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10,553 results for “measurements”
FT42 Savary Jeune (11) 11-key tenoroon: measurements, photos, endoscopic video
<p>Dataset of FT42 Savary Jeune (11) 12-key tenoroon containing detailed external and internal measurements, photos, and an endoscopic video. </p>
Data archive for the journal article: "Comparison of co–located rBC and EC mass concentration measurements during field campaigns at several European sites"
<p>Data archive accompanying the peer-reviewed journal article "Comparison of co–located rBC and EC mass concentration measurements during field campaigns at several European sites". In January 2021 this article was accepted for publication in the journal <em>Atmospheric Measurement </em><em>Techniques</em>. Data are uploaded in the form of Igor 8.0 graphics source files (.pxp) and data exported to Excel spreadsheet (.xlsx).</p>
FTIR measurements of formic acid (2010-2012)
<p>The ground-based Fourier Transform InfraRed (FTIR) total column measurements of formic acid (HCOOH) reported here have been derived from high-resolution (between 0.004 and 0.011 cm<sup>-1</sup>) IR solar absorption spectra recorded regularly, under clear-sky conditions, at a suite of sites located at various latitudes. Most of them are affiliated with the Network for the Detection of Atmospheric Composition Change (NDACC; <a href="http://www.ndacc.org">http://www.ndacc.org</a>).</p> <p>End users of this data set are invited to contact the authors to make sure they are using the data properly and check about the possible availability of more recent products.</p>
Data from: Transformation of measurement uncertainties into low-dimensional feature vector space
<p>Advances in technology allow the acquisition of data with high spatial and temporal resolution. These datasets are usually accompanied by estimates of the measurement uncertainty, which may be spatially or temporally varying and should be taken into consideration when making decisions based on the data. At the same time, various transformations are commonly implemented to reduce the dimensionality of the datasets for post-processing, or to extract significant features. However, the corresponding uncertainty is not usually represented in the low-dimensional or feature vector space. A method is proposed that maps the measurement uncertainty into the equivalent low-dimensional space with the aid of approximate Bayesian computation, resulting in a distribution that can be used to make statistical inferences. The method involves no assumptions about the probability distribution of the measurement error and is independent of the feature extraction process as demonstrated in three examples. In the first two examples Chebyshev polynomials were used to analyse structural displacements and soil moisture measurements; while in the third, principal component analysis was used to decompose global ocean temperature data. The uses of the method range from supporting decision making in model validation or confirmation, model updating or calibration and tracking changes in condition, such as the characterisation of the El Niño Southern Oscillation. </p>
Data from: Long-term, high frequency in situ measurements of intertidal mussel bed temperatures using biomimetic sensors
At a proximal level, the physiological impacts of global climate change on ectothermic organisms are manifest as changes in body temperatures. Especially for plants and animals exposed to direct solar radiation, body temperatures can be substantially different from air temperatures. We deployed biomimetic sensors that approximate the thermal characteristics of intertidal mussels at 71 sites worldwide, from 1998-present. Loggers recorded temperatures at 10–30 min intervals nearly continuously at multiple intertidal elevations. Comparisons against direct measurements of mussel tissue temperature indicated errors of ~2.0–2.5 °C, during daily fluctuations that often exceeded 15°–20 °C. Geographic patterns in thermal stress based on biomimetic logger measurements were generally far more complex than anticipated based only on 'habitat-level' measurements of air or sea surface temperature. This unique data set provides an opportunity to link physiological measurements with spatially- and temporally-explicit field observations of body temperature.
Data for: A Benchmark Engineering Methodology to Measure the Overhead of Application-Level Monitoring
<p>Application-level monitoring frameworks, such as Kieker, provide insight into the inner workings and the dynamic behavior of software systems. However, depending on the number of monitoring probes used, these frameworks may introduce significant runtime overhead. Consequently, planning the instrumentation of continuously operating software systems requires detailed knowledge of the performance impact of each monitoring probe.</p> <p>In this paper, we present our benchmark engineering approach to quantify the monitoring overhead caused by each probe under controlled and repeatable conditions. Our developed MooBench benchmark provides a basis for performance evaluations and comparisons of application-level monitoring frameworks. To evaluate its capabilities, we employ our benchmark to conduct a performance comparison of all available Kieker releases from version 0.91 to the current release 1.8.</p> <p>This dataset supplements the paper and contains the raw experimental data as well as several generated diagrams for each experiment.</p>
SDSS Photometric Measurements with Labels
<p>The Sloan Digital Sky Survey (SDSS) is a comprehensive survey of the northern sky. This dataset contains a subset of this survey, namely the photometric measurements and spectroscopic labels of around 2.8 million objects. The dataset was generated by submitting an SQL query to the DR12 catalog on the SDSS CasJobs site.</p> <p>Each row in the dataset corresponds to an object in the sky. There are 14 columns:</p> <ul> <li>The first two columns contain the right ascension (<strong>ra</strong>) and declination (<strong>dec</strong>) of an object. These two coordinates uniquely determine its position.</li> <li>The third column (<strong>class</strong>) is the spectroscopic class (Star, Galaxy, and Quasar) as determined by expert opinion. This can be the target vector in a classification model.</li> <li>There are 11 columns that we can use as feature vectors. These are the different PSF and Petrosian magnitude and colour measurements: <ul> <li><strong>psfMag_r_w14</strong>: The PSF magnitude measurement in r-band, assuming the object is a point source.</li> <li><strong>psf_u_g_14</strong>: The difference between the PSF magnitude measurement in u-band and the g-band (i.e. the u-g colour), assuming the object is a point source.</li> <li><strong>psf_g_r_14</strong>: The difference between the PSF magnitude measurement in g-band and the r-band (i.e. the g-r colour), assuming the object is a point source.</li> <li><strong>psf_r_i_14</strong>: The difference between the PSF magnitude measurement in r-band and the i-band (i.e. the r-i colour), assuming the object is a point source.</li> <li><strong>psf_i_z_14</strong>: The difference between the PSF magnitude measurement in i-band and the z-band (i.e. the i-z colour), assuming the object is a point source.</li> <li><strong>petroMag_r_w14</strong>: The Petrosian magnitude measurement in r-band, assuming the object is an extended source.</li> <li><strong>petro_u_g_w14</strong>: The difference between the Petrosian magnitude measurement in the u-band and the g-band (i.e. the u-g colour), assuming the object is an extended source.</li> <li><strong>petro_g_r_w14</strong>: The difference between the Petrosian magnitude measurement in the g-band and the r-band (i.e. the g-r colour), assuming the object is an extended source.</li> <li><strong>petro_r_i_w14</strong>: The difference between the Petrosian magnitude measurement in the r-band and the i-band (i.e. the r-i colour), assuming the object is an extended source.</li> <li><strong>petro_i_z_w14</strong>: The difference between the Petrosian magnitude measurement in the i-band and the z-band (i.e. the i-z colour), assuming the object is an extended source.</li> <li><strong>petroRad_r</strong>: The size measurement of the object in r-band in arc seconds.</li> </ul> </li> </ul> <p>The measurements have been corrected for dust extinction (the scattering of light by the galactic dust) using the correction set provided by Wolf (2014). Each feature has also been standardised to have zero mean and unit variance.</p> <p>For the code to generate this dataset, please go to the Github repo: https://github.com/chengsoonong/mclass-sky</p> <p>If you use the SDSS data in your papers, please see here for instructions on how to cite: http://www.sdss.org/collaboration/citing-sdss/</p> <p>Please also cite this upload if you have used this particular pre-processed dataset.</p>
ONERA silver TEEY measurements
<p>This document shows three TEEY measurements made in the spatial environment department (DESP) of ONERA Toulouse. It has been measure by Mohamed Belhaj and Thomas Gineste on both clean and technical silver samples in 2014. </p>
FIGURE 2. Landmarks used for obtain the linear measurements, 1 in Two new species of the genus Xenotoca Hubbs and Turner, 1939 (Teleostei, Goodeidae) from central-western Mexico
FIGURE 2. Landmarks used for obtain the linear measurements, 1 to 9 standard length (SL); 1 to 5 head length (HL); 4 to 17 head high (HH); 1 to 2 preorbital length (PrOL); 3 to 5 postorbital length (POL); 2 to 3 eye diameter (ED); 8 to 11 body least depth (BLD); 13 to 14 pelvic-anal fin distance (PAD); 14 to 6 pelvic-dorsal fin distance (PDD); 14 to 15 pelvic-pectoral fin distance (PPD); 6 to 13 dorsal-anal fin distance (DAD); 6 to 12 dorsal fin origin to anal fin posterior extent distance (DOAE); 7 to 13 dorsal fin posterior extent to anal fin origin distance (DEAO); 7 to 9 end of dorsal fin-hypural plate distance (EDHP); 9 to 12 end of the anal fin-hypural plate distance (EAHP); 6 to 7 dorsal fin base length (DFL); 12 to 13 anal fin base length (AFL); 15 to 16 pectoral fin base length (PFL); 10 to 12 caudal peduncle length (CPL).
Demo measurement using DIAPASON and Sentinel-1 after the 14 November 2016 earthquake in New Zealand
<p>Processing of Sentinel-1A acquisitions of 3rd and 15th Nov 2016 with CNES DIAPASON processing chain, integrated by TRE Altamira on ESA's Geohazard Exploitation Platform. Track 52, Ascending Orbit Direction.</p> <p>Contains modified Copernicus Sentinel data 2016</p>
PsPM-BAER: Skin conductance fluctuations in a public speaking paradigm with a repeated-measures design
<p>This dataset contains four skin conductance response (SCR) measurements of 60 s duration from each of 40 healthy male university students (18-35 years) who participated in a public speaking anticipatory anxiety paradigm with a repeated-measures factorial design.</p>
The Kühtai dataset: 25 years of lysimetric, snow pillow and meteorological measurements
<p>This dataset presents long-term observations from an experimental snow lysimeter plot in Kühtai (Austrian Alps). The data set includes 15 minutes data of snow water equivalent from a 10 m² snow pillow, snow melt outflow from a 10 m² snow lysimeter placed at the same location as the pillow, meteorological data (precipitation, incoming global radiation, reflected short wave radiation, air temperature, relative air humidity and wind speed), and other data (snow depths, snow temperatures at seven heights) from the period October, 1990 – May, 2015. All data have been quality checked, and gaps in the meteorological data have been filled in.</p>
Dataset supplementing the publication Einhäuser, W., Thomassen, S., & Bendixen, A. (2017). Using binocular rivalry to tag foreground sounds: towards an objective visual measure for auditory multistability. Journal of Vision, 17:34, 1-19.
<p>These files supplement the publication Einhäuser, W., Thomassen, S., & Bendixen, A. (2017). Using binocular rivalry to tag foreground sounds: towards an objective visual measure for auditory multistability. Journal of Vision, 17:34, 1-19. The data are free for scientific use, provided this reference is appropriately cited.</p> <p>exp1_data.mat contains all the data of experiment 1 as cell arrays of size 8x16x8 (subject x block x trial) or 8x16 (subject x block). Specifically:<br> xEye: the horizontal eye position in raw (pixel coordinates)<br> gain: the OKN slow phase gain computed from the xEye data as described in the paper; in audio-visual blocks the sign is chosen such that positive gain corresponds to the direction of the grating associated with the low tone; in unambiguous visual blocks (1,16) positive sign corresponds to the direction of the grating.<br> ixLow, ixHigh, ixNone, ixBoth: indices for xEye and gain of the same subject and block for which the button corresponding to the low tone, the high tone, both buttons or no button was pressed.</p> <p>exp2_data.mat and exp3_data.mat contain the data of experiment 2 and experiment 3, respectively, and are organized analogously to exp1_data.mat.</p> <p>figure3.m through figure6.m use these data to plot the respective paper figures to exemplify usage of the data.</p> <p> </p>
Solar eclipse radio frequency measurements
<p>Measurements of the carrier frequency of the NIST radio station WWV on 10 MHz, as performed in north suburban Milwaukee, Wisconsin, during the solar eclipse of August 21, 2017. Details are in the file "readme.pdf".</p> <p>Steven Reyer, WA9VNJ, approx. Lat/Long = 43.218, -87.951. WAV file start time = 1400 UTC. Antenna is a DX Engineering RF-PRO-1B aimed north-south, receiver is a Yaesu FT-857D locked to a Trimble Thunderbolt GPS via an XRef-FT oscillator interface. I tuned the radio to 9999.00 kHz USB and listened for the resulting nominal 1000 Hz tone, which was measured by Spectrum Lab software, doing 512k-point FFTs, overlapping 75%, resulting in a measurement every 12 seconds. </p> <p> </p> <p> </p>
Received Signal Srength (RSS) urban measurements from GSM and UMTS networks for cellular-based positioning
<p>GSM (2G) and UMTS (3G) urban measurement data for cellular-based positioning. The data has been collected in Tampere city, Finland with a mobile phone with proprietary software. The data is given in Matlab *mat format and it contains a cell variable called BS_grid* which shows the GPS coordinates (x,y,z) (converted in local coordinates, in meter values) and the collected RSS value (in dB) per transmitter (i.e., BS or Node B). Each cell contains a N x 4 matrix, whose rows are [x y z RSS]. N is the number of measurements points in which the corresponding Base Station were heard. The size of the BS_grid* cells is equal to the number of heard Base Stations in the measured area. </p> <p>Example of research results based on these measurements can be found for example in:</p> <ul> <li>H. Nurminen, J. Talvitie, S. Ali-Loytty, P. Muller, E.S. Lohan, R. Piche, M. Renfors, "Statistical path loss parameter estimation and positioning using RSS measurements", Journal of Global Positioning Systems, vol. 12(1), 2013, ISSN 1446-3156.</li> </ul>
FTIR-ATR and VSC measurements on cremated archaeological bone from Aakre Kivivare tarand grave, Estonia
<p>This dataset is described in paper bt Lillak <em>et al. </em>in prep "FTIR spectroscopy and VSC-based colour assessment dataset for comparative analysis of cremated bones".</p> <p>This dataset comprises four excel files: </p> <p>Bone list of bones from Aakre Kivivare <em>tarand </em>cemetery. This list is in Estonian.</p> <p>Bone list of bones from Viimsi I <em>tarand </em>cemetery. This list is in English.</p> <p>Bone list template with a table heading proposal, both in Estonian and English.</p> <p>A table of excavated <em>tarand </em>cemeteries, when and by whom they were excavated and whether and where these bones are stored. </p>
Modeling and measuring how codon usage modulates the relationship between burden and yield during protein overexpression in bacteria
<p>Additional data from experiments associated with paper revisions.</p> <p>Also added codon optimizer script.</p>
TCOM-CH4: TOMCAT CTM and Occultation Measurements based daily zonal stratospheric methane profile dataset [1991-2021] constructed using machine-learning
<p>Methodology: </p> <p><span>he </span><strong><span>TOMCAT simulation</span></strong><span> was conducted at a T64L32 resolution, consistent with previous work by Dhomse et al. (2021, 2022), covering the period from 2000 to 2024. These simulations utilized </span><strong><span>ERA-5 reanalysis data</span></strong><span>.</span></p> <h3><span>CH4 Profile Processing and Bias Correction</span></h3> <p><strong><span>Collocated CH4 profiles</span></strong><span> are organized into five distinct latitude bins:</span></p> <ul> <li> <p><strong><span>NH polar</span></strong><span>: </span><span><span><span><span><span>9</span><span>0<span><span><span><span><span><span><span>∘</span></span></span></span></span></span></span></span></span></span></span></span><span>N - </span><span><span><span><span><span>5</span><span>0<span><span><span><span><span><span><span>∘</span></span></span></span></span></span></span></span></span></span></span></span><span>N</span></p> </li> <li> <p><strong><span>NH mid-lat</span></strong><span>: </span><span><span><span><span><span>2</span><span>0<span><span><span><span><span><span><span>∘</span></span></span></span></span></span></span></span></span></span></span></span><span>N - </span><span><span><span><span><span>7</span><span>0<span><span><span><span><span><span><span>∘</span></span></span></span></span></span></span></span></span></span></span></span><span>N</span></p> </li> <li> <p><strong><span>Tropics</span></strong><span>: </span><span><span><span><span><span>4</span><span>0<span><span><span><span><span><span><span>∘</span></span></span></span></span></span></span></span></span></span></span></span><span>S - </span><span><span><span><span><span>4</span><span>0<span><span><span><span><span><span><span>∘</span></span></span></span></span></span></span></span></span></span></span></span><span>N</span></p> </li> <li> <p><strong><span>SH mid-lat</span></strong><span>: </span><span><span><span><span><span>7</span><span>0<span><span><span><span><span><span><span>∘</span></span></span></span></span></span></span></span></span></span></span></span><span>S - </span><span><span><span><span><span>2</span><span>0<span><span><span><span><span><span><span>∘</span></span></span></span></span></span></span></span></span></span></span></span><span>S</span></p> </li> <li> <p><strong><span>SH polar</span></strong><span>: </span><span><span><span><span><span>9</span><span>0<span><span><span><span><span><span><span>∘</span></span></span></span></span></span></span></span></span></span></span></span><span>S - </span><span><span><span><span><span>5</span><span>0<span><span><span><span><span><span><span>∘</span></span></span></span></span></span></span></span></span></span></span></span><span>S</span></p> </li> </ul> <p><span>Initially, </span><strong><span>differences between TOMCAT and satellite measurements</span></strong><span> (primarily ACE-FTS data) are calculated for each zonal bin across 51 height levels (ranging from </span><span><span><span><span><span>10</span><span>,</span><span><span>km</span></span></span></span></span></span><span> to </span><span><span><span><span><span>60</span><span>,</span><span><span>km</span></span></span></span></span></span><span>). It is important to note that unlike previous versions that might have used both HALOE and ACE measurements, this version exclusively utilizes </span><strong><span>ACE-FTS data</span></strong><span>, which is why the dataset starts from 2000.</span></p> <p><strong><span>Separate XGBoost regression models</span></strong><span> are then trained for these CH4 differences at each height level within a given latitude bin. These trained models are subsequently used to estimate </span><strong><span>CH4 bias corrections</span></strong><span> for all daytime TOMCAT grids (9132 days), specifically sampled at 1:30 PM local time at the equator. This yields grid-specific bias corrections that are applied to the original TOMCAT profiles.</span></p> <p><strong><span>Height-resolved CH4 profile data</span></strong><span> are then interpolated onto 28 standard pressure levels (from </span><span><span><span><span><span>300</span><span>,</span><span><span>hPa</span></span></span></span></span></span><span> to </span><span><span><span><span><span>0.1</span><span>,</span><span><span>hPa</span></span></span></span></span></span><span>), using pressure levels directly from the TOMCAT grids. For overlapping latitude bins, values are averaged to ensure smoother fields near boundary regions.</span></p> <h3><span>Data Files</span></h3> <p><span>The dataset includes two files containing daily mean zonal mean CH4 profiles:</span></p> <ul> <li> <p><code><span>zmch4_TCOM_hlev_T2Dz_2000-2024_V1.1.nc</span></code><span>: Contains </span><strong><span>height level data</span></strong><span> (</span><span><span><span><span><span>10</span><span>,</span><span><span>km</span></span></span></span></span></span><span> to </span><span><span><span><span><span>60</span><span>,</span><span><span>km</span></span></span></span></span></span><span>).</span></p> </li> <li> <p><code><span>zmch4_TCOM_plev_T2Dz_2000-2024_V1.1.nc</span></code><span>: Contains </span><strong><span>pressure level data</span></strong><span> (</span><span><span><span><span><span>300</span><span>,</span><span><span>hPa</span></span></span></span></span></span><span> to </span><span><span><span><span><span>0.1</span><span>,</span><span><span>hPa</span></span></span></span></span></span><span>).</span></p> </li> </ul> <h3><span>Reference Publication</span></h3> <p><span>This methodology, incorporating only ACE-FTS data and various minor algorithmic developments, is based on the following publication:</span></p> <p><span>Dhomse, S. S. and Chipperfield, M. P.: Using machine learning to construct TOMCAT model and occultation measurement-based stratospheric methane (TCOM-CH4) and nitrous oxide (TCOM-N2O) profile data sets, Earth Syst. Sci. Data, 15, 5105–5120, </span><a title="null" href="https://doi.org/10.5194/essd-15-5105-2023"><span>https://doi.org/10.5194/essd-15-5105-2023</span></a><span>, 2023.</span></p>
TCOM-N2O: TOMCAT CTM and Occultation Measurements based daily zonal stratospheric nitrous oxide profile dataset [1991-2021] constructed using machine-learning
<p>Methodology: </p> <p><span>The </span><strong><span>TOMCAT simulation</span></strong><span> was conducted at a T64L32 resolution, consistent with previous work by Dhomse et al. (2021, 2022), covering the period from 2000 to 2024. These simulations utilized </span><strong><span>ERA-5 reanalysis data</span></strong><span>.</span></p> <h3><span>N2O Profile Processing and Bias Correction</span></h3> <p><strong><span>Collocated N2O profiles</span></strong><span> are organized into five distinct latitude bins:</span></p> <ul> <li> <p><strong><span>NH polar</span></strong><span>: </span><span><span><span><span><span>9</span><span>0<span><span><span><span><span><span><span>∘</span></span></span></span></span></span></span></span></span></span></span></span><span>N - </span><span><span><span><span><span>5</span><span>0<span><span><span><span><span><span><span>∘</span></span></span></span></span></span></span></span></span></span></span></span><span>N</span></p> </li> <li> <p><strong><span>NH mid-lat</span></strong><span>: </span><span><span><span><span><span>2</span><span>0<span><span><span><span><span><span><span>∘</span></span></span></span></span></span></span></span></span></span></span></span><span>N - </span><span><span><span><span><span>7</span><span>0<span><span><span><span><span><span><span>∘</span></span></span></span></span></span></span></span></span></span></span></span><span>N</span></p> </li> <li> <p><strong><span>Tropics</span></strong><span>: </span><span><span><span><span><span>4</span><span>0<span><span><span><span><span><span><span>∘</span></span></span></span></span></span></span></span></span></span></span></span><span>S - </span><span><span><span><span><span>4</span><span>0<span><span><span><span><span><span><span>∘</span></span></span></span></span></span></span></span></span></span></span></span><span>N</span></p> </li> <li> <p><strong><span>SH mid-lat</span></strong><span>: </span><span><span><span><span><span>7</span><span>0<span><span><span><span><span><span><span>∘</span></span></span></span></span></span></span></span></span></span></span></span><span>S - </span><span><span><span><span><span>2</span><span>0<span><span><span><span><span><span><span>∘</span></span></span></span></span></span></span></span></span></span></span></span><span>S</span></p> </li> <li> <p><strong><span>SH polar</span></strong><span>: </span><span><span><span><span><span>9</span><span>0<span><span><span><span><span><span><span>∘</span></span></span></span></span></span></span></span></span></span></span></span><span>S - </span><span><span><span><span><span>5</span><span>0<span><span><span><span><span><span><span>∘</span></span></span></span></span></span></span></span></span></span></span></span><span>S</span></p> </li> </ul> <p><span>Initially, </span><strong><span>differences between TOMCAT and satellite measurements</span></strong><span> (primarily ACE-FTS data) are calculated for each zonal bin across 51 height levels (ranging from </span><span><span><span><span><span>10</span><span>,</span><span><span>km</span></span></span></span></span></span><span> to </span><span><span><span><span><span>60</span><span>,</span><span><span>km</span></span></span></span></span></span><span>).</span></p> <p><strong><span>Separate XGBoost regression models</span></strong><span> are then trained for these N2O differences at each height level within a given latitude bin. These trained models are subsequently used to estimate </span><strong><span>N2O bias corrections</span></strong><span> for all daytime TOMCAT grids (9132 days), specifically sampled at 1:30 PM local time at the equator. This yields grid-specific bias corrections that are applied to the original TOMCAT profiles.</span></p> <p><strong><span>Height-resolved N2O profile data</span></strong><span> are then interpolated onto 28 standard pressure levels (from </span><span><span><span><span><span>300</span><span>,</span><span><span>hPa</span></span></span></span></span></span><span> to </span><span><span><span><span><span>0.1</span><span>,</span><span><span>hPa</span></span></span></span></span></span><span>), using pressure levels directly from the TOMCAT grids. For overlapping latitude bins, values are averaged to ensure smoother fields near boundary regions.</span></p> <h3><span>Data Files</span></h3> <p><span>The dataset includes two files containing daily mean zonal mean N2O profiles:</span></p> <ul> <li> <p><code><span>zmn2o_TCOM_hlev_T2Dz_2000-2024_V1.1.nc</span></code><span>: Contains </span><strong><span>height level data</span></strong><span> (</span><span><span><span><span><span>10</span><span>,</span><span><span>km</span></span></span></span></span></span><span> to </span><span><span><span><span><span>60</span><span>,</span><span><span>km</span></span></span></span></span></span><span>).</span></p> </li> <li> <p><code><span>zmn2o_TCOM_plev_T2Dz_2000-2024_V1.1.nc</span></code><span>: Contains </span><strong><span>pressure level data</span></strong><span> (</span><span><span><span><span><span>300</span><span>,</span><span><span>hPa</span></span></span></span></span></span><span> to </span><span><span><span><span><span>0.1</span><span>,</span><span><span>hPa</span></span></span></span></span></span><span>).</span></p> </li> </ul> <h3><span>Reference Publication</span></h3> <p><span>This methodology, incorporating only ACE-FTS data and various minor algorithmic developments, is based on the following publication:</span></p> <p><span>Dhomse, S. S. and Chipperfield, M. P.: Using machine learning to construct TOMCAT model and occultation measurement-based stratospheric methane (TCOM-CH4) and nitrous oxide (TCOM-N2O) profile data sets, Earth Syst. Sci. Data, 15, 5105–5120, </span><a title="null" href="https://doi.org/10.5194/essd-15-5105-2023"><span>https://doi.org/10.5194/essd-15-5105-2023</span></a><span>, 2023.</span></p>
Data for "Combining 13C, 15N, and 2H tracer to measure feeding and metabolic activity in marine, shallow-water sponges – A pilot study"
<p>This dataset includes raw data used in the paper "Combining <sup>13</sup>C, <sup>15</sup>N, and <sup>2</sup>H tracer to measure feeding and metabolic activity in marine, shallow-water sponges – A pilot study" (JEMBE).</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.