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1,167 results for “Real-world”
Real-world Clinical Patterns Of Care And Outcomes Among AfME mRCC Patients Receiving Sunitinib as First Line Therapy.
ClinicalTrials.gov study NCT03140176. IPD Sharing: NO. Countries: 4. Publications: 0.
A Study to Describe the Real-world Effectiveness, Safety and Patterns of Use of Dupilumab in Patients With Chronic Spontaneous Urticaria
ClinicalTrials.gov study NCT07316114. IPD Sharing: YES. Countries: 1. Publications: 0.
A Study of Vedolizumab in Adults With Ulcerative Colitis (UC) or Crohn's Disease (CD) in Real-World Practice
ClinicalTrials.gov study NCT04890262. IPD Sharing: YES. Countries: 1. Publications: 0.
Real-World Outcomes Study on Subjects Treated With Radiofrequency Ablation
ClinicalTrials.gov study NCT04873817. IPD Sharing: NO. Countries: 6. Publications: 0.
A Study to Compare How Effective is Encorafenib Plus Binimetinib in Real-world and Clinical Trial Settings
ClinicalTrials.gov study NCT05954546. IPD Sharing: NO. Countries: 1. Publications: 0.
A Multi-center, Real-World Clinical Trial of caIMR on Two Specific Kinds of Patients.
ClinicalTrials.gov study NCT06945094. IPD Sharing: UNDECIDED. Countries: 0. Publications: 9.
Data from: Understanding real-world brake activity: A key to assessing non-tailpipe emission sources for sustainable transportation and communities
Open the record for dataset details and reuse information.
Immune correlates of anti-BCMA CAR-T products idecabtagene vicleucel and ciltacabtagene autoleucel in a real-world cohort of patients with multiple myeloma [scRNA-seq]
GEO Series GSE298012. Homo sapiens. 2 samples. Type: Expression profiling by high throughput sequencing.
Mobile EEG identifies the re-allocation of attention during real-world activity
<p>All the datasets from the three studies reported in the scientific article "Mobile EEG identifies the re-allocation of attention during real-world activity" <a href="https://www.nature.com/articles/s41598-019-51996-y">10.1038/s41598-019-51996-y </a></p>
On the Diversity and Frequency of Code Related to Mathematical Formulas in Real-World Java Projects
<p>Supplementary Material to: On the Diversity and Frequency of Code Related to Mathematical Formulas in Real-World Java Projects. The article available at: https://www.sciencedirect.com/science/article/abs/pii/S0164121220302533</p>
Applying a Multi-platform Architectural Conformance Solution in a Real-World Microservice-based System
<p>Microservice architectures are composed of a set of independent microservices that execute well-defined functionalities, allowing each one to be developed in different programming languages and data management technologies. The problem, however, is that such heterogeneity implies in a harder verification process of communication among microservices and the architectural designs of each microservice. Thereupon, this paper proposes and evaluates a multiplatform architectural conformance solution for the microservice architecture. For this purpose, (i) we specify an architectural constraint language, called DCL+---adapted from the DCL (Dependency Constraint Language) language; (ii) we propose a multiplatform process that restricts the communication between the microservices and verifies the architectural projects of each one of them; (iii) we develop DCL+check, a tool that implements the proposed solution; (iv) we apply our process in a large real-world application composed of eleven microservices, developed in two different languages (JavaScript and Java). As result, we found 16 communication and 171 structural design violations. The communication violations occurred in general due to the lack of knowledge of the developers about the restrictions of communication among the modules of the orchestrator system and other microservices, as well as the evolution of two microservices. </p>
Data from: Real-world study of efficacy, risk management and reasons for discontinuation of natalizumab for treatment of multiple sclerosis in Russia
Background. NTZ is approved in Russia for the treatment of highly active relapsing remitting multiple sclerosis and is reimbursed via federal budget program. However, no data about NTZ treatment in Russia and the effect of federal reimbursement have been performed so far. Objective. To characterize the population of patients receiving natalizumab and assess the efficacy and risk-management plan (RMP) implementation of NTZ therapy in routine clinical practice in Russia. Methods. We analyzed data for 334 patients, who received at least one infusion of NTZ. Relapse rate, MRI activity, NEDA-3 status after 2 years were assessed. Anti-JC virus antibodies status and RMP implementation were evaluated. Drop-out rate and reasons for therapy discontinuation were analyzed. Results. Patients switched to natalizumab in Russia are mainly female (63%), with median EDSS score of 3.5 and high disease activity: 93% had at least 1 relapse and 58% had both T1Gd+ and new T2 lesion an year before therapy initiation. Introduction of federal reimbursement allowed to start therapy with natalizumab for patients with less relapses. The only predictor of 6-month progression was EDSS score at the baseline of therapy (HR=2.1375, 95%CI 1.0026 – 4.5570, p=0.0492). 82% patients reached NEDA-3 at 24 month of therapy. 25% of patients discontinued NTZ for reasons: tolerability (14.5%), JCV antibody status (61%), and patient's decision (17%). RMP was implemented in only 36% patients. Conclusion. Natalizumab appeared to have high efficacy in Russian clinical practice. Federal reimbursement allowed less active patients to start natalizumab. More efforts should be done to improve RMP implementation.
Figure 2 from: Neumann M, Ehnert F (2024) Knowledge re-integration in real-world laboratories to transform cities and communities: report on workshop designs. Research Ideas and Outcomes 10: e124018. https://doi.org/10.3897/rio.10.e124018
Figure 2 Template of the online whiteboard for the handbook workshops (translated into English).
Figure 3 from: Neumann M, Ehnert F (2024) Knowledge re-integration in real-world laboratories to transform cities and communities: report on workshop designs. Research Ideas and Outcomes 10: e124018. https://doi.org/10.3897/rio.10.e124018
Figure 3 Flashlight at the end of the DF transfer workshop; photo: Torsten Görg.
Figure 4 from: Neumann M, Ehnert F (2024) Knowledge re-integration in real-world laboratories to transform cities and communities: report on workshop designs. Research Ideas and Outcomes 10: e124018. https://doi.org/10.3897/rio.10.e124018
Figure 4 On-site excursion during the SLS transfer workshop; photo: Nicole Herzog.
Using a real-world network to model the trade-off between stay-at-home restriction, vaccination, social distancing and working hours on COVID-19 dynamics
<p>These datasets have been obtained through thousands of well-carried simulations by using the agent-based model based on a time-dynamic graph with stochastic transmission events. </p> <p>These simulations have been conducted on MATLAB 2019a.</p>
Spike-in and real-world proteomics data sets used in publication of PRONE
<p>Spike-in and real-world data sets used in the evaluation study by Arend et al. (see reference), and some utilized in the vignettes of PRONE, an R package designed for preprocessing, normalization, and performance evaluation of normalization methods of proteomics data. </p> <h3>Overview of the Data Sets</h3> <p>Due to the unavailability of proteomics quantification data from all original publications, data were extracted from alternative sources, which are also listed in the table below. Please refer to the paper's supplementary material and GitHub repository (https://github.com/lisiarend/PRONE.Evaluation) for more comprehensive information on the data sets. A processed metadata file and the protein quantification data file are provided for all data sets. The original quantification data used to generate these two files for each data set are consistently provided in the `original_data` directory of each data set.</p> <p> </p> <div> <table> <tbody> <tr> <td> <p>Data Set</p> </td> <td> <p>Type</p> </td> <td> <p>Quantification Type</p> </td> <td> <p>Raw Data (ID)</p> </td> <td> <p>Quantification Data</p> </td> </tr> <tr> <td> <p>dS1</p> </td> <td> <p>UPS1 spike-in </p> <p>(4 levels)</p> </td> <td> <p>LFQ</p> </td> <td> <p>Tabb et al. <a href="https://www.zotero.org/google-docs/?gkmf6j">[1]</a></p> </td> <td> <p>Välikangas et al. <a href="https://www.zotero.org/google-docs/?Tt81W8">[10]</a> </p> </td> </tr> <tr> <td> <p>dS2</p> </td> <td> <p>UPS1 spike-in </p> <p>(6 levels)</p> </td> <td> <p>LFQ</p> </td> <td> <p>Ramus et al. <a href="https://www.zotero.org/google-docs/?HP8b9X">[2]</a> (PXD001819)</p> </td> <td> <p>Graw et al. <a href="https://www.zotero.org/google-docs/?aiEVfL">[11]</a></p> </td> </tr> <tr> <td> <p>dS3</p> </td> <td> <p>E.coli spike-in </p> <p>(5 levels)</p> </td> <td> <p>LFQ</p> </td> <td> <p>Shen et al. <a href="https://www.zotero.org/google-docs/?zHIDZy">[3]</a></p> <p>(PXD003881)</p> </td> <td> <p>Sticker et al. <a href="https://www.zotero.org/google-docs/?aZ7oZu">[12]</a></p> </td> </tr> <tr> <td> <p>dS4</p> </td> <td> <p>E.coli spike-in </p> <p>(2 levels)</p> </td> <td> <p>LFQ</p> </td> <td> <p>Cox et al. <a href="https://www.zotero.org/google-docs/?scM19M">[4]</a></p> <p>(PXD00279)</p> </td> <td> </td> </tr> <tr> <td> <p>dS5</p> </td> <td> <p>E.coli spike-in </p> <p>(3 levels)</p> </td> <td> <p>TMT 10-plex (1)</p> </td> <td> <p>Zhu et al. <a href="https://www.zotero.org/google-docs/?qAz08E">[5]</a></p> <p>(PXD013277)</p> </td> <td> <p>Phil Wilmarth <a href="https://www.zotero.org/google-docs/?i7xmgP">[13]</a></p> </td> </tr> <tr> <td> <p>dS6</p> </td> <td> <p>yeast spike-in </p> <p>(3 levels)</p> </td> <td> <p>TMT 11-plex (1)</p> </td> <td> <p>O’Connell et al. <a href="https://www.zotero.org/google-docs/?C2XoaJ">[6]</a></p> <p>(PXD007683)</p> </td> <td> <p>Ammar et al. <a href="https://www.zotero.org/google-docs/?UWcyly">[14]</a></p> </td> </tr> <tr> <td> <p>dR1</p> </td> <td> <p>Osteogenic differentiation of hPCLSCs (4 time points)</p> </td> <td> <p>TMT 6-plex (3)</p> </td> <td> <p>Li et al. <a href="https://www.zotero.org/google-docs/?BouGSs">[8]</a> (PXD020908)</p> </td> <td> <p>MaxQuant executed in-house</p> </td> </tr> <tr> <td> <p>dR2</p> </td> <td> <p>Prospective Ovarian JHU Proteome</p> </td> <td> <p>TMT 10-plex (13)</p> </td> <td> <p>Hu et al. <a href="https://www.zotero.org/google-docs/?kVn4pf">[9]</a></p> <p>(PDC000110)</p> </td> <td> <p>MaxQuant executed in-house</p> </td> </tr> <tr> <td> <p>dR3</p> </td> <td> <p>AROM+ transgenic vs. wild-type mice</p> </td> <td> <p>LFQ</p> </td> <td> <p>Vehmas et al. <a href="https://www.zotero.org/google-docs/?qwYM2A">[7]</a></p> <p>(PXD002025)</p> </td> <td> </td> </tr> <tr> <td> <p>dR4</p> </td> <td> <p>Mycobacterium tuberculosis</p> <p>(healthy, disease vs. treated)</p> </td> <td> <p>TMT 10-plex (2)</p> </td> <td> <p>Schmidt et al. <a href="https://www.zotero.org/google-docs/?m1VHIG">[15]</a></p> <p>(PXD030883)</p> </td> <td> </td> </tr> </tbody> </table> </div> <h3>References</h3> <p>[1] D. L. Tabb et al., ‘Repeatability and Reproducibility in Proteomic Identifications by Liquid Chromatography−Tandem Mass Spectrometry’, J. Proteome Res., vol. 9, no. 2, pp. 761–776, Feb. 2010, doi: 10.1021/pr9006365.<br>[2] C. Ramus et al., ‘Spiked proteomic standard dataset for testing label-free quantitative software and statistical methods’, Data Brief, vol. 6, pp. 286–294, Mar. 2016, doi: 10.1016/j.dib.2015.11.063.<br>[3] X. Shen et al., ‘IonStar enables high-precision, low-missing-data proteomics quantification in large biological cohorts’, Proc. Natl. Acad. Sci., vol. 115, no. 21, pp. E4767–E4776, May 2018, doi: 10.1073/pnas.1800541115.<br>[4] J. Cox, M. Y. Hein, C. A. Luber, I. Paron, N. Nagaraj, and M. Mann, ‘Accurate Proteome-wide Label-free Quantification by Delayed Normalization and Maximal Peptide Ratio Extraction, Termed MaxLFQ *’, Mol. Cell. Proteomics, vol. 13, no. 9, pp. 2513–2526, Sep. 2014, doi: 10.1074/mcp.M113.031591.<br>[5] Y. Zhu et al., ‘DEqMS: A Method for Accurate Variance Estimation in Differential Protein Expression Analysis *’, Mol. Cell. Proteomics, vol. 19, no. 6, pp. 1047–1057, Jun. 2020, doi: 10.1074/mcp.TIR119.001646.<br>[6] J. D. O’Connell, J. A. Paulo, J. J. O’Brien, and S. P. Gygi, ‘Proteome-Wide Evaluation of Two Common Protein Quantification Methods’, J. Proteome Res., vol. 17, no. 5, pp. 1934–1942, May 2018, doi: 10.1021/acs.jproteome.8b00016.<br>[7] A. P. Vehmas et al., ‘Liver lipid metabolism is altered by increased circulating estrogen to androgen ratio in male mouse’, J. Proteomics, vol. 133, pp. 66–75, Feb. 2016, doi: 10.1016/j.jprot.2015.12.009.<br>[8] J. Li et al., ‘Dynamic proteomic profiling of human periodontal ligament stem cells during osteogenic differentiation’, Stem Cell Res. Ther., vol. 12, no. 1, p. 98, Feb. 2021, doi: 10.1186/s13287-020-02123-6.<br>[9] Y. Hu et al., ‘Integrated Proteomic and Glycoproteomic Characterization of Human High-Grade Serous Ovarian Carcinoma’, Cell Rep., vol. 33, no. 3, p. 108276, Oct. 2020, doi: 10.1016/j.celrep.2020.108276.<br>[10] T. Välikangas, T. Suomi, and L. L. Elo, ‘A systematic evaluation of normalization methods in quantitative label-free proteomics’, Brief. Bioinform., vol. 19, no. 1, pp. 1–11, Jan. 2018, doi: 10.1093/bib/bbw095.<br>[11] S. Graw et al., ‘proteiNorm – A User-Friendly Tool for Normalization and Analysis of TMT and Label-Free Protein Quantification’, ACS Omega, vol. 5, no. 40, pp. 25625–25633, Oct. 2020, doi: 10.1021/acsomega.0c02564.<br>[12] A. Sticker, L. Goeminne, L. Martens, and L. Clement, ‘Robust Summarization and Inference in Proteome-wide Label-free Quantification’, Mol. Cell. Proteomics, vol. 19, no. 7, pp. 1209–1219, Jul. 2020, doi: 10.1074/mcp.RA119.001624.<br>[13] ‘understanding_IRS’. Accessed: Mar. 07, 2024. [Online]. Available: https://pwilmart.github.io/IRS_normalization/understanding_IRS.html<br>[14] C. Ammar, M. Gruber, G. Csaba, and R. Zimmer, ‘MS-EmpiRe Utilizes Peptide-level Noise Distributions for Ultra-sensitive Detection of Differentially Expressed Proteins[S]’, Mol. Cell. Proteomics, vol. 18, no. 9, pp. 1880–1892, Sep. 2019, doi: 10.1074/mcp.RA119.001509.<br>[15] F. Biadglegne et al., ‘Mycobacterium tuberculosis Affects Protein and Lipid Content of Circulating Exosomes in Infected Patients Depending on Tuberculosis Disease State’, Biomedicines, vol. 10, no. 4, p. 783, Mar. 2022, doi: 10.3390/biomedicines10040783.</p>
Data and code for "Human neural dynamics of real-world and imagined navigation"
<ul> <li>Please download the zip file</li> <li>Unzip it on your computer</li> <li>Run the plot_FigureX.m scripts in Matlab</li> <li>The scripts generate the panels for the manuscript's figures</li> <li>Figure panels are saved in subfolders for your review</li> </ul>
GF6SRD: a real-world cross-sensor remote sensing super-resolution dataset
<p>We used PMS imagery and WFV imagery from Gaofen-6 satellite to construct the real-world cross-sensor remote sensing dataset, GF6SRD. This dataset is proposed in the paper "DESAT: A Distance Enhanced Strip Attention Transformer for Remote Sensing Super-Resolution"</p> <p>The code of this paper is also available at https://github.com/afomyj/DESAT</p> <p>If you find our work helpful, please cite:</p> <p>Mao Y, He G, Wang G, Yin R, Peng Y, Guan B. DESAT: A Distance-Enhanced Strip Attention Transformer for Remote Sensing Image Super-Resolution. <em>Remote Sensing</em>. 2024; 16(22):4251. https://doi.org/10.3390/rs16224251</p> <p>The exposed portion of this resource is part of GF6SRD, containg 2000 WFV-PMS image pairs for training and 500 pairs for testing. The full GF6SRD dataset can be obtained under appropriate request to the coresponding author, He, Guojin. Email: hegj@aircas.ac.cn</p>
Synthetic Training Dataset for Real-World Terminal Strip Object Detection
<p>This dataset provides synthetic training data for the real-world industrial application of terminal strip object detection to investigate the sim-to-real generalization performance of modern object detectors based on state-of-the-art image synthesis methods. It consists of 30.000 randomly generated synthetic images of terminal strips covering 36 different terminal blocks in five colors and additional accessories such as plug-in bridges, test adapters, end covers and markings. Except from the markings and the DIN rail all objects of the terminal strips are labeled with a bounding box and the respective object class for supervised learning. Additionally, 300 real images of terminal strips were taken and manually labeled for the real-world test.</p> <p>If you use this datset for your research, please consider citing this: <a href="https://arxiv.org/abs/2403.04809">Investigation of the Impact of Synthetic Training Data in the Industrial Application of Terminal Strip Object Detection</a></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.