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Figure 8 from: Ziv G, Beckmann M, Bullock J, Cord A, Delzeit R, Domingo C, Dreßler G, Hagemann N, Masó J, Müller B, Neteler M, Sapundzhieva A, Stoev P, Stenning J, Trajković M, Václavík T (2020) BESTMAP: behavioural, Ecological and Socio-economic Tools for Modelling Agricultural Policy. Research Ideas and Outcomes 6: e52052. https://doi.org/10.3897/rio.6.e52052
Figure 8 BESTMAP ABM concept. Individual farmer's decision-making is modelled using the RAA (orange elements). Each farmer agent has an internal state that is characterized by goals & needs (profit maximization, safety first, risk aversion, etc.), knowledge (education, farming strategies, beliefs about consequences of different practices, etc.), values (social norms, strength of beliefs, value of conservation or biodiversity, etc.) and assets (monetary resources, production means, information, etc.). These shape farmer's attitude towards different behaviours (e.g. pro or contra organic farming), perceived norm (social pressure or other farmer's behaviour) and perceived behavioural control (agent's capacity to perform certain behaviours). Every farmer agent can perceive the state of the farming system (e.g. yields, ESS provision), the behaviour of other farmers as well as exogenous drivers (e.g. price or policy changes). Evaluating these lead to changes in attitudes or perceived norm, which can expand or reduce its behavioural options. Each behavioural option represents an intention to perform a certain behaviour (e.g. adopting a different crop choice or setting aside land). Using a multi-objective utility function, the farmer agent will select a specific behavioural option that will change the state of its farm's fields and provide some economic benefit to the farmer. Specifically, farmer's decisions can lead to changes in cropping system, farm and field size, or in farm ownership, which allows us to analyse/model structural change.
Figure 4 from: Ziv G, Beckmann M, Bullock J, Cord A, Delzeit R, Domingo C, Dreßler G, Hagemann N, Masó J, Müller B, Neteler M, Sapundzhieva A, Stoev P, Stenning J, Trajković M, Václavík T (2020) BESTMAP: behavioural, Ecological and Socio-economic Tools for Modelling Agricultural Policy. Research Ideas and Outcomes 6: e52052. https://doi.org/10.3897/rio.6.e52052
Figure 4 BESTMAP conceptual framework for policy impact assessment modelling, combining actions at EU scale (green), and activities within a representative set of case studies (orange; demonstrated in five areas within BESTMAP). The framework combines workshops (ovals), modelling (rounded rectangles) and interviews (ovals) producing several datasets (curved rectangles) and an online interactive dashboard (barrel shape). At the EU level, BESTMAP will co-design with policy-makers and stakeholders policy scenarios which, with existing scenarios of climate change and other global events, will input into the global economic models (here DART-BIO CGE model, but also MAGNET, CAPRI etc.). Georeferenced data layers ("Case Study Base Layers"; CBL) will be collated from existing sources including the Land Parcel Identification System (LPIS), Farm Accountancy Data Network (FADN), CORINE land-use/land cover, INSPIRE Geoportal, Copernicus Land Monitoring, national/regional datasets and existing remote sensing products. This will be used to define 'prototype' Farm System Archetypes (FSAs) which help stratify and design the interview campaign in each of the case studies. BESTMAP will collect demographic, behavioural characteristics and socio-economic information using semi-structured interviews (with harmonized protocols). Ecosystem services models (here the InVEST biophysically based models, but simpler e.g. capacity matrix or more complex e.g. SWAT models can in be used in the future) will estimate ESS, environmental/climatic impacts and biodiversity provided by different farming units. The 'bundle' of ESS, geo-statistically modelled socio-economics and behavioural characteristics will add to the CBL and define the final typology of FSAs in the case study. A generic ABM template based on the RAA behavioural theory will be locally adapted by the same interviews/surveys, and driven by the outputs (prices and costs of commodities, energy etc.) from the global economic model (DART-BIO) as well as narratives and drivers arising from a national/regional workshop interpreting the EU-level policy. The change in FSAs resulting from those scenarios via the ABM will translate to change in ESS and socio-economics defining the FSA 'bundles' including their uncertainty. Those impacts at the case study level will be translated into stakeholders-defined policy indicators (e.g. of the SDGs) and visualized using a standard interactive web-based data portal policy dashboard, the use of which will be the focus of training and dissemination activities.
Figure 1 from: Ziv G, Beckmann M, Bullock J, Cord A, Delzeit R, Domingo C, Dreßler G, Hagemann N, Masó J, Müller B, Neteler M, Sapundzhieva A, Stoev P, Stenning J, Trajković M, Václavík T (2020) BESTMAP: behavioural, Ecological and Socio-economic Tools for Modelling Agricultural Policy. Research Ideas and Outcomes 6: e52052. https://doi.org/10.3897/rio.6.e52052
Figure 1 Top-level conceptual framework of BESTMAP. BESTMAP combining existing global/EU scale (green) models used by the EC, in particular Partial Equilibrium (PE) and Computable General Equilibrium (CGE) models with regional analyses (yellow) by formalizing engagement with stakeholders to define scenarios; using existing geospatial data and empirical data collection to map farming systems based on a novel concept of Farming System Archetypes (FSA) namely farms with a characteristic bundle of ESS, biodiversity, socio-economics and behavioural characteristics of decision-making agents (i.e. farmers); linking economic (typically global) large scale economic based PE/CGE to agent-based models; describing changes in FSAs's distribution, ESS, biodiversity and socio-economics in a representative sample of case study areas.
Figure 5 from: Ziv G, Beckmann M, Bullock J, Cord A, Delzeit R, Domingo C, Dreßler G, Hagemann N, Masó J, Müller B, Neteler M, Sapundzhieva A, Stoev P, Stenning J, Trajković M, Václavík T (2020) BESTMAP: behavioural, Ecological and Socio-economic Tools for Modelling Agricultural Policy. Research Ideas and Outcomes 6: e52052. https://doi.org/10.3897/rio.6.e52052
Figure 5 BESTMAP demonstration case studies: overview of CS locations in the context of Land Systems Archetypes mapped by Levers et al. 2018 at 3km resolution for Europe. No land system archetypes shown for Serbia as the mapping included EU countries only. Insets show the exact boundaries of CS areas.
Figure 2 from: Ziv G, Beckmann M, Bullock J, Cord A, Delzeit R, Domingo C, Dreßler G, Hagemann N, Masó J, Müller B, Neteler M, Sapundzhieva A, Stoev P, Stenning J, Trajković M, Václavík T (2020) BESTMAP: behavioural, Ecological and Socio-economic Tools for Modelling Agricultural Policy. Research Ideas and Outcomes 6: e52052. https://doi.org/10.3897/rio.6.e52052
Figure 2 The conceptualization of Farming System Archetypes (FSAs) in BESTMAP is an extension of land-use intensity framework from Erb et al. 2013 to include the land manager/farmer and its behavioural characteristics (based here on the RAA). FSA have a typical 'bundle' of ESS, outputs, outcomes, inputs and farmer characteristics.
Replication materials for "The Impact of a National Research Assessment on the Publications of Sociologists in Italy" to appear in Science and Public Policy
<p>Replication materials for "The Impact of a National Research Assessment on the Publications of Sociologists in Italy" to appear in Science and Public Policy</p> <p>DOI: 10.1093/scipol/scab013</p> <p><strong>More information on data on GitHub page</strong>: https://github.com/akbaritabar/SPP-ANVUR-sociologists-2021</p> <p> </p> <p><strong>Introduction to data</strong></p> <p>Data were collected from Scopus in September 2016 and included all records published by Italian sociologists between 2006 and 2015. This period covered five full years before and after ANVUR, whose original call for participation was on 7 November 2011. By considering five years before and after the call, we aimed to trace pre-existing behaviour and examine scientists’ reactions to institutional policies.</p>
Figure 2 from: Balzan MV, Tomaskinova J, Collier MJ, Dicks L, Geneletti D, Grace M, Longato D, Sadula R, Stoev P, Sapundzhieva A (2020) Building capacity for mainstreaming nature-based solutions into environmental policy and landscape planning. Research Ideas and Outcomes 6: e58970. https://doi.org/10.3897/rio.6.e58970
Figure 2 (a) Assessing the relationship between green infrastructure cover (GI) in each local council and average ES capacity and (b) population density (Adapted from: Balzan 2017).
Data from: In an age of open access to research policies: physician and public health NGO staff research use and policy awareness
Introduction: Through funding agency and publisher policies, an increasing proportion of the health sciences literature is being made open access. Such an increase in access raises questions about the awareness and potential utilization of this literature by those working in health fields. Methods: A sample of physicians (N=336) and public health non-governmental organization (NGO) staff (N=92) were provided with relatively complete access to the research literature indexed in PubMed, as well as access to the point-of-care service UpToDate, for up to one year, with their usage monitored through the tracking of web-log data. The physicians also participated in a one-month trial of relatively complete or limited access. Results: The study found that participants' research interests were not satisfied by article abstracts alone nor, in the case of the physicians, by a clinical summary service such as UpToDate. On average, a third of the physicians viewed research a little more frequently than once a week, while two-thirds of the public health NGO staff viewed more than three articles a week. Those articles were published since the 2008 adoption of the NIH Public Access Policy, as well as prior to 2008 and during the maximum 12-month embargo period. A portion of the articles in each period was already open access, but complete access encouraged a viewing of more research articles. Conclusion: Those working in health fields will utilize more research in the course of their work as a result of (a) increasing open access to research, (b) improving awareness of and preparation for this access, and (c) adjusting public and open access policies to maximize the extent of potential access, through reduction in embargo periods and access to pre-policy literature.
Supplementary material 1 from: Egloff W, Agosti D, Patterson D, Hoffmann A, Mietchen D, Kishor P, Penev L (2016) Data Policy Recommendations for Biodiversity Data. EU BON Project Report. Research Ideas and Outcomes 2: e8458. https://doi.org/10.3897/rio.2.e8458
MS841: Biodiversity data publishing legal framework report
Supplementary material 1 from: Wetzel F, Hoffmann A, Häuser C, Vohland K (2016) 1st EU BON Stakeholder Roundtable (Brussels, Belgium): Biodiversity and Requirements for Policy. Research Ideas and Outcomes 2: e8600. https://doi.org/10.3897/rio.2.e8600
Acronyms - 1st EU BON Stakeholder Roundtable
Supplementary material 3 from: Egloff W, Agosti D, Patterson D, Hoffmann A, Mietchen D, Kishor P, Penev L (2016) Data Policy Recommendations for Biodiversity Data. EU BON Project Report. Research Ideas and Outcomes 2: e8458. https://doi.org/10.3897/rio.2.e8458
MS241: Specification for registry and metadata catalogue
Supplementary material 2 from: Egloff W, Agosti D, Patterson D, Hoffmann A, Mietchen D, Kishor P, Penev L (2016) Data Policy Recommendations for Biodiversity Data. EU BON Project Report. Research Ideas and Outcomes 2: e8458. https://doi.org/10.3897/rio.2.e8458
MS971: Data sharing agreement
Figure 1 from: Vanderhoeven S, Adriaens T, Desmet P, Strubbe D, Backeljau T, Barbier Y, Brosens D, Cigar J, Coupremanne M, De Troch R, Eggermont H, Heughebaert A, Hostens K, Huybrechts P, Jacquemart A, Lens L, Monty A, Paquet J, Prévot C, Robertson T, Termonia P, Van De Kerchove R, Van Hoey G, Van Schaeybroeck B, Vercayie D, Verleye T, Welby S, Groom Q (2017) Tracking Invasive Alien Species (TrIAS): Building a data-driven framework to inform policy. Research Ideas and Outcomes 3: e13414. https://doi.org/10.3897/rio.3.e13414
Figure 1 - A visual description of the TrIAS workflow through work packages. Work package 1 generates the input data; Work package 2 creates indicators and summaries of the data; Work package 3 uses the data and generates models and predications of future distributions; Work package 4 involves experts using the information from the other work packages, together with their own experience to create impact assessments.
Figure 2 from: Neylon C (2017) Building a Culture of Data Sharing: Policy Design and Implementation for Research Data Management in Development Research. Research Ideas and Outcomes 3: e21773. https://doi.org/10.3897/rio.3.e21773
Figure 2 - The Cultural Science model of Hartley and Potts (2014).The co-creation of culture and group in the context of an external environment.
CONCEPTUAL FOUNDATIONS OF PRODUCT POLICY DEVELOPMENT AND APPLICATION OF MARKETING RESEARCH IN INTERNATIONAL MARKETING
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Figure 1 from: Wetzel F, Hoffmann A, Häuser C, Vohland K (2016) 1st EU BON Stakeholder Roundtable (Brussels, Belgium): Biodiversity and Requirements for Policy. Research Ideas and Outcomes 2: e8600. https://doi.org/10.3897/rio.2.e8600
Figure 1 - EU BON Work Packages (WP) with the three sections (a) Data Sources and Infrastructure, (b) Science and Application and (c) Policy and Dialogue. The Stakeholder Roundtables are a specific task in the WP 6 that targets the stakeholder engagement and science-policy dialogue (credits: Pensoft).
Figure 4 from: Wetzel F, Hoffmann A, Häuser C, Vohland K (2016) 1st EU BON Stakeholder Roundtable (Brussels, Belgium): Biodiversity and Requirements for Policy. Research Ideas and Outcomes 2: e8600. https://doi.org/10.3897/rio.2.e8600
Figure 4 - Participants from science, policy and international networks at the 1st EU BON stakeholder roundtable in Brussels (credit: EU office of the Leibniz Association).
Figure 3 from: Wetzel F, Hoffmann A, Häuser C, Vohland K (2016) 1st EU BON Stakeholder Roundtable (Brussels, Belgium): Biodiversity and Requirements for Policy. Research Ideas and Outcomes 2: e8600. https://doi.org/10.3897/rio.2.e8600
Figure 3 - The challenge of integrating biodiversity data from remote sensing and in-situ (freshwater, marine, terrestrial).
How to make a difference in science policy with your research - Webinar
<p>As part of Project Ô's communication efforts, two international events were organised aimed at extended networks with a focus on political communication and science advocacy relevant to water reuse. These online public events were recorded and edited for publication on the YouTube channel.</p> <p>--</p> <p>On Monday 21 November 2022, a Project Ô-organised webinar was offered on the topic of how to make a difference in science policy with research. It was organised by the Institute for Methods Innovation on behalf of the project, working in collaboration with renowned science policy strategist Dr Andrew George (Sigma Xi). This live event was aimed at water sustainability researchers, professionals and others interested in the links between research and public policy.</p>
Data from: In an age of open access to research policies: physician and public health NGO staff research use and policy awareness
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ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.