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708
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Dataset results
708 results for “Global dataset”
Mechanisms of crystalline silica-induced pulmonary toxicity revealed by global gene expression profiling (A549 cells dataset 1)
GEO Series GSE30180. Homo sapiens. 10 samples. Type: Expression profiling by array.
A Transcriptomic Dataset of Liver Tissues from Global and Liver-Specific Bmal1 Knockout Mice
GEO Series GSE284601. Mus musculus. 24 samples. Type: Expression profiling by high throughput sequencing.
Mechanisms of crystalline silica-induced pulmonary toxicity revealed by global gene expression profiling (A549 cells dataset 2)
GEO Series GSE30200. Homo sapiens. 20 samples. Type: Expression profiling by array.
Mechanisms of crystalline silica-induced pulmonary toxicity revealed by global gene expression profiling (A549 cells dataset 3)
GEO Series GSE30213. Homo sapiens. 10 samples. Type: Expression profiling by array.
Mechanisms of crystalline silica-induced pulmonary toxicity revealed by global gene expression profiling (A549 cells dataset 5)
GEO Series GSE30215. Homo sapiens. 10 samples. Type: Expression profiling by array.
Uranium isotope fractionation in non-sulfidic anoxic settings and the global uranium isotope mass balance - Dataset
<p>All geochemical data published in Cole, D.B., Planavsky, N.J., Longley, M., Boning, P., Wilkes, D., Wang, X., Swanner, E.D., Wittkop, C., Loydell, D., Busigny, V., Knudsen, A., Sperling, E.A. Uranium isotope fractionation in non-sulfidic anoxic settings and the uranium isotope mass balance. Global Biogeochemical Cycles, 2020</p>
Supporting datasets used in the paper entitled "Black carbon absorption efficiency under preindustrial and present-day conditions simulated by a size- and mixing-state-resolved global aerosol model"
<p>This archive contains datasets used in the paper entitled "Black carbon absorption efficiency under preindustrial and present-day conditions simulated by a size- and mixing-state-resolved global aerosol model".</p>
Global degradation corrected 0.05 degree GOME-2 SIF datasets (derived from JJ datasets)
<p>8-day instrument degradation corrected 0.05 degree GOME-2 SIF datasets on a global scale from 2010 to 2018. JJ dataset from the spatially downscaled sun-induced fluorescence global product proposed by Gregory Duveiller in 2020 is corrected based on a pseudo-invariant method and then masked. Mean value composite method is used to produce monthly data. Files are organized in TIF format.</p>
Global degradation corrected 0.05 degree GOME-2 SIF datasets (derived from PK datasets)
<p>8-day instrument degradation corrected 0.05 degree GOME-2 SIF datasets on a global scale from 2010 to 2018. PK dataset from the spatially downscaled sun-induced fluorescence global product proposed by Gregory Duveiller in 2020 is corrected based on a pseudo-invariant method and then masked. Mean value composite method is used to produce monthly data. Files are organized in TIF format.</p>
A Global Forest Burn Severity Dataset from Landsat Imagery (2003–2016)
<p>Global Forest Burn Severity database provides information on the amounts of biomass that are consumed by wildfire between 2003 and 2016. Based on the Global Fire Atlas product, when and where forest fires occurred during that period are firstly determined and then overlay the available Landsat surface reflectance products to obtain pre-fire and post-fire normalized burn ratios (NBRs) for each burned pixel, designating the difference between pre-fire NBR and post-fire NBR as dNBR and the relative difference as RdNBR. The dNBR and RdNBR for each individual forest fire are saved as GeoTiff file.</p>
Dataset for "Effects of Forbush Decreases on the Global Electric Circuit"
<p>Here we present the figures and datasets of all events in the paper "Effects of Forbush Decreases on the Global Electric Circuit".</p> <p>Detailed descriptions of the results can be found in the paper.</p>
Supporting datasets used in the paper entitled "Substantial uncertainties in Arctic aerosol simulations by microphysical processes within the global climate-aerosol model CAM-ATRAS"
<p>This archive contains datasets used in the paper entitled "Substantial uncertainties in Arctic aerosol simulations by microphysical processes within the global climate-aerosol model CAM-ATRAS".</p>
A Global Dataset of Monthly Non-water-limited Canopy Resistance from 1982 to 2014
<p>Non-water-limited canopy resistance is the key paramter in many ecohydrological models. However, yet there is still no such data over large scales, and consequently privious studies usually assumed it to be 0 or 70 s m-1, which would induce large uncertainties in model output. Here the non-water-limited canopy resistance dataset is estimated based on a Jarvis-type canopy resistance model and optimized parameters, using leaf area index, solar radiation, air temperature, air CO2 concentration, vapor pressure deficit, wind speed and root zone soil water content as the drivers. </p>
Datasets for paper "Global biochemical profiling of fast-growing Antarctic bacteria isolated from meltwater ponds by high-throughput FTIR spectroscopy"
<p><span>Fourier transform infrared (FTIR) spectroscopy is a biophysical techniq</span><span>u<span>e used for non-destructive biochemical profiling of biological samples. It can provide comprehensive information about the total cellular biochemical profile of microbial cells. In this study, FTIR spectroscopy was used to perform biochemical characterization of twenty-nine bacterial strains isolated from the Antarctic meltwater ponds. The bacteria were grown on two forms of brain heart infusion (BHI) medium: agar at six different temperatures </span>(4, 10, 18, 25, 30, and 37 °C) and on broth at 18 °C. Multivariate data analysis approaches such as principal component analysis (PCA) and correlation analysis were used to study the difference in biochemical profiles induced by the cultivation conditions.</span> <span>The observed results indicated a strong correlation between FTIR spectra and the phylogenetic relationships among the studied bacteria. The most accurate taxonomy-aligned clustering was achieved with bacteria cultivated on agar</span><span>. Cultivation on two forms of BHI medium provided biochemically different bacterial biomass. The impact of temperature on the total cellular biochemical profile of the studied bacteria was </span><span>species-specific</span><span>, however, similarly for all bacteria, lipid spectral region was the least affected while polysaccharide region was the most affected by different temperatures. <span>T</span>he biggest temperature-triggered changes of the cell chemistry were detected for bacteria with a wide temperature tolerance such <em><span>Pseudomonas lundensis </span></em><span>strains<em> </em>and<em> Acinetobacter lwoffii </em>BIM B-1558. </span></span></p>
LEGACY DATASET: Reconstructed global long-term contiguous solar-induced fluorescence (LCSIF), MODIS period (2001-2021)
<p><strong>This is a legacy version of the dataset and is no longer recommended for use. Please access the latest version of LCSIF and LCREF with the following links:</strong></p> <ul> <li>LCSIF-AVHRR v3.1 (1982-2000): <a href="https://doi.org/10.5281/zenodo.13922371" target="_blank" rel="noopener">10.5281/zenodo.13922371</a></li> <li>LCSIF-AVHRR v3.1 (2001-2022): <a href="https://doi.org/10.5281/zenodo.13922367" target="_blank" rel="noopener">10.5281/zenodo.13922367</a></li> <li>LCSIF-MODIS v3.1 (2001-2022): <a href="https://doi.org/10.5281/zenodo.13922379" target="_blank" rel="noopener">10.5281/zenodo.13922379</a></li> <li>LCREF-AVHRR v3.1 (1982-2022): <a href="https://doi.org/10.5281/zenodo.11905960" target="_blank" rel="noopener">10.5281/zenodo.11905960</a></li> <li>LCREF-MODIS v3.1 (2001-2022): <a href="https://doi.org/10.5281/zenodo.11657459" target="_blank" rel="noopener">10.5281/zenodo.11657459</a></li> </ul> <p>Thanks a lot. Please feel free to reach out to Jianing Fang (<a href="mailto:jf3423@columbia.edu">jf3423@columbia.edu</a>) if you have any questions.</p>
Global ocean carbon uptake enhanced by rainfall : CO2 flux datasets
<p>1) NETCDF files containing the annual mean maps of the CO2 flux diagnostics considering the different effects of rain over the period 2008-2018 (Parc et al. 2024)</p> <ul> <li>map_statflux_REF.nc : Diagnostic reference flux taking into the ocean skin effect and formation of diurnal warm layers (Bellenger et al. 2017)</li> </ul> <p>- Diagnostics based on the ERA5 reanalysis rain dataset (Hersbach et al. 2020) : </p> <ul> <li>map_statflux_KR_rERA5.nc : Diagnostic flux integrating the impact of rain-induced turbulence (Harrison et al. 2012) to the reference flux</li> <li>map_statflux_DIL_DS1_rERA5.nc : Diagnostic flux integrating the impact of rain-induced dilution using Bellenger et al. (2017) parametrization to the reference flux</li> <li>map_statflux_DIL_DS2_rERA5.nc : Diagnostic flux integrating the impact of rain-induced dilution using Supply et al. (2020) parametrization to the reference flux</li> <li>map_statflux_INT_DS1_rERA5.nc : Diagnostic flux integrating the combined effect of rain-induced turbulence (Harrison et al. 2012) and dilution using Bellenger et al. (2017) parametrization to the reference flux</li> <li>map_statflux_INT_DS2_rERA5.nc : Diagnostic flux integrating the combined effect of rain-induced turbulence (Harrison et al. 2012) and dilution using Supply et al. (2020) parametrization to the reference flux</li> <li>map_statflux_WD_rERA5.nc : Additional diagnostic CO2 flux due to wet deposition (Komori et al. 2007)</li> </ul> <p>- Diagnostics based on the IMERG satellite-based rain dataset (Huffman et al. 2023) : </p> <ul> <li>map_statflux_KR_rIMERG.nc : Diagnostic flux integrating the impact of rain-induced turbulence (Harrison et al. 2012) to the reference flux</li> <li>map_statflux_DIL_DS1_rIMERG.nc : Diagnostic flux integrating the impact of rain-induced dilution using Bellenger et al. (2017) parametrization to the reference flux</li> <li>map_statflux_DIL_DS2_rIMERG.nc : Diagnostic flux integrating the impact of rain-induced dilution using Supply et al. (2020) parametrization to the reference flux</li> <li>map_statflux_INT_DS1_rIMERG.nc : Diagnostic flux integrating the combined effect of rain-induced turbulence (Harrison et al. 2012) and dilution using Bellenger et al. (2017) parametrization to the reference flux</li> <li>map_statflux_INT_DS2_rIMERG.nc : Diagnostic flux integrating the combined effect of rain-induced turbulence (Harrison et al. 2012) and dilution using Supply et al. (2020) parametrization to the reference flux</li> <li>map_statflux_WD_rIMERG.nc : Additional diagnostic CO2 flux due to wet deposition (Komori et al. 2007)</li> </ul> <p>All these files contain three variables : </p> <ul> <li>MFLUX : Annual mean of diagnostic flux (gC/m2/yr)</li> <li>SFLUX : Standard deviation of diagnostic flux</li> <li>WEIGHT : Number of data time steps used for the statistics</li> </ul> <p>2) Excel file containing the monthly means of the global ocean CO2 sink (PgC/y) corresponding to all the different diagnostics previously described (Parc et al. 2024) : GlobalOceanSink_2008-2018_rain_monthly_diagnostics.xlsx</p> <p>References : </p> <ul> <li><em>Bellenger, H. et al. Extension of the prognostic model of sea surface temperature to rain‐induced cool and fresh lenses. J. Geophys. Res. Oceans 122, 484–507 (2017).</em></li> <li><em>Hersbach, H. et al. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 146, 1999–2049 (2020).</em></li> <li><em>Harrison, E. L. et al. Nonlinear interaction between rain- and wind-induced air-water gas exchange. J. Geophys. Res. Oceans 117, (2012).</em></li> <li><em>Supply, A., Boutin, J., Reverdin, G., Vergely, J.-L. & Bellenger, H. Variability of Satellite Sea Surface Salinity Under Rainfall. in Satellite Precipitation Measurement (eds. Levizzani, V. et al.) vol. 69 1155–1176 (Springer International Publishing, Cham, 2020).</em></li> <li><em>Komori, S., Takagaki, N., Saiki, R., Suzuki, N. & Tanno, K. The Effect of Raindrops on Interfacial Turbulence and Air-Water Gas Transfer. in Transport at the Air-Sea Interface (eds. Garbe, C. S., Handler, R. A. & Jähne, B.) 169–179 (Springer Berlin Heidelberg, Berlin, Heidelberg, 2007). doi:10.1007/978-3-540-36906-6_12.</em></li> <li><em>Huffman, G., Stocker, E. F., Bolvin, D. T., Nelkin, E. J. & Tan, J. GPM IMERG Final Precipitation L3 Half Hourly 0.1 degree x 0.1 degree V07. NASA Goddard Earth Sciences Data and Information Services Center https://doi.org/10.5067/GPM/IMERG/3B-HH/07 (2023).</em></li> </ul>
Global natural and planted forests dataset (added tiles 300-400)
Open the record for dataset details and reuse information.
Low-resolution Global Dataset of BaP based on IAP-AACM model (1° × 1°)
<p>This dataset contains the annual- and monthly-averaged BaP simulations in the atmosphere based on the IAP-AACM with a resolution of 1° × 1°.</p>
Dataset of global climatic soil water contents and consecutive dry days
<p>The climatic water content is calculated as previously described [1, 2] using the average number of consecutive dry days obtained from precipitation timeseries (MSWEP [3]), potential evapotranspiration [4] (based on WorldClim [5]) and soil information (SoilGrids [6]).<br> The maps are generated at 0.1° resolution with a global extent of -180 to 180 °E and -60 to 90 °N.</p> <p>References<br> [1] Bickel, Samuel, Xi Chen, Andreas Papritz, and Dani Or. “A Hierarchy of Environmental Covariates Control the Global Biogeography of Soil Bacterial Richness.” Scientific Reports 9, no. 1 (August 20, 2019): 1–10. https://doi.org/10.1038/s41598-019-48571-w.<br> [2] Bickel, Samuel, and Dani Or. “Soil Bacterial Diversity Mediated by Microscale Aqueous-Phase Processes across Biomes.” Nature Communications 11, no. 1 (January 8, 2020): 1–9. https://doi.org/10.1038/s41467-019-13966-w.<br> [3] Beck, Hylke E., Eric F. Wood, Ming Pan, Colby K. Fisher, Diego G. Miralles, Albert I. J. M. van Dijk, Tim R. McVicar, and Robert F. Adler. “MSWEP V2 Global 3-Hourly 0.1° Precipitation: Methodology and Quantitative Assessment.” Bulletin of the American Meteorological Society 100, no. 3 (March 2019): 473–500. https://doi.org/10.1175/BAMS-D-17-0138.1.<br> [4] Jensen, M. E., and H. R. Haise. “Estimating Evapotranspiration from Solar Radiation.” Proceedings of the American Society of Civil Engineers, Journal of the Irrigation and Drainage Division 89, no. 0023 (1963): 15–41.<br> [5] Fick, Stephen E., and Robert J. Hijmans. “WorldClim 2: New 1-Km Spatial Resolution Climate Surfaces for Global Land Areas: New Climate Surfaces for Global Land Areas.” International Journal of Climatology 37, no. 12 (October 2017): 4302–15. https://doi.org/10.1002/joc.5086.<br> [6] Hengl, Tomislav, Jorge Mendes de Jesus, Gerard B. M. Heuvelink, Maria Ruiperez Gonzalez, Milan Kilibarda, Aleksandar Blagotić, Wei Shangguan, et al. “SoilGrids250m: Global Gridded Soil Information Based on Machine Learning.” PLOS ONE 12, no. 2 (February 16, 2017): e0169748. https://doi.org/10.1371/journal.pone.0169748.</p> <p> </p>
Global Soil Moisture Dataset From a Multitask Model (GSM3)
<p>Global Soil Moisture Dataset From a Multitask Model (GSM3).</p> <p>Shortname: GSM3<br> Longname: Global Soil Moisture Dataset From a Multitask Model<br> Version: 1.0<br> Format: GeoTIFF<br> Spatial Coverage: Global<br> Temporal Coverage: 2015-01-01 to 2020-12-31<br> File Size: ~10.3 MB per file<br> Data Resolution<br> Spatial: 9-km<br> Temporal: Daily<br> Coordinate Reference System (CRS): EPSG:6933 - WGS 84 / NSIDC EASE-Grid 2.0 Global</p>
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.