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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 2 from: Brincalepe Salvador R, Cavallari DC (2020) Taxonomy and distribution of enigmatic "helicoid" Polygyratia Gray, 1847 (Gastropoda, Stylommatophora). Zoosystematics and Evolution 96(1): 91-101. https://doi.org/10.3897/zse.96.51047

Figure 2 Internal lamellae in the shell of Polygyratia polygyrata.A. Broken specimen showing the second set of internal lamellae, FMNH 40632 (FMNH). B–F. CT scan image showing the internal structures of an intact shell (MZSP 129318). B. Whole shell showing the two sets of internal lamellae. C, D. First set of internal lamellae. E, F. Second set of internal lamellae.Scale bar: 2 cm (A, B).

Figure 1 from: Brincalepe Salvador R, Cavallari DC (2020) Taxonomy and distribution of enigmatic "helicoid" Polygyratia Gray, 1847 (Gastropoda, Stylommatophora). Zoosystematics and Evolution 96(1): 91-101. https://doi.org/10.3897/zse.96.51047

Figure 1 Shells of Polygyratia polygyrata, showing the variation in conchological characters such as size, number of whorls and aperture shape, as well as colouration. Scale bar = 2 cm. A. Holotype, NHMW-MO-14371 (NHMW). B.AMNH 43887 (AMNH). C.AMNH 119005 (AMNH). D.SMF 26248 (SMF). E. Example of a weathered specimen, which acquires a washed-off ochre/brown colouration, MNZ M.233517 (MNZ). F. Possible holotype of Polygyratia charybdis, NHMD 615842 (NHMD).

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.

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Supplementary material 1 from: Brincalepe Salvador R, Cavallari DC (2020) Taxonomy and distribution of enigmatic "helicoid" Polygyratia Gray, 1847 (Gastropoda, Stylommatophora). Zoosystematics and Evolution 96(1): 91-101. https://doi.org/10.3897/zse.96.51047

: Data type: image

Figure 4 from: He Y, Xu X-M, Zhou Y, Liu Q-R (2020) Onosma fuyunensis (Boraginaceae), a new species from Xinjiang, China. PhytoKeys 144: 11-22. https://doi.org/10.3897/phytokeys.144.33287

Figure 4 Distribution map of O. fuyunensis. Red points denote localities. Illustrated by Feng Xue and Yi He.

Figure 1 from: He Y, Xu X-M, Zhou Y, Liu Q-R (2020) Onosma fuyunensis (Boraginaceae), a new species from Xinjiang, China. PhytoKeys 144: 11-22. https://doi.org/10.3897/phytokeys.144.33287

Figure 1 Type specimens of Onosma gmelinii (A syntype, W 1899-0213498), O. simplicissima (B lectotype, LINN No. 187.1) and O. fuyunensis (C holotype, BNU 0041549 D isotype, BNU 0041547).

Figure 3 from: He Y, Xu X-M, Zhou Y, Liu Q-R (2020) Onosma fuyunensis (Boraginaceae), a new species from Xinjiang, China. PhytoKeys 144: 11-22. https://doi.org/10.3897/phytokeys.144.33287

Figure 3 Characters comparison of O. fuyunensis and related species A nutlets of O. gmelinii (in adaxial and abaxial view) B nutlets of O. fuyunensis (in adaxial and abaxial view) C flowers of O. fuyunensisD corolla of O. gmeliniiE corolla of O. simplicissimaF scanning electron micrograph of nutlets of O. gmeliniiG scanning electron micrograph of nutlets of O. fuyunensisH scanning electron micrograph of pollens of O. gmeliniiI scanning electron micrograph of pollen of O. fuyunensis. Photo by Yi He.

Figure 2 from: He Y, Xu X-M, Zhou Y, Liu Q-R (2020) Onosma fuyunensis (Boraginaceae), a new species from Xinjiang, China. PhytoKeys 144: 11-22. https://doi.org/10.3897/phytokeys.144.33287

Figure 2 Photographs of O. fuyunensis. A Habitat B inflorescence during late flowering season C habit D basal leaves (show spreading bristles) E leaves in abaxial view (show netted venation) F scanning electron micrographs of leaves in adaxial view. Photo by Yi He.

High level features for LHC Olympics 2020 R&D Dataset

High level feature sets for the LHC Olympics 2020 R&D dataset.  import pandas as pd features_sig=pd.read_hdf("LHCORD_sig_features.h5") features_sig.keys() ['pxj1', 'pyj1', 'pzj1', 'mj1', 'tau1j1', 'tau2j1', 'tau3j1', 'pxj2', 'pyj2', 'pzj2', 'mj2', 'tau1j2', 'tau2j2', 'tau3j2'] The features are the 3-momenta, invariant masses and N-jettiness variables (tau^1, tau^2, tau^3) for the two leading R=1 jets in each event. These were computed using fastjet v3.3.0.

Data and R code for Anthropogenic stressors impact fish sensory development and survival via thyroid disruption

The zip document contains all the data and R code used in the manuscript "Anthropogenic stressors impact fish sensory development and survival via thyroid disruption".

Figure 8 from: Bragança PHN, van Zeeventer RM, Bills R, Tweddle D, Chakona A (2020) Diversity of the southern Africa Lacustricola Myers, 1924 and redescription of Lacustricola johnstoni (Günther, 1894) and Lacustricola myaposae (Boulenger, 1908) (Cyprinodontiformes, Procatopodidae). ZooKeys 923: 91-113. https://doi.org/10.3897/zookeys.923.48420

Figure 8 "Lacustricola" myaposae colouration in life: A male SAIAB 208915, 33.0 mm SLB female SAIAB 208915, 35.2 mm SL; from Nseleni River, KwaZulu-Natal, Republic of South Africa.

Figure 7 from: Bragança PHN, van Zeeventer RM, Bills R, Tweddle D, Chakona A (2020) Diversity of the southern Africa Lacustricola Myers, 1924 and redescription of Lacustricola johnstoni (Günther, 1894) and Lacustricola myaposae (Boulenger, 1908) (Cyprinodontiformes, Procatopodidae). ZooKeys 923: 91-113. https://doi.org/10.3897/zookeys.923.48420

Figure 7 "Lacustricola" myaposae preserved colouration: A male SAIAB 96591, 31.7 mm SLB female SAIAB 96591, 35.7 mm SL; from Nseleni River, KwaZulu-Natal, Republic of South Africa.

Figure 6 from: Bragança PHN, van Zeeventer RM, Bills R, Tweddle D, Chakona A (2020) Diversity of the southern Africa Lacustricola Myers, 1924 and redescription of Lacustricola johnstoni (Günther, 1894) and Lacustricola myaposae (Boulenger, 1908) (Cyprinodontiformes, Procatopodidae). ZooKeys 923: 91-113. https://doi.org/10.3897/zookeys.923.48420

Figure 6 Osteological plate of "L." johnstoni (SAIAB 35820) from the Upper Shire River, Mangochi, Malawi. A Lachrymal B neurocranium, ventral view C neurocranium dorsal view D ventral post-cleithrum and first pleural rib, lateral view E anal-fin radials and proximal radials, left lateral view F caudal-fin skeleton, left lateral view G dorsal-fin radials and proximal radials, left lateral view H left branchial arches ventral portion, ventral view I right dorsal portion of branchial arches, ventral view J urohyal, left lateral view K left basipterygium, dorsal view L left shoulder girdle, lateral view M right hyoid bar, lateral view N left jaws, jaws suspensorium and opercular apparatus, lateral view. Scale bars: 1 mm.