dataesr/scientific-paragraphs-categorization huggingface dataset:
We aimed to address this descriptively and statistically by including research effort and number of IAS as individual terms across countries and regions. Hence, all environmental and socio-economic effects can be considered in the context of research effort and invasion rates, and are thereby over and above the strength of these terms. We also present reported costs corrected for research effort in our dataset. However, we caution that our proxies probably do not capture all aspects of regional capacities to report IAS and their impacts, and therefore our results are likely still influenced by differences in reporting capacity. Secondly, publication language may influence perceived cost flows, where regions with greater reporting effort in common languages (English), such as North America, are better represented in our dataset.
Although updates to InvaCost now include data in 21 non-English languages, regions such as Asia and Africa remain heavily underrepresented, with numerous countries having no costs in InvaCost  [20]. Whilst our analyses accounting for research effort would have controlled for some of these biases, they could not account for entirely missing data. Thirdly, purchasing power affects the cost of damage and management incurred by a region; all else being equal, regions with higher purchasing power (such as Asia, Europe, and North America) would inherently incur higher costs. Given this inherent bias and the probable link between lower economic output and research capacity, invasion costs from lower-income countries are likely particularly underestimated. Therefore, research investments in low-to-middle income nations should be promoted to bridge these gaps and support biosecurity.
Cost data and processing We extracted cost data from the latest version of the InvaCost database (version 4. 1, publicly available at 10. 6084/m9. figshare. 12668570  [20, 40] ). InvaCost has been generated following a systematic, standardized methodology to collate invasion costs from peer-reviewed scientific articles, official reports, grey literature, and stakeholder and expert elicitation. Following a thorough and hierarchical screening of each source document for relevance, costs were extracted, standardized to a common currency (2017 USA dollars/US$), and adjusted for inflation through the Consumer Price Index (https://data. worldbank. org/indicator/FP. CPI. TOTL?end=2017&start=1960) to be comparable across space and over time  [40]. Costs were categorized under a range of descriptive fields pertaining to the original source (such as title, authors, and publication year), spatial and temporal coverage (such as period of estimation and study area), cost estimation methodology (such as method reliability and acquisition method) and the cost estimates per se (such as nature and typology of cost relating to damage and/or management costs).
Detailed information on all descriptive variables can be found in an online repository of the InvaCost database (https://doi. org/10. 6084/m9. figshare. 12668570, "Descriptors4. 1. xlsx"). Costs can occur over varying periods; for example, a one-off cost associated with a one-time eradication effort versus a multi-year cost associated with recurrent, annually estimated damages to crop production. To homogenize the temporal occurrence of these cost entries in the database, they were all converted to annual costs using the expandYearlyCosts function of the invacost R package  [41]. This function provides annualized cost estimates for all entries, based upon the probable starting and ending years of the cost occurrence provided in the database ('Probable_starting_year_adjusted' and 'Probable_ending_year_adjusted' columns).
For example, a single cost entry of $5,000 that occurred between 2000 and 2009 would be transformed to 10 entries following expansion, each amounting to $500 per year. Thereby, if costs are reported over a multiannual period, the total cost over that given period is divided by the number of years, resulting in an equal annual cost per year that does not inflate the overall cost. Accounting for these dimensions of costs also allowed for assessments of the dynamics of cost occurrence over time  [41]. Furthermore, for this analysis, we considered costs with impact years between 1960 and 2020, given limited InvaCost data before 1960, and constraints on the availability of relevant socio-economic variables beyond this period (see Predictor variables). We further considered species-specific cost entries only, thus excluding those for diverse (where costs were reported collectively for multiple taxa) or unspecific (where species-level information was missing) taxa, where 1,557 or 11.
Species origins As a first step in determining species' countries of origin, we employed a web scraping script to gather data from the Centre for Agriculture and Bioscience International (CABI) Invasive Species Compendium (ISC, www. cabi. org/isc), the International Union for Conservation of Nature (IUCN) Global Invasive Species Database (GISD, http://www. iucngisd. org/gisd/) and the Global Biodiversity Information Facility (GBIF, www. gbif. org) (see www. github. com/emmajhudgins/Givers_Takers for more information). CABI's ISC contains a variety of information on IAS around the world, including their current distribution and countries of origin  [18]. Our script searched using the species names as entered within InvaCost (harmonized using the GBIF. org Backbone Taxonomy;  [40] ) as well as synonyms in the Integrated Taxonomic Information System (ITIS) database via the taxize R package  [42].
If a species match was found within the CABI ISC, we searched for a "Distribution Table" portion of the species entry. If found, we extracted country or region (within country) names tagged as "Native" within this table. GISD contains geographical information for many IAS and was used as an alternative to CABI where distributional data were missing. Our script searched for GISD distributional data points tagged as "Native" and compiled them at the country level. Finally, we checked for matching entries in GBIF -a global database of all types of species distribution -tagged as "Native" at the country level within the occ_search function of the rgbif package version 3. 6. 0  [43]. We used present day political border definitions for each country as defined by ISO3C codes in the countrycode package  [44].
Next, where possible, we used country-scale origins to infer continental regions. Countries designated in InvaCost to be part of Central America were assigned to North America (and we refer to them henceforth as North America). Following InvaCost protocols, overseas territories were linked with the continent that matched their geographic, rather than political, designation. As exceptions, Turkey and Russia were identified as multi-continent sender and recipient countries. Origin continents within Turkey and Russia were selected on a case-by-case basis for each species, considering published data on the finer-scale distribution of each species within these countries as well as the continental designation of other countries listed (for example, if all other origin countries listed were European, we considered the native range to be European; see Supplementary Table  5  for details of species impacted).
In these two country cases, we classified recipient regions based on human population, because of the role of humans in transporting IAS  [45]  and incurring economic impacts  [15]. Since most of Turkey's population is in Asia and the majority of Russia's population is in Europe, we assigned them accordingly to these continents. As a third exception, China's Special Autonomous Regions (Hong Kong and Macau) and Taiwan were merged with mainland China due to them representing a much smaller landmass, as well as being strongly linked to China politically, economically, and geographically. All origin assignments were checked manually by co-authors (where we ensured that there existed ≥ 1 reliable source[s] that agreed on the origin continent at least) or were entered for the first time when information was unavailable from GISD and CABI, using available literature and databases.
This set of species does not have clear native ranges due to their long domestication and/or hybridization history. In contrast, we opted to retain species such as the European rabbit (Oryctolagus cuniculus) with a well-defined native range  [46]. The remaining 459 unique species were recorded in six origin and recipient continents, amounting to 4,107 cost entries reported across 539 independent publications (expanded to 8,060 total entries; Figure  2 ). When subset to entries with a country-level resolution, our dataset was further restricted to 412 unique species in 223 origin and 80 recipient countries, corresponding to 3,685 raw cost entries, 436 unique publications, and 7,112 expanded entries. Overseas territories were removed from this portion of the analysis because they lacked trade volume, GDP, and/or population data, which were implemented in models (see Predictor variables).
Predictor variables We separated our analysis by decade. Then, from InvaCost version 4. 1, we generated a variety of predictor variables that we hypothesized would influence the magnitude of cost flow to and from different locations (where the cost flow from Region A to B refers to the costs of IAS in Region B that are due to native species from Region A). Firstly, we extracted the number of unique cost references associated with each receiving country in each decade, as a proxy for research effort ("Reference_ID" field in the InvaCost database). Secondly, we summed the total number of species involved in the cost flows between countries for each decade. Thirdly, we summed the total cost, incurred between 1960 and 2020, of IAS originating from each country. Beyond these InvaCost-specific predictors, we employed several external variables hypothesized to influence the magnitude of cost flows due to biological invasions  [47].
We extracted the total volume of imported goods (in metric tonnes) for each country pair from the Centre d'Études Prospectives et d'Informations Internationales' (CEPII) Base pour l'Analyse du Commerce International (BACI) database  [48]  for the years 1995-1999 inclusive and 2015-2019 inclusive, selecting the HS92 designation of harmonized import and export records (see Supplementary Table  6  example data from 10 pairs). We calculated the mean annual flow of goods between each country pair for the 1995-1999 period and dubbed this 'historical trade'. Historical trade can be more predictive of present-day invasion risk due to invasion lags (see  [47] ), but we note that consistent import data are not available for the entire period of our cost data. The mean annual flows for 2015-2019 reflect recent trade (prior to the COVID-19 pandemic).
To assess the role of origin and recipient biodiversity in dictating the flow of invasion impacts, we downloaded species richness data for each country from Mongabay, which tallies species richness for amphibian, bird, fish, mammal, reptile, country (collinear with tourism, research effort, and recipient country area) from our model (all r>0. 70)  [53]. We considered human population and economic output instead of human population density and output per capita, because the qualifying variables (surface land area for human population and human population for output) were already included in the model. Moreover, ratio variables are well-known to cause spurious effects if there is a correlation between the denominator of the ratio and the response variable  [54]. Our approach therefore allows for greater non-linearity and flexibility in considering these independent variables and their partial effects.
Statistical modelling We built predictive models of the cost flow between each country pair for all complete (nonzero) flows recorded in InvaCost (Figure  2 ). To do this, we first summed our cost data within each decade and within each sender-recipient country combination, employing the countrycode R package  [44]  to ensure consistent country naming by converting all InvaCost country records to ISO3C codes. All models were fit as generalized additive models (GAMs) using the mgcv package  [55] , where all quantitative predictors and the cost flows (in millions of $) were logarithmically scaled. Decade was included as a thin-plate smoother term with five knots (a maximum of four inflection points in its functional form) to de-trend the cost flows for consistent variability in time. This variability could be due, for instance, to periods of global economic growth and decline.
Acknowledgements The authors thank the  French National Research Agency  ( ANR-14-CE02-0021 ) and the  BNP-Paribas Foundation Climate Initiative  for funding the  InvaCost  project and the  InvaCost database development. The present work was conducted in the frame of the  InvaCost workshop conducted  in November 2019 (Paris, France) and funded by the  AXA Research Fund Chair of Invasion Biology  and is part of the  Alien-Scenario project  funded by  BiodivERsA  and  Belmont-Forum  call 2018 on biodiversity scenarios. EJH is funded by a  Fonds de Recherche du Québec -Nature et   Téchnologies Postdoctoral Fellowship. RNC is funded through a  Leverhulme Early Career Fellowship  ( ECF-2021-001 ) from the  Leverhulme Trust. DM was supported by the  Czech Science Foundation  (project no. 19-13142S , and  EXPRO  no.
Results For 1D NMR measurements with solvent presaturation during the recovery delay (zgpr), 20 metabolites were successfully quantified by all processing strategies. Some metabolites could not be quantified by some methods. For internal referencing with TSP, only one half of the metabolites were quantified with a trueness below 5%. With peak integration and external calibration, about 90% of the metabolites were quantified with a trueness below 5%. The NMRProcFlow integration module allowed the quantification of several additional metabolites. The number of quantified metabolites and quantification trueness improved for some metabolites with deconvolution tools. Trueness and precision were not significantly different between zgpr-and NOESYpr-based spectra for about 70% of the variables. Conclusion External calibration performed better than TSP internal referencing.
Both zgpr and NOESYpr pulse sequences belong to the same class of solvent presaturation methods. They are the most widely used in quantitative NMR metabolomics and are among the most robust and accurate methods. Popular selective refocusing methods such as excitation-sculpting are not recommended in metabolomic studies, since they are less robust and induce a distortion of peak areas based on J-modulation  (Giraudeau et al. , 2015). Spectra pre-processing steps that lead to Fourier-transformed, phased and baseline-corrected spectra usable for quantitative analysis can be performed with equipmentrelated software, commercial processing software or with open tools. For spectra processing steps that aim at extracting peak areas and calculating absolute concentrations, various approaches have been described.
Although peak integration after peak picking is largely used, tools performing peak fitting with reference compound spectra acquired with the same standard operating procedure or spectral decomposition (often named 'deconvolution') are also available to deal with possible resonance overlapping. For instance, Chenomx NMR Analysis software (Chenomx, Edmonton, Canada) performs peak fitting, while Mnova (Mestrelab Research, Santiago de Compostela, Spain) and the MetaboDecon1D open package  (Häckl et al. , 2021)  perform deconvolution. To determine absolute concentrations in the NMR tube, two strategies prevail: referencing with an internal standard (dissolved in the sample or more rarely as an external reference using a reference material contained in a separate solution) or calibration with dedicated solutions of commercial compounds  (Cullen et al.
Metabolite quantification For the pre-processed zgpr spectra, each operator quantified the urinary metabolites with the TopSpin integration module (Bruker BioSpin, Karlsruhe, Germany), using either internal referencing with TSP or external calibration with the calibration-range solutions, as well as his/her favourite in-house tool or open-access or commercial NMR software (Online Resource 2). The integration regions for each metabolite were chosen by each operator independently focussing on non-overlapping peaks whenever possible and are indicated in Online Resources 3-4 and at https:// doi. org/ 10. 57745/ J0Y81K.
Quantification using TopSpin integration module and internal referencing with TSP Each operator chose one signal per metabolite and integrated it using the TopSpin integration module (Bruker BioSpin, Karlsruhe, Germany). The metabolite concentration was then calculated according to expression (1). Cx is the metabolite concentration, Ix the integral of the metabolite peak, Nx the number of protons contributing to the signal, Cs the standard concentration, Is the integral of the standard proton signal, and Ns the number of protons contributing to the standard proton signal.
Quantification using Mnova deconvolution tool One operator used MestReNova software (Mnova, Mestrelab Research, Santiago de Compostela, Spain) with the Simple Mixture Analysis plugin for semi-automatic metabolite quantification, using the received 1D spectra. This software was used to define the integration regions as peaks or multiplets, using a signal deconvolution tool (GSD) to extract the integrals and thus quantify the concentrations of the identified metabolites. The synthetic urine concentration measurements were based on a library of spectra of the calibrationrange solutions and their concentrations.
Quantification using MetaboHUB in-house deconvolution tool "NMRDeconvR" One operator used an in-house application based on a dedicated R package (https:// github. com/ INRA/ Rnmr1D), hereafter named "NMRDeconvR". Its main functions are noise reduction, baseline correction, automatic ppm calibration based on TSP or DSS and deconvolution. The latter is based on the search for peaks from the second derivative of the signal, then on the construction of a spectrum modelled as a sum of Voigt pseudo-function shapes, followed by an optimisation step based on the Levenberg-Marquardt algorithm (Levenberg 1944). In the present experiment, only local baseline correction and deconvolution were used. Quantification was performed using Jupyter Notebooks  (Kluyver et al. , 2016) , a useful tool for collaborative open data  (Mendez et al.
Statistical analyses A principal component analysis (PCA) of the synthetic urine data was performed on mean-centred and unit-variance scaled data using R scripts [biostatflow. org, v2. 9,  (Jacob et al. , 2020) ]. The theoretical synthetic urine sample-obtained based on gravimetric concentrations-was added on the scores plot. Precision (coefficient of variation expressed as a percentage, n = 3 spectra) and relative trueness (ratio of the mean measured value based on the three urine spectra minus the theoretical value over the theoretical value, expressed as a percentage) values were calculated for each metabolite quantified by each operator based on his/her data obtained using the three zgpr or NOESYpr pre-processed spectra. Mean and standard deviations were calculated for the spectra processing strategies performed by several operators.
Results This study involving several sites and operators evaluated the impact of processing and quantification software and methods. Several operators performed the same two protocols for peak integration with TopSpin followed by TSP referencing (zgpr spectra) or external calibration (zgpr and NOESYpr spectra). For these protocols, the peak integration boundaries were purposely operator-dependent. NMRProc-Flow and the two deconvolution tools were used by a single operator each. Quantification results were first analysed separately for each type of spectra and then compared.
Spectra observation and annotation The synthetic urine spectra contained about 410-420 detectable (peak picking with TopSpin software) and 350-360 quantifiable (i. e. with a signal-to-noise ratio over 10) peaks for the zgpr and NOESYpr spectra. The quality of the baseline correction was similar in both spectra. The quantifiable peaks in the zgpr urine spectra were 1. 1 to 1. 3 times more intense than those in the NOESYpr spectra (Online Resource 5). The synthetic urine zgpr and NOESYpr spectra were annotated by each operator based on a comparison with the proton NMR spectra of pure compounds recorded using the same experimental parameters (Fig. 2 , Online Resource 6). An interactive annotated zgpr-spectrum is available at http:// pmb-borde aux. fr/ nmrAn not/ urine. html. The four most intense signals corresponded to creatinine, trimethylamine-N-oxide, creatine and citric acid.
Quantifications based on zgpr spectra The spectral regions selected for peak integration (PI1 and PI2) or deconvolution (DC2) are listed in Online Resource 3 and available at https:// doi. org/ 10. 57745/ J0Y81K, respectively. For most metabolites, the operators chose the same spectral regions. Some metabolites were not quantifiable by all methods, commercial or in-house NMR software or tools (Online Resource 7). Twenty metabolites and the sums of creatine and creatinine or lactic acid and threonine were successfully quantified using all quantification strategies (Table  1 ). To obtain an overview of these quantification data, a PCA was performed on these 22 concentrations (Online Resource 8). The 'theoretical' sample, based on gravimetric metabolite concentrations, was plotted in the PC1 × PC2 plane using the loadings values of the PCA performed without it.
e. better, than 5%. Among the metabolites with an absolute trueness above 5%, glucose and pyroglutamate were the most underestimated, and 2-hydroxybutyric acid and formic acid were the most overestimated (Table  2 ). Glucose was clearly impacted by residual water presaturation. Several resonances were impacted by peak overlap, e. g. the singlet at 1. 92 ppm for acetic acid with resonances of the lysine multiplet at 1. 97-1. 87 ppm, resulting in impaired quantification of lysine for several operators and a slight overestimation of acetic acid quantification for all operators. However, with TopSpin integration module and external calibration, about 90% of the metabolites and metabolite sums were quantified with an absolute trueness better than 5%. The quantification trueness improved for several metabolites with deconvolution, either with Mnova or NMRDeconvR and external calibration (all 20 individual metabolites common to all strategies quantified with an absolute value of trueness better than 5%).
Finally, this study highlights the importance of inter-laboratory studies in the development of reliable processing methods to obtain accurate quantitative data in NMR metabolomics. Here, as a first step, we used a single data set and different operators to assess multiple processing software. Further steps would require complementary experimental designs. Using several NMR datasets analysed by a single operator with all the processing methods and associated software used in the present study would provide a more robust comparison of these processing methods. Using a set of identical samples distributed to several laboratories instructed to use a single acquisition and quantification method to quantify metabolites would provide a measure of inter-laboratory variability.
Data availability NMR spectra and their metadata have been deposited in data. gouv. fr repository, https:// doi. org/ 10. 57745/ J0Y81K. An interactive annotated zgpr spectrum is available at http:// pmb-borde aux. fr/ nmrAn not/ urine. html. Software availability NMRDeconvR tool is available at https:// github. com/ INRA/ Rnmr1D.