dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements This research was supported by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) in the framework of a  Sonderforschungsbereich  (project numbers  316099922  and  396774617-TRR 212 ) and the priority programme " Antarctic Research with Comparative Investigations in Arctic Ice Areas "  SPP 1158  (project number  424119118 ). It was also funded by  Norwegian Antarctic Research Expeditions (NARE)  programme. This work contributes to the Ecosystems project of the  British Antarctic Survey, Natural Environmental Research Council , and is part of the  Polar Science for Planet Earth Programme. The  Department of Environmental Affairs  provided logistical support for research at  Marion Island  and the  Department of Science and Technology of South Africa  provided funding through the  National Research Foundation (NRF).
Data and methods To investigate the relationship between air pollution, FF consumption, and economic growth at the country level, we used AOD, FFCO 2 emission, and GDP as their proxies, respectively. AOD was used as a proxy of air pollution in multiple studies  (Hu and Rao 2009 , Xiao et al 2015 , Li and He 2018 , Zhao et al 2018). Due to the lack of a dense dataset of satellite XCO 2 measurements, we used the global monthly FFCO 2 emissions at a 1 • × 1 • resolution from ODIAC version 2019  (Oda et al 2018). ODIAC combined satellite nightlight data, national energy consumption statistics, and power plant emission/location profiles to estimate the global spatial extent of FFCO 2 emission. ODIAC has been applied by the international research communities for a wide range of research, like CO 2 flux inversions  (Maksyutov et al 2013) , urban emission estimation (Lauvaux et al 2016), and observing system design experiments  (Ye et al 2020).
We calculated the annual country-level FFCO 2 emissions by summing up grid points in the country masks which is generated by rasterizing an ArcGIS counties data package. Since we focus on FF emission in this study, we applied a 0. 1 g C m -2 d -1 (C = Carbon) FF emission mask to select urbanized areas of a given country where anthropogenic sources dominate the observed enhancements and exclude the other CO 2 sources like biomass burning. The determination of the mask threshold and its impact are shown in figures S1 and S2, which are available online at https://stacks. iop. org/ERL/16/014006/mmedia. All country-level variables (GDP, AOD, FFCO 2 emission, PM 2. 5 and NO 2 ) in the following analysis are filtered by 0. 1 g C m -2 d -1 emission mask. We used emission masks instead of a land-use cover map because land-use cover only provides a description of the urban area, which often excludes point source emissions such as power plants.
Hence, we use emission masks to include all possible areas that contribute significantly to FF emissions. The country-level GDP and GDP per capita (constant 2010 US$) were obtained from World Development Indicators (WDI) published by the World Bank. Since current world economy is still heavily dependent on high carbon energy sources (Jean-Baptiste and Ducroux 2003), FFCO 2 emissions remain a robust indicator of economic development. For AOD, we chose the AOD data from MODIS due to high spatial resolution and the longest temporal range compared to other satellites. The MODIS Aerosol Product, 'MOD04_L2-MODIS/Terra Aerosol 5-Min L2 Swath 10 km' (Levy and Hsu 2015), starting available from 24 February 2000, was used for the analyses. To ensure data quality, only data points with AOD_550_Dark_Target_Deep_Blue_Combined_QA _Flag 2 and 3 (good and very good) are kept.
Non-Green GDP may be a better metric to reflect the effects of anthropogenic activities on air quality. However, the global countrylevel non-green GDP database is not robust enough due to the complexity of measuring and tracking the fraction of the GDP from greener energies. We focused our analysis on the anthropogenic drivers when discussing the relationship between AOD, FFCO 2 emission, and GDP. Although we applied 0. 1 g C m -2 d -1 FFCO 2 emission mask to screen out AOD and CO 2 natural sources, some caveats need to be pointed out. Firstly, this method cannot perfectly separate areas affected by natural and anthropogenic sources. Secondly, the results for small-sized countries may not be significant due to the limited number of data points in the analyses after applying our emission masks.
Data Availability Any data that support the findings of this study are included within the article (and any supplementary information files). ArcGIS countries data package: https://www. arcgis. com/home/item. html?id=f9a37f7add4e402a9df162 30b26272ed AOD from MODIS: https://ladsweb. modaps. eosd is. nasa. gov/missions-and-measurements/products/M OD04_L2/ NO 2 from OMI: https://www. temis. nl/airpollution /no2col/no2regioomimonth_qa. php PM 2. 5 from CAMS: https://ads. atmosphere. coper nicus. eu/cdsapp#!/dataset/cams-global-reanalysis-eac4-monthly?tab=form FFCO 2 emission from ODIAC: http://db. cger. nies. go. jp/dataset/ODIAC/DL_odiac2019. html WDI from World Bank: http://datatopics. worldb ank. org/world-development-indicators/.
Rheological Profiles of Milks Rheological measurements of the milky extracts were carried out on each sample using a dynamic rheometer (RHEOPLUS/32 V3. 40 21004531-33056 Anton Paar, MCR 301 Physica, Virginia, USA). An amount of 15 g of each suspension of the milky extracts whose dry matter and starch contents Polymers 2020, 12, 1404 5 of 13 varied from 17% to 19% and 3% to 12%, respectively, were subjected to a temperature ramp (from 50 to 90 • C at a speed of 2 • C/min), the initial temperature being fixed at 50 • C and gradually increasing in steps of 2 • C/min for a maximum of 90 • C. The temperature of the samples was maintained at this level for 5 min, then they were cooled to 50 • C in steps of 2 • C/min and maintained at this level for 3 min. The rheological profile data for the processed beverages was automatically saved by the computer connected to the rheometer and the rheological profile curves were also generated by the computer.
g. , crop yields and cropland area), suggests that the situation for the agricultural supply side remains uncertain. First, information on agricultural production technologies remains limited; for example, the costs of technology development and agricultural production, management, and land conversion, possible technologies, and their regional availability vary across models with different assumptions. The development of a global agricultural production database would help to narrow the range of model uncertainty. Second, the distribution of energy crop productivity remains uncertain. More knowledge of the projected bioenergy potential would enable us to assess the bioenergy potential and its consequences on food security at the regional level more accurately (see  Rose et al. , this  issue, for more discussion of bioenergy supply comparison).
In Crowdsourcing-based approaches, citizen scientists and biologists identify the species in a collaborative way, based on dierent computer-based tools. Computer vision and machine learning based approaches identify the species automatically using the extracted relevant features from the species images. In the rst step, the species images are transformed to a tractable domain and stored in the training dataset. Then, an unsupervised learning algorithm is trained using the training dataset and will be used to identify the species. Keybased approaches allow users to identify the species through an identication key that is considered dichotomous if there is only two descriptions in each step, and polytomous in the other case. Identication key takes the form of a decision tree and is designed to guide the users to nd the corresponding species name, through successive identication steps.
Finally a group of expert users will validate the identications. Figure  1  shows The Xper3 represents a collaborative knowledge base platform that allows creating interactive identication keys. In the SPIPOLL, the set of required steps can represent an identication path (IP) leading to the species name. The length of IP varies from species to another, according to the characteristics values (CV) of the species. Figure  2  shows three IPs of three dierent ies from the SPIPOLL database. From this gure, we can see that the two last ies that share the most characteristics have longer IPs than the rst y that has specic characteristics (presence of metallic reections). The two last ies might considered also harder for identication than the rst y, due the resemblance between them. Hence, we can assume that the presence of certain CV on the IP can aect the ease identication degree of the insect.
Conclusion In this paper, we have studied the relationship between the insect identication ease degree and the identication key. 193 characteristics values (CV) paths have been extracted for a group of 134 insects from the SPIPOLL database. For each CV we have calculated an ease identication score (IES) according to the average of IES of the insects that have this CV. Then for each insect, we have used the obtained IES of the CV to calculate an estimated IES to construct an estimated ranked list of insects. Finally, we have compared the obtained estimated ranking list with the ground truth ranked list of insects. The obtained results showed a high correlation between the estimated IES and the ground truth IES, which prove that the CV can be used to estimate the IES of the SPIPOLL insects.
Acknowledgments This study was supported by l' Agence Nationale de la Recherche (ANR) , the  National Museum of Natural History (MNHN)  and the  Oce for Insects and their Environment (Opie). We thank  Mathieu De Flores  ( Opie ) for his precious help with all the entomological work and  Grégoire LOÏS  for providing us the SPIPOLL data. Further, we are grateful to all active SPIPOLL users who helped with data collection:  Barbara Mai ,  Gilles Jardinier ,  Sagittaire06 ,  MichelMarly ,  Ascalaf07 ,  Janmar, Prisca and Leonlebourdoun.
Analysis of C:N stoichiometry in seeds The C:N stoichiometry in seeds was analysed using an orthogonal regression, which minimizes the sum of squared orthogonal distances from the data points to the regression line. C% in seeds was plotted along the y-axis and N% in seeds was plotted along the x-axis. Slope and intercept of the regression line were computed using the gx. rma function of the R package rgr in R version 3. 5. 0. Delta seed composition is the orthogonal distance between a data point and the regression line and is determined by the following formula: DSC=DiffN×DiffC/√(DiffN 2 ×DiffC 2 ), where DiffN and DiffC are the differences between the data point and the regression line onto N% and C% scales, respectively. DSC is recorded as a positive value or a negative value if the differences, DiffN and DiffC, are either positive or negative.
Statistical analysis Three-way ANOVA (R software package) was used to assess the effects of the experiment, genotype, stress condition, and their interactions on the trait variation. Each contrast between the control condition and the other conditions was tested by using estimated marginal means with the function 'contrast' of the R package emmeans. Pairwise differences between genotypes were tested by using estimated marginal means with the function CLD of the R package emmeans. Broad-sense heritability (h 2 ) of trait was estimated from expected mean squares using the following equation: h 2 = σ Gen Ä σ Gen + σ Gen×Cond n + σ Gen×Exp k + σ Cond×Exp n×k + σ Gen×Con×Exp n×k + σE n×k ä where σ Gen , σ Gen×Cond , σ Gen×Exp , σ Cond×Exp , σ Gen×Con×Exp , and σ E are respectively the variances of genetic, genetic×condition interaction, genetic×experiment interaction, condition×experiment interaction and genetic×condition×experiment interactions and residuals; n and k are the numbers of experiment and condition respectively.
Acknowledgements The IJPB benefits from the support of  Saclay Plant Sciences-SPS  ( ANR-17-EUR-0007 ). This work has benefited from the support of IJPB's Plant Observatory technological platforms. We thank plant gardeners and students for taking care of the plants and for the help provided during the harvests:  Joël Talbotec ,  Philippe Marechal ,  Nicolas Legay ,  Aurélia Lornac ,  Nicolas Clement ,  Nibu Varghese ,  Caroline Koler , and  Colas Lescombes.
Bat sampling We produced foraging-commuting habitat availability and connectivity maps for P. pipistrellus using data from the French Bat Monitoring Programme (FBMP) for 2008 to 2013. The FBMP is coordinated by the French National Museum of Natural History and follows a standardized data recording methodology  (Kerbiriou et al. , 2018)  (Appendix S1). The sampling scheme consists of randomly chosen 2x2 km squares from a 2x2 km grid. Within each square, observers selected and visited 10 points: at least five of these points were representative of the habitats of the square and the others were located in 'favorable' places for bats such as along the edge of woods. Each point was sampled using a continuous recording of 6 minutes and the ten points of a site were sampled on the same night and always in the same order at each visit.
Observers recorded bats only when weather conditions were favorable (i. e. , no rain, temperature higher than 12°C and wind speed of less than 5 m/sec; Appendix S1). Observers conducted the sampling during peak daily activity, i. e. beginning thirty minutes after dusk (FBMP recommendations). It generally took less than 3 hours to sample the 10 recording points of the square  (Vandevelde, Bouhours, Julien, Couvet, & Kerbiriou, 2014). Data was recorded from 2008 to 2013, between the 16 th June and the 5 th August, during the reproduction period. We extracted the 224 intramural Parisian points from the FBMP database for 2008 to 2013 (Figure  1 ). Depending on observers' availability over the years, the sampling effort at each point varied from one to five years. The dataset included 552 recordings at 224 points  (139 points in 2008, 158 points in 2009, 72 points in 2010, 30 points in 2011, 68 points in 2012, 77 points in 2013).
Conductance maps Conductance maps depict the ease of movement across the mapped area that varies, for example, with land cover, habitat quality or slope. Conductance maps are used as input data to model connectivity under the Circuit approach. We used the habitat availability maps as conductance maps. This data-based approach is expected to produce more realistic conductance values than would be obtained using expert opinion. We changed the resolution of the two habitat availability maps from 2 m to 20 m by averaging the values of the cells. This change in resolution was needed to be coherent with the bat's perceptual grain, defined as the grain at which an organism responds to the heterogeneity of the landscape  (Wade, Mckelvey, & Schwartz, 2015; Wiens & Milne, 1989 ) (see Appendix S3 for details).
Building connectivity maps We modelled connectivity using Circuitscape, a program that uses circuit theory to model connectivity in heterogeneous landscapes  (McRae & Beier, 2007). The Circuit theory approach provides a continuous estimate of connectivity within the area studied, i. e. , for each pixel, integrating all possible pathways. We identified two areas, i. e. , Boulogne and Vincennes woods, as habitat patches known to house roosts of viable populations of the common pipistrelle and able to act as source for smaller populations within intramural Paris. Furthermore, we know that intramural Paris houses roosts of the common pipistrelle, but their location is unknown. We therefore created and randomly placed a fishnet of points distant of 2 km, each point simulating a possible location of an intramural roost and a potential foraging area.
This multi-scale qualitative approach allowed us to investigate how adaptation funding is structured, used, and allocated from the local level to the national one. The data collection process and methods are developed below. We first conducted an analysis of secondary sources with a focus on existing policies, government documents, and research or project reports related to climate change adaptation policy and its implementation both at the national and local levels in Vietnam. The objective of this analysis was to determine the formal adaptation funding structure, key actors, the funding flows and mechanisms, and the extent to which adaptation actions funded by the government contribute to improve adaptive capacity and reduce climate vulnerability. It used references from a search on key bibliographical repositories (mainly Webofscience, Science direct, Wiley online, Emerald, Taylor and Francis, Springerlink, Proquest, Sagepub, JSTOR, Routledge handbook online, Cabdirect, MDPI, Water alternative, Social services database, Project Muse, Pubmed, SSRN, DOIJ, and Ecology and Society) using the keywords "adaptation, Vietnam, Mekong".
Open-ended questions were designed to gather qualitative data on availability and sources of adaptation funding at the local level, how existing funding has been allocated to adaptation efforts, and on the factors influencing the way adaptation funding is used. In most cases, informants preferred not to record the interview. Therefore, researchers took notes during and after each interview. Finally, we conducted an in-depth ethnographic fieldwork between 2019 and 2020 in a rural commune in the Vietnam northwest upland, where so-called "ethnic minorities" suffered from an unprecedented destructive flashflood in 2018. The research team is composed of four anthropologists, three Vietnamese and one French. All the interviews were conducted in Vietnamese with the informant's freely given, informed consent to participate in the study.
2 billion for the second phase (2016-2025)  [38]. International financial support to SPRCC is directed to the central budget before being distributed to climate change projects led by national ministries and provinces. In most projects funded by SPRCC at the local level, there is often some contribution from the provincial government in the form of a counterpart fund. The central government generally adjoins its own resources to international funding. Figure  1  describes the process for the allocation of adaptation funds. Provinces and national ministries submit their proposals once a year to MoNRE, which reviews, evaluates, and selects prioritized projects with the support and guidance of the National Committee on Climate Change. Proposals developed by provinces and ministries need to be consistent with the list of prioritized actions identified in national policies (such as the NTPRCC-GG, National Strategy on Climate Change, the plan for the implementation of the Paris Agreement, and the SPRCC).
Acknowledgments: We thank  Linh Huynh Nguyen  ( IRD Vietnam ) for providing us a crucial bibliographical database on environmental changes and environmental policies in Vietnam and the Mekong area. The  French Agency for Development (AFD) in Hanoi as well as French National Research Institute for Sustainable Development (IRD)  have been key supporters of this work, which is part of the broader project  GEMMES Vietnam , dealing with climate impacts and adaptation strategies in Vietnam. We would also like to thank  Axel Marx , editor of the special issue " Adapting to Climate Change, The interplay between International and Domestic Institutions in the context of Climate Finance " for the organization of a preliminary workshop with all potential contributors at the end of 2019 and for very useful comments on a first version of the paper.
Materials and methods The economic data for mussel aquaculture were collated from the Economic Performance of the EU Aquaculture Sector Report  (STECF 2018)  for those countries that reported data by segment for the period 2010-2016. In particular, the following variables were collated: number of enterprises, total value of assets, total employment, employment measured in full time equivalents (FTE), total sales volume, turnover, other  income, personnel costs, imputed value of unpaid labour, energy costs, repair and maintenance costs, livestock costs, other operational costs and annual depreciation. This report provided data from eight EU mussel producing countries: Denmark, France, Germany, Ireland, Italy, the Netherlands, Slovenia and Spain. Data by country were aggregated at the segment (production method) level.
Hence, the longline technique reflects the combined data from Denmark, Ireland and Italy; bottom technique the combined data from Germany, Ireland and the Netherlands; raft technique the combined data from Slovenia and Spain; and 'bouchot' data from France. According to  FAO (2019) , these eight countries represented 91% of the weight and 78% of the value of EU mussel aquaculture production in 2016. The following economic performance indicators were then estimated based on STECF (2018): Gross value added ðGVAÞ ¼ turnover þ other incomeÀ energy costs À repair and maintenance costs À live stock costs À other operational costs (1)   2018 ) data and analyses were complemented with a SWOT analysis of the EU mussel sector summarizing the expert knowledge. This national expert knowledge was used to produce the country profiles in the Appendix 1, aiming to provide further insights for the main EU mussel producing countries, including those for which economic data were not reported here and also to provide qualitative explanation to the observed changes over time.
2020). Given the short cycle of the mussel, producers cannot replenish their stocks and mussel production in hatcheries is not allowed in France. As with a lot of environmental hazard causing shellfish mortalities, the prevention methods or the tools for reducing the economic consequence are limited. Financial difficulties are important (drop in sales, net loss of turnover), while cleaning of leaseholds (remove the 16 Strategic Use of Competitiveness towards Consolidating the Economic Sustainability of the European Seafood sector, project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 635188. mussels) causes significant costs. These mortalities are forced certain mussel farmers to dismiss employees or remove the seasonal contracts in order to reduce the costs of wage.
4 per kg since 2011, being between the lowest among the EU mussel producing countries (Eurostat 2019). While producers are able to cover the operational costs, profit estimation depends mostly on the assumptions used for the estimation of the opportunity cost for unpaid labour  (Avdelas et al. 2015; Theodoridis et al. 2017). Theodoridis et al. (2017)  collected data in 2013 and 2014 from three regions (Chalastra, Kymina, and Makrygialos) in the Thermaikos Gulf through a survey of 66 mussel farms. According to  Theodoridis et al. (2017) , labour cost represented 31%, variable costs including sprat collection (38%) and fixed costs (28%). Theodorou et al. (2014)  collected data from eight farms in 2008 and modelled the costs by farm size. According to  Theodorou et al. (2014) , labour cost represented 25%, variable costs (22%), fixed costs (14%) and 17 However, there are many elements that render these production estimations too uncertain.
1 cycle sequencing kit (Applied Biosystems, Waltham, MA, USA). For bacterial strains, sequencing was carried out only in one direction using the 8F primer, and for algal sequences both the forward and the reverse strand were sequenced and manually assembled. Sequence identification was carried out using RDP classifier  (Wang et al. 2007)  for bacterial 16S rRNA gene sequences, and BLAST searches against the NCBI nt database (July 2017) for algal sequences. They were further aligned together with reference sequences from the NCBI nt database using the MAFFT server  (Katoh et al. 2002)  and the G-INS-i algorithm. All positions with less than 95% site coverage were eliminated. Phylogenetic analyses were carried out with MEGA 7  (Kumar et al. 2016 ) using the Maximum Likelihood method based on the GTR+G+I model and 1,000 bootstrap replicates.