dataesr/scientific-paragraphs-categorization huggingface dataset:
Sample and Data Firstly, exploratory research was conducted in the study region build on in-depth interviews with key actors. This research enabled understanding the origins of the EI innovation and how it evolved in the study region. It helped to identify the innovation pioneers and the actors currently involved in its development and support. It became clear that the EIs could be understood and implemented quite differently by different winegrowers. Next, individual structured interviews to farmers or farm managers, and representatives of commercial grape and wine farms were made. The interviewees were selected through snowball sampling  [57]. The number of interviewees was determined by exhausting the introduction of new information. The interview script was developed within the purview of the H2020 AgriLink Project  [58]  and included 50 questions that could be either open or closed and whose purpose was to obtain both qualitative and quantitative information in addition to being structured to: 1.
Narratives were treated according to the participants' age group, school education, extent of the vineyard area, and business model. Table  2  shows the characterisation of these 20 interviewees, the dataset used by this paper that has applied content analysis to the farmer's narratives describing the respective innovation path. The transcripts of non-recorded interviews did not allow recovery of the farmer's narratives with comparable structure and detail and, hence, were not used in this paper. Narratives collected in the interviews were treated using content analysis  [59, 60]  which was carried out using Iramuteq software (Interface de R pour les Analyses Multidimensionnelles de Textes et de Questionnaires), 0. 7 alpha 2 version. This software allows for an in-depth analysis of the lexically component of the interviewee's narratives.
It also uses multivariate statistics to classify the interviewee's discourses. The transcription of the narratives constitutes the dataset for the textual analysis, which is known as the corpus  [61, 62]. This software was used to obtain the following results  [63] : (1) identification of categories of interviewees' discourses according to lexical roots of words through the Reinert Method, displayed through a dendrogram of Descending Hierarchical Classification (DHC); and (2) a Similitude Analysis that identifies the co-occurrences between words, helping to identify the patterns of connectedness among the words and the structure of social representations underpinning the speeches. The Reinert Method  [63]  is a divisive hierarchical clustering algorithm that identifies clusters by maximising the chi-squared distance between different clusters.
It enables creating classes of textual segments (from the corpus), presenting similar semantical discourses built on similar vocabulary, which is different from other classes' vocabulary. The Similitude Analysis is based on graph theory  [64]. A graph consists of an ideal mathematical model to study the relationships between discrete objects, allowing for the identification of co-occurrence among words. The software Iramuteq enables visualisation of the relationship between words in each class and how they are related with other classes. The content analysis was driven by the MLP framework to assist the identification of different innovation pathways building on the winegrower's textual narratives on how they understand and implement the EIs agroecological innovation.
Henceforth, two business models are distinguished: (1) the commercial family farm vine-growers; and (2) the corporate winegrowers, including mostly SMEs. The interviewee's differentiation according their business model, "family vine-growers" or "corporate winegrowers", was associated with the farmer's narratives in the content analysis. Based on this classification, we have analysed the content of the interviews, which has generated a corpus that is described as follows: 20 texts (the narratives) arranged in 1397 segments with 10,174 occurrences (words). The application of the Reinert method, with the Iramuteq software, makes it possible to group the interviewees' speeches according to their social representations because, by lexically associating the words, it also groups the corpus into stable categories.
The French Riviera is very often threatened by flash floods. These hydro-meteorological events, which are fast and violent, have catastrophic consequences on life and properties. The development of forecasting tools may help to limit the impacts of these extreme events. Our purpose here is to demonstrate the possibility of using b-flood (a subset of the Basilisk library http://basilisk. fr/) which is a 2D tool based on the shallow water equations and adaptive mesh refinement. The code is first validated on analytical test cases describing different flow regimes. It is then applied on the Toce river valley physical model produced by ENEL-HYDRO in the framework of the CADAM project and on a flash flood case over the urbanized Toce produced during the IMPACT project. Finally, b-flood is applied on the flash flood of October 2015 on Cannes city in south-east France, which demonstrates the feasibility of using a software based on the shallow water equations and mesh refinement for flash flood simulation on small watersheds (less than 100km 2 ) and on predictive computational time scale.
, 2019). For these reasons, we have chosen to use a fully-integrated model based on the 2D shallow water equations with rain source terms solved thanks to a finite volume scheme on an adaptive meshes using the software Basilisk (http://basilisk. fr/). The originality of the model is to gather in the same code both the fluvial and urban flood configurations using up-to-date numerical schemes with  (Buttinger-Kreuzhuber et al. , 2019; Horváth et al. , 2020)  a forecasting computation time, thanks to the automatic adaptive mesh refinement. In the first part we present the model, the numerical method and the adaptive mesh technique. In the second part we first exhibit the ability of the Basilisk software to catch different types of flow regimes for analytical solutions developed in  (Mac-Donald et al.
Rain The rain is simply treated as: S rain = R where R is the local intensity of the rain in m. s -1. This source term is added in the mass equation (  1 ) and was validated in a previous study on Basilisk  (Kirstetter et al. , 2016). Note that it is possible to integrate rain from Meteo-France Panthere RADAR data directly in the code thanks to the read_lamedeau() function (cf. (METEO-FRANCE, 2020)).
Adaptive Mesh Refinement (AMR) The b-flood software takes advantage, among others, of the adaptive mesh refinement (AMR) technique developed on Basilisk by S. Popinet  (Popinet, 2015). This process is very well explained in  van Hooft et al. (2018)  as well as in the sandbox of Van Hooft on the Basilisk website (van Hooft, 2020). We recall here the general mechanism, drawing heavily on the previously cited publications. The adapt_wavelet() function allows to refine or to enlarge the mesh according to the error estimated by the algorithm between two levels of refinement on one or more scalar or vector fields. The error criterion and the fields concerned are set by the user. If the error estimated is greater than the user-defined criterion, the algorithm will refine the cell into four smaller daughter cells.
The practical use of this function is detailed in the 3 examples of real cases published below. In addition to the adaptive refinement process, it is possible to refine certain parts of the mesh statically using the refine() and coarsen() functions. (Popinet, 2013). This means that it takes advantage of all the features of Basilisk: it is completely free and open-source. In addition to the AMR described above, it also allows parallel computing. Experienced users will be able to develop their own modules while simple users will be able to copy/paste the sample scripts given in the rest of this article. It is still recommended to learn how to use the basics of Basilisk before using b-flood, thanks to the various tutorials available on the website (basilisk. fr). 3 Evaluation of the performance on test cases.
Analytical test cases It is important to ensure that the numerical schemes we use are consistent. We test them on complete benchmarks, i. e. the transition from subcritical flow, when the wave velocity is higher than the flow velocity, to supercritical flow, as well as the transition from supercritical to subcritical flow which is characterized by the presence of a shock. The following test cases can be found in the software SWASHES published in  (Delestre et al. , 2013). They are designed to test the validity of implementation of source terms and the consistency of the numerical scheme by comparing the numerical solution with the analytical one.
The delay time does not exceed 5 seconds for all the stations and its maximum value is on station P2. We can see in figure  16  that the dynamics modeling therefore remains convincing. The results produced allow us to conclude on the validity of our simulations in the case of urban flooding on impermeable soil. 4 Real case: flood of October 2015 in Cannes on the French Riviera Here we demonstrate the possibility of using b-flood, a software based on shallow water equations and mesh refinement, in a real situation of flash flood on small watersheds (less than 100km 2 ). For this, we simulate the case of the flash flood that took place in Cannes (France) on October 3, 2015  (Carrega, 2016; Saint-Martin et al. , 2018). The city of Cannes is located in the south-east of France. The Saturday third of October 2015, between 6 pm and 11 pm, a huge rain fell on the Alpes-Maritimes department in France: at some points, 200 mm of rain in less then 3 hours were recorded.
Acknowledgements. We would like to thank  F. Bourgin  for the initial idea of this paper. We would like to thank the  IGN  for providing us with the topographic data as well as  Météo-France  for providing us with the data of their RADAR PANTHERE. We also thank the  AXA Foundation for Research fund  for their financial support at the very beginning of this study. 19 https://doi. org/10. 5194/gmd-2021-15Preprint. Discussion started: 19 March 2021 c Author(s)  2021. CC BY 4. 0 License.
We introduce Multiple Interface String Alignment (MISA), a visualization tool to display coherently various sequence and structure based statistics at protein-protein interfaces (SSE elements, buried surface area, ∆ASA, B factor values, etc). The amino-acids supporting these annotations are obtained from Voronoi interface models. The benefit of MISA is to collate annotated sequences of (homologous) chains found in different biological contexts i. e. bound with different partners or unbound. The aggregated views MISA/SSE, MISA/BSA, MISA/∆ASA etc make it trivial to identify commonalities and differences between chains, to infer key interface residues, and to understand where conformational changes occur upon binding. As such, they should prove of key relevance for knowledge based annotations of protein databases such as the Protein Data Bank.
MISA We assume a collection of crystal structures of the bound and unbound types. We call a polypeptide chain in these structures a chain instance. We also assume that a multiple sequence alignment is available for the sequences of the instances of interest. Practically, we expect two settings: (i) chain instances with identical sequence: since numberings used in the respective PDB files are identical, the identity alignment is used; (ii) homologous proteins: a multiple sequence alignment providing a coherent numbering of amino acids (a. a. ) is taken for granted. The terminology used in the sequel is as follows. We study chain instances and compare them against one another using one MISA for each so-called MISA id. A MISA is based on interface strings (i-string), which following conventions for sequence alignments, use the one-letter code of (a.
More precisely, recall that the Voronoi model identifies pairs of atoms, one on each partner, which are either directly in contact as their Voronoi cells are neighbor, or are contacting a common crystallographic water molecule. Therefore, an a. a. contributing at least one interface atom is called an interface residue/a. a. Practically, we use the sbl-intervor-ABW-atomic. exe executable from the package Space_filling_model of the Structural Bioinformatics Library (  [19] , http://sbl. inria. fr). Interface strings (i-strings). In the sequel, we present MISAs informally, referring the reader to SI Section Supplemental: formal specification of the method. We represent each chain instance with a so-called interface string encoding properties of its interface amino acids. The interface string of a chain instance is a character string with one character per residue, and is actually defined from all instances with the same MISA id.
Residue part of the consensus interface: • Residue not found in the crystal structure: displayed with the star '*'. • Residue found at the interface for this particular chain: displayed with the uppercase one letter code if the a. a. matches the consensus a. a. , or with the lowercase letter otherwise. • Residue not found at the interface for this particular chain, even though the corresponding position contributes to the consensus interface (for other chains): displayed in an italicized uppercase/lowercase letter. (Note that this is the case for all residues of unbound structures-as no partner implies no interface. ) Practically, it is often sound to restrict the study to a range of amino-acids on the chains studied, whence the following: Definition. 3 A window series for a MISA id is a sequence of integer ranges of residue ids {(B i , E i )} used to restrict the display of the i-string.
Colored MISAs We further color the letters of a MISA to encode properties of biological/biophysical interest. MISA/SSE: coloring based on Secondary Structure Elements. The type of SSE a given a. a. belongs to is especially useful when comparing bound and unbound structures, to assess perturbations in the hydrogen bonding network. Practically, we use the dictionary of SSE from  [21]. MISA/BSA: coloring based on Buried Surface Area. Consider a chain instance in a complex. The BSA of this chain is defined as the accessible surface area (ASA)  [11]  of this chain in the partner alone minus the ASA of the chain in the complex. We report the BSA on a per residue basis, computed using the algorithm from  [22]. Remark 1 The Voronoi interface model identifies privileged contacts between atoms of the partners.
Availability and practical matters MISAs are implemented within the Multiple_interface_string_alignment package of the Structural Bioinformatics Library (  [19] , http://sbl. inria. fr), see https://sbl. inria. fr/doc/Multiple_interface_string_alignment-user-manual. html. The calculation and the analysis of MISAs is based on four scripts-see online documentation: • sbl-misa. py: building MISAs from a description of complexes and possibly unbound structures. • sbl-misa-mix. py: mixing selected colored MISAs into a single (html) file. • sbl-misa-bsa. py: displaying the BSA of all (or selected user defined) residues. • sbl-misa-diff. py: comparing i-strings and associated properties (in particular BSA) of two interfaces. Has two modes: comparing two i-strings; comparing one i-string and a list of interface residues typically coming from a publication.
Comparing the RBD of SARS-Cov-1 and SARS-Cov-2 in complex with ACE2 Baseline. The spikes of SARS-Cov-1 and SARS-Cov-2 are highly homologous proteins binding ACE2. Pairwise alignment shows that the overall percent protein sequence identity is about 76%  [40]. The S1 subunit is less conserved (64% identity) than S2 (90% identity). Within S1, the receptor-binding domain (RBD) is 74% identical. The initial analysis of the complex between the RBM of SARS-Cov-1 and its target ACE2 was first carried out in  [41]. In this work, it is noted that the BSA at the interface is ∼ 1700Å. These recent publications take the 14 a. a. identified in  [41]  for granted. Note that in furthering these analysis, we sometimes refer to the jupyter notebook which accompanies our software, and propose selected analysis using the four scripts of the Multiple_interface_string_alignment package.
This reminds us a well known fact, namely that solvation by water molecules with low temperature factors (80 units in our case) may significantly change the interface model  [14]. Beyond these global values, MISA/BSA gather BSA values for all atoms of the same a. a. (Fig. S1 ). Two interesting facts emerge. First, both for SARS-Cov-1 and SARS-Cov-2, the residues which bury the largest ASA are found at both endpoints of the fifth stretch (Fig. S1 ), contributing to the stabilization of the interface. For SARS-Cov-1, one notes in particular: Y475, T486, Y491; and for SARS-Cov-2: F486, Y489, T500, Y505. We also note that some of these a. a. bury a great deal of SAS (Fig. S2 ), in particular Y475 for SARS-Cov-1, and Y489 for SARS-Cov-2. Interface composition. We proceed with the inspection of the number of interface a.
The analysis of MISA/∆ ASAcomplements this view (Fig. S2 ). For SARS-Cov-1, the interface a. a. have a balanced behavior, some gaining some loosing ASA, in the mild range (-10. +30Å 2 ). For SARS-Cov-2 on the other hand, the scale is shifted towards negative values (< -150. + 35Å 2 ). In other words, the binding entails a compactification of the RBD in SARS-Cov-2. Speaking of conformational changes, we further our analysis with a clustering of structures in terms of lRMSD, which is of special interest to assess the conformational changes between the up and down conformation of the RBD. In this case, no significant difference is observed in terms of lRMSD computed from all backbone atoms (Fig. S3 ). Phrased differently, MISAs show subtle details at the individual a. a. level, which are overlooked by global lRMSD calculation.
Interestingly, the first point use MISAs prepared by hand [28, Fig. 2A ]. The systematic nature our automatically generated MISA/SSE shows modest albeit interesting refinements. First, one sees immediately that the regions of the RBD targeted by ACE2 and CR3022 are disjoint (Figs. 3  and 4 ; jupyter notebook). Second, using the two complexes RBD-CR3022 in the asymmetric unit of the crystal, we note that the interface involves 33 rather than 28 a. a. which bury surface area. Those omitted from [28, Fig. 2A ] are sitting on the boundary of the interface though (Fig. 3 (B, VdW balls)). MISA/BSA and MISA/∆ ASAalso immediately identify those a. a. with the largest contribution to BSA (Fig. S4  and Fig. S5 ). Interestingly too, we note that binding with CR3022 triggers significant conformational changes with significant loss (Y380) or gain (H519) of ASA, as evidence by MISA/∆ ASA.
Main steps and associated classes Step 1. : Parsing the input Main class : class MISA_preprocessor Parse the specification file and gather the structural data sbl-misa. py starts by reading the input files, and possibly runs sbl-intervor-ABW-atomic. exe and/or sbl-vorlume-pdb. exe. (The script controls that every structure has a coherent number of chains among the different complex specifications, and if it is not the case, it runs only on the complete structures). It parses the input specification file, and infers the MISA id of each chain. Step 2. : Constructing the MISA Main class : classes MISA_constructor, MISA Initialize the MISA by gathering the chains with the same MISA id and aligning them In the following, we assume a mapping between the original indexing of the residues in each chain, and the renumbering in the MSA.
Also, real tissue contrast (e. g. caused by differential grey and white matter myelination), may also impair segmentation algorithms that rely on withintissue homogeneity. The signal intensity bias can be reduced during processing (e. g. using sqrt(T1w*T2w) approach in the HCP-NHP pipeline). Unfortunately, image types required for biasfield correction are sometimes not acquired in studies of NHPs. For example, in the PRIME-DE database  (Milham et al. , 2018) , of the 16 imaging centers that provided structural MRI data, 10 centers provided both T1w and T2w images required for biasfield correction. Combining biasfield corrected and uncorrected structural data may have an adverse effect on the statistical power. Alternatively, we recommend acquiring structural images using a single-loop receive-only coil or bird-cage coil to substantially reduce the B1biasfield  (Li et al.
97 ± 0. 01 (N=5, for an exemplar test-retest pial and white matter surfaces and dense cortical thickness maps, see Supp. Fig. 4a-d  and 5a-d ). Reliability was comparable with YA-HCP (Supp. Fig. 6a-d ). Taken together, the high reproducibility within subjects and variation of cortical thickness across subjects (at RIKEN dataset) suggests there is meaningful biological variation in the macaque population. The extent of variation in regional cortical thickness due to measurement bias (i. e. environment, subject positioning, B1+ and B1-biasfield, sequence protocol and segmentation)  (Han et al. , 2006)  or biological differences (i. e. gender, body weight, head size, age and genetic) in the sampled multi-center macaque population remains unknown (Fig. 2c ). To determine the primary sources of variation, harmonized data analysis is needed to explicitly remove center-and scanner-related effects from the multi-center data while preserving biologically-relevant covariates in the data  (Fortin et al.
B0 field-map ─ preserving spatial fidelity A B0 field-map enables distortion correction of functional and diffusion (echo-planar) images so that they represent the physical space of the imaged animal. Unfortunately, currently B0 fieldmaps are sometimes neglected in studies of NHPs. For example, in the open PRIME-DE database  (Milham et al. , 2018) , of the 19 imaging centers that provided resting-state fMRI (rfMRI) data, only seven provided B0 field-maps and only five provided EPI echo-spacing (time between neighboring k-space lines). Combining corrections for B0 distortion and its interaction of motion may further increase spatial fidelity and statistical power  (Andersson et al. , 2001 (Andersson et al. , , 2003;; Cusack et al. , 2003). Figure  3  demonstrates the geometric distortion of spin-echo (SE) EPI acquired with leftright (L-R) (Fig.
5 ). Interestingly, FC distributions become more skewed for longer scan durations (Fig. 6 ), in line with the skewed distribution of cortico-cortical connection weights  (Markov et al. , 2014). Functional connectivity (Z-transformed Pearson's correlation coefficient) between a seed point (single grayordinate seed) in the default mode network area and the rest of the cortical mantle. In the macaque two sessions each 51 minutes are acquired whereas in the Young Adult Human Connectome Project (YA-HCP) four sessions were acquired with each 15 min length. Both macaque and human BOLD fMRI data were acquired with repetition time ≈0. 7 sec  (Autio et al. , 2020; Smith et al. , 2013)  and data was preprocessed using HCP and non-human primate (NHP)-HCP pipelines  (Autio et al. , 2020; Donahue et al.
, 2018; Glasser et al. , 2013) , including FreeSurfer  (Fischl, 2012)  and ICA-FIX processing  (Griffanti et al. , 2017 (Griffanti et al. , , 2014;; Salimi-Khorshidi et al. , 2014). In the PRIME-DE database, rfMRI scan length varies between 8 min and 155 min per subject, while repetition times range between 0. 7 sec and 2. 6 sec  (Milham et al. , 2018). These, and other factors (i. e. receiver coil, imaging parameters, and anesthesia protocol), result in resting-state FC Z distributions that vary widely across sites (Fig. 6b, c ). The length of the imaging session and number of temporal data points also has implications for data-driven analyses of fMRI timeseries. For example, ICA separates multivariate fMRI timeseries into subcomponents that are non-Gaussian and statistically independent  (Beckmann et al.