dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements and funding We thank  Vincent Calcagno  and  Pablo Lechón-Alonso  for helpful comments and discussion. J. Timothy Wootton  helped inspire this work. Z. R. M. was supported by the  National Science Foundation Graduate Research Fellowship Program  ( DGE-1746045 ). M. C. and F. M. are supported by the  CNRS 80 prime  project  KARATE.
V. ACKNOWLEDGMENT We thank Junsang Doh and collaborators for providing the data for the migration of T lymphocytes of sinusoidal wavy surfaces. N. S. G. is the incumbent of the Lee and William Abramowitz Professorial Chair of Biophysics, and acknowledges support by the Ben May Center for Theory and Computation, and the Israel Science Foundation (Grant No. 207/22). This research is made possible in part by the historic generosity of the Harold Perlman Family. R. K. S. acknowledges the support from ANR (ANR-19-CE11-0002-03). A. I. and S. P. were supported by the Slovenian Research Agency (ARRS) through the Grant No. J3-3066 and J2-4447 and Programme No. P2-0232. This work was supported by the Marie Sk lodowska-Curie Actions, Individual Fellowship, Project: 846449 (to W. X. ) and the "Initiatives d'excellence" (Idex ANR-11-IDEX-0005-02) transverse project BioMechanOE (TP5) (to W.
X. ). This work was supported by the European Research Council (Grant No. Adv-101019835 to B. L. ), LABEX Who Am I? (ANR-11-LABX-0071 to B. L. and W. X. ) and the Ligue Contre le Cancer (Equipe labellisée 2019 to B. L. and W. X. ) and the ANR PRC LUCELL grant (ANR-19-CE13-0014-01 to B. L. and W. X. ), and DIM-ELICIT 2019: Equipment support, Région Ile-de-France (to W. X. and B. L. ). B. L. and W. X. acknowledge the ImagoSeine core facility of the IJM, member of IBiSA and France-BioImaging (ANR-10-INBS-04) infrastructures. The research of Carsten Beta and C. M. T. has been partially funded by the Deutsche Forschungsgemeinschaft (DFG), Project-ID No. 318763901-SFB1294. S. G. acknowledges funding from FEDER Prostem Research Project no. 1510614 (Wallonia DG06), the F. R. S. -FNRS Epiforce Project no.
Image analysis Images were loaded into TissueMAPS (www. tissuemaps. org, code available at https://github. com/pelkmanslab/TissueMAPS) where ROIs were grouped as 2x3 grids per well. Illumination correction based on averaged intensity statistics across all images and maximum intensity projection along the Z dimension was performed in TissueMaps. Image processingwas done in TissueMaps v0. 6. 3 for the following steps: (i) gaussian smoothing with a filter-size of 5 pixels; (ii) Otsu-thresholding in a user-defined range using the red (H2B-RFP) channel, followed by binary mask filling and filtering objects < 200 pixels in area to get segmentation; (iii) feature measurements using the measure_morphology, measure_intensity and measure_texture modules in TissueMaps for both channels without the smoothing applied (see list Table  1 ); (iv) classification of missegmentations by interactive training of SVMs (Support Vector Machine) in TissueMAPS.
Characterizing and understanding the processes that shape the structure of ecological networks, which represent who interacts with whom in a community, has many implications in ecology, evolutionary biology, and conservation. A highly debated question is whether and how the structure of a bipartite ecological network differs between antagonistic (e. g. , herbivory) and mutualistic (e. g. , pollinators) interaction types. Here, we tackle this question by using a multiscale characterization of network structure, machine learning tools, and a large database of empirical and simulated bipartite networks. Contrary to previous studies focusing on global structural metrics, such as nestedness and modularity, which concluded that antagonistic and mutualistic networks cannot be told apart from only their structure, we find that they can be told apart with a multiscale characterization of their structure.
Recently, using a large and diverse dataset of empirical networks with different types of ecology (e. g. ant-plant, bacteria-phage, hostparasitoid,…), Michalska-Smith and Allesina (2019) showed that the structure of ecological networks is extremely heterogeneous and that global measures of network structure including nestedness and modularity are not sufficient to discriminate antagonistic and mutualistic networks  [20]. Song and Saavedra (2020)  suggested that the confounding effect of environmental conditions indeed prevents telling antagonistic and mutualistic networks apart from such global metrics unless environmental differences are accounted for  [24]. Macro-scale metrics of network structure, like nestedness and modularity, are not the only way to characterize network structure. The nestedness/modularity paradigm, as the main method to characterize bipartite network structure, has recently been questioned  [25].
PCAs performed on spectral density values (Fig. 3B ) or motif frequencies (Fig. 3C ) were better at separating antagonistic versus mutualistic empirical networks than when using the global metrics, but only slightly so (Fig. 3A ). Antagonistic networks appeared to be spread on the projection, while mutualistic networks were more clustered, which is potentially linked to the high number of pollination networks in the database (Supplementary Fig. 6 7 ). Unsupervised K-means classifications on the principal component axes were not sufficient to confidently classify mutualistic and antagonistic networks: mutualistic networks were rather well classified (F-scores=0. 76 for the spectral density and 0. 81 for the motifs; Fig. 3B-C ), but not antagonistic ones (Fscores = 0. 55 for spectral density and 0.
As the best classification was reached using ANN on motif frequencies, we used this approach for the remaining analyses. We tested the potential effect of overfitting and found that it had a limiting impact on the accuracy of the classifications (Supplementary Fig. 8 ). The classification was affected by the over-representation of mutualistic networks in our database, but only slightly so: when equalizing the number of networks per interaction type in the training set, antagonistic networks were better classified (from 58 to 68% correct classifications on average) while there were more misclassifications for mutualistic networks (from 78 to 70% correct classifications on average; Supplementary Fig. 9A ). Finally, we explored whether the ANN learned singularities of the types of ecology rather than learning the mutualistic/antagonistic interaction type.
When we excluded a given type of ecology during the ANN training, the percentage of correct classifications of these networks decreased significantly, but remained quite good (Supplementary Table  7B ): for instance, 65% of the seed dispersal networks and 63% of the plant-ant networks were accurately classified when none of these networks were used during training, compared to 79% and 66% respectively when they were included (Supplementary Table  7B ; Table  1 ). Thus, the ANN classifiers not only learned from the antagonistic/mutualistic dichotomy but also partially "learned" the singularities of the networks (i. e. data linkage). Accordingly, removing one type of ecology (e. g. seed dispersal networks) from the training set did not significantly decrease the accuracy of our ANN classifier, except when removing pollination or host-parasite networks that represent the majority of the mutualistic and antagonistic networks in our database, respectively (Supplementary Table  7A ).
Confidence in the supervised classifications of the empirical networks To overcome the limited size of our database of empirical networks, we introduced a measure of the classification confidence for each empirical network: we replicated 10,000 ANN classifications with different empirical training and test sets (see Methods). When looking at the confidence in the classification of each empirical network, we found that 69% (52% and 17% with high and low confidence respectively; F-score =0. 74) of the antagonistic networks and 89% (76% and 13% with high and low confidence respectively; F-score =0. 86) of the mutualistic ones were accurately classified in the majority of the ANN classification replicates (Table  1 ). The percentage of accurately classified networks varied according to the type of ecology.
Ability of simulated networks to improve the performance of classifiers The rather small size of our empirical database limits the performance of classifiers. We tested whether simulated networks can help improve these performances. We used simulations of the BipartiteEvol model, a recently developed individual-based eco-evolutionary model that mimics the emergence of mutualistic or antagonistic bipartite interaction networks (  [38] ; see Methods). Unfortunately, both unsupervised K-means classifications (results not shown) and ANN (Supplementary Table  8B ) trained on BipartiteEvol simulated networks were not able to properly classify the mutualistic versus antagonistic empirical networks, no matter the metric used (results not shown). Mutualistic networks, in particular, were often wrongly classified as antagonistic.
Our analyses confirm that global metrics, including the modular/nested dichotomy, are not sufficient to discriminate between mutualistic and antagonistic network structures  [20, 21]. We did not find a significant difference in modularity values between mutualistic and antagonistic networks. We did find that empirical mutualistic networks tend to be more nested and less connected than antagonistic networks, as previously suggested by  [3, 12] , but this dichotomy is not consistent enough (i. e. , there are too many exceptions) to provide a robust classification criterion. There are several empirical examples of exceptions to the nested/modular dichotomy in the literature, such as a nested structure in host-parasite antagonistic networks  [18]. Within our database, some pollination and mycorrhizal networks are known to be modular  [9, [39] [40] [41] [42] [43] , while they assemble mainly mutualistic interactions.
The efficiency of supervised classification approaches strongly depends on the size and quality of the dataset used for training. We used one of the largest publicly available databases of empirical networks, but it contained only 343 networks in total. In addition, it had a strong unbalanced representation of the different types of ecology, with 80% of the mutualistic networks represented by pollination networks and 60% of the antagonistic networks represented by host-parasite networks. There was also a strong imbalance in the representation of interaction intimacy, which is known to impact network structure  [5, 17, 23, 46] : mutualistic networks were mainly low intimacy networks, except ant-plants (n=3) or mycorrhizal networks (n=9), and antagonistic networks were mainly high intimacy networks, except herbivory networks (n=23).
Unfortunately, the database of weighted empirical networks is even smaller than that of binary networks. The efficiency of supervised classification approaches can also strongly depend on the type, architecture, and tuning of the classifier. For example, the overfitting we noticed in the ANN (which did not seem to negatively impact our results) could potentially be avoided by using other regularization technics, such as dropout  [49]. We chose to use simple, few-layers neural networks implemented in R for their availability and ease of usage to the large community of R users, as well as because our dataset for training was rather small. However, several other machine learning tools exist and could be tested, although they may require larger training sets; graph neural networks in particular have been specifically designed to learn information from graphs such as ecological networks  [50].
Classifying these networks would thus require using a different training dataset. A potential source of difference might come from species delineation in microbial lineages in such databases, which rely on the clustering into operational taxonomic units (OTUs) of short marker gene sequences (e. g. the small subunit rRNA gene or the ITS marker). This difference in species delimitation in networks involving microbial groups compared to networks involving macro-organisms (e. g. pollination and herbivory)  [19]  may affect network structure. This may explain why the classification of mycorrhizal networks was not very accurate in our study. One approach to tackle this would be to incorporate more microbial networks with known interaction types in the training of the supervised classifier, which would be highly recommended anyway given the importance of the type of ecology in the classification accuracy.
Empirical interaction networks We analyzed 343 local or regional scales bipartite interaction networks with well-known interaction types. We obtained these networks by downloading the networks from  [20] , among which some come from the Web of Life database (web-oflife. es  [61] ). We kept networks containing at least 10 species in each guild, as the structure of small networks is difficult to characterize  [9] , in particular in terms of motif composition (preliminary results not shown). In addition, we gathered 9 mycorrhizal networks that also fit this size criterion: 6 were collected from individual papers  [39, 41, 42, [62] [63] [64]  and 3 from the database generated by  [65]. Among the 343 networks, 216 were mutualistic and 127 antagonistic. They comprised 9 different types of ecology: bacteria-phage (antagonistic; n=17), seed dispersal (mutualistic; n=29), hostparasite (antagonistic; n=77), host-parasitoid (antagonistic; n=10), plant-mycorrhiza (mutualistic; n=9), ant-plant (mutualistic; n=3), herbivory (antagonistic; n=23), anemone-fish (mutualistic; n=1), and pollination (mutualistic; n=174).
Lastly, we also verified whether our results were impacted by the over or under-representation of some types of ecology in the training set (e. g. the pollination networks represent ~50% of the complete database). To do so, we trained the classifier with the same number of networks per type of ecology (from 5 to 20 networks for each type of ecology; if a type of ecology had fewer networks than the number per type of ecology, all networks were included in the training set) and compared the classification accuracy with the one obtained with same-size training sets composed of randomly chosen networks (see Supplementary Fig. 8B ). If the classifiers learn to separate antagonistic from mutualistic networks regardless of ecology, then an over-or under-representation of some ecologies should not impact the results.
Acknowledgments We thank  I. Overcast ,  J. Voznica ,  L. Aristide ,  S. Lambert ,  I. Quintero ,  C. Fruciano ,  J. Clavel ,  A. Silva , for comments on the first version of the manuscript. We also thank  S. Chaffron ,  C. Bowler ,  C. Nef , and  E. Thébault  for fruitful discussions. The authors declare no conflict of interest.
interaction) and compared the classification accuracy with the one obtained with the same-size training sets composed of randomly chosen networks. In each condition, we replicated the classification 50 times with different training sets. Finally, we investigated if the classifiers were strictly learning to separate antagonistic from mutualistic networks or if they also learned the potential structural singularities of each type of ecology from our database. To do so, we removed one by one each type of ecology from the training test (e. g. pollination networks), we trained the classifiers on 50 independent training sets constituted by 80% of the remaining networks, and we (i) tested the classification on the 20% remaining networks (e. g. are other networks still as well classified as when pollination networks were not used in the training?) and (ii) tested the classification on the networks from the removed type of ecology (e.
Rupture strength and deformation A uniaxial compression test was performed on gel cylinders of 1. 5 cm height and 2 cm diameter until the gels broke, using a TA-plus texture analyzer (Lloyd Instruments, Elancourt, France), equipped with a flat cylindrical probe (4 cm diameter) and a 100 N load cell. The probe compression speed was 1 mm. s -1. Data were recorded throughout the compression and analyzed using the Nexygen plus software (Lloyd Instruments, Elancourt, France). Force (N) and distance (cm) were measured at the time of the breakage; the distance measurement was used to calculate the deformation (expressed in %), dividing this value by the initial height of the gel sample. All experiments were triplicated.
Texture profile analysis (TPA test) TPA-type tests were performed on gel cylinders 1. 5 cm height and 2 cm diameter to access hardness, cohesiveness and adhesiveness (stickiness) parameters. The tests were performed using the aforementioned TA-plus texture analyzer, equipped with a 1. 2 cm-diameter cylindrical probe and a 100 N load cell. A double compression cycle test was performed up to 33% deformation (5 mm) at a compression rate of 1 mm. s - 1. A rest period (5 sec) was applied between the two compressions. The previously mentioned Nexygen plus software was used to record compression strength and distance throughout the test. The hardness value was determined as the maximum force (N) required to apply a 33% deformation during the first compression. Cohesiveness (dimensionless), corresponding to the ability of the gel sample to recover its original shape after the deformation stops (deformation memory), was calculated by dividing the area under the curve (AUC) of the second compression by the AUC of the first compression.
The stability of the emulsions was determined by the Turbiscan Stability Index (TSI) (Eq. (  5 )), using the Turbiscan software: TSI = ∑ i ∑ h |scan i (h) -scan i-1 (h)| H (5) with scan i (h) the light intensity of the sample height at the scan time i; scan i-1 (h) the light intensity of the sample height at the previous scan time; and H the total sample height. The TSI aggregates all destabilization phenomena occurring in the sample during backscattering. The lower the TSI values, the higher the stability of the emulsion.
Statistical analysis The data set for physicochemical characteristics and protein solubility index was subjected to multiple comparison of means (lsmeans adjust Tukey Contrast) with significance level set at p < 0. 05, using R software (version 4. 0. 3). All results were expressed as mean ± SD. Experimental design and statistical analyses were performed using Statgraphics software (Statgraphics Technologies, Inc. ), including performing regression analysis on the experimental data and plotting response surfaces. For each experimental factor, the variance was divided into components (linear, quadratic, and interaction) to assess the relative importance of these components and the suitability of the following second-order polynomial function: Y = β 0 + ∑ 3 i=1 β i X i + ∑ 3 i=1 β ii X i 2 + ∑ 3 i,j=1 β ij X i X j (6) with Y the estimated response; β 0 , a constant, β i , β ii , β ij regression coefficients of the model; X i , X j , two independent variables among pH, protein concentration and added NaCl (ionic strength).
As a reminder, the experimental design carried out for GGRP is different from that of WRP, in that the protein concentrations tested were higher for GGRP (Table  1 ). Indeed, the two ingredients have different critical concentrations, i. e. minimum protein concentrations for gelation. Only 5% protein of WRP is enough to form a gel, while it is necessary to increase protein concentration up to 12% for GGRP. This difference may be related to their different protein composition (Table  2 ), leading to different protein networks. In order to assess the relative impact of pH, protein concentration, and ionic strength on the gelling properties of WRP and GGRP, regression analyses of all experimental data have been carried out for all gel properties. Except the cohesiveness model for WRP, the resulting quadratic models (Table  3 ) reveal high R 2 values: 79.
Acknowledgments The authors thank the following organizations for financial support:  Campus France  ( Program Polonium 2019 -2020 ), the  French National Research Agency  ( BioWatts  project  ANR-15-CE05-0003-01 ,  ImABic  project  ANR-16-CE19-0007-03 ), The  Auvergne Rhones Alpes programs "cooperation international " and " Pack ambition international " (AZ), and  JSPS KAKENHI  Grant Number  18H01719  and  22K18912  (ST).
Acknowledgements. The fieldwork was performed within a memorandum of understanding between CRDA Nabeul and INR-GREF. We gratefully acknowledge  Imed Belouafi  ( CTV Haouaria ),  Tarek Ajmi ,  Rim Louati ,  Jihen Zrili  ( INRGREF ) for participating in the fieldwork. The cooperation of the farmers in the Lebna region by providing access to their land is highly appreciated. Financial support. This research was supported by the  PRIMA   ALTOS  project ( ANR-18-PRIM-0011-01 ) and by public funds received from the  MESRS. This work was also supported by the  H2020-MSCA-RISE-2018   ACCWA  project and by funds received from  MESRS ,  IRESA  and  IRD  under the framework of LMI NAILA. Review statement. This paper was edited by  Christophe Cudennec  and reviewed by two anonymous referees.
The session lasted 2 hours and was transcribed into a word processing package to allow for NVivo content analysis  (Bazeley and Jackson, 2013). The transcripts were independently read, analyzed, and compared, leading to interrater reliability measured with Cohen's kappa coefficient (0. 82). Using a categorization process suggested by  Brocato, Voorhees, and Baker (2012) , recurring themes in the data were identified by listing items that reflected similar characteristics. We first open-coded all the data, which provided the basis for developing the coding framework. Our codes became more specific as we progressed through the data analysis.
Research design The fsQCA is developed on a dataset based on a structured questionnaire filled out by French consumers. The questionnaire was adapted to a Google Moduli and shared on social networks, a data-collection channel boosted during the pandemic and still very trendy among scholars  (Flanagan and Priyadarshini, 2021; Kim and Hall, 2022). To intercept consumers with a minimum buying experience, the link to the online questionnaire was shared only on French Facebook thematic pages discussing zero-waste and pro-environmental shopping topics. The participation was voluntary to control for opportunistic responses, with no remuneration. Respondents declaring no previous experience in package-free shopping were thanked and excluded from the survey. The final dataset is composed of 233 completed and valid questionnaires.
fsQCA procedure and results The first step to developing the fuzzy set qualitative comparative analysis (fsQCA) is the definition of the property space, which defines the maximum number of combinations leading to the outcome. The property space is derived by the binary combinations (presence/absence) of the variables considered antecedents of the outcome (2 8 ). Scales have been calibrated following the conventional set-membership calibration procedure for configuration analysis, namely, 6: full membership; 4: indifference point; 2: non-membership. Due to the good numerosity of the dataset (higher than 50 responses), the minimum number of three best-fit cases, and the 0. 8 "quasi-sufficiency" threshold value were set in the analysis  (Ragin, 2008). The truth table shows all the possible combinations of variables determining consumers' intention to buy package-free products (Table  3 ).