dataesr/scientific-paragraphs-categorization huggingface dataset:
16S rRNA PhylOgENy The phylogenetic position of the three isolates was determined based on comparative analysis of their 16S rRNA genes with Brucellaceae members. The 16S rRNA gene was amplified for each strain using universal primer sets 27F/1492R and 518F/800R. Direct sequencing of purified products was conducted by Macrogen with sequencing primers (27F, 1492R, 518F and 800R) and an ABI 3730xl automated sequencer (Applied Biosystems). Sequences were deposited in the NCBI GenBank database under the accession numbers MZ227818 (C130915_07 T ), MZ227819 (C150915_16) and MZ227820 (C150915_17). The 16S rRNA gene sequences were aligned with mafft version 7. 31  [10]  to the 16S rRNA sequences from the genomes of 19 Brucellaceae type strains. These genomes were downloaded from GenBank as genome assemblies or raw sequence reads, depending on the availability (Supplementary Table  S1 , available in the online version of this article).
Downloaded raw sequence reads were assembled using SPAdes version 3. 13. 1  [11]  with default parameters, and scaffolds shorter than 200 bp or with a read coverage lower than 20  ([10]  for Paenochrobactrum pullorum) were discarded. Pairwise similarities (gaps excluded) were calculated from the alignment, using the software package ugene version 37. 1  [12]. The 16S rRNA gene sequences of the three strains were 100 % identical, and exhibited more than 99 % similarity to the 16S rRNA genes of all the other Pseudochrobactrum type strains. The most similar sequences were those of P. lubricantis CCUG 56963 T and P. saccharolyritum CCUG 33852 T , with one and two differences out of the 1406 aligned nucleotides, respectively. A maximum-likelihood (ML) phylogenetic tree was reconstructed from the multiple alignment using RaXML version 8.
OvERAll gENOME fEATuRES The complete genomes of strains C130915_07 T , C150915_16 and C150915_17 were sequenced using the PacBio RSII sequencing system (Pacific Biosciences) and assembled by GenoScreen (Lille, France). The complete genome sequences were annotated using Prokka 1. 12  [15]  with default parameters. The genome sequences were deposited in the NCBI GenBank database under the accession numbers CP075358-CP075362 (C130915_07 T ), CP075353-CP075357 (C150915_16) and CP075348-CP075352 (C150915_17). The genomes of the three strains are extremely similar and consist of a chromosome of ~3. 46 Mbp with ~51. 6 mol% G+C content carrying 3104-3127 protein-coding genes, 15 rRNA genes and 58 tRNA genes. All three strains also carry four plasmids. The first plasmid is 432 kbp long with ~51. 4 mol% G+C content and carries 391-392 protein-coding genes and two tRNA genes (Phe gaa and Thr ggt ) unique to this replicon (Supplementary Table  S2 ).
GC-MS analyses were performed in a Shimadzu GC-MS QP 2010 Ultra system, using a fused-silica DB-5HT capillary column (30m×0. 25 mm internal diameter, 0. 1 µm film thickness) from J and W Scientific. The oven was heated from 120 °C (1. 0 min) to 300 °C (15 min) at 5 °C min -1. The injection was performed at 300 °C, and the transfer line was kept at 300 °C. Compounds - - - - - l-Arabinose - - + - - d-Glucose + - + + + d-Mannose + - + - + l-Malate + - + + + Citrate + - - - - Maltose - - - - - N-Acetyl-d-glucosamine + + - (+) + d-Mannitol - - - - - Phenylacetate - - - - - l-Lactate - + + + - d-Sorbitol - - - - - Adonitol - - - - - l-Histidine (-) - + - + *Data from [1-3]. were identified by mass fragmentography and by comparing their mass spectra with those of the Wiley and NIST libraries. The fatty acid profile (Table  2 ) of strain C130915_07 T was composed of C 19 : cyclo (1.
Funding information Research at the  University of Navarra  was supported by the  ISTUN Institute of Tropical Health, University of Navarra Health funders (Fundación la   CAIXA -LCF/PR/PR13/11080005 ) and  Fundación Caja Navarra ,  Fundación María Francisca de Roviralta ,  Ubesol and Inversiones Garcilaso de la Vega S. L)  and  MINECO  grants  AGL2014-58795-C4-1-R (MINECO/AEI/FEDER)  and  PID2019-107601RA-C32  ( MCIN/AEI/ 10. 1303910. 13039/501100011033 ). M. L. -B. is recipient of the PhD. Fellowships  Formación de Profesorado Universitario (FPU)  funded by  Ministerio de Ciencia, Innovación y Universidad (Spanish Government ) and Ayuda predoctoral from  Gobierno de Navarra. Work at  INRAE  was supported by  Agence Nationale de la Recherche  Grant  ASTRID-Maturation ANR-14-ASMA-0002-02. Work at IRNAS was supported by  Consejo Superior de Investigaciones Científicas  (Grant  202040E185 ).
Fish were shown to exhibit unexpected high activity and displacements  (Daněk et al. , 2014). But no behavioural response to hypoxia has ever been studied on adult European catfish in natural conditions. In an experiment set up to assess the space use and activity cycles of the European catfish, 40 large individuals (subadults and adults whose body length ranged in 727 -2150 mm) have been tracked by acoustic telemetry for three years in a 104-ha shallow eutrophic lake located in South-Eastern France. Within this period, the lake experienced a severe hypoxia, leading to the mortality of many fish from different species, including carp, eel and some small European catfish. However, fourteen months later, our telemetry tracking data revealed that all the 40 tagged catfish were still alive. Therefore, we aimed to analyse how large European catfish individuals could have resisted to hypoxia in natural conditions.
Physical and chemical lake conditions Hourly vertical profiles of water temperature (0. 5, 3 and 5 m above the bottom) and dissolved oxygen concentration (DOC) (0. 5, 1. 5, 2. 5 and 3. 5 m above the bottom) were recorded at the deepest point in the lake (location 42 on Figure  1 ). HOBO data loggers U22 were used for temperature and U26 for DOC. The dissolved oxygen saturation rate (DOS) was calculated from DOC and temperature values. At 0. 5 m above the bottom, there were no records from 09-02 03:00 to 09-04 12:00, because of a sensor failure. 0. 5-1. 5 m, 2. 5-3 m and 3. 5-5 m heights of measurements above the bottom are named bottom, middle and surface, respectively, in the following text. In addition, vertical profiles of temperature and DOC were recorded on 09-04 with a YSI Exo2 multiparameter sonde at different locations in the lake to get a spatial picture of what happened all over the lake at different depths (Figure  1 ).
The mean hourly wind speed at a standard height of 10 meters above ground (10-m wind) was measured at the meteorological station of Salon-de-Provence, located 24 km east of the lake and representative of the weather conditions on the lake. The meteorological data were provided by Météo-France, the French meteorological institute, and available from the INRAE CLIMATIK platform (https://intranet. inrae. fr/climatik/, in French) managed by the AgroClim laboratory of Avignon, France.
Space use metrics Fish have been continuously tracked from December 2017 but, here, we focused on ten days throughout August-September of 2019, when a severe hypoxic event occurred. Individual raw positions were interpolated using the R package trajr  (McLean & Skowron Volponi, 2018)  for each quarter hour between the first and the last position to get synchronised individual tracks. Interpolated positions from gaps in raw positions longer than 1 hour were discarded. These tracks were all together plotted on the lake map to create a video of the catfish displacements, useful to get insights on catfish space use (Online Resource 1). Distances between consecutive positions were computed and set to zero if less than the telemetry system mean position error (7. 4 m). To represent fish swimming activity, for each individual and each hour, the mean speed (m/h) was calculated.
A mean daily speed (m/h) was calculated for each individual if at least seven hourly mean speeds were available in a day. The mean distance of each individual to all others was calculated using the R package spatstat  (Baddeley et al. , 2015). The distance to shore of each individual was calculated using the R package rgeos  (Bivand & Rundel, 2019). These distances were calculated for every quarter hour and averaged over every hour and every day. The individual daily home ranges were estimated with an Epanechnikov kernel as the utilization distribution with probability levels of 95% and 50%; the home range 50% is often referred to as the core area  (Powell, 2000). These both metrics were also estimated for all pooled individuals over different periods of the ten days to map the areas used by the fish.
These spatial analyses were conducted using the R package adehabitatHR  (Calenge, 2006). The video (Online Resource 1) showed an attractive location where individuals gathered. To identify possible differences in individual behaviours during the aggregation dynamics, we analysed inputoutput movements of every fish when they could be identified on video recording. We could extract the time when the fish reached the aggregation and stayed inside for 34 among 40 fish and the time when it definitively left (i. e. it did never swim back to the aggregation location) for 30 among 40 fish.
Statistical analyses We applied an algorithm to detect possible breakpoints corresponding to structural changes in the 2month time series of DOC and of the average hourly speed of all 40 individuals  (Zeileis et al. , 2003). This was done with the strucchange R package  (Zeileis et al. , 2002). Then, we compared the statistically detected breakpoints in each time series to identify potential concomitance between changes in fish swimming activity and DOC dynamics. Among these periods bounded by the breakpoints, one comprised the severe hypoxia. The same algorithm was re-run on the mean hourly speed within this period to detect possible different levels of catfish activity depending on the hypoxia severity. DOC was compared among these different periods of activity by Kruskal-Wallis tests and pairwise comparisons by Fisher's least significant difference (R package agricolae, de Mendiburu, 2020).
Means among days and among sizes by day were pairwise compared by using the emmeans R package  (Lenth, 2016). The model fitting was assessed with regards to the homogeneity and normality of the residuals  (Zuur et al. , 2009)  and to the percentage of explained variance  (Hastie & Tibshirani, 1990). For mean individual distance to others, as we could not find a reliable model, we compared the distributions between days and between size classes among days by using Kruskal-Wallis tests and, if significantly different, we made pairwise comparisons by Fisher's least significant difference (R package agricolae, de Mendiburu, 2020). All statistical analyses were made with R version 3. 6. 3 (R Core Team, 2020).
g. via thinning, calving, and/or topographic unpinning), or the shelf collapses, the upstream ice can accelerate, leading to increased mass loss  (Rott et al. , 2002; Scambos et al. , 2004; Rignot et al. , 2004; Konrad et al. , 2018). (Bindschadler et al. , 2008)  overlain on REMA  (Howat et al. , 2019) , with the shelf boundary from the MEaSURES dataset  (Mouginot et al. , 2017). Inset shows location in Antarctica, and the basin which drains through the shelf. Figure created using Quantarctica  (Matsuoka et al. , 2021). ridges and ice rumples  (Stephenson and Zwally, 1989). Only ~30 % of the shelf is considered to be passive ice that does not provide any buttressing force  (Fürst et al. , 2016). The largest of the outlet glaciers is the Denman Glacier, which contains a sea level rise equivalent of 1.
Satellite datasets We use daily passive microwave observations of surface brightness temperature (T B ) at 19 GHz from two successive versions of the Advanced Microwave Scanning Radiometer (AMSR), called AMSR-E (May 2002-October 2011) and AMSR-2 (May 65 2012-present). The 19 GHz observations have an underlying footprint of ~14-16 km x 22-27 km, but are pre-processed to a regular 12. 5 km southern stereographic polar grid using the drop-in-the-bucket method with daily averaged T B (Meier https://doi. org/10. 5194/tc-2022-94 Preprint. Discussion started: 11 May 2022 c Author(s) 2022. CC BY 4. 0 License.
Self-organising maps A self-organising map (SOM) is a machine learning approach that can simplify multi-dimensional datasets  (Kohonen, 1990 (Kohonen, , 2001)). SOMs have been widely used in many fields, including climate science (e. g. Hewitson and Crane, 2002; Sheridan and Lee, 2011; Cassano et al. , 2016; Gibson et al. , 2017; Udy et al. , 2021) , but have not been used to investigate surface melt in Antarctica before. A SOM algorithm produces a set of outputs that represent important variability in the input data. We refer to these outputs as patterns, because, for our dataset, each output represents a typical spatial distribution (i. e. "pattern") of melting and nonmelting pixels across the shelf, taken at a daily resolution. SOMs work by comparing each input (here a daily melt observation) against a set of self-adaptive reference nodes, which ultimately become the output melt patterns.
Implementation with binary surface melt data We apply the SOM approach using the kohonen package in R  (Wehrens and Buydens, 2007; Wehrens and Kruisselbrink, 2018). Daily maps of binary melt status in the AMSR datasets are flattened to 1D vectors and combined into a matrix in which each Owing to the binary nature of our input data, we use the "Tanimoto" similarity measure. Within the kohonen package, this setting represents the Hamming Distance divided by the total length of the input vector; conceptually, this is the fraction of times in which the input disagrees with the reference pattern. Implementation of the SOM algorithm requires the definition of multiple initiating parameters, the most crucial here being how many output patterns are required. We run a series of sensitivity experiments for each of the parameters (Fig.
S4d  & S5), the additional patterns are near-replicas of the nine patterns shown in Fig. 2 , and would be considered analogous when interpreting the results given the above constraints. Overall, the similarity of the outputs strengthens our confidence in the results, and our interpretations remain unchanged between sensitivity tests. Were the current analysis to be repeated on an alternative shelf, or even on the Shackleton Ice Shelf using a different underlying dataset, there is no expectation that the same settings would produce the most optimal output for that particular case, and sensitivity tests for SOM parameters should be run again.
Sensor comparisons Few previous studies have investigated surface melt on the Shackleton Ice Shelf in detail, but there is some evidence of heterogeneous melt behaviour (see e. g. fig. 3  of  Liu et al. , 2006; fig. 2b of Tedesco and Monaghan, 2009; and fig. 6 of Trusel et al. , 2012). However, results can be sensor-dependent. For example, a longer melt season is observed in the north of the shelf with an active microwave sensor (ASCAT), but to the south-east with a passive sensor (AMSR-2) (see fig. 2d  of  Zheng and Zhou, 2020). The frequency of the sensor also determines how many melt days are observed (see  fig. 3 & fig. 6 of Leduc-Leballeur et al. , 2020). Whilst it can be tempting to try to establish which of the various datasets is 'correct', we advocate building on the approach of Leduc-Leballeur et al.
AMSR-SSMIS comparison We compare our AMSR melt observations against those from the SSMIS sensors, and find that the two datasets only agree on the SOM melt pattern ~30 % of the time overall (Fig. 8 ). Because the assigned SOM patterns represent the observed melt well for both datasets (Fig. S2   Firstly, a third of all pattern 9 days in the AMSR dataset are mapped as pattern 8 days in the SSMIS observations. These two patterns are very similar, and the melt observations for each often only differ in the melt status of a couple of pixels. For many purposes, these patterns could therefore be considered equivalent between the sensors, though pattern 9 represents truer shelf-wide melt. Secondly, the sensors disagree on when the melt season begins and ends each summer (i. e. the melt onset and freeze-up dates).
Nearly a quarter of all days across the AMSR-defined melt seasons occur beyond the SSMIS-defined dates. Many of these days also have little melt in the AMSR observations (i. e. pattern 1), but, on average, there are 3. 5 days each summer with extensive melt (mainly patterns 2 & 4) on days outside of the SSMIS-defined season. A clear diurnal melt cycle exists in East Antarctica  (Picard and Fily, 2006) , suggesting that the afternoon passes of the AMSR sensors are more likely to observe melt than the evening and morning passes of the SSMIS observations. Because we use a binary definition of melt, the effects of a diurnal cycle are likely to be more pronounced away from the height of the melt season, and could thus explain this discrepancy. Thirdly, patterns 3 and 5-7 are rarely observed in the SSMIS dataset at all, and are only observed occurring simultaneously in both datasets on 11 days between them over the entire study period.
Differences in the spatial resolution of the sensors can become very important for melt observations in less homogeneous areas of an ice shelf, particularly if there are changes in elevation within the footprint  (Johnson et al. , 2020). The rarity of patterns 3, 6, and 7 in the SSMIS dataset is likely because of the sensor's coarser resolution as each of these three patterns show extensive melt along the steep grounding line. Likewise, pattern 7 in the AMSR data often corresponds to pattern 4 in the SSMIS data, and thus represents a reduction in melt along the grounding line between the two sensors. However, the grounding line does not remain melt-free in all SSMIS observations, indicating that sufficiently intense melt can compensate for higher elevation topography within the sensor's underlying footprint.
Together, the different resolutions and overpass times of the sensors can explain why the two datasets don't always agree, and suggest that discrepancies between different sensors can potentially provide additional information on the diurnal variability and relative intensity of melt. Future work can build on our SOM-based approach to help identify and understand such differences in further detail, and also extend comparisons to include other datasets used in the melt literature, such as ASCAT, SMOS, and Sentinel-1 (e. g. Bevan et al. , 2018; Zhou et al. , 2019; Johnson et al. , 2020; Leduc-Leballeur et al. , 2020; Liang et al. , 2021).
Melt days and melt fluxes In this study, we use the concept of melt days, based on a binary definition of whether liquid water is observable at the shelf surface when the satellite passes overhead. At sufficiently large spatial scales, the number of melt days correlates well with meltwater production  (Trusel et al. , 2012). Visually inspecting maps of averaged AMSR melt season duration (Fig. 6c ) and RACMO melt fluxes (Fig. 6f ) shows a broad general agreement in their respective spatial variability, but correlations between the two are statistically insignificant. This discrepancy is in part because of the coarser resolution of the RACMO dataset, and also because of differences along the grounding line. Although we use melt days in the current work, our SOM-based approach can be adapted to incorporate melt fluxes in future work.
Future work can use our approach to investigate the spatial and temporal variability of surface melt on other Antarctic ice shelves in greater detail than previously possible (see Code & Availability). Our approach can also be adapted to exploit publicly available satellite datasets to investigate surface melt across the cryosphere worldwide. et al. , 2021). REMA  (Howat et al. , 2019)  is available online at https://www. pgc. umn. edu/data/rema/. Author contributions. DS conceived of the study, performed the analysis, and wrote the manuscript, with guidance and input from all authors at each stage. GP processed the passive microwave satellite data. All authors contributed to discussions and commented on the manuscript. Competing interests. The authors declare no competing interests. Acknowledgements.
Code and data availability. The AMSR-E and AMSR-2 melt data will be made available online for the final paper. The R code used in this analysis can be accessed online at: github. com/polarSaunderson/ShackletonSOM; a doi link will be provided for the final paper. RACMO2. 3p3 model output (van Dalum et al. , 2021)  is publicly available online at: https://doi. org/10. 5281/zenodo. 5512077. LIMA  (Bindschadler et al. , 2008)  and MEaSURES  (Mouginot et al. , 2017)  are publicly available online and also accessible within Quantarctica (Matsuoka.
Introduction Exploring, interlinking, and analysing scientific literature is crucial for producing new knowledge and tools, improving our understanding of the world, and addressing the fundamental challenges of our time. It is also a very challenging task, since the literature is composed by millions of heterogeneous articles in natural language. Researchers and other stakeholders typically explore this complex space by using academic search engine (e. g. , Google Scholar, Scopus, PubMed, Semantic Scholar), which however allow only basic queries and simply returns list of relevant documents that need to be manually analysed. This limitations constitute a significant bottleneck in the knowledge flow of the scientific process  [1, 2]. The main issue is that current systems lack a good representation of the underline scientific knowledge and thus cannot support more sophisticated queries about the entities described in the literature such as methods, tasks, and materials as well as their relations (e.
• A novel module to check the validity of the triples based on SciB-ERT  [30] , a state-of-the-art transformer model for the scientific domain. The code of SCICERO is publicly available at https://github. com/danilo-dessi/ SKG-pipeline, in order to ensure full reproducibility and allow the community to further build on our effort. We evaluated SCICERO against several alternative solutions on a benchmark composed by 3,600 triples in the field of the Information Retrieval (IR), Natural Language Processing (NLP), and Machine Learning (ML). We show that SCICERO yields the best performance in terms of F1 and discuss the effect of its different components (Section 5. 2). We also demonstrate that the number of source documents of a statement can be used to estimate its reliability (Section 5. 3), allowing users to choose different compromises between coverage and accuracy.
In summary, the main research contributions of this paper are: • We introduce SCICERO, a new approach that integrates Semantic Web best practices, NLP, and Machine Learning for building KGs of scientific concepts from research papers. • We release a ground truth 6  of 3,600 triples covering the field of Information Retrieval, Natural Language Processing, and Machine Learning. • We perform an evaluation of SCICERO in terms of precision, recall, and F-measure. • We provide a use case of SCICERO on a big dataset of scientific literature for producing a Computer Science Knowledge Graph. • We make available the full source code of SCICERO at https:// github. com/danilo-dessi/SKG-pipeline. The remainder of this paper is organized as follows. Section 2 discusses the related work. SCICERO is detailed in Section 3.
g. , AI-KG  [25] , ORKG  [2] , and Nanopublications  [33] ), and ii) KGs that describe metadata such as authors, venues, citations, and research topics (e. g. , AIDA  [34] , OpenAlex 7  , the Microsoft Academic Graph (MAG)  8  , AMiner  [35] , Open Academic Graph 9  , Scholarly-data. org [36] , Scopus 10  , Semantic Scholar 11  , OpenCitations  [37] , Dimensions  12  , Core  [38] ). KGs can be generated either manually or automatically. For example, ORKG and Nanopublications are manually built: they require researchers and practitioners to create RDF triples to describe scientific literature. Conversely, AI-KG is automatically generated and curated. SCICERO contributes to the automatic generation of scientific KGs and focuses on the Computer Science domain, although it would be straightforward to extend it to other domains as well.