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.
37
datasets available to search
ShareScore release 0.7.1
Dataset results
37 results for “animal behaviour”
Data from: Behavioural compass: animal behaviour recognition using magnetometers
Open the record for dataset details and reuse information.
Seek and learn: automated identification of microevents in animal behaviour using envelopes of acceleration data and machine learning
Open the record for dataset details and reuse information.
Systematic review of validation of supervised machine learning models in accelerometer-based animal behaviour classification literature
Open the record for dataset details and reuse information.
Data from: Give the machine a hand: a Boolean time-based decision-tree template for rapidly finding animal behaviours in multi-sensor data
1. The development of multi-sensor animal-attached tags, recording data at high frequencies, has enormous potential in allowing us to define animal behaviour. 2. The high volumes of data, are pushing us towards machine-learning as a powerful option for distilling out behaviours. However, with increasing parallel lines of data, systems become more likely to become processor limited and thereby take appreciable amounts of time to resolve behaviours. 3. We suggest a Boolean approach whereby critical changes in recorded parameters are used as sequential templates with defined flexibility (in both time and degree) to determine individual behavioural elements within a behavioural sequence that, together, makes up a single, defined behaviour. 4. We tested this approach, and compared it to a suite of other behavioural identification methods, on a number of behaviours from tag-equipped animals; sheep grazing, penguins walking, cheetah stalking prey and condors thermalling. 5. Overall behaviour recognition using our new approach was better than most other methods due to; (i) its ability to deal with behavioural variation and (ii) the speed with which the task was completed because extraneous data are avoided in the process. 6. We suggest that this approach is a promising way forward in an increasingly data-rich environment and that workers sharing algorithms can provide a powerful library for the benefit of all involved in such work.
Data from: Social behaviour and collective motion in plant-animal worms
Social behaviour may enable organisms to occupy ecological niches that would otherwise be unavailable to them. Here we test this major evolutionary principle by demonstrating self-organizing social behaviour in the plant-animal, Symsagittifera roscoffensis. These marine aceol flat worms rely for all of their nutrition on the algae within their bodies: hence their common name. We show that individual worms interact with one another to co-ordinate their movements so that even at low densities they begin to swim in small polarized groups and at increasing densities such flotillas turn into circular mills. We use computer simulations to: (1) determine if real worms interact socially by comparing them with virtual worms that do not interact and (2) show that the social phase transitions of the real worms can occur based only on local interactions between and among them. We hypothesize that such social behaviour helps the worms to form the dense biofilms or mats observed on certain sun-exposed sandy beaches in the upper intertidal of the East Atlantic and to become in effect a super-organismic seaweed in a habitat where macro-algal seaweeds cannot anchor themselves. S. roscoffensis, a model organism in many other areas in biology (including stem cell regeneration), also seems to be an ideal model for understanding how individual behaviours can lead, through collective movement, to social assemblages.
Data from: Behaviourally mediated predation avoidance in penguin prey: in situ evidence from animal-borne camera loggers
Predator dietary studies often assume that diet is reflective of the diversity and relative abundance of their prey. This interpretation ignores species-specific behavioural adaptations in prey that could influence prey capture. Here, we develop and describe a scalable biologging protocol, using animal-borne camera loggers, to elucidate the factors influencing prey capture by a seabird, the gentoo penguin (Pygoscelis papua). From the video evidence, we show, for the first time, that aggressive behavioural defence mechanisms by prey can deter prey capture by a seabird. Furthermore, we provide evidence demonstrating that these birds, which were observed hunting solitarily, target prey when they are most discernible. Specifically, birds targeted prey primarily while ascending and when prey were not tightly clustered. In conclusion, we show that prey behaviour can significantly influence trophic coupling in marine systems because despite prey being present, it is not always targeted. Thus, these predator-prey relationships should be accounted for in studies using marine top predators as samplers of mid to lower trophic level species.
Supplementary material 1 from: Zidar P, Fišer Ž (2022) Avoidance behaviour toxicity tests should account for animal gregariousness: a case study on the terrestrial isopod Porcellio scaber. In: De Smedt P, Taiti S, Sfenthourakis S, Campos-Filho IS (Eds) Facets of terrestrial isopod biology. ZooKeys 1101: 87-108. https://doi.org/10.3897/zookeys.1101.76711
Table S1–S7
Data from: Social behaviour and collective motion in plant-animal worms
Open the record for dataset details and reuse information.
Data from: Behaviourally mediated predation avoidance in penguin prey: in situ evidence from animal-borne camera loggers
Open the record for dataset details and reuse information.
Data from: Give the machine a hand: a Boolean time-based decision-tree template for rapidly finding animal behaviours in multi-sensor data
Open the record for dataset details and reuse information.
Data from: Finding a home in the noise: cross-modal impact of anthropogenic vibration on animal search behaviour
Open the record for dataset details and reuse information.
Data from: A camera-based method for estimating absolute density in animals displaying home range behaviour
Open the record for dataset details and reuse information.
Data from: Predicting animal behaviour using deep learning: GPS data alone accurately predict diving in seabirds
1.In order to prevent further global declines in biodiversity, identifying and understanding key habitats is crucial for successful conservation strategies. For example, globally, seabird populations are under threat and animal movement data can identify key at-sea areas and provide valuable information on the state of marine ecosystems. To date, in order to locate these areas, studies have used Global Positioning System (GPS) to record position and are sometimes combined with Time Depth Recorder (TDR) devices to identify diving activity associated with foraging, a crucial aspect of at-sea behaviour. However, the use of additional devices such as TDRs can be expensive, logistically difficult, and may adversely affect the animal. Alternatively, behaviours may be resolved from measurements derived from the movement data alone. However, this behavioural analysis frequently lacks validation data for locations predicted as foraging (or other behaviours). 2.Here, we address these issues using a combined GPS and TDR dataset from 108 individuals by training deep learning models to predict diving in European shags, common guillemots and razorbills. We validate our predictions using withheld data, producing quantitative assessment of predictive accuracy. The variables used to train these models are those recorded solely by the GPS device: variation in longitude and latitude, altitude, and coverage ratio (proportion of possible fixes acquired within a set window of time). 3.Different combinations of these variables were used to explore the qualities of different models, with the optimum models for all species predicting non-diving and diving behaviour correctly over 94% and 80% of the time, respectively. We also demonstrate the superior predictive ability of these supervised deep-learning models over other commonly used behavioural prediction methods such as hidden Markov models. 4.Mapping these predictions provides useful insights into the foraging activity of a range of seabird species, highlighting important at sea locations. These models have the potential to be used to analyse historic GPS datasets and further our understanding of how environmental changes have affected these seabirds over time.
Data from: A novel biomechanical approach for animal behaviour recognition using accelerometers
Open the record for dataset details and reuse information.
Data from: Predicting animal behaviour using deep learning: GPS data alone accurately predict diving in seabirds
Open the record for dataset details and reuse information.
Baboon travel progressions as a social spandrel in collective animal behaviour.
<p>Data for all travel progressions as identified by Papadoupoulou et al. 2023</p>
Sample Dataset for YOLO-Behaviour: A simple, flexible framework to automatically quantify animal behaviours from videos
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.