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opencc-by-4.0Jan 2020View details →

Data for paper titled : Comparing Clothing-Mounted Sensors with Wearable Sensors for Movement Analysis and Activity Classification (published in Sensors (MDPI))

Data for paper titled : Comparing Clothing-Mounted Sensors with Wearable Sensors for Movement Analysis and Activity Classification (published in Sensors (MDPI))

opencc-by-4.0May 2017View details →

Dataset: Analysis of timing variability in human movements by aligning parameter curves in time

Supplementary Data for Analysis of timing variability in human movements by aligning parameter curves in time article Dataset associated with the following publication: Maurer, L. K., Maurer, H., & Müller, H. (2017). Analysis of timing variability in human movements by aligning parameter curves in time. ------------------------------------------------------------------------------- The data files are structured in the following way: (1) Basic subject information (age, sex) can be found in the file subject_data.txt (tabulator separated text file). (2) The folder parameter_curves contains the angle trajectories of all trials structured in blocks of 50 trials (sometimes less than 50 because of data cleaning procedures deleating corrupted data and trials in which participants released accidentally [with zero velocity]). Each participant performed five practice days with four blocks of 50 trials, i.e. 20 blocks. File names contain subject (1,...,14), day (1,...,5), and block (1,...,4) information. Within the tabulator separated text files each column contains the angle trajectory of one trial consisting of 1000 values (sampled with 1000 Hz). Index 600 is the moment when participants released the virtual ball.

dryad40/100

Landscape composition and life-history traits influence bat movement and space use: analysis of 30 years of published telemetry data

Using temperate bats, a group of particular conservation concern, we investigated how morphological traits, habitat specialization and environmental variables affect home range sizes and daily foraging movements, using a compilation of 30 years of published bat telemetry data in Northern America and Europe for the period 1988 – 2016. We compiled data on home range size and mean daily distance between roosts and foraging areas at both colony and individual levels from 166 studies of 3,129 radiotracked individuals of 49 bat species. We calculated multi-scale habitat composition and configuration in the surrounding landscapes of all studied roosts. Using mixed models, we examined the effects of habitat availability and spatial arrangement on bat movements, while accounting for body mass, aspect ratio, wing loading and habitat specialization. We found a significant effect of landscape composition on home range size and mean daily distance at both colony and individual levels. On average, home ranges were up to 42% smaller in the most habitat-diversified landscapes while mean daily distances were up to 30% shorter in the most forested landscapes. Bat home range size significantly increased with body mass, wing aspect ratio and wing loading, and decreased with habitat specialization.

opencc-zeroSep 2022View details →

Fig. 5 in Comparative movement analysis for a sympatric dhole and golden jackal in a human-dominated landscape

Fig. 5. Dhole (column A) and jackal (column B) step length and turning angle distributions of encamped (black) and exploratory (grey) behavioral states. Turning angles (in degrees) for both the encamped and exploratory states are plotted on the same polar plot for each species.

Fig. 4 in Comparative movement analysis for a sympatric dhole and golden jackal in a human-dominated landscape

Fig. 4. Comparison of both species' daily activity patterns. Smoothing was achieved by averaging over 4 hour time intervals. The 95% confidence intervals were estimated from the standard error of the mean step length.

Fig. 3 in Comparative movement analysis for a sympatric dhole and golden jackal in a human-dominated landscape

Fig. 3. Autocorrelation function (ACF) of: A, the dhole; and B, the jackal step length. Data points above the dotted line are classified as autocorrelated.

Fig. 2 in Comparative movement analysis for a sympatric dhole and golden jackal in a human-dominated landscape

Fig. 2. Decile-shaded isopleths of convex hull home ranges for the dhole and jackal in Khao Ang Rue Nai Wildlife Sanctuary, Thailand.

Fig. 1 in Comparative movement analysis for a sympatric dhole and golden jackal in a human-dominated landscape

Fig. 1. Dhole and jackal relocations overlaid on a land cover map of Khao Ang Rue Nai Wildlife Sanctuary, Thailand.

Fig. 6. A in Comparative movement analysis for a sympatric dhole and golden jackal in a human-dominated landscape

Fig. 6. A, Semi-variance comparison of dhole and jackal positions. Since the dhole was monitored for a shorter time than the jackal (due to collar malfunctions), we present comparative data for this shorter time period. B, The complete jackal semi-variogram. Both semi-variograms are limited in scope to the first two thirds of the data, since estimates in the last third of the semi-variogram has very large confidence intervals. Semi-variance and 95% confidence intervals (CI) estimated from the standard error of the mean semivariance, were smoothed using a moving average over 20 lags.

publicAug 2024View details →

dryad40/100

An information theory framework for movement path segmentation and analysis

Open the record for dataset details and reuse information.

dryad40/100

Landscape composition and life‐history traits influence bat movement and space use: Analysis of 30 years of published telemetry data

Open the record for dataset details and reuse information.

publicDec 2021View details →

opencc-zeroOct 2015View details →

Supporting information: Analysis of attentional bias towards attractive and unattractive body regions among overweight males and females: An eye-movement study.

This is the data set as supporting information, provided as .csv file. Further information is available on request.

opencc-zeroDec 2021View details →

Data from: Landscape composition and life-history traits influence bat movement and space use: analysis of 30 years of published telemetry data

Aim: Animal movement determines home range patterns, which in turn affect individual fitness, population dynamics and ecosystem functioning. Using temperate bats, a group of particular conservation concern, we investigated how morphological traits, habitat specialization and environmental variables affect home range sizes and daily foraging movements, using a compilation of 30 years of published bat telemetry data. Location: Northern America and Europe. Time period: 1988 – 2016. Major taxa studied: Bats. Methods: We compiled data on home range size and mean daily distance between roosts and foraging areas at both colony and individual levels from 166 studies of 3,129 radiotracked individuals of 49 bat species. We calculated multi-scale habitat composition and configuration in the surrounding landscapes of the 165 studied roosts. Using mixed models, we examined the effects of habitat availability and spatial arrangement on bat movements, while accounting for body mass, aspect ratio, wing loading and habitat specialization. Results: We found a significant effect of landscape composition on home range size and mean daily distance at both colony and individual levels. On average, home ranges were up to 42% smaller in the most habitat-diversified landscapes while mean daily distances were up to 30% shorter in the most forested landscapes. Bat home range size significantly increased with body mass, wing aspect ratio and wing loading, and decreased with habitat specialization. Main conclusions: Promoting bat movements through the landscape surrounding roosts at large spatial scales is crucial for bat conservation. Forest loss and overall landscape homogenization lead temperate bats to fly farther to meet their ecological requirements, by increasing home range sizes and daily foraging distances. Both processes might be more detrimental for smaller, habitat-specialized bats, less able to travel increasingly longer distances to meet their diverse needs.

opencc-by-4.0Mar 2022View details →

Analysis of Smooth Pursuit Eye Movements in Clinical Context by Tracking the Target and Eyes

Eyemove dataset obtained at Teikyo University. If you use the dataset, please state clearly that you have used our data. The mp4 files are the video of the examination. Excel files are the position of the optic disc analyzed by SSD and the ocular position data analyzed by VOG.

opencc-zeroMay 2022View details →

Data from: Network analysis of sea turtle movements and connectivity: a tool for conservation prioritization

Aim: Understanding the spatial ecology of animal movements is a critical element in conserving long-lived, highly mobile marine species. Analysing networks developed from movements of six sea turtle species reveals marine connectivity and can help prioritize conservation efforts. Location: Global. Methods: We collated telemetry data from 1,235 individuals and reviewed the literature to determine our dataset's representativeness. We used the telemetry data to develop spatial networks at different scales to examine areas, connections, and their geographic arrangement. We used graph theory metrics to compare networks across regions and species and to identify the role of important areas and connections. Results: Relevant literature and citations for data used in this study had very little overlap. Network analysis showed that sampling effort influenced network structure and the arrangement of areas and connections for most networks was complex. However, important areas and connections identified by graph theory metrics can be different than areas of high data density. For the global network, marine regions in the Mediterranean had high closeness while links with high betweenness among marine regions in the South Atlantic were critical for maintaining connectivity. Comparisons among species-specific networks showed that functional connectivity was related to movement ecology, resulting in networks composed of different areas and links. Main conclusions: Network analysis identified the structure and functional connectivity of the sea turtles in our sample at multiple scales. These network characteristics could help guide the coordination of management strategies for wide-ranging animals throughout their geographic extent. Most networks had complex structures that can contribute to greater robustness, but may be more difficult to manage changes when compared to simpler forms. Area-based conservation measures would benefit sea turtle populations when directed towards areas with high closeness dominating network function. Promoting seascape connectivity of links with high betweenness would decrease network vulnerability.

opencc-zeroMay 2024View details →

Kinematic characteristics analysis of highly difficult movements "324C+1D" of the first level Wushu athletes

The data is the kinematic data obtained from the analysis of "324C+1D", a difficult Wushu movement completed by the first-level athletes of Chinese competitive Wushu routine, by using SIMI sports software. It is mainly used to study the kinematic characteristics of the difficult Wushu movement "324C+1D".

opencc-by-4.0May 2018View details →

Data from: Individual Movement - Sequence Analysis Method (IM-SAM): characterising spatio-temporal patterns of animal trajectories across scales and landscapes

Dataset included in Zenodo supports the analyses performed in "Individual Movement - Sequence Analysis Methods (IM-SAM) characterising spatio-temporal patterns of animal trajectories across scales and landscapes." The dataset includes one RDS file, that can be easily loaded into R using the readRDS function. The RDS file consists out of a list including two objects per animal: <ul> <li>Object 1 contains a data frame with the real and simulated sequences for an animal. e.g., ls[[1]][[1]] </li> <li>Object 2 contains the home range in raster format of an animal. e.g., ls[[1]][[2]]</li> </ul> The data frames in object 1 contain real habitat use sequences and corresponding simulated habitat use sequences generated in the home range of the specific individual (900 simulated sequences: 6 habitat selection rules x 3 selection coefficients x 50 repetitions). Open and closed habitats are respectively encoded by 0 and 1. The first 96 columns of each row in a data frame represent a 16-day habitat use sequence, with a fixed 4-hour relocation interval (0, 4, 8, 12, 16 and 20h). Column names are named as follows: Day_1_0h, Day_1_4h,..., Day_16_20h. In the next columns we provide the selection coefficients (columns 97-99), the habitat selection rules (or pattern, columns 100-102) and the number of missing values (mvs, columns, 103-104) for each of the real and simulated sequences. Note that simulated sequences have no missing values (i.e. values are always 0.00) and for real sequences there is no selection coefficient or habitat selection rule (i.e. values are always xxx). Rownames of simulated sequences are composed out of the habitat selection rule (c, o, a24, a33, a42 and u), the selection coefficient (5, 10, 50) and the replicate (1 to 50), separated by dashes. For example, the first simulated sequence in the first data frame (ls[[1]][[1]][1,]) is described as a24_10_1. The rownames of real sequences instead are composed out of the individuals' identifier, the biweekly period (1 to 23) and the year. For example, the first real sequence in the first data frame (ls[[1]][[1]][901,]) is described as 1_5_2006.

opencc-by-4.0Jul 2021View details →

Mocap video examples for the analysis of Sign Language movements

These mocap videos support my PhD thesis "Extracting human characteristics from motion: the case of identity in Sign Language" carried out from October 2018 to October 2021. The original mocap data is taken from the <a href="https://www.ortolang.fr/market/corpora/mocap1/">MOCAP1</a> corpus of French Sign Language. The videos have been generated using Python code available as part of the <a href="https://github.com/felixbgd/PLmocap">PLmocap</a> library.