dataesr/scientific-paragraphs-categorization huggingface dataset:
The values of the edit costs for each edit operation have a major impact on the computation of GED and its performance, including its stability  [17, 22]. Besides fixing costs provided a priori by domain experts for a given dataset, methods are proposed to optimize these costs, e. g. , by aligning metrics in the graph to target spaces  [23]. Analyzing the optimized edit costs helps further explore its relevance to the stability of GED heuristics. The remaining part of the paper is organized as follows: Section 2 introduces widely used GED heuristics paradigms. Section 3 defines a measure of the (in)stability of these heuristics, as well as two factors of critical influence. Then, Section 4 gives experiments and analyses. Finally, Section 5 concludes the work and open perspectives.
Experiments In this section, we conduct experimental analyses on the GED stability. First, the influence of the two factors introduced in Section 3. 2 is examined, and then the relevance of stability and prediction performance of GED heuristics is verified, taking advantage of a state-of-the-art edit costs learning strategy. Five well-known public datasets are applied in the experiments: Alkane and Acyclic are composed of acyclic molecules modeled respectively as unlabeled and vertex-labeled graphs, while MAO, Monoterpens, and MUTAG consist of cycles-included molecules represented as graphs labeled on both vertices and edges. The sizes of datasets vary from 68 to 286 (https://brunl01. users. greyc. fr/CHEMISTRY/, accessed on 9 September 2022). We exploit two multi-start heuristics, mbipartite and mIPFP, to evaluate the stability as described in Section 2.
Effects of the Two Factors Figure  2  shows the effect of the two factors, namely "# of solutions" and the ratio between vertex and edge edit costs R ec , on the relative error E r defined in  (5) , considering the mbipartite and mIPFP heuristics on five datasets. The Figure  2a ,c on the left side exhibit how E r drops with the increase of the "# of solutions" m. In most cases, E r drops rapidly when the solution number increases from 1 to around a certain number N, and reaches at a relatively small value; the tendency mitigates afterwards. N is around 20 for mbipartite and 10 for mIPFP. Take datasets Alkane and Acyclic for examples. When using mbipartite, E r 's on these two datasets drop respectively from 0. 23 to 0. 08, and from 0. 12 to 0. 03, as m increases from 1 to 100; when using mIPFP, E r 's drop respectively from 0.
08 to 5 × 10 -6 , and from 0. 03 to 7. 2 × 10 -7. This result indicates that an adequately large number of solutions is necessary, thus a trade-off decision between stability and time complexity needs to be made for different applications. The Figure  2b ,d on the right side reveal the relation between E r and the ratio R ec. The edge costs are set to 1 and the vertex costs to be the ratio value (for insertions, removals, and substitutions). The removal costs of vertices (resp. edges) are set to 0 if vertices (resp. edges) are not labeled. For both mbipartite and mIPFP, E r is relatively large when the ratio is smaller than 1, namely when edge costs are bigger than vertex costs, and drops with the increase of the ratio. We can observe that a larger ratio leads to higher stability. Take datasets Alkane and Acyclic for examples.
When using mbipartite, E r 's on these two datasets drop respectively from 0. 5 to 0. 16, and from 0. 34 to 0. 07, as R ec increases from 0. 1 to 10; when using mIPFP, E r 's drop respectively from 0. 03 to 5. 6 × 10 -3 , and from 0. 02 to 6. 9 × 10 -4. A possible cause of this phenomenon is that large edge costs amplify the arbitrariness of the edge edit operations. For graphs with n vertices, there are n 2 possible edges that can be inserted, removed, and substituted, which causes more uncertainty when constructing edit paths and computing their costs. Taking IPFP for instance, large edge costs lead to a big cost matrix Q in (4), implying the possibility of more variance on the value of the term gx Qx. Many edit costs given by domain experts are in accordance with this empirical rule, such as the ones in  [17].
2 and Figure  2  for more details). Therein, the colors represent datasets (i. e. , blue for Alkane and orange for Acyclic), and shapes represent different edit costs, with as traffic  [35]  c3 , infrared vision  [36]  c4 , sensors  [37]  c5 , and graphs  [38]  c6. Thus it is suitable for the current experiments. Experiments on Alkane and Acyclic show that optimized costs gain a significant improvement in accuracy compared to random or expert costs  [1]. Table  1  summarizes the optimized values of edit costs. We then examine the stability of the two heuristics when using these edit costs. Figure  3  demonstrates the relations between the relative errors E r and the ratios R ec between vertex and edge costs (See Section 3. 2 and Figure  2  for more details). Therein the colors represent datasets (i.
e. , blue for Alkane and orange for Acyclic), and shapes represent different edit costs, with ✖, , and respectively for the expert costs, the optimized costs using the Euclidean and Manhattan distances. When applying mIPFP, for the expert costs, R ec 's for the two datasets are both 1, and E r 's are 0. 02 for Alkane and 0. 013 for Acyclic; when using the Euclidean distance, R ec = 34. 21, E r = 0. 002 for Alkane, and R ec = 2. 6, E r = 0. 002 for Acyclic; when using the Manhattan distance, R ec = 3. 11, E r = 2. 33 × 10 -3 for Alkane, and R ec = 2. 55, E r = 2. 68 × 10 -3 for Acyclic. It can be observed that the optimized costs correspond to much higher ratios and an order of magnitude lower relative errors than the expert costs. Similar conclusions can be observed for mbipartite as well.
223 We then examine the stability of the two heuristics when using these edit costs. Figure  3  224 demonstrates the relations between the relative errors E r and the ratios R ec between vertex 225 and edge costs (See Section 3. 2 and Figure  2  for more details). Therein the colors represent 226 datasets (i. e. , blue for Alkane and orange for Acyclic), and shapes represent different edit 227 costs, with ✖, , and respectively for the expert costs, the optimized costs using the 228 Euclidean and Manhattan distances. When applying mIPFP, for the expert costs, R ec 's 229 for the two datasets are both 1, and E r 's are 0. 02 for Alkane and 0. 013 for Acyclic; when 230 using the Euclidean distance, R ec = 34. 21, E r = 0. 002 for Alkane, and R ec = 2. 6, E r = 0. 002 231 for Acyclic; when using the Manhattan distance, R ec = 3.
11, E r = 2. 33 × 10 -3 for Alkane, 232 and R ec = 2. 55, E r = 2. 68 × 10 -3 for Acyclic. It can be observed that the optimized costs 233 correspond to much higher ratios and an order of magnitude lower relative errors than 234 the expert costs. Similar conclusions can be observed for mbipartite as well. Thus, an 235 empirical conclusion can be derived: the obtained optimized edit costs correspond to higher 236 stability of GEDs, while obtaining a higher performance. respectively for the expert costs, the optimized costs using the Euclidean and Manhattan distances. When applying mIPFP, for the expert costs, R ec 's for the two datasets are both 1, and E r 's are 0. 02 for Alkane and 0. 013 for Acyclic; when using the Euclidean distance, R ec = 34. 21, E r = 0. 002 for Alkane, and R ec = 2.
Conclusions and Future Work In this paper, we conducted analyses of the GED heuristics' stability, which is the first time it is formally investigated in the literature. After defining an (in)stability measure, namely, the relative error, we show the strong connection of the stability with the number of random initial candidates of multi-start GED heuristics and the relation between vertex and edge edit costs. Experiments on five datasets and two GED heuristics indicate that the proper choice of these factors can reduce the relative error by more than an order of magnitude. A further investigation indicates higher stability of GED computation corresponds to the optimized edit costs and thus better prediction performance, where an edit cost learning algorithm is applied to optimize the performance and the k-nearest neighbor regression for prediction.
There are still several challenges to address in future work. First, examining other influential factors and conducting theoretical analyses can help deepen understanding of the stability of the heuristics. Second, it will be helpful to perform more thorough experiments, including other state-of-the-art GED heuristics on datasets from a wider range of fields and statistical properties. Third, higher stability comes with the cost of higher time complexity. Methods that can better balance the stability, time complexity, and prediction performance in practice need to be developed.
Funding: This work has been partially supported by the  University of Rouen Normandy ,  INSA Rouen Normandy , the  "Centre National de la Recherche Scientifique" (CNRS) , the  European Regional Development Fund (ERDF) ,  Labex SynOrg  ( ANR-11-LABX-0029 ),  Carnot Institut I2C , the graduate school for research  XL-Chem  ( ANR-18-EURE-0020 XL CHEM ), the  "Région Normandie", China Scholarship Council (CSC) , and the  French national research agency (ANR)  under the grant  APi  ( ANR-18-CE23-0014 ).
a European-wide dataset to uncover adaptive traits of Listeria monocytogenes to diverse ecological niches Benjamin Félix et al. #  Listeria monocytogenes (Lm) is a ubiquitous bacterium that causes listeriosis, a serious foodborne illness. In the nature-to-human transmission route, Lm can prosper in various ecological niches. Soil and decaying organic matter are its primary reservoirs. Certain clonal complexes (CCs) are over-represented in food production and represent a challenge to food safety. to gain new understanding of Lm adaptation mechanisms in food, the genetic background of strains found in animals and environment should be investigated in comparison to that of food strains. twenty-one partners, including food, environment, veterinary and public health laboratories, constructed a dataset of 1484 genomes originating from Lm strains collected in 19 European countries.
This work was made possible due to the LISTADAPT consortium which included (i) seven national reference laboratories (NRLs) for surveillance of Lm in food, animals and the environment (AT, CZ, DK, FR, IT, NO and SE) and (ii) three research laboratories at INRAE (the French National Research Institute for Agriculture, Food and Environment). Out of the seven NRLs, two are also national public health laboratories (AT and CZ) that are in charge of the surveillance of clinical strains isolated in outbreaks and sporadic cases. In addition, 14 institutes from 12 countries participated as external partners providing isolates. In this data descriptor, we present a dataset of 1484 high-quality draft genomes originating from Lm strains isolated in 19 European countries within the framework of the LISTADAPT project.
The constructed dataset cover a wide genetic diversity of Lm since it includes about 79 different CCs and singleton STs including the most prevalent CCs in Europe  15  and worldwide  13, 17. The strains were collected from natural environment (wild animals and natural environment), primary production (farm environment and farm animals with or without listeriosis symptoms) until FPE and food products. The constructed dataset provides a better understanding of the Lm transmission routes from the farm/natural environment to food and FPE and improves our understanding of its ecology. The dataset may also help to assess the importance of animal and food strains for human infection. Moreover, it can be used by the scientific community (i) to improve our understanding of the Lm population structure and the Lm evolutionary history, (ii) to facilitate the detection of the emerging Lm clones and (iii) to identify genetic traits related to the adaptation of Lm to particular ecological niches (ecophysiology).
Construction of the LIStaDapt dataset (n = 1484). In order to build a dataset of Lm draft genomes suitable for investigating the adaptive traits of Lm to diverse ecological niches, we gathered a curated collection of Lm draft genomes. Strains isolated over the period 2010-2020 were preferred, regardless of their origin of isolation. We considered two geographic levels, (i) the 27 EU countries including Norway and Switzerland, heterogenous in size, population, climate, ecology and economical activities and (ii) based on country borders four European regions roughly equal in terms of surface area without consideration for other criteria (South-West, Central-South, Eastern and Northern). We included strains that were distributed evenly among these four European regions. The strain were gathered from already available strain collections and extensive sampling campaigns (Fig.
1 ). The LISTADAPT dataset was divided into two main ecological compartments: (i) C1 compartment, which included strains from animals and the natural/farm environment (n = 756), and (ii) C2 compartment, which included strains from food (n = 728) (Table  1 ). Strains selected from the initial collection of the LISTADAPT consortium. At the beginning of the LISTADAPT project, the consortium had access to a collection of about 8000 food and animal Lm strains obtained from collaborative projects or national surveillance. Most of these strains were isolated from food, whereas the remainder were isolated from animals (C1 compartment: animal and environmental strains) with a substantial under-representation of certain animal species. This compartment mainly included strains isolated from animals showing listeriosis-related symptoms.
27  (Table  2 ) were isolated from city outskirts parks in Poland, distant from the city center. Similarly, the 47 strains from birds (mainly seagulls) (Hellström et al. )  10  were isolated from localities from on the outskirts of Helsinki, Finland (Table  2 ). Strains obtained from existing microbial collections (n = 648)To increase the size and representativeness of the Lm genome dataset the LISTADAPT consortium performed an extensive review of all recent collections of published and unpublished Lm strains and then contacted researchers in charge of these collections. Finally, 14 external partners, food and veterinary laboratories and research institutes, all dealing with Lm hazards in Europe, collaborated with the LISTADAPT consortium (Tables  2  and 3 ). The initial collection included more strains from animals with listeriosis-associated clinical symptoms than without symptoms.
In order to reduce the number of strains originating from animals with listeriosis while maintaining maximum diversity of the dataset, we adopted an original method to select the strains based on metadata (e. g. , type of sample, geographic location, time of isolation, molecular typing data such as PFGE profiles, animal species and geographic sampling location). This method relies on Gower's coefficient (GC), which is a dissimilarity measure: the "distance" between two units is the sum of all the variable-specific distances (associated with metadata categories). The GC metric enables the combination of numeric and categorical data and enables applying weights to each variable, effectively altering the importance of each metadata category (e. g. , geographical region as a more important category than year of isolation).
The three steps are: (i) calculating the dissimilarity matrix based on Gower's distance (ii) clustering the dissimilarity matrix with hierarchical clustering (agglomerative bottom-up approach of clustering) and (iii) assessing clusters with the "Silhouette" method. The silhouette plot displays a measure of how close each point in one cluster is to the points in the  neighboring clusters. An R script available at https://github. com/lguillier/LISTADAPT/tree/master/metada-ta2assocation was used to perform the selection of strains based on this method. This script takes as input a Comma Separated Values (CSV) file that includes strain ID and metadata information, then outputs a CSV file of selected strains. In the present study, we constructed a large dataset comprising 301 animal and environmental Lm strains from six European countries and published collections (Table  2 ), as well as 347 animal and environmental Lm strains from 12 European countries that were obtained from non-published collections (Table  3 ).
The food strains (C2 compartment) were classified according to the five main categories of risk food matrices for Lm defined by the European Food Safety Authority (EFSA)  31  : dairy products (n = 119), fish and fishery products (n = 165), meat products (n = 246), vegetables and fruits (n = 95), and composite dishes (food products combining several food categories) (n = 103). Six NRL project partners (AGES, ANSES, DTU, IZSAM, SLV and VRI) were instructed to target a maximum of 30 strains per food category from their strain collections, preferring strains isolated in the last Strains isolated from silage were considered as originating from the farm environment since silage mainly includes fermented forage crops collected directly from fields. 10 years. This time period was extended to the under-represented categories (vegetables and fruits); the final dataset included strains originating from the 2002-2020 period.
We excluded raw materials from the selection based on the assumption that they could be contaminated by strains originating from farms or animals. The 728 strains from C2 compartment were isolated along the food chain, from food processing plants to food retail in several EU countries (Table  1 ), detailed strain information were provided in Figshare File 1  26  Complete LIStaDapt dataset (n = 1484). The final LISTADAPT strain dataset that we constructed in collaboration with external partners was balanced with regard to the two main compartments: C1 (animals/ environment, n = 756) and C2 (food/FPE, n = 728) (Table  1 ). The geographic distribution covered 19 of the 27 EU countries plus Norway and Switzerland (Figs. 1  and 2 ). Although the C1 compartment (n = 756) covered a 41-year period (1978-2019), most of the strains (75%) were isolated since 2010.
Standard strain nomenclature. In order to facilitate data sharing between partners, we adopted a standard nomenclature for strain identification (ID). This nomenclature was used as metadata codification to allow for fast identification of the geographic region and detailed isolation source of the strains (e. g. , wild animal, food product or farm environment). In more detail, the LISTADAPT code has between 10 and 15 characters; the first two letters (level 1) correspond to the country code (ISO 3166-1-alpha-2 code), which is followed by a code detailing the origin of the strain and the sample type (level 2 to 4, depending on the nature of the source). Briefly, level 2 details the type of sample (e. g. , animal species, environment and food categories) and level 3 details the nature of the sample (e.
g. , type of animal sample, type of food and nature of environmental sample). The level 4 gives additional information about the sample (e. g. type of preparation for aliments and health status of the animals). The code ends with a sequential number for each country, generated when the strain was added to the collection. For example: the strain DE-RDE-CP-13 was isolated in Germany (DE) from a roe deer (RDE) as a clinical strain (CP) and it was the 13 th strain isolated from Germany included in the dataset. The Supplementary Table  S2  provides a detailed overview of the employed LISTADAPT codification.
Whole Genome Sequencing and genomes data analysis. The next generation sequencing (NGS) paired-reads (2 × 150 bp) were generated during the project with Illumina platforms. Four LISTADAPT partners (AGES, IZSAM, ANSES and DTU) mainly performed the sequencing. Figshare File 1  26  lists the sequencing technology and the center which performed the library preparation and produced the sequences. The genomes were all de novo assembled and annotated with a harmonized in-house workflow named ARTwork (Assembly of reads and typing workflow)  32  used in the ANSES Laboratory for Food Safety. In addition to de novo assembly, the ARTwork pipeline also performs genome annotation using Prokka  33. This whole genome sequencing (WGS) workflow has been described in detail in previous publications  32, [34] [35] [36]  , including the integrated bioinformatics tools and their corresponding versions, enabling repeatability and comparability of the results 2 (Table  5 ).
Assembled genome files were made publicly available in FASTA format through Figshare  37. Quality control of WGS data. Poor-quality reads or assemblies as well as contaminations can significantly affect gene prediction and cluster analyses  38, 39. Different WGS metrics and quality criteria were thus employed in the ARTwork pipeline to ensure high-quality WGS data. Reads with an estimated depth of coverage < 30 × (as estimated by BBmap 40 ) as well as contigs and scaffolds with a length of < 200 bp were excluded (n = 22). Draft genomes with a total length outside the range of 2. 7-3. 3 Mb and with a total number of scaffolds > 200 (n = 46) were also excluded. In addition, inter-and intra-species contamination of reads was determined using the recently developed ConFindr software (v0. 5. 1)  41.
Since recently demonstrated, inter-and-intra species contamination of 10 single nucleotide variants (SNVs) assessed by ConFindr in the conserved core genes does not significantly impact cluster analysis  39. We decided to exclude all genomes presenting SNVs lower than this cut-off (n = 12) as well as various read-or assembly-related errors (n = 34). The employed WGS metrics and quality criteria of the complete LISTADAPT genome dataset are reported in Figshare File 1  26. In total, 114 sequenced genomes were of unsatisfactory quality after quality control and were thus excluded from the final dataset. After quality control of NGS and WGS data, the final LISTADAPT dataset included 1484 genomes. Metadata and WGS data sharing. All metadata and WGS data collected herein were centralized and processed with standardized criteria for common nomenclature and NGS/WGS quality control before sharing between project partners.
Reads normalized to 100 × coverage, draft assemblies (contigs and scaffolds) and annotated genomes (Genome Feature Format, GFF, and Genbank format, GBK) were also centralized at the MongoDB database located at ANSES (Maisons-Alfort Laboratory for Food Safety) providing quickly available, ready-to-use data. Raw (non-normalized) reads for all the Lm strains sequenced in the LISTADAPT collection (n = 1484) were submitted to the NCBI Sequence Read Archive (SRA) for sharing with the LISTADAPT project's partners. Raw (non-normalized) reads for 67 Lm food strains obtained from previous publications  19, 42  were submitted to the NCBI Sequence Read Archive (SRA) database and were linked to their existing accession numbers in Figshare File 1  26. Interactive access to strain metadata and MLST types is available through Microreact 44 , a recently developed online tool for visualizing and sharing spacio-temporal and genetic distributions of strains (Fig.
2 , accession link: https://microreact. org/project/8YtGBqEqhosJtysXTVY79M-figure-2-distribution-of-the-whole-listadaptdataset-n1484-by-geographic-region-time-and-genetic-diversity). The dataset interactive map was generated using either the exact GPS coordinate, regional GPS coordinate or national GPS coordinate according to the level of details available for each strain. An annual timescale was used. The core genome MLST (Moura et al. )  tree was generated from the draft genome assemblies using pairwise categorical difference and single linkage method in BioNumerics. The tree revealed three main clades corresponding to Lm phylogenetic lineages. Each clade included several clusters corresponding to MLST types (CC and singleton ST). Circles in shade of blue show food product isolates (clear blue: fish product, greeblue: dairy products, blue: composite dishes, deep blue: meat products).