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Figure 1 from: Müller C, Bräutigam A, Eilers EJ, Junker RR, Schnitzler J-P, Steppuhn A, Unsicker SB, van Dam NM, Weisser WW, Wittmann MJ (2020) Ecology and Evolution of Intraspecific Chemodiversity of Plants. Research Ideas and Outcomes 6: e49810. https://doi.org/10.3897/rio.6.e49810
Figure 1 Conceptual framework of the proposed RU on the ecology and evolution of intraspecific plant chemodiversity. We will study chemical variation in different plant parts (flowers, nectar, pollen, leaves, phloem sap; roots will be included in a potential second funding period), among plant individuals within populations and among populations (left) as well as consequences on the plant-associated community (right) over space and time. The projects will focus on the tree Populus nigra and the herbs Solanum dulcamara and Tanacetum vulgare (lower panel, from left to right).
Figure 1 from: Hardy H, Knapp S, Allan EL, Berger F, Dixey K, Döme B, Gagnier P-Y, Frank J, Haston EM, Holstein J, Kiel S, Marschler M, Mergen P, Phillips S, Rabinovich R, Sanchez Chillón B, Sorensen MV, Thines M, Trekels M, Vogt R, Wilson S, Wiltschke-Schrotta K (2020) SYNTHESYS+ Virtual Access - Report on the Ideas Call (October to November 2019). Research Ideas and Outcomes 6: e50354. https://doi.org/10.3897/rio.6.e50354
Figure 1 Indicative proposal flow for SYNTHESYS+ Virtual Access proposals. Blue boxes indicated proposer(s) actions/responsibilities; violet boxes VA coordinator actions/responsibilities; green boxes indicate where communication and discussion between proposers and VA coordinators will be paramount; white (no fill) boxes indicate post-proposal actions. Double-ended arrows indicate where back-and-forth will occur between proposer(s) and VA coordinator(s).
Figure 3 from: Borsch T, Stevens A-D, Häffner E, Güntsch A, Berendsohn WG, Appelhans MS, Barilaro C, Beszteri B, Blattner FR, Bossdorf O, Dalitz H, Dressler S, Duque-Thüs R, Esser H-J, Franzke A, Goetze D, Grein M, Grünert U, Hellwig F, Hentschel J, Hörandl E, Janßen T, Jürgens N, Kadereit G, Karisch T, Koch MA, Müller F, Müller J, Ober D, Porembski S, Poschlod P, Printzen C, Röser M, Sack P, Schlüter P, Schmidt M, Schnittler M, Scholler M, Schultz M, Seeber E, Simmel J, Stiller M, Thiv M, Thüs H, Tkach N, Triebel D, Warnke U, Weibulat T, Wesche K, Yurkov A, Zizka G (2020) A complete digitization of German herbaria is possible, sensible and should be started now. Research Ideas and Outcomes 6: e50675. https://doi.org/10.3897/rio.6.e50675
Figure 3 Mass throughput digitization station designed for museum objects at the Botanic Garden and Botanical Museum Berlin.
Figure 2 from: Borsch T, Stevens A-D, Häffner E, Güntsch A, Berendsohn WG, Appelhans MS, Barilaro C, Beszteri B, Blattner FR, Bossdorf O, Dalitz H, Dressler S, Duque-Thüs R, Esser H-J, Franzke A, Goetze D, Grein M, Grünert U, Hellwig F, Hentschel J, Hörandl E, Janßen T, Jürgens N, Kadereit G, Karisch T, Koch MA, Müller F, Müller J, Ober D, Porembski S, Poschlod P, Printzen C, Röser M, Sack P, Schlüter P, Schmidt M, Schnittler M, Scholler M, Schultz M, Seeber E, Simmel J, Stiller M, Thiv M, Thüs H, Tkach N, Triebel D, Warnke U, Weibulat T, Wesche K, Yurkov A, Zizka G (2020) A complete digitization of German herbaria is possible, sensible and should be started now. Research Ideas and Outcomes 6: e50675. https://doi.org/10.3897/rio.6.e50675
Figure 2 Number of digitized and non-digitized specimens held by 62 herbaria in Germany that were surveyed in 2019 (herbarium abbreviations according to Index Herbariorum).
Figure 1 from: Borsch T, Stevens A-D, Häffner E, Güntsch A, Berendsohn WG, Appelhans MS, Barilaro C, Beszteri B, Blattner FR, Bossdorf O, Dalitz H, Dressler S, Duque-Thüs R, Esser H-J, Franzke A, Goetze D, Grein M, Grünert U, Hellwig F, Hentschel J, Hörandl E, Janßen T, Jürgens N, Kadereit G, Karisch T, Koch MA, Müller F, Müller J, Ober D, Porembski S, Poschlod P, Printzen C, Röser M, Sack P, Schlüter P, Schmidt M, Schnittler M, Scholler M, Schultz M, Seeber E, Simmel J, Stiller M, Thiv M, Thüs H, Tkach N, Triebel D, Warnke U, Weibulat T, Wesche K, Yurkov A, Zizka G (2020) A complete digitization of German herbaria is possible, sensible and should be started now. Research Ideas and Outcomes 6: e50675. https://doi.org/10.3897/rio.6.e50675
Figure 1 Proportion of digitized specimens in the surveyed herbaria in Germany (left) and proportion of image and text data digitization vs. "text data only" (data) among the digitized specimens (right).
Figure 1 from: Stehle M, Lasseck M, Khorramshahi O, Sturm U (2020) Evaluation of acoustic pattern recognition of nightingale (Luscinia megarhynchos) recordings by citizens. Research Ideas and Outcomes 6: e50233. https://doi.org/10.3897/rio.6.e50233
Figure 1 Verified L. megarhynchos recordings in groups based on the ConfS (10-20%, N=10; 20-30%, N=10; 30-40%, N=10; 40-50%, N=10; 50-60%, N=21; 60-70%, N=11; 70-80%, N=11; 80-90%, N=3).
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.
Figure 2 from: Heintz-Buschart A, Guerra C, Djukic I, Cesarz S, Chatzinotas A, Patoine G, Sikorski J, Buscot F, Küsel K, Wegner C-E, Eisenhauer N (2020) Microbial diversity-ecosystem function relationships across environmental gradients. Research Ideas and Outcomes 6: e52217. https://doi.org/10.3897/rio.6.e52217
Figure 2 Overview of the sample collection. Decomposition, as a central soil function, and the biodiversity of involved microbial communities will be studied in a large, international monitoring network (sampling locations indicated in a) to assess the generality of microbial biodiversity-function relationships across climates (mean annual air temperature and annual precipitation in b) and land use and cover types (c).
Figure 1 from: Heintz-Buschart A, Guerra C, Djukic I, Cesarz S, Chatzinotas A, Patoine G, Sikorski J, Buscot F, Küsel K, Wegner C-E, Eisenhauer N (2020) Microbial diversity-ecosystem function relationships across environmental gradients. Research Ideas and Outcomes 6: e52217. https://doi.org/10.3897/rio.6.e52217
Figure 1 A Environmental change and heterogeneity determine the biodiversity and the functioning of ecosystems (modified after Eisenhauer et al. 2016). In addition, changes in biodiversity can have significant effects on ecosystem functioning, which can be masked by strong environmental gradients. Only by accounting for environmental heterogeneity can the role of biodiversity for ecosystem functioning be wholly realized (Grace et al. 2016). B Hypothesized positive relationship between microbial diversity and decomposition (overall positive relationship with confidence intervals; no real data was used to create this figure). The diversity gradient in soil microbes is supposed to be caused by different environmental conditions. The different grey lines indicate BEF relationships across different experimental and environmental gradients.
Supplementary material 6 from: Chodacki J, Habermann T, Hendricks G, Kaiser K, Kemp J, Meadows A, Paglione L, Pentz E, Ratner H, Reeves P, Schneider J, Stacey S, Urberg M (2020) Metadata 2020 Workathon Proceedings - Sept 2019. Research Ideas and Outcomes 6: e52878. https://doi.org/10.3897/rio.6.e52878
Value Map for Content Directors
Supplementary material 1 from: Chodacki J, Habermann T, Hendricks G, Kaiser K, Kemp J, Meadows A, Paglione L, Pentz E, Ratner H, Reeves P, Schneider J, Stacey S, Urberg M (2020) Metadata 2020 Workathon Proceedings - Sept 2019. Research Ideas and Outcomes 6: e52878. https://doi.org/10.3897/rio.6.e52878
The Solution Builders Audience Profile
Supplementary material 3 from: Chodacki J, Habermann T, Hendricks G, Kaiser K, Kemp J, Meadows A, Paglione L, Pentz E, Ratner H, Reeves P, Schneider J, Stacey S, Urberg M (2020) Metadata 2020 Workathon Proceedings - Sept 2019. Research Ideas and Outcomes 6: e52878. https://doi.org/10.3897/rio.6.e52878
The Policy/ Society Influencers Audience Profile
Supplementary material 5 from: Chodacki J, Habermann T, Hendricks G, Kaiser K, Kemp J, Meadows A, Paglione L, Pentz E, Ratner H, Reeves P, Schneider J, Stacey S, Urberg M (2020) Metadata 2020 Workathon Proceedings - Sept 2019. Research Ideas and Outcomes 6: e52878. https://doi.org/10.3897/rio.6.e52878
Value Map for Solution Builders
ScienceDex guides
Understand access before you commit
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.