dataesr/scientific-paragraphs-categorization huggingface dataset:
12) 0. 084 (1. 20) 0. 060 (1. 00) 0. 060 (1. 07) CC 1/2 0. 999 (0. 563) 1. 000 (0. 596) 0. 999 (0. 646) 1. 00 (0. 643) Structure refinement Resolution (Å) 38. 24 R work (%) 13. 0 (24. 3) 14. 4 (25. 5) 14. 6 (25. 2) 12. 6 (22. 6) R free (%) 15. 5 (23. 3) 17. 0 (28. 2) 18. 2 (25. 7) 17. 4 (25. 3) N. of atoms 1272 1184 1140 1253 Average atomic B-factor (Å 2 ) 12. 8 13. 8 13. 9 17. 6 R. m. s deviations Bond lengths (Å) 0. 019 0. 020 0. 016 0. 010 Bond angles (°) 2. 2 2. 2 2. 2 1. 6 PDB code 7QF2 7QF3 7QF4 7QF5.
Atlantic multidecadal variability is a coherent mode of natural climate variability occurring in the North Atlantic Ocean, with strong impacts on human societies and ecosystems worldwide. However, its periodicity and drivers are widely debated due to the short temporal extent of instrumental observations and competing effects of both internal and external climate factors acting on North Atlantic surface temperature variability. Here, we use a paleoclimate database and an advanced statistical framework to generate, evaluate, and compare 312 reconstructions of the Atlantic multidecadal variability over the past millennium, based on different indices and regression methods. From this process, the best reconstruction is obtained with the random forest method, and its robustness is checked using climate model outputs and independent oceanic paleoclimate data.
e. , regression) to extend back the AMV. In this respect, two previous studies  22, 44  reconstructed the AMV over the last millennium using a principal component regression method and continental proxy data located in North Atlantic bordering regions. However, these statistical models were calibrated using raw NASST anomalies  22, 44. As a result, the obtained reconstructions inevitably included the direct radiative effect of external forcings  44  , and thus does not correspond to the AMV as defined in the present study  2, 7, 25. In addition, former reconstruction studies  22, 44  do not include any statistical tests for the selection of the proxy records, which means that some of them may not be proper predictors of the AMV. The present study thus relies on statistically objective methods in the selection of proxy data  45  from the PAGES 2k database  46  ("Methods").
This selection is realized separately for the three AMV definitions and timeframes investigated ("Methods"). The year 850 C. E. was chosen since it is the starting point for the last millennium climate simulations. Such simulations will be used later to further evaluate and validate our reconstruction in a pseudo-proxy framework  48. The observed time series for the three AMV indices all reach back to 1870 C. E. , the first year for which gridded SST observations are available  40. The selection of the proxy records is made from a large set of annually resolved proxies from the Northern Hemisphere, which includes the PAGES 2k database  46  and 41 others  45  (Supplementary Table  1 ). This database (hereafter P2k + ) consists of 457 records and was constructed using different quality criteria (Methods).
A comparison with independent ocean proxy records from the Ocean2k database  46  with multi-annual to multidecadal resolution, in order to assess whether our reconstruction agrees with those slowly varying oceanic changes (see Supplementary Fig. 8 , "Methods" and Supplementary Note 2 for further details). 3. Two pseudo-proxy experiments based on climate model outputs  48  to assess (i) the robustness and reliability of the reconstruction method, and (ii) the potential effect of nonstationarities in the connection between the proxies and the AMV (see Supplementary Figs. 9 and 10, "Methods", and Supplementary Note 3 for further details). In addition, we independently performed a NASST reconstruction where no radiative forcing effect is removed from the predictand (NASST) and predictors (proxy records) with the same optimal model selection (Methods, Supplementary Fig.
Selected proxy records for the best reconstruction The nested RF reconstruction of the AMV uses a total of 55 proxy records from the Northern Hemisphere (Fig. 2a, b ) selected based on their correlation with the AMV F index. Their final weights, given by RF variables' importance ("Methods"), and their temporal availability, are shown in Fig. 2c, d. We identify three main groups of records with similarly distributed weights: Central Asia, Europe, and Western North America. The relatively low number of European proxies contrasts with previous regression-based reconstructions that preselected proxies solely on the basis of their geographic proximity to the North Atlantic  22, 44. The fact that only five proxy records from Europe are used could be due to their reduced presence in the proxy record database we consider (<10% of the total of P2k + ), or could simply indicate that the climate signal and seasonality of most European proxies are not representative of the AMV signatures (Fig.
1 ). Among the five selected European proxies, one of them has a large weight (>3. 5%) and interestingly covers the whole reconstruction period (Fig. 2c, d ). It corresponds to a time series of tree ring growth measurements in the European Alps  54  , which is strongly correlated with summer instrumental temperature over the historical period (r = 0. 7, P < 0. 01, Supplementary Table  4 ). The other four European proxy records are related to summer or annual temperature and precipitation, and are thus in agreement with the well-documented fingerprints of the AMV on European summer temperatures  4, 5  , also highlighted here in Fig. 1. The proxy data from Asia and western North America are strongly represented in the PAGES 2k  46  database, which may partly explain their relatively large presence in the reconstruction.
Instrumental AMV indices Historical AMV indices have been calculated using annually resolved observations of SST data from the Hadley Center dataset (HadISST) for the period 1870-2019. Three instrumental AMV indices are defined as the spatially averaged SST over the North Atlantic region, differing in the way the externally forced signal is removed, either beforehand or afterward. The first index (AMV TS , Fig. 1a ) uses the global averaged SST anomalies between 60°S and 60°N as a proxy for the externally forced signal that is subtracted from NASST. A second index (AMV T ; Fig. 1a ), is built using climate model historical simulations to isolate the forced component in NASST. It is calculated with a signal-to-noise maximizing empirical orthogonal function ("Methods") that is then removed by linear regression from an estimate of its 10-year smoothed effect at each grid point of the North Atlantic.
Proxy records database To select the proxy records of this study, a first large dataset is made by merging the PAGES 2k database (686 records) with other proxy records in neighboring continents of the North Atlantic published by different studies (Supplementary Table  1 ). Duplicates from the three sources are removed leading to a final database of 727 proxy records (P2k-ALL). The proxy records from P2k-ALL used in the reconstruction have been selected to fulfill the following conditions: (i) they are annually resolved, (ii) they are located in the Northern hemisphere (latitude >0°), and (iii) they are significantly correlated at the 95% confidence level with at least one historical time series of either annual or seasonal precipitation or surface temperatures from the nearest grid point within the CRU TS4 historical dataset.
First analyses singled out a proxy record in Asia named "Asia. -MOR1JU" which had abnormally large RF weights (more than 5 times higher than the second) such that S CE scores were almost doubled due to its inclusion. The reason for this behavior was not clear but highlighted a limitation of the nonlinear method. Whatsoever, since hundreds of proxy records are tested against the AMV indices, such a statistical anomaly does not appear unlikely. In order to prevent biasing the reconstruction toward this single proxy record, we decided to remove it from the database used in this study. In the next sections, we describe how different reconstructions are compared and how a final nested reconstruction of the AMV is obtained. These reconstructions also use correlation tests to select proxies from P2k+ that are significantly correlated with a given AMV index, for a given learning and reconstruction period.
Composites of ocean proxy records time series Since corals are often very short records and ocean sediment cores have too low temporal resolution (preventing them to meet the requirement of being annually resolved), there is a shortage of ocean records contributing to the reconstructions, which are almost exclusively based on terrestrial records. Interestingly, the low-frequency part in the annually resolved reconstructions can be verified against the ocean records from the Ocean2K database that have not been used in the reconstruction. To avoid overfitting, correlation significance shown in Supplementary Fig. 8a  is only calculated for the preindustrial period (before 1870) with an AR(1) correction for significance tests to avoid false detections due to the low resolution of some proxies ("Methods").
Each pseudo-proxy mimics a specific real-world proxy by taking, in the model, the variable and season (or annual values) with the largest absolute correlations between the real proxy record and the closest grid points from the CRU TS4 dataset (Supplementary Table  4 ). Gaps and missing values in real proxy records are also reproduced in the pseudo-proxy time series. For both pseudo-proxy experiments presented below, and for the sake of reducing computational costs, nested reconstructions have been made with a 20-year timestep for the inferior boundary (from 850-1987 to 1850-1987) instead of the 1-year one that is used for the real reconstructions. The first pseudo-proxy experiment consists of reconstructing the AMV within model simulations using the same proxy records and RF method as for the real-world reconstruction, and for each timestep of the nested reconstruction.
Acknowledgements This study benefited from the  IPSL Prodiguer-Ciclad  and Camelot supercomputing facilities, supported by  CNRS ,  UPMC Labex L-IPSL. This work was also sponsored by  NWO Exact and Natural Sciences  for the use of  SurfSARA supercomputing facilities (Amsterdam)  under project  2020. 022. Authors were also funded by  EU-H2020   TiPES  (Grant no. 820970 , S. M. , contribution no. 164 ),  Blue Action  (Grant Agreement no. 727852 , D. S. , G. G, G. M. , and J. M. ),  EUCP  (Grant Agreement no 776613, D. S. and J. M. ),  ROADMAP  (Grant Agreement no. 116020 , J. M. , G. M. , and G. G. )  ARCHANGE  ( ANR-18-MPGA-0001 , J. M. and G. G. ) research programmes.
Data availability The whole data generated and treated in this study have been deposited on a Zenodo repository: https://zenodo. org/record/ 4896670#. YLjdOS2w3dc. The source and treated data to compute figures in this study are provided in the Source Data file. The generated data in this study are provided in the Data file. Simulation outputs for the LME experiments can be accessed from the NCAR Climate Data gateway website: https://www. earthsystemgrid. org/. The CRU TS4 datasets can be accessed from the CRU data download webpage: https://crudata. uea. ac. uk/cru/data/hrg/#current. The HadISST data can be accessed from the Met Office Hadley Center website: https:// www. metoffice. gov. uk/hadobs/hadisst/. For the historical experiments of CMIP5 and CMIP6 model outputs, data can be accessed from the ESGF website: https://esgf-node.
Choice of Movement Trajectories: In our speed experiments, we wanted to isolate the impact of speed and chose a simple Linear horizontal trajectory. In our trajectory experiments, we added Irregular trajectories. To extract ecologically valid irregular trajectories, we analyzed data from real 2018/2019 Premier League championship games. The data was provided by Footovision  [31] -a performance analytic company-using state-of-the-art video tracking of players. We picked a typical game between Leeds United and Swansea and chose 7 trajectories of walking soccer players and 7 trajectories of running soccer players as seen from a birds-eye-view of the field using a custom visual analytics tool  [73]. For each trajectory we calculated 2 variations: a 180°clockwise rotation and a mirror on the y axis.
CONCLUSIONS Our research agenda was meant to apply to a broad set of visualization in motion scenarios and as such covers a small and wide-ranging set of aspects to consider. In particular, we included characteristics of motion, spatial relationship between viewer and visualization, and factors of situation, context, and design. As the research space will be explored more, each of the broader scenarios outlined in Section 2 deserves their own individual research agenda and design space. We propose visualization in motion as an umbrella under which the impact of relative motion on the experience with and perception of visualizations can be discussed and analyzed, hoping that our common vocabulary can promote such discussions. Many nascent areas of visualization are impacted and already contain visualizations in motion: data physicalization, wearable visualization, mobile visualization, embedded and situated visualization, for example-are areas that all broadly relate to a vision for making visualizations more ubiquitous in our daily life.
ACKNOWLEDGMENTS This work was partly supported by the  Agence Nationale de la Recherche (ANR) , grant number  ANR-19-CE33-0012. Image credits: All images copyright of the person granting permission: Fig. 1a released under cba, Fig. 1b: SportBuzzBusiness [88], Fig. 1c:  Dario Rodighiero , Fig. 1d:  Eddie Camp of Respect New Haven. , Fig. 2: Footovision [31].
Optical simulations We simulated 2D layered structures and then modelled their optical response using finite difference time domain calculations (FDTD) in a commercial software (LUMERICAL FDTD SOLUTIONS; v. 211, http://www. lumerical. com), using code adapted from  Middleton et al. (2020). We tested whether the optical parameters (layer thickness and periodicity) of the photonic structures observed in L. strigocamara could reflect short wavelengths similar to the measured reflectances. We altered the Viburnum tinus parameters (layer thickness: 88 nm; periodicity: 190 nm) to match those measured from L. strigocamara cross-sectional images (layer thickness: 105. 3 nm; periodicity: 110. 7 nm) and retained the same degree of angular disorder (sigmaPhi = 6). Five replicate multilayered structures were generated to consider the inhomogeneity of the structure and the average simulated reflectance was reported.
Acknowledgements We are grateful to  Isabel Molina  for helpful discussions, and to  Adrian Carper  and  Deane Bowers  for the assistance of their butterflies in the pollination of Lantana strigocamara flowers during the winter growing season. We would also like to thank  Maria Murace  and  Lara Valenti , who assisted with obtaining fruits for optical measurements, and  Kayleigh Dodson  for collecting a voucher specimen;  Gianni Jacucci , who provided instruction in optical simulations; and  Franck Dahlem , who assisted with scanning electron force measurements. We appreciate the helpful and constructive comments of three anonymous reviewers, which greatly improved the manuscript. We are indebted to the  Boulder Electron Microscopy Service  for their assistance with data collection and for the use of their facilities, and to the  Nutritional and Isotopic Ecology Laboratory at the University of Colorado-Boulder  for use of their equipment.
MAS-A and SDS gratefully acknowledge funding from  NSF -DBI 1907293  and  DEB-1553114 , respectively. SV acknowledges a Biotechnology and  Biological Sciences Research Council (BBSRC)   David Phillips Fellowship  ( BB/K014617/1 ), the  European Research Council (ERC)  ( ERC-St -Grant  014H2020 639088 ), and the  ERC BiTe  ( 101001637 ). GTvdK acknowledges  Marie Skłodowska-Curie  grant agreement no. 722842  (ITN Plant-inspired  Materials and Surfaces-PlaMatSu ). YO thanks the  NanoBio-ICMG platform  ( FR 2607 ) for granting access to the electron microscopy facility.
2008; Essl et al. 2015; Katsanevakis et al. 2013; Pyšek et al. 2011; Nunes et al. 2015; García-Berthou et al. 2005; Pergl et al. 2017). Global databases of IAS such as the IUCN's Global Invasive Species Database (GISD, www. iucng isd. org) and the CABI Invasive Species Compendium (CABI ISC, www. cabi. org/ isc) list between 34 and 80 different pathways through which alien species can be introduced to new locations. Examples of pathways include horticulture (e. g. purple loosestrife, Lythrum salicaria;  Maki and Galatowitsch 2004) , agriculture (e. g. sisal hemp, Agave sisalana;  Ortega et al. 2019) , pet trade (e. g. Burmese python, Python bivittatus;  Wilson et al. 2011)  and biofouling (e. g. zebra mussel, Dreissena polymorpha;  Carlton 2008). Such lists are neither exhaustive nor static; as societies evolve and economic activities continue to grow, and so more pathways are expected to emerge.
Whilst a number of publications have examined the links between ecological impacts and pathways of IAS (e. g. Pergl et al. 2017; Saul et al. 2017) , there are currently no studies assessing relationships between pathways and economic impacts of IAS. Although economic impact may overlook aspects of ecological impacts, it is a very useful metric of the impact of IAS, as it can be quantitative, and if costs are standardized, they can be compiled across regions or taxa and compared between pathways. A better understanding of the economic costs of invasions is also a key way to raise global awareness about IAS, optimise transboundary legislation and help the prioritisation of management actions  (Diagne et al. 2020a). In this paper, we investigate pathways of economically-harmful IAS using the most up-to-date compilation of monetary cost information on IAS-the InvaCost database  (Diagne et al.
0, openly available at https:// doi. org/ 10. 6084/ m9. figsh are. 12668 570. v4), which is the most complete and up-to-date global dataset of the reported economic costs attributable to biological invasions  (Diagne et al. 2020b). InvaCost has been built by a combination of both systematic literature searches (e. g. , specific search strings used in Web of Science and Google Scholar) and direct solicitations (e. g. , stakeholders, scientific experts) in more than 10 languages, to gather any cost information available in written documents. After ensuring the relevance of each document, cost information was collated, standardised to a common and up-to-date currency in the database (i. e. 2017 US dollars), and finally classified into categories using a range of descriptive fields (complete description and details on these descriptive fields are available at https:// doi.
org/ 10. 6084/ m9. figsh are. 12668 570) (See Table  1  for a description of the fields used in this study). This updatable and publicly available resource contains 13,123 cost entries (as of June 2021), therefore providing an essential basis for worldwide research and policymaking targeting IAS  (Diagne et al. 2020a; 2020b). The 'living' nature of the dataset allows for the taxonomic and geographic gaps in cost information to be addressed over time, and then keeps all users and stakeholders informed on cost dynamics and distribution  (Diagne et al. 2021a).
Cost data collection and filtering We used successive filters from the InvaCost database to identify relevant cost entries for our analysis and obtain a conservative and realistic estimate of costs. First, we extracted costs empirically "observed" in the invaded environment and left out all "potential" costs (not yet actually realised but rather expected and/or predicted over time within or beyond their actual distribution area). Second, we then retained costs classified as "high" reliabilitytherefore discarding "low" reliability costs-thereby keeping only cost estimates either provided by officially pre-assessed documents (peer-reviewed articles This states -at the time of the estimation -whether the reported cost was actually observed (i. e. , cost actually incurred) or potential (i. e. , not incurred but expected cost) Cost type (Type_of_cost_merged column) Categories of the Type of cost column reassigned into damage (economic losses due to direct and/ or indirect impacts of invaders, such as yield loss, health injury, land alteration, infrastructure damage, or income reduction), management (monetary resources allocated to mitigate the spread or impacts of invaders, such as prevention, control, research, long-term management, eradication) or mixed (when costs included both 'damage' and 'management' components); every cost for which the exact nature of cost was not clearly defined was assigned to unspecified Management type (Management_type column) Pre-invasion management (monetary investments for preventing successful invasions in an area-including quarantine or border inspection, risk analyses, biosecurity management, etc.
Diverse/Unspecified is assigned when costs are incurred over multiple regions or the geographic location was not specified 1 3 Vol:. (  1234567890 ) and official reports) or associated with an estimation methodology that was deemed reproducible when building the database  (Diagne et al. 2020b). Lastly, we focused on cost estimates exclusively attributed to individual species, therefore multi-species costs or genus-level costs were removed (e. g. when the value in the Species field included "sp. " or "spp. " or was simultaneously associated with several species without any possibility to disentangle specific contribution of each taxon to the overall cost). Following these filtering steps, our dataset (hereafter called fil-tered_subset) contained 7,175 entries (Supplementary Material 1). Finally, we extracted the list of individual species with recorded costs.
Collection and compilation of pathway information Pathways were categorised using  Harrower et al. (2018) , a guidance document for interpreting the Convention on Biological Diversity (CBD, 2014) pathway classification framework (Fig. 1 ). This pathway classification system has limitations, especially regarding uncertainty linked to subcategories  (Faulkner et al. 2016 (Faulkner et al. , 2020;; Pergl et al. 2020 ), however McGrannachan et al. (2021)  suggested that it is a reliable framework for reporting on IAS pathways at a global level. Pathway mechanisms and categories are defined, and subcategories are listed in Supplementary Material 2. We compiled pathway data for each of the 606 species with reported economic costs in our filtered_subset (Supplementary Material 3, which contains all columns hereafter mentioned) mainly using information from CABI ISC (www.
cabi. org/ isc/) and the GISD (http:// www. iucng isd. org/ gisd), resulting in a total of 478 species with information on their specific pathways. When the pathway information needed was not available in one of these repositories, we opportunistically extended our searches to other databases on biological invasions (e. g. the Galapagos Species Checklist), and performed targeted searches in the published literature. Pathway descriptions provided in databases or publications were recorded, along with the source of the data (CABI ISC, GISD, etc. ), in our pathways dataset. These descriptions do not always match the CBD pathway sub-categories. Each pathway description was initially matched to the CABI ISC pathway description and then classified into the mechanisms, categories, and subcategories of the CBD scheme using the published guidelines for the scheme (i.