dataesr/scientific-paragraphs-categorization huggingface dataset:
IV. CASE STUDY To prove the effectiveness of the proposed methodology, we used four different DNNs: ResNet-20, ResNet-32  [18] , DenseNet-121, and DenseNet-161  [19]. The number of parameters and neurons of such DNN models are shown in Table  I. All the DNNs were trained using CIFAR-10 [20], a well-known dataset used for the task of image classification. This dataset is composed of 60k 32x32 coloured instances, 50k training images and 10k test images. The DNN models are trained and tested with a 32-bit single-precision floating-point representation by using the PyTorch framework on a Linux server equipped with a dual Intel Xeon CPU E5-2680 v3 and 256 GB of RAM. To demonstrate the validity of the methodology, fault injection (FI) campaigns are performed. We resorted to the crashed neurons fault model: we model faulty neurons as neurons that completely stop their activity and the propagation of values.
The set consisted of two repeats of 75 m with a dive start at their preferred stroke (four swimmers at front crawl, four swimmers at backstroke, and four swimmers at butterfly) and with a one-anda-half-minute rest between the first and the second repeat. The 200 m pace was calculated for each swimmer in relation to their personal best, and reference times for the 75 m repeats were given to swimmers before the commencement of the experiment. If variations greater than 1% in swimming velocity were observed in the first of the two 75 m repeats (compared to goal times), then some feedback was given to swimmers for the second repeat. Only data from the second repeat were collected. The set (2 × 75 m) was repeated three times in a fixed order with a minimum of 15 min of active rest between The experimental protocol was part of a national team training camp, and dedicated staff guaranteed that swimmers maintained the same routine (sleep, diet, and lifestyle) for the 24 h preceding the test.
Two video cameras (Sony DCR-HC20E) recording at a frame rate of 50 Hz and situated on the public stands 5 m from the starting or turning wall (approximately 4 m above and 10 m away from the swimmers lane) were employed to record the experiment. Videos were analyzed with Kinovea software 0. 8. 27  (Copyright © 2006 -2011, Joan Charmant & Contrib. ) , including the manual digitization of the swimmers' head emersion from underwater by an experienced observer. Six control points represented by colored floating buoys surrounding the swimming-pool lanes were employed for calibration purposes, as previously done by  Veiga et al. (2010). This allowed converting two-dimensional screen coordinates into real-space coordinates of the plane formed by the water's surface. Two experienced coaches using a manual stopwatch Seiko SVAS003 (Seiko Watch Corporation, Japan) collected total time in seconds of each swimming repeat.
The distance traveled underwater (m) in each of the three swimming laps was calculated from the starting or turning wall to the point of the swimmers' head emersion, underwater time (s) was calculated from wall contact to head emersion, average underwater velocity (m/s) was obtained by dividing underwater distance and underwater time, and also the number of underwater undulatory kicks (n) was counted from the trials' video footage. Finally, average surface swimming velocity (m/s) was computed from the total time and distance of the repeats minus the underwater segments. The distribution of data was examined for normality using the Shapiro-Wilk test, and all parameters were graphed for visual inspection to screen for outliers. Two outliers were presented in the total underwater distance, and they were then excluded for statistical analyses.
a b s t r a c t Agricultural Long-Term Experiments (LTEs) are crucial agricultural research infrastructures for monitoring the long term effects of management and environment on crop production and soil resources. We have compiled the meta-information of 616 LTEs from 30 different countries across Europe with a duration of typically 20 years, including clustered information of the European LTEs in different categories (management operations, land use, duration, status, etc. ). It consists of the updated version of the dataset published by  Grosse et al. , (2020)  but is extended by further LTE metadata, categories and research themes. Each set of metadata consists of up to 49 different attributes (categorical or numeric). Collected attributes were analyzed according to several research themes, including fertilization, crop rotation and tillage treatments.
Value of the Data • The data comprises the most comprehensive metadata set of LTEs and other experiments in Europe through the combination of independent datasets of the BonaRes and EJP SOIL projects. • The dataset primarily offers a wide selection of the LTE locations, research themes, and sources that the scientists and decision-makers can use for possible collaborations, even with LTE holders, intending to analyze the soil ecosystem services. • It is a significant source of information about various LTEs for soil and environmental researchers, research institutions, local authorities, along with access to relevant information for future studies and cooperation. • This LTE data collection established a new basic framework in this study to make data from different LTEs com parable and appraisable, even though there are no standard procedures for LTE set-up, treatment designs, minimum information and data management.
For instance, LTE-based data were acquired and managed by various national, international or global initiatives and networks, including GLTEN (Global long-term experiment network), ILTER (International long-term ecological research), IOSDV (International Organic Nitrogen Fertilization Experiments), NLFT (National Long-term Fertilization Trials, Hungary), RetiBio 2 (Italy), and the projects BonaRes and EJP SOIL. While each organization has its data management plan, the variety of actors makes that LTE-related information was dispersed across different databases, data holders and publications, which makes making it challenging to access and reuse the data. Since 2016, an extensive metadata collection on LTEs has been compiled within the BonaRes project. The basis of this collection was the BonaRes factsheet and LTE metadata template for the structured collection of metadata and the BonaRes definition of which field experiments have the status long-term experiment.
The BonaRes definition is as follows: "field experiments with a minimum duration of 20 years and a static design"  [1]. EJP SOIL data has been built very closely on the Factsheet and metadata template developed and proven in BonaRes to collect metadata. However, there are differences in the definition. EJP definition: "running field experiments focusing on (sustainable) soil management and a minimum duration of 5-10 years with a statistically sound design, a control treatment and regular monitoring of crop/soil parameters. We have not filtered this dataset using the strict BonaRes defaults but also integrated LTE meeting the EJP definition. However, we offer simple filtering possibilities based on the duration of the experiment, and thus every user has the possibility to decide for himself.
In this case, we found that expressing the differing LTE definitions of both BonaRes and EJP SOIL would be relevant for researchers to select the experiment duration used in their own research. As the leading research initiatives, the BonaRes and EJP SOIL research groups have recently merged their LTE metadata databases of the European LTEs. Several LTE parameters and features for a time span of more than 150 yrs (1843-2022) were aggregated and published. The dataset comprises an updated number of the LTEs and revised research categories compared to the data published by Grosse et al. [1 , 3]. Recently the complete dataset consists of metadata for 616 LTEs from 30 countries in Europe ( Fig. 1  ), that can be divided into five groups; (i) trial information (i. e. site, country, duration, status), (ii) land use (i.
e. , arable land, grassland), (iii) research theme, (iv) management operations (i. e. tillage, fertilization, crop rotation), and (v) basic soil parameters (i. e. types, texture). Collected LTE data were merged and visualized through an online overview map ( https: //lte. bonares. de ) running under the BonaRes Repository ( https://datenzentrum. bonares. de ). The overall dataset comprises the complete list of the LTEs and their attributes. The file includes 616 rows compatible with the listed LTEs and 49 columns of attributes in various categories. Besides the LTE-related attributes, the file consists of the cited literature and source information for each LTE. A comprehensive EXCEL sheet is provided as supplemental information along with the published dataset: https://doi. org/10. 20387/bonares-40kc-2661.
The article displays two tables; (1) Table  1  includes the list, and description of the categories used for displaying into the Bonares LTE overview map, (2) Table  2  offers the ranges of values or the choices of the LTEs for each country and main categories. While merging the BonaRes and EJP SOIL LTE sets, 19 doubles became visible. Fifteen of these LTEs are located in Germany, and one is from Switzerland, Belgium, Denmark, and Italy. Duplications of these LTEs were excluded from the dataset to prevent redundancies. Following the exclusion of duplicates, the final number of the LTEs were defined. The majority of the LTEs in Europe are located in Germany ( n = 299), Sweden ( n = 51), Austria ( n = 31), Great Britain ( n = 27). A considerable number of LTEs are also located in Mediterranean countries, including Italy (18), France (18), Spain (14) and Turkey (3), respectively.
Experimental Design, Materials and Methods The study comprised the LTEs in Europe and merged the data of three datasets. Additional data were identified through a comprehensive bibliographic review and personal communication with numerous LTE holders. Articles from scientific journals, books, research notes and websites were selected addressing the research themes and parameters of the agricultural LTEs. At first, we conducted a systematic, keyword-based search in Web of Science (WoS) Core Collection ( https://apps. webofknowledge. com/ ) and SCOPUS ( https://www. scopus. com/ ) as well as the Google Scholar databases to create an overview of available LTE information. The bibliographic review was based on the English keywords "long-term field experiment", "long-term experiment", "long-term field trial", and "long-term trial" in title, abstract or keywords  [2].
However, as especially older publications are written in national journals; also national (German) keywords were applied, such as "Dauerfeldversuch", "Dauerdüngungsversuch", "Dauerversuch", "Langzeitfeldversuch" and "Langzeitversuch" to precisely identify the LTEs in Germany where the majority of LTEs are located  [2]. Articles representing LTE information were selected if they addressed at least the e. g. site, duration, land use and management operations. Thus, we recorded the metadata contents for each LTE as inputs listed for the dataset. Both English and German keywords were compatible with the multilingual thesaurus AGROVOC (FAO) that assisted in finding and linking LTEs in related topics. Data collection was carried out in 30 European countries and described based on AGROVOC keywords ( https://agrovoc.
fao. org/browse/agrovoc/en/ ). Besides the bibliographic review, the metadata of the considerable number of LTEs was received by personal communication. Factsheets were forwarded to many LTE holders, and the filled versions were discussed via e-mails, phone calls, and online meetings. The LTE metadata was uploaded to the overview map once the information on the factsheet was agreed upon for the related experiment. The data for the additional LTE were collected using the EJP SOIL metadata template developed from the keyword trees of the knowledge library and adapted for the need of the consortium, which consisted of a multi-sheet excel file with data validation and controlled vocabulary through drop-down lists. Copies of the template were automatically made using Google Drive API. Links to templates were distributed to national representatives within the EJP SOIL consortium then sent to the LTE owners.
A script was implemented to download the templates and perform automatic checks. The automated checks included controlling the type of the values, if a value was inside the drop-down list or if it was a new choice added by the user. The automatic check review also verified the quality of the data; a. o. consistency of the index across the different tables of the template and their uniqueness. All templates that passed the automatic review were merged together to form the final database. The database was then exported to the format described above to be further processed and merged with datasets  [1 , 3 , 4].
Tables How data were acquired The dataset was acquired by (i) a bibliographic review, (ii) personal communication with the LTE holders through the BonaRes factsheet  [5]  and of data collection The BonaRes datasets ( https://doi. org/10. 20387/BonaRes-3tr6-mg8r , https://doi. org/10. 20387/bonares-eff3-0mb4 ), collected from scientific journals, books, research notes and websites, were further improved and merged with a dataset from EJP SOIL ( https://doi. org/10. 20387/bonares-jwhj-z839 ). Personal communication with numerous LTE holders was set up by exchanging the factsheets information. Collected LTE metadata in the study were included.
X-ray data We retrieved archival XMM-Newton observations of A399-401 from the Science Archive 5. In particular, we made use of three pointings: 0112260101 (covering A399), 0112260301 (covering A401), and 0112260201 (covering the region between the two clusters). The European Photon Imaging Camera (EPIC) observations were processed with the XMM-Newton Scientific Analysis System (SAS v16. 1. 0) and the Extended Source Analysis Software (ESAS). After filtering bad time intervals due to soft proton flares, we produced an EPIC mosaic image in the 0. 5-2. 0 keV band combining the three ObsIDs. This was used to compare the X-ray and radio (from LOFAR) surface brightnesses of the observed emission. For a detailed analysis of the XMM-Newton observations, we refer to  Sakelliou & Ponman (2004).
The Pan-STARRS1 Surveys (PS1) and the PS1 public science archive have been made possible through contributions by the Institute for Astronomy, the University of Hawaii, the Pan-STARRS Project Office, the Max-Planck Society and its participating institutes, the Max Planck Institute for Astronomy, Heidelberg and the Max Planck Institute for Extraterrestrial Physics, Garching, The Johns Hopkins University, Durham University, the University of Edinburgh, the Queen's University Belfast, the Harvard-Smithsonian Center for Astrophysics, the Las Cumbres Observatory Global Telescope Network Incorporated, the National Central University of Taiwan, the Space Telescope Science Institute, the National Aeronautics and Space Administration under Grant No. NNX08AR22G issued through the Planetary Science Division of the NASA Science Mission Directorate, the National Science Foundation Grant No.
Acknowledgements. This publication is part of the project  CORTEX  ( NWA. 1160. 18. 316 ) of the research programme  NWA-ORC  which is (partly) financed by the  Dutch Research Council (NWO). This work made use of the Dutch national e-infrastructure with the support of the SURF Cooperative using grant no. EINF-1287. RJvW acknowledges support from the  ERC   Starting Grant ClusterWeb 804208. AB acknowledges support from the  VIDI research programme  with project number  639. 042. 729 , which is financed by the  Netherlands Organisation for Scientific Research (NWO) , and from the  ERC Starting  Grant  DRANOEL 714245. RC acknowledges support from  INAF  mainstream.
Furthermore, in  [16] , authors also aim to partition a dataset by finding the doubly-stochastic matrix that best approximates the dataset similarity matrix. Finally, in  [17] , authors propose to use doubly-stochastic scaling to perform co-clustering, by exploiting the piecewise constant shapes of the scaling factors that are expected to approximate the joint densities between the random variables inferring the data, and random variables inferring the partitions. We remark that all these studies use the doubly-stochastic scaling as a step of a whole pipeline and for a very specific purpose: achieving uniform marginals in the flow table  [14] , obtaining staircase-like singular vectors  [15]  or scaling factors  [17] , or approximating a similarity matrix  [16]. On the other hand, our present method is a wider-purpose preprocessing that can be used prior to any community detection method.
Acknowledgements We would like to thank  Jean-Francois Marcotorchino  for his feedback at an early stage of this work. We also would like to thank  Mario Arioli  and  Renaud Lambiotte , for their constructive comments on this work when reviewing LlG's PhD dissertation. We are also grateful to the reviewers from the Neurocomputing Journal for their helpful remarks and suggestion that have helped to improve the quality of this study.
, 2020). A filtering step was carried out to remove chimeras based on the quality of their taxonomic assignments. Finally, the retained reads were homogenized by random selection (10,000 reads for 16S rRNA gene sequences) to compare the datasets efficiently and avoid biased community comparisons. More detailed information about sequencing data quality is given in Supplementary Figure  1. The retained high-quality reads were used for taxonomyindependent analyses, to determine alpha-diversity metrics (e. g. , richness), and taxonomy-based analysis is performed using USEARCH  (Edgar, 2010)  against the SILVA 16S rRNA reference database (r132)  (Quast et al. , 2012). The raw datasets are available in the EBI database system under project accession number PRJEB52689.
pca function of the ade4 package in R  (Chessel et al. , 2004). Discrimination between treatments, years or treatments x years was tested using a Monte Carlo permutation test (1,000 permutations, bca and randtest function, ade4 package). The significance threshold was set at P < 0. 001. Alpha diversity was analyzed using Hill numbers generated by vegan package  (Chao et al. , 2010; Oksanen et al. , 2013). The Hill numbers make it possible to interpret alpha diversity in a linear way by progressively taking into account the abundance of OTUs (q-value), between rare and dominant OTUs. Thus, q = 0 represents richness, q = 1 the exponential of Shannon entropy and q = 2 the inverse Simpson index. The variations of the soil microbial biomass and diversity indices (Hill indices q0, q1, and q2) over time were tested for each organic material treatment by means of a Kruskal-Wallis test with Bonferroni correction.
The relative abundances of bacterial and archaeal phyla were determined, and then a Bray-Curtis dissimilarity matrix was computed. Then, NMDS was applied using the metaMDS function in the vegan package  (Oksanen et al. , 2013 ; 1,000 permutations). Individuals were plotted in the NMDS space according to NMDS1 and NMDS2 axes, which depicted the differences in the bacterial and archaeal community compositions. The significance of the differences between treatments and sampling times were assessed using a PERMANOVA approach was used to test for the relative effect of organic material treatment, time and organic material treatment x time (adonis2 function, vegan package; 1,000 permutations, significance threshold: P < 0. 001). To better understand the sources of variations of the composition of bacterial and archaeal communities, phylum, soil physicochemical properties and fertilization practices data were fitted in the NMDS space using the envfit function (vegan package; 1,000 permutations).
Acknowledgments This work, through the involvement of technical facilities of the  GenoSol platform of the infrastructure ANAEE-Services , received a grant from the  French  state through the  National Agency for Research under the program  " Investments for the Future"  (reference  ANR-11-INBS-0001 ), as well as a grant from the  Regional Council of Burgundy. The La Mare field experiment is conducted within a collaboration between  INRAE  and  CIRAD  and is part of the  INRAE SOERE PRO  (network of mid-term experiments dedicated to the study of impacts of organic waste product recycling), certified and funded in 2013 by  ALLENVI (Alliance Nationale de Recherche pour l'Environnement ) and integrated as a service of the infrastructure " Investment for the future" AnaEE-France , overseen by the  French National Research Agency  ( ANR 11-INBS-0001 ).
Lung Brm were studied by surface staining with a mixture of PerCP Vio700-anti-IgM (130-106-012, Miltenyi), and PerCP Vio700-anti-IgD (130-103-797, Miltenyi), APC-H7-anti-CD19 (560143, BD Biosciences), PE-anti-CD38 (102708, BioLegend Europe BV), PE-Cy7-anti-CD62L (ab25569, Ab-Cam), BV711-anti-CD69 (740664, BD Biosciences), BV421-anti-CD73 (127217, BioLegend Europe BV), and FITC-anti-CD80 (104705, BioLegend Europe BV and yellow Live/Dead (Invitrogen). Cells were incubated with appropriate mixtures for 25 min at 4 C, washed in PBS containing 3% fetal calf serum and fixed with paraformaldehyde 4% after an overnight incubation at 4 C. Samples were acquired in an Attune NxT cytometer (Invitrogen) and data analyzed by FlowJo software (Treestar).
ACKNOWLEDGMENTS The authors are grateful to  Dr. Marie José Quentin-Millet  and  Estelle Besson  ( TheraVectys ) for discussion and advice, to  Magali Tichit  and  Sabine Maurin  for excellent technical assistance in preparing histological sections, to  Sébastien Chardenoux  ( CIGM, Institut Pasteur ) for excellent technical assistance in B6. K18-hACE2 IPÀTHV mouse transgenesis, and to  Marie Laure Loriquet  and  Dr. Paul-Henri Consigny  ( Institut Pasteur Medical Center ) for their gift of the leftover Moderna vaccines, not unusable in humans, so that this study did not deprive any individual of a vaccine dose during the COVID-19 pandemic. The  SARS-CoV2 variant Delta/2021/I7. 2 200 was supplied by the  Virus and Immunity Unit (Institut Pasteur, Paris, France ) headed by  Olivier Schwartz.
(2. 2) and (2. 3) only seven of them are independent. This fact must be taken into account when mapping all integrals into topologies, and we use the program Apart  [54]  to implement partialfraction relations systematically. Once all integrals have been sorted into topologies, we reduce them to MIs using integration-by-parts (IBP) relations  [55, 56]. We perform the IBP reductions with publicly available codes such as FIRE  [57] , LiteRed  [58]  and Kira  [59]. After IBP reduction, we find 76 master integrals that contribute to the two-loop corrections to the process in eq. (2. 1). There are 19 four-point integrals (see fig. 1 ), 37 three-point integrals (see fig. 2 ), 10 two-point integrals (see fig. 3 ) and 10 integrals that factorise into a product of one-loop integrals (see fig. 4 ). The factorised master integrals are trivial to evaluate, and we will not discuss them further.