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6,766 results for “project”
Local Geohistory Project: Open Data
<p>The Local Geohistory Project aims to educate users and disseminate information concerning the geographic history and structure of political subdivisions and local government. This repository contains the data used to populate the <a href="https://www.localgeohistory.pro/en/">project website</a>. The tab-separated values (TSV) files containing the data are available in the <strong>data</strong> folder, and metadata is available in the <strong>metadata</strong> folder.</p> <p>Currently, the open dataset only contains information related to New Jersey and Pennsylvania, with several scattered events concerning neighboring jurisdictions, mostly that currently border either state.</p> <p>This repository does not contain the application code, which can be found in the <a href="https://github.com/localgeohistoryproject/application">Application repository</a>, nor does it contain the table data for the bundled <strong>calendar</strong> extension.</p>
End-user's survey results on needs and expectations for next- generation Energy Performance Certificates (H2020 X-tendo project)
<p>The SPSS data file consists of survey data from the X-tendo project on the end-user needs and expectations from next-generation energy performance certificates.</p>
Characterization of SRF (XRF portable analyser) prepared for an aluminium scrap pre-heating system (REVaMP project)
<p>Open access to experimental data generated by the REVaMP project (GA 869882, Horizon 2020, European Union) along the research of the combustion of a SRF, prepared from ASR, to be used as alternative fuel in a scrap pre-heater at an aluminium refinery plant. Research pertaining to WP1, Deliverable D1. <br> Underlying data for the publication Acha, E. et al. Combustion of a Solid Recovered Fuel (SRF) Produced from the Polymeric Fraction of Automotive Shredder Residue (ASR). Polymers 2021, 13, 3807. https://doi.org/10.3390/polym13213807. Data related to Figure 1 in the article.</p> <p>Subject: Representative samples of SRF were manually sorted into categories of plastics, wood, textile, foam and others, and directly analyzed by the Thermo Fisher Scientific portable analyser Niton™, X-Ray Fluorescence (XRF).</p>
Complementary material of the WorkStream 1 of project 4SECURail
<p>Deliverables, models, artifacts developed within WP2 of the Shift2Rail project 4SECURAil.</p>
Neolithic Settlements in Central Europe: Data from the Project 'Lifestyle as an Unintentional Identity in the Neolithic'
<p>This repository contains data set submitted to <strong>Journal of Open Archaeology Data</strong>. The data set originated in course of the Czech Science Foundation project n. 19-16304S entitled <strong>Lifestyle as an unintentional identity in the Neolithic</strong>.</p> <p>The data set comprises of over 2100 Neolithic settlement sites from Central Europe (mainly Czech Republic with small parts of Slovakia and Austria). Each site is is defined by spatial coordinates and information about relative chronology phase. The time span is 4900 BCE to 3300 BCE.</p> <p>The data set consists of the following files:</p> <ul> <li> <p><strong>sites.csv</strong> – is a main list of sites with unique identifiers in the id field, id starting with B means the site is from the eastern part of the Bohemia section of the study area and in case of id starting with M the site is from the Morava river catchment. Field orig_id contains identifier by which the site is referenced in cited works and field site contains the site name;</p> </li> <li> <p><strong>pot_traditions.csv</strong> – contains site ids, field chrono giving the general pottery tradition and field period listing occurence of the site in one of the nine time slices;</p> </li> <li> <p><strong>pot_groups.csv</strong> – has same fields as the previous file with the difference in chrono field that contains information on detailed pottery groups;</p> </li> <li> <p><strong>references.csv</strong> – list of references, where possible, the excavation reports are linked to their source in the Digital Archive of the Archaeological Map of the Czech Republic (<a href="https://digiarchiv.aiscr.cz/">https://digiarchiv.aiscr.cz/</a>). Column ref_id is linked through file references_sites.csv to the database of sites in sites.csv file.</p> </li> <li> <p><strong>references_sites.csv</strong> – connects files references.csv and sites.csv</p> </li> </ul> <p>Geodata (in S-JTSK / Krovak East North coordinate reference system):</p> <ul> <li> <p><strong>site_locations.gml</strong> and site_locations.xsd – settlement sites locations. The id field gives a unique identifier for each site, column accuracy gives how accurately the site location is defined, value 1 meaning accurate location (instrumentally measured), value 2 is precision in hundreds of meters, i.e. the site location is known by the local name, street name or so and value 3 means the location is not very accurate, in approx. 1 km range. The field surface is TRUE if the site is defined based on surface survey only and field altitude gives altitude in meters;</p> </li> <li> <p><strong>study_area.gml</strong> and study_area.xsd – polygon giving the borders of the area where data was initially collected;</p> </li> <li> <p><strong>regions.gml</strong> and regions.xsd – polygons giving the extent of the two studied regions, (1) eastern part of Bohemia and (2) Morava river drainage basin;</p> </li> <li> <p><strong>raw_material_sources.gml</strong> and raw_material_sources.xsd – locations of raw material sources as points or lines. Points are based on places where prehistoric procurement activities are known or the outcrops of the given raw materials are present. Lines give the border of the raw material occurrence in case of erratic flint or river courses, in which the raw materials can be procured. The rm column gives an abbreviated name of the raw material and type field is either l for chipped stone tools or p for polished stone tools.</p> </li> </ul> <p>Vocabularies:</p> <ul> <li> <p><strong>voc_periods.csv</strong> – contains period labels;</p> </li> <li> <p><strong>voc_pot_traditions.csv</strong> – contains pottery traditions labels, where possible, field periodo_link maps the period to AMCR Periods Vocabulary at Periodo (<a href="http://n2t.net/ark:/99152/p0wctqt">http://n2t.net/ark:/99152/p0wctqt</a>);</p> </li> <li> <p><strong>voc_pot_groups.csv</strong> – contains pottery groups labels, same fields as previous file;</p> </li> <li> <p><strong>voc_pot_groups_facets.csv</strong> – general labels for pottery groups;</p> </li> <li><strong>voc_raw_materials.csv</strong> – list of raw material abbreviations in the rm column of raw_material_sources.gml file with full names.</li> </ul>
Datasets for HiTIME project D2.3 and D2.4
<p>Development of capability for scanning tunneling microscopy combined with two-port near-field microwave scanning of topological insulator nanoribbons (Bi2Se3). Data used for HiTIMe deliverables D2.3 and D2.4.</p>
Data provided for the Preenacting Climate Change Scenarios project 2021
<p>CMIP6 model output data processed using the scripts provided here: https://github.com/lukasbrunner/preenact/</p>
Students' perceived obstacles with Forced Online Distance Learning during the CoVID-19 outbreak and their preferences to continue with the introduced teaching methods after the reopening of the University of Maribor [Project documentation]
<p>The outbreak of COVID -19 forced most universities into distance education. Three didacticians and researchers from the University of Maribor, Slovenia: Kosta Dolenc, Mateja Ploj Virtič and Andrej Šorgo formed a self-initiated initiative project group during the COVID -19 epidemic and started the first project with the working title: The Side Effects of Forced Online Distance Education (FODE).</p> <p>The aim of the second study, conducted during the first wave of the epidemic in March 2020, was to investigate the response of university students to the new situation. The project documentation provided for the Forced Online Distance Learning (FODL) consists of:</p> <ul> <li>abstract,</li> <li>instrument,</li> <li>copy of the descriptive statistics,</li> <li>and SPSS dataset.</li> </ul>
Forced Continuance Intention Model of Distance Online Teaching during CoVID-19 outbreak at University of Maribor, Slovenia [Project documentation]
<p>The outbreak of COVID -19 forced most universities into distance education. Three didacticians and researchers from the University of Maribor, Slovenia: Kosta Dolenc, Mateja Ploj Virtič and Andrej Šorgo formed a self-initiated initiative project group during the COVID -19 epidemic and started the project with the working title: The Side Effects of Forced Online Distance Education (FODE).</p> <p>The aim of the first study, conducted during the first wave of the epidemic in March 2020, was to investigate the response of university teachers to the new situation. The project documentation provided for the Forced Online Distance Teaching (FODT) consist of:</p> <ul> <li>abstract,</li> <li>instrument,</li> <li>copy of the descriptive statistics, and</li> <li>SPSS dataset.</li> </ul>
Educational transformation and network learning dataset – qualitative data from an international collaborative EU-project
<p>We are releasing our dataset of workshop outcomes acquired from the annual consortium conferences organized by the international “NextFood” consortium. The purpose of this project is to develop new ways of educating the future sustainability leaders of the agrifood sector, making sure that the professionals (farmers, advisers, businesses, students) have the right set of skills and competences needed to tackle the sustainability challenges we face ahead. Data gathering started from May 2018 yielding considerable amount of data on achievements, challenges and action plans related to educational transformation. This dataset will be updated by the time of project finalization. This work was funded by the European Union, through the Horizon 2020 project “NextFood”, Grant agreement No. 771738.</p>
Water risks to hydropower projects in the face of climate change
<p>This repository hosts the main outputs from an analysis using the <a href="https://waterriskfilter.org/">WWF Water Risk Filter</a> to demonstrate how one such tool can be used to screen for a variety of risks at a global scale, including risks to riverine ecosystems from both climate change and hydropower as well as risks to hydropower projects — and operators, owners, and investors — from climate change and potential regulatory or reputational risk arising from negative impacts to ecosystems. The study <a href="https://www.mdpi.com/2073-4441/14/5/721">Using the WWF Water Risk Filter to Screen Existing and Projected Hydropower Projects for Climate and Biodiversity Risks (DOI 10.3390/w14050721) </a>was published in the special issue of the MDPI journal Water: <a href="https://www.mdpi.com/journal/water/special_issues/hydrometeorological_hazards">"Hydro-Meteorological Hazards under Climate Change"</a>.</p> <p>This product incorporates data from the GRanD v1.3 database which is © Global Water System Project (2011), and from the FHReD database beta version, both datasets available at <a href="http://globaldamwatch.org/">globaldamwatch.org</a> . The source code used in this study is available at <a href="https://github.com/rafaexx/hydropowerClimateChange">https://github.com/rafaexx/hydropowerClimateChange</a></p> <p>See the interactive maps using this data at <a href="https://rcamargo.shinyapps.io/HydropowerClimateChange">https://rcamargo.shinyapps.io/HydropowerClimateChange</a></p>
Mapping Building BioData.pt Indicators against the performance and impact assessment frameworks for research infrastructures of OECD, ESFRI and RI-PATHS project
<p>"Buiding BioData.pt" indicators observed in international frameworks for performance and impact assessment of research infrastructures, namely, OECD, ESFRI and RI-PATHS.</p>
Air exchange rate dataset of the BELUVA project
<p>This dataset is extracted from the DFG (German Research Foundation) funded project BELUVA. The project is about the determination of air exchange rates in naturally ventilated barns and validation of prediction model.</p> <p>Series of numerical air flow simulations inside and around naturally ventilated barns were undergone for five different parameter, the length to width ratio of the barn (LW), the side curtains position (Curt), the incoming air temperature (T), the incoming air velocity magnitude (Vel) and the incoming air velocity direction (Theta), see the figures “parameters_tree” and “Convection_and_Temp-Vel-pair”. More details can be found in the following papers:</p> <p>On Finding the Right Sampling Line Height through a Parametric Study of Gas Dispersion in a NVB (<a href="https://doi.org/10.3390/app11104560">https://doi.org/10.3390/app11104560</a>)</p> <p>A Parametric Model for Local Air Exchange Rate of Naturally Ventilated Barns (<a href="https://doi.org/10.3390/agronomy11081585">https://doi.org/10.3390/agronomy11081585</a>)</p> <p>50 planes of equal distance inside the barn from 0.39 m to 11.66 m height have been designed. Each plan contains a grid of points 100 along the width of the barn and 2, 3 or 4 x 100 points along the length L=2, 3 or 4 x W respectively.</p> <p>Each file contains the coordinates of the grid points of one plan as well as the corresponding, ammonia concentration, carbon dioxide concentration and the Cartesian velocity components. The nomenclature of the file is as follows:</p> <p>Yplane_LW_Curt_Conv_Temp_Vel_Theta_Yx-yz</p> <p>LW: the length to width ratio, LW2, LW3 or LW4 corresponding to L=2, 3, 4 x W</p> <p>Curt: the curtain position, Open (without curtain), OpenUp (curtain from floor to middle opening height) or OpenDown (curtain from roof to middle opening height)</p> <p>Conv: the flow convection type, For (forced convection), Mix (mixed convection), Nat (natural convection)</p> <p>Temp: the temperature, written x-y meaning x.y in Celsius (°C), for example 30-0 means 30°C</p> <p>Vel: the air velocity magnitude, written x-y meaning x.y in m/s, for example 2-7 means 2.7 m/s</p> <p>Theta: the air inlet angle, 0, 45 or 90 deg, see figure “Inlet-angle_and_Length-to-width-ratio”</p> <p>Yx-yz: x-yz referring to the height of the plane x.yz, for example 3-15 means 3.15 m</p> <p>The python script “contour.py” represents an example of use of the dataset. Here it serves to obtain contour plots of the temperature, velocity magnitude and ammonia and carbon dioxide concentrations from any case LW_Curt_Conv_Temp_Vel_Theta_Yx-yz. Once the command “python contour.py” is given in the terminal, the user just needs to follow the instructions and the contour plots will be created in the folder “Plots”.</p>
Demo showing what RELIANCE project has achieved on Open Science, FAIR and EOSC
<p>This demo shows what we have achieved on Open Science and FAIR. </p> <p> </p> <p>- Starting from <a href="https://beta.explore.openaire.eu/">OpenAIRE EXPLORE</a>, we search for "Copernicus air quality" and find lots of resources, mostly publications and only 2 software. The reason is that to be "classified" as "Software", we have to add specific metadata when publishing.</p> <p>- The "Software" we found is a "EOSC Jupyter notebook" created by <a href="https://orcid.org/0000-0003-3979-3645">Simone Mantovani</a> with a DOI and additional metadata so that OpenAIRE explore can "associate" it to a specific EOSC service, namely <a href="https://www.egi.eu/services/notebooks/">EGI Notebook</a>. </p> <p>- When we click on "<a href="https://marketplace.eosc-portal.eu/services/egi-notebooks?q=EGI+Notebook">EOSC Service: EGI Notebook</a>", we are re-directed directly to the service that has been used to generate the original scientific results we found in OpenAIRE explore.</p> <p>- Any EOSC service needs to be requested and you have to plave an "order" to get access to it, where you may have to explain why you would like to access this EOSC service. To authenticate to any EOSC service, you can use for instance your <a href="https://orcid.org/">ORCID </a>identifier. if you do not have one, we suggest to register: this is very handy for EOSC services and you keep your ORCID identifier when you move from one institution to another (in addition, your institutional login may not work).</p> <p>- You will get notified by email (check your SPAM folder!) when you got access to an EOSC service.</p> <p>- We login to EGI notebook using ORCID identifier and upload (manually) the jupyter notebook we found in OpenAIRE (following the link e.g. from zenodo (<a href="https://doi.org/10.5281/zenodo.5554786">https://doi.org/10.5281/zenodo.5554786</a>)</p> <p>- The Jupyter notebook uses CAMS European air quality analysis from Copernicus Atmosphere Monitoring Service. The input data is accessible through an external service called the <a href="https://reliance.adamplatform.eu/">ADAM platform</a> (Advanced geospatial Data Management platform). It hosts datacubes (easy and fast access to large amount of data).</p> <p>- We can re-execute the Jupyter notebook but more importatnly we can create derivative work. However, make sure you check the license of the original result you find in OpenAIRE explore: it needs to have a license that allows you to create derivative work. Also make sure the Jupyter notebook is well documented.</p> <p>- We duplicate the Jupyter notebook and customize it. To bring the Open Science aspect from the beginning and not only when publishing the Jupyter Notebook, we need to use storage that can be shared. We use another service called "<a href="https://www.egi.eu/services/datahub/">EGI datahub</a>".</p> <p>- As when collaboratively writing scientific papers, we agree on how to organize the data: we create an "input folder" (containing all the input datasets used in the Jupyter notebook), an "output" folder with all the outputs we generate and a tool folder with the Jupyter notebook.</p> <p>- The new analysis is very similar to the previous one but over a different geographical area (France). </p> <p>- Finally, we create a Research Object that aggrgate all the resources. We use another external service called <a href="https://reliance.rohub.org/">RoHub </a> (Research Object Hub) and create and "executable Research Object" which we hope will be found, accessed and reused!</p> <p> </p>
Visual-Evoked Potential (VEP) Event-Related Files from the General Anesthesia and Brain Activity (GABA) Study and Infant Sibling Project (ISP)
<p>HAPPE+ER software was optimized for developmental data using a subset of EEG files from 4-month and 10-month old infants in the General Anesthesia and Brain Activity (GABA) Study. While medically necessary, 1-2 million infants each year undergo general anesthesia – a process that sedates brain activity and impacts early sensory experiences during a time typically characterized by rapid neurocognitive development. The GABA study examines sensory and socioemotional neurodevelopment longitudinally from infancy through childhood in individuals who have and who have never undergone general anesthesia during different windows in the first year of life. The GABA study was carried out in accordance with the recommendations of the Institutional Review Board at Boston Children’s Hospital. All caregivers provided assent for their child’s participation in the GABA study and for the release of the deidentified data. </p> <p>To facilitate the use and understanding of HAPPE+ER software, we have provided a subset of the validation files from the GABA study to serve as a tutorial dataset for how to run event-related potential (ERP) data through this automated processing pipeline. Five files (a.raw - e.raw) are from four 4-month and one 10-month old infants during a pattern reversal visual-evoked potential (VEP) paradigm. Pattern reversal occurred every 500 milliseconds. The pattern stimulus onset is indicated in each file by the code: vep+. Data was collected using a 128-channel EGI HydroCel Geodesic Sensor Net and EGI Net Amps 400, sampled at 1000Hz with an online reference to channel CZ. </p> <p>We have also included a subset of files from the Infant Sibling Project (ISP), an investigation examining infants at high versus low familial risk for autism spectrum disorder over the first 3 years of life. Baseline EEG data was collected while a young child sat in a parent’s lap watching a research assistant blow bubbles or show toys for several minutes. The Infant Sibling Project was carried out in accordance with the recommendations of the Institutional Review Board at Boston University and Boston Children’s Hospital (#X06-08-0374), with written informed consent from all caregivers prior to their child’s participation in the study. All files here have been deidentified, including alteration of exact acquisition dates. Acquisition times have not been altered. For additional information about data collection paradigms, and sample studies published on the larger ISP data set, please see the following references:</p> <ol> <li>Levin, A. R., Varcin, K. J., O’Leary, H. M., Tager-Flusberg, H., and Nelson, C. A. (2017). EEG power at 3 months in infants at high familial risk for autism. J. Neurodev. Disord. 9, 1–13.</li> <li>Gabard-Durnam, L.J., Wilkinson, C., Kapur, K. et al. Longitudinal EEG power in the first postnatal year differentiates autism outcomes. Nat Commun 10, 4188 (2019). <a href="https://doi.org/10.1038/s41467-019-12202-9">https://doi.org/10.1038/s41467-019-12202-9</a></li> </ol> <p>Here we provide a subset of the full dataset with a simulated VEP signal added into the data, as example files for HAPPE+ER. To create these files, we selected a subset of 39 spatially-distributed channels in the baseline EEG files and created sixteen 30-second files using continuous segments of relatively artifact-free (clean) baseline data from the full-length files. Next, from 30-second sections of the same individuals’ EEG that were artifact-laden, we ran ICA and extracted artifact independent components (identified by an expert and labeled artifact by both ICLabel and MARA automated algorithms). We inserted the artifact ICs into that individual’s clean 30-second data segment to create an additional 16 artifact-added files. We then selected a channel from a simulated VEP dataset (included here as simulated_full.set) with a stereotyped and prominent simulated VEP waveform, in this case Oz, and added its timeseries (included here as simulated_singleChan.set) to each channel of the clean and artifact-added files to create two VEP datasets with a known ERP morphology (sim-artifact_a-p and sim-clean_a-p). For additional information about the creation of this simulated data and VEP data with a known ERP morphology, please refer to Monachino et al., in revision; DOI: https://doi.org/10.1101/2021.07.02.450946.</p> <p>Additional files included below are the HAPPE+ER data and pipeline quality metric output spreadsheets for the five GABA study data files for an example run, the output spreadsheet containing the ERP timeseries from the generateERPs script, the .mat file containing the parameter settings for HAPPE+ER for that run, an Excel file with the bad channels for each file, and a tutorial document illustrating the results of this example run. </p>
Centroid Moment Tensor solutions for the earthquake dataset of the project IMAGINE_IT
<p>The project IMAGINE_IT (PI Dr. Dimitri Komatitsch) received 40 million CPU-hours on the Tier-0 GENCI/TGCC CURIE supercomputer as a winner of the 9th PRACE consortium call (2014). </p> <p>The awarded computational resources allowed us to construct a new 3D tomographic model for the Italian lithosphere, <em>Im25</em>,<em> </em>by combining spectral-element three-dimensional wavefield simulations and an adjoint-state method.</p> <p>To obtain the final model <em>Im25, </em>we performed 25 adjoint tomography iterations. Moreover, two additional source inversion iterations have been performed in order to improve the earthquake source parameter estimates and reduce the misfit between observed and synthetic seismograms: one inversion using the 3D wavespeed model considered as starting model of the tomographic procedure, and one inversion for the improved wavespeed model at iteration 12 (<em>Im12</em>). </p> <p>The presented table contains the Centroid Moment Tensor parameters of the163 earthquakes considered in the IMAGINE_IT project for: the initial (Time Domain Moment Tensor; http://terremoti.ingv.it/) source solution based on a 1D wavespeed model (iter=0), the source inversion solution with the starting 3D wavespeed model (iter=1), and the source inversion solution with model <em>Im12</em> (iter=2). <strong> </strong></p>
Ash (Fraxinus excelsior L.) in vitro survival data for the UKs Living Ash Project
<p><em>In-vitro</em> propagation and survival data sets (including nursery survival) of the ash plants generated i.e. <em>Fraxinus excelsior</em> L., plus the PCR primers used and conditions applied.</p> <p>Surveyed from a range of ash seed material taken from across the UK, and held at the UK ash collection hosted by the Earth Trust in Oxfordshire, UK.</p> <p>A more detailed analysis of this data is currently expected to be be published in the <em>Annals of Forest Science</em>, and which has already provisionally accepted this work for publication, subject to the underlying data being made available i.e. here</p> <p>The data deposited here represents the underlying data that will be presented in graphical form in the forthcoming paper by Fenning et al., plus the associated metadata and statistical analyses, along with the original .jpg of the photos used.</p>
DIAMOND project Open Datasets
<p>The folder includes the datasets collected through the H2020 DIAMOND project titled ‘Revealing fair and actionable knowledge from data to support women’s inclusion in transport systems.’ (Grant Agreement No 824326), focusing on each Use Case of the project:</p> <ul> <li>Use Case I: Public Transport Infrastructures (Railways);</li> <li>Use Case II: (Emotion in) Autonomous Passenger Car;</li> <li>Use Case III: Vehicle (Bike) Sharing Fleet Management;</li> <li>Use Case IV: Employment of Women in Rail Industry and Freight/CSR Protocols.</li> </ul> <p>Data collection campaigns have been based on the methodological approach of the DIAMOND project. </p> <p>This relies on the Fairness Characteristics (FCs) identified through Thematic Analysis and on a series of interdisciplinary tools and methods. </p> <p>The heterogeneous and disaggregated datasets gathered through the executed data collection campaigns are classified in following categories:</p> <ul> <li>Structured data (Use Cases I, III and IV);</li> <li>Observations (Use Cases I and III);</li> <li>UESI questionnaires (all Use Cases);</li> <li>Social media data (Use Cases I, II and III);</li> <li>DAD survey questionnaires (all Use Cases);</li> <li>Recommendations (All Use Cases);</li> <li>Validation of the Toolkit4Fairness (All Use cases).</li> </ul> <p>The folder includes the document 'Deliverable 4.1 Datasets description', which is aimed at reporting the datasets collected for the H2020 DIAMOND project including information about the identification of dataset sources, data collection tools, data collection timeline and responsible partners of the Consortium for each data collection activity.</p>
Soybean yield projections in Europe under historical (1981-2010) and future climate (2050-2059 and 2090-2099 for RCP4.5 and RCP8.5)
<p><strong>General information</strong></p> <p>This dataset contains soybean yield projections in Europe under historical (1981-2010) and future climate with moderate (RCP 4.5) to intense (RCP 8.5) warming, up to the 2050s and 2090s time horizons. The data has been generated by <em>Guilpart et al. (2022) Data-driven projections suggest large opportunities to improve Europe's soybean self-sufficiency under climate change, Nature Food. </em>All details can be found in this paper. A brief summary is provided below.</p> <p><strong>Summary of soybean yield projections methodology</strong></p> <p>Yield projections have been performed using data-driven relationships between climate and soybean yield derived from machine-learning (Random Forest). The Random Forest model was trained using (i) the the global dataset of historical yields updated version (Iizumi et al. 2014a), which includes grid-wise soybean yields worldwide with the grid size of 1.125 degree over 1981-2010, and (ii) the global retrospective meteorological forcing dataset tailored for agricultural application (GRASP, Iizumi et al. 2014b), which covers the period 1961–2010 at the same spatial resolution as yield data, i.e. a grid size of 1.125 degree. Time-detrended soybean yield data was related (using Random Forest) to 35 climate variables defined at a monthly time step over the seven months of the soybean growing season, plus the fraction of irrigated area, i.e. a total of 36 variables. The 35 climate variables are monthly mean daily minimum and maximum temperatures (<em>Tmin</em> and <em>Tmax</em>, degree Celsius), monthly total precipitation (<em>rain</em>, mm month<sup>-1</sup>), monthly mean daily total solar radiation (<em>solar</em>, MJ m<sup>-2</sup> day<sup>-1</sup>), monthly mean air vapor pressure (VP, hPa). The fitted model showed high R² (higher than 0.9) and low RMSE (0.35 t ha<sup>-1</sup>) between observed and predicted yields based on cross-validation.</p> <p>Then, soybean yield projections under historical over whole Europe have been performed using the GRASP climate data, and yield projections under future climate have been performed using 16 climate change scenarios consisting of bias-corrected data of eight Global Circulation Models (GCM; GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, MIROC5, MIROC-ESM, MIROC-ESM-CHEM, MRI-CGCM3, and NorESM1-M, used in the Coupled Model Intercomparison phase 5 (CMIP5) and two Representative Concentration Pathways (RCPs; 4.5 and 8.5 W m<sup>-2</sup>). Soybean growing season used for projections is April to October. All projections assumed irrigated fraction equals to zero. Projections are shown only on agricultural area (cropland plus pasture), in the year 2000. Soybean yield is expressed in tons per hectare.</p> <p><strong>Files description</strong></p> <ul> <li><em>RF_soybean_historical_GRASP_median_1981_2010.nc</em> : random forest projections of soybean yield in Europe for the historical (1981-2010) period using GRASP climate data. This file contains the median yield (in tons per hectare) over 1981-2010.</li> <li><em>RF_soybean_rcp45_median_2050_2059.nc : </em>random forest projections of soybean yield in Europe for the 2050-2059 time period under RCP4.5. This file contains the median yield (in tons per hectare) over 2050-2059 and the 8 GCMs.</li> <li><em>RF_soybean_rcp45_median_2090_2099.nc : </em>random forest projections of soybean yield in Europe for the 2090-2099 time period under RCP4.5. This file contains the median yield (in tons per hectare) over 2090-2099 and the 8 GCMs.</li> <li><em>RF_soybean_rcp85_median_2050_2059.nc : </em>random forest projections of soybean yield in Europe for the 2050-2059 time period under RCP8.5. This file contains the median yield (in tons per hectare) over 2050-2059 and the 8 GCMs.</li> <li><em>RF_soybean_rcp85_median_2090_2099.nc : </em>random forest projections of soybean yield in Europe for the 2090-2099 time period under RCP8.5. This file contains the median yield (in tons per hectare) over 2090-2099 and the 8 GCMs.</li> </ul> <p><strong>References</strong></p> <p>Guilpart N. <em>et al.</em> (2022)<strong> </strong>Data-driven projections suggest large opportunities to improve Europe's soybean self-sufficiency under climate change, <em>Nature Food</em>.</p> <p>Iizumi T. <em>et al.</em> (2014a) Historical changes in global yields: Major cereal and legume crops from 1982 to 2006. <em>Glob. Ecol. Biogeogr.</em> 23, 346–357.</p> <p>Iizumi T. <em>et al</em>. (2014b). A meteorological forcing data set for global crop modeling: Development, evaluation, and intercomparison. <em>J. Geophys. Res. Atmos. Res.</em> 119, 363–384.</p>
HYDRO-CSI, Project 1.2: In-stream hydrology. Part 1: Groundwater measurements
<p>The continuous exchange of water between surface water and groundwater is a key environmental process controlling the transport and the fate of nutrients, solutes and pollutants in river networks. The dynamics of the near-stream groundwater has a non-negligeable role on controlling flow direction and solutes exchange between the stream water with the adjacent groundwater, however it is rarely considered in solute transport experiments. Despite the amount of individual studies, we are still uncertain about how the physical processes controlling in-stream solutes transport change with different hydrologic conditions and how these processes can be inferred by modelling outcomes.</p> <p>In this project we investigated groundwater and stream interactions in order to characterize the physical processes that control the water and solute exchange in the river corridor and their variability over time. To do so, we drilled 36 wells in the near-stream domain, and 7 piezometers in the stream channel. We observed the water level and electrical conductivity every 15 min at 22 of the 36 wells with a water level sensor (Orpheus Mini, OTT, Kempten, Germany, resolution of 1 mm and accuracy of ±0.05% FS) over a period of 32 months (July 2018 - March 2021).</p> <p>The dataset includes the following files:</p> <p>> "Raw groundwater measurements.xlsx" <br> This file includes groundwater table elevation measured as depth from the upper limit of the well (time step of 15 minutes, Orpheus Mini, OTT, Kempten, Germany, resolution of 1 mm and accuracy of ±0.05% FS). Every excel file includes also Electrical Conductivity measurements (μS/cm) and Voltage (V) of the instruments. Every sheet in this .xlsx file reports measurement for one sensor in the specific observation-well where it was placed.</p> <p>> "Groundwater elevation data - wells metadata and fixed groundwater table elevation.xlsx"<br> This file includes the raw groundwater table measurements measured via the OTT, information on the well network, elevation and location of the observation wells, location of subsurface layers, and suggested correction of the groundwater table measurements for short periods with missing data.</p> <p>> "Hand-measurements and metadata.xlsx"<br> This file includes the list of in-situ inspections and hand-measurements of the groundwater table conducted over the entire observation period in every well and piezometer of the groundwater-monitoring well network. Every sheet includes details on the instruments measuring the groundwater table, their offset with hand-measured data, information on their re-calibration, and calculation of the groundwater elevation above the reference plane after each hand-measurement.</p>
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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