dataesr/scientific-paragraphs-categorization huggingface dataset:
2B ). Although FORCIS contains fewer species per sample than the coretop synthesis ForCenS (6 vs. 15), it contains more species than ForCenS when using the same taxonomic level in both databases, i. e. , 46 vs. 40 species, respectively. The main reason for this difference is the coarser size fraction in ForCenS, which is limited to ≥150 μm 181 vs. the finer size fractions in FORCIS that extend down to 30 μm; only these latter finer size fractions include small-sized species such as S. globigerus, N. vivans, O. riedeli, T. clarkei, T. fleisheri and T. parkerae, which are not included in ForCenS  181, 184. Moreover, more species are documented in FORCIS compared to core-top sediment databases (e. g. , CLIMAP  185  , Brown Foraminiferal Database 186 , ForCens), and the use of species names is not fully complementary between this study and the earlier databases.
Usage Notes Filtering of data in the FORCIS database allows the user to select particular datasets (e. g. , by latitude, longitude, season, ocean basin, year). Seasons were distinguished between the Northern Hemisphere (defining Autumn by September, October, November; Winter by December, January, February; Spring by March, April, May and Summer by June, July, August) and Southern Hemisphere (defining Spring by September, October, November; Summer by December, January, February; Autumn by March, April, and Winter by June, July, August). The type of original count data was kept in FORCIS as reported in the original study (raw, number concentration (ind/ m -3 ), percentage concentration (%), bin or fluxes). Data are presented as counts of the identified specimens or total abundance of all the species found in the sample including unidentified specimens.
In the latter case, the count is reported in the column unidentified_specimens. In most cases, the number concentration is given by the data contributors, in others, the sampled volume of seawater could be calculated for vertical tows using the surface area of the net times the depth interval. When the total number of Foraminifera or volume of sampled seawater are not provided, the number concentration cannot be calculated (see column on subsample_absolute_abundance_available). The number concentration reported in FORCIS are raw numbers corrected for split and the filtered volume when available, but are not standardized for either the mesh size or sieve size fraction. This is important since different sizes will significantly affect number and percentage concentrations (e. g. , Berger, 1969).
The column subsample_count_type gives the type of count reported in the database. All the counts reported as 0 (zero) in the original study were kept in the FORCIS database, which means that the respective species was not found in the sample. However, the absence of species has not always been consistently recorded because of different counting procedures (e. g. , researchers working in the polar areas have not consistently reported the absence of tropical species). To express this, the column subsample_all_shells_present_were_counted helps the user to identify in which datasets a species may have been present but was not counted. For subsamples with "complete" taxonomic coverage, the entry in this column is "true". All counts without clear location (nine subsamples) and/or date of sampling (274 subsamples) were kept in the database even though they cannot be used directly for spatial and time-series analyses.
A note has been associated with the corresponding subsamples. The number_of_species_counted was calculated when all the species were counted in the subsample and provided for both levels of taxonomy (in block 3 and block 4) based on the number of planktonic Foraminifera species observed in each subsample. The number of benthic species was included in FORCIS when given in the original data source but is not included in the calculation of Foraminifera diversity. Finally, the FORCIS database will be open for any new data entry, and the FORCIS project warmly welcomes any new data published or provided by any contributor by submitting the data through our website (https:// forcis. cerege. fr/).
acknowledgements This research is a product of the FORCIS group funded by the synthesis center CESAB of the  French Foundation for Research on Biodiversity  (FRB; www. fondationbiodiversite. fr) and co-funded by  INSU LEFE program , and the  Max Planck Institute for Chemistry (MPIC)  in Mainz. We appreciate the help of all data providers and modelers who contributed in designing and building the FORCIS database, especially  Marine Courtois  and  Romain Suarez  thanks to the  Labex OT-Med/Institut ITEM. We thank the editor and two reviewers for their helpful feedback on a previous version of this manuscript.
Counts Identifier: species_name Foreign key (s): 'site_id' , 'profile_id' , 'cast_id' , 'sample_id' and 'subsample_id' Publications Primary key: 'ref_id' Number of variables: 2 Primary key: 'sample_id' Foreign key (s): 'site_id' , 'profile_id' and 'cast_id' Number of variables: 22Each sample in this table is characterized by its depth range, volume of water filtered (for net data), coordinates, segment length (for CPR data), date of sampling, and in situ temperature and salinity. Species Number of variables: 3The different species' original names were kept in the FORCIS database as they were given by the data contributor or in publication/manuscript and listed in the table species. This species names list was formatted according to 2 levels of taxonomy: level 1 "validated taxonomy" and level 2 "lumped taxonomy".
Sampling_devices Number of variables: 3There are only four main sampling device types comprising this table, which are: net (plankton tow), CPR, sediment traps, and pump. Count_type Number of variables: 2Count data in FORCIS are reported in different units: raw as number of individuals, absolute (ind/m 3 ), relative (%) and fluxes (ind/m 2 /day). The binned data were also included in the FORCIS database as a range of values (raw as number of individuals, absolute (ind/m 3 ), relative (%) or fluxes (ind/m 2 /day)).
, 2011; Ranf, 2017). This has been observed in Arabidopsis thaliana  (Schellenberger et al. , 2021)  as well as in other Brassicaceae of agricultural interest or other plants commonly used in agriculture like grapevines, cherry-tomato fruit, or rapeseed  (Varnier et al. , 2009; Sanchez et al. , 2012; Yan et al. , 2016; Monnier et al. , 2018). The nature of membrane lipids can vary greatly across different organisms leading to tremendous structural diversity: as of 2022, more than 47,000 unique lipid structures have been reported in the LIPID MAPS Structure Database (LSMD)  (Sud et al. , 2006; Liebisch et al. , 2020). Despite the great complexity of the plasma membrane composition, only three classes of lipids constitute the vast majority of eukaryotic membranes: glycerolipids (including phospholipids), sphingolipids, and sterols, offering the possibility to prepare simplified models for biophysical studies  (Deleu et al.
Materials and methods All simulations were run using GROMACS software  (Abraham et al. , 2015). The structural properties of membrane thickness, membrane area and area per lipid were calculated by FATSLiM software  (Buchoux, 2017). VMD  [158]  was used for visualisation. Graphs and images were created with Xmgrace, Inkscape  (Project, 2020) , GNUplot  (Janert, 2016)  and PyMol  (DeLano, 2002).
System setup Coarse Grained (CG) simulations were carried out using the MARTINI 2 force field  (Marrink, de Vries, and Mark, 2004; Marrink et al. , 2007). Membranes were built with the insane. py script available in the Martini web page  (Wassenaar et al. , 2015). The script was adapted to add RLs in the membranes. All CG simulations were performed with a time step of 14 fs under periodic boundary conditions applied in all directions for a total simulation time length of 15 μs. For long range interactions, the reaction-field option was used. For the short-range electrostatic and van der Waals interactions, the Verlet cutoffs  (Páll and Hess, 2013)  were fixed to 1. 1 nm. All systems were studied at 299 K with the V-rescale thermostat  (Bussi, Donadio, and Parrinello, 2007)  in combination with a time constant of 1.
Analysis CG order parameters were calculated by the python script doorder-gmx5. py available on the MARTINI website  (Ingolfsson, 2022)  using the equation: P 2 1 2 3 cos ² < Θ > -1 ( ) P 2 considers the angle θ formed by the bonds of two consecutive CG beads and the normal of the membrane (axis z). Values of 1, -0. 5 and 0 correspond to a perfect alignment, anti-alignment and random orientation with respect to the membrane normal, respectively. Data were analysed by GROMACS tools (gmx command): density, rdf (for radial distribution function), and (for lateral diffusion). More details about how the calculations are performed are described in detail in GROMACS manual  (Lindahl and Spoel. 2022). Ergosterol flip-flops were calculated using LiPyphilic toolkit  (Smith and Lorenz, 2021). A successful flip-flop event was defined as a translocation from one leaflet to another followed by permanence in the destination leaflet for at least 10 ns, as previously done for other works  (Gu, Baoukina, and Tieleman, 2019).
e. the ability of the individuals of the species to spread  (Hui et al. 2016). The ability to spread and develop high population densities may however not necessarily proxy a measurable impact on biodiversity  (Ricciardi & Cohen, 2007). Hence, both spread and proven impacts described in peer-reviewed research were used as indicators of 'invasiveness'. Then, by direct contact with an expert in EU risk assessments for arising alien species (Dr. Elena Tricarico), one additional article and online links to EPPO (European and Mediterranean Plant Protection Organization), EASIN (European Alien Species Information Network), and the InvaCost database, which is the rst comprehensive database on the costs of invasive species worldwide  (Diagne et al. 2021)  were obtained and scrutinized.
Data analysis First, the taxonomic identities of all potential non-native orthopterans from the identi ed literature and databases were extracted. The correctness of the obtained taxa list was then checked against GBIF (Global Biodiversity Information Facility;  Lane & Edwards, 2007)  to identify potentially wrongly named species. Using GBIF, we further identi ed the presence of basionyms and synonyms for each species, and then checked if (i) any of the identi ed non-native Orthoptera were considered as established in any European country by cross-checking if they were recorded in the dataset Established Non-native species in Europe  (Casties et al. 2016; Seebens, 2021; Briski et al. unpublished data)  and ultimately (ii) the native range was identi ed using GBIF and the website of the Centre for Agriculture and Bioscience International  (CABI, 2020; Osdaghi, 2023).
Whether a species is potentially invasive or not was determined based on the described spread and proven impact (i. e. a measurable damage). This information was obtained from the collected literature as well as using the invasive species compendium of CABI, 2020 (ISC,  Osdaghi, 2023) , the inventory of alien invasive species in Europe (DAISIE;  Lane & Edwards, 2007) , the Global Invasive Species Database  (GISD, Poorter et al. 2005) , the Global Register of Introduced and Invasive Species (GRIIS;  Pagad et al. 2018) , and InvaCost  (Diagne et al. 2021). For each alien species, following data were collected (if available): species name, basionyms, status (established, casual), native range, rst introduction to Europe, distribution, pathways of introduction, the species being potentially invasive, impacts, and sources of found information.
Results The 33 identi ed articles contained information on non-native and/or invasive species for Europe. Two websites also provided information about non-native Orthoptera in Europe. The majority of data on non-native Orthoptera was retrieved from the EASIN database, while the EPPO database did not deliver any hits. The EU Regulation 1143/2014 on Invasive Alien Species also did not list any Orthoptera species. All three databases, i. e. the Global Invasive Database (GISD), the Global Register of Introduced and Invasive Species (GRIIS) and the Inventory of Alien Invasive Species in Europe (DAISIE), also did not list any nonnative/invasive Orthoptera, nor impacts which could be attributed to those species. Only the database InvaCost  (Diagne et al. 2021)  recorded monetary impacts for four invasive Orthoptera species, including S.
italicus and L. migratoria are also listed as invasive  (Stahi & Gaibu, 2010). This nding highlights the staggering lack of information on the impacts incurred by several of these species in Europe, additionally undermined by inconsistencies among the identi ed literature and publicly accessible databases. The absence of knowledge which results in underestimation of non-native orthopterans as a phytosanitary threat in Europe must be completed rapidly.
Although not all non-native Orthoptera species are considered as established in Europe, anecdotal evidence of impacts does exist for several species, while others have already established European populations and/or spread substantially. The two species which are undoubtedly invasive due to proven impacts and spread, namely D. maroccanus and S. gregaria, are not indicated as invasive in larger databases except CABI (2020). They are both widely distributed in Europe and impacts on the environment and economy were also reported (CABI, 2020). Furthermore, in CABI (2020) both invasiveness and impacts were clearly documented. Another article from Moldova also classi es C. italicus and L. migratoria as invasive for this region  (Stahi & Gaibu, 2010) , but no database has information on this. Based on high reproduction rates or impacts recorded in other countries or continents, we further identi ed six other Orthoptera species which could be potentially invasive as they (a) occur in several European countries outside their native range and are able to spread across a substantially large area (see Figures  1, 2 ), while concomitantly (b) being known for negative impacts in several European and non-European countries.
Inconsistencies in the available information Although there was much information about non-native Orthoptera species in Europe, in many cases the information from the databases, articles, and websites was not identical. This makes it more di cult to make absolute and secure statements. As mentioned in the results, all three databases on invasive species did not list any non-native/invasive Orthoptera species. Also, InvaCost, the largest and only database of invasive species costs, did not report cost entries for Orthoptera in Europe, yet pieces of information was available for invasive Orthoptera in India and North America, namely S. gregaria, the Mole Cricket Scapteriscus spp. and the Tawny Mole Cricket Neoscapteriscus vicinus. The extrapolated cost for these three listed species is about 4 million dollars.
Interestingly, S. gregaria is also recorded as invasive in Europe, but so far, no costs have been calculated  (Renault et al. 2022). The absence of orthopterans from the EPPO-the European and Mediterranean Plant Protection Organizationpest list, which predominantly contains Coleoptera and Diptera, is very surprising. It is possible that various cereal crops that could be affected by orthopterans are not included in the EPPO Database, that the search did not yield any hits, or that Orthoptera is simply not listed as potential pests due to a paucity of accessible information. When searching in EASIN for Orthoptera, some species were recorded, but among them were also several species of Coleoptera and Hemiptera which were falsely listed as Orthoptera. EASIN also reported information about the species, but they do not attribute a 'high impact' to any of the listed species.
At the same time, no risk assessment seems to have been conducted or is publicly available. Even the EU Regulation 1143/2014 on Invasive Alien Species did not list any relevant Orthoptera species. In addition, GBIF lists information on almost every relevant Orthoptera species, and provides a series of graphical displays of recorded occurrences. However, data in GBIF are affected by a low taxonomic resolution and biased information. In the case or invasive orthoptera, GBIF is likely not up-to-data, as highlighted by the species' distribution, records also likely contain insecurities as the underlying data are collected from different sources, using different methods like human observations without actually catching the species, and thereby likely introducing errors and spatial biases  (Yesson et al.
2007; Troudet et al. 2017). In addition, it is also possible that species have been misidenti ed and as a result, the data become incorrect. This all leads to inaccuracy of the data. It is also noticeable that the species L. migratoria and C. italicus, which according to  Stahi & Gaibu (2010)  are considered as invasive in Moldova, do not appear from the data in GBIF. There are no records of occurrences in this region in GBIF. This may be due to several reasons, as mentioned above, GBIF may not be up to date and/or may not have data for these and possibly other species from south-eastern Europe. The discrepancy in the data may also have occurred due to unequal data collection and misclassi cation and misidenti cation of species. The heatmap of L. migratoria (Figure  1 , bottom left), which was created with the GBIF data, also shows a confusing picture, because compared to various articles, the heatmap here shows only a relatively small distribution area, but many articles considered the most widespread species among locusts and grasshoppers  (Chapuis et al.
2009; Zhang et al. 2009). It should therefore be kept in mind that GBIF could have incomplete data available. CABI (2020) furthermore does not offer information for a large number of relevant species and unfortunately not all data is freely accessible, but it does offer a variety of peer-reviewed information  (Turbelin et al. 2021). Both databases, GBIF and CABI, are often contrary in their available information. One problem that arises from this is some non-native Orthoptera species recorded in GBIF show wide distributions, which would indicate-albeit some uncertainty due to the problems arising from GBIF and a wide distribution does not necessarily indicate impact-a certain degree of invasiveness (depending on the classi cation of 'invasiveness'; see  Ricciardi & Cohen, 2007 ). Yet, impact or risk assessments  (Bacher et al.
8000-times that of sugar  (Palazzo et al. , 2011; Wal et al. , 2019). In parallel with food safety, there is significant concern relating to environmental impact of foods (e. g. ,  Behrens et al. , 2017; Ibarrola-Rivas and Nonhebel, 2022). However, existing food-based or dietary studies do not explicitly include the impact of NNSs. Indeed, the authors are aware of only six LCA for NNSs, limited to the ingredient level; five studies for steviol glycosides  (PureCircle, 2015; Cargill, 2021; Gantelas et al. , 2022; Milovanoff and Kicak, 2022; Suckling et al. , 2023b) , one for aspartame in the World Food LCA Database (WFLDB,  Nemecek et al. , 2019) ; and one further study for the sweetness enhancer, thaumatin  (Suckling et al. , 2023a). For the WFLDB aspartame LCA, the precursor amino acids are produced by bacterial fermentation and combined to make aspartame via chemical synthesis.
Environmental impact data of aspartame from the WFLDB LCA are reported in Section 4 by way of comparison to those from this study and to understand differences. Another production route, one based primarily on chemical synthesis, is also widely used  (Yagasaki and Hashimoto, 2008)  and at present an LCA study of this is lacking, a gap which needs to be filled. Understanding the sustainability of the foods and their ingredients is increasingly important, especially if there is a desire to replace added sugar with alternatives such as NNSs for health reasons alone. However, in order to understand potential ramifications for sustainability of making the swap at a dietary level, it is first necessary to have sufficient LCAs at an ingredient level. At present, the existing studies are predominantly related to stevia variants, with other NNSs underrepresented.
CSV"). The research questions of this study are: 1) What is the environmental impact of producing 1 kg aspartame, and subsequently 1 kg neotame? 2) What is the sensitivity of the baseline impact results to process data uncertainty? 3) What is the net environmental impact of the two NNSs in terms of replacing an equivalent sweetness of added sugar? This research is part of the EU-funded project SWEET (Sweeteners and sweetness enhancers: Impact on health, obesity, safety, and sustainability, www. sweetproject. eu, grant agreement No. 774293).
Overview of production process and LCA Fig. 1  shows the main LCA modelling processes used to calculate environmental impact of 1 kg aspartame, and separately, 1 kg neotame. The system boundary is marked with a dashed box and flows of resources and emissions accounted for indicated. All emissions relating to background processes are also accounted for. For aspartame production, the two main inputs are L-phenylalanine methyl ester hydrogen chloride (L-PheMEHCl), and N-carbobenzoxy-l-aspartic acid (Z-Asp acid). Results of this study are reported in terms of impacts of producing each of the four materials. However, neither L-PheMEHCl nor Z-Asp acid were found in the Ecoinvent database. Therefore, it was necessary to develop production pathways back to materials which were available. The process of achieving this is described in subsequent sections.
Impact assessment was conducted using SimaPro 9. 3 software and Ecoinvent 3. 3 database  (Wernet et al. , 2016) , and the ReCiPe 2016 Midpoint (Hierarchist) method  (Huijbregts et al. , 2016). Environmental impact within all ReCiPe 2016 impact categories is reported, with focus on global warming potential, land use, water consumption and mineral resource scarcity.
Functional unit, goal, and scope The goal of the LCA was to assess environmental impact of producing aspartame (E951), and subsequently neotame (E961), from a chemical production process. The LCA was cradle-to-factory-gate, and functional units are, separately, "production of 1 kg purified aspartame" and "production of 1 kg purified neotame". Both NNSs can be used in a variety of applications and, therefore, their use in food or beverage products was omitted as out of scope. The factory was not modelled as being in a particular location and, therefore, global mixes of materials and energy were used where possible. The study included all steps in synthesis of materials, from those present in the Ecoinvent database, up to purified aspartame and neotame. All foreground data for materials consumption and emissions were derived from literature, and further data relating to energy consumption were calculated through study of stoichiometric reaction equations and known chemical properties.
Assumption: energy consumption Literature will state reaction conditions in terms of heating, cooling, or temperature, but rarely the energy required to achieve those conditions. Sometimes, it is possible to use chemical process software (e. g. , Aspen Plus) to calculate energy requirements, but only if the materials are present in the databases. At the time this study was conducted, many required materials could not be found in the Aspen Plus database. An alternative is to use a baseline quantity for calculations, as tested by  Huber et al. (2022) , which used 0. 5 kWh/kg product. For this study energy consumption was estimated based upon stochiometric equations, molecular structure, and phase changes, and reported masses used within reactions. Therefore, Microsoft's Excel was used to estimate energy consumption as follows: 1) Enthalpy: an estimate of heat energy emitted or consumed by a reaction was made based upon the molecular structure and mass of both reagents and products of each synthesis step.