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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:

Evaluation of sampling frequency, window size and sensor position for classification of sheep behaviour
E. K. Walton, Christy Casey, Jürgen Mitsch, et al.
Royal Society Open Science (2018) Vol. 5, Iss. 2, pp. 171442-171442
Open Access | Times Cited: 95

Showing 26-50 of 95 citing articles:

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
Assessing sheep behavior through low-power microcontrollers in smart agriculture scenarios
L. H. P. Nóbrega, Pedro Gonçalves, Mário Antunes, et al.
Computers and Electronics in Agriculture (2020) Vol. 173, pp. 105444-105444
Closed Access | Times Cited: 29
A pipeline with pre-processing options to detect behaviour from accelerometer data using Machine Learning tested on dairy goats.
Sarah Mauny, Joon Kwon, N.C. Friggens, et al.
Peer Community Journal (2025) Vol. 5
Open Access
Time Series Classification to Improve Poultry Welfare
Alireza Abdoli, Amy C. Murillo, Chin‐Chia Michael Yeh, et al.
2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA) (2018), pp. 635-642
Open Access | Times Cited: 28
Inclusion of features derived from a mixture of time window sizes improved classification accuracy of machine learning algorithms for sheep grazing behaviours
Shuwen Hu, Aaron Ingham, Sabine Schmoelzl, et al.
Computers and Electronics in Agriculture (2020) Vol. 179, pp. 105857-105857
Closed Access | Times Cited: 27
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
Behavior Classification and Analysis of Grazing Sheep on Pasture with Different Sward Surface Heights Using Machine Learning
Zhongming Jin, Leifeng Guo, Hang Shu, et al.
Animals (2022) Vol. 12, Iss. 14, pp. 1744-1744
Open Access | Times Cited: 16
Exploring the Potential of Machine Learning Algorithms Associated with the Use of Inertial Sensors for Goat Kidding Detection
Pedro Gonçalves, M.R. Marques, A.T. Belo, et al.
Animals (2024) Vol. 14, Iss. 6, pp. 938-938
Open Access | Times Cited: 3
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
Lameness Detection in Cows Using Hierarchical Deep Learning and Synchrosqueezed Wavelet Transform
Delaram Jarchi, Jasmeet Kaler, Saeid Sanei
IEEE Sensors Journal (2021) Vol. 21, Iss. 7, pp. 9349-9358
Open Access | Times Cited: 19
What Are Sheep Doing? Tri-Axial Accelerometer Sensor Data Identify the Diel Activity Pattern of Ewe Lambs on Pasture
Seer J. Ikurior, Nelly Marquetoux, Stephan T. Leu, et al.
Sensors (2021) Vol. 21, Iss. 20, pp. 6816-6816
Open Access | Times Cited: 19
Evaluating Behavior Recognition Pipeline of Laying Hens Using Wearable Inertial Sensors
Kaori Fujinami, Ryo Takuno, Itsufumi Sato, et al.
Sensors (2023) Vol. 23, Iss. 11, pp. 5077-5077
Open Access | Times Cited: 8
Fitbit for Chickens?
Alireza Abdoli, Sara Alaee, Shima Imani, et al.
(2020)
Open Access | Times Cited: 20
Determination of ewe behaviour around lambing time and prediction of parturition 7 days prior to lambing by tri-axial accelerometer sensors in an extensive farming system
R. Sohi, Fazel Almasi, Hien D. Nguyen, et al.
Animal Production Science (2022) Vol. 62, Iss. 17, pp. 1729-1738
Open Access | Times Cited: 12
Sensor-based Detection and Classification of Soccer Goalkeeper Training Exercises
Juan Haladjian, Daniel Schlabbers, Sajjad Taheri, et al.
ACM Transactions on Internet of Things (2020) Vol. 1, Iss. 2, pp. 1-20
Closed Access | Times Cited: 17
Evaluation of the tri-axial accelerometer to identify and predict parturition-related activities of Debouillet ewes in an intensive setting
Sara C Gurule, Colin Tobin, Derek W. Bailey, et al.
Applied Animal Behaviour Science (2021) Vol. 237, pp. 105296-105296
Open Access | Times Cited: 16
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
SheepIT, an E-Shepherd System for Weed Control in Vineyards: Experimental Results and Lessons Learned
Pedro Gonçalves, L. H. P. Nóbrega, António Monteiro, et al.
Animals (2021) Vol. 11, Iss. 9, pp. 2625-2625
Open Access | Times Cited: 15
Use of an ear-tag accelerometer and a radio-frequency identification (RFID) system for monitoring the licking behaviour in grazing cattle
Gamaliel Simanungkalit, Jamie Barwick, Frances Cowley, et al.
Applied Animal Behaviour Science (2021) Vol. 244, pp. 105491-105491
Closed Access | Times Cited: 15
Forestry 4.0 and Industry 4.0: Use case on wildfire behavior predictions
Jesús N.S. Rubí, Paulo H.P. de Carvalho, Paulo Roberto de Lira Gondim
Computers & Electrical Engineering (2022) Vol. 102, pp. 108200-108200
Closed Access | Times Cited: 11
Classification of Behaviour in Conventional and Slow-Growing Strains of Broiler Chickens Using Tri-Axial Accelerometers
Justine Pearce, Yu‐Mei Chang, Dong Xia, et al.
Animals (2024) Vol. 14, Iss. 13, pp. 1957-1957
Open Access | Times Cited: 2
PD-YOLO: A study of daily behavioural detection in housed sheep
Jie Wang, Yahong Zhai, Lan Zhu, et al.
PLoS ONE (2024) Vol. 19, Iss. 11, pp. e0313412-e0313412
Open Access | Times Cited: 2
A Pilot Study Using Accelerometers to Characterise the Licking Behaviour of Penned Cattle at a Mineral Block Supplement
Gamaliel Simanungkalit, Jamie Barwick, Frances Cowley, et al.
Animals (2021) Vol. 11, Iss. 4, pp. 1153-1153
Open Access | Times Cited: 14
Evaluation of proper sensor position for classification of sheep behaviour through accelerometers
M. Decandia, S.P.G. Rassu, Vasilis Psiroukis, et al.
Small Ruminant Research (2021) Vol. 201, pp. 106445-106445
Closed Access | Times Cited: 14
Identification of reindeer fine-scale foraging behaviour using tri-axial accelerometer data
Heidi Rautiainen, Moudud Alam, Paul G. Blackwell, et al.
Movement Ecology (2022) Vol. 10, Iss. 1
Open Access | Times Cited: 10

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