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19 results for “KPIs”
REMOTE: DEMO Greece KPIs
<p>This dataset includes the KPIs of the DEMO Greece of the REMOTE project (GA 779541)</p>
REMOTE: DEMO Spain KPIs
<p>This dataset includes the KPIs of the DEMO Spain of the REMOTE project (GA 779541)</p>
CZ DEMO KPIs in OneNet Project
<p>The file contains data on KPIs from CZ demonstration projects (CZ DEMO) . Data reflect number of flexibility providers included in the register detailing increase of flexibility available for flexibility provisions. Evaluation of the KPI (called “Increase of flexibility providing units”) is provided at the first sheet (first KPI – “Number of FSP”). Data for this particular KPI are included in the same excel file at the section “FSP”. Information on conclusion, data format and explanation are included in the <a href="https://www.onenet-project.eu/wp-content/uploads/2024/03/OneNet_D10.5_V1.0.pdf">Deliverable 10.5 section</a> (section 2.1.2).</p> <p> </p>
OneNet project - KPIs repository
<p>This data set is the result of the work carried out in the OneNet project related to the evaluation of demonstrators' results.<br> <br>The data set consists of the KPI values that were achieved in all OneNet demonstrators, as well as their characterization and a brief description of the objectives for calculating each KPI. In the complementary document, the calculation methodologies and formulas of each KPI are reported.</p>
Papers and KPIs for the Evaluation of Renewable Energy Communities' Performance
<p>This database contains the methodology used to explore and identify the papers that containes KPIs related to the evaluation of RECs performance. This methodology is divided into three phases: </p> <p>1.1) <em>Papers Exploration – </em>Comprehensive search of papers in the field of RECs using Scopus and Web Of Science databases;</p> <p>1.2) <em>Papers Screening</em> – Initial screening of collected literature based on research domain and accessibility;</p> <p>1.3) <em>Papers Eligibility</em> – Further filtering papers by extracting those that explicitly define KPIs through mathematical formulations in the context of the RECs.</p> <p> In the <em>Papers Exploration</em> step, the authors conducted a systematic review of the state-of-the-art of literature on performance metrics in the context of the renewable energy community. The search was conducted in March 2024 using the search engines Scopus and Web Of Science (the used queries are detailed explain in thte database). The output of this phase is a large database of the most recent and relevant studies, cataloged by the following information: authors, article title, abstract, author keywords, index keywords, and year of publication. At this stage, only journal articles and research works published after 2010 were considered. In the <em>Papers Screening</em> phase, the articles are further filtered by the authors screening manually all papers based on keywords, titles, and abstracts, removing articles not relevant to the context of the RECs. In addition, articles for which it was not possible to access the full text are excluded. In the <em>Papers Eligibility</em> phase, the articles are entirely read to identify those articles that directly address the use of performance metrics. The eligibility criterion used by the reviewers’ team refers to the explicit definition of KPIs through mathematical formulas combined with their direct usage to evaluate RECs’ performances. The main objective of this phase is therefore to identify those articles that explicitly define and use KPIs, so that they can later be collected and labeled, based on their definition and usage.<br><br></p> <p>In additions, KPIs are extracted from the papers deemed elegible generating Tables A1, A2, A3 and A4. In these tables, similar KPIs are aggregated together in one single mathematical definition based on the methodology described in <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4991932">Key Performance Indicators for Renewable Energy Communities: A Comprehensive Review by Lorenzo Giannuzzo, Minuto Francesco Demetrio, Daniele Salvatore Schiera, Samuele Branchetti, Carlo Petrovich, Angelo Frascella, Nicola Gessa, Andrea Lanzini :: SSRN</a></p>
KPIs to measure the impact of Food loss and Waste prevention strategies
<p>These data correspond to the process of KPIs definition to measure the impact of Food Loss and Waste prevention strategies in FOODRUS. Both the initial long list of KPIs that started the process, and the results of 3 different surveys delivered to experts and stakeholders are included here.</p> <p> </p>
WeLive First Phase KPIs
<p>WeLive First Phase KPIs</p>
KPIs for the validation of the GreenSoul behavioural models
<p>This document presents both the methodology followed to select the Key Performance Indicators (KPI) and the final KPIs selected to measure the impact achieved by the actions carried on during the pilots. To select the KPIs we first have prepared a comprehensive list of KPIs to consider, then we follow a Delphi Method to reach a consensus among a panel of experts about the how to score every KPI in several aspects and finally we perform a descriptive statistical analysis to select the best set of KPIs.</p>
FOODRUS KPIs sheets
<p>Sheets with information on the FOODRUS KPIs, including: general description, calculation methodology, and observations.</p>
Network operator KPIs time series dataset
<p>This dataset contains the measurements of different key performance indicators (KPIs) of the usage of a network operator's infrastructure. It provides time series with the evolution of the KPIs measured every 5 minutes for a time interval greather than one month. The measurements correspond to different operator locations. Four different KPIs are provided in the dataset: aggregated Internet traffic (in bits per second), downstream traffic (in bits per second), number of active client sessions, and Virtual Private Network (VPN) traffic (in bits per second).</p> <p>The results have been anonymized, the time frame has been shifted so that the first timestamp of each time series is 0 and the values of the KPIs have been scaled, so that they range from 0 to 1000 in each time series.</p>
Valencia Demo Site KPIs
<p>Dataset containing all action and intervention indicators (KPI) in Valencia Demo Site for the MAtchUP project.</p>
KPIs to measure the impact of Food loss and Waste prevention strategies
<p>These data correspond to the process of KPIs definition to measure the impact of Food Loss and Waste prevention strategies in FOODRUS. Both the initial long list of KPIs that started the process, and the results of 3 different surveys delivered to experts and stakeholders are included here.</p>
KPIs for Building
<p>List of Key Performance Indicators, , characterizing the different aspects of building and district performances selected based on existing building related databases (such as the Observatory of the EU Building Stock), literature research, or defined ex novo, in order to give a broader vision on the performances and their affecting parameters</p>
Tampere KPIs (from City Performance Monitor)
<p>This dataset contains all the Tampere KPIs monitored through the Tampere Performance Monitor and exported in JSON format.</p>
Genova KPIs (from City Performance Monitor)
<p>This dataset contains all the GEnova KPIs monitored through the City Performance Monitor and exported in JSON format.</p>
Eindhoven KPIs (from City Performance Monitor)
<p>This dataset contains all the Eindhoven KPIs monitored through the CIty Performance Monitor and exported in JSON format.</p>
REMOTE: DEMO Norway KPIs
<p>This dataset includes the KPIs of the DEMO Norway of the REMOTE project (GA 779541)</p>
Spark Data containing logs and metrics (KPIs) for Hades
<p>Please make sure to cite our paper whenever you use the data in your research:</p><p>@inproceedings{DBLP:conf/icse/LeeYCSYL23, author = {Cheryl Lee and Tianyi Yang and Zhuangbin Chen and Yuxin Su and Yongqiang Yang and Michael R. Lyu}, title = {Heterogeneous Anomaly Detection for Software Systems via Semi-supervised Cross-modal Attention}, booktitle = {45th {IEEE/ACM} International Conference on Software Engineering, {ICSE} 2023, Melbourne, Australia, May 14-20, 2023}, pages = {1724--1736}, publisher = {{IEEE}}, year = {2023}, url = {https://doi.org/10.1109/ICSE48619.2023.00148}, doi = {10.1109/ICSE48619.2023.00148}, timestamp = {Wed, 19 Jul 2023 10:09:12 +0200}, biburl = {https://dblp.org/rec/conf/icse/LeeYCSYL23.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} }</p><p> </p><p> </p>
The Impact of Remote Training on Colonoscopy KPIs
ClinicalTrials.gov study NCT06101836. IPD Sharing: NO. Countries: 1. Publications: 0.
ScienceDex guides
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