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311 results for “function dataset”
Dataset to the article 'Redox-zoning in high-energy subterranean estuaries as a function of storm floods, temperatures, seasonal groundwater recharge and morphodynamics'
<p>This repository contains model input files, and pre- and post-processing scripts for the research article:</p> <p>Janek Greskowiak, Stephan L. Seibert, Vincent E.A. Post, Gudrun Massmann (2023), Redox-zoning in high-energy subterranean estuaries as a function of storm floods, temperatures, seasonal groundwater recharge and morphodynamics, Estuarine, Coastal and Shelf Science, https://doi.org/10.1016/j.ecss.2023.108418</p> <p><br> Data:</p> <p>The folder Laserascannerdata_top_and_hydr_heads contains five cross-shore topography profiles of a high energy beach on Spiekeroog Island, Germany, including a python-script that assembles them to a daily times-series over one year and that calculates the tide-averaged hydraulic heads in the intertidal zone as detailed in Greskowiak and Massmann (2021), The impact of morphodynamics and storm floods on pore water flow and transport in the subterranean estuary, Hydrological Processes, 35:e14050, https://doi.org/10.1002/hyp.14050</p> <p><br> Model input:</p> <p>The file Dynamod_1_redox_basecase.zip contains a folder with the model input textfiles (SEAWAT and PHT3D) for the case that considers all dynamic influence factors investigated in the paper, i.e., seasonal meteoric groundwater recharge, stormfloods, temperature-dependence of reaction rates and morphodynamics.</p> <p>The files pht3d_ph.dat and pht3d_datab.dat are PHT3D model specific files defining the reacants and reactions, and have to be copied into the model folder before PHT3D is started.</p> <p><br> Run models:</p> <p>First the SEAWAT model needs to be run with the name file 'Dynamod_1_redox_basecase.nam'. This creates the mt3d link file 'mt3d_link.ftl'. After that, PHT3D has to be started with the name file 'pht3d.nam'</p> <p>Note that running the models will generate 22 Gybte output.</p> <p><br> Pre-processing:</p> <p>All model input files were generated with the python-script Dynamod_1_redox_basebase.py using Flopy, a python-based groundwater modelling user-interface:<br> https://www.usgs.gov/software/flopy-python-package-creating-running-and-post-processing-modflow-based-models</p> <p><br> Post-processing:</p> <p>For the figures 3,4 and S1, S2 in the paper, the corresponding python scripts are provided in this database. Note that with respect to Figures 5 and S3, only the results for the case with all dynamic influence factors (Figure S3_h) are being plotted.</p> <p> </p> <p>Other model output:<br> Animation_A1_Dynamod_redox.mp4 is a video animation showing the dynamic modelled salinity and temperature distribution, and redox zoning in the subterranean estuary all dynamic influence factors investigated in the paper, i.e., seasonal meteoric groundwater recharge, stormfloods, temperature-dependence of reaction rates and morphodynamics.</p>
Dataset: Bridging Time-series Image Phenotyping and Functional-Structural Plant Modeling to Predict Adventitious Root System Architecture
<p>Dataset for Bridging Time-series Image Phenotyping and Functional-Structural Plant Modeling to Predict Adventitious Root System Architecture manuscript submitted to Plant Phenomics. The dataset contains raw and processed root architecture images, RhizoVision trait outputs, and the associated R scripts for statistical analysis and model parameterization.</p>
Dataset of scientific article: "Effect of miR-34a on expression of clock and clock-controlled genes in human cancer DLD1 and Lovo cells with different backgrounds in respect to p53 functionality and 17β-estradiol-mediated regulation"
<p>The dataset includes "raw" data used for the calculation of gene expression, wound healing test, fluorescence intensity, and MTS test.</p> <p> </p>
Supplementary shapefile dataset for the paper "Mars Crater Production Function based on Mapped Crater Populations"
<p>This <span>shapefile </span>dataset includes the counted craters within each area for the paper "Mars Crater Production Function based on Mapped Crater Populations".</p>
Receiver-function datasets of the passive-source seismic profiles in the northeastern Tibetan plateau contributed by LRC
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Dataset from: Functional traits explain both seedling and adult plant spatial patterns in gypsum annual species
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Ruffed Grouse Resource Selection Function and Survival Datasets
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Dataset for: Relative contribution of high and low elevation soil microbes and nematodes to ecosystem functioning
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Patch-clamp dataset (SyncroPatch384PE) for control variants used in a calibrated functional patch clamp assay to enhance clinical variant interpretation in KCNH2-related long QT syndrome
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Dataset from: Functional divergence of mammalian TFAP2a and TFAP2b transcription factors for bidirectional sleep control
<p><span>Sleep is a conserved behavioral state that is found in all animals that have a nervous system. Invertebrates typically show quiet sleep, whereas in mammals, sleep is more complex, and consists of periods of non-rapid-eye-movement sleep (NREMS) and REM sleep (REMS). We previously found that the transcription factor AP-2 is required for sleep<i> </i>in<i> C. elegans </i>and <i>Drosophila</i> and that the <i>C. elegans </i>AP-2 gene <i>aptf-1</i> is required for sleep-active neurons to induce sleep. In mammals, several paralogous AP-2 transcription factors exist. Sleep-controlling genes are often conserved. However, little is known about how sleep genes evolved from controlling simpler types of sleep to govern complex mammalian sleep. Here, we studied the roles of <i>Tfap2a</i> and <i>Tfap2b</i> in sleep control in mice. Consistent with our results from <i>C. elegans</i> and <i>Drosophila</i>, the AP-2 transcription factors <i>Tfap2a</i> and <i>Tfap2b</i> also control sleep in mice. Surprisingly, however, the two AP-2 paralogs play contrary roles in sleep control. <i>Tfap2a</i> reduction of function causes stronger delta and theta power in both baseline and homeostasis analysis, thus indicating increased sleep quality, but did not affect sleep quantity. By contrast, <i>Tfap2b</i> reduction of function decreased NREM sleep time specifically during the dark phase, reduced NREMS and REMS power, and caused a weaker response to sleep deprivation. Consistent with the observed signatures of decreased sleep quality, stress resistance and memory were impaired in <i>Tfap2b</i> mutant animals. Also, the circadian period was slightly shortened. Taken together, AP-2 transcription factors control sleep behavior also in mice, but the role of the <i>AP-2</i> genes functionally diversified to allow for a bidirectional control of sleep quality. Divergence of AP-2 transcription factors might perhaps have supported the evolution of more complex types of sleep.</span></p>
Dataset associated to Tailored glycosylated anode surfaces: Addressing the exoelectrogen bacterial community via functional layers for microbial fuel cell applications
<p>This file contains the dataset associated to the published research article "<a href="https://www.sciencedirect.com/science/article/pii/S1567539420302887">Tailored glycosylated anode surfaces: Addressing the exoelectrogen bacterial community via functional layers for microbial fuel cell applications</a>". The dataset contains Atomic Force Microscopy, electrochemistry, Microbial Fuel Cells power output and water contact angle raw data from their relative instruments. This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. <a href="https://www.sciencedirect.com/science/article/pii/S1567539420302887#gp005">799175</a> (HiBriCarbon). The results of this publication reflect only the authors' view and the Commission is not responsible for any use that may be made of the information it contains. This publication has also emanated from research conducted with the financial support of Science Foundation Ireland under Grant No. <a href="https://www.sciencedirect.com/science/article/pii/S1567539420302887#gp010">13/CDA/2213</a>. The authors also thank the France-Ireland PHC ULYSSES programme for support, project 36028UB. JAB acknowledges support from the Irish Research Council under Grant No. <a href="https://www.sciencedirect.com/science/article/pii/S1567539420302887#gp015">GOIPG/2014/399</a>. </p>
Dataset related to the article "Human Cardiac Mesenchymal Stromal Cells From Right and Left Ventricles Display Differences in Number, Function, and Transcriptomic Profile"
<p>This record contains raw data related to the article "Human Cardiac Mesenchymal Stromal Cells From Right and Left Ventricles Display Differences in Number, Function, and Transcriptomic Profile". </p> <p><strong>Background:</strong> Left ventricle (LV) and right ventricle (RV) are characterized by well-known physiological differences, mainly related to their different embryological origin, hemodynamic environment, function, structure, and cellular composition. Nevertheless, scarce information is available about cellular peculiarities between left and right ventricular chambers in physiological and pathological contexts. Cardiac mesenchymal stromal cells (C-MSC) are key cells affecting many functions of the heart. Differential features that distinguish LV from RV C-MSC are still underappreciated.</p> <p><strong>Aim:</strong> To analyze the physiological differential amount, function, and transcriptome of human C-MSC in LV versus (vs.) RV.</p> <p><strong>Methods:</strong> Human cardiac specimens of LV and RV from healthy donors were used for tissue analysis of C-MSC number, and for C-MSC isolation. Paired LV and RV C-MSC were compared as for surface marker expression, cell proliferation/death ratio, migration, differentiation capabilities, and transcriptome profile.</p> <p><strong>Results:</strong> Histological analysis showed a greater percentage of C-MSC in RV vs. LV tissue. Moreover, a higher C-MSC amount was obtained from RV than from LV after isolation procedures. LV and RV C-MSC are characterized by a similar proportion of surface markers. Functional studies revealed comparable cell growth curves in cells from both ventricles. Conversely, LV C-MSC displayed a higher apoptosis rate and RV C-MSC were characterized by a higher migration speed and collagen deposition. Consistently, transcriptome analysis showed that genes related to apoptosis regulation or extracellular matrix organization and integrins were over-expressed in LV and RV, respectively. Besides, we revealed additional pathways specifically associated with LV or RV C-MSC, including energy metabolism, inflammatory response, cardiac conduction, and pluripotency.</p> <p><strong>Conclusion:</strong> Taken together, these results contribute to the functional characterization of RV and LV C-MSC in physiological conditions. This information suggests a possible differential role of the stromal compartment in chamber-specific pathologic scenarios.</p>
Dataset: Modelling Seepage Erosion in Porous Media with a Linear Decay Function
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Dataset: Trends in physical functioning in acute lymphoblastic leukemia and non-Hodgkin lymphoma survivors across three decades
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Dataset for publication: "Potential compensatory mechanisms preserving cardiac function in myotubular myopathy"
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MarNemaFunDiv - a first comprehensive dataset of functional traits for marine nematodes
<p><span>Matrix of nematode genera associated with 16 functional traits</span><strong><span> </span></strong></p>
Receiver function dataset
<p>This Dataset include raw receiver functions for crustal anisotropy measurement in South China</p>
Dataset for the paper: Predicting protein functions using positive-unlabeled ranking with ontology-based priors
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Boston & Corniolo Datasets - road segmentation - described in "An Enhanced Loss Function for Semantic Road Segmentation in Remote Sensing Images""
<p>In Corniolo.rar the masks (values {0,1}) are saved in the png files</p>
Dataset for "Investigating the Relationship Between Cognitive Functions and Clinical and Individual Characteristics in Iranian Multiple Sclerosis Patients"
<p>Instruction and tests are in persian Laguage. </p>
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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.