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Dataset results
155 results for “DCs”
Liquid Resin Infusion (LRI) manufacturing and Spring_In monitoring by FBGs, DCs and 3D CMM meassurements
<p>ELADINE project is aiming to implement a numerical tool that can reduce reoccurring costs of low-volume production in composite manufacturing of primary structural elements and thus reducing overall manufacturing effort and carbon emissions. A<strong> primary goal of this project is to eliminate tolerance non-compliancy in the manufactured structures caused by natural and unavoidable post-manufacturing distortions, typical for composite materials</strong>. These distortions might render otherwise qualitative components unusable due to their final geometry.</p> <p>Objectives of the Numerical model validation are:</p> <ul> <li>To understand the dominant factors which affects the spring-in phenomenon.</li> <li>To provide the simulation tool with the required values of the properties that influence on spring-in.</li> <li>To verify the simulation tool ability to predict spring-in for a variety of conditions.</li> <li>To develop a procedure of adapting and embedding sensors (dielectric and fiber optic) to obtain proper, useful and accurate signals of the manufacturing parameters (T, degree of cure, strain).</li> <li>To develop interpretation procedures of the signal/curves of sensors to obtain on-line process monitoring information.</li> </ul> <p>To obtain the data to feed and develop the numerical tool able to estimate the component distortions after its manufacturing, a combination of Fiber Optic Sensors (FOS) based on Fiber Bragg Grating (FBG) technology, Dielectric Curing sensors (DC) and 3D scanning were used to monitor the composite coupon manufacturing and the distortions the days after being demoulded. During the manufacturing process embedded FBGs and DC sensors were used to monitor the coupon temperature and strain distribution and resin curing evolution. After the manufacturing and the demolding, the distortions evolution were monitored by the embedded FBGs and by 3D CMM measurements.</p> <p><strong>In the ELADINE project, the distortion monitoring was made to two Out-of-Autoclave manufacturing technologies: liquid resin infusion (LRI) and oven cured pre-preg</strong>. For both material systems, slightly curved coupons and C-shaped coupons were the geometries selected as representative for the Skin and spars of the wing box. The Skin coupon was curved panel with a 1475 mm radius (with edge rise of 7,65 mm) that was thought to best replicate the wing profile geometry. The C-spar coupon geometry selected for the study was a non-tapered spar section with two different angle with radius of curvature of 5mm and 12mm. This geometry was chosen to simplify measuring and comparisons with wing demo. Furthermore, three different thickness are studied for the Skin coupons and two for the C-spar coupons which were selected from different zones along the wing. Moreover, a C-spar coupon with variable thickness was studied, as a simulation of the transition between zones with different thickness in the wing.</p> <p><strong>In this dataset, the data from the FBGs, DCs and 3D CMM meassurements for the LRI manufacturing process and spring_in distortions monitoring is included.</strong></p>
DCSsim (simulated) and DCSsub (sub-sampled) ChIP-seq data for benchmarking DCS tools.
<p>These data are the results from five independent runs of DCSsim and DCSsub for TF, sharp and broad mark signals in 50:50 and 100:0 regulation scenarios.</p> <p> </p> <p>Simulated data from DCSsim: simulated_ChIP-seq_data.zip</p> <ul> <li>Set1: TF 50:50</li> <li>Set2: TF 100:0</li> <li>Set3: Sharp mark 50:50</li> <li>Set4: Sharp mark 100:0</li> <li>Set5: Broad mark 50:50</li> <li>Set6: Broad mark 100:0</li> </ul> <p> </p> <p>Sub-sampled data from DCSsub: sub-sampled_ChIP-seq_data.zip</p> <ul> <li>Set1: Cebpa-ChIP-seq 50:50</li> <li>Set2: Cebpa-ChIP-seq 100:0</li> <li>Set3: H3K27ac-ChIP-seq 50:50</li> <li>Set4: H3K27ac-ChIP-seq 100:0</li> <li>Set5: H3K36me3-ChIP-seq 50:50</li> <li>Set6: H3K36me3-ChIP-seq 100:0</li> </ul>
Pre-Preg (PP) Manufacturing and Spring-in monitoring through FBGs, DCs and 3D CMM measurements
<p>ELADINE project is aiming to implement a numerical tool that can reduce reoccurring costs of low-volume production in composite manufacturing of primary structural elements and thus reducing overall manufacturing effort and carbon emissions. A<strong> primary goal of this project is to eliminate tolerance non-compliancy in the manufactured structures caused by natural and unavoidable post-manufacturing distortions, typical for composite materials</strong>. These distortions might render otherwise qualitative components unusable due to their final geometry.</p> <p>Objectives of the Numerical model validation are:</p> <ul> <li>To understand the dominant factors which affects the spring-in phenomenon.</li> <li>To provide the simulation tool with the required values of the properties that influence on spring-in.</li> <li>To verify the simulation tool ability to predict spring-in for a variety of conditions.</li> <li>To develop a procedure of adapting and embedding sensors (dielectric and fiber optic) to obtain proper, useful and accurate signals of the manufacturing parameters (T, degree of cure, strain).</li> <li>To develop interpretation procedures of the signal/curves of sensors to obtain on-line process monitoring information.</li> </ul> <p>To obtain the data to feed and develop the numerical tool able to estimate the component distortions after its manufacturing, a combination of Fiber Optic Sensors (FOS) based on Fiber Bragg Grating (FBG) technology, Dielectric Curing sensors (DC) and 3D scanning were used to monitor the composite coupon manufacturing and the distortions the days after being demoulded. During the manufacturing process embedded FBGs and DC sensors were used to monitor the coupon temperature and strain distribution and resin curing evolution. After the manufacturing and the demolding, the distortions evolution were monitored by the embedded FBGs and by 3D CMM measurements.</p> <p><strong>In the ELADINE project, the distortion monitoring was made to two Out-of-Autoclave manufacturing technologies: liquid resin infusion and oven cured Pre-Preg (PP)</strong>. For both material systems, slightly curved coupons and C-shaped coupons were the geometries selected as representative for the Skin and spars of the wing box. The Skin coupon was curved panel with a 1475 mm radius (with edge rise of 7,65 mm) that was thought to best replicate the wing profile geometry. The C-spar coupon geometry selected for the study was a non-tapered spar section with two different angle with radius of curvature of 5mm and 12mm. This geometry was chosen to simplify measuring and comparisons with wing demo. Furthermore, three different thickness are studied for the Skin coupons and two for the C-spar coupons which were selected from different zones along the wing. Moreover, a C-spar coupon with variable thickness was studied, as a simulation of the transition between zones with different thickness in the wing.</p> <p><strong>In this dataset, the data obtained from the FBGs, DCs and 3D CMM meassurements during a PP manufacturing process and spring-in distortions monitoring can be found.</strong></p>
Elastic electron scattering cross sections of ethanol in the energy range 30 eV to 800 eV: Differential (DCS), Integral (ICS) and Momentum Transfer Cross Sections (MTCS)
<h3>Cross section datasets on the elastic electron scattering of ethanol from our publication Eur. Phys. J. D 77, 52 (2023)</h3> <p>The elastic differential cross sections (DCS) are given in the energy range 30-800 eV in the full angular range: 30°-150° experimental, 0°-25° and 155°-180° extrapolated experimental data using the IAM-SCAR+I model.</p> <p>The integral elastic (ICS) and momentum transfer cross sections (MTCS) are given for energies 60-800 eV.</p> <p>Additional information can be found in the README.txt or the publication.</p>
Simulation data DC Waste Heat Reuse - Multiple DCs connected to river water / ATES system
<p>CATALYST D7.5 report - SBP-DC-TC-7 - Scenario 7</p> <p>Compilation of simulation result data on DC waste heat reuse for a DC chain, connected to river water / ATES free cooling system.</p> <p>Scenario 7: Workload federated DCs - DC Waste Heat Reuse - EleENGctrical Flexibility - IT Load Migration</p>
Effect of DCS on Post-Endodontic Pain
ClinicalTrials.gov study NCT06632015. IPD Sharing: NO. Countries: 1. Publications: 1.
A Double Blind Clinical Trial of DCS for Food Anxiety
ClinicalTrials.gov study NCT01996644. IPD Sharing: Not stated. Countries: 1. Publications: 1.
SSCS and DCS fastq data for ALR conplastic mice mitochondrial DNA profiling
<p>The SSCS and DCS fq files for ALR conplastic mice associated with https://doi.org/10.5281/zenodo.10127101 repo. </p>
SSCS and DCS fastq data for B6 (wildtype) conplastic mice mitochondrial DNA profiling
<p>The SSCS and DCS fq files for B6 (wildtype) mice associated with https://doi.org/10.5281/zenodo.10127101 repo. </p>
SSCS and DCS fastq data for FVB conplastic mice mitochondrial DNA profiling
<p>The SSCS and DCS fq files for FVB conplastic mice associated with https://doi.org/10.5281/zenodo.10127101 repo. </p>
SSCS and DCS fq data for NZB conplastic mice mitochondrial DNA profiling
<p>The SSCS and DCS fq files for NZB conplastic mice associated with https://doi.org/10.5281/zenodo.10127101 repo. </p>
SSCS and DCS fastq data for AKR conplastic mice mitochondrial DNA profiling
<p>The SSCS and DCS fq files for AKR conplastic mice associated with https://doi.org/10.5281/zenodo.10127101 repo. </p>
P2 Solo Lambda + DCS + Ladder
<p>Note: If you're looking for ladder sequences, you probably want <a href="../doi/10.5281/zenodo.10020101">this dataset</a> instead.</p> <p>This is a random 1M read subset from an ONT P2 Solo sequencing run (i.e. 250 of the FAST5 files produced from the run, sampled randomly), called using dorado v0.2.4 stereo duplex with model 'dna_r10.4.1_e8.2_400bps_sup@v4.1.0'. Called reads with move tables (as BAM files) are provided in this release; Pod5 files to accompany these will be provided in a subsequent release.</p> <p>This was a P2 Solo run with libraries prepared using SQK-LSK114. Three samples were combined together on the same run (without barcoding):</p> <ul> <li> <p>19 µl Lambda DNA</p> </li> <li> <p>1 µl DNA CS</p> </li> <li> <p>1 µl <a href="https://assets.fishersci.com/TFS-Assets/LSG/manuals/MAN0000843_trackit-1kb-plusDNAladder_PI.pdf">TrackIt 1kb Plus DNA Ladder</a></p> </li> </ul> <p>Here are read length statistics from this 1M subset:</p> <ul> <li>Total sequences: 1000000</li> <li>Total length: 1736.546619 Mb</li> <li>Longest sequence: 565.449 kb</li> <li>Shortest sequence: 1 b</li> <li>Mean Length: 1.736 kb</li> <li>Median Length: 534 b</li> <li>N10: 3361 sequences; L10: 48.485 kb</li> <li>N50: 41078 sequences; L50: 8.015 kb</li> <li>N90: 474390 sequences; L90: 544 b</li> </ul> <p>Here are the run statistics from the full sequencing run (which produced approximately 2.5 TB of FAST5 files):</p> <ul> <li>Run time: 73 hours (as two runs of 21 hours + 52 hours)</li> <li>Yield (bases): 120 Gb</li> <li>Yield (reads): 69 M</li> <li>Duplex rate (pairs): 7.27%</li> <li>Duplex rate (split reads): 1.72%</li> </ul> <p>The library was loaded onto the flow cell without dilution, as we assumed a mean read length of around 15 kb, rather than the actual mean read length of around 1.7 kb. This probably contributed to the duplex rate being so low; ONT have mentioned that overloading a flow cell can reduce the duplex rate.</p>
Impact of Fluticasone and Salmeterol on Airway Dendritic Cells (DCs) in Smokers
ClinicalTrials.gov study NCT00908362. IPD Sharing: Not stated. Countries: 1. Publications: 2.
A Clinical Trial on HB-Vac Activated-DCs Combined With Peg-IFN or NAs in CHB
ClinicalTrials.gov study NCT01935635. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
iTBS-DCS in Fibromyalgia
ClinicalTrials.gov study NCT05395494. IPD Sharing: NO. Countries: 1. Publications: 0.
Immunogenicity and Safety of DCs in Breast Cancer
ClinicalTrials.gov study NCT03450044. IPD Sharing: NO. Countries: 1. Publications: 20.
TRANSCRANIAL DIRECT CURRENT STIMULATION (t-DCS) AS ADD-ON TO NEUROREHABILITATION OF PISA SYNDROME IN PARKINSON DISEASE
ClinicalTrials.gov study NCT04620863. IPD Sharing: NO. Countries: 1. Publications: 19.
Dataset for the paper "Fast Multi-Distance Time-Domain NIRS and DCS System for Clinical Applications"
Open the record for dataset details and reuse information.
Therapeutic Role of Transcranial DCS in Alzheimer
ClinicalTrials.gov study NCT03313518. IPD Sharing: NO. Countries: 0. Publications: 1.
ScienceDex guides
Understand access before you commit
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.