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624 results for “HD”
HD-SIM-RBV: a synthetic dataset with model-based simulations of blood volume changes during hemodialysis
<p>The HD-SIM-RBV dataset is a synthetic (model-based) dataset generated to enable the study of blood volume (BV) or relative blood volume (RBV) changes during hemodialysis (HD).</p> <p>The dataset includes the profiles of BV changes during a standard 4-hour HD session simulated using a lumped-parameter, physiologically-based model of the cardiovascular system and the whole-body water and solute kinetics in 5,000 virtual patients with randomly adjusted values of 90 physiological parameters.</p> <p>For each of the 90 selected parameters, a random value was drawn from a normal distribution with the mean equal to the baseline value used originally in the model (with a few exceptions) and the standard deviation (SD) assumed at the level of 10%, 20%, or 40% of the baseline value, depending on the nature of the given parameter and the likelihood of its variation in the population (for some parameters, SD was set below 10% - see Parameters.xlsx). Only values within ±2SD from the mean were accepted. </p> <p>Ultrafiltration was set randomly within ±1 L from the assigned fluid overload. All other parameters as well as dialysis settings were kept constant for all virtual patients (at the levels used in our previous work - see the references below).</p> <p> </p> <p>When using the dataset, please cite the associated conference paper:</p> <p>Pstras L, Waniewski J. A Model-Based Dataset for In-Silico Exploration of the Patterns of Relative Blood Volume Changes During Hemodialysis. 2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology, 149-150, 2023, doi: 10.1109/IEEECONF58974.2023.10404528.</p>
HD-EEGtask(Dataset 2)
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Composite X-EUV + optical model spectrum of the planet-hosting star HIP 67522 (HD 120411)
<p>Composite spectrum of HIP 67522 obtained by joining a Phoenix photospheric spectrum with the X-EUV spectrum synthesized from the reconstructed plasma Emission Measure Distribution (EMD) vs. temperature in chromosphere, transition region, and corona. The FITS file contains 3 extensions with the spectrum, the EMD, and the plasma chemical abundances, derived from the analysis of X-ray and FUV high-resolution spectra, obtained with simultaneous observations with XMM-Newton and HST.</p> <p>In the attached figure, the upper panel shows the specific flux at Earth, while the bottom panel is the photon flux at a distance of 1 AU. In green the Phoenix spectrum resampled to a wavelength resolution of 1 Angstrom, down to 1700 A; the XUV spectrum in the range 1-1700 A instead has a resolution of 0.01 A. The green and blue segments in the upper panel, at about 200 nm, mark the Phoenix model flux and the observed flux integrated over the OM UVM2 band.</p>
HD-EEGtask(Dataset 1)
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Data and model for 'JWST transmission spectroscopy of HD 209458b: a super-solar metallicity, a very low C/O, and no evidence of CH4, HCN, or C2H2'
<p>Supplementary materials for https://arxiv.org/abs/2310.03245 </p> <p>include:</p> <p>1. <strong>spectra_final.csv: </strong>transmission spectrum reduced by Eureka! and SPARTA (Figure 6), the best-fit model presented in Figure 1(a).</p> <p>2. <strong>Opacities </strong>used in the retrieval that are compatible with PLATON described in section 3.</p> <p>All opacity numpy pickle files are generated by <code>Python 3.9.7</code> and <code>Numpy 1.24.2</code>.</p> <p> </p> <p>**Bestfit in spectra_final.csv and all opacities are updated on Jan 23, 2024</p> <p>For any additional data requests or questions, please contact: qiaox@uchicago.edu</p>
Parameter fields for the Hydrological Discharge (HD) model at 0.5° and 5 Min. horizontal resolution
<p><strong>HD model parameter files</strong></p> <p>This dataset comprises global parameter data that are necessary to run the Hydrological Discharge (HD) model Vs. 5.1, which has been published on <a href="http://doi.org/10.5281/zenodo.5707587">Zenodo</a>. The HD model calculates the lateral transport of water over the land surface to simulate discharge into the oceans. The HD model parameter dataset comprises parameter fields at 0.5° global resolution and at 5 Min. resolution (global, Europe). Details for both resolutions are provided below. </p> <p><strong>Authors</strong>: Stefan Hagemann, Tobias Stacke <br> <strong>Copyright 2021</strong>: Institute of Coastal Systems - Analysis and Modelling, Helmholtz-Zentrum Hereon<br> <strong>License</strong>: under the Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0; https://creativecommons.org/licenses/)</p> <p><br> <strong>HD model parameter file at 5 Min resolution: hdpara_vs5_1.nc</strong></p> <p>River directions and digital elevation data were provided by Bernhard Lehner (pers. comm., 2014) and were derived from the HydroSHEDS (Lehner et al., 2006) database and from the Hydro1K dataset for areas north of 60°N (https://lta.cr.usgs.gov/HYDRO1K).<br> For a number of rivers (most of them north of 60°N), flow directions and model orography were manually corrected based on available GIS data, such as from DIVA (https://www.diva-gis.org/gdata), CCM River and Catchment Database<br> (Vogt et al. 2007), SMHI (Swedish Meteorological and Hydrological Institute), NVE (Norges vassdrags- og energidirektorats), SYKE (Finnish Environment Institute).</p> <p>This corrected dataset is referred to as HDvs5 in the following.<br> The HD model parameters for overland flow, base flow and river flow are generated as described in Hagemann and Dümenil (1998) and Hagemann et al. (2020). However, different to the HD model vs. 4 described in Hagemann et al. (2020), Vs5 utilizes inland water fractions from the ESA CCI Water Bodies Map v4.0 (Lamarche et al. 2017) and wetland fractions from the Global Lakes and Wetlands Database (Lehner and Döll 2004) instead of the previously used lake and wetlands fractions. Changes from Vs. 5.0 to Vs. 5.1 are provided in the file history_data.md that should be previewed below.</p> <p>The HD parameter dataset contains 14 variables which are shortly described in the following table.</p> <ul> <li> FLAG | Land sea mask | -</li> <li> FDIR | Flow direction | - | defined as written below</li> <li> ALF_K | HD model parameter Overland flow k | d-1</li> <li> ALF_N | HD model parameter Overland flow n | -</li> <li> ARF_K | HD model parameter Riverflow k | d-1</li> <li> ARF_N | HD model parameter Riverflow n | -</li> <li> AGF_K | HD model parameter Baseflow flow k | d-1</li> <li> AREA | Grid cell area | m-2 | based on own computation</li> <li> FILNEW | River flow target indices for longitudes | -</li> <li> FIBNEW | River flow target indices for latitudes | -</li> <li> DISTANCE | Distance between gridboxes in flow direction | m</li> <li> RIVERLENGTH | Distance between gridbox and the river mouth (or final sink) | km</li> <li> CAT_AREA | Upstream catchment area of gridbox | km²</li> <li> CAT_ID | Catchment ID of gridbox | -</li> </ul> <p> <em> Flow directions in variable FDIR are defined as on the Num Pad of a PC keyboard: </em> </p> <ul> <li> 7 8 9</li> <li> \ | /</li> <li> \|/</li> <li> 4--5--6</li> <li> /|\</li> <li> / | \</li> <li> 1 2 3</li> </ul> <p> Special directions: 5 = Sink point, i.e. no outflow<br> 0 = River mouth point in the ocean <br> -1 = Ocean point, but no river mouth</p> <p>Forcing data masks file: masks_5min.nc</p> <p>In the offline HD model version, this file is usually only used to obtain the grid information of the forcing data, i.e. of surface runoff and drainage (subsurface runoff). However, it contains four variables that are read in by the model, and that are actually used un coupled applications within the MPI-ESM. Even though these variables are not used in the HD model offline version, it was decided to keep them in order to allow future developments regarding the usage of these data and to keep some consistency with the HD model code implemented in MPI-ESM.</p> <ul> <li> ALAKE | Lake fraction within a grid box | Lamarche et al. 2017</li> <li> GLAC | Glacier fraction within a grid box | Hagemann 2002</li> <li> SLF | Land fraction within a grid box | Lamarche et al. 2017</li> <li> SLM | Land Sea Mask | Lamarche et al. 2017</li> </ul> <p>For simplicity, the data provided at the HD model resolution. Hence, these masks can be used when the forcing data are interpolated to the HD model resolution before they are read during the model run.</p> <p>This tar archive also include a subset of this global dataset for the European domain, hdpara_vs5_0_euro5min.nc.</p> <p> </p> <p><strong>HD model parameter file at 0.5° resolution: hdpara_vs1_11.nc</strong></p> <p>In addition, a global 0.5° HD parameter file hdpara_vs1_11 included. This is an update of the previous version 1.10 that was consistent to the parameter files used in previous offline and coupled applications of the HD model at 0.5° resolution (see, e.g. studies cited in Sect. 2.1 of Hagemann et al., 2020). Compared to the previous version 1.10, Vs. 1.11 now also utilzes the ESA water bodies and GLWD wetlands database such as in the 5 Min vs. 5.1 (see above). In additon, some flow directions have been updated. Except for DISTANCE and RIVERLENGTH, it comprises the same variables as for the 5 Min. version, but flow directions and parameters are generated as described in Hagemann and Dümenil (1998) and Hagemann and Dümenil Gates (2001). Here, the 0.5 degree mask file mask_05.nc comprises those masks that were utilized in the HD parameter generation. Only the land fraction is taken from Hagemann (2002) where the HD land sea mask indicates land.</p> <p><br> <strong>References</strong></p> <ul> <li>Hagemann, S., L. Dümenil (1998) A parameterization of the lateral waterflow for the global scale. Clim. Dyn. 14 (1), 17-31</li> <li>Hagemann, S., L. Dümenil Gates (2001) Validation of the hydrological cycle of ECMWF and NCEP reanalyses using the MPI hydrological discharge model, J. Geophys. Res. 106, 1503-1510</li> <li>Hagemann, S., 2002: An improved land surface parameter dataset for global and regional climate models, MPI Report No. 336, Max Planck Institute for Meteorology, Hamburg, Germany</li> <li>Hagemann, S., T. Stacke and H. Ho-Hagemann (2020) High resolution discharge simulations over Europe and the Baltic Sea catchment. Front. Earth Sci., 8:12. doi: 10.3389/feart.2020.00012.</li> <li>Lamarche, C., Santoro, M., Bontemps, S., d’Andrimont, R., Radoux, J., Giustarini, L., Brockmann, C., Wevers, J., Defourny, P. and Arino, O. (2017) Compilation and validation of SAR and optical data products for a complete and global map of inland/ocean water tailored to the climate modeling community. Remote Sensing, 9(1), p.36.</li> <li>Lehner, B., P. Döll (2004) Development and validation of a global database of lakes, reservoirs and wetlands.</li> <li>J. Hydrol., 296: 1-22, doi:10.1016/j.jhydrol.2004.03.028.</li> <li>Vogt, J.V. et al. (2007): A pan-European River and Catchment Database. European Commission - JRC, Luxembourg, (EUR 22920 EN) 120 pp.</li> </ul> <p> </p>
Dataset for "Implementation of disequilibrium chemistry to spectral retrieval code ARCiS and application to 16 exoplanet transmission spectra. Indication of disequilibrium chemistry for HD 209458b and WASP-39b"
<p>This is the supplemental materials for the Astronomy & Astrophysics publication "Implementation of disequilibrium chemistry to spectral retrieval code ARCiS and application to 16 exoplanet transmission spectra. Indication of disequilibrium chemistry for HD 209458b and WASP-39b". Please refer to "README.md" for details.</p>
The Water-ice Feature in Near-infrared Disk-scattered Light around HD 142527: Micron-sized Icy Grains Lifted up to the Disk Surface?
<p>This is a reproduction package for the paper "The Water-ice Feature in Near-infrared Disk-scattered Light around HD 142527: Micron-sized Icy Grains Lifted up to the Disk Surface?" by Tazaki et al. (2021). In this repository, you will find the data files used to make figures in the paper. Source codes and scripts are included as well.</p>
Reproduction package for the paper "Exploring the directly imaged HD 1160 system through spectroscopic characterization and high-cadence variability monitoring"
<p>This is a basic reproduction package for the paper <a href="https://doi.org/10.1093/mnras/stae1315">"Exploring the directly imaged HD 1160 system through spectroscopic characterization and high-cadence variability monitoring" by Sutlieff et al. (2024)</a>. It aims to provide the most important data products to check and reproduce the main results of the paper.</p>
RPC-Net Dataset. Simultaneous HD-sEMG Recordings on the Forearm and angles of a 29-DOF Hand Kinematic Model
<p>The dataset in this repository comprises data acquired during the doctoral research project of Giovanni Rolandino at the Nuffield Department of Surgical Sciences, University of Oxford. Five sub-datasets make up the repository:</p> <p>DS1: Simultaneous acquisition of high-density surface electromyography (HD-sEMG) signals from the forearm and hand position kinematics. Data were recorded from 12 healthy subjects while they cycled through 16 hand poses. HD-sEMG was acquired with traditional gel electrode arrays.</p> <p>DS2: A similar protocol to DS1 was followed, but the HD-sEMG was acquired using a novel dry-electrode array. This dataset includes 16 subjects. Whereas DS1 included data from a single session for each subject, DS2 includes two sessions, acquired hours to days apart; these sessions are identified as s1 and s2.</p> <p>DS3: This subset consists of two parts. DS3.a repeats the protocol used in DS2 with 4 subjects, introducing repositioning between trials. DS3.b includes the results of the real-time assessment of RPC-Net, a shallow neural network trained with data from DS3.a to estimate hand position from HD-sEMG activity. DS3.b contains the real-time output recorded during prompt-matching tasks and the corresponding targets.</p> <p>DS4: This subset includes data related to the assessment of RFC-Net, a shallow neural network designed to estimate hand position from neck muscle activation. Experiments were performed on 8 healthy participants and 8 participants with tetraplegia. DS4.a includes the data used for training the network, while DS4.b includes data from the testing phase of the algorithm. DS4.b.s1 includes results from a cursor control task, and DS4.b.s2 includes results from a virtual hand control task.</p> <p>AD1: Additional data related to the electrical validation of the dry-electrode array.</p> <p>Code for processing the data in this repository is available on Dropbox:<br>https://www.dropbox.com/scl/fo/nkvbse7evo0k8ou1utn7i/AMDh_MOZQJ6gCwDXGPadmZ0?rlkey=ynoix3anpc81v24hogn3fymb4&st=xrqx07q2&dl=0</p> <p>For additional information, readers are referred to the original papers detailing acquisition protocols and processing procedures:</p> <p>1) G. Rolandino, M. Gagliardi, T. Martins, G. L. Cerone, B. Andrews, J. J. FitzGerald. Developing RPC-Net: Leveraging High-Density Electromyography and Machine Learning for Improved Hand Position Estimation. IEEE Transactions on Biomedical Engineering, 71(5):1617-1627, May 2024. doi:10.1109/TBME.2023.3346192.</p> <p>2) G. Rolandino, C. Zangrandi, T. Vieira, G. L. Cerone, B. Andrews, J. J. FitzGerald. HDE-Array: Development and Validation of a New Dry Electrode Array Design to Acquire HD-sEMG for Hand Position Estimation. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 32:4004-4013, 2024. doi:10.1109/TNSRE.2024.3490796.</p> <p>3) G. Rolandino, T. Vieira, G. L. Cerone, B. Andrews, J. J. FitzGerald. Performance of a ML-Based 3-DoF Kinematic Model in Estimating Hand Position from High-Density EMG. Presented at IFESS Conference, Bath, UK, September 2024.</p> <p>4) G. Rolandino, L. Lion, T. Vieira, G. L. Cerone, B. Andrews, J. J. FitzGerald. Artificial Neural Networks for HD-sEMG-Based Hand Position Estimation: Addressing Inter- and Intra-Subject Variability. Submitted to IEEE Transactions on Neural Systems and Rehabilitation Engineering, July 2025.</p> <p>5) G. Rolandino, G. Parisi, T. Vieira, G. L. Cerone, B. Andrews, J. J. FitzGerald. Real-Time Hand Kinematic Estimation with HD-sEMG and Artificial Neural Networks: Feasibility and Effects of Multi-Subject Training and Visual Feedback. Submitted to IEEE Transactions on Neural Systems and Rehabilitation Engineering, July 2025.</p> <p>6) G. Rolandino, V. Taboni Lisboa, T. Vieira, A. Cliquet Jr., B. Andrews, J. J. FitzGerald. HD-sEMG-Based Control Using Neck Muscles and Shallow Neural Networks: Assessing Performance in Rehabilitation-Oriented Tasks. Submitted to IEEE Transactions on Neural Systems and Rehabilitation Engineering, July 2025.</p> <p>This dataset benefited from the support of all listed authors and arose from collaborations between the Oxford Neural Interfacing Group; LISiN (Politecnico di Torino, Turin, Italy); the Department of Orthopedics, Rheumatology and Traumatology (University of Campinas, SP, Brazil); and the Oxford Robotics Institute (University of Oxford, Oxford, UK). Part of this work was funded by the John Fell Oxford University Press Research Fund.</p> <p>The corresponding author is available for questions or clarification at g.rolandino@protonmail.com.</p>
Multi-Crystal XRD on Cyanophage S-2L HD phosphohydrolase (DatZ) collected at room temperature with the Plate Screener
<p>Multi-Crystal XRD on DatZ crystals collected at room temperature with the CRIBLEUR Plate Screener during 2022_Run2_Plate on 2022-03-23 from an in-situ MiTeGen plate. Sweeps of 35.636 degrees (value to be confirmed) were collected on each crystal, for a total of 19 data sets. </p>
Dataset of comprehensive Full-notch creep tests (FNCT) of selected high-density polyethylene (PE-HD) materials
<p>The dataset provided in this repository comprises data obtained from a series of full-notch creep tests (FNCT) performed on selected high-density polyethylene (PE-HD) materials (for further details, see section 1 Materials in this document) in accordance with the corresponding standard ISO 16770 [1]. </p><p>The FNCT is one of the mechanical testing procedures used to characterize polymer materials with respect to their environmental stress cracking (ESC) behavior. It is widely applied for PE-HD materials, that are predominantly used for pipe and container applications. It is based on the determination of the time to failure for a test specimen under constant mechanical load in a well-defined and temperature controlled liquid environment. The test device used here also allows for continuous monitoring of applied force, specimen elongation and temperature.</p>
Dataset: The Home Depot, Inc. (HD) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dynamic HD Model Relative Orography Corrections
<p>Relative orography corrections for MPI's Dynamic HD model version 3.0.</p>
Subgenus Lestes (15-28). L. sponsa: 15. Left side of head of ♂ showing ridge behind antennal cavity; 16. pterostigma of right forewing; 17-18. anal appendages dorsally and from left; 19. prophallus; 20. terminal segments and ovipositor sheath and vulvar scale at its base (all Germany except prophallus from Japan). L. barbarus (Morocco): 21. right anal appendages from above; 22. prophallus. L. dryas (California): 23. anal appendages dorsally, 24. prophallus. L. macrostigma (Turkey): 25, 26. the same. L. virens (Germany): 27, 28. the same. f = flange, hd = hood, li = ligula, sc = scoop, sh = shelf. in A revision of African Lestidae (Odonata) (excerpt)
Subgenus Lestes (15-28). L. sponsa: 15. Left side of head of ♂ showing ridge behind antennal cavity; 16. pterostigma of right forewing; 17-18. anal appendages dorsally and from left; 19. prophallus; 20. terminal segments and ovipositor sheath and vulvar scale at its base (all Germany except prophallus from Japan). L. barbarus (Morocco): 21. right anal appendages from above; 22. prophallus. L. dryas (California): 23. anal appendages dorsally, 24. prophallus. L. macrostigma (Turkey): 25, 26. the same. L. virens (Germany): 27, 28. the same. f = flange, hd = hood, li = ligula, sc = scoop, sh = shelf.
FIG. 1 in The camel remains from site HD-6 (Ra's al-Hadd, Sultanate of Oman): an opportunity for a critical review of dromedary findings in eastern Arabia
FIG. 1. — The Late Stone Age, Bronze and Iron Age sites with camel remains in eastern Arabia mentioned in the text.
FIG. 10 in The camel remains from site HD-6 (Ra's al-Hadd, Sultanate of Oman): an opportunity for a critical review of dromedary findings in eastern Arabia
FIG. 10. — Sha'ib Musamma open-air site, Central Arabia. Rock engraving of group hunting of one-humped camel (Camelus dromedarius) (Spassov & Stoytchev 2004).
FIG. 7 in The camel remains from site HD-6 (Ra's al-Hadd, Sultanate of Oman): an opportunity for a critical review of dromedary findings in eastern Arabia
FIG. 7. — LSI-values calculated on the astragalus of dromedaries from Arabian Peninsula (empty triangles referring to hybrids).
FIG. 4. — A in The camel remains from site HD-6 (Ra's al-Hadd, Sultanate of Oman): an opportunity for a critical review of dromedary findings in eastern Arabia
FIG. 4. — A plan of HD-6. The map displays the main occupation levels labeled as Period I (3100–2700 BC) and dromedary remains (Plan: Valentina Azzarà).
FIG. 3 in The camel remains from site HD-6 (Ra's al-Hadd, Sultanate of Oman): an opportunity for a critical review of dromedary findings in eastern Arabia
FIG. 3. — Third lower molar of Camelus dromedarius founded in an external sandy layer to a stone structure from the saline component. It presents large areas of combustion: A, lingual side; B, labial side; C, occlusal side (Photo: Elena Maini).
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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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.