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Global patterns of the leaf economics spectrum in wetlands
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Data from: Economics, life history and international trade data for seven turtle species in Malaysia and Indonesian farms
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Data from: Evidence of economical territory selection in a cooperative carnivore
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Plant growth forms determine root resource acquisition strategy along ‘fast-slow’ economics spectrum in a temperate forest community
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Supplementary material from: Melchior A (2019) Russia in world trade: Between globalism and regionalism. Russian Journal of Economics 5(4): 354-384. https://doi.org/10.32609/j.ruje.5.49345
: Data type: Table
Figure 3 from: Pujade-Villar J, Wang Y, Zhang W, Mata-Casanova N, Lobato-Vila I, Dénes A-L, László Z (2020) A new Diplolepis Geoffroy (Hymenoptera, Cynipidae, Diplolepidini) species from China: a rare example of a rose gall-inducer of economic significance. ZooKeys 904: 131-146. https://doi.org/10.3897/zookeys.904.46547
Figure 3 Distribution of the 11 species of Diplolepis of the Palearctic; in yellow, the species distributed exclusively in the Eastern Palearctic. Palaearctic map obtained from https://www.google.com/maps/@57.7164944,49.0396796,9792440m/data=!3m1!1e3. The inset image pointing out in red the Gansu Province (and thus the collecting location) was obtained from https://en.wikipedia.org/wiki/Gansu.
Figure 4 from: Pujade-Villar J, Wang Y, Zhang W, Mata-Casanova N, Lobato-Vila I, Dénes A-L, László Z (2020) A new Diplolepis Geoffroy (Hymenoptera, Cynipidae, Diplolepidini) species from China: a rare example of a rose gall-inducer of economic significance. ZooKeys 904: 131-146. https://doi.org/10.3897/zookeys.904.46547
Figure 4 a forewing of D. flaviabdomenisb forewing of D. hunanensisc forewing of D. minoriabdomenis, and d forewing of D. nr japonicae head in frontal view of D. nr japonica (reused from Wang et al. 2013) f head in dorsal view of D. nr japonica (reused from Wang et al. 2013) g head in frontal view of D. hunanensish lateral mesosoma of D. minoriabdomenisi mesoscutum of D. nr japonica.
Figure 5 from: Pujade-Villar J, Wang Y, Zhang W, Mata-Casanova N, Lobato-Vila I, Dénes A-L, László Z (2020) A new Diplolepis Geoffroy (Hymenoptera, Cynipidae, Diplolepidini) species from China: a rare example of a rose gall-inducer of economic significance. ZooKeys 904: 131-146. https://doi.org/10.3897/zookeys.904.46547
Figure 5 Bayesian inference (BI) tree of the Diplolepis species that have available mitochondrial COI sequences. Numbers on the branches represent posterior probabilities (PP).
Figure 2 from: Pujade-Villar J, Wang Y, Zhang W, Mata-Casanova N, Lobato-Vila I, Dénes A-L, László Z (2020) A new Diplolepis Geoffroy (Hymenoptera, Cynipidae, Diplolepidini) species from China: a rare example of a rose gall-inducer of economic significance. ZooKeys 904: 131-146. https://doi.org/10.3897/zookeys.904.46547
Figure 2 Diplolepis abei Pujade-Villar & Wang ♀ sp. nov. a–b galls c dissected gall with last instar larva d forewing e lateral and ventral view of the 3rd instar larva f lateral and ventral view of the last instar larva g lateral habitus.
Figure 1 from: Pujade-Villar J, Wang Y, Zhang W, Mata-Casanova N, Lobato-Vila I, Dénes A-L, László Z (2020) A new Diplolepis Geoffroy (Hymenoptera, Cynipidae, Diplolepidini) species from China: a rare example of a rose gall-inducer of economic significance. ZooKeys 904: 131-146. https://doi.org/10.3897/zookeys.904.46547
Figure 1 Diplolepis abei Pujade-Villar & Wang ♀ sp. nov. a head in frontal view b genae in dorsal view c antenna d mesosoma in lateral view e mesosoma in dorsal view f mesosoma in dorso-lateral view g propodeum h metasoma in lateral view.
Figure 3 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 3 The Land Systems Archetypes mapped by Levers et al. (2018) for one of BESTMAP case study areas in Catalonia. Land Systems Archetypes were derived at 3km resolution for Europe based on CORINE land cover, downscaled ecological and economic data. BESTMAP aims to map Farming System Archetypes (FSAs) at the farm level.
Figure 7 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 7 BESTMAP workflow to map FSAs, defined as having a characteristic bundle of ESS (and dis-services e.g. pollution), biodiversity, socio-economic and behavioural characteristics. Geospatial datasets (the Case Study Base Layer) will be used to approximate these FSAs (the "Proto-FSAs" clusters) in the first step; followed by interviews, field data collection and modelling to assign values on ESS, biodiversity, socio-economic aspects and behaviour for a stratified sample of proto-FSAs farms. The proto-FSAs will likely share "socio-economic-environmental" space and will be split/merged to minimize (but not eliminate) that overlap by changing the clustering in Case Study Base Layer space, defining the final FSAs and their spatial distribution.
Figure 9 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 9 BESTMAP dashboard mock-up, visualizing the impact of the policies through the policy indicators.
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.
The effect of renewable and nuclear energy consumption on decoupling economic growth from CO2 emissions in Spain
<p>This study examines the relationship between renewable and nuclear energy consumption, carbon dioxide emissions and economic growth by using the Granger causality and non-linear impulse response function in a business cycle in Spain. We estimate the threshold vector autoregression (TVAR) model on the basis of annual data from the period 1970‒2018, which are disaggregated into quarterly data. Our analysis reveals that economic growth and CO<sub>2</sub> emissions are positively correlated during expansions but not during recessions. Moreover, we find that rising nuclear energy consumption leads to decreased CO<sub>2</sub> emissions during expansions, while the impact of increasing renewable energy consumption on emissions is negative but insignificant. In addition, there is a positive feedback between nuclear energy consumption and economic growth, but unidirectional positive causality running from renewable energy consumption to economic growth in upturns. Our findings do indicate that both nuclear and renewable energy consumption contribute to a reduction in emissions; however, the rise in economic activity, leading to a greater increase in emissions, offsets this positive impact of green energy. Therefore, a decoupling of economic growth from CO<sub>2</sub> emissions is not observed. These results demand some crucial changes in legislation targeted at reducing emissions, as green energy alone is insufficient to reach this goal.</p>
Figure 45 in Economically Beneficial Ground Beetles. The specialized predators Pheropsophus aequinoctialis (L.) and Stenaptinus jessoensis (Morawitz): Their laboratory behavior and descriptions of immature stages (Coleoptera: Carabidae: Brachininae)
Figure 45. Pupa of P. aequinoctialis, dorsal (left), ventral (middle), and left (right) lateral aspects
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
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.