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.
1,117
datasets available to search
ShareScore release 0.9.0
Dataset results
1,117 results for “1980”
Santa Barbara Channel Marine BON Darwin Core Archive: Nearshore kelp forest integrated benthic cover, 1980-ongoing
The Santa Barbara Channel Marine Biodiversity Observation Network (SBCMBON) tracks long-term patterns in species abundance and diversity. This dataset contains cover of kelp forest sessile invertebrates, understory macroalgae, and substrate types by integrating data from four contributing projects working in the kelp forests of the Santa Barbara Channel, USA. Divers collect data on using either uniform point contact (UPC) or random point contact (RPC) methods. The four contributing projects are two research projects: The Santa Barbara Coastal LTER (SBC LTER) and the Partnership for Interdisciplinary Studies of Coastal Oceans (PISCO), the kelp forest monitoring program of the Santa Barbara Channel National Park, and the San Nicolas Island monitoring program supported by USGS. Together, these projects have recorded data for more than 200 species at approximately 100 sites on both the mainland coast and on the Santa Barbara Channel Islands. Sampling began in 1982 and is ongoing. Data were collected by human observation (divers using SCUBA) during regular surveys. Percent cover is recorded for taxa where individuals cannot be counted. Cover can be calculated from the data here as the fraction of total points at which the taxon was present x 100. With UPC and RPC methods, multiple species can be recorded at any given point. The total percent cover of all species combined using this method can exceed 100%; however, the percent cover of any single species cannot exceed 100%. See Methods for information on integration and data processing. MBON is funded by National Aeronautics and Space Administration (NASA), Bureau of Ocean Energy Management (BOEM), and National Oceanic and Atmospheric Administration (NOAA). This dataset is formatted as a Darwin Core Archive (DwC-A, occurrence core). This is a derived data product and see provenance for the source data. For users who are interested in using all or part of this integrated datasets, please contact data owners to discuss your
Santa Barbara Channel Marine BON Darwin Core Archive: Nearshore kelp forest integrated quad and swath cover, 1980-ongoing
The Santa Barbara Channel Marine Biodiversity Observation Network (SBCMBON) tracks long-term patterns in species abundance and diversity. This dataset contains counts of algae and invertebrates (both sessile and mobile) by integrating data from four contributing projects working in the kelp forests of the Santa Barbara Channel, USA. The four contributing projects are two research projects: The Santa Barbara Coastal LTER (SBC LTER) and the Partnership for Interdisciplinary Studies of Coastal Oceans (PISCO), the kelp forest monitoring program of the Santa Barbara Channel National Park, and the San Nicolas Island monitoring program supported by USGS. Together, these projects have recorded data for more than 200 species at approximately 100 sites on both the mainland coast and on the Santa Barbara Channel Islands. Sampling began in 1982 and is ongoing. Data were collected by human observation (divers using SCUBA) during regular surveys. The data table documents the number of organisms and the area over which that number was counted for calculation of areal abundance. Data were collected by human observation (divers using SCUBA) during regular surveys. The algae and invertebrate counts record the number of taxa found in each plot, including quad (small square plots such as 1 or 2 m2) and swath (large linear plots such as 60 m2). See Method and protocol for information on integration and data processing. MBON is funded by National Aeronautics and Space Administration (NASA), Bureau of Ocean Energy Management (BOEM), and National Oceanic and Atmospheric Administration (NOAA). This dataset is formatted as a Darwin Core Archive (DwC-A, occurrence core). This is a derived data product and see provenance for the source data. For users who are interested in using all or part of this integrated datasets, please contact data owners to discuss your research interests, data-related issues or any other questions. A recommended citation for the data package is available from the down
University of Michigan Biological Station Weather Observations 1980 to Present
This data set includes daily year-round weather measurements and observations recorded at the University of Michigan Biological Station (UMBS) from 1980-present. Data primarily serves the purpose of benefitting UMBS researchers needing access to weather data as a confounding variable. Parameters include maximum and minimum temperatures, precipitation, snowfall, snowpack, and estimated cloud cover and type recorded daily at approximately 08:00. Maximum and minimum temperatures were measured at the Biological Station campus with National Weather Service liquid-in-glass thermometers housed in a Weather Service (a.k.a., Cotton Region) shelter located on State Road. Precipitation was measured with either a Belfort Rainfall Transmitter 5915 or an ETI NOAH IV Total Precipitation Gauge in the “UV Field” located immediately northwest of the Biological Station campus. Snowfall and snowpack were also measured in the UV Field on snowboards using National Weather Service according to National Weather Service snow measurement guidelines. Cloud cover was estimated in oktas and classified into one of the ten basic cloud types.
Daily climatological observations from Sapelo Island, Georgia, from June 1980 through June 2001
Daily summaries of air temperature and precipitation were collected from the National Weather Service weather station on Sapelo Island, Georgia, from June 1980 through June, 2001. The station (NWS Station 97808) is located at the University of Georgia Marine Institute (UGAMI), and has been in continuous operation since 1957. Observations are logged and transmitted to the NWS by UGAMI personnel, and archived by the NOAA National Climatic Data Center (http://www.ncdc.noaa.gov/). The monitoring station is described on the Georgia Coastal Ecosystems web site at http://gce-lter.marsci.uga.edu/lter/research/mon/ugami.htm.
Gap-filled daily precipitation at the 15 long-term NPP sites at Jornada Basin LTER, 1980-ongoing
This package contains gap-filled daily precipitation values for the 15 NPP sites at Jornada Basin LTER in southern New Mexico, USA. Sites were selected to represent the 5 major ecosystem types in the Chihuahuan Desert (upland grasslands, playa grasslands, mesquite-dominated shrublands, creosotebush-dominated shrublands, tarbush-dominated shrublands). For each ecosystem type, three sites were selected to represent the range in variability in production and plant diversity; thus the locations are not replicates. Gap-filled daily precipitation was calculated for the period from 1980 to 2020 at each site using the closest rain gauges that provided a minimum resolution of daily precipitation data. The Methods section and attached documents describe this process in detail. The rain gauges used are described, with respect to their relationship to NPP sites, in the attached "daily_gapfill_ppt_gauge_usage.csv" file. Although automated weather stations became operational at all NPP sites in 2013 (except P-SMAL, in 2017), updates to this data package are ongoing and are intended to gap-fill any missing or invalid data from the weather stations.
Record of storm events and associated water levels for the Virginia Coast Reserve, 1980-2013
This empirical storm record for the Virginia Coast Reserve was created using a 34-year record of hourly wave hindcast data - including wave height (Hs) and wave period (Tp) - from the USACE's Wave Information Studies buoy offshore Hog Island in the Virginia Coast Reserve (Station 63183, 22 m water depth) and hourly records of water level from the nearest NOAA tide gauge (Station 8631044, Wachapreague, VA). The record includes wave and water level statistics for each event relevant for coastal modeling applications: storm start and end times, duration, total water level, still water level, as well as concurrent tidal amplitude, non-tidal residual, Hs, and Tp. The raw data is processed by first removing the 1 yr running median, which accounts for non-stationarity in wave and water level parameters due to inter-annual and decadal variability while maintaining seasonality. The median of the last 3 years is then applied to the entire time series such that the new time series is representative of the current climate. A year-by-year tidal analysis is performed to obtain the tidal amplitude and non-tidal residual. Lastly, water elevations are calculated following the run-up equations of Stockdon et al. (2006). Storm events are then extracted from the corrected time series by conditioning on Hs: events are identified as periods of 8 or more consecutive hours with deep-water significant wave heights greater than 2.1 m, which is the minimum monthly averaged wave height for periods in which waters levels exceeded the measured average dune toe elevation (1.9 m NAVD88) of barriers in the Virginia Coast Reserve. In total, we identify 282 independent sea-storm events over the 34-year record, resulting in an average of 8.3 events per year. See Reeves et al. (2021; https://doi.org/10.1029/2021GL092958) and supplementary information therein for complete details of the methodology.
LAMASUS NUTS-level economic data 1980-2021
<p>This dataset comprises spatial and temporal economic data compiled from the Annual Regional Database of the European Commission (ARDECO) and education attainment from Eurostat, covering the period from 1980 to 2021(2024). The dataset consists of three files, each corresponding to a different level of NUTS coding (NUTS 1-3) according to the 2016 NUTS specification.</p> <p>For each file, the following columns are included: <br><br>Identifier:</p> <ol> <li>NUTS Code: The unique identifier for the NUTS (2016) region</li> <li>Year: The year of the data point</li> </ol> <p>Variables:</p> <p> 3. - 8. Hours Worked by NACE sector in 1000 hours (empHour_*)<br> 9. - 15. Employment by NACE sector in 1000 jobs (emp_*)<br> 16. Total employment in 1000 jobs (empl)<br> 17. GDP at constant prices ref. 2015 in mio EUR (gdp)<br> 18. - 23. GVA by NACE sector at constant prices ref. 2015 in mio EUR (gva_*)<br> 24. Total Labour Force in 1000 jobs (labour)<br> 25. Total Population (Regional Accounts) in persons (pop)<br> 26. - 31. Compensation of Employees by NACE sector at constant prices ref. 2015 in mio EUR (wage_*)<br> 32. Share of low education workers in per cent (loweduc) [not available for NUTS3]<br> 33. Share of high education workers in per cent (terteduc) [not available for NUTS3]</p> <p>The temporal dimension is yearly, ranging from 1980 to 2021(2024). The spatial dimension is identified by NUTS codes (2016), with granularity ranging from level 1 to level 3.</p> <div> <p>This dataset has been created as part of LAMASUS Project under the scope of Deliverable 3.2 titled "Database on EU policies and payments for agriculture, forest, and other LUM related drivers ". The data is directly linked to the work described on pages 45-47 belonging to section 3.3 Sectoral Income and Employment. The full text of the deliverable can be accessed via: <a href="https://www.lamasus.eu/wp-content/uploads/LAMASUS_D3.2_policy-and-payment-database.pdf">https://www.lamasus.eu/wp-content/uploads/LAMASUS_D3.2_policy-and-payment-database.pdf.</a></p> <p>Please note that this dataset is intended for research and analysis in the fields of climatology, environmental science, and related disciplines. Users are encouraged to cite this dataset appropriately if utilized in academic or scientific publications.</p> </div>
Data for publication "Benefits of open access to researchers from lower-income countries: A global analysis of reference patterns in 1980–2020"
<p>Data to reproduce figures for the publication "Benefits of open access to researchers from lower-income countries: A global analysis of reference patterns in 1980–2020" (DOI: 10.1177/01655515241245952). Each file contains the data underlying the figure corresponding to the file name.</p>
Zonal mean data of the SOCOLv4 reference experiment (1980-2018)
<p>Zonal mean data of the SOCOLv4 reference experiment (1980-2018). O3, NOx, ClOx, HOx, BrOx, Temperature.</p>
ERA-NUTS: meteorological time-series based on C3S ERA5 for European regions (1980-2021)
<p><strong># ERA-NUTS (1980-2021)</strong></p> <p>This dataset contains a set of time-series of meteorological variables based on <a href="https://climate.copernicus.eu/climate-reanalysis">Copernicus Climate Change Service (C3S) ERA5 reanalysis</a>. The data files can be downloaded from here while notebooks and other files can be found on the <a href="https://github.com/energy-modelling-toolkit/era-nuts-code">associated Github repository</a>.</p> <p>This data has been generated with the aim of providing hourly time-series of the <strong>meteorological variables</strong> commonly used for power system modelling and, more in general, studies on energy systems.</p> <p>An example of the analysis that can be performed with ERA-NUTS is shown <a href="https://youtu.be/zVeF8Dv6jlE">in this video</a>.</p> <p><strong>Important</strong>: <em>this dataset is still a work-in-progress, we will add more analysis and variables in the near-future. If you spot an error or something strange in the data please tell us <a href="mailto:matteo.de-felice@ec.europa.eu">sending an email</a> or opening an Issue in the <a href="https://github.com/energy-modelling-toolkit/era-nuts-code">associated Github repository</a>.</em></p> <p><strong>## Data</strong><br> The time-series have hourly/daily/monthly frequency and are aggregated following the <a href="https://ec.europa.eu/eurostat/web/nuts/background">NUTS 2016 classification</a>. NUTS (Nomenclature of Territorial Units for Statistics) is a European Union standard for referencing the subdivisions of countries (member states, candidate countries and EFTA countries).</p> <p>This dataset contains NUTS0/1/2 time-series for the following variables obtained from the <strong>ERA5 reanalysis data</strong> (in brackets the name of the variable on the Copernicus Data Store and its unit measure):</p> <p> - <strong>t2m</strong>: 2-meter temperature (`2m_temperature`, Celsius degrees)<br> - <strong>ssrd</strong>: Surface solar radiation (`surface_solar_radiation_downwards`, Watt per square meter)<br> - <strong>ssrdc</strong>: Surface solar radiation clear-sky (`surface_solar_radiation_downward_clear_sky`, Watt per square meter)<br> - <strong>ro</strong>: Runoff (`runoff`, millimeters)<br> - <strong>sd</strong>: Snow depth (`sd`, meters)<br> <br> There are also a set of derived variables:<br> - <strong>ws10</strong>: Wind speed at 10 meters (derived by `10m_u_component_of_wind` and `10m_v_component_of_wind`, meters per second)<br> - <strong>ws100</strong>: Wind speed at 100 meters (derived by `100m_u_component_of_wind` and `100m_v_component_of_wind`, meters per second)<br> - <strong>CS</strong>: Clear-Sky index (the ratio between the solar radiation and the solar radiation clear-sky)<br> - <strong>RH</strong>: Relative Humidity (computed following Lawrence, BAMS 2005 and Alduchov & Eskridge, 1996)<br> - <strong>HDD</strong>/<strong>CDD</strong>: Heating/Cooling Degree days (derived by 2-meter temperature the <a href="https://ec.europa.eu/eurostat/cache/metadata/en/nrg_chdd_esms.htm">EUROSTAT definition</a>.</p> <p>For each variable we have <strong>367 440 hourly samples</strong> (from 01-01-1980 00:00:00 to 31-12-2021 23:00:00) for <strong>34/115/309 regions</strong> (NUTS 0/1/2).<br> <br> The data is provided in two formats:</p> <p> - NetCDF version 4 (all the variables hourly and CDD/HDD daily). NOTE: the variables are stored as `int16` type using a `scale_factor` to minimise the size of the files.<br> - Comma Separated Value ("single index" format for all the variables and the time frequencies and "stacked" only for daily and monthly)<br> <br> All the CSV files are stored in a zipped file for each variable.</p> <p><strong>## Methodology</strong></p> <p>The time-series have been generated using the following workflow:</p> <p> 1. The NetCDF files are downloaded from the Copernicus Data Store from the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=form">ERA5 hourly data on single levels from 1979 to present</a> dataset<br> 2. The data is read in R with the <a href="http://www.meteo.unican.es/climate4R">climate4r</a> packages and aggregated using the function `/get_ts_from_shp` from <a href="https://github.com/matteodefelice/panas">panas</a>. All the variables are aggregated at the NUTS boundaries using the average except for the runoff, which consists of the sum of all the grid points within the regional/national borders.<br> 3. The derived variables (wind speed, CDD/HDD, clear-sky) are computed and all the CSV files are generated using R<br> 4. The NetCDF are created using `xarray` in Python 3.8.</p> <p><strong>## Example notebooks</strong></p> <p>In the folder `notebooks` on the <a href="https://github.com/energy-modelling-toolkit/era-nuts-code">associated Github repository</a> there are two Jupyter notebooks which shows how to deal effectively with the NetCDF data in `xarray` and how to visualise them in several ways by using matplotlib or the <a href="https://github.com/kavvkon/enlopy">enlopy</a> package.</p> <p>There are currently two notebooks:</p> <p> - <strong>exploring-ERA-NUTS</strong>: it shows how to open the NetCDF files (with Dask), how to manipulate and visualise them.<br> - <strong>ERA-NUTS-explore-with-widget</strong>: explorer interactively the datasets with [<a href="https://jupyter.org/">jupyter</a>]() and <a href="https://ipywidgets.readthedocs.io/en/stable/">ipywidgets</a>.</p> <p>The notebook `exploring-ERA-NUTS` is also available rendered as HTML.<br> <br> <strong>## Additional files</strong></p> <p>In the folder `additional files`on the <a href="https://github.com/energy-modelling-toolkit/era-nuts-code">associated Github repository</a> there is a map showing the spatial resolution of the ERA5 reanalysis and a CSV file specifying the number of grid points with respect to each NUTS0/1/2 region.</p> <p><strong>## License</strong></p> <p>This dataset is released under <a href="https://creativecommons.org/licenses/by/4.0/">CC-BY-4.0 license</a>.</p> <p><strong>## Changelog</strong></p> <p><strong>2022-04-08 </strong>Added Relative Humidity (RH)<br> <strong>2022-03-07 </strong>Added the missing month in CDD/HDD <br> <strong>2022-02-08 </strong>Updated the wind speed and temperature data due to missing months. </p> <p> </p>
ERA5-Land selected indicators daily aggregates for Africa, 1980
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1980.</p> <p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p> <p>For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.</p> <p>Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.</p>
CLDF dataset derived from Z'graggen's "Madang Comparative Wordlists" from 1980
<p>Cite the source of the dataset as:</p> <blockquote> <p>Z'graggen, J A. (1980) A comparative word list of the Northern Adelbert Range Languages, Madang Province, Papua New Guinea. Canberra: Pacific Linguistics.</p> </blockquote>
Heatwave indexes 1980-2018
<p>Heatwaves were defined as minimum temperature exceeding the 99th percentile of minimum temperature for the reference period (1986-2005) for four or more days.</p> <p>From this definition, four heatwave indicators are derived:</p> <p>Number of heatwave instances</p> <p>Total number of days of heatwave</p> <p>Heatwave degree-days over threshold, being the sum of the differences between the daily temperatures and the 99th percentile threshold</p> <p>Mean length of heatwaves</p> <p>Heatwave mean degrees over threshold, being the heatwave degree-days divided by the total number of days.</p> <p> </p> <p>These definitions were applied to ECMWF ERA5 temperature data at 0.5˚ grid resolution for the years 1980-2018. Each indicator is provided in a separate file.</p> <p> </p>
NASA Eulerian Snow On Sea Ice Model Version 1.1 (NESOSIMv1.1) data: 1980 - 2024
<p><strong>Repository updates</strong></p> <p><em>Update on Sep 12th 2024: The repository now includes NESOSIM v1.1 output from September 1st 2022 to April 30th 2023 and September 1st 2023 to April 30th 2024 </em></p> <p><em>Update on Sep 5th 2022: </em>The repository now includes NESOSIM v1.1 output from September 1st 2021 to April 30th 2022</p> <p><em>Update on June 7th 2022: </em>The repository now includes NESOSIM v1.1 output from September 1st 2021 to March 31st 2022</p> <p><em>Update on March 8th 2021: </em>The gridded forcing files are now available in the gridded_forcings.zip file. Data are stored as Python pickles which can be easily read in by the core NESOSIM source code. </p> <p><em>Update on March 8th 2021: </em>The repository now includes zip files of gridded forcing (snowfall, winds, ice drift, ice concentration, initial conditions) as well as gridded Operation IceBridge snow depths. </p> <p><em>Update on January 30th 2021: </em>The repository now also includes a NESOSIM v1.1. daily gridded snow climatology using the mean (np.nanmean) of all data available between September 1 2010 and April 30 2020.</p> <p><strong>Overview</strong></p> <p>NESOSIM is a three-dimensional, two-layer (vertical), Eulerian snow on sea ice budget model developed with the primary aim of producing daily estimates of the depth and density of snow on sea ice across the polar oceans through the winter accumulation season, generally September through April (Petty et al., 2018).</p> <p>This repository contains model output from September 1st 1980 to April 30th 2021 [and September 1st 2021 to March 31st 2022 as of June 7th 2022] based on the NESOSIM v1.1 code release which is available on GitHub (https://github.com/akpetty/NESOSIM/tree/v1.1) and archived through Zenodo (10.5281/zenodo.4448355). More information about changes between the v1.0 and v1.1 model framework can be found in those links.</p> <p>A preprint is now available in <em>The Cryosphere Discuss</em> explaining these upgrades and their impacts on ICESat-2 winter Arctic sea ice thickness estimates (Petty et al., 2022). </p> <p><strong>Data production:</strong></p> <p>Data are re-initialized at the end of summer each year (September 1st) using summer near-surface air temperature-scaled initial snow depths and run through until the end of April of the following year. The 1987-1988 winter is missing due to the lack of passive-microwave derived ice concentration data available during this period. Daily data are generated on a 100 km x 100 km North Polar Stereographic grid (EPSG: 3413) across the entire Arctic Ocean including the peripheral seas.</p> <p><strong>Forcings:</strong></p> <p><em>NB: Recent year runs often require the use of near-real-time data products, so the underlying forcings used in this v1.1 release can change in time, as noted below:</em></p> <ul> <li>Snowfall: European Center for Medium Range Weather Forecasts (ECMWF) ERA5 (https://cds. climate.copernicus.eu, September 1 1980 onwards2) + CloudSat scaling (Cabaj et al., 2020).</li> <li>Near-surface winds: ECMWF ERA5 (https://cds. climate.copernicus.eu, September 1 1980 onwards).</li> <li>Sea ice drift: NSIDC Polar Pathfinder v4 (https://nsidc.org/data/nsidc-0116, September 1 1980 to April 30 2019), OSI SAF merged (https://osi-saf.eumetsat.int/products/osi-405-c, September 1 2019 onwards).</li> <li>Sea ice concentration: Final v3 NSIDC Climate Data Record (https://nsidc.org/data/g02202/versions/3/, September 1 1980 to December 31 2020), and near-real-time v2 NSIDC Climate Data Record (https://nsidc.org/data/g10016, January 1 2021 onwards).</li> <li>Near-surface air temperature (to derive temperature-scaled initial conditions): ECMWF ERA5 (https://cds. climate.copernicus.eu, September 1 1980 onwards).</li> </ul> <p><em>The forcings used to generate each winter dataset are described in a new 'forcings' variable in each NetCDF file. </em></p> <p><strong>Operation IceBridge snow depths:</strong></p> <p>The repository now also includes the gridded Operation IceBridge snow depths we used for calibration purposes, as described in Petty et al., (2022). The data contained within <em>gridded_oib_snowdepths.zip</em> includes the daily gridded data on the NESOSIM v1.1 100 km domain, ordered by day of collection. Data are stored as Python pickles and text files and include estimates derived from the following snow depth algorithms: SRLD (2009-2015): snow radar layer detection, JPL (2009-2015): Jet Propulsion Laboratory, GSFC (2009-2015): Goddard Space Flight Center, NSIDC (2009-2012): archived NASA GSFC data on the NSIDC, QL (2013-2019): NSIDC quick-look data based on the GSFC algorithm. MEDIAN (2010-2015): consensus snow depth from median of GSFC, JPL and SRLD. </p> <p><strong>References:</strong></p> <p>Cabaj, A., P. J. Kushner, C. G. Fletcher, S. Howell, A. Petty (2020), Constraining reanalysis snowfall over the Arctic Ocean using CloudSat observations, Geophysical Research Letters, 47, doi:10.1029/2019GL086426.</p> <p>Petty, A. A., M. Webster, L. N. Boisvert, T. Markus (2018), The NASA Eulerian Snow on Sea Ice Model (NESOSIM) v1.0: Initial model development and analysis, Geosci. Model Dev., doi: 10.5194/gmd-11-4577-2018.</p> <p>Petty A. A., N. Keeney, A. Cabaj, P. Kushner, M. Bagnardi (2023), Winter Arctic sea ice thickness from ICESat-2: upgrades to freeboard and snow loading estimates and an assessment of the first three winters of data collection, The Cryosphere, 17, 127–156, doi: 10.5194/tc-17-127-2023.</p>
Australian CDHWs based on AWAP 1980 - 2014
<p>Data published in Ridder, N.N., Pitman, A.J. and Ukkola, A.M., 2022. High impact compound events in Australia. <em>Weather and Climate Extremes</em>, <em>36</em>, p.100457. <a href="https://doi.org/10.1016/j.wace.2022.100457">https://doi.org/10.1016/j.wace.2022.100457</a></p>
Modeled dynamic and thermodynamic sea ice growth in the Arctic 1980-2019 from NAOSIM
<p>This data set is related to the paper "Evidence for an Increasing Role of Ocean Heat in Arctic Winter Sea Ice Growth" by Ricker et al. (2021). Please refer to this study for further details.</p> <p>Ricker, R., Kauker, F., Schweiger, A., Hendricks, S., Zhang, J., & Paul, S. (2021). Evidence for an Increasing Role of Ocean Heat in Arctic Winter Sea Ice Growth, Journal of Climate, 34(13), 5215-5227. Retrieved Nov 24, 2022, from https://journals.ametsoc.org/view/journals/clim/34/13/JCLI-D-20-0848.1.xml</p>
Tropical cyclone and equatorial wave data in ERA5 (1980-2018)
<p><strong>The dataset consists of global tropical cyclone (TC) track and equatorial wave data derived from ERA5 (1980-2018). The dataset was originally used to produce the paper "Equatorial waves as useful precursors to tropical cyclone occurrence and intensification" published in Nature Communications in 2023. </strong>See the Methods section in the article for details.</p> <p><strong>Note</strong>: the TC data are produced by <strong>Kevin Hodges</strong> (Reading University) using the TRACK method (Hodges and Emerton, 2015; Hodges et al., 2017); the wave data are produced by <strong>Gui-Ying Yang</strong> (Reading University) based on the method in Yang et al., 2003. ERA5 data is generated by ECMWF and distributed by the C3S CDS. ERA5 data used in this paper are archived from <a href="https://cds.climate.copernicus.eu/#!/search?text=ERA5&type=dataset">https://cds.climate.copernicus.eu/#!/search?text=ERA5&type=dataset</a>.</p> <p> </p> <p><strong>TC track data </strong>(ERA5 TC-matched.zip)</p> <p>TCs are identified and tracked by cyclonic vorticity centres in the ECMWF fifth generation climate reanalysis (ERA5) from six‐hourly atmospheric data, using the TRACK method (Hodges and Emerton, 2015; Hodges et al., 2017). The TRACK scheme used in this paper includes the following processes. First, the vertical average of the relative vorticity between 850 and 600 hPa is obtained. This is then spatially filtered using spherical harmonics to T63 resolution; the large‐scale background with total wavenumbers n ≤ 5 is removed. Vorticity maxima in the Northern Hemisphere and minima in the Southern Hemisphere are determined on the T63 grid and then used as starting points to obtain the off‐grid locations using B‐spline interpolation and maximisation methods. Then, in the first instance, all positive vorticity centres that exceed 0.5 Cyclonic Vorticity Unit (CVU, with 1.0 CVU=1.0*10<sup>-5</sup> s<sup>-1</sup>) in the range 0°–60°N (negative vorticity centres that are below -0.5 CVU in the range 0°–60°S) are identified through the data time series. The tracking is performed by first initialising a set of tracks using a nearest neighbour method and then refining them by minimising a cost function for track smoothness subject to adaptive constraints on track smoothness and displacement distance in a time step. After the tracking is complete, the full T63 vorticity maxima at levels from 850 hPa up to 200 hPa (850, 700, 600, 500, 400, 300 and 200 hPa) are added to the tracks using a recursive search within a 5° radius (geodesic) of the tracked centre. This is used to test for the existence of a coherent vertical structure and a warm core.</p> <p>A matching process is applied to match the ERA5 tracks against the observed tracks, the Best Track from IBTrACS. An ERA5 track is matched to an IBTrACS track if the mean spatial separation is ≤5° over the corresponding paired track points and it is the track with the smallest separation. This process ensures that the ERA5 tracks are those storms actually ‘observed’ in IBTrACS (but they may have different lifecycle). This means that the ERA5 storm tracks have an extended lifecycle consistent, allowing the analysis of the “pre-TC” features. ERA5 “pre-TCG” is the first point of the above identified TC track in ERA5, which is at an earlier stage than the genesis in IBTrACS (normally the first track point reaching the tropical storm intensity). On average, the ERA5 pre-TCG events are 4.6 days earlier in time, and 2.7 CVU weaker in relative vorticity of the vortex, than the TCG events at the observed TCG time.</p> <p> </p> <p><strong>Wave data (</strong>qvr_coefficient_YYYY_k3-40_p2-10_28plev_era5.nc<strong>)</strong></p> <p>In this dataset, dynamical equatorial waves are derived by projecting global wind and geopotential height data onto an orthogonal basis defined by the horizontal equatorial wave structures obtained from the theory of disturbances to a resting atmosphere on the equatorial <em>β</em>-plane. Six‐hourly horizontal winds and geopotential height in ERA5 are used here. This method identifies horizontal wind (<em>u, v</em>) and geopotential height (<em>Z</em>) structures associated with distinct equatorial waves. Potential equatorial waves are identified by projecting <em>u</em>, <em>v</em> and <em>Z</em> in the tropics (24°S–24°N) at each pressure level onto the different equatorial wave modes, using their sinusoidal structure in the zonal direction and parabolic cylinder functions in the meridional direction. These basis functions used for the wave projection are orthogonal, meaning that the wave structures here are orthogonal since they are pre-described as a series of the basis functions. In the parabolic cylinder functions, the meridional trapping scale is y<sub>0</sub>=6°. Before the projection, a broad-band spectral filter, with wavenumber 3 to 40 and period 2 to 10 days, is applied to separate eastward and westward moving waves.</p> <p>This ERA5 wave dataset contains three equatorial wave modes: westward-moving mixed Rossby-gravity (WMRG) and meridional mode number n=1 and 2 Rossby (R1 and R2) waves. The dataset spans 39 years from 1980 to 2018 covering all seasons, with a 6-hourly interval at 1° resolution on 28 pressure levels from 1000 to 70 hPa. </p> <p> </p> <p>Reference </p> <p>Hodges, K. I., & Emerton, R. (2015). The prediction of Northern Hemisphere tropical cyclone extended life cycles by the ECMWF ensemble and deterministic prediction systems. Part I: Tropical cyclone stage. <em>Monthly Weather Review</em>, 143(12), 5091-5114.</p> <p>Hodges, K., Cobb, A., & Vidale, P. L. (2017). How well are tropical cyclones represented in reanalysis datasets?. <em>Journal of Climate</em>, 30(14), 5243-5264.</p> <p>Yang, G. Y., Hoskins, B., & Slingo, J. (2003). Convectively coupled equatorial waves: A new methodology for identifying wave structures in observational data. <em>Journal of the atmospheric sciences</em>, 60(14), 1637-1654.</p> <p>Feng, X., Yang, G.Y., Hodges, K., & Methven, J. (2023). Equatorial waves as useful precursors to tropical cyclone occurrence and intensification. <em>Nature Communications</em>.</p>
Greenland Ice Sheet modeled firn properties from SNOWPACK and the Community Firn Model (1980-2020)
<p>This dataset contains model output from the physics-based SNOWPACK firn model and the semi-empirical Community Firn Model (CFM) over the Greenland Ice Sheet from 1980 through 2020. Included are individual density profiles for locations with firn density observations as well as firn air content (FAC) calculated over different depth intervals. Data for both models are supplied. These data are used in a manuscript to be submitted to The Cryosphere journal (see Thompson-Munson et al., in review).</p>
Datos ERA5 Pichilemu 1980-2020
<p>Datos asociados el proyecto RISUE RED21992 del Ministerio de Educación de Chile.</p> <p>Los datos corresponden a salidas grilladas del reanálisis ERA5 con resolución temporal horaria entre los años 1980 y 2020 y dominio espacial entre 33.75°S y 34.75°S y entre 72.5°W y 71.25°W con resolución de 0.25 grados, equivalente a matrices numéricas de 5x6 celdas.</p> <p>Se incluyen las siguientes variables:</p> <ul> <li>Viento componente zonal (oeste-este) a 10 metros del suelo (10u)</li> <li>Viento componente meridional (sur-norte) a 10 metros del suelo (10v)</li> <li>Temperatura del aire a 2m (2t)</li> <li>Temperatura de punto de rocío a 2m (2d)</li> <li>Tasa de precipitación conectiva (crr) </li> <li>Tasa de precipitación de gran escala (lsrr)</li> </ul>
Waves Hindcast on the Senegalese Coast over the Last Four Decades (from 1980 to 2021). [A Dataset use in : SAMOU, M.S.; BERTIN, X.; SAKHO, I.; LAZAR, A.; SADIO, M.; DIOUF, M.B. Wave Climate Variability along the Coastlines of Senegal over the Last Four Decades. Atmosphere 2023]
<p>Computed from the WW3 Model, the last Four Decades Wave Hindcast is available on the Senegalese Coast through this present Dataset. Covering the period 1980 to 2021, this high resolution hindcast, both spatial (0.05x0.05) and temporal (1 h) provided all the wave parameters such as: the significant wave heights, the mean wave periods, the wave directions and the peak wave periods (to compute from wave frequencies) with an hourly interval.</p> <p>More details on this data (e.g., model implementation and validation) can be obtained in: SAMOU, M.S.; BERTIN, X.; SAKHO, I.; LAZAR, A.; SADIO, M.; DIOUF, M.B. Wave Climate Variability along the Coastlines of Senegal over the Last Four Decades. <em>Journal Atmosphere 2023</em>].</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.