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1,819 results for “Experimental data”
Data used in the extension of the conference paper "An Experimental Performance Assessment of Galileo OSNMA"
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Experimental signals and processed data used in the research work "On-chip phonon-magnon reservoir for neuromorphic computing"
<p>The data set includes the raw experimental data, processed experimental data, and numerically modeled dependencies in the respective folders:</p><p>The raw experimental data (magnon readout, as measured) are given for all processed signals presented in the respective figures (folders 'Figure2', 'Figure3' and 'Sup Figure1') and used for the ANN training (folder '3x3Sets & AugmentedVisualShapes'). </p><p>The signals presented in the folders '\3x3Sets & AugmentedVisualShapes\3x3SET##' are named following the scheme shown in Fig. 3a. The signals in the folder \3x3Sets & AugmentedVisualShapes\RandomizedShapes' are simulated using the procedure described in the Methods section as "Drawing of randomized visual shapes". The sets of calculated statistical parameters used for the shapes' recognition are in the folder ''\3x3Sets & AugmentedVisualShapes\Parameters'</p><p>The waveforms for the trajectories formed by randomly selected 4, 5, 6, and 7 discrete positions and their statistical parameters are presented in the corresponding folder. </p>
Data from: Crossed effects of helminth infection and lead exposure on fitness: an experimental study in feral pigeons (Columba livia)
<p><span>Living organisms are exposed to multiple environmental factors that can affect their fitness. The effects of these simultaneous stressors can be additive or can interact in negative synergistic or antagonistic ways to affect the health of exposed individuals. Parasites can accumulate pollutants in their own tissues and have been shown to increase the tolerance of their hosts to different pollutants (antagonistic interaction between parasites and pollutants). We tested the existence of combined antagonistic effects between intestinal parasites and lead exposure on urban feral pigeons (<em>Columba livia</em>) which are known to be exposed to trace metal pollution and harbor a wide variety of internal and external parasites. </span><span>We experimentally exposed feral pigeons to two treatments: an anthelmintic treatment to eliminate intestinal nematode parasites; an exposure to lead for a period of 6 months. We tested the effects of these crossed treatments on several components of fitness: immunocompetence, reproduction, and body mass. </span><span>Our findings suggest that the overall effects of lead exposure, either alone or in combination with the presence of intestinal parasites (without </span><span>anthelmintic </span><span>treatment) were negative, through either additive or synergistic means. </span><span>In the absence of putative antagonistic effects between lead exposure and with helminths, the detoxification hypothesis could not be confirmed. </span><span>Our results reveal the existence of negative combined effects between pollutant exposure and intestinal parasites, highlighting the importance of accounting for multiple stress factors when studying the effects of exposure to pollutants and/or other environmental stressors on the fitness of organisms.</span></p>
FLIGHTED: Inferring Fitness Landscapes from Noisy High-Throughput Experimental Data (Part 1)
<p>Data for FLIGHTED (Inferring Fitness Landscapes from Noisy High-Throughput Experimental Data). This data contains the model weights for FLIGHTED-Selection and FLIGHTED-DHARMA, the training data for both, and fits on the GB1 landscape. It does not contain anything related to the TEV protease landscape.</p> <p>The data is arranged in the following folders:</p> <ol> <li>DHARMA_Input: contains the input for the DHARMA models, with the canvas sequence, the DHARMA reads, and the FACS data.</li> <li>DHARMA_Models: contains the weights, hyperparameters, and model training history for the FLIGHTED-DHARMA model.</li> <li>Fitness_Landscapes: the GB1 landscape, with and without FLIGHTED, as well as splits published by FLIP.</li> <li>Landscape_Models: models trained on the GB1 landscape with and without FLIGHTED under the various FLIP splits. Each model folder contains hyperparameters, training history, and predictions on the test set which can be used to evaluate model performance. Raw model parameters are not provided for fine-tuned models due to size; contact us if you want them.</li> <li>FLIGHTED_Selection: contains the weights, hyperparameters, and model training history for the FLIGHTED-Selection model.</li> <li>Selection_Simulations: contains the simulated training data for the FLIGHTED-Selection model.</li> </ol>
FLIGHTED: Inferring Fitness Landscapes from Noisy High-Throughput Experimental Data (Part 2)
<p>Data for FLIGHTED (Inferring Fitness Landscapes from Noisy High-Throughput Experimental Data). This data contains the TEV protease landscape and models trained on it. All other FLIGHTED data is in the other Zenodo repository (refer to the paper for details).</p> <p>The data is arranged in the following folders:</p> <ol> <li>TEV_Landscape: contains the TEV landscape (in flighted_fitnesses.csv) and splits thereof in Splits/. The main files for model training are flighted_fitnesses.csv and the files labeled one_vs_rest, two_vs_rest, and three_vs_rest. The files labeled three_vs_rest_control within Splits/ and the read count CSV files refer to further information about the read count in the landscape; see the Supplement for details. The dictionary files are the original raw data prior to processing with FLIGHTED.</li> <li>TEV_Models: contains models trained on the TEV landscape under the various splits. Each model folder contains hyperparameters, training history, and predictions on the test set which can be used to evaluate model performance. Raw model parameters are not provided for fine-tuned models due to size; contact us if you want them. The control_run/ refers to the run described in the supplement on just read counts.</li> </ol>
Experimental Data for Dynamic Flow Reactions of 1,2,4-Triazole with Acrylonitrile
<p>The reaction input flow rates, temperatures and concentrations are provided for time intervals of roughly 12 s. At each of these points, the concentration of four species was measured by online benchtop NMR analysis and these concentrations are provided.</p> <p>Experiments 1-7 and the self-optimization data are detailed in the manuscript (part 1).</p>
Experimental data for "The interaction between plastics and microalgae affects community assembly and nutrient availability"
<p>Dataset of the experimental data obtained for the study “The interaction between microplastics and microalgae affects community assembling and nutrient availability” (published on Communications Earth and Environment, DOI: 10.1038/s43247-024-01706-y). This include 5 different tabular files, which are listed below:</p> <p><strong>Algae growth</strong> Values of chlorophyll fluorescence (as a proxy of algal biomass growth, in arbitrary units) in all treatments between days 1 and 17 of the experiment in the 4 different replicates (shown as different columns).</p> <p><strong>Biofilm growth on plastic</strong> Measures of biofilm coverage (in % of plastic fragment's surface) via image analysis after optical microscopy and chlorophyll fluorescence via spectroscopy (after the analysis of 3 replicates per batch, relative standard deviation below 20%). Data at day 0 indicate the fragments before the incubation with the pelagic community. Data are shown for each treatment containing plastic (i.e., <em>plastic</em>, <em>biofilm</em> and <em>dispersal</em>).</p> <p><strong>Nutrient concentrations</strong> Nutrient concentration in every replicate at different days from the beginning of the experiment. Data are average values after three measure replicates (relative standard deviation below 5%). Data below LODs are shown as LOD/2.</p> <p><strong>Pelagic community composition </strong>Counting values of the different algal species from optical microscopy measurement of all treatments after 5, 8 and 15 days (average values after 3 replicates of measures (relative standard deviation below 25%). The inoculum of the pelagic community before the beginning of the experiment is also included. Species not present in the community or not detected are shown as ND.</p> <p><strong>Photosynthetic efficiency </strong>Values of photosynthetic efficiency (measured with pulse-amplitude-modulated fluorescence) in all treatments at day 5, 8 and 15 of the experiment in the 4 different replicates (shown as different columns).</p>
Experimental Data - Physicochemical Properties of 20 Ionic Liquids Prepared by the Carbonate-Based IL (CBILS) Process
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Experimental and computational data of "Nanoconfinement-induced electrochemical ion-solvent cointercalation in pillared titanate host materials"
<p>Electrochemical ion-solvent cointercalation reactions are an avenue to reach improved kinetics compared to the corresponding intercalation of desolvated ions. Here, we demonstrate the impact of different structural pillar molecules on the electrochemical Li<sup>+</sup> intercalation mechanism in expanded hydrogen titanate (HTO) electrode materials. We show that interlayer-expansion of HTO with organic pillars can enable cointercalation reactions. Their electrochemical reversibility is drastically improved when non-cross-linking pillars are employed that expand and separate the host material’s individual layers, underlining the impact of the electrochemo-mechanics of the nanoconfined interlayer space. This pillared HTO structure results in an increased Li<sup>+</sup> storage capacity and reversibility compared to pristine HTO. We derive structural models of the pillared HTO host materials based on combined experiments and theoretical calculations, and employ electrochemical operando experiments to unambiguously demonstrate the nanoconfinement-induced cointercalation mechanism in pillared HTO electrode materials. The work demonstrates the potential of nanoconfined pillar molecules to modify host materials and enable highly reversible cointercalation reactions with improved capacity and kinetics.</p>
Data sharing of: Sulfur inventory of the young lunar mantle constrained by experimental sulfide saturation of Chang'e-5 mare basalts and a new sulfur solubility model for silicate melts in equilibrium with sulfides of variable metal–sulfur ratio
<p>Data sharing of: Sulfur inventory of the young lunar mantle constrained by experimental sulfide saturation of Chang’e-5 mare basalts and a new sulfur solubility model for silicate melts in equilibrium with sulfides of variable metal–sulfur ratio</p>
Data from: Experimental evidence for neonicotinoid driven decline in aquatic emerging insects
<p>There is an ongoing unprecedented loss in insects, both in terms of richness and biomass. The usage of pesticides, especially neonicotinoid insecticides, has been widely suggested to be a contributor to this decline. However, the risks of neonicotinoids to natural insect populations have remained largely unknown due to lack of field-realistic experiments. Here, we used an outdoor experiment to determine effects of field-realistic concentrations of the commonly applied neonicotinoid thiacloprid on the emergence of naturally assembled aquatic insect populations. Following application, all major orders of emerging aquatic insects (Coleoptera, Diptera, Ephemeroptera, Odonata, Trichoptera) declined strongly in both abundance and biomass. At the highest concentration (10 μg/L), emergence of most orders was nearly absent. Diversity of the most species-rich family, Chironomidae, decreased by 50% at more commonly observed concentrations (1 μg/L) and was generally reduced to a single species at the highest concentration. Our experimental findings thereby showcase a causal link of neonicotinoids and the ongoing insect decline. Given the urgency of the insect decline, our results highlight the need to reconsider the mass usage of neonicotinoids to preserve freshwater insects as well as the life and services depending on them.</p>
Data set supplementing "Interactive versus static decision support tools for COVID-19: An experimental comparison"
<p>This is the de-identified data set used to conduct the analyses in the preprint submitted to JMIR Public Health and Surveillance under the title "Interactive versus static decision support tools for COVID-19: An experimental comparison" (https://doi.org/10.2196/preprints.33733).</p> <p>This data set contains the appraisal of 196 respondents (without decision support, with static or with interactive decision support) to appropriate social and care-seeking behavior for seven fictitious descriptions of patients. Additionally, this data contains participants'</p> <ul> <li>gender</li> <li>educational background</li> <li>previous medical training</li> <li>affinity for technology</li> <li>experience with COVID-19 related medical questions</li> <li>perceived threat from COVID-19</li> <li>answers to a COVID-19 knowledge test</li> <li>accuracy</li> <li>decision certainty (after deciding)</li> <li>mental effort</li> <li>ratings of <ul> <li>the decision support tool's usefulness </li> <li>ease of use</li> <li>trust</li> <li>future intention to use the tool</li> </ul> </li> </ul>
Experimental source data for "A photosensitiser-polyoxometalate dyad that enables the decoupling of light- and dark-reactions for delayed on-demand solar hydrogen production"
<p>Experimental data for the manuscript "A photosensitiser-polyoxometalate dyad that enables the decoupling of light- and dark-reactions for delayed on-demand solar hydrogen production"</p>
Tipping points and multiple drivers in changing aquatic ecosystems: A review of experimental studies (Data)
<p>The ecological concepts of tipping points and multiple stressors are both important concepts for the management of natural ecosystems. However, very few studies have attempted to combined both concepts and evaluate tipping points in the context of multiple stressors. The data available here have been published in a literature review (Limnology & Oceanography). In this review we (1) develop a historical and current perspective of tipping point studies in terrestrial, freshwater and marine ecological systems; (2) portray the research effort in different freshwater and marine habitats; and (3) explore the results of experimental studies focusing on tipping points measured at the individual, communities, ecosystem level, as well as ecosystem functions and services in a context of single and multiples stressors.</p>
Data Set: The influence of roughness on experimental fault mechanical behaviour and associated microseismicity
<p>This is the data set used to create the figures for the submitted manuscript, The influence of roughness on experimental fault mechanical behaviour and associated microseismicity. Submitted July 2022 to the Journal of Geophysical Research: Solid Earth. Article DOI: 10.1029/2022JB025113</p>
Full data for 'Experimental phase diagram of zero-bias conductance peaks in superconductor/semiconductor nanowire devices'
<p>This repository contains experimental data for the following paper:<br> Experimental phase diagram of zero-bias conductance peaks in superconductor/semiconductor nanowire devices<br> Authors: Jun Chen, Peng Yu, John Stenger, Moïra Hocevar, Diana Car, Sébastien R. Plissard, Erik P.A.M. Bakkers, Tudor D. Stanescu, Sergey M. Frolov</p> <p>Content of this repository: </p> <p>Readme file. </p> <p>/RawData/<br> Original data obtained at the time of measurement for devices 1014-841 and 1115A4. ZBP phase diagram data was measured on device 1014-841; Hard gap data was measured on device 1115A4</p> <p>/Measurement notes/<br> All the measurement data was summarized in powerpoints, catagorized by the name of the device.</p> <p>/Data of paper figures/<br> All the organized data files for the figures in the main text and supplementary information.</p> <p>Data file types:<br> data_NNN.dat - the original data file obtained at the time of the experiment<br> dataNNN.py - the original QTLab data acquisition script saved with data<br> data_NNN.set - settings of measurement instruments at the time of measurement<br> data_NNN.meta - auxillary file necessary for plotting data using SpyView (see below) <br> data_NNN.MTX - a simple 2D/3D matrix format developed for Spyview</p> <p>NNN stands for dataset number, automatically indexed by QTLab</p> <p>How to plot data:</p> <p>1) Spyview - a free data plotting program written by Gary Steele</p> <p>Data in this repository can be simply dropped into Spyview for plotting. </p> <p>Spyview also produces and can read .mtx files which are available for some of the data in this repository.</p> <p>https://nsweb.tn.tudelft.nl/~gsteele/spyview/</p> <p><br> 2) QTPlot - a Python plotter written by Ruben van Gulik</p> <p>Data in this repository can be directly opened with QTPlot, which will read axis labels.</p> <p>https://github.com/Rubenknex/qtplot</p> <p>Note: requires PyQT4</p>
Detect, Fix, and Verify TensorFlow API Misuses - SANER 2022 - Experimental Data
<p>Detect, Fix, and Verify TensorFlow API Misuses - SANER 2022</p> <p>Experimental data</p>
Biological experimental raw data from: The Incubascope : A simple, compact and large field of view microscope for long-term
<p>Optical imaging has rapidly evolved in the last decades. Sophisticated microscopes allowing optical sectioning for 3D imaging or sub-diffraction resolution are available. Due to price and maintenance issues, these microscopes are often shared between users in facilities. Consequently, long term access is often prohibited and does not allow to monitor slowly evolving biological systems or to validate new models like organoids. Preliminary coarse long-term data that do not require do acquisition of terabytes of high-resolution images are important as a first step. By contrast with expansive all-in-one commercialized stations standard microscopes equipped with incubator stages offer a more cost-effective solution despite imperfect long run atmosphere and temperature control. Here, we present the Incubascope, a custom-made compact microscope that fits into a table-top incubator. It is cheap and simple to implement user-friendly and yet provides high imaging performances. The system has a field of view of 5.5×8 mm2, a 3 µm resolution, a 10 frames/second acquisition rate, and is controlled with a Python-based graphical interface. We exemplify the capabilities of the Incubascope on biological applications such as the hatching of Artemia salina eggs, the growth of the slime mold Physarum polycephalum and of encapsulated spheroids of mammalian cells.</p>
Data from: Effective specialist or jack of all trades? Experimental evolution of a crop pest in fluctuating and stable environments
<p>Understanding pest evolution in agricultural systems is crucial for developing effective and innovative pest control strategies. Types of cultivation, such as crop monocultures versus polycultures or crop rotation, may act as a selective pressure on pests' capability to exploit the host's resources. In this study, we examined the herbivorous mite <em>Aceria tosichella</em> (commonly known as wheat curl mite), a widespread wheat pest, to understand how fluctuating versus stable environments influence its niche breadth and ability to utilize different host plant species. We subjected a wheat-bred mite population to replicated experimental evolution in a single-host environment (either wheat or barley), or in an alternation between these two plant species every three mite generations. Next, we tested the fitness of these evolving populations on wheat, barley, and on two other plant species not encountered during experimental evolution, namely rye and smooth brome. Our results revealed that the niche breadth of <em>A. tosichella</em> evolved in response to the level of environmental variability. The fluctuating environment expanded the niche breadth by increasing the mite's ability to utilize different plant species, including novel ones. Such an environment may thus promote flexible host-use generalist phenotypes. However, the niche expansion resulted in some costs expressed as reduced performances on both wheat and barley as compared to specialists. Stable host environments led to specialized phenotypes. The population that evolved in a constant environment consisting of barley increased its fitness on barley without the cost of utilizing wheat. However, the population evolving on wheat did not significantly increase its fitness on wheat, but decreased its performance on barley. Altogether, our results indicated that, depending on the degree of environmental heterogeneity, agricultural systems create different conditions that influence pests' niche breadth evolution, which may in turn affect the ability of pests to persist in such systems.</p>
Raw data to "Macrophage Plasticity and Polarization Are Altered in the Experimental Model of Multiple Sclerosis"
<p>Background: Macrophages have been identified as one of the major effectors of inflammation and demyelination in both MS and its animal model, experimental autoimmune encephalomyelitis (EAE). However, the activation and heterogeneity of macrophages in MS is not fully understood.</p> <p>Results: We performed a complete immunophenotyping of M1 and M2 macrophages from EAE and control mice through polychromatic flow cytometry. We found that M1 macrophages possessed a higher proinflammatory profile in EAE compared to control mice, since they expressed higher levels of activation/co-stimulatory markers (iNOS, CD40 and CD80) (Table 1), cytokines/chemokines (IL-6, IL-12, CCL2 and CXCL10) (Table 2) and a higher expression of Toll-like receptors (TLR2) 2 and 5 (Table 3). On the contrary, M2 of EAE mice lost their M2-like phenotype by showing a decreased expression of their signature markers CD206, CD11c, CD44 and CCL22, and a concomitant upregulation of several M1 makers such as iNOS, CD40, CD80/CD86 (Table 1) and proinflammatory CCL22 (Table 2) and TLR4 and 8.</p> <p>Conclusions: Our data account for a phenotypic alteration of M1/M2 balance during MS and this can be of crucial importance not only for a better understanding of the immunopathology of this neurodegenerative disease but also to potentially develop new macrophage-centered therapeutic strategies</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.