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114 results for “WP2”
Eumeplat WP2 dataset
<p>Aggregated data for EUMEPLAT Work Package 2.2 - Platformization of News in 10 Countries</p>
Results of quasi static test on CFRP/CFRP joints and CFRP/Al joint with different surface treatments within project _Spoke11_WP2_Task2.3_MOST
<p>Quasi-static single lap joint test on CFRP CFRP joints and CFRP aluminum joints. Two different epoxy adhesives have been used and different chemical surface treatments used in order to try to enhance the mechanical resistance of the single lap joint.</p>
Chinese Intellectual Debates and Writings WP2 - list of analysed sources
<p>The Excel file contains a list of resources studied for the WP2 Chinese Intellectual Debates and Writings of the project Chinese Conceptualisation of the Rule of Law (CLAW): Challenges for the International Legal Order, funded by the OP JAC Project "MSCA Fellowships at Palacký University II." CZ.02.01.01/00/22_010/0006945 at Palacký University Olomouc, Czech Republic. The list includes bibliographical details of the fifty most cited articles (for each keyword) listed in the CNKI database and published between 1979 and 2022. The selected keywords are 法治 / <em>fazhi</em> and 依法治国 / <em>yifazhiguo</em>. The table contains the following information: original title in Chinese, English title or translation, author(s) name in pinyin and Chinese characters, journal name, publication date, number of citations, number of downloads, permalink, and citation in APA.</p> <p>The second version includes minor language corrections.</p> <p>The content analysis of listed resources will be used as a base to generate new open-access content.</p>
WP2 T2.4 MEDIA T07 Live pilot 2 raw files, Cyclocross Soudal Leuven
<p>Two zip files - Directive and Omnidirectional. Inside the first one there is a proxy file of the TV broadcast of the filmed cyclocross men’s elite race. The zip file Omni has the 4 cameras and their respective raw files, that would require further editing to create a product in the form of a simulated live cyclocross experience, using either the ImmersiaTV plug-in or Cinegy Live.</p>
MADFORWATER: WP2: Adaptation of wastewater treatment technologies for agricultural reuse: Task2.4: Industrial wastewater treatment: Treatment of different types of wastewater by means of innovative resins: Subset3
<p>This dataset contains the data underlying the following publication: Li Qimeng, Wu Ji, Hua Ming, Zhang Guang, Li Wentao, Shuang Chendong, Li Aimin. (2017). Preparation of Permanent Magnetic Resin Crosslinking by Diallyl Itaconate and Its Adsorptive and Anti-fouling Behaviors for Humic Acid Removal. <em>Scientific Report. </em> <a href="https://doi.org/10.1038/s41598-017-17360-8">https://doi.org/10.1038/s41598-017-17360-8</a></p>
MADFORWATER: WP2: Adaptation of wastewater treatment technologies for agricultural reuse: Task2.4: Industrial wastewater treatment: Treatment of different types of wastewater by means of innovative resins: Subset1
<p>This dataset contains the data underlying the following publication: Li Wen-Tao, Cao Meng-Jie, Young Tessora, Ruffino Barbara, Dodd Michael, Li Ai-Min, Korshin Gregory. (2017).</p> <p>Application of UV absorbance and fluorescence indicators to assess the formation of biodegradable dissolved organic carbon and bromate during ozonation. Water Research 2017, 111, 154-162. <a href="http://dx.doi.org/10.1016/j.watres.2017.01.009">http://dx.doi.org/10.1016/j.watres.2017.01.009</a>.</p>
WP2 T2.4 MEDIA T12 Live pilot 3, MNM radio studio, visual radio pilot
<p> Live pilot 3, MNM radio studio, visual radio pilot</p> <p>3 files:<br> mnm-360-1.mp4: host of the show view<br> mnm-360-2.mp4: total view of the studio<br> mnm-360-3.mp4: dashboard of the host<br> </p> <p> </p>
MADFORWATER: WP2: Adaptation of technologies for efficient water management and treated wastewater reuse in agriculture: Task 2.3–Agro-industrial wastewater treatment: Subtask 2.3.1. –Treatment of olive mill wastewater (OMWW): Aerobic biological treatment in sequenced batch reactors (SBRs): Subset 1
<p>This dataset contains the data underlying the following publication: Fatma AROUS, Chadlia HAMDI, Souhir KMIHA, Nadia KHAMMASSI, Amani AYARI, Mohamed NEIFAR, Tahar MECHICHI, Atef JAOUANI (2018). Treatment of olive mill wastewater through employing sequencing batch reactor: Performance and microbial diversity assessment. 3 Biotech 2018, 8, 481. https://doi.org/10.1007/s13205-018-1486-6.</p>
PERCEIVE: WP2: Evaluation of EU citizens' perception of the EU project: Task2.1: Data relative to regional performance of the Cohesion Policy and institutional quality
<p>1. PERCEIVE regional panel datasets - secondary data collected from Eurostat, EU Commission on Strutural Fund Expenditures and quality of government for NUTS 1, 2 and 3 regions from 1990-2015, (STATA files). See codebook for more detail about variables</p> <p>2. Flash Eurobarometer survey data on "Awarness of EU Regional Policy" and questionaires (STATA files)</p> <p>3. Standard Eurobaromter survey data, annual, from 2000-2016 and questionaires (STATA files)</p> <p>4. Expenditure data on EU Structural Funds, latest three budget periods (2000-2020) (Excel file)</p> <p>5. codebook for all regional data</p>
SmartAqua4FuturE (SAFE) Horizone Europe project_WP2_T2.3.1_Keywater Fisheries_Vermiculture Dataset
Open the record for dataset details and reuse information.
IRS WP2 Task 2.4: Heat Flux Experiments With Magnet
<p>This folder contains 12 videos documenting heat flux experiments conducted in the PWK1 plasma wind tunnel facility at the Institute of Space Systems, University of Stuttgart, as part of the Horizon 2020 MEESST project. The videos showcase experiments designed to test the heat flux mitigation potential of an active magnetic shield during atmospheric entry.</p> <p>The setup features a magnetohydrodynamic (MHD) plasma probe, visible on the right side of the frame, which houses a high-temperature superconducting (HTS) coil. High-enthalpy air plasma flow is generated by the self-field magnetoplasmadynamic plasma generator, RD5, located on the left side of the frame. The MHD plasma probe is positioned 517.5 mm downstream from the anode exit plane of the RD5 generator.</p> <p>During testing, the magnetic (B) field created by the HTS solenoid within the MHD plasma probe was varied from 0 A to 50 A, with 50 A producing a magnetic field strength of approximately 0.67 T at the stagnation point. The probe was subjected to six different B-field strengths across two test conditions that simulated the thermochemical environments of early entry phases for two types of Earth reentry: hyperbolic (227 kW) and high-elliptic (181 kW).</p>
BIOFRUITNET - Boosting Innovation in Organic FRUIT production through knowledge NETworks - WP2 - Database of ready for practice materials
<p>In the project BIOFRUITNET (Boosting Innovation in Organic FRUIT production through knowledge NETworks, HORIZON 2020 Grant Agreement No. 862850), WP2, a database of technical and scientific publications concerning plant health care have been compiled. The materials in the database were collected by participants in the project (SLU, HortiAdvice, UCPH, GRAB, FIBL, FÖKO, INHORT, VSUO, LAIMBURG, CIHEM-IAMB, ECOVALIA). Materials were chosen for their relevance to organic fruit growers and advisors. The materials in the collection are describing ready for practice methods and tools, covering <strong>stone fruits, pome fruits and citrus fruits</strong>, within the selected main topics: <strong>pests, plant diseases and plant nutrition</strong>. Materials were collected from March to September 2020. The materials are research articles, technical guidelines, advisory material, pamphlets, videos, posters, podcasts etc. Project homepage: <a href="https://biofruitnet.eu/">https://biofruitnet.eu/</a></p>
Rat gadoxetate MRI signal dataset for the IMI-WP2-TRISTAN Six Test Compounds study
<p>Dataset to accompany the manuscript "Use of In Vivo Imaging and Physiologically-Based Kinetic Modelling to Predict Hepatic Transporter Mediated Drug–Drug Interactions in Rats" by Nicola Melillo, Daniel Scotcher, J. Gerry Kenna, Claudia Green, Catherine D. G. Hines, Iina Laitinen, Paul D. Hockings, Kayode Ogungbenro, Ebony R. Gunwhy, Steven Sourbron, John C. Waterton, Gunnar Schuetz, and Aleksandra Galetin (referred to as the 'Six Test Compounds' study).</p> <p><strong>data.zip</strong></p> <p>After extracting the contents within the data.zip folder, the MRI signal data acquired in this study are provided in csv format and can be found inside the <code>data/SixTestCompounds/01_signals</code> subfolder. Each csv file contains the liver and spleen region of interest (ROI) time curve data for a single rat at a specific site and day, after administration of the test compound of interest.</p> <p>The corresponding filename for each file is formatted as a string containing study descriptors (metadata) separated by underscores, i.e., filename = <code>compound_site_RatNumber_dayNumber_dataType</code>, e.g., <code>Asunaprevir_E_Rat2_2_Signals</code>.</p> <p><strong>results.zip</strong></p> <p>The results and figures relating to this manuscript were created using the outputs contained in <code>results/SixTestCompounds/2022-09-01</code>.</p> <p><code>01_model_ouputs</code> contains all outputs generated as a result of the tracer kinetic model fitting. Within this, plotted signal time curves for each acquistion per rat may be found in <code>figures/per_rat</code>, while average deltaR1 curves per drug may be found in <code>figures/per_drug</code>. All estimated parameter variables from the fitting are stored in <code>all_parameters.csv</code>. The folder <code>relaxation_rates_and_signals</code> contains the fitted MRI signal data in a similar format to the original csv data in <code>data/01_signals</code>. Additional columns have been included showing the MRI signals converted to R1.</p> <p><code>02_effect_sizes</code> contains results of the statistical analysis performed on the tracer kinetic modelling output. This is summarised in tabular format in <code>effect_sizes.csv</code>, while graphical distributions are provided within the <code>figures/</code> folder. <code>fit_errors.txt</code> contains an ID list for any computational fitting errors found during quality control of the tracer kinetic modelling.</p>
EUREKA WP2: Regional Interview Transcripts
ZIP file containing transcripts in English of 40 interviews conducted with farmers, foresters, advisors and other professionals across Europe by several EUREKA partners in the summer of 2020. The focus of the interviews was to discuss the (digital) information needs of potential users of the EU-FarmBook knowledge platform. This work informed the development of a set of representative 'user personas' and associated 'user journeys' to aid in the design of the EU-FarmBook. See Deliverable 2.2 for a full report of the findings.
EUREKA WP2: Regional Interview Transcripts
ZIP file containing transcripts in English of 40 interviews conducted with farmers, foresters, advisors and other professionals across Europe by several EUREKA partners in the summer of 2020. The focus of the interviews was to discuss the (digital) information needs of potential users of the EU-FarmBook knowledge platform. This work informed the development of a set of representative 'user personas' and associated 'user journeys' to aid in the design of the EU-FarmBook. See Deliverable 2.2 for a full report of the findings.
META-DATA for IEA Wind Task 46. WP2: Atmospheric drivers of wind turbine blade leading edge erosion: Ancillary variables
<p>Leading edge erosion (LEE) of wind turbine blades has been identified as a major factor in decreased wind turbine blade lifetimes and energy output over time. Accordingly, the International Energy Agency Wind Technology Collaboration Programme (IEA Wind TCP) created Task 46 to undertake cooperative research in the key topic of blade erosion.</p> <p>This report is a product of WorkPackage 2 <strong>Climatic conditions driving blade erosion. </strong></p> <p>The objectives of the work summarized in this report are to:</p> <ul> <li>Summarize efforts to elucidate critical atmospheric co-stressors that may accelerate leading edge erosion and hence for which meta-data regarding observations should be collated.</li> <li>Briefly describe and summarize additional data pertaining to those LEE co-stressors from sites that were the focus of analyses of hydrometeors in the report “Atmospheric drivers of wind turbine blade leading edge erosion: Hydrometeors” (Pryor et al. 2021)</li> </ul> <p>Accompanying this report is a detailed spreadsheet that summarizes the meta-data regarding these co-stressor variables.</p>
Rat gadoxetate MRI signal dataset for the IMI-WP2-TRISTAN Reproducibility study
<p>Dataset to accompany the manuscript "Assessment of hepatic transporter function in rats using dynamic gadoxetate-enhanced MRI: A reproducibility study" by Ebony R. Gunwhy, Catherine D. G. Hines, Claudia Green, Iina Laitinen, Sirisha Tadimalla, Paul D. Hockings, Gunnar Schütz, J. Gerry Kenna, Steven Sourbron, and John C. Waterton (referred to as the 'Reproducibility' study).</p> <p><strong>data.zip</strong></p> <p>After extracting the contents within the data.zip folder, the MRI signal data acquired in this study are provided in csv format and can be found inside the <code>data/Reproducibility/01_signals</code> subfolder. Each csv file contains the liver and spleen region of interest (ROI) time curve data for a single rat from a specific substudy and day, after administration of the test compound of interest.</p> <p>The corresponding filename for each file is formatted as a string containing study descriptors (metadata) separated by underscores, i.e., filename = <code>substudy_</code><code>compound_site_RatNumber_dayNumber_dataType</code>, e.g., <code>1_Rifampicin_D_Rat5_2_Signals</code>.</p> <p><strong>results.zip</strong></p> <p>The results and figures relating to this manuscript were created using the outputs contained in <code>results/Reproducibility/2023-04-17</code>.</p> <p><code>01_model_ouputs</code> contains all outputs generated as a result of the tracer kinetic model fitting. Within this, plotted signal time curves for each acquistion per rat may be found in <code>figures/per_rat</code>, while average deltaR1 curves per substudy may be found in <code>figures/per_substudy</code>. All estimated parameter variables from the fitting are stored in <code>all_parameters.csv</code>. The folder <code>relaxation_rates_and_signals</code> contains the fitted MRI signal data in a similar format to the original csv data in <code>data/01_signals</code>. Additional columns have been included showing the MRI signals converted to R1. <code>fit_errors.txt</code> contains an ID list for any computational fitting errors found during quality control of the tracer kinetic modelling.</p> <p><code>02_analyses</code> contains results of the statistical analysis performed on the tracer kinetic modelling output. These are summarised in tabular csv format within the <code>repeatability/ </code>and <code>reproducibility/</code> folders, while graphical distributions are provided within the <code>figures/</code> folder. Summary results for benchmark values and from the mixed ANOVA are also provided in <code>benchmarks.csv </code>and<code> mixed_anova.csv</code>, respectively. Please see the manuscript for further details on these results.</p>
WP2 dataset (Foss)
<p>The dataset contains the spectral data acquired on waste wood samples using a Foss DS2500 spectrophotometer (Foss A/S, Hillerød, Denmark). Waste wood samples were collected in different locations in Italy and Denmark during September and October 2020. The sampling involved recycling centres in Danish and in Italian municipalities, a panel board company and a company producing blocks for pallets. The sampling was carried out from static lots, focusing on collecting large pieces of woody material for ensuring the assignment into the proper waste wood category. </p> <p>In fact, in order to choose the proper reuse of the material according to its characteristics, the collected waste wood was sorted into three main waste wood categories according to the possible reuse of the material: i) virgin wood (VW), i.e. clean wood, that can be used both for panel board production and energy applications; ii) treated wood (TW), consisting in wood mixed with glue/resins (panel board, plywood, oriented-strand board, etc.) that can be reused for panel board production or energy applications in big combustion plants provided with gas cleaning filters; and iii) disposal wood (DW), consisting of impregnated and painted wood that needs to be sent to the disposal and cannot be reused. The collected samples are also divided in 12 sub-categories, containing a more detailed description of the material quality. </p> <p>The file ‘WP2_Foss_040723_v01.csv’ contains the spectral dataset while ‘Legend_WP2_Foss_040723_v01.csv’ contains a legend with the acronyms used. In detail ‘WP2_Foss_040723_v01.csv’ contains the following information in the columns:</p> <ul> <li>sample code: it is reporting the sample code where the first three letters indicate the sampling location, the successive two letters are the waste wood subcategory and the last number the NIR replicate (please have a look at Legend_WP2_Foss_040723_v01.csv for complete information);</li> <li>category: virgin wood corresponding to 0, treated wood corresponding to 1 and disposal wood corresponding to 2; </li> <li>subcategory: it is a more detailed description of the waste wood category (please have a look at Legend_WP2_Foss_040723_v01.csv file);</li> <li>spectral data: absorbance values for each sample from 400 nm to 2499.5 nm.</li> </ul> <p>The aim behind this dataset is to characterize the waste wood material based on its quality and consequently determine its most suited applications (WP2 of WoodSpec project). In fact, the classification of waste wood samples is a key-point for improving the sorting of the material and its reuse, avoiding expensive landfill. Having knowledge of the waste wood properties and characteristics could help in deciding the best recycling option.</p> <p>If you would like know more about the data, or to use these data, please refer to our article in Analytica Chimica Acta, doi: https://doi.org/10.1016/j.aca.2023.341564</p> <p> </p> <p><em>Funding: The project leading to this application has received funding from theEuropean Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 838560.</em></p> <p><em>Terms of use: These data are provided "as is", without any warranties of any kind. The data are provided under the Creative Commons Attribution 4.0 International license.</em></p>
PROLIFIC_790157_IRIS_WP2_UAE_protein
<p>Technical data regarding the extraction of protein assisted by ultrasound.</p>
WP2 T2.4 MEDIA T05 Cyclocross Commentator Paul and Michel start
<p>Commentator Paul and Michel_start.zip: codec mp4 (60MB/s), taken from 4 camera GOPRO rig,<br> raw material suitable for stitching software (Videostitch)</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.