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Cell biology, microbiology, ecology, biodiversity, species data, evolutionary biology
27,493 datasets
Models of seabed sediment mobilisation examine the intermediate disturbance hypothesis for Australia's continental shelf. The analysis predicts the spatial distribution of an ecological disturbance index based on wave, tide, and cyclone energy. This work by the Australian Ocean Data Network represents a first-of-its-kind analysis for any continental shelf.
Spatial layers describe Forest Canopy Extent likelihood from 1995 to 2019 in New South Wales Regional Forest Agreements areas. The data is derived from 25-meter resolution Landsat-based grids from the National Carbon Accounting System and processed using land use masking and fuzzy-logic certainty analysis. This dataset is superseded by a newer state-wide version covering 1995 to 2020.
Endogenous mNeonGreen tagging data for protein subcellular localisation in the bloodstream form of the eukaryotic pathogen Trypanosoma brucei. The master deposition includes summary tables of localisations, primer sequences, and DOI indexing for microscopy data spread across multiple Zenodo repositories. Clare Halliday from the University of Oxford led this targeted tagging project, analogous to the genome-wide TrypTag procyclic form study.
A survey of the Solitary Islands Key Ecological Feature was conducted from August 7th to 16th, 2012. The survey collected forward-facing mono video, stereo video, and downward-facing stills, with GPS and USBL positional tracking. It was conducted by the NERP Marine Biodiversity Hub in collaboration with the New South Wales Office of Environment and Heritage.
Parks Australia provides a review examining six major invasive ant abatement programs across multiple Australian regions, including Arnhem Land and Christmas Island. The review evaluates management strategies, biodiversity impacts, and community engagement, noting programs rely on surveillance and toxic baiting. It was published via the Australian Marine Parks Science Atlas and last updated on 2026-07-16.
Quantitative abundance data for fish, megafaunal invertebrates, and algal cover collected via transect surveys across temperate mainland Australia. The data was collected by the Australian Ocean Data Network, primarily from studies on Marine Protected Areas (MPAs) and surveys in locations like the Port Lincoln district. Methods are based on transects at 5 m and/or 10 m depths, with temporal replication at a yearly scale as described in Edgar and Barrett (1997).
A 2026 compilation integrates benthic habitat datasets from research, government, industry, and community sources across Australia. The National Benthic Habitat Layer uniformly classifies these disparate datasets under a national scheme. It is maintained as a 'live' asset by the Australian Ocean Data Network and will continue to develop with new validated data.
Natural England's 2022 dataset categorizes areas in Swindon and Wiltshire into four risk zones for great crested newt conservation. Red zones contain key populations on a regional, national, or international scale, including designated Sites of Special Scientific Interest. The data is derived from multiple sources including land cover maps, Ordnance Survey data, and local council and environmental trust inputs.
Natural England's 2020 dataset categorizes areas in South and East Yorkshire by risk to great crested newt populations. It defines four zones—red, amber, green, and white—based on species occurrence and the likely impact of development. The data integrates multiple sources including land cover mapping, soils data, and ecological records from several regional trusts and centers.
Natural England's dataset categorizes areas in North and West Yorkshire by risk to great crested newt populations from development. It defines red, amber, green, and white zones based on population importance and habitat connectivity. The data is derived from multiple environmental sources including Ordnance Survey, the Centre for Ecology & Hydrology, and the Freshwater Habitats Trust.
Natural England's 2020 dataset categorizes areas in Lancashire by risk to great crested newt populations, from key red zones to rare white zones. The data is derived from multiple environmental sources including Ordnance Survey, Natural Environment Research Council, and the Freshwater Habitats Trust. It identifies zones based on species occurrence and the likely impact of development.
A 2022 dataset from Natural England categorizes areas in Greater Manchester based on great crested newt occurrence and development impact risk. It defines four risk zones—red, amber, green, and white—based on population importance and habitat connectivity. The data is derived from land cover mapping and other environmental sources.
Natural England's 2020 dataset categorizes areas in Dorset by risk to Great Crested Newt populations from development. It defines four zones—red, amber, green, and white—based on population importance and habitat connectivity. The data is derived from multiple environmental sources including land cover maps and Ordnance Survey data.
Natural England's 2021 dataset categorizes areas in Derbyshire by the likely impact of development on great crested newt populations. It defines four risk zones—red, amber, green, and white—based on species occurrence and habitat connectivity. The data integrates multiple sources, including Ordnance Survey, Cranfield University soils data, and local wildlife trust monitoring.
Natural England's 2020 dataset categorizes areas in Cambridgeshire by the distribution of great crested newts and the likely impact of development. It defines four risk zones—red, amber, green, and white—based on population importance and habitat connectivity. The data incorporates information from multiple environmental sources, including the Centre for Ecology & Hydrology and the Freshwater Habitats Trust.
More than 30 years of twice-monthly vegetation index data, from 1981 to 2014, provides a consistent long-term record for monitoring plant life cycles. The dataset combines Advanced Very High Resolution Radiometer (AVHRR) data from 1981-1999 with Moderate Resolution Imaging Spectroradiometer (MODIS) data from 2000-2014, calculating Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI2) at a 0.05-degree spatial resolution. It includes 12 Science Datasets per file, containing the indices, quality assurance flags, input surface reflectance, and viewing geometry.
Data from IMEF studies in the Hunter and Murrumbidgee Rivers, testing the hypothesis that environmental flows can scour biofilms and improve habitat. The dataset was published by the NSW Department of Climate Change, Energy, the Environment and Water and was last updated on 2026-06-12. It likely contains measurements related to biofilm, silt, and invertebrate populations before and after flow events.
The Great Barrier Reef is the focus of this dataset, which defines ecologically relevant targets for sediment loads and desired seagrass meadow conditions. It was produced by the Australian Ocean Data Network, compiling historical and new data with statistical models and the eReefs coastal model. The data was last updated on 2026-07-13.
Terrestrial fluxes of nitrous oxide (N2O), methane (CH4), and ecosystem respiration (CO2) were measured monthly from October 2018 to September 2019 in mature oil palm plantations on mineral soil in Riau, Indonesia. A total of 54 static chambers across nine plots tested three understory vegetation management treatments: normal, reduced, and enhanced complexity. The dataset also includes soil moisture measurements taken around each chamber.
Plant census and microenvironment dataset from Mt. Baldy, Colorado, USA, 2014-2017 comprises a long-term study of alpine plant community dynamics. The data include annual census records for all plants in 50 plots, individual-level demographic estimates, and high-resolution microenvironmental and functional trait measurements. It covers several thousand individuals across approximately twenty species and highlights an apparent pattern of demographic decline.