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219 results for “in-situ data”
In-situ grazing-incidence X-ray diffraction data of the crystallization process of organic-inorganic methylammonium lead bromide perovskite (MAPbBr3) via employing an isopropanol antisolvent. Raw Data
<p>The dataset contains 400 diffraction images from a 40 second in-situ grazing-incidence wide-angle X-ray scattering measurement of the crystallization process of organic-inorganic methylammonium lead bromide perovskite (MAPbBr3) on a glass substrate. The crystallization is initiated via employing an isopropanol antisolvent during the spin-coating of the perovskite precursor solution. 40 µL of MAPbBr3 solution (4:1 DMF/DMSO solvent mixture) was applied on plasma-cleaned glass substrate in a chamber with kapton windows. The two-phase spin-coating regime included 10 seconds at 1000 rpm followed by 30 seconds at 2000 rpm, 200 µL of antisolvent was dispensed at t = 30 s.</p> <p> </p> <p> </p> <p>The data was acquired at the P08 Beamline at PETRA III (DESY Hamburg). Acquisition parameters:</p> <p> </p> <ul> <li> <p>X-ray wavelength: 0.6888 nm</p> </li> <li> <p>Sample detector distance: 809 mm</p> </li> <li> <p>Incidence angle: 0.5 deg.</p> </li> <li> <p>Detector model: XRD 1621 CN3 EHS</p> </li> <li> <p>Acquisition rate : 10 frames per second (10 Hz)</p> </li> <li> <p>Direct beam position (pixels): 545, 222</p> </li> </ul>
Structure matters – Direct in-situ observation of cluster nucleation at atomic scale in a liquid phase (supplementary data)
<p>This a dataset of scanning transmission electron microscopy data showing Pt clusters nucleating in an ionic liquid. For each of the 4 movies there is the raw data (uncompressed .tif and compressed as .avi) and denoised versions (uncompressed .tif and compressed as .avi).</p> <p>This data is for the article "Structure matters – Direct in-situ observation of cluster nucleation at atomic scale in a liquid phase" published in ChemNanoMat (2020), by Trond R. Henninen, Debora Keller and Rolf Erni. (https://onlinelibrary.wiley.com/doi/full/10.1002/cnma.202000503)</p> <p><strong>Movie 1:</strong> Homogeneous nucleations of two clusters in a suspended thin film of ionic liquid. </p> <p><strong>Movie 2: </strong>Heterogeneous nucleation of a ca 8-9 atom cluster near the edge of a nanodroplet supported on a carbon film.</p> <p><strong>Movie 3: </strong>Heterogeneous nucleation of multiple clusters in a nanodroplet. Shortly after nucleation, they coalesce to form disordered nanoclusters.</p> <p><strong>Movie 4:</strong> Heterogeneous nucleation and dissolution cycles of spherical particles in a nanodroplet.</p>
Rating curves based on satellite altimetry and in-situ discharge data
<h1>Context: </h1> <p>The ESA river discharge Climate Change Initiative (CCI) project is a precursor study. It aims to derive long term climate data records (at least over 20-years) of river discharge for some selected river basins (and some locations in the river network) using satellite remote sensing observations (altimetry and multispectral images) and ancillary data. It aims to provide a proof-of-concept for the feasibility for a potential River Discharge ECV product to meet the requirements for the <a href="https://gcos.wmo.int/en/essential-climate-variables/rivers/" target="_blank" rel="noopener">Global Climate Observing System</a>. This project covers precursor activities towards the production of data products that address the GCOS-defined requirements for the River Discharge ECV.</p> <h1>Data description :</h1> <p>Just as in-situ stage measurements can be used to gauge river discharge, altimetry-derived water surface elevation (WSE) can serve as an alternative means of estimating river discharge when discharge time series data is available. Several methodologies have been documented for deriving discharge time series from multimission altimetry observations and supplementary data (Biancamaria et al., 2024). At least two approaches will be used, depending on the available in situ discharge and altimetry water surface elevation (WSE) time series:</p> <p>⋅ <strong><em>Method 1</em>: </strong>The preferred approach relies on the altimetry water surface elevation time series and in situ discharge time series to create a rating curve (RC) characterized by a power relationship between these two variables following a Bayesian approach (Rantz et al., 1982). However, this method necessitates a significant overlap period between discharge data and radar altimetry measurements (e.g., Biancamaria et al., 2011; Papa et al., 2012), or it requires the assumption that the rating curve remains valid and consistent when discharge data is only available prior to the altimetry observation period.</p> <p>⋅ <em><strong>Method 2:</strong></em> The final option, in cases where there is no temporal overlap between in-situ or simulated discharge and water surface elevation data, assumes that the validity and stability of the rating curve persist across the various time periods covered by the two datasets. Both of these time periods should be sufficiently long to encompass a wide range of events. With this assumption, Tourian et al. (2013, 2017) introduced a method for calculating the rating curve, not based on the time series of discharge and water surface elevation, but on the distribution of their quantiles. This method has been adopted by a limited number of recent studies (e.g., Belloni et al., 2021). However, it’s important to note that this methodology naturally introduces higher errors when compared to the preferred approach. For this reason, this methodology will be validated over some stations with various hydrological dynamics and satisfying previous methods (overlap period exists between WSE and Q).</p> <h1>Approaches to derive Rating Curve (RC) :</h1> <h2>Bayesian Approach :</h2> <p>The Bayesian method is a robust statistical approach used for constructing a rating curve, frequently applied in the field of hydrology when the goal is to estimate unknown parameters from observed data, while taking into consideration the associated uncertainty in these estimates. </p> <p>According to this, the estimation of the rating curve using the Bayesian method involves several steps:</p> <ul> <li>The initial step entails defining a probabilistic model that describes the relationship between observed data and the parameters we aim to estimate. In many hydrological applications, the relationship between discharge data (Q) and water surface elevation data (WSE) is often expressed as a power function:</li> </ul> <p><em> Q = a⋅(WSE-z</em><em>0</em><em>)</em><sup><em>b</em></sup></p> <p>Here, <em>a, z0</em> and <em>b</em> are the parameters of the rating curve. <em>a,</em> is a scaling coefficient governing the magnitude of the Q-WSE relationship, <em>b,</em> characterizes the nature of this relationship, and <em>z0</em>, represents the height of the free surface above the reference point, corresponding to the river bottom's altitude. The power relationship is especially pertinent due to its consistency with numerous hydrodynamic phenomena. The exponent b within the equation allows for the representation of distinctive flow characteristics, including factors like roughness and channel geometry. Moreover, it offers adaptability in modelling to accommodate variations in flow characteristics, whether they are turbulent or laminar. This relationship, despite its mathematical simplicity, facilitates the fine-tuning of model adjustments in accordance with observed data (Chow, 1959).</p> <ul> <li>The second step involves the use of prior normal distributions, reflecting our prior knowledge about these parameters. These distributions can either be informative or uninformative, depending on our level of knowledge. The limits and ranges for a, z0 and b can vary depending on the specific context of the study, the dataset used, and the characteristics of the river or channel being analysed.</li> </ul> <p><u>- Coefficient “a”</u>: adjustment parameter for the rating curve representing the scaling factor for discharge. Its value can significantly fluctuate based on various factors such as the characteristics of the river or channel, hydraulic conditions, and other influencing factors. Consequently, "a" must be non-negative and constrained within a sensible range specific to the system under study. Following the Manning equation, “a” must be equal to W/n*S<sup>1/2</sup> (Chow et al., 1988) where W is the river’s width (m), n the Manning’s roughness coefficient and S the slope (m/m). Given the considerable variability in river width and slope across different stations, a feasible range for this coefficient can be considered as:</p> <p> a ∈ [0; 3000]</p> <p><u>- Coefficient “b”</u>: adjustment parameter representing the exponent of the rating curve and indicating the hydraulic condition of the study site. Like "a," this value must comply with physical constraints and cannot be negative. Following the Manning equation, “b” must be equal to 5/3 for reference hydraulic condition (Rantz et al., 1982). To accommodate the variability in system characteristics across sites, the following range values can be considered for this coefficient:</p> <p> b ∈ [0; 5]</p> <p><u>- Coefficient “z0”</u>: offset or the elevation at which discharge begins. It should be within the range of elevations relevant to your study. For this <em>reason, the value</em> cannot exceed the minimum value of water surface elevation (WSE) and the range value need to consider of the variability in term of water depth over the sites. A feasible range for this coefficient can be considered as:</p> <p> z0 ∈ [min(WSE)-30; min(WSE)]</p> <ul> <li>The final step involves parameter estimation. The posterior distribution of the parameters yields probabilistic estimates of the rating curve parameters in the form of mean values (optimal values) and credibility intervals (95th percentiles). This accounts for the uncertainty associated with these parameters and is achieved through Markov Chain Monte Carlo (MCMC) sampling from the posterior distribution. Two commonly employed MCMC algorithms are "NUTS" (No-U-Turn Sampler) and "Metropolis-Hastings." The Metropolis-Hasting sampler "MH" algorithm, which is relatively simple and efficient where a balance between exploration and exploitation is desired. This algorithm can be adapted to sample from discrete state spaces.</li> </ul> <h2>Quantile approach : </h2> <p>The Quantile approach employs statistical modelling using quantile functions to create a rating curve, eliminating the necessity for overlapping measurements. This algorithmic method enables the estimation of river discharge using satellite altimetry, even in instances where there are no in situ measurements within the altimeter's timeframe. This approach has undergone application and validation in diverse river basins spanning different climatic zones, such as the Amazon, Brahmaputra, Danube, Niger, and Ob (Tourian et al., 2013).</p> <p>Assuming a stationary flow behaviour and no modification in the river bathymetry both at the altimetry virtual station and at the in-situ gage, this approach ensures the utilization of historical in situ data in current applications. This method computes the quantile functions of the altimetry water surface elevation on one hand and of the discharge time series on the other hand. Then a scatter plot of these in-situ discharge quantiles versus altimetry water surface elevation quantiles is computed to establish the rating curve using the bayesian approach described previously.</p> <h1>File description :</h1> <table> <tbody> <tr> <td><strong>Column name</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>basin-station</td> <td>Basin name in capital letters and Station name in capital letters separated by "_" and where spaces have been replaced by "-".</td> </tr> <tr> <td>lon</td> <td>Longitude in decimal degrees [-180,180] with 4 decimals - corresponding to the insitu discharge station.</td> </tr> <tr> <td>lat</td> <td>Latitude in decimal degrees [-90,90] with 4 decimals – corresponding to the insitu discharge station.</td> </tr> <tr> <td>a</td> <td>Adjustment parameter for the rating curve representing the scaling factor for discharge. Number with 3 decimals.</td> </tr> <tr> <td>b</td> <td>Adjustment parameter representing the exponent of the RC and indicating the hydraulic condition of the study site. Number with 3 decimals.</td> </tr> <tr> <td>z0</td> <td>Offset of the elevation at which discharge begins. Number with 3 decimals.</td> </tr> <tr> <td>a_sd</td> <td>Standard deviation of the coefficient "a". Number with 3 decimals.</td> </tr> <tr> <td>b_sd</td> <td>Standard deviation of the coefficient "b". Number with 3 decimals.</td> </tr> <tr> <td>z0_sd</td> <td>Standard deviation of the coefficient "z0". Number with 3 decimals.</td> </tr> <tr> <td>period</td> <td>Period used to compute the rating curve under the format %Y-%m-%d where the start and the end dates are separated by ":"</td> </tr> <tr> <td>nb</td> <td>Number of overlap dates to compute the rating curve.</td> </tr> <tr> <td>Methodology</td> <td>Methodology used to compute the rating curve. The first part describes the approach used to compute the RC and the second part, separated by “_”, describes the algorithm used. To avoid any issue for the reader the spaces have been replaced by “-”. At the end 2 approaches has been used: “Overlap-approach” or “Quantile-approach” and 2 algorithms: “Bayesian-algorithm” or “Multiple-algorithms” designed for Arctic rivers experiencing frozen periods. </td> </tr> <tr> <td>Source</td> <td>In-situ data sources to compute the rating curve. If multiple sources has been used, the sources are separate by "/"</td> </tr> </tbody> </table> <p>---------</p> <p><em>THE DATASET IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR </em><em>IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,</em><br><em>FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE </em><em>AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER </em><em>LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, </em><em>OUT OF OR IN CONNECTION WITH THE DATASET OR THE USE OR OTHER DEALINGS IN THE </em><em>DATASET.</em></p>
Lemming Mesocosm (Denmark): in-situ fluorescence chlorophyll-a calibration, underlying data
<p>These data files include 2 years (2018-2020) of high-frequency in-situ chlorophyll-a and phycocyanin fluorescence sensor (Turner Designs, Cyclops 7F) data, together with data from various tests conducted with these sensors. Morever, in-vitro chlorophyll-a data for 2018-2020 period is also included.</p> <p>These data are collected in Lemming mesocosm site, Denmark, where there are 24 tanks with 2 nutrient and 3 temperature treatments (2x3 factorial design). There is data from 24 in-situ chlorophyll-a and 12 in-situ phycocyanin fluorescence sensors.</p> <p>In this Dataset folder, there are:</p> <ul> <li>one <strong>Metadata </strong>(*.xlsx)<strong> </strong>file with 3 sheets; <ul> <li>"<em>Descriptive</em>": comprises information on location, authors, study period and the main instrument used, </li> <li>"<em>Structural</em>": includes detailed information on each dataset.</li> <li>"<em>Relational</em>": includes a figure showing the relations between the datasets</li> </ul> </li> <li>thirteen files (*.csv) in LemCP_DataORE that were used to; <ul> <li>conduct in-situ fluorescence sensor tests (i.e. blank variation, linearity check, DOC effect check), </li> <li>calibrate 24 in-situ fluorescence chlorophyll-a sensors</li> <li>plot various figures</li> </ul> </li> </ul>
Lemming Mesocosm (Denmark): in-situ fluorescence chlorophyll-a calibration, extended data
<p>These files include a scheme showing the sensor and tank (mesocosm) system, plots from high frequency in-situ chlorophyll-a and phycocyanin fluorescence data and in-vitro chlorophyll-a data collected in Lemming mesocosm site, Denmark, where there are 24 tanks with 2 nutrient and 3 temperature treatments (2x3 factorial design). These plots are based on 24 in-situ chlorophyll-a and 12 in-situ phycocyanin fluorescence sensors from 24 tanks/mesocosms. There is also a table showing the steps for cleaning the high-frequency data.</p>
Data: Homochiral metal-organic frameworks coated double-plasmon active optical fiber for in-situ enantioselective detection
<p>This dataset is focused on utilization of optical fiber with double-plasmon activity (ensured by a spatially separated gold and silver nanocoating of the fiber core) and subsequent surface grafting by HMOFs for enantioselective capture of organic enantiomers.</p>
Data-Mining of In-Situ TEM Experiments: Towards Understanding Nanoscale Fracture
<p>Datasets for the publication in the "Computational Materials Science". This is essentially a snapshot of the gitlab repository https://gitlab.com/computational-materials-science/public/publication-data-and-code/2022-data-mining-of-in-situ-tem-experiments that might contain additional updates and scripts. A version of the manuscript can also be found at https://arxiv.org/abs/2206.11355</p>
Extensive crowdsourced dataset of in-situ evaluated binaural soundscapes of private dwellings containing subjective sound-related and situational ratings along with person factors to study time-varying influences on sound perception — research data
<p><strong>Abstract:</strong></p> <p>The soundscape approach highlights the role of situational factors in sound evaluations; however, only a few studies have applied a multi‐domain approach including sound‐related, person‐related, and time‐varying situational variables. Therefore, we conducted a study based on the Experience Sampling Method to measure the relative contribution of a broad range of potentially relevant acoustic and non‐auditory variables in predicting indoor soundscape evaluations. Here we present the comprehensive dataset for which 105 participants reported temporally (rather) stable trait variables such as noise sensitivity, trait affect, and quality of life. They rated 6.594 situations regarding the soundscape standard dimensions, perceived loudness, and the saliency of its sound components and evaluated situational variables such as state affect, perceived control, activity, and location. To complement these subject‐centered data, we additionally crowdsourced object‐centered data by having participants make binaural measurements of each indoor soundscape at their homes using a low‐(self‐)noise recorder. These recordings were used to compute (psycho‐)acoustical indices such as the energetically averaged loudness level, the A‐weighted energetically averaged equivalent continuous sound pressure level, and the A‐weighted five‐percent exceedance level. This complex hierarchical data can be used to investigate time‐varying non‐auditory influences on sound perception and to develop soundscape indicators based on the binaural recordings to predict soundscape evaluations.</p> <p><strong>Content:</strong></p> <ul> <li><a href="https://zenodo.org/record/7858848/files/01%20StudyDescription.pdf">01 StudyDescription.pdf </a> <ul> <li>Description of the field study.</li> <li>Information about the methods and materials used.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/02%20Dataset.csv">02 Dataset.csv</a> <ul> <li>The dataset, consisting of 93 variables describing 6594 observations taken by 105 participants.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/03%20VariableDescriptions_EnglishPersonQuestionnaire.pdf">03 VariableDescriptions_EnglishPersonQuestionnaire.pdf</a> <ul> <li>Descriptions of all variables, their measurement scale, scale ranges and levels.</li> <li>Questions and task descriptions of the Experience Sampling Method questionnaire in German language with an English translation.</li> <li>English translations of questions asked in the person questionnaire.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/04%20ESM-Questionnaire.pdf">04 ESM-Questionnaire.pdf</a> <ul> <li>Screenshots of the original Experience Sampling Method questionnaire with English translations.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/05%20PersonQuestionnaire_OriginalGermanVersion.pdf">05 PersonQuestionnaire_OriginalGermanVersion.pdf</a> <ul> <li>Original version of the person questionnaire in German language.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/06%20HelpTexts.pdf">06 HelpTexts.pdf</a> <ul> <li>Descriptions of the study task.</li> <li>Explanations of the scales used in the questionnaire.</li> <li>Explanations of the sound categories and the soundscape composition.</li> <li>Explanation of the operation of the recording device.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/AcousticFeatures_README.md">AcousticFeatures_README.md</a> <a href="https://zenodo.org/api/files/3d784540-c0f4-412f-8742-df1db6f5401d/TimeSeries_and_Spectrograms_README.md?versionId=9291496c-d2c6-4151-96f1-a2ad99e1a540"> </a> <ul> <li>Descriptions of the structure of the AcousticFeatures_xxx.csv and .zip files.</li> <li>Analyis settings used in Artemis Suite to generate the acoustic features.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/AcousticFeatures_SingleValues.csv">AcousticFeatures_SingleValues.csv</a> <ul> <li>All acoustic features, aggregated to single values per feature, recording, and channel.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/AcousticFeatures_Spectra.csv">AcousticFeatures_Spectra.csv</a> <ul> <li>Time-averaged 1/3 octave spectra of each channel of each recording, A-weichted and un-weighted.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/AcousticFeatures_Spectrograms.zip">AcousticFeatures_Spectrograms.zip</a> <ul> <li>13188 .csv files with un-weighted spetrograms of each channel of each recording.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/AcousticFeatures_TimeSeries.zip">AcousticFeatures_TimeSeries.zip</a> <ul> <li>A .csv file containing LAeq and LZeq time series of each channel of each recording.</li> </ul> </li> </ul> <p><strong>Publications refering to this dataset:</strong></p> <p>Versümer, Siegbert; Steffens, Jochen; Weinzierl, Stefan (currently under review): "The role of loudness predictions, personal and situational factors in day-to-day loudness assessments of indoor soundscapes."</p> <p><strong>Funding:</strong></p> <p>This study was sponsored by the German Federal Ministry of Education and Research. “FHprofUnt” funding code: 13FH729IX6. </p> <p><strong>License: </strong></p> <p>CC 4.0 BY, <a href="https://creativecommons.org/licenses/by/4.0/legalcode">https://creativecommons.org/licenses/by/4.0/legalcode</a></p> <p><strong>Version history:</strong></p> <p>Details can be found in the <a href="https://zenodo.org/api/files/a15d6a91-1a35-4b5e-a7ec-da8a9bcbee2b/Changelog.md">Changelog.md</a> file.</p> <ul> <li> V.01.0. March 7, 2023: Initial publication. <a href="https://doi.org/10.5281/zenodo.7193938">https://doi.org/10.5281/zenodo.7193938</a></li> <li> V.01.1. April 25, 2023. <a href="https://doi.org/10.5281/zenodo.7858848">https://doi.org/10.5281/zenodo.7858848</a></li> </ul>
HiP-RI: High-resolution spatial assessment of precipitation using in-situ and remote sensing data in the Cordillera Blanca, Peru
<p>The HiP-RI product was obtained from CHIRP, PERSIANN and GPM datasets, also vegetation products (NDVI-BOKU), topography (DEM SRTM) and data from 38 meteorological stations (2012-2020) were used to estimate precipitation in the Cordillera Blanca, northern sector of the Peruvian Andes. The observed data underwent quality control. A Gaussian filter, resampling and temporal homogenization at monthly scale were applied to the raster data. Subsequently, a linear regression model was built with the different datasets that served as predictors for precipitation spatialization. This allowed obtaining the best R2 values between the in situ data and those estimated with the model (HiP-RI). The results obtained were satisfactory with R2 values higher than 0.60 and an RMSE = 54%.</p>
In-situ XANES data for the calcination of Ca(II)/Ce(IV)-doped LaPO4 monazite
<p>These files include raw data from high temperature in-situ HERFD-XANES measurements at the ROBL beamline at the ESRF as well as laboratory PXRD data of the solid solution La1-xCax/2Cex/2PO4 produced by co-precipitation and a solid state route. All necessary background information will be found in the journal article this data will be linked to.</p>
Experimental data related to the publication: "In-situ analysis of the effect of residual fcc phase and special grain boundaries on the deformation dynamics in pure cobalt"
<p>The article figures were produced solely from these data sets employing data processing methods described therein. For experimental conditions and naming conventions please refer to the paper.</p> <p><br>1. Deformation data files contained within "deformation_data.zip":</p> <p>The .zip archive contains five files related to five samples of thermally treated cobalt:<br>def_co600.csv<br>def_co800.csv<br>def_co1100.csv<br>def_co1100-10c.csv<br>def_co1100-20c.csv</p> <p>The data were recorded during compression of the above-listed samples at room temperature. </p> <p><br>2. Acoustic emission (AE) data files contained within "AE_data.zip":</p> <p>The .zip archive contains four files related to four samples of thermally treated cobalt:<br>AE_co600.wav<br>AE_co800.wav<br>AE_co1100.wav<br>AE_co1100-20c.wav</p> <p>The AE data were recorded in continuous mode ("data streaming" at 2 MHz) during compression of the above-listed samples at room temperature. </p> <p> </p> <p>3. Electron back-scatter diffraction (EBSD) data files contained within "EBSD_data.zip":</p> <p>The .zip archive contains fifty-three .osc files related to samples of as-drawn and thermally treated cobalt within four folders:<br>0c - as-drawn and annealed samples (i.e. without thermal cycling)<br>10c - annealed samples after thermal cycling of 10 cycles<br>20c - annealed samples after thermal cycling of 20 cycles<br>ex-situ_def - ex-situ EBSD during deformation of selected samples</p> <p>The .osc data files represent EBSD data after clean-up procedures described in detail in the manuscript.</p> <p> </p> <p> </p> <p> </p>
In-situ data recorded from thaumatin crystals on Diamond Light Source VMXi
<p>Data collected as part of routine beamline commissioning, from samples of thaumatin grown in 0.1 M Sodium citrate, 0.75M Sodium / Potassium tartrate. Data were collected in unattended mode with positions identified <em>via</em> SynchWeb from photographs taken with a Formulatrix imaging system (picture included.) Each data set consists of 200 images taken with an Eiger 2X 4M detector at a distance of 186mm, with exposure time of 2 ms, wavelength 0.97950 angstroms and 2% transmission with a DMM beam. Automated processing combined data using the xia2 multiplex tool to give a sufficiently complete data set for structure solution and refinement.</p>
Processed data used for JGR publication "Role of Midwater Mixed Waves in the Loop Current Separation Events from A Coupled Ocean-Atmosphere Regional Model and In-Situ Observations"
<p>This is the processed dataset used in the JGR publication "Role of Midwater Mixed Waves in the Loop Current Separation Events from A Coupled Ocean-Atmosphere Regional Model and In-Situ Observations" by Xiao Ge.</p> <p>Please contact the author (gexiao@tamu.edu) for all the original/processed outputs of R-CESM, and use the following original papers as citations.</p> <p>The dataset used in this research includes:</p> <p>1. Loop Current Dynamics 2009-2011: LC_*.nc is the processed (reorganized) data for each in-situ station, * represents their station ID</p> <ul> <li>https://digital.library.unt.edu/ark:/67531/metadc955416/</li> <li>https://www.sciencedirect.com/science/article/pii/S0377026516301348?via%3Dihub</li> <li>https://search.dataone.org/view/%7BBD2513E6-3B34-4B7C-BCB9-3C4ED5E8D0FB%7D</li> </ul> <p>2. Regional Community Earth System Model, R-CESM: <a href="https://zenodo.org/api/records/13932074/draft/files/h.nc/content" target="_blank" rel="noopener noreferrer">h.nc</a> is the bathymetry data of R-CESM; cmpr_*.nc files are provided as examples of the original R-CESM outputs; pvsf_prho_*.nc are the processed (subsampled at the target region and interpolated on potential density layers, derived stream function, potential vorticity, and relative vorticity) R-CESM outputs used in this research; and <a href="https://zenodo.org/uploads/13932074" target="_blank" rel="noopener noreferrer">LC_pv_40hlp_2013.nc</a> is the example of organized processed R-CESM (pvsf_prho_*.nc files) containing potential vorticity and relative vorticity for figures plotting</p> <ul> <li>https://journals.ametsoc.org/view/journals/bams/102/9/BAMS-D-20-0024.1.xml?tab_body=fulltext-display</li> </ul> <p> </p> <p> </p> <p> </p> <p> </p>
COLA-hires: High-resolution (0.5x0.625) regional carbon fluxes inferred from in-situ and OCO-2 data
<p>This dataset contains high-resolution CO<sub>2</sub> inversion estimate in North America, East Asia, and Europe at 0.5x0.625 resolution from 2015 to 2018 using the Carbon in Ocean-Land-Atmosphere (COLA) system. The in-situ observations obtained from NOAA obspack and the land-nadir/land-glint retrevials from OCO-2 are assimilated.</p>
Synthetic in-situ T/S data over 1993-2018 from a NEMO-based simulation of the IMHOTEP project
<p>"Synthetic observations" of in-situ Temperature and Salinity profiles as a function of depth have been extracted online during the production of the global, NEMO-based experiment ** IMHOTEP-GAIc**, at every single time and location (in x,y,z dimensions) where a true in-situ profile exists in the ENACT-4 database (Good et al 2013) over the simulation period: 1980-2018. This global ocean/sea-ice/iceberg simulation uses the NEMO model, and has a horizontal resolution of 1/4°. The atmospheric forcing applied at the surface is based on the JRA reanalysis (Kobayashi et al., 2015) and varies over the full range of time-scales from 6 hours to multi-decadal. The freshwater runoff forcing applied to the experiment is fully-variable (daily to multi-decadal) based on the ISBA-CTRIP hydrographic reanalysis for rivers (Decharme et al., 2019) and from altimeter data and regional GCM simulations for the liquid and solid discharges from the Greenland ice-sheet (Mouginot et al 2019). These runoffs are only climatological around Antarctica. The synthetic in-situ T/S dataset from the model is available over the period 1993-2018.</p><p>See the README file for more information. And online documentation is also available here: https://doc-imhotep.readthedocs.io/en/latest/6-Synthetic-Obs.html</p>
Data Accompanying "Fracturing process of concrete under uniaxial and triaxial compression: insights from in-situ x-ray mechanical tests"
<p>These are the datasets analysed in "Fracturing process of concrete under uniaxial and triaxial compression: insights from <em>in-situ</em> x-ray mechanical tests" by Stamati <em>et. al.</em> (submitted on June 2020 in Cement and Concrete Research)</p> <p>The datasets contain the reconstructed x-ray 3D images of selected tests and the corresponding DVC analysis.</p> <p>Note that details regarding the DVC analysis can be found in the paper.</p> <ul> <li>Each folder contains a separate test; uniaxial compression ("<em>C-02"</em>) and triaxial compression at 5MPa ("<em>TX5-01"</em>), 10MPa ("T<em>X10-01</em>") and 15MPa ("<em>TX15-01</em>") confining pressures.</li> <li>For each test, folder 01 contains the first scan, where 01 is the reference state for the DVC analyis. Folder 01 also contains the image of the labelled largest aggregates used for the discrete DVC. Folrders 02-XX contain the scans at intermediate loading steps and the corresponding DVC analysis; "<em>reg</em>" contains the registration result in downscaled 2-binning images. "<em>ddic</em>" contains the discrete DVC computation. "<em>ldic</em>" contains the local DVC computation, before and after the merge and filtering of the grid and discrete DVC fields. "<em>strains</em>" contains the strain fields coming from the corrected merged DVC field.</li> <li>Folder "<em>stressStrain</em>" contains the stress-strain curves measured during the tests after subtracting the displacement corresponding to the loading system.</li> <li>Folder "<em>doubleScanUniaxial</em>" contains the two reconstructed images of the "repeated scan" of the uniaxial tests, from which the DVC measurement uncertainties were evaluated.</li> </ul>
In-situ high temperature FTIR, Raman data and breakdown temperature for phlogopite
<p>This dataset contains all new data corresponding to figures in the manuscript, including in situ high temperature FTIR, Raman data, and breakdown temperature from previous studies and this study.</p>
Data from: A pioneering experimental investigation of a novel in-situ dynamic characterization of the tensile/compression stress-strain mechanism on human plantar soft tissue
<p><span>We have conducted the first in-situ and in-vivo dynamic mechanical test on human plantar soft tissue. A dynamic mechanical analysis (DMA)-like device has been invented to perform the in-situ and in-vivo stress-strain tests on living plantar in order to characterize the material mechanism of biological soft tissue, whereas it is nearly impossible to prepare a sample from a living body for classical tests. A series of pioneering tests of tensile/compression on the heel of ten volunteers are reported, with the reference of tests on mimic foot model made by silicon rubber, standard silicon rubber brick sample, and finite elementary analysis. In addition to demonstrating the effectiveness of the device and approach, interesting correlations between the results and clinic data were found, suggesting considerable potential for the invention in future research.</span></p>
In-situ Liaodong Bay Sea Ice Data for the Study of Remotely Estimating Absorptive Substances within Sea Ice
<p>This dataset was used for a study trying to develop remote rensing retrieval model and analyse the uncertainties of impurity content in sea ice from the optics perspective.</p>
Data for "Measurement report: Comparison of airborne in-situ measured, lidar-based, and modeled aerosol optical properties in the Central European background – identifying sources of deviations"
<p>A unique set of data is presented, derived from measurements conducted at the rural central European observatory at Melpitz, Germany. Data derived from remote sensing (lidar), airborne platforms (helicopter, balloon), and ground-based in-situ methods is included. Measured and Mie-modeled optical aerosol parameters are presented in the dry- and ambient state. Modeled optical parameters are based on Mie-theory. For ambient state hygroscopic growth simulations are utilized.</p>
ScienceDex guides
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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.