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opencc-by-4.0Dec 2023View details →

EAT example applications

These are example applications for the <a href="../doi/10.5281/zenodo.10306435">Ensemble and Assimilation Tool (EAT).</a> Instructions for running these examples can be found in the contained <code>README.md</code>. This updated version uses Jupyter notebooks for all three applications. It requires EAT version 1.1 or higher.

embargoedcc-by-4.0Jun 2024View details →

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"] (anonymized version - third 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 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"]. 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: 10 to 16 years old students and some parents. Number of participants: 231 (206 adolescents + 25 adults). Year of the study: 2018 - 2019. Place of the study: New Caledonia. The accelerometer .csv files with a 1 second epoch and extracted from raw .bin files are available in open datasets: <ul> <li><a title="Open dataset - first part" href="https://doi.org/10.5281/zenodo.12615468" target="_blank" rel="noopener">anonymized version - first part</a></li> <li><a title="Open dataset - second part" href="https://doi.org/10.5281/zenodo.12638746" target="_blank" rel="noopener">anonymized version - second part</a></li> <li><a title="Open dataset - third part" href="https://doi.org/10.5281/zenodo.12682660" target="_blank" rel="noopener">anonymized version - third part</a></li> </ul> The accelerometer raw .bin files are available in restricted datasets: <ul> <li><a title="Restricted dataset - first part" href="https://doi.org/10.5281/zenodo.11594645" target="_blank" rel="noopener">non-anonymized version - first part</a></li> <li><a title="Restricted dataset - second part" href="https://doi.org/10.5281/zenodo.12638965" target="_blank" rel="noopener">non-anonymized version - second part</a></li> <li><a title="Restricted dataset - third part" href="https://doi.org/10.5281/zenodo.12661429" target="_blank" rel="noopener">non-anonymized version - third part</a></li> </ul> Other participant characteristics (age, place of living, cultural community and socio-economic status) are available in a <a title="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" [en: "Eating cultures and behaviors of young people in French-speaking Pacific countries in the 21st century: the example of New Caledonia"] (non-anonymized information version)" href="https://doi.org/10.5281/zenodo.12195186" target="_blank" rel="noopener">restricted non-anonymized dataset</a>. 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>

embargoedcc-by-4.0Jun 2024View details →

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"] (anonymized version - second part)

opencc-by-4.0Sep 2021View details →

Taste sensitivity and eating behaviour preadolescent children-Extended data

This data set contained children's detection threshold responses, their eating behaviour score based on CEBQ (Child Eating Behaviour Questionnaire), and food propensity based on FPQ (Food Propensity Questionnaire). In addition, this extended data also provides the raw questionnaires of CEBQ and FPQ used in the study.

Appendix List of samples of deep frozen frog legs with purchase date, collection number, haplotype number, taxonomic identification, tibia length (TL) and estimated snout vent length (SVL). in Which frog's legs do froggies eat? The use of DNA barcoding for identification of deep frozen frog legs (Dicroglossidae, Amphibia) commercialized in France

Appendix List of samples of deep frozen frog legs with purchase date, collection number, haplotype number, taxonomic identification, tibia length (TL) and estimated snout vent length (SVL).

Fig. 2. Minimum spanning network depicting relationships among 16S in Which frog's legs do froggies eat? The use of DNA barcoding for identification of deep frozen frog legs (Dicroglossidae, Amphibia) commercialized in France

Fig. 2. Minimum spanning network depicting relationships among 16S haplotypes of Fejervarya cancrivora (Gravenhorst, 1829). The size of each circle is proportional to the haplotype frequency and the lengths of the connecting lines are proportional to the number of mutations. Colors refer to distinct regions (Indonesia: Java, Sumatra, Bali, Kalimantan, Bangka; Malaysia; Taiwan) and commercialized frogs of unknown origin are in black.

Fig. 3. Histograms. A in Which frog's legs do froggies eat? The use of DNA barcoding for identification of deep frozen frog legs (Dicroglossidae, Amphibia) commercialized in France

Fig. 3. Histograms. A. Snout vent length (in mm) in adult Fejervarya cancrivora (Gravenhorst, 1829) from samples collected for scientific purposes (Boulenger 1920) and collection specimens as mentioned in Material and methods. B. Snout vent length estimated from tibia length of genetically identified frog legs from French supermarkets (specimen list, see Appendix).

Fig. 1 in Which frog's legs do froggies eat? The use of DNA barcoding for identification of deep frozen frog legs (Dicroglossidae, Amphibia) commercialized in France

Fig. 1. Phylogeny of Indonesian species of Fejervarya and Limnonectes recovered by the Bayesian analysis (GTR + I + G model). Hoplobatrachus rugulosus (Wiegmann, 1834) and Occidozyga laevis (Günther, 1858) were used as outgroups. Numbers on nodes represent Bayesian posterior probabilities, * indicates a value higher than 0.98. Only values higher than 0.75 are represented. h01 to h18 indicate the 18 haplotypes from frozen frog legs recovered in this study.

Figure 3 in You are what you eat: native versus exotic Crotalaria species (Fabaceae) as host plants of the Ornate Bella Moth, Utetheisa ornatrix (Lepidoptera: Erebidae: Arctiinae)

Figure 3. Rates of development of Utetheisa ornatrix larvae on different species of native and exotic Crotalaria in Florida and effect of leaves versus beans in the diet (see text for details): (A) partial development of larvae on the native C. rotundifolia versus exotic C. lanceolata; (B, C) partial development of larvae on the native C. pumila versus exotic C. lanceolata; (D, E) development of larvae on the exotic C. spectabilis/retusa versus exotic C. lanceolata; (F) development of larvae on C. incana (native to U. ornatrix range in the Neotropics, but introduced to Florida) versus exotic C. lanceolata. (F – based on data from Sourakov and Locascio 2013).

Figure 4 in You are what you eat: native versus exotic Crotalaria species (Fabaceae) as host plants of the Ornate Bella Moth, Utetheisa ornatrix (Lepidoptera: Erebidae: Arctiinae)

Figure 4. Fore wing size of Utetheisa ornatrix raised on different species of native and exotic Crotalaria and effect of leaves versus beans in the diet (see text for details): (A) Fore wing size of

Figure 2 in You are what you eat: native versus exotic Crotalaria species (Fabaceae) as host plants of the Ornate Bella Moth, Utetheisa ornatrix (Lepidoptera: Erebidae: Arctiinae)

Figure 2. (A) Understorey of the Florida hammock habitat occupied with invasive exotic Crotalaria spectabilis; (B) a clearing in a secondary Florida habitat, overgrown with exotic Crotalaria pallida; (C, D) mature larvae of U. ornatrix prefer pods of C. spectabilis over leaves; (E) carpenter ants are attracted to the extrafloral nectaries of C. lanceolata; (F, G) larva of U. ornatrix on C. pumila and a pod destroyed by it; (H) mature larva of U. ornatrix inside a pod of C. incana; (I, J) pods of C. pallida are numerous and large and provide ample food and shelter for U. ornatrix; (K) empty pods of C. spectabilis in December with all of their seeds consumed by U. ornatrix larvae; (L) in December, C. retusa becomes the preferred hostplant of U. ornatrix in the C. spectabilis-dominated habitat, when the latter declines; similarly, C. pumila becomes preferred for oviposition in C. lanceolata-dominated habitat; (M) the seeds of C. retusa are well protected by thick walls of the pod; here, a third instar larva is unable to penetrate it; (N) onset of the ultimate instar; (O–Q) prepupa-to-pupa development of U. ornatrix.

Figure 1 in You are what you eat: native versus exotic Crotalaria species (Fabaceae) as host plants of the Ornate Bella Moth, Utetheisa ornatrix (Lepidoptera: Erebidae: Arctiinae)

Figure 1. (A) In the wild population of U. ornatrix, adult moth landing on the flower of exotic Crotalaria retusa, Micanopy, Florida; (B) a typical size of a moth from a wild population at Cross Creek, Florida, resulting from larval feeding on C. rotundifolia leaves (top) and its offspring raised in the laboratory on beans of C. spectabilis (bottom) (fore wing length = 20 mm); (C) a single egg batch split in two (experimental and control groups) prior to hatching; (D) hostplant preference test using mature larvae of U. ornatrix inside a tray; (E) differences in pod size and seed volume in six Crotalaria species found in Florida; (F) difference in sprouting rate under similar conditions: native Crotalaria pumila shows much slower sprouting rate than introduced invasive Crotalaria species; (G) upland pine habitat on the University of Florida campus overtaken by thousands of exotic Crotalaria lanceolata plants with a sporadic native C. pumila in the midst (October 2014); (H) U. ornatrix eggs on C. lanceolata; (I) first instar larvae; (J) third instar larva.

opencc-by-4.0Jun 1970View details →

FIGURE2 in Stance and gait in the flesh-eating dinosaur Tyrannosuurus

FIGURE2. The knee-joint of a megalosaurian to show the femoral condyle (indicated by an arrow) inserted between the tibia and fibula. This is similar to the knee-jointof Tyrannosaurus.

opencc-by-4.0Aug 2019View details →

Wrist-mounted IMU data towards the investigation of free-living human eating behavior - the Free-living Food Intake Cycle (FreeFIC) dataset

Introduction The Free-living Food Intake Cycle (FreeFIC) dataset was created by the <a href="http://mug.ee.auth.gr">Multimedia Understanding Group</a> towards the investigation of in-the-wild eating behavior. This is achieved by recording the subjects’ meals as a small part part of their everyday life, unscripted, activities. The FreeFIC dataset contains the $3D$ acceleration and orientation velocity signals ($6$ DoF) from $22$ in-the-wild sessions provided by $12$ unique subjects. All sessions were recorded using a commercial smartwatch ($6$ using the Huawei Watch 2™ and the MobVoi TicWatch™ for the rest) while the participants performed their everyday activities. In addition, FreeFIC also contains the start and end moments of each meal session as reported by the participants. Description FreeFIC includes $22$ in-the-wild sessions that belong to $12$ unique subjects. Participants were instructed to wear the smartwatch to the hand of their preference well ahead before any meal and continue to wear it throughout the day until the battery is depleted. In addition, we followed a self-report labeling model, meaning that the ground truth is provided from the participant by documenting the start and end moments of their meals to the best of their abilities as well as the hand they wear the smartwatch on. The total duration of the $22$ recordings sums up to $112.71$ hours, with a mean duration of $5.12$ hours. Additional data statistics can be obtained by executing the provided python script stats_dataset.py. Furthermore, the accompanying python script viz_dataset.py will visualize the IMU signals and ground truth intervals for each of the recordings. Information on how to execute the Python scripts can be found below. <pre><code># The script(s) and the pickle file must be located in the same directory. # Tested with Python 3.6.4 # Requirements: Numpy, Pickle and Matplotlib # Calculate and echo dataset statistics $ python stats_dataset.py # Visualize signals and ground truth $ python viz_dataset.py</code></pre> FreeFIC is also tightly related to Food Intake Cycle (FIC), a dataset we created in order to investigate the in-meal eating behavior. More information about FIC can be found <a href="https://zenodo.org/record/4421861">here</a> and <a href="https://mug.ee.auth.gr/intake-cycle-detection/">here</a>. Publications If you plan to use the FreeFIC dataset or any of the resources found in this page, please cite our work: <pre><code>@article{kyritsis2020data, title={A Data Driven End-to-end Approach for In-the-wild Monitoring of Eating Behavior Using Smartwatches}, author={Kyritsis, Konstantinos and Diou, Christos and Delopoulos, Anastasios}, journal={IEEE Journal of Biomedical and Health Informatics}, year={2020}, publisher={IEEE}}</code></pre> <pre><code>@inproceedings{kyritsis2017automated, title={Detecting Meals In the Wild Using the Inertial Data of a Typical Smartwatch}, author={Kyritsis, Konstantinos and Diou, Christos and Delopoulos, Anastasios}, booktitle={2019 41th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)}, year={2019}, organization={IEEE}} </code></pre> Technical details We provide the FreeFIC dataset as a <a href="https://docs.python.org/3/library/pickle.html">pickle</a>. The file can be loaded using Python in the following way: <pre><code class="language-python">import pickle as pkl import numpy as np with open('./FreeFIC_FreeFIC-heldout.pkl','rb') as fh: dataset = pkl.load(fh)</code></pre> The dataset variable in the snipet above is a dictionary with $5$ keys. Namely: <ul> <li>'subject_id'</li> <li>'session_id'</li> <li>'signals_raw'</li> <li>'signals_proc'</li> <li>'meal_gt'</li> </ul> The contents under a specific key can be obtained by: <pre><code class="language-python">sub = dataset['subject_id'] # for the subject id ses = dataset['session_id'] # for the session id raw = dataset['signals_raw'] # for the raw IMU signals proc = dataset['signals_proc'] # for the processed IMU signals gt = dataset['meal_gt'] # for the meal ground truth </code></pre> The sub, ses, raw, proc and gt variables in the snipet above are lists with a length equal to $22$. Elements across all lists are aligned; e.g., the $3$rd element of the list under the 'session_id' key corresponds to the $3$rd element of the list under the 'signals_proc' key. sub: list Each element of the sub list is a scalar (integer) that corresponds to the unique identifier of the subject that can take the following values: $[1, 2, 3, 4, 13, 14, 15, 16, 17, 18, 19, 20]$. It should be emphasized that the subjects with ids $15, 16, 17, 18, 19$ and $20$ belong to the held-out part of the FreeFIC dataset (more information can be found in the publication titled "A Data Driven End-to-end Approach for In-the-wild Monitoring of Eating Behavior Using Smartwatches" by Kyritsis et al). Moreover, the subject identifier in FreeFIC is in-line with the subject identifier in the FIC dataset (more info <a href="https://zenodo.org/record/4421861">here</a> and <a href="https://mug.ee.auth.gr/intake-cycle-detection/">here</a>); i.e., FIC’s subject with id equal to $2$  is the same person as FreeFIC’s subject with id equal to $2$. ses: list Each element of this list is a scalar (integer) that corresponds to the unique identifier of the session that can range between $1$ and $5$. It should be noted that not all subjects have the same number of sessions. raw: list Each element of this list is dictionary with the 'acc' and 'gyr' keys. The data under the 'acc' key is a $N_{acc} \times 4$ numpy.ndarray that contains the timestamps in seconds (first column) and the $3D$ raw accelerometer measurements in $g$ (second, third and forth columns - representing the $x, y $ and $z$ axis, respectively). The data under the 'gyr' key is a $N_{gyr} \times 4$ numpy.ndarray that contains the timestamps in seconds (first column) and the $3D$ raw gyroscope measurements in ${degrees}/{second}$(second, third and forth columns - representing the $x, y $ and $z$ axis, respectively). All sensor streams are transformed in such a way that reflects all participants wearing the smartwatch at the same hand with the same orientation, thusly achieving data uniformity. This transformation is in par with the signals in the FIC dataset (more info <a href="https://zenodo.org/record/4421861">here</a> and <a href="https://mug.ee.auth.gr/intake-cycle-detection/">here</a>). Finally, the length of the raw accelerometer and gyroscope numpy.ndarrays is different $(N_{acc} \neq N_{gyr})$. This behavior is predictable and is caused by the Android platform. proc: list Each element of this list is an $M\times7$  numpy.ndarray that contains the timestamps, $3D$ accelerometer and gyroscope measurements for each meal. Specifically, the first column contains the timestamps in seconds, the second, third and forth columns contain the $x,y$ and $z$ accelerometer values in $g$ and the fifth, sixth and seventh columns contain the $x,y$ and $z$ gyroscope values in ${degrees}/{second}$. Unlike elements in the raw list, processed measurements (in the proc list) have a constant sampling rate of $100$ Hz and the accelerometer/gyroscope measurements are aligned with each other. In addition, all sensor streams are transformed in such a way that reflects all participants wearing the smartwatch at the same hand with the same orientation, thusly achieving data uniformity. This transformation is in par with the signals in the FIC dataset (more info <a href="https://zenodo.org/record/4421861">here</a> and <a href="https://mug.ee.auth.gr/intake-cycle-detection/">here</a>). No other preprocessing is performed on the data; e.g., the acceleration component due to the Earth's gravitational field is present at the processed acceleration measurements. The potential researcher can consult the article "A Data Driven End-to-end Approach for In-the-wild Monitoring of Eating Behavior Using Smartwatches" by Kyritsis et al. on how to further preprocess the IMU signals (i.e., smooth and remove the gravitational component). meal_gt: list Each element of this list is a $K\times2$ matrix. Each row represents the meal intervals for the specific in-the-wild session. The first column contains the timestamps of the meal start moments whereas the second one the timestamps of the meal end moments. All timestamps are in seconds. The number of meals $K$ varies across recordings (e.g., a recording exist where a participant consumed two meals). Ethics and funding Informed consent, including permission for third-party access to anonymised data, was obtained from all subjects prior to their engagement in the study. The work has received funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement No 727688 - <a href="https://bigoprogram.eu/">BigO: Big data against childhood obesity</a>. Contact Any inquiries regarding the FreeFIC dataset should be addressed to: Dr. Konstantinos KYRITSIS Multimedia Understanding Group (MUG) Department of Electrical & Computer Engineering Aristotle University of Thessaloniki University Campus, Building C, 3rd floor Thessaloniki, Greece, GR54124 Tel: +30 2310 996359, 996365  Fax: +30 2310 996398 E-mail: kokirits [at] mug [dot] ee [dot] auth [dot] gr

opencc-by-4.0Sep 2017View details →

Wrist-mounted IMU data towards the investigation of in-meal human eating behavior - the Food Intake Cycle (FIC) dataset

Introduction The Food Intake Cycle (FIC) dataset was created by the <a href="http://mug.ee.auth.gr">Multimedia Understanding Group</a> towards the investigation of in-meal eating behavior. The FIC dataset contains the triaxial acceleration and orientation velocity signals ($6$ DoF) from $21$ meal sessions provided by $12$ unique subjects. All meals were recorded in the restaurant of Aristotle University of Thessaloniki using a commercial smartwatch, the Microsoft Band $2$™ for ten out of the twenty-one meals and the Sony Smartwatch $2$™ for the remaining meals. In addition, the start and end moments of each food intake cycle as well as of each micromovement are annotated throughout the FIC dataset. Description A total of $12$ subjects were recorded while eating their launch at the university’s cafeteria. The total duration of the $21$ meals sums up to $246$ minutes, with a mean duration of $11.7$ minutes. Each participant was free to select the food of their preference, typically consisting of a starter soup, a salad, a main course and a desert. Prior to the recording, the participant was asked to wear the smartwatch to the hand that he typically uses in his everyday life to manipulate the fork and/or the spoon. A GoPro™ Hero $5$ camera was already set at the table of the participant using a small, $23$ cm in height, tripod facing the participant, including both the food tray and upper body part in it’s field of view. The purpose of video recording was to obtain ground truth data by manually annotating the IMU sequences based on the video stream. Participants were also asked to perform a clapping hand movement both at the start and end of the meal, for synchronization purposes (as this movement is distinctive in the accelerometer signal). No other instructions were given to the participants. It should be noted that the FIC dataset does not contain instances related with liquid consumption or eating without the fork, knife and spoon (e.g. eating directly with hands). The accompanying python script viz_dataset.py will visualize the IMU signals and food intake cycle (i.e., bite) ground truth intervals for each of the recordings. Information on how to execute the Python scripts can be found below. <pre><code class="language-python"># The script(s) and the pickle file must be located in the same directory. # Tested with Python 3.6.4 # Requirements: Numpy, Pickle and Matplotlib # Visualize signals and ground truth $ python viz_dataset.py</code></pre> FIC is also tightly related to FreeFIC, a dataset we created in order to investigate the in-the-wild eating behavior. More information on FreeFIC can be found <a href="https://zenodo.org/record/4421951">here</a> and <a href="https://mug.ee.auth.gr/free-food-intake-cycle-detection/">here</a>. Annotation Micromovements For all recordings, the start and end points of all $6$ micromovements of interest were manually labeled. The micromovements of interest include: <ul> <li>pick food, wrist manipulates a fork to pick food from the plate</li> <li>upwards, wrist moves upwards, towards the mouth area</li> <li>downwards, wrist moves downwards, away from the mouth area</li> <li>mouth, wrist inserts food in mouth</li> <li>no movement, wrist exhibits no movement</li> <li>other movement, every other wrist movement</li> </ul> The annotation process was performed in such a way that the start and end times of each micro-movement span the whole meal session, without overlapping each other. Food intake cycles For all recordings, we annotated the start and end points for each intake cycle (i.e. every bite). Each food intake cycle starts with a p, ends with a d and contains an m micromovement. Publications If you plan to use the FIC dataset or any of the resources found in this page, please cite our work: <pre><code>@article{kyritsis2019modeling, title={Modeling Wrist Micromovements to Measure In-Meal Eating Behavior from Inertial Sensor Data}, author={Kyritsis, Konstantinos and Diou, Christos and Delopoulos, Anastasios}, journal={IEEE journal of biomedical and health informatics}, year={2019}, publisher={IEEE}}</code></pre> <pre><code>@inproceedings{kyritsis2017food, title={Food intake detection from inertial sensors using lstm networks}, author={Kyritsis, Konstantinos and Diou, Christos and Delopoulos, Anastasios}, booktitle={International Conference on Image Analysis and Processing}, pages={411--418}, year={2017}, organization={Springer}}</code></pre> <pre><code>@inproceedings{kyritsis2017automated, title={Automated analysis of in meal eating behavior using a commercial wristband IMU sensor}, author={Kyritsis, Konstantinos and Tatli, Christina Lefkothea and Diou, Christos and Delopoulos, Anastasios}, booktitle={2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)}, pages={2843--2846}, year={2017}, organization={IEEE}}</code></pre> Technical details We provide the FIC dataset as a <a href="https://docs.python.org/3/library/pickle.html">pickle</a>. The file can be loaded using Python in the following way: <pre><code class="language-python">import pickle as pkl import numpy as np with open('./FIC.pkl','rb') as fh: dataset = pkl.load(fh)</code></pre> The dataset variable in the snipet above is a dictionary with $6$ keys. Namely: <ul> <li>'subject_id'</li> <li>'session_id'</li> <li>'signals_raw'</li> <li>'signals_proc'</li> <li>'meal_gt'</li> <li>'bite_gt'</li> </ul> The contents under a specific key can be obtained by: <pre><code>sub = dataset['subject_id'] # for the subject id ses = dataset['session_id'] # for the session id raw = dataset['signals_raw'] # for the raw IMU signals proc = dataset['signals_proc'] # for the processed IMU signals mm = dataset['mm_gt'] # for the micromovement ground truth bite = dataset['bite_gt'] # for the bite ground truth</code></pre> The sub, ses, raw, proc, mm and gt variables in the snipet above are lists with a length equal to $21$. Elements across all lists are aligned; e.g., the 3rd element of the list under the 'session_id' key corresponds to the 3rd element of the list under the 'signals_proc' key. sub: list Each element of the sub list is a scalar (integer) that corresponds to the unique identifier of the subject that can take values between $1$ and $12$. Moreover, the subject identifier in FIC is in-line with the subject identifier in the FreeFIC dataset (information available <a href="https://zenodo.org/record/4421951">here</a> and <a href="https://mug.ee.auth.gr/free-food-intake-cycle-detection/">here</a>); i.e., FIC’s subject with id equal to 2  is the same person as FreeFIC’s subject with id equal to 2. ses: list Each element of this list is a scalar (integer) that corresponds to the unique identifier of the session that can range between 1 and $3$. It should be noted that not all subjects have the same number of sessions. raw: list Each element of this list is dictionary with the 'acc', 'gyr' and 'offset' keys. The data under the 'acc' key is a $N_{acc}\times4$ numpy.ndarray that contains the timestamps in seconds (first column) and the $3D$ raw accelerometer measurements in $g$ (second, third and forth columns - representing the $x, y$ and $z$ axis, respectively). The data under the 'gyr' key is a $N_{gyr} \times 4$ numpy.ndarray that contains the timestamps in seconds (first column) and the $3D$ raw gyroscope measurements in $degrees/second$(second, third and forth columns - representing the $x, y$ and $z$ axis, respectively). All sensor streams are transformed in such a way that reflects all participants wearing the smartwatch at the same hand with the same orientation, thusly achieving data uniformity. This transformation is in par with the signals in the FreeFIC dataset (information available <a href="https://zenodo.org/record/4420039">here</a> and <a href="https://mug.ee.auth.gr/free-food-intake-cycle-detection/">here</a>). Finally, the length of the raw accelerometer and gyroscope numpy.ndarrays is different $N_{acc} \neq N_{gyr}$. This behavior is predictable and is caused by the Android/MS Band platforms. The offset key contains a float that is used to align the IMU sensor streams with the videos that were used for annotation purposes (videos are not provided). proc: list Each element of this list is an $M \times 7$  numpy.ndarray that contains the timestamps, $3D$ accelerometer and gyroscope measurements for each meal. Specifically, the first column contains the timestamps in seconds, the second, third and forth columns contain the $x,y$ and $z$ accelerometer values in $g$ and the fifth, sixth and seventh columns contain the $x, y$ and $z$ gyroscope values in $degrees/second$. Unlike elements in the raw list, processed measurements (in the proc list) have a constant sampling rate of 100 Hz and the accelerometer/gyroscope measurements are aligned with each other. In addition, all sensor streams are transformed in such a way that reflects all participants wearing the smartwatch at the same hand with the same orientation, thusly achieving data uniformity. This transformation is in par with the signals in the FreeFIC dataset (information available <a href="https://zenodo.org/record/4420039">here</a> and <a href="https://mug.ee.auth.gr/free-food-intake-cycle-detection/">here</a>). No other preprocessing is performed on the data; e.g., the acceleration component due to the Earth's gravitational field is present at the processed acceleration measurements. The potential researcher can consult the article "Modeling Wrist Micromovements to Measure In-Meal Eating Behavior from Inertial Sensor Data" by Kyritsis et al. on how to further preprocess the IMU signals (i.e., smooth and remove the gravitational component). mm: list Each element of this list is a $K \times 3$ numpy.ndarray. Each row represents a single micromovement interval. The first column contains the timestamps of the start moments in seconds, the second column the timestamps of the end moments in seconds and the third column a number representing the type of the micromovement. The identifier to micromovement mapping is provided below: $[1] \rightarrow$ no movement $[2] \rightarrow$ upwards $[3] \rightarrow$ downwards $[4] \rightarrow$ pick food $[5] \rightarrow$ mouth $[6] \rightarrow$ other movement bite: list Each element of this list is a $L\times2$ numpy.ndarray. Each row represents a single food intake event (i.e., a bite). The first column contains the start moments while the second column contains the end moments of each intake event. Both the start and end moments are provided in seconds. Ethics and funding Informed consent, including permission for third-party access to anonymised data, was obtained from all subjects prior to their engagement in the study. The work has received funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement No 727688 - <a href="https://bigoprogram.eu/">BigO: Big data against childhood obesity</a>. Contact Any inquiries regarding the FIC dataset should be addressed to: Dr. Konstantinos KYRITSIS Multimedia Understanding Group (MUG) Department of Electrical & Computer Engineering Aristotle University of Thessaloniki University Campus, Building C, 3rd floor Thessaloniki, Greece, GR54124 Tel: +30 2310 996359, 996365  Fax: +30 2310 996398 E-mail: kokirits [at] mug [dot] ee [dot] auth [dot] gr

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Figure 3 in Ecological niche differentiation among Aztec fruit-eating bat subspecies (Chiroptera: Phyllostomidae) in Mesoamerica

Figure 3. Niche overlap values for Schoener's D and Hellinger's I compared to a null distribution: (a) Artibeus a. aztecus (yellow) vs. A. a. minor (blue), (b) A. a. aztecus vs.A. A. major (red), (c) A. a. minor vs. A. a. major.

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Figure 2 in Ecological niche differentiation among Aztec fruit-eating bat subspecies (Chiroptera: Phyllostomidae) in Mesoamerica

Figure 2. Maxent predicted potential distribution for (a) Artibeus a. aztecus, (b) A. a. minor, and (c) A. a. major.