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18 results for “MADIA”
MADIA_732678_SCRIBA_Bonding images and protocol_01
<p>ONLY METADATA</p> <p>Collection of protocol and images on bonding procedure of:</p> <ul> <li>PDMS on several substrates (glass, silicon wafer, SU-8);</li> <li>a polystyrene transparent top layer on sensors substrate.</li> </ul> <p>Data produced between from April 2017 to November 2017.</p>
MADIA_732678_SERGAS_samples_01
<p>MEATADATA ONLY<br> <br> Clinical and biological samples (blood) obtained from Parkinson patients and clinical and biological samples (blood and cerebrospinal fluid_CSF) samples obtained from the Alzheimer patients.</p>
MADIA_732678_SCRIBA_AFM images_01
<p>ONLY METADATA<br> Collection of AFM Images of magnetic nanoparticles in microfluidic channels. Data produced between June 2017 to July 2017</p>
MADIA_732678_SCRIBA_Finite elements simulation_01
<p>ONLY METADATA<br> <br> Collection of .mph files with all the performed simulation models on transport of diluted species in a background fluid. Dataset is composed of documents with the description of physics boundary conditions, model equation settings and results. Furthermore, dataset is composed of relevant scientific articles used as references. Data produced between from January 2017 to May 2017.</p>
MADIA_732678_SCRIBA_CAD 2Dand3D_01
<p>ONLY METADATA</p> <p>Collection of CAD drawings in scale (2D and 3D) and images of:</p> <ul> <li>microfluidic components: microfluidic channels, aggregation chamber, preconcentration chamber;</li> <li>3D printed rigid cartridge equipped with liquid ports i.e. connection with actuating pumps for aggregation chamber prototype (linear setup);</li> <li>elastomeric valves realized through operative protocol for thermoforming of microfluidic devices;</li> <li>master realized through UV lithography performed upon structural SU8 layer;</li> <li>master realized through 3D printer for PDMS replica;</li> <li>prototype of the introduction chamber realized and integrated with the support for the actuators and valves.</li> </ul> <p>Data produced between from February 2017 to December 2018</p>
MADIA_732678_INN_patents_02
<p>Collection of .csv files with raw patent data, analysed to obtain landscaping information on technologies overlapping with the project. Used to produce the deliverable “Patent and scientific literature study M24” D7.6 Patent landscaping. The data where obtained on <a href="http://www.thelens.org">www.thelens.org</a></p> <p>The queries used to obtain the data were:</p> <p>A</p> <p>title:(neurodegenerative early diagnosis) OR abstract:(neurodegenerative early diagnosis) OR claims:(neurodegenerative early diagnosis) AND classification_ipcr:((G01N*) OR (H01L*))</p> <p>B</p> <p>title:(functionalized magnetic nano*) OR abstract:(functionalized magnetic nano*) OR claims: (functionalized magnetic nano*) AND classification_ipcr:((G01N*) OR (B82Y*))</p> <p>C</p> <p>title:((microfluidic) AND (chamber OR apparatus OR device)) OR abstract:((microfluidic) AND (chamber OR apparatus OR device)) OR claims:((microfluidic) AND (chamber OR apparatus OR device)) AND classification_ipcr:((B01J*) OR (B81B*))</p> <p>D</p> <p>title:((magnetic nano*) AND (sensor OR detector)) OR abstract:((magnetic nano*) AND (sensor OR detector)) OR claims: ((magnetic nano*) AND (sensor OR detector)) AND classification_ipcr:((G01R*) OR (G11B*) OR (H01L*) OR (H01F*))</p>
Meteorological variables for Agriculture: a Dataset for the Italian Area (MADIA)
<p> </p> <p>The dataset is the supplementary material for the following journal paper:</p> <p>Parisse B.*, Alilla R., Pepe A.G., De Natale F., <em>MADIA - Meteorological variables for Agriculture: a Dataset for the Italian Area,</em> Data in Brief, 46 (2023), 108843, <a href="http://doi.org/10.1016/j.dib.2022.108843">10.1016/j.dib.2022.108843</a>, (<a href="https://www.sciencedirect.com/science/article/pii/S2352340922010460">https://www.sciencedirect.com/science/article/pii/S2352340922010460</a>)</p> <ol> </ol> <p> </p> <p><strong>Abstract</strong></p> <p>The <strong>MADIA gridded dataset</strong> provides the series of the main <strong>agro-meteorological </strong>variables derived from ERA5 hourly surface data, across the Italian domain for the period <strong>1981-2022</strong>, and their respective 1981-2010 and 1991-2020 <strong>climate normals</strong>,<strong> </strong>as well as the following statistics on the 30-year dekadal values of each variable: absolute minimum and maximum, 5<sup>th</sup>, 10<sup>th</sup>, 50<sup>th</sup>, 90<sup>th</sup>, 95<sup>th</sup> percentiles. Temporal and spatial resolutions are <strong>10-daily</strong> and <strong>0.25 degrees</strong> respectively. The dataset contains time series of minimum, average and maximum air temperature, minimum and maximum air relative humidity, wind speed, solar radiation, precipitation and reference evapotranspiration according to the FAO Penman-Monteith method. The dataset is provided in both <strong>NetCDF </strong>and <strong>csv </strong>format. In addition, discovery and description metadata are provided. In order to facilitate the data reuse for computing statistics at Italian <strong>NUTS 2 and 3</strong> levels, a complementary vector file is provided which reports the cell weight in terms of fraction covered of each administrative unit considered. Another vector file is included with the <strong>ERA5 cell polygons</strong> covering the Italian country for visualizing and mapping csv data. </p> <p>A <strong>daily version of the MADIA dataset</strong> (only in csv format) is also available on Zenodo at <a href="http://doi.org/10.5281/zenodo.7621453">https://doi.org/10.5281/zenodo.7621453</a>.</p> <p>Both MADIA datasets will be periodically updated.</p> <p><strong>Attached content</strong></p> <p>A ZIP archive composed by the following folders</p> <ol> <li>nc_data: annual time series from 1981 to 2022 and climate normals (1981-2010 and 1991-2020) in NetCDF format</li> <li>csv_data: annual time series from 1981 to 2022 and climate normals (1981-2010 and 1991-2020) in csv format</li> <li>metadata: discovery and description metadata </li> <li>shp_data: two complementary vector layers with the NUTS2-3 cover fractions and the ERA5 cell polygons for Italy</li> </ol> <p><strong>Acknowledgments</strong></p> <p>This work was supported by the Italian Ministry of Agricultural, Food and Forestry Policies (AgriDigit-Agromodelli, DM n. 36502 of 20/12/2018)</p>
Madia elegans D.Don (BR0000024747967)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Meteorological variables for Agriculture: daily time series for the Italian Area (MADIA daily)
<p> </p> <p><strong>Abstract</strong></p> <p>The <strong>MADIA daily gridded dataset</strong> provides the series of the main <strong>agro-meteorological </strong>variables derived from ERA5 hourly surface data, with a spatial resolution of 0.25 degrees, across the Italian domain for the period <strong>1981-2022</strong>. The dataset contains time series of minimum, average and maximum air temperature, minimum and maximum air relative humidity, wind speed, solar radiation, precipitation and reference evapotranspiration according to the FAO Penman-Monteith method. Data is provided at daily temporal resolution and in <strong>csv </strong>format (every cell is identified by the latitude/longitude coordinates of its centre). The dataset is annotated with discovery and description metadata. A vector file is included with the <strong>ERA5 cell polygons </strong>covering the Italian country for visualizing and mapping csv data. In order to facilitate the data reuse for computing statistics at Italian NUTS 2 and 3 levels, a complementary vector file which reports the cell weight in terms of fraction covered of each administrative unit considered, as well as its altitude, is provided in:</p> <ul> <li>Parisse Barbara, Alilla Roberta, Pepe Antonio Gerardo, & De Natale Flora. (2022). <em>Meteorological variables for Agriculture: a dataset for the Italian Area (MADIA)</em> [Data set]. Zenodo. <a href="http://10.5281/zenodo.6868944">https://doi.org/10.5281/zenodo.6868944</a> </li> </ul> <p>Further details on methods applied for data processing are available in:</p> <ul> <li>Parisse B., Alilla R., Pepe A.G., De Natale F., <em>MADIA - Meteorological variables for Agriculture: a Dataset for the Italian Area</em>, Data in Brief, 46 (2023), 108843, <a href="https://doi.org/10.1016/j.dib.2022.108843">10.1016/j.dib.2022.108843</a>, (<a href="https://www.sciencedirect.com/science/article/pii/S2352340922010460">https://www.sciencedirect.com/science/article/pii/S2352340922010460</a>)</li> </ul> <p>The MADIA daily dataset will be periodically updated.</p> <p><strong>Attached content</strong></p> <p>A ZIP archive composed by the following folders:</p> <ol> <li>csv_data: daily time series for each year from 1981 to 2022 in csv format</li> <li>metadata: discovery and description metadata</li> <li>shp_data: a complementary vector layer with the ERA5 cell polygons for Italy</li> </ol> <p><strong>Acknowledgments</strong></p> <p>This work was supported by the Italian Ministry of Agricultural, Food and Forestry Policies (AgriDigit-Agromodelli, DM n. 36502 of 20/12/2018)</p>
H. J. Andrews Experimental Forest site, station Andrews Watershed 1, study of plant cover of Madia gracilis in units of percent on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from H. J. Andrews Experimental Forest (AND) contains plant cover of Madia gracilis measurements in percent units and were aggregated to a yearly timescale.
H. J. Andrews Experimental Forest site, station Andrews Watershed 3, study of plant cover of Madia gracilis in units of percent on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from H. J. Andrews Experimental Forest (AND) contains plant cover of Madia gracilis measurements in percent units and were aggregated to a yearly timescale.
MADIA_732678_UniBi_magnetores_01
<p>Collection of text files containing the measured value during magnetosensor characterisation</p>
MADIA_732678_INN_patents_01
<p>Raw data on patents use to conduct a landscape analysis</p>
MADIA_732678_CNR-BO_EXP_CNRSERGAS_01
<p>Collection of raw experimental data sets with related technical report of <em>in-vitro</em> experiments on nanoparticles-amyloids aggregation in the aggregation chamber performed at the clinical laboratory of SERGAS</p>
MADIA_732678_CNR-BO-CAD_01
<p>CAD drawnings of mechanical components of the aggregation chamber in the two competing geometries currently evaluated. Files are stored in.ipt and .dwg format as generated by the used CAD/CAM software. For easy consultation all the technical drawings have been copied into .pdf format. File size are in the range of 100 KB-1MB</p>
MADIA_732678_CNR-BO_DIFF_01
<p>Data set derived from theoretical calculations on design of inductive sensors of magnetic nanoparticles. The sensors are intended as a reference system for the MADIA spintronic sensors in the high concentration regime. Reference are avaialable in the scientific literature as research articles or book chapters (open access)</p>
MADIA_732678_CNR-BO_AGGCH_01
<p>In-house realized experimental instrumentation and prototypes of aggregation chamber in two different geometries. Two mechanical linear actuators for controlled handling of magnetic nanoparticles in capillaries with speeds in the range of 100 μm/sec- 1 mm/sec and below 100 μm/sec, repectively. They are used to experimentally test operational parameters (speed, permanence time) and geometries (configuration, relative distance of magnets) of the aggregation chamber. A prototype of aggregation chamber in serpentine geometry (manufactured by Scriba Nanotechnologie srl). A prototype of aggregation chamber in helicoidal geometry in-house fabricated by numerical-controlled machines .</p>
MADIA_732678_EBRI_samplesNov292018_01
<p>No description available</p>
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