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Dataset results
29 results for “FLC”
CT-LaSTAF continous FLC from AMT29 in 2019
<p><strong>AMT29 / DY110</strong></p><p><strong>Nov / Dec 2019</strong></p><p><strong>Atlantic Ocean </strong></p><p>See also: <a href="https://www.amt-uk.org/">https://www.amt-uk.org/</a></p><p>AMT29 in Nov/Dec 2019 was the first field deployment of LabSTAF, consequently not all functions were running as they do in the present instrument, most notably there was no automated acquisition of photosynthetic excitation profiles and associated spectral correction of data.</p><p>DWM measurements were run automatically, but no PEC correction applied to the data.</p><p>The instrument was run in continuous FLC mode along the entire transect, the FLC are 'static'.</p><p>The folder <strong>raw_rsf</strong> contains the rsf files acquired with timestamp in UTC.</p><p>Blanks of MQ and 0.2um filtrate were run daily.</p><p>The folder <strong>auxiliary</strong> contains the cruise report, the cruisetrack, the troubleshoot log, the blank measurements, and the setting files used. As outlined in the cruise report, many auxiliary parameters (nutrients, particulate absorption, HPLC pigments, 14C-based and O2-based primary production, in line and above water optics) were measured.</p><p> </p><p> </p>
AUV-Based Multi-Sensor Dataset: Forward-Looking Camera (FLC) and Forward-Looking Sonar (FLS) Observations in the Red Sea
<p><strong>Context</strong></p> <p>This dataset is the first part of a dataset collection comprised of forward-looking sonar (FLS) and forward-looking camera (FLC) underwater images. The entire data was collected during the years 2021-2023 using 2 underwater vehicles in both the Red Sea and the Mediterranean along the Israeli shoreline, depicting both man-made and natural underwater environments. The data is part of a research project aimed at developing fusion models for improved obstacle detection and navigation in autonomous underwater vehicles.</p> <p><strong>Content</strong></p> <p>This dataset consists of FLC and FLS images and their metadata, collected by the ALICE-AUV. Both sensors were installed in the front payload section in a configuration having aligned fields of view to achieve matching pairs of data. The data was collected to train and evaluate a complete perception and obstacle avoidance framework.</p> <p>A series of diving sessions were performed in the Red Sea, off the coast of Eilat, Israel. The experiments focused on two main sites: A "Sunboat" shipwreck and the Eilat-Ashkelon Pipeline Company (EAPC) pier pillars. The "Sunboat" shipwreck is a 40-meter long vessel resting at a depth of approximately 12 meters, with the surrounding seabed at a depth of 18-24 meters. This dataset contains approximately 8,000 FLC-FLS sample pairs from the first session conducted at the "Sun boat" shipwreck site on September 3, 2023. The data was recorded at depths ranging from 10 to 15 meters.</p> <p>The dataset is organized into separate sessions, each representing a specific dive or experiment. Within each session, the data is further categorized into modalities: camera (FLC images), sonar (FLS images), and navigation (dead reckoning data). The navigation data is derived from a combination of GPS, DVL, and IMU sensors, providing estimated positions when GPS is unavailable. Inside each modality directory, you will find the corresponding data files in PNG format for images and CSV format for navigation data. The file names follow a sequential numbering scheme (e.g., 00001.png, 00002.png, etc.). Each modality directory also contains a CSV file (e.g., camera.csv) that maps each data file to its respective timestamp. Additionally, the samples.json file documents the relationship between uni-modal and multi-modal samples, allowing for easy association of data from different modalities.</p> <p>By providing synchronized and aligned camera and sonar imagery, along with corresponding navigation data, this dataset enables researchers to explore novel algorithms and techniques for multi-modal sensor fusion in the context of autonomous underwater vehicles.</p> <p><strong>Technical Details</strong></p> <ul> <li>Sonar: Blueprint Oculus M1200d <ul> <li>Operating frequency: 1.2 MHz (low frequency mode)</li> <li>Maximum range: 40 m (set to 20 m for this dataset)</li> <li>Horizontal aperture: 130°</li> <li>Vertical aperture: 20°</li> <li>Number of beams: 512</li> <li>Angular resolution: 0.6°</li> <li>Beam separation: 0.25°</li> <li>Image resolution: 902x497 pixels</li> <li>Coordinate system: Polar</li> </ul> </li> <li>Camera: Allied-Vision Manta G-917 <ul> <li>Image dimensions: 3384x2710 pixels (downscaled to 1692x1355 for this dataset)</li> <li>Sensor type: CCD Progressive</li> <li>Sensor bit depth: 12-bit</li> <li>Captured bit depth: 8-bit</li> <li>Camera model: Pinhole with Plumb Bob (Brown–Conrady) distortion coefficients</li> <li>Focal length (fx, fy): (1638.36157, 1641.95202)</li> <li>Principal point (cx, cy): (1705.03529, 1380.27954)</li> <li>Radial distortion coefficients (k1, k2, k3): (-0.124823, 0.048851, 0.000000)</li> <li>Tangential distortion coefficients (p1, p2): (0.000259, -0.002945)</li> </ul> </li> <li>Navigation: <ul> <li>Data format: CSV</li> <li>Contains fused dead reckoning data based on GPS, DVL, and IMU sensors</li> <li>Columns: <ul> <li>timestamp: Unix timestamp (seconds)</li> <li>latitude: Latitude (degrees)</li> <li>longitude: Longitude (degrees)</li> <li>altitude: Altitude (meters)</li> <li>yaw: Yaw angle (degrees)</li> <li>pitch: Pitch angle (degrees)</li> <li>roll: Roll angle (degrees)</li> <li>velocity_x: Velocity along the x-axis (meters per second)</li> <li>velocity_y: Velocity along the y-axis (meters per second)</li> <li>velocity_z: Velocity along the z-axis (meters per second)</li> <li>depth: Depth (meters)</li> </ul> </li> </ul> </li> <li>Frame rate: 2 Hz for both sonar and camera</li> </ul> <p>More datasets from this collection will be uploaded in the future, and a link to access them will be provided on this page.</p> <p><strong>Acknowledgements</strong></p> <p>The data in this repository is part of the DeeperSense project that received funding from the European Commission, Program H2020-ICT-2020-2 ICT-47-2020, Project Number: 101016958.</p>
Computation Time, Searched Nodes and Path Length for Navigation Using Improved A-Star, Directional ORCA and FLC-ORCA
<p>Naivation simulation using Improved A*, Directional ORCA and Novel FLC-ORCA method. The computational times, searched nodes and path length are recorded for comparison. A simulation study for Robotic Navigation Aid (RNA)</p>
CT-LabSTAF continous FLC from DY149 in 2022
<p><strong>CT-LabSTAF</strong></p><p><strong>DY149 </strong></p><p><strong>10-31 March 2022 </strong></p><p><strong>North Atlantic</strong></p><p>LabSTAF deployments during DY149 primarily focused on deployments of the first in situ instrument and 14C-uptake comparisons. However, two instruments were run continuously connected to the underway water supply and only this data is included here.</p><p>Both instruments were run in Auto-FLC mode and PEP and DWM were automatically run and applied before each FLC. Different FLC protocols were run, the data is saved in accordingly named subfolders.</p><p>The folder <strong>raw_rsf</strong> contains one folder per instrument within each are subfolders with the raw rsf files.</p><p>The folder<strong> auxiliary</strong> contains a folder with the cruisetrack and underway data (temp, sal, etc). The cruise report will be uploaded when it becomes available.</p>
Dataset of 'Field Line Curvature (FLC) Scattering in the Dayside Off-Equatorial Minima Regions'
<p>The data used for each figure in the paper ('Field Line Curvature (FLC) Scattering in the Dayside Off-Equatorial Minima Regions') are publicly available.</p>
Data from: Independent FLC mutations as causes of flowering time variation in Arabidopsis thaliana and Capsella rubella
Capsella rubella is an inbreeding annual forb closely related to Arabidopsis thaliana, a model species widely used for studying natural variation in adaptive traits such as flowering time. Although mutations in dozens of genes can affect flowering of A. thaliana in the laboratory, only a handful of such genes vary in natural populations. Chief among these are FRIGIDA (FRI) and FLOWERING LOCUS C (FLC). Common and rare FRI mutations along with rare FLC mutations explain a large fraction of flowering-time variation in A. thaliana. Here we document flowering time under different conditions in 20 C. rubella accessions from across the species' range. Similar to A. thaliana, vernalization, long photoperiods and elevated ambient temperature generally promote flowering. In this collection of C. rubella accessions, we did not find any obvious loss-of-function FRI alleles. Using mapping-by-sequencing with two strains that have contrasting flowering behaviors, we identified a splice-site mutation in FLC as the likely cause of early flowering in accession 1408. However, other similarly early C. rubella accessions did not share this mutation. We conclude that the genetic basis of flowering-time variation in C. rubella is complex, despite this very young species having undergone an extreme genetic bottleneck when it split from C. grandiflora a few tens of thousands of years ago.
Dataset: lhp1 FLC-Venus time course imaging – Hybrid protein assembly-histone modification mechanism for PRC2-based epigenetic switching and memory
<p>The histone modification H3K27me3 plays a central role in Polycomb-mediated epigenetic silencing. H3K27me3 recruits and allosterically activates Polycomb Repressive Complex 2 (PRC2), which adds this modification to nearby histones, providing a read/write mechanism for inheritance through DNA replication. However, for some PRC2 targets, a purely histone-based system for epigenetic inheritance may be insufficient. We address this issue at the Polycomb target Flowering Locus C (FLC) in Arabidopsis thaliana, as a narrow nucleation region of only ~three nucleosomes within FLC mediates epigenetic state switching and subsequent memory over many cell cycles. To explain the memory's unexpected persistence, we introduce a mathematical model incorporating extra protein memory storage elements with positive feedback that persist at the locus through DNA replication, in addition to histone modifications. Our hybrid model explains many features of epigenetic switching/memory at FLC and encapsulates generic mechanisms that may be widely applicable.</p>
Data from: Independent FLC mutations as causes of flowering time variation in Arabidopsis thaliana and Capsella rubella
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Dataset: lhp1 FLC-Venus time course imaging – Hybrid protein assembly-histone modification mechanism for PRC2-based epigenetic switching and memory
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Antagonistic activities of co-transcriptional regulators within an early developmental window sets quantitative FLC expression
GEO Series GSE166728. Arabidopsis thaliana. 9 samples. Type: Expression profiling by high throughput sequencing.
Divergence of regulatory networks governed by the orthologous transcription factors FLC and PEP1 in Brassicaceae species.
GEO Series GSE89889. Arabidopsis thaliana; Arabis alpina. 10 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Small RNA-sequencing of primary fibrolamellar carcinoma (FLC), FLC patient-derived xenograft, and maturational liver lineage stages
GEO Series GSE114974. Homo sapiens. 36 samples. Type: Non-coding RNA profiling by high throughput sequencing.
Transcriptomic characterization of FLC-mutant engineered human hepatocyte organoids
GEO Series GSE215785. Homo sapiens. 31 samples. Type: Expression profiling by high throughput sequencing.
Nucleosome positioning dynamics during the cold-induced silencing of the Arabidopsis FLC gene using targeted MNase-seq
GEO Series GSE274066. Arabidopsis thaliana. 14 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Components of the autonomous flowering pathway form a complex that suppresses FLC transcription by regulating multiple histone modifications [RNA-seq]
GEO Series GSE193252. Arabidopsis thaliana. 10 samples. Type: Expression profiling by high throughput sequencing.
DRP-104 (Glutamine Antagonist) in Combination With Durvalumab in Patients With Advanced Stage Fibrolamellar Carcinoma (FLC)
ClinicalTrials.gov study NCT06027086. IPD Sharing: NO. Countries: 1. Publications: 0.
Flaxseed Lignan-Enriched Complex (FLC) for the Treatment of Patients With Ulcerative Colitis (UC): A Pilot Assessment
ClinicalTrials.gov study NCT02201758. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Vancomycin in Patients With Unresectable Fibrolamellar Hepatocellular Carcinoma (FLC) Oral
ClinicalTrials.gov study NCT04025567. IPD Sharing: Not stated. Countries: 1. Publications: 0.
RNA-sequencing of fibrolamellar carcinoma (FLC) cell line treated with miR-375 mimic
GEO Series GSE125602. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
Data from: Floral regulators FLC and SOC1 directly regulate expression of the B3-type transcription factor TARGET OF FLC AND SVP 1 at the Arabidopsis shoot apex via antagonistic chromatin modifications
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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.