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1,709 results for “Reflectivity”
Landsat-based Spectral Indices for pan-EU 2000-2022 - Annual predictor P25 (2014): Reflectances bands, NDVI and NDWI
<h2><strong>Data information</strong></h2> <p>This dataset provides the P25 (percentile 25) values of corresponding predictors for the year 2014. The P25 values are calculated from the bimonthly values of six corresponding predictors throughout the year. The predictors in this subset include seven reflectance bands: red, green, blue, NIR, SWIR1, SWIR2, and thermal; as well as two spectral indices: NDVI and NDWI.</p> <h2><strong>As a part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>
User study data: Nudges to Mitigate Confirmation Bias during Web Search for Opinion Formation, automatic vs. reflective study
<p>Data of two user studies (282 and 307 participants), investigating the risks and benefits of warning labels with and without obfuscations to mitigate confirmation bias during web search on debated topics.</p> <p> </p> <p>Study Variables (study 1 and study 2)</p> <p> </p> <p> display_con: Search result display<br> - Study 1<br> - 1: targeted warning label with obfuscation<br> - 2: random warning label with obfuscation<br> - 3: regular (no intervention)<br> - Study 2<br> - 1: targeted warning label with obfuscation<br> - 2: targeted warning label without obfuscation<br> - 3: random warning label with obfuscation<br> - 4: random warning label without obfuscation<br> - 5: regular (no intervention)<br>- CRT_cat: Cognitive reflection<br> - 1: intuitive<br> - 2: analytic<br>- topic: Assigned debated topic<br> - 1: Is drinking milk healthy for humans? <br> - 2: Is homework beneficial?<br> - 3: Should people become vegetarian?<br> - 4: Should students have to wear school uniforms?<br>- clicksup_prop: Clicks on attitude-confirming (AC) search results (proportion of all clicks)<br>- clickwarn_prop: Clicks on warning label (WL) search results (proportion of all clicks)<br>- show_clicked: Clicks on show-button (number of clicks, only in conditions with obfuscation)<br>- accuracy_bias: Accuracy bias estimation (Difference between a) observed bias (as the proportion of attitude-confirming clicks) and b) perceived bias (reported in the post-interaction questionnaire and re-coded into values from 0 to 1), positive values indicate an overestimation of bias)<br>- att_change: Attitude change (Difference between attitude reported in the pre-interaction questionnaire and the post-interaction questionnaire. Negative values indicate an attitude change in the attitude-opposing direction, while positive values indicate an attitude strengthening in the attitude-supporting direction.)<br>- knowledge_1: Self-reported prior knowledge (Reported on a seven-point Likert scale ranging from non-existent to excellent as a response to how they would describe their knowledge on the topic they were assigned to)<br>- N_clicks: Cumulative clicks (Number of all clicks on search results)<br>- NFC: Need for Cognition (Mean response to 4-item subset of the NFC questionnaire)<br>- UX_usability: Usability (Mean of responses on a seven-point Likert scale to the module "usability"from the meCUE 2.0 questionnaire)<br>- UX_usefulness: Usefulness (Mean of responses on a seven-point Likert scale to the module "usefulness"from the meCUE 2.0 questionnaire)</p>
CLDF dataset reflecting Zariquiey, Blum et al.'s "Tracing the Evolution of Panoan Languages in Parallel with Archaeological Changes in the Ucayali Basin", work in progress.
<p>Cite the source of the dataset as:</p> <blockquote> <p>Zariquiey, Roberto and Blum, Frederic and Valenzuela, Pilar and Koile, Ezequiel and Blasi, Damian and Gray, Russell and List, Johann-Mattis. "Tracing the Evolution of Panoan Languages in Parallel with Archaeological Changes in the Ucayali Basin" (work in progress).</p> </blockquote>
IODP Expedition 367 Color reflectance
Color reflectance data were measured on section halves using an integration sphere and a UV-VIS spectrophotometer mounted on the Section Half Multisensor Logger (SHMSL). Spectral counts are recorded in the range of 380 to 700 nm, covering the visible spectrum, and binned in ~2 nm bins. Spectral data are reduced from spectra and recorded in tristimulus XYZ values, CieLAB L*a*b* values, and other units.
Soil characteristics and spectral reflectance data of six agricultural fields in Switzerland
<p>Soil characteristics and spectral reflectance data of six agricultural fields in Switzerland collected within the EJP Soil project STEROPES. The data in the .csv files is organized as relational database with the database schema depicted in DB_Schema.pdf. The file headers (marked with #) contain metadata.</p>
Time-lapse electrical resistivity tomography and seismic reflection imaging of a shallow ground-water aquifer (0-50 m): Mississippi River levee seepage across the Duncan Point bar, Baton Rouge, Louisiana, U.S.A.
<p>The electrical resisitivity raw data files are slightly processed to remove bad data points but can be inverted using tomographic inversion code. </p> <p>The seismic data were assembled in Seismic Unix format, a shortened version of the SEG-Y format (Society of Exploration Geophysicists Exchange Format-Y https: //seg. org/Publications/SEG-Technical-Standards), that has the 3200-byte EBCDIC and 400-byte tape header removed. The data uploaded online (<a href="https://zenodo.org/records/14776025">https://zenodo.org/records/14776025</a>) is a CMP brute-stacked seismic section. </p> <p>During data collection, shotpoint location changed proceeding along a 136-degree azimuth (south-easterly direction), and spaced every 1 m.</p> <p>A total of 48, horizontal-component 28-Hz nominal geophones were placed every one meter and shotpoints were located half-way between geophones. Geophones remained fixed at their locations throughout the survey and so the CMP spacing is nominally 0.5-m but fold varies linearly from a value of 1 from either side of the survey to a central maximum of 24. The seismic source consisted of a partially buried 20-lb steel I-beam struck repeatedly on either side three times by an 8-lb sledge hammer. Data of the same striking polarity were added in-phase in the field. Data with opposing polarity at each shotpoint location were subtracted later to enhance SH-wave data and suppress converted SH-to-P waves.</p> <p>Seismic processing is minimal and consists of standard surface-wave muting, elimination of bad seismic traces, normal moveout, bandpass filtering (between 12 Hz and 50 Hz) and preliminary stacking with trace mixing every 3 CMPs. The data were stacked with a single velocity throughout that ranged from 80 m/s (Vs) at 0.2 s, to 100 m/s at 0.35 s and reached 180 m/s at 0.5 s of two-way traveltime.</p> <p> </p>
Leaf reflectance and traits of floating and emergent macrophytes
<p>This dataset includes leaf samples from six floating and emergent macrophyte species common in temperate areas, covering different phenological stages, seasons, and environmental conditions, and measured leaf reflectance (400-2500 nm) and leaf traits (dealing with photophysiology, pigments, and structure). Data were collected along three years (2016-2018) from three temperate shallow lakes surrounded by wetlands and hosting abundant macrophyte communities, located in central and southern Europe: Lake Hídvégi or Kis-Balaton (Hungary), Mantua lakes system (Italy), and Lake Varese (Italy).</p> <p>Leaf photophysiological parameters derived from chlorophyll fluorescence measured with a PAM-2500 chlorophyll fluorometer (Heinz Walz GmbH, Germany).</p> <p>Leaf pigments were derived from spectrophotometric readings of absorbance of leaf extracts in acetone 80%.</p> <p> </p>
Reflection Ultrasound Computed Tomography (RUCT) Data
<p>Data for Reflection Ultrasound Computed Tomography (RUCT) Delay and Sum Algorithm</p> <p>Data is shared for "pyruct" package tests and as supporting files of the research article indicated below.</p> <p>"pyruct" package can be found in "https://github.com/berkanlafci/pyruct"</p> <p>If you use this data in your research, please cite the following paper:</p> <p>B. Lafci, J. Robin, X. L. Deán-Ben and D. Razansky, "Expediting Image Acquisition in Reflection Ultrasound Computed Tomography," in IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, doi: <a href="https://ieeexplore.ieee.org/document/9768674">10.1109/TUFFC.2022.3172713</a>.</p> <p>"nct" means number of consecutive transducer elements used in transmission event. Please use the files with "nct_1" tags for the full acquisition and reconstruction.</p>
Multi-channel seismic reflection profiles SALTFLU (Salt deformation and sub-salt fluid circulation in the Algero-Balearic abyssal plain) - Pre-Stack Kirchhoff Time & Depth Migration 2022
<p>This archive contains sections of reprocessed multi-channel seismic reflection profiles SALTFLU, acquired south of Ibiza (Spain) in 2012 with the OGS Explora (pre-stack Kirchhoff time and depth stacks, and migration velocities in SEG-Y format). It also contains the cruise report describing the survey acquisition in 2012. Connected articles describe the processing flow applied to this dataset and interpretations led by the first author. </p> <p>Field File Identification and Shot Numbers (FFID, SHOTNO) are linearly interpolated by matching the CMP numbers before and after migration. Bytes 73-76 and 77-80 are identical to bytes 181-184 and 185-188 and contain the CMP coordinates.</p> <p> </p> <p> </p> <p> </p>
The SPIN covid19 RMRIO dataset: Global trade network data for the years 2016-2026 reflecting macroeconomic effects of the covid19 pandemic - B. Data for 2020 - 2026 - Covid scenario
<p>The SPIN covid19 RMRIO dataset is a time series of MRIO tables covering years from 2016-2026 on a yearly basis. The dataset covers 163 sectors in 155 countries.</p> <p>This repository includes data for years from 2020 to 2026 (<em>covid</em> scenario).<br> Code, method material and data for years 2016-2019 are stored in the following repository: <a href="http://doi.org/10.5281/zenodo.5713811">10.5281/zenodo.5713811</a><br> Data for the <em>counterfactual</em> scenario are stored in the following repository: <a href="https://doi.org/10.5281/zenodo.5713839">10.5281/zenodo.5713839</a></p> <p>Tables are generated using the <a href="https://github.com/TBeaufils/SPIN">SPIN method</a>, based on the <a href="https://doi.org/10.5281/ZENODO.3993659">RMRIO tables</a> for the year 2015, GDP, imports and exports data from the <a href="https://data.imf.org/?sk=4c514d48-b6ba-49ed-8ab9-52b0c1a0179b">International Financial Statistics</a> (IFS) and the World Economic Outlooks (WEO) of <a href="https://www.imf.org/en/Publications/WEO/weo-database/2019/October">October 2019</a> and <a href="https://www.imf.org/en/Publications/WEO/weo-database/2021/April">April 2021</a>.</p> <p>The <em>covid</em> scenario is in line with April 2021 WEO's data and includes the macroeconomic effects of Covid 19.</p> <p>All tables are labelled in 2015 US$ and valued in basic prices.</p>
The SPIN covid19 RMRIO dataset: Global trade network data for the years 2016-2026 reflecting macroeconomic effects of the covid19 pandemic - A. Code and data for 2016-2019
<p>The SPIN covid19 RMRIO dataset is a time series of MRIO tables covering years from 2016-2026 on a yearly basis. The dataset covers 163 sectors in 155 countries.</p> <p>This repository includes data for years from 2016 to 2019 (<em>hist</em> scenario) and the corresponding labels.<br> Data for years 2020 to 2026 are stored in the corresponding repositories:</p> <ul> <li><em>covid</em>: <a href="https://doi.org/10.5281/zenodo.5713825">10.5281/zenodo.5713825</a></li> <li><em>counterfactual: </em><a href="https://doi.org/10.5281/zenodo.5713839">10.5281/zenodo.5713839</a></li> </ul> <p>Tables are generated using the <a href="https://github.com/TBeaufils/SPIN">SPIN method</a>, based on the <a href="https://doi.org/10.5281/ZENODO.3993659">RMRIO tables</a> for the year 2015, GDP, imports and exports data from the <a href="https://data.imf.org/?sk=4c514d48-b6ba-49ed-8ab9-52b0c1a0179b">International Financial Statistics</a> (IFS) and the World Economic Outlooks (WEO) of <a href="https://www.imf.org/en/Publications/WEO/weo-database/2019/October">October 2019</a> and <a href="https://www.imf.org/en/Publications/WEO/weo-database/2021/April">April 2021</a>.</p> <p>From 2020 to 2026, the dataset includes two diverging scenarios. The <em>covid</em> scenario is in line with April 2021 WEO's data and includes the macroeconomic effects of Covid 19. The<em> counterfactual</em> scenario is in line with October 2019 WEO's data and simulates the global economy without Covid 19. Tables from 2016 to 2019 are labelled as <em>hist</em>.</p> <p>The <em>Projections</em> folder includes the generated tables for years from 2016 to 2019 (<em>hist</em> scenario) and the corresponding labels.<br> The <em>Sources </em>folder contains the data records from the IFS and WEO databases. The <em>Method data</em> contains the data files used to generate the tables with the SPIN method and the following Python scripts:</p> <ul> <li><em>SPIN_covid19_MRIO_files_preparation.py</em> generates the data files from the source data.</li> <li><em>SPIN_covid19_RMRIO runs.py</em> is the command to run the SPIN method and generate the dataset.</li> <li><em>figures.py</em> is a script to produce figures reflecting the consistency of the projected tables and the evolution of macroeconomic figures in the 2016-2026 period for a selection of countries.</li> </ul> <p>All tables are labelled in 2015 US$ and valued in basic prices.</p>
The SPIN covid19 RMRIO dataset: Global trade network data for the years 2016-2026 reflecting macroeconomic effects of the covid19 pandemic - C. Data for 2020 - 2026 - Counterfactual scenario
<p>The SPIN covid19 RMRIO dataset is a time series of MRIO tables covering years from 2016-2026 on a yearly basis. The dataset covers 163 sectors in 155 countries.</p> <p>This repository includes data for years from 2020 to 2026 (<em>counterfactual</em> scenario).<br> Code, method material and data for years 2016-2019 are stored in the following repository: <a href="http://doi.org/10.5281/zenodo.5713811">10.5281/zenodo.5713811</a><br> Data for the <em>covid</em> scenario are stored in the following repository: <a href="https://doi.org/10.5281/zenodo.5713825">10.5281/zenodo.5713825</a></p> <p>Tables are generated using the <a href="https://github.com/TBeaufils/SPIN">SPIN method</a>, based on the <a href="https://doi.org/10.5281/ZENODO.3993659">RMRIO tables</a> for the year 2015, GDP, imports and exports data from the <a href="https://data.imf.org/?sk=4c514d48-b6ba-49ed-8ab9-52b0c1a0179b">International Financial Statistics</a> (IFS) and the World Economic Outlooks (WEO) of <a href="https://www.imf.org/en/Publications/WEO/weo-database/2019/October">October 2019</a> and <a href="https://www.imf.org/en/Publications/WEO/weo-database/2021/April">April 2021</a>.</p> <p>The<em> counterfactual</em> scenario is in line with October 2019 WEO's data and simulates the global economy without Covid 19.</p> <p>All tables are labelled in 2015 US$ and valued in basic prices.</p>
Diffuse reflectance spectra of coated plates and corresponding plots transformed Kubelka-Munk function versus the energy of light (eV)
<p>The link contains UV-DRS results of TiO<sub>2</sub>/Fe<sub>2</sub>O<sub>3</sub> layered composites (from commercial nanoparticles) and corresponding bandgap energies</p>
IODP Expedition 379 Color reflectance
Color reflectance data were measured on section halves using an integration sphere and a UV-VIS spectrophotometer mounted on the Section Half Multisensor Logger (SHMSL). Spectral counts are recorded in the range of 380 to 700 nm, covering the visible spectrum, and binned in ~2 nm bins. Spectral data are reduced from spectra and recorded in tristimulus XYZ values, CieLAB L*a*b* values, and other units.
Radar Reflectivity at Whillans Ice Plain
<p>This is an extracted data product for radar bed reflectivity from Whillans Ice Plain, West Antarctica. The original data are hosted by the Center for Remote Sensing of Ice Sheets (CReSIS; see associated citation below). The files here can be recalculate and are meant to be used within a set of computational notebooks here:<br>https://doi.org/10.5281/zenodo.10859135</p> <p>There are two csv files included here, each structured as a Pandas dataframe. You can load them in Python like:<br><code>df = pd.read_csv('./Picked_Bed_Power.csv')</code></p> <p>The first file, 'Picked_Bed_Power.csv' is the raw, uncorrected power from the radar image at the bed pick provided by CReSIS. There are also other useful variables for georeferencing, flight attributes, etc.</p> <p>The second file, 'Processed_Reflectivity.csv' is processed from the first file. Processing includes: 1) a spreading correction; 2) an attenuation correction; and, 3) a power adjustment flight days based on compared power at crossover points. This file also has identifiers for regions including "grounded ice", "ungrounded ice", and "subglacial lakes".</p>
IODP Expedition 371 Color reflectance
Color reflectance data were measured on section halves using an integration sphere and a UV-VIS spectrophotometer mounted on the Section Half Multisensor Logger (SHMSL). Spectral counts are recorded in the range of 380 to 700 nm, covering the visible spectrum, and binned in ~2 nm bins. Spectral data are reduced from spectra and recorded in tristimulus XYZ values, CieLAB L*a*b* values, and other units.
IODP Expedition 360 Color reflectance
Color reflectance data were measured on section halves using an integration sphere and a UV-VIS spectrophotometer mounted on the Section Half Multisensor Logger (SHMSL). Spectral counts are recorded in the range of 380 to 700 nm, covering the visible spectrum, and binned in ~2 nm bins. Spectral data are reduced from spectra and recorded in tristimulus XYZ values, CieLAB L*a*b* values, and other units.
IODP Expedition 397 Color reflectance
Color reflectance data were measured on section halves using an integration sphere and a UV-VIS spectrophotometer mounted on the Section Half Multisensor Logger (SHMSL). Spectral counts are recorded in the range of 380 to 700 nm, covering the visible spectrum, and binned in ~2 nm bins. Spectral data are reduced from spectra and recorded in tristimulus XYZ values, CieLAB L*a*b* values, and other units.
Landsat-based Spectral Indices for pan-EU 2000-2022 - Bimonthly predictor (2000-05-01/2000-06-30): Reflectance bands
<h2><strong>Data Information</strong></h2> <p>This dataset includes seven reflectance bands: blue, green, red, nir, swir1, swir2, and thermal, for the period 2000-05-01/2000-06-30.</p> <h2><strong>As a Part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>
Landsat-based Spectral Indices for pan-EU 2000-2022 - Bimonthly predictor (2022-03-01/2022-04-30): Reflectance bands
<h2><strong>Data Information</strong></h2> <p>This dataset includes seven reflectance bands: blue, green, red, nir, swir1, swir2, and thermal, for the period 2022-03-01/2022-04-30.</p> <h2><strong>As a Part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>
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