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Reference data and documentation for Skills4EOSC Deliverable D6.1 Mapping of existing professional networks
<p>This record presents the data underlying <strong>Skills4EOSC Deliverable D6.1 Mapping of existing professional networks</strong> and relevant documentation of the search string.</p>
Uncertainty in Migration Scenarios. QuantMig Project Deliverable D9.2 Data Description
<p>This open data deposit contains the data and code accompanying used in the report: Barker and Bijak (2021), Uncertainty in Migration Scenarios, QuantMig Project Deliverable D9.2. The cover note should be read in conjunction with the report, available via www.quantmig.eu, and with the individual readme files in the data folders that can be found within this Zenodo repository (DOI: 10.5281/zenodo.7709443).</p>
MAGIC Deliverable D6.5: Shale gas development in the EU 10Km radius well grid scenario
<p>Geo data set of escenario of shale gas implementation in Europe. Developed for WP 6 of the <a href="https://magic-nexus.eu/">MAGIC-Nexus project</a>. It derives from a Geomodel of wells and a database of shale gas played developed by the <a href="https://ec.europa.eu/jrc/sites/jrcsh/files/pl1-britze.pdf">EUOGA </a>project. </p> <p><strong>DB Fields------------------------------------------</strong></p> <p>WELLid: Id of the well</p> <p>RBid: Id of the River Basin in which the well is located</p> <p>RBtxtINT: Name of the River Basin - English</p> <p>RBtxt: Name of the River Basin - Country's Name</p> <p>GWid: Groundwater basin ID</p> <p>PADid: ID of the extraction pad</p> <p>Formation: Shale formation</p> <p>Age: of the well </p> <p>Depth_avg: Average depth of the shale (inherited)</p> <p>Mature_avg: Average matureness of the shale (inherited)</p> <p>TOC_avg: Average Organic content of the shale (inherited)</p> <p>ThickGross: Gross Thickness of the shale play in meters (inherited)</p> <p>ThickNet_m: Net Thickness of the shale play in meters (inherited)</p> <p>EUOGA_Basi: Basin of the well according ot the EUOGA project database (inherited)</p> <p>Basin_inde: Id of the shale basin (inherited)</p> <p>NGS_Basin: Id of the BAsin as stated by the national geological service</p> <p>Shale_CP: Shale country </p> <p>RF_Maturit: Reference Maturity</p> <p>RF_Depth: Reference Depth</p> <p>CNTR_CODE, Country code</p> <p>NUTS_NAME: Name of the NUTS region</p> <p>NUTid: ID of the NUTS region</p> <p>x,y Coordinates of the well</p>
DuneFront deliverable D4.1 - Physical boundary conditions over European coasts
<p>The European project DuneFront (<a href="https://dunefront.eu">https://dunefront.eu</a>, <a href="https://cordis.europa.eu/project/id/101135410">https://cordis.europa.eu/project/id/101135410</a>) is working to improve coastal protection across Europe by using Nature-based Solutions (NbS), such as Dune-Dike hybrids (DD-hybrids), to defend coastlines from extreme weather and rising sea levels. Within this project, this “Physical Boundary Conditions” deliverable focuses on collecting and mapping key physical boundary conditions that affect the effectiveness of these solutions. The aim was to create a consistent, Europe-wide, high-quality dataset that helps understand how DD-hybrids are, and will be, affected by waves, tides, weather patterns, and climate change. </p> <p>The dataset is made of 5 geopackage files, each duplicated in csv format for accessibility. See the deliverable report (pdf) for detailed information on each file, use notes, and literature references. We explicitly recommend the use of the gpkg files over csv for any GIS application, for reasons that are detailed in the report.</p>
Audio Commons Ground Truth Data for deliverables D4.4, D4.10 and D4.12
<p>This dataset contains the ground truth data used to evaluate the musical <strong>pitch</strong>, <strong>tempo</strong> and <strong>key </strong>estimation algorithms developed during the AudioCommons H2020 EU project and which are part of the <a href="https://www.audiocommons.org/2018/07/15/audio-commons-audio-extractor.html">Audio Commons Audio Extractor tool</a>. It also includes ground truth information for the <strong>single-event<em>ness</em> </strong>audio descriptor also developed for the same tool.</p> <p>This ground truth data has been used to generate the following documents:</p> <ul> <li><strong>Deliverable D4.4</strong>: Evaluation report on the first prototype tool for the automatic semantic description of music samples</li> <li><strong>Deliverable D4.10</strong>: Evaluation report on the second prototype tool for the automatic semantic description of music samples</li> <li><strong>Deliverable D4.12</strong>: Release of tool for the automatic semantic description of music samples</li> </ul> <p>All these documents are available in the <a href="https://www.audiocommons.org/materials/">materials section </a>of the AudioCommons website.</p> <p>All ground truth data in this repository is provided in the form of CSV files. Each CSV file corresponds to one of the individual datasets used in one or more evaluation tasks of the aforementioned deliverables. This repository <strong>does not include the audio files</strong> of each individual dataset, but includes references to the audio files. The following paragraphs describe the structure of the CSV files and give some notes about how to obtain the audio files in case these would be needed.</p> <p><br> <strong>Structure of the CSV files</strong></p> <p>All CSV files in this repository (with the sole exception of <em>SINGLE EVENT - Ground Truth.csv</em>) feature the following 5 columns:</p> <ol> <li><strong>Audio reference</strong>: reference to the corresponding audio file. This will either be a string withe the <strong>filename</strong>, or the <strong>Freesound ID </strong>(for one dataset based on Freesound content). See below for details about how to obtain those files. </li> <li><strong>Audio reference type</strong>: will be one of <em>Filename</em> or <em>Freesound ID</em>, and specifies how the previous column should be interpreted. </li> <li><strong>Key annotation</strong>: tonality information as a string with the form "RootNote minor/major". Audio files with no ground truth annotation for tonality are left blank. Ground truth annotations are parsed from the original data source as described in the text of deliverables D4.4 and D4.10.</li> <li><strong>Tempo annotation</strong>: tempo information as an integer representing beats per minute. Audio files with no ground truth annotation for tempo are left blank. Ground truth annotations are parsed from the original data source as described in the text of deliverables D4.4 and D4.10. Note that integer values are used here because we only have tempo annotations for <em>music loops</em> which typically only feature integer tempo values.</li> <li><strong>Pitch annotation</strong>: pitch information as an integer representing the MIDI note number corresponding to annotated pitch's frequency. Audio files with no ground truth pitch for tempo are left blank. Ground truth annotations are parsed from the original data source as described in the text of deliverables D4.4 and D4.10.</li> </ol> <p>The remaining CSV file, <em>SINGLE EVENT - Ground Truth.csv</em>, has only the following 2 columns:</p> <ul> <li><strong>Freesound ID</strong>: sound ID used in Freesound to identify the audio clip.</li> <li><strong>Single Event: </strong>boolean indicating whether the corresponding sound is considered to be a single event or not. Single event annotations were collected by the authors of the deliverables as described in deliverable D4.10.</li> </ul> <p> </p> <p><strong>How to get the audio data</strong></p> <p>In this section we provide some notes about how to obtain the audio files corresponding to the ground truth annotations provided here. Note that due to licensing restrictions we are not allowed to re-distribute the audio data corresponding to most of these ground truth annotations.</p> <ul> <li><strong>Apple Loops (APPL)</strong>: This dataset includes some of the music loops included in Apple's music software such as Logic or GarageBand. Access to these loops requires owning a license for the software. Detailed instructions about how to set up this dataset are <a href="https://github.com/ffont/ismir2016/blob/master/docs/create_dataset.md#appl">provided here</a>. </li> <li><strong>Carlos Vaquero Instruments Dataset (CVAQ)</strong>: This dataset includes single instrument recordings carried out by <a href="https://www.linkedin.com/in/carlosvaquero/">Carlos Vaquero</a> as part of this <a href="http://mtg.upf.edu/node/2609">master thesis</a>. Sounds are available as Freesound packs and can be downloaded at this page: https://freesound.org/people/Carlos_Vaquero/packs</li> <li><strong>Freesound Loops 4k (FSL4)</strong>: This dataset set includes a selection of music loops taken from Freesound. Detailed instructions about how to set up this dataset are <a href="https://github.com/ffont/ismir2016/blob/master/docs/create_dataset.md#instructions-for-setting-up-datasets">provided here</a>.</li> <li><strong>Giant Steps Key Dataset (GSKY)</strong>: This dataset includes a selection of previews from Beatport annotated by key. Audio and original annotations <a href="https://github.com/GiantSteps/giantsteps-key-dataset">available here</a>.</li> <li><strong>Good-sounds Dataset (GSND)</strong>: This dataset contains monophonic recordings of instrument samples. Full description, original annotations and audio are <a href="https://zenodo.org/record/820937#.XEYMiy2ZN25">available here</a>.</li> <li><strong>University of IOWA Musical Instrument Samples (IOWA)</strong>: This dataset was created by the Electronic Music Studios of the University of IOWA and contains recordings of instrument samples. The dataset is available upon request by <a href="http://theremin.music.uiowa.edu/MIS.html">visiting this website</a>.</li> <li><strong>Mixcraft Loops (MIXL)</strong>: This dataset includes some of the music loops included in Acoustica's Mixcraft music software. Access to these loops requires owning a license for the software. Detailed instructions about how to set up this dataset are <a href="https://github.com/ffont/ismir2016/blob/master/docs/create_dataset.md#mixl">provided here</a>.</li> <li><strong>NSynth Dataset Test and Validation sets (NSYT and NSYV)</strong>: NSynth is a large-scale and high-quality dataset of annotated musical notes built with synthesized sounds by Google's Magenta team. Full dataset description including original annotations and audio files is <a href="https://magenta.tensorflow.org/datasets/nsynth">available here</a>.</li> <li><strong>Philarmonia Orchestra Sound Samples Dataset (PHIL)</strong>: This includes thousands of free, downloadable sound samples specially recorded by Philharmonia Orchestra players. Audio files are freely downloadable from the <a href="http://www.philharmonia.co.uk/explore/sound_samples">philarmonia orchestra website</a>.</li> <li><strong>Freesound Single Events Dataset (SINGLE EVENT)</strong>: This includes a selection of Freesound audio clips representing audio signals containing either a single audio <em>event</em> or multiple ones. Original audio files can be retrieved by downloading individual audio clips from Freesound using the ID identifier provided in the CSV file. A similar procedure to that described <a href="https://github.com/ffont/ismir2016/blob/master/docs/create_dataset.md#getting-fsl4-by-downloading-content-from-freesound">here</a> could be followed.</li> </ul>
Audio Commons Estimation Results Data for deliverables D4.4, D4.10 and D4.12
<p>This dataset contains the results of running the automatic audio annotation algorithms for <strong>pitch</strong>, <strong>tempo</strong> and <strong>key </strong>used for the evaluation of algorithms developed during the AudioCommons H2020 EU project and which are part of the <a href="https://www.audiocommons.org/2018/07/15/audio-commons-audio-extractor.html">Audio Commons Audio Extractor tool</a>. It also includes estimation results information for the <strong>single-event<em>ness</em> </strong>audio descriptor also developed for the same tool.</p> <p>These estimation results data has been used to generate the following documents:</p> <ul> <li><strong>Deliverable D4.4</strong>: Evaluation report on the first prototype tool for the automatic semantic description of music samples</li> <li><strong>Deliverable D4.10</strong>: Evaluation report on the second prototype tool for the automatic semantic description of music samples</li> <li><strong>Deliverable D4.12</strong>: Release of tool for the automatic semantic description of music samples</li> </ul> <p>All these documents are available in the <a href="https://www.audiocommons.org/materials/">materials section </a>of the AudioCommons website.</p> <p>All data in this repository is provided in the form of CSV files. Each CSV file corresponds to the analysis results of one musical task and one of the individual datasets used in the aforementioned deliverables. This repository <strong>does not include the audio files </strong>of each individual dataset, but includes references to the audio files. The following paragraphs describe the structure of the CSV files and give some notes about how to obtain the audio files in case these would be needed.</p> <p><br> <strong>Structure of the CSV files</strong></p> <p>All the CSV files in this repository (with the sole exception of <em>SINGLE EVENT - Estimation Results Truth.csv</em>) are named according to the following convention: "<em>DATASET_NAME</em> - <em>ESTIMATION_TASK</em> Estimation Results.csv". Therefore, estimation results for pitch, tempo and tonality music tasks are separated in different files. All these files share the same structure for the first 2 CSV columns:</p> <ol> <li><strong>Audio reference</strong>: reference to the corresponding audio file. This will either be a string withe the <strong>filename</strong>, or the <strong>Freesound ID </strong>(for one dataset based on Freesound content). See below for details about how to obtain those files. </li> <li><strong>Audio reference type</strong>: will be one of <em>Filename</em> or <em>Freesound ID</em>, and specifies how the previous column should be interpreted. </li> </ol> <p>The rest of the columns include the estimation results for each one of the algorithms included in the evaluation of each music facet. For <strong>each algorithms two columns</strong> are reserved, the first one containing the actual <strong>estimation</strong> and the second one the <strong>confidence</strong> of this estimation (see CSV file previews below). The format of actual estimations depends on the musical task, check the description of the <a href="https://zenodo.org/deposit/2545728">corresponding ground truth dataset</a> for more information on that. The confidence value is a float number, typically in the range from 0.0 to 1.0. It can happen that one or both columns are empty for a given analysis algorithm and CSV row. This will be the case if the algorithm could not successfully produce an estimation for the audio file row corresponding to the CSV row.</p> <p>The remaining CSV file, <em>SINGLE EVENT - Estimation Results.csv</em>, has the following 4 columns:</p> <ul> <li><strong>Freesound ID</strong>: sound ID used in Freesound to identify the audio clip.</li> <li><strong>ACExtractorV2</strong>: single-event<em>ness</em> estimation of the algorithm included in the second version of the Audio Commons Audio Extractor tool (bool).</li> <li><strong>ACExtractorV2-opt</strong>: single-event<em>ness</em> estimation of the algorithm included in the second version of the Audio Commons Audio Extractor tool with optimized parameters (bool).</li> <li><strong>ACExtractorV3</strong>: single-event<em>ness</em> estimation of the algorithm included in the third version of the Audio Commons Audio Extractor tool (bool).</li> </ul> <p> </p> <p><strong>How to get the audio data</strong></p> <p>In this section we provide some notes about how to obtain the audio files corresponding to the estimation results provided here. Note that due to licensing restrictions we are not allowed to re-distribute the audio data corresponding to most of these automatic annotations.</p> <ul> <li><strong>Apple Loops (APPL)</strong>: This dataset includes some of the music loops included in Apple's music software such as Logic or GarageBand. Access to these loops requires owning a license for the software. Detailed instructions about how to set up this dataset are <a href="https://github.com/ffont/ismir2016/blob/master/docs/create_dataset.md#appl">provided here</a>.</li> <li><strong>Carlos Vaquero Instruments Dataset (CVAQ)</strong>: This dataset includes single instrument recordings carried out by <a href="https://www.linkedin.com/in/carlosvaquero/">Carlos Vaquero</a>as part of this <a href="http://mtg.upf.edu/node/2609">master thesis</a>. Sounds are available as Freesound packs and can be downloaded at this page: https://freesound.org/people/Carlos_Vaquero/packs</li> <li><strong>Freesound Loops 4k (FSL4)</strong>: This dataset set includes a selection of music loops taken from Freesound. Detailed instructions about how to set up this dataset are <a href="https://github.com/ffont/ismir2016/blob/master/docs/create_dataset.md#instructions-for-setting-up-datasets">provided here</a>.</li> <li><strong>Giant Steps Key Dataset (GSKY)</strong>: This dataset includes a selection of previews from Beatport annotated by key. Audio and original annotations <a href="https://github.com/GiantSteps/giantsteps-key-dataset">available here</a>.</li> <li><strong>Good-sounds Dataset (GSND)</strong>: This dataset contains monophonic recordings of instrument samples. Full description, original annotations and audio are <a href="https://zenodo.org/record/820937#.XEYMiy2ZN25">available here</a>.</li> <li><strong>University of IOWA Musical Instrument Samples (IOWA)</strong>: This dataset was created by the Electronic Music Studios of the University of IOWA and contains recordings of instrument samples. The dataset is available upon request by <a href="http://theremin.music.uiowa.edu/MIS.html">visiting this website</a>.</li> <li><strong>Mixcraft Loops (MIXL)</strong>: This dataset includes some of the music loops included in Acoustica's Mixcraft music software. Access to these loops requires owning a license for the software. Detailed instructions about how to set up this dataset are <a href="https://github.com/ffont/ismir2016/blob/master/docs/create_dataset.md#mixl">provided here</a>.</li> <li><strong>NSynth Dataset Test and Validation sets (NSYT and NSYV)</strong>: NSynth is a large-scale and high-quality dataset of annotated musical notes built with synthesized sounds by Google's Magenta team. Full dataset description including original annotations and audio files is <a href="https://magenta.tensorflow.org/datasets/nsynth">available here</a>.</li> <li><strong>Philarmonia Orchestra Sound Samples Dataset (PHIL)</strong>: This includes thousands of free, downloadable sound samples specially recorded by Philharmonia Orchestra players. Audio files are freely downloadable from the <a href="http://www.philharmonia.co.uk/explore/sound_samples">philarmonia orchestra website</a>.</li> <li><strong>Freesound Single Events Dataset (SINGLE EVENT)</strong>: This includes a selection of Freesound audio clips representing audio signals containing either a single audio <em>event</em>or multiple ones. Original audio files can be retrieved by downloading individual audio clips from Freesound using the ID identifier provided in the CSV file. A similar procedure to that described <a href="https://github.com/ffont/ismir2016/blob/master/docs/create_dataset.md#getting-fsl4-by-downloading-content-from-freesound">here</a> could be followed.</li> </ul>
Deliverable T2.2 soil threats and soil ecosystem services of interest in SERENA.xlsx
<p>The internal EJP SOIL project SERENA contributed to the evaluation of soil multifunctionality aiming at providing assessment tools for land planning and soil policies at different scales. By co-working with relevant stakeholders, the project provided co-developed indicators and associated cookbooks to assess and map them, to report both on soil degradation, soil-based ecosystem services and their bundles, under actual conditions and for climate and land-use changes, at the regional, national, and European scales. </p> <p>In T2.2 we discussed the definitions of soil threats (ST) and soil-based ecosystem services (SES) to be further analysed in SERENA. In this process we kept information from the literature review and the results of the national prioritisation of ST and SES. This dataset description is an Excel with sheets of literature search and national prioritisation from the participating countries. Please read metadata at the fist sheet. Furthermore, the last sheet shows the results of our discussion on definitions that we had to clarify before the prioritisation. </p>
Deliverable 2.3 Design Load Case Database for Code-to-Code Comparison
<p>In work package 2 of FLOATECH a detailed validation and verification of the capabilities of QBlade-Ocean was performed. Thereby, three wind turbine models mounted on floating substructures with differing characteristics serve as the means for the validation.This dataset contains Floating Offshore Wind Turbine (FOWT) calculations in various design situations, computed with three different codes. In more detail, three floating platform archetypes are used in the code-to-code comparison ongoing in work package 2: a semi-submersible-type floater and a spar-type floater as well as the Hexafloat® concept recently proposed by Saipem®. The three test-cases are the NREL 5MW RWT mounted on the DeepCwind semi-submersible platform, the DTU 10MW RWT mounted on the SOFTWIND spar-type platform and the DTU 10MW RWT mounted on the Hexafloat® platform. <br> More details regarding the dataset structure and the testcases can be found in the accompanying document. </p> <p> </p> <p><em>Changelog:</em> </p> <p>Version 5.0.0</p> <ul> <li>Changed structural damping ratio (increased) in OpenFAST results (SOFTWIND and Hexafloat models) to match structural damping ratios in QBlade</li> <li>Added wave time series at platform undisplaced position</li> <li>Added TurbSim input files for wind field generation. TurbSim v2.0.0 (https://www.nrel.gov/wind/nwtc/turbsim.html - National Renewable Energy Laboratory)</li> <li>Added detailed DLC and simulation information in "DLC&SimulationList.xlsx"</li> </ul> <p>Version 4.0.0</p> <ul> <li>Correction of bugs in QB 5MWOC4 and 10MWSOFT models: wind shear exponent in wind fields (0.11 -> 0.14)</li> <li>Update of hydrodynamic database used in OpenFAST and QBlade results of 10MWSOFT model </li> </ul> <p> </p> <pre><code>Data in version 5.0.0 is used in WES publication: Papi, F., Troise, G., Behrens de Luna, R., Saverin, J., Perez-Becker, S., Marten, D., Ducasse, M.-L., and Bianchini, A.: A Code-to-Code Comparison for Floating Offshore Wind Turbine Simulation in Realistic Environmental Conditions: Quantifying the Impact of Modeling Fidelity on Different Substructure Concepts, Wind Energ. Sci. Discuss. [preprint], https://doi.org/10.5194/wes-2023-107, in review, 2023.</code></pre> <p> </p>
Deliverable D-IA.2.2.OH-Harmony-Cap.2.1: Completed Pilot Survey
<p>This is a public deliverable of One Health EJP Joint Research Project,<strong><em> </em></strong><strong><em>Integrative Action-2.2,</em></strong> <strong>OH-HARMONY-CAP: </strong>One Health Harmonisation of Protocols for the Detection of Foodborne Pathogens and AMR Determinants. <a href="https://onehealthejp.eu/jip-oh-harmony-cap/"><strong>https://onehealthejp.eu/jip-oh-harmony-cap/</strong></a></p> <p>The purpose is to develop an integrated One Health map (OHLabCap ) of the levels of system capability/capacity/interoperability for each of the EU MS that is repeatable and sustainable. The first step is to develop of a pilot survey, targeting NRLs and the primary diagnostic services (primary sector). The pilot survey covers: 1. six priority bacteria and ten priority parasites have been chosen, as model organisms, together with the antimicrobial resistance (AMR) testing of <em>Salmonella</em> and <em>Campylobacter</em>. 2. 63 questions incorporating capability, capacity, and interoperability</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>
Deliverable 2.1 Aero-hydro-elastic model definition - SOFTWIND 10 MW FOWT (wave-tank SIL version)
<p>For the detailed validation and verification of the capabilities of QBladeOcean in work package 2 of FLOATECH, a detailed definition of the models is needed. This database presents the QBladeOcean model of the DTU 10MW Reference Wind Turbine mounted on the SOFTWIND floater.</p> <p>Update V2.0.0: <br>Structure files are modified according to the requirements of the QBladeCE version</p> <p>Update V3.0.0:<br>- Added controller from SOFTWIND experiments (Modified from DTU 10MW to have oO star controller parameters)<br>- Modified mooring line length<br>- Shifted platform COG slightly towards centerline<br>- Modified blade definition to AD14 blade def.<br>- Included STATICBUOYANCY flag</p> <p>Update V3.1.0:<br>- Included ADVANCEDBUOYANCY flag<br>- Corrected excitation file (.3), previously: incorrect assignment of wave headings and excitation force coefficients<br>- Addition of mean drift file (.8)<br>- Corrected error in added mass matrix entry [4,2] (sway-roll coupling)</p> <p>Update V3.2.0:<br>- DELTA_DIR_DIFF 1-->20<br>- STATICBUOYANCY --> true</p> <p>Update V3.3.0:<br>- updated Substructure .txt file to format compatible with new QBlade version 2.0.6.4+<br>- extrapolation stretching activated<br>- depth dependent drag coefficient of 0.6 until z = -4m<br>- adjusted "DAMP_[-]" paremeter in the "MOORELEMENTS" table of ths Substructure .dat file to be zero due to numerical instabilities</p>
Deliverable 2.1 Aero-hydro-elastic model definition - OC5 5MW MSWT
<p>For the detailed validation and verification of the capabilities of QBladeOcean in work package 2 of FLOATECH, a detailed definition of the models is needed. This database presents the QBladeOcean model of the MARIN Stock Wind Turbine mounted on the OC5 floater.</p> <p>Update V2.0.0; Structure files are modified according to the requirements of the QBladeCE version</p> <p>Update V3.0.0; Bugfix mooring line 3 definition</p> <p>Update V4.0.0;<br>- TOWERDRAG activated and set to cd = 0.5<br>- Inlcusion of Non-linear QTF forces<br>- Modified RAYLEIGHDMP 0.05 --> 0.01</p> <p>Update V5.0.0;<br>- updated Substructure .dat file to a format compatible with QBlade version 2.0.6.4+<br>- extrapolation stretching activated<br>- depth dependent drag coefficient of 0.6 until z = -4m<br>- enhanced model to improve low frequency pitch excitation --> additional parameters in HYDROJOINTCOEFF table<br>- adjusted "DAMP_[-]" paremeter in the "MOORELEMENTS" table of the Substructure .dat file to be zero due to numerical instabilities</p>
Deliverable D2 (D1.2) - FEBID of SiOx with lateral feature size of 20 nm
<p>Direct local nanofabrication of silicon oxide by Focused Electron Beam Induced Deposition (FEBID) can have a significant impact in the fabrication of nanoscale circuits for creating insulating barriers and may act as a mask in etching steps. It is known that SiOx can be grown by FEBID using Si precursors such as 2, 4, 6, 8, 10-pentamethyl-cyclopenta-siloxane (PMCPS) combined with water. High-resolution deposition of lines and dots by FEBID has been achieved in different electron microscope systems. However, high-resolution SiOx deposits using PMCPS combined with water has not been reported yet.<br> FEBID is out carried in an eLINE system (Raith) equipped with a Schottky-type electron emitter. The precursor gas is supplied in the chamber through a five-needle gas injection system (GIS). The reservoir’s temperature of PMCPS and water is 30°C. The GIS is positioned approximately 500μm above the sample. A Si/SiO2 (native) substrate with 5nm Pt on top was used. Single pixel lines with a length of 1μm were patterned with an electron beam energy of 30keV, a beam current of 32pA, a 10μs dwell time and at 10mm working distance. The Pt was deposited in order to enhance the contrast between the substrate and the deposited SiOx.</p>
Deliverable D2 (D1.4) - Functional quantum device grown with FEBID
<p>Superconductors are one of the building blocks for most nanodevices used in quantum applications. The direct local writing aspect of the Focused Electron Beam Induced Deposition (FEBID) technique allows for flexible deposition of any materials and especially metals and superconductors. As clean bulk material tungsten (W) has a superconducting transition temperature Tc = 15 mK only and a critical field Bc = 0.12 mT. Tc can be increased by introducing disorder and carbon incorporation to about 5.5 K and Bc to 9.5 T using e.g. ion- induced deposition techniques [3, 4]. Superconducting tungsten fabricated by FEBID has been so far reported only twice in the literature [1, 2]. Here we report the use of tungstenhexacarbonyl W(CO)6 molecules as a precursor for fabricating superconducting tungsten by FEBID.</p>
Mapping of samples – Deliverables in WP3: Novel pyrolysis oil from non-food/feed biomass
<p>In the H2020-project BioMates (www.biomates.eu, see chapter 4 “Funding and disclaimer”), RISE produced samples from ablative fast pyrolysis of herbaceous biomass in a TRL 5-plant. A dedicated document provides identifiers for relevant liquid samples and their blends [1]. The document at hand maps it to the substances reported to be used in the public deliverables and public deliverable summaries connected to Work Package 3 “Technology scale up and validation” of the BioMates-project, as far as mapping is not provided in the documents themselves.</p>
Optical Cluster set definitions associated with CERTO project deliverable 4.2
<p>This set of files consists of pickle and csv files that describe the optical water class sets computed as part of the CERTO project ( https://certo-project.org ). A written description and discussion of these clusters is provided in Deliverable 4.2 from the CERTO project.</p>
Supporting material of deliverable D2.1 (StretchBio_D.2.1_Report on relation deformation-force for the nanopillars)
<p>Nanopillars - Analytical vs numerical bending - Spreadsheet</p> <p>Nanopillars - Deformation_pillars_on_SiO2substrate_figure10 at deliverable D2.1</p> <p>Nanopillars - vonMises_stress_pillars_on_SiO2substrate_figure14 at deliverable D2.1</p>
Deliverable 1.2: Collection of national reports on the citizens' future visions
<p>This deliverable presents the 30 national reports on the citizens’ future visions from the National Citizen Vision Workshops (NCVs), held as a part of the CIMULACT project.</p> <p>The main objective of CIMULACT is to add to the relevance and accountability of the European Research & Innovation (R&I) agenda by engaging citizens and multi-actors in the actual formulation of the European Union’s R&I agenda. The NCVs contributed to this process by engaging citizens in formulating their visions for a desirable and sustainable future.</p> <p>Over a three month period (November 2015 until January 2016) 30 NCVs were held in 30 European countries (28 EU member states, as well as Switzerland and Norway). At each NCV citizens met for a full day to formulate and debate their visions for desirable and sustainable futures. All together 179 visions were formulated during the NCVs by more than 1000 citizens.</p> <p>The national reports on the citizens’ future visions each includes a summary of the NCV process and presents the original and unedited visions (raw visions and six<a href="#_ftn1">[1]</a> final visions) for each country. In addition, all reports include information on participant data. The summaries and the final visions are to be found in the national language and translated into English.</p> <p>The national reports on the citizens’ future visions offer a unique opportunity to identify the European citizens’ wishes, needs and demands for a sustainable and desirable future. The reports may inspire and give input to experts, policy- and decision makers all over Europe, hereby enhancing Responsible Research and Innovation (RRI) in the European Union.</p> <p>In the following, we introduce the necessary background information on how to read the national reports and interpret the visions. In addition we give a resume of the methodology and the NCV process. A deeper analysis of the results is to be found in Deliverable 1.3 –Vision Catalogue and Devliverable 2.1 –First draft of the societal needs research programme scenarios. </p> <p>CIMULACT is a three-year project funded by the Horizon 2020 Framework Program of the European Union. The project began in June 2015.</p> <p> </p> <p><a href="#_ftnref1">[1]</a> Ireland is an exception, since this country only formulated 5 visions.</p>
Deliverable 1.3 -Vision Catalogue - Encompassing the visions from all 30 countries
<p>This deliverable presents an English translation of the 179 visions elaborated by more than 1000 citizens during the National Citizen Vision Workshops (NCVs), arranged as a part of the CIMULACT project.</p> <p>The main objective of CIMULACT is to add to the relevance and accountability of the European Research and Innovation (R&I) agenda by engaging citizens and multi-actors in the actual formulation of the European Union’s R&I agenda. The NCVs contributed to this process by engaging citizens in formulating their visions for desirable and sustainable futures.</p> <p>Over a three month period (November 2015 until January 2016) 30 NCVs were held in 30 European countries (28 EU member states, as well as Switzerland and Norway). At each NCV 25-42 (36 on average) citizens met for a full day to formulate and debate their visions for a desirable and sustainable future.</p> <p>The visions represent the final product of the NCVs and are the result of a comprehensive and intensive vision building process in each of the participating countries. The visions were originally formulated in the citizens’ national language, but for simplicity all visions have been translated into English. The original visions and national reports from each NCV are to be found elsewhere (Deliverable 1.2 - Collection of national reports on the citizens’ future visions).</p> <p>The present deliverable documents the European citizens’ wishes, needs and demands for a desirable future. The visions enable dialogue between the citizens and the European policy- and decision makers, hereby enhancing Responsible Research and Innovation (RRI) in the European Union.</p> <p>CIMULACT is a three-year project funded by the Horizon 2020 Framework Program of the European Union. The project was kicked-off in June 2015</p> <p> </p>
Larval dispersal histogram data used for ATLAS deliverable D1.6: Biologically realistic Lagrangian dispersal and connectivity
<p>Larval dispersal histogram data for ATLAS deliverable D1.6 "Biologically realistic Lagrangian connectivity" (https://www.eu-atlas.org/resources/atlas-partners-document-area/atlas-deliverables/455-d1-6-biologically-realistic-lagrangian-connectivity/file). Tar archive files are ordered by ATLAS case study source region and with folders by larval behaviour type. The numbered behaviour types are described in deliverable D1.6. Each netcdf histogram file, e.g. hists_age_21.nc, contains the histogram for larvae of a single age in 5-day steps, from 00 (0 days) to 37 (185 days).</p> <p>Within each file histogram file, particle counts in each Viking20 model grid-cell are contained in a 4-d array with dimensions (launch month, lauch year, model gridsquare y index, model gridsquare x index). The Viking20 grid in the North Atlantic is the ORCA tripolar grid. Details of the model mesh are in the included file viking20_mesh_mask.tgz</p> <p>Histograms are in netcdf files:</p> <p>============================</p> <p>$ ncdump -h hists_age_00.nc<br> netcdf hists_age_00 {<br> dimensions:<br> coordinate = 4 ;<br> coordinate_1 = 50 ;<br> coordinate_2 = 1719 ;<br> coordinate_3 = 1784 ;<br> variables:<br> int64 coordinate(coordinate) ;<br> coordinate:units = "month" ;<br> coordinate:long_name = "Launch month" ;<br> int64 coordinate_1(coordinate_1) ;<br> coordinate_1:units = "year" ;<br> coordinate_1:long_name = "Launch year" ;<br> int64 coordinate_2(coordinate_2) ;<br> coordinate_2:units = "index" ;<br> coordinate_2:long_name = "J index" ;<br> int64 coordinate_3(coordinate_3) ;<br> coordinate_3:units = "index" ;<br> coordinate_3:long_name = "I index" ;<br> int64 data(coordinate, coordinate_1, coordinate_2, coordinate_3) ;<br> data :long_name = "particle count" ;</p> <p>// global attributes:<br> :Conventions = "CF-1.6" ;<br> }</p> <p>==========================================</p> <p> </p> <p> </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.