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175 results for “h2020”
H2020 Platone German Demonstrator Use Case 1 Asset and Topolgy Data
<p>This dataset asset and topology data of the field test setup. The dataset gives details about:</p> <p>- Low Voltage (LV) network</p> <p>- Tranformer located in the secondary substation</p> <p>- Community Battery Energy Storage System (CBES) connected to the LV-busbar in the 2nd. Substation</p> <p>- PV and number of households located in the energy community</p> <p>- PV installed generation power of the community</p> <p>- Domestic Storages and Inverter</p> <p> </p>
H2020 Platone German Demonstrator Use Case 1 Market Data
<p>This dataset contains settings of the Local Energy Management System, that have been set via a Graphical User Interface (GUI). The dataset contain follwowing data:</p> <p>H2020_Platone_GER_Market__ALF-C_GUI_Timestamp</p> <p>H2020_Platone_GER_Market__ALF-C_GUI_Use Case</p> <p>H2020_Platone_GER_Market__ALF-C_GUI_UC_ID</p> <p>H2020_Platone_GER_Market__ALF-C_GUI_Option</p> <p>H2020_Platone_GER_Market__ALF-C_GUI_Priority</p> <p>H2020_Platone_GER_Market__ALF-C_GUI_Submission_Time</p> <p>H2020_Platone_GER_Market__ALF-C_GUI_UC_Start_Date</p> <p>H2020_Platone_GER_Market__ALF-C_GUI_UC_End_Date</p> <p> </p> <p> </p>
H2020 Platone Italian Demonstrator Use Case 1-2 Measurements
<p>Description of the database "areti_profile_flexibility_customer_2021":</p> <p>Data about the flexibility measurement of the users involved in the trial.</p> <p>In the database you will find: </p> <p>Date: Measurement date (Mmm dd, yyyy);<br> Timestamp: Measurement time (hh:mm:ss.sss @UTC);<br> pod: Point of Delivery of the users' place (PoD) identification code;<br> measures.energy.absorbedActiveEnergy.value: Quarter-hour sample of active energy absorbed (kWh) [as per ID 6 in Tab. A.5 - CEI 13-82];<br> measures.energy.injectedActiveEnergy.value: Quarter-hour sample of active energy injected (kWh) [as per ID 7 in Tab. A.5 - CEI 13-82];<br> measures.energy.absorbedInductiveReactiveEnergy.value: Quarter-hour sample of inductive reactive energy when active energy absorbed (kVARh) [as per ID 9 in Tab. A.5 - CEI 13-82]; <br> measures.energy.absorbedCapacitiveReactiveEnergy.value: Quarter-hour sample of capacitive reactive energy when active energy absorbed (kVARh) [as per ID 10 in Tab. A.5 - CEI 13-82]; <br> measures.energy.injectedInductiveReactiveEnergy.value: Quarter-hour sample of inductive reactive energy when active energy injected (kVARh) [as per ID 11 in Tab. A.5 - CEI 13-82]; <br> measures.energy.injectedCapacitiveReactiveEnergy.value: Quarter-hour sample of capacitive reactive energy when active energy injected (kVARh) [as per ID 12 in Tab. A.5 - CEI 13-82];<br> measures.power.activePower.value: Quarter-hour average of active power exchange (kW) [as per ID 16 in Tab. A.5 - CEI 13-82];</p> <p> </p> <p>(Useful link to consult Italian UC:</p> <p><a href="https://eur01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fsmart-grid-use-cases.github.io%2Fdocs%2Fusecases%2Fplatone%2Fuc-it-1-voltage-management%2F&data=04%7C01%7Cfabio.bastianelli%40mail-bip.com%7C0bb5a42aac0c49cb876708d9f2c89510%7Cbb1a63ebeb09471aa00537b07792a5b5%7C0%7C0%7C637807765572787908%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=ptAsS52VBberHmZqIzYEXZs1PQrXQ6TDz6mNK%2FNWnk0%3D&reserved=0">https://smart-grid-use-cases.github.io/docs/usecases/platone/uc-it-1-voltage-management/</a></p> <p><a href="https://eur01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fsmart-grid-use-cases.github.io%2Fdocs%2Fusecases%2Fplatone%2Fuc-it-2-congestion-management%2F&data=04%7C01%7Cfabio.bastianelli%40mail-bip.com%7C0bb5a42aac0c49cb876708d9f2c89510%7Cbb1a63ebeb09471aa00537b07792a5b5%7C0%7C0%7C637807765572787908%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=xWNOqSeS5JxDoBEWZ4aB63gLmsnTA8YGGfCOoLjo1eo%3D&reserved=0">https://smart-grid-use-cases.github.io/docs/usecases/platone/uc-it-2-congestion-management/</a></p> <p><a href="https://platone-h2020.eu/data/deliverables/864300_M12_D1.1.pdf">https://platone-h2020.eu/data/deliverables/864300_M12_D1.1.pdf</a>)</p>
GERONTE H2020 project - GERDAT005 - Intrinsic capacity evaluation and intervention protocol
<p><strong>The present document is a dataset generated as part of Deliverable D1.1. of the GERONTE project, which has received funding from the European Union’s Horizon 2020 Programme under Grant Agreement N°945218. It aims to provide the geriatric oncology professional community with a protocol for the evaluation of intrinsic capacity and frailty, with subsequent interventions aimed at optimizing health status and support for older patients with multimorbidity and cancer.</strong></p> <p>GERONTE is a 5-year research and innovation project (April 2021 to Mars 2026) funded by the European Union within the framework of the H2020 Research and Innovation programme, in response to the health societal challenge topic SC1-BHC-24-2020 “Healthcare interventions for the management of the elderly multimorbid patient”. The overall aim of GERONTE is to improve quality of life - defined as well-being on three levels: global health status, physical functioning and social functioning- for older multimorbid patients, while reducing overall costs of care. To this end, GERONTE will co-design, test, and prepare for deployment an innovative cost-effective patient-centred holistic health management system, hereafter referred to as the GERONTE intervention. GERONTE intervention will rely on an ICT based application for real-time collection and integration of standardised clinical and home patient-reported data. GERONTE intervention will be demonstrated in the context of care of multimorbid patients having cancer as a dominant morbidity, and be adaptable to any other combination of morbidities.</p> <p>An important component of Geronte is to take account of intrinsic capacity. Most older patients who are diagnosed with cancer also suffer from other illnesses and impairments that could affect their prognosis, priorities and ability to tolerate and benefit from treatment. For tailored oncologic decision making, it is essential to obtain a complete overview of the patient’s health status. This dataset contains the protocol for evaluation intrinsic capacity and potential interventions for impairments or vulnerabilities that were identified in this evaluation. It is going to be used in the assessment and management of older patients with multimorbidity and cancer, within the GERONTE care pathway.</p>
In situ smartphone radiometry of Lake Balaton, MONOCLE H2020 project
<p>This dataset includes in situ radiometric data collected from Lake Balaton and Kis-Balaton, Hungary during July 3<sup>rd</sup> – July 5<sup>th</sup>, 2019 within the framework of Horizon 2020 MONOCLE project.</p> <p>The Python code used to analyse the data and produce the summary tables, and which should be used to read the data in, can be found here: <a href="https://github.com/burggraaff/smartphone-water-colour">https://github.com/burggraaff/smartphone-water-colour</a></p> <p>The data are structured as follows:</p> <p>"Balaton_2019070x" - These folders contain the RAW and JPEG smartphone images, sorted by station, time, and smartphone. Each low-level subfolder includes the RAW and JPEG images and a CSV file with the derived radiance, R_rs, and other values.</p> <p>"Discarded_data" - These folders contain RAW and JPEG smartphone images that were not used, organised in the same way as "Balaton_2019070x". They are included here for completeness. Each subfolder contains a file explaining why the data were not used.</p> <p>"Greycard" - This folder contains the RAW smartphone images used to characterise the angular response of the grey card. Also included are the derived mean/uncertainty values in .NPY format.</p> <p>"BALATON_2019_STATION_LOG" - This worksheet contains the station log of the MONOCLE field campaign at Lake Balaton in 2019, during which our field data were taken.</p> <p>"balaton_xxx_18pct.csv" - These CSV files contain summaries of the data derived from the smartphone RAW images, including radiance, R_rs, etc. They are most easily read in using the Python code linked above. These files were generated by stacking the individual CSV files from each station/time/smartphone. The filename indicates the smartphone and data type.</p> <p>"balaton_Samsung_Galaxy_S8_raw_replicates.csv" - This CSV file contains the relative uncertainty (R/G/B and band ratios, in %) and absolute uncertainty (hue angle and FU) in replicate Galaxy S8 measurements, as described in the paper.</p> <p>"greycard_data_Maine.csv" - This CSV file contains the data used to determine the spectral response of the grey card. It includes spectral measurements of the surface irradiance on a white panel and the grey card, and the downwelling irradiance measured with a cosine collector.</p> <p>"README.txt" - Description of the data.</p> <p>"So-Rad_Balaton2019.csv" - This CSV file contains the (ir)radiance data and R_rs from the So-Rad on 3 July 2019, processed using the 3C method, as described in the paper.</p> <p>"wisp_Balaton_20190703_20190705_table.csv" - This CSV file contains the (ir)radiance data and R_rs from the WISP-3 on 3-5 July 2019, processed using the Mobley method, as described in the paper.<br> </p>
H2020 ENODISE: UTWE Configuration A Experimental Databases with Mitigation
<p>This database contains the datasets for the aeroacoustic experiments conducted at the University of Twente on the H2020 ENODISE project, Task 6.1 (experimental databases with mitigation), Configuration A1 - Wall mounted.</p> <p><br>Configuration A1 consists of a propeller ingesting a zero-pressure-gradient boundary layer. Far-field acoustics are measured using two microphone arrays, to assess directivity and sound pressure levels. Different treatments are used at the flat plate underneath the propeller to attenuate the noise reflected by the plate. Two main strategies are analyzed: tuned quarter-wavelength resonator arrays - to reduce tonal noise - and a broadband noise absorber (metal foam). The quarter-wavelength resonator arrays are tested in different geometrical distributions. </p> <p><br>That forms part of deliverables D6.4, D6.5, and D6.6 - see reports for further information regarding measurement techniques. Included are descriptive READMEs. The data is in the HDF5 format and comprises mean, spectral, and other processed data types in engineering units. CAD files are also added, to properly describe the setup and its components.</p>
REASSURE (H2020 731591) Datasets AES128 for Understanding Leakage Detection - Section 2
<p>Datasets collection for the Section 2 of "Understanding Leakage Detection" tutorial (presented also at CARDIS 2018). These datasets have been collected adopting the ChipWhisperer Lite board, running the popular AES Furious implementation. The code and the additional material of the tutorial can be found at http://reassure.eu/leakage-detection-tutorial/.</p> <p> </p>
Dataset generated to evaluate in situ sampling strategies to reconstruct fine-scale ocean currents in the context of SWOT satellite mission (H2020 EuroSea project)
<p><strong>Dataset generated in Subtask 2.3.1 of the H2020 EuroSea project.</strong></p> <ul> <li> <p><em>H2020 EuroSea project:</em><br> The H2020 EuroSea project aims at improving and integrating the European Ocean Observing and Forecasting System (see official website: <a href="https://eurosea.eu/">https://eurosea.eu/</a>). It has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 862626).</p> </li> <li> <p><em>Task 2.3:</em><br> Task 2.3 has the objective to improve the design of multi-platform experiments aimed to validate the Surface Water and Ocean Topography (SWOT) satellite observations with the goal to optimize the utility of these observing platforms. Observing System Simulation Experiments (OSSEs) have been conducted to evaluate different configurations of the in situ observing system, including rosette and underway CTD, gliders, conventional satellite nadir altimetry and velocities from drifters. High-resolution models have been used to simulate the observations and to represent the “ocean truth”. Several methods of reconstruction have been tested: spatio-temporal optimal interpolation, machine-learning techniques, model data assimilation and the MIOST tool. The planned OSSEs are detailed in this public report <a href="https://doi.org/10.3289/eurosea_d2.1">Barceló-Llull et al. (2020)</a> and the complete analysis is available here <a href="https://doi.org/10.3289/eurosea_d2.3">Barceló-Llull et al. (2022)</a>. Contributors to Task 2.3 are CSIC (Spain), CLS (France), SOCIB (Spain), IMT-Atlantique (France) and Ocean-Next (France).</p> </li> <li> <p><em>Subtask 2.3.1:</em><br> Subtask 2.3.1 aims to evaluate different in situ sampling strategies to reconstruct fine-scale ocean currents (~20 km) in the context of SWOT. An advanced version of the classic optimal interpolation used in field experiments, which considers the spatial and temporal variability of the observations, has been applied to reconstruct different configurations with the objective to evaluate the best sampling strategy to validate SWOT.</p> </li> <li> <p><em>Where?</em><br> The analysis focuses on two regions of interest: (i) the western Mediterranean Sea and (ii) the Subpolar North West Atlantic. In the western Mediterranean Sea, the target area is located within a swath of SWOT, while in the North West Atlantic the region of study includes a crossover of SWOT during the fast-sampling phase.</p> </li> </ul> <p><strong>Report with the full analysis</strong></p> <p>The complete analysis can be found in this report: <a href="https://doi.org/10.3289/eurosea_d2.3">Barceló-Llull et al. (2022)</a>.</p> <p><strong>Codes for the analysis</strong></p> <p>The codes generated to develop Subtask 2.3.1 can be found on GitHub: <a href="https://github.com/bbarcelollull/EuroSea_subTask_2.3.1">https://github.com/bbarcelollull/EuroSea_subTask_2.3.1</a></p> <p><strong>The dataset</strong></p> <p>The dataset includes:</p> <p>1) Model outputs used to simulate the observations in different configurations in both regions of study. The folder "2D_model_outputs" contains 2D data used to simulate SSH observations for the analysis of the temporal correlation scale (<a href="https://doi.org/10.3289/eurosea_d2.3">Barceló-Llull et al., 2022</a>, p. 28-42). The folder "3D_model_outputs" contains 3D model outputs used to simulate observations of temperature and salinity. Note that eNATL60 outputs have been interpolated onto a new regular grid. </p> <p>2) Simulated configurations (or sampling strategies) in each region (PKL file format).</p> <p>3) Observations simulated in each configuration in both regions of study. The observations simulated are temperature and salinity. ADCP horizontal velocities are also simulated, however for eNATL60 they will be corrected in the future to account for the rotated original axes. File format: region_configuration_period_model.nc. The folder "SSH" includes the simulated SSH observations for the analysis of the temporal correlation scale (<a href="https://doi.org/10.3289/eurosea_d2.3">Barceló-Llull et al., 2022</a>, p. 28-42).</p> <p>4) Reconstructed fields with the spatio-temporal optimal interpolation. File format: region_configuration_period_model_stOI_Lx_Lt_cd_YYYYMMDDhhmm_var.nc (stOI = spatio-temporal optimal interpolation, Lx = spatial correlation scale, Lt = temporal correlation scale, cd = map on the central date of the sampling, YYYYMMDDhhmm = date and time of the map, var = variable interpolated (temperature and salinity) or the derived variables (dynamic height, geostrophic velocities and the Rossby number)).</p> <p>5) Compared fields (ocean truth from model outputs vs. reconstructed fields) for each region and model (PKL file format).</p> <p> </p>
H2020 Platone German Demonstrator - Baseline Active Power Exchange at Grid Connection Point (Medium Voltage/Low Voltage)
<p>The given data are computed values for the active power exchange at the medium (MV)/low voltage grid connecting feeder (active power). The data are provided as 15-minutes mean values in kilowatt. The computed indicate the power exchange that would have been measured, in case no use case would have been applied in the field (control of batteries).</p> <p><strong>Data Description:</strong></p> <ul> <li>p_tei_c_mean = arithmetic mean of p_tei computed in 1-minute intervals devided by number of samples available for computing within 15 minutes (p_tei_count)</li> <li>p_tei_c_min = the minimum value (1-minute mean) computed within the period of p_tei_mean (15-minutes)</li> <li>p_tei_c_max = the maximum value (1-minute mean) computed within the period of p_tei_mean period (15-minutes)</li> </ul> <p><strong>Field Test Setup</strong></p> <p>The field test setup of the demonstrator consists of a MV/LV substation, 89 households, 450kW of installed PV generation capacity, a large scale battery with 300 kW and 850 kWh capacity. </p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 864300</p>
i-Dreams H2020 EU Project: Sample dataset
<p>The overall objective of the <em>i</em>-DREAMS project is to setup a framework for the definition, development, testing and validation of a context-aware safety envelope for driving (‘Safety Tolerance Zone’), within a smart Driver, Vehicle & Environment Assessment and Monitoring System (<em>i</em>-DREAMS). Taking into account driver background factors and real-time risk indicators associated with the driving performance as well as the driver state and driving task complexity indicators, a continuous real-time assessment is made to monitor and determine if a driver is within acceptable boundaries of safe operation. Moreover, safety-oriented interventions were developed to inform or warn the driver real-time in an effective way as well as on an aggregated level after driving through an app- and web-based gamified coaching platform. The conceptual framework, which was tested in a simulator study and three stages of on-road trials in Belgium, Germany, Greece, Portugal and the United Kingdom on a total of 600 participants representing car, bus, and truck drivers, respectively. Specifically, the Safety Tolerance Zone (STZ) is subdivided into three phases, i.e. ‘Normal driving phase’, the ‘Danger phase’, and the ‘Avoidable accident phase’. For the real-time determination of this STZ, the monitoring module in the<em> i</em>-DREAMS platform continuously register and process data for all the variables related to the context and to the vehicle. Regarding the operator, however, continuous data registration and processing are limited to mental state and behavior. Finally, it is worth mentioning that data related to operator competence, personality, socio-demographic background, and health status, are collected via survey questionnaires. More information of the project can be seen from project website: https://idreamsproject.eu/wp/</p> <p>This dataset contains naturalistic driving data of various trips of participants recruited in i-Dreams project. Various different types of events are recorded for different intensity levels such as headway, speed, acceleration, braking, cornering, fatigue and illegal overtaking. Running headway, speed, distance, wipers use, handheld phone use, high beam use and other data is also recorded. Driver characteristics are also available but not part of this sample data. In the i-Dreams project, raw data for a particular trip was collected via CardioID gateway, Mobileye, wristband or CardioWheel. These trip data are fused using a feature-based data fusion technique, namely geolocation through synchronization and support vector machines. The system provided by CardioID integrates several data streams, generated by the different sensors that make up the inputs of the i-Dreams system. The sample dataset is fused, processed as well as aggregated to produce consistent time series data of trips for a particular time interval such as 30 secs/ 60 secs or 2- minutes intervals. More datasets can be acquired for analysis purposes by following the data acquisition process given in the data description file.</p>
EVIDENT H2020– Environmental data for Sweden cities Dataset
<p>EVIDENT H2020- Environmental data for Swedish Cities Dataset</p> <p>Environmental data from 615 cities in Sweden</p> <p>Weather, in combination with residential characteristics and electricity consumption, might be useful to consider in association with other datasets. In the instance of EVIDENT, they will be examined in combination with electricity consumption to establish the correlation with weather and to examine if weather conditions contribute and should be considered for policy development.</p> <p>The data have been collected from 18.10.2021 to 4.05.2023 and refer to 615 Swedish cities. The collection has been carried out with agents created and by calling in API. In the file "Sweden_Cities_Avg_DaySect.xlsx", all cities have averaged from all measures. Also, the day has been divided into 3 sections and the averages apply to each section of the day.</p> <p>In each city, on average, there are 6 measurements per day. The source dataset is "swedish_cities_environmental.csv.". example</p> <table> <tbody> <tr> <td>country</td> <td>city</td> <td>temperature</td> <td>feels_like</td> <td>temp_min</td> <td>temp_max</td> <td>pressure</td> <td>humidity</td> </tr> </tbody> </table> <table> <tbody> <tr> <td>wind_speed</td> <td>wind_deg</td> <td>sunrise</td> <td>sunset</td> <td>weather_description</td> </tr> </tbody> </table> <p>There are 2 more datasets, "swedish cities environmental_tranformDay.csv" and "swedish cities environmental_week.csv", and refer to transformations made in the original dataset.<br> The first file is about the day analysis, where the day has been divided into 3 sections and depending on the time of the measurement, a new column has been created in the Day section and can take values 0,1,2. In addition, there is the column day_hours which is the duration of the day in seconds from sunrise to sunset. Finally, there is pressure, humidity and wind speed. In the second file, the column weekday has been added and relates to the day of the week (e.g. Monday), and the daily analysis has been removed.</p> <p>More information can be found on the public deliverables of the EVIDENT project <a href="https://evident-h2020.eu/deliverables/">https://evident-h2020.eu/deliverables/</a>. More specifically, the experiment's theoretical framework and motivation are described in are described in deliverable D1.2 <a href="https://evident-h2020.eu/wp-content/uploads/2021/12/EVIDENT_D1.2_Assessing_behavioural_biases_and_financial_literacy.pdf">Assessing behavioural biases and financial literacy</a> and deliverable <strong>D1.3</strong> <a href="https://evident-h2020.eu/wp-content/uploads/2022/03/EVIDENT_D1.3_Specification_of_Big_Data_Analytics.pdf">Specifications of Big Data Analytics</a>, in section 4 while the final design is reported in <strong>D3.2</strong> <a href="http://evident-h2020.eu/wp-content/uploads/2023/01/EVIDENT_D3.2_Implementation-of-preparatory-actions-for-RCT-surveys-and-serious-game.pdf">Implementation of preparatory actions for RCT, surveys and serious game</a>.</p>
REASSURE (H2020 731591) ECC Dataset
<p>Datasets collection for ECC (C25519) side-channel traces, as part of REASSURE H2020 731591 project.</p> <p>The set “REASSURE_c25519_arithm_6k + PatternExtract From 5997 traces + StaticAlign.trs” contains electromagnetic traces coming from 5997 executions of Curve25519 $\mu$NaCl Montgomery Ladder scalar multiplication:<br> <a href="https://eur03.safelinks.protection.outlook.com/?url=http%3A%2F%2Fmunacl.cryptojedi.org%2Fcurve25519-cortexm0.shtml&data=02%7C01%7Cdavide.bellizia%40uclouvain.be%7C8c9e51578a42428bd87008d80c926b2f%7C7ab090d4fa2e4ecfbc7c4127b4d582ec%7C0%7C0%7C637273171350333478&sdata=m8uJEnfiU%2BAw7s%2Bt%2BKX%2Fgf8RcJM849UH2p2%2B%2FGHJtOs%3D&reserved=0">http://munacl.cryptojedi.org/curve25519-cortexm0.shtml</a><br> running on the Pi\~{n}ata target:<br> <a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.riscure.com%2Fproduct%2Fpinata-training-target%2F&data=02%7C01%7Cdavide.bellizia%40uclouvain.be%7C8c9e51578a42428bd87008d80c926b2f%7C7ab090d4fa2e4ecfbc7c4127b4d582ec%7C0%7C0%7C637273171350333478&sdata=39fNqShJ8l5BoZ4HMtHLgviylORABJyq4Ri1wOQGpOI%3D&reserved=0">https://www.riscure.com/product/pinata-training-target/</a><br> which is a 32-bit STM32F4 microcontroller with an ARM-based architecture, running at the clock frequency of 168 MHz.<br> <br> The implementation employs arithmetic-based conditional swap and is additionally protected with projective coordinate re-randomization and scalar randomization.<br> Each trace from the dataset represent a single iteration of the Montgomery Ladder scalar multiplication that is cut from the whole execution trace; such trace is labeled with the corresponding cswap condition bit.<br> Observe that a full scalar can be trivially recovered from the cswap condition bits used in the 255 Montgomery Ladder iterations.<br> Furthermore, all these cut traces (5997*255=1529235) are aligned to exploit the leakage efficiently.<br> Details about the implementation and how the traces are aligned are in:<br> <a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Feprint.iacr.org%2F2016%2F923.pdf&data=02%7C01%7Cdavide.bellizia%40uclouvain.be%7C8c9e51578a42428bd87008d80c926b2f%7C7ab090d4fa2e4ecfbc7c4127b4d582ec%7C0%7C0%7C637273171350343476&sdata=i3GZpv26FuYFwvOSxGjtXm2EU%2Be5xy5XCHEw%2BkrDuKE%3D&reserved=0">https://eprint.iacr.org/2016/923.pdf</a><br> <br> The set “REASSURE_c25519_arithm_6k + PatternExtract From 100 traces + StaticAlign.trs” contains a part of the 5997 set, but limited to the first 100 full traces.<br> <br> The set “REASSURE_c25519_arithm_6k_100 full traces.trs” contains the full 100 traces (before division).<br> <br> Each traces is in the TRS format that is described under the following links:<br> <a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com%2FRiscure%2Fpython-trsfile&data=02%7C01%7Cdavide.bellizia%40uclouvain.be%7C8c9e51578a42428bd87008d80c926b2f%7C7ab090d4fa2e4ecfbc7c4127b4d582ec%7C0%7C0%7C637273171350343476&sdata=v78Tofs4%2BWvvSYhtqF7dW2uaU1nanllTXA5RyGshjWU%3D&reserved=0">https://github.com/Riscure/python-trsfile</a><br> <a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com%2FRiscure%2Fjava-trsfile&data=02%7C01%7Cdavide.bellizia%40uclouvain.be%7C8c9e51578a42428bd87008d80c926b2f%7C7ab090d4fa2e4ecfbc7c4127b4d582ec%7C0%7C0%7C637273171350353467&sdata=Nw%2FAamSU1RQaoCeIC80786IKn4f%2BuOl7wfyjNk5R1C4%3D&reserved=0">https://github.com/Riscure/java-trsfile</a><br> <a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com%2FRiscure%2FJlsca&data=02%7C01%7Cdavide.bellizia%40uclouvain.be%7C8c9e51578a42428bd87008d80c926b2f%7C7ab090d4fa2e4ecfbc7c4127b4d582ec%7C0%7C0%7C637273171350353467&sdata=sVPz0gh96ivEqMNxUNN4C36RV9ulAE%2BOu5DQ0w44Pic%3D&reserved=0">https://github.com/Riscure/Jlsca</a><br> <br> Moreover, note that each trs file include a short description inside the file itself.</p>
Inter- and transdisciplinary projects in FP7 and H2020 (May 2019)
<p>The metadata of interdisciplinary (IDR) and transdisciplinary (TDR) projects conducted under the European Union framework programs (FP7 & Horizon 2020) were collected from the Cordis database (<a href="https://cordis.europa.eu/">https://cordis.europa.eu/</a>). SHAPE-ID research team used periodic data dumps, stored in EU open data portal (<a href="https://data.europa.eu/euodp/en/data/dataset/cordisfp7projects">https://data.europa.eu/euodp/en/data/dataset/cordisfp7projects</a> and <a href="https://data.europa.eu/euodp/en/data/dataset/cordisH2020projects">https://data.europa.eu/euodp/en/data/dataset/cordisH2020projects</a>). </p> <p>The data dump from <strong>May 2019</strong> was used, so the FP7 database is complete while H2020 projects were still being added periodically.</p> <p>CORDIS files were subsequently queried for interdisciplinar* or transdicsiplinar*, matched against title or abstract (“objective”). This procedure allowed for creating two subsets:</p> <p>FP7_projects_May2019_IDR_TDR.csv 1750 FP7 projects. Out of 1699 IDR projects, interdisciplinar* featured in 40 titles and 1679 abstracts. Out of 56 TDR projects transdisciplinar* featured in 2 project titles and 54 abstracts.</p> <p>1912 H2020 projects (as of May 2019). Out of 1837 IDR projects, interdisciplinar* featured in 57 titles and 1820 abstracts. Out of 85 TDR projects transdisciplinar* featured in 2 project titles and 85 abstracts.</p> <p><strong>Description of the files </strong></p> <p>CSV files contain the same fields as CORDIS database data dumps: id, acronym, status, programme, topics, framework Programme, title, startDate, endDate, projectUrl, objective, totalCost, ecMaxContribution, call, fundingScheme, coordinator, coordinatorCountry, participants, participantCountries, subjects.</p> <p>Additional fields:</p> <p>IDR - project features interdiciplinary research (1 = yes, 0 = no)</p> <p>TDR - project features transdiciplinary research (1 = yes, 0 = no)</p> <p>Title_Interdisciplinar* - frequency of “interdisciplinar*” in the project title</p> <p>Objective_interdisciplinar*- frequency of “interdisciplinar*” in the project objective</p> <p>Title_transdisciplinar* - frequency of “interdisciplinar*” in the project title</p> <p>Obj_transdisiplinar* - frequency of “interdisciplinar*” in the project objective</p> <p>Reference data (countries, funding schemes/types of action, subjects (SIC codes)) can be found in this dataset: <a href="https://data.europa.eu/euodp/en/data/dataset/cordisref-data">https://data.europa.eu/euodp/en/data/dataset/cordisref-data</a></p> <p> </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>
H2020 Platone German Demonstrator Use Case 1 Measurement Data
<p>This dataset contains measurement datas collected from mesurements devices in the field (substation, battery storage, etc) during the application of Use Case 1 (Islanding/Maximization of local self-consumption).</p> <p> </p>
H2020 Platone Greek Demonstrator PV_generation_20190227_20200506
<p>This dataset contains PV generation (kWh) of 7 producers (connected to the medium voltage (MV) level, 20kV) from 27/02/2019 to 06/05/2020 in 15 min intervals and contains the following fields:</p> <p>Customer_id</p> <p>Value_KWh</p> <p>Timestamp</p>
GERONTE H2020 project - GERDAT006 - Dataset of symptoms and proms for specific cancer types and gender
<p>This dataset describes a series of symptoms, potentially indicative of treatment-related complications, destabilised comorbidity or functional decline, to be used in the Geronte project for symptoms monitoring in older patients with multimorbidity during and after their cancer treatment</p>
GERONTE H2020 project - GERDAT004 - Dataset of self-management recommendations
<p><strong>The present document is a dataset generated as part of Deliverable D1.1. of the GERONTE project, which has received funding from the European Union’s Horizon 2020 Programme under Grant Agreement N°945218. It aims to provide the geriatric oncology professional community with a dataset of self-management recommendations that can be used in the care for older patients with cancer and multimorbidity.</strong></p> <p>GERONTE is a 5-year research and innovation project (April 2021 to Mars 2026) funded by the European Union within the framework of the H2020 Research and Innovation programme, in response to the health societal challenge topic SC1-BHC-24-2020 “Healthcare interventions for the management of the elderly multimorbid patient”. The overall aim of GERONTE is to improve quality of life - defined as well-being on three levels: global health status, physical functioning and social functioning- for older multimorbid patients, while reducing overall costs of care. To this end, GERONTE will co-design, test, and prepare for deployment an innovative cost-effective patient-centred holistic health management system, hereafter referred to as the GERONTE intervention. GERONTE intervention will rely on an ICT based application for real-time collection and integration of standardised clinical and home patient-reported data. GERONTE intervention will be demonstrated in the context of care of multimorbid patients having cancer as a dominant morbidity, and be adaptable to any other combination of morbidities.</p> <p>Patient empowerment by supporting self-management is an important component of the Geronte care pathway. As patients will be monitoring themselves at home, to register side-effects of treatment, decompensation of comorbidities and signs of functional decline, they will also be faced with questions about how to deal with the issues that they are having. While one important component of the care pathway is early signalling of complications to allow for early intervention by health care professionals, there is also a lot that patients can do for themselves at home to enhance their life-style, decrease burden of signs and symptoms or to improve outcomes.</p> <p>This led to the composition of a dataset to be included in the GERONTE care pathway, which is presented here.</p>
GERONTE H2020 project - GERDAT003 - Core intrinsic capacity dataset
<p><strong>The present document is a dataset generated as part of Deliverable D1.1. of the GERONTE project, which has received funding from the European Union’s Horizon 2020 Programme under Grant Agreement N°945218. It aims to provide the geriatric oncology professional community with a dataset of intrinsic capacity/frailty data to be included and assessed in the evaluation of older patients with cancer and multimorbidity.</strong></p> <p>GERONTE is a 5-year research and innovation project (April 2021 to Mars 2026) funded by the European Union within the framework of the H2020 Research and Innovation programme, in response to the health societal challenge topic SC1-BHC-24-2020 “Healthcare interventions for the management of the elderly multimorbid patient”. The overall aim of GERONTE is to improve quality of life - defined as well-being on three levels: global health status, physical functioning and social functioning- for older multimorbid patients, while reducing overall costs of care. To this end, GERONTE will co-design, test, and prepare for deployment an innovative cost-effective patient-centred holistic health management system, hereafter referred to as the GERONTE intervention. GERONTE intervention will rely on an ICT based application for real-time collection and integration of standardised clinical and home patient-reported data. GERONTE intervention will be demonstrated in the context of care of multimorbid patients having cancer as a dominant morbidity, and be adaptable to any other combination of morbidities.</p> <p>An important component of the GerOnTe care pathway was to determine which intrinsic capacity/frailty information is need for optimizing treatment decision making and the subsequent care trajectory. We developed a list of comorbidities and intrinsic capacity/frailty itmes from literature and subsequently asked an expert panel to determine which of these were relevant for oncologic decision making and care. This led to the composition of a dataset to be included in the GERONTE care pathway, which is shown in this dataset. These data can to be included in the evaluation of older patients with cancer and multimorbidity to determine the feasibility of treatment and additional care needs</p>
GERONTE H2020 Project - GERDAT002 - Composition of the health care professional consortium
<p><strong>The present document is a dataset generated as part of Deliverable D1.1. of the GERONTE project, which has received funding from the European Union’s Horizon 2020 Programme under Grant Agreement N°945218. It aims to provide the geriatric oncology professional community</strong> <strong>with a dataset of core health care professionals that should be involvoed in the evaluation and treatment trajectories of older patients with cancer and multimorbidity.</strong></p> <p>GERONTE is a 5-year research and innovation project (April 2021 to Mars 2026) funded by the European Union within the framework of the H2020 Research and Innovation programme, in response to the health societal challenge topic SC1-BHC-24-2020 “Healthcare interventions for the management of the elderly multimorbid patient”. The overall aim of GERONTE is to improve quality of life - defined as well-being on three levels: global health status, physical functioning and social functioning- for older multimorbid patients, while reducing overall costs of care. To this end, GERONTE will co-design, test, and prepare for deployment an innovative cost-effective patient-centred holistic health management system, hereafter referred to as the GERONTE intervention. GERONTE intervention will rely on an ICT based application for real-time collection and integration of standardised clinical and home patient-reported data. GERONTE intervention will be demonstrated in the context of care of multimorbid patients having cancer as a dominant morbidity, and be adaptable to any other combination of morbidities.</p> <p>An important component of the GerOnTe care pathway was to determine which health care professionals should be included in the health care professional consortium (HPC) providing care for the patient. Beforehand, we had considered the option of four core members and at least eight other participants depending on the patient’s specificities or profile.</p> <p>Based on clinical experience, we developed a list of 15 potential participants, including general practitioner, one or more oncology specialists (such as surgeons, medical oncologists, radiotherapists), geriatrician, oncology nurse, social worker, clinical pharmacist, physiotherapist, anaesthesiologist, home care nurse, dietician, occupational therapist, spiritual helpers/clerics, psychologist/psychiatrist, palliative care specialist, organ-specific physician(s) such as cardiologist, pulmonologist, nephrologist, rheumatologist etc.</p> <p>This list was presented to the expert panel, and they were asked to determine whether or not these participants should be involved in decision-making and/or the subsequent oncologic care trajectory; experts could specify if these participants should be involved for all patients, only in specific situations/profiles, or did not need to be involved.</p> <p>The results of this expert panel survey and the subsequent composition of the health care professional consortium forms the basis of this dataset.</p>
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