dataesr/scientific-paragraphs-categorization huggingface dataset:
(3) environment type(s) of the IAS causing impact (aquatic, semiaquatic, terrestrial or diverse/unspecified). We compared the total cumulative and mean annual costs of damage and management costs, as well as the different management categories defined above, and how they evolved over time. This was done using the summarizeCosts function of the 'invacost' R package  (Leroy et al. , 2021) , thereby determining the decadal and absolute annual average expenditures for (i) total management, (ii) total damage, (iii) pre-invasion management, (iv) post-invasion management and (v) knowledge funding.
Statistical modelling We built two models to examine the predictiveness of biosecurity investment for the total management or damage cost incurred by a nation in a given decade (n = 7 decades 1960-2020; n = 102 countries; n = 198 [country + decade] pairs). To do this, we first summed our cost data within each decade and within each country, employing the 'countrycode' R package  (Arel-Bundock et al. , 2018)  to ensure consistent country naming by converting all InvaCost country records to ISO3C codes. In both models, we included several other factors hypothesised to influence economic impacts of biological invasion  (Haubrock et al. , 2021a (Haubrock et al. , , 2021b;; Kourantidou et al. , 2021) , including the Gross Domestic Product (GDP) and human population of each country in 2014 (from the World Bank, adapted from  Sardain et al.
, 2019) , as well as total volume of imports in metric tonnes (BACI CEPII 2021; http://www. cepii. fr/cepii/en/bdd_modele/ presentation. asp?id=37), either using the annual average from 2015 to 2019 or a historical annual average from 1995 to 1999 (given our data were grouped by decade and that historical trade can be more predictive of present day invasion risk due to invasion lags; see  Latombe et al. , 2021). We also included a measure of the total invader burden in a country from the sTwist first record database  (Seebens et al. , 2017) , calculated as the cumulative number of species first records up to that decade. Models were formulated as generalised additive models (GAM) using the R package 'mgcv'  (Wood, 2011)  in order to include temporal terms as thin plate smoothers. This allowed us to detrend our cost data with respect to time and to explore potential non-monotonic relationships between cost and lags in management.
e. damage cost (for management model only), pre-invasion management spending, post-invasion management spending (for damage model only), knowledge funding, number of unique cost references, current trade imports, historical trade imports, population size, GDP, species richness, first invasion records, decade, and time since first management record]. Models were checked for high concurvity (the GAM equivalent of multicollinearity;  Wood, 2011)  and when predictor variables were highly correlated (r > 0. 8), the predictor with the greatest correlation with the response variable was retained. All code and derived data are available at github. com/emmajhudgins/biosecurity_invacost. In addition to the two main GAM models, we examined trends in the degree of proactive (i. e. pre-invasion) and reactive (i.
Cost of inaction We set out to quantify the additional cost incurred due to delayed management action (i. e. the cost of inaction) on a global scale using the filtered dataset. We applied the cost model developed by  Ahmed et al. (2021) , which predicts the temporal dynamics of potential damage and inaction costs by fitting cost curves to damage and management cost data (Supplementary Material 3). Globally aggregated damage costs were reported on an annual basis from 1960 to 2020, with a maximum reported cost value of $216. 6 billion in 2004. Management costs were also reported yearly over the same time period, with a maximum cost of $7. 4 billion in 2002. To make predictions of the cost of inaction across all delay scenarios, we used the total damage and management costs per year in the filtered data.
Geographic distribution Considering the unfiltered dataset (i. e. including low reliability and potential costs), expenditure for managing invasions has totalled $307. 9 billion between 1960 and 2020 worldwide, based on 20,758 expanded cost entries from the InvaCost database (hereafter, n); a value that sums to only 6% of the total recorded damages incurred from IAS worldwide ($5118. 6 billion, n = 10,494) (Supplementary Material 4). Total management costs were greatest in North America ($92. 9 billion) and Oceania ($92. 0 billion), then Asia ($48. 9 billion), with the remainder each contributing under $30 billion. Considering total costs, all regions, except Antarctic-Subantarctic (damage cost = $0. 9 million; management cost = $6. 1 million; represented mainly by French Southern and Antarctic Lands, and South Georgia), spent less on management than was incurred in damages, ranging from 3-times less in Oceania to 92-times less in South America (Supplementary Material 4).
The filtered dataset (i. e. excluding low reliability and potential costs) revealed qualitatively similar patterns. Management spending totalled $95. 3 billion (n = 15,864), which is only 8% of the total incurred from damages ($1130. 6 billion, n = 4872) (Fig. 1a ). Total management costs were greatest in North America ($51. 6 billion, i. e. 54%) and Oceania ($28. 7 billion, i. e. 30%), with remaining regions spending under $5 billion. Across all regions except Antarctic-Subantarctic (damage cost = $0. 9 million; management cost = $6. 1 million), damage costs were substantially higher than management costs, ranging from 3-times higher in Africa to 77times higher in Asia (Fig. 1a ). Within this filtered management spending of $95. 3 billion, the investment in pre-invasion management is low ($2.
8 billion, n = 579), with approximately 25-times less invested than in post-invasion management and with considerably fewer database entries ($72. 7 billion, n = 12,439). In turn, $15. 6 billion (n = 1143) has been invested in knowledge funding actions, with the remaining costs mixed in type ($4. 2 billion, n = 1703) (Fig. 1b ). Within management spending, all regions (except Antarctic-Subantarctic: pre-invasion = $1. 0 million; post-invasion = $0. 8 million) invested less in pre-invasion management than in post-invasion Fig. 1. Distribution of (a) total damage and total management costs and (b) management cost types across geographic regions, considering filtered (i. e. highly reliable, observed) costs in 2017 US$. The "Diverse/Unspecified" category encompasses cost estimates that could not be assigned to a single geographic region.
Discussion Management costs for IAS have totalled at least $95. 3 billion worldwide in the last 60 years , and are potentially as high as $307. 9 billionat least one order of magnitude lower than damage costs over the same period ($1130. 6 billion to $5118. 6 billion). While these cost figures derived from the InvaCost database should not be perceived as exact totals  (Diagne et al. , 2021a) , they represent the most up-to-date and exhaustive overview of management costs globally and allow us to investigate largescale patterns. Our results reveal largely disparate, inadequate and rarely proactive management investments for addressing current and future IAS impacts. Consistent and marked differences in management typology were found, with proactive pre-invasion investments comprising a tiny fraction ($2.
Early-stage investments as the exception Our results showed that damage costs not only largely outweigh management costs overall, but also that within management expenditures, proactive, pre-invasion management investments are much lower than reactive, post-invasion expenditures. Relatively low levels of proactive investment may be interpreted in two, potentially non-exclusive waysdepending on the success of these management actions. On the one hand, this result might reveal cost-efficient preventive actions so far, confirming that prevention is better and cheaper than 'cure' regarding biological invasions  (Leung et al. , 2002). This is further supported by management having three-times more expanded database entries than damage, but 12-times lower cost, indicating that management is generally cheaper than damage incurred.
, 2016; Diagne et al. , 2021a). They could also reflect language barriers, with the InvaCost database capturing costs in 'only' 22 languages, e. g. missing many African and Asian ones  (Angulo et al. , 2021a). In addition, it is worth noting that the significant difference in price levels between areas (e. g. labour costs for similar management actions are likely cheaper in most African countries when compared with those in Europe or North America) may contribute to discrepancies in reported costs among regions. Further monetary comparisons at the macroeconomic scale should require reliance on indicators such as the purchasing power parity, which are still prevented by very limited information for most countries and/or years from official sources such as the World Bank  (Diagne et al. , 2020b).
Gaps and evidence-based recommendations Our work allowed us to identify a number of knowledge gaps as well as avenues for further improvements, for both research and applied purposes (summarised in Table  1 ). Importantly, we are aware that the trends and patterns in InvaCost only reflect a sample of the true costs at the time of writing this paper. Our work should therefore be considered as a baseline that will need to be updated and improved as new cost information is both generated and collated in this living database. The suite of data available is highly uneven and changing rapidly, with temporal trends showing a significant increase in the number of cost-reporting documents available over time  (Haubrock et al. , 2022) , paralleling increasing cost magnitudes and invasion rates  (Seebens et al.
g. the case of rodents,  Diagne et al. , 2021c). We therefore advocate for future work to adopt a consistent framework for estimating and reporting different management expenditures dedicated to invasions (see  Robertson et al. , 2020). Second, while "mixed" management costs (an unattributable combination of pre-and post-invasion management, or mixed with knowledge funding) formed a significant proportion (11%) of the expanded cost entries in the filtered dataset, a number of cost entries (n = 124) were not retained due to their classification as "unspecified" (i. e. it was not possible to identify and classify any specific actions taken with respect to the stage at which they occurred). Moreover, costs that were mixed between "damage" and "management" were excluded entirely because it was not possible to reliably distinguish and split the expenditure reported into specific categories.
7 years was found to have caused an additional cost of $1247. 2 billion compared to a scenario with immediate management. Although management costs were initially reported in the year 1960, this was for a relatively small number of species (4 from a total of 366 species and country combinations). Species and country-specific delay times are centrally distributed around the mean time delay of 10. 7 years, and this provides a more accurate representation of the time of management introduction at the global level (Supplementary Material 3). Moreover, had management actions never occurred, inaction costs in excess of $3423. 6 billion would have accrued to the present day. Although coarsely modelled at the global scale considering that management spending was pooled across species and countries that had significantly varied delay times (Supplementary Material 3), this very concretely illustrates The InvaCost database has allowed the launch of several dozen projects to better inform on the economic costs of biological invasions worldwide and provides a 'living' platform for cost reporting  (Diagne et al.
Acknowledgments 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 work on InvaCost database development. The present work was conducted in the frame of InvaCost workshop carried in November 2019 (Paris, France) and 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 was funded through a  Leverhulme Early Career Fellowship  ( ECF-2021-001 ) from the  Leverhulme Trust  and a  Humboldt Postdoctoral Fellowship  from the  Alexander von Humboldt Foundation. DAA is funded by the  Kuwait Foundation for the Advancement of Sciences (KFAS)  ( PR1914SM-01 ) and the  Gulf University for Science and Technology (GUST)  internal seed funds ( 187092 & 234597 ).
CA was funded by the  French National Centre for Scientific Research (CNRS). TWB acknowledges funding from the  European Union 's  Horizon 2020 research and innovation programme Marie Skłodowska-Curie fellowship  (Grant No. 747120 ). FE was funded through the  2017-2018 Belmont Forum and BiodivERsA joint call for research proposals , under the  BiodivScen ERA-Net COFUND programme , and with the funding organisation  Austrian Science Foundation FWF  (grant I  4011-B32 ). NK is funded by the basic project of  Sukachev Institute of Forest SB RAS, Russia  (Project No. 0287-2021-0011 ; data mining) and the  Russian Science Foundation  (project No. 21-16-00050 ; data analysis).
Within species analyses: which genes are specific to particular zooid types? We sequenced mRNA from microdissected zooids from seven different siphonophore species (at least two replicate colonies) and mapped these short-read libraries to previously published transcriptomes  [26]. We collected RNA-seq data from, where possible, 5 different zooids and one specialized tissue, the pneumatophore, as well as unique zooids specific to Agalma elegans (B palpons), Physalia physalis (tentacular palpon), and Bargmannia elongata (yellow and white gastrozooids), and where possible developing and mature zooids (Table  S1 ). Due to species availability, we were not able to sample more than one replicate for some zooids -single replicates were excluded from downstream DGE analyses (see Fig. S1 ). The first component of variation that we assessed was among technical replicates.
Methods All scripts for the analyses are available in a git repository at https://github. com/dunnlab/Siphonophore_ Expression. Additional data files required for analyses that are too large for the github repository can be found on Figshare with the following DOIs: 10. 6084/m9. figshare. 14838384, 10. 6084/m9. figshare. 14838372, 10. 6084/m9. figshare. 14838315, 10. 6084/m9. figshare. 14838090, 10. 6084/m9. figshare. 14829183. The most recent commit at the time of the analysis presented here was fc063e9.
Differential gene expression Short read libraries were mapped to previously published transcriptomes  [26]  using Agalma v 2. 0. 0  [58, 59] , which uses a number of existing tools for transcript quantification, including RSEM (which uses Bowtie)  [20, 60]. Using the agalmar package (https://github. com/caseywdunn/agalmar), we filtered out genes that were flagged as being rRNA, and selected only protein coding genes. We also only considered genes that were greater than 0 in at least two libraries. Differential gene expression analyses, including normalization, were conducted in R, using the DESeq2 package  [61]. Libraries that were found to be outliers based on mean Cook's distance were removed from the DESeq object and from downstream analyses and normalization. Testing for differential expression was conducted using the results() function in DESeq2.
Genes were considered to be significantly differentially expressed if adjusted p-values (Bonferroni correction) were less than 0. 05. Differential expression analyses were only conducted on zooids/tissue with two or more replicates. GO annotations were retrieved for each of the reference translated transcriptomes  [26]  using the PANNZER2 web server  [62]. The PANNZER2 format was modified to match the gene2GO format required for the package topGO  [63]. Gene set enrichment analyses were carried out within species using the R package GOseq  [64] , which takes gene length into account. Over and underrepresented categories were calculated using the Wallenius approximation, and p-values were adjusted using the Benjamini and Hochberg method. Categories with an adjusted p-value below 0. 05 are considered enriched.
Gene set enrichment analyses were also conducted at the gene tree level, considering representative GO terms for particular gene trees. Representative GO terms were selected based on the frequency of occurrence among genes in the gene tree. As gene lengths vary among species and genes in the gene tree, the GOseq approach could not be used, and topGO was used to detect GO terms that are enriched based on Fisher's exact test. This approach assumes that each gene tree has an equal probability of having genes shared among species that are detected as differentially expressed, however results may be biased by a number of factors, including mean gene length among genes in the gene tree  [64]. Putative toxin genes were identified using blastp from the ToxProt dataset last accessed 13 May 2021). See Supplementary Information for R package version numbers.
Speciation Tree Filtering For Speciation Tree Filtering (STF) analyses, single copy orthologs were obtained using Orthofinder 2 (v2. 4. 0)  [33]. Linear models used in STF analyses were constructed using lm(), following methods and code developed by Breschi et al. 2016  [34] , https://github. com/abreschi/Rscripts/blob/master/anova. R. All SVGs and TVGs are genes for which both species and tissue explain greater than 75% of variance. Additionally, in SVGs, the proportion of variance explained by species is two times greater than that explained by tissues; while in TVGs the proportion of variance explained by tissues is two times greater than that explained by species.
2 , step 4). To examine if expression values in our dataset evolve under Brownian Motion (BM), we simulated data set of random expression values using fastBM() from the phytools package with empirically derived mean and standard-deviation values for each gene tree. Under this model, expression variance accumulates among linages on the gene tree as a function of time, and is used as a model of evolution under drift, as well as some forms of natural selection  [74] [75] [76].
Acknowledgements This work was supported by the  National Science Foundation  ( DEB-1256695 , the  Waterman Award ). CM was also supported in part by a  RI-EPSCoR Fellowship ,  NSF   EPS-1004057. Analyses were conducted with computational resources and services at the  Center for Computation and Visualization at Brown University , supported in part by the  NSF   EPSCoR   EPS-1004057  and the  State of Rhode Island. Analyses were also conducted at the  Yale Center for Research Computing (YCRC). We would like to thank  Steve Haddock ,  Lynne Christianson , and the whole crew of the  Western Flyer  for their help and expertise. Additional thanks are due to  Samuel Church  for his assistance during a research cruise, and for productive conversations about comparative gene expression methods.
The co-authors also worked with Indigenous peoples from multiple countries and in partnership with an organisation working with Indigenous Australians. Results and discussion: Over 100 people from multiple disciplines and countries have been involved in co-designing STARDIT since 2019. STARDIT is the first open access web-based data-sharing system which standardises the way that information about initiatives is reported across diverse fields and disciplines, including information about which tasks were done by which stakeholders. STARDIT is designed to work with existing data standards. STARDIT data will be released into the public domain (CC0) and integrated into Wikidata; it works across multiple languages and is both human and machine readable. Reports can be updated throughout the lifetime of an initiative, from planning.
However, there is currently no standardised way to share information about initiatives and problem-solving across and between fields such as health, environment, basic science, manufacturing, education, media and international development. A multi-disciplinary international team of over 100 citizens, experts and data-users has been involved in co-creating STARDIT to help everyone in the world share, find and understand information about collective human actions, which are referred to as 'initiatives'. STARDIT is an open access data-sharing system to standardise the way that information about initiatives is reported, including information about which tasks were done by different people. Reports can be updated at all stages, from planning to evaluation, and can report impacts in many languages, using Wikidata.
Objective The objective of STARDIT is to address current limitations and inconsistencies in sharing data about initiatives. The STARDIT tool features standardised data reporting about initiatives, including who has been involved, what tasks they did, and any impacts observed. STARDIT is designed to support a culture of partnership across disciplines and beyond, and is, wherever possible, aligned and interoperable with existing reporting models and frameworks such as those used in health, environment, manufacturing, publishing, government policy, education, arts and international development (see Table  1 ). In addition, the STARDIT Preference Mapping (STARDIT-PM) tool provides a standardised way to report information about    [158] ; information about data custodianship  [50] , information about any potential risks (to human health or lifeforms, natural or cultural heritage); information about who assessed those risks and how, information about informed consent process, information about any cultural sensitivities or restrictions (including relevant information about gender, clan, tribe or other culturally constructed groupings)  [159] [160] [161] , information about relevant laws and lore  [50] , ethics processes and their impacts (including who was involved and how)  [162] , reporting impacts and outcomes from dissemination  [89]  Tangible cultural heritage (including cultural property  [163] ) Reporting: Experts involved in production, other people involved in production process, resources involved in production process (including relevant DNA information to verify products from plants, animals and fungi), ingredients, funding for resources (for example demonstrating it is 'slavery free'), process reporting (including Good Manufacturing Practice), regulation and authorisation processes (for example medicines and medical devices), code and algorithm checking (for example, autonomous vehicles) process for designing impact assessment, impact assessment (including human and environmental), experts involved in dismantling process (including recycling), other people involved in dismantling process and disposal, evaluation of product according to transparently-decided outcome measures Reporting.
Social care and services Other services different stakeholders' preferences, including preferences for power-sharing and methods of involving people during an initiative (see section 'Mapping preferences for involvement'). In alignment with the UNESCO Recommendation on Open Science  [43] , the co-created values of the STAR-DIT project state that designs and code should always be open access and relevant licences should always be those which allow others to build on and improve the project, while maintaining central control over quality (such as the Creative Commons Attribution-ShareAlike 4. 0 International license (CC BY-SA 4. 0) and the GNU General Public License (GPL) 3. 0 for code. STARDIT data will released into the public domain (CC0) and integrated into Wikidata, which is a free and open knowledge base for collaboratively editing structured data  [44].