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1,211 results for “instrument”
FIGURES 1–8. Figures 1–4 in Rearing mining flies (Diptera: Agromyzidae) from host plants as an instrument for associating females with males, with the description of seven new species
FIGURES 1–8. Figures 1–4: Agromyza abiens Zetterstedt; 1: mine in Cynoglossum officinale leaf; 2–3: posterior segments of puparium; 2: lateral view; 3: posterior view. 4: posterior spiracles (posterior view). Figures 5–8: A. albipennis Meigen; 5: mine with larva (indicated by arrow) in Elymus repens leaf; 6–7: posterior segments of puparium; 6: lateral view; 7: posterior view. 8: posterior spiracles (posterior view).
Poker: Visual Instrumentation of Reactive Programs With Programmable Probes (video demonstration)
<p>This video is supplementary material for a paper submitted to the 2021 workshop on reactive and event-based languages and systems (REBLS 2021): https://2021.splashcon.org/home/rebls-2021</p> <p>Music: <a href="https://www.bensound.com/royalty-free-music">https://www.bensound.com/royalty-free-music</a> </p> <p> </p>
Required instrumentation and data acquisition system
<p>Required instrumentation and data acquisition system</p>
Dataset Effects of repeated ultrasonic instrumentation on single-unit crowns
<p>Dataset of the restoration quality assessments</p>
CTAO Instrument Response Functions - version prod3b-v2
<p><strong>CTAO Instrument Response Functions - version prod3b-v2</strong></p> <p><strong>**Please check the CTA webpage (<a href="https://www.cta-observatory.org/science/cta-performance/">https://www.cta-observatory.org/science/cta-performance/</a>)) for the most recent instrument response functions.**</strong></p> <p><strong>**Figures and files in this repository are superseded by newer versions and provided for archival reasons.**</strong></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.</p> <p>This data repository provides access to performance evaluation and instrument response functions (IRFs) for CTA.</p> <p>- IRF version: prod3b-v2<br> - Telescope model and site configuration: <a href="https://zenodo.org/record/6219128">prod3b model</a><br> - Publication date: April 2019<br> - Archived webpage with performance figures included: [CTAO Performance Description (file Website.md)](Website.md)<br> - Licence: his work is licensed under a [Creative Commons Attribution 4.0 International License](LICENSE).</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 https://www.cta-observatory.org/science/cta-performance/ (version prod3b-v2; [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/5163273/export/hx">https://zenodo.org/record/5163273/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 (v6.9+, 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 two zenith angles (20 deg and 40 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, as FITS tables, and for some on-axis IRFs also as simple ASCII files. 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> </p> <p>File Naming (examples):</p> <p>- CTA-Performance-prod3b-v2-North-20deg-average-50h.root: IRF for CTA Northern site on La Palma, 20 deg zenith angle, azimuth-averaged pointing, optimised for 50 hours of observation time<br> - CTA-Performance-prod3b-v2-South-20deg-average-50h.root: IRF for CTA Southern site in Paranal, 20 deg zenith angle, azimuth-averaged pointing, optimised for 50 hours of observation time<br> - CTA-Performance-prod3b-v2-South-40deg-S-30m.root: RF for CTA Southern site, 40 deg zenith angle, South pointing, optimised for 30 minutes of observation time</p> <p>List of files:</p> <p>- fits/CTA-Performance-prod3b-v2-FITS.tar.gz - IRFs in FITS format (making use of the HEASARC’s caldb indexing) - includes IRFs for 20 deg, 40 deg, and 60 deg zenith angle, average, north and south pointing<br> - root/CTA-Performance-prod3b-v2-20deg-ROOT.tar.gz - IRFs in ROOT format for 20 deg zenith angle, azimuth-averaged, north and south pointing<br> - root/CTA-Performance-prod3b-v2-40deg-ROOT.tar.gz - IRFs in ROOT format for 40 deg zenith angle, azimuth-averaged, north and south pointing<br> - root/CTA-Performance-prod3b-v2-60deg-ROOT.tar.gz - IRFs in ROOT format for 60 deg zenith angle, azimuth-averaged, north and south pointing<br> - ascii/CTA-Performance-prod3b-v2-20deg-ASCII.tar.gz - (selected) IRFs in ASCII format for 20 deg zenith angle, azimuth-averaged, north and south pointing<br> - ascii/CTA-Performance-prod3b-v2-40deg-ASCII.tar.gz - (selected) IRFs in ASCII format for 40 deg zenith angle, azimuth-averaged, north and south pointing<br> - ascii/CTA-Performance-prod3b-v2-60deg-ASCII.tar.gz - (selected) IRFs in ASCII format for 60 deg zenith angle, azimuth-averaged, north and south pointing</p> <p>### CTA Science Performance Requirements</p> <p><strong>**Performance requirements for CTA are currently under review. The attached requirements are preliminary and subject to change.**</strong></p> <p>The following documents summarise the science performance requirements for CTA. These requirements correspond to the baseline implementation of CTA. Values for the requirements on differential sensitivity, angular and energy resolution are provided in plain text files.</p> <p>- Requirements description (CTA-SPE-SCI-00000-0001_Issue_1_SystemLevelSciencePerformanceReqs.pdf)<br> - CTA-Performance-Requirements.tar.gz (ascii files)</p> <p>## References</p> <p>- [1] https://www.ikp.kit.edu/corsika/<br> - [2] Bernloehr, K. 2008, Astroparticle Physics, 30, 149</p> <p>## Acknowledgements</p> <p>We would like to thank the computing centres that provided resources for the generation of the Instrument Response Functions:</p> <p>- CAMK, Nicolaus Copernicus Astronomical Center, Warsaw, Poland<br> - CETA-GRID, Resource Center CETA-CIEMAT, Trujillo, Spain<br> - CIEMAT-LCG2, CIEMAT, Madrid, Spain<br> - CYFRONET-LCG2, ACC CYFRONET AGH, Cracow, Poland<br> - DESY-ZN, Deutsches Elektronen-Synchrotron, Standort Zeuthen, Germany<br> - GRIF, Grille de Recherche d’Ile de France, Paris, France<br> - IN2P3-CC, Centre de Calcul de l’IN2P3, Villeurbanne, France<br> - IN2P3-CPPM, Centre de Physique des Particules de Marseille, Marseille, France<br> - IN2P3-LAPP, Laboratoire d Annecy de Physique des Particules, Annecy, France<br> - INFN-FRASCATI, INFN Frascati, Frascati, Italy<br> - INFN-T1, CNAF INFN, Bologna, Italy<br> - INFN-TORINO, INFN Torino, Torino, Italy<br> - MPIK, Heidelberg, Germany<br> - M3PEC, Mesocentre Aquitain, Bordeaux, France<br> - OBSPM, Observatoire de Paris Meudon, Paris, France<br> - PIC, port d’informacio cientifica, Bellaterra, Spain<br> - prague_cesnet_lcg2, CESNET, Prague, Czech Republic<br> - praguelcg2, FZU Prague, Prague, Czech Republic<br> - SE-SNIC-T2, The Swedish WLCG Tier 2 InitiativeStockholm, Sweden</p> <p> </p>
RAÄ-2021-3144 instrument report µXRF in situ
<p>XRF maps and point analyses carried out by the heritage laboratory from the the Swedish National Heritage Board on two Wari khipus from the Museums of World Culture, Gothenburg, Sweden in the context of project Dnr RAÄ-2021-3144.</p>
YM2413-MDB: A Multi-Instrumental FM Video Game Music Dataset with Emotion Annotations
<p>YM2413-MDB is an 80s FM video game music dataset with multi-label emotion annotations. It includes 669 audio and MIDI files of music from Sega and MSX PC games in the 80s using YM2413, a programmable sound generator based on FM. The collected game music is arranged with a subset of 15 monophonic instruments and one drum instrument. They were converted from binary commands of the YM2413 sound chip. Each song was labeled with 19 emotion tags by two annotators and validated by three verifiers to obtain refined tags</p> <p>For more detailed information about the dataset, please refer to our paper: <a href="https://arxiv.org/abs/2211.07131">YM2413-MDB: A Multi-Instrumental FM Video Game Music Dataset with Emotion Annotations</a>.</p> <p><strong>File Description</strong></p> <p><strong>1) Pure data</strong></p> <p>- original_vgms: crawled vgm files from <a href="https://www.smspower.org/">SMS POWER</a> and <a href="https://vgmrips.net/packs/">VGMRIPs</a></p> <p>- wav: rendered vgm files using <a href="https://github.com/vgmrips/vgmplay">VGMPlay</a></p> <p> </p> <p><strong>2) MIDI data</strong></p> <p>- midi/vgmplay_log_to_midi: converted midi files</p> <p>- midi/adjust_tempo: add postprocessing(metrically aligned using wav_downbeat files) after midi conversion</p> <p>- midi/adjust_tempo_remove_delayed_inst: add postprocessing(metrically aligned using wav_downbeat files, remove delayed instrument) after midi conversion</p> <p> </p> <p><strong>3) Metadata</strong></p> <p>- emotion_annotation/verified_annotation.csv: contains emotion annotation for each songs</p> <p>- tags_kor_eng.txt: Korean <-> English tag dictionary</p> <p> </p> <p><strong>4) Useful middle-time step data</strong></p> <p>- wav_downbeat: extracted downbeat values using TCNBeatTracker of <a href="https://github.com/CPJKU/madmom">madmom</a></p> <p>- vgm_txts: disassembled vgm files as txt using <a href="https://github.com/vgmrips/vgmtools#vgm-text-writer-vgm2txt">vgm2txt</a></p> <p>- ydr: YM2413 Disassembly Raw(YDR). command list of vgm files. generated by reading vgm_txts</p> <p> </p> <p><strong>Update Log</strong></p> <p>- version 1.0.1: Fix ticks per beat value adjust to tempo where tempo values are not 150. Also, madmom downbeat files are updated from DBNBeatTracker(ISMIR, 2015) to TCNBeatTracker(Newer one EUSIPCO, 2019).</p> <p>- version 1.0.2: <strong><a href="https://github.com/jech2/YM2413-MDB/issues/2">Wrong emotion tag issue in the verification annotation file was fixed.</a></strong></p>
Supplementary Data for Manuscript : "Application of OSL surface exposure dating with the use of two-dimensional OSL laser scanning instruments and energy-dispersive x-ray spectroscopy'
<p>Contains all supplementary works mentioned in the manuscript.</p>
Particle Size Distribution from a LISST 100-X instrument for three transect cruises across the North Pacific Ocean
<p>Particle size distribution data (numbers per liter per micrometer) over 32 logarithmically-spaced size bins from a Laser In-Situ Scattering and Transmissometry sensor, LISST 100X Type B (Sequoia Scientific). Data are from three transect cruises in the North Pacific funded by the Simons Foundation (Gradients 1, 2, and 3). Cruises transited approximately along 158W, and between 21N and 42N. LISST data was processed using spherical kernel inversion and corrected for background particle concentrations as in White et al (2015):</p> <p>White, A. E., Letelier, R. M., Whitmire, A. L., Barone, B., Bidigare, R. R., Church, M. J., and Karl, D. M. (2015), Phenology of particle size distributions and primary productivity in the North Pacific subtropical gyre (Station ALOHA), <em>J. Geophys. Res. Oceans</em>, 120, 7381– 7399, doi:<a href="https://doi.org/10.1002/2015JC010897">10.1002/2015JC010897</a>.</p>
A comparative study on the polar cap hot patch and cold patch by using multi-instrument observations
<p>The support videos of paper<A comparative study on the polar cap hot patch and cold patch by using multi-instrument observations> is stored here. and will be updated if needed.</p>
Supplementary Documents for Manuscript 'Investigating the use of two-dimensional OSL laser scanning instruments and energy-dispersive x-ray spectroscopy for OSL exposure dating'
<p>Appendix Data for Manuscript 'Investigating the use of two-dimensional OSL laser scanning instruments and energy-dispersive x-ray spectroscopy for OSL exposure dating' - with added "Readme's" for relevant databases.</p>
Replication package for: Weak Instruments in Instrumental Variables Regression: Theory and Practice
<p>The package contains all the code necessary to reproduce the figures and tables in </p> <p><a href="https://www.annualreviews.org/doi/abs/10.1146/annurev-economics-080218-025643">Weak Instruments in Instrumental Variables Regression: Theory and Practice</a></p> <p>Isaiah Andrews, James H. Stock, Liyang Sun</p> <p>Annual Review of Economics 2019 11:1, 727-753</p> <p>Detailed instructions are given in a readme therein. Section A is an overview on the directory structure of the replication files. Section B describes features collected from articles and specifications in the AER sample.</p>
China traditional music instrument dataset
<p>The FolkMusic dataset is a Chinese traditional music dataset mainly used for training instrument recognition models and performance evaluation. The dataset covers 15 traditional Chinese musical instruments, including Ba, Flute, Dongxiao, Erhu, Guqin, Guzheng, Hulusi, Liuqin, Pipa, Sanxian, Sheng, Suona, Yangqin, Zhongruan, and Falling Qin. The music clips in each instrument are saved as .mp3 files, which are recorded via two channels with a sampling rate of 44100Hz. The duration of these music clips are 3s, and a single instrument plays each music clip.</p>
The surgical instrument dataset in the paper A Lightweight Segmentation Network for Endoscopic Surgical Instruments Based on Edge Refinement and Efficient Self-Attention
<p>In the paper A Lightweight Segmentation Network for Endoscopic Surgical Instruments Based on Edge Refinement and Efficient Self-Attention, we collected and annotated the surgical instrument dataset.</p>
Anexos de la comunicación: Design and validation of an instrument for the evaluation of educational innovation experiences published in professional teaching journals.
<p>Anexos de la comunicación: Design and validation of an instrument for the evaluation of educational innovation experiences published in professional teaching journals, presentada para el VII Congreso Internacional sobre Aprendizaje, Innovación y Cooperación (CINAIC 2023). 18-20 Octubre 2023, Madrid, ESPAÑA</p> <p> </p>
Supplementary Documents for Manuscript 'Investigating the use of two-dimensional OSL laser scanning instruments and energy-dispersive x-ray spectroscopy for OSL exposure dating'
<p>This file contains all the supplementary material for the manuscript 'Investigating the use of two-dimensional OSL laser scanning instruments and energy-dispersive x-ray spectroscopy for OSL exposure dating' sent to Radiation Measurements on 8/20/2023.</p>
Our processed EndoVis2017 dataset in the paper A Lightweight Segmentation Network for Endoscopic Surgical Instruments Based on Edge Refinement and Efficient Self-Attention
<p>Our processed EndoVis2017 dataset is used for the paper "A Lightweight Segmentation Network for Endoscopic Surgical Instruments Based on Edge Refinement and Efficient Self-Attention".</p>
Our processed Kvasir-Instrument dataset in the paper A Lightweight Segmentation Network for Endoscopic Surgical Instruments Based on Edge Refinement and Efficient Self-Attention
<p>Our processed Kvasir-Instrument dataset is used for the paper "A Lightweight Segmentation Network for Endoscopic Surgical Instruments Based on Edge Refinement and Efficient Self-Attention".</p>
Monte Carlo Arithmetic Instrumented DeepGOPlus Protein Function Predictions
<p>This dataset contains the perturbed protein function predictions by the DeepGOPlus model excluding the Diamond tool component. The model was perturbed with Verrou, an implementation of Monte Carlo Arithmetic (MCA), a stochastic arithmetic technique that injects noise into a program that simulates changes in a user's execution environment. The folders contain pkl files that can be read with the Pandas python library to load up dataframes containing the predictions and original values. Each file is one MCA sample run across the entire DeepGOPlus test set.</p> <p>The folder "Verrou_All" contains predictions where the entirety of the model was instrumented with MCA.</p> <p>The folder "Verrou_TF" contains predictions where only the Tensorflow library was instrumented with MCA.</p> <p>The folder "Fuzzy_Python" contains predictions where only the Python interpreter was instrumented with MCA.</p> <p>The folder "VPREC_Outbound_Mode" contains predictions where the virtual precision of the floating point operations was reduced witht the VPREC precision simulator tool in outbound mode.</p> <p>The folder "VPREC_Inbound_Mode" contains predictions where the virtual precision of the floating point operations was reduced witht the VPREC precision simulator tool in inbound mode.</p> <p>More information can be found by consulting <a href="https://arxiv.org/abs/2212.06361">this paper</a> or <a href="https://github.com/big-data-lab-team/deepgoplus-stability">this Github repository</a>.</p>
Jupiter's ground-based observations on 2018 May 24th-27th from the Very Large Telescope Imager and Spectrometer instrument
<p>The Very Large Telescope Imager and Spectrometer (VISIR) instrument on ESO's Very Large Telescope (VLT) has been used to support the NASA's Juno mission since 2016. The present dataset was collected at the European Organisation for Astronomical Research in the Southern Hemisphere under ESO programme 0101.C-0073(A)). The 2018 May 24th-27th dataset provides a comprehensive view of Jupiter's pole-to-pole thermal, chemical, and aerosol structure ; including the Great Red Spot; and the auroral-related heating in the southern polar stratosphere; after retrieval calculations.</p> <p>Here, we provide the destriped, cleaned, calibrated and projected data of 2018 May 24h to 27th dataset.</p> <p> </p> <p>For the 7.9µm filter, we have added the radial Doppler velocities files.</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.