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615 results for “tuning”
Matlab code for " Prior expectation enhances sensorimotor behavior by modulating population tuning and subspace activity in sensory cortex"
<p>Here are the data and code supporting the findings of the paper, 'Prior expectation enhances sensorimotor behavior by modulating population tuning and subspace activity in sensory cortex.'</p>
Molecular and structural insights into H2 indicator supraparticles: lowering the limit of detection by tuning incorporated catalyst nanoparticles
<p>Raw data from DRIFTS experiments, TEM and SEM microscopy, Zeta potential measurements, and TGA measurements for the article "Molecular and structural insights into H2 indicator supraparticles: lowering the limit of detection by tuning incorporated catalyst nanoparticles" published in Chemistry of Materials <a href="https://doi.org/10.1021/acs.chemmater.3c01105">https://doi.org/10.1021/acs.chemmater.3c01105</a>.</p>
No More In-Context Learning? Exploring Parameter-Efficient Fine-Tuning Techniques for Code Generation with Large Language Models
<p>Data and models part of the replication package of the ICSE 24 submission entitled "<em>No More In-Context Learning? Exploring Parameter-Efficient Fine-Tuning Techniques for Code Generation with Large Language Models</em>".</p>
Data accompanying the paper "Improving the near-surface wind and turbulence at the edge of the orographic drag grey zone by tuning the roughness length"
<p>This repository contains outputs from simulations (with observations) launched with the ALARO-HR18 model utilizing the TOUCANS two-energy turbulence scheme and different roughness length configurations. The results are provided in three separate files, i.e., "acf_data.zip", "fluxes.zip", and "verif_data.zip".</p> <p>1) "acf_data.zip" contains four ASCII files with the name "acf_DATE_TYPE.txt", where DATE=YYYYMMDD and TYPE=mountain or flat. Each file includes the model data from surroundings of approx. 200 x 200 km around the chosen point (flat terrain or mountain), and provides data on longitude (LON), latitude (LAT), zonal wind (U-WIND), meridional wind (V-WIND) and wind speed (W). The data included in this file were used to compute the 2D spatial autocorrelation function for different cases and locations shown in Fig.3.</p> <p>2) "fluxes.zip" contains individual ASCII files with the name "FTYPE_EXP.txt", where FTYPE=uw_flux or vw_flux, and EXP is one of OBS, ORL, REF, EXP1, EXP2, EXP3 and EXP4. Each file contains turbulent momentum flux data at four vertical levels (5 m; UW005 and VW005, 60 m; UW060 and VW060; 100 m; UW100 and VW100 and 180 m; UW180 and VW180). The description of EXP is following:</p> <p>OBS; observed momentum fluxes from the Cabauw tower, the Netherlands, corresponding to five different cases, i.e., matching with 3D model simulations starting at 00 UTC on 6, 10 and 16th February 2020, as well as 16 and 25th August 2020. Each column corresponds to one vertical level, while the rows represent hourly values (up to +72 hours in advance), added case by case.</p> <p>ORL; predicted fluxes obtained with the old roughness length configuration described in the paper.</p> <p>REF; predicted fluxes obtained with the new roughness length (NRL) configuration as described in the paper and without tuning the vegetation roughness length (C1=0.25, C2=1.0, C3=6, FA=0 and OGWD scheme on).</p> <p>EXP1; is the same as REF but with C2=1.5.</p> <p>EXP2; is the same as REF but with C2=1.875.</p> <p>EXP3; is the same as REF but with C2=1.875 and C3=3.</p> <p>EXP4; is the same as REF but with C2=1.875 and C3=1.5.</p> <p>The data included in this file were used to prepare inputs to Table 2.</p> <p><strong>NOTE:</strong> The measurements taken at the Cabauw tower are under the care of "Koninklijk Nederlands Meteorologisch Instituut" (KNMI). They are added to this repository only to confirm related results presented in the paper. For any personal use of these data, contact the responsible KNMI staff.</p> <p>3) "verif_data.zip" contains individual ASCII files used to validate the impact of various tunable parameters and final settings of the NRL configuration on 10-m wind, i.e., for preparation of Fig. 6-8. and Fig. 9-12. Individual ASCII files are named "WS_EXP_SDATE_ENDDATE_all", where:</p> <ul> <li>a) EXP is one of C1s, C1f, C2s, C2f, C3s, C3f, ORL, NRLs, NRLf, FAs, FAf, GWDon and GWDoff. "s" and "f" correspond to the starting and the final setup related to sensitivity studies performed in the paper (Fig. 6-8.) or starting and final version of the NRL configuration (Fig. 10-12). The results from FAs, FAf, GWDon and GWDoff are only briefly commented on within the paper but not shown.</li> <li>b) SDATE=YYYYMMDD (starting date of the validation period), and</li> <li>c) EDATE=YYYYMMDD (ending date of the validation period).</li> </ul> <p>Each ASCII file contains the following fields (column-wise): SYNOP number of station (location; for explanation cf. "synop_list.txt"), latitude (lat), longitude (lon), altitude of the station (h), date to which data refers to (date), forecast lead time to which data refers to (lead time), predicted 10-m wind (fcst) and measured 10-m wind (obs).</p> <p><strong>CAUTION:</strong> Be aware that all times are given in UTC and that they are consistently used where predicted and observed data are used.</p> <p>In case you have any questions, contact the corresponding author.</p>
Aversive stimulus-tuned responses in the CA1 of the dorsal hippocampus (dataset 8)
<p>Multichannel electrophysiology data for the manuscript with the same title.</p> <p>(2306_24)</p>
Data and code for ""Smoking gun" signatures of topological milestones in trivial materials by measurement fine-tuning and data postselection"
<p><strong>Brief description</strong></p> <p>This repository contains data, code, and other materials for ""Smoking gun" signatures of topological milestones in trivial materials by measurement fine-tuning and data postselection". Files are zipped by project.</p> <p><strong>Data formats</strong></p> <ul> <li>MTX: A simple 2D/3D matrix format developed for <a href="https://nsweb.tn.tudelft.nl/~gsteele/spyview/">Spyview</a>.</li> <li>DAT: Plain-text tabular data.<br> DAT and MTX files can be plotted with Spyview, qtplot (a portable version for Windows can be downloaded <a href="https://github.com/cover-me/qtplot/releases/download/2020.09.21/qt_plot.2020.09.21.7z">here</a>), Jupyter notebooks in ZIP files or <a href="https://github.com/cover-me/qtview">here</a>.</li> <li>SET: Instrument settings.</li> <li>PY: Measurement scripts.</li> <li>IPYNB or HTML: Jupyter notebooks with code and figures. IPYNB can be previewed on this <a href="https://kokes.github.io/nbviewer.js/viewer.html">page</a>.</li> </ul> <p><strong>Additional repositories</strong></p> <p>MATLAB codes for theoretical simulations in Ref 63: https://github.com/frolovgroup/</p> <p>Full data and code from the Shapiro step project: [https://zenodo.org/record/6416083](Data and code for "smash junction" papers)</p>
The Efficacy of a Frequency-tuned Electromagnetic Field Treatment in Facilitating the Recovery of Subacute Ischemic Stroke Patients - a Pivotal Study (THE "EMAGINE" STUDY)
ClinicalTrials.gov study NCT05044507. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Comparison of 50% Tilt and Tuned Waveforms in Single-Coil Active Can Configuration
ClinicalTrials.gov study NCT00874445. IPD Sharing: Not stated. Countries: 1. Publications: 5.
Tuning in to Kids® for Parents of Preschool-aged Children in Portugal
ClinicalTrials.gov study NCT07350083. IPD Sharing: NO. Countries: 1. Publications: 0.
Electronic Diabetes Tune-Up Group (eDTU) for African Americans
ClinicalTrials.gov study NCT06321029. IPD Sharing: YES. Countries: 1. Publications: 1.
Tuning in to Kids in Norwegian FUS Kindergartens
ClinicalTrials.gov study NCT03985124. IPD Sharing: NO. Countries: 1. Publications: 1.
Tuning in to Kids in School - Competence Building and Collaboration in the Team Around Primary School Students
ClinicalTrials.gov study NCT06501300. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Surgical Prehabilitation on Autonomic Nervous System (TUNE)
ClinicalTrials.gov study NCT06398301. IPD Sharing: NO. Countries: 1. Publications: 19.
Comparison of Fixed Tilt and Tuned Waveforms in Right-sided Implants
ClinicalTrials.gov study NCT00873691. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Tuned Versus Untuned Ankle-foot Orthoses in Children and Adolescents With Cerebral Palsy
ClinicalTrials.gov study NCT03547674. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Participatory Research for Fine-tuning of a 2.0 System to Optimise Home Management of Oral Cancer Therapies.
ClinicalTrials.gov study NCT02921724. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.
Data from: Mechanosensation is evolutionarily tuned to locomotor mechanics
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The evolution of sexual signaling is linked to odorant receptor tuning in perfume-collecting orchid bees
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Data from: Causes and consequences of repeatability, flexibility and individual fine-tuning of migratory timing in pike
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Data from: Behavioural tuning in a tropical amphibian along an altitudinal gradient
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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.