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419 results for “capture data”

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dryad36/100

Large-scale integration of single-cell transcriptomic data captures transitional progenitor states in mouse skeletal muscle regeneration

Skeletal muscle repair is driven by the coordinated self-renewal and fusion of myogenic stem and progenitor cells. Single-cell gene expression analyses of myogenesis have been hampered by the poor sampling of rare and transient cell states that are critical for muscle repair, and do not inform the spatial context that is important for myogenic differentiation. Here, we demonstrate how large-scale integration of single-cell and spatial transcriptomic data can overcome these limitations. We created a single-cell transcriptomic dataset of mouse skeletal muscle by integration, consensus annotation, and analysis of 23 newly collected scRNAseq datasets and 88 publicly available single-cell (scRNAseq) and single-nucleus (snRNAseq) RNA-sequencing datasets. The resulting dataset includes more than 365,000 cells and spans a wide range of ages, injury, and repair conditions. Together, these data enabled identification of the predominant cell types in skeletal muscle, and resolved cell subtypes, including endothelial subtypes distinguished by vessel-type of origin, fibro/adipogenic progenitors defined by functional roles, and many distinct immune populations. The representation of different experimental conditions and the depth of transcriptome coverage enabled robust profiling of sparsely expressed genes. We built a densely sampled transcriptomic model of myogenesis, from stem cell quiescence to myofiber maturation and identified rare, transitional states of progenitor commitment and fusion that are poorly represented in individual datasets. We performed spatial RNA sequencing of mouse muscle at three time points after injury and used the integrated dataset as a reference to achieve a high-resolution, local deconvolution of cell subtypes. We also used the integrated dataset to explore ligand-receptor co-expression patterns and identify dynamic cell-cell interactions in muscle injury response. We provide a public web tool to enable interactive exploration and visualization of the data. Our work supports the utility of large-scale integration of single-cell transcriptomic data as a tool for biological discovery.

opencc-zeroOct 2021View details →

zenodo36/100

Capture-recapture data from mouse enclosure experiment

Capture-recapture data from mouse enclosure experiment. This data can be analyzed using R code at https://github.com/barrettlabecoevogeno/mouse-recapture.

opencc-by-4.0Nov 2018View details →

zenodo36/100

Labelled and unlabelled hand acceleration data captured unobtrusively from PD patients and Healthy Controls

The dataset contains acceleration signals captured in-the-wild via the IMU sensor embedded in modern smartphones, for the purpose of detecting tremorous episodes, related to Parkinson's Disease (PD). It contains two different groups of subjects: <ul> <li>tremor_sdata.pickle --> A group of 45 subjects that have been subjected to neurological examination (the same dataset as https://zenodo.org/record/3519213)</li> <li>tremor_gdata.pickle --> A group of 454 subjects who just self-reported their PD status</li> </ul> All subjects contributed accelerometer data using their personal smartphones, for a period spanning many months. Tri-axial acceleration values were recorded automatically whenever a phone call was realized. The recording lasted for 75 seconds at the most. Each phone call thus resulted in one recorded accelerometer signal, also referred to as session. Each subject contributed a different amount of sessions depending on the number of phone calls they realized during the data collection period as well as their participation time (they were free to drop-out at any time). A detailed description of the capturing process as well as analysis results, can be found in the related research article. The data is presented as a python dictionary, indexed by the subject ids. Each element of the dictionary is a list with the following significance: <table> <thead> <tr> <th scope="col">Index</th> <th scope="col">Meaning</th> </tr> </thead> <tbody> <tr> <td>0</td> <td>List of np.arrays, Each array contains the power spectral density for an acceleration segment of 5s duration</td> </tr> <tr> <td>1</td> <td>Dictionary, Denotes subject updrs</td> </tr> <tr> <td>2</td> <td>List of str containing a unique identifier of the acceleration session that each segment in the other lists belongs to</td> </tr> <tr> <td>3</td> <td>List of np.arrays, Each array contains the pre-processed acceleration values for a 5s segment</td> </tr> </tbody> </table>   The subject updrs is represented as dictionary containing the following tremor-related annotation values (FOR THE FIRST GROUP ONLY): * updrs16: scalar int The value related to tremor as described in item 16 of the part II of the MDS-UPDRS scale, as reported by the subject. * updrs20_right: scalar int in range [0, 4] The value related to rest tremor in the right hand as described in item 20 of the part III of the MDS-UPDRS scale, as reported by the attending neurologist. * updrs20_left: scalar int in range [0, 4] Same as above but for left hand. * updrs21_right: scalar int in range [0, 4] The value related to action/postural tremor in the right hand as described in item 21 of the part III of the MDS-UPDRS scale, as reported by the attending neurologist. * updrs21_left: scalar int in range [0, 4] Same as above but for left hand. * sp_expert: scalar int in range [0, 1] A binary tremor annotation created by a group of signal processing experts, upon visually examining the contributed signals in both time and frequency domain and taking into consideration the UDPRS scores of each subject. This was necessary due to the intermittent nature of tremor, as well as a number of considerations related to the in-the-wild nature of the data capturing process. For more details, we refer the reader to the dataset description in the related research article. A '1' value indicates that the subject has tremor. A '0' value indicates that the subject doesn't have tremor. * pd_status: scalar int in range [0, 1] A '1' value indicates that the subject is a PD patient. A '0' value indicates that the subject is a Healthy Control Note: Each annotation value refers to the subject as a whole, and not in any one session.

opencc-by-4.0Nov 2022View details →

zenodo36/100

tellingsounds/lama-data: LAMA Data - Capturing Entities (Persons, Topics, Music, etc.) in Austrian audio(-visual) archive material

Data entered into LAMA (Linked Annotations for Media Analysis), a research software for capturing and visualizing the interaction of music and its contexts, developed by the Telling Sounds project.

openother-openDec 2022View details →

dryad36/100

Speciation in coastal basins driven by staggered headwater captures: Dispersal of a species complex, Leporinus bahiensis, as revealed by genome-wide SNP data

Past sea level changes and geological instability along watershed boundaries have largely influenced fish distribution across coastal basins, either by dispersal via palaeodrainages now submerged or by headwater captures, respectively. Accordingly, the South American Atlantic coast encompasses several small and isolated drainages that share a similar species composition, representing a suitable model to infer historical processes. Leporinus bahiensis is a freshwater fish species widespread along adjacent coastal basins over narrow continental shelf with no evidence of palaeodrainage connections at low sea level periods. Therefore, this study aimed to reconstruct its evolutionary history to infer the role of headwater captures in the dispersal process. To accomplish this, we employed molecular-level phylogenetic and population structure analyses based on Sanger sequences (5 genes) and genome-wide SNP data. Phylogenetic trees based on Sanger data were inconclusive, but SNPs data did support the monophyletic status of L. bahiensis. Both COI and SNP data revealed structured populations according to each hydrographic basin. Species delimitation analyses revealed from 3 (COI) to 5 (multilocus approach) MOTUs, corresponding to the sampled basins. An intricate biogeographic scenario was inferred and supported by Approximate Bayesian Computation (ABC) analysis. Specifically, a staggered pattern was revealed and characterized by sequential headwater captures from basins adjacent to upland drainages into small coastal basins at different periods. These headwater captures resulted in dispersal throughout contiguous coastal basins, followed by deep genetic divergence among lineages. To decipher such recent divergences, as herein represented by L. bahiensis populations, we used genome-wide SNPs data. Indeed, the combined use of genome-wide SNPs data and ABC method allowed us to reconstruct the evolutionary history and speciation of L. bahiensis. This framework might be useful in disentangling the diversification process in other neotropical fishes subject to a reticulate geological history.

opencc-zeroJan 2023View details →

dryad36/100

Data from: Sampling from commercial vessel routes can capture marine biodiversity distributions effectively

Collecting fine-scale occurrence data for marine species across large spatial scales is logistically challenging but is important to determine species distributions and for conservation planning. Inaccurate descriptions of species ranges could result in designating protected areas with inappropriate locations or boundaries. Optimising sampling strategies, therefore, is a priority for scaling up survey approaches using tools such as environmental DNA (eDNA) to capture species distributions. In a marine context, commercial vessels, such as ferries, could provide sampling platforms allowing access to under-sampled areas and repeatable sampling over time to track community changes. However, sample collection from commercial vessels could be biased and may not represent biological and environmental variability. Here, we evaluate whether sampling along Mediterranean ferry routes can yield unbiased biodiversity survey outcomes, based on perfect knowledge from a stacked species distribution model (SSDM) of marine megafauna from online data repositories. Simulations to allocate sampling point locations were carried out representing different sampling strategies (random vs regular), frames (ferry routes vs unconstrained) and number of sampling points. SSDMs were remade from different sampling simulations and compared to the 'perfect knowledge' SSDM to quantify the bias associated with different sampling strategies. Ferry routes detected more species and were able to recover known patterns in species richness at smaller sample sizes better than unconstrained sampling points. However, to minimise potential bias, ferry routes should be chosen to cover the variability in species composition and its environmental predictors in the SSDMs. The workflow presented here can be used to design effective sampling strategies using commercial vessel routes globally, including for eDNA analyses. This approach has potential to provide a cost-effective method to access remote oceanic areas on a regular basis and can recover meaningful data on spatiotemporal biodiversity patterns.

opencc-zeroJan 2023View details →

dryad36/100

Data from: Long-term capture data uncover shifts in the daily activity patterns of Amazonian birds

Although the duration of biological rhythms varies from milliseconds to decades, daily cycles are especially widespread for individual organisms. These circadian rhythms are synchronized by light-dark cycles, which govern predictable changes in other microclimate variables. Despite the fixed, regular nature of these abiotic cycles at low latitudes, little is known about how different animals structure activity along these gradients. We used 25 years of capture-recapture data and ~25,000 unique captures to characterize 'activity' (time of capture) for 65 species of Amazonian birds in the lowest forest stratum (0–2.5 m). To quantify vertical niche breadths, we also measured foraging heights for these same species in the field. Activity peaked within the first two hours after sunrise for most species (71%), while others peaked 1–3 hours later (29%). This difference was associated with an apparent preference for specific vertical strata, as terrestrial and near-ground birds peaked ~1 hour after sunrise, while birds from higher strata delayed their first bouts of activity in the lower understory (by an average of 40 to 100 minutes). Peak capture times also correlated with long-term abundance trends, as only species with the earliest capture times showed strong declines, suggesting that associations with mid- or late-morning microclimates may contribute to resiliency in birds typical of higher strata. Because arboreal species largely avoid the lower understory in the early morning, when terrestrial birds are active, we infer that they are poorly adapted to low-light environments. However, with their broad vertical niches and reliance on brighter microhabitats, we suggest that an arboreal lifestyle diminishes specialization and may increase the capacity to cope with a wider range of daily microclimate conditions. We predict that subtle, but important, shifts in daily activity patterns would materialize if other biodiversity-monitoring programs could analyze similar temporal datasets.

opencc-zeroMar 2023View details →

dryad36/100

Data for: Capturing complex interactions in disease ecology with simplicial sets

Here we provide archived code for: Code for Capturing Complex Interactions in Disease Ecology with Simplicial Sets. Ecology Letters. The code provided can be used to generate the figures used in the manuscript as well as to generate appendix 3 in the supplementary materials. In the paper, we describe how higher-order network approaches can be applied in disease ecology research. We explain what simplicial sets are; why their use would be beneficial in different subject areas; where these areas are: social, transmission, movement/spatial and ecological networks; and when using them would help most in each context. To demonstrate their application, we develop a novel approach to identify how pathogens persist within a host population (see code for Appendix 3 in this repository). We also provide an overview of how to use simplicial sets, highlighting specific metrics, generative models and software. Finally, we synthesize key research questions simplicial sets will help us answer and highlight the methodological developments required.

opencc-zeroMar 2023View details →

zenodo36/100

GNSS-Acoustic Data Capturing the Mw 8.2 July 28, 2021 Chignik Earthquake

GNSS-Acoustic surveys collected at a site, SEM1, along the Alaska Subduction Zone southwest of Kodiak, AK from 2018-2021. These data contain positions close to the trench of the subduction zone both prior to and following the July 28, 2021 Mw 8.2 Chignik earthquake. Contains raw data files, data extractions in a text column format, and processed site positions.

opencc-by-4.0Mar 2023View details →

zenodo36/100

Data for Process Design and Energy Assessment of an Onboard Carbon Capture System with Boilers or Heat Pumps for Additional Steam Generation

1. Supporting file includes main stream information used in Aspen HYSYS model, process simulations of boiler and heat pump for model construction. 2. Supporting file also includes main information used in ProMAX model, process simulations of carbon capture process for model construction.

opencc-by-4.0Mar 2023View details →

zenodo36/100

Using a low-cost 2D LiDAR Sensor to capture 3D Data - Raw Data

Raw Data for an upcoming publication in the MDPI Journal of Sensors, titled: "Using a low-cost 2D LiDAR Sensor to capture 3D Data"

opencc-by-4.0Apr 2023View details →

dryad36/100

Data for: Capturing synchronization with complexity measure of ordinal pattern transition network constructed by Crossplot

To evaluate the synchronization of bivariate time series has been a hot topic and a number of measures have been proposed. In this work, by introducing the ordinal pattern transition network (OPTN) into the crossplot, a new method for measuring the synchronisation of bivariate time series is proposed. After the crossplot been partitioned and coded, the coded partitions are defined as network nodes and a directed weighted network is constructed based on the temporal adjacency of the nodes. The crossplot transition entropy (CPTE) of the network is proposed as an indicator of the synchronization between two time series. To test the characteristics and performance of the method, it is used to analyse the unidirectional coupled Lorentz model and compared it with existing methods. The results showed the new method had the advantages of easy parameter setting, efficiency, robustness, good consistency and suitable for short time series. Finally, EEG data from auditory evoked potential EEG-Biometric dataset are investigated, and some useful and interesting results are obtained.

opencc-zeroJun 2023View details →

zenodo36/100

CARRT - Motion Capture Data for Robotic Human Upper Body Model

As advancements in the study of human activity have progressed in recent years, researchers have directed their attention towards analyzing human daily activities to investigate a diverse range of performance metrics unconsciously optimized by individuals while engaged in specific tasks. To replicate these movements in robotic systems based on human models, researchers have developed a framework for robot motion planning capable of utilizing various optimization methods to reproduce such motions through human demonstrations. In this process, capturing the movements of the human body and the objects involved in the demonstrations is imperative, as they provide essential information for the motion planning procedure. The objective of this dataset is to present human motion data while performing activities of daily living. This dataset encompasses comprehensive and precise whole-body motion data of individuals collected using a Vicon motion capture system, which facilitated the development of a full-body model integrated into OpenSim and MATLAB. The dataset comprises nine different daily living activities and eight Range of Motion activities performed by ten healthy participants. A publicly accessible whole-body human motion database has been established, encompassing raw motion data in .c3d format, motion data in .csv format for the OpenSim model, and post-processed motion data for the MATLAB model.

openJun 2023View details →

dryad36/100