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OpenAIRE Graph: Dataset for research communities and initiatives
<p>This dataset contains metadata records of the OpenAIRE Graph relevant for the research communities and initiatives collaborating with OpenAIRE and with a public Community Gateway on <a href="https://connect.openaire.eu">OpenAIRE CONNECT</a> as of July 2025.</p> <p>Each file is a tar archive containing gzip files with one json per line. Each json is compliant to the schema available at <a href="https://doi.org/10.5281/zenodo.14891476" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.14891476</a></p> <table style="width: 100%; height: 822.939px;"> <tbody> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;"><strong>Research community name</strong></td> <td style="width: 38.9414%; height: 19.5938px;"><strong>File name</strong></td> <td style="width: 23.1067%; height: 19.5938px;"><strong>URL to the gateway</strong></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;">Argo France</td> <td style="width: 38.9414%; height: 19.5938px;">argo-france.tar</td> <td style="width: 23.1067%; height: 19.5938px;"><a href="https://argo-france.openaire.eu/">https://argo-france.openaire.eu</a></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;">Aurora University Alliance</td> <td style="width: 38.9414%; height: 19.5938px;">aurora.tar</td> <td style="width: 23.1067%; height: 19.5938px;"><a href="https://aurora.openaire.eu">https://aurora.openaire.eu</a></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;">Transport Research (EC projects BE OPEN and SciLake)</td> <td style="width: 38.9414%; height: 19.5938px;">beopen.tar</td> <td style="width: 23.1067%; height: 19.5938px;"><a href="https://beopen.openaire.eu">https://beopen.openaire.eu</a></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;">CIVICA Alliance</td> <td style="width: 38.9414%; height: 19.5938px;">civica.tar</td> <td style="width: 23.1067%; height: 19.5938px;"><a href="https://civica.openaire.eu" target="_blank" rel="noopener">https://civica.openaire.eu</a></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;">COVID 19</td> <td style="width: 38.9414%; height: 19.5938px;">covid-19.tar</td> <td style="width: 23.1067%; height: 19.5938px;"><a href="https://covid-19.openaire.eu">https://covid-19.openaire.eu</a></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;">DARIAH EU</td> <td style="width: 38.9414%; height: 19.5938px;">dariah.tar</td> <td style="width: 23.1067%; height: 19.5938px;"><a href="https://dariah.openaire.eu">https://dariah.openaire.eu</a></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;">Digital Humanities and Cultural Heritage</td> <td style="width: 38.9414%; height: 19.5938px;">dh-ch.tar</td> <td style="width: 23.1067%; height: 19.5938px;"><a href="https://dh-ch.openaire.eu">https://dh-ch.openaire.eu</a></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;">Virtual Twins in Health (EC project EDITH)</td> <td style="width: 38.9414%; height: 19.5938px;">dth.tar</td> <td style="width: 23.1067%; height: 19.5938px;"><a href="https://dth.openaire.eu">https://dth.openaire.eu</a></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;">European Digital Innovation Hubs Network ADRIA</td> <td style="width: 38.9414%; height: 19.5938px;">edih-adria.tar</td> <td style="width: 23.1067%; height: 19.5938px;"><a href="https://edih-adria.openaire.eu">https://edih-adria.openaire.eu</a></td> </tr> <tr style="height: 39.1875px;"> <td style="width: 33.9453%; height: 39.1875px;">European Geothermal Research and Innovation Search Engine </td> <td style="width: 38.9414%; height: 39.1875px;">egrise.tar</td> <td style="width: 23.1067%; height: 39.1875px;"><a href="https://egrise.openaire.eu">https://egrise.openaire.eu</a></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;">ELIXIR Greece</td> <td style="width: 38.9414%; height: 19.5938px;">elixir-gr.tar</td> <td style="width: 23.1067%; height: 19.5938px;"><a href="https://elixir-gr.openaire.eu">https://elixir-gr.openaire.eu</a></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;">Energy Planning (EC project SciLake)</td> <td style="width: 38.9414%; height: 19.5938px;">energy-planning_1.tar, energy_planning_2.tar</td> <td style="width: 23.1067%; height: 19.5938px;"><a href="https://energy-planning.openaire.eu/">https://energy-planning.openaire.eu</a></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;">Energy Research (EC project Enermaps)</td> <td style="width: 38.9414%; height: 19.5938px;">enermaps.tar</td> <td style="width: 23.1067%; height: 19.5938px;"><a href="https://enermaps.openaire.eu">https://enermaps.openaire.eu</a></td> </tr> <tr style="height: 39.1875px;"> <td style="width: 33.9453%; height: 39.1875px;">European University for Smart Urban Coastal Sustainability</td> <td style="width: 38.9414%; height: 39.1875px;">eu-conexus.tar</td> <td style="width: 23.1067%; height: 39.1875px;"><a href="https://eu-conexus.openaire.eu/">https://eu-conexus.openaire.eu</a></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;">European University of Technology+</td> <td style="width: 38.9414%; height: 19.5938px;">eut.tar</td> <td style="width: 23.1067%; height: 19.5938px;"><a href="https://eut.openaire.eu/">https://eut.openaire.eu</a></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;">EUTOPIA Alliance</td> <td style="width: 38.9414%; height: 19.5938px;">eutopia.tar</td> <td style="width: 23.1067%; height: 19.5938px;"><a href="https://eutopia.openaire.eu/">https://eutopia.openaire.eu</a></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;">FORTHEM Alliance</td> <td style="width: 38.9414%; height: 19.5938px;">forthem.tar</td> <td style="width: 23.1067%; height: 19.5938px;"><a href="https://forthem.openaire.eu">https://forthem.openaire.eu</a></td> </tr> <tr> <td style="width: 33.9453%;">[NEW] GoTriple </td> <td style="width: 38.9414%;">gotriple_1.tar, gotriple_2.tar</td> <td style="width: 23.1067%;"><a href="https://gotriple.openaire.eu/">https://gotriple.openaire.eu/</a></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;">Heritage Science (EC project IPERION HS)</td> <td style="width: 38.9414%; height: 19.5938px;">heritage-science.tar</td> <td style="width: 23.1067%; height: 19.5938px;"><a href="https://heritage-science.openaire.eu/">https://heritage-science.openaire.eu</a></td> </tr> <tr style="height: 39.1875px;"> <td style="width: 33.9453%; height: 39.1875px;">Institut national de recherche en informatique et en automatique (EC project GraspOS)</td> <td style="width: 38.9414%; height: 39.1875px;">inria.tar</td> <td style="width: 23.1067%; height: 39.1875px;"><a href="https://inria.openaire.eu" target="_blank" rel="noopener">https://inria.openaire.eu</a></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;">IPERION HS</td> <td style="width: 38.9414%; height: 19.5938px;">iperionhs.tar</td> <td style="width: 23.1067%; height: 19.5938px;"><a href="https://iperionhs.openaire.eu">https://iperionhs.openaire.eu</a></td> </tr> <tr style="height: 39.1875px;"> <td style="width: 33.9453%; height: 39.1875px;">Knowmad Institut</td> <td style="width: 38.9414%; height: 39.1875px;">knowmad_1.tar, knowmad_2.tar, knowmad_3.tar, knowmad_4.tar, knowmad_5.tar, knowmad_6.tar, knowmad_7.tar, knowmad_8.tar, knowmad_9.tar</td> <td style="width: 23.1067%; height: 39.1875px;"><a href="https://knowmad.openaire.eu/">https://knowmad.openaire.eu</a></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;">LifeWathc ERIC</td> <td style="width: 38.9414%; height: 19.5938px;">lifewatch-eric.tar</td> <td style="width: 23.1067%; height: 19.5938px;"><a href="https://lifewatch-eric.openaire.eu/">https://lifewatch-eric.openaire.eu</a></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;">European Marine Science</td> <td style="width: 38.9414%; height: 19.5938px;">mes.tar</td> <td style="width: 23.1067%; height: 19.5938px;"><a href="https://mes.openaire.eu">https://mes.openaire.eu</a></td> </tr> <tr style="height: 39.1875px;"> <td style="width: 33.9453%; height: 39.1875px;">Atmospheric Research Community (EC project NEANIAS)</td> <td style="width: 38.9414%; height: 39.1875px;">neanias-atmospheric.tar</td> <td style="width: 23.1067%; height: 39.1875px;"><a href="https://neanias-atmospheric.openaire.eu/">https://neanias-atmospheric.openaire.eu</a></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;">Space Research Community (EC project NEANIAS)</td> <td style="width: 38.9414%; height: 19.5938px;">neanias-space.tar</td> <td style="width: 23.1067%; height: 19.5938px;"><a href="https://neanias-space.openaire.eu/">https://neanias-space.openaire.eu</a></td> </tr> <tr style="height: 39.1875px;"> <td style="width: 33.9453%; height: 39.1875px;">Underwater Research Community (EC project NEANIAS)</td> <td style="width: 38.9414%; height: 39.1875px;">neanias-underwater.tar</td> <td style="width: 23.1067%; height: 39.1875px;"><a href="https://neanias-underwater.openaire.eu/">https://neanias-underwater.openaire.eu</a></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;">Netherlands Research Portal</td> <td style="width: 38.9414%; height: 19.5938px;">netherlands_1.tar</td> <td style="width: 23.1067%; height: 19.5938px;"><a href="https://netherlands.openaire.eu/">https://netherlands.openaire.eu</a></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;">[NEW] Neuroscience<br>(EC project SciLake - former Neuroinformatics community is now a subcommunity of Neuroscience)</td> <td style="width: 38.9414%; height: 19.5938px;">neuroscience_1.tar</td> <td style="width: 23.1067%; height: 19.5938px;"><a href="https://neuroscience.openaire.eu">https://neuroscience.openaire.eu</a></td> </tr> <tr style="height: 39.1875px;"> <td style="width: 33.9453%; height: 39.1875px;">North American Studies</td> <td style="width: 38.9414%; height: 39.1875px;">north-american-studies.tar</td> <td style="width: 23.1067%; height: 39.1875px;"><a href="https://north-american-studies.openaire.eu">https://north-american-studies.openaire.eu</a></td> </tr> <tr style="height: 39.1875px;"> <td style="width: 33.9453%; height: 39.1875px;">Rural Digital Europe (EC project DESIRA)</td> <td style="width: 38.9414%; height: 39.1875px;">rural-digital-europe.tar</td> <td style="width: 23.1067%; height: 39.1875px;"><a href="https://rural-digital-europe.openaire.eu/">https://rural-digital-europe.openaire.eu</a></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;">Sustainable Development Solutions Network - Greece </td> <td style="width: 38.9414%; height: 19.5938px;">sdsn-gr.tar</td> <td style="width: 23.1067%; height: 19.5938px;"><a href="https://sdsn-gr.openaire.eu/">https://sdsn-gr.openaire.eu</a></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;">Technological University Network</td> <td style="width: 38.9414%; height: 19.5938px;">tunet.tar</td> <td style="width: 23.1067%; height: 19.5938px;"><a href="https://tunet.openaire.eu">https://tunet.openaire.eu</a></td> </tr> <tr> <td style="width: 33.9453%;">[NEW] UNITE! University Alliance</td> <td style="width: 38.9414%;">unite.tar</td> <td style="width: 23.1067%;"><a href="https://unite.openaire.eu">https://unite.openaire.eu</a></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 33.9453%; height: 19.5938px;">University of the Arctic (UArctic) </td> <td style="width: 38.9414%; height: 19.5938px;">uarctic_1.tar, uarctic_2.tar</td> <td style="width: 23.1067%; height: 19.5938px;"><a href="https://uarctic.openaire.eu/">https://uarctic.openaire.eu</a></td> </tr> </tbody> </table>
Initial Sample of HYPERNETS Hyperspectral Water Reflectance Measurements for Satellite Validation at Lake Garda, GAIT site (Italy)
<p>The HYPERNETS project (www.hypernets.eu) has the overall aim to ensure that high quality in situ measurements are available to support the (VNIR/SWIR) optical Copernicus products. Therefore, it established a new autonomous hyperspectral spectroradiometer (HYPSTAR® - www.hypstar.eu) dedicated to land and water surface reflectance validation with instrument pointing capabilities. In the prototype phase, the instrument is being deployed at 24 sites covering a range of water and land types and a range of climatic and logistic conditions. This dataset provides the first published data for the HYPERNETS site in Lake Garda in Italy (GAIT). It is a subset of the complete data record which consists of the best quality GAIT measurements which could be used for satellite validation. </p><p>The provided NetCDF files are the L2A hypernets products with water leaving radiance and reflectances, with and without NIR Similarity Correction (see Ruddick et al., 2006, DOI:<a href="http://dx.doi.org/10.2307/3841124">10.2307/3841124</a>). The reflectance in the L2A products is the Water Reflectance without NIR Similarity Correction (referred to as reflectance_nosc in the file) defined as:</p><p>\(\rho_wnosc=\pi (Lu-\rho_FLd)/E_d\)</p><p>where Lu is the upwelling radiance (at 40° zenith angle, and, 90° or 135° azimuth angle relative to the sun), Ld is the downwelling radiance (at 140° zenith angle, and, 90° or 135° azimuth angle relative to the sun). Ed is the (hemispherical) downwelling irradiance (i.e. including both direct solar and diffuse sky irradiance).</p><p>For the GAIT site, the reflectance corrected for the NIR Similarity correction (epsilon, see Ruddick et al., 2006) is also provided:</p><p>\(\rho_w=\pi (Lu-\rho_FLd)/E_d-\epsilon\)</p><p>These reflectances have dimensions of wavelength and series, where each series is a set of measurements for the computation of a water reflectance measurement. In addition to variables for wavelength and bandwidth, the files also contain variables that provide for each series the acquisition time, viewing and solar angles, and quality flags (typically no flags are set in the data provided in this dataset). These NetCDF files also contain further relevant metadata as attributes. See https://hypernets-processor.readthedocs.io/ for further info.</p><p>The HYPSTAR®-SR (Standard Range) instruments deployed at each land HYPERNETS site consist of a VNIR sensor and autonomously collect data between 380-1000 nm at various viewing geometries and send it to a central server for quality control and processing. The VNIR sensor spans 1330 channels between 380 and 1000 nm with a FWHM of 3 nm. The hypernets_processor (Goyens et al. 2021, DOI: <a href="https://doi.org/10.1109/IGARSS47720.2021.9553738">10.1109/IGARSS47720.2021.9553738</a>; De Vis et al. in prep.) automatically processes all this data into various products, including the L2A surface reflectance product provided here. The current dataset is limited to the 400-900 nm range. Uncertainties are not yet included.</p><p>To obtain this dataset, we start from the full GAIT data record and omit all the data that do not pass all the quality checks performed as part of the hypernets_processor. In addition, an additional screening procedure was developed to supply the best quality data suitable for satellite validation:</p><p>1. The coefficient of variation in water reflectance is below 10% in the 580-600 nm range</p><p>2. The water reflectance (after correction for the NIR similarity) at 500 nm is below 0.1</p><p> </p>
Initial Sample of HYPERNETS Hyperspectral Water Reflectance Measurements for Satellite Validation from the LPAR site (Argentina)
<p>The HYPERNETS project (www.hypernets.eu) has the overall aim to ensure that high quality in situ measurements are available to support the (VNIR/SWIR) optical Copernicus products. Therefore, it established a new autonomous hyperspectral spectroradiometer (HYPSTAR® - www.hypstar.eu) dedicated to land and water surface reflectance validation with instrument pointing capabilities. In the prototype phase, the instrument is being deployed at 24 sites covering a range of water and land types and a range of climatic and logistic conditions. This dataset provides the first published data for the HYPERNETS site in Rio de La Plata, LPAR, in Argentina. It is a subset of the complete data record which consists of the best quality LPAR measurements which could be used for satellite validation. </p> <p>The provided NetCDF files are the L2A hypernets products with water leaving radiance and reflectances (without NIR Similarity Correction, see Ruddick et al., 2006, DOI:<a href="http://dx.doi.org/10.2307/3841124">10.2307/3841124</a>). The reflectance in the L2A products is the Water Reflectance (referred to as reflectance_nosc in the file) defined as:</p> <p><span class="math-tex"><em>ρ</em><em>w</em><em>n</em><em>o</em><em>s</em><em>c</em>=<em>π</em>(<em>L</em><em>u</em>−<em>ρ</em><em>F</em><em>L</em><em>d</em>)/<em>E</em><em>d</em></span></p> <p>where Lu is the upwelling radiance (at 40° zenith angle, and, 90° or 135° azimuth angle relative to the sun), Ld is the downwelling radiance (at 140° zenith angle, and, 90° or 135° azimuth angle relative to the sun). Ed is the (hemispherical) downwelling irradiance (i.e. including both direct solar and diffuse sky irradiance).</p> <p>These reflectances have dimensions of wavelength and series, where each series is a set of measurements for the computation of a water reflectance measurement. In addition to variables for wavelength and bandwidth, the files also contain variables that provide for each series the acquisition time, viewing and solar angles, and quality flags (typically no flags are set in the data provided in this dataset). These NetCDF files also contain further relevant metadata as attributes. See https://hypernets-processor.readthedocs.io/ for further info.</p> <p>The HYPSTAR®-SR (Standard Range) instruments deployed at each land HYPERNETS site consist of a VNIR sensor and autonomously collect data between 380-1000 nm at various viewing geometries and send it to a central server for quality control and processing. The VNIR sensor spans 1330 channels between 380 and 1000 nm with a FWHM of 3 nm. The hypernets_processor (Goyens et al. 2021, DOI: <a href="https://doi.org/10.1109/IGARSS47720.2021.9553738">10.1109/IGARSS47720.2021.9553738</a>; De Vis et al. in prep.) automatically processes all this data into various products, including the L2A surface reflectance product provided here. The current dataset is limited to the 400-900 nm range. Uncertainties are not yet included.</p> <p>To obtain this dataset, we start from the full LPAR data record and omit all the data that do not pass all of the quality checks performed as part of the hypernets_processor. In addition, an additional screening procedure was developed to supply the best quality data suitable for satellite validation:</p> <p>1. The coefficient of variation in water reflectance is below 10% in the 400-900 nm range</p> <p>2. The water reflectance at 500 nm is below 0.1</p>
Initial Sample of HYPERNETS Hyperspectral Water Reflectance Measurements for Satellite Validation from the VEIT site (Italy)
<p>The HYPERNETS project (www.hypernets.eu) has the overall aim to ensure that high quality in situ measurements are available to support the (VNIR/SWIR) optical Copernicus products. Therefore, it established a new autonomous hyperspectral spectroradiometer (HYPSTAR® - www.hypstar.eu) dedicated to land and water surface reflectance validation with instrument pointing capabilities. In the prototype phase, the instrument is being deployed at 24 sites covering a range of water and land types and a range of climatic and logistic conditions. This dataset provides the first published data for the HYPERNETS site at Aqua Alta, Venice in Italy (VEIT). It is a subset of the complete data record which consists of the best quality VEIT measurements which could be used for satellite validation. </p> <p>The provided NetCDF files are the L2A hypernets products with water leaving radiance and reflectances, with and without NIR Similarity Correction (see Ruddick et al., 2006, DOI:<a href="http://dx.doi.org/10.2307/3841124">10.2307/3841124</a>). The reflectance in the L2A products is the Water Reflectance without NIR Similarity Correction (referred to as reflectance_nosc in the file) defined as:</p> <p><span class="math-tex">\(\rho_wnosc=\pi (Lu-\rho_FLd)/E_d\)</span></p> <p>where Lu is the upwelling radiance (at 40° zenith angle, and, 90° or 135° azimuth angle relative to the sun), Ld is the downwelling radiance (at 140° zenith angle, and, 90° or 135° azimuth angle relative to the sun). Ed is the (hemispherical) downwelling irradiance (i.e. including both direct solar and diffuse sky irradiance).</p> <p>For the VEIT site, the reflectance corrected for the NIR Similarity correction (epsilon, see Ruddick et al., 2006) is also provided:</p> <p><span class="math-tex">\(\rho_w=\pi (Lu-\rho_FLd)/E_d-\epsilon\)</span></p> <p>These reflectances have dimensions of wavelength and series, where each series is a set of measurements for the computation of a water reflectance measurement. In addition to variables for wavelength and bandwidth, the files also contain variables that provide for each series the acquisition time, viewing and solar angles, and quality flags (typically no flags are set in the data provided in this dataset). These NetCDF files also contain further relevant metadata as attributes. See https://hypernets-processor.readthedocs.io/ for further info.</p> <p>The HYPSTAR®-SR (Standard Range) instruments deployed at each land HYPERNETS site consist of a VNIR sensor and autonomously collect data between 380-1000 nm at various viewing geometries and send it to a central server for quality control and processing. The VNIR sensor spans 1330 channels between 380 and 1000 nm with a FWHM of 3 nm. The hypernets_processor (Goyens et al. 2021, DOI: <a href="https://doi.org/10.1109/IGARSS47720.2021.9553738">10.1109/IGARSS47720.2021.9553738</a>; De Vis et al. in prep.) automatically processes all this data into various products, including the L2A surface reflectance product provided here. The current dataset is limited to the 400-900 nm range. Uncertainties are not yet included.</p> <p>To obtain this dataset, we start from the full VEIT data record and omit all the data that do not pass all of the quality checks performed as part of the hypernets_processor. In addition, an additional screening procedure was developed to supply the best quality data suitable for satellite validation:</p> <p>1. The coefficient of variation in water reflectance is below 10% in the 580-600 nm range</p> <p>2. The water reflectance (after correction for the NIR similarity) above 800 nm is below 0.01</p> <p> </p>
Initial Sample of HYPERNETS Hyperspectral Water Reflectance Measurements for Satellite Validation at Berre coastal lagoon, BEFR site (France)
<p>The HYPERNETS project (www.hypernets.eu) has the overall aim to ensure that high quality in situ measurements are available to support the (VNIR/SWIR) optical Copernicus products. Therefore, it established a new autonomous hyperspectral spectroradiometer (HYPSTAR® - www.hypstar.eu) dedicated to land and water surface reflectance validation with instrument pointing capabilities. In the prototype phase, the instrument is being deployed at 24 sites covering a range of water and land types and a range of climatic and logistic conditions. This dataset provides the first published data for the HYPERNETS site at Etang de Berre in France (BEFR). It is a subset of the complete data record which consists of the best quality BEFR measurements which could be used for satellite validation. </p> <p>The provided NetCDF files are the L2A hypernets products with water leaving radiance and reflectances, with and without NIR Similarity Correction (see Ruddick et al., 2006, DOI:<a href="http://dx.doi.org/10.2307/3841124">10.2307/3841124</a>). The reflectance in the L2A products is the Water Reflectance without NIR Similarity Correction (referred to as reflectance_nosc in the file) defined as:</p> <p><span class="math-tex"><em>ρ</em><em>w</em><em>n</em><em>o</em><em>s</em><em>c</em>=<em>π</em>(<em>L</em><em>u</em>−<em>ρ</em><em>F</em><em>L</em><em>d</em>)/<em>E</em><em>d</em></span></p> <p> </p> <p>where Lu is the upwelling radiance (at 40° zenith angle, and, 90° or 135° azimuth angle relative to the sun), Ld is the downwelling radiance (at 140° zenith angle, and, 90° or 135° azimuth angle relative to the sun). Ed is the (hemispherical) downwelling irradiance (i.e. including both direct solar and diffuse sky irradiance).</p> <p>For the BEFR site, the reflectance corrected for the NIR Similarity correction (epsilon, see Ruddick et al., 2006) is also provided:</p> <p><span class="math-tex"><em>ρ</em><em>w</em>=<em>π</em>(<em>L</em><em>u</em>−<em>ρ</em><em>F</em><em>L</em><em>d</em>)/<em>E</em><em>d</em>−<em>ϵ</em></span></p> <p> </p> <p>These reflectances have dimensions of wavelength and series, where each series is a set of measurements for the computation of a water reflectance measurement. In addition to variables for wavelength and bandwidth, the files also contain variables that provide for each series the acquisition time, viewing and solar angles, and quality flags (typically no flags are set in the data provided in this dataset). These NetCDF files also contain further relevant metadata as attributes. See https://hypernets-processor.readthedocs.io/ for further info.</p> <p>The HYPSTAR®-SR (Standard Range) instruments deployed at each land HYPERNETS site consist of a VNIR sensor and autonomously collect data between 380-1000 nm at various viewing geometries and send it to a central server for quality control and processing. The VNIR sensor spans 1330 channels between 380 and 1000 nm with a FWHM of 3 nm. The hypernets_processor (Goyens et al. 2021, DOI: <a href="https://doi.org/10.1109/IGARSS47720.2021.9553738">10.1109/IGARSS47720.2021.9553738</a>; De Vis et al. in prep.) automatically processes all this data into various products, including the L2A surface reflectance product provided here. The current dataset is limited to the 400-900 nm range. Uncertainties are not yet included.</p> <p>To obtain this dataset, we start from the full BEFR data record and omit all the data that do not pass all of the quality checks performed as part of the hypernets_processor. In addition, an additional screening procedure was developed to supply the best quality data suitable for satellite validation:</p> <p>1. The coefficient of variation in water reflectance is below 10% in the 500-600 nm range</p> <p>2. The water reflectance (after correction for the NIR similarity) between 700-900 nm is below 0.01</p>
Initial Sample of HYPERNETS Hyperspectral Water Reflectance Measurements for Satellite Validation at the mouth of the Gironde Estuary, MAFR site (France)
<p>The HYPERNETS project (www.hypernets.eu) has the overall aim to ensure that high quality in situ measurements are available to support the (VNIR/SWIR) optical Copernicus products. Therefore, it established a new autonomous hyperspectral spectroradiometer (HYPSTAR® - www.hypstar.eu) dedicated to land and water surface reflectance validation with instrument pointing capabilities. In the prototype phase, the instrument is being deployed at 24 sites covering a range of water and land types and a range of climatic and logistic conditions. This dataset provides the first published data for the HYPERNETS site at the Gironde Estuary, MAGEST Network, in France (MAFR). It is a subset of the complete data record which consists of the best quality MAFR measurements which could be used for satellite validation. </p> <p>The provided NetCDF files are the L2A hypernets products with water leaving radiance and reflectances without NIR Similarity Correction (see Ruddick et al., 2006, DOI:<a href="http://dx.doi.org/10.2307/3841124">10.2307/3841124</a>). The reflectance in the L2A products is the Water Reflectance without NIR Similarity Correction (referred to as reflectance_nosc in the file) defined as:</p> <p><span class="math-tex"><em>ρ</em><em>w</em><em>n</em><em>o</em><em>s</em><em>c</em>=<em>π</em>(<em>L</em><em>u</em>−<em>ρ</em><em>F</em><em>L</em><em>d</em>)/<em>E</em><em>d</em></span></p> <p>where Lu is the upwelling radiance (at 40° zenith angle, and, 90° or 135° azimuth angle relative to the sun), Ld is the downwelling radiance (at 140° zenith angle, and, 90° or 135° azimuth angle relative to the sun). Ed is the (hemispherical) downwelling irradiance (i.e. including both direct solar and diffuse sky irradiance).</p> <p>These reflectances have dimensions of wavelength and series, where each series is a set of measurements for the computation of a water reflectance measurement. In addition to variables for wavelength and bandwidth, the files also contain variables that provide for each series the acquisition time, viewing and solar angles, and quality flags (typically no flags are set in the data provided in this dataset). These NetCDF files also contain further relevant metadata as attributes. See https://hypernets-processor.readthedocs.io/ for further info.</p> <p>The HYPSTAR®-SR (Standard Range) instruments deployed at each land HYPERNETS site consist of a VNIR sensor and autonomously collect data between 380-1000 nm at various viewing geometries and send it to a central server for quality control and processing. The VNIR sensor spans 1330 channels between 380 and 1000 nm with a FWHM of 3 nm. The hypernets_processor (Goyens et al. 2021, DOI: <a href="https://doi.org/10.1109/IGARSS47720.2021.9553738">10.1109/IGARSS47720.2021.9553738</a>; De Vis et al. in prep.) automatically processes all this data into various products, including the L2A surface reflectance product provided here. The current dataset is limited to the 400-900 nm range. Uncertainties are not yet included.</p> <p>To obtain this dataset, we start from the full MAFR data record and omit all the data that do not pass all of the quality checks performed as part of the hypernets_processor. In addition, an additional screening procedure was developed to supply the best quality data suitable for satellite validation:</p> <p>1. The coefficient of variation in water reflectance is below 10% in the 600-700 nm range</p>
Initial Sample of HYPERNETS Hyperspectral Surface Reflectance Measurements for Satellite Validation from the IFEVA site in Argentina
<p>The HYPERNETS project (www.hypernets.eu) has the overall aim to ensure that high quality in situ measurements are available to support the (VNIR/SWIR) optical Copernicus products. Therefore, it established a new autonomous hyperspectral spectroradiometer (HYPSTAR® - www.hypstar.eu) dedicated to land and water surface reflectance validation with instrument pointing capabilities. In the prototype phase, the instrument is being deployed at 24 sites covering a range of water and land types and a range of climatic and logistic conditions. This dataset provides the first published data for the HYPERNETS site at IFEVA in Buenos Aires Argentina (IFAR). It is a subset of the complete data record which consists of the best quality IFAR measurements which could be used for satellite validation. </p> <p>The provided NetCDF files are the L2A hypernets products with surface reflectances, their associated uncertainties and error-correlation information. The reflectance in the L2A products is the Hemispherical-directional Reflectance Factor (HDRF) defined as: HDRF = π L / E where L is the directional upwelling radiance (with field of view of 5 degrees) and E is the (hemispherical) downwelling irradiance (i.e. including both direct solar and diffuse sky irradiance). These reflectances have dimensions of wavelength and series, where each series is a set of measurements for a given geometry (combination of viewing zenith and azimuth angle). In addition to variables for wavelength and bandwidth, the files also contain variables that provide for each series the acquisition time, viewing and solar angles, number of valid scans used, and quality flags (typically no flags are set in the data provided in this dataset). These NetCDF files also contain further relevant metadata as attributes. See https://hypernets-processor.readthedocs.io/ for further info.</p> <p>The IFAR site is a temporary test site located in the Agronomy Faculty campus in Buenos Aires city, Argentina (34.592322°S, 58.479017°W). The venue is managed by the IFEVA (Agricultural Physiology and Ecology Research Institute) and characterized by natural pastures with different treatments distributed in 16 patches of 7mx7m. The HYPSTAR®-XR sensor has been deployed in June 2021 at the top of a 2.4 m high tripod that is pointing to one of the patches where the vegetation has no specific treatment (natural) and is cut regularly every year in February. Data is collected every 30 min between 14:00 and 18:00 hs UTC (11:00 to 15:00 local time).</p> <p>The HYPSTAR®-XR (eXtended Range) instruments deployed at each land HYPERNETS site consist of a VNIR and a SWIR sensor and autonomously collect data between 380-1700 nm at various viewing geometries and send it to a central server for quality control and processing. The VNIR sensor spans 1330 channels between 380 and 1000 nm with a FWHM of 3 nm and the SWIR sensor has 220 channels between 1000 and 1700 nm with a FWHM of 10 nm. The hypernets_processor (Goyens et al. 2021; De Vis et al. in prep.) automatically processes all this data into various products, including the L2A surface reflectance product provided here. All of the products have associated uncertainties (divided into random and systematic uncertainties, including error-correlation information) which were propagated using the CoMet toolkit (www.comet-toolkit.org). </p> <p>To obtain this dataset, we start from the full IFAR data record and omit all the data that do not pass all of the quality checks performed as part of the hypernets_processor. In addition, an additional screening procedure was developed to remove outliers and only supply the best quality data suitable for satellite validation. To remove the outliers, a sigma-clipping method is used. First reflectances are extracted in separate 2 hour windows throughout the day (to account for BRDF differences due to different solar position) for 4 different wavelengths (500, 900, 1100 and 1600 nm). Outliers in these reflectances are then identified by iteratively calculating the mean reflectance trend with time (by binning the data per maximum 30 data points), calculating the standard deviation from this trend, and masking any data that is more than 3 standard deviations away from the trend. This process is repeated on the unmasked data until the standard deviation does not vary by more than 5% between two iterations. The masks for the 4 different wavelengths are then combined (keeping only measurements for which none of the 4 wavelengths is an outlier). The reflectances and associated uncertainties for any masked series (i.e. a geometry that is masked either by the sigma-clipping procedure or from the masks of the hypernets_processor) are replaced by NaNs. Any sequence that has more than half of its series masked is removed entirely. </p>
Initial Sample of HYPERNETS Hyperspectral Water Reflectance Measurements for Satellite Validation at the measurement tower MOW1, M1BE site (Belgium)
<p>The HYPERNETS project (www.hypernets.eu) has the overall aim to ensure that high quality in situ measurements are available to support the (VNIR/SWIR) optical Copernicus products. Therefore, it established a new autonomous hyperspectral spectroradiometer (HYPSTAR® - www.hypstar.eu) dedicated to land and water surface reflectance validation with instrument pointing capabilities. In the prototype phase, the instrument is being deployed at 24 sites covering a range of water and land types and a range of climatic and logistic conditions. This dataset provides the first published data for the HYPERNETS site at the measurement pole near the <em>Zeebrugge</em> harbour 3.65km from land, often called MOW1, in Belgium (M1BE). It is a subset of the complete data record which consists of the best quality M1BE measurements which could be used for satellite validation. </p> <p>The provided NetCDF files are the L2A hypernets products with water leaving radiance and reflectances, with and without NIR Similarity Correction (see Ruddick et al., 2006, DOI:<a href="http://dx.doi.org/10.2307/3841124">10.2307/3841124</a>). The reflectance in the L2A products is the Water Reflectance without NIR Similarity Correction (referred to as reflectance_nosc in the file) defined as:</p> <p><span class="math-tex">\(\rho_wnosc=\pi (Lu-\rho_FLd)/E_d\)</span></p> <p>where Lu is the upwelling radiance (at 40° zenith angle, and, 90° or 135° azimuth angle relative to the sun), Ld is the downwelling radiance (at 140° zenith angle, and, 90° or 135° azimuth angle relative to the sun). Ed is the (hemispherical) downwelling irradiance (i.e. including both direct solar and diffuse sky irradiance).</p> <p>For the M1BE site, the reflectance corrected for the NIR Similarity correction (epsilon, see Ruddick et al., 2006) is also provided:</p> <p><span class="math-tex">\(\rho_w=\pi (Lu-\rho_FLd)/E_d-\epsilon\)</span></p> <p>These reflectances have dimensions of wavelength and series, where each series is a set of measurements for the computation of a water reflectance measurement. In addition to variables for wavelength and bandwidth, the files also contain variables that provide for each series the acquisition time, viewing and solar angles, and quality flags (typically no flags are set in the data provided in this dataset). These NetCDF files also contain further relevant metadata as attributes. See https://hypernets-processor.readthedocs.io/ for further info.</p> <p>The HYPSTAR®-SR (Standard Range) instruments deployed at each land HYPERNETS site consist of a VNIR sensor and autonomously collect data between 380-1000 nm at various viewing geometries and send it to a central server for quality control and processing. The VNIR sensor spans 1330 channels between 380 and 1000 nm with a FWHM of 3 nm. The hypernets_processor (Goyens et al. 2021, DOI: <a href="https://doi.org/10.1109/IGARSS47720.2021.9553738">10.1109/IGARSS47720.2021.9553738</a>; De Vis et al. in prep.) automatically processes all this data into various products, including the L2A surface reflectance product provided here. The current dataset is limited to the 400-900 nm range. Uncertainties are not yet included.</p> <p>To obtain this dataset, we start from the full M1BE data record and omit all the data that do not pass all of the quality checks performed as part of the hypernets_processor. In addition, an additional screening procedure was developed to supply the best quality data suitable for satellite validation:</p> <p>1. The coefficient of variation in water reflectance is below 10% in the 500-600 nm range</p> <p>2. The water reflectance at 500 nm is below 0.1</p> <p>The data consists of 73 spectra ranging from 20230226T1431 till 20230429T1502.</p> <p>Coordinates of the site are the following:</p> <p>site_latitude = 51.360548<br> site_longitude = 3.118246</p> <p>The site is owned by Afdeling Kust (https://www.agentschapmdk.be/nl).</p> <p> </p>
Initial Sample of HYPERNETS Hyperspectral Surface Reflectance Measurements for Satellite Validation from the Wytham Woods site in the United Kingdom
<p>The HYPERNETS project (www.hypernets.eu) has the overall aim to ensure that high quality in situ measurements are available to support the (VNIR/SWIR) optical Copernicus products. Therefore, it established a new autonomous hyperspectral spectroradiometer (HYPSTAR® - www.hypstar.eu) dedicated to land and water surface reflectance validation with instrument pointing capabilities. In the prototype phase, the instrument is being deployed at 24 sites covering a range of water and land types and a range of climatic and logistic conditions. This dataset provides the first published data for the Wytham Woods HYPERNETS site in the United Kingdom (WWUK). It is a subset of the complete data record which consists of the best quality WWUK measurements which could be used for satellite validation. </p> <p>The provided NetCDF files are the L2A hypernets products with surface reflectances, their associated uncertainties and error-correlation information. The reflectance in the L2A products is the Hemispherical-directional Reflectance Factor (HDRF) defined as: HDRF = π L / E where L is the directional upwelling radiance (with field of view of 5 degrees) and E is the (hemispherical) downwelling irradiance (i.e. including both direct solar and diffuse sky irradiance). These reflectances have dimensions of wavelength and series, where each series is a set of measurements for a given geometry (combination of viewing zenith and azimuth angle). In addition to variables for wavelength and bandwidth, the files also contain variables that provide for each series the acquisition time, viewing and solar angles, number of valid scans used, and quality flags (typically no flags are set in the data provided in this dataset). These NetCDF files also contain further relevant metadata as attributes. See https://hypernets-processor.readthedocs.io/ for further info.</p> <p>The WWUK site is a deciduous broadleaf forest comprised primarily of Oak, Hazel, Ash, Sycamore and Beech. It is located approximately 5 km North-West of Oxford, UK and has an extensive history of scientific research. The site follows the typical seasonal dynamics of a temperate forest with distinctive periods of leaf-off, green up and senescence across the growing season. The HYPERNETS site itself (51.777206 degrees N, 1.338494 W), is located at a height of 28 m upon a flux tower in the centre of the forest. The HYPSTAR®-XR sensor was installed in October 2021. Data are collected e very 30 minutes between 9am and 6pm local time between viewing zenith angles of 0 and 30 degrees.</p> <p>The HYPSTAR®-XR (eXtended Range) instruments deployed at each land HYPERNETS site consist of a VNIR and a SWIR sensor and autonomously collect data between 380-1700 nm at various viewing geometries and send it to a central server for quality control and processing. The VNIR sensor spans 1330 channels between 380 and 1000 nm with a FWHM of 3 nm and the SWIR sensor has 220 channels between 1000 and 1700 nm with a FWHM of 10 nm. The hypernets_processor (Goyens et al. 2021; De Vis et al. in prep.) automatically processes all this data into various products, including the L2A surface reflectance product provided here. All of the products have associated uncertainties (divided into random and systematic uncertainties, including error-correlation information) which were propagated using the CoMet toolkit (www.comet-toolkit.org). </p> <p>To obtain this dataset, we start from the full WWUK data record and omit all the data that do not pass all of the quality checks performed as part of the hypernets_processor. In addition, two additional screening procedures are developed to remove outliers and only supply the best quality data suitable for satellite validation. For Wytham wood, sequences are only supplied that match a typical vegetation spectrum. As such, data is only provided between April and October during the leaf-on period. Reflectances are then tested against three parameters to check that they are vegetation spectrum. Firstly, that there is a peak in the green portion of the visible wavebands (560 nm). Secondly, that a red edge is detected. Finally, the Normalized Difference Vegetation Index (NDVI) is calculated. Spectra with an NDVI of less than 0.42 are removed from the final data set.</p> <p>After the vegetation quality flag are applied, a sigma-clipping method is used to remove outliers. First reflectances are extracted in separate 2 hour windows throughout the day (to account for BRDF differences due to different solar position) for 4 different wavelengths (500, 900, 1100 and 1600 nm). Outliers in these reflectances are then identified by iteratively calculating the mean reflectance trend with time (by binning the data per maximum 30 data points), calculating the standard deviation from this trend, and masking any data that is more than 3 standard deviations away from the trend. This process is repeated on the unmasked data until the standard deviation does not vary by more than 5% between two iterations. The masks for the 4 different wavelengths are then combined (keeping only measurements for which none of the 4 wavelengths is an outlier). The reflectances and associated uncertainties for any masked series (i.e. a geometry that is masked either by the sigma-clipping procedure or from the masks of the hypernets_processor) are replaced by NaNs. Any sequence that has more than half of its series masked is removed entirely. </p>
Regional Heat Vulnerability Map and Cooling Solutions: A webtool of the Healthy Urban Environments Initiative
## Regional Heat Vulnerability Map and Cooling Solutions The regional heat vulnerability map and cooling solutions webtool offers two data sources for equitable heat mitigation. The dashboard layers vulnerability data onto land surface temperature regional rankings to identify areas with high and low heat exposure and vulnerability as well as the existing assets in each census block group. Additional layers can be added into the heat vulnerability map to highlight how heat affects critical infrastructures including schools, mobile home parks, parking lots, public transportation stops, pedestrian thoroughfares, and bikeways. The solutions tab showcases a variety of heat mitigation solutions and the research behind them. Heat-related solutions and resources from urban Maricopa County are included, including solutions funded through the Healthy Urban Environment Initiative. The data catalogued here are the underlying data that populate the webtool. ## Healthy Urban Environment (HUE) Initiative - Overview HUE is a solutions-focused research, policy and technology incubator to create healthier communities across Maricopa County (central Arizona, USA) through collaboration between researchers, practitioners and community members. As such, HUE funded rapid development, testing and deployment of heat-mitigation and air-quality improvement strategies and technologies. Heat emerged as the urgent focus, as urban centers across the desert Southwest continue to grow in size and density, aggravating existing challenges posed by the expansion of the built environment. In Phoenix, AZ, this expansion of the built environment creates conditions which magnify the intensity and duration of heat – making it difficult for residents to achieve thermal comfort throughout the day and night. Further, the legacies of urban sprawl and transportation planning in the Phoenix, Arizona metropolitan area have contributed to challenges with atmospheric pollutants. Importantly, urban heat and air qua
Soil factors predict initial plant colonization on Puerto Rican landslides
Tropical storms are the principal cause of landslides in montane rainforests, such as the Luquillo Experimental Forest (LEF) of Puerto Rico . A storm in 2003 caused 30 new landslides in the LEF that we used to examine prior hypotheses that slope stability and organically enriched soils are prerequisites for plant colonization. We measured slope stability and litterfall in 1 m2 plots 8-13 months following landslide formation. At 13 months we also measured microtopography, soil characteristics (organic matter, particle size, total nitrogen, and water holding capacity), elevation, distance to forest edge, and canopy cover, as well as plant aboveground biomass, plant cover, and root biomass. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
SIRAH-CoV2 initiative: RNA binding domain of nucleocapsid phosphoprotein (PDB id:6VYO)
<p>This dataset contains the trajectory of a 10 microseconds-long coarse-grained molecular dynamics simulation of SARS-CoV2 RNA binding domain of the nucleocapsid phosphoprotein in its APO form with Zn ions bound (PDB id:6VYO, Bioassembly 1). Simulations were performed using the SIRAH force field running with the Amber18 package at the Uruguayan National Center for Supercomputing (ClusterUY) under the conditions reported in <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00006">Machado et al. JCTC 2019</a>, adding 150 mM NaCl according to <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00953">Machado & Pantano JCTC 2020</a>. Zinc ions were parameterized as reported in <a href="https://pubs.acs.org/doi/10.1021/acs.jcim.0c00160">Klein et al. 2020</a>.</p> <p>The file 6VYO_SIRAHcg_rawdata.tar contains all the raw information required to visualize (on VMD), analyze, backmap, and eventually continue the simulations using Amber18 or higher. Step-By-Step tutorials for running, visualizing, and analyzing CG trajectories using <a href="https://academic.oup.com/bioinformatics/article/32/10/1568/1743152">SirahTools</a> can be found at www.sirahff.com. Additionally, the file 6VYO_SIRAHcg_10us_prot.tar contains only the protein coordinates, while 6VYO_SIRAHcg_10us_prot_skip10ns.tar contains one frame every 10ns.</p> <p>To take a quick look at the trajectory:</p> <p>1- Untar the file 6VYO_SIRAHcg_10us_prot_skip10ns.tar</p> <p>2- Open the trajectory on VMD using the command line:</p> <p>vmd 6VYO_SIRAHcg_prot_10us_skip10ns.prmtop 6VYO_SIRAHcg_prot_10us_skip10ns.ncrst 6VYO_SIRAHcg_prot_10us_skip10ns.nc -e sirah_vmdtk.tcl</p> <p>Note that you can use normal VMD drawing methods as vdw, licorice, etc., and coloring by restype, element, name, etc. </p> <p>This dataset is part of the SIRAH-CoV2 initiative.</p> <p>For further details, please contact Florencia Klein (fklein@pasteur.edu.uy) or Sergio Pantano (spantano@pasteur.edu.uy).</p>
SIRAH-CoV2 initiative: Main Protease (PDB id:6LU7)
<p>This dataset contains the trajectory of a 15 microseconds-long coarse-grained molecular dynamics simulation of SARS-CoV2 Main protease in its APO form (PDB id: 6LU7, Bioassembly 1). Simulations have been performed using the SIRAH force field running with the Amber18 package at the Uruguayan National Center for Supercomputing (ClusterUY) under the conditions reported in <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00006">Machado et al. JCTC 2019</a>, adding 150 mM NaCl according to <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00953">Machado & Pantano JCTC 2020</a>. </p> <p>The files 6LU7_SIRAHcg_rawdata1.tar, 6LU7_SIRAHcg_rawdata2.tar, and 6LU7_SIRAHcg_rawdata3.tar, contain all the raw information required to visualize (on VMD), analyze, backmap, and eventually continue the simulations using Amber18 or higher. Step-By-Step tutorials for running, visualizing, and analyzing CG trajectories using <a href="https://academic.oup.com/bioinformatics/article/32/10/1568/1743152">SirahTools</a> can be found at www.sirahff.com.</p> <p>Additionally, the file 6LU7_SIRAHcg_15us_prot.tar contains only the protein coordinates, while 6LU7_SIRAHcg_15us_prot_skip10ns.tar contains one frame every 10ns.</p> <p>To take a quick look at the trajectory:</p> <p>1- Untar the file 6LU7_SIRAHcg_15us_prot_skip10ns.tar</p> <p>2- Open the trajectory on VMD using the command line:</p> <p>vmd 6LU7_SIRAHcg_prot.prmtop 6LU7_SIRAHcg_prot.ncrst 6LU7_SIRAHcg_prot_15us_skip10ns.nc -e sirah_vmdtk.tcl</p> <p>Note that you can use normal VMD drawing methods as vdw, licorice, etc., and coloring by restype, element, name, etc. </p> <p>This dataset is part of the SIRAH-CoV2 initiative.</p> <p>For further details, please contact Sergio Pantano (spantano@pasteur.edu.uy).</p>
SIRAH-CoV2 initiative: NSP9 RNA binding protein (PDBid:6W4B)
<p>This dataset contains the trajectory of a 10 microseconds-long coarse-grained molecular dynamics simulation of SARS-CoV2 NSP9 RNA binding protein (PDB id: 6W4B, Bioassembly 1). Simulations have been performed using the SIRAH force field running with the Amber18 package at the Uruguayan National Center for Supercomputing (ClusterUY) under the conditions reported in <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00006">Machado et al. JCTC 2019</a>, adding 150 mM NaCl according to <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00953">Machado & Pantano JCTC 2020</a>. </p> <p>The file 6W4B_SIRAHcg_rawdata.tar contains all the raw information required to visualize (on VMD), analyze, backmap, and eventually continue the simulations using Amber18 or higher. Step-By-Step tutorials for running, visualizing, and analyzing CG trajectories using <a href="https://academic.oup.com/bioinformatics/article/32/10/1568/1743152">SirahTools</a> can be found at www.sirahff.com.</p> <p>Additionally, the file 6W4B_SIRAHcg_10us_prot.tar contains only the protein coordinates, while 6W4B_SIRAHcg_10us_prot_skip10ns.tar contains one frame every 10ns.</p> <p>To take a quick look at the trajectory:</p> <p>1- Untar the file 6W4B_SIRAHcg_10us_prot_skip10ns.tar</p> <p>2- Open the trajectory on VMD using the command line:</p> <p>vmd 6W4B_SIRAHcg_prot.prmtop 6W4B_SIRAHcg_prot.ncrst 6W4B_SIRAHcg_prot_10us_skip10ns.nc -e sirah_vmdtk.tcl</p> <p>Note that you can use normal VMD drawing methods as vdw, licorice, etc., and coloring by restype, element, name, etc. </p> <p>This dataset is part of the SIRAH-CoV2 initiative.</p> <p>For further details, please contact Sergio Pantano (spantano@pasteur.edu.uy).</p>
SIRAH-CoV2 initiative: Nucleocapsid protein N-terminal RNA binding domain (PDB id:6M3M)
<p>This dataset contains the trajectory of a 10 microseconds-long coarse-grained molecular dynamics simulation of SARS-CoV2 Nucleocapsid protein N-terminal RNA binding domain (PDB id:6M3M). Simulations have been performed using the SIRAH force field running with the Amber18 package at the Uruguayan National Center for Supercomputing (ClusterUY) under the conditions reported in <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00006">Machado et al. JCTC 2019</a>, adding 150 mM NaCl according to <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00953">Machado & Pantano JCTC 2020</a>. </p> <p>The files 6M3M_SIRAHcg_rawdata.tar contains all the raw information required to visualize (on VMD), analyze, backmap, and eventually continue the simulations using Amber18 or higher. Step-By-Step tutorials for running, visualizing, and analyzing CG trajectories using <a href="https://academic.oup.com/bioinformatics/article/32/10/1568/1743152">SirahTools</a> can be found at www.sirahff.com.</p> <p>Additionally, the file 6M3M_SIRAHcg_10us_prot.tar contains only the protein coordinates, while 6M3M_SIRAHcg_10us_prot_skip10ns.tar contains one frame every 10ns.</p> <p>To take a quick look at the trajectory:</p> <p>1- Untar the file 6M3M_SIRAHcg_10us_prot_skip10ns.tar</p> <p>2- Open the trajectory on VMD using the command line:</p> <p>vmd 6W4B_SIRAHcg_prot.prmtop 6W4B_SIRAHcg_prot.ncrst 6W4B_SIRAHcg_prot_10us_skip10ns.nc -e sirah_vmdtk.tcl</p> <p>Note that you can use normal VMD drawing methods as vdw, licorice, etc., and coloring by restype, element, name, etc. </p> <p>This dataset is part of the SIRAH-CoV2 initiative.</p> <p>For further details, please contact Florencia Klein (fklein@pasteur.edu.uy) or Sergio Pantano (spantano@pasteur.edu.uy).</p>
SIRAH-CoV2 initiative: Papain-like Protease (PDB id:6W9C)
<p>This dataset contains the trajectory of a 10 microseconds-long coarse-grained molecular dynamics simulation of SARS-CoV2 Papain-like protease in its APO form with Zn ions bound (PDB id: 6W9C). Simulations have been performed using the SIRAH force field running with the Amber18 package at the Uruguayan National Center for Supercomputing (ClusterUY) under the conditions reported in <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00006">Machado et al. JCTC 2019</a>, adding 150 mM NaCl according to <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00953">Machado & Pantano JCTC 2020</a>. Zinc ions were parameterized as reported in <a href="https://pubs.acs.org/doi/10.1021/acs.jcim.0c00160">Klein et al. 2020</a>.</p> <p>The files 6W9C_SIRAHcg_rawdata_0-5us.tar, 6W9C_SIRAHcg_rawdata_5-10us.tar, contain all the raw information required to visualize (on VMD), analyze, backmap, and eventually continue the simulations using Amber18 or higher. Step-By-Step tutorials for running, visualizing, and analyzing CG trajectories using <a href="https://academic.oup.com/bioinformatics/article/32/10/1568/1743152">SirahTools</a> can be found at www.sirahff.com.</p> <p>Additionally, the file 6W9C_SIRAHcg_10us_prot.tar contains only the protein coordinates, while 6W9C_SIRAHcg_10us_prot_skip10ns.tar contains one frame every 10ns.</p> <p>To take a quick look at the trajectory:</p> <p>1- Untar the file 6W9C_SIRAHcg_10us_prot_skip10ns.tar</p> <p>2- Open the trajectory on VMD using the command line:</p> <p>vmd 6W9C_SIRAHcg_prot.prmtop 6W9C_SIRAHcg_prot.ncrst 6W9C_SIRAHcg_10us_prot_skip10ns.nc -e sirah_vmdtk.tcl</p> <p>Note that you can use normal VMD drawing methods as vdw, licorice, etc., and coloring by restype, element, name, etc. </p> <p>This dataset is part of the SIRAH-CoV2 initiative.</p> <p>For further details, please contact Florencia Klein (fklein@pasteur.edu.uy) or Sergio Pantano (spantano@pasteur.edu.uy).</p>
SIRAH-CoV2 initiative: S1 Receptor Binding Domain in complex with human antibody CR3022 (PDBid: 6W41)
<p>This dataset contains the trajectory of a 12 microseconds-long coarse-grained molecular dynamics simulation of SARS-CoV-2 receptor binding domain in complex with a human antibody CR3022 (PDB id: 6W41). Simulations have been performed using the SIRAH force field running with the Amber18 package at the Uruguayan National Center for Supercomputing (ClusterUY) under the conditions reported in <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00006">Machado et al. JCTC 2019</a>, adding 150 mM NaCl according to <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00953">Machado & Pantano JCTC 2020</a>. Glycans have been removed from the structures.</p> <p>The file 6W41_SIRAHcg_rawdata.tar contains all the raw information required to visualize (on VMD), analyze, backmap, and eventually continue the simulations using Amber18 or higher. Step-By-Step tutorials for running, visualizing, and analyzing CG trajectories using <a href="https://academic.oup.com/bioinformatics/article/32/10/1568/1743152">SirahTools</a> can be found at www.sirahff.com.</p> <p>Additionally, the file 6W41_SIRAHcg_12us_prot.tar contains only the protein coordinates, while 6W41_SIRAHcg_12us_prot_skip10ns.tar contains one frame every 10ns.</p> <p>To take a quick look at the trajectory:</p> <p>1- Untar the file 6W41_SIRAHcg_12us_prot_skip10ns.tar</p> <p>2- Open the trajectory on VMD using the command line:</p> <p>vmd 6w41_SIRAHcg_prot.prmtop 6w41_SIRAHcg_prot.ncrst 6w41_SIRAHcg_prot_12us_skip10ns.nc -e sirah_vmdtk.tcl</p> <p>Note that you can use normal VMD drawing methods as vdw, licorice, etc., and coloring by restype, element, name, etc. </p> <p>This dataset is part of the SIRAH-CoV2 initiative.</p> <p>For further details, please contact Martín Soñora (msonora@pasteur.edu.uy) or Sergio Pantano (spantano@pasteur.edu.uy).</p>
SIRAH-CoV2 initiative: NSP16 - NSP10 Complex (PDB id:6W4H)
<p>This dataset contains the trajectory of a 10 microseconds-long coarse-grained molecular dynamics simulation of SARS-CoV2 NSP16 - NSP10 Complex with Zn ions bound (PDB id: 6W4H). Simulations have been performed using the SIRAH force field running with the Amber18 package at the Uruguayan National Center for Supercomputing (ClusterUY) under the conditions reported in <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00006">Machado et al. JCTC 2019</a>, adding 150 mM NaCl according to <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00953">Machado & Pantano JCTC 2020</a>. Zinc ions were parameterized as reported in <a href="https://pubs.acs.org/doi/10.1021/acs.jcim.0c00160">Klein et al. 2020</a>.</p> <p>The file 6W4H_SIRAHcg_rawdata.tar contains all the raw information required to visualize (on VMD), analyze, backmap, and eventually continue the simulations using Amber18 or higher. Step-By-Step tutorials for running, visualizing, and analyzing CG trajectories using <a href="https://academic.oup.com/bioinformatics/article/32/10/1568/1743152">SirahTools</a> can be found at www.sirahff.com.</p> <p>Additionally, the file 6W4H_SIRAHcg_10us_prot_Zn.tar contains only the protein coordinates, while 6W4H_SIRAHcg_10us_prot_Zn_skip10ns.tar contains one frame every 10ns.</p> <p>To take a quick look at the trajectory:</p> <p>1- Untar the file 6W4H_SIRAHcg_10us_prot_Zn_skip10ns.tar</p> <p>2- Open the trajectory on VMD using the command line:</p> <p>vmd 6w4h_SIRAHcg_prot.prmtop 6w4h_SIRAHcg_prot.ncrst 6w4h_SIRAHcg_prot_Zn_10us_skip10ns.nc -e sirah_vmdtk.tcl</p> <p>Note that you can use normal VMD drawing methods as vdw, licorice, etc., and coloring by restype, element, name, etc. </p> <p>This dataset is part of the SIRAH-CoV2 initiative.</p> <p>For further details, please contact Martín Soñora (msonora@pasteur.edu.uy) or Sergio Pantano (spantano@pasteur.edu.uy).</p> <p> </p>
SIRAH-CoV2 initiative: ORF7A enconded accessory protein (PDB id:6W37)
<p>This dataset contains the trajectory of a 10 microseconds-long coarse-grained molecular dynamics simulation of SARS-CoV2 ORF7a encoded accessory protein (PDB id: 6W37). Simulations have been performed using the SIRAH force field running with the Amber18 package at the Uruguayan National Center for Supercomputing (ClusterUY) under the conditions reported in <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00006">Machado et al. JCTC 2019</a>, adding 150 mM NaCl according to <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00953">Machado & Pantano JCTC 2020</a>. </p> <p>The file 6W37_SIRAHcg_rawdata.tar contains all the raw information required to visualize (on VMD), analyze, backmap, and eventually, continue the simulations using Amber18 or higher. Step-By-Step tutorials for running, visualizing, and analyzing CG trajectories using <a href="https://academic.oup.com/bioinformatics/article/32/10/1568/1743152">SirahTools</a> can be found at www.sirahff.com.</p> <p>Additionally, the file 6W37_SIRAHcg_10us_prot.tar contains only the protein coordinates, while 6W37_SIRAHcg_10us_prot_skip10ns.tar contains one frame every 10ns.</p> <p>To take a quick look at the trajectory:</p> <p>1- Untar the file 6W37_SIRAHcg_10us_prot_skip10ns.tar</p> <p>2- Open the trajectory on VMD using the command line:</p> <p>vmd 6w37_SIRAHcg_prot.prmtop 6w37_SIRAHcg_prot.ncrst 6w37_SIRAHcg_prot_10us_skip10ns.nc -e sirah_vmdtk.tcl</p> <p>Note that you can use normal VMD drawing methods as vdw, licorice, etc., and coloring by restype, element, name, etc. </p> <p>This dataset is part of the SIRAH-CoV2 initiative.</p> <p>For further details, please contact Martín Soñora (msonora@pasteur.edu.uy) or Sergio Pantano (spantano@pasteur.edu.uy).</p>
SIRAH-CoV2 initiative: co-factor complex of NSP7 and the C-terminal domain of NSP8 from SARS CoV-2 (PDBid:6WIQ)
<p>This dataset contains the trajectory of a 10 microseconds-long coarse-grained molecular dynamics simulation of the co-factor complex of NSP7 and the C-terminal domain of NSP8 from SARS CoV-2 (PDBid:6WIQ). Simulations have been performed using the SIRAH force field running with the Amber18 package at the Uruguayan National Center for Supercomputing (ClusterUY) under the conditions reported in <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00006">Machado et al. JCTC 2019</a>, adding 150 mM NaCl according to <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00953">Machado & Pantano JCTC 2020</a>. </p> <p>The files 6WIQ_SIRAHcg_rawdata_0-5us.tar, and 6WIQ_SIRAHcg_rawdata_5-10us.tar, contain all the raw information required to visualize (on VMD), analyze, backmap, and eventually continue the simulations using Amber18 or higher. Step-By-Step tutorials for running, visualizing, and analyzing CG trajectories using <a href="https://academic.oup.com/bioinformatics/article/32/10/1568/1743152">SirahTools</a> can be found at www.sirahff.com.</p> <p>Additionally, the file 6WIQ_SIRAHcg_10us_prot.tar contains only the protein coordinates, while 6WIQ_SIRAHcg_10us_prot_skip10ns.tar contains one frame every 10ns.</p> <p>To take a quick look at the trajectory:</p> <p>1- Untar the file 6WIQ_SIRAHcg_10us_prot_skip10ns.tar</p> <p>2- Open the trajectory on VMD using the command line:</p> <p>vmd 6WIQ_SIRAHcg_prot.prmtop 6WIQ_SIRAHcg_prot.ncrst 6WIQ_SIRAHcg_10us_prot_skip10ns.nc -e sirah_vmdtk.tcl</p> <p>Note that you can use normal VMD drawing methods as vdw, licorice, etc., and coloring by restype, element, name, etc. </p> <p>This dataset is part of the SIRAH-CoV2 initiative.</p> <p>For further details, please contact Florencia Klein (fklein@pasteur.edu.uy) or Sergio Pantano (spantano@pasteur.edu.uy).</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.