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zenodo40/100

Farmer adaptive behavior and risk management in EU agriculture

Risk and risk management are essential elements of agriculture and affect the wellbeing of farm households. Farmers react to production, market and institutional risks and challenges by taking measures on or off the farm. Such risk management measures are often costly and have implications for up- and downstream industries as well as the environment. The risk exposure of European farms is increasing. For example, climate change will increase the frequency and magnitude of extreme weather events like droughts, heatwaves and heavy rainfalls that potentially have detrimental effects on agricultural production. Thus, the adaptive capacity and risk management options in European agriculture need to be improved. Policy shall support this process. Policies are needed to support a diversity of risk management solutions and not only focus on a few solutions. Strategies to cope with risk often go beyond the level of the individual farm. Cooperation, learning and sharing of risks play a vital role in European agriculture and shall be strengthened. Thus, coordinated policies targeting beyond the individual farm and considering all the stakeholders involved in the risk management strategies are needed to ensure their effective implementation. Moreover, policies need to facilitate to take full advantage of the rapid technological progress and improved data availability (e.g. based on satellite imagery) to develop a wider set of risk management strategies.

opencc-by-4.0Aug 2019View details →

zenodo40/100

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-4.0Aug 2019View details →

zenodo40/100

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

opencc-by-4.0Sep 2017View details →

zenodo40/100

Dataset of Concurrent EEG, ECG, and Behavior with Multiple Doses of transcranial Electrical Stimulation

Supporting materials for the GX Dataset.   The GX Dataset is a dataset of combined tES, EEG, physiological, and behavioral signals from human subjects. Publication A full data descriptor is published in <a href="https://doi.org/10.1038/s41597-021-01046-y">Nature Scientific Data</a>. Please cite this work as: <blockquote> Gebodh, N., Esmaeilpour, Z., Datta, A. et al. Dataset of concurrent EEG, ECG, and behavior with multiple doses of transcranial electrical stimulation. Sci Data 8, 274 (2021). https://doi.org/10.1038/s41597-021-01046-y </blockquote>   Descriptions A dataset combining high-density electroencephalography (EEG) with physiological and continuous behavioral metrics during transcranial electrical stimulation (tES; including tDCS and tACS). Data includes within subject application of nine High-Definition tES (HD-tES) types targeted three brain regions (frontal, motor, parietal) with three waveforms (DC, 5Hz, 30Hz), with more than 783 total stimulation trials over 62 sessions with EEG, physiological (ECG or EKG, EOG), and continuous behavioral vigilance/alertness metrics (CTT task). Acknowledgments Portions of this study were funded by X (formerly Google X), the Moonshot Factory. The funding source had no influence on study conduction or result evaluation. MB is further supported by grants from the National Institutes of Health: R01NS101362, R01NS095123, R01NS112996, R01MH111896, R01MH109289, and (to NG) NIH-G-RISE T32GM136499. We would like to thank Yuxin Xu and Michaela Chum for all their technical assistance.   Extras For downsampled data (1 kHz ) please see (in .mat format): <ul> <li><a href="https://doi.org/10.5281/zenodo.3840614">Experiment 1</a> : https://doi.org/10.5281/zenodo.3840614</li> <li><a href="https://doi.org/10.5281/zenodo.3840616">Experiment 2</a> : https://doi.org/10.5281/zenodo.3840616</li> </ul>   Code used to import, process, and plot this dataset can be found here: <ul> <li><a href="https://github.com/ngebodh/GX_tES_EEG_Physio_Behavior">GitHub</a> : <a href="https://github.com/ngebodh/GX_tES_EEG_Physio_Behavior">https://github.com/ngebodh/GX_tES_EEG_Physio_Behavior</a></li> </ul>   Additional figures for this project have been shared on Figshare. Trial-wise figures can be found here: <ul> <li><a href="https://figshare.com/articles/figure/Dataset_of_Concurrent_EEG_ECG_and_Behavior_with_Multiple_Doses_of_transcranial_Electrical_Stimulation-_Stimulation_Trials_PSD/14810517">PSD</a>: <a href="https://doi.org/10.6084/m9.figshare.14810517.v1">https://doi.org/10.6084/m9.figshare.14810517.v1</a></li> <li><a href="https://figshare.com/articles/figure/Dataset_of_Concurrent_EEG_ECG_and_Behavior_with_Multiple_Doses_of_transcranial_Electrical_Stimulation-_Stimulation_Trials_Topoplots/14810478">Topoplots During Stimulation:</a> <a href="https://doi.org/10.6084/m9.figshare.14810478">https://doi.org/10.6084/m9.figshare.14810478</a></li> <li><a href="https://doi.org/10.6084/m9.figshare.14810442.v1">Voltage timeseries, spectrogram and behavior:</a> <a href="https://doi.org/10.6084/m9.figshare.14810442">https://doi.org/10.6084/m9.figshare.14810442.v1</a></li> </ul>   The full dataset is also provided in <a href="https://bids.neuroimaging.io/">BIDS</a> format here: <ul> <li><a href="https://doi.org/10.18112/openneuro.ds003670.v1.1.0">Data in BIDS format:</a> https://doi.org/10.18112/openneuro.ds003670.v1.1.0</li> </ul> Data License  <a href="https://creativecommons.org/licenses/by/4.0/">Creative Common 4.0 with attribution (CC BY 4.0)</a>   NOTE Please email ngebodh01@citymail.cuny.edu with any questions.

opencc-by-4.0May 2020View details →

zenodo40/100

Video files linked in Coleman and Burge, "Association behavior between sand tiger sharks and round scad is driven by mesopredators"

Videos referred to in the Results, Table 1, Figure 2, and Figure 3 of Coleman and Burge "Association behavior between sand tiger sharks and round scad is driven by mesopredators" are included here. Table S2 lists a description of each video (Descriptions), date (Date of occurrence) and time (Clock time of occurrence) of footage, a timing reference to the description within the video (video time), and a link to Youtube (Video reference) of the same footage. Note that file uploads for videos for views of the field site at Frying Pan Tower (in Materials and Methods and Table S2; <a href="https://www.youtube.com/playlist?list=PLK1g13VpyT6oYUJL7U3hRPlt2U5L_mcKL">https://www.youtube.com/playlist?list=PLK1g13VpyT6oYUJL7U3hRPlt2U5L_mcKL</a>) are not included with these uploads as no data or observations are derived from these videos.

opencc-by-4.0Jan 2021View details →

zenodo40/100

Figs 21–22 in Termitotrox icarus sp. nov. (Coleoptera: Scarabaeidae): a new termitophilous beetle from Myanmar with observations of carrying behavior by host termites

Figs 21–22. Single large egg in a female Termitotrox icarus sp. nov. (21 – dorsal viewT 22 – lateral view).

opencc-by-4.0Jul 2020View details →

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Figs 18–19 in Termitotrox icarus sp. nov. (Coleoptera: Scarabaeidae): a new termitophilous beetle from Myanmar with observations of carrying behavior by host termites

Figs 18–19. Carrying behavior by host termite Odontotermes proformosanus Ahmad, 1965 to Termitotrox icarus sp. nov. (observed in laboratory).

opencc-by-4.0Jul 2020View details →

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Figs 14–17 in Termitotrox icarus sp. nov. (Coleoptera: Scarabaeidae): a new termitophilous beetle from Myanmar with observations of carrying behavior by host termites

Figs 14–17. Habitat of Termitotrox icarus sp. nov. 14–15 – termite mound of host termite Odontotermes proformosanus Ahmad, 1965T 16 – a fungus garden chamber of host termiteT 17 – alive specimen of Termitotrox icarus sp. nov. walking on the wall of fungus garden chamber.

opencc-by-4.0Jul 2020View details →

zenodo40/100

Figs 9–13 in Termitotrox icarus sp. nov. (Coleoptera: Scarabaeidae): a new termitophilous beetle from Myanmar with observations of carrying behavior by host termites

Figs 9–13. Body parts of Termitotrox icarus sp. nov. (paratype male). 9 – epipharynx, ventral viewT 10 – elytra, dorsal viewT 11 – abdomen, ventral viewT 12 – pygidium, postero-lateral viewT 13 – aedeagus, dorsal view. Scale bars: 0.05 mm (Fig. 9)T 0.20 mm (Fig. 10)T 0.10 mm (Figs 11–13).

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zenodo40/100

Figs 3–8 in Termitotrox icarus sp. nov. (Coleoptera: Scarabaeidae): a new termitophilous beetle from Myanmar with observations of carrying behavior by host termites

Figs 3–8. Habitus of Termitotrox icarus sp. nov. (holotype and paratypes). 3 – holotype male, dorsal viewT 4 – paratype male, ventral viewT 5 – paratype female, dorsal viewT 6 – ditto, ventral viewT 7 – holotype male, antero-lateral viewT 8 – paratype male, dorsal view (elytra removed). Scale bar = 1.0 mm.

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Figs 1–2 in Termitotrox icarus sp. nov. (Coleoptera: Scarabaeidae): a new termitophilous beetle from Myanmar with observations of carrying behavior by host termites

Figs 1–2. Terminology used in species description. Head: CFS – clypeofrontal suture, CO – clypeal outline, CF – clypeofrons, GE – gena, AN – antenna, VE – vertex. Pronotum: ALL – anterolateral lobe, MC – median costa, SLC – sublateral costa, CD – central depression, PMC – paramedian costa, LC – lateral costa, MGC – marginal costa, BMS – basomedian section, BP – base of pronotum. Elytra: IS – interstria, ES – elytral stria, is1–is9 – interstriae 1 to 9, AS – apicosutural area, EPI – epipleuron. Ventral structures of thorax: PR – propectus, PLAP – posterolateral areas of propectus, PPS – postprosternal surface, MSV – mesoventrite, MTV – metaventrite. Abdomen: av1–av5 – abdominal ventrite 1 to 5, Py – pygidium. Legs: PF – profemur, PRT – protrochanter, PT – protibia, PRTA – protarsus, AC – anterior claw.

opencc-by-4.0Jul 2020View details →

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Data from: Multimodal in situ datalogging quantifies inter-individual variation in thermal experience and persistent origin effects on gaping behavior among intertidal mussels (Mytilus californianus)

In complex habitats, environmental variation over small spatial scales can equal or exceed larger-scale gradients. This small-scale variation may allow motile organisms to mitigate stressful conditions by choosing benign microhabitats, whereas sessile organisms may rely on other behaviors to cope with environmental stresses in these variable environments. We developed a monitoring system to track body temperature, valve gaping behavior, and posture of individual mussels (Mytilus californianus) in field conditions in the rocky intertidal zone. Neighboring mussels' body temperatures varied by up to 14°C during low tides. Valve gaping during low tide and postural adjustments, which could theoretically lower body temperature, were not commonly observed. Rather, gaping behavior followed a tidal rhythm at a warm, high intertidal site; this rhythm shifted to a circadian period at a low intertidal site and for mussels continuously submerged in a tidepool. However, individuals within a site varied considerably in time spent gaping when submerged. This behavioral variation could be attributed in part to persistent effects of mussels' developmental environment. Mussels originating from a wave-protected, warm site gaped more widely, and they remained open for longer periods during high tide than mussels from a wave-exposed, cool site. Variation in behavior was modulated further by recent wave heights and body temperatures during the preceding low tide. These large ranges in body temperatures and durations of valve closure events - which coincide with anaerobic metabolism - support the conclusion that individuals experience "homogeneous" aggregations such as mussel beds in dramatically different fashion, ultimately contributing to physiological variation among neighbors.

opencc-zeroDec 2016View details →

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Data from: Has gene expression neofunctionalization in the fire ant antennae contributed to queen discrimination behavior?

Queen discrimination behavior in the fire ant Solenopsis invicta maintains its two types of societies: colonies with one (monogyne) or many (polygyne) queens, yet the underlying genetic mechanism is poorly understood. This behavior is controlled by two supergene alleles, SB and Sb, with ~600 genes. Polygyne workers, having either the SB/SB or SB/Sb genotype, accept additional SB/Sb queens into their colonies but kill SB/SB queens. In contrast, monogyne workers, all SB/SB, reject all additional queens regardless of genotype. Because the SB and Sb alleles have suppressed recombination, determining which genes within the supergene mediate this differential worker behavior is difficult. We hypothesized that the alternate worker genotypes sense queens differently because of the evolution of differential expression of key genes in their main sensory organ, the antennae. To identify such genes, we sequenced RNA from four replicates of pooled antennae from three classes of workers: monogyne SB/SB, polygyne SB/SB, and polygyne SB/Sb. We identified 81 differentially expressed protein-coding genes with 13 encoding potential chemical metabolism or perception proteins. We focused on the two odorant perception genes: an odorant receptor SiOR463 and an odorant binding protein SiOBP12. We found that SiOR463 has been lost in the Sb-genome. In contrast, SiOBP12 has an Sb-specific duplication, SiOBP12b', which is expressed in the SB/Sb worker antennae, while both paralogs are expressed in the body. Comparisons with another fire ant species revealed that SiOBP12b' antennal expression is specific to S. invicta and suggests that queen discrimination may have evolved, in part, through expression neofunctionalization.

opencc-zeroNov 2019View details →

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Dataset for article: Jaber-Lopez, T., Garcia-Gallego, A., Perakakis, P., Georgantzis, N. (2014). Physiological and behavioral patterns of corruption. Frontiers in Behavioral Neuroscience

Dataset and matlab analysis scripts for article: Dataset for article: Jaber-Lopez, T., Garcia-Gallego, A., Perakakis, P., Georgantzis, N. (2014). Physiological and behavioral patterns of corruption. Frontiers in Behavioral Neuroscience

opencc-zeroFeb 2015View details →

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A QUESTIONNAIRE FOR THE ASSESSMENT OF VIOLENT BEHAVIORS IN YOUNG COUPLES: THE ITALIAN VERSION OF DATING VIOLENCE QUESTIONNAIRE (DVQ)

In the last years, intimate partner violence (IPV) became a relevant problem for community and for social life, particularly in young people. Its correct assessment and evaluation in the population is mandatory. Our objectives were: Confirm factor structure of Dating Violence Questionnaire (DVQ) and investigate its convergent and divergent validity. The DVQ along with other personality measures were filled by a sample of 418 university students (Females = 310) of average age of 23 y.o. (SD = 4.71). A subsample of participants (223 students) consented in being involved also in retest and filled also the Revised Eysenck Personality Questionnaire (short form) and a brief scale for describing the behavior of the (past) partner after the breaking of the relationship (BRS). The 8-factor structure, with respect to the two other competing models, reported better fit indexes and showed significant correlations with other personality measures. Personality traits, both Neuroticism and Psychoticism, correlated with Sexual Violence, while Detachment correlated only with Neuroticism and Coercion, Humiliation and Physical Violence correlated with only Psychoticism. Extraversion did not report significant relationships with any of the 8 DVQ factors. Also the predictive validity of DVQ was satisfactory with the partner violent reaction to the break of relationship predicted positively predicted by Coercion (b = 0.22) and by Humiliation (b = 0.20) and negatively by Emotional Punishment (b = -0.18). The present results indicate a good factor structure of the questionnaire, and interesting correlations with personality traits, allowing to identify psychological aspects with a predisposing role for anti-social aggressive behaviors. Further studies will be aimed at ascertaining other possible determinants of intimate partner violence and the weight of cultural aspects.

opencc-zeroMar 2015View details →

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Dataset and Code: Pain and sickness behavior associated with corneal lesions in dairy calves

This is the dataset and code for the analysis of sickness behaviors in calves within corneal lesions (IBK or pinkeye).

opencc-by-4.0Jun 2015View details →

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A dataset of 3D fly (Drosophila melanogaster) flight trajectories to study the role of neuropeptide degradation in visuo-motor behaviors.

As part of a wide study on the role of neuropeptides in the visuo-motor behavior of Drosophila melanogaster, we exposed three fly strains with impaired neuropeptide degradation function, and corresponding controls, to different visual stimuli. Find further details in the provided README.

opencc-by-4.0Aug 2015View details →

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FIGURE 1 in Description of a new species of Lutosa Walker, 1869 (Orthoptera: Anostostomatidae: Lutosinae) from the Brazilian Atlantic Forest, with biological and behavioral information

FIGURE 1. Lutosa horribilis sp. nov. holotype Ƌ. A) habitus in dorsal view; B) same in lateral view; C) head in frontal view; D) head and pronotum in dorsal view; E) same in lateral view; F) fore tibia in right lateral view; G) fore tibia in left lateral view; H) apex of hind tibia and base of tarsus in dorsal view; I) Pro, meso and metasternum in ventral view; J) apex of abdomen in dorsal view; K) same in ventral view; L) same in lateral view.

opencc-zeroDec 2016View details →

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On the Understandability of Semantic Constraints for Behavioral Software Architecture Compliance: A Controlled Experiment

Software architecture compliance is concerned with the alignment of implementation with its desired architecture and detecting potential inconsistencies. The study is specifically concerned with behavioral architecture compliance. That is, the focus is on semantic alignment of implementation and architecture. In particular, the study evaluates three representative approaches for describing semantic constraints in terms of their understandability, namely natural language descriptions as used in many architecture documentations today, a structured language based on specification patterns that abstract underlying temporal logic formulas, and a structured cause-effect language that is based on Complex Event Processing. We conducted a controlled experiment with 190 participants using a simple randomized design with one alternative per experimental unit.

opencc-by-4.0Feb 2017View details →

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Data from "Asymmetries in behavioral and neural responses to spectral cues demonstrate the generality of auditory looming bias"

Supporting material for Baumgartner et al. (2017): "Asymmetries in behavioral and neural responses to spectral cues demonstrate the generality of auditory looming bias" in Proc Natl Acad Sci USA; www.pnas.org/cgi/doi/10.1073/pnas.1703247114

opencc-by-sa-4.0Jul 2017View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

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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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record