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1,421 results for “Behavioral data”
Data for: Mercury contamination challenges the behavioral response of a keystone species to Arctic climate change
<p>Combined effects of multiple, climate change-associated stressors are of mounting concern, especially in Artic ecosystems. Elevated mercury (Hg) exposure in Arctic animals could affect behavioural responses to changes in foraging landscapes linked to climate change, generating interactive effects on behaviour and population resilience. We investigated this hypothesis in the little auk (<em>Alle alle</em>), a keystone Artic seabird. We compiled behavioural data using accelerometers, and quantified blood mercury and environmental conditions (sea surface temperature (SST), sea ice coverage (SIC)) across multiple years. These datasets contain the behavioral, blood Hg and environmental data (SST, SIC) used in our analyses. Details about the datasets are found in the accompanying word document.</p>
Open synthetic data on travel and charging demand of battery electric cars: An agent-based simulation on three charging behavior archetypes
<p><strong>Background</strong></p> <p>Battery electric vehicles (BEVs) are crucial for a sustainable transportation system. As more people adopt BEVs, it becomes increasingly important to accurately assess the demand for charging infrastructure. However, much of the current research on charging infrastructure relies on outdated assumptions, such as the assumption that all BEV owners have access to home chargers and the "Liquid-fuel" mental model. To address this issue, we simulate the travel and charging demand on three charging behavior archetypes. We use a large synthetic population of Sweden, including detailed individual characteristics, such as dwelling types (detached house vs. apartment) and activity plans (for an average weekday). This data repository aims to provide the BEV simulation's input, assumptions, and output so that other studies can use them to study sizing and location design of charging infrastructure, grid impact, etc.</p> <p>A journal paper published in Transportation Research Part D: Transport and Environment details the method to create the data (particularly Section 2.2 BEV simulation).</p> <p><a href="https://doi.org/10.1016/j.trd.2023.103645">https://doi.org/10.1016/j.trd.2023.103645</a></p> <p><strong>Methodology</strong></p> <p>This data product is centered on the 1.7 million inhabitants of the Västra Götaland (VG) region, which includes the second largest city in Sweden, Gothenburg. We specifically simulated 284,000 car agents who live in VG, representing 35% of all car users and 18% of the total population in the region. They spend their simulation day (representing an average weekday) in a variety of locations throughout Sweden.</p> <p>This open data repository contains the core model inputs and outputs. The numbers in parentheses correspond to the data sets. We use individual agents' activity plans (1) and travel trajectories from MATSim simulation for the BEV simulation (2), in which we consider overnight charger access (3), car fleet composition referencing the current private car fleet in Sweden (4), and Swedish road network with slope information (5) with realistic BEV charging & discharging dynamics. For the BEV simulation, we tested ten scenarios of charging behavior archetypes and fast charging powers (6). The output includes the time history of travel trajectories and charging of the simulated BEVs across the different scenarios (7).</p> <p><strong>Data description</strong></p> <p>The current data product covers seven data files.</p> <p><strong>(1) Agents' experienced activity plans</strong></p> <p>File name: 1_activity_plans.csv</p> <table> <tbody> <tr> <td> <p><strong>Column</strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Data type</strong></p> </td> <td> <p><strong>Unit</strong></p> </td> </tr> <tr> <td> <p>person</p> </td> <td> <p>Agent ID</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>act_id</p> </td> <td> <p>Activity index of each agent</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>deso</p> </td> <td> <p>Zone code of Demographic statistical areas (DeSO)<sup>1</sup></p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>POINT_X</p> </td> <td> <p>Coordinate X of activity location (SWEREF99TM)</p> </td> <td> <p>Float</p> </td> <td> <p>meter</p> </td> </tr> <tr> <td> <p>POINT_Y</p> </td> <td> <p>Coordinate Y of activity location (SWEREF99TM)</p> </td> <td> <p>Float</p> </td> <td> <p>meter</p> </td> </tr> <tr> <td> <p>act_purpose</p> </td> <td> <p>Activity purpose (work, home, other)</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>mode</p> </td> <td> <p>Transport mode to reach the activity location (car)</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>dep_time</p> </td> <td> <p>Departure time in decimal hour (0-23.99)</p> </td> <td> <p>Float</p> </td> <td> <p>hour</p> </td> </tr> <tr> <td> <p>trav_time</p> </td> <td> <p>Travel time to reach the activity location</p> </td> <td> <p>String</p> </td> <td> <p>hour:minute:second</p> </td> </tr> <tr> <td> <p>trav_time_min</p> </td> <td> <p>Travel time in decimal minute</p> </td> <td> <p>Float</p> </td> <td> <p>minute</p> </td> </tr> <tr> <td> <p>speed</p> </td> <td> <p>Travel speed to reach the activity location</p> </td> <td> <p>Float</p> </td> <td> <p>km/h</p> </td> </tr> <tr> <td> <p>distance</p> </td> <td> <p>Travel distance between the origin and the destination</p> </td> <td> <p>Float</p> </td> <td> <p>km</p> </td> </tr> <tr> <td> <p>act_start</p> </td> <td> <p>Start time of activity in minute (0-1439)</p> </td> <td> <p>Integer</p> </td> <td> <p>minute</p> </td> </tr> <tr> <td> <p>act_time</p> </td> <td> <p>Activity duration in decimal minute</p> </td> <td> <p>Float</p> </td> <td> <p>minute</p> </td> </tr> <tr> <td> <p>act_end</p> </td> <td> <p>End time of activity in decimal hour (0-23.99)</p> </td> <td> <p>Float</p> </td> <td> <p>hour</p> </td> </tr> <tr> <td> <p>score</p> </td> <td> <p>Utility score of the simulation day given by MATSim</p> </td> <td> <p>Float</p> </td> <td> <p>-</p> </td> </tr> </tbody> </table> <p>1 <a href="https://www.scb.se/vara-tjanster/oppna-data/oppna-geodata/deso--demografiska-statistikomraden/">https://www.scb.se/vara-tjanster/oppna-data/oppna-geodata/deso--demografiska-statistikomraden/</a></p> <p> </p> <p><strong>(2) Travel trajectories</strong></p> <p>File name: 2_input_zip</p> <p>Produced by MATSim simulation, the zip folder contains ten files (events_batch_X.csv.gz, X=1, 2, …, 10) of input events for the BEV simulation. They are the moving trajectories of the car agents in their simulation days.</p> <table> <tbody> <tr> <td> <p><strong>Column</strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Data type</strong></p> </td> <td> <p><strong>Unit</strong></p> </td> </tr> <tr> <td> <p>time</p> </td> <td> <p>Time in second in a simulation day (0-86399)</p> </td> <td> <p>Integer</p> </td> <td> <p>Second</p> </td> </tr> <tr> <td> <p>type</p> </td> <td> <p>Event type defined by MATSim simulation<sup>2</sup></p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>person</p> </td> <td> <p>Agent ID</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>link</p> </td> <td> <p>Nearest road link consistent with (5)</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>vehicle</p> </td> <td> <p>Vehicle ID identical to person</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> </tbody> </table> <p><sup>2 </sup>One typical episode of MATSim simulation events: Activity ends (actend) -> Agent’s vehicle enters traffic (vehicle enters traffic) -> Agent’s vehicle moves from previous road segment to its next connected one (left link) -> Agent’s vehicle leaves traffic for activity (vehicle leaves traffic) -> Activity starts (actstart)</p> <p> </p> <p><strong>(3) Overnight charger access</strong></p> <p>File name: 3_home_charger_access.csv</p> <table> <tbody> <tr> <td> <p><strong>Column</strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Data type</strong></p> </td> <td> <p><strong>Unit</strong></p> </td> </tr> <tr> <td> <p>person</p> </td> <td> <p>Agent ID</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>home_charger</p> </td> <td> <p>Whether an agent has access to a home garage charger/living in a detached house (0=no, 1=yes)</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>(4) Car fleet composition</strong></p> <p>File name: 4_car_fleet.csv</p> <table> <tbody> <tr> <td> <p><strong>Column</strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Data type</strong></p> </td> <td> <p><strong>Unit</strong></p> </td> </tr> <tr> <td> <p>person</p> </td> <td> <p>Agent ID</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>income_class</p> </td> <td> <p>Income group (0=None, 1=below 180K, 2=180K-300K, 3=300K-420K, 4=above 420K)</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>car</p> </td> <td> <p>Car model class (B=40 kWh, C=60 kWh, D=100 kWh)</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> </tbody> </table> <p> </p> <p>(<strong>5) Road network with slope information</strong></p> <p>File name: 5_road_network_with_slope.shp (5 files in total)</p> <table> <tbody> <tr> <td> <p>Column</p> </td> <td> <p>Description</p> </td> <td> <p>Data type</p> </td> <td> <p>Unit</p> </td> </tr> <tr> <td> <p>length</p> </td> <td> <p>The length of road link</p> </td> <td> <p>Float</p> </td> <td> <p>meter</p> </td> </tr> <tr> <td> <p>freespeed</p> </td> <td> <p>Free speed</p> </td> <td> <p>Float</p> </td> <td> <p>km/h</p> </td> </tr> <tr> <td> <p>capacity</p> </td> <td> <p>Number of vehicles</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>permlanes</p> </td> <td> <p>Number of lanes</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>oneway</p> </td> <td> <p>Whether the segment is one-way (0=no, 1=yes)</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>modes</p> </td> <td> <p>Transport mode (car)</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>link_id</p> </td> <td> <p>Link ID</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>from_node</p> </td> <td> <p>Start node of the link</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>to_node</p> </td> <td> <p>End node of the link</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>count</p> </td> <td> <p>Aggregated traffic (number of cars travelled per day)</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>slope</p> </td> <td> <p>Slope in percent from -6% to 6%</p> </td> <td> <p>Float</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>geometry</p> </td> <td> <p>LINESTRING (SWEREF99TM)</p> </td> <td> <p>geometry</p> </td> <td> <p>meter</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>(6) Simulation scenarios specifying the parameter sets</strong></p> <p>File name: 6_scenarios.txt</p> <table> <tbody> <tr> <td> <p><strong>Parameter set</strong></p> <p><strong>(paraset)</strong></p> </td> <td> <p><strong>Strategy 1</strong></p> </td> <td> <p><strong>Strategy 2</strong></p> </td> <td> <p><strong>Strategy 3</strong></p> </td> <td> <p><strong>Fast charging power (kW)</strong></p> </td> <td> <p><strong>Minimum parking time for charging (min)</strong></p> </td> <td> <p><strong>Intermediate charging power (kW)</strong></p> </td> </tr> <tr> <td> <p>0</p> </td> <td> <p>0.2</p> </td> <td> <p>0.2</p> </td> <td> <p>0.9</p> </td> <td> <p>150</p> </td> <td> <p>5</p> </td> <td> <p>22</p> </td> </tr> <tr> <td> <p>1</p> </td> <td> <p>0.2</p> </td> <td> <p>0.2</p> </td> <td> <p>0.9</p> </td> <td> <p>50</p> </td> <td> <p>5</p> </td> <td> <p>22</p> </td> </tr> <tr> <td> <p>2</p> </td> <td> <p>0.3</p> </td> <td> <p>0.3</p> </td> <td> <p>0.9</p> </td> <td> <p>150</p> </td> <td> <p>5</p> </td> <td> <p>22</p> </td> </tr> <tr> <td> <p>3</p> </td> <td> <p>0.3</p> </td> <td> <p>0.3</p> </td> <td> <p>0.9</p> </td> <td> <p>50</p> </td> <td> <p>5</p> </td> <td> <p>22</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>(7) Time history of travel trajectories and charging of the simulated BEVs</strong></p> <p>File name: 7_output.zip</p> <p>Produced by the BEV simulation, the zip folder contains four files (parasetX.csv.gz, X=1, 2, 3, 4) corresponding to the four parameter sets specified in (6). They are the moving trajectories of the car agents with simulated energy and charging time history in their simulation days.</p> <table> <tbody> <tr> <td> <p><strong>Column</strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Data type</strong></p> </td> <td> <p><strong>Unit</strong></p> </td> </tr> <tr> <td> <p>person</p> </td> <td> <p>Agent ID</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>home_charger</p> </td> <td> <p>Whether an agent has access to a home garage charger/living in a detached house (0=no, 1=yes)</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>car</p> </td> <td> <p>Car model class (B=40 kWh, C=60 kWh, D=100 kWh)</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>seq</p> </td> <td> <p>Sequence ID of time history by agent</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>time</p> </td> <td> <p>Time (0-86399)</p> </td> <td> <p>Integer</p> </td> <td> <p>Second</p> </td> </tr> <tr> <td> <p>purpose</p> </td> <td> <p>Valid for activities (home, work, school, other)</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>type</p> </td> <td> <p>Event type defined by MATSim simulation</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>link</p> </td> <td> <p>Link ID (link_id in File 5)</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>distance_driven</p> </td> <td> <p>Cumulative driven distance in the simulation day</p> </td> <td> <p>Float</p> </td> <td> <p>km</p> </td> </tr> <tr> <td> <p>energy_1</p> </td> <td> <p>Energy consumed while driving (-) or charging (+) (Strategy 1)</p> </td> <td> <p>Float</p> </td> <td> <p>kWh</p> </td> </tr> <tr> <td> <p>energy_2</p> </td> <td> <p>Energy consumed while driving (-) or charging (+) (Strategy 2)</p> </td> <td> <p>Float</p> </td> <td> <p>kWh</p> </td> </tr> <tr> <td> <p>energy_3</p> </td> <td> <p>Energy consumed while driving (-) or charging (+) (Strategy 3)</p> </td> <td> <p>Float</p> </td> <td> <p>kWh</p> </td> </tr> <tr> <td> <p>charger_1</p> </td> <td> <p>Power rating of the charger (Strategy 1)</p> </td> <td> <p>Float</p> </td> <td> <p>kW</p> </td> </tr> <tr> <td> <p>charger_2</p> </td> <td> <p>Power rating of the charger (Strategy 2)</p> </td> <td> <p>Float</p> </td> <td> <p>kW</p> </td> </tr> <tr> <td> <p>charger_3</p> </td> <td> <p>Power rating of the charger (Strategy 3)</p> </td> <td> <p>Float</p> </td> <td> <p>kW</p> </td> </tr> <tr> <td> <p>soc_1</p> </td> <td> <p>State of charge (0-1, Strategy 1)</p> </td> <td> <p>Float</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>soc_2</p> </td> <td> <p>State of charge (0-1, Strategy 2)</p> </td> <td> <p>Float</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>soc_3</p> </td> <td> <p>State of charge (0-1, Strategy 3)</p> </td> <td> <p>Float</p> </td> <td> <p>-</p> </td> </tr> </tbody> </table> <p> </p>
Data of Chinese treatment group for the research work "Disentangling material, social, and cognitive determinants of human behavior and belief".
<p>This repository contains data files of Chinese treatment group for the research work "Disentangling material, social, and cognitive determinants of human behavior and belief".</p>
Data from "Behavioral flexibility is associated with changes in structure and function distributed across a frontal cortical network in macaques"
<p>DATA FILES from the study below:</p> <p><strong><a href="https://www.biorxiv.org/content/10.1101/603530v1">Behavioral flexibility is associated with changes in structure and function distributed across a frontal cortical network in macaques</a></strong></p> <p>Jérôme Sallet, MaryAnn P Noonan, Adam Thomas, Jill X O’Reilly, Jesper Anderson, Georgios KPapageorgiou, Franz X Neubert, Bashir Ahmed, Jackson Smith, Andrew H Bell, Mark J Buckley, LéaRoumazeilles, Steven Cuell, Mark E Walton, Kristine Krug, Rogier B Mars, Matthew FS Rushworth</p> <p>bioRxiv 603530; doi: <a href="https://doi.org/10.1101/603530">https://doi.org/10.1101/603530</a></p> <p>*.nii.gz files could be opened with FSLeyes -<a href="https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FSLeyes)">https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FSLeyes)</a></p> <p>Dara are also available from : https://www.jeromesallet.org/data-ofc-reversal-learning</p>
Data from: Multiple spawning run behavior and population consequences in migratory striped bass Morone saxatilis
<p>Multiple spawning runs cause different contingents within the same population to experience varying demographic fates that can stabilize populations through the portfolio effect. Multiple spawning runs are reported here for the first time for striped bass, an economically important coastal species, which is well known for plastic estuarine and shelf migration behaviors. Adult Hudson River Estuary striped bass (n=66) were tagged and tracked with acoustic transmitters from two known spawning reaches separated by 90 km. Biotelemetry recaptures for two years demonstrated that each reach was associated with separate spawning runs. Time series of spawning run trajectories were examined via nonparametric dynamic time warping and revealed two dominant time series centroids, each associated with the two spawning reaches. In 2017, the lower reach run occurred earlier than the higher reach run, but difference in timing was not observed in 2018. The majority (84%) of returning adults in 2018 showed the same run behaviors exhibited in 2017. The two spawning run may have been cued differently by temperatures, where warming lagged 1-week at the higher reach in comparison to the lower reach. The two spawning runs exhibited similar Atlantic shelf migration patterns with strong summer fidelity to Massachusetts Bay and winter migrations to the southern US Mid-Atlantic Bight. Still, in 2017, differing times of departure from spawning reaches into nearby shelf waters likely caused the early spawning run to experience substantially higher mortality than the later run. Anecdotal evidence suggests that higher fishing effort is exerted on the early-spawning run as it first enters shelf fisheries. Thus, as in salmon, multiple spawning runs by striped bass can lead to differential demographic outcomes, contributing to overall population dynamics.</p>
Behavioral data and analyses of competitive interactions between invasive and native ant species [from Cordonnier et al. 2021, Animals]
<p>This README accompanies the files "data_Cordonnier_Animals.txt" & "script_Cordonnier_Animals.txt"</p> <p> </p> <p>Associated publication : </p> <p>The native ant <em>Lasius niger</em> can limit the access to resources of the invasive Argentine ant</p> <p>M. Cordonnier, O. Blight, E. Angulo, and F. Courchamp</p> <p>Published in <em>Animals</em></p> <p> <br> ********************************** CONTENTS *****************************<br> The data are in table form with TABs as variables field delimiters so they can be readily imported in any statistical package or spreadsheet program. Please, contact me if you need the file formatted otherwise. </p> <p> </p> <p>*******************************************************************************<br> Variable names and descriptions</p> <p> </p> <p>Status_Lh status of Linepithema humile (Colonizer or Resident) </p> <p>opp species of the opponent</p> <p>combirc combination of status and species interacting</p> <p>temp temperature during the test</p> <p>hygro hygrometry during the test</p> <p>categ interacting species combination</p> <p>n_deadtot_opp total number of dead opponent workers</p> <p>t_50dead_opp time when 50% of the opponent mortality load have been diagnosed</p> <p>t_interact time of the first interaction between L. humile and opponent workers</p> <p>t_maxfights time when the maximal number of simultaneous fights occurs</p> <p>ET_fights standard deviation of the numbers of fights over time</p> <p>mean_fights mean number of simultaneous fights during the contest</p> <p>n_deadtot_Lh total number of dead workers of L. humile</p> <p>t_50dead_Lh time when 50% of the L. humile mortality load have been diagnosed</p> <p>t_arena_opp time of the opponent entrance in the arena</p> <p>t_bait_opp time of opponent resources’ discovery</p> <p>t_maxarena_opp time when the max. number of opponent workers occurs in the arena</p> <p>mean_arena_opp mean number of opponent workers simultaneously present in the whole arena</p> <p>t_maxbait_opp time when the maximal number of opponent workers on the bait occurs</p> <p>mean_bait_opp mean number of opponent workers on the bait</p> <p>t_arena_Lh time of the entrance in the arena of L. humile</p> <p>t_maxarena_Lh time when the max. number of workers of L. humile occurs in the arena</p> <p>mean_arena_Lh mean number of L. humile workers simultaneously present in the whole arena</p> <p>n_totprey_Lh total number of preys brought by L. humile</p> <p>t_bait_Lh time of resources’ discovery by L. humile</p> <p>t_maxbait_Lh time when the maximal number of L. humile individuals on the bait occurs</p> <p>ETbait_Lh standard deviation of the numbers of L. humile workers on the bait over time</p> <p>mean_bait_Lh mean number of L. humile workers on the bait</p> <p>t_50prey_Lh time when 50% of the final prey load</p> <p> </p> <p>******************************** CONTACT *********************************<br> Please contact me at:</p> <p>Marion Cordonnier<br> e-mail: marion.cordonnier@hotmail.com</p> <p>*******************************************************************************</p> <p> </p>
Wrist-mounted IMU data towards the investigation of free-living human eating behavior - the Free-living Food Intake Cycle (FreeFIC) dataset
<p><strong>Introduction</strong></p> <p>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 <em>in-the-wild</em> 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 <span class="math-tex">\(3D\)</span> acceleration and orientation velocity signals (<span class="math-tex">\(6\)</span> DoF) from <span class="math-tex">\(22\)</span> in-the-wild sessions provided by <span class="math-tex">\(12\)</span> unique subjects. All sessions were recorded using a commercial smartwatch (<span class="math-tex">\(6\)</span> 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.</p> <p><strong>Description</strong></p> <p>FreeFIC includes <span class="math-tex">\(22\)</span> in-the-wild sessions that belong to <span class="math-tex">\(12\)</span> 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 <span class="math-tex">\(22\)</span> recordings sums up to <span class="math-tex">\(112.71\)</span> hours, with a mean duration of <span class="math-tex">\(5.12\)</span> hours. Additional data statistics can be obtained by executing the provided python script <em>stats_dataset.py</em>. Furthermore, the accompanying python script <em>viz_dataset.py </em>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.</p> <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> <p>FreeFIC is also tightly related to Food Intake Cycle (FIC), a dataset we created in order to investigate the <em>in-meal</em> 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>.</p> <p><strong>Publications</strong></p> <p>If you plan to use the FreeFIC dataset or any of the resources found in this page, please cite our work:</p> <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> <p><strong>Technical details</strong></p> <p>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:</p> <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> <p>The dataset variable in the snipet above is a dictionary with <span class="math-tex">\(5\)</span> keys. Namely:</p> <ul> <li>'subject_id'</li> <li>'session_id'</li> <li>'signals_raw'</li> <li>'signals_proc'</li> <li>'meal_gt'</li> </ul> <p>The contents under a specific key can be obtained by:</p> <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> <p>The <em>sub</em>, <em>ses</em>, <em>raw</em>, <em>proc </em>and <em>gt </em>variables in the snipet above are lists with a length equal to <span class="math-tex">\(22\)</span>. Elements across all lists are aligned; e.g., the <span class="math-tex">\(3\)</span>rd element of the list under the 'session_id' key corresponds to the <span class="math-tex">\(3\)</span>rd element of the list under the 'signals_proc' key.</p> <p><em>sub</em>: list<br> 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: <span class="math-tex">\([1, 2, 3, 4, 13, 14, 15, 16, 17, 18, 19, 20]\)</span>. It should be emphasized that the subjects with ids <span class="math-tex">\(15, 16, 17, 18, 19\)</span> and <span class="math-tex">\(20\)</span> belong to the held-out part of the FreeFIC dataset (more information can be found in <span class="math-tex">\( \)</span>the publication titled "A Data Driven End-to-end Approach for In-the-wild Monitoring of Eating Behavior Using Smartwatches" by Kyritsis <em>et al).</em> 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 <span class="math-tex">\(2\)</span> is the same person as FreeFIC’s subject with id equal to <span class="math-tex">\(2\)</span>.</p> <p><em>ses: </em>list<br> Each element of this list is a scalar (integer) that corresponds to the unique identifier of the session that can range between <span class="math-tex">\(1\)</span> and <span class="math-tex">\(5\)</span>. It should be noted that not all subjects have the same number of sessions.</p> <p><em>raw</em>: list<br> Each element of this list is dictionary with the 'acc' and 'gyr' keys.<br> The data under the 'acc' key is a <span class="math-tex">\(N_{acc} \times 4\)</span> numpy.ndarray that contains the timestamps in seconds (first column) and the <span class="math-tex">\(3D\)</span> raw accelerometer measurements in <span class="math-tex">\(g\)</span> (second, third and forth columns - representing the <span class="math-tex">\(x, y \)</span> and <span class="math-tex">\(z\)</span> axis, respectively). The data under the 'gyr' key is a <span class="math-tex">\(N_{gyr} \times 4\)</span> numpy.ndarray that contains the timestamps in seconds (first column) and the <span class="math-tex">\(3D\)</span> raw gyroscope measurements in <span class="math-tex">\({degrees}/{second}\)</span>(second, third and forth columns - representing the <span class="math-tex">\(x, y \)</span> and <span class="math-tex">\(z\)</span> 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 <span class="math-tex">\((N_{acc} \neq N_{gyr})\)</span>. This behavior is predictable and is caused by the Android platform.</p> <p><em>proc: </em>list<br> Each element of this list is an <span class="math-tex">\(M\times7\)</span> numpy.ndarray that contains the timestamps, <span class="math-tex">\(3D\)</span> accelerometer and gyroscope measurements for each meal. Specifically, the first column contains the timestamps in seconds, the second, third and forth columns contain the <em><span class="math-tex">\(x,y\)</span></em> and <span class="math-tex">\(z\)</span> accelerometer values in <span class="math-tex">\(g\)</span><strong> </strong>and the fifth, sixth and seventh columns contain the <em><span class="math-tex">\(x,y\)</span></em> and <span class="math-tex">\(z\)</span> gyroscope values in <span class="math-tex">\({degrees}/{second}\)</span>. Unlike elements in the <em>raw </em>list, processed measurements (in the <em>proc</em> list) have a constant sampling rate of <span class="math-tex">\(100\)</span> 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>). <em>No other preprocessing is performed on the data</em>; 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 <em>et al. </em>on how to further preprocess the IMU signals (i.e., smooth and remove the gravitational component).</p> <p><em>meal_gt: </em>list<br> Each element of this list is a<strong> <span class="math-tex">\(K\times2\)</span></strong> 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<strong> </strong>whereas the second one the timestamps of the meal end moments. All timestamps are in seconds. The number of meals <span class="math-tex">\(K\)</span> varies across recordings (e.g., a recording exist where a participant consumed two meals).</p> <p><strong>Ethics and funding</strong></p> <p>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>.</p> <p><strong>Contact</strong></p> <p>Any inquiries regarding the FreeFIC dataset should be addressed to:</p> <p>Dr. Konstantinos KYRITSIS</p> <p>Multimedia Understanding Group (MUG)<br> Department of Electrical & Computer Engineering<br> Aristotle University of Thessaloniki<br> University Campus, Building C, 3rd floor<br> Thessaloniki, Greece, GR54124</p> <p>Tel: +30 2310 996359, 996365 <br> Fax: +30 2310 996398<br> E-mail: kokirits [at] mug [dot] ee [dot] auth [dot] gr</p>
Wrist-mounted IMU data towards the investigation of in-meal human eating behavior - the Food Intake Cycle (FIC) dataset
<p><strong>Introduction</strong></p> <p>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 <em>in-meal</em> eating behavior. The FIC dataset contains the triaxial acceleration and orientation velocity signals (<span class="math-tex">\(6\)</span> DoF) from <span class="math-tex">\(21\)</span> meal sessions provided by <span class="math-tex">\(12\)</span> unique subjects. All meals were recorded in the restaurant of Aristotle University of Thessaloniki using a commercial smartwatch, the Microsoft Band <span class="math-tex">\(2\)</span>™ for ten out of the twenty-one meals and the Sony Smartwatch <span class="math-tex">\(2\)</span>™ 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.</p> <p><strong>Description</strong></p> <p>A total of <span class="math-tex">\(12\)</span> subjects were recorded while eating their launch at the university’s cafeteria. The total duration of the <span class="math-tex">\(21\)</span> meals sums up to <span class="math-tex">\(246\)</span> minutes, with a mean duration of <span class="math-tex">\(11.7\)</span> 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 <span class="math-tex">\(5\)</span> camera was already set at the table of the participant using a small, <span class="math-tex">\(23\)</span> 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 <em>viz_dataset.py </em>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.</p> <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> <p>FIC is also tightly related to FreeFIC, a dataset we created in order to investigate the <em>in-the-wild </em>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>.</p> <p><strong>Annotation</strong></p> <p><em>Micromovements</em></p> <p>For all recordings, the start and end points of all <span class="math-tex">\(6\)</span> micromovements of interest were manually labeled. The micromovements of interest include:</p> <ul> <li><strong>p</strong>ick food, wrist manipulates a fork to pick food from the plate</li> <li><strong>u</strong>pwards, wrist moves upwards, towards the mouth area</li> <li><strong>d</strong>ownwards, wrist moves downwards, away from the mouth area</li> <li><strong>m</strong>outh, wrist inserts food in mouth</li> <li><strong>n</strong>o movement, wrist exhibits no movement</li> <li><strong>o</strong>ther movement, every other wrist movement</li> </ul> <p>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.</p> <p><em>Food intake cycles</em></p> <p>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 <strong>p</strong>, ends with a <strong>d </strong>and contains an <strong>m </strong>micromovement.</p> <p><strong>Publications</strong></p> <p>If you plan to use the FIC dataset or any of the resources found in this page, please cite our work:</p> <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> <p><strong>Technical details</strong></p> <p>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:</p> <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> <p>The <em>dataset </em>variable in the snipet above is a dictionary with <span class="math-tex">\(6\)</span> keys. Namely:</p> <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> <p>The contents under a specific key can be obtained by:</p> <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> <p>The <em>sub</em>, <em>ses</em>, <em>raw</em>, <em>proc, mm </em>and<em> gt </em>variables in the snipet above are lists with a length equal to <span class="math-tex">\(21\)</span>. Elements across all lists are aligned; e.g., the <span class="math-tex">3</span>rd element of the list under the 'session_id' key corresponds to the <span class="math-tex">3</span>rd element of the list under the 'signals_proc' key.</p> <p><em>sub</em>: list<br> Each element of the sub list is a scalar (integer) that corresponds to the unique identifier of the subject that can take values between <span class="math-tex">\(1\)</span> and <span class="math-tex">\(12\)</span>. 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 <span class="math-tex">2</span> is the same person as FreeFIC’s subject with id equal to <span class="math-tex">2</span>.</p> <p><em>ses: </em>list<br> Each element of this list is a scalar (integer) that corresponds to the unique identifier of the session that can range between <span class="math-tex">1</span> and <span class="math-tex">\(3\)</span>. It should be noted that not all subjects have the same number of sessions.</p> <p><em>raw</em>: list<br> Each element of this list is dictionary with the 'acc', 'gyr' and 'offset' keys.<br> The data under the 'acc' key is a <span class="math-tex"><span class="math-tex">\(N_{acc}\times4\)</span></span> numpy.ndarray that contains the timestamps in seconds (first column) and the <span class="math-tex"><em><span class="math-tex">\(3D\)</span></em></span> raw accelerometer measurements in <span class="math-tex">\(g\)</span> (second, third and forth columns - representing the <span class="math-tex">\(x, y\)</span> and <span class="math-tex"><em><span class="math-tex">\(z\)</span></em></span> axis, respectively). The data under the 'gyr' key is a <span class="math-tex"><span class="math-tex">\(N_{gyr} \times 4\)</span></span> numpy.ndarray that contains the timestamps in seconds (first column) and the <span class="math-tex">\(3D\)</span> raw gyroscope measurements in <span class="math-tex">\(degrees/second\)</span>(second, third and forth columns - representing the<span class="math-tex"><em> <span class="math-tex">\(x, y\)</span></em></span> and <span class="math-tex">\(z\)</span> 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 <span class="math-tex"><span class="math-tex">\(N_{acc} \neq N_{gyr}\)</span></span>. 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).</p> <p><em>proc: </em>list<br> Each element of this list is an <span class="math-tex">\(M \times 7\)</span> numpy.ndarray that contains the timestamps, <span class="math-tex"><em><span class="math-tex">\(3D\)</span></em></span> accelerometer and gyroscope measurements for each meal. Specifically, the first column contains the timestamps in seconds, the second, third and forth columns contain the <span class="math-tex">\(x,y\)</span> and <span class="math-tex"><em><span class="math-tex">\(z\)</span></em></span> accelerometer values in <span class="math-tex"><em><span class="math-tex">\(g\)</span></em></span><strong> </strong>and the fifth, sixth and seventh columns contain the <span class="math-tex">\(x, y\)</span> and <span class="math-tex"><em><span class="math-tex">\(z\)</span></em></span> gyroscope values in <span class="math-tex"><em><span class="math-tex">\(degrees/second\)</span></em></span>. Unlike elements in the <em>raw </em>list, processed measurements (in the <em>proc</em> list) have a constant sampling rate of <span class="math-tex">100</span> 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>). <em>No other preprocessing is performed on the data</em>; 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 <em>et al. </em>on how to further preprocess the IMU signals (i.e., smooth and remove the gravitational component).</p> <p><em>mm</em>: list<br> Each element of this list is a <span class="math-tex">\(K \times 3\)</span> 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:<br> <span class="math-tex">\([1] \rightarrow\)</span> <strong>n</strong>o movement<br> <span class="math-tex">\([2] \rightarrow\)</span> <strong>u</strong>pwards<br> <span class="math-tex">\([3] \rightarrow\)</span> <strong>d</strong>ownwards<br> <span class="math-tex">\([4] \rightarrow\)</span> <strong>p</strong>ick food<br> <span class="math-tex">\([5] \rightarrow\)</span> <strong>m</strong>outh<br> <span class="math-tex">\([6] \rightarrow\)</span> <strong>o</strong>ther movement</p> <p><em>bite</em>: list<br> Each element of this list is a <strong><span class="math-tex">\(L\times2\)</span></strong> 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.</p> <p><strong>Ethics and funding</strong></p> <p>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>.</p> <p><strong>Contact</strong></p> <p>Any inquiries regarding the FIC dataset should be addressed to:</p> <p>Dr. Konstantinos KYRITSIS</p> <p>Multimedia Understanding Group (MUG)<br> Department of Electrical & Computer Engineering<br> Aristotle University of Thessaloniki<br> University Campus, Building C, 3rd floor<br> Thessaloniki, Greece, GR54124</p> <p>Tel: +30 2310 996359, 996365 <br> Fax: +30 2310 996398<br> E-mail: kokirits [at] mug [dot] ee [dot] auth [dot] gr</p>
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.
Data from: Has gene expression neofunctionalization in the fire ant antennae contributed to queen discrimination behavior?
<p>Queen discrimination behavior in the fire ant <i>Solenopsis invicta</i> 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, <i>SB</i> and <i>Sb,</i> with ~600 genes. Polygyne workers, having either the <i>SB/SB</i> or <i>SB/Sb </i>genotype, accept additional <i>SB/Sb</i> queens into their colonies but kill <i>SB/SB</i> queens. In contrast, monogyne workers, all <i>SB/SB</i>, reject all additional queens regardless of genotype. Because the <i>SB</i> and <i>Sb</i> 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 <i>SB/SB</i>, polygyne <i>SB/SB,</i> and polygyne <i>SB/Sb</i>. 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<i> SiOR463</i> and an odorant binding protein <i>Si</i><i>OBP12</i>. We found that <i>SiOR463</i> has been lost in the <i>Sb</i>-genome. In contrast, <i>SiOBP12</i> has an <i>Sb</i>-specific duplication, <i>SiOBP12b'</i>, which is expressed in the <i>SB/Sb</i> worker antennae, while both paralogs are expressed in the body. Comparisons with another fire ant species revealed that <i>SiOBP12b'</i> antennal expression is specific to <i>S. invicta</i> and suggests that queen discrimination may have evolved, in part, through expression neofunctionalization.</p>
Data from "Asymmetries in behavioral and neural responses to spectral cues demonstrate the generality of auditory looming bias"
<p>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</p>
Dataset of Concurrent EEG, ECG, and Behavior with Multiple Doses of transcranial Electrical Stimulation-Exp1-Data Downsampled
<p><strong>GX Dataset downsampled - Experiment 1</strong></p> <p>The GX Dataset is a dataset of combined tES, EEG, physiological, and behavioral signals from human subjects.<br>Here the GX Dataset for <strong>Experiment 1</strong> is downsampled to 1 kHz and saved in .MAT format which can be used in both MATLAB and Python.</p> <p><strong>Publication</strong></p> <p>A full data descriptor is published in <a href="https://doi.org/10.1038/s41597-021-01046-y">Nature Scientific Data</a>. <strong>Please cite this work as</strong>:</p> <blockquote> <p>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</p> </blockquote> <p><strong>Descriptions</strong></p> <p>A dataset combining high-density electroencephalography (EEG) with physiological and continuous behavioral metrics during transcranial electrical stimulation (tES). 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, EOG), and continuous behavioral vigilance/alertness metrics.</p> <p><strong>Acknowledgments</strong></p> <p>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.</p> <p><strong>Extras</strong></p> <p>Back to <a href="https://doi.org/10.5281/zenodo.4456079">Full GX Dataset</a> : https://doi.org/10.5281/zenodo.4456079</p> <p> </p> <p>For downsampled data (1 kHz ) please see (in .mat format):</p> <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> <p> </p> <p>Code used to import, process, and plot this dataset can be found here:</p> <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> <p> </p> <p>Additional figures for this project have been shared on Figshare. Trial-wise figures can be found here:</p> <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> <p> </p> <p>The full dataset is also provided in <a href="https://bids.neuroimaging.io/">BIDS</a> format here:</p> <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> <p><strong>Data License </strong><br><a href="https://creativecommons.org/licenses/by/4.0/">Creative Common 4.0 with attribution (CC BY 4.0)</a></p> <p> </p> <p><strong>NOTE</strong></p> <p><strong>Please email ngebodh01@citymail.cuny.edu with any questions.</strong></p> <p> </p> <p><br><strong>Updates</strong></p> <ul> <li><strong>Version 2.1.0</strong> <ul> <li>Behavioral data (ptracker) adjusted for all files. Previous version had one participant's data overwriting all behavioral data when downsampled. Mat format now explicitly >v7.3 </li> </ul> </li> <li><strong>Version 2</strong> <ul> <li>Stimulation trigger labels now adjusted. Previous labels were missmatched for Experiment 1's data. </li> </ul> </li> </ul> <p> </p>
Data set for "Distributed and specific encoding of sensory, motor and decision information in the mouse neocortex during goal-directed behavior"
<p>Data set for: Oryshchuk A, Sourmpis C, Weverbergh J, Asri R, Esmaeili V, Modirshanechi A, Gerstner W, Petersen CCH, Crochet S (2024) Distributed and specific encoding of sensory, motor and decision information in the mouse neocortex during goal-directed behavior. Cell Reports 43: 113618. https://doi.org/10.1016/j.celrep.2023.113618</p> <p> </p> <p>There are 2 files in this upload:</p> <p> </p> <p>1. The file named "2024_Oryshchuk_CellReports.pdf" is the Open Access pdf of the online publication in Cell Reports.</p> <p> </p> <p>2. The file named " Oryshchuk _data_code.zip" (~1.8 GB) is a zipped version of a folder "Oryshchuk _data_code" (~2.3 GB), which contains the preprocessed data analyzed in the study along with the Matlab and Python codes used to generate the published figures. To access the data and codes, first unzip the file.</p> <p>· The subfolder “Atlas” contains templates from the Allen Mouse Brain Reference Altas of anatomical brain sections used to map the location of the silicon probes (Supplementary Figure S1).</p> <p>· The subfolder “Clustering-master” contains the Matlab codes used for the clustering on neuronal activity (Figure 1). The output is the data structure ‘Data_Clustering.mat’ file already provided in the folder ‘Data’.</p> <p>· The subfolder “Code” contains the main Matlab codes used to analyze the data and plot the figures. The ouput from the clustering and decoding analyses are provided in the ‘Data’ folder, thus the Matlab codes can be run independently, without running the ‘clustering’ or ‘decoding’ codes first.</p> <p>· The subfolder “Data” contains the Matlab data structures containing the electrophysiological and behavioral data from whisker rewarded (‘DataWR.mat’) and non-rewarded (‘DataWnonR.mat’) mice, the behavioral data for optogenetic inactivation in rewarded mice, the clustering results (‘Data_Clustering.mat’) and a subfolder containing the results from the decoding analyses (“Decoding”).</p> <p>· The subfolder “decoding” contains the Python codes used for the decoding analyses. The required configuration can be found in the file ‘requirements.txt’. To run the codes, follow instructions from the ‘README.md’ file.</p> <p>· The subfolder “Figures” will be populated with figures saved in .png and .eps formats as well as a ‘Methods.txt’ files when running the main Matlab codes.</p> <p>· The subfolder “Functions” contains subfunctions used by the main Matlab codes to analyze the data and plot the figures.</p> <p>· The subfolder “Results” will be populated with Matlab data structures as well as a ‘.xlsx’ files when running the main Matlab codes.</p> <p>When running the code, you need to set the Matlab file path to be "Oryshchuk _data_code". In addition, you should add the folder "Oryshchuk_data_code" with subfolders to the Matlab path. Some parts of the code rely upon previous results, and need to be executed sequentially in the order of the figure panels in the journal publication. Please note that some of the code can take several hours to execute.</p>
Data from: Mating environments mediate the evolution of behavioral isolation during ecological speciation
<p>The evolution of behavioral isolation is often the first step towards speciation. While past studies show that behavioral isolation will sometimes evolve as a by-product of divergent ecological selection, we lack a more nuanced understanding of factors that may promote or hamper its evolution. The environment in which mating occurs may be important in mediating whether behavioral isolation evolves for two reasons. Ecological speciation could occur as a direct outcome of different sexual interactions being favored in different mating environments. Alternatively, mating environments may vary in the constraint they impose on traits underlying mating interactions, such that populations evolving in a 'constraining' mating environment would be less likely to evolve behavioral isolation than populations evolving in a less constraining mating environment. In the latter, mating environment is not the direct cause of behavioral isolation but rather permits its evolution only if other drivers are present. We test these ideas with a set of 28 experimental fly populations, each of which evolved under one of two mating environments and one of two larval environments. Counter to the prediction of ecological speciation by mating environment, behavioral isolation was not maximal between populations evolved in different mating environments. Nonetheless, mating environment was an important factor as behavioral isolation evolved among populations from one mating environment but not among populations from the other. Though one mating environment was conducive to the evolution of behavioral isolation, it was not sufficient: assortative mating only evolved between populations adapting to different larval environments within that mating environment, indicating a role for ecological speciation. Intriguingly, the mating environment that promoted behavioral isolation is characterized by less sexual conflict compared to the other mating environment. Our results suggest that mating environments plays a key role in mediating ecological speciation via other axes of divergent selection.</p>
Data from: Effects of past mating behavior versus past ejaculation on male mate choice and male attractiveness
<p>Past reproductive effort allows males to assess their ability to acquire mates, but it also consumes resources that can reduce their future competitive ability. Few studies have examined how a male's reproductive history affects his subsequent mate choice; and, to date, no study has determined the relative contribution of past mating behavior and past ejaculate production because these two forms of investment are naturally highly correlated. Here, we disentangled the relative effects of past mating behavior and past ejaculate production in male mosquitofish (<em>Gambusia holbrooki</em>) by experimentally preventing some males from ejaculating when trying to mate. We assessed the effect of mating behavior on mate choice by comparing males that had previously been with or without access to females and male rivals for 16 weeks; and assessed the effect of ejaculation on mate choice by comparing males that either could or could not ejaculate when they had access to females for 16 weeks. We compared (1) time females spent with each male, (2) total distance males swam, (3) total time males spent inspecting females, (4) proportion of time males spent with the solitary females in separate models, with age (week 8, week 16) and treatment of reproductive history ("naive male", "mating only male" and "mating and ejaculation males") as fixed factors. </p> <p>We showed that reproductive history did not affect male attractiveness, but it did affect male mate choice. Somewhat surprisingly, in two-choice trials males from all three treatments preferred females in the vicinity of a rival over solitary females. This preference was marginally stronger for males engaging in previous mating behavior but was unaffected by past ejaculate production. This is the first study to quantify the relative influence of pre- and post-copulatory reproductive investment on male mate choice.</p>
Data from: Exponential history integration with diverse temporal scales in retrosplenial cortex supports hyperbolic behavior
<p>Animals rely on their experience to guide their next choice. In foraging-type tasks guided by history-dependent value, these experiences are typically integrated such that the weights of past events initially decay quickly over time but show a longer tail than expected by exponential decay. Rather, such integration is better described by a hyperbolic function. Hyperbolic integration affords sensitivity to both recent environmental dynamics and long-term trends, however the mechanism by which the brain implements this hyperbolic integration is unknown. We trained mice on a history-dependent, value-based decision task and found that the mice indeed showed hyperbolic decay on their weighting of past experience. However, the activity of history-encoding cortical neurons showed weighting with exponential decay. In resolving this apparent mismatch, we observed that cortical neurons encode history information heterogeneously across a wide variety of exponential time-constants, with the retrosplenial cortex (RSC) overrepresenting longer time-constants compared to other areas. A model that combines these diverse timescales of exponential history integration can recreate the heavy-tailed, hyperbolic history integration observed in behavior. In particular, time-constants of RSC neurons best matched the behavior, and optogenetic inactivation of RSC uniquely reduced the use of history information. These results indicate that behavior-relevant history information is maintained in neurons across multiple timescales in parallel, and suggest that the neural population in RSC is a critical reservoir of this information guiding decision-making.</p>
Data from: Offspring behavioral outcomes following maternal allergic asthma in the IL-4-deficient mouse
<p>Background: Maternal allergies and asthma during pregnancy have been associated with increased risk of ASD and ADHD to the child. Previous rodent studies have demonstrated that inducing a T helper-2 (Th2)-mediated allergic response during pregnancy leads to an offspring behavioral phenotype characterized by decreased social interaction and increased stereotypies. Interleukin-4 is a key signal in the Th2 immune cascade, but its role in fetal brain development and subsequent impacts of maternal allergic asthma (MAA) on offspring behavioral deficits have yet to be determined.</p> <p>Objective: In this study, we investigated whether the absence of IL-4 signaling would mitigate MAA-induced behavioral changes.</p> <p>Methods: C57BL/6J and Interleukin-4 knockout (IL-4 KO) mice were sensitized to ovalbumin and exposed to repeated allergic asthma aerosol inductions throughout pregnancy. Offspring were assessed on Juvenile Reciprocal Social Interaction, Elevated Plus Maze, Open Field Exploration, Novel Object Recognition, Forced Swim, Marble-burying, and Grooming tasks.</p> <p>Results: MAA during pregnancy resulted in decreased social interactions in male C57 offspring and impaired memory performance in both male and female C57 mice. These deficits were not observed in IL-4 KO mice exposed to MAA. However, we observed genotype effects in IL-4 KO mice including altered motor performance and anxiety-associated responses.</p> <p>Conclusion: MAA-induced social and cognitive behavioral alterations are IL-4 dependent. IL-4KO offspring display genotype-specific differences suggesting IL-4 signaling is important for typical developmental processes.</p>
Data and code for: Behavioral plasticity shapes participation in a mixed-species flocking community of birds
<p>Behavioral plasticity can modulate the costs and benefits of sociality, and thus may play a prominent role in mediating competition and facilitation during social interactions in mixed-species groups. However, investigations of assembly patterns of mixed-species groups typically treat species' behavioral attributes as static rather than dynamic features that can change in social contexts. We investigate four axes of behavioral plasticity that may modulate interaction within mixed-species groups: 1) species' selective preference for joining certain groups, 2) species' ability to flexibly change their behavior in response to groupmates' behavior, and 3) shifts and/or 4) expansions of species' niche occupancy when foraging with conspecifics versus when foraging with heterospecifics. We assess variation in these axes of behavioral plasticity in an Australian mixed-species avian community. All species had selective preferences for flocks of certain strata, and some flexibly matched their flockmates' foraging strata. Three species exhibited patterns of niche shift, and one species showed niche expansion. These findings suggest that species converge in strata in mixed-species flocks despite the potential for increased competition and emphasize that species can plastically react to changes in their social environment in numerous ways. Acknowledgment of such plasticity is likely integral to understanding the nuances of heterospecific interactions.</p>
Reproduction code and data for the plot of "Synthesizing survival robot behavior through reinforcement learning for homeostasis"
<pre># Reproduction code and data for the plot of "Synthesizing survival robot behavior through reinforcement learning for homeostasis"<br>Author: Naoto Yoshida<br><br>How to use:<br>1. Clone https://github.com/ugo-nama-kun/journalpaper_robot_2024 from github.<br>2. Extract data_20241119.zip in the cloned repository.<br>3. Run each plot_Fig*.py</pre>
Raw data: Nanogels with tailored hydrophobicity and their behavior at air water interfaces
<p>Raw data for Journal article: "Nanogels with tailored hydrophobicity and their behavior at air/water interfaces"</p> <p>Abstract:</p> <p>The interfacial behavior of micro-/nanogels is governed to a large extent by the hydrophobicity of their polymeric network. Prevailing studies to examine this influence mostly rely on external stimuli like temperature or pH to modulate the colloidal hydrophobicity. Here, a sudden transition between hydrophilic and hydrophobic state prevents systematic and gradual modulation of hydrophobicity. This limits important correlations between interfacial behavior and quantitative physicochemical measures for network hydrophobicity. To address this challenge, we introduce a nanogel platform that allows accurate tuning of hydrophobicity on a molecular level. For this, via post-functionalization of active ester-based particles, we prepare poly(N-(2-hydroxypropyl)methacrylamide) (PHPMA) nanogels as a hydrophilic benchmark and introduce gradually varied amounts of hydrophobic propyl or dodecyl moieties to increase the nanogel hydrophobicity. We study the deformation and arrangement of these particles at an air/water interface and correlate the results with quantitative measures for nanogel hydrophobicity. We observe that increasing hydrophobicity of nanogels, either by increasing the hydrophobic moiety ratio or the alkyl chain length, leads to decreased particle deformability and aggregation of an interfacially-adsorbed monolayer. Contrary to what may be intuitively assumed, these changes are not gradual, but rather occur suddenly above a threshold in hydrophobicity. Our study further shows that the effect of hydrophobicity affects the nanogel properties differently in bulk and when adsorbed at liquid interfaces. Thus, this study establishes the transition of interfacial behavior between soft gel-like particles to a solid spherical morphology triggered by the increase in hydrophobicity.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.
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.
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.
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.
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.