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.
13,453
datasets available to search
ShareScore release 0.9.0
Dataset results
13,453 results for “australia”
AusEFlux: Empirical upscaling of OzFlux eddy covariance flux tower data over Australia
<p>AusEFlux (<strong>Aus</strong>tralian <strong>E</strong>mpirical <strong>Flux</strong>es) is a high resolution (500 metre) gridded estimate of Gross Primary Productivity (GPP), Ecosystem Respiration (ER), Net Ecosystem Exchange (NEE), and Evapotranspiration over the Australian continent for the period January 2003 to Present. These datasets provide a benchmark for assessment against Land Surface Model simulations, and a means for monitoring of Australia’s terrestrial carbon cycle at an unprecedented high-resolution.</p> <p><strong>Version 2.1 </strong>of AusEFlux has just been released (as of May 2025) and was created to <strong>operationalise</strong> the research datasets published in this <a href="https://doi.org/10.5194/bg-20-4109-2023">EGU Biogeosciences publication.</a> In order to operationalise these datasets, changes to the input datasets were required to align the data sources with datasets that are regularly and reliably updated, along with general improvements. The datasets provided on Zenodo have been reprojected to 5 km resolution to facilitate easier uploading and sharing, but<strong> full resolution datasets (both v1.1 and v2.1) can be accessed freely through <a href="https://thredds.nci.org.au/thredds/catalog/ub8/au/AusEFlux/catalog.html">NCI's THREDDS portal.</a></strong></p> <p><strong>Two Jupyter Notebooks</strong> have been created (one for GPP and one for NEE) that demonstrate the differences between the research datasets (v1.1) and the operational datasets (v2.1), including showing the differences in specifications and inputs. You can view/download these notebooks using the links below:</p> <p><a href="https://nbviewer.org/github/cbur24/AusEFlux/blob/master/notebooks/analysis/Compare_AusEFlux_versions_GPP.ipynb">GPP comparison between versions</a></p> <p><a href="https://nbviewer.org/github/cbur24/AusEFlux/blob/master/notebooks/analysis/Compare_AusEFlux_versions_NEE.ipynb">NEE comparisons between versions</a></p> <p>Each dataset contains three variables:</p> <ul> <li>"<flux>_median": represents the 'best-estimate' of a given flux, the units are gC/m<sup><sub>2</sub></sup>/mon<sup>-1</sup></li> <li>"<flux>_25th_percentile": represents the lower uncertainty bound, the units are gC/m<sup><sub>2</sub></sup>/mon<sup>-1</sup></li> <li>"<flux>_75th_percentile": represents the upper uncertainty bound, the units are gC/m<sup><sub>2</sub></sup>/mon<sup>-1</sup></li> </ul> <p><span><strong>Version Guide</strong>:</span></p> <p><em>v1.0:</em> DO NOT USE THIS VERSION. There was a mistake in the modelling of ecosystem respiration, so this version of the dataset should not be used. As of version 1.1, the error has been rectified.</p> <p><em>v1.1: </em>This version of the datasets are those used to inform the EGU Publication linked above. Its time range is 2003-July 2022, and its spatial resolution is 5 km on Zenodo, but the 1 km resolution datasets can be accessed through <a href="https://thredds.nci.org.au/thredds/catalog/ub8/au/AusEFlux/catalog.html">NCI's THREDDS portal</a>.</p> <p><em>v2.0: <strong>IMPORTANT NOTE:</strong> a bug in the modelling of vegetation height resulted in data artefacts in the NEE and ER fluxes over very tall mesic forests in this version. This resulted in unnaturally high ER and lower than expected NEE (less negative than would be expected). This issue has been rectified in version 2.1.</em> <strong>Version 2 datasets represent the operational version of the datasets</strong>, it includes several improvements over version 1.1. Its time-range is 2003-2024 (and will be updated annually), and its spatial resolution is 500m. A 5 km reprojected version of the dataset is included here on Zenodo, but the 500 metre datasets can be accessed through<a href="https://thredds.nci.org.au/thredds/catalog/ub8/au/AusEFlux/catalog.html"> NCI's THREDDS portal.</a></p> <p><strong>v2.1: </strong>This version is a patch to version 2.0 to remove a bug in the modelling of vegetation height. <strong>It is recommended to use this version </strong>over v2.0. 500 metre resolution datasets can be accessed through<a href="https://thredds.nci.org.au/thredds/catalog/ub8/au/AusEFlux/catalog.html"> NCI's THREDDS portal.</a></p>
Evaluation of dynamically downscaled CMIP6-CCAM models over Australia
<p>Downscaled CCAM-CMIP6 model data used in the evaluation of CCAM-CMIP6 models against AGCD observations:</p><ol><li>Data required for daily evaluation of precipitation and temperature variables, and calculation of Perkins skill score</li><li>Data required for evaluation of bias for precipitation and temperature variables</li><li>Data required for KGE skill score</li></ol>
Phlorest phylogeny derived from Bouckaert et al. 2018 'The origin and expansion of Pama–Nyungan languages across Australia'
<p>Cite the source of the dataset as:</p> <blockquote> <p>Bouckaert RR, Bowern C & Atkinson QD. 2018. The origin and expansion of Pama–Nyungan languages across Australia. Nature Ecology and Evolution. 2: 741–749</p> </blockquote>
Data from the Untethered Balloon Launch of a Holographic Imager Into Cloud Near Adelaide, South Australia in August 2020
<p>This dataset was obtained from an untethered balloon launch into cloud near Adelaide, South Australia in August 2020. The balloon payload train consisted of a holographic imager, polarsonde, webcam, and meteorological sensors.</p> <p>Data is organised as follows:</p> <ol> <li>A netcdf file consisting of; <ul> <li>Raw RS41 radiosonde and data logger temperature and relative humidity profiles</li> <li>Mean number density (30-second averaged) from the holographic imager </li> <li>Mean equivalent diameter (30-second averaged) from the holographic imager </li> <li>Raw polarsonde co and cross polarisation signals</li> </ul> </li> <li>A selection of focussed particle images </li> <li>A selection of webcam images</li> <li>A figure summarising the results of HYSPLIT back trajectory modelling for the launch (FigS1)</li> <li>A figure showing the HIMAWARI Cloud Type classification for the region surrounding the launch site (FigS2)</li> </ol> <p>This dataset can be used to reproduce the key figures of the following paper submitted to Atmospheric Measurement Techniques (under review 2023): </p> <p>'A Light-Weight Holographic Imager for Cloud Microphysical Studies from an Untethered Balloon'.</p> <p>Additional details about the instruments and measurements can be found in that publication.</p>
Observation based gridded annual runoff estimates over Victoria, Australia
<p>The dataset provides observation-based interpolated gridded annual runoff estimates over Victoria, Australia during 1982 - 2012. The methodology extended the R package <em>rtop</em> to allow <em>top-kriging</em> with external drift by employing spatial variability of gridded rainfall estimates. This dataset can be useful to estimate runoff at ungauged or poorly gauged catchments in Victoria. The full paper with the methodology is available at https://mssanz.org.au/modsim2021/papers/K11/weligamage.pdf</p> <p> </p>
2019 search and interaction log from the data catalogue: Research Data Australia
<p>In order to provide a better support to user's data discovery activity, we analysed a data search log in order to understand how data seekers interact with a data search system when they search for data. The data search log is from the research data discovery portal: <a href="https://researchdata.edu.au">Research Data Australia (RDA)</a>. RDA is the data discovery service of the Australian Research Data Commons (ARDC). ARDC is supported by the Australian Government through the National Collaborative Research Infrastructure Strategy Program.</p> <p>Please read the research paper "<a href="https://doi.org/10.1108/JD-12-2021-0245">Large-scale Analysis of Query Logs to Profile Users for Dataset Search</a>" for detailed description and analysis of the datasets, and the software "<a href="https://zenodo.org/record/6321621#.Yh79Tt9xUmA">Python code for processing and clustering a data search log</a>" for the data process and analysis.</p> <p>The search log consists of the entire user-front activity log data for the duration of January to December 2019. During this period, the catalogue contained about 150,000 metadata records of datasets.</p> <p>The dataset (2019_search_log_sessioned.txt) was generated from raw log data with following steps:</p> <ul> <li>Remove entries that were likely from machines instead of human users. Those recorded machine activities may result from downstream aggregators who harvested metadata from RDA by directly sending queries to the catalogue URL instead of using the API endpoint.</li> <li>Identify search sessions from a user - a search session includes all activities a user conducts with a search system in order to satisfy a (information/data) search needs. We followed the following steps to identify search sessions. First, we identified a user by IP address, where a unique IP address was considered a single user. We recognise the limitation of this approach, as several users may share the same IP address, however the IP address is the only information available for identifying a user. <br> Past research in log analysis usually apply the following two methods to identify a session: 30 minutes from the same IP address, and/or more than 30 minutes of inactivity between the current activity event and its immediate preceding event. We examined both methods carefully for our log data and concluded that both ended with large unwanted sessions from machine activities. Therefore, we take a brutal approach, by taking only a session from an IP address with a maximum 30 minutes duration.</li> <li>We also removed sessions whose 40% of activities resulted in ’page not found’ or whose activities were all about accessing grants. Within a session, we removed "duplicated" activities that were exactly as their precedent activity with less than one second time span (this could have been a result of reloading a page).</li> </ul> <p>The dataset (id_to_title_subject.csv) lists title and subject headings per record id.</p> <p> </p>
Environmental and physiochemical controls on coral calcification along a latitudinal temperature gradient in Western Australia
<p>Supplementary data for: Environmental and physiochemical controls on coral calcification along a latitudinal temperature gradient in Western Australia</p>
Data and outputs for chapter 'The impacts of the 2019-20 wildfires on Australian fungi ' in 'Australia's Megafires: Biodiversity Impacts and Lessons from 2019-2020
<p><strong>Dataset includes raw data downloaded from the following sources: fungi_data.csv</strong></p> <ul> <li>Atlas of Living Australia occurrence download: https://doi.org/10.26197/ala.9e0ca388-9da2-4096-b1a3-26e2aaa51d8a. Accessed 2021-09-16. GBIF.org (16 September 2021)</li> <li>GBIF Occurrence Download https://doi.org/10.15468/dl.secenk</li> <li>Fungimap (https://fungimap.org.au/ (data obtained directly from Fungimap Inc.)</li> <li>MycoPortal (https://mycoportal.org/portal/index.php)</li> <li>iNaturalist (https://www.inaturalist.org/home)</li> </ul> <p><strong>Output files from point and polygon overlap with fire layer:</strong></p> <ul> <li>Fungi and fire analysis point overlap.xlsx</li> <li>Fungi and fire analysis polygon overlap.xlsx</li> </ul> <p> </p> <p> </p>
Data set: Australia's hidden radiation - phylogenomic analysis reveals rapid Miocene radiation of blindsnakes
<p>This repository contains the additional raw data to accompany our paper entitled "Australia’s hidden radiation: phylogenomic analysis reveals rapid Miocene radiation of blind snakes."</p> <p>This project is part of the AusARG Initiative funded by BioPlatforms Australia.</p> <p>Raw sequences data can be downloaded from the BioPlatforms downloads portal: https://data.bioplatforms.com/dataset?q=ticket%3ABPAOPS-1196</p> <p><strong>Information about files</strong></p> <ol> <li>ASTRAL_tree_SqCL_AHE.tre - output from ASTRAL-III just with SqCL data + outgroups</li> <li>ASTRAL_tree_SqCL_AHE_Ramphotyphlops.tre - same with above but also including additional <em>Ramphotyphlops </em>genes.</li> <li>mcmctree_1.txt - mcmcfile output from MCMCTree analysis using all SkewT or SkewNormal distribution priors.</li> <li>mcmctree_2.txt - mcmcfile output from MCMCTree analysis using SkewT, SkewNormal, and cauchy distribution priors. **This is the tree used in our publication**</li> <li>mcmctree_strategy1.tre - output phylogeny 1</li> <li>mcmctree_strategy2.tre - output phylogeny 2</li> <li>IQTREE_gcf_scf.nex - gene concordance and site factors for mcmctree_strategy2.tre</li> </ol> <p>tree_data/ folder contains concatenated gene trees (IQTREE) and corresponding shortcut coalescent method (ASTRAL-III) tree.</p> <p>Should there be questions regarding the code and data set, please contact the corresponding author.</p>
Low Frequency Ambient Noise Dynamics and Trends: Cape Leeuwin, Australia
<p>Data files and code for : Low Frequency Ambient Noise Dynamics and Trends: Cape Leeuwin, Australia</p> <p>R version 4.X used for plots and analysis. Packages: entropy, foreach, rEDM</p> <ol> <li>Plots.R : Create plots for manuscript. </li> <li>MI.R : Evaluate lagged Mutual Information of random data segments</li> <li>CCM.R : Convergent cross mapping of Ambient Noise with teleconnections</li> <li>EMM_Surrogate.R : Evaluate CCM significance with randomized surrogates</li> </ol> <p>Python 3.X used for plots and analysis. Packages: pandas, matplotlib, emd, pyEDM</p> <ol> <li>Figure5,py Figure14.py : Create plots for manuscript</li> <li>EMD.py : Compute empirical mode decompositions</li> </ol> <p> </p>
The Identification of Extinct Megafauna in Rock art Using Geometric Morphometrics: A Genyornis newtoni Painting in Arnhem Land, Northern Australia?
<p>Raw data files used for the analysis of a contentiously identified rock-art image located in Arnhem Land, Northern Australia. The data were used to test a novel approach to quantifying species identification in rock art images to assess the extent to which an image resembles other rock art of sound identification or anatomical images of visually similar species.</p> <p>Included files are the raw coordinate data files ("[feature] PCA file", .txt format) for use in Morphologika2, and formatted files for use in CVAGen8 ("[feature]" x1y1 file for CVA", .x1y1 format; "[feature] group file", .txt format).</p> <p>Files produced using software by Rohlf (2015) and Sheets (2014)</p>
Data and code from: Climate-based prediction of carbon fluxes from deadwood in Australia
This repository contains the code for the publication 'Climate-based prediction of carbon fluxes from deadwood in Australia'.
Monthly Standardized Precipitation Evapotranspiration Index (SPEI) for Australia at 0.05 degree from 1982 to 2014
<p>This monthly SPEI dataset in 1-48 scale is calculated using R's <a href="https://cran.r-project.org/web/packages/SPEI/index.html">SPEI </a>package in 'kernel -- rectangular', 'distribute -- log-Logistic' and 'fit -- ub-pwm' mode, with <a href="http://www.csiro.au/awap/">AWAP'</a>s monthly rainfall and <a href="http://www.bom.gov.au/water/landscape/">ALWB</a>'s potential evapotranspiration.</p>
Fair Data Awareness Survey - Australia - 2017
<p>This record describes a survey around the awareness of the FAIR data principles, undertaken in Australia in 2017 by ANDS, Nectar and RDS. ANDS (Australian National Data Service), Nectar (National eResearch Collaboration Tools and Resources), and RDS (Research Data Services) are NCRIS facilities. NCRIS is an Australian Federal Government investment in research infrastructure. ANDS(ands.org.au), Nectar(nectar.org.au) and RDS(rds.edu.au) have integrated their work in line with proposals laid out in the NCRIS Roadmap (https://docs.education.gov.au/node/43736), early in 2017.</p> <p>The survey was conducted as a Google Form, and analysed in a 12 page report (see Summary of Full Results - attached). Results of the demographics and quantitative responses are shared attached to this record. The qualitative responses are not shared, for reasons of confidentiality.</p> <p><strong>Background (from Summary Report)</strong></p> <p>ANDS/RDS/Nectar undertook a baseline survey to assess level of awareness around FAIR in the research community at eResearch Australasia conference (Oct 2017) and through an online survey. The online survey was closed a few weeks later on 16.11.17. A list of questions is provided (see Are you FAIR aware? Google Form.pdf). There were 249 responses.</p>
Relationship between Australia suicide and Catalonia GDP
<p>Experiment that uses open data from 2000 to 2014.</p> <p><br> Datasets used:</p> <ul> <li>Suicide people in AUS:<br> https://data.oecd.org/healthstat/suicide-rates.htm</li> <li>Catalonia GDP by demand components (2000-2016):<br> https://www.kaggle.com/xavier14/catalonia-gdp-by-demand-components-20002016</li> </ul> <p>This Experiment compares these two datasets and make a chart for comparison.</p>
Differential Interferogram of the September 16 2018 Mw 5.3 earthquake Lake Muir, Perth, Australia
<p>A moderate earthquake of Mw 5.3 (M<sub>L</sub> 5.7) occured on September 16 2018 near the Lake Muir region, Perth, SW Australia. Despite Australia being in a mostly stable continental interior, moderate or strong shallow crustral earthquakes occured the past years. Due to shallow faulting and low relief/semi-arid conditions in most regions of Australia, even moderate events lead to surficial deformation in form of mapped surface ruptures or deformation identified by radar satellites (InSAR).</p> <p>The Sep.16 earthquake produced a distinctive surficial deformation pattern, identified in an interferometric pair of Sentinel-1 Copernicus radar images (Descending orbit, September 14 - September 26). Sentinel-1 TOPS Interferogram and Line-of-Sight (LOS) displacement were produced using SNAP and DIAPASON tools in the <a href="https://geohazards-tep.eo.esa.int">Geohazards Exploitation Platform</a>. Color fringes on interferogram represent each a ~2.8cm displacement. Displacement (unwrapped) grid files are also provided.</p> <p>InSAR analysis show co-seismic rupture along a NNE-SSW reverse fault plane, consistent with published moment tensors (USGS). LOS profiles show a 5-15cm displacement across a fault rupture that propagated to the surface. Hundreds of metres of fractures and surface ruptures were reported by local farmers' accounts and photographs to the ABC South West Australia news agency.</p>
Regional model results (combined) for the six transition potentials (one for Africa, Australia, Asia, Europe, North America, and South America)
<p>Results for the six regional models showing areas of high potential to transition from tree cover to tree cover loss to areas of low potential to transition.</p>
National Checklists 2017: Australia Species List
Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See <p></p>https://github.com/diatomsRcool/checklists for details<p></p>A list of species from Australia collected using effechecka and geonames polygons
National Checklists 2019: Australia Species List
Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See <p></p>https://github.com/diatomsRcool/checklists for details.<p></p>A list of species from Australia collected using effechecka and geonames polygons
MALDI-TOF-MS spectra of archaeological bone fragments from Bandicoot Bay, Barrow Island (Australia) for ZooMS (Zooarchaeology by Mass Spectrometry)
<p>MALDI-TOF-MS spectra for archaeological bone fragments from Bandicoot Bay, Barrow Island, Western Australia. All spectra are uploaded in .mzml format. </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.