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1,211 results for “Instruments”
Evaluating an instrument of the research software related to software use and disclosure - Dimension 1 - Dataset of Focus Groups
<p>Artifacts used for data collection and analysis of the focus groups sessions during the evaluation of an instrument for research software related to software use and disclosure - dimension 1.</p>
Brazilian Rhythmic Instruments Dataset (BRID)
<p>The Brazilian Rhythmic Instruments Dataset (BRID) is a curated collection of audio recordings featuring traditional Brazilian rhythmic instruments, developed for Music Information Retrieval (MIR) research. It comprises 367 tracks, including 274 solo and 93 multi-instrument recordings, encapsulating ten instrument classes and five primary Brazilian rhythm styles: samba, partido alto, samba-enredo, capoeira, and marcha. BRID is suitable to study rhythm, beat tracking, and rhythmic pattern recognition in Brazilian music. </p>
Data for Manuscript: Instrumental Validity of the Motion Detection Accuracy of a Smartphone Based Training Game
<p><strong>Background: </strong>In the project TRIMOTEP we developed a low-cost augmented reality training game. Aim of the training game ist to support patients after total hip replacement in their rehabilitation. The project was funded by the Austrian Research Promotion Agency (FFG, grant number 862050). As hardware the training game uses a headset, an android smartphone and a step board. The goal of the training game is to dodge animals and objects while performing exercises. A current version of the training game can be downloaded here: https://trimotep.fh-joanneum.at/exer-game-ar_walker/ . The training game is based on Google ARCore and uses a movement detection approach to recognise different exercises. To detect movements ARCore uses the smartphone inbuilt inertial measurement unit and the front camera (https://developers.google.com/ar/discover). In order to investigate the possibilities of the training game, it is necessary to examine the accuracy of movement detection in more detail.</p> <p><strong>Data: </strong>To investigate the accuracy, comparative measurements were carried out with 30 healthy subjects. During the measurements, the subjects motion was recorded simultaneously with the training game and an optoelectronic motion capture system (Vicon). Two trials were recorded with each subject.</p> <p>First Trial: subjects followed a protocol</p> <p>Second Trial: subjects played the training game for one minute</p> <p>The training game measures the movement of the smartphone (and therefore of the headset and the head). The optoelectronic motion capture system uses a marker set consisting of four markers. Those markers are labeled HMD_F, HMD_B, HMD_R, HMD_L. Markers HMD_R and HMD_L as well as HMD_B and HMD_F form an axis in a karthesian coordinate system. This coordinate system is rotated by 8 degrees compared to the training game along the transversal axis.</p> <p><strong>Structure of the Data Set:</strong> The data set includes an excel sheet with general data of the subjects and a figure showing the tilt between the two coordinate systems. Further one folder contains the measurement data of the training game as json files. Another folder contains the measurement data of the optoelectronic motion capturing system as csv files.</p> <p> </p> <p>For further information or help to process the data please contact:</p> <p>Bernhard Guggenberger, bernhard.guggenberger2@fh-joanneum.at</p>
CTAO Instrument Response Functions - prod5 version v0.1
<p>CTAO Instrument Response Functions - prod5 version v0.1</p> <p>The CTA Observatory (CTAO) will provide very wide energy range and excellent angular resolution and sensitivity in comparison to any existing gamma-ray detector. Energies down to 20 GeV will allow CTAO to study the most distant objects. Energies up to 300 TeV will push CTAO beyond the edge of the known electromagnetic spectrum, providing a completely new view of the sky. This data repository provides access to performance evaluation and instrument response functions (IRFs) for CTA.</p> <ul> <li>IRF version: prod5 v0.1</li> <li>Telescope model and site configuration: <a href="https://zenodo.org/record/6218687">prod5-model</a></li> <li>Publication date: Sep 2021</li> <li>Archived webpage with performance figures included: <a href="/cta-science/montecarlo-results/public-irfs-zenodo/cta-prod5-zenodo/-/blob/preview/Website.md">CTAO Performance Description (file Website.md)</a></li> <li>Licence: this work is licensed under a <a href="/cta-science/montecarlo-results/public-irfs-zenodo/cta-prod5-zenodo/-/blob/preview/LICENSE">Creative Commons Attribution 4.0 International License</a>.</li> </ul> <p>Please use the contact address open-data@cta-observatory.org for any inquiries.</p> <p>Citation and Acknowledgements:</p> <p>In cases for which the CTA instrument response functions are used in a research project, we ask to add the following acknowledgement in any resulting publication:</p> <p>"This research has made use of the CTA instrument response functions provided by the CTA Consortium and Observatory, see <a href="https://www.cta-observatory.org/science/cta-performance/">https://www.ctao-observatory.org/science/cta-performance/</a> (version prod5 v0.1; [citation]) for more details."</p> <p>Please use the following BibTex Entry for [citation] in the reference section of your publication:<br> <a href="https://zenodo.org/record/5499840/export/hx">https://zenodo.org/record/5499840/export/hx</a></p> <p>Description</p> <p>Monte Carlo Simulations:</p> <p>The performance values are derived from detailed Monte Carlo (MC) simulations of the CTA instrument based on the CORSIKA air shower code (v7.71, with the hadronic interaction models QGSjet-II-04 and URQMD, [1]) and telescope simulation tool sim_telarray [2]. A power- law gamma-ray spectrum with photon index 2.62 was assumed in the calculations, although none of the instrument response functions (e.g. differential flux sensitivities, effective areas, angular or energy resolutions) depends on the assumed spectral shape of the gamma-ray source. Background cosmic-ray spectra of proton and electron/positron particle types are modelled according to recent measurements from cosmic-ray instruments.</p> <p>Nominal telescope pointing is assumed, with all telescopes pointing directions parallel to each other (performance estimation for other pointing modes, e.g. divergent pointing will be provided in the future). Performance estimations are available for three zenith angles (20 deg, 40 deg, and 60 deg), and for each zenith angle for two different azimuth angles (corresponding to pointing towards the magnetic North and South). There are significant performance differences found between the two azimuthal pointing directions (especially for the Northern site) as the impact of the geomagnetic field is large enough to influence notably the air shower development. For general studies, the use of the azimuth-averaged instrument response functions is recommended.</p> <p>Instrument Response Functions (IRFs):</p> <p>The analysis has been tuned to maximize the performance in terms of flux sensitivity. The optimal analysis cuts depend on the duration of the observation, therefore the IRFs are provided for 3 different observation times, from 0.5 to 50 h. IRFs are provided as binned histogram or FITS tables. It should be stressed, that the full potential of CTA in terms of angular and energy resolution is not revealed by these IRFS, due to the focus on the optimisation for best flux sensitivity.</p> <p>In general all histograms are binned with a 0.2-binning on the logarithmic energy axis (5 bins per decade); some selected histograms (e.g. effective areas or energy migration matrices) are provided with a finer binning. Effective area and energy migration matrix are available in a double version: one for the case in which there is no a priori knowledge of the true direction of incoming gamma rays (e.g. for the observation of diffuse sources), and another for observations of point-like objects (including among the analysis cuts one on the angle between the true and the reconstructed gamma-ray direction).</p> <p>IRFs are provided in ROOT format and as FITS tables. The FITS tables can be used directly as input to science analysis tools. The values of the IRFs are identical for the different file format, with one exception: the angular point-spread function is approximated by a Gaussian function for the FITS tables, while the ROOT files contain the full distribution.</p> <p>Telescope layouts are preliminary and subject to change. The following array layouts (Alpha configuration) have been assumed:</p> <ul> <li> CTA South with 14 MSTs and 37 SSTs (see [figure](figures/CTA-Performance-prod5-v0.1-South-Alpha-Layout.png))</li> <li> CTA North with 4 LSTs and 9 MSTs (see [figure](figures/CTA-Performance-prod5-v0.1-North-Alpha-Layout.png))</li> </ul> <p>Two zip files are uploaded:</p> <ul> <li>full archive with IRFs in FITS and ROOT format: cta-prod5-zenodo-v0.1.zip</li> <li>partial archive with IRFs in FITS format only: cta-prod5-zenodo-fitsonly-v0.1.zip</li> </ul> <p>File Naming (examples):</p> <ul> <li>Prod5-North-40deg-AverageAz-4LSTs09MSTs.18000s-v0.1.root: IRF for CTA Northern site on La Palma, 40 deg zenith angle, azimuth-averaged pointing, optimised for 5 hours of observation time</li> <li>Prod5-South-20deg-AverageAz-14MSTs37SSTs.180000s-v0.1.fits.gz: IRF for CTA Southern site in Paranal, 20 deg zenith angle, azimuth-averaged pointing, optimised for 50 hours of observation time</li> </ul> <p>List of files:</p> <p>FITS format:</p> <ul> <li>fits/CTA-Performance-prod5-v0.1-North-20deg.FITS.tar.gz</li> <li>fits/CTA-Performance-prod5-v0.1-North-40deg.FITS.tar.gz</li> <li>fits/CTA-Performance-prod5-v0.1-North-60deg.FITS.tar.gz</li> <li>fits/CTA-Performance-prod5-v0.1-South-20deg.FITS.tar.gz</li> <li>fits/CTA-Performance-prod5-v0.1-South-40deg.FITS.tar.gz</li> <li>fits/CTA-Performance-prod5-v0.1-South-60deg.FITS.tar.gz</li> </ul> <p>ROOT format:</p> <ul> <li>root/CTA-Performance-prod5-v0.1-North-20deg.tar.gz</li> <li>root/CTA-Performance-prod5-v0.1-North-40deg.tar.gz</li> <li>root/CTA-Performance-prod5-v0.1-North-60deg.tar.gz</li> <li>root/CTA-Performance-prod5-v0.1-South-20deg.tar.gz</li> <li>root/CTA-Performance-prod5-v0.1-South-40deg.tar.gz</li> <li>root/CTA-Performance-prod5-v0.1-South-60deg.tar.gz</li> </ul> <p>IRFs for subarrays of e.g., MSTs only are in the files named MSTSubArray (similar for all other telescope types).</p> <p>References</p> <ul> <li>[1] <a href="https://www.ikp.kit.edu/corsika/">https://www.ikp.kit.edu/corsika/</a></li> <li>[2] Bernloehr, K. 2008, Astroparticle Physics, 30, 149</li> </ul> <p>Acknowledgements</p> <p>We would like to thank the computing centres that provided resources for the generation of the Prod 5 Instrument Response Functions (IRFs):</p> <ul> <li>CAMK, Nicolaus Copernicus Astronomical Center, Warsaw, Poland</li> <li>CIEMAT-LCG2, CIEMAT, Madrid, Spain</li> <li>CYFRONET-LCG2, ACC CYFRONET AGH, Cracow, Poland</li> <li>DESY-ZN, Deutsches Elektronen-Synchrotron, Standort Zeuthen, Germany</li> <li>GRIF, Grille de Recherche d’Ile de France, Paris, France</li> <li>IN2P3-CC, Centre de Calcul de l’IN2P3, Villeurbanne, France</li> <li>IN2P3-CPPM, Centre de Physique des Particules de Marseille, Marseille, France</li> <li>IN2P3-LAPP, Laboratoire d Annecy de Physique des Particules, Annecy, France</li> <li>INFN-FRASCATI, INFN Frascati, Frascati, Italy</li> <li>INFN-T1, CNAF INFN, Bologna, Italy</li> <li>INFN-TORINO, INFN Torino, Torino, Italy</li> <li>MPIK, Heidelberg, Germany</li> <li>OBSPM, Observatoire de Paris Meudon, Paris, France</li> <li>PIC, port d’informacio cientifica, Bellaterra, Spain</li> <li>prague_cesnet_lcg2, CESNET, Prague, Czech Republic</li> <li>praguelcg2, FZU Prague, Prague, Czech Republic</li> <li>UKI-NORTHGRID-LANCS-HEP, Lancaster University, United Kingdom</li> </ul>
Raw measurements data of piezo actuator displacements, in a form of a groove pattern, obtained with a laser interferometer and a roundness instrument
<p><strong>A brief description of the repository content</strong></p> <p>The data presented here were obtained during an experimental calibration of the Taylor Hobson 130 (Leicester, UK) roundness instrument with the Thorlabs LPS710M (Thorlabs, Newton, NJ, USA) piezo actuator driven by Thorlabs PPC001 Piezo Controller and controlled with Kinesis® (Thorlabs) software. Before the calibration, the piezo actuator itself was calibrated with the Renishaw XL-80 interferometer system (Renishaw, Wotton-under-Edge, UK) along with the Renishaw small optics kit (A-8003-3244). These data are included in the repository as well.</p> <p><strong>Description of the dataset</strong></p> <p>Inside the .zip file, the data obtained with the Renishaw XL-80 laser interferometer and the Taylor Hobson 130 roundness instrument are stored in the “Renishaw_XL-80” and “Taylor_Hobson_130” folders, respectively. Each of these folders contains six subfolders: “0.24um”, “0.75um”, “2.4um”, “7.5um”, “24um”, and “75um”, which include the measurements data obtained with these devices (not simultaneously) while Thorlabs LPS710M piezo actuator was performing displacements simulating a groove pattern. The names of subfolders correspond to the groove’s depth.</p> <p>The data from the measurements performed with the Renishaw XL-80 interferometer were exported with the Laser XL system’s software and are saved with the extension “.RTX”. Two data files are available for each depth being considered that correspond to two measurement series. Inside each file, 40 s recording is stored. Eight grooves should be visible (nominal values: 1.5 s groove width, 3 s distance between subsequent grooves). The data were acquired with a 50 kS/s sampling rate.</p> <p>The data from the measurements performed with the Taylor Hobson 130 roundness instrument were exported with the Ultra® (Taylor Hobson) software and are saved with the extension “.SBF”. 30 data files are available for each depth being considered - 15 files for each of two measurement series. Inside each file, 10 s recording is stored. Two grooves should be visible (nominal values: 1 s groove width, 4 s distance between subsequent grooves). The data were acquired with a 360 S/s sampling rate.</p> <p>Caution:</p> <ul> <li>No synchronisation between the piezo actuator and XL-80 interferometer or Taylor Hobson 130 roundness instrument was used. Thus, the first or the last groove within the response to the simulated pattern might be too short to be considered valid.</li> <li>The square excitation of the piezo actuator was used. Thus, ringing oscillations near the grooves’ edges are present.</li> <li>The motion of the piezo actuator might happen to be initiated while the acquisition already had started. Thus, the first response inside each file should be analysed carefully.</li> <li>The piezo actuator did not hold time dependencies properly. The widths of simulated grooves usually differ from their nominal values to some extent.</li> </ul> <p><strong>Acknowledgement</strong></p> <p>This dataset was obtained within the 18RP01 ProbeTrace project. This project (18RP01 – ProbeTrace) has received funding from the EMPIR programme co-financed by the Participating States and from the European Union's Horizon 2020 research and innovation programme.</p> <p>Project title: Traceability for contact probe and stylus instrument measurements<br> Funder name: European Metrology Programme for Innovation and Research (EMPIR)<br> Funder ID: 10.13039/100014132<br> Grant number: 18RP01 ProbeTrace<br> Link to project homepage: http://probetrace.org/</p>
Dataset for "How Instrument Transformers Influence Power Quality Measurements: A Proposal of Accuracy Verification Tests"
<p>This is dataset for paper published:</p> <p>Crotti, Gabriella, Yeying Chen, Huseyin Çayci, Giovanni D’Avanzo, Carmine Landi, Palma Sara Letizia, Mario Luiso, Enrico Mohns, Fabio Muñoz, Renata Styblikova, and Helko van den Brom. 2022. "How Instrument Transformers Influence Power Quality Measurements: A Proposal of Accuracy Verification Tests" <em>Sensors</em> 22, no. 15: 5847. https://doi.org/10.3390/s22155847</p> <p> </p> <p>Excel file provides data in the time domain for tests performed on the inductive VT</p> <p> </p>
Recording and analysing physical control variables used in clarinet playing: A Musical Instrument Performance Capture and Analysis Toolbox (MIPCAT)
<p>Measuring fine-grained physical interaction between the human player and the musical instrument can significantly improve our understanding of music performance. This article presents a Musical Instrument Performance Capture and Analysis Toolbox (MIPCAT) that can be used to capture and to process the physical control variables used by a clarinettist while performing music. This includes both a measurement apparatus with sensors and a software toolbox for analysis. Several of the components used here can also be applied in other musical contexts. Applied to the clarinet, the instrument sensors record blowing pressure, reed position, tongue contact and sound pressures in the mouth, mouthpiece and barrel. Radiated sound and multiple videos are also recorded to allow details of the embouchure and the instrument’s motion to be determined. The software toolbox can synchronise measurements from different devices, extract time-variable descriptors, segment by notes and excerpts, and summarise descriptors per note, phrase or excerpt. An example of its application is given showing how to compare performances from different musicians.Measuring fine-grained physical interaction between the human player and the musical instrument can significantly improve our understanding of music performance. This article presents a Musical Instrument Performance Capture and Analysis Toolbox (MIPCAT) that can be used to capture and to process the physical control variables used by a clarinettist while performing music. This includes both a measurement apparatus with sensors and a software toolbox for analysis. Several of the components used here can also be applied in other musical contexts. Applied to the clarinet, the instrument sensors record blowing pressure, reed position, tongue contact and sound pressures in the mouth, mouthpiece and barrel. Radiated sound and multiple videos are also recorded to allow details of the embouchure and the instrument’s motion to be determined. The software toolbox can synchronise measurements from different devices, extract time-variable descriptors, segment by notes and excerpts, and summarise descriptors per note, phrase or excerpt. An example of its application is given showing how to compare performances from different musicians.Measuring fine-grained physical interaction between the human player and the musical instrument can significantly improve our understanding of music performance. This article presents a Musical Instrument Performance Capture and Analysis Toolbox (MIPCAT) that can be used to capture and to process the physical control variables used by a clarinettist while performing music. This includes both a measurement apparatus with sensors and a software toolbox for analysis. Several of the components used here can also be applied in other musical contexts. Applied to the clarinet, the instrument sensors record blowing pressure, reed position, tongue contact and sound pressures in the mouth, mouthpiece and barrel. Radiated sound and multiple videos are also recorded to allow details of the embouchure and the instrument’s motion to be determined. The software toolbox can synchronise measurements from different devices, extract time-variable descriptors, segment by notes and excerpts, and summarise descriptors per note, phrase or excerpt. An example of its application is given showing how to compare performances from different musicians.</p>
Venusian bow shock crossings manually identified from measurements by the ASPERA-4 and MAG instruments onboard Venus Express
<p>Bow shock crossings at Venus identified manually from the ASPERA-4 and MAG instruments onboard Venus Express for the full mission from 2006 to 2014.</p> <p>A detailed description of the dataset can be found in the paper "Influence of solar wind variations on the shapes of Venus’ plasma boundaries based on Venus Express observations" by Signoles et al.</p> <p>The boundary crossings by Venus Express are determined from combining the measurements of both ion, electron and magnetic field measurements. The bow shock is identified from the sharp increase in the magnetic field magnitude, and the increase in electron and ion temperature. The ion composition boundary is identified from the decrease in magnetosheath protons and electrons, and the appearance of planetary heavy ions.</p> <p>The dataset contains 5193 identified bow shock crossings and 2679 identified ion composition boundary crossings.</p> <p>For more information on the dataset contact: M. Persson, moaperssonphd at gmail.com</p>
Spectra belonging to XRF instrument report. Identification of ink components through XRF analysis of Azzolino documents.
<p>Accumulated spectra. Details given in supporting information </p> <p><a href="https://journals.plos.org/plosone/article/file?type=supplementary&id=10.1371/journal.pone.0283539.s002">S2 File. </a>XRF instrument report.</p> <p>Identification of ink components through XRF analysis of Azzolino documents.</p> <p><a href="https://doi.org/10.1371/journal.pone.0283539.s002">https://doi.org/10.1371/journal.pone.0283539.s002</a></p> <p>(DOCX)</p> <p>Belonging to publication </p> <p>Lagerqvist Alidoost A, Hacke M, Winther T, Sandström T (2023) A closer look at the Azzolino collection. PLOS ONE 18(4): e0283539. <a href="https://doi.org/10.1371/journal.pone.0283539">https://doi.org/10.1371/journal.pone.0283539</a></p>
XRF maps and line scans project files belonging to XRF instrument report. Identification of ink components through XRF analysis of Azzolino documents.
<p>RTX Project files. Details given in supporting information </p> <p><a href="https://journals.plos.org/plosone/article/file?type=supplementary&id=10.1371/journal.pone.0283539.s002">S2 File. </a>XRF instrument report.</p> <p>Identification of ink components through XRF analysis of Azzolino documents.</p> <p><a href="https://doi.org/10.1371/journal.pone.0283539.s002">https://doi.org/10.1371/journal.pone.0283539.s002</a></p> <p>(DOCX)</p> <p>Belonging to publication </p> <p>Lagerqvist Alidoost A, Hacke M, Winther T, Sandström T (2023) A closer look at the Azzolino collection. PLOS ONE 18(4): e0283539. <a href="https://doi.org/10.1371/journal.pone.0283539">https://doi.org/10.1371/journal.pone.0283539</a></p>
O2-O2, SO2, BrO, and IO differential slant column densities (dSCDs) measured by the University of Colorado Multi-AXis Differential Optical Absorption Spectroscopy (CU MAX-DOAS) instrument at Maido Observatory during April 29, 2018 and May 4, 2018
<p>Description: O<sub>2</sub>-O<sub>2</sub>, SO<sub>2</sub>, BrO, and IO differential slant column densities (dSCDs) measured by the University of Colorado Multi-AXis Differential Optical Absorption Spectroscopy (CU MAX-DOAS) instrument at Maido Observatory during April 29, 2018 and May 4, 2018.</p> <p>Instrument: University of Colorado Multi-AXis Differential Optical Absorption Spectroscopy (CU MAX-DOAS)<br> Instrument reference: Coburn et al. (2011); doi:10.5194/amt-4-2421-2011<br> Instrument contact: Christopher F. Lee (christopher.f.lee@colorado.edu)<br> Instrument PI: Rainer Volkamer (rainer.volkamer@colorado.edu)<br> <br> Measurement site: Maido Observatory, Reunion Island<br> Longitude: 55.384 degrees East<br> Latitude: 21.080 degrees South<br> Altitude: 2160 meters above sea level<br> Azimuth angle: Approximately 100 degrees clockwise from north<br> <br> The detection limit is defined as (2*Measured RMS) / (Maximum differential absorption cross section), where RMS = root-mean-square noise of spectral signal not accounted for by DOAS fit parameters [optical density units]. The maximum differential absorption cross sections used are 7.0e-21 [cm<sup>2</sup>] for SO<sub>2</sub>, 2.6e-17 [cm<sup>2</sup>] for BrO, and 3.5e-17 [cm<sup>2</sup>] for IO. Detection limits for SO<sub>2</sub> dSCDs, BrO dSCDs, and IO dSCDs are only reported during periods of significant SO<sub>2</sub> detection. BrO to SO<sub>2</sub> ratios are only reported during periods when both BrO dSCDs and SO<sub>2</sub> dSCDs are above the detection limit.</p> <p>Local time (RET) is UTC+4.<br> <br> Column 1: UTC start datetime (yyyy-mm-dd HH:MM:SS)<br> Column 2: UTC center datetime (yyyy-mm-dd HH:MM:SS)<br> Column 3: UTC stop datetime (yyyy-mm-dd HH:MM:SS)<br> Column 4: Elevation angle above the horizon (degrees)<br> Column 5: O<sub>2</sub>-O<sub>2</sub> dSCD [molec<sup>2</sup> cm<sup>-5</sup>]<br> Column 6: Spectral fit error for O<sub>2</sub>-O<sub>2</sub> dSCD [molec<sup>-2</sup> cm<sup>-5</sup>]<br> Column 7: SO<sub>2</sub> dSCD [molec cm<sup>-2</sup>]<br> Column 8: Spectral fit error for SO<sub>2</sub> dSCD [molec cm<sup>-2</sup>]<br> Column 9: Detection limit for SO<sub>2</sub> dSCD [molec cm<sup>-2</sup>]<br> Column 10: BrO dSCD [molec cm<sup>-2</sup>]<br> Column 11: Spectral fit error for BrO dSCD [molec cm<sup>-2</sup>]<br> Column 12: Detection limit for BrO dSCD [molec cm<sup>-2</sup>]<br> Column 13: IO dSCD [molec cm<sup>-2</sup>]<br> Column 14: Spectral fit error for IO dSCD [molec cm<sup>-2</sup>]<br> Column 15: Detection limit for IO dSCD [molec cm<sup>-2</sup>]<br> Column 16: Ratio of BrO dSCDs to SO<sub>2</sub> dSCDs<br> Column 17: Error in ratio of BrO dSCDs to SO<sub>2</sub> dSCDs</p>
NOBEL-BOX: A Ship-Based Low-Cost Instrument for Real-Time Ocean Monitoring and Analysis
<p>This data is the measurement result obtained from the NOBEL-BOX instrument. The principle of NOBEL-BOX is to attach sensors in a container connected to a microcontroller and then measure directly. This data results from measurements using fresh water and sea water mixed to see the response from NOBEL BOX. Furthermore, data was also obtained from sea measurements in Pangandaran, West Java, Indonesia. These measurements include pH, water and water temperature, dissolved oxygen, TDS, and salinity. The use of this parameter is to see the condition of the sea so that it becomes a reference in mitigating and managing the ocean.</p>
Data and software: 'Survey Operations for the Dark Energy Spectroscopic Instrument'
<p>Supplementary material to the DESI publication "Survey Operations for the Dark Energy Spectroscopic Instrument".</p><p>The main "figures.py" script generates the figures in the paper from the included data files. The software relies on the DESI software stack available at github.com/desihub, and conveniently available at NERSC in the default DESI environment.</p><p>Some documentation on the code implementing the survey and for running the survey simulations is available here:</p><ul><li>https://desisurvey.readthedocs.io/en/latest/</li><li>https://surveysim.readthedocs.io/en/latest/</li></ul><p>Contents:</p><ul><li>Configuration files<ul><li>config-main-actual.yaml: a yaml file giving the configuration for the survey simulation software (https://github.com/desihub/surveysim/releases/tag/0.12.5), specifying things like interexposure times and downtime, using the actual long shutdowns we had.</li><li>config-main-nominal.yaml: a yaml file giving the configuration for the survey simulation software, using the nominal long shutdowns we planned for.</li><li>config-main.yaml: same as config-main-nominal.yaml, except with the default name looked for by the survey simulation software.</li><li>rules-main.yaml: configuration rules file controlling how the next tile selector selects files for observation, implementing our depth-first strategy.</li></ul></li><li>Software files<ul><li>figures.py: main routines for generating the plots in this publication. Running `python figures.py` will create all of the figures used in this paper.</li><li>precess.py: set of routines implementing the precession of the Earth's pole used by figures.py for accurate airmass calculations.</li><li>downtime.py: routines used by figures.py for computing statistics about DESI's time usage, in particular the downtime.</li><li>util_efs.py: utility routines, primarily for making plots used by figures.py</li><li>util_efs_c.pyx: cython routines used by util_efs.py</li><li>margincomputations.py: routines for computing survey margin; i.e., how well we did relative to how much time was available.</li></ul></li><li>Data files<ul><li>Survey<ul><li>exposures-daily-main-20220614.ecsv: list of main survey exposures taken through 2022-06-14, following processing by the offline pipeline.</li><li>exposures-20220614.ecsv: list of main survey exposures taken through 2022-06-14, before processing by the offline pipeline. This largely duplicates exposures-daily, but contains much less information and contains more records for rare cases when exposures cannot be processed by the offline pipeline.</li><li>tiles-4112-packing-20210405-decorated-fixed.fits: file giving the geometry of DESI tile centers on the sky, plus associated information, like the extinction and stellar density at those locations.</li><li>tiles-daily.csv</li></ul></li><li>Simulation<ul><li>ephem_2019-01-01_2027-12-31.fits: ephemerides for DESI site for 2019 through 2027, generated by the desisurvey software ephemerides module. https://github.com/desihub/desisurvey</li><li>desi-status-end-nominal.ecsv: tile file giving completion of each DESI tile, for a survey simulation using the nominal configuration file.</li><li>desi-status-end-actual-noslew.ecsv: tile file giving completion of each DESI tile, for a survey simulation using the actual configuration file, without slew optimization</li><li>desi-status-end-actual.ecsv: tile file giving the completion of each DESI tile, for a survey simulation using the actual configuration file.</li><li>exposures_actual-noslew.fits: exposures generated by survey simulation software using the 'actual' configuration, with slew optimization disabled.</li><li>exposures_actual.fits: exposures file generated by survey simulation software using the 'actual' configuration.</li><li>exposures_nominal.fits: exposures file generated by survey simulation software using the 'nominal' configuration.</li><li>stats_actual.fits: statistics file generated by survey simulation software using the 'actual' configuration, containing information about DESI time usage.</li><li>stats_actual-noslew.fits: same as stats_actual.fits, except with slew time optimization disabled.</li><li>stats_nominal.fits: same as stats_actual.fits, except with the nominal survey sim configuration file.</li><li>tiles-daily.csv: tile file with detailed information on all tiles completed by the survey</li><li>tiles-main-20220614.ecsv: tile file with tile completeness information on 20220614</li><li>tiles-main.ecsv: tile file with completeness information on tiles in the survey</li><li>performance_current.csv.gz: performance information detailing state of DESI instrument every second, used for tracking downtime statistics.</li></ul></li></ul></li></ul><p>Dependencies: The included software uses the 'surveysim,' 'desisurvey,' and 'desimodel' packages, available on github through the desihub organization. Otherwise it depends on the usual astronomy software stack: numpy scipy matplotlib astropy. Alternatively, people with access to NERSC can load the default DESI environment and pull in all needed dependencies.</p>
IODP Expedition 392 Rig instrumentation
<p>Operational rig information data were measured using a variety of sensors and compiled using the RigWatch software package. Approximately 50 channels of drilling/coring data are captured in real time during the expedition. Data are presented as ASCII files extracted from the proprietary RigWatch data files and are presented by expedition. RigWatch data in time or depth domain can be imported into graphics and analysis programs to be merged and correlated with core physical properties data to enhance assessment of poor core recovery intervals.</p>
Fifty years of instrumental surface mass balance observations at Vostok Station, central Antarctica
<p>The database of snow buildup, density and accumulation rate values as observed at the accumulation-stake farms in the vicinity of Vostok station (central East Antarctica) since January 1970.</p> <p>The reference for the data: Ekaykin A.A., Lipenkov V.Ya., Tebenkova N.A. Fifty years of instrumental surface mass balance observations at Vostok Station, central Antarctica. - J. of Glaciology, 2023, 1–13. https://doi.org/10.1017/jog.2023.53.</p> <p> </p>
Catch bond kinetics are instrumental to cohesion of fire ant rafts under load
Open the record for dataset details and reuse information.
Effects of relational and instrumental messaging on human perception of rattlesnakes
Open the record for dataset details and reuse information.
Data from: Efficiency of using electric toothbrush as an alternative to tuning fork for artificial buzz pollination is independent of instrument buzzing frequency
Open the record for dataset details and reuse information.
Associated dataset for "Instrumental Evaluation of Sensor Self-Noise in Binaural Rendering of Spherical Microphone Array Signals"
<p>The conducted instrumental evaluation utilizes the Real-Time Spherical Microphone Renderer (<a href="https://github.com/AppliedAcousticsChalmers/ReTiSAR">ReTiSAR</a>) for binaural reproduction in Python. The at that time employed code state should be used in order to exactly reproduce the rendering results in this data set. The frozen code state for this data set is available at:<br> <a href="https://github.com/AppliedAcousticsChalmers/ReTiSAR/releases/tag/v2020.FA">https://github.com/AppliedAcousticsChalmers/ReTiSAR/releases/tag/v2020.FA</a></p> <p>Download the rendering pipeline and follow the setup instructions! Use the here included Conda environment file when setting up the Python environment. In this way, you will obtain exactly the same Python setup as utilized in the instrumental evaluation in the publication:</p> <pre><code class="language-bash">conda env create --file ReTiSAR_environment_freeze.yml</code></pre> <pre><code class="language-bash">source activate ReTiSAR_FA_freeze</code></pre> <p>Directory "SMA sampling grids":</p> <ul> <li>Visualization of spatial arrangement (like Figure 4) for all investigated spherical microphone array rendering configurations (Table 1)</li> </ul> <p>Shell script "record_snr.sh":</p> <ul> <li>Record the input and output signals of the rendering pipeline for sound field (target / wanted) and self-noise (unwanted) components for all configurations at multiple head orientations</li> <li>All captured signals are contained in the "SNR" directory</li> </ul> <p>Matlab script "calculate_snr.m":</p> <ul> <li>Visualize the raw captured input and output signals (like Figure 1 for all configurations)</li> <li>Visualize the resulting signal-to-noise ratio (like Figure 2 for all configurations)</li> <li>Visualize the comparison of the resulting signal-to-noise ratio of all configurations (Figure 3, also for the resulting SNR from signals with A-weighting)</li> <li>All generated plots are contained in the "SNR" directory</li> </ul> <p>Shell script "record_noise.sh":</p> <ul> <li>Record the calibration and noise signals of the mh acoustic Eigenmike 32 spherical microphone array in the anechoic chamber at Chalmers University of Technology (Appendix)</li> <li>All captured signals are contained in the "EM32 measurements" directory</li> <li>Pictures of the measurement setup are contained in the "Pictures" subdirectory</li> </ul> <p>Matlab script "calculate_EM32_noise_levels.m":</p> <ul> <li>Determine the resulting target signal sensitivity and equivalent input noise levels for the investigated pre-amplification gains (Table 2)</li> <li>Visualize the statistical distribution of the individual raw and weighted SMA channels (like Figure 6 for all configurations)</li> <li>Visualize the spatial distribution of the individual raw and weighted SMA channels for all configurations</li> <li>Visualize the smoothed and averaged magnitude spectra of the individual raw and weighted SMA channels (like Figure 5 for all configurations)</li> </ul>
Data archive for the peer-reviewed journal article "Detailed characterization of the CAPS single scattering albedo monitor (CAPS PMssa) as a field-deployable instrument for measuring aerosol light absorption with the extinction-minus-scattering method"
<p>Data archive accompanying the peer-reviewed journal article "Detailed characterization of the CAPS single scattering albedo monitor (CAPS PMssa) as a field-deployable instrument for measuring aerosol light absorption with the extinction-minus-scattering method". In 2020 this article was accepted for publication in the journal <em>Atmospheric Measurement Techniques</em>. Data are uploaded in the form of ascii text files, Igor Pro experiment files (.pxp), and Jupyter notebook files. In addition, a Jupyter notebook file is included containing an implementation of the error model used in the paper.</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.