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2,587 results for “Movement”
Upper limb movements can be decoded from the time-domain of low-frequency EEG
<p>How neural correlates of movements are represented in the human brain is of ongoing interest and has been researched with invasive and non-invasive methods. In this study, we analyzed the encoding of single upper limb movements in the time-domain of low-frequency electroencephalography (EEG) signals. Fifteen healthy subjects executed and imagined six different sustained upper limb movements. We classified these six movements and a rest class and obtained significant average classification accuracies of 55% (movement vs movement) and 87% (movement vs rest) for executed movements, and 27% and 73%, respectively, for imagined movements. Furthermore, we analyzed the classifier patterns in the source space and located the brain areas conveying discriminative movement information. The classifier patterns indicate that mainly premotor areas, primary motor cortex, somatosensory cortex and posterior parietal cortex convey discriminative movement information. The decoding of single upper limb movements is specially interesting in the context of a more natural non-invasive control of e.g., a motor neuroprosthesis or a robotic arm in highly motor disabled persons.</p>
BRAIN Journal-Evolving Spiking Neural Networks for Control of Artificial Creatures-Figure 8. Flowchart for movements of creature
<p>Window time has a fixed length between and and is an appropriate time interval [5]. So in each time window the total number of spikes in each three neurons is compared with other three neurons and artificial creature moves toward direction that the respective neurons fired maximum number of spikes. These fixed time<br> windows consist of 600 time-steps. Each time step is 0.5 ms. Flowchart in Figure 8 shows details.</p>
BRAIN Journal-Evolving Spiking Neural Networks for Control of Artificial Creatures-Figure 7. Effect of different type of movement on the image in the vision of artificial creature.
<p>Figure 7a, Figure 7b, Figure 7c and Figure 7d, shows effect of different type of movement on the image in the vision of artificial creature if food be on vision boundaries. As mentioned each part of the image equal 7.5 degree.<br> Therefore 15 degree left or right rotation locomotion equivalent two parts shift toward left or right.<br> For motion to forward direction, size of the image has been reduplicated so that each part has been become to the two similar parts. Then half of new image in right side and left side has been deleted in order to create new close image in vision. Accordingly, if food be on vision boundaries, the number of black parts of the image for the food object in ultimate location is 2, by one movement to forward direction the number of these parts become to 4, by one movement to forward direction the number of these parts become to 8 and so on. After four movements to forward all part of the image is black and the creature is succeed find the food object.</p>
BRAIN Journal-Evolving Spiking Neural Networks for Control of Artificial Creatures-Figure 15. Movements a typical successful creature to find food
<p>Figure 15 (a, b, c, d, e, f, g, h) illustrates movements of a typical successful creature for finding one food object.</p> <p>We believe that this study can be a step forward in understanding the morphology of artificial creatures. Also this paper suggests more complex artificial life examination by adding different part to these networks similar to different segments of the brain such as: vision, locomotion, hippocampus and communication in a future work. Because of the different axonal conduction delay between every two neurons in the neural network of artificial creatures in this paper, our next study is to enhance the artificial lives by STDP learning.</p>
Chronic wasting disease alters the movement behavior and habitat use of mule deer during clinical stages of infection
<p>Integrating host movement and pathogen data is a central issue in wildlife disease ecology that will allow for a better understanding of disease transmission. We examined how adult female mule deer (<em>Odocoileus hemionus</em>) responded behaviorally to infection with chronic wasting disease (CWD). We compared movement and habitat use of CWD-infected deer (<em>n</em> = 18) to those that succumbed to starvation (and were CWD-negative by ELISA and IHC; <em>n</em> = 8) and others in which CWD was not detected (<em>n</em> = 111, including animals that survived the duration of the study) using GPS collar data from two distinct populations collared in central Wyoming, USA during 2018–2022. CWD and predation were the leading causes of mortality during our study (32 of 91 deaths attributed to CWD and 27 of 91 deaths attributed to predation). Deer infected with CWD moved slower and used lower elevation areas closer to rivers in the months preceding death compared with uninfected deer that did not succumb to starvation. Although CWD-infected deer and those that died of starvation moved at similar speeds during the final months of life, CWD-infected deer used areas closer to streams with less herbaceous biomass than deer that died of starvation. These behavioral differences may allow for the development of predictive models of disease status from movement data, which will be useful to supplement field and laboratory diagnostics or when mortalities cannot be quickly retrieved to assess cause-specific mortality. Furthermore, identifying individuals that are sick before predation events could help to assess the extent to which disease mortality is compensatory with predation. Finally, infected animals began to slow down around four months prior to death from CWD. Our approach for detecting the timing of infection-induced shifts in movement behavior may be useful in application to other disease systems to better understand the response of wildlife to infectious disease.</p>
Data and code for 3D-ARM-Gaze: a public dataset of 3D Arm Reaching Movements with Gaze information in virtual reality
<p>This repository contains data and code for</p> <p>Lento B., Segas E., Leconte V., Doat E., Danion F., Péteri R., Benois-Pineau J., de Rugy A. (2024). <strong>3D-</strong><strong>ARM</strong><strong>-Gaze</strong><strong>: a </strong><strong>public </strong><strong>dataset of </strong><strong>3D </strong><strong>A</strong><strong>rm </strong><strong>R</strong><strong>eaching </strong><strong>M</strong><strong>ovements</strong><strong> </strong><strong>with Gaze information</strong><strong> </strong><strong>in </strong><strong>virtual reality</strong><strong>. </strong>doi:</p> <p>It contains a dataset <strong>(DBAS22_DataOnline </strong>folder) of natural arm movements together with visual and gaze information when reaching objects in a wide reachable space from a precisely controlled, comfortably seated posture. More details could be find in the link publication (see Related identifiers section).</p> <p>The <strong>DBAS22_DocOnline</strong> folder contains all the documentation files. The <strong>MainDataExplained </strong>file lists and describes the variables recorded during the experimental phases. In the <strong>SummaryOfFiles </strong>document, you will find descriptions for all the files within the <strong>DBAS22_DataOnline</strong> folder, and at the bottom, there is also a file tree that illustrates the file structure. The <strong>DBAS22FilesWorkflow </strong>document offers an overview of the workflow of experimental file creation during the experiment.</p> <p>The <strong>DBAS22_CodeOnline</strong> folder contains all the scripts to perform data analysis, listed and described in the files <strong>CodeExplanations </strong>and <strong>DependenciesRelations</strong>. The <strong>GuideInstall </strong>file contains information needed to run the Python code files.</p> <p>The <strong>DBAS22_CodeOnline</strong> folder also contains the DataPlayer Unity project. Instructions for running the project are provided in the <strong>DataPlayerGuide </strong>file and SupplementaryVideo2 (see Related identifiers section for more details). The folder <strong>DBAS22_DataPlayer_StandAloneApp </strong>contains the standalone version of the DataPlayer, which doesn't require any software installation.</p> <p>The <strong>DBAS22_VideoOnline</strong> folder contains all the videos. </p>
Fig. 5 in Taxonomic groups with lower movement capacity may present higher beta diversity
Fig. 5. Similarity in species composition of birds among the 16 localities sampled in Minas Gerais, Brazil, based on the Jaccard coefficient of similarity and subsequent cluster analysis (UPGMA). Obs.: dashed line (significance level: 0.5 or 50%) (AIU, Aiuruoca; BOC, Bocaina de Minas; CAM, Camanducaia; CAX, Caxambu; DEL, Delfim Moreira; EXT, Extrema; GUA, GuaxupÉ; MAR, Maria da FÉ; MON, Monte Belo; MVE, Monte Verde; PAS, Passa Quatro; POÇ, Poços de Caldas; POU, Pouso Alegre; SGS, SÃo Gonçalo do SapucaÍ; SRJ, Santa Rita de Jacutinga; VIR, VirgÍnia).
Fig. 6 in Taxonomic groups with lower movement capacity may present higher beta diversity
Fig. 6. Similarity in species composition of primates among the 16 localities sampled in Minas Gerais, Brazil, based on the Jaccard coefficient of similarity and subsequent cluster analysis (UPGMA). Obs.: dashed line (significance level: 0.5 or 50%) (AIU, Aiuruoca; BOC, Bocaina de Minas; CAM, Camanducaia; CAX, Caxambu; DEL, Delfim Moreira; EXT, Extrema; GUA, GuaxupÉ; MAR, Maria da FÉ; MON, Monte Belo; MVE, Monte Verde; PAS, Passa Quatro; POÇ, Poços de Caldas; POU, Pouso Alegre; SGS, SÃo Gonçalo do SapucaÍ; SRJ, Santa Rita de Jacutinga; VIR, VirgÍnia).
Data from: Individual energetics scale up to community coexistence: Movement, metabolism and biodiversity dynamics in fragmented landscapes
<p>Unraveling the intricate mechanisms that govern community coexistence remains a daunting challenge, particularly amidst ongoing environmental change. To understand the response of individual animals to environmental change, physiology and individual metabolism are often studied. However, this perspective is currently largely lacking in community ecology. We argue that the integration of individual metabolism into community theory can offer new insights into coexistence. We present the first individual-based metabolic community model for a terrestrial mammal community to simulate energy dynamics and home range behavior in different environments. Using this model, we investigate how ecologically similar species coexist and maintain their energy balance under food competition. Only if individuals of different species are able to balance their incoming and outgoing energy over the long-term will they be able to coexist. After thoroughly testing and validating the model against real-world patterns such as of home range dynamics and field metabolic rates, we applied it as a case study to scenarios of habitat fragmentation - a widely discussed topic in biodiversity research. First, comparing single-species simulations with community simulations, we find that the effect of habitat fragmentation on populations is strongly context-dependent. While populations of species living alone in the landscape were mostly positively affected by fragmentation, the diversity of a community of species was highest under medium fragmentation scenarios. Under medium fragmentation, energy balance and reproductive investment were also most similar among species. We therefore suggest that similarity in energy balance among species promotes coexistence. We argue that energetics should be part of community ecology theory, as the relative energetic status and reproductive investment can reveal why and under what environmental conditions coexistence is likely to occur. As a result, landscapes can potentially be protected and designed to maximize coexistence. The metabolic community model presented here can be a promising tool to investigate other scenarios of environmental change or other species communities to further disentangle global change effects and preserve biodiversity.</p>
Movement data set for trust assessment (Drapebot robot cell/Profactor)
<p>In the Drapebot project, a worker collaborates with a large industrial manipulator in two tasks: collaborative transport of carbon fibre patches and collaborative draping. To realize data-driven trust assessement, the worker is equipped with a motion tracking suit and the body movement data is labeled with the trust scores from two standard Trust questionnaire (1. Trust perception scale - HRI, Schaefer 2016; 2. Trust in industrial human robo collaboration, Charalambous, et.al. 2016).</p> <p>For this data set, data has been collected for the draping task from 21 participants all familiar with working with large industrial manipulators. For all sessions, body tracking was performed using the Xsens MVN Awinda tracking suit. It consists of a tight-fitting shirt, gloves, headband, and a series of straps used to attach 17 IMUs to the participant. After calibration the system uses inverse kinematics to track and log the movements of the participant at a rate of 60 Hz. The measurements include linear and angular speed, velocity, and acceleration of every skeleton tracking point (see <a href="https://www.xsens.com/hubfs/Downloads/Manuals/MVN_real-time_network_streaming_protocol_specification.pdf">XSENS manual</a> for a detailed description of avaiable measurements).</p> <p><strong>Data organization</strong></p> <p>There are 21 files for 21 participants. The name of the files is PID01, where the number 01 is the participant. Each file contains all the data that was generated from the XSENS motion capture system. The files are xlsx files and for each sheet inside the excel file there are different types of data:</p> <ul> <li>Segment Orientation - Quat</li> <li>Segment Orientation - Euler</li> <li>Segment Position</li> <li>Segment Velocity</li> <li>Segment Acceleration</li> <li>Segment Angular Velocity</li> <li>Segment Angular Acceleration</li> <li>Joint Angles ZXY</li> <li>Joint Angles XZY</li> <li>Ergonomic Joint Angles ZXY</li> <li>Ergonomic Joint Angles XZY</li> <li>Center of Mass</li> <li>Sensor Free Acceleration</li> <li>Sensor Magnetic Field</li> <li>Sensor Orientation - Quat</li> <li>Sensor Orientation - Euler</li> </ul> <p>See also: <a href="https://base.movella.com/s/article/Output-Parameters-in-MVN-1611927767477?language=en_US">https://base.movella.com/s/article/Output-Parameters-in-MVN-1611927767477?language=en_US</a></p> <p>For more information on each specific data and/or sensors please see the xsens manual (Link above)</p> <p><strong>Data Annotation</strong></p> <p>In each .xlsx file the first tab (sheet) is called "Markers". It annotates the starting frame of the individual tasks. The annotations are pickup, draping, return and some files may contain a also a "fail" annotation. Failed attempts should not be taken into consideration for model training.</p> <p>The file trustscores.xlsx includes the results of the trust questionaires for each participant (scores for the individual items as well as the calculated overall trust scores).</p> <p>Items for Trust perception scale - HRI, Schaefer 2016:</p> <ul> <li>Which % of time does the robot <ul> <li>Function successfully</li> <li>Act consistently</li> <li>Communicate with people</li> <li>Provide feedback</li> <li>Malfunction</li> <li>Follow directions</li> <li>Meet the needs of the mission</li> <li>Perform exactly as instructed</li> <li>Have errors</li> </ul> </li> <li>Which % of the time is the robot: <ul> <li>Unresponsive</li> <li>Dependable</li> <li>Reliable</li> <li>Predictable</li> </ul> </li> </ul> <p>Items for Trust in industrial human robo collaboration, Charalambous, et.al. 2016:</p> <ul> <li>The way the robot moved made me uncomfortable</li> <li>I felt I could rely on the robot to do what it was supposed to do </li> <li>The speed at which the gripper picked up and released the components made me uneasy </li> <li>I felt safe interacting with the robot</li> <li>I knew the gripper would not drop the components</li> <li>The size of the robot did not intimidate me</li> <li>The robot gripper did not look reliable</li> <li>I was comfortable the robot would not hurt me</li> <li>I trusted that the robot was safe to cooperate with</li> <li>The gripper seemed like it could be trusted</li> </ul> <p> </p> <p><span>K.</span><span> </span><span>E.</span><span> </span><span>Schaefer,</span><span> </span><span>Measuring</span><span> </span><span>Trust</span><span> </span><span>in</span><span> </span><span>Human</span><span> </span><span>Robot</span><span> </span><span>Interactions: </span><span>Development</span><span> </span><span>of</span><span> </span><span>the</span><span> </span><span>“Trust</span><span> </span><span>Perception</span><span> </span><span>Scale-HRI”</span><span>.</span><span> </span><span>Boston, </span><span>MA:</span><span> </span><span>Springer</span><span> </span><span>US,</span><span> </span><span>2016,</span><span> </span><span>pp.</span><span> </span><span>191–218.</span><span> </span></p> <p><span>G. Charalambous, S. Fletcher, and P. Webb, “The development of </span><span>a scale to evaluate trust in industrial human-robot collaboration,” </span><span>International Journal of Social Robotics</span><span>, vol. 8, pp. 193–209, 2016.</span></p>
Movement data set for trust assessment (Drapebot robot cell/Dallara)
<p>In the Drapebot project, a worker collaborates with a large industrial manipulator in two tasks: collaborative transport of carbon fibre patches and collaborative draping. To realize data-driven trust assessement, the worker is equipped with a motion tracking suit and the body movement data is labeled with the trust scores from two standard Trust questionnaire (1. Trust perception scale - HRI, Schaefer 2016; 2. Trust in industrial human robo collaboration, Charalambous, et.al. 2016).</p> <p>For this data set, data has been collected for the draping task in a near-production setting from 5 participants all familiar with carbon-fibre draping. For all sessions, body tracking was performed using the Xsens MVN Awinda tracking suit. It consists of a tight-fitting shirt, gloves, headband, and a series of straps used to attach 17 IMUs to the participant. After calibration the system uses inverse kinematics to track and log the movements of the participant at a rate of 60 Hz. The measurements include linear and angular speed, velocity, and acceleration of every skeleton tracking point (see <a href="https://www.xsens.com/hubfs/Downloads/Manuals/MVN_real-time_network_streaming_protocol_specification.pdf">XSENS manual</a> for a detailed description of avaiable measurements).</p> <p><strong>Data organization</strong></p> <p>There are 5 files for 5 participants. The name of the files is PID01, where the number 01 is the participant. Each file contains all the data that was generated from the XSENS motion capture system. The files are xlsx files and for each sheet inside the excel file there are different types of data:</p> <ul> <li>Segment Orientation - Quat</li> <li>Segment Orientation - Euler</li> <li>Segment Position</li> <li>Segment Velocity</li> <li>Segment Acceleration</li> <li>Segment Angular Velocity</li> <li>Segment Angular Acceleration</li> <li>Joint Angles ZXY</li> <li>Joint Angles XZY</li> <li>Ergonomic Joint Angles ZXY</li> <li>Ergonomic Joint Angles XZY</li> <li>Center of Mass</li> <li>Sensor Free Acceleration</li> <li>Sensor Magnetic Field</li> <li>Sensor Orientation - Quat</li> <li>Sensor Orientation - Euler</li> </ul> <p>See also: <a href="https://base.movella.com/s/article/Output-Parameters-in-MVN-1611927767477?language=en_US">https://base.movella.com/s/article/Output-Parameters-in-MVN-1611927767477?language=en_US</a></p> <p>For more information on each specific data and/or sensors please see the xsens manual (Link above)</p> <p><strong>Data Annotation</strong></p> <p>In each .xlsx file the first tab (sheet) is called "Markers". It annotates the starting frame of the individual tasks. The annotations are pickup, draping, return and some files may contain a also a "fail" annotation. Failed attempts should not be taken into consideration for model training.</p> <p>The file trustscores.xlsx includes the results of the trust questionaires for each participant (scores for the individual items as well as the calculated overall trust scores).</p> <p>Items for Trust perception scale - HRI, Schaefer 2016:</p> <ul> <li>Which % of time does the robot <ul> <li>Function successfully</li> <li>Act consistently</li> <li>Communicate with people</li> <li>Provide feedback</li> <li>Malfunction</li> <li>Follow directions</li> <li>Meet the needs of the mission</li> <li>Perform exactly as instructed</li> <li>Have errors</li> </ul> </li> <li>Which % of the time is the robot: <ul> <li>Unresponsive</li> <li>Dependable</li> <li>Reliable</li> <li>Predictable</li> </ul> </li> </ul> <p>Items for Trust in industrial human robo collaboration, Charalambous, et.al. 2016:</p> <ul> <li>The way the robot moved made me uncomfortable</li> <li>I felt I could rely on the robot to do what it was supposed to do </li> <li>The speed at which the gripper picked up and released the components made me uneasy </li> <li>I felt safe interacting with the robot</li> <li>I knew the gripper would not drop the components</li> <li>The size of the robot did not intimidate me</li> <li>The robot gripper did not look reliable</li> <li>I was comfortable the robot would not hurt me</li> <li>I trusted that the robot was safe to cooperate with</li> <li>The gripper seemed like it could be trusted</li> </ul> <p> </p> <p>K. E. Schaefer, Measuring Trust in Human Robot Interactions: Development of the “Trust Perception Scale-HRI”. Boston, MA: Springer US, 2016, pp. 191–218.</p> <p>G. Charalambous, S. Fletcher, and P. Webb, “The development of a scale to evaluate trust in industrial human-robot collaboration,” International Journal of Social Robotics, vol. 8, pp. 193–209, 2016.</p>
Movement data set for trust assessment (Drapebot robot cell/DLR)
<p>In the Drapebot project, a worker collaborates with a large industrial manipulator in two tasks: collaborative transport of carbon fibre patches and collaborative draping. To realize data-driven trust assessement, the worker is equipped with a motion tracking suit and the body movement data is labeled with the trust scores from two standard Trust questionnaire (1. Trust perception scale - HRI, Schaefer 2016; 2. Trust in industrial human robo collaboration, Charalambous, et.al. 2016).</p> <p>For this data set, data has been collected for the draping task from 21 participants all familiar with working with large industrial manipulators. For all sessions, body tracking was performed using the Xsens MVN Awinda tracking suit. It consists of a tight-fitting shirt, gloves, headband, and a series of straps used to attach 17 IMUs to the participant. After calibration the system uses inverse kinematics to track and log the movements of the participant at a rate of 60 Hz. The measurements include linear and angular speed, velocity, and acceleration of every skeleton tracking point (see <a href="https://www.xsens.com/hubfs/Downloads/Manuals/MVN_real-time_network_streaming_protocol_specification.pdf">XSENS manual</a> for a detailed description of avaiable measurements).</p> <p><strong>Data organization</strong></p> <p>There are 21 files for 21 participants. The name of the files is PID01, where the number 01 is the participant. Each file contains all the data that was generated from the XSENS motion capture system. The files are xlsx files and for each sheet inside the excel file there are different types of data:</p> <ul> <li>Segment Orientation - Quat</li> <li>Segment Orientation - Euler</li> <li>Segment Position</li> <li>Segment Velocity</li> <li>Segment Acceleration</li> <li>Segment Angular Velocity</li> <li>Segment Angular Acceleration</li> <li>Joint Angles ZXY</li> <li>Joint Angles XZY</li> <li>Ergonomic Joint Angles ZXY</li> <li>Ergonomic Joint Angles XZY</li> <li>Center of Mass</li> <li>Sensor Free Acceleration</li> <li>Sensor Magnetic Field</li> <li>Sensor Orientation - Quat</li> <li>Sensor Orientation - Euler</li> </ul> <p>See also: <a href="https://base.movella.com/s/article/Output-Parameters-in-MVN-1611927767477?language=en_US">https://base.movella.com/s/article/Output-Parameters-in-MVN-1611927767477?language=en_US</a></p> <p>For more information on each specific data and/or sensors please see the xsens manual (Link above)</p> <p><strong>Data Annotation</strong></p> <p>In each .xlsx file the first tab (sheet) is called "Markers". It annotates the starting frame of the individual tasks. The annotations are pickup, draping, return and some files may contain a also a "fail" annotation. Failed attempts should not be taken into consideration for model training.</p> <p>The file trustscores.xlsx includes the results of the trust questionaires for each participant (scores for the individual items as well as the calculated overall trust scores).</p> <p>Items for Trust perception scale - HRI, Schaefer 2016:</p> <ul> <li>Which % of time does the robot <ul> <li>Function successfully</li> <li>Act consistently</li> <li>Communicate with people</li> <li>Provide feedback</li> <li>Malfunction</li> <li>Follow directions</li> <li>Meet the needs of the mission</li> <li>Perform exactly as instructed</li> <li>Have errors</li> </ul> </li> <li>Which % of the time is the robot: <ul> <li>Unresponsive</li> <li>Dependable</li> <li>Reliable</li> <li>Predictable</li> </ul> </li> </ul> <p>Items for Trust in industrial human robo collaboration, Charalambous, et.al. 2016:</p> <ul> <li>The way the robot moved made me uncomfortable</li> <li>I felt I could rely on the robot to do what it was supposed to do </li> <li>The speed at which the gripper picked up and released the components made me uneasy </li> <li>I felt safe interacting with the robot</li> <li>I knew the gripper would not drop the components</li> <li>The size of the robot did not intimidate me</li> <li>The robot gripper did not look reliable</li> <li>I was comfortable the robot would not hurt me</li> <li>I trusted that the robot was safe to cooperate with</li> <li>The gripper seemed like it could be trusted</li> </ul> <p> </p> <p>K. E. Schaefer, Measuring Trust in Human Robot Interactions: Development of the “Trust Perception Scale-HRI”. Boston, MA: Springer US, 2016, pp. 191–218.</p> <p>G. Charalambous, S. Fletcher, and P. Webb, “The development of a scale to evaluate trust in industrial human-robot collaboration,” International Journal of Social Robotics, vol. 8, pp. 193–209, 2016.</p>
Figure 6 in Group Movement in Entomopathogenic Nematodes: Aggregation Levels Vary Based on Context
Figure 6: Average IJ movement in each of the 3 species when corner placed, in both conspecific and heterospecific conditions. Sc = Steinernema carpocapsae, Sf = Steinernema feltiae, Sg = Steinernema glaseri. The solid line within each box indicates the median, black diamonds indicate the arithmetic mean, and black circles indicate outliers (observations with values> 1.5 * the interquartile range).
Figure 3 in Group Movement in Entomopathogenic Nematodes: Aggregation Levels Vary Based on Context
Figure 3: Index of Dispersion for each of the three species when applied alone in the center of dispersal boxes. Values of D> 1 indicate increasing aggregation. Solid line within each box indicates the median, black diamonds indicate the arithmetic mean, and black circles indicate outliers (observations with values> 1.5 * the interquartile range).
Figure 2 in Group Movement in Entomopathogenic Nematodes: Aggregation Levels Vary Based on Context
Figure 2: Pyrex experimental arenas to assess responses when nematodes were added to opposite corners. Arenas filled with approximately 1200 g of sand at 10% moisture. A: Heterospecific experiment arena, where corners have different species of IJs. B: Conspecific test arena, where a single species of IJ was placed at one corner. C: 5 x 5 sampling grid; samples were collected at each circle.
Figure 5 in Group Movement in Entomopathogenic Nematodes: Aggregation Levels Vary Based on Context
Figure 5: Aggregation shown by each of the 3 species when corner placed, in both conspecific and heterospecific conditions. Increasing values of D indicate increasing aggregation. Sc = Steinernema carpocapsae, Sf = Steinernema feltiae, Sg = Steinernema glaseri. The solid line within each box indicates the median, black diamonds indicate the arithmetic mean, and black circles indicate outliers (observations with values> 1.5 * the interquartile range).
Figure 1 in Group Movement in Entomopathogenic Nematodes: Aggregation Levels Vary Based on Context
Figure 1: Polypropylene experimental arenas to assess introduction at a common point. Arenas filled with approximately 1200 g of sand at 10% moisture. Image shows introduction point on 60mm filter paper and sample locations.
Figure 4 in Group Movement in Entomopathogenic Nematodes: Aggregation Levels Vary Based on Context
Figure 4: Aggregation shown by each of the three species when center placed, in both conspecific (alone) and heterospecific conditions. Increasing values of D indicate increasing aggregation. Sc = Steinernema carpocapsae, Sf = Steinernema feltiae, Sg = Steinernema glaseri. The solid line within each box indicates the median, black diamonds indicate the arithmetic mean, and black circles indicate outliers (observations with values> 1.5 * the interquartile range). NOTE that y-axis scale changes significantly across the three panels.
Figure 7. 2007-2008 in Conservation considerations revealed by the movements of post-nesting green turtles from the Republic of the Marshall Islands
Figure 7. 2007-2008 post-nesting movement of a 101 cm CCL green turtle ID 40702, "Loj5", from Erikub Atoll, Republic of the Marshall Islands to pelagic waters east of Erikub. Loj5 traveled a total distance of 4,212 km, in the 278 days the satellite tag transmitted.
Figure 6. 2007 in Conservation considerations revealed by the movements of post-nesting green turtles from the Republic of the Marshall Islands
Figure 6. 2007 post-nesting movement of a 100 cm CCL green turtle ID 40605, "Loj4", from Erikub Atoll, Republic of the Marshall Islands to Pohnpei, Federated States of Micronesia. Loj4 traveled a total distance of 4,039 km, in the 162 days the satellite tag transmitted.
ScienceDex guides
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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.