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 0.7.1
Dataset results
2 results for “sub-Antarctic marine ecosystems”
Beagle kelp maps from: One of the least disturbed marine coastal ecosystems on Earth: Spatial and temporal persistence of Darwin's sub-Antarctic giant kelp forests
<p>Aim: Marine habitats and their dynamics are difficult to systematically monitor, particularly those in remote locations. This is the case with the sub-Antarctic ecosystem of the giant kelp <i>Macrocystis pyrifera</i>, which was already noted by Charles Darwin in his accounts on the <i>Voyage of the Beagle</i> and recorded on the nautical charts made during that expedition. We combined these and other nautical charts from the 19th and early 20th centuries with surveys conducted in the 1970s and 1980s and satellite detection algorithms from 1984 to 2019, to analyse kelp distribution through time and the factors that correlate with it.</p> <p>Location: Marine ecoregions of Channels and Fjords of Southern Chile, Falkland Islands (Malvinas), and the island of South Georgia.</p> <p>Taxon: <i>Macrocystis pyrifera</i>.</p> <p>Methods: We characterised 309 giant kelp forests by their coastal geospatial attributes. Statistically significant variables were included in a conditional inference tree to predict kelp forest size. Sea surface temperature (SST) records were analysed to confirm temperature ranges over the last four decades. Nautical charts, historical surveys, aerial photogrammetry, unmanned aerial vehicle (UAV) surveys and satellite imagery were overlaid to assess spatial distribution of kelp forest canopies, spanning the period 1829–2020.</p> <p>Results: Considering the extensive natural and human caused changes over the last two centuries, this diverse kelp ecosystem is remarkably persistent. We found that the ocean currents and wave exposure, combined with the geomorphological settings of the coastline are the most critical factors predicting the extent of the kelp forests.</p> <p>Main conclusions: We have described the long-term ecological persistence of the kelp forests in this vastly under-studied region that offers a conceptual biogeographical model supporting the global importance proposed by Charles Darwin 200 years ago (Darwin, 1845). In the current context of global change, the need for conservation of this persistent and well-preserved marine ecosystem has never been more important.</p>
Beagle kelp maps from: One of the least disturbed marine coastal ecosystems on Earth: Spatial and temporal persistence of Darwin’s sub-Antarctic giant kelp forests
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.