dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Acquisition and Analysis Experiments were carried out on a Bruker Equinox 55 spectrometer (Karlsruhe, Germany) equipped with a liquid-nitrogen-cooled DTGS (deuterated tri glycine sulfate) detector, and continuously purged with N 2 during data acquisition. For each experiment, 128 scans were realized with a 4 cm -1 resolution at room temperature A demountable cell (Bruker) equipped with CaF 2 windows was used for all experiments. At least two independent measurements were realized to assure results accuracy.
Molecular Dynamics Simulations All MD simulations were performed using the GROMACS suite  [79, 80]. Maestro software (Schrödinger, LLC, New York, NY, USA) was used to build atomistic versions of deprotonated mono-and di-RL that were then submitted to the Automated Topology Builder website  [81, 82]  in order to obtain a basic topology. These ATB-built topologies were manually refined in order to be more compliant with the Slipids forcefield  [83]  that was used for simulations. In particular, Slipids parameters were used to describe the aliphatic carbons from both RLs as this forcefield simulates lipid chain order parameters well  [84]. The sugar moiety was described by the means of GLYCAM06 forcefield  [85]  in conjunction with AMBER ff99SB-ILDN  [85]. The TIP3P model  [86]  was used to describe water molecules.
Data Acquisition and Analysis All experiments were carried out on a Bruker Avance Biospin 300 WB (7. 05 T) equipped with a CP-MAS 7-mm probe. First 2 H NMR spectra of PLPC/POPC-2 H 31 (1/1), POPC/POPC-2 H 31 (1/1), and soy PC/POPE-2 H 31 /soy PI/soy PG/β-sitosterol/soy glucosylceramide (2. 6/2. 5/0. 6/0. 6/3. 2/0. 5) were recorded varying the temperature from 10 to 38 • C using an increment of 4 • C. Before each acquisition, samples were allowed 30 min to equilibrate at defined temperature. For this study, temperature calibration of the probe was performed previously using methanol-2 H 4 /methanol (96/4) and ethylene glycol/DMSO-2 H 6 (80/20) samples according to a standard protocol  [96]. In each case, a small decrease of spectral width was noted with increasing temperature. This diminution reflects the increase of global lipids dynamics caused by the augmentation of temperature (e.
Contributions: S. B. , S. R. and C. S. conceptualized the project. N. M. , A. L. F. , S. B. and C. S. designed the methodology for NMR investigations and conducted the validation and formal analysis. M. D. (Magali Deleu) designed the methodology for FTIR investigations, and did the validation and formal analysis. S. B. , with the support of M. D. (Manuel Dauchez), designed the methodology for MD simulations, and did the validation and formal analysis. All authors helped with writing the manuscript and approved the final version. Funding: This research was supported by the Hauts de France (HdF) Council and European Regional Development Fund, and was realized in the context of the MAELIA project. N. M. 's Ph. D. thesis was co-funded by the HdF Council and ERDF. The HdF council and ERDF also co-founded the equipment utilized in UTC and UPJV within CPER 2007-2020.
N. M. 's post-doctoral position was funded through a grant in partnership with the SAS PIVERT, within the frame of the French Institute for the Energy Transition (Institut pour la Transition Energétique (ITE) PIVERT (www. institut-pivert. com)) as part of the Investments for the Future ("Investissements d'Avenir"), by the French Government under the reference ANR-001-01. Molecular dynamics simulations were supported by the French HPC Center ROMEO (URCA) and by the MATRICS platform (UPJV). This works was also founded by the University of Liège (Fonds Spéciaux de la Recherche, Action de Recherche Concertée-Project FIELD) and the F. R. S. -F. N. R. S. (National Funds for Scientific Research, Belgium) via the CDR grant # J. 0086. 18. M. D. (U Liege) thanks the F. R. S. -F. N. R. S. for her position as Senior Research Associate.
Acknowledgments: We are grateful to  SFR  Condorcet  CNRS 3417  for providing a fruitful space for discussion, to  Stéphan Dorey  from  Unité   RIBP-EA 2069 ,  Université de Reims Champagne Ardenne, Reims, France , for providing purified RL mixture, and to  Dominique Cailleu  for his welcoming access to the UPJV NMR analytical platform. We also thank  Wesley Wilson  and  Pauline Trezel  for their involvement in this work during their internships and  Aubanel Monnier  for his help on graphical abstract realization.
Author Contributions: S. B. , S. R. and C. S. conceptualized the project. N. M. , A. L. F. , S. B. and C. S. designed the methodology for NMR investigations and conducted the validation and formal analysis. M. D. (Magali Deleu) designed the methodology for FTIR investigations, and did the validation and formal analysis. S. B. , with the support of M. D. (Manuel Dauchez), designed the methodology for MD simulations, and did the validation and formal analysis. All authors helped with writing the manuscript and approved the final version. Funding: This research was supported by the Hauts de France (HdF) Council and European Regional Development Fund, and was realized in the context of the MAELIA project. N. M. 's Ph. D. thesis was co-funded by the HdF Council and ERDF. The HdF council and ERDF also co-founded the equipment utilized in UTC and UPJV within CPER 2007-2020.
A simple module for estimating canopy radiation interception over the growing season is used to simulate the photosynthesis and transpiration on each FU. In QualiTree (see Lescourret et al. 30  for the core of the model, Mirás-Avalos et al. 42  for the associated light interception model and Rahmati et al. 21 for energy balance calculation and water transfer formalization within the plant), the carbon processes are assimilation, exchange within the tree, utilization for organ growth, mobilization and replenishment of reserves, and metabolic transformation of various sugars in the fruit. Water processes are transpiration, flow within the tree and fruit water inflows and outflows. Rather than adopting a single view, QualiTree uses several approaches for carbon partitioning within the tree. At the tree level, QualiTree uses a teleonomic approach by considering, based on the root-shoot functional balance principle, that the tree tends to restore the C balance between leafy shoots and fine roots.
The concept of actuarial senescence (defined here as the increase in mortality hazards with age) is often confounded with life span duration, which obscures the relative role of agedependent and age-independent processes in shaping the variation in life span. We use the opportunity afforded by the Species360 database, a collection of individual life span records in captivity, to analyze age-specific mortality patterns in relation to variation in life span. We report evidence of actuarial senescence across 96 mammal species. We identify the life stage (juvenile, prime-age, or senescent) that contributes the most to the observed variation in life span across species. Actuarial senescence only accounted for 35%-50% of the variance in life span across species, depending on the body mass category.
From a theoretical viewpoint as well, actuarial senescence should have a limited demographic impact compared with mortality earlier in life  [22, 23] , which expectedly decouples the variation in the rate of actuarial senescence from the variation in life span. Thus, some life span variation across species should be caused by either age-independent mortality (i. e. , factors that affect all age classes in the same way) or early-life mortality (e. g. , factors that mostly affect young adults)  [24]. To the best of our knowledge, these hypotheses have never been tested empirically, and the relative contributions of the successive life stages to variation in life span across species have never been quantified. We took advantage of a unique dataset of accurate individual longevities from mammals that were born and died in zoological institutions to fill that gap.
Datasets We restricted the analyses to females. Demographic data were obtained from the Zoological Information Management Systems (ZIMS) database from the Species360 organization (https://www. species360. org/) under the 2017-06 data agreement. This unique organization collates and standardizes information from approximately 6. 5 million individuals belonging to >21,000 species (mostly vertebrates) across >1,000 zoological and aquaria institutions in over 90 countries. For each species, following previous works  [38, 53] , we considered that captive individuals constitute a metapopulation representing how the species performs in captivity. We did not access information about the cause of death and hereafter study life span variation irrespective of the cause of death (as commonly done in life span studies  [54] ).
The database documents the precise age at death, at least for individuals born in captivity, which we exclusively focused on. Because of the participatory nature of the data gathering by the ZIMS, some records are incomplete-i. e. , it is not always possible to decipher whether an individual's death failed to be reported or it is still alive. To circumvent that issue, we only analyzed the data from extinct cohorts, which are cohorts for which all individuals have been documented as dead or are past the maximum age ever reported for the species. From an initial set of 144 species  [38, 55] , we selected those for which at least 25 females were documented to have reached the age of 1 year, at least five had reached the predicted age at which 90% of an average cohort would be dead (denoted Â10% ; Fig 1; explained below), and the model-selection procedure (explained below) yielded evidence of actuarial senescence.
The final dataset featured 96 species (S5 Data). This relatively stringent selection procedure ensured that the observed role of senescence in shaping life span was not influenced by the inclusion of species that do not exhibit any actuarial senescence and that the sample size to estimate the life span and actuarial senescence was large. On average, 220 study individuals per species reached the age of 1 year (range: 35-1,247 for Alcelaphus buselaphus and P. leo, respectively) and 18 individuals reached the age of Â10% (range: 5-98, also for A. buselaphus and P. leo).
Measuring life span The maximum recorded life span in a longevity dataset typically depends on the sample size, meaning that the maximum recorded life span is a potentially biased metric of life span  [58, 59]. Instead, we used the age at which 90% of a cohort was predicted to be dead, also called the "10% life span":  [58, 59]. From Eq 1, it follows that A 10% ¼ xjl x ð Þ ¼ 0:1 f g Â10% ¼ if lð ÂÞ � 0:1 Â10% ¼ À logð0:1Þ âJ if lð ÂÞ > 0:1 and lð ÔÞ � 0:1 Â10% ¼ Â À logð0:1Þ þ âJ Â â0 if lð ÔÞ > 0:1 Â10% ¼ Ô þ 1 b log 1 À b â0 ðlogð0:1Þ þ âJ Â þ â0 ÔÞ " # ð2Þ 8 > > > > > > > > > < > > > > > > > > > :.
We report both analyses hereafter for the sake of comparability. The outcomes of both were qualitatively similar in our case. C ij ¼ r ij s i ð � y i Þs j ð � y j Þvarðy i Þvarðy j Þ p i ¼ X j C ij X k X j C kj ð3Þ f i ¼ X j jC ij j X k X j jC kj j Body mass as a measure of the species-specific rank along the slow-fast continuum of life histories In several instances, we used body mass to rank species along the slow-fast continuum of life histories instead of a more reliable metric like the generation time  [62, 63]. This is because our dataset did not include known outliers of the mass-pace of life relationship (i. e. , no bats and no mole rats) and because we did not have access to independent generation-time data for all the species in our sample, meaning that using generation time would have introduced statistical correlations between predictors and dependent variables.
We kept generating values until we obtained, for each species in the dataset, 10 simulated mortality curves that had a life span within 1 year of the focal species' life span. This enforced that the distribution of life span in the resulting simulated datasets was the same as observed in the real dataset. We thereby obtained 500 simulated datasets of 960 species with a life span distribution similar to the observed life span distribution across the 96 species of our dataset. We analyzed each of the simulated datasets in the same way as the real data. This yielded a null-expected distribution for all the quantities that we manipulated, including the decomposition of the variance in life span. If the observed proportion of the variance in life span explained by θ i matched the expected proportion, with a statistical threshold of 0.
Acknowledgments The dataset of longevity in captivity that supports the findings of this study is available from Species360 (https://www. species360. org/). We thank  D. Conde  for facilitated access to the data. Restrictions apply to the availability of these data, which were used under license for the current study (Species360 Research Data Agreement # 2017-06 ) and so are not publicly available. Data are, however, available from the authors upon request and with permission of Species360.
With an increase in the number of Linked Open Data datasets, insufficient interlinking quality can lead to a decrease in overall data quality. Therefore, it is necessary to keep the interlinking quality as high as possible. One of the main ways to link datasets is to use owl:sameAs links, i. e. to indicate that two things are the same. But with its strict semantics, there is a lot of misuse of owl:sameAs in the wild. Indeed, identity is often relative and depends on the context of use. We therefore propose an approach that enables considering the characteristics of involved datasets to interlink datasets thanks to owl:sameAs statements. The experimental results performed on real-world datasets show that the proposed approach is promising.
Introduction Linked Data 1  (LD) datasets, also called Knowledge Bases (KBs), use ontologies backed by Description logics (DL) and OWL (see  [9] ) to define their schema. owl:sameAs 2  links are the most commonly used links to link datasets together and discover new knowledge. The role owl:sameAs from the OWL ontology language states that two things (two individuals, instances or resources) are the same and that all statements about one instance are also true for the other (the indiscernibility of identicals). In the Semantic Web field, retrieving all interchangeable knowledge between two things is still a major challenge. For example, suppose we have two KBs, each containing an instance 'Paris', the French capital. One can link these two instances thanks to the role owl:sameAs. Therefore, the knowledge about Paris in the first KB can be used with Paris in the second KB.
Introduction Linked Data 1 (LD) datasets, also called Knowledge Bases (KBs), use ontologies backed by Description logics (DL) and OWL (see  [9] ) to define their schema. owl:sameAs 2 links are the most commonly used links to link datasets together and discover new knowledge. The role owl:sameAs from the OWL ontology language states that two things (two individuals, instances or resources) are the same and that all statements about one instance are also true for the other (the indiscernibility of identicals). In the Semantic Web field, retrieving all interchangeable knowledge between two things is still a major challenge. For example, suppose we have two KBs, each containing an instance 'Paris', the French capital. One can link these two instances thanks to the role owl:sameAs. Therefore, the knowledge about Paris in the first KB can be used with Paris in the second KB.
There are many ways to find such linksets (see Definition 2) of pairwise identical things between datasets. The most obvious is by using OWL semantics itself, e. g. by using roles such as owl:hasKey, owl:FunctionalProperty, owl:InverseFunctionalProperty, etc. Another way to find such links is to use frameworks based on similarity measures between instances. However, owl:sameAs has a strict semantics  3  (see Table  1 ) and is proved to be often misused in the wild (see  [8] ). A lot of owl:sameAs links are in fact inappropriate whether they are simply wrong, or because they are contextdependent. For example, the city of Paris is geographically the same as the department of Paris, but they are administratively different. So, in a geographic context, the statement holds, but in an administrative context it does not.
Related work There are several ways to handle the identity problem. The first one is the historical way that has as a main goal to retrieve (create) a maximum of good links. A second way is to find wrong identity links among existing ones. By using either semantics or statistics, it is possible to find links that may be erroneous. The term instance matching refers to the problem of finding equivalent resources. The goal is to produce links between a source dataset and a target dataset. For each couple of instances, a similarity score is produced and if the score is above some (user defined) threshold, the link is validated. Frameworks like Silk  [19]  or KnoFuss  [14]  allow creating links between datasets after a configuration step. Ferraram et al. [6]  published a complete survey and more recently Achichi et al.
Identity link assessment approaches consist of checking if an existing link is true or false. Methods from those approaches may also be used to find links. Guéret et al. [7]  proposed to use classical network measures to assess existing links. De Melo  [5]  proposed to use the unique name assumption within datasets (i. e. an instance has one name within a dataset) to spot sets of instances linked by owl:sameAs where at least one link may be wrong. Next, a linear programming algorithm is used to check if the link is wrong. In Papaleo et al. [15] , the authors proposed a logical approach that tries to detect logical conflicts by using semantics features like functional properties in small subgraphs containing the two involved instances to assess. Hence, this approach strongly relies on semantics.
Paulheim  [16]  proposed to use data mining methods. Links are represented in an embedded space, then an outlier detection algorithm is used to detect links that may be wrong. Valdestilhas et al. [18]  use a combination of semantics and graph partitioning algorithms to detect erroneous transitive properties. Raad et al. [17]  proposed to use community detection on identity links network to detect erroneous links. Also using network structure, Idrissou et al. [10]  proposed to combine several network metrics to find wrong links across multiple datasets. Our work aims at finding identity links by considering the involved datasets structures. In other words, we use explicit characteristics (from the ontology) like functional properties, disjoint class axiom, i. e. all available semantics that can help us in our task.
We also use implicit properties, i. e. how properties are used in datasets. A property is explicitly defined in the ontology by its domain, its range, the fact that it can have a literal value or an object value (i. e. another IRI) and is implicitly defined within a dataset by the way it is used (e. g. how many instances use a given property?). Those explicit and implicit features may be of great help to discover new identity links. Concerning the implicit features, we propose to combine similarities function in conjunction with weights according to the use of each property within datasets. For instances of a given concept, we use two types of weights. One representing the importance of a property and one the discriminating power of this property. Our work is complementary to the ones we have just seen.
Results We have developed a prototype in C# that can be run either on Linux or Windows machines. It is also available as a Docker container. We choose the open source library dotNetRDF  7  to handle SPARQL, OWL and RDF parts. All experiments are performed on a computer with an I7 processor (3. 10 GHz) and 16 Go of RAM. All our code and datasets can be found on this Github repository so our experiments are reproducible: https://github. com/ PHParis/im_prototype. For each experiment, we have calculated the precision, recall and F-measure with result linkset as follows: • True positive (tp) : number of alignments predicted (by our approach) that are actually true • False positive ( f p) : number of alignments predicted and actually wrong • False Negative ( f n) : number of true alignment not found among those predicted by our approach • Precision = tp tp+ f p • Recall = tp tp+ f n • F-measure = 2×Precision×Recall Precision+Recall 5.
First experiment For the first experiment, we performed an instance matching task on real-world and well-known datasets. Thus, we used subsets of DBpedia and Wikidata. More precisely, we used DBpedia Wikidata 8  in place of Wikidata since it is expressed with DBpedia ontology. Hence, we respect the following condition from Definition 1: T 1 T 2 (source TBox must be a subset of target TBox). The version of DBpedia used is "2016-10" and the version of DBpedia Wikidata is "03. 30. 2015". Now we describe how we built our two test KBs from real-world datasets. In the source dataset KB s , we have selected instances of persons from DBpedia having at least 15 homonyms (according to rdfs:label) in Wikidata. We arbitrarily chose 15 homonyms to have a sufficient challenge without having to recover too many instances (the scaling of our approach is not our main concern at the moment).
We queried all triples mentioning one of these instances to construct the source dataset. In the target dataset KB t , we retrieved all homonyms (belonging to DBpedia Wikidata) of instances from our DBpedia selection. For example, the instance dbpedia:John Williams has the label "John Williams" and there are 88 distinct instances in Wikidata with the label "John Williams". The DBpedia selection contains all triples having dbpedia:John Williams as subject or object and the DBpedia Wikidata contains all triples having one of the 88 instances as subject or object. From both source and target datasets, we obviously deleted owl:sameAs links after having assessed them (none of them were erroneous). There were 36 owl:sameAs. Those links were then used as a gold standard. The source KB contains 277 instances, 3468 triples and 36 candidate instances belonging to the concept to be matched with the target KB.
Second experiment The goal of this experiment is two folds. First, we want to compare our approach with state of the art approaches. Secondly, other approaches we selected all use advanced terminology or structural comparison techniques (see Sect. 2) to perform their task. Hence, we want to prove that a structure-based approach with simple string matching can perform as well as these approaches that use more advanced techniques. To compare our approach with others (see Sect. 2), we performed tests using the SPIMBENCH SANDBOX task from OAEI 2017  9. This task has a source and a target dataset and a gold standard. A concept name is provided, so only the instances of this concept might be linked. SPIMBENCH SANDBOX datasets are alterations of an original one through value-based, structure-based, and semantics-aware transformations.
Discussion In the first experiment, results are good since we obtain 91. 7% for all measures. Good links are well found, indicating that our approach is promising. Precision and recall are the same because, several times, a wrong candidate has been selected instead of the good one. This candidate selection issue is due to similarity scores that are too close between the good candidate and the (wrongly) selected one. It seems that our aggregation functions (see Section 4. 2. 2) are responsible for both true and false negative. In the second experiment, for the SPIMBENCH SANDBOX dataset, our approach performed well since recall is 99. 6% and precision 85. 4%. We reached the same F-Measure as I-Match which was the best competitor on F-measure. In the same way as in the first experiment, several times, a wrong candidate has been selected instead of the correct one.