dataesr/scientific-paragraphs-categorization huggingface dataset:
Southeast Asia and national comparisons The monetary impact of invasions recorded in Southeast Asia totalled US$ 16. 89 billion between 1960 and 2020. Amongst these, Singapore ranked fifth relative to other countries in terms of reported costs, with two recorded invasive alien species and seven recorded cost entries in InvaCost. Notably, Brunei had the lowest number of recorded Table  1. Comparison of recorded invasive alien species and their costs amongst countries in Southeast Asia. Proportions of species with reported costs, relative to numbers of known reported alien species originating from the Global Alien Species First Records Database  (Seebens et al. 2018; accessed in June 2020) , are also displayed.
Recorded species Database entries  entries (1), species (1) and costs (US$ 6. 7 million), while Thailand had the highest costs (US$ 5. 2 billion) and most recorded entries (13) according to InvaCost (Table  1 ), suggesting considerable spatial heterogeneity in the region (Figure  3 ). In countries where lists of known invasive alien species were available  (Liu et al. 2021 ), all had reported costs for 10% or less of known invasive alien species, with Singapore having the lowest proportion of aliens with costs (< 3%). We further identified a significant correlation between trade volume and the number of recorded entries in InvaCost (Suppl. material 5). When the number of records from Singapore is related to the volume of trade imports (Figure  4 ), which has been shown to be strongly related to cost entries  (Haubrock et al.
For all these reasons, it could be assumed that the presented costs may represent potentially a massive underestimation of the real economic costs of biological invasions in Singapore and Southeast Asia. Our work also reveals a considerable taxonomic bias in the reported economic impacts of the 142 reported alien animal species in Singapore. The weighting of costs towards taxa in the database does not reflect the 'true' taxonomic composition of alien species in Singapore. Freshwater fishes and reptiles together make up the majority of alien species in this country (61%)  (Yeo and Chia 2010) , but no cost data were found for any of these taxa here. Yet,  Yeo and Chia (2010)  present anecdotal information that several non-native plant species (e. g. the South American water hyacinth, Eichhornia crassipes) are likely to have necessitated regular management at various scales, sometimes at considerable (yet unquantified) financial cost.
This information, however, mostly relied on  Wee and Corlett (1986) , who, although most likely being outdated, listed 34 potentially invasive plant species present in Singapore. Nevertheless, these two accounts together are only about one quarter of the 648 species listed by  GRIIS (Kwek et al. 2020) , underlining the gap of cost reporting for invasive species in Singapore. Whilst we cannot exclude that some existing cost data may have not been captured by the InvaCost database, this taxonomic discrepancy should be discussed. Singapore has a history of freshwater species introductions  (Yeo and Chia 2010; Ng et al. 2010; Liew et al. 2012; Ng and Yeo 2012; Kwik et al. 2013; Ng et al. 2015 Ng et al. , 2016a, b), b). Accidental releases/escapes aside, key drivers of intentional releases can often be cultural (e.
Acknowledgements The authors acknowledge the  French National Research Agency  ( ANR-14-CE02-0021 ) and the  BNP-Paribas Foundation Climate Initiative  for funding the  InvaCost  project that allowed the construction of the InvaCost database. The present work was conducted following a workshop funded by the  AXA Research Fund Chair of Invasion Biology  and is part of the  AlienScenario  project funded by  BiodivERsA and Belmont-Forum  call 2018 on biodiversity scenarios. RNC is funded by a research fellowship from the  Alexander von Humboldt foundation. CD is funded by the  BiodivERsA-Belmont Forum  Project " Alien Scenarios " ( BMBF/PT DLR 01LC1807C ). The authors also acknowledge the constructive and helpful comments of the anonymous reviewers. Further, the authors acknowledge  Wen Xiong ,  Axel E.
Measurements were made with a laser telemeter. Traffic volume was calculated using the TADARIDA-L software  (Bas et al. , 2017)  to identify and count vehicle passes. Sound event detection was done in the low frequency mode, and any acoustic sequence of 5 s or less that contained an uninterrupted sound event with a duration superior to 1. 2 s was counted as one vehicle pass, even if several vehicles followed each other very closely. This threshold was chosen based on a verification of false and true positives and negatives on 100 random sound sequences from different study sites stratified by sound duration (unpublished data).
Sampling rate was set at 192 kHz, trigger at 6 dB above background noise and trigger window at 2. 5 sec. Species identification was performed based on echolocation calls, which carry enough information to allow the identification of the majority of European bat species, depending on the quality and the context of the recordings  (Barataud, 2015). We used the SonoChiro software (Biotope/MNHN, France) to automatically sort sequences by species, and then verified most of the sequences manually on Syrinx (John Burt, USA) (except for sequences classified as Pipistrellus which are too numerous for a detailed verification, and because SonoChiro has a very low error rate for P. pipistrellus and P. pygmaeus in the Mediterranean region according to our experience). Plecotus species were grouped in Plecotus sp.
Three-dimensional positioning of bat calls Bat three-dimensional flight paths were generated from the three-dimensional source location of echolocation calls recorded on the four microphones. After species acoustic identification, call location was achieved by (1) measuring time of arrival differences (TOAD) of each call between pairs of microphones and (2) deducing the coordinates of the sound source by comparing those field TOAD (TOAD F ) with theoretical TOAD (TOAD T ). Indeed, since the speed of sound in the air is known (here we approximated it to 340 m/s), TOADs of a sound source recorded by at least four microphones can be used to calculate the location of the source (see  Koblitz, 2018). TOAD F were calculated by measuring the starting time of bat calls using the SonoChiro software (Biotope/MNHN, France).
Call association between pairs of microphones was achieved using the R (R Core Team, 2014) function find. matches of the Hmisc package  (Harrell, 2018). Because there are four microphones, six TOAD F per call are calculated. TOAD T were calculated for each simulated point of a matrix of 40 x 40 x 40 m with a one-meter resolution and centred around the centroid of the 4 microphones, inputting the same microphone configurations as the ones used in the field. The dimensions of this matrix were chosen according to the spatial range of our equipment (i. e. maximal distance of detection of a bat position) for the location of middle-range echolocators (e. g. Pipistrellus pipistrellus). This range is dependent on the acoustic range of the individual recorded and the position of the individual in relation to the microphones, i.
e. accuracy is maximal at the centre of the device and minimal at the far edges. The position of the bat was deduced from the comparison of the differences between the six TOAD F and the six TOAD T using the R (R Core Team, 2014) function find. matches of the Hmisc package  (Harrell, 2018). The closest TOAD match was selected as a candidate bat position. During test calibrations of our setting with different microphone configurations, we calculated that TOAD F resulting in a position more than 10 m away from the centroid of the microphones had a difference with the real position larger than one meter. Imprecise positions were systematically reconstructed away from the centroid, which means for example that a bat flying in reality at 15 m from the centroid could be located with our device at 17 m (away from the centroid), but never at 13 m (toward the centroid).
Model 1 -Local bat density The density of the most common species in our dataset (i. e. occurrence per night larger than 30 % and occurrence per site larger than 50 %) was modelled as a negative binomial distribution. The response variable was the median number (among the four microphones) of five second intervals per night within which a species was identified. This acoustic activity was then used as a proxy of bat density (number of acoustic sequences per night within the acoustic range of the setting)  (Froidevaux et al. , 2017).
Model 3 -Bat-vehicle co-occurrence The probability of bats flying through the zone at risk gives a spatial evaluation of risk. To make a more precise risk assessment, bat-vehicle co-occurrence (i. e. temporal evaluation of risk) was also modelled. For bat flight trajectories at risk only, a proxy for the probability of bats avoiding vehicles was modelled using bat-vehicle co-occurrence as the binomial response variable (1 = bat-vehicle co-occurrence; 0 = presence of a bat while absence of vehicle). To do this, the time lag between an acoustic sequence containing a bat and the closest sequence containing a vehicle pass was calculated using the function find. matches of the Hmisc package  (Harrell, 2018). If the time lag was lower than 10 s, we considered that there was a batvehicle co-occurrence (1).
Model selection We used the R (R Core Team, 2014) package glmmTMB  (Brooks et al. , 2017)  to model each response variable in generalised linear mixed models (GLMM). When sample size of a given species was too small, we did not model the species response. In addition, three bat guilds were created based on the adaptation of species to clutter of the environment, which is strongly linked to sonar features  (Aldridge and Rautenbach, 1987; Denzinger et al. , 2018). Species were thereby split into the guilds "short-range echolocator" (SRE), "mid-range echolocator" (MRE) or "long-range echolocator" (LRE) according to the definition of Frey-Ehrenbold et al. (2013)   All descriptive variables were normalised if necessary and scaled to follow a normal distribution and to compare their effects. Thus, distance to vegetation and traffic were normalised using the square root function.
Discussion This study aimed at disentangling the different mechanisms that influence bat-vehicle collision risks: (1) density of individuals recorded from the road edge, (2) position in the zone at risk (low flight height over the road), (3) bat-vehicle co-occurrence, and (4) flight path orientation. Disentangling those mechanisms was possible thanks to three-dimensional acoustic flight path tracking of bat behaviour at roads on a large scale (French Mediterranean region) on a large amount of data (122,294 bat passes). Our results demonstrate heterogeneity in the influence of explanatory variables on the four response variables, depending on species.
Time of year Our results show typical activity patterns throughout the year with peak density in summer or autumn, that seemingly drive the number of bat passes at collision risk per night (the product of quantitative models), that also shows a peak in summer or autumn. However, it is the first time to our knowledge that it is demonstrated that flight proportion in the zone at risk at roads increases in autumn (for several species and guilds). An increased flight proportion in the zone at risk in autumn could partly be attributed to the naïve behaviour of juveniles, which after birth and emancipation suddenly increase population sizes at the end of the summer  (Dietz et al. , 2009) , and that are necessarily present in our dataset even if we cannot assess their proportion. Juveniles are indeed more vulnerable to road collisions than adults  (Fensome and Mathews, 2016).
Acknowledgments This study was a collaboration between Biotope and the  Muséum national d'Histoire naturelle (Paris, France)  in the form of a PhD thesis funded by  Biotope  and the  Association Nationale de la Recherche et de la Technologie. We would like to thank  Bruno Sanchez ,  Dominique Guicheteau  ( Réserve naturelle nationale de la Plaine des Maures ),  Ugo Schumpp  and  Julien Penvern  for their precious help during field work. We thank  Fiona Mathews  for her comments before submission, as well as  Gloriana Chaverri ,  Brock Fenton ,  Mark Brigham  and two anonymous other reviewers for their comments. All of them greatly improved the quality of the manuscript. We are grateful to  Kate Derrick  for proofreading the manuscript.
The nodal data are corrected for the geophone instrument response (damping factor of 0. 7 at a 5 Hz corner frequency). An in-depth analysis of the ground motions in terms of frequency content, spatial variability of signal-to-noise ratios, and the comparison between DAS and nodal seismometers was performed by  Wang et al. (2018). These authors also performed a preliminary beamforming analysis using the data from the nodal seismometer array but did not attempt to make a comparison with DAS data. In the present study, we retrieve and interpret the same dataset as was analysed by  Wang et al. (2018) , and so we build upon the conclusions drawn from this previous study.
In the current dataset, this corresponds to the direction running from the DAS interrogator to the opposite end of the cable. Let u(x i ) denote the three-component ground velocity field at location x i along the cable, with subscript i denoting the channel index. The longitudinal (alongcable) strain rate recorded by DAS on the channel corresponds to the gauge-long segment x i , x i+1 is ε(x i+1 , x i ) = 1 L ( u(x i+1 ) -u(x i )) • n i , where n i = 1 L (x i+1 -x i ) is the unit vector pointing from x i to x i+1. In the local reference frame associated with the cable, u in Eq. (  8 ) is defined as the projection of the particle velocity in the direction of increasing channel numbers along the cable: u = u • n. Since the cable curves and is laid out in a zigzag pattern, even parallel cable segments may have unit vectors pointing in opposite directions.
To demonstrate that the proposed integration method is accurate, we also select the longest quasilinear segment in the DAS array (cyan segment in Fig. 13a ) for inspection of the waveforms. As can be seen in Fig. 13b  and c , the particle velocity obtained from the integration of the DAS strain rates up to the last channel is practically identical to the waveforms recorded by the terminal nodal seismometer, demonstrating the accuracy of the method. The comparison is shown in Fig. 13  up to 2 Hz, but the accuracy persists up to 5 Hz. This segment is 230 m in length, and consequently the wavefield changes substantially from the start to the end of the segment (particularly for the S wave; Fig. 13c ). Nonetheless, the difference between the converted DAS and the nodal data is minimal, which is encouraging for the integration of DAS strain rates over even longer distances (potentially up to several kilometres).
, 2018)  further attest to this. Singh et al. (2020)  proposed that a correction term (the "coupling tensor") may be inverted for, so that the amplitudes of the recordings may be adjusted to represent the "true" velocity gradients. Since the true particle velocity field is known (being recorded by the nodal array), this offers an interesting perspective for future analysis of the Brady Hot Springs dataset. 4. Lastly, even when considering the directional sensitivity of DAS, the locally recorded strain rates appear to be highly incoherent, while the particle velocity measurements themselves exhibit very strong coherence (compare Fig. 3  with Fig. 8 ). The superposition of multiple orthogonal components of the particle velocity field may lead to additional destructive interference, even though this was not clearly seen in our analysis in Sect.
, 2019; Sladen et al. , 2019). For many civil monitoring applications, such as traffic density monitoring  (Liu et al. , 2018)  and vehicle tracking  (Wiesmeyr et al. , 2020) , some of the issues pointed out in the previous section do not apply, as the signals of interest arrive at the DAS fibre at a shallow (or zero) inclination. However, for the purpose of localising deep or distant sources, the inclination sensitivity of DAS starts to become directly relevant. A second unfavourable aspect of the PoroTomo dataset is that the DAS array is deployed within a relatively small region (1500 by 500 m), which limits the resolution of beamforming methods (being proportional to the span of the array). Sparse L-shaped and quasi-linear array configurations provide a much larger array span, at the expense of increased source azimuth ambiguity inherent to linear arrays.
This approach warrants the design of "hybrid" arrays comprising both DAS and nodal seismometer arrays. Code and data availability. Python scripts that reproduce the results and figures in this paper are available at https://doi. org/10. 6084/m9. figshare. 12899288 (van den Ende and Ampuero, 2020). Author contributions. MPAvdE conceptualised the study and performed the analyses. JPA supervised MPAvdE and contributed to methodology and interpretation. Both authors discussed and prepared the contents of the paper. Competing interests. The authors declare that they have no conflict of interest. Special issue statement. This article is part of the special issue "Fibre-optic sensing in Earth sciences". It is not associated with a conference. Acknowledgements. The authors thank Lingsen Meng for sharing his MUSIC back-projection codes, from which the beamforming codes used in this study were derived.
The nodal seismometer data were processed using ObsPy  (Beyreuther et al. , 2010) , and generic data manipulations were performed with NumPy  (Harris et al. , 2020). Data visualisation was done using Matplotlib  (Hunter, 2007). The authors are grateful for the constructive comments of the handling editor Philippe Jousset, two anonymous reviewers, and Eileen Martin. Financial support. This work was supported by the French government through the UCA JEDI Investments in the Future project managed by the National Research Agency (ANR) with the reference number ANR-15-IDEX-01. Review statement. This paper was edited by Philippe Jousset and reviewed by two anonymous referees.
The data presented in the following sections correspond to an experiment (three replicates, 81 plants with 63 infected / 18 healthy); the data of the second experiment are presented in Fig. S4 , Fig. S6 , Table  S1. A larger number of infected plants was monitored given the greater variability expected in the case of the interaction with the pathogen. Automated phenotyping included weighing for the estimation of transpiration and imaging. Analysis of acquired images (Fig. S1 ) allowed to estimate the position (vertical) of the plant center of gravity (Fig. S2  and Fig. S3 ) and the chlorophyll content  (Liang et al. , 2017). Plants were also manually sampled at different days post inoculation  (1, 3, 4, 5, 7, 8)  to determine organ fresh/dry weight, as well as organ and xylem biochemical profiling and bacterial population (CFU).
Dynamic profiling of tomato xylem fluid metabolites during R. solanacearum infection Figure 5 PCA showed that some tomato xylem metabolites concentration vary during infection by R. solanacearum. In order to get a dynamic view of these changes, we monitored metabolite concentrations over several days in healthy and infected plants. The kinetic data for the 7 compounds with statistical differences for one time point or more are represented in Figure  5  (and in Fig. S8  for alanine). We noticed that statistical differences were mostly observed at 3, 4 or 5 dpi, certainly due to a higher number of plants analyzed and sometimes high variability. For additional compounds tested, no statistical difference was observed (Fig. S8 ). Putrescine was detected when plant infection resulted in high bacterial proliferation (between 3 and 5 dpi, see Figure  1 ) and its concentration stabilized in the last days of infection (7 and 8 dpi) when bacterial multiplication started to slow down.
Acknowledgments LG was funded by a PhD grant from the  French Ministry of National Education and Research. AE was funded by the  French Laboratory of Excellence TULIP  ( ANR-10-LABX-41  and  ANR-11-IDEX-0002-02 ). The study was funded by the  French Laboratory of Excellence (LABEX)  project  TULIP  ( ANR-10-LABX-41  and  ANR-11-IDEX-0002-02 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We thank  TPMP (Toulouse Plant Microbe Phenotyping) platform (Castanet-Tolosan, France)  and its staff  Fabrice Devoilles  for their technical support in plant cultures and imaging, as well as HiTMe (High-Throughput Metabolic phenotyping) platform ( Villenave d'Ornon, France ) for biochemical analysis of organ metabolites. We acknowledge  MetaToul (Metabolomics & Fluxomics Facitilies, Toulouse, France , www.
metatoul. fr)platform, which is part of the  MetaboHUB-ANR-11-INBS-0010 national infrastructure  (www. metabohub. fr) and its staff  Cécilia Berges ,  Edern Cahoreau  and  Lindsay Peyriga  for access to NMR facilities. We also thank  Patrick Barberis  for his expert skills in the construction of the triple mutant strain.
Methods To develop a formal tool to assess the impact of health research aligned with RRI, we utilized the key principles identi ed from the RRI literature that were applied in building the ve dimensions of the CRIF. These criteria included participatory governance, effective and inclusive stakeholder engagement, shared mission and agenda and collective measurement for the bottom-up evaluation of health research impacts that advance collaboration between science and society and integrate scienti c excellence with patients' needs and social responsibility. The MULTI-ACT Scorecard was accomplished by drawing from lessons learned through the whole multistakeholder engagement process carried out by the MULTI-ACT project. First, an initial database of the health research impact indicators was built through a meta-review and systematic literature review to explore existing health research impact frameworks and indicators already being used in the health sector and multi-stakeholder organizations.
Initial collection of impact indicators: Meta-review and systematic literature reviews The analysis of the academic and non-academic literature on health research impact and multistakeholder initiatives has informed the development of an initial database on health research impact indicators (Figure  2 ). This database formed the basis for the development of MSC. A tentative meta-review was conducted on previous health research impact frameworks proposing indicators and measurement dimensions on health research assessment (e. g. [26] [27] [28] ). These frameworks have some limitations concerning their suitability for assessing the impact of multistakeholder initiatives (e. g. lack of patient engagement and stakeholder participation in de ning the indicators, limited coverage of multi-dimensional impacts or implementation of indicators in practice).
Therefore, we also performed the literature review on academic and non-academic research (not limited to health research) impact studies focused on multi-stakeholder initiatives between 1992 and 2017. We identi ed 19 documents proposing speci c indicators for measuring research impact for more detailed analysis. In particular, we collected proposed indicators from the payback model  [29] , the expected monetary value  [30] , the Research Impact Framework  [31] , the Research Excellence Framework  [32] , logic models  [33, 34] , the Canadian Academy of Health Sciences model  [35] , and the hybrid forms of these frameworks  [36] [37] [38] [39] [40] [41] [42] [43] [44] [45] [46] [47] [48]. The selection of documents was combined with an additional systematic literature review in the Scopus database to gather other speci c impact indicators used in practice in health sector organizations and the pharma industry.