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56 results for “Actors”
Organized actors at the biodiversity science-policy-society interface
<p>This database was developed in the context of the Deliverable 2.1 of the BioAgora project 'Developing the Science Service for European Research and Biodiversity Policymaking' (<a href="https://bioagora.eu/)">https://bioagora.eu/)</a>. BioAgora is a collaborative European project funded by the Horizon Europe programme (Horizon Europe research and innovation programme, grant agreement No. 101059438). The project's main outcome is intended to be the development of a Science Service for Biodiversity, the principal EU mechanism to connect research and knowledge on biodiversity to the needs of policy making through a continuous dialogue. The ultimate goal of BioAgora and of the Science Service is to support the implementation of the Biodiversity Strategy for 2030, and more broadly the sustainability transition required by the EU Green Deal. The BioAgora project was launched in July 2022 for a duration of 5 years. It gathers a Consortium of 22 partners, from 13 European countries, led the Finnish Environment Institute (Syke). Partners represent a diversity of actors coming from academia, public authorities, SMEs, and associations. Views and opinions expressed are those of the authors only and do not necessarily reflect those of the European Union or the European Commission. Neither the European Union nor the granting authority can be held responsible for them. </p> <p>In order to develop the database, a thorough desk search was conducted to compile an extensive, albeit not exhaustive, list of organizations operating at the science-policy-society interface in the context of biodiversity and sustainability. In collecting the list, we focused on actors operating at EU level, although we also included particularly relevant international, regional or national organized actors. The desk search built upon the work already developed in the context of two pan-European projects, funded by the Seventh framework programme of the European Community: ‘Developing a Knowledge Network for European Expertise on biodiversity and ecosystem services to inform policy making and economic sectors (KNEU, 2010-2014, grant 265299) and ‘Establishing a European Knowledge and Learning Mechanism to Improve the Policy-Science-Society Interface on Biodiversity and Ecosystem Services’ (Eklipse, 2016-2020, grant 690474). The two above-mentioned projects preceded the BioAgora project in that they aimed at understanding and improving the effectiveness of the biodiversity science-policy(-society) interface in Europe. Such projects had thus already compiled extensive databases of relevant organizations in Europe (including national and international actors, in addition to EU level actors), and quantified the relevance of such organizations based on votes cast by project members and based on interviews with key organizations. The database developed through the desk search conducted was further refined with suggestions for relevant organizations provided by BioAgora’s participants and by the representatives of the organizations interviewed during the other steps of the data collection. The data collection processes started in September 2022 and was updated until June 2024. Note that the categories for network types (Columns E-F) are not mutually exclusive. For further details about the development of the database please see Deliverable 2.1 (<a href="https://bioagora.eu/deliverables/">https://bioagora.eu/deliverables/</a>). </p>
Data from the "The Psychology of Professional and Student Actors: Creativity, Personality, and Motivation"
<p>Data associated with:</p> <p>Dumas, D., Doherty, M., Organisciak, P. (2020) "The Psychology of Professional and Student Actors: Creativity, Personality, and Motivation". PLOS ONE.</p> <p>Description of work associated with this data:</p> <blockquote> <p>As a profession, acting is marked by a high-level of economic and social riskiness concomitantly with the possibility for artistic satisfaction and/or public admiration. Current understanding of the psychological attributes that distinguish professional actors is incomplete. Here, we compare samples of professional actors (n = 104), undergraduate student actors (n = 100), and non-acting adults (n = 92) on 26 psychological dimensions and use machine-learning methods to classify participants based on these attributes. Nearly all of the attributes measured here displayed significant univariate mean differences across the three groups, with the strongest effect sizes being on Creative Activities, Openness, and Extraversion. A cross-validated Least Absolute Shrinkage and Selection Operator (LASSO) classification model was capable of identifying actors (either professional or student) from non-actors with a 92% accuracy and was able to sort professional from student actors with a 96% accuracy when age was included in the model, and a 68% accuracy with only psychological attributes included. In these LASSO models, actors in general were distinguished by high levels of Openness, Assertiveness, and Elaboration, but professional actors were specifically marked by high levels of Originality, Volatility, and Literary Activities.</p> </blockquote>
Photonics Actors Database
<p>The data are collected from the European countries in which the partners of the EPRISE project are based, and predominantly includes photonics Academia, Clusters and Companies who are active in the four EPRISE project target markets: agriculture, food, medical technologies and pharmaceuticals.</p> <p>The list is not intended to be exhaustive, while it is continuously updated by the project consortium.</p>
Combining internal and external motivations in multi-actor governance arrangements for biodiversity and ecosystem services
<p>These files provide the original survey data of the paper on motivations for biodiversity conservation in Europe. This paper analyses the possibility of building a mutually supportive dynamics between internally and<br /> externally motivated behaviour for biodiversity conservation and ecosystem services provision. To this<br /> purpose a face to face survey amongst 169 key actors of 34 highly successful and prominent biodiversity<br /> arrangements in seven EU countries was conducted. The main<br /> finding of the paper is the feasibility of<br /> combining inherently intrinsically motivated behaviours (providing enjoyment, pleasure from<br /> experimentation and learning, aesthetic satisfaction) and internalized extrinsic motivations (related<br /> to the identification with the collective goals of conservation policy) through a common set of governance<br /> features. Successful initiatives that combine internal and external motivations share the following<br /> features: inclusive decision making processes, a broad monitoring by “peers” beyond the core staff of the<br /> initiatives, and a context that is supportive for the building of autonomous actor competences. These<br /> findings are in line with the psycho-sociological theory of motivation, which shows the importance of a<br /> psycho-social context leading to a subjective perception of autonomy and a sense of competence of the<br /> actors.</p>
agroBRIDGES Multi-Actor Framework Engagement Data Analysis
<p>The multi-actor framework employed in agroBRIDGES foresees the creation of a regional Multi-Actor Platform (MAP) in the 12 focal European regions and countries of the project (Beacon Regions) as well as a Stakeholder Reference Group (SRG) at European level with a view to engaging stakeholders in the project’s activities.</p> <p>In this way, this dataset contains the engagement analysis of the MAP and the SRG members that have been selected as active participants in the activities carried out in the frame of the <a href="https://www.agrobridges.eu/">agroBRIDGES</a> H2020 project, focused on building bridges between producers and consumers, thanks to the <a href="https://www.agrobridges.eu/toolbox/">agroBRIDGES Toolbox</a> developed and other supporting activities organized throughout the project. To achieve project objectives, results from MAPs and SRG management have been tracked during the first half of the project, and will continue to be monitored until the end of the project, in collaboration with all Beacon Region Leaders and the SRG Manager.</p> <p>In this first round of monitoring, with respect to the SRG, 20 stakeholders have been engaged, of which approximately 50% have had a high engagement, 30% a medium engagement, and 20% a low-to-none engagement. As part of the planned engagement analysis and based on the data provided by each of the Beacon Region Leaders, the initial and long-term number of MAP members, as well as the evolution in their level of engagement, have been monitored., getting a total of 179 MAP members currently engaged in the project. In a first evaluation, high engagement was generally achieved in approximately 80% of cases, with less engagement from some educational bodies and consumers.</p> <p>In the second round of monitoring, the MAPs were expanded to a total of 199 stakeholders in 12 countries, while the SRG synthesis remained unchanged. The activities of the project supported higher engagement of the MAPs, while collaboration with the SRG continued in an ad hoc basis, as pan-European activities were less frequently organised than regional ones.</p> <ul> <li>The countries involved are: <ul> <li>Denmark</li> <li>Finland</li> <li>France</li> <li>Greece</li> <li>Ireland</li> <li>Italy</li> <li>Latvia</li> <li>Lithuania</li> <li>Netherlands</li> <li>Poland</li> <li>Spain</li> <li>Turkey</li> </ul> </li> </ul> <p>The dataset contains:</p> <ul> <li><strong>agroBRIDGES_StakeholderEngagement_2022.12.27_v1</strong>: Spreadsheet in .xlsx format, containing a table with all MAP members, classified by type of organization, country, and including the level of engagement of each of them.</li> <li><strong>agroBRIDGES_StakeholderEngagement_2022.12.27_v1</strong>: Spreadsheet in .xlsx format, containing a table with all MAP members, classified by type of organization, country, and including the level of engagement of each of them.</li> <li><strong>agroBRIDGES_StakeholderEngagement_2023.12.19_v2</strong>: Spreadsheet in .xlsx format, containing a table with all MAP members, classified by type of organization, country, and including the level of engagement of each of them. The data is updated up to December 2023.</li> <li><strong>agroBRIDGES_MAP-Engagement_2023.12.19_v2:</strong> Spreadsheet in .xlsx format, where all MAP Engagement data are collected, classified by country. This file also includes a tab with indicators of how the level of engagement has been assigned. The data is updated up to December 2023.</li> <li><strong>agroBRIDGES_SRG-Engagement_2022.12.27_v1</strong>: Spreadsheet in .xlsx format, where all data concerning SRG Engagement is collected. This file also includes a tab with indicators of how the level of engagement has been assigned.</li> </ul>
Fig. 2 in A closer look at the main actors of Neotropical floodplain food webs: functional classification and niche overlap of dominant benthic invertebrates in a floodplain lake of Paraná River
Fig. 2. Cluster plot depicting trophic similarity (Morisita index) among species of dominant benthic invertebrates in a floodplain lake of ParanÁ River, Argentina. Dotted line depicts the threshold similarity of 0.6.
Fig. 3 in A closer look at the main actors of Neotropical floodplain food webs: functional classification and niche overlap of dominant benthic invertebrates in a floodplain lake of Paraná River
Fig. 3. Non Metric Multidimensional scaling plot. Circles depicts taxa classified as gatherer collectors (Aulodrilus pigueti, Pristina leidyi, Dero vagus, Nais communis, Pelomus sp., Cladopelma sp., Endotribelos sp., Polypedilum sp., Chironomus sp., Parachironomus sp., Phaenopsectra sp., Americabaetis sp., Baetis sp., Campsurus violaceus, Hyalella curvispina, Crynellus sp.) [Triangles: Tanypodinae (Coelotanypus sp., Procladius sp. and Ablabesmyia (Karelia); inverted triangle: Sympetrum sp.; square: Monopelopia sp.; cross: Pomacea canaliculata].
Fig. 1 in A closer look at the main actors of Neotropical floodplain food webs: functional classification and niche overlap of dominant benthic invertebrates in a floodplain lake of Paraná River
Fig. 1. Relative importance (IRI) of food items for analyzed taxa of dominant benthic invertebrates in a floodplain lake of ParanÁ River, Argentina (parenthesis indicate sample size).
Asynchronous Workload Balancing through Persistent Work-Stealing and Offloading for a Distributed Actor Model Library
<p>With dynamic imbalances caused by both software and ever more complex hardware, applications and runtime systems must adapt to dynamic load imbalances. We present a diffusion-based, reactive, fully asynchronous, and decentralized dynamic load balancer for a distributed actor library. With the asynchronous execution model, features such as remote procedure calls, and support for serialization of arbitrary types, UPC++ is especially feasible for the implementation of the actor model. While providing a substantial speedup for small- to medium-sized jobs with both predictable and unpredictable workload imbalances, the scalability of the diffusion-based approaches remains below expectations in most presented test cases.</p> <p>Actor-UPCXX is a high-performance computing library based on the actor model to enable the use of the actor model for HPC simulations. The source code can be found at: https://github.com/TUM-I5/Actor-UPCXX</p>
Deliverable 3.2 Programmes and concepts for all citizen and multi-actor consultations
<p>This deliverable includes the results obtained during 50 National Research & Policy workshops (NRPs) held as part of the second consultation phase of the CIMULACT project. At least one workshop was held in each of 30 participating European countries (28 EU member states + Norway and Switzerland) in the period September 2nd until October 8th 2016.</p> <p>CIMULACT stands for ‘Citizen and Multi-Actor Consultation on Horizon 2020’.1 The project engages citizens, along with a wide range of other actors, in redefining the European Research and Innovation agenda and thereby making it more relevant and accountable to society.</p> <p>While the first phase of the CIMULACT project aimed at collecting visions for sustainable and desirable futures formulated by European citizens. The second phase of the project aimed at transforming these visions into research and policy options.</p> <p>The scope of the NRPs was twofold: 1) Test, validate, enrich and prioritise the research programme scenarios developed during the first phase of the project (WP2.1), 2) Experiment with methods for co-creating research and policy recommendations by citizens and multi-actors.</p> <p>Consulting different target groups and using a variety of concepts for consulting these groups the research programme scenarios have been enriched by a diverse group of citizens and societal actors with a high diversity of perspectives. All together 977 European citizens and multi-actors were consulted during the NRPs.</p> <p>In order to understand the context in which the NRPs were held it is central to understand the structure of the CIMULACT project. For the same reason the first half of this summary presents the CIMULACT project and its results until the NRPs, while the second half of the summary will focus on the results of the NRPs.</p> <p>The programmes, concepts, and outcome of the consultations are presented in national reports which are to be found in extension of the summary. </p>
Entrevistas a actores locales en Constitución y Licantén
<p><span>Fotografías con las entrevistas a actores locales en Licantén y Constitución a raíz de las inundaciones. Fotografías tomadas por Gabriela Cortés, Simón Inzunza, Nikole Guerrero, Yvonee Merino y Nicolás Pérez. Fecha de captura: 12, 13 y 14 de julio de 2023.</span></p>
Actors and Satellites in the African Earth Observations Sector: Insights from the 2021 Radiant Earth ML for EO Market Map and the Union of Concerned Scientists Database
<p>The database of organizational actors, "Actors and Satellites in the African Earth Observations Sector: Insights from the 2021 Radiant Earth ML for EO Market Map and the Union of Concerned Scientists Database" analyzed in "<span>Whose Priorities? Examining Inequities in Earth </span><span>Observation Advancements Across Africa" </span>this study, is available on Zenodo, an open-access repository developed under the European OpenAIRE program. The dataset comprises information on 310 space-centric earth observation organizations, including headquarters locations. For the 31 organizations in our sample, we provide additional details including the African countries where their projects are active, the type of initiative or program, other focus areas, organizational classification (commercial, government, or nongovernmental), funding source (public or private), organizational type (research, startup, or established industry), capabilities (data analysis, data storage, image labeling, competition platforms), involvement in early warning systems, data accessibility, availability of global products, and whether they build commercial satellites.</p> <p>This open sharing of the compiled organizational data aims to promote transparency, reproducibility, and additional investigations into the evolving landscape of earth observation activities globally and across Africa. Analyses of this dataset's relationships, funding flows, and priorities can provide further insights to guide equitable advancement of earth observation capabilities.</p>
Sentences with negative actors: negative strength quantified
<p>Files: data1.xml,data3.xml,data3.xml (3 annotators) - XML validiert</p> <p>- 439 sentences <br> - target: a negative cause (an actor etc.) represented by the Lemma<br> - id: sentence number<br> - string: the plain sentence<br> - strength: negativity strength of the target<br> - labels 0-3<br> - 0 no negative entity found (or parsing error)<br> - 1 slightly negative, 2 negative, 3 stronly negative<br> <br> - 115 out of 439 sentences with tag 0: i.e. sentences do not contain a negative actor<br> - different reasons (see the paper below): modal, future tense etc. but also parsing errors</p> <p><br> Data source: Facebook posts of the AfD, a German right-wing party</p> <p>Examples:</p> <p>no actor here: passive voice<br> <sent><id>1</id><target>Junge</target><strength>0</strength><string>"Verletzt wurde auch ein 11-jähriger Junge . "</string></sent><br> stronly negative:<br> <sent><id>411</id><target>Euro</target><strength>3</strength><string>"Der Euro ruiniert Europa . "</string></sent><br> negative:<br> <sent><id>214</id><target>Merkel</target><strength>2</strength><string>"Merkel verantwortet zusätzliche 50 Milliarden Sozialkosten bis 2018 . "</string></sent><br> slightly negative:<br> <sent><id>154</id><target>Meuthen</target><strength>1</strength><string>"Meuthen schadet der Partei . "</string></sent></p> <p>References:</p> <p>@inproceedings{nodalida,<br> month = {Juni},<br> author = {Manfred Klenner and Anne G{\"o}hring and Sophia Conrad},<br> booktitle = {Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa)},<br> address = {Reykjavik, Iceland},<br> title = {Getting Hold of Villains and other Rogues},<br> publisher = {Virtual Event},<br> pages = {435--439},<br> year = {2021},<br> language = {english},<br> url = {https://doi.org/10.5167/uzh-204265},<br> abstract = {In this paper, we introduce the first corpus specifying negative entities within sentences. We discuss indicators for their presence, namely particular verbs, but also the linguistic conditions when their prediction should be suppressed. We further show that a fine-tuned Bert-based baseline model outperforms an over-generating rule-based approach which is not aware of these further restrictions. If a perfect filter were applied, both would be on par.}<br> }<br> </p>
Dataset on involved actors and their roles in the governance of innovative contracts for agri-environmental and climate schemes
<p>In the presented dataset we have analyzed 19 innovative contracts for agri-environmental and climate schemes from six European countries. The selected contracts represent examples of four different contract types: result-based, collective, land tenure, and value chain contracts.</p> <p>For the analysis we used a three step approach:</p> <p>Step 1: We employed a method mix combining literature review, web search, and expert consultation to identify potential case examples for the innovative contracts, resulting in a final sample of four to five contracts per contract types including contracts from Belgium, France, Germany, Ireland, the Netherlands, and the United Kingdom.</p> <p>Step 2: We designed a survey structured in accordance with Elinor Ostrom’s institutional analysis and development (IAD) framework to collect detailed information on overall 34 variables for each contract, yielding a data matrix with 646 data points.</p> <p>Step 3: We conducted an in-depth analysis for each contract on the involved actors and their roles in contract governance roles, generating a detailed inventory of 179 identified actors where each actor was assigned one or several out of 16 possible governance roles, creating a data matrix of 2,864 data points.</p> <p>The dataset generated through these three steps contains 84 data files, including tables, figures, maps, and one text file.</p> <p>The dataset can be re-used by all interested in the institutional and governance analysis of innovative contracts for agri-environmental and climate schemes.</p> <p>The dataset is linked to Work package 2 (WP2), Task 2.2 (T2.2), and Deliverable 2.2 (D2.2) of the research and innovation action ‘Contracts2.0’ (<a href="https://www.project-contracts20.eu/">https://www.project-contracts20.eu/</a>) funded under grant agreement no. 818190 through the European Union’s Horizon 2020 program.</p> <p>This dataset contains the information required to reproduce the results presented in the following research paper: Sattler, C., Barghusen, R., Bredemeier, B., Dutilly, C., Prager, K. (2023). Institutional analysis of actors involved in the governance of innovative contracts for agri-environmental and climate schemes. Global Environmental Change 80:102668. <a href="https://doi.org/10.1016/j.gloenvcha.2023.102668">https://doi.org/10.1016/j.gloenvcha.2023.102668</a></p> <p>The dataset has been compiled to the best of our knowledge based on the sources available to us.</p>
Non-State Actors and Energy Transition. Prospects on the Role of Private Sector in the UN Agenda 2030.
<p>Conference on January 27th, 2020.</p> <p>International Environmental Law and Climate Change Lecture.</p> <p>Room BLB 1_University of Houston Law Center.</p> <p>Houston, Texas.</p>
WP2 Task 2.4 Multi Actor Approach survey results and analysis
<p>WP2 Task 2.4 Multi Actor Approach survey results and analysis</p>
WP2 Task 2.4 Multi Actor Approach dataset final categorisation
<p>WP2 Task 2.4 Multi Actor Approach dataset final categorisation</p>
The Role of Actors in Platform Ecosystems: Selected Studies and Coding Results
<p>13th International Conference on Software Business (ICSOB 2022).</p> <p> </p> <p>Paper: The Role of Actors in Platform Ecosystems: A Systematic Literature Review and Comparison Across Platform Types</p> <p> </p> <p>File: Selected Studies and Coding Results.</p>
Increasing the value of wind - From passive to active actors in the power markets (On-line Appendix)
<p>Mathematical formulations and input data for the evaluation of the different market participation strategies.</p>
Process Integration Multi-Actor Multi-Criteria Optimization (PI-MAMCO) Framework: Case Study on Palm Oil Based Complex (POBC)
<p>The dataset includes the complete workflow for the PI-MAMCO method implementation, demonstrated on a POBC integration case study. The files are structured as follows:</p> <ol> <li> <p><strong>NOTEBOOKS Folder</strong>: This folder contains the Jupyter notebooks that constitute the entire workflow of our case study. These notebooks import necessary model equations, data, and functions from the "MODEL" folder. Running these notebooks with the correct Python package versions will reproduce the exact results presented in our submitted article.</p> </li> <li> <p><strong>MODEL Folder</strong>: This folder includes all model equations, data, and functions required by the Jupyter notebooks. The scripts in this folder are integral to executing the PI-MAMCO method as outlined in our research.</p> </li> <li> <p><strong>OUTPUTS Folder</strong>: The results generated by running the notebooks are saved in this folder. This includes all outputs reported in our manuscript as well as additional results not included in the paper.</p> </li> <li><strong>OUTPUTS_GENERATED Folder</strong>: The files contained in this folder are the expected results from executing the case study notebook.</li> </ol> <p><em>Reproducibility</em>: To ensure the reproducibility of our results, please ensure that you are using the correct versions of the required Python packages. Detailed instructions for setting up the Python environment are provided within the notebook and the readme.txt.</p> <p>Contained are also outputs from the <a href="https://www.sciencedirect.com/science/article/pii/S0957417422003931" target="_blank" rel="noopener">IRenE procedure</a>, used for the literature review (<em>01_extracted_keywords.xlsx</em> ; <em>02_sampling_results.csv</em>)</p>
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