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103 results for “Seamless”
Plug and play stability for intracortical brain-computer interfaces: A one-year demonstration of seamless brain-to-text communication
<p>Intracortical brain-computer interfaces (iBCIs) have shown promise for restoring rapid communication to people with neurological disorders such as amyotrophic lateral sclerosis (ALS). However, to maintain high performance over time, iBCIs typically need frequent recalibration to combat changes in the neural recordings that accrue over days. In this study, we propose a method: Continual Online Recalibration with Pseudo-labels (CORP), that enables self-recalibration of communication iBCIs without interrupting the user. We evaluated CORP with one clinical trial participant. CORP achieved a stable decoding accuracy of 93.84% in an online handwriting iBCI task, significantly outperforming other baseline methods.</p> <p>This dataset contains 21 sessions of recorded neural activities used for the evaluation. It has been formatted for developing and evaluating machine learning models. There 5 more sessions heldout for a planned iBCI stability competition. They will be released in the future.</p> <p>We also provide a pretrained RNN seed model and a laugnage model to preproduce the results in our paper.</p>
Latitude and longitude grids for global oceanic seamless POC concentration products
Open the record for dataset details and reuse information.
Spatiotemporal seamless global surface soil moisture
<p>Temporal resolution is from 31 March 2015 to 31 May 2023 and spatial resolution is 0.25°.</p>
Seamless integration of bioelectronic interface in an animal model via in vivo polymerization of conjugated oligomers
<p>Leveraging the biocatalytic machinery of living organisms for fabricating functional bioelectronic interfaces, in vivo, defines a new class of micro-biohybrids enabling the seamless integration of technology with living biological systems. Previously, we have demonstrated the in vivo polymerization of conjugated oligomers forming conductors within the structures of plants. Here, we expand this concept by reporting that Hydra, an invertebrate animal, polymerizes the conjugated oligomer ETE-S both within cells that expresses peroxidase activity and within the adhesive material that is secreted to promote underwater surface adhesion. The resulting conjugated polymer forms electronically conducting and electrochemically active μm-sized domains, which are inter-connected resulting in percolative conduction pathways extending beyond 100 μm, that are fully integrated within the Hydra tissue and the secreted mucus. Furthermore, the introduction and in vivo polymerization of ETE-S can be used as a biochemical marker to follow the dynamics of Hydra budding (reproduction) and regeneration. This work paves the way for well-defined self-organized electronics in animal tissue to modulate biological functions and in vivo biofabrication of hybrid functional materials and devices.</p>
ChinaHighO3: Big Data Seamless 10 km Ground-level MDA8 O3 Dataset for China
<p>ChinaHighO<sub>3</sub> is one of the series of long-term, full-coverage, high-resolution, and high-quality datasets of ground-level air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from the big data (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence by considering the spatiotemporal heterogeneity of air pollution. </p> <p>This is the big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 10 km (i.e., D10K, M10K, and Y10K) ground-level maximum 8-hour average (MDA8) O<sub>3</sub> dataset in China from 1979 to 2020. This dataset yields a high quality with a cross-validation coefficient of determination (CV-R<sup>2</sup>) of 0.87, a root-mean-square error (RMSE) of 17.10 µg m<sup>-3</sup>, and a mean absolute error (MAE) of 11.29 µg m<sup>-3</sup> on a daily basis.</p> <p>If you use the ChinaHighO<sub>3</sub> dataset for related scientific research, please cite the corresponding reference (Wei et al., RSE, 2022; He et al., 2022):</p> <ul> <li> <p>Wei, J., Li, Z., Li, K., Dickerson, R., Pinker, R., Wang, J., Liu, X., Sun, L., Xue, W., and Cribb, M. <a href="https://weijing-rs.github.io/publications/Wei_et_al-RSE-2022.pdf">Full-coverage mapping and spatiotemporal variations of ground-level ozone (O<sub>3</sub>) pollution from 2013 to 2020 across China</a>. <em>Remote Sensing of Environment</em>, 2022, 270, 112775. https://doi.org/10.1016/j.rse.2021.112775</p> </li> <li> <p>He, L., Wei, J., Wang, Y., Shang, Q., Liu, J., Yin, Y., Frankerberg, C., Jiang, J., Li, Z., and Yung, Y. <a href="https://weijing-rs.github.io/publications/He_et_al-EF-2022.pdf">Marked impacts of pollution mitigation on crop yields in China</a>. <em>Earth's Future</em>, 2022, 10, e2022EF002936. https://doi.org/10.1029/2022EF002936</p> </li> </ul> <p><strong>Note that access to this dataset is now restricted, as a longer-term (2000 to present), high-resolution (1 km), and higher quality ChinaHighO<sub>3</sub> dataset is now available: <a href="http://doi.org/10.5281/zenodo.10477125">http://doi.org/10.5281/zenodo.10477125</a></strong></p> <p><strong>More CHAP datasets of different air pollutants can be found at: <a href="https://weijing-rs.github.io/product.html">https://weijing-rs.github.io/product.html</a></strong></p>
ELITE land surface temperature: seamless 1km LST over China (2014)
<p>The <strong>E</strong>ssential therma<strong>L</strong> <strong>I</strong>nfrared remo<strong>T</strong>e s<strong>E</strong>nsing (<strong>ELITE</strong>) product suite currently has four types of products, including land surface temperature (LST: clear-sky and all-sky), emissivity (NBE: narrowband emissivity; BBE: broadband emissivity; and spectral emissivity), the component of surface radiation and energy budget (SLUR: surface longwave upwelling radiation; SLDR: surface longwave downward radiation SLDR; SLNR: surface longwave net radiation), and the component of Earth’s radiation budget (OLR; outgoing longwave radiation; RSR: reflected solar radiation). The spatial-temporal resolutions of the ELITE products are mainly determined by the employed satellite data sources. For more information about ELITE products, please refer to the website (<a href="https://elite.bnu.edu.cn">https://elite.bnu.edu.cn</a>).</p> <p>This dataset is the ELITE seamless 1km LST over China landmass (2002-2020). Firstly, a look-up-table-based empirical retrieval algorithm is developed for retrieving microwave LST from AMSR-E/AMSR2 observations. Then, AMSR-E/AMSR2 LST is downscaled using the geographically weighted regression to obtain 1km LST. Finally, the multi-scale kalman filter is used to fuse MODIS LST and AMSR-E/AMSR2 LST to generate a 1km seamless LST data set. The ground valuation results show that the root mean square error (RMSE) of the 1km seamless LST is about 3K. In addition, the spatial distribution of the 1km seamless LST is consistent with MODIS LST and CLDAS LST.</p> <p>This is the seamless LST dataset in 2014. Please <a href="https://zenodo.org/record/8274917"><em><strong>click here</strong></em></a> to download the ELITE LST product in 2013 and <a href="https://zenodo.org/record/8274959"><em><strong>click here</strong></em></a> to download the ELITE LST product in 2015.</p> <p> </p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: China</li> <li>Temporal Coverage: 2014</li> <li>Spatial Resolution: 1 KM</li> <li>Temporal Resolution: 2 times per day</li> <li>Data Format: hdf</li> <li>Scale: 0.02</li> </ul> <p><strong>Citation </strong>(Please cite these papers when using the data)<strong>:</strong></p> <ol> <li>Xu, S., & Cheng, J. (2021). A new land surface temperature fusion strategy based on cumulative distribution function matching and multiresolution Kalman filtering. Remote Sensing of Environment, 254, 112256</li> <li>Zhang, Q., Wang, N., Cheng, J., & Xu, S. (2020). A Stepwise Downscaling Method for Generating High-Resolution Land Surface Temperature From AMSR-E Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 5669-5681 </li> <li>Zhang, Q., & Cheng, J. (2020). An Empirical Algorithm for Retrieving Land Surface Temperature From AMSR-E Data Considering the Comprehensive Effects of Environmental Variables. Earth and Space Science, 7, e2019EA001006. https://doi.org/10.1029/2019EA001006 </li> </ol> <p> </p> <p>If you have any questions, please contact Prof. Jie Cheng (<a href="mailto:eliteqrs@126.com">eliteqrs@126.com</a>).</p>
The Outcomes of Seamless ADL Training Between Occupational Therapist and Nurse in Stroke Patients
ClinicalTrials.gov study NCT02361307. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Plug and play stability for intracortical brain-computer interfaces: A one-year demonstration of seamless brain-to-text communication
Open the record for dataset details and reuse information.
SGD-SST: Seamless Global Daily Sea Surface Temperature Products
<p><strong>Seamless Global Daily SNPP-VIIRS Sea Surface Temperature </strong><strong>Products from 2020 to 2024</strong></p>
Seamless Follow up and Support System for Frail Elderly Living at Home
ClinicalTrials.gov study NCT03468647. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Phase II/III Seamless Clinical Study of MG-K10 Humanized Monoclonal Antibody Injection in Treatment of Seasonal Allergic Rhinitis
ClinicalTrials.gov study NCT06846385. IPD Sharing: NO. Countries: 1. Publications: 0.
Veterans Affairs Seamless Phase II/III Randomized Trial of STAndard Systemic theRapy With or Without PET-directed Local Therapy for Oligometastatic pRosTate Cancer
ClinicalTrials.gov study NCT04787744. IPD Sharing: NO. Countries: 1. Publications: 0.
The SEAMLESS Study: Smartphone App-based Mindfulness for Cancer Survivors
ClinicalTrials.gov study NCT03557762. IPD Sharing: NO. Countries: 1. Publications: 0.
Collaborative Seamless Care in Oncology : Measure and Reinforce Safety and Adherence to Oral Cancer Treatment
ClinicalTrials.gov study NCT01370980. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Effects of Manualized Treatment in a Seamless System
ClinicalTrials.gov study NCT01372033. IPD Sharing: Not stated. Countries: 1. Publications: 0.
The "Seamless" Patient:From Preoperative Preparation to Postoperative Rehabilitation
ClinicalTrials.gov study NCT06699446. IPD Sharing: NO. Countries: 1. Publications: 0.
Co-design of a Seamless Person-centered Intervention to Optimize Medication Use Across Healthcare Levels
ClinicalTrials.gov study NCT05421143. IPD Sharing: YES. Countries: 1. Publications: 0.
Seamless Controlled Trial To Evaluate Safety And Immunogenicity of Chikungunya Vaccine in LatinAmerica and Asia
ClinicalTrials.gov study NCT04566484. IPD Sharing: NO. Countries: 5. Publications: 0.
ChinaHighPM10: Big Data Seamless 10 km Ground-level PM10 Dataset for China (Closed)
<p>ChinaHighPM<sub>10</sub> is one of the series of long-term, full-coverage, high-resolution, and high-quality datasets of ground-level air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from the big data (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence by considering the spatiotemporal heterogeneity of air pollution. </p> <p>This is the big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 10 km (i.e., D10K, M10K, and Y10K) ground-level ground-level PM<sub>10</sub> products in China from 2013 to 2020. This dataset yields a high quality with a cross-validation coefficient of determination (CV-R<sup>2</sup>) of 0.86 and a root-mean-square error (RMSE) of 24.34 µg m<sup>-3</sup> on a daily basis.</p> <p><strong>Note that this dataset is closed access since a longer-term, high-resolution (1 km), and higher quality ChinaHighPM<sub>10</sub> dataset is available at: <a href="http://doi.org/10.5281/zenodo.3752465">http://doi.org/10.5281/zenodo.3752465</a></strong></p> <p><strong>More CHAP datasets of different air pollutants can be found at: <a href="https://weijing-rs.github.io/product.html">https://weijing-rs.github.io/product.html</a></strong></p>
ChinaHighPM2.5: Big Data Seamless 10 km Ground-level PM2.5 Dataset for China (Closed)
<p>ChinaHighPM<sub>2.5</sub> is one of the series of long-term, full-coverage, high-resolution, and high-quality datasets of ground-level air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from the big data (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence by considering the spatiotemporal heterogeneity of air pollution. </p> <p>This is the big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 10 km (i.e., D10K, M10K, and Y10K) ground-level PM<sub>2.5</sub> dataset in China from 2013 to 2020. This dataset yields a high quality with a cross-validation coefficient of determination (CV-R<sup>2</sup>) of 0.91 and a root-mean-square error (RMSE) of 12.67 µg m<sup>-3</sup> on a daily basis.</p> <p><strong>Note that this dataset is closed access since a longer-term, high-resolution (1 km), and higher quality ChinaHighPM<sub>2.5</sub> dataset is available at: <a href="http://doi.org/10.5281/zenodo.6398971">http://doi.org/10.5281/zenodo.6398971</a></strong></p> <p><strong>More CHAP datasets of different air pollutants can be found at: <a href="https://weijing-rs.github.io/product.html">https://weijing-rs.github.io/product.html</a></strong></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
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DANDI Archive for NWB datasets
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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.