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2,045 results for “Plan S”
Additional data of the PhD thesis "Merge-and-Shrink Abstractions for Classical Planning: Theory, Strategies, and Implementation"
<p>The original data set for the thesis "Merge-and-Shrink Abstractions for Classical Planning: Theory, Strategies, and Implementation" by Sievers, 2017, available under https://doi.org/10.5281/zenodo.1164137, accidentally did not include the data of one experiment, namely the raw data of the parsed data contained in "sota-symba-spmas-eval". It can be found in this data set.</p>
Experimental data from the PhD thesis "Counterexample-guided Cartesian Abstraction Refinement and Saturated Cost Partitioning for Optimal Classical Planning"
<p>The three data sets contain the raw experiment data, parsed values and basic reports for the three parts of the thesis. For each experiment there are two directories. The first directory contains the raw data of all experiment runs. The code directories and benchmark files have been removed to avoid duplication and save space. The second directory (*-eval) contains "properties" file with all parsed values and an HTML report.</p>
Nicosia, Cyprus. Bedesten. Plan of the building prepared 1980-81.
<p>Nicosia, Cyprus. Bedesten. Plan of the building prepared 1980-81.</p>
PDDL benchmarks for oversubscription planning
<p>Benchmark suite for oversubscription planning.</p> <p>The benchmarks were created in a similar fashion to the procedure described by Domshlak and Mirkis (2015), based on the collection of classical International Planning Competition (IPC) domains. The bounds are set to 25%, 50%, 75%, or 100% of the best known solution cost for the classical planning task obtained from planning.domains (Muise 2016). Each goal in the original problem has a utility of 10 and a randomly chosen 5% of the facts have a uniformly distributed integer utility in the range [1, 5].</p> <p>For more details, see:</p> <p>Michael Katz, Emil Keyder, Florian Pommerening and Dominik Winterer. Oversubscription Planning as Classical Planning with Multiple Cost Functions. In <em>Proceedings of the 29th International Conference on Automated Planning and Scheduling (ICAPS 2019)</em>. 2019.</p>
Planning as Optimization: Online Learning of Situations and Optimal Configurations - SASO 2019 - Accompanying material
<p>These files are accompanying material for our submission "" to SASO 19:</p> <p>Many approaches apply optimization techniques in SASs, mostly within the planning procedure, to generate new system configurations or adaptation plans. We performed an analysis of these techniques based on approaches published during the last ten years in conferences and journals related to self-adaptive systems (SASs), namely ACM Transactions on Autonomous and Adaptive System (TAAS), the International Conference on Autonomic Computing and Communications (ICAC), the International Conferences on Self-Adaptive and Self-Organizing Systems (SASO), the International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS), and the Symposium on the Foundations of Software Engineering (FSE). We identified the use of 29 different techniques in 51 publications. This list shows that a large set of techniques from different classes such as probabilistic, combinatorial, evolutionary, stochastic, mathematical, and meta-heuristic optimization are applied in SASs.</p> <p> </p> <p>We provide two files:</p> <p>- List of References (SASO - References - Planning_as_Optimization.pdf)</p> <p>- Dataset (SASO - Dataset - Planning_as_Optimization.xlsx)</p>
Can the "Gazelles Plan" support gazelles growth? —Evidence from the Beijing Zhongguancun in China
<p> In order to solve the problem of “Financing difficulty” commonly faced by small and medium-sized enterprises(SMEs) in China, the Beijing Zhongguancun Science Park(ZSP) Management Committee has formulated the “Gazelles Plan” to support strong innovated, rapid growing firms with good development prospects in strategic emerging industries. However, few studies have examined the impact mechanism of the “Gazelles Plan” on firm growth at the micro-enterprise level. This paper aims to take the implementation of the Beijing Zhongguancun of the “Gazelles Plan” as a quasi-natural experiment, and integrates the 2006-2008 Gazelle Firm List issued by Beijing Zhongguancun Science Park Management Committee and Chinese Industrial Enterprise Database during period 2004-2008, and scientifically evaluates the net effect of the “Gazelles Plan” and the mechanism of its policy tools by using the PSM-DID method. We find that the implementation of the “Gazelles Plan” has indeed promoted the rapid growth of firms, indicating that scientific support policies for high-growth firms play an important role in promoting firm growth. The mechanism test of the policy tools shows that “Bank credit” and “Commercial credit”, as two important policy tools of the “Gazelles Plan”, have different effects on firm growth. Bank credit significantly promotes firm growth, while the effect of commercial credit on the rapid growth of firms has not yet emerged. Our findings provide theoretical foundation and empirical evidence for government departments to formulate policies to foster gazelles growth.</p>
AEROARMS Control-based local optimization methods for planning
<p>This package allows to perform a Dynamic (Re-)Planning within the Set-Based Task-Priority Inverse-Kinematics (SBTP-IK) framework. It is ROS-based and it is provided to work with the Kinova Jaco2 7DOFs but it is can be easily adapted to another robot system changing the files describing the robot kinematics such as Denvait Hartenberg parameters.</p> <p>The proposed approach is based on merging control-based local optimization methods inside the planning algorithms. For the experiments, a fixed-based 7 DOFs manipulator has been considered in two different scenarios. In the first case, a static environment has been considered. In the second one, the user places an obstacle on the manipulator’s path in real-time. Thus, a re-planning results necessary to manage this change in the environment.</p> <p> </p>
Experimental data of the paper "Counterexample-Guided Abstraction Refinement for Pattern Selection in Optimal Classical Planning"
<p>This dataset contains experimental data used in the paper. They were generated<br> using Downward-Lab (see https://doi.org/10.5281/zenodo.399255). The scripts<br> used to run the experiments can be found in the software bundle for this paper<br> (see https://doi.org/10.5281/zenodo.2628703).</p> <p>Directories without the "-eval" ending contain raw data, distributed over a<br> subdirectory for each experiment. Each of these contain a subdirectory tree<br> structure "runs-*" where each planner run has its own directory. For each run,<br> there are symbolic links to the input PDDL files domain.pddl and problem.pddl<br> (can be resolved by putting the benchmarks directory to the right place), the<br> run log file "run.log" (stdout), possibly also a run error file "run.err"<br> (stderr), the run script "run" used to start the experiment, and a "properties"<br> file that contains data parsed from the log file(s).</p> <p>Directories with the "-eval" ending contain a "properties" file, which contains<br> a JSON directory with combined data of all runs of the corresponding<br> experiment. In essence, the properties file is the union over all properties<br> files generated for each individual planner run.</p>
Experimental data and benchmarks of the paper "Merge-and-Shrink Task Reformulation for Classical Planning"
<p>This dataset contains both benchmarks and data used in the paper.</p> <p>PDDL benchmark files can be found in the file optimal-strips-benchmarks.tar.gz<br> and satisficing-strips-benchmarks.tar.gz, which contain the STRIPS domains from<br> the sequential optimal/satisficing tracks of all IPCs as collected in the<br> repository https://bitbucket.org/aibasel/downward-benchmarks.</p> <p>All other files in this dataset contain raw and processed data of all<br> experiments, which were generated using Downward-Lab (see<br> https://doi.org/10.5281/zenodo.399255). The scripts used to run the experiments<br> can be found in the software bundle for this paper (see<br> https://doi.org/10.5281/zenodo.3232878).</p> <p>Directories without the "-eval" ending contain raw data, distributed over a<br> subdirectory for each experiment. Each of these contain a subdirectory tree<br> structure "runs-*" where each planner run has its own directory. For each run,<br> there are symbolic links to the input PDDL files domain.pddl and problem.pddl<br> (can be resolved by putting the benchmarks directory to the right place), the<br> run log file "run.log" (stdout), possibly also a run error file "run.err"<br> (stderr), the run script "run" used to start the experiment, and a "properties"<br> file that contains data parsed from the log file(s).</p> <p>Directories with the "-eval" ending contain a "properties" file, which contains<br> a JSON directory with combined data of all runs of the corresponding<br> experiment. In essence, the properties file is the union over all properties<br> files generated for each individual planner run.</p>
Accountability simulator path planning data
<p>Measurement data of developed path planning algorithms.</p> <p>Regards execution time, path length and differences of conducted modifications.</p>
Feedback on the draft implementation guidance of Plan S
<p>Launched in September 2018, cOAlition S is an initiative of an international consortium of research funders to make full and immediate Open Access to research publications a reality. It is built around Plan S, which consists of one target and 10 principles. In November 2018, cOAlition S released a draft guidance on the implementation of Plan S for public feedback. This process, which lasted from November 2018 to February 2019, collected more than 600 feedback statements from universities, learned societies, publishers, scholarly associations, and individual scholars from more than 40 countries. The documents include all responses and accompanying statements received during the process. Responses have been analysed and an updated guidance on implementation was adopted and published in May 2019. In order to facilitate the download of the full results, a Zip file (002_Plan S_Feedback_all_files) has been added in version 3. Please visit cOAlition S website for more information: <a href="https://www.coalition-s.org/">https://www.coalition-s.org/</a></p>
Experimental data from the PhD thesis "Certifying Planning Systems: Witnesses for Unsolvability"
<p>The zipfile contains the raw experiment data used in the PhD thesis "Certifying Planning Systems: Witnesses for Unsolvability". It is split in three folders:</p> <p>1) combined: contains the condensed data of all experiments as a json-file named properties, as well as a html page reporting the most important attributes</p> <p>2+3) downward and fd-hc-refinement: contains the raw data of all experiments run with certifying-downward/certifying-fd-hc-refinement, as well as a json-file each for the condensed data in the *-eval directory. The json-file from 1) is a combination of those two json-files.</p> <p>Furthermore, it contains the downward-lab (https://doi.org/10.5281/zenodo.790461) scripts used for generating the experiments, showing with which configurations and limits the experiments were run.</p>
Rédiger un Plan de Gestion de Données (PGD) : supports d'atelier (JNSO)
<p><strong>Avertissement au sujet des documents diffusés dans ce jeu de données</strong></p> <p>Ces supports ont été conçus en prévision d'un atelier ayant lieu au cours de la 2ème édition des JNSO, à destination des personnes accompagnant la rédaction de plans de gestion des données. Les participants devaient prendre connaissance d'un projet fictif dans un des 3 ensembles disciplinaires proposés (sciences biomédicales, sciences humaines et sociales et 2 projets en sciences et techniques), puis ils devaient identifier ou imaginer les éléments à faire figurer dans la trame du plan de gestion des données proposée par l'ANR. Les exemples ou "corrigés" proposés ne sont pas des modèles ou plans idéaux, il s'agit plutôt d'exemples concrets, relativement synthétiques, afin de permettre aux participants d'en prendre connaissance dans le temps de l'atelier.</p> <p>Les projets proposés sont pour la plupart fictifs, mais sont inspirés de projets réels. Les intervenants ont proposés les projets suivants :</p> <ul> <li>Romain Féret : TEMGAE (sciences humaines et sociales)</li> <li>Kenneth Maussant : TERAMiLI (sciences et techniques)</li> <li>Yvette Lafosse : ATLAS (sciences biomédicales)</li> <li>Véronique Stoll : Open Sol (sciences et techniques)</li> </ul> <p><strong>Résumé de l'atelier proposé</strong></p> <p>Lors de son appel à projets générique 2019, l'ANR a instauré l'obligation (pour tout projet de recherche financé) d'établir un plan de gestion de données (PGD) comme livrable à 6 mois, mis à jour ensuite régulièrement au cours du projet. Dès lors, le PGD devient un outil de gestion de projet incontournable pour les équipes de recherches, y compris parmi les communautés les plus novices en la matière. Cette évolution importante dans les pratiques quotidiennes des chercheurs appelle en conséquence un accompagnement pour la rédaction du PGD, avec éventuellement une attention particulière à apporter aux champs disciplinaires les moins matures en termes de bonnes pratiques de gestion de données.<br> L'objectif de cet atelier est de proposer à la fois une vision "stratégie établissement/services d'accompagnement" pour le soutien des équipes de recherche, mais également une approche "chercheur/utilisateur" avec une présentation détaillée du modèle générique de PGD proposée par l'ANR (disponible dans DMP OPIDoR), intégrant les bonnes pratiques à mettre en place dès le début et tout le long d’un projet.<br> Parmi les outils d'accompagnement, nous présenterons des ressources nationales telles DoRANum et DMP OPIDoR. Des initiatives à l'échelle de l'établissement seront également présentées (Université de Lille, Observatoire de Paris).</p>
Text-fig. 1. The plan of Za Hájovnou Cave, survey of Tomica, A. (Musil 2005a, supplemented). in Middle Pleistocene Stratigraphy Of The Deposits In Za Hájovnou Cave (Javoříčko Karst, Northern Moravia, Czech Republic)
Text-fig. 1. The plan of Za Hájovnou Cave, survey of Tomica, A. (Musil 2005a, supplemented).
Text-fig. 1. Ground plan of Za Hájovnou Cave (according Tomica, supplemented by Musil, 2005). in Panthera Fossilis (Reichenau, 1906) (Felidae, Carnivora) From Za Hájovnou Cave (Moravia, The Czech Republic): A Fossil Record From 1987-2007
Text-fig. 1. Ground plan of Za Hájovnou Cave (according Tomica, supplemented by Musil, 2005).
Data on planned eHV transmission lines in Germany
<p>Contains manually created lists of planned eHV AC and DC lines in Germany based on the Grid development plan 2021. The main source was the Grid development plan 2021, second draft by the German transmission system operators.</p> <p>In addition, a shapefile including lines that will be replaced by these lines is included.</p> <p>The data was created for the data processing tool <a href="https://github.com/openego/eGon-data">eGon-data </a>within the research project hybit. </p> <p>We thank the Federal Ministry of Education and Research (BMBF) for funding the project hybit (FKZ: 03SF0687A).</p>
Support data for article "The concept of optimal planning of a linearly oriented segment of the 5G network"
<p>Support data for article</p> <p>V. Kovtun, K. Grochla, E. Zaitseva, and V. Levashenko, “The concept of optimal planning of a linearly oriented segment of the 5G network,” PLOS ONE, vol. 19, no. 4. Public Library of Science (PLoS), p. e0299000, Apr. 17, 2024. doi: 10.1371/journal.pone.0299000.</p> <p>This research is part of the project No. 2022/45/P/ST7/03450 co-funded by the National Science Centre and the European Union Framework Programme for Research and Innovation Horizon 2020 under the Marie Skłodowska-Curie grant agreement No. 945339.</p>
D1.2 Data Management Plan (DMP)
<p>This deliverable constitutes the Data Management Plan (DMP) of the NANOFACTS project. It is the first version of DMP, describing data management life cycle for all research and publication data. This deliverable is introducing ways to produce, collect, process, save, exchange, share, and publish research data in line with the EC “Guidelines on FAIR Data Management in Horizon 2020”.</p>
Data related to Mapping user-related comfort conditions to identify urban planning issues for improved quality of places in cities: The case of Ljubljana, Slovenia
<p>The data consists of parameters including air temperature, noise levels, humidity, and air quality (PM 2.5), measured using a portable ICT device while cycling through the city of Ljubljana between August 1 and August 30, 2022. The data is available as raw CSV files, with each measurement session stored in a separate file. An additional QGIS Project file (qgz) is included, along with maps (in jpg format in ppt) that visually represent the results presented in the article.</p>
Data for Paper: A rigorous optimization method for long-term multi-stage investment planning: Integration of hydrogen into a decentralized multi-energy system
<p>Data containing the results and figures presented in the paper "A rigorous optimization method for long-term multi-stage investment planning: Integration of hydrogen into a decentralized multi-energy system" by Luka Bornemann and Jelto Lange and Martin Kaltschmitt, submitted to the Journal Energy Reports.</p>
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