Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
270
datasets available to search
ShareScore release 0.9.0
Dataset results
270 results for “Climatology”
Polar low climatology over the Nordic Seas for 1995-2009
<p>Polar low climatology over the Nordic and Barents seas based on satellite passive microwave data from September 1995 to April 2009. </p>
Mediterannean Sea-Temperature and Salinity Annual Climatology
<p>Mediterranean Sea Annual Climatology computed from SeaDataNet V2 data set with the use of DIVA software tool 4.6.11. The Climatology covers the geographical region W6.25-E36.5, N30-N40 on 30 standards depths from 5-4000m. The observations cover the period 1950-2015.</p>
Mediterannean Sea-Temperature and Salinity Annual Climatology for six periods (1950-1959, 1960-1969, 1970-1979, 1980-1989, 1990-1999, 2000-2015)
<p>Mediterranean Sea Annual Climatology for six periods (1950-1959, 1960-1969, 1970-1979, 1980-1989, 1990-1999, 2000-2015) using the SeaDataNet V2 aggregated dataset and the DIVA software tool 4.6.11. The Climatology covers the geographical region W6.25-E36.5, N30-N40 and is computed on 30 standards depths from 5-4000m. The observations are from the period 1950-2015.</p>
Mediterannean Sea-Temperature and Salinity Monthly Climatology
<p>Mediterranean Sea Monthly Climatology using the SeaDataNet V2 aggregated dataset and the DIVA software tool 4.6.11. The Climatology covers the geographical region W6.25-E36.5, N30-N40 and is computed on 31 standards depths from 0-4000m. The observations are from the period 1950-2015.</p>
Mediterannean Sea-Temperature and Salinity Annual Climatology for 57 running decades from 1950-1959 to 2006-2015
<p>Mediterranean Sea Annual Climatology for 57 running decades from 1950-1959 to 2006-2015 using the SeaDataNet V2 aggregated dataset and the DIVA software tool 4.6.11. The Climatology covers the geographical region W6.25-E36.5, N30-N40 and is computed on 30 standards depths from 5-4000m. The observations are from the period 1950-2015.</p>
Mediterannean Sea-Temperature and Salinity Seasonal Climatology for six periods (1950-1959, 1960-1969, 1970-1979, 1980-1989, 1990-1999, 2000-2015)
<p>Mediterranean Sea Seasonal Climatology for six periods (1950-1959, 1960-1969, 1970-1979, 1980-1989, 1990-1999, 2000-2015) using the SeaDataNet V2 aggregated dataset and the DIVA software tool 4.6.11. The Climatology covers the geographical region W6.25-E36.5, N30-N40 and is computed on 31 standards depths from 0-4000m. The observations are from the period 1950-2015.</p>
Mediterannean Sea-Temperature and Salinity Seasonal Climatology
<p>Mediterranean Sea Seasonal Climatology using the SeaDataNet V2 aggregated dataset and the DIVA software tool 4.6.11. The Climatology covers the geographical region W6.25-E36.5, N30-N40 and is computed on 31 standards depths from 0-4000m. The observations are from the period 1950-2015.</p>
Mediterannean Sea-Temperature and Salinity Seasonal Climatology for 57 running decades from 1950-1959 to 2006-2015
<p>Mediterranean Sea Seasonal Climatology for 57 running decades from 1950-1959 to 2006-2015 using the SeaDataNet V2 aggregated dataset and the DIVA software tool 4.6.11. The Climatology covers the geographical region W6.25-E36.5, N30-N40 and is computed on 31 standards depths from 0-4000m. The observations are from the period 1950-2015.</p>
Climatological radiation covariance between temperature and radiative heating anomaly composites
Open the record for dataset details and reuse information.
Supporting Data and Code for "Managing to Climatology: Improving semi-arid agricultural risk management using crop models and a dense meteorological network"
<p>Without reliable seasonal climate forecasts, farmers and managers in other weather-sensitive sectors might adopt practices that are optimal for recent climate conditions. To demonstrate this principle, crop simulation models driven by a dense meteorological network were used to identify climate-optimal planting dates for U.S. Southern High Plains (SHP) un-irrigated agriculture. This method converted large samples of SHP growing season weather outcomes into climate-representative cotton and sorghum yield distributions over a range of planting dates. Best planting dates were defined as those that maximized median cotton lint (April 24) and sorghum grain (July 1) yields. Those optimal yield distributions were then converted into corresponding profit distributions reflecting 2005-2019 commodity prices and fixed production costs. Both crop's profitability under variable price conditions and current SHP climate conditions were then compared based on median profits and loss probability, and through stochastic dominance analyses that assumed a slightly risk-averse producer.</p>
Drought assessment of China in 2002–2017 based on a Detrended Combined Climatological Deviation Index
<p>Detrended-CCDI.zip:The monthly drought results were identified by Detrended-CCDI from April 2002 to August 2017.Detrended-CCDI was calculated using GRACE data and precipitation data after the seasonal decomposition of time series based on the Loess (STL) method.</p> <p>Drought frenquency.zip:Drought frequency is the “n/m” ratio per pixel. “n” is the number of consecutive months (more than or equal to 3 months) in which Detrended-CCDI was less than or equal to -0.53, and “m” is the total number of months in the study period.</p> <p>Drought trend.zip:Drought trend was obtained using the modified Mann-Kendall (MMK) trend test method.</p> <p>Regional drought events.zip:A regional drought event is defined when the area where Detrended-CCDI is less than or equal to - 0.53 exceeds 100,000 km<sup>2</sup> and the duration is more than 3 months. The severity of drought events is expressed by the average cumulative value of monthly Detrended-CCDI pixels.</p> <p>The China Flood and Drought Disaster Bulletin.zip:2002-2017 the China Flood and Drought Disaster Bulletins were from Ministry of Water Resources of the People's Republic of China</p>
MERIS FRS L2 CDOM absorption monthly climatology Western Baltic Sea
<p>MERIS FRS L2 (full resolution level 2) product from 2003 to 2012 was used to create a monthly climatology of CDOM absorption for the Western Baltic Sea region. The MERIS FRS L2 product was processed with the C2RCC algorithm (Doerffer and Schiller, 2007) which has been trained with data-sets from European coastal waters. Full details of the post processing of the MERIS data into a climatology can be found in Röhrenbach (Bachelor Thesis, 2019). A monthly climatology for the complete time frame of the MERIS archive was created and includes the mean value, standard deviation and number of observations for each point.</p>
Collection of The tropical cyclones best-track data (CMABSTdata) and the daily surface climatological stations dataset for China (V3.0) from CMA
<p>This dataset contains the tropical cyclones best-track data from the China Meteorological Administration (CMABSTdata)<strong>[1] </strong>and the daily surface climatological stations dataset for China (V3.0)<strong>[2] </strong>from 1979-2019 from China Meteorological Administration.</p> <p>[1] Y<span>ing, M., Zhang, W., Yu, H., Lu, X., Feng, J., Fan, Y., . . . Chen, D. (2014). An overview of the China Meteorological Administration tropical cyclone database. <em>Journal of Atmospheric and Oceanic Technology, 31</em>(2), 287-301. </span></p> <p>[2]<span>(CMA, http://data.cma.cn)</span></p>
Climatologies for the IceNet Preprocessing Tool
<p>Climatologies for the IceNet Preprocessing Tool</p>
Supporting Data and Code for "Managing to Climatology: Improving semi-arid agricultural risk management using crop models and a dense meteorological network"
Open the record for dataset details and reuse information.
Data from: Extending the climatological concept of ‘Detection and Attribution’ to global change ecology in the Anthropocene
Open the record for dataset details and reuse information.
Data from: Climatologies at high resolution for the earth's land surface areas
Open the record for dataset details and reuse information.
Polar Radiant Energy in the Far InfraRed Experiment (PREFIRE) Surface Emissivity Sorted All-sky Climatology from PREFIRE Satellite 2 R01
Polar Radiant Energy in the Far InfraRed Experiment (PREFIRE) Surface Emissivity Sorted All-sky Climatology from PREFIRE Satellite 2 (PREFIRE_SAT2_3-SFC-SORTED=ALLSKY) contains monthly climatologies of the Level 2 PREFIRE Surface Emissivity from the PREFIRE Satellite 2 collection (PREFIRE_SAT2_2B_SFC), which is derived from data collected by the PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE) aboard PREFIRE-SAT2. Dual CubeSats each carry a PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE), a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation from approximately 5 to 53 µm. Most polar emissions are in the FIR but have not been measured on a large scale. PREFIRE aims to fill knowledge gaps in the global energy budget by more accurately characterizing polar emissions. This information will then be assimilated into global circulation and other models to predict future conditions more accurately.PREFIRE_SAT1_3-SFC-SORTED-ALLSKY climatologies include 1) gridded, time-averaged spectral emissivity sorted by surface type, and 2) gridded standard deviations of time-averaged spectral emissivity sorted by surface type. The grids are 1°x1° and time averaging is currently monthly. Spatially, the cross-track dimension is retained, which consists of 8 distinct tracks. Full swath climatologies will be generated in addition to ascending- and descending-only subsets to account for diurnal variability. The purpose of this collection is to identify surface emissivity behaviors based on surface type and to assimilate PREFIRE surface emissivity data into climate models to further understand and more accurately predict future climates.The data format is NetCDF4.The sorted surface emissivity climatologies for the sister instrument aboard PREFIRE-SAT1 can be found in the PREFIRE_SAT1_3-SFC-SORTED-ALLSKY collection.
Polar Radiant Energy in the Far InfraRed Experiment (PREFIRE) Surface Emissivity Sorted All-sky Climatology from PREFIRE Satellite 1 R01
Polar Radiant Energy in the Far InfraRed Experiment (PREFIRE) Surface Emissivity Sorted All-sky Climatology from PREFIRE Satellite 1 (PREFIRE_SAT1_3-SFC-SORTED=ALLSKY) contains monthly climatologies of the Level 2 PREFIRE Surface Emissivity from the PREFIRE Satellite 1 collection (PREFIRE_SAT1_2B_SFC), which is derived from data collected by the PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE) aboard PREFIRE-SAT1. Dual CubeSats each carry a PREFIRE Thermal Infrared Spectrometer (TIRS-PREFIRE), a push broom spectrometer with 63 channels measuring mid- and far-infrared (FIR) radiation from approximately 5 to 53 µm. Most polar emissions are in the FIR but have not been measured on a large scale. PREFIRE aims to fill knowledge gaps in the global energy budget by more accurately characterizing polar emissions. This information will then be assimilated into global circulation and other models to predict future conditions more accurately.PREFIRE_SAT1_3-SFC-SORTED-ALLSKY climatologies include 1) gridded, time-averaged spectral emissivity sorted by surface type, and 2) gridded standard deviations of time-averaged spectral emissivity sorted by surface type. The grids are 1°x1° and time averaging is currently monthly. Spatially, the cross-track dimension is retained, which consists of 8 distinct tracks. Full swath climatologies will be generated in addition to ascending- and descending-only subsets to account for diurnal variability. The purpose of this collection is to identify surface emissivity behaviors based on surface type and to assimilate PREFIRE surface emissivity data into climate models to further understand and more accurately predict future climates.The data format is NetCDF4.The sorted surface emissivity climatologies for the sister instrument aboard PREFIRE-SAT2 can be found in the PREFIRE_SAT2_3-SFC-SORTED-ALLSKY collection
Surface seawater concentration of acetone: monthly climatology
Acetone is one of the most abundant oxygenated volatile organic compounds (VOCs) in the atmosphere. The oceans impose a strong control on atmospheric acetone, yet the oceanic fluxes of acetone remain poorly constrained. The air-sea exchange of acetone is largely controlled by the surface seawater concentration of acetone. This dataset consists of the surface seawater concentration of acetone, predicted by an observationally trained machine-learning algorithm (random forest). The observationally trained machine-learning algorithm is discussed in Wang et al. (2019). The training dataset includes ship-borne observations from a number of previous studies: Yang et al. (2014a); Yang et al. (2014b); Dixon et al. (2014); Beale et al. (2013); Kameyama et al. (2010); Hudson et al. (2007); Marandino et al. (2005); Marandino et al. (Knorr06). This dataset is in 0.9*1.25 (degree*degree) horizontal resolution (finite volume), and can be used to calculate the bi-directional air-sea exchange of acetone.
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.