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.
50
datasets available to search
ShareScore release 0.9.0
Dataset results
50 results for “ecological trap”
Data from: Under the snow: a new camera trap opens the white box of subnivean ecology
Open the record for dataset details and reuse information.
High blood parasite infection rate and low fitness suggest ecological traps for pied flycatchers breeding near forest water-bodies
Open the record for dataset details and reuse information.
Data from: Discovery and exploitation of a natural ecological trap for a mosquito disease vector
Open the record for dataset details and reuse information.
Data from: Ecological traps for large-scale invasive species control: predicting settling rules by recolonising American mink post-culling
Open the record for dataset details and reuse information.
Increased microclimatic variation in artificial nests does not create ecological traps for a secondary cavity breeder, the European roller
Open the record for dataset details and reuse information.
Data from: Forbidden fruit: human settlement and abundant fruit create an ecological trap for an apex omnivore
Habitat choice is an evolutionary product of animals experiencing increased fitness when preferentially occupying high-quality habitat. However, an ecological trap (ET) can occur when an animal is presented with novel conditions and the animal's assessment of habitat quality is poorly matched to its resulting fitness. We tested for an ET for grizzly (brown) bears using demographic and movement data collected in an area with rich food resources and concentrated human settlement. We derived measures of habitat attractiveness from occurrence models of bear food resources and estimated demographic parameters using DNA mark–recapture information collected over 8 years (2006–2013). We then paired this information with grizzly bear mortality records to investigate kill and movement rates. Our results demonstrate that a valley high in both berry resources and human density was more attractive than surrounding areas, and bears occupying this region faced 17% lower apparent survival. Despite lower fitness, we detected a net flow of bears into the ET, which contributed to a study-wide population decline. This work highlights the presence and pervasiveness of an ET for an apex omnivore that lacks the evolutionary cues, under human-induced rapid ecological change, to assess trade-offs between food resources and human-caused mortality, which results in maladaptive habitat selection.
Data from: Machine learning to classify animal species in camera trap images: applications in ecology
Motion‐activated cameras ("camera traps") are increasingly used in ecological and management studies for remotely observing wildlife and are amongst the most powerful tools for wildlife research. However, studies involving camera traps result in millions of images that need to be analysed, typically by visually observing each image, in order to extract data that can be used in ecological analyses. We trained machine learning models using convolutional neural networks with the ResNet‐18 architecture and 3,367,383 images to automatically classify wildlife species from camera trap images obtained from five states across the United States. We tested our model on an independent subset of images not seen during training from the United States and on an out‐of‐sample (or "out‐of‐distribution" in the machine learning literature) dataset of ungulate images from Canada. We also tested the ability of our model to distinguish empty images from those with animals in another out‐of‐sample dataset from Tanzania, containing a faunal community that was novel to the model. The trained model classified approximately 2,000 images per minute on a laptop computer with 16 gigabytes of RAM. The trained model achieved 98% accuracy at identifying species in the United States, the highest accuracy of such a model to date. Out‐of‐sample validation from Canada achieved 82% accuracy and correctly identified 94% of images containing an animal in the dataset from Tanzania. We provide an r package (Machine Learning for Wildlife Image Classification) that allows the users to (a) use the trained model presented here and (b) train their own model using classified images of wildlife from their studies. The use of machine learning to rapidly and accurately classify wildlife in camera trap images can facilitate non‐invasive sampling designs in ecological studies by reducing the burden of manually analysing images. Our r package makes these methods accessible to ecologists.
Data from: Forbidden fruit: human settlement and abundant fruit create an ecological trap for an apex omnivore
Open the record for dataset details and reuse information.
Data from: Machine learning to classify animal species in camera trap images: applications in ecology
Open the record for dataset details and reuse information.
Camera trap data accompanying paper titled: "Deep learning-based ecological analysis of camera trap images is impacted by training data quality and size"
<p>ZIp file that contains two camera trap datasets that support the experiments of the paper title: "Deep learning-based ecological analysis of camera trap images is impacted by training data quality and size". The paper is currently under submission.</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.