dataesr/scientific-paragraphs-categorization huggingface dataset:
For PPP-based standardisation, it was not possible to perform the process for all cost estimates as PPP data do not exist for all countries and/or specific periods (we mentioned NA in the database when such information was missing). In summary, we used the following formula to convert and standardise each cost estimate: = × M C I C ( / ) V F F e with Ce = Converted cost estimate (to 2017 US dollars based on exchange rate or Purchase Power Parity), M V = Cost estimate (either the 'Raw cost estimate local currency' extracted from analysed paper or the 'Cost per year local currency' transformed by us), C F = Conversion factor (either the official market exchange rate or the purchasing power parity, in US dollars), I F = Inflation factor since the year of cost estimation, calculated as CPI 2017 / CPIy with CPI corresponding to the Consumer Price Index and y corresponding to the year of the cost estimation (' Applicable year').
Data summary. InvaCost currently contains 2419 cost estimates (1215 from peer-reviewed articles, 1204 from grey materials), collected from 849 references, of which 1769 estimates were deemed as of high reliability. In total, twenty currencies are reported in our database, the majority being US dollars, n = 1348 cost estimates. Not all cost estimates were successfully converted to 2017 US$ as (i) conversion data from official sources are available only since 1960 (cost estimates range from 1945 to 2017 in InvaCost) or simply not found for some years and countries, and/or (ii) cost data are sometimes simultaneously associated with several countries, constraining the PPP-based standardisations. Hence, respectively 2416 and 2126 estimates were successfully converted using market exchange rates and PPPs.
cbd. int/ doc/decisions/cop-13/cop-13-dec-13-en. pdf) advocating the incorporation of invasion science knowledge into management planning. In addition to offer unique opportunities for future research, InvaCost will provide a strong quantitative and evidence-based support for impacts of invasive species reported in other databases such as the Global Register of Introduced and Invasive Species (GRIIS)  20  , helping refine information in this database. Also, invasive populations recorded in InvaCost but data deficient in the GRIIS should be ultimately classified in that database. Additionally, InvaCost could be considered as another data-based component, adding novel and significant information on invader impacts categorised by the Socio-Economic Impact Classification of Alien Taxa (SEICAT)  36.
The latter is a classification system, applicable across a broad range of taxa and spatial scales, providing a consistent procedure for translating the broad range of measures and types of impacts into ranked levels of socio-economic impacts, assigning alien taxa on the basis of the best available evidence of their documented deleterious impacts. Quantitative support provided by InvaCost will strongly contribute to impact classification. Ultimately, integrating data from these diverse sources could allow a complete description of the overall impacts of biological invasions at regional and global scales. Caveats and directions for further database improvement. Rather than claiming exhaustiveness of data collated, we highlight that InvaCost should be considered as the most current, standardised, accurate and globally representative repository of various economic losses and expenditures documented for the largest possible set of invaders.
We are aware that our database can be improved in at least three ways. First, InvaCost mostly does not include publications and reports not yet available in electronic format and/ or using non-English language, leaving open the possibility of increasing data comprehensiveness and limiting potential biases. Indeed, local reports as well as research results from some countries (e. g. , China, Russia) are likely to be published in non-English language  37. Again, accessing grey literature is challenging as it is not systematically digitalised and/or included in well-curated bibliographic databases  29. We strongly encourage future users of InvaCost to help gathering this currently unreachable information when possible. Furthermore, some mistakes might have occurred despite our best efforts when constructing InvaCost.
In this regard, we advocate for regular public updates of InvaCost in order to improve it both quantitatively (by adding currently inaccessible or missed information) and qualitatively (if errors are identified). Second, as the distribution and impacts of invaders are inherently dynamic for a number of reasons  38  , InvaCost should further consider the status of the species recorded for their economic impacts in order to improve both the relevance and the usefulness of the database. As an illustration, InvaCost likely includes invasive populations currently extirpated from particular areas after successful eradication campaign(s) as well as those still established but for which impacts are locally reduced as a result of management efforts. Attempting to obtain and integrate such information into InvaCost was beyond the scope of this work.
Nonetheless, it should be reciprocally beneficial to establish connections between InvaCost and other databases such as the GRIIS that provides a harmonised, open source, multi-taxon database including verified information on the continued presence of introduced and invasive species for most countries  20. In light of such additional information, the value of InvaCost will be its application for policy purposes, such as identification of exotic invaders that are currently associated with Table  3. Quantitative summary of information recorded in InvaCost according to the 'Geographic region' of the cost estimates. Central America includes cost data from the Carribean area. 'Mixed' contains data concomitantly associated with two or more geographic regions. Taxonomic units refers either to a single species or, if any, to a unique group of species for which specific contribution to the whole cost is not possible to disentangle.
economic losses in particular areas. Also, crossing information between databases may allow the refinement of the descriptor 'Spatial scale' we propose here. Third, we would recommend, for a future updated version of InvaCost that would require screening back all the materials, to improve the ' Acquisition method' , 'Implementation' and 'Reliability' descriptors, to pay attention to the specificity of "avoided costs" and to create a new descriptor for 'non-market values'. We detail these possibilities below. Improving descriptors. An improved version of the ' Acquisition method' could lead to a subdivision of the extrapolation category into spatial, temporal and spatio-temporal extrapolation. This would allow simultaneous refinement from the currently binary 'Implementation' descriptor (observed vs potential) into several levels of certainty regarding the incurred cost (e.
g. taking into consideration the temporality (past/current or predicted) of the onset of the cost and of the status of the invasive species in the study area). The next step for deeming the 'Reliability' of the cost estimates recorded in InvaCost would consist of assessing the repeatability of the methodology used, by adapting the approach previously developed by Bradshaw et al. 14. The latter evidenced that assuming the reproducibility of published methods should not rely only on the nature of the materials and recognized the qualitative nature of the procedure, although applying this approach to InvaCost was constrained by the large sample size and high diversity in our database (Bradshaw et al. 's study focused on a single taxonomic class). Also, because InvaCost involves several collaborators and potential future contributors, consistent and objective criteria should be further defined to cope with the large array of materials, methods and situations encountered.
Some avoided costs are probably already included in InvaCost but they are likely underestimated because keywords such as "savings" or "benefits" were not included in the search strings. Also, even if they are sometimes mentioned as "benefits" in the literature, care should be taken not to confuse these avoided-costs with the benefits incurred by direct use or exploitation of invasive species. The latter have been ignored in InvaCost since they were relatively few (and beyond of the scope of this database), but might constitute a twin project. A new 'Non-market values' descriptor. The means dedicated to preventing or managing an invasion (e. g. manual removal of invasive plants) and certain economic losses and damage due to an invasion (e. g. the value of crop losses or the repair costs of damaged infrastructures due to an invasive insect) are observable on markets.
Moreover, the very principle of giving a value to "benefit from nature" through economic valuation is not necessarily acknowledged by the entirety of scientific and civil communities  39, 42. For future analysis, the 'non-market values' should not be systematically aggregated with the other economic costs gathered in InvaCost. It is to note that while some non-market values are probably already included in InvaCost within the losses and damage 'Type of cost' , the loss of non-market values is probably largely underestimated in the database because they were not the primary focus of InvaCost and therefore the related keywords were not included in the search strings. These possible ways of improvement call for completion and/or refinement of existing entries as well as integration of newly published or acquired data by future contributors in InvaCost, with the aim to consolidate its long-term relevance (cf.
When a relevant material was found in more than one bibliographic repository, we considered it once only, as coming only from the first repository where it was chronologically recorded (respectively WoS, GS, Go and TC). The last spreadsheet (Cited references) contains the cited references collected during the full-text analysis of relevant materials. In each spreadsheet, each reference recorded is associated with a 'Reference_ID' that allows correspondence with InvaCost  30. Cells with missing or unavailable information were marked as 'NA'. All cost estimates gathered in the abovementioned references were compiled in a second Excel workbook called InvaCost_database uploaded on Figshare  30. It contains a dataset with a spreadsheet recording all the cost estimates arranged in such a way that each line refers to information retrieved from one bibliographic reference and related to a single cost estimate associated with one taxonomic group (generally at specific level) in one determined location (regardless of the geographic scale), and related to one specific period of time.
technical Validation Considerable effort was made to ensure the highest possible degree of reliability in our database development. Each step was undertaken by at least two researchers to mitigate individual subjectivity when dealing with the documents. The whole process carefully followed the recommendations on rule-based search operations for collecting and synthesising relevant evidence  43  , as those reported in PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses)  44. Furthermore, extensive literature searches within several major bibliographic resources ensured that the collected sample was representative to the greatest extent possible. We also accounted for possible publication bias (i. e. , the propensity for journals to publish studies with positive, hypothesis-affirming, or significant results rather than negative, contentious, or non-significant findings  45  ) by searching both published and grey literature.
Usage Notes InvaCost is expected to be of interest for a broad range of actors directly or indirectly interested in biological invasions (governments, researchers, conservation agencies, etc. ). We stored the full dataset in a public repository to facilitate global access. We wish to highlight that InvaCost is open to corrections and updates from authors as well as any interested contributor through a systematic, standardised process. We are willing to receive any feedback that could improve our database. Any reader or user can therefore add new information and/or correct existing ones within InvaCost. The contributors are expected to send either a message, or data as a spreadsheet containing specific information corresponding to each column (Online-only Table  1 ) of InvaCost to our e-mail address (updates@invacost.
fr). We encourage the future contributors to give as many narrative elements as possible in the 'Details' column that can contribute to better understanding of the cost estimates or to support choices made for completing the database, in order to allow backtracking investigations and facilitate the review process. Contributors should provide access to the document from which information was taken. We strive to establish a collaborative community (including experts as well as non-expert contributors) and an online platform in order to sustain regular reviews of InvaCost. The intent is to ensure transparency of the process as well as relevance of this database as it is expanded and updated. Regularly-updated versions will be dated and permanently stored in the same repository as the original version, with a unique stable DOI and unique version numbering for each release.
Each contributor bringing relevant additional information will be acknowledged in the updated version that will be released in the online repository (Column 'Contributors'). Contributors are also encouraged to contact the authors in order to obtain supplementary information on how to use or update the database in case that information is not available here. Users interested in working on the data described here are also asked to cite this manuscript as well as the specific version release of the database used, along with its DOI if necessary. The latter information should be systematically provided with the updated version downloaded from the public repository. We emphasise that users who aim to perform statistical analyses should take care to extract (from the database) and prepare relevant information based on the research question.
One should keep in mind that duplicate or overlapping cost entries (e. g. multiple cost estimates for a single invasive species in a same location over a similar period) may exist in the database, and that these should be identified before any analysis. Importantly, neither the raw cost estimates ('Raw_cost_estimate_2017_USD_exchange_rate' , 'Raw_cost_estimate_2017_USD_PPP') nor the cost estimates per years ('Cost_estimate_per_year_2017_USD_exchange_rate' , 'Cost_estimate_per_year_2017_USD_ PPP') should be simply summed since they do not necessarily cover the same time range or spatial scale, and they were not necessarily categorised within a working framework directly implementable for all types of studies. These columns should be used with complementary information on the 'Period of estimation' , 'Time_range' and 'Occurrence' columns to take into account the temporal extent of the costs.
acknowledgements We express all our gratitude to  Corey J. A. Bradshaw  for sharing his experience and expertise with our teamwork. We thank  Sabrina Kumschick ,  Shyama Pagad  and  Frederic Simard  for their valuable assistance and advice during all this work. We are grateful to  Jean-Michel Salles  for bringing valuable expertise in economic aspects. We also wish to thank every collaborator and colleague for help in gathering relevant materials worldwide when they were contacted. We are particularly indebted to the colleagues that gave us constructive feedback. We acknowledge financial support from the  French National Research Agency (ANR ,  ANR-14-CE02-0021 ) and the  Foundation BNP Paribas  for the  INVACOST  project,  Biodiversa Eranet  for the AlienScenario programme and  AXA Research Fund for the Invasion Biology Chair.
Apparent viscosity of Acacia gum dispersions The apparent viscosity of the aqueous phase of Acacia gum dispersions before emulsion preparation was measured at 25 °C using a rotating stress-controlled rheometer (RheoCompass MCR 702, Anton Paar, Les Ulis, France) equipped with a sanded cylindrical geometry (cup diameter: 22 mm; bob diameter 19. 997 mm). Rheological measurements could not be performed on emulsions because of their instability in several experimental conditions. The apparent viscosity of samples was measured at increasing shear rates from 0. 1 to 1000 s -1. For all dispersions, the flow curves showed a Newtonian behavior with a plateau at high shear rate (data not shown). The apparent viscosity obtained at a shear rate of 100 s -1 was used to compare samples.
Experimental design Response surface methodology was used to evaluate the effect of two independent variables on the droplet size, the delay time and creaming index of emulsions. The first independent variable was the Table  1  Matrix of the central composite design of two variables in units along with the experimental response and the validation experiments. 1 , the coded and uncoded independent variables were listed. Eight experimental settings and four central points were carried out randomly. The repeatability of the emulsification method was estimated by repeating the center points (4 times). The experimental design and data analysis were generated using Modde 12 (Sartorius Stedim Biotech, Sweden). For each variable, a second-order polynomial equation was constructed to estimate the responses and optimize the parameter (Eq.
Each diluted protein sample was then loaded on a sample well (10 µg of protein/well) on top of a precast 4-20% polyacrylamide gel electrophoresis (mini-protein TGX precast gel, Bio-Rad Laboratories, Dublin, Ireland). Electrophoresis was carried out at a constant current of 200 V for 45 min. Gels were rinsed with distilled water and were stained with Bio-safe Coomassie G250 stain (Bio-Rad Laboratories, Dublin, Ireland). A molecular weight (MW) marker (kit Precision Plus Protein Standards 10-250 kD, Bio-Rad Laboratories, Dublin, Ireland) was employed for MW calibration. The gels were scanned by an Image Scan II gel imager using the LabScan software (GE Healthcare, Velizy Villacoublay, France).
Acknowledgements Sameh Obeid's postdoctoral fellowship was funded by  CNIEL (French Dairy Interbranch Organization, Paris, France)  in the  Moon project (scientific coord. C. Lopez ,  INRA ). The authors thank all the members of CNIEL's committee on dairy lipids for stimulating discussions about milk fat globules and dairy processing. Christelle Lopez  warmly thanks  Mr  and  Mrs Denoual  from  Gaec des Fougères  for providing milk samples. The authors are grateful to the  INRA STLO Platform  (https://www6. rennes. inra. fr/plateforme_lait). Maryvonne Pasco  and  Jordane Ossemond  ( INRA-STLO, Rennes ) are acknowledged for their contribution in gel-electrophoresis analysis of MFG surface proteins and CLSM observations of AFM tips, respectively. Grégory Francius  ( LCPME, CNRS, Nancy, France ) and  Jennifer Burgain  ( LIBio, Université de Lorraine, Nancy, France ) are thanked for useful advice on analysis of AFM retraction force curves using the WLC model and on AFM colloidal probes, respectively.
08 ppm for at least 12 signals out of 15. Only the carbons of the acid function or near the acid function (i. e. , C5, C6, C13) exhibited a higher chemical shift variation; -a DEPT sequence confirmed the number of hydrogens linked to each carbon. It should be point out that the chemical shift values of carbons, measured on spectra recorded using high field spectrometers, were given with two decimal places. Nevertheless, it occasionally arose that chemical shift values were given with only one decimal. In such a case, although it is not mathematically correct, comparison of data given with one decimal and those given with two decimals unambiguously allowed identification of compounds. Table  2. 13 C NMR data (400 MHz, CDCl 3 ) of compounds 152 and 153.
Gas Chromatography (GC) Analysis GC analyses were performed on a Clarus 500 FID gas chromatograph (PerkinElmer, Courtaboeuf, France) equipped with two fused silica gel capillary columns (50 m, 0. 22 mm, film thickness 0. 25 m), BP-1 (polydimethylsiloxane) and BP-20 (polyethylene glycol). The oven temperature was programmed from 60 to 220 • C at 2 • C/min and then held isothermal at 220 • C for 20 min, injector temperature: 250 • C; detector temperature: 250 • C; carrier gas: hydrogen (1. 0 mL/min); split: 1/60. The relative proportions of the oil constituents were expressed as percentages obtained by peak area normalization, without using correcting factors. Retention indices (RIs) were determined relative to the retention times of a series of n-alkanes with linear interpolation ('Target Compounds' software of PerkinElmer).
UHPLC-DAD and UHPLC-ESI-MS Analysis Plant extract solution (at 20 mg mL -1 in water) was prepared and then diluted five times with water before analysis by UHPLC-DAD (Thermo Scientific™ Dionex™ UltiMate™ 3000 BioRS equipment with WPS-3000TBRS auto sampler, TCC-3000RS column compartment set at 35 °C, and Chromeleon 7 software, from Thermofisher Scientific, Waltham, MA, USA) and by UHPLC-ESI-MS (Synapt G2-S equipment with ESI operating in resolution mode and MassLynx 4. 1 software, from Waters Corp. , Milford, MA, USA). For both methods, a Kinetex C18 100A 100 × 2. 1 mm, 2. 6 μm column in association with a security guard ultra-cartridge was used (from Phenomenex Inc. , Torrance, CA, USA). A binary solvent system was used. It consisted of water/formic acid (1‰, v/v) mixture as solvent A and acetonitrile/formic acid (1‰, v/v) mixture as solvent B.
ESI(-) FT-ICR-MS Analysis The dried extract was dissolved into 2 mL ultrapure water in glass vial and put for 2 min in an ultrasonic bath at RT. The resulting aqueous solution was recovered and transferred in a 2. 5 mL vial for 2 min centrifugation at 14,000 rpm. The solution was then diluted 200 times in methanol for direct injection in the mass spectrometer (12T FT-ICR Solarix equipment and FTMS-Control V2. 2. 0 software, from Bruker Daltonics, Bremen, Germany). External calibration of the mass spectrometer was performed with arginine clusters (at 10 mg L -1 in methanol) prior to acquisition. The methanolic solution was infused at 2 μL min -1 flow rate in the ESI source (Apollo II, Bruker Daltonics, Bremen, Germany) that was used in negative-ion mode with a capillary voltage set at 3. 6 kV.
The flow rate, the drying gas temperature, and the nebulizer gas pressure were kept at 4 L min -1 , 180 °C, and 2. 2 bar, respectively. MS spectra resulting from 300 scans that were accumulated over a m/z 122-100 range and with a 4 megaword time-domain, were then processed using Data Analysis 5. 0 software (Bruker Daltonics, Bremen, Germany). An internal calibration was realized using well-known CxHyOz anions (composed of fatty acids and sugars), with mass accuracy values lower than 500 ppb. Lists of peaks were generated at signal-to-noise ratio ≥4 and then exported. Signals related to satellite and magnetron peaks were removed using an algorithm initially developed by Kanawati et al. [29]. As the samples were all analyzed in replicates, only m/z features observed at least 60% were kept and aligned in a matrix with 0.
5 ppm tolerance. As previously reported  [30] , the achieved matrix was processed for assignment by computing the average m/z values with 0. 5 ppm annotation tolerance. Eventually, CHO, CHON, CHOS, CHONS, and CHOCl molecular series were achieved. Perseus software was used to perform principal component analysis (PCA). The chemical composition description yielded for the different samples was detailed by means of heteroatom class distribution and van Krevelen diagrams representing the hydrogen-to-carbon and oxygen-to-carbon atomic ratios. It was possible to distinguish various biochemical compounds such as lipids, amino acids, carbohydrates, and polyphenols according to their location on the van Krevelen diagram  [4, 31]. Peaks were putatively assigned via MassTRIX with metabolite compounds observed in the Arabidopsis thaliana  [32].