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1,058 results for “ARDS”
Acute Respiratory Distress Syndrome-Database of Genes (ARDS-DB)
<p>To better understand the gene level associations that are most relevant to Acute Respiratory Distress Syndrome (ARDS), a comprehensive resource is needed. There’s currently no freely available database dedicated to ARDS that provides comprehensive gene lists from experimentally verifiable studies, gene function, gene location, and additional metadata for tracking related link out resources. The need for such a database is only accentuated by the steep rise in ARDS cases due to the 2020 Coronavirus pandemic, in which infected patients admitted to the ICU develop ARDS at a rate of 67% to 85%, calling for an increase into ARDS research. </p> <p>Our goal was to develop such a resource for use by the scientific community to enhance our studies of ARDS and associated genes. Our first step was to perform data mining and curation of scientific literature through a robust review process. Subsequent steps enabled us to refine our data by capturing specific metadata and incorporating these into our database. The version 1 of the database will provide users with access to the database flat file with current genes, gene location, chromosomal information, and more in a freely accessible and downloadable format. Future project goals are to develop a standalone web portal that will integrate the gene level information with network analysis, and other visualizations for users. </p> <p> </p>
Sentinel-3 NDVI ARD and Long Term Statistics (1999-2019) from the Copernicus Global Land Service over Lombardia
<p>Sentinel-3 NDVI Analysis Ready Data (ARD) (C_GLS_NDVI_20220101_20220701_Lombardia_S3_2.nc) product provided by the Copernicus Global Land Service [3]. The file C_GLS_NDVI_20220101_20220701_Lombardia_S3_2_masked.nc is derived from C_GLS_NDVI_20220101_20220701_Lombardia_S3_2.nc but values have been scaled (raw_value * ( 1/250) - 0.08) and values lower then -0.08 and greater than 0.92 have been removed (set to missing values).</p> <p>The original dataset can also be discovered through the OpenEO API[5] from the CGLS distributor VITO [4]. Access is free of charge but an <a href="https://aai.egi.eu/">EGI registration</a> is needed.</p> <p>The file called Italy.geojson has been created using the Global Administrative Unit Layers <a href="https://data.apps.fao.org/map/catalog/srv/eng/catalog.search#/metadata/9c35ba10-5649-41c8-bdfc-eb78e9e65654">GAUL G2015_2014</a> provided by FAO-UN (see <a href="https://data.apps.fao.org/map/catalog/srv/api/records/9c35ba10-5649-41c8-bdfc-eb78e9e65654/attachments/GAUL2015_Documentation.zip">Documentation</a>). It only contains information related to Italy.</p> <p> </p> <p>Further info about drought indexes can be found in the Integrated Drought Management Programme [5]</p> <p>[1] <a href="https://www.sciencedirect.com/science/article/abs/pii/027311779500079T">Application of vegetation index and brightness temperature for drought detection</a> [2] <a href="https://en.wikipedia.org/wiki/Normalized_difference_vegetation_index">NDVI</a> [3] <a href="https://land.copernicus.eu/global/index.html">Copernicus Global Land Service</a> [4] <a href="https://vito.be/en">Vito</a> [5] <a href="https://openeo.org/">OpenEO</a> [5] <a href="https://www.droughtmanagement.info/indices">Integrated Drought Management</a></p>
crowdsourced body parameters of workshop attendants at the Helmholtz MT ARD ST3 meeting
<p>This data set was crowdsourced at the 2021 Helmholtz MT ARD ST3 meeting from attendants of the Machine Learning Tutorial on Sep 30, 2021. For more details on the event, see<br> https://indico.desy.de/event/28823/</p> <p>The CSV contains 4 columns:</p> <p>- is_female : fill with 1 if participant is female, filled with 0 if not female</p> <p>- shoesize_europe : your european shoesize</p> <p>- weight_kg : your weight in kilograms</p> <p>- height_cm : your height in centimeters</p> <p>Participants were encouraged to +1 or -1 to individual body properties in case they do not feel confident providing their true numbers.</p>
Echinochloa oryzoides (Ard.) Fritsch (BR0000015241269V)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Historical time-series reconstruction benchmark dataset of Landsat bi-monthly aggregates from GLAD ARD-2 at 30-m resolution with stratified sampling based on ESA CCI
<h2>Description</h2> <p>Historical time-series reconstruction benchmark dataset presented here is designed for evaluating and comparing the performance of time series reconstruction methods in the context of land cover change detection. The dataset is based on the European Space Agency Climate Change Initiative (ESA CCI) land cover dataset, which has been aggregated into 18 classes to facilitate analysis. The dataset includes information on land cover dynamics from 2000 to 2020, focusing on identifying and characterizing changes in land cover over time.</p> <h3><strong>Data Collection and Processing:</strong></h3> <p>The dataset is derived from the ESA CCI land cover dataset, which provides information on land cover classes at a global scale. The original dataset, containing 37 land cover classes, was aggregated into 18 classes based on similarity. Pixels with stable land cover over the study period and pixels with one or multiple land cover changes were identified and grouped into strata for sampling purposes.</p> <p>Sampling points were selected using a stratified sampling design, ensuring representation across different land cover classes and change scenarios. Approximately 2600 points were selected from each stratum, resulting in a total of 51,978 sampling points. The selected points were uniformly distributed along the strata, with spatial variations accounted for.</p> <p>Bimonthly time series data were extracted for each sampling point from 1997 to 2022, capturing temporal dynamics in land cover. Artificial gaps were introduced into the time series data to simulate real-world data loss, allowing for the evaluation of time series reconstruction methods under varying gap densities.</p> <p>The time series values were extracted from Landsat GLAD imagery using the specified spectral bands, including blue, green, red, NIR, SWIR1, SWIR2, and thermal bands. Additionally, a clear quality band was also extracted.</p> <h3>Data Details</h3> <ul> <li><strong>Time Period:</strong> 1997-01-01 to 2022-12-31</li> <li><strong>Type of Data: </strong>R data frame / Geopackage points.</li> <li><strong>Collection/Derivation:</strong> Derived from Landsat ARD v2, processed with Scikit-map.</li> <li><strong>Coordinate Reference System:</strong> EPSG:4326</li> <li><strong>Bounding Box:</strong> All the globe</li> <li><strong>File Format:</strong> RDS</li> </ul> <p> </p> <h3><strong>Reclassified Classes of ESA CCI Land Cover Dataset</strong></h3> <table> <tbody> <tr> <td> <div> <div> <p><strong>Aggregated Class Code</strong></p> </div> </div> </td> <td> <div> <div> <p><strong>Aggregated Class Label</strong></p> </div> </div> </td> <td> <div> <div> <p><strong>Original ESA CCI Classes</strong></p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>10</p> </div> </div> </td> <td> <div> <div> <p>Cropland rainfed</p> </div> </div> </td> <td> <div> <div> <p>10, 11, 12</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>30</p> </div> </div> </td> <td> <div> <div> <p>Mosaic cropland | natural vegetation</p> </div> </div> </td> <td> <div> <div> <p>30, 40</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>50</p> </div> </div> </td> <td> <div> <div> <p>Tree cover broadleaved evergreen</p> </div> </div> </td> <td> <div> <div> <p>50</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>60</p> </div> </div> </td> <td> <div> <div> <p>Tree cover broadleaved deciduous</p> </div> </div> </td> <td> <div> <div> <p>60, 61, 62</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>70</p> </div> </div> </td> <td> <div> <div> <p>Tree cover needleleaved evergreen</p> </div> </div> </td> <td> <div> <div> <p>70, 71, 72</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>80</p> </div> </div> </td> <td> <div> <div> <p>Tree cover needleleaved deciduous</p> </div> </div> </td> <td> <div> <div> <p>80, 81, 82</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>90</p> </div> </div> </td> <td> <div> <div> <p>Tree cover mixed leaf type</p> </div> </div> </td> <td> <div> <div> <p>90</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>100</p> </div> </div> </td> <td> <div> <div> <p>Mosaic tree and shrub | herbaceous cover</p> </div> </div> </td> <td> <div> <div> <p>100, 110</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>120</p> </div> </div> </td> <td> <div> <div> <p>Shrubland</p> </div> </div> </td> <td> <div> <div> <p>120, 121, 122</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>150</p> </div> </div> </td> <td> <div> <div> <p>Sparse vegetation</p> </div> </div> </td> <td> <div> <div> <p>150, 151, 152, 153</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>160</p> </div> </div> </td> <td> <div> <div> <p>Tree cover flooded</p> </div> </div> </td> <td> <div> <div> <p>160, 170</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>180</p> </div> </div> </td> <td> <div> <div> <p>Shrub or herbaceous cover flooded</p> </div> </div> </td> <td> <div> <div> <p>180</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>200</p> </div> </div> </td> <td> <div> <div> <p>Bare areas</p> </div> </div> </td> <td> <div> <div> <p>200, 201, 202</p> </div> </div> </td> </tr> </tbody> </table> <p>In the table, each row represents a reclassified land cover class, identified by a unique code. The 'Original ESA CCI Classes' column lists the specific land cover classes from the European Space Agency Climate Change Initiative dataset that are grouped together to form each broader category. Note that land cover classes not listed in this table were retained in their original value and were not reclassified.</p> <h3><strong>File Format</strong></h3> <p>The dataset comprises observations spanning from January 1997 to November 2022, capturing data for 51,978 samples.</p> <ul> <li>blue.rds: Time series data for the blue spectral band.</li> <li>green.rds: Time series data for the green spectral band.</li> <li>red.rds: Time series data for the red spectral band.</li> <li>nir.rds: Time series data for the near-infrared (NIR) spectral band.</li> <li>swir1.rds: Time series data for the shortwave infrared 1 (SWIR1) spectral band.</li> <li>swir2.rds: Time series data for the shortwave infrared 2 (SWIR2) spectral band.</li> <li>thermal.rds: Time series data for the thermal infrared band.</li> <li>clear.rds: Time series data for the clear quality band, used for masking out cloudy observations.</li> </ul> <p>How open the files in R:</p> <p><code>blue <- readRDS("blue.rds")</code></p> <p>To open the files in Python, you need to the <code>pyreadr</code> library:</p> <p><code>import pyreadr</code><br><code>blue = pyreadr.read_r('blue.rds')</code></p> <p> </p>
Fig. 9. Core ARD 3 in The Use of Testate Amoebae in Monitoring Peatland Restoration Management: Case Studies from North West England and Ireland
Fig. 9. Core ARD 3 selected percentage testate amoebae diagram, data are presented as percentages of the total testates in each level. The diagram has been subdivided into zones to better aid interpretation. Note that in the older literature (including all the more accessible identification guides) Archerella flavum is refered to as Amphitrema flavum.
Fig. 8. Core ARD 2 in The Use of Testate Amoebae in Monitoring Peatland Restoration Management: Case Studies from North West England and Ireland
Fig. 8. Core ARD 2 selected percentage testate amoebae diagram, data are presented as percentages of the total testates in each level. The diagram has been subdivided into zones to better aid interpretation. Note that in the older literature (including all the more accessible identification guides) Archerella flavum is refered to as Amphitrema flavum.
Fig. 7. Core ARD 1 in The Use of Testate Amoebae in Monitoring Peatland Restoration Management: Case Studies from North West England and Ireland
Fig. 7. Core ARD 1 selected percentage testate amoebae diagram, data are presented as percentages of the total testates in each level. The diagram has been subdivided into zones to better aid interpretation. Note that in the older literature (including all the more accessible identification guides) Archerella flavum is refered to as Amphitrema flavum and Padaungiolla lageniformis is called Nebela lageniformis.
Sagina apetala Ard. subsp. apetala L. (BR0000014453168)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Sagina apetala Ard. subsp. apetala L. (BR0000012276806)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Sagina apetala Ard. subsp. apetala L. (BR0000010036525)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Sagina apetala Ard. subsp. apetala L. (BR0000012574513)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Sagina apetala Ard. subsp. apetala L. (BR0000012276974)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Sagina apetala Ard. subsp. apetala L. (BR0000012277070)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Sagina apetala Ard. subsp. apetala L. (BR0000015265272V)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Sagina apetala Ard. subsp. apetala L. (BR0000012406777)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Sagina apetala Ard. subsp. apetala L. (BR0000014434549)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Sagina apetala Ard. subsp. apetala L. (BR0000012220946)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Sagina apetala Ard. subsp. apetala L. (BR0000012574414)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Sagina apetala Ard. subsp. apetala L. (BR0000010036655)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
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)
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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.