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855
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ShareScore release 0.9.0
Dataset results
855 results for “model system”
Effect of COPI depletion on senescence model systems
GEO Series GSE224070. Homo sapiens. 42 samples. Type: Expression profiling by high throughput sequencing.
Single cell RNA sequencing of femur bone marrow cells from the NZBWF1 systemic lupus erythematosus mouse model and its progenitors.
GEO Series GSE174728. Mus musculus. 9 samples. Type: Expression profiling by high throughput sequencing.
Integration of human stem cell-derived in vitro systems and mouse preclinical models identifies complex pathophysiologic mechanisms in retinal dystrophy [mouse_retina_scRNAseq]
GEO Series GSE220626. Mus musculus. 6 samples. Type: Expression profiling by high throughput sequencing.
In vitro Modeling of the Human Dopaminergic System using spatially arranged ventral Midbrain-Striatum-Cortex Assembloids [Smartseq]
GEO Series GSE219246. Homo sapiens. 22 samples. Type: Expression profiling by high throughput sequencing.
Genome-wide analysis of atypical NK cell subset in mouse model of Systemic Lupus Erythematosus (SLE)
GEO Series GSE63829. Mus musculus. 6 samples. Type: Expression profiling by array.
The female-biased factor VGLL3 drives cutaneous and systemic autoimmunity: RNA-seq analysis of the K5-Vgll3 transgenic mouse model of cutaneous and systemic lupus
GEO Series GSE128453. Mus musculus. 22 samples. Type: Expression profiling by high throughput sequencing.
Functional genomics of the human epididymis: further characterization of efferent ducts and model systems by single cell RNA-seq analysis.
GEO Series GSE235009. Homo sapiens. 4 samples. Type: Expression profiling by high throughput sequencing.
A model system for assessing the ability of tag sequencing to detect genes specific for malignant B-cells
GEO Series GSE40310. Homo sapiens. 8 samples. Type: Expression profiling by high throughput sequencing.
Human Ex Vivo Lung Perfusion: A Model System to study Lung diseases and direct targeted therapies
GEO Series GSE146436. Homo sapiens. 26 samples. Type: Expression profiling by high throughput sequencing.
Village In a Dish: A Model System for Population-scale hiPSC Studies [Affymetrix]
GEO Series GSE224950. Homo sapiens. 20 samples. Type: SNP genotyping by SNP array; Genome variation profiling by SNP array.
Contribution of Veillonella parvula to Pseudomonas aeruginosa mediated pathogenicity in a murine tumor model system
GEO Series GSE58388. Veillonella parvula DSM 2008; Mus musculus; Pseudomonas aeruginosa PA14. 12 samples. Type: Expression profiling by high throughput sequencing.
Aquaplanet experiment data for Webb, M. J., & Lock, A. P. (2020). Testing a physical hypothesis for the relationship between climate sen-sitivity and double-ITCZ bias in climate models.Journal of Advances in Modeling Earth Systems, 12,e2019MS001999.https://doi.org/10.1029/2019MS001999
<p><strong>Aquaplanet experiment data from Webb and Lock (2020)</strong></p> <p><br> Webb, M. J., & Lock, A. P. (2020). Testing a physical hypothesis for the relationship between climate sen-sitivity and double-ITCZ bias in climate models.Journal of Advances in Modeling Earth Systems, 12,e2019MS001999.https://doi.org/10.1029/2019MS001999</p> <p>CSV files containing data from Figs 1(b) and 2(a-d)</p> <p>Figure 2b:</p> <p>APEQ.Precipitation_mmperday.zonal.csv<br> APEQ_2LW_Cloud.Precipitation_mmperday.zonal.csv<br> APEQ_3LW_Cloud.Precipitation_mmperday.zonal.csv</p> <p>Figure 3a:</p> <p>APEQ.w700.zonal.csv<br> APEQ_3LW_Cloud.w700.zonal.csv<br> APEQ_2LW_Cloud.w700.zonal.csv</p> <p>Figure 3b:</p> <p>APEQ.Estimated_Inversion_Strength_K.zonal.csv<br> APEQ_2LW_Cloud.Estimated_Inversion_Strength_K.zonal.csv<br> APEQ_3LW_Cloud.Estimated_Inversion_Strength_K.zonal.csv</p> <p>Figure 3c:</p> <p>APEQ.Net_CRE_Wperm2.zonal.csv<br> APEQ_2LW_Cloud.Net_CRE_Wperm2.zonal.csv<br> APEQ_3LW_Cloud.Net_CRE_Wperm2.zonal.csv</p> <p>Figure 3d:</p> <p>APEQ4K-APEQ.Net_CRE_Feedback_Wperm2perK.zonal.csv<br> APEQ4K_2LW_Cloud-APEQ_2LW_Cloud.Net_CRE_Feedback_Wperm2perK.zonal.csv<br> APEQ4K_3LW_Cloud-APEQ_3LW_Cloud.Net_CRE_Feedback_Wperm2perK.zonal.csv</p> <p>Any queries please contact Mark Webb mark.webb@metoffice.gov.uk</p> <p> </p>
S1_raw_images paper "Use of dual-flow bioreactor to develop a simplified model of nervous-cardiovascular systems crosstalk: a preliminary assessment
<p>Original uncropped and unadjusted images underlying all blot or gel results reported in a submission’s figures and Supporting Information files.</p>
Data from: Modeling spatial patterns of soil respiration in maize fields from vegetation and soil property factors with the use of remote sensing and geographical information system
To examine the method for estimating the spatial patterns of soil respiration (Rs) in agricultural ecosystems using remote sensing and geographical information system (GIS), Rs rates were measured at 53 sites during the peak growing season of maize in three counties in North China. Through Pearson's correlation analysis, leaf area index (LAI), canopy chlorophyll content, aboveground biomass, soil organic carbon (SOC) content, and soil total nitrogen content were selected as the factors that affected spatial variability in Rs during the peak growing season of maize. The use of a structural equation modeling approach revealed that only LAI and SOC content directly affected Rs. Meanwhile, other factors indirectly affected Rs through LAI and SOC content. When three greenness vegetation indices were extracted from an optical image of an environmental and disaster mitigation satellite in China, enhanced vegetation index (EVI) showed the best correlation with LAI and was thus used as a proxy for LAI to estimate Rs at the regional scale. The spatial distribution of SOC content was obtained by extrapolating the SOC content at the plot scale based on the kriging interpolation method in GIS. When data were pooled for 38 plots, a first-order exponential analysis indicated that approximately 73% of the spatial variability in Rs during the peak growing season of maize can be explained by EVI and SOC content. Further test analysis based on independent data from 15 plots showed that the simple exponential model had acceptable accuracy in estimating the spatial patterns of Rs in maize fields on the basis of remotely sensed EVI and GIS-interpolated SOC content, with R2 of 0.69 and root-mean-square error of 0.51 µmol CO2 m−2 s−1. The conclusions from this study provide valuable information for estimates of Rs during the peak growing season of maize in three counties in North China.
Data and R script for publication: The LOKI underwater imaging system and an automatic identification model for the detection of zooplankton taxa in the Arctic Ocean
<p>This is a www.zenodo.org data and R script upload for publication:</p> <p> </p> <p>Schmid, M.S. et al., The LOKI underwater imaging system and an automatic identification model for the detection of zooplankton taxa in the Arctic Ocean. Methods in Oceanography (2016), http://dx.doi.org/10.1016/j.mio.2016.03.003</p> <p> </p> <p>Downloadable script: Script_Schmid_Mio_2016_data_upload.R</p> <p>Downloadable data: Schmid_2016_MIO_CGlac.csv</p>
Finite Volume Community Ocean Model-based Arctic Ocean Forecast System: A Comprehensive Assessment of Sea Ice Forecast Results without Data Assimilation
<p>The dataset contains sea ice forecast results from the Finite Volume Community Ocean Model-based Arctic Ocean Forecast System (FVCOM-AOFS) for the period 2019-2020. The output result of the system is presented as daily mean values.</p><p>Each netCDF file within the dataset includes the following variables: lon, lat, lonc, latc, aice, vice, uuice, and vvice. Specifically, the variables of lon and lat represent the longitude and latitude of the unstructured triangular grid. The variables of lonc and latc represent the longitude and latitude of the unstructured triangular cell. The variables of aice and vice indicate sea ice concentration and sea ice thickness. The variables of uuice and vvice indicate eastward and northward sea ice drift velocity. The scalars including aice and vice use lon and lat coordinates, and the vectors including uuice and vvice use lonc and latc coordinates. </p>
Supplementary material for "The comparative role of physical system processes in Hudson Strait ice stream cycling: a comprehensive model-based test of Heinrich event hypotheses"
<p>This supplementary material for "The comparative role of physical system processes in Hudson Strait ice stream cycling: a comprehensive model-based test of Heinrich event hypotheses" includes two animations showing a full Hudson Strait surge cycle with the default GSM heat flux.</p>
The dataset of article "Analysis of Uncertainties and Associated Convective Processes in Simulations of Extreme Precipitation over Cities with a Regional Earth System Model: A Case Study"
<p>The "out<i>EXP02.nc" and "out</i>EXP11.rar" are two examples of 11 simulations' output file in the article, in each of the file, 9 variables (horizontal wind u, horizontal wind v, vertical speed, height, temperature, pressure, precipitation, longitude and latitude) from outputs of simulation are included, the time interval is 2 hours.</p><p>The MERRA-2 dataset provides the initial and boundary conditions of chemical fields in simulations.</p><p>The era5 dataset provides the meteorological initial and boundary conditions in simulations.</p><p> </p>
Consumer-driven phosphorus recycling by fish in aquatic systems: retention and discarding of digested P by a cyprinid model under different conditions
<p>Semi-purified research diet recipes, experimental design, fish stock and experimental feeding protocols are given in supplementary methods text file. Including calculations and formulas in both supplementary methods and main document text files. The dataset contains data of pre-trial, experiment 1 to 4 included in the manuscript. Parameters include: Experiment; Group; Replicate; Initial tank biomass; Final tank biomass; Feed digestible N; Feed bioavailable P; Food N: P (mass); Food N: P (molar); Non-faecal P loss; Non-faecal N loss; Non-faecal N:P loss (mass); Non-faecal N:P loss (molar); Body protein content; Body N content; Body P content; Body N:P (mass); Body N:P (molar); PUE; DRP losses. Groups include: experiment 1 to 3 diets (control, T1 to T3), pre-trial scaly versus scaleless, experiment-4 fed versus starved. </p>
Figure 6 from: Landel S, Lymer G, Pasterk M, Guiraud M, Worley K (2024) A report on recommendations for the most suitable financial contribution model for the Distributed System of Scientific Collections Research Infrastructure (DiSSCo-RI). Research Ideas and Outcomes 10: e117217. https://doi.org/10.3897/rio.10.e117217
Figure 6 Simulating inflation, between 2024 and 2040 – Basic number: 2% inflation per year.
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.