Find research datasets worth reusing

opencc-by-4.0Dec 2014View details →

Sony AIBO robot dog accelerometer

Dataset taken from https://www.cs.unm.edu/~mueen/robot_dog.txt Stored on Zenodo as backup for Stumpy Fast Pattern Matching Tutorial https://stumpy.readthedocs.io/en/latest/Tutorial_Pattern_Searching.html .

opencc-by-4.0Dec 2014View details →

Sony AIBO robot dog accelerometer query

Dataset taken from https://www.cs.unm.edu/~mueen/carpet_query.txt Stored on Zenodo as backup for Stumpy Fast Pattern Matching Tutorial https://stumpy.readthedocs.io/en/latest/Tutorial_Pattern_Searching.html .

opencc-zeroJan 2024View details →

Monitoring mobility in older adults using a global positioning system (GPS) smartwatch and accelerometer: A validation study

Background There is interest in identifying the most reliable method for detecting early mobility limitations. Accelerometry and Global Positioning System (GPS) could provide insight into declines in mobility, but few studies have used this multi-sensor approach to monitor mobility in older adults. Methods Thirty-two volunteers (66.2±6.3 years) agreed to participate in our validation study. We conducted two experiments to determine the validity of the TicWatch S2 and Pro 3 Ultra GPS models against the Qstarz receiver in measuring life-space mobility, trip frequency, duration, and mode. We also assessed the accuracy of the TicWatch in measuring step count and agreement with the ActiGraph wGT3X-BT for activity counts and sedentary behavior. Participants wore devices simultaneously for three consecutive days and recorded activity and trip information. Results The TicWatch Pro 3 Ultra GPS performed better than the S2 model and was similar to the Qstarz in all tested trip-related measures, and it was able to estimate both passive and active trip modes. Both models showed similar results to the Qstarz in life-space-related measures. The TicWatch S2 demonstrated good to excellent overall agreement with the ActiGraph algorithms for the time spent in sedentary and non-sedentary activities, with 84% and 87% agreement rates, respectively. Under supervised conditions, the TicWatch Pro 3 Ultra GPS measured step count consistently with the gold standard observer, with a bias of 0.4 steps. The thigh-worn ActiGraph algorithm accurately classified sitting and lying postures (97%) and standing postures (90%). Conclusion Our multi-sensor approach to monitoring mobility has the potential to capture both accelerometer-derived movement data and trip/life-space data only available through GPS. In this study, we found that the TicWatch models are valid devices for capturing GPS and raw accelerometer data, making them useful tools for assessing real-world mobility in older adults and advancing our knowledge of early mobility decline.

opencc-zeroJan 2024View details →

Data from: Exploring deep learning techniques for wild animal behaviour classification using animal-borne accelerometers

1: Machine learning-based behaviour classification using acceleration data is a powerful tool in bio-logging research. Deep learning architectures such as convolutional neural networks (CNN), long short-term memory (LSTM), and self-attention mechanism as well as related training techniques have been extensively studied in human activity recognition. However, they have rarely been used in wild animal studies. The main challenges of acceleration-based wild animal behaviour classification include data shortages, class imbalance problems, various types of noise in data due to differences in individual behaviour and where the loggers were attached, and complexity in data due to complex animal-specific behaviours, which may have limited the application of deep learning techniques in this area. 2: To overcome these challenges, we explored the effectiveness of techniques for efficient model training: data augmentation, manifold mixup, and pre-training of deep learning models with unlabelled data, using datasets from two species of wild seabirds and state-of-the-art deep learning model architectures. 3: Data augmentation improved the overall model performance when one of various techniques (none, scaling, jittering, permutation, time-warping, and rotation) was randomly applied to each data during mini-batch training. Manifold mixup also improved model performance, but not as much as random data augmentation. Pre-training with unlabelled data did not improve model performance. The state-of-the-art deep learning models, including a model consisting of four CNN layers, an LSTM layer, and a multi-head attention layer, as well as its modified version with shortcut connection, showed better performance among other comparative models. Using only raw acceleration data as inputs, these models outperformed classic machine learning approaches that used 119 handcrafted features. 4: Our experiments showed that deep learning techniques are promising for acceleration-based behaviour classification of wild animals and highlighted some challenges (e.g. effective use of unlabelled data). There is scope for greater exploration of deep learning techniques in wild animal studies (e.g. advanced data augmentation, multimodal sensor data use, transfer learning, and self-supervised learning). We hope that this study will stimulate the development of deep learning techniques for wild animal behaviour classification using time-series sensor data. This abstract is cited from the original article "Exploring deep learning techniques for wild animal behaviour classification using animal-borne accelerometers" in Methods in Ecology and Evolution (Otsuka et al., 2024). Please see README for the details of the datasets.

GENEActiv accelerometer files collected in Vanuatu during FALAH project (anonymized version - second part)

<a title="GENEActiv" href="https://activinsights.com/technology/geneactiv/" target="_blank" rel="noopener">GENEActiv</a> accelerometer .csv and .RData files converted with a 1 second epoch from raw GENEActiv .bin files recorded in Vanuatu during <a title="FALAH website" href="https://falah.unc.nc/" target="_blank" rel="noopener">FALAH</a> project. Devices are 60-Hz triaxial accelerometers. This dataset also contains participantCharacteristics.csv that povides basic information about participants and read_a_binFile_share.R that is a short R code aiming at converting and saving accelerometer data from .bin files in 1 second epoch .csv files (consider the Methods section). Participant characteristics: 13 to 17 years old students. Number of participants: 72. Year of the study: 2023. Place of the study: Vanuatu. The accelerometer .csv and .RData files with a 1 second epoch and extracted from raw .bin files are available in the restricted datasets: <ul> <li><a title="Anonymized dataset (first part)" href="https://doi.org/10.5281/zenodo.14043332" target="_blank" rel="noopener">anonymized version (first part)</a></li> <li><a title="Anonymized dataset (second part)" href="https://doi.org/10.5281/zenodo.14089478" target="_blank" rel="noopener">anonymized version (second part)</a></li> </ul> The accelerometer raw .bin files are available in the restricted datasets: <ul> <li><a title="Non-anonymized dataset (first part)" href="https://doi.org/10.5281/zenodo.14043547" target="_blank" rel="noopener">non-anonymized version (first part)</a></li> <li><a title="Non-anonymized dataset (second part)" href="https://doi.org/10.5281/zenodo.14089527">non-anonymized version (second part)</a></li> </ul> Other participant characteristics (age, place of living, ...) and responses to questionnaires are available in <a title="Information and questionnaire associated with GENEActiv accelerometer files collected in Vanuatu during FALAH project (non-anonymized information)" href="https://doi.org/10.5281/zenodo.14189884" target="_blank" rel="noopener">a restricted non-anonymized dataset</a>.

GENEActiv accelerometer files collected in Vanuatu during FALAH project (non-anonymized version - first part)

<a title="GENEActiv" href="https://activinsights.com/technology/geneactiv/" target="_blank" rel="noopener">GENEActiv</a> accelerometer .csv files converted with a 1 second epoch from raw GENEActiv .bin files recorded in Vanuatu during <a title="FALAH website" href="https://falah.unc.nc/" target="_blank" rel="noopener">FALAH</a> project. Devices are 60-Hz triaxial accelerometers. This dataset also contains participantCharacteristics.csv that povides basic information about participants and read_a_binFile_share.R that is a short R code aiming at converting and saving accelerometer data from .bin files in 1 second epoch .csv files (consider the Methods section). Participant characteristics: 13 to 17 years old students. Number of participants: 72. Year of the study: 2023. Place of the study: Vanuatu. The accelerometer .csv and .RData files with a 1 second epoch and extracted from raw .bin files are available in the restricted datasets: <ul> <li><a title="Anonymized dataset (first part)" href="https://doi.org/10.5281/zenodo.14043332" target="_blank" rel="noopener">anonymized version (first part)</a></li> <li><a title="Anonymized dataset (second part)" href="https://doi.org/10.5281/zenodo.14089478" target="_blank" rel="noopener">anonymized version (second part)</a></li> </ul> The accelerometer raw .bin files are available in the restricted datasets: <ul> <li><a title="Non-anonymized dataset (first part)" href="https://doi.org/10.5281/zenodo.14043547" target="_blank" rel="noopener">non-anonymized version (first part)</a></li> <li><a title="Non-anonymized dataset (second part)" href="https://doi.org/10.5281/zenodo.14089527">non-anonymized version (second part)</a></li> </ul> Other participant characteristics (age, place of living, ...) and responses to questionnaires are available in <a title="Information and questionnaire associated with GENEActiv accelerometer files collected in Vanuatu during FALAH project (non-anonymized information)" href="https://doi.org/10.5281/zenodo.14189884" target="_blank" rel="noopener">a restricted non-anonymized dataset</a>.

GENEActiv accelerometer files collected in Vanuatu during FALAH project (non-anonymized version - second part)

opencc-by-4.0Oct 2021View details →

Supplementary material S28: Modal response of the petri-dish and accelerometer.

Accelerometer modal response, with petri-dish, petri-dish and lid, and alone. An artificial vibration was driven at a 25% magnitude of the PC maximum output volume with a frequency sweep from 0 to 24kHz, via an electromagnetic shaker, to the accelerometer (green), accelerometer glued to the petri-dish (black) and the accelerometer glued to the petri-dish with the lid placed on (red). These figures demonstrate the linearity of the recording system in both linear and logarithmic scales. A large deviation from linearity is seen at 18kHz, where our signals of interest are seldom seen. The only other discrepancy is seen at 500Hz, where the petri-dish exhibits a resonance. The electromagnetic shaker is incapable of vibrating the accelerometer beyond 21kHz, causing the large drop of response seen at ultra-high frequencies.

opencc-by-4.0Oct 2021View details →

Supplementary material S29: Accelerometer sensitivity and linearity at varying signal magnitudes.

Accelerometer sensitivity and linearity with frequency. Artificial vibrations with three different magnitudes were driven with a frequency sweep from 0 to 24 kHz, with an electromagnetic shaker. The accelerometer outputs (a) are modulated by both the shaker and the crystal responses. The ratio of any two curves (signal = 6 divided by signal = 3 for (b), and signal = 9 divided by signal = 3 for (c)) allows the estimation of the accelerometer’s linearity alone. These figures demonstrate the remarkable linearity of our sensor, except for the bandwidth between 14-18 kHz and frequencies beyond 23 kHz, where up to 10% deviation can be seen. Our signals of interest seldom or never overlap with these frequency bands.

opencc-by-4.0May 2021View details →

Instrument response files for seismic stations in South Korea (accelerometer)

To do[1.01] I have found something wrong in poles and zeros for the KG and KN networks. Please do not use them until update. I am sorry for it. (14 November 2023) Example 1 (Seismic Analysis Code)Examples to deconvolve the instrument response from raw data are below, using the Seismic Analysis Code (SAC, version sac-101.6a).  1. A unit of output is 'm/s2'r $input rtr rmean taper TRANS FROM POLEZERO S $pzfile TO none FREQ 0.05 0.1 1.5 3.0 w acc.sac q 2. A unit of output is 'm/s'r $input rtr rmean taper TRANS FROM POLEZERO S $pzfile TO none FREQ 0.05 0.1 1.5 3.0 TRANS FROM ACC TO VEL w vel.sac q 3. A unit of output is 'm'r $input rtr rmean taper TRANS FROM POLEZERO S $pzfile TO none FREQ 0.05 0.1 1.5 3.0 TRANS FROM ACC TO VEL rtr rmean taper TRANS FROM VEL TO NONE w disp.sac q Note that the unit of the output is incoherent with the SAC header 'IDEP'.Example 2 (StationXML with obspy)#this is same as the code for the velocity seismometers. Update notes<ul><li>[1.01]<ul><li>Typo at INPUT UNIT for KG is modified (M/S -> M/S**2)</li><li>Stations KS.CE2A, KS.HA2B are added</li><li>StationXML file is added (ksgn.xml)</li></ul></li><li>[1.00] The files for the KG network are made based on the logs until 12 April 2019 (personal communication with the Korea Institute of Geoscience and Mineral Resources).</li></ul>OthersVelocity seismometer <a href="https://doi.org/10.5281/zenodo.3700312">https://doi.org/10.5281/zenodo.3700312</a> Accelerometer <a href="https://doi.org/10.5281/zenodo.3872436">https://doi.org/10.5281/zenodo.3872436</a> LicenseThis distribution follows the Creative Commons Attribution 4.0 International. You are free to modify and redistribute the files.

opencc-zeroJan 2022View details →

Ecological inference using data from accelerometers needs careful protocols

1. Accelerometers in animal-attached tags have proven to be powerful tools in behavioural ecology, being used to determine behaviour and provide proxies for movement-based energy expenditure. Researchers are collecting and archiving data across systems, seasons and device types. However, in order to use data repositories to draw ecological inference, we need to establish the error introduced according to sensor type and position on the study animal and establish protocols for error assessment and minimization. 2. Using laboratory trials, we examine the absolute accuracy of tri-axial accelerometers and determine how inaccuracies impact measurements of dynamic body acceleration (DBA) in human participants, with DBA as the main acceleration-based proxy for energy expenditure. We then examine how tag type and placement affect the acceleration signal in birds, using (i) pigeons Columba livia flying in a wind tunnel, with tags mounted simultaneously in two positions, and (ii) back- and tail-mounted tags deployed on wild kittiwakes Rissa tridactyla. Finally, we (iii) present a case study where two generations of tag were deployed using different attachment procedures on red-tailed tropicbirds Phaethon rubricauda foraging in different seasons. 3. Bench tests showed that individual acceleration axes required a two-level correction to eliminate measurement error. This resulted in DBA differences of up to 5% between calibrated and uncalibrated tags for humans walking at a range of speeds. Device position was associated with greater variation in DBA, with upper- and lower back-mounted tags varying by 9% in pigeons, and tail- and back-mounted tags varying by 13% in kittiwakes. The largest variation occurred between tropicbirds tagged in different seasons, where DBA varied by 25%, which may be due to tag attachment procedures. In general, the tropicbird study highlights the difficulties of attributing changes in signal amplitude to a single factor, when confounding influences tend to covary. 4. Accelerometer accuracy, tag placement, and attachment critically affect the signal amplitude and thereby the ability of the system to detect biologically meaningful phenomena. We propose a simple method to calibrate accelerometers that can be executed under field conditions. This should be used prior to deployments and archived with resulting data. We also suggest a way that researchers can assess accuracy in previously collected data, and caution that variable tag placement and attachment can increase sensor noise and even generate trends that have no biological meaning.

opencc-zeroMay 2024View details →

Data for: Domestic cat accelerometer data calibrated with behaviours

Observing animals in the wild often poses extreme challenges, but animal-borne accelerometers are increasingly revealing unobservable behaviours. Automated machine learning streamlines behaviour identification from the substantial datasets generated during multi-animal, long-term studies, however, the accuracy of such models depends on the qualities of the training data. We examined how data processing influenced the predictive accuracy of random forest (RF) models, leveraging the easily observed domestic cat (Felis catus) as a model organism for terrestrial mammalian behaviours. Nine indoor domestic cats were equipped with collar-mounted tri-axial accelerometers, and behaviours were recorded alongside video footage. From this calibrated data, eight datasets were derived with; (i) additional descriptive variables; (ii) altered frequencies of acceleration data (40 Hz vs. a mean over 1 second); and (iii) standardised durations of different behaviours. These training datasets were used to generate RF models which were validated against calibrated cat behaviours before identifying behaviours of five free-ranging tag-equipped cats. These predictions were compared to those identified manually to validate the accuracy of the RF models for free-ranging animal behaviours. RF models accurately predicted the behaviours of indoor domestic cats (F-measure up to 0.96) with discernible improvements observed with post-data-collection processing. Additional variables, standardized durations of behaviours, and higher recording frequencies improved model accuracy. However, prediction accuracy varied with different behaviours, where high-frequency models excelled in identifying fast paced behaviours (e.g. locomotion), while lower frequency models (1 Hz) more accurately identified slower, aperiodic behaviours such as grooming and feeding, particularly when examining free-ranging cat behaviours. While RF modelling offered a robust means of behaviour identification from accelerometer data, field validations were important to validate model accuracy for free-ranging individuals. Future studies may benefit from employing similar data processing methods that enhance RF behaviour identification accuracy, with extensive advantages for investigations into ecology, welfare, and management of wild animals.

opencc-by-4.0May 2024View details →

Accelerometer, gyroscope and pressure data associated with behaviors of free-ranging hawksbill sea turtles (Eretmochelys imbricata)

<div> <div> <div> <div> <div> In this paper, we explored the use of transfer learning across species and taxa, employing fully convolutional neural networks to predict the behaviors of critically endangered hawksbill sea turtles from acceleration data. For this purpose, fully convolutional neural networks (V-net and U-net) were pre-trained on a dataset of green turtles (Zenodo link) and human data (Intensive Care Unit (ICU) HAR dataset, <a href="https://doi.org/10.24432/C54S4K" target="_new" rel="noreferrer">https://doi.org/10.24432/C54S4K</a>) before being fine-tuned on the hawksbill dataset. The results reveal a 8% and 4% improvement in F1-score with transfer learning from the green turtle and human datasets, respectively, compared to training the models from random weight initialization (without transfer learning).  </div> </div> </div> </div> </div>   The dataset comprised the raw acceleration, gyroscope and depth sequence of 6 free-ranging hawksbill sea turtles associated with the behaviors. The indiviuals were equipped with a on-board video recorder combined with an accelerometer, gyroscope, magnetometer and luminosity, temperature and depth sensors using four suction cups and an automatic release system over a two-day periods (see Jeantet et al. 2020 for details and the associated article). The accelerometer, gyroscope, magnetometer recorded at 20 Hz and the pressure, temperature and luminosity sensors at 1 Hz. The cameras were programmed to record until nightfall (6 pm) and resume at daybreak (6 am). The magnetometer, luminosity and temperature data are provided but not used in the associated study.    <div> <div> <div> <div> <div> For each individual, the data collected by the devices was correlated with observed behaviors from video recordings. Unlabeled sequences, mostly comprising night recordings, were excluded, resulting in the creation of one file per day of deployment for each individual.  </div> </div> </div> </div> </div> <div> <div> <div> <div> <div> To process the depth data and increase the sampling rate to 20 Hz, we used a linear interpolation technique. We called this new variable "Pressure_corr". Additionally, we calculated the pressure difference ("Pressure_diff") between each measuring point (originally at 1 Hz). </div> </div> </div> </div> </div>   "In total, 69.7 hours of multi-sensor sequences were labelled from six different hawksbill turtles (approximately 11.6 hours of recording per individual, max = 17.8 hours, min = 6.3 hours, standard deviation = 3.6 hours). The predominant behavior observed in the videos was Feeding, totaling over 38.6 hours, followed by Resting and Swimming, with 19.1 hours and 7.9 hours, respectively. The other behaviors were expressed in minority (Breathing: 2.2 hours, Gliding: 1 hour, Scratching: 0.8 hour and Other: 0.1 hour). "    The folder contains 10 Python matrices, each with 15 columns (AccX, AccY, AccZ, GyrX, GyrY, GyrZ, MagX, MagY, MagZ, Depth, Light, Temperature, Pressure_corr, Pressur_diff, Behavior) and a number of rows corresponding to the deployment duration. The title of each file indicates the camera number used (CC-09-XX) and the deployment day (DD-MM-YYYY), with the last digit specifying whether the matrix corresponds to the first or second day of deployment.   The folder also contains two dictionaries (behInd_to_behName, behName_to_behInd) that specify the behaviors associated with each number used as a label in the Behavior column. Additionally, there is a dictionary (dico_info) that provides the names of the matrix columns and the frequence of recording.

opencc-zeroJun 2024View details →

Data from: Identification of reindeer fine-scale foraging behaviour using tri-axial accelerometer data

Animal behavioural responses to the environment ultimately affect their survival. Monitoring animal fine-scale behaviour may improve understanding of animal functional response to the environment and provide an important indicator of the welfare of both wild and domesticated species. In this study, we illustrate the application of collar-attached acceleration sensors for investigating reindeer fine-scale behaviour. Using data from 19 reindeer, we tested the supervised machine learning algorithms random forests, support vector machines, and hidden Markov models to classify reindeer behaviour into seven classes: grazing, browsing low from shrubs or browsing high from trees, inactivity, walking, trotting, and other behaviours. We implemented leave-one-subject-out cross-validation to assess generalizable results on new individuals. Our main results illustrated that hidden Markov models were able to classify collar-attached accelerometer data into all our pre-defined behaviours of reindeer with reasonable accuracy while random forests and support vector machines were biased towards dominant classes. Random forests using 5-second windows had the highest overall accuracy (85%), while hidden Markov models were able to best predict individual behaviours and handle rare behaviours such as trotting and browsing high. We conclude that hidden Markov models provide a useful tool to remotely monitor reindeer and potentially other large herbivore species behaviour. These methods will allow us to quantify fine-scale behavioural processes in relation to environmental events.

opencc-by-4.0May 2024View details →

Classification of African ground pangolin behaviour based on accelerometer readouts: validation of bio-logging methods

Data and R Scripts for the manuscript titled "Classification of African ground pangolin behaviour based on accelerometer readouts: validation of bio-logging methods". Code is for labelling accelerometer data and running a random forest model. Script01. Label the accelerometer data with behavioural labels from BORIS Script02. Create summary metrics and resample frequencies. This includes code adapted from (Clark, 2019; Clark et al., 2022). https://ore.exeter.ac.uk/repository/handle/10871/120152, https://www.int-res.com/abstracts/meps/v701/p145-157/ Script03. Run random forest for each frequency and smoothing window. AccelerometerData.zip containes Files grouped by individual. For each individual there is: Accelerometer_data: Accelerometer data. ID: BORIS behaviour output ID_labs: Labelled accelerometer data

restrictedcc-by-4.0Jun 2024View details →

Information associated with GENEActiv accelerometer files collected during the project entitled "Cultures et comportements alimentaires de la jeunesse dans les pays francophones du Pacifique au XXIème siècle: exemple de la Nouvelle-Calédonie" [Eng: "Eating cultures and behaviors of young people in French-speaking Pacific countries in the 21st century: the example of New Caledonia"] (non-anonymized information)

Additional restricted participant characteristics of the following datasets: <ul> <li><a title="GENEActiv accelerometer files collected during the project entitled "Cultures et comportements alimentaires de la jeunesse dans les pays francophones du Pacifique au XXIème siècle: exemple de la Nouvelle-Calédonie" (non-anonymized version - first part)" href="https://doi.org/10.5281/zenodo.11594645" target="_blank" rel="noopener">GENEActiv accelerometer files collected during the project entitled "Cultures et comportements alimentaires de la jeunesse dans les pays francophones du Pacifique au XXIème siècle: exemple de la Nouvelle-Calédonie" [en: "Eating cultures and behaviors of young people in French-speaking Pacific countries in the 21st century: the example of New Caledonia"] (non-anonymized version - first part)</a></li> <li><a title="GENEActiv accelerometer files collected during the project entitled "Cultures et comportements alimentaires de la jeunesse dans les pays francophones du Pacifique au XXIème siècle: exemple de la Nouvelle-Calédonie" (non-anonymized version - second part)" href="https://doi.org/10.5281/zenodo.12638965" target="_blank" rel="noopener">GENEActiv accelerometer files collected during the project entitled "Cultures et comportements alimentaires de la jeunesse dans les pays francophones du Pacifique au XXIème siècle: exemple de la Nouvelle-Calédonie" [en: "Eating cultures and behaviors of young people in French-speaking Pacific countries in the 21st century: the example of New Caledonia"] (non-anonymized version - second part)</a></li> <li><a title="GENEActiv accelerometer files collected during the project entitled "Cultures et comportements alimentaires de la jeunesse dans les pays francophones du Pacifique au XXIème siècle: exemple de la Nouvelle-Calédonie" (non-anonymized version - third part)" href="https://doi.org/10.5281/zenodo.12661429" target="_blank" rel="noopener">GENEActiv accelerometer files collected during the project entitled "Cultures et comportements alimentaires de la jeunesse dans les pays francophones du Pacifique au XXIème siècle: exemple de la Nouvelle-Calédonie" [en: "Eating cultures and behaviors of young people in French-speaking Pacific countries in the 21st century: the example of New Caledonia"] (non-anonymized version - third part)</a></li> <li><a title="GENEActiv accelerometer files collected during the project entitled "Cultures et comportements alimentaires de la jeunesse dans les pays francophones du Pacifique au XXIème siècle: exemple de la Nouvelle-Calédonie" (anonymized version - first part)" href="https://doi.org/10.5281/zenodo.12615468" target="_blank" rel="noopener">GENEActiv accelerometer files collected during the project entitled "Cultures et comportements alimentaires de la jeunesse dans les pays francophones du Pacifique au XXIème siècle: exemple de la Nouvelle-Calédonie" [en: "Eating cultures and behaviors of young people in French-speaking Pacific countries in the 21st century: the example of New Caledonia"] (anonymized version - first part)</a></li> <li><a title="GENEActiv accelerometer files collected during the project entitled "Cultures et comportements alimentaires de la jeunesse dans les pays francophones du Pacifique au XXIème siècle: exemple de la Nouvelle-Calédonie" (anonymized version - second part)" href="https://doi.org/10.5281/zenodo.12638746" target="_blank" rel="noopener">GENEActiv accelerometer files collected during the project entitled "Cultures et comportements alimentaires de la jeunesse dans les pays francophones du Pacifique au XXIème siècle: exemple de la Nouvelle-Calédonie" [en: "Eating cultures and behaviors of young people in French-speaking Pacific countries in the 21st century: the example of New Caledonia"] (anonymized version - second part)</a></li> <li><a title="GENEActiv accelerometer files collected during the project entitled "Cultures et comportements alimentaires de la jeunesse dans les pays francophones du Pacifique au XXIème siècle: exemple de la Nouvelle-Calédonie" (anonymized version - third part)" href="https://doi.org/10.5281/zenodo.12682660" target="_blank" rel="noopener">GENEActiv accelerometer files collected during the project entitled "Cultures et comportements alimentaires de la jeunesse dans les pays francophones du Pacifique au XXIème siècle: exemple de la Nouvelle-Calédonie" [en: "Eating cultures and behaviors of young people in French-speaking Pacific countries in the 21st century: the example of New Caledonia"] (anonymized version - third part)</a></li> </ul> Participant characteristics: 10 to 16 years old students (n = 206) and parents (n = 25). Number of participants: 231. Year of the study: 2018 - 2019. Place of the study: New Caledonia. When using this dataset, please cite the following reference: <a title="Wattelez et al. 2025" href="https://doi.org/10.1016/j.dib.2024.111228" target="_blank" rel="noopener">G. Wattelez, S. Frayon, O. Galy, Assessing physical activity/behavior of adolescents living in the Pacific with accelerometer data: 231 GENEActiv records in New Caledonia, Data in Brief 58 (2025) 111228, doi: 10.1016/j.dib.2024.111228</a>

GENEActiv accelerometer files collected in Vanuatu during FALAH project (anonymized version - first part)