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1,211 results for “Instruments”
Data and code for "Tweezepy: A Python package for calibrating forces in single-molecule video-tracking instruments"
<p>Data and code for "Tweezepy: A Python package for calibrating forces in single-molecule video-tracking instruments."</p> <p>Data includes representative real and simulated bead trajectories used in the manuscript.</p> <p>Code includes all simulations, analysis, and plot details for the Figures in the manuscript. </p> <p>See included README.txt for more details.</p>
Data for creating figures to the paper "Assessing Net Growth of Phytoplankton Biomass on Hourly to Annual Timescales Using the Geostationary Ocean Color Instrument."
<p>Processed data to generate figures for the paper "Assessing Net Growth of Phytoplankton Biomass on Hourly to Annual Timescales Using the Geostationary Ocean Color Instrument."</p> <p>The rate at which microscopic ocean plants, or phytoplankton, consume carbon dioxide represents a gap in scientific knowledge that needs to be filled in order to better model the earth system. To aid in this understanding we use a novel technique that allows us to track the growth behavior of phytoplankton in the Yellow Sea and the East Sea-Japan Sea. This is enabled by using satellite data from the Geostationary Ocean Color Imager, which has the unprecedented ability to collect quality biological information from the ocean surface each daylight hour. We find that the results, while in agreement with local observations and other satellite studies, also contain information about how phytoplankton change over daily to annual cycles and how native communities adapt in response to the annual solar cycle. This information is useful to the ocean modeling community, that seeks to understand various ways in which phytoplankton communities affect the cycling of Earth’s carbon.</p>
Appearances of Erkki Kurenniemi's Electronic Musical Instruments
<p>The excel spreadsheet includes notes about the appearances of the electronic musical instruments designed by the Finnish electroacoustic music pioneer Erkki Kurenniemi. The spreadsheet will be updated regularly when new information is found and checked. PI and contact information: Mikko Ojanen / https://orcid.org/0000-0002-7833-9659</p>
Datasets for paper "Evaluating the PurpleAir monitor as an aerosol light scattering instrument"
<p>The data sets included will allow the user to reproduce the plots and analyses described in Ouimette et al. (2022). The Collocated*csv file contains data from multiple collocated PurpleAirs that sampled for a few days. The data in this file was used in the precision analysis in section 2.2.9 of the paper. The other files contain nephelometer and PurpleAir data from Mauna Loa (MLO) and Table Mountain (BOS) and DMPS size distribution files from BOS. Their contents are described in the README.TXT file.</p>
ASTRI Mini-Array Instrument Response Functions (Prod2, v1.0)
<p><strong>Aim:</strong></p> <p>This data repository provides access to a set of Instrument Response Functions (IRFs) of the ASTRI Mini-Array, saved in a FITS data file. The IRFs can be used as input to science analysis tools for high-level scientific analysis purposes.</p> <p><strong>Citations:</strong></p> <p>In the case the present ASTRI Mini-Array Instrument Response Functions (IRFs) are used in a research project, we kindly ask to add the following acknowledgement in any resulting publication:</p> <p>"This research has made use of the ASTRI Mini-Array Instrument Response Functions (IRFs) provided by the ASTRI Project [citation]."</p> <p>Please use the following BibTex Entry for [citation] in the reference section of your publication: <a href="https://zenodo.org/record/6827882/export/hx">https://zenodo.org/record/6827882/export/hx</a></p> <p><strong>Instrument:</strong></p> <p>The ASTRI Mini-Array is an international project led by the Italian National Institute for Astrophysics (INAF) to build and operate an array of nine 4-m class Imaging Atmospheric Cherenkov Telescopes (IACTs) at the <em>Observatorio del Teide</em> (Tenerife, Spain) [1]. The telescopes are an evolution of the dual-mirror ASTRI-Horn telescope, successfully installed and tested since 2014 at the INAF “M.C. Fracastoro” observing station in Serra La Nave (Mt. Etna, Italy) [2][3].</p> <p>The ASTRI Mini-Array is designed to perform deep observations of the galactic and extragalactic gamma-ray sky in the TeV and multi-TeV energy band, with a differential sensitivity that surpass the one of current Cherenkov telescope facilities above a few TeV, extending the energy band well above hundreds of TeV [4].</p> <p>The main science goals of the ASTRI Mini-Array in the very high-energy (VHE) gamma-ray band encompass both galactic and extragalactic science [5][6][7]. Important synergies with other ground-based gamma-ray facilities in the Northern Hemisphere and space-borne telescopes are foreseen.</p> <p><strong>Monte Carlo Simulations:</strong></p> <p>The IRFs of the ASTRI Mini-Array were obtained from a dedicated Monte Carlo (MC) production (dubbed ASTRI Mini-Array Prod2, version 1.0). Air showers initiated by gamma rays, protons and electrons were simulated using the CORSIKA package [8] (version 6.99), while the response of the array telescopes was simulated using the sim_telarray package [9] (version 2018-11-07).</p> <p>The layout of the ASTRI Mini-Array telescopes considered in the MC simulations is based on the actual telescope positions at the Teide Observatory site (28.30°N, 16.51°W, 2390 m a.s.l.). The nominal telescope pointing configuration, in which all telescopes point to the same sky position, was assumed in all MC simulations. Air showers produced by the primaries were simulated as coming from a zenith angle of 20° and an azimuth angle of 0° and 180° (corresponding to telescope pointing directions toward the geomagnetic North and South, respectively). Although not-negligible differences in performance (on the order of ≤15% at a zenith angle of 20°) are found between the two azimuthal pointing directions, the final IRFs were obtained by averaging between the two directions. Finally, all MC simulations were generated with a night sky background (NSB) level corresponding to dark sky conditions at the Teide Observatory site.</p> <p><strong>Monte Carlo data reduction and analysis:</strong></p> <p>The MC simulations were reduced and analysed with A-SciSoft [10][11] (version 0.3.1), the scientific software package of the ASTRI Project. The calibration and reconstruction of the MC events were achieved with the standard methods implemented in the data reduction pipeline (see [10][11] for more details). In particular, the background rejection and energy reconstruction were achieved with a procedure based on the Random Forest method [12], while the arrival direction of each shower was estimated from a weighted intersection of the major axes of the images from different telescopes. After the full reconstruction of the MC events, the background (proton and electron) events were re-weighted according to recent experimental measurements of their spectra, while gamma-ray events with a power-law gamma-ray spectrum with a photon index of 2.62. This approach follows a similar procedure adopted in [13].</p> <p>The final analysis cuts were based on the background rejection, shower arrival direction, and event multiplicity parameters. They were defined, in each considered energy bin and off-axis bin, by optimising the flux sensitivity for 50 hr exposure time. Then, five standard deviations (5σ, with σ defined as in Eq. 17 of [14]) were required for a detection in each energy bin and off-axis bin, considering the same exposure time (as in the cut optimization procedure) and a ratio of the off-source to on-source exposure equal to 5. In addition, the signal excess was required to be larger than 10 and at least 5 times the expected systematic uncertainty in the background estimation (assumed to be ∼1%). It should be noted that these analysis cuts, based on the best flux sensitivity, do not provide the best angular and energy resolution achievable by the system. Other analysis cuts, which take into account both differential flux sensitivity and angular/energy resolution in the cut optimization process, may actually provide better performance [4].</p> <p><strong>Instrument Response Functions (IRFs):</strong></p> <p>The IRFs are saved in a FITS data file [15] which contains the following quantities (FITS tables): effective collection area ("EFFECTIVE AREA" table), angular resolution ("POINT SPREAD FUNCTION" table), energy resolution ("ENERGY DISPERSION" table), and residual background rate ("BACKGROUND" table). These quantities are provided as a function of the energy and the off-axis. The energy bins are logarithmic and range between 10<sup>-0.7 </sup>~ 0.2 TeV and 10<sup>2.5 </sup>~ 316 TeV. Five energy bins per decade are used for the angular resolution and residual background rate, while ten energy bins per decade for the effective collection area. In the case of energy resolution, the energy migration matrix is provided with a much finer energy binning. The off-axis bins are linearly spaced between 0° and 6°, with a bin width equal to 1°. In the case of the residual background rate, a 2-dimensional squared spatial binning is used, which ranges between 0° and 6° with a bin width equal to 0.2° in each direction.</p> <p>The IRFs can be used as input to science analysis tools and, in particular, are compliant with the input/output (I/O) data format requested by the science analysis tools Gammapy [16] and ctools [17].</p> <p><strong>Dataset:</strong></p> <p>The dataset consists of one file: "astri_100_43_008_0502_C0_20_AVERAGE_50h_SC_v1.0.lv3.fits".</p> <p>The naming convention is: astri_[ARRAY_ID]_[ORIG_ID]_[REL_ID]_[PACKET_TYPE]_[CLASS_CUT]_[ZENITH]_[AZIMUTH]_[ EXPOSURE_TIME]_[AIM]_[VERSION].lv3.fits</p> <p>where:</p> <ul> <li>[ARRAY_ID] = 100 (100 = ASTRI Mini-Array with 9 telescopes)</li> <li>[ORIG_ID] = 43 (4 = INAF-OAR; 3 = AIV/AIT MC simulations)</li> <li>[REL_ID] = 008 (008 = MC prod2, v1.0)</li> <li>[PACKET_TYPE] = 0502 (0502 = IRF3)</li> <li>[CLASS_CUT] = C0 (C0 = cuts based on sensitivity maximisation)</li> <li>[ZENITH] = 20 [deg]</li> <li>[AZIMUTH] = AVERAGE [deg]</li> <li>[EXPOSURE_TIME] = 50h</li> <li>[AIM] = SC (SC = SCience)</li> <li>[VERSION]= v1.0</li> </ul> <p><strong>Acknowledgments:</strong></p> <p>This work was conducted in the context of the ASTRI Project thanks to the support of the Italian Ministry of University and Research (MUR) as well as the Ministry for Economic Development (MISE) with funds specifically assigned to the Italian National Institute of Astrophysics (INAF). We acknowledge support from the Brazilian Funding Agency FAPESP (Grant 2013/10559-5) and from the South African Department of Science and Technology through Funding Agreement 0227/2014 for the South African Gamma-Ray Astronomy Programme. The Instituto de Astrofisica de Canarias (IAC) is supported by the Spanish Ministry of Science and Innovation (MICIU). This work has also been partially supported by H2020-ASTERICS, a project funded by the European Commission Framework Programme Horizon 2020 Research and Innovation action under grant agreement n. 653477. This work has gone through the internal ASTRI review process.</p> <p>We would also like to thank the computing centres that provided resources for the generation of the Monte Carlo (MC) simulations used to produce the ASTRI Mini-Array Instrument Response Functions (IRFs) released in this work:</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> <p><strong>References:</strong></p> <ol> <li>Scuderi, S. et al., "The ASTRI Mini-Array of Cherenkov telescopes at the Observatorio del Teide", Journal of High Energy Astrophysics 35, 52–68 (2022).</li> <li>Giro, E. et al., "First optical validation of a Schwarzschild Couder telescope: the ASTRI SST-2M Cherenkov telescope", A&A 608, A86 (Sept. 2017).</li> <li>Lombardi, S. et al., "First detection of the Crab Nebula at TeV energies with a Cherenkov telescope in a dual-mirror Schwarzschild-Couder configuration: the ASTRI-Horn telescope", A&A 634, A22 (Feb. 2020).</li> <li>Lombardi, S. et al., "Performance of the ASTRI Mini-Array at the Observatorio del Teide", in [37th International Cosmic Ray Conference. 12-23 July 2021. Berlin], 884 (Mar. 2022).</li> <li>Vercellone, S. et al., "ASTRI Mini-Array core science at the Observatorio del Teide", Journal of High Energy Astrophysics 35, 1–42 (2022).</li> <li>D’Aì, A. et al., "Galactic Observatory Science with the ASTRI Mini-Array at the Observatorio del Teide", Journal of High Energy Astrophysics 35, 139–175 (2022).</li> <li>Saturni, F. et al., "Extragalactic Observatory Science with the ASTRI Mini-Array at the Observatorio del Teide", Journal of High Energy Astrophysics 35, 91–111 (2022).</li> <li>Heck, D. et al., [CORSIKA: a Monte Carlo code to simulate extensive air showers.], Report FZKA 6019 (1998).</li> <li>Bernlöhr, K., "Simulation of imaging atmospheric Cherenkov telescopes with CORSIKA and sim_telarray", Astropart. Phys. 30, 149–158 (Oct. 2008).</li> <li>Lombardi, S. et al., "ASTRI SST-2M prototype and mini-array data reconstruction and scientific analysis software in the framework of the Cherenkov Telescope Array", in [Software and Cyberinfrastructure for Astronomy IV], Chiozzi, G. and Guzman, J. C., eds., Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series 9913, 991315 (July 2016).</li> <li>Lombardi, S. et al., "ASTRI data reduction software in the framework of the Cherenkov Telescope Array", in [Software and Cyberinfrastructure for Astronomy V], Guzman, J. C. and Ibsen, J., eds., Society of Photo- Optical Instrumentation Engineers (SPIE) Conference Series 10707, 107070R (July 2018).</li> <li>Breiman, L., "Random Forests", Machine Learning 45, 5–32 (Jan. 2001).</li> <li>Cherenkov Telescope Array Observatory, & Cherenkov Telescope Array Consortium. (2021). CTAO Instrument Response Functions - prod5 version v0.1 (v0.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5499840</li> <li>Li, T.-P. and Ma, Y.-Q., "Analysis methods for results in gamma-ray astronomy", ApJ, 272, 317 (1983)</li> <li>Pence, W. D. et al., "Definition of the Flexible Image Transport System (FITS), version 3.0", A&A 524, A42 (Dec. 2010).</li> <li>Deil, C. et al., "Gammapy - A prototype for the CTA science tools", in [35th International Cosmic Ray Conference (ICRC2017)], International Cosmic Ray Conference 301, 766 (Jan. 2017).</li> <li>Knödlseder, J. et al., "GammaLib and ctools. A software framework for the analysis of astronomical gamma- ray data", A&A 593, A1 (Aug. 2016).</li> </ol>
The dataset for publication "Characterization of scintillating materials in use for brachytherapy fiber based dosimeters" by S. Commeti, et al., Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 2022.
<p>This dataset is related to paper journal paper with DOI: <a href="http://dx.doi.org/10.1016/j.nima.2022.167083">10.1016/j.nima.2022.167083</a>.</p> <p>The dataset contains raw txt file and matlab files on the transmittance and the attenuation of Gadox and YVO specimens. </p> <p>Data files were prepared by agnieszka.gierej@vub.be</p>
Simulated coherent diffraction from 2NIP (SPB-SFX instrument, 3 fs, 4.96 keV European XFEL pulses)
<p>Simulated diffraction from 2NIP</p> <p>Input: https://dx.doi.org/10.5281/zenodo.886061 (photon-matter interaction)</p> <p>Simulation code: singFEL</p>
Leap Motion Hand Gestures for Interaction with 3D Virtual Music Instruments (LMHGIf3DVMI)
<p>The aim of the dataset is to investigate machine learning real-time gesture recognizer captured with a Leap Motion sensor to control the performance of a virtual 3D musical instrument. The dataset includes from 10-15 samples for each of the 8 gesture classes collected from 10 participants (5 female and 5 male) using the Leap Motion sensor.</p> <p> </p>
All-sky information content analysis for novel passive microwave instruments - data
<p>This dataset is the underlying data for the article:</p> <p>Grützun, V., S. A. Buehler, L. Kluft, M. Brath, J. Mendrok, and P. Eriksson (in press, 2018), All-sky Information Content Analysis for Novel Passive Microwave Instruments in the Range from 23.8 GHz up to 874.4 GHz, Atmos. Meas. Tech., doi:10.5194/amt-2017-377. </p> <p>Please refer to that article for a description of the scientific background of the data and to the attached README file for a technical documentation. </p> <p>Contact: Verena Grützun, verena.gruetzun@uni-hamburg.de<br> </p>
Irish Traditional Music Instruments Thesaurus, Extended Version
<p>A Simple Knowledge Organisation System (SKOS) thesaurus. Incorporates extended instruments used in contemporary Irish traditional music. Developed for use at the Irish Traditional Music Archive. Contains Irish language and English terms.</p>
Irish Traditional Music Instruments Thesaurus, Core Version
<p>A Simple Knowledge Organisation System (SKOS) thesaurus. Incorporates core instruments used in contemporary Irish traditional music. Developed for use at the Irish Traditional Music Archive. Contains Irish language and English terms.</p>
MEaSUREs blue band total column water vapor sample data for the Ozone Monitoring Instrument
<p>This dataset contains the MEaSUREs OMI Total Column Water Vapor (TCWV) data and their related data used in the paper titled “Development of the MEaSUREs blue band water vapor algorithm – Towards a long-term data record” by Wang et al. (2023). The unzipped archive contains the following three directories. </p> <ol> <li>OMI-H2O-L2/ contains the MEaSUREs Level 2 data (in molecules/cm2) in netCDF4 format for January and July 2005 and 2006. Selected supporting data are also included in each file.</li> <li>OMI-H2O-L3/ contains MRaSUREs Level 3 data (0.25 degree by 0.25 degree, in molecules/cm2) generated using the standard filtering criteria in netCDF4 format for January and July 2005 and 2006. Selected supporting data are also included.</li> <li>Model3_ncresult/ contains netCDF4 formatted files for the MEaSUREs OMI TCWV data (in mm), the AMSR_E TCWV data sampled onto the corresponding OMI pixel locations, and the LightGBM model 3 predictions for the OMI pixels.</li> </ol> <p>The linux command ‘ncdump -h filename’ can be used to examine the contents of netCDF4 files. Due to the current size limit of Zenodo, only a small subset of the MEaSUREs data is archived here. The full dataset will be released elsewhere, e.g., NASA EARTHDATA GES DISC.</p>
WICE-DB - Web Instrument Count Experiment
<p>This dataset consist of 12 hand selected musical stimuli to evaluate source count experiments.</p> <p>However, to better study the influence of vibrato we require extended control over certain parameters such as note duration, vibrato duration, exact fundamental frequency, vibrato rate, vibrato extend, reproducibility, loudness or expression.</p> <p>As mentioned in the previous chapter, vibrato techniques vary across instruments. Instruments such as violin and saxophone are known for their distinct frequency modulations. Other instruments such as the English horn and the flute are more close to amplitude modulations.</p> <p>We generated the notes using a software sampler which allows us to control the parameters such as the vibrato. All our test stimuli have a duration of three seconds. Items were equalized in loudness by using an iterative calculation of the loudness algorithm of the time-varying Zwicker model. We rendered 29 notes of C4, resulting in 841 unique unison instrument mixtures per pitch class.</p>
Early Instrumental Meteorological Measurements in Switzerland
<p>This dataset encompasses early instrumental measurement series that have been gathered and digitised in the context of the Swiss National Science Foundation project Nr. 169676 “Swiss Early Instrumental Measurements for Studying Decadal Climate Variability (CHIMES)”. The newest version of the dataset (v2_2020-04) contains an update of certain URLs indicated on the description (first page) of each PDF-Document. It does not contain any new measurement series compared to the original version.</p> <p>An overview on the individual series on ZENODO is given in the summary table in the description PDF-file.</p> <p>The dataset, organised alphabetically by location name is provided as a .zip-file. General information and terms of use are indicated on the first page of each file.</p> <p>To find further information about all Swiss measurement series (also the ones not published on ZENODO) consider the full inventory provided separately as CSV-file.</p> <p> </p>
Image of sky region around Crab obtained by JEM-X1 instrument in the energy range 3-20 keV.
<p>https://www.astro.unige.ch/cdci/astrooda_?DEC=22.0145&E1_keV=3&E2_keV=20&RA=83.633083333333&T1=2003-03-15T23%3A27%3A40.0&T2=2019-03-16T00%3A03%3A15.0&T_format=isot&detection_threshold=20&instrument=jemx&jemx_num=1&osa_version=OSA10.2&product_type=jemx_image&query_status=new&query_type=Real&radius=5&src_name=Crab&use_scws=no</p> <p> </p>
Data set associated to the publication "An active source seismo-acoustic experiment using tethered balloons to validate instrument concepts and modelling tools for atmospheric seismology"
<p>Data set of the scientific publication entitled "An active source seismo-acoustic experiment using tethered balloons to validate instrument concepts and modelling tools for atmospheric seismology":</p> <p>Seismological sensors</p> <p>Microphones</p> <p>Barometers</p> <p>Accelerometers</p> <p>Detailed test report.</p>
Electron density and altitude of the main ionospheric peak of Mars as observed by Mars Express instruments. Archived data for the paper "Seasonal and geographical variability of the Martian ionosphere from Mars Express observations", submitted to JGR-Planets
<p>This repository contains archived data for the manuscript "Seasonal and geographical variability of the Martian ionosphere from Mars Express observations", published in Journal of Geophysical Research-Planets. Details about the methods to generate the data can be found in the paper.</p> <p>5 data files plus 2 readme text files are included.</p> <p>The file MEx_ionpeak.dat (described in the readme file README_ionpeak.txt) contains the peak electron densities and peak altitudes resulting from 34539 observations. Each record includes 14 columns. The content of each column is:</p> <p>Column 1: Instrument providing the observation (MARSIS or MaRS)<br> Column 2: Mars Year at which the observation was obtained (from MY27 to MY33)<br> Column 3: Solar Longitude (Ls) of the observation (unit: degrees)<br> Column 4: Latitude of the observation (unit: degrees)<br> Column 5: Longitude of the observation (unit: degrees)<br> Column 6: Solar Zenith Angle (SZA) of the observation (unit: degrees)<br> Column 7: F10.7 solar proxy index at 1 Astronomic Unit (unit: solar flux units)<br> Column 8: Peak electron density measured by the instrument (unit: cm-3)<br> Column 9: Peak electron density at the subsolar point, i.e., corrected for the SZA variation (unit: cm-3)<br> Column 10: Peak electron density at the subsolar point and at F10.7 (1AU)=100, i.e., corrected for the SZA and the solar radiation output variations (unit: cm-3)<br> Column 11: Peak electron density at the subsolar point, at F10.7 (1AU)=100 and corrected for the seasonal variation (unit: cm-3)<br> Column 12: Peak altitude measured by the instrument (unit: km)<br> Column 13: Peak altitude at the subsolar point, i.e. corrected for the SZA variation (unit: km)<br> Column 14: Peak altitude at the subsolar point and corrected for the seasonal variation (unit: km)</p> <p> </p> <p>The files eprofiles_MaRS.dat, eprofiles_MARSIS_prof1.dat, eprofiles_MARSIS_prof2.dat and eprofiles_MARSIS_prof3.dat contain 4 electron density profiles. They are described in the file README_eprofiles.txt. Each file includes 2 columns, the first one being the altitude (unit: km) and the second one the electron density (unit: cm-3).</p> <p> </p> <p>Contact: Francisco Gonzalez-Galindo, ggalindo@iaa.es<br> </p>
IODP Expedition 368X 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>
Data availability. Multivariate data analysis. Validation of an instrument for the evaluation of teaching digital competence.
<p><strong>Data availability</strong>. Multivariate data analysis. Validation of an instrument for the evaluation of teaching digital competence. </p><ul><li>SPSS DATA. Multivariate data analysis. Validation of an instrument for the evaluation of teaching digital competence (spss data.sav). The data presented in this file contains the data imported wiyh the Software IBM SPSS Statistics, versión 28.0.1.1(15).</li><li>EXCEL DATA. Multivariate data analysis. Validation of an instrument for the evaluation of teaching digital competence (spss data.sav). The data presented in this file contains the data imported wiyh the Software IBM SPSS Statistics, versión 28.0.1.1(15).</li><li>Data of Project factorial.xlsx (The data presented in this file contains the results of the statistical analysis carried out with the Software Microsoft Excel).</li><li>Data Project reliability.xlsx (The data presented in this file contains the results of the statistical analysis carried out with the Software Microsoft Excel).</li><li>FIGURES. Multivariate data analysis. Validation of an instrument for the evaluation of teaching digital competence (Figure 1.jpeg, Figure 2.jpeg, Figure 3 and Figure 4.jpeg).</li></ul>
Effects of relational and instrumental messaging on human perception of rattlesnakes
<p>We tested the effects of relational and instrumental message strategies on US residents' perception of rattlesnakes—animals that tend to generate feelings of fear, disgust, or hatred but are nevertheless key members of healthy ecosystems. We deployed an online survey to social media users (n=1,182) to describe perceptions of rattlesnakes and assess the change after viewing a randomly selected relational or instrumental video message. An 8–item, pre– and post– Rattlesnake Perception Test (RPT) evaluated perception variables along emotional, knowledge, and behavioral gradients on a 5–point Likert scale; the eight responses were combined to produce an Aggregate Rattlesnake Perception (ARP) score for each participant. We found that people from Abrahamic religions (i.e., Christianity, Judaism, Islam) and those identifying as female were associated with low initial perceptions of rattlesnakes, whereas agnostics and individuals residing in the Midwest region and in rural residential areas had relatively favorable perceptions. Overall, both videos produced positive changes in rattlesnake perception, although the instrumental video message led to a greater increase in ARP than the relational message. The relational message was associated with significant increases in ARP only among females, agnostics, Baby Boomers (age 57–75), and Generation–Z (age 18–25 to exclude minors). The instrumental video message was associated with significant increases in ARP, and this result varied by religious group. ARP changed less in those reporting prior experience with a venomous snake bite (to them, a friend, or a pet) than in those with no such experience. Our data suggest that relational and instrumental message strategies can improve people's perceptions of unpopular and potentially dangerous wildlife, but their effectiveness may vary by gender, age, religious beliefs, and experience. These results can be used to hone and personalize communication strategies to improve perceptions of unpopular wildlife species.</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.