.</td> </tr> <tr> <td>year</td> <td>Indicator for each ENSANUT wave (2000, 2006, 2012, 2016, 2018).</td> </tr> <tr> <td>svy_weights_raking_2030</td> <td>Complex survey weight, constructed for the baseline sample (ENSANUT 2018) to replicate the expected age and sex distribution in 10-year age groups for 2030.</td> </tr> <tr> <td>svy_weights_raking_2040</td> <td>Complex survey weight, constructed for the baseline sample (ENSANUT 2018) to replicate the expected age and sex distribution in 10-year age groups for 2040.</td> </tr> <tr> <td>body_weight_final_2030_Nordpred</td> <td>Simulated body weight by 2030 based on MEGs projections of the Nordpred-based fit (kg). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>body_weight_final_2040_Nordpred</td> <td>Simulated body weight by 2040 based on MEGs projections of the Nordpred-based fit (kg). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>BMI_final_2030_Nordpred</td> <td>Simulated body mass index by 2030 based on MEGs projections of the Nordpred-based fit (kg/m<sup>2</sup>). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>BMI_final_2040_Nordpred</td> <td>Simulated body mass index by 2040 based on MEGs projections of the Nordpred-based fit (kg/m<sup>2</sup>). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>obes_final_2030_Nordpred</td> <td>Indicator of obesity by 2030, based on MEGs projections of the Nordpred-based fit (1 = yes, 0 = no). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>obes_final_2040_Nordpred</td> <td>Indicator of obesity by 2040, based on MEGs projections of the Nordpred-based fit (1 = yes, 0 = no). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>body_weight_final_2030_Gompertz</td> <td>Simulated body weight by 2030 based on MEGs projections of the Gompertz model (kg). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>body_weight_final_2040_Gompertz</td> <td>Simulated body weight by 2040 based on MEGs projections of the Gompertz model (kg). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>BMI_final_2030_Gompertz</td> <td>Simulated body mass index by 2030 based on MEGs projections of the Gompertz model (kg/m<sup>2</sup>). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>BMI_final_2040_Gompertz</td> <td>Simulated body mass index by 2040 based on MEGs projections of the Gompertz model (kg/m<sup>2</sup>). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>obes_final_2030_Gompertz</td> <td>Indicator of obesity by 2030, based on MEGs projections of the Gompertz model (1 = yes, 0 = no). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>obes_final_2040_Gompertz</td> <td>Indicator of obesity by 2040, based on MEGs projections of the Gompertz model (1 = yes, 0 = no). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>body_weight_final_2030_linear</td> <td>Simulated body weight by 2030 based on MEGs projections of the linear fit (kg). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>body_weight_final_2040_linear</td> <td>Simulated body weight by 2040 based on MEGs projections of the linear fit (kg). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>BMI_final_2030_linear</td> <td>Simulated body mass index by 2030 based on MEGs projections of the linear model (kg/m<sup>2</sup>). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>BMI_final_2040_linear</td> <td>Simulated body mass index by 2040 based on MEGs projections of the linear model (kg/m<sup>2</sup>). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>obes_final_2030_linear</td> <td>Indicator of obesity by 2030, based on MEGs projections of the linear model (1 = yes, 0 = no). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>obes_final_2040_linear</td> <td>Indicator of obesity by 2040, based on MEGs projections of the linear model (1 = yes, 0 = no). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>body_weight_final_2030_rootSquare</td> <td>Simulated body weight by 2030 based on MEGs projections of the root square fit (kg). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>body_weight_final_2040_rootSquare</td> <td>Simulated body weight by 2040 based on MEGs projections of the root square fit (kg). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>BMI_final_2030_rootSquare</td> <td>Simulated body mass index by 2030 based on MEGs projections of the root square fit (kg/m<sup>2</sup>). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>BMI_final_2040_rootSquare</td> <td>Simulated body mass index by 2040 based on MEGs projections of the root square fit (kg/m<sup>2</sup>). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>obes_final_2030_rootSquare</td> <td>Indicator of obesity by 2030, based on MEGs projections of the root square fit (1 = yes, 0 = no). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>obes_final_2040_rootSquare</td> <td>Indicator of obesity by 2040, based on MEGs projections of the root square fit (1 = yes, 0 = no). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> </tbody> </table>
Data set for the publication entitled "An exact system of generation for face-hobbed hypoid gears: Application to high reduction hypoid gear drives"
<p>This data set includes 16 files corresponding to the paper with doi:10.1016/j.mechmachtheory.2022.105115 plus a readme.txt file. See the readme.txt file for a description of the contents of the 16 files.</p>
Data : Effects of Fructooligosaccharides (FOS) on the Immune Response of the Shrimp Penaeus vannamei and on the Reduction in Vibrio spp. and Pseudomonas spp. in Cultures of Post-Larvae.
<p>Data related with Corrales Barrios, Y.; Roncarati, A.; Martín Ríos, L.D.; Rodríguez González, M.; González Salotén, M.; López Zaldívar, Y.; Arenal, A. Effects of Fructooligosaccharides (FOS) on the Immune Response of the Shrimp <em>Penaeus vannamei</em> and on the Reduction in <em>Vibrio</em> spp. and <em>Pseudomonas</em> spp. in Cultures of Post-Larvae. Microbiol. Res. 2023, 14,</p>
Data from: Allometric brain reduction in an insular, dwarfed population of black-tailed deer
<p>Insular populations of mammals have been shown to undergo drastic morphological changes relative to mainland counterparts, and these adaptations can provide insight into the evolutionary effects of predation and competition. Selection has been shown to favor more energetically efficient body plans in insular mammals, even when this entails the reduction of anti-predator defenses, but few studies have focused on morphological effects within the same species. Previous research has established that insular large mammals tend to reduce in body size, and that size reduction may not scale isometrically across all body parts. The brain has been a particular subject of interest due to its high energy requirements. Here, we report that an extant, dwarfed island population of black-tailed deer (<em>Odocoileus hemionus columbianus</em>) exhibits significantly reduced brain mass relative to body mass in comparison to their mainland conspecifics, with brain mass 4.9% smaller in the island population for a given body mass. Further, in the dwarfed population, orbital area was 4.1% smaller for a given body mass, but this reduction was not significant. Foramina magna reduced isometrically. In resource-limited insular environments, negative allometry of the brain is consistent with predictions of reduced investment in energetically costly organs. This study is, to our knowledge, the first to examine the morphological effects of insularity on brain size in two conspecific populations, and these findings suggest that selection toward reduced brain size may act relatively quickly after isolation.</p>
Data from: Patterns in antipredator armature reduction and maintenance in isolated spring populations of an amphipod crustacean
<p class="xmsonormal"><span>Organisms colonizing new habitat can undergo adaptive change due to novel selective landscapes encountered in the new environment. Examples in nature where development of the same traits has repeatedly occurred on multiple independent occasions upon colonizing a novel habitat represent instances of parallel evolution. Here we test whether the colonization of spring habitat by the principally lacustrine amphipod crustacean <em>Pallaseopsis quadrispinosa </em>has resulted in parallel evolution in armature traits using empirical data on morphology and mitochondrial DNA and through a breeding experiment. Analysis of mtDNA CO1 sequences shows that the spring populations share no common history and have evolved in isolation from each other and from their neighbouring lake populations since deglaciation approximately 12000 years ago and are now fixed for different haplogroups. Dorsal spines and lateral projections were absent or less developed in all spring populations than in lake populations. Variation in armature development also could be explained by predator presence as populations with fish predators exhibited more developed spines than those without fish. In a laboratory breeding experiment, hybrid Spring X Lake F1 offspring had intermediate development of armature when compared to offspring of Lake X Lake and Spring X Spring matings. The results support the hypothesis that armature reduction has independently evolved on multiple occasions in <em>P. quadrispinosa. </em>Recent research has questioned the degree to which parallel evolution actually explains variance in traits. Taking into account predation regime, sexual dimorphism and mineral composition of the trait, a more precise understanding of the factors influencing parallel evolution emerges.</span></p>
Data from: Reduction in nitrification during the early transition from conventional to organic farming practices
<p>Contributors</p> <ul> <li>@JacobRPrice</li> </ul>
ANGIOTENSIN AGENTS AND REDUCTION OF THE PRESCRIPTION OF ANTIDEPRESSANT DRUGS: A RETROSPECTIVE COHORT STUDY USING REAL-WORLD DATA
ClinicalTrials.gov study NCT04899206. IPD Sharing: UNDECIDED. Countries: 1. Publications: 26.
Impact of a Data-driven Monitor Alarm Reduction Strategy Implemented in Safety Huddles
ClinicalTrials.gov study NCT02458872. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Data from: Great tits do not compensate over time for a radio-tag-induced reduction in escape-flight performance
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Data from: Fire-induced population reduction and landscape opening increases gene flow via pollen dispersal in Pinus halepensis
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Data from: Reductions in body size of benthic macroinvertebrates as a precursor of the Early Toarcian (Early Jurassic) extinction event in the Lusitanian Basin, Portugal
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ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.