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30,948 results for “Profile”
Profiling of linear B-cell epitopes against human coronaviruses in pooled sera sampled early in the COVID-19 pandemic
<p>Background: Antibodies play a key role in the immune defence against infectious pathogens. Understanding the underlying process of B cell recognition is not only of fundamental interest; it supports important applications within diagnostics and therapeutics. Whereas the nature of conformational B cell epitope recognition is inherently complicated, linear B cell epitopes offer a straightforward approach that potentially can be reduced to one of peptide recognition.</p> <p>Methods: Using an overlapping peptide approach representing the entire proteomes of the seven main coronaviruses known to infect humans, we analysed sera pooled from eight PCR-confirmed COVID-19 convalescents and eight pre-pandemic controls. Using a high-density peptide microarray platform, 13-mer peptides overlapping by 11 amino acids were in situ synthesised and incubated with the pooled primary serum samples, followed by development with secondary fluorochrome-labelled anti-IgG and -IgA antibodies. Interactions were detected by fluorescence detection. Strong Ig interactions encompassing consecutive peptides were considered to represent "high-fidelity regions" (HFRs). These were mapped to the coronavirus proteomes using a 60% homology threshold for clustering.</p> <p>Results: We identified 333 human coronavirus derived HFRs. Among these, 98 (29%) mapped to SARS-CoV-2, 144 (44%) mapped to one or more of the four circulating common cold coronaviruses (CCC), and 54 (16%) cross-mapped to both SARS-CoV-2 and CCCs. The remaining 37 (11%) mapped to either SARS-CoV or MERS-CoV. Notably, the COVID-19 serum was skewed towards recognising SARS-CoV-2-mapped HFRs, whereas the pre-pandemic was skewed towards recognising CCC-mapped HFRs. In terms of absolute numbers of linear B cell epitopes, the primary targets are the ORF1ab protein (60%), the spike protein (21%), and the nucleoprotein (15%) in that order; however, in terms of epitope density, the order would be reversed.</p> <p>Conclusion: We identified linear B cell epitopes across coronaviruses, highlighting pan-, alpha-, beta-, or SARS-CoV-2-corona-specific B cell recognition patterns. These findings could be pivotal in deciphering past and current exposures to epidemic and endemic coronavirus. Moreover, our results suggest that pre-pandemic anti-CCC antibodies may cross-react against SARS-CoV-2, which could explain the highly variable outcome of COVID-19. Finally, the methodology used here offers a rapid and comprehensive approach to high-resolution linear B-cell epitope mapping, which could be vital for future studies of emerging infectious diseases.</p>
Achoanus hulleyi, n.sp. I. Head of male in profile; 2. Head of female in profile: 3. Wing. in A New Genus And Species Of Syrphidae (Diptera) From South Africa
Achoanus hulleyi, n.sp. I. Head of male in profile; 2. Head of female in profile: 3. Wing.
NOAA PSL thermodynamic profiles retrieved from a combination of active and passive remote sensors and numerical weather prediction models with the optimal estimation physical retrieval TROPoe at Platteville, CO, USA
<p>This dataset contains retrieved profiles of thermodynamic variables obtained using the Tropospheric Remotely Observed Profiling via Optimal Estimation (TROPoe) physical retrieval from various combinations of input data collected by passive and active remote sensing instruments, in-situ surface platforms, and numerical weather prediction models deployed at the Platteville, CO, USA, site in fall 20221-winter 2022. Among the employed instruments are Microwave Radiometers (MWRs), Infrared Spectrometers (IRS), Radio Acoustic Sounding Systems (RASS), ceilometers, surface sensors, and information from the operational Rapid Refresh numerical weather prediction model.</p> <p>The dataset also includes 15 radiosounding launched for assessing the retrievals.</p> <p>For further information, please see:</p> <p>Bianco, L., Adler, B., Bariteau, L., Djalalova, I. V., Myers, T., Pezoa, S., Turner, D. D., and Wilczak, J. M.: Sensitivity of thermodynamic profiles retrieved from ground-based microwave and infrared observations to additional input data from active remote sensing instruments and numerical weather prediction models, Atmos. Meas. Tech. Discuss. [preprint], https://doi.org/10.5194/amt-2023-263, in review, 2024.</p>
Blood DNA Methylation Profiling Identifies Cathepsin Z Dysregulation in Pulmonary Arterial Hypertension
<p>Ulrich, A., Wu, Y., Draisma, H., Wharton, J., Swietlik, E. M., Cebola, I., Vasilaki, E., Balkhiyarova, Z., Jarvelin, M. R., Auvinen, J., Herzig, K. H., Coghlan, J. G., Lordan, J., Church, C., Howard, L. S., Pepke-Zaba, J., Toshner, M., Wort, S. J., Kiely, D. G., Condliffe, R., … Rhodes, C. J. (2024). <a href="https://pubmed.ncbi.nlm.nih.gov/38184627/"><strong>Blood DNA methylation profiling identifies cathepsin Z dysregulation in pulmonary arterial hypertension</strong></a>. <em>Nature communications</em>, <em>15</em>(1), 330. https://doi.org/10.1038/s41467-023-44683-0</p> <p>Maternal educational attainment (MEA) shapes offspring health through multiple potential pathways. Differential DNA methylation may provide a mechanistic understanding of these long-term associations. We aimed to quantify the associations of MEA with offspring DNA methylation levels at birth, in childhood and in adolescence. Using 37 studies from high-income countries, we performed meta-analysis of epigenome-wide association studies (EWAS) to quantify the associations of completed years of MEA at the time of pregnancy with offspring DNA methylation levels at birth (n = 9 881), in childhood (n = 2 017), and adolescence (n = 2 740), adjusting for relevant covariates. MEA was found to be associated with DNA methylation at 473 cytosine-phosphate-guanine sites at birth, one in childhood, and four in adolescence. We observed enrichment for findings from previous EWAS on maternal folate, vitamin-B12 concentrations, maternal smoking, and pre-pregnancy BMI. The associations were directionally consistent with MEA being inversely associated with behaviours including smoking and BMI. Our findings form a bridge between socio-economic factors and biology and highlight potential pathways underlying effects of maternal education. The results broaden our understanding of bio-social associations linked to differential DNA methylation in multiple early stages of life. The data generated also offers an important resource to help a more precise understanding of the social determinants of health.</p>
High-Resolution Beach Profile Time Series: Morecambe Bay, 2007-2022
<p><strong>Description:</strong> This dataset provides a comprehensive collection of beach profile measurements for Morecambe Bay, covering a 16-year period from 2007 to 2022. The data was collected and shared by Sefton Council (Sefton MBC), UK, as part of the coastal monitoring programme. Captured biannually during spring and autumn, the data offers valuable insights into the bay's coastal morphology and dynamics.</p> <p><strong>Data Description:</strong></p> <ul> <li><strong>Beach Transects:</strong> Detailed profiles are provided for various locations along the Morecambe Bay coastline. Station numbers are used to identify each measurement point on a dedicated map (included within the data).</li> <li><strong>Temporal Coverage:</strong> The dataset encompasses measurements conducted biannually, capturing seasonal variations in beach profiles.</li> </ul>
Aligning Data Management Plans with Community Standards using FAIR Implementation Profiles
<p>Here you can find the files corresponding to our submission titled 'Aligning DMPs with Community Standards using FIPs'.</p> <p>- VU DMP template and the mapping is included in the folder /VU-DMP-template-and-mapping</p> <p>- All the FAIR Implementation Profiles are included in the folder /FIPs.</p> <p>- The knowledge model we created for the project, and a small demo of the interface are in the folder /KM-and-demo.</p> <p>- The folder /user-study consists of the following:</p> <p> a) The mock DMPs we provided to the participants of this research are in /mock_DMPs.</p> <p> b) We downloaded the resulting DMPs after participants completed their DMPs, they are in the folder /resulting_DMPs.</p> <p> c) Survey results can be found in the folder /survey_results.</p> <p> d) Some Python scripts were used for the analysis of the survey results. They are in the folder /Python_script_for_analysis.</p> <p><br>The project is open source under the license CC-BY 4.0.</p> <p>Contact: Shuai Wang (shuai.wang@vu.nl)</p> <p> </p>
Assessment of Self-Medication Practices and Safety Profile of Medicines Utilisation among Pregnant Women attending Antenatal Clinics in Freetown, Sierra Leone: A Multicentre Cross-Sectional Descriptive Study
<p><strong><span>Background: </span></strong><span>Despite the potential fetal and maternal risks of self-medication, studies on self-medication practice and the safety profile of medicines used during pregnancy are scarce in our setting. This study determined the self-medication practice and safety profile of medicines used among pregnant women.</span></p> <p><strong><span>Methods: </span></strong><span>This cross-sectional study was conducted in face-to-face interviews among 345 pregnant women at three hospitals in Sierra Leone. Data were analyzed using descriptive statistics and binary logistic regression to <span>determine the prevalence and associated factors of self-medication. </span></span></p> <p><strong><span>Results: </span></strong><span>A total of 345 pregnant women participated in the study. The prevalence of self-medication among pregnant women with conventional and/or herbal medicine was </span><span>132 (38.3%)</span><span>. Also, 93 (75%) of the conventional medicines (CMs) were categorized as probably safe, of which paracetamol 36 (29.0%) was commonly used, followed by amoxicillin 23 (18.5%) and antimalarials 22 (17.7%) for common illnesses such as </span><span>headache 30 (25.4%), </span><span>urinary tract infection </span><span>23 (19.4%)<span> and malaria 22 (18.6%). The common reason for self-medication was previous experience with the disease 24 (27.3%). </span><em>Luffa acutangula</em> 19 (30.2%) <span>was the most used herbal medicine (HM), </span>and Oedema 30 (47.6%) was the most reported ailment. <span>Among the HM users,</span> 34 (54.0%)<span> believe they are more effective than CMs. Secondary school education (AOR = 2.128, 95%CI = 1.191 – 3.804, p = 0.011), tertiary education (AOR = 2.915, 95%CI = 1.104 – 7.693, p = 0.031), monthly income of greater than NLe 1,000 (AOR = 4.084, 95% CI = 1.269 – 13.144, p = 0.018), and perceived maternal illness (AOR = 0.367, CI = 0.213 – 0.632, p = <0.001) were predictors of self-medication.</span></span></p> <p><strong><span>Conclusion: </span></strong><span>Self-medication practice was highly prevalent and was associated with educational status, monthly income, and maternal illness during pregnancy. Therefore, intervention programs should be designed and implemented to minimize the practice and risk associated with self-medication among pregnant women.</span></p>
Transcriptomic profiles of resected 50 pancreatic adenocarcinoma samples
<p><span><span>An aggregated retrospective database with standardized clinicopathological variables was created for patients resected </span><span>in Erasme and Pitié Salpêtrière hospitals. </span>RNA was extracted from the scrapped sections with the ALLPrep FFPE tissue kit<sup>©</sup> following the manufacturer’s instructions for semi-automated RNA extraction via Qiacube instrument (Qiagen, Venlo, The Netherlands). RNA samples were run on an Agilent 2100 bioanalyzer using the RNA 6000 Pico LabChip kit (Agilent, Diegem, Belgium). The bioanalyzer electropherograms were analyzed by Agilent 2100 Expert Software to determine the RNA quantity and quality. RNA samples with DV200 >30% were selected and 100 ng of RNA was used for the library preparation. NGS libraries were prepared using the QuantSeq Library Prep Kit for Illumina (Lexogen) as per manufacturer recommendations’. The libraries were sequenced on NovaSeq using NovaSeq 6000 S2 Reagent Kit with 100 bp single reads.</span></p>
Supplementary Videos to Transient Attracting Profiles in the Great Pacific Garbage Patch
<h1>Summary</h1> <p>This repository contains supplementary videos to the publication Transient Attracting Profiles in the Great Pacific Garbage Patch (<a href="https://doi.org/10.5194/egusphere-2024-1215" target="_blank" rel="noopener">Kunz et al., 2024, preprint</a>). TRansient Attracting Profiles (TRAPs, <a href="http://doi.org/10.1038/s41467-020-16281-x" target="_blank" rel="noopener">Serra et al. (2020)</a>, <a href="https://doi.org/10.1063/1.4951720" target="_blank" rel="noopener">Serra and Haller (2016)</a>) have been computed using the TRAPs detection software by <a href="https://github.com/MattiaSerra/TRAPs" target="_blank" rel="noopener">Mattia Serra (2020)</a>. Raw TRAPs were further post-processed, tracked and analysed using software by <a href="https://github.com/kunzluca/trapsgpgp" target="_blank" rel="noopener">Luca Kunz (2024)</a>. The videos provided here display:</p> <ol> <li><strong>SV1_TRAPS_TRAJECTORIES.mp4</strong>: Coloured lines represent the trajectories of propagating TRAPs, colours indicate the estimated total lifetime of a TRAP. Ephemeral TRAPs appear as flickering points.</li> <li><strong>SV2_TRAPS_VORTICITY_DRIFTERS.mp4</strong>: Black lines indicate TRAPs upon a colourmap of the relative voriticity field ζ. Filled and empty black circles represent the 24-hourly positions of drogued and undrogued GDP drifters. The video shows the simultaneous motion of TRAPs, drifters and the relative vorticity field.</li> <li><strong>SV3_TRAPS_VORTICITY_EDDIES.mp4</strong>: Black lines indicate TRAPs upon a colourmap of the relative voriticity field ζ. Blue lines indicate the speed contour of cyclonic, pink lines the speed contour of anticyclonic mesoscale eddy detections. The video shows the simultaneous propagation of TRAPs, mesoscale eddy detections and the relative vorticity field.</li> </ol> <p>For related datasets, see: <a href="https://doi.org/10.5281/zenodo.10993736">https://doi.org/10.5281/zenodo.10993736</a></p> <h1>Source data</h1> <p>Raw TRAPs and relative vorticity are derived from daily snapshots of near-surface geostrophic + Ekman currents from the product Global Total Surface and 15m Current (COPERNICUS-GLOBCURRENT) from Altimetric Geostrophic Current and Modeled Ekman Current Reprocessing that is provided by the E.U. Copernicus Marine Service (<a href="https://doi.org/10.48670/moi-00050" target="_blank" rel="noopener">CMEMS, 2022a</a>). Surface drifter positions have been consulted from the Global Drifter Program (<a href="https://doi.org/10.25921/7ntx-z961" target="_blank" rel="noopener">Lumpkin and Centurioni, 2019</a>). Mesoscale eddy detections have been consulted from the Mesoscale Eddy Trajectory Atlas (META3.2 DT) which is produced by SSALTO/DUACS and distributed by AVISO+ with support from CNES, in collaboration with IMEDEA (<a href="https://doi.org/10.24400/527896/a01-2022.005.220209" target="_blank" rel="noopener">AVISO+, 2022</a>).</p> <h1>Acknowledgements</h1> <p>This work is a contribution to the project <a href="https://www.trr-energytransfers.de/research/area-l/l3" target="_blank" rel="noopener">L3 Meso- to submesoscale turbulence in the ocean</a> of the Collaborative Research Centre TRR 181 Energy Transfer in Atmosphere and Ocean funded by the German Research Foundation (DFG) and has been conducted in collaboration with <a href="https://theoceancleanup.com/" target="_blank" rel="noopener">The Ocean Cleanup</a>.</p> <h1>Please cite</h1> <p>When using the videos, please cite</p> <p>Kunz, L., Griesel, A., Eden, C., Duran, R., and Sainte-Rose, B.: Transient Attracting Profiles in the Great Pacific Garbage Patch, Ocean Sci., 20, 1611–1630, <a href="https://doi.org/10.5194/os-20-1611-2024">https://doi.org/10.5194/os-20-1611-2024</a>, 2024. </p> <p>and provide a link to the repository.</p> <p> </p>
Material Dependent Influence of Ring/Spot Beam Profiles in Laser Powder Bed Fusion
<p>Raw data associated with a paper submission.<br>"Material Dependent Influence of Ring/Spot Beam Profiles in Laser Powder Bed Fusion"</p> <p>Contained are all the Matlab scripts used for the simulations, as well as csv's of any data plotted in graphs.</p>
Intervention effect of person profiles for people with advanced dementia: Stepped-wedge cluster randomised trial
<p>Dataset related to study <a href="https://kofam.ch/en/snctp-portal/searching-for-a-clinical-trial/study/53440">SNCTP000003941</a></p> <div><strong># Project title</strong></div> <div>Intervention effect of person profiles for people with advanced dementia: Stepped-Wedge Cluster Randomised Trial.</div> <div> </div> <div><strong># Brief project overview</strong></div> <div>This is Data coming out of the IPOS-Dem project, a Stepped-wedge Cluster Randomised Trial (SW-CRT). Please make shure to check out the SW-CRT protocol for more details at <a href="https://www.doi.org/10.1111/jan.14953">https://www.doi.org/10.1111/jan.14953</a>. </div> <div> </div> <div>We are currently working on our main study (SW-CRT) analysis. While we remain dedicated to open science, our priority is to ensure the utmost integrity of our ongoing research.</div> <div> </div> <div><strong># Contributors</strong></div> <div>Frank Spichiger, Andrea Koppitz, André Meichtry</div> <div> </div> <div><strong>## Repository overview</strong></div> <div>|-- readme.md</div> <div>|-- documentation</div> <div> |--readme.md</div> <div> |--Datadictionary.html</div> <div>|-- data</div> <div> |--readme.md</div> <div> |--IPOS-Dem_CH-SW-CRT_PLD_IPOS-Dem.feather</div> <div> |--IPOS-Dem_CH-SW-CRT_PLD_locf_IPOS-Dem.feather</div> <div> |--IPOS-Dem_CH-SW-CRT_PLD_Qualidem.feather</div> <div> |--IPOS-Dem_CH-SW-CRT_PLD_locf_Qualidem.feather</div> <div> |--csv</div> <div> |--IPOS-Dem_CH-SW-CRT_PLD_IPOS-Dem.csv</div> <div> |--IPOS-Dem_CH-SW-CRT_PLD_locf_IPOS-Dem.csv</div> <div> |--IPOS-Dem_CH-SW-CRT_PLD_Qualidem.csv</div> <div> |--IPOS-Dem_CH-SW-CRT_PLD_locf_Qualidem.csv</div> <div>|-- analysis</div> <div> |--readme.md</div> <div> |--240229_Analysis_Main.html</div> <div> |--240229_Analysis_Main.qmd</div> <div> |--grateful-refs.bib</div> <div> |--output</div> <div> |--models-REML.csv</div> <div> |--models.csv</div> <div> |--subscale_estimates.csv</div> <div> |--figures</div> <div> |--Datashowcasing-1.png</div> <div> |--DEBI-Predictions-Plot-1.png</div> <div> |--DPII-Plot-1.png</div> <div> |--Model_1-Plots-1.png</div> <div> |--Model_1-Plots-2.png</div> <div> |--Model_2-Plots-1.png</div> <div> |--Model_2-Plots-2.png</div> <div> |--Model_3-Plot-1.png</div> <div> |--Model_4-Plot-1.png</div> <div> |--Model_5-Plot-1.png</div> <div> </div> <div> </div> <div><strong># Applicable instructions</strong></div> <div>The R markdown files were generated using <a href="https://www.r-project.org">R 4.2.2</a> in <a href="https://posit.co/downloads/">RStudio 2023.12.1</a> for MacOS X you can run them using free software but will need to install R, Rstudio and the packages cited at the end of the quarto or the rendered html we used.</div> <div> </div> <div><strong># Additional resources</strong></div> <div>- Main study protocol: <a href="https://www.doi.org/10.1111/jan.14953">https://www.doi.org/10.1111/jan.14953</a></div> <div>- QUALIDEM measure used: <a href="https://doi.org/10.1186/1477-7525-11-91">https://doi.org/10.1186/1477-7525-11-91</a> </div> <div>- Pos-Pal consortium with more information on the measure: <a href="https://www.pos-pal.org">https://www.pos-pal.org</a></div> <div>- IPOS-Dem translation and adaption: <a href="https://www.doi.org/10.1186/s41687-022-00420-7">https://www.doi.org/10.1186/s41687-022-00420-7</a></div> <div>- IPOS-Dem inter-rating reliability: <a href="https://doi.org/10.1371/journal.pone.0286557">https://doi.org/10.1371/journal.pone.0286557</a> </div>
How can CotB2V80L alter the product profile? Supporting Information
<p>Docking results, electrostatic maps and volume calculation.</p> <p>CHARMM scripts for electrostatic calculation and python scripts for Monte Carlo integration on pocket volume and assement of extra volume.</p>
Dataset for Interactive Profiling Narrative (IPN) with Toxicity Tolerance Score of each individual user to different categories of toxicity
<p>he dataset is the collection of the results of the Interactive Profiling Narrative that was developed as a part of the project Listener Aware Content Detoxification.<br>It contains the toxicity tolerance scores of each individual user to different categories.<br>High scores indicate that the user is extremely sensitive to the particular category where as low scores indicate that the user isn't that triggered by the category.Medium scores show moderate tolerance.</p> <p>Columns:<br>1.UserId: The unique id given to each user (helps preserve anonymity).<br>2.Race: The user's toxicity tolerance to the category race. High score means racist comment trigger them. <br>3.Sex: The user's toxicity tolerance to the category sexuality. High scores indicate sensitivity to comments on sexual identity.<br>4.Body_image: The user's sensitivity to comments regarding body image. High scores indicate that remarks on body appearance strongly affect the user.<br>5.Disability:The user's sensitivity to comments about disabilities. A high score means the user is highly sensitive to potentially ableist remarks.<br>6.Religion_culture: The user's sensitivity to content involving religion or culture. High scores show the user is easily triggered by insensitive comments about religious or cultural aspects.<br>7.Physical_abuse: User's sensitivity to comments regarding physical abuse. High scores indicate the user is strongly affected by remarks on physical abuse.<br>8.Mental_health: This measures the user's sensitivity to comments on mental health. High scores suggest that the user is particularly affected by comments stigmatizing mental health issues.<br>9.Politics:The user's sensitivity to political content. High scores indicate that political discussions or comments are likely to evoke a strong reaction in the user</p>
Dataset for Formulation of minitablets with personalised dissolution profile by fluid-bed granulation of drug nanosuspensions
<p>Source data for all graphs in the manuscript titled "Formulation of minitablets with personalised dissolution profile by fluid-bed granulation of drug nanosuspensions" by Mutylo et al., submitted to International Journal of Pharmaceutics. The data sets contains values for particle size distributions, tablet uniformity, drug loading, and dissolution curves.</p>
Video Supplement for Himes et al. (2024): "Using neural networks for near-real-time aerosol retrievals from OMPS Limb Profiler measurements"
<p>This archive contains the video supplement for</p> <p>Using neural networks for near-real-time aerosol retrievals from OMPS Limb Profiler measurements</p> <p>by Himes et al. (2024), submitted to Atmospheric Measurement Techniques. The file contains an animation of the V2.1 and NRT average retrieved extinction coefficient between 19.5--21.5 km at 997 nm for the 2024 Ruang eruptions.</p>
Vertical profiles of leaf photosynthesis and leaf traits, and soil nutrients in two tropical rainforests in French Guiana before and after a three-year nitrogen and phosphorus addition experiment
<p>We provide a comprehensive dataset of vertical profiles of photosynthetic capacity and important leaf traits, including leaf N and P concentrations, from two three-year, large-scale fertilisation experiments conducted in two tropical rainforests in French Guiana. These data present a unique source of information to further improve model representations of the roles of N, P, and other leaf nutrients, in photosynthesis in tropical forests. To further facilitate the use of our data in syntheses and model studies, we provide an elaborate list of ancillary data, including important soil properties and nutrients, along with the leaf data. As environmental drivers are key to improve our understanding of carbon (C)-nutrient cycle interactions, this comprehensive dataset will aid to further enhance our understanding of how nutrient availability interacts with C uptake in tropical forests.</p>
Seasonal cycles of the 234Th profiles and each term of the 234Th diagnostic equation from a realistic simulation at SEATS
<p>This dataset provides the diagnostic results of the <sup>234</sup>Th equation from a physical-biogeochemical-<sup>234</sup>Th model simulation at SEATS. It includes two files: (file1)seasonal_mean_of_vertical_profiles.mat and (file2) each term of the <sup>234</sup>Th diagnostic equation.mat. In file1, seasonal cycle of tracers (e.g., temperature, salinity, particulate <sup>234</sup>Th and total <sup>234</sup>Th) at SEATS were saved. File 2 provides the diagnostic results of the <sup>234</sup>Th equation at SEATS, including the rate of <sup>234</sup>Th over time, the deficit of <sup>238</sup>U-<sup>234</sup>Th term, the particle sinking term, the vertical advection and diffusion term. </p>
DisFull_Lnc: discover full-length long noncoding RNAs without transcriptional initiation profiles
<p>DisFull_Lnc aims to identify full-length lncRNAs directly from raw RNA-seq data without H3K4me3 profiles from the corresponding sample.</p>
Phase profiles of the Sorter 1 (unwrapper)
<p>Off axis electron holography Images of the main tip of the Sorter 1 collected at the Titan-Holo. This dataset has been collected to study the phase profile of the electron beam after its interaction with the main tip of the electrostatic Sorter 1 (or unwrapper). In particular said phase profile images are the ones denoted the the letter P at the end of the file name.</p>
Analysis of microRNA expression profiles in peri-miniscrew implant crevicular fluid in orthodontics
<p> This study systematically evaluated microRNA (miRNA) expression patterns in peri-miniscrew implant crevicular fluid (PMICF) in orthodontic patients.</p>
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.