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1,211 results for “instrument”
Figure 14 from: Tomaskinova J, Geneletti D, Dicks LV, Grace M, Collier M, Longato D, Stoev P, Sapundzhieva A, Balzan MV (2021) Capacity-building as an instrument to foster the implementation of nature-based solutions. Research Ideas and Outcomes 7: e77666. https://doi.org/10.3897/rio.7.e77666
Figure 14 Group work on planning and designing NbS in the Marsa Sports Complex case study.
Figure 13 from: Tomaskinova J, Geneletti D, Dicks LV, Grace M, Collier M, Longato D, Stoev P, Sapundzhieva A, Balzan MV (2021) Capacity-building as an instrument to foster the implementation of nature-based solutions. Research Ideas and Outcomes 7: e77666. https://doi.org/10.3897/rio.7.e77666
Figure 13 Group work on planning and designing NbS in the Hal-Ferħ case study.
Figure 1 from: Tomaskinova J, Geneletti D, Dicks LV, Grace M, Collier M, Longato D, Stoev P, Sapundzhieva A, Balzan MV (2021) Capacity-building as an instrument to foster the implementation of nature-based solutions. Research Ideas and Outcomes 7: e77666. https://doi.org/10.3897/rio.7.e77666
Figure 1 Pie chart showing the gender diversity distribution of keynote speakers and panellists.
Figure 11b from: Tomaskinova J, Geneletti D, Dicks LV, Grace M, Collier M, Longato D, Stoev P, Sapundzhieva A, Balzan MV (2021) Capacity-building as an instrument to foster the implementation of nature-based solutions. Research Ideas and Outcomes 7: e77666. https://doi.org/10.3897/rio.7.e77666
Figure 11b Introduction to self-organised group work based on three actual case studies. -
Figure 11a from: Tomaskinova J, Geneletti D, Dicks LV, Grace M, Collier M, Longato D, Stoev P, Sapundzhieva A, Balzan MV (2021) Capacity-building as an instrument to foster the implementation of nature-based solutions. Research Ideas and Outcomes 7: e77666. https://doi.org/10.3897/rio.7.e77666
Figure 11a Introduction to self-organised group work based on three actual case studies. -
Musical Instrument Rondador 3D
El rondador is a Ecuadorian musical instrument that is made from a local plant called "Carrizo" This especific model has 25 tubes each one with different heights and thickness. The .rar file contains: - High and low meshes in .fbx and .obj format - PBR textures maps (2K resolution )for any 3d software, unity 5 and unreal engine 4. - .blend file - hdri image file Source: Objaverse 1.0 / Sketchfab
Supplemental Table S1: Survey instrument
<p>Supplemental Table S1: Survey instrument. This table is a supporting document for the publication "Assigned group work is associated with increased student motivation and perceptions of belonging in an asynchronous online physiology laboratory course". Authors: Shea E. Carr, Thad E. Wilson, Stacey A. Slone, Leila W. Karanja, Jennifer L. Osterhage. </p>
Data and software for "Correcting Turbulence-induced Errors in Fiber Positioning for the Dark Energy Spectroscopic Instrument"
<p>Supplementary material to the DESI publication "Correcting Turbulence-induced Errors in Fiber Positioning for the Dark Energy Spectroscopic Instrument".</p> <p>The main "turbfigures.py" script generates the figures in the paper from the included data files.</p> <p>Contents:</p> <p><strong>Software files</strong></p> <ul> <li>turbfigures.py: Turbulence plotting and analysis plotting routines.</li> <li>turbulence.py: Analysis routines called by turbfigures.py. The live version of this code in production for desi is in the desimeter package (https://github.com/desihub/desimeter/blob/main/py/desimeter/turbulence.py)</li> </ul> <p><strong>Data files</strong></p> <ul> <li>coord-summary-20240124-pm.fits <ul> <li>Measured centroids of fibers in for 400 consecutive images of the focal plane while at zenith. File contains the following columns: <ul> <li>expid - exposure ID</li> <li>location - "location" of fiber in focal plane; ranges from 0 - 10000. Fibers on petal 0 have numbers between 0 and 1000, etc. There are only 500 positioners per petal, so most locations are not populated.</li> <li>move - in real data, this indexes over the moves in a DESI positioning loop; garbage information here.</li> <li>fpa_{x,y} - measured position of the fiber in this exposure</li> <li>req_{x,y} - requested position of the fiber in this exposure; not used for this data set where positioners are fixed in location</li> <li>{x,y}turb - empty in this file; gets filled in by turbfigures.py with measured turbulence</li> <li>flags_cor - flags indicating that a positioner or fiber may be problematic</li> <li>postype - flags indicating that a location corresponds to a real positioner vs. a fiducial</li> <li>expected_{x,y} - empty in this file; gets filled in by turbfigures.py with "expected" positions of each fiber, so that fpa_{x,y} - expected_{x,y} is a noisy estimate of the turbulence seen by the fiber.</li> </ul> </li> <li>Note that this file has an awkward structure. It has 10000 rows, one for each possible location. Most fields are then 400 element arrays that gives the corresponding values, corresponding to the 400 exposures present in the file.</li> </ul> </li> <li>coordinates-stats.ecsv <ul> <li>statistics of positioning accuracy and turbulence amplitude in DESI positioning loops. Measured RMSes are the 5-sigma clipped root-mean-square positioning offset in 2D: sqrt(mean(dx^2 + dy^2)). Measured medians are median(sqrt(dx^2 + dy^2)).</li> <li>Contents: <ul> <li>coord_filename - file name of DESI coordinates file statistics were drawn from</li> <li>rms_turbulence - RMS for the turbulent contribution to the positioning error</li> <li>rms_positioning - RMS for positioning after removing turbulence</li> <li>rms_total - Total RMS</li> <li>med_turbulence - median turbulence in exposure</li> <li>med_positioning - median positioning error in exposure</li> <li>med_total - median total turbulence + positioning error in exposure</li> <li>expid - exposure id number</li> </ul> </li> </ul> </li> </ul> <p>Dependencies: The included software uses the DESI software stack and otherwise the usual python astronomy packages: numpy scipy matplotlib astropy. Alternatively, people with access to NERSC can load the default DESI environment and pull in all needed dependencies.</p>
Audio samples from "A Physical Intelligent Instrument using Recurrent Neural Networks"
<p>These are the audio samples from chapter 3 and 4 of my thesis "A Physical Intelligent Instrument using Recurrent Neural Networks".</p>
Investigating CNN-Based Instrument Family Recognition for Western Classical Music Recordings
<p>This repository contains the data used for experiment 2 (both patch- and file-based) to reproduce the results from the ISMIR paper.</p> <p>If you wish to know more about the dataset and experiment 1, please contact us.</p>
ORGANIZING FINANCIAL INSTRUMENT ACCOUNTING IN ECONOMIC ENTITIES IN ACCORDANCE WITH INTERNATIONAL STANDARDS
Open the record for dataset details and reuse information.
Supplementary Material for 'Generation Z Doctors' Preferences Towards Pharmaceutical Marketing': Survey Instrument and Response Distribution
<p>This supplementary material accompanies the study 'Generation Z Doctors and the Future of Pharma Marketing in India.' It includes the complete survey instrument used to gather insights from Generation Z doctors, as well as detailed response distribution tables. The material provides additional data that supports the quantitative and qualitative analyses presented in the main manuscript, offering a comprehensive understanding of the engagement preferences, decision-making paradigms, and ethical considerations influencing Generation Z doctors' interactions with pharmaceutical marketing. This dataset is intended to facilitate further research and enhance the transparency of the study's findings.</p>
Deep-Learned Broadband Encoding Stochastic Filters for Computational Spectroscopic Instruments
<p>Abstract</p> <p>Computational spectroscopic instruments with broadband encoding stochastic (BEST) filters allow the reconstruction of the spectrum at high precision with only a few filters. However, conventional design manners of BEST filters are often heuristic and may fail to fully explore the encoding potential of BEST filters. The parameter constrained spectral encoder and decoder (PCSED)—a neural network-based framework—is presented for the design of BEST filters in spectroscopic instruments. By incorporating the target spectral response definition and the optical design procedures comprehensively, PCSED links the mathematical optimum and practical limits confined by available fabrication techniques. Benefiting from this, a BEST-filter-based spectral camera presents a higher reconstruction accuracy with up to 30 times enhancement and better tolerance to fabrication errors. The generalizability of PCSED is validated in designing metasurface- and interference-thin-film-based BEST filters.</p> <p> </p> <p>Please refer to https://github.com/Hao-Laboratory/PCSED for the source code for data analysis and visualization.</p>
Input files accompanying the paper "Instrumenting Stellar Oscillation Codes with Parfait Models"
<p>A zip archive containing input files accompanying the paper "Instrumenting Stellar Oscillation Codes with Parfait Models" by R. H. D. Townsend and R. V. Kuenzi.</p>
Instrument part 2
Parts of Chinese instrument scanned by Jewelry scanner JS-300. Orignal is made of rosewood Source: Objaverse 1.0 / Sketchfab
Instrument Design and Validation to Asses the Perception of Wellbeing Associated to Plain Water.
ClinicalTrials.gov study NCT01982981. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Effectiveness of a Massage Instrument Compared to Placebo for Upper Trapezius Muscle Pain
ClinicalTrials.gov study NCT01167036. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Relationship Between Central Corneal Thickness and Intraocular Pressure Measures Instruments.
ClinicalTrials.gov study NCT00347191. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Effects of Instrument Assisted Soft Tissue Mobilization in Addition to Conventional Physiotherapy in Knee Arthrofibrosis
ClinicalTrials.gov study NCT06172231. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Frailty as an INstrument for Evaluation of Elderly Patients With Non ST Elevation Myocardial Infarction (NSTEMI)
ClinicalTrials.gov study NCT01049997. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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.