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9,672 results for “Australian”
AusTraits: a curated plant trait database for the Australian flora
<p>AusTraits is a transformative database, containing measurements on the traits of Australia's plant taxa, standardised from hundreds of disconnected primary sources. So far, data have been assembled from > 300 distinct sources, describing > 500 plant traits and > 34,000 taxa.</p> <p>To handle the harmonising of diverse data sources, we use a reproducible workflow to implement the various changes required for each source to reformat it suitable for incorporation in AusTraits. Such changes include restructuring datasets, renaming variables, changing variable units, changing taxon names. While this repository contains the harmonised data, the raw data and code used to build the resource are also available on the project's GitHub repository, <a href="https://github.com/traitecoevo/austraits.build/">https://github.com/traitecoevo/austraits.build/</a>.</p> <p>Further information on the project is available at the project website <a href="https://austraits.org">austraits.org</a> and in the associated publication (see below).</p> <p><strong>CONTRIBUTORS</strong></p> <p>The project is jointly led by Dr Daniel Falster (UNSW Sydney), Dr Rachael Gallagher (Western Sydney University), Dr Elizabeth Wenk (UNSW Sydney), and Dr Hervé Sauquet (Royal Botanic Gardens and Domain Trust Sydney), with input from > 300 contributors from over > 100 institutions (see full list above). The project was initiated by Dr Rachael Gallagher and Prof Ian Wright while at Macquarie University.</p> <p>We are grateful to the following institutions for contributing data Australian National Botanic Garden, Brisbane Rainforest Action and Information Network, Kew Botanic Gardens, National Herbarium of NSW, Northern Territory Herbarium, Queensland Herbarium, Western Australian Herbarium, South Australian Herbarium, State Herbarium of South Australia, Tasmanian Herbarium, Department of Environment Land Water and Planning Victoria and the Royal Botanic Gardens Victoria.</p> <p>AusTraits has been supported by investment from the Australian Research Data Commons (ARDC), via their "Transformative data collections" (https://doi.org/10.47486/TD044) and "Data Partnerships" (https://doi.org/10.47486/DP720, https://doi.org/10.47486/DP720A) programs; and grants from the Australian Research Council (FT160100113, DE170100208, FT100100910) and Macquarie University, The ARDC is enabled by National Collaborative Research Investment Strategy (NCRIS).</p> <p><strong>ACCESSING AND USE OF DATA</strong></p> <p>The compiled AusTraits database is released under an open source licence (CC-BY), enabling re-use by the community.</p> <p>A requirement of use is that users cite the AusTraits resource paper, which includes all contributors as co-authors:</p> <blockquote> <p>Falster, Gallagher et al (2021) <em>AusTraits, a curated plant trait database for the Australian flora</em>. Scientific Data 8: 254, <a href="https://doi.org/10.1038/s41597-021-01006-6">https://doi.org/10.1038/s41597-021-01006-6</a></p> </blockquote> <p>In addition, we encourage users you to cite the original data sources, wherever possible.</p> <p>Note that under the license data may be redistributed, provided the attribution is maintained.</p> <p>The downloads below provide the data in two formats:</p> <ul> <li>austraits-X.X.X.zip: data in plain text format (.csv, .bib, .yml files). Suitable for anyone, including those using Python.</li> <li>austraits-X.X.X.rds: data as compressed R object. Suitable for users of R (see below).</li> <li> <div>austraits-X.X.X-flattened.rds: contains a flattened version of the dataset for direct loading in R; all data tables are joined into a wider format</div> </li> <li> <div>austraits-X.X.X-flattened.parquet: contains a flattened version of the dataset in parquet format; all data tables are joined into a wider format </div> </li> </ul> <p>For R users, access and manipulation of data is assisted with the <a href="http://github.com/traitecoevo/austraits">austraits R package</a>. The package can both download data and provides examples and functions for running queries.<br><br><strong>STRUCTURE OF AUSTRAITS</strong></p> <p>The compiled AusTraits database contains a series of relational tables and files. These elements include all the data, contextual information submitted with each contributed datasets, database schema, and trait definitions. The file dictionary.html provides the same information in textual format. Similar information is available at <a href="https://traitecoevo.github.io/traits.build-book/">https://traitecoevo.github.io/traits.build-book/</a>.</p> <p><strong>CONTRIBUTING</strong></p> <p>We envision AusTraits as an on-going collaborative community resource that:</p> <ol> <li>Increases our collective understanding the Australian flora;</li> <li>Facilitates accumulation and sharing of trait data;</li> <li>Builds a sense of community among contributors and users; and</li> <li>Aspires to fully transparent and reproducible research of the highest standard.</li> </ol> <p>As a community resource, we are very keen for people to contribute. Assembly of the database is managed on GitHub at <a href="https://github.com/traitecoevo/austraits.build/">https://github.com/traitecoevo/austraits.build/</a>.</p> <p>Here are some of the ways you can contribute:</p> <p><strong>Reporting Errors</strong>: If you notice a possible error in AusTraits, please <a href="https://github.com/traitecoevo/austraits.build/issues">post an issue on GitHub</a>.</p> <p><strong>Refining documentation:</strong> We welcome additions and edits that make using the existing data or adding new data easier for the community.</p> <p><strong>Contributing new data</strong>: We gladly accept new data contributions to AusTraits. See full instructions on how to contribute at <a href="https://github.com/traitecoevo/austraits.build/">https://github.com/traitecoevo/austraits.build/</a>.</p>
Soil visible–near infrared (vis–NIR) spectra for the Biomes of Australian Soil Environments (BASE) soil microbial diversity database
<p>Visible–near infrared spectra of 695 soil samples collected in the Biomes of Australian Soil Environments (BASE) soil microbial diversity project (Bissett et al., 2016). The spectra represent reflectance values from 2151 wavelengths that range from 350 nm to 2500 nm with a 1 nm interval. The dataset has unique sample identification numbers and the date of sampling, which can be related to the BASE (Australian Microbiome) database (https://data.bioplatforms.com/organization/australian-microbiome)</p>
Data from: Land use, season, and parasitism predict metal concentrations in Australian flying fox fur
<p>There are two .csv files in this upload. The "Pteropus_metal_data_wide.csv" file contains metal concentrations (reported in ng/g) measured in fur samples collected from <em>Pteropus </em>flying foxes (<em>P. alecto</em>, <em>P. conspicillatus</em>, <em>P. poliocephalus</em>). Flying foxes were captured from 2015-2018 at multiple sites across Australia. The file also contains capture information (e.g. date, location) and biological information (e.g. species, sex, age class, parasitism) for each flying fox. The "Pteropus_metadata.csv" file provides further details on all column names in the primary data file, including the specific metals that were quantified. Detailed information on the study methods and results can be found in the associated Science of the Total Environment publication, "Land use, season, and parasitism predict metal concentrations in Australian flying fox fur" by Sánchez et al.</p>
Data set: Morphological evolution and niche conservatism across a continental radiation of Australian blindsnakes
<h1>Repository for "Morphological evolution and niche conservatism across a continental radiation of Australian blindsnakes"</h1> <p>---</p> <p>These data scripts were used to perform analyses included in the research paper "Morphological evolution and niche conservatism across a continental radiation of Australian blindsnakes" </p> <p>Main questions for the study:</p> <p>1. What are the main axes of morphological variation?<br>2. Does variation in morphology among species correlate with their current environments? <br>3. Are lineages that occupy ecologically similar habitats morphologically convergent? <br>4. Is speciation predominantly allopatric or sympatric? <br>5. Do sister species have greater morphological and ecological niche overlap than expected relative to non-sister species pairs?</p> <h2>## Data structure</h2> <p>Contents in the data folder is archived as a zip and can be downloaded from Zenodo (for all versions see https://zenodo.org/doi/10.5281/zenodo.10397830). Once you unzip the zipped files, you will see three folders and some files that are no in any folders. </p> <p>/data/ - files that were manually created and the phylogeny</p> <p>/data/script_generated_data/ - A combination of processed data needed to run the analyses </p> <p>/data/dorsal/ - photographs of the head from the dorsal view. These photos were used for digitising landmarks and semilandmarks. </p> <p>/data/worldclim2_30s/ - cropped and merged annual temperature from WorldClim2 (Fick and Hijmans 2017), soil bulk density from <a href="https://esoil.io/TERNLandscapes/Public/Pages/SLGA/GetData.html">Soil and Landscape Grid of Australia</a>, and Global Aridity Index from Zomer et al. (2022). <br><br>/DREaD/ - contains some files required to replicate DREaD analysis</p> <h2>## Code/Software</h2> <p>All scripts can be run using open source software. Scripts should be run in order to create necessary files that will be saved in /data/script_generated_data/ for further scripts. R is required to run R scripts (.R).</p> <h3>### /Code</h3> <p> - utility/*.R - scripts for custom functions. These are sourced in other scripts.<br> - DREaD/*.R - scripts associated with DREaD analyses<br> - 00_linear_measurement_shaperatio.R - script used to account for sexual dimorphism and calculate conventional PCA. Addresses Q1.<br> - 01_model_fitting.R - script used to address Q2 and plot visualisations.<br> - 02_convergence.R - this script calculates Ct1-4 and C5 scores. Addresses Q3.<br> - 02_convergence_model_fitting.R - this script evaluates fit of different evolutionary models to traits. Addresses Q3.<br> - 02_convergence_test_simulations.R - simulation studies to show that our phylogeny has sufficient power to detect convergence.<br> - 03_niche_enmtools_bias_account.R - calculates ecological niche models (ENMs) for each species using MAXENT. Runs Age-Overlap Correlation tests for geography and ENMs. Partially addresses Q4.<br> - 03_DREaD_Blindsnakes_AS.R - script to run DREaD analysis. <br> - 03_morpho_niche_overlap_plots.R - Runs Age-Overlap Correlation tests for body shape and snout shape. Plots AOCs. Partially addresses Q4. <br> - 04_pairwise_distance_test.R - Binomial tests between sister and non-sister pairs for ENMs and Geographic Range. Partially addresses Q5<br> - 04_morpho_pairwise.R - Binomial tests between sister and non-sister pairs for body shape and snout shape. Partially addresses Q5</p> <h2>## Contact</h2> <p>Should you have questions about these scripts or would like to request raw data, please do not hesitate to contact Sarin Tiatragul (contact information can be found in the paper) or on Github (https://github.com/stiatragul/blindsnakemorphoevo)</p> <h2>## References</h2> <p><a name="ref-fickWorldClim2017"></a>Fick, S. E., and R. J. Hijmans. 2017. <a href="https://doi.org/10.1002/joc.5086">WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas</a>. International Journal of Climatology 37:4302–4315.</p> <p><a name="ref-zomerVersion2022"></a>Zomer, R. J., J. Xu, and A. Trabucco. 2022. <a href="https://doi.org/10.1038/s41597-022-01493-1">Version 3 of the global aridity index and potential evapotranspiration database</a>. Scientific Data 9:409.</p>
South East Australian Coastal Ocean Forecast System (SEA-COFS)
<p>A suite of high resolution hydrodynamic ocean models for south eastern Australia that form the South East Australian Coastal Ocean Forecast System (SEA-COFS).<br> <br> The modelling suite includes output from several different configurations of the Regional Ocean Modeling System hydrodynamic simulation of the East Australian Current (EAC) System. These various model configurations include free running hindcast models, data assimilating state estimates, ocean forecasts and various nested high resolution runs. There is also a biogeochemical configuration of the fennel model on the EAC parent grid.<br> <br> At the time of upload there were four model grids (check later versions for new and revised / extended grids).</p> <p>The SEA-COFS domain covers the southeastern Australia oceanic region from 25.1-41.5 S and 147.1-162.2 E.</p> <p><br> <strong>SEACOFS_EAC_Grid.nc</strong> Is the parent grid of the EAC Domain 2.5-6km resolution</p> <ul> <li>Kerry, C. G. and M. Roughan, (2020). A high-resolution, 22-year, free-running, hydrodynamic simulation of the East Australia Current System using the Regional Ocean Modeling System. UNSW.dataset. <a href="https://doi.org/10.26190/5e683944e1369"> 10.26190/5e683944e1369</a>. </li> <li>Kerry, C. G. and M. Roughan,<strong> </strong>Powell, Brian, Oke, Peter (2020). A high-resolution reanalysis of the East Australian Current System assimilating an unprecedented observational data set using 4D-Var data assimilation over a two-year period (2012-2013). Version 2017. UNSW.dataset. <a href="https://doi.org/10.26190/5ebe1f389dd87"> 10.26190/5ebe1f389dd87</a>. <br> Kerry, C. , Powell, B. Roughan, M. and Oke, P. (2016) <a href="http://www.geosci-model-dev.net/9/3779/2016/gmd-9-3779-2016.pdf">Development and evaluation of a high-resolution reanalysis of the East Australian Current region using the Regional Ocean Modelling System (ROMS 3.4) and Incremental Strong-Constraint 4-Dimensional Variational (IS4D-Var) data assimilation</a>. Geosci. Model Dev, 9, 3779-3801, 10.5194/gmd-9-3779-2016<br> </li> </ul> <p><strong>SEACOFS_CoffsHarbour_Grid.nc</strong> Coffs Harbour Region 0.75-1km resolution</p> <ul> <li>Kerry, C., Roughan, M., & Powell, B. (2020). <a href="https://doi.org/10.1016/j.jmarsys.2019.103286">Predicting the submesoscale circulation inshore of the East Australian Current</a>. Journal of Marine Systems (Vol. 204, p. 103286)</li> </ul> <p><strong>SEACOFS_HSM_Grid.nc</strong> - Hawkesbury Shelf Model (HSM) 750m resolution </p> <ul> <li>Ribbat N., M. Roughan, B. Powell, C. Kerry, S. Rao, (2020). A high-resolution (750m) free-running hydrodynamic simulation of the Hawkesbury Shelf region off Southeastern Australia (2012-2013) using the Regional Ocean Modeling System. UNSW.dataset. <a href="https://doi.org/10.26190/5ec35ca34752e">DOI: 10.26190/5ec35ca34752e</a>. <a href="https://researchdata.ands.org.au/high-resolution-750m-ocean-modeling/1460879">Data access and more information.</a></li> </ul> <p><strong>SEACOFS_Narooma_Grid.nc </strong> Narooma Model 0.75-1km resolution<br> <br> <strong>EAC_BGC Fennel Model </strong></p> <ul> <li>Rocha, C., Edwards, C. A., Roughan, M., Cetina-Heredia, P., & Kerry, C. (2019) <a href="https://doi.org/10.5194/gmd-12-441-2019">A high-resolution biogeochemical model (ROMS 3.4 + bio_Fennel) of the East Australian Current system</a> Geosci. Model Dev., 12, 441-456</li> </ul> <p> </p>
Open-data release of aggregated Australian school-level information. Edition 2016.1
<p>The file set is a freely downloadable aggregation of information about Australian schools. The individual files represent a series of tables which, when considered together, form a relational database. The records cover the years 2008-2014 and include information on approximately 9500 primary and secondary school main-campuses and around 500 subcampuses. The records all relate to school-level data; no data about individuals is included. All the information has previously been published and is publicly available but it has not previously been released as a documented, useful aggregation. The information includes:<br /> (a) the names of schools<br /> (b) staffing levels, including full-time and part-time teaching and non-teaching staff<br /> (c) student enrolments, including the number of boys and girls<br /> (d) school financial information, including Commonwealth government, state government, and private funding<br /> (e) test data, potentially for school years 3, 5, 7 and 9, relating to an Australian national testing programme know by the trademark 'NAPLAN'<br /> <br /> Documentation of this Edition 2016.1 is incomplete but the organization of the data should be readily understandable to most people. If you are a researcher, the simplest way to study the data is to make use of the SQLite3 database called 'school-data-2016-1.db'. If you are unsure how to use an SQLite database, ask a guru.<br /> <br /> The database was constructed directly from the other included files by running the following command at a command-line prompt:<br /> <em>sqlite3 school-data-2016-1.db < school-data-2016-1.sql</em><br /> Note that a few, non-consequential, errors will be reported if you run this command yourself. The reason for the errors is that the SQLite database is created by importing a series of '.csv' files. Each of the .csv files contains a header line with the names of the variable relevant to each column. The information is useful for many statistical packages but it is not what SQLite expects, so it complains about the header. Despite the complaint, the database will be created correctly.<br /> <br /> Briefly, the data are organized as follows.<br /> (a) The .csv files ('comma separated values') do not actually use a comma as the field delimiter. Instead, the vertical bar character '|' (ASCII Octal 174 Decimal 124 Hex 7C) is used. If you read the .csv files using Microsoft Excel, Open Office, or Libre Office, you will need to set the field-separator to be '|'. Check your software documentation to understand how to do this.<br /> (b) Each school-related record is indexed by an identifer called 'ageid'. The ageid uniquely identifies each school and consequently serves as the appropriate variable for JOIN-ing records in different data files. For example, the first school-related record after the header line in file 'students-headed-bar.csv' shows the ageid of the school as 40000. The relevant school name can be found by looking in the file 'ageidtoname-headed-bar.csv' to discover that the the ageid of 40000 corresponds to a school called 'Corpus Christi Catholic School'.<br /> (3) In addition to the variable 'ageid' each record is also identified by one or two 'year' variables. The most important purpose of a year identifier will be to indicate the year that is relevant to the record. For example, if one turn again to file 'students-headed-bar.csv', one sees that the first seven school-related records after the header line all relate to the school Corpus Christi Catholic School with ageid of 40000. The variable that identifies the important differences between these seven records is the variable 'studentyear'. 'studentyear' shows the year to which the student data refer. One can see, for example, that in 2008, there were a total of 410 students enrolled, of whom 185 were girls and 225 were boys (look at the variable names in the header line).<br /> (4) The variables relating to years are given different names in each of the different files ('studentsyear' in the file 'students-headed-bar.csv', 'financesummaryyear' in the file 'financesummary-headed-bar.csv'). Despite the different names, the year variables provide the second-level means for joining information acrosss files. For example, if you wanted to relate the enrolments at a school in each year to its financial state, you might wish to JOIN records using 'ageid' in the two files and, secondarily, matching 'studentsyear' with 'financialsummaryyear'.<br /> (5) The manipulation of the data is most readily done using the SQL language with the SQLite database but it can also be done in a variety of statistical packages.<br /> (6) It is our intention for Edition 2016-2 to create large 'flat' files suitable for use by non-researchers who want to view the data with spreadsheet software. The disadvantage of such 'flat' files is that they contain vast amounts of redundant information and might not display the data in the form that the user most wants it.<br /> (7) Geocoding of the schools is not available in this edition.<br /> (8) Some files, such as 'sector-headed-bar.csv' are not used in the creation of the database but are provided as a convenience for researchers who might wish to recode some of the data to remove redundancy.<br /> (9) A detailed example of a suitable SQLite query can be found in the file 'school-data-sqlite-example.sql'. The same query, used in the context of analyses done with the excellent, freely available R statistical package (http://www.r-project.org) can be seen in the file 'school-data-with-sqlite.R'.</p>
Thirty-eight years of CO 2 fertilization have outpaced growing aridity to drive greening of Australian woody ecosystems
<p>Data and code for "Thirty-eight years of CO 2 fertilization have outpaced growing aridity to drive greening of Australian woody ecosystems"</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>
MacFarlane Australian Anthropogenic Mercury Emissions
<p><strong>Australian anthropogenic mercury emissions inventory.</strong></p> <p>A detailed description of the emissions is provided in MacFarlane et al., currently (as of March 2022) in review for <em>Environmental Science: Processes and Impacts</em> and available as a pre-print on EarthArXiv (<a href="https://doi.org/10.31223/X5RK84">https://doi.org/10.31223/X5RK84</a>).</p> <p>The dataset posted here includes:</p> <ul> <li>Total annual emissions for each sector (kg), summed over Australia as a whole, as a .csv file</li> <li>Gridded emissions for each sector (kg/m<sup>2</sup>/s), as netcdf (.nc) files</li> </ul> <p>The netcdf files are provided in GEOS-Chem compliant format, with metadata included within the files. Note that the gridded files do not all have the same horizontal resolution, with distributed emissions at 0.25° resolution and point-source emissions at 0.1° resolution.</p>
Data associated with the publication "Interannual variability in the Australian carbon cycle over 2015-2019, based on assimilation of OCO-2 satellite data".
<p>This dataset refers to the publication "Interannual variability in the Australian carbon cycle over 2015-2019, based on assimilation of OCO-2 satellite data". https://doi.org/10.5194/acp-2022-15.</p> <p> </p>
Australian waste account 2007-2021
<p>This dataset updates the previous release from 2019 to 2021, in line with the <a href="https://www.dcceew.gov.au/environment/protection/waste/national-waste-reports/2022">National Waste Database</a> update. The previous release is available here: https://zenodo.org/records/5646740. This release also incorporates LGA waste generation data where it is available (<a href="https://www.epa.nsw.gov.au/-/media/epa/corporate-site/resources/wastestrategy/23p4660-lg-warr-report-2021-22.pdf">NSW </a>and <a href="https://www.vic.gov.au/victorian-local-government-waste-data-dashboard">Victoria</a>).</p> <p>Updated notes: </p> <p>The National Waste Database (https://www.awe.gov.au/environment/protection/waste/national-waste-reports/2020) is a repository for Australia's solid waste data. This collection of waste data is useful however has some issues: The timeseries is not complete, as some years are missing. Allocation to industries is very coarse, there are only 3 waste generating entities: construction and demolition, commercial and industrial, and municipal (households). Further, not all reporting regions (States and Territories) provide data at the same resolution of material type.</p> <p>We have created an open source dataset in an attempt to solve some of these issues. Missing years are filled using linear interpolation. The regional resolution is disaggregated to SA2 regions using the ABS Business Register (https://www.abs.gov.au/Ausstats/abs@.nsf/0/49658AFA6CC395CECA2583A700121A41). Municipal (households) waste is split into SA2s from state totals using population. The ABS Waste Account is used to establish a relationship between waste types and generating sectors (https://www.abs.gov.au/statistics/environment/environmental-management/waste-account-australia-experimental-estimates/latest-release).</p> <p>The data is published as labelled flat files (.csv). The dataset dimensions are:</p> <p>- years: 2007 - 2021,</p> <p>- regions: 2310 SA2 (2016) ASGS regions,</p> <p>- entities: 116 generating entities; 115 SUPG (supply-use product group) industries + 1 households,</p> <p>- waste_types: 69 waste material types,</p> <p>- treatments: 5 waste treatment methods</p> <p>This dataset is made available under a Creative Commons Attribution 4.0 International License. https://creativecommons.org/licenses/by/4.0/</p>
Comparative wordlist prompts for Australian languages
<p>An aligned version of three wordlists, Sutton & Walsh, Curr, and Bates. See also David Nash's item (https://zenodo.org/record/1476467#.W9uXR3ozbUI) of Various Australian wordlist schemes.</p>
Water Body Checklists 2019: Great Australian Bight Species List
Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.<p></p>List of species collected from the Great Australian Bight using effechecka and a modified polygon from the International Hydrographic Association. A filter was applied (based on data from WoRMS) to remove all non-marine taxa.
Water Body Checklists: Great Australian Bight Species List
Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.<p></p>List of species collected from the Great Australian Bight using effechecka and a modified polygon from the International Hydrographic Association. A filter was applied (based on data from WoRMS) to remove all non-marine taxa.
Data from the National Prioritisation of Australian plant species after the 2019-2020 bushfires
<p>Data for 26,062 native Australian plant species assessed against ten post-fire recovery criteria. Details of criteria and methods available in Gallagher, R. V. (2020) <em>National prioritisation of Australian plants affected by the 2019–2020 bushfire season.</em> Report to the Commonwealth Dartement of Agriculture, Water and Environment. https://www.environment.gov.au/system/files/pages/289205b6-83c5-480c-9a7d-3fdf3cde2f68/files/final-national-prioritisation-australian-plants-affected-2019-2020-bushfire-season.pdf </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>
COALMOD-World 2.0 data, results, figures for: Stranded Assets in the Coal Export Industry? The Case of the Australian Galilee Basin
<p>This dataset contains all COALMOD-World 2.0 data for Hauenstein et al. (2023): New coal mines in the Australian Galilee Basin are not economically viable and are prone to become stranded assets (doi.org/10.1016/j.oneear.2023.07.005).</p> <p>With the input data files and the GAMS scenario file the model (https://github.com/chauenstein/COALMOD-World_v2.0) can be run to reproduce the model results.</p> <p>Furthermore, the output.zip folder contains the results file, the R code to compile the figures, and PDFs of the figures.</p>
Gene annotations of Amphibolurus muricatus (jacky dragon), Intellagama lesueurii (Australian water dragon), Phrynocephalus przewalskii (Przewalski's toadhead agama), and Phrynocephalus vlangalii (Ching Hai toadhead agama)
<p><strong>Annotation file and associated FASTA files for <em>A. muricatus</em> assembly AmpMurF_3.0</strong><br> • AmpMurF3.gff3.tar.gz: EVidenceModeler (EVM) gene model annotation file.<br> • AmpMurF3.cds.tar.gz: EVM gene models coding sequences.<br> • AmpMurF3.pep.tar.gz: EVM gene models coding sequences translated into amino acid sequences.</p> <p><strong>Annotation file and associated FASTA files for <em>A. muricatus</em> assembly AmpMurM_3.0</strong><br> • AmpMurM3.gff3.tar.gz: EVidenceModeler (EVM) gene model annotation file.<br> • AmpMurM3.cds.tar.gz: EVM gene models coding sequences.<br> • AmpMurM3.pep.tar.gz: EVM gene models coding sequences translated into amino acid sequences.</p> <p><strong>Annotation file and associated FASTA files for <em>I. lesueurii</em> (Australian water dragon; assembly EWD_hifiasm_HiC generated as part of the AusARG consortium)</strong><br> • Intellagama_lesueurii.evm.final.add_replace_buscoV5_homolog.final.gff3.tar.gz: EVidenceModeler (EVM) gene model annotation file.<br> • Intellagama_lesueurii.evm.final.add_replace_buscoV5_homolog.final.cds.fa.tar.gz: EVM gene models coding sequences.<br> • Intellagama_lesueurii.evm.final.add_replace_buscoV5_homolog.final.pep.fa.tar.gz: EVM gene models coding sequences translated into amino acid sequences.</p> <p><strong>Annotation file and associated FASTA files for <em>P. przewalskii</em> (Przewalski’s toadhead agama; see PMID ID 30808754 and CNGBdb accession no. CNP0000203) </strong><br> • Phrynocephalus_przewalskii.evm.final.add_replace_buscoV5_homolog.gff3.tar.gz: EVidenceModeler (EVM) gene model annotation file.<br> • Phrynocephalus_przewalskii.evm.final.add_replace_buscoV5_homolog.cds.fa.tar.gz: EVM gene models coding sequences.<br> • Phrynocephalus_przewalskii.evm.final.add_replace_buscoV5_homolog.pep.fa.tar.gz: EVM gene models coding sequences translated into amino acid sequences.</p> <p><strong>Annotation file and associated FASTA files for <em>P. vlangalii</em> (Ching Hai toadhead agama; see PMID ID 30808754 and CNGBdb accession no. CNP0000203)</strong><br> • Phrynocephalus_vlangalii.evm.final.add_replace_busco_homolog.gff3.tar.gz: EVidenceModeler (EVM) gene model annotation file.<br> • Phrynocephalus_vlangalii.evm.final.add_replace_busco_homolog.cds.fa.tar.gz: EVM gene models coding sequences.<br> • Phrynocephalus_vlangalii.evm.final.add_replace_busco_homolog.pep.fa.tar.gz: EVM gene models coding sequences translated into amino acid sequences.</p>
CLDF dataset derived from Bowern et al.'s "Diversity in the Numeral Systems of Australian Languages" from 2012
<p>Cite the source of the dataset as:</p> <blockquote> <p>Bowern, Claire, and Jason Zentz (2012): Diversity in the Numeral Systems of Australian Languages. Anthropological Linguistics, 2012. http://www.jstor.org/stable/23621076</p> </blockquote>
F igs 1-4 in New Species of Australian Lestidae (Odonata)
F igs 1-4—Thoracic patterns of lestids: (1) Austrolestes psyche, lateral view; (2) Austrolestes minjerriba, lateral view; (3) Indolestes obiri, ♂, darkest specimen, slightly anterolateral view; (4) I. obiri, ♀, slightly anterolateral view. Scale 2 mm.
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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.