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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Assessing the effects of sampling frequency on behavioural classification of accelerometer data
Jenna L. Hounslow, Lauran R. Brewster, Karissa O. Lear, et al.
Journal of Experimental Marine Biology and Ecology (2019) Vol. 512, pp. 22-30
Open Access | Times Cited: 41

Showing 1-25 of 41 citing articles:

Deep transfer learning in sheep activity recognition using accelerometer data
Natasa Kleanthous, Abir Hussain, Wasiq Khan, et al.
Expert Systems with Applications (2022) Vol. 207, pp. 117925-117925
Open Access | Times Cited: 48
A survey of machine learning approaches in animal behaviour
Natasa Kleanthous, Abir Hussain, Wasiq Khan, et al.
Neurocomputing (2022) Vol. 491, pp. 442-463
Open Access | Times Cited: 39
Classification of behaviors of free-ranging cattle using accelerometry signatures collected by virtual fence collars
Erik Versluijs, Laura J. Niccolai, Mélanie Spedener, et al.
Frontiers in Animal Science (2023) Vol. 4
Open Access | Times Cited: 20
Challenges of machine learning model validation using correlated behaviour data: Evaluation of cross-validation strategies and accuracy measures
Bence Ferdinandy, L. Gerencsér, Luca Corrieri, et al.
PLoS ONE (2020) Vol. 15, Iss. 7, pp. e0236092-e0236092
Open Access | Times Cited: 49
Identifying animal behaviours from accelerometers: Improving predictive accuracy of machine learning by refining the variables selected, data frequency, and sample duration
Carolyn E. Dunford, Nikki J. Marks, Rory P. Wilson, et al.
Ecology and Evolution (2024) Vol. 14, Iss. 5
Open Access | Times Cited: 5
Using tri-axial accelerometer loggers to identify spawning behaviours of large pelagic fish
Thomas M. Clarke, Sasha K. Whitmarsh, Jenna L. Hounslow, et al.
Movement Ecology (2021) Vol. 9, Iss. 1
Open Access | Times Cited: 31
Accelerometer sampling requirements for animal behaviour classification and estimation of energy expenditure
Hui Yu, Florian T. Muijres, Jan Severin te Lindert, et al.
Animal Biotelemetry (2023) Vol. 11, Iss. 1
Open Access | Times Cited: 13
The role of individual variability on the predictive performance of machine learning applied to large bio-logging datasets
Marianna Chimienti, Akiko Kato, Olivia Hicks, et al.
Scientific Reports (2022) Vol. 12, Iss. 1
Open Access | Times Cited: 17
Prey ingestion rates revealed by back-mounted accelerometers in Eurasian spoonbills
Tamar Lok, Matthijs van der Geest, Roeland A. Bom, et al.
Animal Biotelemetry (2023) Vol. 11, Iss. 1
Open Access | Times Cited: 11
Classification of behaviour with low-frequency accelerometers in female wild boar
Thomas Ruf, Jennifer Krämer, Claudia Bieber, et al.
PLoS ONE (2025) Vol. 20, Iss. 2, pp. e0318928-e0318928
Open Access
Practical guidelines for validation of supervised machine learning models in accelerometer‐based animal behaviour classification
Oakleigh Wilson, David S. Schoeman, Andrew P. Bradley, et al.
Journal of Animal Ecology (2025)
Open Access
Limitations of using surrogates for behaviour classification of accelerometer data: refining methods using random forest models in Caprids
Eleanor R. Dickinson, Joshua P. Twining, Rory P. Wilson, et al.
Movement Ecology (2021) Vol. 9, Iss. 1
Open Access | Times Cited: 23
Predicting moose behaviors from tri-axial accelerometer data using a supervised classification algorithm
T. Kirchner, Olivier Devineau, Marianna Chimienti, et al.
Animal Biotelemetry (2023) Vol. 11, Iss. 1
Open Access | Times Cited: 9
Human action recognition using Lie Group features and convolutional neural networks
Linqin Cai, Chengpeng Liu, Rongdi Yuan, et al.
Nonlinear Dynamics (2020) Vol. 99, Iss. 4, pp. 3253-3263
Closed Access | Times Cited: 23
A framework for energy-efficient equine activity recognition with leg accelerometers
Anniek Eerdekens, Margot Deruyck, Jaron Fontaine, et al.
Computers and Electronics in Agriculture (2021) Vol. 183, pp. 106020-106020
Open Access | Times Cited: 20
Evaluating the constraints governing activity patterns of a coastal marine top predator
Evan E. Byrnes, Ryan Daly, Vianey Leos‐Barajas, et al.
Marine Biology (2021) Vol. 168, Iss. 1
Closed Access | Times Cited: 19
Feature Extraction, Selection, and K-Nearest Neighbors Algorithm for Shark Behavior Classification Based on Imbalanced Dataset
Yu Yang, Hen‐Geul Yeh, Wenlu Zhang, et al.
IEEE Sensors Journal (2020) Vol. 21, Iss. 5, pp. 6429-6439
Closed Access | Times Cited: 18
DANA
Mohammad Malekzadeh, Richard G. Clegg, Andrea Cavallaro, et al.
Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies (2021) Vol. 5, Iss. 3, pp. 1-27
Open Access | Times Cited: 16
Wildlife tourism has little energetic impact on the world's largest predatory shark
Adrienne Gooden, Thomas M. Clarke, Lauren Meyer, et al.
Animal Behaviour (2023) Vol. 207, pp. 247-265
Open Access | Times Cited: 6
Assessment of Machine Learning Models to Identify Port Jackson Shark Behaviours Using Tri-Axial Accelerometers
Julianna Kadar, Monique A. Ladds, Joanna Day, et al.
Sensors (2020) Vol. 20, Iss. 24, pp. 7096-7096
Open Access | Times Cited: 15
Using accelerometers to infer behaviour of cryptic species in the wild
Laura Benoit, Nadège Bonnot, Lucie Debeffe, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2023)
Open Access | Times Cited: 4
Detecting changes in fish behaviour in real time to alert managers to thresholds of potential concern
Matthew J. Burnett, Vanessa Süßle, Terence Saayman, et al.
River Research and Applications (2023) Vol. 40, Iss. 1, pp. 129-147
Open Access | Times Cited: 4
Discrimination of ingestive behavior in sheep using an electronic device based on a triaxial accelerometer and machine learning
Magno do Nascimento Amorim, Sílvia Helena Nogueira Turco, Daniel dos Santos Costa, et al.
Computers and Electronics in Agriculture (2024) Vol. 218, pp. 108657-108657
Closed Access | Times Cited: 1
Classification of African ground pangolin behaviour based on accelerometer readouts: validation of bio-logging methods
Jessica Harvey‐Carroll, Dáire Carroll, Cara-Marie Trivella, et al.
Animal Biotelemetry (2024) Vol. 12, Iss. 1
Open Access | Times Cited: 1
The Use of Triaxial Accelerometers and Machine Learning Algorithms for Behavioural Identification in Domestic Dogs (Canis familiaris): A Validation Study
Cushla Redmond, Michelle Smit, I. Draganova, et al.
Sensors (2024) Vol. 24, Iss. 18, pp. 5955-5955
Open Access | Times Cited: 1
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