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1,760 results for “phase 2”
Dataset related to ADHD phase 2 of the project "Transition care between adolescent and adult services for young people with chronic health needs in Italy", funded by the Italian Ministry of Health (RF-2019-12371228).
<p>Dataset related to ADHD phase 2 of the project "Transition care between adolescent and adult services for young people with chronic health needs in Italy", funded by the Italian Ministry of Health (RF-2019-12371228).</p>
FIGURE 2. Magnolia javieri. A. Flower after male phase. B-C. Flowering branches and leaves. D. Dehiscing fruit. E in Three new species of Magnolia (Magnoliaceae) endemic to the north-wet-arc in the Maya Highlands of Guatemala
FIGURE 2. Magnolia javieri. A. Flower after male phase. B-C. Flowering branches and leaves. D. Dehiscing fruit. E. Abaxial side of leaf. F. Seedlings. Photographs by F. Archila.
Spectral characterization of a supercontinuum source based on self-phase modulation in an aqueous K$_{2}$ZnCl$_{4}$ salt solution
<p>Spectral data and Matlab scripts for analysis, associated with the article "Spectral characterization of a supercontinuum source based on self-phase modulation in an aqueous K$_{2}$ZnCl$_{4}$ salt solution", submitted to Applied Optics.</p>
Figure 2 in Placebo-Controlled Phase 3 Trial of a Recombinant Glycoprotein 120 Vaccine to Prevent HIV-1 Infection
Figure 2. Self-reported risk behaviors by treatment arm and month of visit. STDs, sexually transmitted diseases.
CSRM Level 2 dataset: Rayleigh wave phase and group velocity maps in continental China
<p>This dataset contains the Rayleigh wave phase/group velocity maps at the period range from 8 to 70 s. These maps are obtained by travel time tomography using the <strong>639,171</strong> inter-station and <strong>54,792 </strong>event-station Rayleigh wave phase/group velicity dispersion curves (<a href="../records/8103561">data link</a>). The spatial resolution of these maps is ~60 km in the north-south seismic belt and thans-North China Orogen regions, and ~120 km in the rest of the continental China. This dataset results from a project of constructing the high-resolution China Seismological Reference Model (<strong>CSRM-1.0</strong>) in the top 100 km of the crust and uppermost mantle in continental China (<a href="https://doi.org/10.1029/2024JB029520">Xiao et al, JGR, 2024</a>) (<a href="https://doi.org/10.5281/zenodo.11098135">model link</a>).</p> <p>本数据库包含了利用 <strong>8 - 70 s </strong>的中国大陆区域瑞利波相/群速度结构。反演该相/群速度结构用到了 <strong>639,171 </strong>条台站间瑞利波相/群速度频散曲线和 <strong>54,792 </strong>事件-台站间频散曲线(数据链接)。相/群速度图在南北地震带和华北造山带的分辨率约为 <strong>60</strong> km, 在中国大陆其他区域的分辨率约为 <strong>120</strong> km。该数据库源于构建中国大陆区域高精度地壳和上地幔顶部 100 公里三维地震学模型(<strong>CSRM-1.0</strong>)的工作 (<a href="https://doi.org/10.1029/2024JB029520">Xiao et al, JGR, 2024</a>) (<a href="https://doi.org/10.5281/zenodo.11098135">模型链接</a>) 。</p> <p>If you face any problem or issue in the usage of this dataset, please feel free to communicate with the corresponding author Xiao Xiao (<strong>xiaox.seis@gmail.com</strong>). </p>
Dataset related to epilepsy phase 2 of the project "Transition care between adolescent and adult services for young people with chronic health needs in Italy", funded by the Italian Ministry of Health (RF-2019-12371228).
<p>Dataset related to epilepsy phase 2 of the project "Transition care between adolescent and adult services for young people with chronic health needs in Italy", funded by the Italian Ministry of Health (RF-2019-12371228).</p>
Phase separation of SARS-CoV-2 nucleocapsid protein with TDP-43 is dependant on C-terminus domains
Open the record for dataset details and reuse information.
Liquid Phase Electron Microscopy of Bacterial Ultrastructure (2 of 2)
<p>Files from Axon Studios software from study D.radiodurans encapsulated in graphene liquid cells.</p>
Figure 2 in A new Antarctic species of Orchomenella G.O. Sars, 1890 (Amphipoda: Lysianassoidea: Tryphosidae): is phase-contrast micro-tomography a mature technique for digital holotypes?
Figure 2. Scanning electron microscopy of antenna 1 of Orchomenella rinamontiae. Paratype, ♂, 11.3 mm. Scale bar: 200 µm.
The STARS phase 2 study: a randomized controlled trial of gaboxadol in Angelman syndrome
<p><b>Objective</b>: To evaluate safety and tolerability and exploratory efficacy endpoints for gaboxadol (OV101) compared with placebo in individuals with Angelman syndrome (AS).</p> <p><b>Methods</b>: Gaboxadol is a highly selective orthosteric agonist that activates γ-subunit–containing extrasynaptic γ-aminobutyric acid type A (GABA<sub>A</sub>) receptors. In a multicenter, double-blind, placebo-controlled, parallel-group trial, adolescent and adult individuals with a molecular diagnosis of AS were randomized (1:1:1) to 1 of 3 dosing regimens for a duration of 12 weeks: placebo morning dose and gaboxadol 15 mg evening dose (qd); gaboxadol 10 mg morning dose and 15 mg evening dose (bid); or placebo morning and evening dose. Safety and tolerability were monitored throughout the study. Prespecified exploratory efficacy endpoints included adapted Clinical Global Impression–Severity (CGI-S) and Clinical Global Impression–Improvement (CGI-I) scales which documented the clinical severity at baseline and change after treatment, respectively.</p> <p><b>Results</b>: Eighty-eight individuals were randomized. Of 87 individuals (aged 13–45 years) who received at least 1 dose of study drug, 78 (90%) completed the study. Most adverse events (AEs) were mild to moderate, and no life-threatening AEs were reported. Efficacy of gaboxadol, as measured by CGI-I improvement in an exploratory analysis, was observed in gaboxadol qd vs placebo (p = 0.0006).</p> <p><b>Conclusion</b>: After 12 weeks of treatment, gaboxadol was found to be generally well tolerated with a favorable safety profile. The efficacy as measured by the AS-adapted CGI-I scale warrants further studies.</p>
Supplementary material 2 from: Bila Dubaić J, Plećaš M, Raičević J, Lanner J, Ćetković A (2022) Early-phase colonisation by introduced sculptured resin bee (Hymenoptera, Megachilidae, Megachile sculpturalis) revealed by local floral resource variability. NeoBiota 73: 57-85. https://doi.org/10.3897/neobiota.73.80343
Study area – Belgrade (Serbia): basic topography, biogeography, ecological patterns and urbanistic zonation
The machine learning based statistical emulators of GGCMI phase 2
<p>A statistical emulator with machine learning algorithm to reproduce the response of year-to-year variation of four crop yield to CO<sub>2</sub> (C), temperature (T), water (W) and nitrogen (N) perturbations defined in the Global Gridded Crop Model Intercomparison Project (GGCMI) phase 2 experiment.</p>
Supplementary Data to "Simulation of dendritic-eutectic growth with the phase-field method" by Seiz et al. pt. 2
<p>Data from some additional simulations conducted during the review of the paper. The goal was to test whether the theory delineating the dendritic-eutectic regime from the eutectic regime would also be applicable at lower solidification speeds. I chose the gradient G = 99 K/mm for quicker convergence and a target crossover concentration of c_0 = 0.11 to roughly get a factor of 10 slower crossover velocity compared to the previous simulations. The crossover velocity, following the theory, ended up being V= 8.347 um/s. The grid spacing was increased by a factor of 3 motivated by the classical scaling laws VR^2 = const. (R= dendrite tip radius or lamellar spacing) and approximating sqrt(10) as 3. At first, three test concentrations (c_0 = 0.1, 0.11, 0.12) are considered. If the theory is right, only c_0 = 0.1 should yield dendritic-eutectic growth.</p> <p> </p> <p>Even after an increase of grid spacing, having more than 5 diffusion lengths between the solidification front and the boundary would have required in excess of 15k cells in the growth direction. Thus I opted to work with a smaller domain for which I later needed to account. I employed a domain of 1200 um (4k cells) in the growth direction, with the moving cutoff at 600 um, and a width of 270 um. This makes for about 2.5 diffusion lengths (l_d = 239.6 um) between the front and the boundary. This did influence the results, as the simulation with c_0 = 0.11 showed dendritic-eutectic growth. Fitting an exponential ansatz for the concentration c(x) = c_inf + dc exp(-x/l_d) (x starting from the dendrite tip) showed that the apparent far-field concentration c_inf=0.108243 was actually within the dendritic-eutectic regime.</p> <p>In order to approximate a simulation for which c_inf = 0.11, the ansatz was employed to solve for c(x_b = 600um) using the actual diffusion length for l_d and observed tip concentration to determine dc. This led to a boundary value of c(x_b) = 0.115, for which another simulation was run. This simulation did show eutectic growth dominating, though the apparent far-field concentration (c_inf = 0.112216) at simulation end did not match the target concentration, likely due to being not converged yet. However, this point does lie within the eutectic regime as predicted by the theory and thus the theory also works at lower velocities once the problem of finite domain sizes is accounted for.</p> <p> </p> <p>Videos showing the evolution of the four simulations with different boundary concentration are attached, though with somewhat variable time between frames. The videos show a 660 x 270 um view of the simulation, slightly beyond the moving window cutoff.</p> <p>Attached as well is the updated microstructure map (with the new simulations plotted over their apparent far-field concentration) and a plot of the height difference between the maximal observed position of the alpha and theta phase. The latter serves as an easy way to differentiate dominant eutectic from dominant dendritic-eutectic growth, as this difference goes to zero for dominant eutectic growth, but some significant non-zero value for dendritic-eutectic growth.</p>
Archival bundle of the data used for "A 2-phase Strategy For Intelligent Cloud Operations"
<p>This archive contains the data used for the paper</p> <p><strong>A 2-phase Strategy For Intelligent Cloud Operations</strong><br> <a href="mailto:giacomo.lanciano@sns.it">Giacomo Lanciano</a>*, Remo Andreoli, Tommaso Cucinotta, Davide Bacciu, Andrea Passarella</p> <p>Follow the instructions provided in the <a href="https://github.com/giacomolanciano/intelligent-cloud-operations">companion repo</a> to automatically download and decompress the archive. The following files are included:</p> <table> <tbody> <tr> <td><strong>File</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td> <p>amphora-x64-haproxy.qcow2</p> </td> <td> <p>Image used to create Octavia amphorae</p> </td> </tr> <tr> <td> <p>distwalk-disk-load-<INCREMENTAL-ID>/</p> </td> <td> <p>distwalk runs data</p> </td> </tr> <tr> <td> <p>FINAL-rl3-*/</p> </td> <td> <p>Cassandra runs data</p> </td> </tr> <tr> <td> <p>model_dumps/*</p> </td> <td> <p>Dumps of the models used for the validation</p> </td> </tr> <tr> <td> <p>test_load_disk_01-2tpi.dat</p> </td> <td> <p>distwalk load trace used to generate the workload</p> </td> </tr> <tr> <td> <p>ubuntu-20.04-server-distwalk-683d9e7.img</p> </td> <td> <p>Image used to create Nova instances for the scaling group</p> </td> </tr> </tbody> </table> <p>* <em>contact author</em></p>
Composition variations in Cu(In,Ga)(S,Se)2 solar cells: not a gradient, but an interlaced network of two phases
<p><strong>Abstract</strong></p> <p>Record efficiency in chalcopyrite-based solar cells Cu(In,Ga)(S,Se)<sub>2</sub> is achieved using a gallium gradient to increase the band gap of the absorber towards the back side. Although this structure has successfully reduced recombination at the back contact, we demonstrate that in industrial absorbers grown in the pilot line of Avancis, the back part is a source of non-radiative recombination. Depth-resolved photoluminescence (PL) measurements reveal two main radiative recombination paths at 1.04 eV and 1.5-1.6 eV, attributed to two phases of low and high band gap material, respectively. Instead of a continuous change in the band gap throughout the thickness of the absorber, we propose a model where discrete band gap phases interlace, creating an apparent gradient. Cathodoluminescence and Raman scattering spectroscopy confirm this result.</p> <p>Additionally, deep defects associated to the high gap phase reduce the absorber performance. Etching away the back part of the absorber leads to an increase of one order of magnitude in the PL intensity, i.e., 60 meV in quasi Fermi level splitting. Non-radiative voltage losses correlate linearly with the relative contribution of the high energy PL peak, suggesting that reducing the high gap phase could increase the open circuit voltage by up to 180 mV.</p>
Fig. 2 in New Spinicaudatan Species of Late Jurassic Linglongta Phase of Yanliao Biota from Western Liaoning, China
Fig. 2. Stratigraphic column of the Tiaojishan Formation exposed near Daxishan village, Linglongta, Jianchang County, showing the fossil clam shrimp sampling horizons (after Duan et al. 2009).
Visualization of adiabatic gas-liquid flow in a cross-corrugated plate heat exchanger channel: Part 2 - Original photographs, uniform two-phase distribution
<p>These measurement data are obtained and analyzed as part of a research project on adiabatic gas-liquid flow in a cross-corrugated plate heat exchanger channel. (See list of publications below). <br> The following Creative Commons license applies to the research data (images and measurement values) uploaded to the online repositories:<br> CC-BY 4.0<br> Author: Susanne Buscher</p> <p>The measurement data is published in 2 data sets: </p> <p>Data set I: Original image data (4 parts): <br> - uniform gas injection, part 1: https://doi.org/10.5281/zenodo.7985771; <br> - uniform gas injection, part 2: https://doi.org/10.5281/zenodo.7986374; <br> - uniform gas injection, part 3: https://doi.org/10.5281/zenodo.7986384; <br> - non-uniform gas injection (part 4): https://doi.org/10.5281/zenodo.8067163<br> This data set contains the original photographs of the two-phase flow in the cross-corrugated channel obtained with a high-resolution camera. In addition, the corresponding experimental parameters and flow patterns (for part 1-3 only) are included in the CSV files.<br> For uniform and non-uniform gas injection, respectively, the images were stored in sequentially numbered folders. The numbers of the folders correspond to the numbers of the measurement points listed in the attached CSV files with the associated experimental parameters.<br> The image folders are grouped in ZIP archives. Each ZIP archive contains the single-phase reference images which can be used for the two-phase images to conduct background subtraction, because the lighting conditions are equal for all images in one ZIP archive. </p> <p>Data set II: Measurement values and processed image data: <br> - https://doi.org/10.14279/depositonce-17868; <br> This data set contains all measurement values and calculated results of all measurement points in the Excel and CSV files (e.g. pressure drop, volumetric flow rates, void fraction, measurement uncertainties).<br> In addition, the results of the image processing algorithm are included in the Excel and CSV files (e.g. mean bubble diameter, maximum bubble diameter, local film flow ratio, extent of the two-phase distribution across the channel width, measurement uncertainties).<br> The image folders contain the pre-processed images which were the input to the digital image analysis (i.e. the aligned and cropped image section of the channel without inlet, outlet, and peripheral regions and after subtraction of the image background), and the post-processed images visualizing the output of the digital image analysis for this image (i.e. detected objects are inserted as colored regions in the image section; the meaning of colors was explained in the publications of 2022 and 2023). <br> In this dataset, the image folders are also subdivided into measurements with uniform and non-uniform gas injection and designated with the numbers of the measurement points, which are listed in the Excel and CSV files.</p> <p>The two datasets are the supplementary research data for the following publications: <br> - S. Buscher, 2023, Visualization, measurement, and modelling of adiabatic gas-liquid flow in a cross-corrugated plate heat exchanger channel, Doctoral thesis, Technische Universität Berlin, https://doi.org/10.14279/depositonce-17866. (supplemented by data sets I and II) <br> - S. Buscher, 2019, Visualization and modelling of flow pattern transitions in a cross-corrugated plate heat exchanger channel with uniform two-phase distribution, International Journal of Heat and Mass Transfer 144, 118643, https://doi.org/10.1016/j.ijheatmasstransfer.2019.118643. (supplemented by data set I, part 1-3)<br> - S. Buscher, 2021, Two-phase pressure drop and void fraction in a cross-corrugated plate heat exchanger channel: Impact of flow direction and gas-liquid distribution, Experimental Thermal and Fluid Science 126, 110380, https://doi.org/10.1016/j.expthermflusci.2021.110380. (supplemented by the measurement values in the Excel and CSV files of data set II)<br> - S. Buscher, 2022, Digital image analysis of gas-liquid flow in a cross-corrugated plate heat exchanger channel: A feature-based approach on various two-phase flow patterns, International Journal of Multiphase Flow 154, 104149, https://doi.org/10.1016/j.ijmultiphaseflow.2022.104149. (supplemented by data set II)</p>
Fig. 2 in Magnetic Ti C MXene functionalized with β-cyclodextrin as magnetic solid-phase extraction and in situ derivatization for determining 12 phytohormones in oilseeds by ultra-performance liquid chromatography-tandem mass spectrometry
Fig. 2. XRD spectrum (a), FI-TR pattern (b), Raman spectrum (c) of the composite material, and magnetization hysteresis loop of Fe3O4@Ti3C2@β-CD (d).
2023 IEEE SPS Video and Image Processing (VIP) Cup: Ophthalmic Biomarker Detection Phase 2
<p>Ophthalmic clinical trials that study treatment efficacy of eye diseases are performed with a specific purpose and a set of procedures that are predetermined before trial initiation. Hence, they result in a controlled data collection process with gradual changes in the state of a diseased eye. In general, these data include 1D clinical measurements and 3D optical coherence tomography (OCT) imagery. Physicians interpret structural biomarkers for every patient using the 3D OCT images and clinical measurements to make personalized decisions for every patient.</p> <p>Two main challenges in medical image processing has been <em>generalization</em> and <em>personalization</em>.</p> <p>Generalization aims to develop algorithms that work well across diverse patients and scenarios, providing standardized and widely applicable solutions. Personalization, in contrast, tailors algorithms to individual patients based on their unique characteristics, optimizing diagnosis and treatment planning. Generalization offers broad applicability but may overlook individual variations. Personalization provides tailored solutions but requires patient-specific data. While deep learning has shown an affinity towards generalization, it is lacking in personalization.</p> <p>The presence and absence of biomarkers is a personalization challenge rather than a generalization challenge. The variation within OCT scans of patients between visits can be minimal while the difference in manifestation of the same disease across patients may be substantial. The domain difference between OCT scans can arise due to pathology manifestation across patients, clinical labels, and the visit along the treatment process when the scan is taken. Morphological, texture, statistical and fuzzy image processing techniques through adaptive thresholds and preprocessing may prove substantial to overcome these fine-grained challenges. This challenge provides the data and application to address personalization.</p> <p> </p> <p>These files constitute the second phase of the VIP CUP 2023 Challenge at ICIP 2023. This test set has a more general patient base than the first one and as such is a better indicator of the performance of models. This test set was created by taking a subset of the data from a publicly available OCT dataset and then asking our medical partners to provide fine-grained biomarker labels for the competition. We provide the citation for the source of these images below: </p> <p>Kermany D, Goldbaum M, Cai W et al. Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning. Cell. 2018; 172(5):1122-1131. doi:10.1016/j.cell.2018.02.010.</p> <p> </p> <p>This zenodo repository contains the images and submission template file needed for the second phase of the competition.</p>
A Phase II Efficacy Study Comparing 2',3'-Dideoxyinosine (ddI) (BMY-40900) and Zidovudine Therapy of Patients With HIV Infection Who Have Been on Long Term Zidovudine Treatment
ClinicalTrials.gov study NCT00000671. IPD Sharing: Not stated. Countries: 2. Publications: 9.
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.