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1,211 results for “Instruments”
Lake Sunapee Instrumented Buoy: High-Frequency Weather Data, 2007-2022
The Lake Sunapee/Global Lake Ecological Observatory Network (GLEON) instrumented buoy, operated by the Lake Sunapee Protective Association (LSPA www.lakesunapee.org), is equipped with meteorologic (weather) sensors. The Lake Sunapee buoy is located near Loon Island Lighthouse (43.391°N, 72.058°W) during the summer months and in the Lake Sunapee Harbor (43.386°N, 72.081°W) (2010-current) from late fall to spring. During the first few years of data collection (2007-2010), the buoy was located near Loon Island year-round. The meteorological sensors include: a LI-COR LI-190R Quantum Photosynthetically Active Radiation (PAR) sensor (May 2018 - present), an air temperature sensor (HMP50 Vaisala, 2010-present), and a wind speed/direction sensor (WXT511 Vaisala, 2009-present) sensor. These sensors are located approximately 1.7 meters above the water's surface. The buoy is also equipped with below-water surface sensors comprised of water temperature thermistors and dissolved oxygen sensors, and as of 2021, a YSI EXO multiparameter sonde. The data from the below water sensors are available in a separate dataset (EDI data package edi.499). All data in this data package have been QAQC'd to remove obviously errant readings, highly suspicious readings, and artifacts of buoy maintenance.
Lake Sunapee Instrumented Buoy: High Frequency Water Quality Data - 2007-2022
The Lake Sunapee (Global Lake Ecological Observatory Network—GLEON) instrumented buoy, operated by the Lake Sunapee Protective Association (LSPA www.lakesunapee.org), is equipped with a thermistor chain, one optical dissolved oxygen probe suspended at approximately 10 meters depth (installed in 2013), and a multi-parameter sonde (installed in 2021) at 1 meter depth. An optical dissolved oxygen (DO) probe was located at 1.5 meter prior to the 2021 deployment of the multi-parameter probe from the inception of the buoy and the deep DO sensor was at 10.5 meters depth prior to 2021 since deployment. The number of thermistors and below-surface depth of the thermistors has fluctuated throughout the years and the additional water quality sensors have changed over time. The Lake Sunapee buoy is located near Loon Island Lighthouse (43.391°N, 72.058°W) during the summer months and in the Lake Sunapee Harbor (43.386°N, 72.081°W) (2010-current). During the first few years of data collection (2007-2010), the buoy was located near Loon Island year-round. In two instances, HOBO units were deployed at the buoy's location when sensors failed. This occurred in 2015 (in place of the thermistors) and in 2018 (in place of the shallow DO sensor). All data in this data package have been QAQC'd to remove obviously errant readings, highly suspicious readings, and artifacts of buoy maintenance. Additionally, flags have been added per sensor, to indicate calibration and non-calibration of the optical DO probe, location of the buoy, and to document other potential confounding observations. Dissolved oxygen data and data from the multi-parameter probe are “raw”: they are have not been corrected for calibration issues, drift, or fouling. Additional documentation and data are provided including manual DO measurements, visual comparisons of buoy-recorded DO and manual DO measurements, and an overview of how and where DO offsets applied in the data logger program were removed.
Spectral reflectance data of Mercury's surface collected by the Mercury Atmospheric and Surface Composition Spectrometer (MASCS) instrument during orbital observations of the NASA MESSENGER mission between 2011 and 2015 resampled to a [55399 × 396] tabular data format.
<p>MASCS is a three sensor point spectrometer with a spectral coverage from 200 nm to 1450 nm.<br> Single spectra are resamples in to steps to a format useful for our ML application : a datacube with ~400 spectral channel covering the whole surface of Mercury.<br> The final dataset has dimension [N×M] where N is the number of grid cells (360 × 180 = 64, 800) and M is the number of spectral features (396).<br> Due to the incomplete coverage and data filtering, some grid cells are empty.<br> After removing these empty cells, the size of the dataset is [55399 × 396].</p> <p>This specific product is stored as a gzip compressed json, where each element is a grid cell.<br> We are in the process to publish a complete pipeline to produce this product from RAW data on https://github.com/epn-ml/MESSENGER-Mercury-Surface-Cassification-Unsupervised_DLR/ .</p> <p>Spectral reflectance data of Mercury’s surface collected by the Mercury Atmospheric and Surface Composition Spectrometer (MASCS) instrument during orbital observations of the NASA MESSENGER mission between 2011 and 2015.<br> MASCS is a three sensor point spectrometer with a spectral coverage from 200 nm to 1450 nm.<br> Single spectra are resamples in to steps to a format useful for our ML application : a datacube with ~400 spectral channel covering the whole surface of Mercury.<br> The final dataset has dimension [N×M] where N is the number of grid cells (360 × 180 = 64, 800) and M is the number of spectral features (396).<br> Due to the incomplete coverage and data filtering, some grid cells are empty.<br> After removing these empty cells, the size of the dataset is [55399 × 396].</p> <p>0. Pre-filtering<br> We used the most recent dataset that had large-scale photometric corrections and thus was almost free from observation geometry effects.<br> However, extreme geometry are still present and are typically associated with high noise and some residual instrumental effects.<br> Based on our empirical tests, we filtered out observations with an emission/incidence angle ≥80∘.<br> We also calculated the median value per wavelength and per cell grid when constructing the global hyperspectral data cube and filtered out observations falling under the 2nd percentile and above 99.9th percentile to clean some residual geometry effects.<br> With this approach we create an effective noise filter while retaining enough observations to be able to analyse the entirety of the surface of the planet.</p> <p>1. Spectral resmpling<br> Unprocessed MASCS spectra could have 512 or 256 channes, depending on binning.<br> We resampled the data in the spectral dimension to a common wavelength range from 260 nm to 1052 nm with a 4 nm resolution (2 nm spectral sampling), resulting in 396 spectral channels.<br> This approach slightly oversamples the original 4.77 nm spectral resolution and removes some points from the original 200-1050 nm range.<br> The resulting data matrix is expressed in tabular form, with each row representing a single grid cell or pixel on the surface.<br> The elements of each row are the spectral reflectance values from the VIS instrument at 396 (resampled) wavelengths.</p> <p>2. Spatial resmpling<br> The whole dataset of ∼ 5 million spectra is resampled to a planet-wide rectangular grid of 1×1deg in the latitudinal band between ± 80.<br> The cell longitudinal size varies between ∼ 40 km at the equator to a minimum of ∼ 10 km at ±80∘.<br> Thus, the area spanned by each grid cell depends on the latitude. However, the same is true for the acquisition process, where higher spatial resolution is reached near the equator and lower resolution at the poles.</p>
Water level data (15-min frequency) from 18 instrumented playas in the Jornada Basin, southern New Mexico, USA, from 2016-2022
This dataset contains water level data collected at 18 playas starting in June 2016 for a long-term study of playa inundation at the Jornada Basin LTER site in southern New Mexico, U.S.A. Playas are located throughout the Jornada Basin and are instrumented with dataloggers and attached pressure transducers located at the lowest point of each playa. Instantaneous measurements of surface water level are made every 15 minutes. Data are processed to flag errors and identify periods of playa inundation (floods). This is an ongoing study and the dataset will be updated yearly.
Raw spectra measurements of scattered sunlight collected using a MAX-DOAS (Multi-Axis Differential Optical Absorption Spectroscopy) instrument in the austral summer of 2016/17 during the Antarctic Circumnavigation Expedition (ACE).
<p><strong>Dataset abstract</strong></p> <p>To achieve the objectives of the project, we installed a MAX-DOAS (Multi-AXis Differential Optical Absorption Spectroscopy) instrument on the vessel “Akademik Tryoshnikov”. This instrument is based on the DOAS technique, which is used to measure trace gas concentrations in the atmosphere. The method consists of the analysis of the spectral absorption lines that each trace gas produces in the solar spectra. The DOAS technique uses the narrowband features that every trace gas has in their spectral absorption coefficients. This differential cross section is unique and acts like a fingerprint for the trace gases, allowing to differentiate between them and to estimate their concentrations (for further details see Platt and Stutz, 2008).</p> <p>In the past decades, atmospheric chemists have come to realize that halogen species (like Cl, Br or I and their oxides ClO, BrO and IO) exert a powerful influence on the chemical composition of the troposphere and through that influence affect the evolution of pollutants, hence having a significant impact on climate. These reactive halogen species are potent oxidizers for organic and inorganic compounds throughout the troposphere. In particular, halogen cycles can act on several compounds (such as methane, ozone, particles…), all of which are climate forcing agents through direct and indirect radiative effects. Dynamic exchange of halogens between the ocean, sea ice, snowpack and atmosphere is the main driver for the frequent occurrence of Ozone Depletion Events (ODEs) and Atmospheric Mercury Depletion Events (AMDEs) (Saiz-Lopez and von Glasow, 2012).</p> <p>In this dataset we present the raw spectra measurements of scattered sunlight recorded by the MAX-DOAS onboard a research vessel in the Southern Ocean and Atlantic Ocean. Included are position and vessel inclination data. Data coverage is from December 2016 to April 2017.</p> <p><strong>Dataset contents</strong></p> <ul> <li>ace_maxdoas_gps.zip</li> <li>GPS_JDDD.txt, data file, ASCII text</li> <li>ace_maxdoas_inclination.zip</li> <li>Inclination_JDDD.txt, data file, ASCII text</li> <li>ace_maxdoas_spectra-YYYY-MM.zip</li> <li>- MAXDOAS<br> - - WWW<br> - - - JDDD<br> - - - - LiveInfo_DDDhhmmss.WWW, data file, ASCII text<br> - - - - Atmos<br> - - - - - DDDhhmmss_90.WWW, data file, ASCII text<br> - ZENITH<br> - - WWW<br> - - - JDDD<br> - - - - LiveInfo_DDDhhmmss.WWW, data file, ASCII text<br> - - - - Atmos<br> - - - - - DDDhhmmss_90.WWW, data file, ASCII text</li> <li>README.txt, metadata, text</li> <li>data_file_header_gps.txt, metadata, text</li> <li>data_file_header_inclination.txt, metadata, text</li> <li>data_file_header_spectra_atmos.txt, metadata, text</li> <li>data_file_header_spectra_liveinfo.txt, metadata, text</li> </ul> <p>where YYYY is the year and MM is the month. JDDD is the day of the year (Julian day) YYYY in which the file was recorded. hhmmss is the time. WWW is the central wavelength of the measured spectrum in the UV or VIS region.</p> <p><strong>Dataset license</strong></p> <p>This dataset of raw spectra of scattered sunlight measurements from ACE is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
Bromine monoxide (BrO) measurements made using a MAX-DOAS (Multi-AXis Differential Optical Absorption Spectroscopy) instrument in the austral summer of 2016/17 during the Antarctic Circumnavigation Expedition (ACE).
<p><strong>Dataset abstract</strong></p> <p>To achieve the objectives of the project, we installed a MAX-DOAS (Multi-AXis Differential Optical Absorption Spectroscopy) instrument on the vessel “Akademik Tryoshnikov”. This instrument is based on the DOAS technique, which is used to measure trace gas concentrations in the atmosphere. The method consists of the analysis of the spectral absorption lines that each trace gas produces in the solar spectra. The DOAS technique uses the narrowband features that every trace gas has in their spectral absorption coefficients. This differential cross section is unique and acts like a fingerprint for the trace gases, allowing to differentiate between them and to estimate their concentrations (for further details see Platt and Stutz, 2008).</p> <p>In the past decades, atmospheric chemists have come to realize that halogen species (like Cl, Br or I and their oxides ClO, BrO and IO) exert a powerful influence on the chemical composition of the troposphere and through that influence affect the evolution of pollutants, hence having a significant impact on climate. These reactive halogen species are potent oxidizers for organic and inorganic compounds throughout the troposphere. In particular, halogen cycles can act on several compounds (such as methane, ozone, particles…), all of which are climate forcing agents through direct and indirect radiative effects. Dynamic exchange of halogens between ocean, sea ice, snowpack and atmosphere is the main driver for the frequent occurrence of Ozone Depletion Events (ODEs) and Atmospheric Mercury Depletion Events (AMDEs) (Saiz-Lopez and von Glasow, 2012).</p> <p>In this dataset we present the mixing ratio and vertical column density of bromine monoxide (BrO) recorded in the austral summer of 2016/2017 in the Southern Ocean and Atlantic Ocean, averaged over one-hour time periods.</p> <p><strong>Dataset contents</strong></p> <ul> <li>ace_bromine_monoxide_atmospheric_measurements.csv, data file, comma-separated values</li> <li>data_file_header.txt, metadata, text</li> <li>README.pdf, metadata, PDF/A-1a</li> <li>README.txt, metadata, text</li> </ul> <p><strong>Dataset license</strong></p> <p>This dataset of atmospheric bromine monoxide measurements from ACE is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
Iodine monoxide (IO) measurements made using a MAX-DOAS (Multi-AXis Differential Optical Absorption Spectroscopy) instrument in the austral summer of 2016/17 during the Antarctic Circumnavigation Expedition (ACE).
<p><strong>Dataset abstract</strong></p> <p>To achieve the objectives of the project, we installed a MAX-DOAS (Multi-AXis Differential Optical Absorption Spectroscopy) instrument on the vessel “Akademik Tryoshnikov”. This instrument is based on the DOAS technique, which is used to measure trace gas concentrations in the atmosphere. The method consists of the analysis of the spectral absorption lines that each trace gas produces in the solar spectra. The DOAS technique uses the narrowband features that every trace gas has in their spectral absorption coefficients. This differential cross section is unique and acts like a fingerprint for the trace gases, allowing to differentiate between them and to estimate their concentrations (for further details see Platt and Stutz, 2008).</p> <p>In the past decades, atmospheric chemists have come to realize that halogen species (like Cl, Br or I and their oxides ClO, BrO and IO) exert a powerful influence on the chemical composition of the troposphere and through that influence affect the evolution of pollutants, hence having a significant impact on climate. These reactive halogen species are potent oxidizers for organic and inorganic compounds throughout the troposphere. In particular, halogen cycles can act on several compounds (such as methane, ozone, particles…), all of which are climate forcing agents through direct and indirect radiative effects. Dynamic exchange of halogens between ocean, sea ice, snowpack and atmosphere is the main driver for the frequent occurrence of Ozone Depletion Events (ODEs) and Atmospheric Mercury Depletion Events (AMDEs) (Saiz-Lopez and von Glasow, 2012).</p> <p>In this dataset we present the mixing ratio and vertical column density of iodine monoxide (IO) recorded in the austral summer of 2016/2017 in the Southern Ocean and Atlantic Ocean, averaged over one-hour time periods.</p> <p><strong>Dataset contents</strong></p> <ul> <li>ace_iodine_monoxide_atmospheric_measurements.csv, data file, comma-separated values</li> <li>data_file_header.txt, metadata, text</li> <li>README.pdf, metadata, PDF/A1-a</li> <li>README.txt, metadata, text</li> </ul> <p><strong>Dataset license</strong></p> <p>This dataset of atmospheric iodine monoxide measurements from ACE is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
3D-COSI ~ 3D Collection of Surgical Instruments
<h2><strong>COSI - 3D STL Collection of Surgical Instruments</strong></h2><p><i><strong>Due to large file names, we have chosen to use </strong></i><a href="https://www.7-zip.org/"><i><strong>https://www.7-zip.org/</strong></i></a><i><strong> which is 100% free and compatible with WinZip. If you encounter an error using WinZip, it's likely due to large file names, please use 7zip.</strong></i></p><p>Inside the repository, you will find an information overview "<i>Overview.docx", </i>a showcase video "<i>Example video 3D instruments.mp4"</i>, STL files of 103 surgical instruments "<i>Surgical Instruments.7z"</i>, examples of variations of the surgical instruments using Blender add-on or Python script build on the Trimesh library "<i>Blender Part x of 8 ... 7z"</i>, or "<i>Trimesh part x of 9 ... .7z". </i>You will also find the used Blender Add On <i>"MultiMesh.zip", </i>measurements of virtual instruments and settings for the add-on (.xlsx), and the script that was used to perform these measurements inside a single folder, "<i>Scripts, measurements and used Blender settings.7z</i>".</p><p>The proposed data collection consists of 103 3D-scanned medical instruments from the clinical routine, scanned with structured light scanners. The collection consists, for example, of instruments like retractors, forceps, and clamps. The collection is augmented by generating likewise models using 3D software, resulting in an inflated dataset for analysis. The collection can be used for general instrument detection and tracking in operating room settings or a freeform marker-less instrument registration for tool tracking in augmented reality. Furthermore, for medical simulation or training scenarios in virtual reality or mixed reality.<br><br><strong>Related article:</strong><br>Luijten, G., Gsaxner, C., Li, J. <i>et al.</i> 3D surgical instrument collection for computer vision and extended reality. <i>Sci Data</i> <strong>10</strong>, 796 (2023). https://doi.org/10.1038/s41597-023-02684-0</p>
Medley-solos-DB: a cross-collection dataset for musical instrument recognition
<p>Medley-solos-DB<br> =============<br> Version 1.2 March 2019.<br> </p> <p> </p> <p>Created By<br> --------------</p> <p>Vincent Lostanlen (1), Carmine-Emanuele Cella (2), Rachel Bittner (3), Slim Essid (4).<br> <br> (1): New York University<br> (2): UC Berkeley<br> (3): Spotify, Inc.<br> (4): Télécom ParisTech</p> <p> </p> <p><br> Description<br> ---------------</p> <p> </p> <p>Medley-solos-DB is a cross-collection dataset for automatic musical instrument recognition in solo recordings. It consists of a training set of 3-second audio clips, which are extracted from the MedleyDB dataset of Bittner et al. (ISMIR 2014) as well as a test set set of 3-second clips, which are extracted from the solosDB dataset of Essid et al. (IEEE TASLP 2009). Each of these clips contains a single instrument among a taxonomy of eight: clarinet, distorted electric guitar, female singer, flute, piano, tenor saxophone, trumpet, and violin.</p> <p>The Medley-solos-DB dataset is the dataset that is used in the benchmarks of musical instrument recognition in the publications of Lostanlen and Cella (ISMIR 2016) and Andén et al. (IEEE TSP 2019).</p> <p> </p> <p>[1] V. Lostanlen, C.E. Cella. Deep convolutional networks on the pitch spiral for musical instrument recognition. Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), 2016.</p> <p>[2] J. Andén, V. Lostanlen, and S. Mallat. Joint time-frequency scattering. IEEE Transactions in Signal Processing, vol. 67, no. 14, pp. 3704-3718, 2019. doi: 10.1109/TSP.2019.2918992</p> <p> </p> <p><br> Data Files<br> --------------</p> <p>The Medley-solos-DB contains 21571 audio clips as WAV files, sampled at 44.1 kHz, with a single channel (mono), at a bit depth of 32. Every audio clip has a fixed duration of 2972 milliseconds, that is, 65536 discrete-time samples.</p> <p>Every audio file has a name of the form:</p> <p>Medley-solos-DB_SUBSET-INSTRUMENTID_UUID.wav</p> <p> </p> <p>For example:</p> <p>Medley-solos-DB_test-0_0a282672-c22c-59ff-faaa-ff9eb73fc8e6.wav</p> <p>corresponds to the snippet whose universally unique identifier (UUID) is 0a282672-c22c-59ff-faaa-ff9eb73fc8e6, contains clarinet sounds (clarinet has instrument id equal to 0), and belongs to the test set.</p> <p> </p> <p><br> Metadata Files<br> -------------------</p> <p>The Medley-solos-DB_metadata is a CSV file containing 21572 rows (one for each audio clip) and five columns:</p> <p>1. subset: either "training", "validation", or "test"</p> <p>2. instrument: tag in Medley-DB taxonomy, such as "clarinet", "distorted electric guitar", etc.</p> <p>3. instrument id: integer from 0 to 7. There is a one-to-one between "instrument" (string format) and "instrument id" (integer). We provide both for convenience.</p> <p>4. song id: integer from 0 to 226. The track and artist names are anonymized.</p> <p>5. UUID4: universally unique identifier. Assigned and random, and different for every row.</p> <p> </p> <p>The list of instrument classes is:</p> <p>0. clarinet</p> <p>1. distorted electric guitar</p> <p>2. female singer</p> <p>3. flute</p> <p>4. piano</p> <p>5. tenor saxophone</p> <p>6. trumpet</p> <p>7. violin</p> <p> </p> <p><br> Please acknowledge Medley-solos-DB in academic research<br> ---------------------------------------------------------------------------------</p> <p>When Medley-solos-DB is used for academic research, we would highly appreciate it if scientific publications of works partly based on this dataset cite the following publication:</p> <p>V. Lostanlen, C.E. Cella. Deep convolutional networks on the pitch spiral for musical instrument recognition. Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), 2016.</p> <p>The creation of this dataset was supported by ERC InvariantClass grant 320959.</p> <p> </p> <p><br> Conditions of Use<br> ------------------------</p> <p>Dataset created by Vincent Lostanlen, Rachel Bittner, and Slim Essid, as a derivative work of Medley-DB and solos-Db.</p> <p>The Medley-solos-DB dataset is offered free of charge under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) license:<br> https://creativecommons.org/licenses/by/4.0/</p> <p>The dataset and its contents are made available on an "as is" basis and without warranties of any kind, including without limitation satisfactory quality and conformity, merchantability, fitness for a particular purpose, accuracy or completeness, or absence of errors. Subject to any liability that may not be excluded or limited by law, the authors are not liable for, and expressly exclude all liability for, loss or damage however and whenever caused to anyone by any use of the Medley-solos-DB dataset or any part of it.</p> <p> </p> <p><br> Feedback<br> -------------</p> <p>Please help us improve Medley-solos-DB by sending your feedback to:<br> vincent.lostanlen@nyu.edu</p> <p>In case of a problem, please include as many details as possible.</p> <p> </p> <p> </p> <p>Acknowledgement<br> -------------------------<br> We thank all artists, recording engineers, curators, and annotators of both MedleyDB and solosDb.</p>
MAD-EEG: an EEG dataset for decoding auditory attention to a target instrument in polyphonic music
<p>The <em><strong>MAD-EEG Dataset</strong></em> is a research corpus for studying EEG-based auditory attention decoding to a target instrument in polyphonic music. </p> <p>The dataset consists of 20-channel EEG responses to music recorded from 8 subjects while attending to a particular instrument in a music mixture. </p> <p>For further details, please refer to the paper: <em><a href="https://hal.archives-ouvertes.fr/hal-02291882/document">MAD-EEG: an EEG dataset for decoding auditory attention to a target instrument in polyphonic music</a>.</em></p> <p>If you use the data in your research, please reference the paper (not just the Zenodo record):</p> <pre><code>@inproceedings{Cantisani2019, author={Giorgia Cantisani and Gabriel Trégoat and Slim Essid and Gaël Richard}, title={{MAD-EEG: an EEG dataset for decoding auditory attention to a target instrument in polyphonic music}}, year=2019, booktitle={Proc. SMM19, Workshop on Speech, Music and Mind 2019}, pages={51--55}, doi={10.21437/SMM.2019-11}, url={http://dx.doi.org/10.21437/SMM.2019-11} }</code></pre> <p> </p>
Solar Wind properties measured with instruments on the Advanced Composition Explorer (ACE)
<p>Combined ACE/SWEPAM, ACE/Mag, and ACE/SWICS data set<br> ACE/MAG and ACE/SWEPAM data are taken from the ACE Science center (https://izw1.caltech.edu/ACE/ASC/) and binned to the 12-minute time resolution of SWICS.<br> The SWICS data is based on the PHA data and analyzed as described in Berger (2008).<br> This data set is used in the following two publications:<br> Teichmann, S. Heidrich-Meisner, V, Berger, L, Wimmer-Schweingruber, R.F. (2023, submitted), "Influence of solar wind parameters on unsupervised solar wind classification with k-means" source code available: 10.5281/zenodo.7695074<br> Hecht, M, Heidrich-Meisner, V, Berger, L, Wimmer-Schweingruber, R.F. 2023 (in preparation) "Scope and limitations of ad-hoc neural network reconstructions of solar wind parameters", source code available: 10.5281/zenodo.7681047.</p> <p>Contact: Verena Heidrich-Meisner, CAU Kiel heidrich@physik.uni-kiel.de</p> <p>We thank the science teams of ACE/SWEPAM, ACE/MAG as well as<br> ACE/SWICS for developing, maintaining and calibrating the instruments and for providing the respective level 2 and level 1 data products.<br> This work was supported by the Deutsches Zentrum für Luft- und Raumfahrt (DLR) as SOHO/CELIAS 50 OC 2104.</p> <p>Data products description:<br> year: year of observation (int)<br> time: day of year in current year as float<br> yeartime: time in years as float (UTC)<br> vsw: solar wind proton speed in km/s, measured by ACE/SWEPAM (level 2 from ACE Science Center) and rebinned to 12 minute time resolution<br> dsw: solar wind proton density in cm^{-3}, measured by ACE/SWEPAM (level 2 from ACE Science Center)and rebinned to 12 minute time resolution<br> tsw: solar wind proton temperature in K, measured by ACE/SWEPAM (level 2 from ACE Science Center) and rebinned to 12 minute time resolution<br> B: magnetic feld strength in nT, measured by ACE/MAG (level 2 from ACE Science Center)<br> colage: proton-proton collisional age computed as 6.4* 1e8 * dsw /(vsw* tsw**(3/2)) in K^{3/2} s^2 cm^3 km^{-1}<br> dO7_6: ratio of the O7+ to O6+ charge state densities, measured by ACE/SWICS, derived directly from PHA (pulse height analysis) data<br> eO7_6: estimate of the relative error of dO7_6 based on the counting statistics<br> ldO7_6: decadic logarithm of dO7_6<br> elO7_6: estimate of the relative error of the decadic logarithm dO7_6 based on the counting statistics<br> mcsFe: mean charge state of Fe, based on SWICS PHA of Fe8+, Fe9+, Fe10+, Fe11+, and Fe12+ in units of the elementary charge e. At least 10 counts distributed over Fe8+, Fe9+, Fe10+, Fe11+ and Fe12+ are required<br> emcsFe: estimate of the relative error of dO7_the mean Fe charge state in e (assumes 10% relative error for each Fe charge state)<br> cor_hole: coronal hole wind category in the categorization of Xu&Borovsky (2015) The ejecta category is disregarded, see for example Heidrich-Meisner (2020). Entries are 0 or 1, 1 of the data point is assigned to this type.<br> sec_rev: sector reversal plasma wind category in the categorization of Xu&Borovsky (2015) The ejecta category is disregarded, see for example Heidrich-Meisner (2020). Entries are 0 or 1, 1 of the data point is assigned to this type.<br> stream_belt: streamer belt wind category in the categorization of Xu&Borovsky (2015) The ejecta category is disregarded, see for example Heidrich-Meisner (2020). Entries are 0 or 1, 1 of the data point is assigned to this type.<br> ICME: interplanatery coronal mass ejections time periods (with a six hour safety margin before and after each ICME) from the Jian (2006,2011) and Richardson & Cane (2014, 2018) ICME lists. Entries are 0 or 1, 1 of the data point is assigned to this type.<br> totalCountsFe: number of counts in ACE/SWICS distributed over Fe8+-Fe12+<br> The data set is restricted to data points where valid data points are available for all listed data products. Only for the mean charge state of Fe invalid data points are indicated with nan (not a number)</p> <p>References:<br> Berger, L. 2008, PhD thesis, Kiel, Christian-Albrechts-Universität, Diss., 2008<br> Gloeckler, G., Cain, J., Ipavich, F., et al. 1998, in The Advanced Composition Explorer Mission (Springer), 497–539<br> McComas, D., Bame, S., Barker, P., et al. 1998b, in The Advanced Composition Explorer Mission (Springer), 563–612<br> Smith, C. W., L’Heureux, J., Ness, N. F., et al. 1998, in The Advanced Composition Explorer Mission (Springer), 613–632</p> <p>Xu, F. & Borovsky, J. E. 2015, Journal of Geophysical Research: Space Physics, 120, 70<br> Heidrich-Meisner, V., Berger, L., & Wimmer-Schweingruber, R. F. 2020, Astronomy & Astrophysics, 636, A103<br> Jian, L., Russell, C., & Luhmann, J. 2011, Solar Physics, 274, 321<br> Jian, L., Russell, C., Luhmann, J., & Skoug, R. 2006, Solar Physics, 239, 393<br> Richardson, I. G. 2004, Space Science Reviews, 111, 267<br> Richardson, I. G. 2018, Living reviews in solar physics, 15, 1</p> <p>Teichmann, S. Heidrich-Meisner, V, Berger, L, Wimmer-Schweingruber, R.F. (2023), "Influence of solar wind parameters on unsupervised solar wind classification with k-means" source code available: 10.5281/zenodo.7695074<br> Hecht, M, Heidrich-Meisner, V, Berger, L, Wimmer-Schweingruber, R.F. 2023 (in preparation) "Scope and limitations of ad-hoc neural network reconstructions of solar wind parameters", source code available: 10.5281/zenodo.7681047.</p> <p>year/1 time/day of year yeartime/UTC vsw/km/s dsw/cm^{-3} tsw/K B/nT colage/(K^{3/2} s^2 cm^3 km^{-1}) dO7_6/1 eO7_6/1 ldO7_6/1 elO7_6/1 mcsFe/e emcsFe/e cor_hole/bool sec_rev/bool stream_belt/bool ICME/bool totalCountsFe/1</p>
Measurement differences between air temperature instruments used at H.J. Andrews meteorological stations
The PRIMET Horizontal Radiation Shield Comparison (PHRSC) experiment compares the difference between the air temperature measurements of a reference temperature sensor inside a fan aspirated radiation shield and temperature sensors located inside passively aspirated radiation shields including a cotton region shelter, Gill multi-plate shield, and a custom-fabricated model. Observed variables include air temperature, wind speed, and incoming and reflected solar radiation. Data was collected in the field between 2010 and 2017 at the Primary Meteorological Station (PRIMET) at H.J. Andrews Experimental Forest, located in Oregon’s Western Cascades (44.21, -122.26, elevation 430m).
A multi-instrument thermal profile of Edison Eastlake, a Phoenix, Arizona, USA neighborhood, on a summer day in 2019
We have measured the thermal environment conditions of the Edison Eastlake neighborhood in Phoenix, AZ on a clear sky, hot, sunny day of June 19, 2019 to look at the neighborhood microclimate before construction began on the renovation of the public housing units in the community. Measurements were taken with a mobile bio-meteorological weather station, known as MaRTy (Middel & Krayenhoff, 2019), car traverses, four pole-mounted temperature/relative humidity sensors, and two rooftop mounted weather stations. Since the measurement campaign began, construction has been undertaken to redevelop the public housing stock in Edison Eastlake. ### references - Middel, A., & Krayenhoff, E. S. (2019). Micrometeorological determinants of pedestrian thermal exposure during record-breaking heat in Tempe, Arizona: Introducing the MaRTy observational platform. Science of The Total Environment, 687, 137–151. https://doi.org/10.1016/j.scitotenv.2019.06.085
The User Interface and Functionality Charts of Erkki Kurenniemi's Electronic Musical Instruments (EKIS)
<p>This spreadsheet includes data related to user interface and functionality charts of Erkki Kurenniemi's electronic musical instruments. Data covers only musical instruments; not studio equipment. The data set produced as a part of the PhD project "User Stories of Erkki Kurenniemi’s Electronic Musical Instruments" by the author. The data is visualized with a video published in https://vimeo.com/375784663</p> <p>PI and contact information: Mikko Ojanen / https://orcid.org/0000-0002-7833-9659</p> <p>The outlining of charts is based on previous research on DMIs, e.g. by</p> <p>Birnbaum, D., Fiebrink, R., Malloch, J., & Wanderley, M. M. Towards a dimension space for musical devices. <em>Proceedings of the 2005 Conference on New Interfaces for Musical Expression, </em>192-195.</p> <p>Magnusson, T. An Epistemic Dimension Space for Musical Devices. <em>Proceedings of the 2010 Conference on New Interfaces for Musical Expression, </em>43-46.</p> <p>Wanderley, Mortensen M. 2002. Evaluation of input devices for musical expression: Borrowing tools from HCI.<em> Computer Music Journal, </em><em>26</em>(3), 62-76.</p>
An interactive figure of the 2016 and 2020 X-ray light curves of LMC 1968 as observed by the XRT instrument on Swift
<p>This repository contains all the files necessary to create the interactive figure in the Research Note ov Schwarz, Page, Kuin, & Darnley 2020. The figure was created using the <a href="https://aas-timeseries.readthedocs.io/en/latest/">aas-timeseries</a> package of the <a href="https://www.astropy.org">astropy</a> project. The file lmc68.py is the underlying python code while the two lmcrel*.csv are the input files for the 2016 and 2020 eruptions of the recurrent nova LMC 1968 as observed by the XRT instrument on board the Neil Gehrels Swift observatory. A Jupyter notebook is required to preview the interactive figure. The output from the code is saved in the interactive.tar.gz package. It consists of four files:</p> <ul> <li>index.html</li> <li>figure.json</li> <li>data_75e74aca-09f1-4846-966e-9e33c7acc8d3.csv</li> <li>data_5402e718-01cf-4ad7-92a5-7679d4076ed5.csv</li> </ul> <p>The first file, index.html, is the html framework that houses the interactive figure. figure.json contains the interactive figure commands while the two data*csv files are the underlying data. The interactive figure can be viewed if this package is opened on a web server. A copy of this interactive figure is available <a href="https://authortools.aas.org/LMC1968/">here</a> so you can try it out.</p>
Analyses of the works of art and events related to Erkki Kurenniemi's Electronic Musical Instruments (EKIS)
<p>This speadsheet includes data related to analyses of works of art and events related to the Erkki Kurenniemi's electronic musical instruments. The data set produced as a part of the PhD project "User Stories of Erkki Kurenniemi’s Electronic Musical Instruments" by Mikko Ojanen.</p>
IODP Expedition 397T Rig instrumentation
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.
IODP Expedition 383 Rig instrumentation
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.
Immunoglobulin G: SEC-SAXS experiment using in house SAXS instrument with in situ UV-Vis
<p>SEC-SAXS experiment using in house SAXS instrument</p> <p>Sample: Immunoglobuline G<br>Buffer: 50mM Tris, 50 mM NaCl, pH 7.6</p> <table> <tbody> <tr> <td><strong>File Name</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>SAXS_raw.zip</td> <td>Raw, unreduced data (2D images) in HDF5 format</td> </tr> <tr> <td>SAXS_reduced.zip</td> <td>SAXS data, azimutaly reduced (1D curves). Q-vaules are in 1/nm</td> </tr> <tr> <td>SAXS_UV_raw.zip</td> <td>Raw UV-Vis absorption data collected using in-situ spectrometer</td> </tr> <tr> <td>SAXS_UV_processed.zip</td> <td>UV-Vis data collected using in-situ spectrometer. Absorbance at 280nm extracted as a time series</td> </tr> <tr> <td>HPLC_raw.zip</td> <td>Data from HPLC system natively exported from control software (Unicorn)</td> </tr> <tr> <td>HPLC_extracted.zip</td> <td>Data from HPLC system exported to CSV</td> </tr> <tr> <td>DS11_fractions.zip</td> <td>UV-Vis absorption data collected usig micro volume spectrometer (DeNovix DS-11) on fractions collected using fraction collector.</td> </tr> </tbody> </table> <p> </p>
1.3A (Ge337) calibration data for new Ge115 monochromator installed on Echidna Neutron Powder Diffraction Instrument
<p>In early October 2024 the Echidna neutron powder instrument located at the OPAL reactor, ANSTO, installed a new monochromator with Ge115 cut. The present calibration data were collected shortly afterwards from a standard LaB6 sample in a 6mm diameter Vanadium can. The instrument was set to 140 degrees takeoff angle and monochromator angle 85.08 degrees, corresponding to the Ge337 reflection. Raw data in NeXus format are contained in <strong>ECH0034261.nx.hdf</strong>. These data were corrected for variable detector response using the information in <strong>eff_2024-10-06.cif</strong> and pixel vertical positions adjusted according to the table in <strong>vertical_offsets_2024-10-06.txt. </strong>Deviations from the ideal detector 1.25 degree angular spacing were applied using <strong>echidna-Apr2018.ang</strong>. The detector response was then recorrected based on overlapping measurements using the algorithm described in <a href="https://doi.org/10.1107/S1600576718014048">Avdeev and Hester (2018)</a> resulting in a 1D pattern suitable for fitting wavelength and peak shapes. This 1D pattern is provided here as a plain table (<strong>ECH0034261_LaB6.xyd</strong>) and as a pdCIF file (<strong>ECH0034261_LaB6.cif</strong>) including metadata on data collection and reduction. Details of data reduction are described in the above paper, and the data reduction routines used are included in the <a href="https://github.com/Gumtree/Echidna_scripts">Gumtree package as python code</a>.</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.