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ShareScore release 0.9.0
Dataset results
107 results for “ZIP”
Evaluation of Safety of ZyComb® In Patients With Common Cold - ZIP 3000 (XY-005-IM)
ClinicalTrials.gov study NCT00480194. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Profiling the target genes for ZIP repressor in MCF-7
GEO Series GSE13905. Homo sapiens. 2 samples. Type: Genome binding/occupancy profiling by array.
Rain Gauge Rainfall Collection Data obtained at 620 Sao Bento Street, Guarulhos, Sao Paulo, Brazil, Zip Code 07070-000, from Aug. 24, 2015 to Nov, 24, 2015
Open the record for dataset details and reuse information.
Local Genomic Features Predict the Distinct and Overlapping Binding Patterns of the bHLH‐Zip Family Oncoproteins MITF and MYC‐MAX
GEO Series GSE115845. Homo sapiens. 10 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
Genome-wide expression of HD-Zip genes in cassava
GEO Series GSE93098. Manihot esculenta. 24 samples. Type: Expression profiling by high throughput sequencing.
Identification of genes cooperatively regulated by cytokinin and HD-ZIP III transcription factors
GEO Series GSE54469. Arabidopsis thaliana. 8 samples. Type: Expression profiling by array.
Rice gene expression profiling in wild type and transgenic rice plants by over-expressing a class I HD-ZIP gene by Agilent custom 8X60K Microarray analysis
GEO Series GSE154469. Oryza sativa. 4 samples. Type: Expression profiling by array.
North America (SW) Gross Primary Productivity (GPP) Dataset 2018 (GeoTIFF) - ZIP file
<p>Product: Gross Primary Productivity (GPP)</p> <p>Year: 2018</p> <p>Region: North America (southwest)</p> <p>Temporal Scale: 8 Days</p> <p>Spatial Resolution: 500 meters</p> <p>Method: Light-use-efficiency (LUE) approach.</p> <p>Referred Publication: https://www.sciencedirect.com/science/article/pii/S0048969718307149</p> <p>https://onlinelibrary.wiley.com/doi/10.1111/gcb.12261</p> <p>This archive has been uploaded as a .zip file, due to repeated difficulties uploading single GeoTIFFs to Zenodo. </p> <p>NOTE: As ArcPy classifies 'no data' pixels as very low negative values, users should mask all data values below 0 to prevent these affecting statistics.</p>
Amazon Gross Primary Productivity (GPP) Dataset 2018 (GeoTIFF) - ZIP file
<p>Product: Gross Primary Productivity (GPP)</p> <p>Year: 2018 </p> <p>Region: Amazon </p> <p>Temporal Scale: 8 Days</p> <p>Spatial Resolution: 500 meters</p> <p>Method: Light-use-efficiency (LUE) approach.</p> <p>Referred Publication: https://www.sciencedirect.com/science/article/pii/S0048969718307149</p> <p>https://onlinelibrary.wiley.com/doi/10.1111/gcb.12261</p> <p>This archive has been uploaded as a .zip file, due to repeated difficulties uploading single GeoTIFFs to Zenodo. </p> <p><strong>NOTE: As ArcPy classifies 'no data' pixels as very low negative values, users should mask all data values below 0 to prevent these affecting statistics. </strong></p>
Europe Gross Primary Productivity (GPP) Dataset 2018 (GeoTIFF) - ZIP file
<p>Product: Gross Primary Productivity (GPP)</p> <p>Year: 2018 </p> <p>Region: Europe</p> <p>Temporal Scale: 8 Days</p> <p>Spatial Resolution: 500 meters</p> <p>Method: Light-use-efficiency (LUE) approach.</p> <p>Referred Publications: https://www.sciencedirect.com/science/article/pii/S0048969718307149</p> <p>https://onlinelibrary.wiley.com/doi/10.1111/gcb.12261</p> <p>This archive has been uploaded as a .zip file, due to repeated difficulties uploading single GeoTIFFs to Zenodo. </p> <p><strong>NOTE: As ArcPy (used in generation) classifies 'no data' pixels as very low negative values, users should mask all data values below 0 to prevent these affecting statistics. </strong></p>
North America (SW) Gross Primary Productivity (GPP) Dataset 2018 (GeoTIFF) - ZIP file
<p>Product: Gross Primary Productivity (GPP)</p> <p>Year: 2018</p> <p>Region: North America (southwest)</p> <p>Temporal Scale: 8 Days</p> <p>Spatial Resolution: 500 meters</p> <p>Method: Light-use-efficiency (LUE) approach.</p> <p>Referred Publication: https://www.sciencedirect.com/science/article/pii/S0048969718307149</p> <p>https://onlinelibrary.wiley.com/doi/10.1111/gcb.12261</p> <p>This archive has been uploaded as a .zip file, due to repeated difficulties uploading single GeoTIFFs to Zenodo. </p> <p>NOTE: As ArcPy classifies 'no data' pixels as very low negative values, users should mask all data values below 0 to prevent these affecting statistics.</p>
North America (SW) Gross Primary Productivity (GPP) Dataset 2019 (GeoTIFF) - ZIP file
<p>Product: Gross Primary Productivity (GPP)</p> <p>Year: 2019</p> <p>Region: North America (southwest)</p> <p>Temporal Scale: 8 Days</p> <p>Spatial Resolution: 500 meters</p> <p>Method: Light-use-efficiency (LUE) approach.</p> <p>Referred Publication: https://www.sciencedirect.com/science/article/pii/S0048969718307149</p> <p>https://onlinelibrary.wiley.com/doi/10.1111/gcb.12261</p> <p>This archive has been uploaded as a .zip file, due to repeated difficulties uploading single GeoTIFFs to Zenodo. </p> <p><strong>NOTE: As ArcPy classifies 'no data' pixels as very low negative values, users should mask all data values below 0 to prevent these affecting statistics.</strong></p>
Amazon Gross Primary Productivity (GPP) Dataset 2019 (GeoTIFF) - ZIP file
<p>Product: Gross Primary Productivity (GPP)</p> <p>Year: 2019</p> <p>Region: Amazon</p> <p>Temporal Scale: 8 Days</p> <p>Spatial Resolution: 500 meters</p> <p>Method: Light-use-efficiency (LUE) approach.</p> <p>Referred Publication: https://www.sciencedirect.com/science/article/pii/S0048969718307149</p> <p>https://onlinelibrary.wiley.com/doi/10.1111/gcb.12261</p> <p>This archive has been uploaded as a .zip file, due to repeated difficulties uploading single GeoTIFFs to Zenodo. </p> <p><strong>NOTE: As ArcPy classifies 'no data' pixels as very low negative values, users should mask all data values below 0 to prevent these affecting statistics. </strong></p>
Europe Gross Primary Productivity (GPP) Dataset 2019 (GeoTIFF) - ZIP file
<p>Product: Gross Primary Productivity (GPP)</p> <p>Year: 2019</p> <p>Region: Europe</p> <p>Temporal Scale: 8 Days</p> <p>Spatial Resolution: 500 meters</p> <p>Method: Light-use-efficiency (LUE) approach.</p> <p>Referred Publications: https://www.sciencedirect.com/science/article/pii/S0048969718307149</p> <p>https://onlinelibrary.wiley.com/doi/10.1111/gcb.12261</p> <p>This archive has been uploaded as a .zip file, due to repeated difficulties uploading single GeoTIFFs to Zenodo. </p> <p>NOTE: As ArcPy (used in generation) classifies 'no data' pixels as very low negative values, users should mask all data values below 0 to prevent these affecting statistics. </p>
Europe Gross Primary Productivity (GPP) Dataset 2020 (GeoTIFF) - ZIP file
<p>Product: Gross Primary Productivity (GPP)</p> <p>Year: 2020</p> <p>Region: Europe</p> <p>Temporal Scale: 8 Days</p> <p>Spatial Resolution: 500 meters</p> <p>Method: Light-use-efficiency (LUE) approach.</p> <p>Referred Publications: https://www.sciencedirect.com/science/article/pii/S0048969718307149</p> <p>https://onlinelibrary.wiley.com/doi/10.1111/gcb.12261</p> <p>This archive has been uploaded as a .zip file, due to repeated difficulties uploading single GeoTIFFs to Zenodo. </p> <p><strong>NOTE: As ArcPy (used in generation) classifies 'no data' pixels as very low negative values, users should mask all data values below 0 to prevent these affecting statistics. </strong></p>
Australia Gross Primary Productivity (GPP) Dataset 2019 (GeoTIFF) - ZIP file
<p>Product: Gross Primary Productivity (GPP)</p> <p>Year: 2019</p> <p>Region: Australia</p> <p>Temporal Scale: 8 Days</p> <p>Spatial Resolution: 500 meters</p> <p>Method: Light-use-efficiency (LUE) approach.</p> <p>Referred Publication: https://www.sciencedirect.com/science/article/pii/S0048969718307149</p> <p>https://onlinelibrary.wiley.com/doi/10.1111/gcb.12261</p> <p>This archive has been uploaded as a .zip file, due to repeated difficulties uploading single GeoTIFFs to Zenodo. </p> <p><strong>NOTE: As ArcPy classifies 'no data' pixels as very low negative values, users should mask all data values below 0 to prevent these affecting statistics. </strong></p>
North America (SW) Gross Primary Productivity (GPP) Dataset 2020 (GeoTIFF) - ZIP file
<p>Product: Gross Primary Productivity (GPP)</p> <p>Year: 2020 </p> <p>Region: North America (southwest)</p> <p>Temporal Scale: 8 Days</p> <p>Spatial Resolution: 500 meters</p> <p>Method: Light-use-efficiency (LUE) approach.</p> <p>Referred Publication: https://www.sciencedirect.com/science/article/pii/S0048969718307149</p> <p>https://onlinelibrary.wiley.com/doi/10.1111/gcb.12261</p> <p>This archive has been uploaded as a .zip file, due to repeated difficulties uploading single GeoTIFFs to Zenodo. </p> <p><strong>NOTE: As ArcPy classifies 'no data' pixels as very low negative values, users should mask all data values below 0 to prevent these affecting statistics. </strong></p>
Australia Gross Primary Productivity (GPP) Dataset 2018 (GeoTIFF) - ZIP file
<p>Product: Gross Primary Productivity (GPP)</p> <p>Year: 2018 </p> <p>Region: Australia</p> <p>Temporal Scale: 8 Days</p> <p>Spatial Resolution: 500 meters</p> <p>Method: Light-use-efficiency (LUE) approach.</p> <p>Referred Publication: https://www.sciencedirect.com/science/article/pii/S0048969718307149</p> <p>https://onlinelibrary.wiley.com/doi/10.1111/gcb.12261</p> <p>This archive has been uploaded as a .zip file, due to repeated difficulties uploading single GeoTIFFs to Zenodo. </p> <p><strong>NOTE: As ArcPy classifies 'no data' pixels as very low negative values, users should mask all data values below 0 to prevent these affecting statistics. </strong></p>
Australia Gross Primary Productivity (GPP) Dataset 2020 (GeoTIFF) - ZIP file
<p><br>Product: Gross Primary Productivity (GPP)</p> <p>Year: 2020 </p> <p>Region: Australia</p> <p>Temporal Scale: 8 Days</p> <p>Spatial Resolution: 500 meters</p> <p>Method: Light-use-efficiency (LUE) approach.</p> <p>Referred Publication: https://www.sciencedirect.com/science/article/pii/S0048969718307149</p> <p>https://onlinelibrary.wiley.com/doi/10.1111/gcb.12261</p> <p>This archive has been uploaded as a .zip file, due to repeated difficulties uploading single GeoTIFFs to Zenodo. </p> <p><strong>NOTE: As ArcPy classifies 'no data' pixels as very low negative values, users should mask all data values below 0 to prevent these affecting statistics. </strong></p>
Amazon Gross Primary Productivity (GPP) Dataset 2020 (GeoTIFF) - ZIP file
<p>Product: Gross Primary Productivity (GPP)</p> <p>Year: 2020 </p> <p>Region: Amazon</p> <p>Temporal Scale: 8 Days</p> <p>Spatial Resolution: 500 meters</p> <p>Method: Light-use-efficiency (LUE) approach.</p> <p>Referred Publication: https://www.sciencedirect.com/science/article/pii/S0048969718307149</p> <p>https://onlinelibrary.wiley.com/doi/10.1111/gcb.12261</p> <p>This archive has been uploaded as a .zip file, due to repeated difficulties uploading single GeoTIFFs to Zenodo. </p> <p><strong>NOTE: As ArcPy classifies 'no data' pixels as very low negative values, users should mask all data values below 0 to prevent these affecting statistics. </strong></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.