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.
13,452
datasets available to search
ShareScore release 0.7.1
Dataset results
13,452 results for “Australia”
Vegetation survey (BACI and Paired-plots) from arid central Australia for impacts of buffel grass on resident native plant communities
<p>The data set accompanies the accepted paper in Ecosphere. The data set includes two experimental appraoches to assess the spread and impacts of buffel grass, Cenchrus cilairis, in the Aṉangu Pitjantjatjara Yankunytjatjara (APY) Lands of arid central Australia: a Before-After-Control-Impact (BACI) experiment over 25 years at 15 sites (surveyed in 1994-95 and 2018-19), and a spatially paired-plot (randomised-block) experiment at 18 sites (surveyed in 2018-19). Both experiments spanned two geographic regions (~ 300 km apart) and multiple vegetation communities amongst flat plains and rocky hills landforms. Each experimental design has a plant species data set, and a data set that includes site variables and summed relative cover of plant functional groups. Data collection methodology is described in the accompanying paper, and summarised here.</p> <p>Each site was one hectare in size. The ecological data was collected in accordance with standard biological survey methods in South Australia (Heard and Channon 1997), including recording of plant species and cover abundance, life form, height class and habitat variables including percent bare earth, litter, rock/strew and soil type (clay percent). Fire history for the previous 25 years was also available from fire scar mapping. Species cover-abundance was estimated in the field using a modified Braun-Blanquet scale and later converted to a raw continuous variable based on the mid-point of the cover class: 1% (1-10 plants, <5% cover); 2% (sparsely present, <5% cover; 3% (plentiful but <5% cover); 15% (5 to 25% cover class); 37% (25 to 50% cover class); 63% (50 to 75% cover class). Buffel grass was recorded on the same scale. Plant species were vouchered and identification checked post-field by the South Australian Hebarium. Plant taxonomy reflects current names (as of 2015) in the Biological Databases of South Australia and taxonomy was aligned between the 1990s and 2020s decades. Recently some species have been split into multiple species (e.g. <em>Acacia aneura</em>, Mulga) but this latest taxonomy was not adopted to retain taxonomic alignment within the dataset. The raw mid-point percent cover was converted to relative percent cover by dividing each species’ (or groups’) raw cover by the summed cover of all species at that site (including buffel grass + understorey + overstorey species). Classification of plants into functional groups was based on field assessed (1) height class + (2) life form, and literature-derived (3) life strategy (perennial or annual) + (4) Native status to South Australia. Height classes were grouped into overstorey (>1m in height) and understorey (≤1m). Summed relative cover for each functional group per site is included in the site and cover data sets to facilitate modelling of cover with site variables. The plant species data sets is the full list of species and cover abundance recorded at each site which can be used for analysis of community composition, diversity, turnover or individual species change. Sensitive species (one species in this dataset) has had the coordinates denatured by 10km due according to the requirements of the Biological Database of South Australia for sensitive species. All coordinates provided in MGA 52 Eastings and Northings (UTM, Australian National Grid). </p> <p>The authors wish to acknowledge Traditional Owners and Aṉangu Pitjantjatjara Yankunytjatjara (APY) Lands Organisation who gave permission for collaboration, data collection, photographs and reporting on and about their Traditional Lands. Data is jointly the Intellectual Property of Aṉangu as the Traditional Owners and the author team, and approval has been granted for research and publication use with appropriate acknowledgment of Aṉangu and the author team. The 1990s baseline data is also the Intellectual Property of the South Australian Government and is made publicly available under a licencing agreement with the Biological Databases of South Australia (licence number 2412). Many people assisted in the field during the 1990s and 2020s vegetation surveys and are wholly acknowledged. APY Land Management, Alinytjara Wilurara Landscape Board, Central Land Council, Ten Deserts Project, Charles Darwin University, South Australian Department for Environment and Water, State Herbarium of South Australia, Holsworth Wildlife Research Endowment, Jill Landsberg Trust and Ecological Society of Australia all provided either funding and/or in-kind support of the project. Study conducted with APY Executive Board approval, South Australian Scientific Permit Q26782 and Northern Territory Wildlife Permit 63104. </p> <p> </p> <p> </p>
Fish community data obtained from Antillean-Z fish trap deployment in the Eastern Gulf of Shark Bay, Australia from June 2013 to August 2013
This dataset consists of capture data of teleost communities caught in Antillean-Z fish traps deployed in 2013. Also included are anciliary environmental data as well as trap deployment information. This dataset will be used to determine changes in teleost community structure and abundance in response to an extreme temperature event and associated decline in seagrass cover in Shark Bay, Western Australia.
Monthly time series of rainfall, potential evapotranspiration and streamflow for 201 catchments in South-East Australia
<p>The data set contains data for 201 catchments located in South-Eastern Australia. The data was extracted from the datasets collated by Lerat, Thyer et al. (2020) including rainfall and potential-evapotranspiration data obtained from the Bureau of Meteorology Australian Water Outlook website (Frost, Ramchurn et al. 2016) and streamflow data obtained from the Bureau of Meteorology Water Data Online website (Bureau of Meteorology 2019). The data was collected over the period from 1980 to 2018, split into the two sub-periods 1980-1999 (Period 1) and 1999-2018 (Period 2).</p><p> </p><p>Bureau of Meteorology. (2019). "Water Data Online." from <a href="http://www.bom.gov.au/waterdata">http://www.bom.gov.au/waterdata</a>.</p><p>Frost, A. J., A. Ramchurn and A. Smith (2016). "The bureau's operational AWRA landscape (AWRA-L) Model." Bureau of Meteorology Technical Report.</p><p>Lerat, J., M. Thyer, D. McInerney, D. Kavetski, F. Woldemeskel, C. Pickett-Heaps, D. Shin and P. Feikema (2020). "A robust approach for calibrating a daily rainfall-runoff model to monthly streamflow data." Journal of Hydrology<strong>591</strong>: 125129.</p>
Simulated severe convective wind events and environments from the Bureau of Meteorology Atmospheric Regional Projections for Australia (BARPA)
<p>Contacts for further details:</p> <ul> <li>This data record and associated research: Andrew Brown (andrewb1@student.unimelb.edu.au)</li> <li>BARPA data: Chun Hsu Su (chunhsu.su@bom.gov.au), Christian Stassen (Christian.Stassen@bom.gov.au), Harvey Ye (harvey.ye@bom.gov.au)</li> </ul> <h1>Introduction</h1> <p>This record contains data in support of Brown et al. (2024), including post-processed regional climate model data, automatic weather station observations, and post-processed reanalysis data over southeastern Australia for various time periods <strong>over December-Febrary months only</strong> (see descriptions below). This data relates to analysis of severe convective wind gusts in historical and future climate, with analysis scripts in <a href="https://github.com/andrewbrown31/BARPA/tree/main/wind_gust_analysis">this repository</a>. The data are described here according to the directory structure of this record (noting the files and directories have been compressed into <code>barpa_data.tgz</code>), as well as the relevant data sources. </p> <h1>Data sources</h1> <ul> <li><strong>The Bureau of Meteorology Atmospheric Regional Projections for Australia (BARPA)</strong>. A regional climate model containing a regional (BARPA-R) and convection-permitting (BARPAC-M) configuration, with large-scale forcing from ERA-Interim (1990-2015) and ACCESS1-0 using a historical (1985-2005) and RCP8.5 (2039-2059) forcing. See Brown et al. (2024) and Su et al. (2021) for more details. Note that any reuse of this data should properly acknowledge the Australian Bureau of Meteorology. Note also that the BARPA data used here was produced as part of the <a href="https://www.climatechangeinaustralia.gov.au/en/projects/esci/">Electricity Sector Climate Information project</a> (with licence and disclaimers <a href="https://www.climatechangeinaustralia.gov.au/en/projects/esci/risk-assessment/#Disclaimer">here</a>)<em>,</em> with more current BARPA versions (not used here) available at <a href="https://dx.doi.org/10.25914/z1x6-dq28" target="_blank" rel="noopener">https://dx.doi.org/10.25914/z1x6-dq28</a>. </li> <li><strong>Measured wind gusts from automatic weather stations (AWS)</strong>. Gust data is provided by the <a href="http://www.bom.gov.au/climate/data/stations/">Australian Bureau of Meteorology</a>, from 272 AWS locations over 2005-2015. Note that any reuse of this data should properly acknowledge the Australian Bureau of Meteorology.</li> <li><strong>The ERA5 reanalysis </strong>from the European Center for Medium Range Weather Forecasting (Hersbach 2020).</li> <li><strong>The ERA-Interim reanalysis</strong> from the European Center for Medium Range Weather Forecasting (Dee 2011).</li> </ul> <h1>/10min_points</h1> <p>This directory contains .csv files, with wind gust and related environmental data at 10-minute intervals at point locations, corresponding to automatic weather station locations. Data is available over 2005-2015. Files are named in the form <code>barpac_m_aws_<state>.csv</code> and <code>barpac_m_aws_<state>_barpa_r_interp.csv</code>. Here, <state> represents different administrative regions in southeast Australia, including New South Wales (nsw), Victoria (vic), South Australia (sa) and Tasmania (tas). See Figure 1 in Brown et al. (2024) for a map of station locations, that is also included in the /meta directory. The <code>barpa_r_interp</code> suffix indicates that the BARPAC-M wind gusts have been interpolated to the BARPA-R grid for comparison.</p> <p>This data is used in Brown et al. (2024) for evaluation and analysis of BARPA wind gusts in the historical climate (forced by ERA-Interim). The user is directed to that paper for more information on data processing. For the .csv files here, column descriptions are provided in Table 1, below.</p> <h1>/daily_points</h1> <p>This directory contains .csv files, with data associated with daily maximum wind gusts at point locations. This data is derived from the 10min_points data described above, with the same column descriptions in Table 1, below. The different files in this directory are as follows:</p> <ul> <li> <p><code>barpac_m_aws_dmax_obs.csv</code><br>Daily maximum observed wind gust from AWS measurements, with associated wind gust ratio and environmental conditions from ERA5.</p> </li> <li> <p><code>barpac_m_aws_dmax_2p2km.csv</code><br>Daily maximum simulated wind gust from BARPAC-M at closest grid point to AWS location, with associated wind gust ratio, lighting flash count, and environmental conditions from BARPA-R.</p> </li> <li> <p><code>barpac_m_aws_dmax_12km.csv</code><br>Daily maximum simulated wind gust from BARPA-R at closest grid point to AWS location, with associated wind gust ratio, lightning flash count, and environmental conditions.</p> </li> <li> <p><code>barpac_m_aws_dmax_erai.csv</code><br>Daily maximum simulated wind gust from ERA-Interim at closest grid point to AWS location, with associated wind gust ratio and environmental conditions from ERA5.</p> </li> <li> <p><code>barpac_m_aws_dmax_2p2km_barpa_r_interp.csv</code><br>As in <code>barpac_m_aws_dmax_2p2km.csv</code>, but wind gusts are interpolated to the BARPA-R grid prior to calculating the daily maximum.</p> </li> </ul> <h1>/monthly_nc</h1> <p>This directory contains monthly netcdf files at 12 km horizontal grid spacing, with post-processed BARPA data, relating to simulated severe convective wind gusts (from BARPAC-M), and their associated large-scale environments (from BARPA-R). This includes BARPAC-M and BARPA-R data that has been forced by the ACCESS1-0 global climate model, that is intended for analysis of future changes in severe convective wind events and environments. For further information, the user can refer to the internal file metadata, as well as Brown et al. (2024). The files in this directory are as follows (where <experiment> is either <code>hist</code> for historical climate forcing (1985-2005) or <code>rcp</code> for RCP8.5 climate forcing (2039-2059)):</p> <ul> <li> <p><code>barpac_scws_<experiment>_monthly.nc</code><br>Monthly counts of simulated severe convective wind events from BARPAC-M, for each type of environment (see <code>cluster</code> in Table 1).</p> </li> <li> <p><code>barpar_<experiment>_monthly.nc</code><br>Monthly counts of favouable severe convective wind environments from BARPA-R (using <code>bdsd</code>, see Table 1), for each type of environment (see <code>cluster</code> in Table 1).</p> </li> <li> <p><code>barpac_scws_bdsd_<experiment>.nc</code><br>Monthly counts of simulated severe convective wind events from BARPAC-M, that occur under favourable environmental conditions from BARPA-R. For each type of environment (see <code>cluster</code> in Table 1).</p> </li> <li> <p><code>barpac_max_<experiment>_monthly.nc</code><br>Monthly maximum simulated severe convective wind gust, from BARPAC-M, for each type of environment (see <code>cluster</code> in Table 1).</p> </li> </ul> <h1>/daily_nc</h1> <p>This directory contains monthly netcdf files at 12 km horizontal grid spacing, with daily maximum wind gusts from BARPAC-M, as well as the wind gust ratio (see <code>wgr_4</code> in Table 1) and the type of convective environment (from BARPA-R, see <code>cluster</code> in Table 1). This includes BARPA data that has been forced by ERA-Interim and by ACCESS1-0. For further information, the user can refer to the file metadata, as well as Brown et al. (2024). The files in this directory are as follows (where <code><experiment></code> is either <code>historical</code> for historical climate forcing or <code>rcp85</code> for RCP8.5 climate forcing, <code><forcing_model></code> is either <code>erai</code> for ERA-Interim or <code>ACCESS1-0</code>, <<code>date1></code> is the file start date and <code><date2></code> is the file end date):</p> <ul> <li><code>barpa_scw_<forcing_model>_<experiment>_0_<date1>_<date2>.nc</code></li> </ul> <h1>/meta</h1> <p>Lists of AWS stations, for each administrative state, with a file containing column descriptions. Note that not all of the stations listed in these files are used for analysis. Fig1.jpeg is from Brown et al. (2024), showing the BARPAC-M domain (also defines the netcdf file spatial extents), and the location of AWS.</p> <h3>Table 1</h3> <table> <tbody> <tr> <td><strong>Column</strong></td> <td><strong>Name</strong></td> <td><strong>Notes</strong></td> </tr> <tr> <td>stn_id</td> <td>Automatic weather station (AWS) identifier</td> <td> </td> </tr> <tr> <td>time</td> <td>Wind gust time (UTC)</td> <td> </td> </tr> <tr> <td>gust</td> <td>Observed wind gust speed from AWS (m/s)</td> <td>Observed wind gusts are measured at a height of 10 m, and represent a 3-second average. Data is provided as a one-minute maximum, and is resampled to a 10-minute maximum here for comparison with BARPA</td> </tr> <tr> <td>wgr_4</td> <td>Wind gust ratio</td> <td>The observed wind gust ratio, defined as the ratio between <code>gust</code>, and the 4-hour mean from the 10-minute data here.</td> </tr> <tr> <td>time_6hr</td> <td>6-hourly time (UTC)</td> <td>The most recent 6-hourly time step prior to <code>time</code>, associated with environmental diagnostics.</td> </tr> <tr> <td>mu_cape</td> <td>Most unstable convective available potential energy (J/kg)</td> <td>Environmental diagnostic derived from BARPA-R. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>s06</td> <td>Bulk vertical wind shear from the surface to 6 km above ground level (m/s)</td> <td>Environmental diagnostic derived from BARPA-R. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>dcape</td> <td>Downdraft convective available potential energy (J/kg)</td> <td>Environmental diagnostic derived from BARPA-R. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>bdsd</td> <td>Brown and Dowdy (2021) Statistical Diagnostic (BDSD) for identifying favourable severe convective wind environments</td> <td>Environmental diagnostic derived from BARPA-R. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>qmean01</td> <td>Mass-weighted mean mixing ratio from the surface to 1 km (g/kg)</td> <td>Environmental diagnostic derived from BARPA-R. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>Umean06</td> <td>Mass-weighted mean wind speed from the surface to 6 km above ground level (m/s)</td> <td>Environmental diagnostic derived from BARPA-R. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>lr13</td> <td>Temperature lapse rate from 1 km above ground level to 3 km above ground level (◦C/km)</td> <td>Environmental diagnostic derived from BARPA-R. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>cluster</td> <td>Environment type</td> <td> <p>Based on statistical clustering of severe convective wind environments by Brown et al. 2023<br>0: Strong background wind cluster<br>1: Steep lapse rate cluster<br>2: High moisture cluster</p> <p>Derived from BARPA-R</p> </td> </tr> <tr> <td>s06_era5</td> <td>See s06</td> <td>Environmental diagnostic derived from ERA5. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>qmean01_era5</td> <td>See qmean01</td> <td>Environmental diagnostic derived from ERA5. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>Umean06_era5</td> <td>See Umea06</td> <td>Environmental diagnostic derived from ERA5. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>lr13_era5</td> <td>See lr13</td> <td>Environmental diagnostic derived from ERA5. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>bdsd_era5</td> <td>See bdsd</td> <td>Environmental diagnostic derived from ERA5. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>dcape_era5</td> <td>See dcape</td> <td>Environmental diagnostic derived from ERA5. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>mu_cape_era5</td> <td>See mu_cape</td> <td>Environmental diagnostic derived from ERA5. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>cluster_era5</td> <td>See cluster</td> <td> <p>Based on statistical clustering of severe convective wind environments by Brown et al. 2023<br>0: Strong background wind cluster<br>1: Steep lapse rate cluster<br>2: High moisture cluster</p> <p>Derived from ERA5</p> </td> </tr> <tr> <td>wg10_12km_point</td> <td>Simulated wind gust from BARPA-R (m/s)</td> <td>Intended to represent a 10 meter wind gust. See Ma et al. (2018) for gust parameterisation details.</td> </tr> <tr> <td>wgr_12km_point</td> <td>Wind gust ratio from BARPA-R </td> <td>See wgr_4</td> </tr> <tr> <td>wg10_2p2km_point</td> <td>Simulated wind gust from BARPC-M (m/s)</td> <td>Intended to represent a 10 meter wind gust. See Ma et al. (2018) for gust parameterisation details.</td> </tr> <tr> <td>wgr_2p2km_point</td> <td>Wind gust ratio from BARPAC-M (see wgr_4)</td> <td>See wgr_4</td> </tr> <tr> <td>n_lightning_fl</td> <td>Number of daily lightning flashes from BARPAC-M</td> <td>See Brown et al. (2024) for more information.</td> </tr> <tr> <td>erai_wg10</td> <td>Simulated wind gust from ERA-Interim (m/s).</td> <td>Intended to represent a 10 meter wind gust. Note that ERA-Interim is provided in 3-hourly intervals, rather than 10-minute intervals for BARPA.</td> </tr> </tbody> </table> <p> </p>
Sediment size dataset for Australia
<p>This repository contains a dataset of median grain size (d50) for the Australian coastline.</p> <p>The sediment samples were collected by <a href="https://www.sydney.edu.au/science/about/our-people/academic-staff/andrew-short.html">Professor Andrew D. Short</a> during field campaigns between 1979 and 1999. This dataset includes all the <em>sand</em> samples collected in the <em>swash zone</em><em>. </em>More information about this dataset can be found in <a href="https://link.springer.com/book/10.1007/978-3-030-14294-0#bibliographic-information">Australian Coastal Systems book</a>. The beach sand sample collection is physically stored at Geoscience Australia (Canberra) and can be viewed by contacting <a href="mailto:AusGeoSamples@ga.gov.au">AusGeoSamples@ga.gov.au</a>.</p> <p><strong>Dataset description</strong></p> <p>The data is contained in the file <strong>Australia_dataset.geojson</strong>. This geospatial layer contains a linestring for each individual beach/embayment. The coordinate system of the geospatial layer is WGS84.<br> <br> This geospatial layer matches and complements the Australian beach-face slope dataset published here <a href="https://doi.org/10.5281/zenodo.5606216">https://doi.org/10.5281/zenodo.5606216</a> and described in <em><a href="https://doi.org/10.5194/essd-14-1345-2022">Vos et al. 2022</a>. Note that not every beach in the layer contains a sediment size value.</em></p> <p>Each feature has the following attributes:</p> <p><strong>Grain-size and location attributes</strong><br> - <em>d50</em>: Median grain-size in millimetres. Obtained by sieving the sand samples. The original sand samples were donated to Geoscience Australia.<br> - <em>beach_id</em>: Database id for each beach, e.g., aus0001, aus0002, …, aus5255 (same as in <a href="http://doi.org/10.5281/zenodo.5606216">Vos et al. 2022</a>),<br> - <em>ABSAMP_id</em>: id of the sample in the ABSAMP database, e.g., nsw0001, tas001, qld001 etc. See the <a href="https://ecat.ga.gov.au/geonetwork/srv/api/records/d14b2b5b-332d-4e8f-b732-3d01a06866b2">Smartline</a> from Geoscience Australia for the location of each id.<br> - <em>distance_to_sample</em>: Distance in metres between the linestring in the ABSAMP database and the linestring in this layer.<br> - <em>latitude</em>: Latitude of the centroid of the beach in WGS84.<br> - <em>longitude</em>: Longitude of the centroid of the beach in WGS84.<br> - <em>beach_length</em>: Length of the beach or embayment, very long beaches (>50km) were split to optimise memory usage.<br> - <em>coastal_region</em>: Database id corresponding to the 23 coastal regions as identified by <a href="https://www.sciencedirect.com/science/article/pii/S0964569117306129">Thom et al. (2018)</a>.<br> - <em>primary_comp_id</em>: Database id corresponding to the 100 primary sediment compartments as identified by <a href="https://www.sciencedirect.com/science/article/pii/S0964569117306129">Thom et al. (2018)</a>.<br> - <em>secondary_comp_id</em>: Database id corresponding to the 361 secondary sediment Compartments as identified by <a href="https://www.sciencedirect.com/science/article/pii/S0964569117306129">Thom et al. (2018)</a>.</p> <p><strong>Wave climate and tide range attributes</strong><br> - Hs<em>_mean</em>: Mean Significant Wave Height at the closest grid point in the ERA5 re-analysis dataset (computed using 6-hourly time-series between 2010 and 2020).<br> - Hs<em>_max</em>: Max Significant Wave Height at the closest grid point in the ERA5 re-analysis dataset (computed using 6-hourly time-series between 2010 and 2020).<br> - <em>Tp_mean</em>: Mean Peak Wave Period at the closest grid point in the ERA5 re-analysis dataset (computed using 6-hourly time-series between 2010 and 2020).<br> - <em>Wdir_mean</em>: Mean Wave Direction at the closest grid point in the ERA5 re-analysis dataset (computed using 6-hourly time-series between 2010 and 2020).<br> - <em>Wdir_weighted_average</em>: More robust estimator of the Mean Wave Direction obtained by computing the average wave direction weighted by wave energy flux.<br> - <em>hsig_median</em>: Median Significant Wave Height from the closest grid point in the CAWCR re-analysis dataset<br> - <em>mstr</em>: Mean Spring Tide Range at the beach calculated from the closest grid point in the FES2014 global tide model</p> <p><strong>Beach-face slope attributes (same as in <a href="http://doi.org/10.5281/zenodo.5606216">Vos et al. 2022</a>)</strong><br> - <em>beach_slope_average</em>: Average of the beach-face slope at the site, weighted by the width of the confidence bands, value between 0.01 and 0.2<br> - <em>width_ci_average</em>: Average width of confidence band over the comprised transects, value between 0 and 0.19<br> - <em>quality_flag</em>: Quality flag indicating the confidence in the slope estimate at this transect (High, Medium or Low)<br> - <em>prc_msrt_obs</em>: percentage of the Mean Spring Tide Range observed by the satellite-derived shorelines<br> - <em>min_tide_obs</em>: Lowest tide level observed by the satellite-derived shorelines<br> - <em>max_tide_obs</em>: Highest tide level observed by the satellite-derived shorelines<br> - <em>sl_points_average</em>: Average number of datapoints in the shoreline time-series over the comprised transects</p>
A high-resolution, multi-decadal, free-running, hydrodynamic simulation of the East Australia Current System using the Regional Ocean Modeling System (Version 3.0, 1994-2019)
<p>The data is from a Regional Ocean Modelling System free-running, hydrodynamic simulation of the East Australian Current System. The model has a horizontal resolution of 2.5-6 km in the cross-shore direction and 5 km in the alongshore direction, and 30 vertical s-levels. The model domain covers the southeastern Australia oceanic region from 25.1-41.5°S and 147.1-162.2°E, and the grid is orientated 20 degrees clockwise to be predominantly orientated alongshore. The time period covered is 02 Jan 1994 to 28 Feb 2019. The model outputs provided are daily averages of the following variables: Two-dimensional variables: Sea surface height (zeta), barotropic cross-grid velocity (u) and barotropic along-grid velocity (v). Three-dimensional variables: Temperature (temp), salinity (salt), density (rho), cross-grid velocity (u), along-grid velocity (v) and vertical velocity (w), temperature time rate of change (temp_rate), temperature horizontal advection term (temp_hadv), temperature vertical advection term (temp_vadv), temperature horizontal diffusion term (temp_hdiff), temperature vertical diffusion term (temp_vdiff). In this version, the heat budget terms (temp_rate, temp_hadv, temp_vadv, temp_hdiff and temp_vdiff) are set to be zeros on the land.</p> <p> </p> <p>This model is part of the <a href="../records/8294716"><strong>South East Australian Coastal Ocean Forecast System (SEA-COFS)</strong></a> suite of models.</p>
AusENDVI: A long-term NDVI dataset for Australia
<p>AusENDVI (<strong>Aus</strong>tralian <strong>E</strong>mprical <strong>NDVI</strong>) is a monthly, 5-km gridded estimate of NDVI across Australia from 1982-2022. It is built by calibrating and harmonising NOAA's Climate Data Record AVHRR NDVI data to MODIS MCD43A4 NDVI using a gradient boosting ensemble decision tree method. Additionally, the datasets are gapfilled using a synthetic NDVI dataset. The methods are extensively described in an <a href="https://doi.org/10.5194/essd-16-4389-2024">Earth System Science Data publication.</a></p> <p>AusENDVI consists of several datasets, each dataset has a description in the attributes of the NetCDF file that describes its provenance. The naming convention is "AusENDVI_<model_type>_<year_range>_<version>.nc". </p> <ol> <li> <p><em>AusENDVI-clim_gapfilled_1982_2013</em>. Calibrated and harmonised Climate Data Record AVHRR NDVI data from Jan. 1982 to Dec. 2013. This version of the dataset used climate data in the calibration and harmonisation process and has the best agreement statistics with MODIS MCD43A4 NDVI. The dataset has been gap filled using the methods described in the accompanying publication.</p> </li> <li><em>AusENDVI-clim_MCD43A4_gapfilled_1982_2022</em>. This dataset consists of calibrated and harmonised NOAA Climate Data Record AVHRR NDVI data from Jan. 1982 to Feb. 2000, joined with MODIS-MCD43A4 NDVI data from Mar. 2000 to Dec. 2022. This version of the dataset _used climate data_ in the calibration and harmonisation process. The dataset has been gapfilled using the methods described in the accompanying publication</li> <li> <p><em>AusENDVI-noclim_1982_2013</em>. Calibrated and harmonised Climate Data Record AVHRR NDVI data from Jan. 1982 to Dec. 2013. This version of the dataset did not use climate data in the calibration and harmonisation process and the dataset has not been gap filled.</p> </li> <li> <p><em>AusENDVI-synthetic_1982_2022</em>. This dataset consists of synthetic NDVI data that was built by training a model on the joined _AusENDVI-clim_ and _MODIS-MCD43A4 NDVI_ timeseries using climate, woody-cover-fraction, and atmospheric CO2 as predictors. The synthetic NDVI is used for gap filling.</p> </li> </ol> <p>All datasets are in 'EPSG:4326' projection, and have a spatial resolution of 0.05 degrees. Geographic coordinate information is contained in the `spatial_ref` variable. </p> <p>A <strong>Jupyter Notebook </strong>is also provided that shows how to load, plot, QC mask, reproject, and gap-fill AusENDVI datasets. The notebook is effectively a 'readme' file.</p> <ul> <li>The notebook is also available to view/download <a href="https://nbviewer.org/github/cbur24/AusENDVI/blob/main/notebooks/analysis/AusENDVI_loading_example.ipynb">here</a></li> </ul> <p>An open-source <strong>github repository </strong>details the methods used to create these datasets</p> <ul> <li>https://github.com/cbur24/AusENDVI</li> </ul> <p> </p> <p>A few small changes to the datasets were implemented in <strong>version 0.2.0:</strong></p> <ol> <li>All datasets now have their values clipped to the range 0-1</li> <li>The AusENDVI-clim dataset is now gapfilled, and includes a QC layer</li> <li>The merged <em>AusENDVI-noclim_MCD43A4_1982_2022</em> dataset was removed to simplify the number of datasets included in the repository. Users who want to join the 'noclim' and MODIS datasets can do so by clipping out MCD43A4 from the <em>AusENDVI-clim_MCD43A4_gapfilled_1982_2022 </em>dataset.</li> <li>The accompanying Jupyter Notebook 'readme' has been updated.</li> </ol>
Geospatial Modelling of Australia's National Electricity Market - Dataset
<p>This dataset contains information relating to the topology of Australia's largest electricity transmission network, along with details pertaining to the technical and economic characteristics of generators operating within this grid. Information has been compiled from publicly available datasets released by the Australian Energy Market Operator (AEMO) [1, 2] and Geoscience Australia (GA) [3, 4, 5]. Potential applications include the development of economic dispatch, power-flow, and unit commitment models.</p> <p>The network is comprised of 912 nodes, 1406 AC edges, and three HVDC links. Information regarding forward and reverse power-flow limits for two AC interconnectors is also provided. Latitude and longitude coordinates are given for each node, with the network based off of geospatial datasets obtained from GA [3, 4, 5]. Signals for electricity demand at each node were derived using regional load profiles in combination with population data obtained from the Australian Bureau of Statistics (ABS) [6]. Allocation methods outlined in [7, 8] were used to disaggregate regional load profiles according to the geospatial distribution of Australia's population. Construction of the generator dataset involved compiling information obtained from AEMO's Market Management System Data Model (MMSDM) [1] and National Transmission Network Development Plan (NTNDP) [2] datasets. Historic generator dispatch signals were also obtained from AEMO [1], allowing the output of market models to be compared with realised outcomes.</p> <p>For further information regarding the contents of each csv file please refer to <code>dataset_summary.pdf</code>. Jupyter Notebooks at [9] contain the Python code necessary to reproduce these datasets.</p> <p><strong>Version history:</strong></p> <p><strong>v1.3 - Documentation update:</strong></p> <ul> <li>The document summarising datasets, <code>dataset_summary.pdf</code>, has been updated.</li> </ul> <p><strong>v1.2 - Startup cost correction:</strong></p> <ul> <li>Startup cost column labels in <code>generators.csv</code> were mistakenly switched (specifically, SU_COST_WARM and SU_COST_HOT). This has now been corrected.</li> </ul> <p><strong>v1.1 - Transmission line parameters and demand allocation update</strong></p> <ul> <li><strong>Transmission lines: </strong>Line resistance and shunt susceptance values have been updated. Transmission line lengths and voltages have also been added to <code>network_edges.csv</code>. AC interconnector information is split over two files to better capture aggregate flow limits defined over the New South Wales - Victoria interconnector. Connection points for these interconnectors are described in <code>network_ac_interconnector_links.csv</code>, while <code>network_ac_interconnector_flow_limits.csv</code> contains aggregate forward and reverse power flow limits.</li> <li><strong>Demand allocation: </strong>The algorithm used to approximate demand at different nodes has been updated. The new method constructs a Voronoi tessellation based on network nodes. These cells are then overlapped with geospatial ABS population data, which are used to estimate the number of people served by each node.</li> </ul> <p><strong>v1.0 - First release</strong></p>
Paired field measurements of suspended-sediment concentration, turbidity, acoustic backscatter, and particle size compiled from various estuaries in the United States and Australia
<p>Field measurements of suspended-sediment concentration, turbidity, acoustic backscatter, and particle size are compiled from various estuaries in the United States and Australia to investigate the utility of combining optical and acoustic backscatter measurements for the estimation of suspended-sediment concentration under changes in floc particle size and density. </p> <p>Theory, analysis, and interpretation of the data is available in Livsey et al (2023). Data collected from the Chesapeake Bay, US were compiled from Fall et al (2022). Data collected on the Brisbane River were collected by Livsey et al (2022). Data collected for all other locations were compiled from Livsey et al (2022). </p> <p>Data collected by Fall et al (2022) utilized a LISST 100x. Data collected by Livsey et al (2022, 2023) utilized a LISST 200x. Data files for each instrument are provided. </p> <p>Funding for this research was provided by an Advance Queensland Industry Research Fellowship, Queensland University of Technology, and Queensland Department of Environment and Science.</p> <p>References</p> <p>Fall, Kelsey A., Massey, Grace M., and Friedrichs, Carl T., (2020). The importance of organic content to fractal floc properties in estuarine surface waters, insights from video, LISST, and pump sampling: Supporting data. Data. William & Mary. https://doi.org/10.25773/7gbc-794 6739</p> <p>Livsey, D., Turner, R., Grace, P., and Crosswell, & Andy Steven. (2022). Field and laboratory measurements of suspended-sediment particle size and concentration from nine rivers draining to the Great Barrier Reef (1.0). Data. Zenodo. https://doi.org/10.5281/zenodo.6788303</p> <p>Livsey, D., Turner, R., and Grace, P. (2023). Combining optical and acoustic backscatter measurements for monitoring of fine suspended-sediment concentration under changes in particle size and density. Water Resources Research. <a href="https://doi.org/10.1029/2022WR033982">https://doi.org/10.1029/2022WR033982</a></p> <p> </p>
Radar-derived storm characteristics and convective diagnostics associated with hourly maximum measured wind gusts around Australia
<p>The data in this record describes various characteristics associated with hourly measured surface wind gusts across various locations in Australia, with these characteristics and data sources described below.</p> <p>This record provides all data used in the preparation of Brown et al. (2023a), except for lightning data that can be obtained from the <a href="https://wwlln.net/">World Wide Lightning Location Network archive</a></p> <p><strong>Record contents</strong></p> <ul> <li><em>gust_observations.zip</em><br> Within this zip archive, a <em>.csv</em> file is provided for wind gust observations, along with associated storm statistics from radar, and convective diagnostics from a global reanalysis. These data are provided for each of the 20 radar domains listed in Brown et al. (2023a). The <em>.csv</em> files follow the structure: <em>gust_observations_x.csv, </em>where <em>x </em>is the identification number for each radar from the <a href="https://www.openradar.io/operational-network">Australian Unified Radar Archive</a>.<br> </li> <li><em>station_details.csv</em><br> This file provides details on the automatic weather stations that measure the wind gusts, with station identifiers (column=Station_id) consistent between <em>station_details.csv </em>and<em> gust_observations_x.csv</em>.<br> </li> <li><em>Table1.pdf</em> <br> Descriptions of convective diagnostics from reanalysis, that are provided in <em>gust_observations_x.csv</em>. This table has been extracted from the supplementary information of Brown et al. (2023a), and references in this table can be found therein.<br> </li> <li><em>radar_details.pdf</em> <br> Taken from Table 1 from Brown et al. (2023a), showing the details of radars used here for storm statistics in <em>gust_observations_x.csv</em>.<br> </li> <li><em>Fig1.jpeg</em><br> Taken from from Brown et al. (2023a), showing a map of the radar domains used here for storm statistics in <em>gust_observations_x.csv</em>.</li> </ul> <p><strong>Wind gust data</strong></p> <p>Measured wind gusts here represent a 3-second average wind speed, at a height of 10 m above ground level. We also provide some derived quantities from the gust data (see table below). Gust data is provided by the <a href="http://www.bom.gov.au/climate/data/stations/">Australian Bureau of Meteorology</a>, from 204 automatic weather stations (<em>station_details.csv)</em>, chosen to be within 100 km of a weather radar with sufficient archived data. These data are originally provided by the Bureau of Meteorology at 1-minute frequency, representing a maximum over a 1-minute interval, but are resampled in this record to hourly frequency, for comparisons with other hourly data below (see Brown et al. (2023a) for details of this resampling). Note that any reuse of this data should properly acknowledge the Australian Bureau of Meteorology.</p> <p><strong>Radar data</strong></p> <p>Radar data is obtained by the <a href="https://www.openradar.io/">Australian Unified Radar Archive</a> (AURA), produced from operational weather radar within the Australian Bureau of Meteorology network. The level1b data used here is available from the AURA dataset on the Australian NCI under a CC4-BY-NC licence from <a href="https://dx.doi.org/10.25914/5f4c85732ee80">https://dx.doi.org/10.25914/5f4c85732ee80</a>. Various properties derived from radar reflectivity and Doppler velocity data is reported here in association with the wind gust observations. These properties are only reported if there is a storm object within 10 km and 10 minutes of the gust location (see Brown et al. (2023a) for storm object definition). Radar properties are described in the table below.</p> <p><strong>Environmental data</strong></p> <p>Various convective diagnostics are associated with wind gust observations, representing the convective environment and large-scale wind profile. These diagnostics are derived from a combination of pressure-level and surface-level ERA5 data (Hersbach et al. 2020), which is provided at hourly intervals on a 0.25-degree latitude-longitude grid, hosted on the Australian NCI (<a href="http://dx.doi.org/10.25914/5fb115b82e2ba">http:// dx.doi.org/10.25914/5fb115b82e2ba</a>). Details on these convective diagnostics are provided in Brown et al. (2023a), and<strong> </strong>in <em>Table1.pdf</em> as provided in this record.</p> <p><strong>Column descriptions</strong></p> <p>The following table provides descriptions of columns of <em>gust_observations_x.csv</em></p> <table> <thead> <tr> <th scope="col">Column name</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>dt_utc</td> <td>Time of the measured wind gust, from the automatic weather station data (YYYY-MM-DD HH:MM:SS UTC)</td> </tr> <tr> <td>Station_id</td> <td>Identification number of the weather station that measured the gust. See <em>station_details.csv </em>for details of each station</td> </tr> <tr> <td>Wind_gust_observed</td> <td>The measured wind gust speed (m/s)</td> </tr> <tr> <td>Peak_to_mean_wind_gust_ratio</td> <td>Ratio of the measured wind gust to the 4-hour mean at that station (with the window centred on the gust time)</td> </tr> <tr> <td>SCW</td> <td>Is the measured gust a severe convective wind event?<br> 0: Gust is either less than 25 m/s, does not have a storm object within 10 km, or has a peak-to-mean wind gust ratio less than 2.<br> 1: Gust is greater than 25 m/s, has a storm object within 10 km, and has a peak-to-mean wind gust ratio greater than 2.</td> </tr> <tr> <td>Radar_id</td> <td>Radar identification number (see <em>radar_details.pdf)</em></td> </tr> <tr> <td>Storm_speed</td> <td>Translational speed of the parent storm object (m/s). Only defined if Storm_in10km=1</td> </tr> <tr> <td>Storm_angle</td> <td>Angle of parent storm object movement. In units of degrees from N. Only defined if Storm_in10km=1</td> </tr> <tr> <td>Parent_storm_class</td> <td>The type of parent storm associated with a gust. Only defined if Storm_in10km=1. Possible types are:<br> "Non-linear"<br> "Linear"<br> "Cellular"<br> "Cell cluster"<br> "Supercellular"<br> "Embedded supercell"<br> See Brown et al. (2023a) for classification details</td> </tr> <tr> <td>Storm_in10km</td> <td>Is there a radar-derived storm object within 10 km of the gust, observed no more than 10 minutes prior to the gust?<br> 0: No<br> 1: Yes<br> See Brown et al. (2023a) for a definition of "storm object"</td> </tr> <tr> <td>Major_axis_length</td> <td>The length of the major axis of an ellipse fitted to the parent storm object (km). Only defined if Storm_in10km=1</td> </tr> <tr> <td>Minor_axis_length</td> <td>The length of the minor axis of an ellipse fitted to the parent storm object (km). Only defined if Storm_in10km=1</td> </tr> <tr> <td>Local_reflectivity_maxima</td> <td>Number of local reflectivity maxima within the parent storm object. Only defined if Storm_in10km=1</td> </tr> <tr> <td>Maximum_storm_altitude</td> <td>The maximum height of the parent storm radar reflectivity object (km). Only defined if Storm_in10km=1</td> </tr> <tr> <td>Azimuthal_shear</td> <td>Azimuthal shear of the parent storm object derived from radar data (s<sup>-1 </sup>x 1000). Only defined if Storm_in10km=1. See Brown et al (2023a) for a discussion of azimuthal shear and processing applied to this quantity here.</td> </tr> <tr> <td>ERA5_time</td> <td>Time of the ERA5 environmental data that is associated with the measured gust, corresponding to the closest previous hour (YYYY-MM-DD HH:MM:SS UTC).</td> </tr> <tr> <td>ERA5_latitude</td> <td>Location of closest ERA5 (land) grid point to measured gust, in degrees of latitude</td> </tr> <tr> <td>ERA5_longitude</td> <td>Location of closest ERA5 (land) grid point to measured gust, in degrees of longitude</td> </tr> <tr> <td>Environmental_cluster</td> <td>Event type, based on statistical clustering of environmental data (Brown et al. 2023b)<br> 0: Strong background wind cluster<br> 1: Steep lapse rate cluster<br> 2: High moisture cluster</td> </tr> <tr> <td>Umean06</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>Umean01</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>U10</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>WindGust10</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>S06</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>EBWD</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>Umeanwindinf</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>SRHE</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>SRH06</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>DMI</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>LR_subcloud</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>LR_freezing</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>LR03</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>LR13</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>WMSI</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>BDSD</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>ConvGust_wet</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>ConvGust_dry</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>GUSTEX</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>DmgWind</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>DmgWind_fixed</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>DCAPE</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>WMPI</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>WINDEX</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>DowndraftTemp</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>ThetaeDiff</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>TEI</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>WNDG</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>DCP</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>SCP</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>SCP_fixed</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>SHERB</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>SHERBE</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>SWEAT</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>MUCS6</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>MLCS6</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>EffCS6</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>T_Totals</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>K_Index</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>Eff_CAPE</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>Eff_LCL</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>MLCAPE</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>ML_LCL</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>MUCAPE</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>MU_LCL</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>Qmean01</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>Qmean06</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> </tbody> </table> <p><strong>References</strong></p> <p>Brown, A., Dowdy, A., Lane, T. P., & Hitchcock, S. (2023b). Types of Severe Convective Wind Events in Eastern Australia. <em>Monthly Weather Review</em>, <em>151</em>(2), 419–448. https://doi.org/10.1175/MWR-D-22-0096.1</p> <p>Brown, A., A. Dowdy, T. P. Lane, & Hitchcock, S. (2023a). Long-term observational characteristics of different severe convective wind types around Australia. <em>Wea. Forecasting</em>, <a href="https://doi.org/10.1175/WAF-D-23-0069.1">https://doi.org/10.1175/WAF-D-23-0069.1</a>, in press.</p> <p>Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz‐Sabater, J., et al. (2020). The ERA5 Global Reanalysis. <em>Quarterly Journal of the Royal Meteorological Society</em>, qj.3803. https://doi.org/10.1002/qj.3803</p>
Abundance of holothurians (Echinodermata: Holothuroidea) in the Rowley Shoals, Australia, in 2018 and 2023
This dataset represents the first island-scale compilation of sea cucumber (Echinodermata: Holothuroidea) abundance data across all three protected atolls of the Rowley Shoals in the northwest coast of Australia. We specifically quantified holothurian abundance by doing standardized benthic surveys (SCUBA visual census along 50-meter transects) across 34 sites in both 2018 and 2023 as follows: Clerke Reef (n=18 sites), Imperieuse Reef (n=10 sites), and Mermaid Reef (n=6 sites). Given that many species here documented are listed as threatened or vulnerable by the IUCN, this dataset will help inform research and management efforts to protect these unique species and reef ecosystems.
Percent cover, species richness, and canopy height data of seagrass communities in Shark Bay, Western Australia, with accompanying abiotic data, from October 2012 to July 2013
This dataset provides cover, canopy height, and species richness estimates for seagrass communities throughout Shark Bay, as well as ancilliary physical data. These data will be used to determine spatial patterns of seagrass loss and recovery, as well as recovery rates and succisional changes in community composition, if present.
Capture data for sharks caught in standardized drumline fishing in Shark Bay, Western Australia, with accompanying abiotic data, from February 2008 to July 2014.
This dataset provides data on large sharks captured during standardized drumline fishing and is used to monitor shark catch rates, sex ratios and size distributions in Shark Bay.
Capture data for sharks caught in standardized drumline fishing in Shark Bay, Western Australia, with accompanying abiotic data, from January 2012 to April 2014.
This file provides data on standardized drumline fishing effort (gear deployment data) targeting large elasmobranchs in Shark Bay, Western Australia between 2012 and 2014
Count data of air-breathing fauna from visual transect surveys including water temperature, time, sea and weather conditions in Shark Bay Marine Park, Western Australia from February 2008 to July 2014
This dataset provides information on the relative abundances of air breathing fauna (dugongs, dolphins, sea snakes, marine birds, and sea turtles) in the study area of the Eastern Gulf of Shark Bay, Western Australia. The dataset comprises transects that quantify animal abundances in three microhabitats (shallow seagrass banks, seagrass bank edges, and deep sandy channels). These microhabitats vary in their food supply as well as their potential to facilitate or inhibit detection and escape from predators, mainly the tiger shark (Galeocerdo cuvier). As a result these data have been used to examine risk-specific habitat use behaviors of these fauna, in addition to general abundance estimates.
Marine turtles captured during haphazard at-sea surveys in Shark Bay, Australia from February 2008 to December 2013
This dataset provides information on marine turtles captured during long-term, haphazard surveys of our study site in Shark Bay, Western Australia. Taxon found in the study area include Chelonia mydas, Caretta caretta and Eretmochelys imbricata.
Stationary camera observations, set, and environmental data from Shark Bay Marine Park, Western Australia from July 2011 to June 2012
This dataset provides information on stationary video cameras set within the study area from 2011 to 2012, including animals viewed along with relevent environmental and camera data. These data provide insight into teleost communities that utilize various habitats within Shark Bay.
Fish trap catch, set, and environmental data from Shark Bay Marine Park, Western Australia from May 2010 to July 2012
This dataset provides information on fish traps set within the study area from 2010 to 2012, including animals caught, relevent environmental and trap data, animal specific measurements and logging of samples retained. Additionally the dataset contains stable isotope values for individuals that were retained for Stable Isotope Analysis in addition to stomach content data. These data provide insight into teleost communities that utilize various habitats within Shark Bay with further insights into their trophic relationships.
Metagenomics of a pustular microbial mat from Shark Bay, Australia: Raw sequences and assembled MAGs
<p>This data accompanies the paper, "<a href="https://www.nature.com/articles/s43705-022-00128-1">Metagenomic, (bio)chemical, and microscopic analyses reveal the potential for the cycling of sulfated EPS in Shark Bay pustular mats</a>" which looks at the cycling of sulfated polysaccharides in peritidal pustular mats from Shark Bay, Australia. The microbial community was sequenced, assembled, and binned. The raw sequencing reads used in this analysis are the following:</p> <ul> <li>SB_forward_paired_copy.fastq.gz </li> <li>SB_reverse_paired_copy.fastq.gz </li> </ul> <p>The resulting metagenome-assembled genomes (MAGs) are presented in the following folder:</p> <ul> <li>MAGs.zip</li> </ul> <p> </p> <ul> </ul> <p> </p>
Quantifying hail and lightning risk factors using long-term observations around Australia - Hail and Lightning Datasets
<p>These data accompany a paper referenced as doi: 10.1029/2020JD033101. Two spreadsheets are provided as used to derive the hourly and monthly occurrence of hail or lightning events within the domains of the ten radars sites (Melbourne, Wollongong, Gympie, Grafton, Canberra, Marburg, Adelaide, Namoi, Perth, Hobart). Lightning events as defined in this paper (derived from post-processed lightning information) were provided for 2005-2018 and hail data from 1997-2018 (noting that the start date of hail is dependent on when the respective radar site was established). A value of 1 indicates a lightning/hail event occurred during that hour (in UTC +0). Information on processing methods and data are provided in the published paper for the doi listed above.</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.