dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements S. W. acknowledges funding from the  CY Initiative of Excellence (TALENT). H. S. acknowledges funding from the  European Research Council (ERC , Grant  101020872 ) and the  Royal Society  ( RP∖R1∖201082 ). S. W. and S. R. acknowledge funding from the  EUTOPIA Young Leaders Academy. D. F. acknowledges partial funding from the  National Recovery and Resilience Plan  ( NRRP ),  Mission 04 Component 2, Investment 1. 5 -NextGenera-tionEU ,  Call  for tender n. 3277 dated  30/12/2021 , Award Number:  0001052  dated  23/06/2022. X. S. and S. L. thank the  IQST at the University of Stuttgart  for funding through the  Carl Zeiss Foundation , and this work was done in the framework of the newly established DFG graduate training group  GRK2948  on mixed ionic-electronic transport. This work was also partially supported by  Basic Science Research Program  through the  National Research Foundation of Korea (NRF)  funded by the  Ministry of Education  ( 2022R1A6A3A03072886 ).
Next generation sequencing, assembly, datamining and annotation The SMNH 209003 specimen was sent to the Beijing Genomics Institute sin Shenzhen, China. The institute took care of the DNA extraction and performed sequencing on a DNBseq platform, from which ca. 40 million clean 150 bp paired-end reads were obtained. Reads were deposited on Sequence Read Archive (SRA) and are available as part of the BioProject PRJNA947900 (https://www. ncbi. nlm. nih. gov/sra/ PRJNA947900). Assemblies were performed with SPAdes 3. 15. 5  [6]  with two different k-mer of 85 and 125. Contigs of interest were extracted by data mining using blastn commandline  [9] , using as references the sequences indicated in Table  1. After circularisation and trimming, the mitogenome was annotated with the help of MITOS  [8]  and manually curated.
Phylogeny Two different phylogenies were performed. The first one was a multigene phylogeny performed on concatenated amino acid sequences of the mitochondrial proteins from the available mitogenomes of Capsalidae. The second was based on the dataset of partial 28S gene sequences from Perkins et al. [48]  and Mollaret et al. [45]. For the multigene phylogeny, protein sequences were extracted and aligned separately using  MAFFT 7 [36] , trimmed using trimAl with the -automated1 option  [16] , and alignments were then concatenated using Phyutility 2. 7. 1  [53]. For the 28S inferred phylogeny, partial genes were aligned with the complete gene from T. integrum SMNH 209003 using the Clustal function of MEGA X  [38]  and trimmed manually at the endings. Evolutionary models were chosen according to ModelTest-NG v0.
The mitogenome of Tristoma integrum A 14 003 bp long contig with a coverage of 64. 7Â was retrieved after assembly (k-mer 125). Its AT-rich extremities showed redundancies, but also contained repeated sequences. It was trimmed to a circular contig of 13 968 bp, but due to the aforementioned presence of repetitions, this size should be taken with care. The mitogenome (GenBank Accession Number OQ355698) contains 12 protein-coding genes, 2 rRNA and 22 tRNA (Fig. 5 ). It was not possible to find a stop codon for the cox1 gene, which would overlap tRNA-Thr by 9 bp if terminated by a natural stop and showed no obvious clue of a premature termination by A residues to the mRNA. The cox2 gene is terminated by the presence of tRNA-Glu and the addition of A residues to the mRNA.
However, possibly for this reason, the annotation of the cox1 gene of C. cristatus was noted as unverified in GenBank. The overlap between ND4L and ND4 seems to be a constant feature. Neobenedenia melleni (MacCallum, 1927) Yamaguti, 1963, which appears slightly outside the cluster mentioned above but clearly distinct from Benedenia spp. , also displays this 28 bp ND4L/ND4 overlap  [65]. Conversely, neither Benedenia hoshinai Ogawa, 1984  [35]  nor any of the specimens of Benedenia seriolae  (Yamaguti, 1934)  Meserve, 1938  [37, 48]  harbour any such overlap. However, there is an intergenic sequence of ca. 118 bp between ND4L and ND4 in specimens of B. seriolae and this intergenic sequence is 282 bp in length in B. hoshinai.
The cluster of ribosomal RNA genes of Tristoma integrum and 28S inferred phylogeny A 6 273 bp long contig containing the cluster of rRNA genes was found with a high coverage of 805. 91x. After verification on Rfam and trimming, the complete cluster was 5 839 bp long (GenBank: OQ349751), distributed as 1 985 bp (18S), 462 bp (ITS1), 153 bp (5. 8S), 332 bp (ITS2), and 2 907 bp (28S). The 28S gene was compared with the partial 28S genes already available in GenBank for T. integrum. Our sequence of Tristoma integrum was found to be 99. 02% identical with FJ972015 (409 bp from X. gladius collected off Italy  [48] ) and only 96. 66% with AF131715 (329 bp from X. gladius collected off France  [45] ). However, in the case of AF131715, most differences were found in the 3 0 extremity of the sequence and were not shared with FJ972015, and are likely to be errors from early (2000) manual Sanger sequencing.
Other markers available for members of the genus Tristoma The complete histone 3 and elongation factor 1 genes were also obtained from T. integrum SMNH 209003, based on the assembly made with k-mer 85. The intronless histone 3 gene consisted of a 2 695 bp contig with a coverage of 70. 4x. The gene itself, as deposited in GenBank (OQ354712), is 411 bp long. A megablast query on the whole gene returned as best result a partial 258 bp fragment obtained from Pseudonitzschia uku  Yamaguti, 1965 (FJ972129). Manual alignment with MEGA X of the complete gene with the partial 148 bp fragment obtained by Perkins et al. [48]  from T. integrum (FJ972131) also displayed 100% identity. The EF1a gene was found inside a 1 693 bp contig (GenBank: OQ349179) with a coverage of 4. 8x. The gene itself was 1 476 bp long, and contained 2 group I introns (39 bp and 72 bp), leading to a 1362 bp complete coding sequence.
Host DNA and identification From the pool of contigs obtained after assembly with k-mer 125, it was possible to retrieve an 11 425 bp contig with a coverage of 29. 35x, which contained the complete cluster of rRNA of the host, X. gladius. After verification with Rfam and trimming, the complete cluster was 6199 bp long (GenBank: OQ349752), distributed as 1 838 bp (18S), 453 bp (ITS1), 154 bp (5. 8S), 490 bp (ITS2), and 3264 bp (28S). A Megablast query returned as the best result a 3947 bp fragment attributed to the predicted 28S gene of X. gladius (GenBank: XR_005706970), with 99. 69% identity. Alignment with the 656 bp partial 28S gene of X. gladius used for datamining showed 100% identity between both. Using the pool of contigs obtained with assembly k-mer parameter of 125, it was hardly possible to find traces of the  host's mitochondrial genome.
(2009)   [48]  on a smaller taxonomical sampling, who concluded that they could only retrieve monophyly for the Capsalinae. We note that our limited results based on mitogenome proteins also found the Capsalinae monophyletic. The Capsalinae are all parasites of gamefish  [18, 48]  and it is likely that they have evolved within this group of pelagic, high-speed animals. France. Chahinez Bouguerche was supported by the Swedish Taxonomy Initiative, Artdatabanken, Swedish University of Agricultural Sciences within the scope of the project (dha 2019. 4. 3-48). For the research concerning old documents on Tristoma mentioned in the Appendix, we thank Wolfgang Sterrer, Pedro Frade and Helmut Sattmann. For clarification of the copyright and authorisation to reproduce the Figure from the Austrian National Library (Österreichische Nationalbibliothek), we thank Marlies Dornig.
Background DNA methylation is a process by which methyl groups are added to the DNA molecule. Methylation of promoters in uence gene expression, as the addition of a methyl group can prevent the binding of certain transcription factors and repress the transcription of the associated gene. Bioinformatics pipelines are a series of computational steps or work ows designed to analyse biological data such as sequencing data. Typically several steps, such as data preprocessing, quality control, alignment, and downstream analysis are involved. These pipelines can be tailored to a speci c research question and can be customized with various software and tools. GENE-SWitCH is an EU H2020 project with the aim of providing the functional genome annotation in pig and chicken, with many different assays speci cally DNA methylation.
Results We present a pipeline for processing bisul te sequencing data, which uses nf-core methylseq as a foundation. This extension pipeline includes useful steps such as analysing a bam le, methylation calling, methylation visualisation on a genome-wide level, and methylation statistics. Conclusion: Our pipeline is useful for the analysis of bisul te sequencing data, ensuring reproducible results, and stable tool versions. It is easy to use and implement for any given data set and we executed it for analysis of > 80 RRBS and WGBS data sets.
RRBS provides insight into a subset of the genome especially areas of the genome with a high CpG content (promotor and other regulatory regions). Costs for RRBS sequencing are lower in comparison with WGBS, which is a major advantage of using this method over full genome bisul de sequencing  [4]. Given the strong association with regulation of gene expression, DNA methylation has become a research topic of interest and many datasets are produced  [1]. Hence, one of the important aspects of this research is the reproducibility of results. Work ow management systems provide an important resource that contributes to reproducibility of results. Tools such as Next ow  [5]  and Snakemake[6] contribute to data and code management  [7]  together with container platforms such as Singularity and Docker  [8, 9].
The GENE-SWitCH project is an EU H2020 project, with the aim of obtaining the functional genome of two monogastric species (pig and chicken) during three developmental stages in 7 different tissues representative of germ layers. Different assays are used to investigate the functional genome within the project (including WGBS and RRBS datasets)  [13]. Determination of the changes in gene expression, epigenetic states and activity of functional regulatory elements, provide insight into development and the underlying mechanisms regulating the biological process of development. The development and identi cation of suitable pipelines for the analysis of the various data types produced within this research is a strategy implemented within the project to ensure reproducible results. In the current study we aimed to compile the various steps for methylation data processing into an easy to use bioinformatics pipeline to ensure reproducibility of results.
Methylation calling & statistics Methylation calling is performed using Bismark to generate a comprehensive methylation report, which is necessary for downstream analysis and MethylDackel  [20]  which is only used in conjunction with the BWA-meth aligner. In addition to the above processes provided by the original nf-core pipeline, our pipeline includes CGmaptools and MethylKit. CGmaptools  [21]  is used as an additional tool for methylation calling, providing CGmap and ATCGmap output les which include comprehensive information (e. g. non-cytosine context) regarding the methylome provided for downstream analysis. CGmaptools provides insight into genome-wide and chromosomal methylation levels, and coverage of the methylome can be investigated in detail. Additional descriptive statistics on DNA methylation pro les, such as frequency of methylation per CpG and mean CpG coverage are visualized using the MethylKit package  [22].
Performance Performance reports are provided from the pipeline which displays CPU usage, job run time, and memory usage for each individual process in the pipeline. We observed a total of 437. 7 CPU hours for completion of the pipeline. Alignment is the single process which can be done using multi-threading (we used 8 CPUs) and uses the most memory (> 30G) and takes most runtime. Trimming and methylation calling processes use 4 CPU's (~ 4) and indexing as well as Qualimap needs about 20-25G of memory for a single WGBS dataset. This is as expected when evaluating the performance of these tools and processes separately. Run time, CPU and memory use vary depending on the type of data (WGBS vs. RRBS).
Conclusion We present a comprehensive pipeline for the analysis of bisul te sequencing data. We benchmarked the pipeline using a WGBS dataset of pig IPECJ2 cell line. In addition, the pipeline is extensively tested on a variety of datasets from the GENE-SWitCH consortium (7 different tissues in 2 vertebrate species, chicken and pig), about 80 RRBS and WGBS. The processed data from the pipeline can easily be imported into tools for visualization of methylation levels across genomic features. Additionally, this pipeline is currently in DSL1 format, however a newer version of DSL (DSL2) was released. There will be updates to the code in future to accommodate with the continuous release of various tools and programming languages. We believe this pipeline will be of great value for scientists working with bisul te sequencing data as it is easy to use and implement for any given dataset.
Acknowledgements We would like to thank other members of the GENE-SWitCH data analysis group. This work is supported by the  GENE-SWitCH programme  which has received funding from the  European Union 's  Horizon 2020 Research and Innovation Programme  under the grant agreement n°  817998. Funding  GENE-SWitCH  has received funding from the  European Union 's  Horizon 2020 Research and Innovation Programme  under the grant agreement n°  817998.
, 2018). Integrating perspectives, approaches, data, and knowledge from diverse fields poses many challenges. Socioeconomic factors act at different spatial or temporal scales from natural systems  (Bergsten et al. , 2014). For example, ecological regime shifts often proceed quickly and are detected too late to inform management intervention  (Biggs et al. , 2009). Such scale mismatches between ecological and human systems can cause decreases in resilience in socioecological systems, mismanagement of natural resources, and declines in human well-being  (Cumming et al. , 2006). Additionally, varied data processing and analysis practices among disciplines challenge effective integration of datasets for systemic understanding. Interdisciplinary data management often requires special consideration for cultural and traditional knowledge and socioeconomic data, underscoring the need for privacy policies and recognition that standardization is not always possible (or desirable) across disciplines.
Open science practices that support replication and synthesis (e. g. , open science research workflows, freely available datasets and code) are critical for progress but lack support (NASEM, 2021). In addition, shifting emphasis from novelty and speed in funding and publishing toward, for example, distributed experimental networks would facilitate collection of data well suited to testing generalities. Developing incentives and platforms for publishing null or nonsignificant results would ensure that findings from all studies are discoverable, regardless of their outcome. Cross-sectoral and cross-disciplinary working groups that facilitate comparison across ecosystems and integrate empirical with theoretical approaches are one possible way to encourage the broad thinking that would enable progress.
Expanding participation in synthesis science Synthesis science is most powerful when it integrates datasets, perspectives, and insights across regions and ecosystems. By increasing diversity of participants and fostering inclusive research environments, we can better identify and answer research questions that inform just and equitable solutions to pressing challenges. Without prioritizing diversity and inclusion, syntheses risk being biased and less broadly applicable  (Hofstra et al. , 2020) , which may lead to unjust and inequitable outcomes. Although the principles of diversity and inclusion are relevant to any scientific endeavor, they are particularly salient for synthesis given its integrative nature. The regional to global scale of much of synthesis science emphasizes the value of including people, ideas, and data from multiple cultures, geographies, and languages.
Expanding the data and knowledge foundation The quantity of data for use in synthesis science is growing  (Farley et al. , 2018). Efforts are being made to standardize and synthesize data across multiple existing databases (e. g. ,  Jeliazkov et al. , 2020)  and harmonize data across scientific networks (e. g. ,  O'Brien et al. , 2021). These efforts enable research on ecological questions that could not be addressed by any one database  (Bates et al. , 2021). However, dedicated funding to support data curation, management, and archiving is needed to advance existing and future efforts. Careful consideration of the ethics of assembling diverse datasets must be part of any equitable data synthesis future. Some individuals, communities, and institutions may not share their data publicly for privacy, ethical, or safety concerns, or are unable due to legal or policy constraints.
Some researchers may be disadvantaged by current open science norms if, by making their data freely available, they effectively cede control of their intellectual property to others with greater resources who can more rapidly use it. This "information drain" from under-resourced institutions, researchers, and countries risks exacerbating existing inequalities in academic output and rewards. Even if there are barriers to providing equal participation opportunities, it is crucial to follow just, equitable, and inclusive practices in authorship and acknowledgment of the diverse people who generate scientific data and improve standards going forward  (Armenteras, 2021). Many potential synthesis datasets are inaccessible or of limited value due to poor-quality metadata  (Quarati & Raffaghelli, 2020).
Data may be difficult to find or access because they are published in a language that is not native to the investigator, not digitized, or housed in databases with barriers to access  (Haddaway & Bayliss, 2015). Alternative forms of data, including qualitative datasets (e. g. , traditional knowledge, photographs), tend not to be included in public databases  (Moon et al. , 2016; Young et al. , 2018). Limitations in using these forms of information can hinder our ability to identify ecological patterns and processes  (Konno et al. , 2020). Tools that aid scientists in accessing diverse data sources, such as machine learning techniques that help identify relevant data sources across many languages  (Han et al. , 2020) , are key to addressing these hurdles. Ultimately, increased inclusion in synthesis efforts through global collaboration across diverse groups of people is one of the best ways to move forward.
Greater diversity within teams of scientists and practitioners would provide perspectives and contributions that would better frame synthesis-based questions. Such diversity, along with open science practices and technology that foster sharing of data and ideas, offers great potential for the future of synthesis in ecology and environmental science. Achieving greater inclusion goes far beyond inviting more diverse participants to discussions; it requires fundamental changes in the structure of science education and funding to achieve broad participation and leadership, and acknowledgment of those who generate data. Greater inclusion also requires mechanisms ensuring datasets follow the findability, accessibility, interoperability, reusability (FAIR) principles  (Wilkinson et al. , 2016) , efforts to promote shared open access data repositories and tools, and the platforms and skills needed for ongoing iterative interactions and data integration among researchers  (Djenontin & Meadow, 2018; Keeler et al.
Purpose Incisional hernias are common after laparotomies. The aims of this study were to assess the rate of incisional hernia repair after abdominal surgery, recurrence rate, hospital costs, and risk factors, in France. Methods This national, retrospective, longitudinal, observational study was based on the exhaustive hospital discharge database (PMSI). All adult patients (≥ 18 years old) hospitalised for an abdominal surgical procedure between 01-01-2013 and 31-12-2014 and hospitalised for incisional hernia repair within five years were included. Descriptive analyses and cost analyses from the National Health Insurance (NHI) viewpoint (hospital care for the hernia repair) were performed. To identify risk factors for hernia repair a multivariable Cox model and a machine learning analysis were performed.
Previous studies have identified risk factors in small populations in other countries  [7, 9, [14] [15] [16]  and in France Hernia (2023) 27:861-871 1 3  [1]. Identifying risk factors of incisional hernia repair in France and at national level with up to date data could enable the selection of higher-risk patients indicated for specific abdominal prophylactic surgical procedures or for enhanced follow-up to ultimately decrease the burden of incisional hernia repair. Likewise, the economic burden of incisional hernia has been examined in other populations  [8, 11, 12]  but not at national level in France. This national study examined incisional hernia repair (i. e. , any patient requiring a surgical procedure at the hospital) within 5 years of abdominal surgery in France. Our aims were to assess the public health burden of incisional hernia repair (incidence, recurrence, and hospital-related costs) and to identify the risk factors of incisional hernia repair.
Study design and data source A national, retrospective, longitudinal, observational study including all French hospitals was carried out. The study was based on the exhaustive hospital discharge database (PMSI, Programme de Médicalisation des Systèmes d'Information) of the French National Health Insurance (NHI) database (SNDS, Système National des Données de Santé)  [17]. The PMSI database covers all stays in medical, surgical, or obstetric facilities in all public and private hospitals. It includes all medical and surgical procedures undertaken during inpatient stays or outpatient visits. All procedures are coded with the Common Classification of Medical Procedures (CCAM) -the French equivalent of the American Current Procedural Terminology (CPT). In addition, the reasons for hospitalisation and the patient's medical history are documented using one or more ICD-10 codes (as principal diagnosis, PD, related diagnosis, RD, and significant associated diagnosis, SAD).
The index date was defined as the date of the first abdominal surgery during the inclusion period. A historical period of 2 years prior to the index date was considered to capture the patient's medical history. All patients were followed for 5 years from the index surgery, until the end of the study (31-12-2019), or death, whichever occurred first. As the PMSI database exhaustively reports all hospital stays in France, all patients in the PMSI meeting the selection criteria were included and no computation of the study size was performed.
Cost analyses The costs are presented from the point of view of the French NHI and were based on national costs applicable from 2013 to 2019. Costs are in Euros of the corresponding year. The official flat fee of the hospital stay was applied according to the DRG (Diagnosis Related Group or GHM, Groupe Homogène de Malades) code attributed in the PMSI database. These standard tariffs include medical and related procedures, nursing care, treatments (except specific expensive drugs and medical devices), hospitality, and related investment costs for hospitalised patients. Additional costs per day of hospitalisation in an intensive care unit were added where appropriate. For private hospitals, where physicians are paid on a fee-for-service basis, physician fees were identified from the French observatory of real-world spending on healthcare (ENCC, Etude Nationale des Coûts à méthodologie Commune)  [18]  and added to the private DRG tariff.