dataesr/scientific-paragraphs-categorization huggingface dataset:
HIPEs address a handful of key organisms and proteins To further identify the main objects (i. e. organisms or molecules) investigated by the HIPEs, we performed a word occurrence analysis of the title and abstracts combined with word cloud generation and iterative text enrichment for words referring to organisms or molecules (Dataset 2; see Supplementary Methods for details). That pipeline generated a world cloud of the 200 most frequently used words referring to organisms or molecules (Figure  4A ). As expected, the most used words refer to key groups of organisms (pathogen, Arabidopsis, bacterium, fungus, Pseudomonas) or groups of molecules (effector, receptor, NLR, gene, protein, genome). We further quantitatively analyzed Dataset 2 to identify the top five most frequently referred to plant species, pathogen species, immune receptors, and effectors (Figures  4B-E ).
Temporal analysis of model pathogens reveals the increasing importance of plant pathogenic fungi To get a more detailed understanding of the model pathogens addressed in HIPEs, we performed an iterative analytical reading of all HIPEs and categorized them according to the taxonomic group of pathogens they emphasized (if any). This analysis revealed that 80% of the HIPEs emphasize a specific pathogen; we grouped those HIPEs into six categories: bacteria (96 HIPEs), fungi (74), oomycetes (33), viruses (3), nematodes (2), and aphids (1) (Dataset 1; Figure  5A ). Only a handful of HIPEs emphasize aphids, viruses and nematodes, suggesting that the effector biology community revolves mostly around large communities studying bacterial, fungal and oomycete model systems. Notably, the categories 'bacteria' and 'oomycetes' (33% and 13% of the collection, respectively) show a low model diversity, as they mostly address Pseudomonas spp.
and Phytophthora spp. , respectively (Dataset 1). In contrast, the fungal group (28% of the HIPEs) shows a high model diversity, as it addresses 15 different fungal genera (Dataset 1). Interestingly, the size of the categories evolved drastically between 2000 and 2020 (Figure  5B ). Indeed, between the 2000-2005 and the 2016-2020 time periods, studies focusing on bacteria declined from 75% to 21% of the HIPE collection, while studies focusing on oomycetes and fungi increased from 2% to 13% and from 2% to 40%, respectively. These results indicate that in the early 2000s model bacterial systems vastly dominated effector biology, but that over time filamentous pathogens (i. e. fungi and oomycetes) gained importance. This likely reflects the impact of pathogenomics and the possibility for systematic effectors prediction in sequenced genomes of a larger number of filamentous microbes such as fungi.
The influence of publications addressing fungal effectors increased over time: analysis of the 'HIPE-fun' collection To perform an objective analysis of the most prominent fungal species in highly cited publications addressing fungal plant pathogen effectors, we repeated the bibliometric analysis that identified the HIPE collection, by considering only the research articles addressing fungi. This analysis helped to build a collection of 100 HIPEs focusing on fungi (thereafter referred to as 'HIPE-funs'; Dataset 3; see Supplementary Methods, Supplementary Figure  S2  for details), archived on the following public web address: https://www. zotero. org/groups/4410905/ hipe-fun_collection/library. To understand the increase of the number of publications focused on fungi within HIPEs, we extracted the citation rank over years of the HIPE-funs (i.
e. rank of the publication on the Web of Science database based on the number of citations) and the number of publications addressing plant pathogen effectors. This analysis revealed an obvious increase over the years of the citation rank of the HIPE-funs, which coincided with the increasing number of publications addressing plant pathogen effectors in general. Indeed, in 2000-2005 fungal studies ranked 33rd out of 56 publications, while in 2016-2020 they ranked 7th out of 192 publications (Figure  6A ). Thus, publications addressing fungi drastically gained visibility over time. Interestingly, most of those studies (90%) focused on either biotrophic or hemibiotrophic fungi (Figure  6B ). Publications addressing effectors of (hemi)biotrophic fungi gain prominence over time. The HIPE-funs appertain to influential fungal species in the field of molecular plant pathology In 2012, Dean and colleagues surveyed hundreds of mycologists who voted for the most important fungal species in molecular plant pathology based on their scientific and economic importance (495 votes in total).
We performed a comparative analysis to evaluate the correspondence between the fungal species addressed in HIPE-funs and the top 10 list from  Dean et al. (2012). We first identified a total of 21 fungal genera covered in the HIPE-funs. We ranked fungal genera according to HIPE-funs numbers and annual citation rates, and then we compared this ranking with the list by Dean and colleagues (Table  2 ). First, this analysis showed that all top 10 species occur in at least one HIPEfun (Dataset 3). Notably, the genera Magnaporthe and Puccinia appear in the top of both lists (Table  2 ;  Dean et al. , 2012). These genera comprise species that trigger dramatic epidemics on rice and/or wheat, threatening global food security  (Talbot, 2003; Wilson and Talbot, 2009; Figueroa et al. , 2018). That may explain why they represent important fungal models in the field of fungal effector biology.
For instance, our searches missed the key publications that reported the TAL effector code or clusters of virulence effectors in fungal pathogens, because the metadata of the publications (i. e. , the title, the abstract, and the keyword list) did not use the key terms 'pathogen' and 'effector', respectively  (Kämper et al. , 2006; Boch et al. , 2009). This illustrates well the necessity for readers to diversify the search terms while exploring publication databases. It also prompts authors and editors to carefully select keywords that appear in the metadata in order to maximize the discoverability of their study and its visibility among the targeted readership.
('Phytophthora', 'infestans', 'ramorum' and 'sojae'), Xanthomonas sp. ('Xanthomonas', 'campestris' and 'oryzae' if used in a meaningful context), Powdery mildews ('Powdery' and 'mildews'), NLR ('NLR', 'LRR', 'NB-LRR', 'TNL', 'CNL', 'sNLR' and 'hNLR'). NLR: nucleotide-binding leucine-rich-repeat receptor; PRR: pattern recognition receptors. The raw data used to build this figure are available in the Dataset 2.
(A) Evolution of the number of publications related to plant pathogen effectors and the citation rank for HIPE-funs. Each citation rank was calculated based on the mean of rank of publications by time frames (i. e. position of the publication on the Web of Science website based on the number of citations). (B) Pie chart displaying the proportion of each lifestyle of plant pathogenic fungi studied in the HIPE-funs. The categorization of each lifestyle was based on iterative expert reading and association with the pathogenic species studied for the entire HIPE-fun collection. Numbers above lifestyle topics indicate the total number of HIPE-funs for each topic. The raw data used to build this figure are available in the Dataset 3.
Acknowledgments We acknowledge  M. Saubin ,  F. Lauve-Zannini , and the members of the  UMR IAM  for fruitful discussions and continuous support throughout the course of this project. The authors also acknowledge members of the community met over the past ten years at the  different meetings of the Effectome network  (supported by the  INRAE scientific divisions SPE  and  EFPA ) for stimulating discussions on the topic of plant pathogen effectors.
Funding The authors are supported by the Pole Scientifique A2F of the  Universitéde Lorraine  and by the  French "Investissement d'Avenir" program   ANR-11-LABX-0002-01 ,  Lab of Excellence ARBRE. CL was supported by a PhD fellowship from the  Reǵion Lorraine  and the  French National Research Agency  ( ANR-18-CE32-0001 ,  Clonix2D  project).
0005, 0. 5, 1. 5, 11. 5, 35. 5, and 75. 5  individuals. m -  , respectively). The number of fields per CMS with weed data ranged between 7 and 23 (Supp. Table  1 ). Out of the 97 * 2 * 6 = 1164 records of weed communities, 338 were discarded because weeding operations were implemented after the weed survey. We thus obtained 826 weed communities i. e. from 3 to 6 annual data for each of the 97 fields. The dataset contained 155 weed species. The mean annual richness and abundance of weed communities per CMS are presented in Supp. Table  1.
We then computed each proxy at the community level (value/m 2 ) by multiplying values at the plant level by the abundance of each weed species (in plants/m 2 ). For the 338 missing surveys, we attributed the mean value of the proxies estimated for the same crop type in another year in the same field (216 cases) or, if the crop was not present in the field in the other years, in theother fields cropped with the same CMS (122 cases). The contribution of weeds to services (pollination, pest regulation) was estimated throughout the cropping season in order to account for the phenology of the different weed species (see  Yvoz, Cordeau, et al. , 2021)  but were averaged here at the annual scale. The nine annual-fieldscale proxy values were then averaged at the pluriannual scale (i. e. over the six years) and normalised between 0 and 1 by dividing each mean proxy by the maximum value found in the whole dataset.
Stability was estimated by the calculation of the inter-annual coefficient of variation of each proxy over the six years (CVRot) and the intra-annual coefficient of variation (over the cropping season for each of the six years, CVAnn) of the six proxies of services (pollination, pest regulation). All coefficients of variation were normalised, i. e. divided by the maximum value in the dataset. Proxies at the field scale (value per m 2 ) were the sum of the field core and field edge proxy values, weighted by the proportional cover area of the two locations (Eq. (  1 )). Thus, the relative contribution of field edge was equal to the ratio between the proxy value in field edge multiply by the proportion of field edge area and the proxy value at the field scale (Eq. (  2 )). 1. Proxy Field = Proxy FE * Area FE Area Field + Proxy FC * Area FC Area Field (1) Contribution FE = Proxy FE * AreaFE Area Field Proxy Field (2).
Optimal composition and structure of the land-use mosaics The assessment was conducted through the development of scenarii of changes in the composition and the structure of the land-use mosaics in the study area. We developed two sets of simulation based on the existing land-use mosaics. For each field of each simulated land-use mosaics, we randomly attributed a pair of field core and edge weed communities, the two communities being recorded in the same field (data in 97 fields), depending on the CMS attributed to the field and computed the proxies at the field scale (Service. F, -Harm. F and Stability. F) and then at the landscape scale (Service. L, -Harm. L and Stability. L, see section 2. 2. 2). The first set of scenarii combined changes in the structure and composition in the land-use mosaics.
The BIODESERT survey BIODESERT is a flexible and scalable survey of multiple vegetation attributes, soil properties, ecosystem processes and livestock grazing-related variables that has been conducted in drylands worldwide between 2016 and 2019. This survey is coordinated by the Dryland Ecology and Global Change Laboratory at the University of Alicante (Spain, https://maestrelab. com/en/, last access: 17 December 2021) and is mainly supported by the BIODESERT project, funded by the Consolidator Grant Program of the European Research Council for the period 2016-2022 (http://biodesert. The power and relevance of the data to be obtained by a survey like BIODESERT rely on being able to conduct it at a large number of sites globally, which also encourages the widest participation. The survey is divided into a series of measurements that must be conducted by all the members of the network at all sites (denoted as mandatory in Fig.
Cover and diversity survey with quadrats Along each of the four transects located in the field, we also placed 25 consecutive quadrats (1. 5 m × 1. 5 m size; Fig. 4b ) within which we visually assessed the cover of every perennial species relative to quadrat size (to 1 %). These data can be used to assess, among other things, the composition and diversity of perennial plant communities, their spatial association networks, and intransitive interactions (e. g. ,  Maestre et al. , 2012; Soliveres et al. , 2015; Saiz et al. , 2018). A total of 100 quadrats were sampled within each plot. Whenever possible, the presence of perennial species located within the 45 m ×45 m plot, but not recorded in the quadrats, was also recorded.
Five 1. 5 m × 1. 5 m quadrats regularly spaced per transect were selected (20 quadrats per plot; Fig. 4b ). If by chance there were no perennial plants within a given quadrat (i. e. , 100 % bare soil), this was noted in the data sheet, and another quadrat was randomly chosen from within the same transect. https://doi. org/10. 5194/we-22-75-2022 Web Ecol. , 22, 75-96, 2022 2. In each quadrat, the cover of all perennial species was noted (these measurements were obtained from Sect. 2. 2. 2 described above). 3. In each quadrat, the tallest and most well-developed individual of each species was selected. 4. Functional traits were measured on the selected individuals.
Organizational features of the BIODESERT global collaborative survey The BIODESERT survey has been carried out by a global and inclusive network of researchers who have agreed to implement a replicated survey following the standardized protocol described in the previous sections. All network participants have agreed to the following basic rules: 1. Fund project infrastructure and implementation at their own sites. 2. Collaborate well with other members of the team. 3. Implement the survey protocols accurately. All participating sites followed these protocols and used the available templates for data submission. 4. Openly share data associated with the project in a common repository, managed by the Dryland Ecology and Global Change Laboratory. The BIODESERT global survey is supported by steering and publication committees.
The steering committee serves to establish general guidelines for the BIODESERT survey, including protocols, data use and timelines. All the samples and data are centralized at the Dryland Ecology and Global Change Laboratory; all the data will be posted in public repositories such as figshare or Dryad upon the publication of the articles using them. The willingness of many scientists to cooperate and collaborate is what makes BIODESERT possible. Because this network encompasses a diverse group of people from all over the globe and relies on many data contributors, writing scientific papers can be more challenging than with more traditional projects. Thus, we have attempted to lay out ground rules to establish a fair process for establishing authorship and to be inclusive while not diluting the value of the authorship of a paper.
We have established a publications committee (PuC) which ensures communication across projects to avoid overlap of papers, works to provide guidance on procedures and authorship guidelines, and serves as the body of last resort for resolution of authorship disputes within the BIODESERT network. The guidelines for co-authorship of the papers arising from the BIODESERT survey are described below: 1. By contributing data to the mandatory parts of the BIODESERT survey according to data submission protocols, two members of each participating group will automatically be included as co-authors on three core papers if the group follow deadlines and rules for data submission. 2. Members from those groups contributing data to the optional parts of the survey (described in Sects. 2. 2. 5, 2. 5 and 2.
6 above) will also be co-authors on one paper arising from each part of the survey they have contributed to (two co-authors per group that has contributed data). Web Ecol. , 22, 75-96, 2022 https://doi. org/10. 5194/we-22-75-2022 For example, members of a group that has contributed to the tea bag experiment will be co-authors of a paper using these measurements, while members of a group contributing data to all the optional parts of the survey will be co-authors of three additional papers arising from these data (with the limit of two co-authors per group and paper). 3. In addition to the papers described above, all the researchers involved in collecting field data or data processing will be co-authors of a BIODESERT "data paper". This paper will be a "one stop shop" including the whole, raw database gathered during the survey and will be published in an open-access format to maximize the visibility and usability of the data.
4. All the manuscripts derived from the BIODESERT survey will be revised by the co-authors before submission; before this the coordinator will contact the responsible person of each participating group, who will provide the names and affiliations of the members of his/her group who should be included as co-authors in each paper. While members from the PuC will take the lead on the data analysis and writing of the core papers and those arising from the optional parts of the BIODESERT survey, network participants can make proposals to lead additional analyses/articles using BIODESERT data. These proposals will be sent to the coordinator (including title, abstract and potential list of co-authors when doing so), who will discuss them with the PuC. Proposed ideas are reviewed by the PuC to ensure there is sufficient distinction from proposed and ongoing/planned BIODESERT papers.
Competing interests. The contact author has declared that none of the authors has any competing interests. Disclaimer. Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Special issue statement. This article is part of the special issue "Biological soil crusts". It is not associated with a conference. Acknowledgements. We would like to thank all the participants in the BIODESERT field survey and all its associated tasks (laboratory analyses, handling and storage of samples, maintenance of databases sample collections, etc. ) as without them this survey would have not been possible. We also thank Dolores Ruiz-Lupión of MUSHER Scientific Illustration Studio (https://www. musherstudio. com, last access: 23 December 2021) for drawing  Figs.
4, 6, 7, 8 and 9. Financial support. This research has been supported by the European Research Council (ERC grant agreement no. 647038 -BIODESERT) and the Conselleria de Innovación, Universidades, Ciencia y Sociedad Digital, Generalitat Valenciana (grant no. CIDEGENT/2018/041). Nicolas Gross was supported by CAP 20-25 (16-IDEX-0001) and the AgreenSkills+ fellowship program which has received funding from the EU's Seventh Framework Programme under grant agreement no. 996 FP7-609398 (AgreenSkills+ contract). Hugo Saiz is supported by a María Zambrano fellowship funded by the Ministry of Universities and European Union Next Generation plan. Review statement. This paper was edited by Daniel Montesinos and reviewed by Chi Xu and one anonymous referee.
wild-type males were compared by RT-qPCR at the pupal stage (Fig. 4a , Supplementary Data 3). Interestingly, pseudo-males from three different lines expressed Nix at a similar level to wild-type males (SM9 males vs. WT males p-value = 0. 501, 1. 2G males vs. WT males p-value = 0. 391, 3. 1G males vs. WT males p-value = 0. 626). Thus, we inquired if the downstream double switch genes, doublesex (dsx) and fruitless (fru), showed male-specific splicing products in pseudo-males. For this, we performed RT-PCR on pseudo-males of four transgenic lines. Results revealed that pseudo-males displayed the same splicing pattern as WT males for both genes (Supplementary Fig. 4 ). Expression of myo-sex orthologues in Ae. albopictus pseudomales. Contrarily to Ae. aegypti where the lack of the M-linked gene myo-sex resulted in flightless pseudo-males  24  , our Ae.
a Sex ratio comparison between the WT line and the SM9, 1. 2G and 3. 1G transgenic lines. Sex ratio of the WT line was counted manually on N = 3 independent batches of pupae, while sex ratios of transgenic lines were counted on N = 3 independent batches of neonate larvae using COPAS. Grey dots in rectangles represent the total numbers of males and females (right yaxis). Grey dots in the middle part represent the sex ratio of each replicate. Black dots are the estimate values with vertical lines being 95% confidence intervals. Sex ratios were compared using linear generalised mixed-effect model. None of the sex ratios were significantly different from that of the WT line: SM9 vs. WT p-value = 0. 165, 1. 2G vs. WT p-value = 0. 528, 3. 1G vs. WT p-value = 0. 760. b Wing length comparison between N = 38 wild-type males, N = 39 wild-type females and N = 48 Nix-expressing SM9 pseudo-males represented as violin plots with jitter grey data points.
Wing length was measured on ImageJ software from pictures of dissected right wings taken under a binocular microscope. Comparisons were performed using linear model: WT male vs. WT female p-value < 0. 001, SM9 male vs. WT female p-value < 0. 001, WT male vs. SM9 male p-value = 0. 998. genome assembly concerning one of the above-mentioned copies is erroneous possibly due to high similarity between them. Hence, we compared the expression levels of the Ae. albopictus orthologues of myo-sex in Nix-expressing pseudo-males lacking the M-linked copy vs. WT males by RT-qPCR. All myo-sex copies being 100% identical at the cDNA level, RT-qPCR reflects global expression of all copies combined. Our results showed that pseudo-males expressed myo-sex at a level similar to wild-type males (SM9 males vs. WT males p-value = 1.
This result is consistent with the high flexibility of sex determination in Diptera. This flexibility is Fig. 6  Automated sex sorting of Ae. albopictus Nix-expressing transgenic lines. Sorting is performed at the neonate stage using a COPAS device. Presented graphs show the fluorescence profile of a representative sample of each line as log(Green) = f(log(Red)). Fluorescence clusters are detected automatically using the 'kmeans' function from the R package 'stat'. Nix-expressing pseudo-males being tagged with an eGFP marker gene, the top green cluster is composed of males, while females are in the bottom black cluster. a 3728 larvae from the SM9 line carrying an OpIE2-GFP marker. Here automated clustering detected the lowest male larvae as females, which depicts the difficulty of separating males from females at the neonate stage using an OpIE2-GFP marker.
Surviving larvae expressing eGFP and transiently showing red fluorescence were collected and examined again at the pupal stage. All larvae counting and fluorescence sorting were performed using a COPAS SELECT device (Union Biometrica, Belgium) with the provided Biosort software. Mosquito rearing. Aedes albopictus mosquitoes (strain BiA) were collected as larvae from a garden rainwater collector in the city of Bischheim, near Strasbourg (France) in 2018 and maintained in the insectary since then at 25 °C, 75-80% humidity, with a 14-h/10-h light/dark photoperiod. Larvae were reared in pans filled with demineralized water and provided ground TetraMin fish food twice a day. Adult mosquitoes were caged and provided with 10% sugar solution ad libitum. Females were blood-fed on anesthetised mice.