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.
2
datasets available to search
ShareScore release 0.9.0
Dataset results
2 results for “European carp”
Data from: eDNA surveys to detect species at very low densities: a case study of European carp eradication in Tasmania, Australia
1. Eradications of invasive species are usually expensive and difficult to conduct. Knowing when to declare an eradication successful requires distinguishing between failed detection of the target species due to imperfect sensitivity of the detection method and true species absence. This is difficult because the sensitivities of many detection methods are unknown. 2. Environmental DNA (eDNA) methods can be used to detect species by analysing DNA present in environmental samples. eDNA has been promoted as a particularly sensitive and cost-effective way to detect species at low densities and, importantly, the sensitivity of eDNA surveys can be quantified. Nevertheless, the effort and costs involved in detecting species at extremely low densities, such as required during eradication, have not been previously calculated. 3. We evaluated the sensitivity of eDNA surveys in detecting the invasive European carp, Cyprinus carpio, in two lakes in Tasmania, Australia, one in which carp have been eradicated and a second in which carp are currently being eradicated. We determined the sampling effort and associated cost required to detect the species at very low density in these lakes. 4. While our eDNA survey detected the current low density carp population present in in Lake Sorell, we show that an exponential increase in sampling effort and associated cost will be required to confidently detect the species as the population declines. Similarly, while our eDNA survey corroborated the species absence from Lake Crescent, our detection confidence was low. We quantify the survey effort and financial investment required to confidently establish eradication success in Lake Crescent. Synthesis and applications: 5. Estimating the eDNA survey effort and cost required to detect species at a given density will enable practitioners to make informed decisions on the feasibility of implementing such surveys. Quantifying the sensitivity of eDNA surveys will also inform the confidence practitioners should place in the results of eDNA detection surveys to ensure appropriate management actions are implemented and provide a suitable stopping point at which to confidently declare eradication success.09-Jul-2019
Data from: eDNA surveys to detect species at very low densities: a case study of European carp eradication in Tasmania, Australia
Open the record for dataset details and reuse information.
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.