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150 results for “data fusion”
Data from: multiexciton interactions in singlet fission and triplet fusion upconversion dendrimers
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Data from: Efficient summary statistics for detecting lineage fusion from phylogeographic datasets
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Data from: Regulation of vacuole fusion in stomata by dephosphorylation of the HOPS subunit VPS39
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Data from: A phylogenomic analysis of Lonicera and its bearing on the evolution of organ fusion
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Data from: Non-clonal coloniality: genetically chimeric colonies through fusion of sexually produced polyps in the hydrozoan Ectopleura larynx
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Data from: Fission–fusion processes weaken dominance networks of female Asian elephants in a productive habitat
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Detection of fusion transcripts and their genomic breakpoints from RNA sequencing data - Table S03 - All detected SVs.xlsx
<p>Large concatenated results table on all samples of the Dr. Disco study.</p> <p> </p>
Data from: Quantifying uncertainty due to fission-fusion dynamics as a component of social complexity
Groups of animals (including humans) may show flexible grouping patterns, in which temporary aggregations or subgroups come together and split, changing composition over short temporal scales, i.e. fission and fusion). A high degree of fission-fusion dynamics may constrain the regulation of social relationships, introducing uncertainty in interactions between group members. Here we use Shannon's entropy to quantify the predictability of subgroup composition for three species known to differ in the way their subgroups come together and split over time: spider monkeys (Ateles geoffroyi), chimpanzees (Pan troglodytes) and geladas (Theropithecus gelada). We formulate a random expectation of entropy that considers subgroup size variation and sample size, against which the observed entropy in subgroup composition can be compared. Using the theory of set partitioning, we also develop a method to estimate the number of subgroups that the group is likely to be divided into, based on the composition and size of single focal subgroups. Our results indicate that Shannon's entropy and the estimated number of subgroups present at a given time provide quantitative metrics of uncertainty in the social environment (within which social relationships must be regulated) for groups with different degrees of fission-fusion dynamics. These metrics also represent an indirect quantification of the cognitive challenges posed by socially dynamic environments. Overall, our novel methodological approach provides new insight for understanding the evolution of social complexity and the mechanisms to cope with the uncertainty that results from fission-fusion dynamics.
Data from: Lineage fusion in Galápagos giant tortoises
Although many classic radiations on islands are thought to be the result of repeated lineage splitting, the role of past fusion is rarely known because during these events, purebreds are rapidly replaced by a swarm of admixed individuals. Here we capture lineage fusion in action in a Galápagos giant tortoise species, Chelonoidis becki, from Wolf Volcano (Isabela Island). The long generation time of Galápagos tortoises and dense sampling (841 individuals) of genetic and demographic data were integral in detecting and characterizing this phenomenon. In C. becki we identified two genetically distinct, morphologically cryptic lineages. Historical reconstructions show that they colonized Wolf Volcano from Santiago Island in two temporally separated events, the first estimated to have occurred ~199 thousand years ago (KYA). Following arrival of the second wave of colonists, both lineages co-existed for approximately ~53 KY. Within that time they began fusing back together, as microsatellite data reveal widespread introgressive hybridization. Interestingly, greater mate selectivity seems to be exhibited by purebred females of one of the lineages. Forward-in-time simulations predict rapid extinction of the early arriving lineage. This study provides a rare example of reticulate evolution in action, and underscores the power of population genetics for understanding the past, present, and future consequences of evolutionary phenomena associated with lineage fusion.
Data from: Major improvements to the Heliconius melpomene genome assembly used to confirm 10 chromosome fusion events in 6 million years of butterfly evolution
The Heliconius butterflies are a widely studied adaptive radiation of 46 species spread across Central and South America, several of which are known to hybridize in the wild. Here, we present a substantially improved assembly of the Heliconius melpomene genome, developed using novel methods that should be applicable to improving other genome assemblies produced using short read sequencing. First, we whole-genome-sequenced a pedigree to produce a linkage map incorporating 99% of the genome. Second, we incorporated haplotype scaffolds extensively to produce a more complete haploid version of the draft genome. Third, we incorporated ∼20x coverage of Pacific Biosciences sequencing, and scaffolded the haploid genome using an assembly of this long-read sequence. These improvements result in a genome of 795 scaffolds, 275 Mb in length, with an N50 length of 2.1 Mb, an N50 number of 34, and with 99% of the genome placed, and 84% anchored on chromosomes. We use the new genome assembly to confirm that the Heliconius genome underwent 10 chromosome fusions since the split with its sister genus Eueides, over a period of about 6 million yr.
Data from: Fusion or hypertrophy?: The unusual arms of the Petalocrinidae (Ordovician-Devonian; Crinoidea)
The large, triangular or cylindrical second brachial plate of the Petalocrinidae was formed through fusion of brachial plates along the distal margin of the growing arms. Based on the number of ambulacral bifurcations, brachials from the primibrachitaxis through at least the quintibrachitaxis may have been fused to form this large plate. In Petalocrinus, all calcite of fused second brachials assume the same crystallographic orientation, but in Spirocrinus more than one crystal comprises the second brachial plate.
Research Data for FiHi: Fusion of inertial and high-resolution acoustic data for privacy-preserving human activity recognition
<h1><strong>Description</strong></h1> <div>This dataset contains information on 20 different activities collected from 15 participants (20-55 years old) using Wit-motion smart inertial sensors and Double Acoustics guitar pickups. Each participant performs these daily activities in an unrestricted environment, with each activity lasting at least 60 seconds and repeated twice.</div> <div> </div> <div>If you use the dataset in an academic work, please cite: </div> <div> </div> <div><code>@ARTICLE{10980212,</code><br><code> author={Yang, Zhe and Zhang, Ying and Li, Yanjun and Huang, Linchong and Hu, Ping and Lin, Yuexiang},</code><br><code> journal={IEEE Transactions on Instrumentation and Measurement}, </code><br><code> title={Fusion of Inertial and High-resolution Acoustic Data for Privacy-Preserving Human Activity Recognition}, </code><br><code> year={2025},</code><br><code> volume={74},</code><br><code> number={},</code><br><code> pages={1-20},</code><br><code> keywords={Human activity recognition;Sensors;Acoustics;Feature extraction;Privacy;Microphones;Biomedical monitoring;Wireless fidelity;Sensor phenomena and characterization;Sensor fusion;Human activities recognition;inertial sensing;Hi-res audio;attention mechanism},</code><br><code> doi={10.1109/TIM.2025.3565250}}</code></div> <h1><strong>DataSet Information</strong></h1> <h2><strong>1.Original_data.zip</strong></h2> <div>The data was annotated by manually reviewing the audio clips and assigning appropriate activity labels. Timestamping the inertial sensor data with the start time of the audio device recorded by the experimenter ensured correct segmentation and synchronization of the inertial and acoustic data. The total data length for</div> <div>all participants is over 10 hours.</div> <h3><strong>(1) </strong><strong>IMU</strong><strong> DATA</strong></h3> <div>The inertial data (accelerometer and gyroscope) is sampled at 100 Hz and transmitted by Bluetooth to the host computer. These reviewed and annotated original samples from 15 participants are placed in separate csv files. The arrangement of information in each csv file is:</div> <div>Col 1-3: 3D-acceleration data (g)</div> <div>Col 4-6: 3D-gyroscope data (°/s)</div> <h3><strong>(2) Audio DATA</strong></h3> <div>The acoustic data are sampled at 192 kHz by a Steinberg sound card and transmitted by USB cable to the host computer. These original samples are placed in separate wav files, with each file name containing all the necessary information regarding the contents of the file.</div> <div><strong>For example:</strong></div> <div>100801_sitting</div> <div>Participant ID (1-4 digits): 1008, Session ID (5-6 digits): 01, Activity ID: sitting.</div> <div> </div> <h2><strong>2、Processed_data.zip</strong></h2> <div>The last two columns of each file are as follows:</div> <ul> <li> <div>participant_id: such as 100101, 100102, 100201 ...... The last two digits are the Session ID, representing the two sessions from the same participant for the same activity.</div> </li> <li> <div>activity_id: Refer to the ACTIVITY SET below</div> </li> </ul> <h3><strong>(1) </strong><strong>IMU</strong><strong> DATA</strong></h3> <div>The original inertial data is individually aligned with the processed Audio data based on their start times, with any excess data rows at the end being trimmed. Then, these inertial data files are augmented with activity_id and participant_id for identification, and consolidated into a single csv file.</div> <div>The continuous motion signal is segmented into sliding windows, each with a duration of 3 seconds and a step size of 3 seconds. Given the IMU’s sampling rate of 100 Hz, each window of inertial data consists of 300 time steps, with 6 channels of information (3 axes each for accelerometer and gyroscope). Consequently, a single inertial sample is represented by a 300 × 6 dimensional matrix.</div> <h3><strong>(2) Audio DATA</strong></h3> <div>The acoustic signals are processed using the Short Time Fourier Transform (STFT) with a window length of 1024 points and an overlap of 256 points, , which generates n linear spaced frequency bins between n kHz frequency range in the frequency domain. The output contains the estimate of the short-term, time-localized frequency patterns. We examine two levels of privacy protection: 8 ∼ 96 kHz for non-speech sound and 20 ∼ 96 kHz for inaudible sound, with 88 and 76 linear spaced frequency bins, respectively.</div> <div>Under a sample rate of 192 kHz for the original acoustic signal, there are (192000 - 256)/(1024 - 256) ≈ 250 frequency features within a second, while each feature has 88 and 76 dimensions for non-speech (8∼96 kHz) and inaudible (20∼96 kHz) feature, respectively.</div> <h1><strong>ACTIVITY </strong><strong>SET</strong></h1> <div>The activityIDs and corresponding activities are listed in the following:</div> <div>0: use microwave</div> <div>1: brush teeth</div> <div>2: browse video</div> <div>3: drink water</div> <div>4: fry</div> <div>5: lie down</div> <div>6: flush</div> <div>7: go downstairs</div> <div>8: go upstairs</div> <div>9: sit</div> <div>10: stand</div> <div>11: manipulate door</div> <div>12: type</div> <div>13: jump</div> <div>14: run</div> <div>15: walk</div> <div>16: wash hands</div> <div>17: write</div> <div>18: operate light</div> <div>19: eat</div>
[Data] Self-Supervised Bayesian Representation Learning of Acoustic Emissions from Laser Powder Bed Fusion Process for In-situ Monitoring
<div> <div> <div> <p>Different Laser Powder Bed Fusion (LPBF) process spaces were deliberately introduced by employing two distinct 316L stainless steel powder distributions (with particle sizes >45 μm and < 45 μm) and processing them with two sets of laser parameters, resulting in the creation of four datasets [D1, D2, D3, and D4]. These datasets encompass LoF pores, conduction mode, and keyhole formations, each associated with three LPBF regimes denoted as D1, D2, D3, and D4. The experiments utilized a Sisma MYSINT 100 commercial LPBF printer and an airborne AE sensor system with a flat frequency response ranging from 0 to 150 kHz. Validation of the ground truths for the three laser regimes across the four datasets, representing distinct process spaces, was accomplished through the confirmation of cross-sectional images. In the course of fabricating a cube using a powder bed and laser, data acquisition from an AE sensor was triggered when the optical intensity reached a threshold of 0.5 V for each scan length. The photodiode trigger gain was adjusted to saturate at 5 V, and the ensuing continuous-time window, where the optical signal remained at 5 V for 12.5 ms, was calculated and segmented to generate the dataset. Irrespective of the specific regime (Lack of Fusion, Conduction, and Keyhole) or the cube being fabricated (with two powder distributions), the signals obtained during this process were then segmented into a 12.5 ms window comprising 5000 data points. To eliminate any noise, an offline application of a low-pass Butterworth filter with a 150 kHz cut-off frequency was employed, aligned with the frequency response specification of the AE sensor. Each dataset has two files against it [raw/groundtruth label].</p> </div> </div> </div>
MGP: a new 1-hourly 0.25° global precipitation product (2000-2020) based on multi-source precipitation data fusion
<p>The multi-source merged global precipitation product (MGP; 0.25°/ hourly; 2000-2020; 60°N/S), which takes advantage of the complementary strengths of satellite, reanalysis, and gauge data to obtain reliable precipitation estimates over the global land surface, provides a new higher-quality precipitation dataset for data users to realize their respective research purposes and the social and economic activities. The data developers hope that MGP will play an important role in a variety of science communities (e.g., hydrology, meteorology, climatology, ecology, and agriculture).</p>
Terrestrial water storage data based on the generalized three-cornered hat fusion method
<p>We deduct 2004-2009 means from the three GRACE terrestrial water storage (TWS) Mascon products (CSR, JPL and GSFC) to ensure that they have the same reference period. To eliminate the discrepancies induced by the different data and improve the reliability of the results, we estimate the relative uncertainties of the GRACE TWS data using the generalized three-cornered hat method (GTCH), and then fuse them according to their uncertainties using a least squares method.</p>
Data for "Quantifying nocturnal thrush migration using sensor data fusion between acoustics and vertical-looking radar"
<p>This repository contains the data used for the analyses conducted and described in the article "Quantifying nocturnal thrush migration using sensor data fusion between acoustics and vertical-looking radar", as well as the Random Forest classifier built from such data. </p> <p>Files:</p> <p><em>2021_AcousticDataset.xlsx:</em> excel dataset containing the daily number of calls for each of the three thrush species considered recorded in Helsinki (Finland) during the study period (year 2021). The dates refer to UTC time.</p> <p><em>2022_ AcousticDataset.xlsx: </em>excel dataset containing the daily number of calls for each of the three thrush species considered recorded in Helsinki (Finland) during the study period (year 2022) . The dates refer to UTC time.</p> <p><em>RandomForestClassifier.Rdata: </em>R object containing the Random Forest classifier built using the eight most important echo features. The purpose of the classifier is to categorize echoes into 'thrush' and 'non-thrush' classes. </p> <p><em>2021_RadarDataset.rds: </em>R object containing the radar data collected with AVLR BirdScan MR1 in Helsinki (Finland) during the study period (year 2021).</p> <p><em>2022_RadarDataset.rds:</em> R object containing the radar data collected with AVLR BirdScan MR1 in Helsinki (Finland) during the study period (year 2022).</p> <p><em>license.txt: </em>the license applying to the data.</p>
Dynamic implicit modeling of tunnel unfavorable geology based on multi-source data fusion using support vector machine
<p>This is the relevant data of the article "Dynamic implicit modeling of tunnel unfavorable geology based on multi-source data fusion using support vector machine"</p>
Supplementary data "Broad genomic workup including Optical Genome Mapping uncovers a DDX3X::MLLT10 gene fusion in Acute Myeloid Leukemia"
<pre>Supplementary data "Broad genomic workup including Optical Genome Mapping uncovers a DDX3X::MLLT10 gene fusion in Acute Myeloid Leukemia" - OGM Rare Variant Analysis for both time points extracted from Bionano Access RVP Analysis output folder: -> unfiltered annotated SV output -> unfiltered CNV output - Whole Exome Sequencing Gene Panel results as output by Varvis (Limbus) (CSV file) -> WES SNVs -> WES CNVs - Quality metrics + selected results: -> OGM; both time points -> Whole Exome Sequencing; time point 1</pre>
Data for "Nanoparticle reinforced medium entropy CoCrFeNi produced by laser powder bed fusion: Microstructure evolution"
<p>Nanoparticle-reinforced metallic composites produced via laser powder bed fusion (LPBF) offer an economically feasible approach for obtaining high-strength near-net shaped critical components in automotive and aviation industries. This study investigates the equiatomic medium entropy alloy (MEA) CoCrFeNi manufactured by LPBF, incorporating two types of reinforcing particles, titanium nitride (TiN) and titanium oxide (TiO2), with varying sizes and volume concentrations. In this paper, we focus on analyzing the microstructure and texture evolution of all alloys, alongside examining the dissolution, precipitation and phase transitioning of the particles. TiN nanoparticles dissolve in the melt pool and uniformly precipitate as TiO2, forming novel core-shell nanoparticles resistant to coarsening. </p> <p>Here we share the STEM raw data files that were used in the analysis. We also share a general image analysis routine that used python based modules to measure the particle sizes and their volume fraction.</p> <p>In brief, we used the following steps for several images of each sample:<br>(a) Threshold the equalized grayscale image to create a binary image.<br>(b) Calculate the area fraction of the cleaned binary image.<br>(c) Detect contours in the binary image.<br>(d) Extract properties of circles from the contours, such as scaled diameter, and area.<br>(e) Draw circles on the original image using the detected contours.</p> <p><br>For TiN/5/800 samples containing multiple square-shaped particles, we assess the area of these squares and subsequently determine the diameter of a circle possessing an equivalent area.</p> <p>We also share the the raw file and the jupyter notebook for the 4DSTEM experiment conducted on the core-shell nanoparticle.</p>
DAVE - Image and Pointcloud data for Fusion
<p>The dataset was collected during a measurement run on the river Trave in Lübeck, Germany. The data consists of two main components: images and pointclouds from a LiDAR sensor.</p> <p>Pointcloud data was recorded using an RoboSense RS-LIDAR-M1.</p> <p>Image data was recorded using a Hik Vision DS-2CD2T47G2-LSU/SL camera.</p> <p> </p> <div> <div> <div> <p>This publication is a result of the research of the Center of Excellence CoSA and funded by the Federal Ministry for Digital and Transport of the Federal Republic of Germany (Id 19F2225C, DAVE).</p> <p> </p> <p>Project website: https://www.th-luebeck.de/cosa/projekt/dave/</p> </div> </div> </div>
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.