dataesr/scientific-paragraphs-categorization huggingface dataset:
There is a misleading view of how non-English speaking countries are currently dealing with invasions:  Zenni et al. , (2017)  showed how non-English literature reporting world leading efforts was internationally largely ignored, most likely due to well established expert scientific communities of biological invasions pertaining to English speaking countries. Hence, our objective here was to assess the potential gaps and biases in data compiled exclusively from sources written in English. To this end, we used InvaCost, a recently published database that synthesizes the reported economic costs of biological invasions worldwide (N = 2419 cost entries;  Diagne et al. , 2020a). Diagne et al. (in press ) explored the distribution of these costs across space, taxa, and types of expenditure over time, and found that invasions cost a minimum of US$1.
288 trillion (2017 US dollars) from 1970 to 2017 globally. Beyond these results, the authors also found large geographical data gaps, with few data outside North America, Europe, and Australia/New Zealand, and the majority of source documents being scientific peer-reviewed articles. In this sense, InvaCost (hereafter English database) for now consists of English sources exclusively. It is very likely that the studies on economic costs are not as rare as usually admitted, and that this preconception comes from a focus on English sources. In addition, not considering non-English sources can bias economic assessments, and hinder analyses that inform prioritization and expenditure on the management of invasive species. We performed a data search in non-English languages, to compare it with the English database.
Data searching methods We searched costs associated with biological invasions in 15 non-English languages by native speakers (Table  1 ). Following the methodology used to compile the English database  (Diagne et al. , 2020a) , we used two complementary approaches for collating cost information. First, we performed a standardized literature search using three online bibliographic sources successively: ISI Web of Science platform (WoS hereafter; https://webofknowledge. com/), Google Scholar database (https://scholar. google. com/) and the Google search engine (https:// www. google. com/). In the WoS, we used the same search string as those used for the English database, and used the "language" option to retrieve results for each non-English language (Appendix S1). This standardized search method was the only one that was exactly comparable to the methodology used in the English database  (Diagne et al.
, 2020a). Search strings used in Google and Google Scholar were unavoidably slightly different in each language, which was due to inherent linguistic differences and methodological constraints in Google engines  (Haddaway et al. , 2015; Appendix S1). Second, similar to the English database, albeit more targeted, an opportunistic search was carried out in each language (Appendix S1). This included (i) searching web pages of national institutions, NGOs, and other organizations, (ii) seeking specific literature databases of the countries/languages considered, and (iii) contacting official national managers or researchers that could provide cost data. Data were retrieved until May 2020  (Angulo et al. , 2020 ; doi:https:// doi. org/10. 6084/m9. figshare. 12928136). All data were compiled using the same structure as the English database  (Diagne et al.
, 2020a; Appendix S2). Briefly, the database consisted of about 40 columns with four types of information: raw and standardized cost estimates; characteristics of data source documents (e. g. , type of document, authorship, title, year); taxonomic classification of the invasive alien species for which costs were given; and cost characteristics (e. g. , impacted sector, type of cost, spatial and temporal coverage, type of environment in which the cost occurred). We followed the procedures described in  Diagne et al. (2020a)  to screen for duplicates within the non-English database entries and against the English entries, as costs reported in non-English could have been the source of costs reported in English; in which case, exact cost entries were removed. Whenever possible and to ensure validity, each document was checked independently by two co-authors (i.
The non-English database and comparability to the English database Given that the non-English search was performed more recently than the English one (data for the English database -original version ) and the search methods were slightly different, we consider that the two databases could not be fully compared. Thus, we divided the non-English database into two datasets (Fig. 1 ): one containing exclusively costs gathered from documents published before 2018, which could be quantitatively compared with the English database (hereafter called "comparable dataset"); and another one containing data from documents published after 2017 as well as unpublished data obtained from expert requests (i. e. , that was not quantitatively comparable to the English database; hereafter called "non-comparable dataset").
The effect of the proportion of English speakers on the number of costs We analyzed the correlation between the numbers of cost entries of each non-English language per country and the proportion of English speakers per country. To do so, we used the complete non-English database. The number of entries was log10 transformed. We obtained data for the proportion of English speakers for 26 countries from  Amano and Sutherland (2013) and Eberhard et al. (2020). Amano and Sutherland (2013)  obtained the total number of speakers of English as the first or second language from four different sources -including a previous version of Eberhard et al. (  2020 ) -, related it to the national population, and used the maximum value obtained for each country. When no data was available in  Amano and Sutherland (2013) , we referred to  Eberhard et al.
Differences between non-English and English data in cost descriptors Using only the comparable datasets of both non-English and English databases, we evaluated the differences between them in three ways. First, we tested whether the number of entries was different for each of the following cost descriptors: geographic region and type of environment where the cost occurred, spatial scale and impacted sector of the cost, as well as the type of cost. The original categories of the "Spatial_scale" column of the English database (Appendix S2) were re-assigned to three categories as follows: 'supranational' costs (regrouping the original categories of global, intercontinental, continental, and regional, i. e. , costs estimated for more than one country), 'country-level' costs (estimated for a whole country) and 'local-level' costs (regrouping the original categories of site and unit, i.
e. , costs estimated within a country). The original categories of the type of cost of the English database were re-grouped in three categories: cost related to 'damage or loss', cost related to 'management', and 'mixed' costs when both costs categories are reported together or when the type of cost was unspecified meaning that it could not be easily classified under one or the other category (Appendix S3). For the type of economic sector, we used the categories: 'authorities/stakeholders', 'agriculture', 'health', 'environment', 'forestry', 'public and social welfare', 'fishery'; and we merged mixed categories with 'diverse/unspecified'. To assess differences in the taxonomic composition of invasive species between English and non-English entries, we only used the most represented, broad categories: the kingdom Plantae for plants and the phyla Arthropoda, Chordata, and Mollusca for animals.
For the purposes of this analysis, we excluded data assigned to more than one of these categories. To perform all of these comparisons, we fitted generalized linear mixed models with a binomial distribution and a logit link (SAS Institute Inc. , 2018). For this purpose, we added dummy variables for each category within each of the above cost descriptors, with '0' (when the cost entry was not assigned to a specific category) or '1' (when the cost entry was assigned to a specific category). We considered each dummy variable as the dependent variable, and whether they come from the non-English or the English datasets as the independent variable. Because there could be more than one cost estimate within a given document (e. g. , reporting five cost estimates for a given species in different years, or reporting costs for the control of five different aquatic species), entries coming from the same document were not statistically independent.
Thus, we included the "Reference_ID" (the identification code for each document) as a random effect to explicitly model the covariance structure due to cost entries extracted from the same document ("repeated_subject" in Proc Genmod). We also calculated, for each category of the cost descriptors, the percentage that the monetary costs of the comparable non-English dataset represented to the total costs obtained once combining the English and non-English (comparable datasets) (in 2017 US dollars). Fig. 1. Number of cost entries of invasive species in non-English languages and in English (InvaCost database), distinguishing comparable and non-comparable datasets. For each of the non-English datasets, the number of cost entries by language are represented in the bar diagrams. Languages shown: SP, Spanish; FR, French; JP, Japanese; UA, Ukranian; RU, Russian; CH, Chinese.
Differences in invasive species recorded in both databases First, we compared invasive species reported in the non-English and English databases, using both the comparable and the complete datasets. Specifically, we examined whether species with costs in the non-English datasets were already included in the English dataset (shared species), or whether they were only included in the non-English datasets; and similarly for species with costs in the English dataset. Since some cost entries were assigned to multiple species simultaneously, we obtained the complete list of species having reported costs, as follows: we expanded all species contained in these cost entries (in the column "Species"), so that each species was individually considered. In order to avoid over-estimations, we also removed subspecies or genus when the corresponding species was present in the same dataset (e.
g. , we removed Canis lupus dingo when C. lupus was already present; we removed Ludwigia sp. if any Ludwigia species, such as L. grandiflora, was present). When comparing both species lists (non-English and English), if a genus was present in a list (but the species name was missing) while in the other list there were one or more than one species, we considered that only one species was shared between the two databases (e. g. , Rubus sp. appears in the English dataset, while both Rubus glaucus and R. constrictus do in the non-English dataset; so only one shared species was counted). Second, we quantified the contribution of costs reported from species in the non-English relative to the English dataset, and graphically mapped the results. Using only the comparable datasets, we developed an index that reflects the difference between the number of species by country in the non-English and English datasets (that is, for each country, we subtracted the number of species in the English dataset from the number of species in the non-English dataset).
The relevance of non-English documents reporting costs The non-English database includes 5212 cost entries from 356 documents, which covered 10 out of the 15 non-English languages examined in this study (Fig. 1 , doi:https://doi. org/10. 6084/m9. figshare. 12928136). Despite our extensive search efforts, we could not find cost reports in five of the languages we considered. These languages are Arabic and four languages used in India: Hindi, Telugu, Tamil, and Bengali. Some documents obtained directly from Spanish official managers were written in two co-official languages: Catalan and Galician. From the 356 documents collected, 30 were unpublished materials (N = 1635 cost entries), and 149 documents were published after 2017 (N = 1850). This resulted in a total of 2500 entries that were comparable to the English entries (i.
e. , the comparable non-English dataset) and 2712 entries were not comparable (Fig. 1 ). In general, Spanish and French dominated over the other languages, and mostly Spanish from Spain (>85%) rather than from Latin American countries, and French from France (>80%) rather than from francophone African countries. From the English database, the non-comparable dataset consisted of 15 cost entries from six unpublished documents (i. e. , "Type_of_material" column: "Unpublished material") and eight entries from five documents published in 2018. The English comparable dataset had therefore 2396 entries from 838 documents. In relation to the total economic cost, the non-English comparable dataset resulted in US$ 214 billion (sum of the annual estimated costs), and when including the non-comparable dataset, the contribution from the non-English database resulted in US$ 234 billion.
Differences in cost descriptors Compared to the English dataset, the number of entries in the non-English dataset was significantly higher for European countries, and significantly lower for countries from Africa, North and Central America, and Oceania and Pacific Islands (Fig. 3a , Appendix S4). The number of entries in the non-English dataset was significantly higher at the local scale, but significantly lower at the country and global scales compared to the English dataset (Fig. 3b , Appendix S4). With respect to the environment where the cost occurred, the number of cost entries was not significantly different between the non-English and English datasets (Fig. 3c , Appendix S4). The number of entries in the non-English dataset was significantly higher for the authorities and stakeholders, but significantly lower for agriculture, forestry, and public and social welfare sectors than in the English dataset (Fig.
3d , Appendix S4). The number of entries in the non-English dataset was significantly higher for management costs, but significantly lower for damage costs than in the English dataset (Fig. 3e , Appendix S4). Finally, we obtained a significantly higher number of entries for invasive alien plants in the non-English dataset while significantly lower entries for Chordata and Arthropoda, and no difference for Mollusca (Fig. 3f, Appendix S4 ). Regarding the differences in the spatial scale of cost entries between non-English and English comparable datasets, we observed that only African countries had entries (in French) at the supranational scale (Fig. S1a ). Those costs had a higher proportion than those in the English database (12% vs. 5. 3% respectively). In the English database, the proportion of cost entries at the local scale or at the country level were very similar (48.
Differences in invasive alien species recorded in both databases The comparable and non-comparable datasets of the English database had the same species lists. In the non-English database, the non-comparable dataset had a higher number of species than the comparable dataset, resulting overall in more species being listed in the non-English than in the English database. The species lists of the two comparable (English and non-English) datasets shared only 19% of species, and species brought up by the search in non-English languages represented 44% of the total (249 out of 569 species; Fig. 4a ). When considering the full non-English database (comparable and non-comparable datasets), the percentage of shared species remained 19%, but amounted to 54% for species reporting cost only in non-English languages (384 out of 705 species; Fig.
4b ). The difference in species per country between non-English and English datasets varied from -102 to 132 species. Positive values represent more species in the non-English dataset, which was found in 18 countries, with the highest value in Spain (Fig. 4c ). Negative values represent more species in the English dataset, which was found in 5 countries, with the highest (negative) value in the USA (Fig. 4c ). Additionally, for countries with species in one dataset only, positive values were found in 15 countries (i. e. , reporting costs for species only in non-English languages) and negative values occurred in 59 countries (i. e. , reporting costs for species only in English). In both cases, the extreme values were lower: a total of 43 species was the maximum number of species with reported costs only in the non-English dataset, and was found from Russia; and -56 species was the minimum number of species with reported costs only in English and was found from Australia (Fig.
Discussion The relevance of considering non-English languages was substantiated as non-English data: (i) increased the content of the published English database by more than 100% (2500 non-English vs. 2396 English entries), (ii) increased the global cost estimate of invasions by 16% (by ~US$ 214 billion), and (iii) provided costs for 249 new species and 15 new countries. In addition, 135 other species were found by considering 2712 cost entries from non-published sources, directly obtained from practitioners or managers and/or from documents produced after 2017. Moreover, these gaps resulted in an underrepresentation of cost entries (i) associated with European countries, (ii) measured at the local scale, (iii) impacting primarily authorities and stakeholders, (iv) corresponding to management, and/or (v) reported for plants.
Knowledge gaps when considering only English in the costs of invasive species The large number of costs of invasive species reported exclusively in non-English languages highlights the importance of increasing efforts to capture all available literature beyond English only. This is in agreement with previous findings that provide evidence for gaps in global assessment and ecological patterns, e. g. , the assessments of IUCN population status of endangered taxa  (Amano et al. , 2016)  or the use of interviews in conservation biology  (Young et al. , 2018). Here, we also demonstrated that relying on only English sources results in a distorted picture of lower invasion costs. For example, management expenses were under-represented in English versus non-English datasets. This could be explained by the fact that a third of the cost entries in the non-English database were obtained from local managers and/or practitioners.
Country abbreviations: AR, Argentina; BE, Belgium; BZ, Brazil; CA, Canada; CL, Chile; CH, China; CO, Colombia; CY, Cyprus; EC, Ecuador; EG, Egypt; ET, Ethiopia; FR, France; GE, Germany; GH, Ghana; GR, Greece; IR, Ireland; JP, Japan; KY, Kenya; MA, Madagascar; MX, Mexico; MO, Morocco; NL, Netherlands; NI, Nigeria; NZ, New Zealand; PT, Portugal; RU, Russia; SP, Spain; US, United States; VE, Venezuela; ZA, Zambia. c, d, e, f ) and relative amount (g, h, i, j, k, l) of economic costs of invasive alien species in non-English languages and in English (from InvaCost database), by (a, g) geographic regions where the cost occurred, (b, h) spatial scale of the cost, (c, i) environment where the cost occurred, (d, j) impacted sector of the cost, (e, k) type cost, and (f, l) main taxonomic groups. Significant differences in the number of entries between non-English and English are marked with asterisks and highlighted in blue.
and more broadly, with society as a whole. Similar gaps were also found in other applied ecological global databases, such as the Forest Global Earth Observatory (ForestGEO: https://forestgeo. si. edu/) and the Nutrient Network (Nutnet: http://www. nutnet. umn. edu/) (Nuñez et al. , 2019). We also found marked differences in the number of cost entries among languages. This uneven geographic distribution is similar to what  Amano et al. (2016)  reported in the context of biodiversity and conservation, when comparing 16 major languages. These researchers found that 64. 4% of the documents were published in English, followed by 12. 6% in Spanish, 10. 3% in Portuguese, 6% in Chinese, and 3% in French. In our case, and considering together the English and non-English databases, we obtained 43% of cost entries in Spanish, 31.
7% in English, 15% in French, 4. 3% in Japanese, and 1. 5% in Chinese. We observed that Spanish and French represented a large proportion of the cost entries that were not reported in English. Not surprisingly, countries with a high proportion of English speakers were more represented in the English database compared to the non-English database. In multilingual countries, several of them located in Africa or Asia, publishing in the native tongue(s) may not be the most practical or efficient. Indeed, there may be several native tongues within a single country, making it complicated to opt for consensual non-English language(s) to report information. For example, while Kenya and the Netherlands have a similar proportion of English speakers, the non-English speakers in Kenya are linguistically more diverse, where about 70 languages are spoken, whereas for the Netherlands the remaining almost entirely speak Dutch  (Eberhard et al.
0  (Diagne et al. , 2020b) , which included English as well as non-English data, permitted to show that for some continents and countries economic assessments of invasive species mostly rely on non-English data. For instance, in Central and South America over 40% of cost estimates have been published in non-English languages  (Heringer et al. , in press ); among those, in The map shows the number of species that the non-English comparable dataset contributed minus the total number of species of the English comparable dataset by country (turquoise-magenta scale). Countries having only species in the non-English comparable dataset are marked with stripes and countries having only species in the English comparable dataset are marked with a grid. The borders illustrated in the map may not represent the current political reality.
Ecuador 51% of all costs have been published in Spanish  (Ballesteros-Mejia et al. , in press). A similar situation is observed in Asia (reviewed in  Liu et al. , in press) , where all cost estimates from Japan have been reported in Japanese  (Watari et al. , in press) , and cost entries from Russia have predominantly originated from Russian-language documents  (Kirichenko et al. , in press). Costs reported at larger spatial scales were more frequent in the English database, whilst the non-English search added significantly more cost entries at the local scale (~8% of the total money spent on combining English and non-English databases). This is likely due to local researchers and practitioners being more informed on a local level, but maybe not speaking English, or not being encouraged to publish their data in traditional scientific outlets  (Nuñez et al.
, 2014). The non-English database can constitute an essential tool for practitioners (e. g. , searching for cost information associated with specific management types actions or specific species), policy makers (e. g. , searching for damage-related costs in order to motivate, guide and/or prioritize prevention or response actions towards invasive species), and scientists (e. g. , macroecological analyses, data syntheses, or meta-analyses). Our results also show that an English-only search missed a large number of cost entries impacting authorities and stakeholders. Species invasions are context-dependent, with developing countries typically facing challenges different to those by more developed countries. Therefore, the way invasive species are perceived by local populations, stakeholders and leaders, as well as funders, including the nature of their costs, might differ between countries  (Nuñez et al.