dataesr/scientific-paragraphs-categorization huggingface dataset:
Sire traits (region categories LWS, conv(LWS) or conv) on the other hand. For each of these two analyses, the three MGI, KEGG and GOBP databases were queried simultaneously and significant co-annotations were detected (Additional file 6: sheets 5 and 6). These analyses confirmed the main conclusions outlined above. First, the two sets of regions provided a large number of significant co-annotation terms related to body size and growth (but these biological functions were represented by different genes in the two sets of regions, as these were nonoverlapping). Second, biological functions related to early life survival or bone physiology were more significant in the analysis based on signatures expected for Dam traits, while biological functions related to glycogen or lipid metabolism were only significant in the analysis based on signatures expected for Both.
Traits under selection in the two lines When characterizing biological functions through phenotypes from the MGI database, the strongest enrichment was found to be related to body weight, body size and growth and concerned both the Dam and Both. Sire regions (Fig. 5 ). Genes related to these functions were found in many distinct candidate regions (Table  3 ). This provides strong evidence that these functions were under selection because several distinct variants were mobilized rather than a single variant affecting several genes due to hitchhiking. Besides, several of these genes represent potential candidate genes in their region, based on the local values of the statistics used to detect selection or on the expression data available from  [40]  (see Additional file 2: Table  S1 ). These results are consistent with the strong selection for carcass traits that has been applied in the two LW lines in recent decades (Fig.
Funding This study was supported by the  CRB-Anim infrastructure , funded by the  French National Research Agency  (Grant Number  ANR-11-INBS-0003 ) as part of the  Programme d'Investissement d'Avenir. Part of the data were obtained in the  Delisus  project funded by  BIOPORC  ( ADN, Choice Genetics France, Gene+ and Nucleus breeding organizations and IFIP) and French ANR  ( ANR-07-GANI-001 ). The PhD research of CP was conducted as part of the  IMAGE  project, which recieved funding from the  European Union   Horizon 2020 Research and Innovative Program  under Grant Agreement No. 677353. It was also conducted as part of the  Haptitude  project, which was funded by the  INRA SelGen metaprogramme.
Availability of data and materials DNA samples of the animals used in this study are stored at the @BRIDGE platform which, like the National Cryobank, is part of the CRB-Anim infrastructure (see below). Raw sequencing data produced in this study (fastq files) can be found at https:// www. ebi. ac. uk/ ena under accession number PRJEB51909. Genotype datasets resulting from these data (HQSNP and AV) can be found at.
Biological term Database Region category Nb. genes Nb. regions Candidate genes Body weight MGI Dam 8 6 LPIN1, CAMK2A, KITLG, NR3C1 Body weight MGI Both. Sire 16 8 RELN, EPG5, GATA3, NRCAM, IGF2, TNIK, PNPLA8, PLCG1 Body size MGI Dam 9 7 B3GLCT, PDGFRB, LPIN1, KITLG, NR3C1 Body size MGI Both. Sire 11 6 RELN, IGF2, SFRP4, PLCG1 PI3K signaling ptw. KEGG Dam 3 2 PDGFRB, KITLG PI3K signaling ptw. KEGG Both. Sire 5 3 MAGI2, RELN, IGF2 Preweaning lethality MGI Dam 9 8 KATNA1, ARHGAP10, TMTC3, RBM26, SLC6A7 Perinatal lethality MGI Dam 4 4 KITLG, NR3C1, PDGFRB Calcium signaling ptw. KEGG Dam 3 2 PDGFRB, CAMK2A, GNA14 Osteoporosis MGI Dam 4 3 NR3C1 Lipid metabolic process GOBP Both. Sire 5 5 LPIN1, PNPLA8, PLCG1 Fatty acid metabolic process GOBP Both. Sire 3 3 LPIN1, PNPLA8 Insulin secretion KEGG Both.
Climate change is a growing concern for city planners as Urban Heat Islands have an impact on mortality  (Clarke, 1972) , health in general  (Lowe, 2016)  and consumption of energy for building cooling  (Malys et al. , 2012)  among other effects. A first step towards large scale study of urban climate is to define classes based on logical division of the landscape, such as Local Climate Zones (LCZ) defined by Stewart & Oke (2012). A spatial comparison is performed by producing agreement maps between classifications, agreement statistics and a confusion matrix to help qualify and quantify the misclassifications. The lczexplore package aims at comparing different LCZ classifications, but can be used to compare any pair of classifications on geographical units. This software is available as a free and open source R package.
A comparison of a raster stream result and a GIS vector stream approach was proposed by  Muhammad et al. (2022). This comparison relies on rasterising vector data. It uses several tools: QGIS, python scripts and SAGA GIS. As far as we know, the scripts and the automation of the method are not publicly available. The need for automation of map comparison (both raster and vector), and specific features (like standard colors or legends for LCZs, or sensitivity analysis according to confidence) justified the development of lczexplore.
Features General workflow and processing steps The lczexplore package is a set of R functions to import, visualize, group and compare LCZ classifications, even when they don't use the same spatial units to classify the area. These LCZ classifications can come from vector layers or raster layers (the latter will be vectorized by the dedicated import function) Main functions are presented in plain lines, the dashed boxes and arrows represent optional steps. These functions are presented in detail in the next section, and they allow the following steps of exploration: 1. The LCZ classifications (or any other qualitative variables) are imported from a file (GeoJSON or Shapefile format) 2. Each LCZ classification can then be visualized 3. Some LCZ levels may be grouped in broader categories 4. A pair of LCZ classifications (or qualitative variable maps) can then be compared: • a map of agreement/disagreement is produced, • the general agreement and a pseudo-kappa indicator of agreement are computed, • the summed surface of each LCZ type is computed for each classification, • a confusion matrix shows how the levels of one LCZ classification break up into the levels of the other 5.
Sensitivity analysis Some algorithms add a uniqueness value to the LCZ type it assigns to a spatial unit. It can measure if another LCZ level could have been assigned to this unit. Thus, it can be seen as a confidence value of the LCZ type. The lczexplore package allows a sensitivity analysis according to this level of confidence, in order to answer the question: does keeping only geometries with a higher confidence value make the degree of agreement between two classifications higher? This sensitivity analysis is performed considering all LCZ types and within each individual LCZ type. The agreement between classifications for the geometries with a confidence level higher than the threshold, and their numbers, are plotted in blue. The agreement and the numbers of geometries under the threshold are plotted in magenta.
Import and explore categorical variable (other than LCZ classifications) This package focuses on LCZ maps comparison, but more often than not, people working on LCZ maps also describe their area of interest with other categorical indicators. The workflow of comparison of LCZ maps can be used for any pair of maps of categorical variables, under certain limitations: • there must not be more than 36 levels for the categorical variable to explore, • the associated geometries must be (multi) polygons or easily converted to them, (typically, the package would not be suitable to compare road characterization), • the geometries must be topographically valid (this is also true for LCZ). The importQualVar function allows the import of such variables on (multi-) polygons maps. It outputs an sf object that can be fed to the other main functions of the package (showLCZ, compareLCZ, groupLCZ…).
Coding implementation lczexplore is an R package, all its specific functions are coded in R language. It relies on state-of-the art packages: • geographical computation requires the sf package for vector data and the terra package for raster data, • data management mainly requires the following packages: dplyr, tidyr, forcats, rlang and methods packages, • graphical production uses ggplot2, grDevices, cowplot and RColorBrewer, • tests need the tinytest package. Every step corresponds to an R function (see the workflow on figure  1  for the name of the main functions). Every function has an associated file for unitary testing.
Install and load The lczexplore package can be downloaded from https://github. com/orbisgis/lczexplore and installed with the command install_github: library(devtools) devtools::install_github("orbisgis/lczexplore", build_vignettes = TRUE) library(lczexplore) In order to discover the package, vignettes can be browsed via browseVignettes("lczexplore") or by calling them directly: vignette("lczexplore_en") vignette("lczexplore_raster_vector") vignette("lczexplore_alter").
Import the data This example uses two LCZ classifications of French Redon city, produced using a single LCZ classification method (the GeoClimate workflow), with two different input data: OpenStreetMap and the 2. 2 version of the French BDTopo. # Set the path to the example data folder created once you install the package dirPath<-paste0(system. file("extdata",package="lczexplore"),"/") dirPathOSM<-paste0(dirPath,"osm/2022/Redon") dirPathBDT<-paste0(dirPath,"bdtopo_2_2/Redon") # Import into an sf object the data produced with GeoClimate and # the OpenStreetMap data (city of Redon) redonOSM<-importLCZvect( dirPath=dirPathOSM, file="rsu_lcz. geojson", column = "LCZ_PRIMARY", geomID="ID_RSU", confid="LCZ_UNIQUENESS_VALUE") # Import into an sf object the data produced with GeoClimate and # the french BDTopo V2.
Compare the two LCZ classifications To compare the two loaded LCZ classifications, use the compareLCZ function. • the sf1 and column1 arguments allow to specify the sf dataset of the first map, and the column in which the LCZ levels are stored (same for sf2 and column2), • the wf1 and wf2 arguments take strings to specify the workflows used to produce the input data. • The location argument allows to name the area of interest covered by input datasets. The 3 previous arguments will be parsed in the legends of the graphics (except if title is specified) and the name of potential csv output files, • when exWrite=TRUE, the data computed to create the confusion matrix will be written in a csv file in the working directory, • if saveG=TRUE, the four plots created by the function will be written in a png file in the working directory.
Study the impact of confidence on agreement The confidSensib function makes a sensitivity analysis of the agreement according to the threshold of confidence above which one keeps the geometries of the compared maps. To perform this analysis, the confidence value and the identifier of each geometry must be fed to the comparison function. The $data slot is then passed to confidSensib. The saveG allows to set the path to a folder where resulting graphics will be saved. The values of confidence for which the agreement will be computed are determined by the range of confidence values present in the data and by the number of points, set by the nPoints argument. # Path to the folder where compareLCZ stored output data file # mainPath<-system. file("extdata", package = "lczexplore") # (not executed to avoid useless files in the package) # Specification of geomID and confid columns for both dataset, # as confidSensib needs them in the next step comparison<-compareLCZ( sf1=redonBDT,column1="LCZ_PRIMARY", wf1="BDTopo v2.
2", geomID1="ID_RSU", confid1="LCZ_UNIQUENESS_VALUE", sf2=redonOSM, column2="LCZ_PRIMARY", wf2="OpenStreetMap", geomID2="ID_RSU", confid2="LCZ_UNIQUENESS_VALUE",ref=1, repr="standard", exwrite=F, location="Redon") testSourceFact<-comparison$data # Sensitivityanalysis on this compareLCZ output sensitAnalysis<-confidSensib(inputDf=comparison$data, filePath="", nPoints=5, wf1="bdtopo_2_2", wf2="osm", geomID1="ID_RSU", column1="LCZ_PRIMARY", confid1="LCZ_UNIQUENESS_VALUE", geomID2="ID_RSU. 1",column2="LCZ_PRIMARY. 1", confid2="LCZ_UNIQUENESS_VALUE. 1", plot=TRUE, saveG="") # one would use saveG = mainPath to write the results in # the mainPath folder Figure  7  shows the resulting graphics for the agreement per LCZ type (types not present in the dataset are excluded). For each LCZ type, the x-axis shows the minimum confidence threshold.
The y-axis shows the agreement levels. Each cyan point shows the average agreement between classifications for spatial units where the minimum confidence value is greater than the x-axis value. As one wants to be sure the dropped units didn't have a greater agreement, they are showed as magenta triangles. For this particular example, it seems that for the "Open Low" LCZ type, the greater the confidence, the smaller the agreement! This unexpected result is explained by the fact that in our case, the confidence qualifies the method, not the data. In this example, in the OSM dataset which is compared, the building height are mostly estimated using a statistical method  (Bernard et al. , 2022)  as stated above.
An example of qualitative variables The function importQualVar allows you to import maps of qualitative variables on (multi)polygons. For instance, classifications of urban tissue called Urban Typology by Random Forest  (Bocher et al. , 2018)  are available as examples in this package. After the import, the usual functions can be used, as shown in the following code: utrfRedonBDT<-importQualVar(dirPath=paste0( system. file("extdata", package = "lczexplore"), "/bdtopo_2_2/Redon"), file="rsu_utrf_area. geojson", column="TYPO_MAJ") map3<-showLCZ(sf=utrfRedonBDT, column="TYPO_MAJ",repr="alter", title = " UTRF classification of the French city of Redon") utrfRedonOSM<-importQualVar( dirPath=paste0( system. file("extdata", package = "lczexplore"), "/osm/2022/Redon"), file="rsu_utrf_area. geojson", column="TYPO_MAJ", geomID="ID_RSU", confid="UNIQUENESS_VALUE") utrfComparison<-compareLCZ(sf1=utrfRedonBDT, column1="TYPO_MAJ",wf1=" UTRF BDTopo", sf2=utrfRedonOSM, column2="TYPO_MAJ", wf2 = " UTRF OpenStreetMap", location = " Redon",exwrite=FALSE,repr="alter") # Plot the confusion matrix of thes two classifications Licensing, availability and contribution lczexplore is licensed under the GNU Lesser General Public License (v3.
Data collection During the winter of 2021-2022, a list of keywords ("heat stress", "elevated temperature" and "heat" combined with "yield", "grain yield", and "productivity") were searched using different scienti c bibliographic tools, such as Google Scholar, Web of Science, PubMed and AGRICOLA, focused on the effects of heat stress on the yields of crop plants. In addition, lists of cited references in relevant reviews were surveyed (Prasad et al. , 2008; Suzuki et al. , 2014; Sehgal et al. , 2018; Cohen et al. , 2020b). Overall, we extracted and analyzed 186 different case studies taken from 67 different peer-reviewed publications covering various legumes (soybean, chickpea, pea, lentil, mungbean, peanut, common bean, fava bean, cowpea, kidney bean, lima bean and tepary bean) and the three major C3 cereals (barley, wheat and rice).
We decided to focus on the two key parameters in crop physiology: photosynthesis and grain yield. The data on grain yield response were collected from experiments in which the heat stress period took place during the reproductive stages. We compiled data from all publications into a table summarizing the crop family, plant species, experimental parameters (number of replicates, stress temperature and duration of heat stress), yield under control conditions, yield reduction, cumulative heat units (the product of temperature above optimal and duration of heat stress), and photosynthetic rate under stress and under control conditions (Table  S1 ). Experiments that used a short (12 h or less) heat treatment or used fewer than four replicates per treatment were omitted. Both crop yield and photosynthetic rates under stress are presented as percentages of the same parameter under control conditions.
The regression lines in legumes vs. cereals were compared using multiple regression (function lm, relative_yield_at_heat ~ crop_family * cumulative_heat_units or relative_yield_at_heat ~ crop_family * temp_above_optimal) and examining the interaction term. Plots were generated using tidyverse  22  and ggplot2  23. All R code used here is available at https://github. com/ellasiera/Crop_yield_under_stress. See g. S1 for the distribution of temperatures above optimal and stress duration in the collected data.
For that purpose, the rules must be extracted first from the existing planning documents. They can be verified then using, for example, spatial images. The numerous satellites observe our planet on a daily basis producing a lot of those images  13  , and thus there should be no problem in using this source of information. The main challenge of the problem is to develop an approach to automate processing of the information coming from the both. There is no typical solution for that up to now, and our project, Hérelles (https://anr. fr/Projet-ANR-20-CE23-0022) funded by the French national research agency ANR (Agence Nationale de la Recherche: https://anr. fr), aims to fulfill this shortcoming. The application domains of Hérelles are the study of land artificialization and natural risk management.
In order to extract rules from the PLU and PPRn documents in an automatic manner a machine learning method can be employed. A common approach for automatic rule extraction from texts consists of exploiting a supervised learning setting  15, 16. The data must be segmented (i. e. split into segments) and labeled, a classifier is trained then using labeled examples. There are two main challenges in this modeling: how to represent the data and how to actually label it. A segmentation can be performed on the level of words  17  , sentences  18, 19  and fragments  20, 21  (parts of text with any number of sentences). Fragment representation better suits our needs, because the rules of our interest can be longer than one sentence. To use this method we define a fragment as a part of a document separated by one or more empty lines.
Criteria Details Examples Thematic fit To the best of our knowledge, LUPAN is the first labeled corpus in French in open access on the topics of urban planning and natural risk management. It should facilitate development of methods for automated extraction of rules from regulatory documents in the French language. Moreover, annotated text data are much rarer in French than in English (on the date of submission, there are 55 data sets in the French language in the HuggingFace library  24  , one of the biggest collections of publicly available data for NLP, comparing to 525 data sets in English, and only 12 of those sets are multi-label comparing to 152 analogous sets in English), and existing data in the former are mostly related to the biomedical field  [25] [26] [27]  or to more traditional topics such as sentiment analysis  28, 29.
Text extraction. The PLU and PPRI documents originally come in PDF (Portable Document Format: https://www. adobe. com/acrobat/about-adobe-pdf. html). Our first step, therefore, was extraction of the text from the PDF files. It was implemented with the fitz module of the PyMuPDF library (https://github. com/pymupdf/ PyMuPDF) in Python. Each document separated into pages was converted into a plain text file using that library. The code is available in our code repository, which is present in the Code Availability section below. To construct our corpus we used the following sources of the PLU and PPRI documents: https://www. montpellier. fr/2300-reglement-du-plu-pieces-ecrites. htm (ZONE-A, N, AU0, 14AU, 5AU, 4AU1) as a source of PLU Montpellier, http://www. ville-grabels. fr/1637-environnnement.
Data Records The presented data that we constructed are stored at Hérelles Dataverse (https://doi. org/10. 57745/XIVJ65)  32. The corpus is presented by two files: Corpus_Extracted_Segments_Consolidated_Version. zip and Corpus_Manual_ Annotation_Consolidated_Version. zip. The first file includes labeled segments and the second -an annotated corpus, which the segments were extracted from. Corpus_Extracted_Segments_Consolidated_Version. zip contains 1934 labeled segments. Each file in the archive corresponds to the document using which the segments were constructed. The segments are stored as a plain text. In addition to the segments, we provide a manually annotated corpus itself. Corpus_Manual_ Annotation_Consolidated_Version. zip contains an arxiv with 9 text files corresponding to 9 documents summarized in Table  2.
These documents come from the PLU of Grabels, the PLU of Montpellier and the PPRI of Grabels. The name of the file consists of 2 parts: the type of the document (PLU or PPRn) and the name of the area (zone identifier or municipality name). The formats of the files are described in detail in Read_Me_Consolidated_Version. pdf, and the links to the original documents used for constructing the corpus are presented in Corpus_Expert_Links. tab. The both of these files are included at Hérelles Dataverse.
A transfer learning model allows pre-training a classifier on a larger corpus then a smaller corpus is used for fine-tuning the model. The most advanced approach in this category is the model of type BERT (Bidirectional Encoder Representations from Transformers)  35  , which is trained on a large corpus of webpages in English. For other languages there exist special extensions and CamemBERT 36 is among them for the French language. The principal difference with BERT is that CamemBERT was trained on a large corpus in French, also parameters of the model were optimized accordingly. Another alternative for the French language is FlauBERT 37 , which was trained on a very large and heterogeneous French corpus. As a consequence, FlauBERT better suits for some specific tasks while CamemBERT remains to be more general.
As for the classification model, we employed CamemBERT as the most advanced method for text classification. To perform evaluation of our experiments, we used stratified cross-validation implemented as follows. The data were split into 2 parts: 80% of segments were used for learning, and 20% were used for validation. The split was performed in such a way that the proportion of segments in each of the classes remains the same (Table  4 ). Quality measures. To assess the quality of our prediction we used Precision, Recall and F 1 score as widely used metrics in the NLP community. We computed these measures for each of 6 classes in our corpus. We defined precision for class i as the ratio of examples correctly classified as positives (belonging to class i) over all examples classified as positives:  i i i i = + where FN i -false negatives for class i (examples incorrectly classified as not belonging to class i).
We implemented CamemBERT in Python 3. 8 using the CamembertForSequenceClassification model from the HuggingFace library  24. We used AdamW 43 as the most common optimizer for this task with the parameters recommended in  35  : learning rate 2 × 10 -5 and ε = 10 × 10 -8. We also fixed the number of epochs to 10 and the batch size to 16. Finally, we used the scikit-learn library  44    The detailed performance w. r. t. the leaves of the hierarchy is presented in Table  6. As can be seen from the results, the Not pertinent class is the easiest one to predict, which is not surprising taking into account that it takes the biggest part of the corpus. Non-verifiable class is the next one in this comparison, which is not surprising taking into account that it is the second biggest class in the corpus.