dataesr/scientific-paragraphs-categorization huggingface dataset:
Behavioral Measurements To estimate the locomotion activities (speed and travelling distances (horizontal and vertical)), a 3300SL GPS collar (Lotek Wireless, Newmarket, ON, Canada) was placed on the neck of each experimental goat. Collars were programmed to acquire a GPS fix every 5 min (Figure  2 ). GPS collar data were analyzed using GPS software (Model 3000 Host, Lotek Wireless, Newmarket, ON, Canada). The obtained coordinate system (UTM WGS84) was converted to the Moroccan Transverse Mercator using ArcMap 10. x (ESRI, Redlands, CA, USA). The data management tools in ArcMap were used to calculate the coordinates (x, y) in meters for each fixed record. The horizontal (D H ) and vertical (D V ) distances traveled were also estimated. D H was calculated using Euclidean geometry between two consecutive pairs of fixed locations A (x 1 , y 1 ) and B (x 2 , y 2 ), with D H = (x 2x 1 ) 2 + (y 2y 1 ) 2.
D V was calculated from the difference in altitude between successive pairs of altitudinal positions (z 1 , z 2 ). The speed of goats during grazing was calculated by dividing the total traveled distance D T (D T = (D H ) 2 + (D V ) 2 ) by the time interval that elapsed between two consecutive positions. To estimate the number of steps and the time spent lying and standing, a leg sensor (IceTag, IceRobotics Ltd. , Scotland, UK) was placed on the rear left leg of each experimental goat (Figure  2 ). IceTag data were analyzed using IceManager software (IceRobotics Ltd. , Scotland, UK). Standing results from the IceTags (tri-axial accelerometers) included both grazing and non-grazing times. The lying position (when the sensor was horizontal) was a unique activity identified without grazing. The calibration and classification tree analyses were performed to predict the grazing activities of each experimental dairy goat (grazing, lying, resting while standing and walking)  [28, 29].
Therefore, IceTag data were averaged to the same intervals as the GPS collars. To estimate the number of steps and the time spent lying and standing, a leg sensor (IceTag, IceRobotics Ltd. , Scotland, UK) was placed on the rear left leg of each experimental goat (Figure  2 ). IceTag data were analyzed using IceManager software (IceRobotics Ltd. , Scotland, UK). Standing results from the IceTags (tri-axial accelerometers) included both grazing and non-grazing times. The lying position (when the sensor was horizontal) was a unique activity identified without grazing. The calibration and classification tree analyses were performed to predict the grazing activities of each experimental dairy goat (grazing, lying, resting while standing and walking)  [28, 29]. Therefore, IceTag data were averaged to the same intervals as the GPS collars.
A trial was conducted in order to use the data from the GPS collars and leg sensors to estimate times spent grazing/eating, as well as other grazing activities. The calibration study involved the visual observation of eight goats equipped with GPS collars and IceTags over a 3 day period. The observed grazing activities were timed manually, as precisely as possible, to serve in the validation of GPS collar and leg sensor data and the model construction  [30]. Classification and regression tree (CART) analysis was employed to predict the behavior of the experimental goats using the motion index (a proprietary metric for the overall leg activity as measured in three dimensions), the time spent standing and lying, the number of steps acquired from the leg sensors and fixed location activities (x-activity, yactivity), the horizontal distance traveled and the time spent with heads down detected from the GPS collars.
Browsed Diet Quality The temporal variations in the browsed diet quality were determined based on the available data for the chemical composition and digestibility of the forage ingested  [11]  and the diet composition of the experimental goats  [3]. These studies were conducted in the same studied woodland during the same periods. For the forage quality, the analyses were performed on three independent samples of the hand-plucked forage of each plant species consumed by the experimental goats during each grazing season across the two consecutive years. The investigated parameters A trial was conducted in order to use the data from the GPS collars and leg sensors to estimate times spent grazing/eating, as well as other grazing activities. The calibration study involved the visual observation of eight goats equipped with GPS collars and IceTags over a 3 day period.
Browsed Diet Quality The temporal variations in the browsed diet quality were determined based on the available data for the chemical composition and digestibility of the forage ingested  [11]  and the diet composition of the experimental goats  [3]. These studies were conducted in the same studied woodland during the same periods. For the forage quality, the analyses were performed on three independent samples of the hand-plucked forage of each plant species consumed by the experimental goats during each grazing season across the two consecutive years. The investigated parameters for the diet quality were the dry matter (DM), organic matter (OM), crude protein (CP), neutral detergent fiber (NDF), acid detergent fiber (ADF), acid detergent lignin (ADL), condensed tannins (CT) and organic matter digestibility (OMD).
Data Analysis Data were submitted to analysis using SAS software (SAS version 9. x, New York, NY, USA). Before analysis, data expressed in percentages were arcsine-square root-transformed to normalize the distribution  [34]. Browsed-diet-quality data were analyzed according to the GLM procedure. The daily DMI and grazing activities were evaluated according to the PROC MIXED procedure. Individual goats were considered as random factors to prevent this variance from being incorporated into the error term of the analysis. The models contained two fixed variables: the seasons (i. e. , spring, summer and autumn) and year (i. e. , dry (2016) and wet (2017)) and their interaction. Pair-wise comparisons were performed using the Tukey test. All tests were considered statistically significant at p < 0.
Nowadays, it is important to make the results of scientific research accessible in a simple and understandable way according to the Open Science policy. This movement uses tools to enhance findability and interoperability of data. This paper describes the transformation of the meat dictionary published by the French Meat Academy as a book into a machine actionable and freely accessible terminological resource based on the SKOS standard format. This thesaurus contains 1567 concepts describing the meat production chain. This work was carried out by experts in semantic web, meat biology and meat vocabulary. This thesaurus can be used to index articles, journals and datasets, thus facilitating consultation; it can also be used to facilitate interoperability of the indexed datasets and provide contextual definitions for building ontologies, i.
By choosing to represent it using the standard SKOS (Simple Knowledge Organisation System) schema, and to associate rich metadata and identifiers with it, we also make it usable by various computer applications carrying out the indexing of documents, datasets, content annotation, or the display of definitions in context. In this article, we present our approach to the construction and online publication of the Meat Thesaurus through collaborative work between specialists in knowledge and data engineering on one hand and meat science and technology on the other hand.
Choice of the SKOS model The creation of the Meat Thesaurus is based on the terms of the Meat Dictionary by the French Meat Academy. The source data file provided was made of two Word documents, including one for French, with 1357 entries and inserts. The inserts were not kept in the resulting thesaurus. The content for English was integrated in a second phase of the project, after the transformation and organisation of the data for French, as the English version was not available at the beginning. Similar though simpler procedures were used for English. In this paper, we will focus on the processing for data in French. The structure for describing an entry in the source file for French is pretty regular (Fig. 1 ), which allows some automation of the process, although this is made difficult by the presence of a large number of optional elements.
Conversion of the dictionary from its textual form into a structured and formalized resource The work for the conversion of the dictionary into the thesaurus includes the recovery and formalisation of data (the two first elements in Fig. 6 ) which precedes the structuring phase (the third element). Much of the retrieval and transformation work was done semiautomatically and required the following: -An expertise in using regular expressions that we applied in a simple text editor (Notepad++) in order to isolate the dictionary entries from each other (1 per line) and to organise their respective information in a tabular format; -OpenRefine, a free software for data cleaning and transformation. It allowed us to identify and massively correct some encoding errors, and to refine the information segmentation of each entry.
A digital tool with the possibility of doing linkages will therefore be of great benefit to integrate trade-based or cut-based standards with more specific scientific and more generalised industry terms. The meat thesaurus is presented as a useful resource respecting the FAIR principles of open science for indexing specific databases in the meat sector including contextual and national characteristics and for information retrieval from these databases. This will also help to provide a semantic base for the construction of a meat quality ontology that will be further discussed.
Indexing databases The meat thesaurus can be classically used in bibliographic databases to index journal articles, books or any document on meat, its processing, trade and related disciplines. Technological advances over the last decades have dramatically increased the rate of collecting scientific information and data  (Hughes, Bao, Hu, Honavar, & Reecy, 2008). This has led to a proliferation of large biological databases that manipulate concepts like units, scales, or laboratory methods  (Chriki et al. , 2013)  that are defined in different ways, or not defined at all. This is a huge limitation for meta-analysis and modelling approaches  (Hocquette et al. , 2012). It is therefore advantageous for researchers to use already defined concepts and add keywords to the metadata when feeding databases.
This will also help in the reusability and interoperability of research databases. According to ISO 25964-I (International Organisation for Standardization 2011, Clause 4. 1), a thesaurus has as prime function to support information retrieval by guiding the choice of terms for data indexing and information searching. This will enable an indexer and a searcher to choose the same term for the same concept. A thesaurus gives a unified and formalized representation of information in the information retrieval system, reflecting paradigmatic relations between terms. It is an effective tool for thematic retrieval as it provides search precision on specific subjects. As an example, in 2002, the National Agricultural Library Thesaurus (NALT) was established to select controlled vocabulary terms for subjects indexing in databases such as AGRICOLA or PubAg (https://ag class.
nal. usda. gov/). With the emergence of repositories and catalogues for other research products like data, software, and protocols, it has become crucial to use reference concepts to be able to query them in an efficient manner. Yet, thematic wide vocabularies like Agrovoc or NALT are generally not suited to precisely describe data, which often requires finer grained or specific vocabularies for a given scientific field. Some experts who participated in this work are members of the International Meat Research 3G Foundation (https://imr3g. org/). The main aim of this foundation is to strengthen the link between the different actors in the beef sector (farmers, producers, slaughterhouses, wholesalers and retailers). To achieve this, the foundation is establishing an international database with a large number of consumers' scores related to beef palatability and related animal, carcass, cut and muscle data.
These scores are obtained according to standard protocols of the most advanced beef grading system: The Meat Standards Australia methodology (MSA). This system has been undergoing development since the 1990s, always with the same protocols to record the most powerful determinants of beef eating quality on a large scale first in Australia and then across countries such as France, Ireland, Poland, South-Africa, South Korea or USA  (Hocquette et al. , 2020 , Bonny et al. , 2018). Using the MSA standard methodology already contributes to the strategy of the International Meat Research 3G Foundation but is not sufficient to achieve data interoperability and reusability at scale. These international collaborations emphasised the desirability of establishing common standards, description and protocols for data collection and physical collection of cuts, fabrication of consumer samples and untrained consumer testing protocols and control software to ensure data were fully compatible and able to be aggregated.
As a result, agreed descriptive terms and protocols reflecting global data have been endorsed for use in conjunction with the UNECE Bovine Language allowing free universal use in conjunction with IMR3GF accredited assessment training and providing the ability to develop 3G consumer prediction models from appropriate pooled data. To go in that direction, the Meat Research 3G Foundation will ensure that the future database will allow its users to work using a shared language. The main objective of publishing the meat thesaurus was to provide a semantic base specific to meat quality that would sustain the future database. Beyond its use in this database, the meat thesaurus is made available and thus expected to be adopted by the meat supply chain actors who need accurate definitions of specific concepts.
Thanks to the access services provided by Agroportal, it can be incorporated directly in digital applications used by agricultural and consumer devices connected to the Internet. Such applications could then benefit from a standard food vocabulary with a global scope also including regional specificities, French ones in our case. Parts of the meat thesaurus can also be integrated into or mapped with other semantic resources. For instance, the Chinese Agricultural Thesaurus was mapped to AGROVOC to allow users to access the vast repositories of Chinese agricultural knowledge, previously inaccessible to non-Chinese speakers, by allowing them to use vocabulary in languages with which they are familiar as an entry point to the indexed resources  (Liang & Sini, 2006). To summarise, the meat thesaurus described here, freely accessible and following the open science principles is likely to be a useful resource for indexing specific databases in the meat sector.
It not only provides precise definitions of the different concepts specific of the meat sector, but also highlights This assessment of the quantity and of the intramuscular fat distribution is made visually in the muscle Longissimus dorsi, between the 12th and 13th ribs. For the classification of marbling, photographic standards produced by the USDA relationships between them through categories and hierarchical and explicit associated links. This will contribute to a better knowledge of the sector, its organisations, its products, its policies, etc. The advantage of this thesaurus is that it can also be used to index databases and bibliographic resources. This thesaurus covers various aspects of meat: meat-producing animals, slaughter and cutting, marketing, culinary preparations and consumption.
The users of this thesaurus may therefore have different profiles: professionals of the sector, researchers, journalists, students or simple consumers who wish to better know the specificities of the meat sector, in particular in the French context. This thesaurus does exist in English while retaining the specificities of the French meat industry and having the potential to incorporate other regional specificities. In addition, the thesaurus will further be expanded into different languages. It has also the potential to be regularly updated. Indeed, it could happen that the thesaurus does not cover all the requirements of any database which may contain additional variables not defined yet or defined in a different way. In this case, it will be possible to request additions or modifications of concepts in the thesaurus.
Transcriptomics have shed some light on the pathogenesis of cDLBCL, revealing similarities with its human counterpart, but also important differences that should be considered in veterinary and comparative clinical trials. Compared to normal B-cells, cDLBCL present active NF-kB signaling induced by antigen engagement of the B-cell receptor 6. Additionally, up-regulation of several Toll-like receptors suggests a pathogenesis similar to human activated B-cell (ABC) DLBCL and the activation of immune-related signatures is correlated with an inferior outcome. Indeed, dogs with a shorter overall survival and tumor-free interval show a higher expression of transcripts coding for proteins involved in JAK/STAT signaling, microenvironment, immune system and p53 pathway  7. Recent studies have described the mutational spectrum of canine lymphomas and provided a comprehensive catalog of somatic mutations in coding regions.
In human medicine the integration of next generation sequencing technologies in clinical practice holds great promise for personalized medicine but correlations between genotype and phenotype are critical for the interpretation of these analyses. Veterinary oncology has only recently been modelling the same approach but the process has been hampered by several di culties. Firstly, large multi-institutional molecular studies comprising datasets of fully characterized canine tumors often suffer from a lack of funding. Secondly, understanding the link between molecular aberrations and prognosis is challenging since treatment and outcome are strongly in uenced by the owner. Thirdly, even if genetic alterations are de ned, their functional impact and clinical validation are often unknown, thus preventing the identi cation of new therapeutic targets and the prediction of prognosis.
To address these issues and to prospectively inform clinical trials of canine DLBCL, we performed a comprehensive multiomics pro ling of de novo diagnosed cDLBCL with the goal of clarifying the genetic changes within this tumor. Genetic data obtained from WES and WGS were correlated with clinicopathological features. Finally, an integrated model comprising mutations, copy number aberrations (CNAs) and transcriptome was designed to predict overall survival and tumor-free interval. Further, clinically signi cant mutations were validated in an independent cohort of cDLBCL.
Study population and clinical characteristics We enrolled 77 de novo cDLBCL cases with matched normal tissues for WES and 10 cDLBCLs for WGS. A subset of the 77 dogs (n=43) were also analyzed for RNA-seq. Complete patient demographics and clinical presentation are described in Supplementary Data 1. Mixedbreed dogs (n=19; 24. 7%), German shepherds (n=9; 11. 7%), Rottweilers (n=7; 9. 1%) and Golden retrievers (n=4; 5. 2%) were the most common breeds. There were 40 (51. 9%) females and 37 (48. 1%) males. At diagnosis, median age was 7 years (range 3-15 years) and median weight was 30. 8 kg (range, 4. 5-81. 3 kg). Regarding clinical stage, 48 (62. 3%) dogs had stage V disease, 28 (36. 4%) had stage IV and only one (1. 3%) dog had stage III disease. Forty-nine (63. 6%) dogs were asymptomatic at presentation (substage a), while 28 (36.
We ltered the protein coding somatic variants for known cancer genes using the COSMIC dataset (v92, 723 genes) and a total of 172 cancer genes with at least one protein coding variant were retrieved (Supplementary Data 4). By comparing the genetic alterations in cDLBCLs with several human DLBCL datasets, 1902 genetic alterations in driver genes were shared, including MYC and TP53 (Supplementary Data 4). To explore patterns of co-mutation and mutual exclusivity, we examined pairwise overlaps using Fisher's test and mutual exclusion  9  among the top cancer genes. Signi cant pairwise interactions (FDR=0. 1) are depicted in Fig. 5. Notably, PLEC mutations exclusively co-occurred with SETD2, while MAP3K14 mutations were mutually exclusive with TRAF3. The tumor mutational burden (TMB) ranged from 0.
RNA/DNA isolation and sequencing Total RNA and DNA were extracted from all samples, using the AllPrep DNA/RNA Mini Kit (Qiagen, Hilden, Germany) according to the manufacturer's instructions. DNA concentration and quality were measured by Qubit uorometer (Life Technologies Ltd, Paisley, UK) and Agarose gel electrophoresis. RNA concentration and integrity were measured respectively, in a NanoDrop ND-1000 spectrophotometer and a Bioanalyzer 2010 instrument (Agilent Technologies, Palo Alto, CA, USA). A total of 43 non-normalized libraries for RNA sequencing experiments were prepared by using SureSelect Strand Speci c RNA-Seq Library Preparation kit (Agilent Technologies) and single end sequencing (50SE) was carried out on an Illumina HiSeq2500 (Illumina Inc. , San Diego, CA, USA). Raw Illumina sequencing data are deposited in the SRA database (GenBank) under accession number SRP137798.
All Illumina reads were analyzed with FastaQC software in order to assess sequence quality. A total of 154 high quality whole genome sequencing libraries were prepared using the Illumina-compatible KAPA HyperPlus Library Preparation Kit (Roche Sequencing and Life Science, Wilmington, MA). Exome capture was performed using Roche's SeqCap EZ Share Prime Developer Kit (Roche Sequencing and Life Science, Wilmington, MA) for non-human genomes following the SeqCap EZ HyperCap Work ow User's Guide. The developer's reagent (06684335001) was used in place of COT-1 and index-speci c hybridization enhancing oligos were also used. Exome capture was performed using SeqCap EZ Canine Exome Custom Design (canine 140702_canFam3_exomeplus_BB_EZ_HX1 probe set, Roche Sequencing and Life Science, Wilmington, MA) in which probes were designed on the target enrichment design of 150 megabases developed by Broeckx et al.
52. Libraries were quanti ed using the Qubit DNA Assay Kit in a Qubit 2. 0 Fluorometer (Thermo Fischer, Foster City, CA, USA) and quality was assessed using the Bioanalyzer 2100 instrument. For WGS, ten libraries (fragments ranging from 300 to 400 bp) were then pooled and sequenced on an Illumina NovaSeq 6000 platform in a paired-end (150 PE) mode. The chosen target sequencing coverage was 30x and 250x for WGS and WES, respectively. RNA-Seq, WES and WGS reads quality control, alignment and preprocessing Data quality control was rst performed on raw Illumina reads for RNA-Seq, WES and WGS using FastQC v. 0. 11. 9 (https://www. bioinformatics. babraham. ac. uk/projects/download. html) software. RNA-Seq reads trimming, mapping, gene quanti cation and differential expression analysis steps were performed using the pipeline previously described in Aresu et al.
7. We calculated the two prognostic transcriptomic subgroups as previously described  53. Brie y, we retrieved immune gene sets for immune cells, tumor in ltrating lymphocytes (TILs), proin ammatory molecules, cytokines and cytokine receptors, regulatory T-cells and immune checkpoints, and performed single sample GSEA to derive the enrichment score of each immune-related term. By applying unsupervised consensus clustering analysis, we separated cDLBCL into two subgroups (high immunity = "hot" and low immunity = "cold"). For WES and WGS, Trimmomatic v. 0. 36 was used to select high-quality reads and remove adapter sequences. The paired-end clean reads were aligned against the CanFam3. 1 reference genome from UCSC (https://genome-euro. ucsc. edu/cgi-bin/hgGateway?db=canFam3) using bwa-mem software v.