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114 results for “WP2”
ECOBREED WP2 durum wheat data related to Kuzmanovic et al. (2020)
<p>Agronomic data (Sheet 1) and quality data (Sheet 2) of durum wheat (Triticum durum) check varieties and durum wheat breeding lines with multiple alien gene introgressions. The data are related to the publication of Kuzmanovic et al. (2020) Agronomy 10, 486. doi:10.3390/agronomy10040486</p>
Pre-processed data of atlas in EUCP-WP2
<p>Outputs from the probabilistic projection methods developed or assessed in the European Climate Projection system (EUCP) Horizon2020 project. The data can be previewed through our <a href="\"https://eucp-project.github.io/atlas/\"">interactive atlas</a>.</p> <p> </p> <p>For more information, see the <a href="\"https://eucp-project.github.io/atlas/about\"">atlas about page</a>, or the corresponding <a href="\"https://eucp-project.github.io/storyboards/atlas/1_intro\"">storyboard</a>.</p> <p> </p> <p><strong>Preprocessed data of Atlas in EUCP-WP2</strong></p> <p>We provide some notebooks that check the original/raw data, fix/add the metadata using <a href="\"https://cfconventions.org/Data/cf-conventions/cf-conventions-1.9/cf-conventions.html\"">CF-conventions</a> and save data in a NetCDF format. See <a href="\"https://github.com/eucp-project/atlas/blob/main/python/README.md\"">https://github.com/eucp-project/atlas/blob/main/python/README.md</a>.</p> <p>For two of the methods, REA and ClimWIP, pre-calculated weights have also been included. Note that these weights are only valid in the context of this specific model ensemble. Therefore, the original (pre-processed) model data is published together with the weights.</p> <p>The pre-processed data follows the following standards:</p> <p><strong>coordinates</strong></p> <ul> <li>climatology_bounds (climatology_bounds) datetime64[ns] ['2050-06-01', '2050-09-01', '2050-12-01', '2051-03-01']</li> <li>time (time) (datetime64[ns]) [2050-07-16 2051-01-16] # "JJA", "DJF"</li> <li>latitude (lat) (float64) [30, ..., 75]</li> <li>longitude (lon) (float64) [-10, ..., 40]</li> <li>percentile (percentile) (int64) [10, 25, 50, 75, 90]</li> </ul> <p><strong>variables</strong></p> <ul> <li>tas (time, latitude, longitude, percentile) (float64)</li> <li>pr (time, latitude, longitude, percentile) (float64)</li> </ul> <p><strong>attributes</strong></p> <p>The attributes of variables and coordinates are defined as:</p> <ul> <li>"tas": {<br> "description": "Change in Air Temperature",<br> "standard_name": "Change in Air Temperature",<br> "long_name": "Change in Near-Surface Air Temperature",<br> "units": "K",<br> "cell_methods": "time: mean changes over 20 years 2041-2060 vs 1995-2014",<br> },</li> <li>"pr": {<br> "description": "Relative precipitation",<br> "standard_name": "Relative precipitation",<br> "long_name": "Relative precipitation",<br> "units": "%",<br> "cell_methods": "time: mean changes over 20 years 2041-2060 vs 1995-2014",<br> },</li> <li>"latitude": {"units": "degrees_north", "long_name": "latitude", "axis": "Y"},</li> <li>"longitude": {"units": "degrees_east", "long_name": "longitude", "axis": "X"},</li> <li>"time": {<br> "climatology": "climatology_bounds",<br> "long_name": "time",<br> "axis": "T",<br> "climatology_bounds": ["2050-6-1", "2050-9-1", "2050-12-1", "2051-3-1"],<br> "description": "mean changes over 20 years 2041-2060 vs 1995-2014. The mid point 2050 is chosen as the representative time.",<br> },</li> <li>"percentile": {"units": "%", "long_name": "percentile", "axis": "Z"},</li> </ul> <p>The attributes of the data is defined as:</p> <ul> <li>"description": "Contains modified <code>institute</code> <code>method</code> data used for Atlas in EUCP project.",</li> <li>"history": "original <code>institute</code> <code>method</code> data files ...",</li> </ul> <p><strong>output file names</strong></p> <p>output_file_name = <code>prefix_activity_institution-id_source_method_sub-method_cmor-var</code></p> <p>example: atlas_EUCP_CNRM_CMIP6_KCC_cons_tas.nc</p> <p><strong>Reference</strong>:</p> <p><a href="\"https://eucp-project.github.io/atlas/about\"">https://eucp-project.github.io/atlas/about</a></p>
WP2_SIM-TST_DSB-SSBchirpPerformance_V1
<p>WP2_SIM_DSB-SSBperformance_V1.xslx</p> <p>WP2_TST_VCSEL20GHz-S-parametersBTB_V1.txt</p> <p>WP2_TST_VCSEL20GHz-S-parameters10km_V1.txt</p> <p>WP2_TST_VCSEL20GHz-S-parameters24km_V1.txt</p> <p>WP2_TST_VCSEL20GHz-S-parameters34km_V1.txt</p> <p>The results of the simulations performed to assess the performance of a directly-modulated source in different chirp conditions are reported in the Excel file. A standard dual sideband system and a single sideband condition are considered.</p> <p>The S-parameters of a short-cavity high-bandwidth VCSEL measured at various lengths (BTB, 10 km, 24 km, 34 km) are reported in the .txt files. The measured S-parameters are useful for calculating the transfer function of the SM fiber due to the interplay between the source chirp and the fiber chromatic dispersion, needed to obtain the chirp parameters <em>alfa</em> and <em>k</em> of the source.</p>
SOILCARE_database1_WP2_SICS_aspects
<p>This Excel file contains the information gathered from literature searches regarding meta-analysis studies which published results on the impact of soil improving cropping systems. These impacts were considered for 10 crop husbandry management practices and were split in 5 types (see below). Results pertain to the (relative) effect of a specific management practice compared to a reference (conventional) situation; mostly only the main effects were taken. Early results were used in the WP2 deliverable D2.1 (Oenema et al., 2017; see https://www.soilcare-project.eu/resources/deliverables). Later, results were added due to the increase of new information in the literature. All gathered information is available in sheet 'Table', and formed the basis of a scientific paper (in preparation) to be published in a special issue dedicated to SoilCare.</p> <p>Crop Husbandry management practices:<br> 1. Crop type & crop rotations, including intercropping, cover crops and perennial crops<br> 2. Nutrient management<br> 3. Irrigation + fertigation<br> 4. (Controlled) Drainage<br> 5. Tillage<br> 6. Pest management<br> 7. Weed management<br> 8. Crop residue management & mulching<br> 9. Mechanization & technology<br> 10. Landscape management </p> <p>Areas of interest:<br> a) Agronomic effects (typically: yield, crop quality)<br> b) Soil quality & soil health<br> c) Resource use efficiency (mainly: water, nutrients)<br> d) Economic aspects<br> e) Environmental impacts (mainly: losses of greenhouse gases, and leaching)<br> Originally we also had 'Human health' as area of interest, but since hardly any information was available on this subject, this was no longer considered.</p>
ECOBREED WP2 T2.1 Winter common wheat (Triticum aestivum) - Late maturity group
<p>Description of the winter common wheat (Triticum aestivum) late maturity group nursery. Tested within T2.1 in Germany (by Secobra), Czech Republic (by Selgen) and Slovakia (by NPPC) in 2019/2020.</p>
ECOBREED WP2 T2.1 Winter durum wheat (Triticum durum) nursery
<p>Description of the winter durum wheat (Triticum durum) nursery. Tested within T2.1 in Austria (by BOKU), Hungary (by MTA-ATK) and Italy (by UNITUS). Results included from the season 2018/19.</p>
SMARTDEST DATASET WP2 V1
<p>SMARTDEST (https://smartdest.eu) is an EU-funded H2020 research project under the Grant Agreement no. 870753. It brings together 11 universities and 1 innovation centre from seven European and Mediterranean countries. It aims to develop innovative solutions in the face of the conflicts and externalities that are produced by tourism-related mobilities in cities, by informing the design of alternative policy options for more socially inclusive places in the age of mobilities.</p> <p>The SMARTDEST DATASET WP2 V1 includes data and indicators elaborated from different public sources (EUROSTAT, LFS, EU SILC) and some private sources that have been used to provide evidence on the territorial impacts of tourism mobilities and on exclusionary trends across the European space, which are collected in a technical report (SMARTDEST deliverable 2.2).</p> <p> </p> <p> </p>
Audio Commons WP2 - Responses to user survey (Deliverable 2.1)
<p>These are the responses gathered from the user survey described in Deliverable 2.1 of the Audio Commons project.</p> <p>The survey contained 24 questions asking creatives working in music industry about various subjects like demographics, workflows they use and metadata they would like to use when searching for new audio content on the Web.</p> <p>The file formResponses.csv contains the actual responses, column names in the first row, fields separated by commas and enclosed in double quotes if needed (e.g., a comma was in the value).</p> <p>The Timestamp column contains the response timestamp, in DD/MM/YYYY hh:mm:ss format. </p> <p>Each other column is named as one of the survey questions and contain the corresponding responses.</p> <p>For the relevant context, please check the Audio Commons web page (http://www.audiocommons.org/) and specifically Deliverable 2.1.</p>
UWB-IODA project: Datasets associated to WP2
<p>The signal acquisition is carried out using an IR-UWB sensor module Xethru X4, from Novelda, Norway, having the following parameters:<br> Output power −12.6 dBm<br> Center frequency 8.748 GHz<br> Pulse repetition frequency 400 MHz<br> Bandwidth (–10 dB) 2.95 GHz<br> Range resolution 6.4 mm<br> Beamwidth 65°<br> Staggered PRF sequence length 220 cycles<br> No. of antenna arrays per radar chip 1 Tx & 1 Rx</p> <p>The transmitted signals reflected by the human back contain the information related to the motion caused by the human heart beats. The backscattered signals are then stored in the PC through a micro-USB cable. The distance of the human to the radar sensor is kept between 0.5 and 1m due to the short distance between the driver and the seat. During the experiments, the subjects were resting for most of the time and also talking and moving slightly their bodies for certain time intervals.</p> <p>The five data files corresponds to the five subjects involved in the experiments, having the following characteristics:</p> <p>Gender Age (years) Height (cm) Weight (kg) <br> M 33 182 72<br> M 21 173 73<br> M 28 174 77<br> F 26 170 60<br> M 24 165 62</p> <p>All of these individuals were healthy and without any special health conditions or disabilities.</p>
MSCA-IF-896925_MaMo_WP2-A_GIS
<p>This dataset originates from the research activities (WP2-WP3) carried out during the H2020- MSCA-IF 2019 EU-funded project "Materializing Modernity - Socialist and Post-socialist Rural Legacy in Contemporary Albania (MaMo)", GA. no. 896925, implemented by Federica Pompejano (MSCA-IF Researcher) at the Instituti i Antropologjise Kulturore dhe i Studimit te Artit (Akademia e Studimeve Albanologjike), Tirana, Albania. This dataset contains five .qgz files and a set of georeferenced raw data (JPG pictures and GPS tracks) collected during the MaMo fieldwork research activities (WP3). This dataset has been curated by Dr Federica Pompejano. The mapping of buildings and landscapes carried out during the fieldwork activities aimed at providing extensive visual documentation of the rural landscape of the five representative macro-areas selected for the MaMo research project. The mapping activity was carried out by means of the QFIELD App and then synchronized in QGIS Desktop 3.18.2. The QGIS Projects (WP2) have been created by Dr Federica Pompejano (IAKSA-ASA) in collaboration and with the support of Prof. Bianca Federici (Università di Genova - UNIGE) and Dr Ilaria Ferrando (Università di Genova - UNIGE). Unless otherwise specified, the data contained in this dataset are open for public disposal under the terms and conditions described in the CC BY-NC-SA 4.0 license. A copy of each QGIS project is deposited at the Scientific Archive of Ethnography and Folklore (IAKSA-ASA) where all MaMo materials are stored, preserved, and are available for consultation under the Archive's terms and conditions. The Scientific Archive of Ethnography and Folklore (IAKSA-ASA) is in Rruga Naim Frasheri 1, Tirana (Albania). E-mail contact is: iaksa@asa.edu.al.</p> <p>ACKNOWLEDGMENTS: This dataset and all its content are part of a project that has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 896925.</p>
MULTIPLIERS_WP2_Needs analysis in open schooling and interview guidelines_UCY_20231006_v2
<p>This dataset contains data on the review of needs analysis in open schooling. The dataset is composed mainly by the following items:</p> <ul> <li>description of surveyed persons (interviews transcripts; focus groups and written survey with open ending questions)</li> <li>contents of the semi‐structured interview protocol used for conducting both interviews and focus groups. The interview protocols were produced in local languages by partners who did not conduct the interviews in English.</li> <li>summary of the transcripts of interviews with experts in the selected case‐study regions (in English). Interviewees were asked to provide their views and perceptions on OSC network building and open schooling learning projects.</li> <li>summary of the transcripts of focus groups (in English). Each focus group was held in the local language of the beneficiary organising it, recorded with audio equipment and then transcribed. Focus groups were run in Germany, Cyprus, Spain, Slovenia and Sweden.</li> </ul>
Food4Me - personalised nutrition European 9 countries public acceptance survey - Dataset from WP2
<p>This data set consists of a large cross-country survey conducted for the EU funded project Food4Me in 2013. It contains determinants for consumer acceptance and intention to use of personalized nutrition advices based on different levels of specificity and personality of data –self-reported intake, blood analysis and DNA based analysis of optimal nutrition patterns.</p> <p>Data are provided in SPSS and interoperable .CSV format. Surveys and metainformation are added in interoperable .RTF format.</p> <p>The underlying survey is present in 8 languages English, German, Spanish, Norwegian, Dutch, Portuguese, Greek and Polish. Used scales are sourced to the originals in the description file. </p>
ΝEW ABC_ WP2 _ Task 2-1 Mapping the NEW ABC platform requirements_20230609_v1
<p>This data set consists of an analysis of users of existing platforms of other European projects, and the level of engagement they create, to inform the creation and successful use of NEW ABC project platform. This analysis will assess the number and typology of users who access these platforms and what kind of information and material is available to them. The data set will contain numerical and textual tabular data converted into digital format (survey made by online interviews with platform users), quantitative and qualitative data.</p> <p>Content of the files:</p> <ul> <li>file <strong><em>ΝEWABC_WP2_T2-1_Mapping the NEW ABC platform requirements_20230609_v1.csv </em></strong>contains a description of the data collected for each Horizon project relevant to the NEW ABC.</li> </ul> <p> </p> <ul> <li>file <strong>README_</strong><strong><em> ΝEWABC_WP2_T2-1_Mapping the NEW ABC platform requirements_20230609_v1.rtf</em></strong> contains basic information on the dataset uploaded.</li> </ul>
InTheMED WP2 Data Archive - Groundwater Quality Literature Review
<p>The data archive InTheMED_WP2_DS_GWQualityLitReview is part of Task 2.2 “Review and collect the available groundwater quantity and quality data sets in the MED region” and contains a literature review of groundwater quality data collected from various sites in Mediterranean countries. The data includes measurements of different water quality parameters, providing valuable insights into the characteristics of groundwater in different regions. The dataset was compiled from a literature review of published research articles.</p>
ECOBREED WP2 T2.3 Wheat (Triticum aestivum) - Allelopathic activity nursery
<p>Description of the common wheat (Triticum aestivum) allelopathic activity nursery. Tested within T2.3 in Spain (by UVIGO).</p>
PROLIFIC_790157_IRIS_WP2_MAE_protein
<p>Technical data regarding the extraction of protein assisted by microwave.</p>
Dataset of the WP2 Cranfield
<p>C-scan dataset of impact damages on composite coupons and laminates. This dataset contains<br> ultrasonic full-waveform Phased Array (PA) data that were acquired during the experimental<br> assessment of impact damages on composite coupons and laminate conducted by Cranfield in the<br> context of project CompInnova: An Advanced Methodology for the Inspection and Quantification of<br> Damage on Aerospace Composites and Metals using an Innovative Approach (H2020 FET OPEN, Grant<br> Agreement No. 665238). The C-scan data on composite coupons is acquired by using 5MHz wheel probe2 Phased Array (PA)<br> the transducer is in raw CSV format and processed C-scan in PNG image format. The 10MHz PA transducer and novel elastomer wedge which is a part of PA module developed at Cranfield and novel mist couplant delivery are used for scanning both coupons and laminates and raw data is in CSV format and processed data in TIFF image format. Also, a document on information about material and layup of coupons and laminate is also included. The data were used in the deliverable D2.3, D2.4 and D7.1 and D7.2.</p>
Like-a-PRO: WP2:T2.2: Krill protein (Olypep) processing options and attempts
<p>The flow diagram provides an early stage overview of the processes to be further mapped in detail for the Techno-economic analysis (TEA) (WP2:T2.2) and True price method (WP7) in the Like-a-PRO project. The processes are a result of the ongoing work for krill optimization (WP2: T2.1)</p>
ATMO-ACCESS WP2_Success Story_TNA PIRAThe
<p>Users across Europe and beyond have used ATMO-ACCESS services to enhance their research projects. We are collecting stories from users to illustrate their access experience and make the benefits of transnational access more tangible to new users.</p> <p>In this video, you will discover Martin Ramacher - Postdoctoral scientist in the Department of Chemical Transport Modelling at the <a href="https://www.linkedin.com/company/hereon-helmholtz/" target="_self">Helmholtz-Zentrum Hereon-</a>'s ATMO-ACCESS success story. He explains his project PIRAThe " Particulates infiltrating residencies in Athens" undertaken at ATMOS, National observatory of Athens. You will learn how this groundbreaking European project facilitates access to atmospheric research infrastructures and propels scientific innovation.</p> <p>On the programme:</p> <ul> <li>Dr. Martin Ramacher's entertaining testimonial</li> <li>Overview of the scientific results made possible by ATMO-ACCESS</li> <li>Focus on an exposure study based on indoor environment</li> </ul> <p>Find out more here : <a href="https://www.atmo-access.eu/meetourusers/">https://www.atmo-access.eu/meetourusers/</a></p>
WP2: Photochemical model of planetary atmospheres driven by high-energy stellar irradiation
<p>Modeling results of irradiated planetary atmospheres (Locci et al. 2021, <em>Extreme Ultraviolet and X-ray Driven Photochemistry of Gaseous Exoplanets</em>, PSJ, submitted). See <em>Introduction</em> and <em>Readme_Reference_model</em> for more details.</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.