dataesr/scientific-paragraphs-categorization huggingface dataset:
Sequence assembly All data were subjected to quality control check  9  and samples with base qualities greater !30 over 70% of read length were used in the downstream analysis. KmerFreq_HA v2. 0 and Corrector_HA v2. 01 tools of SOAPec_v2. 01  10  were further used to perform K-mer frequency generation and error correction of paired-end and mate-pair data, respectively. SOAPdenovo-127mer (v2. 04)  10  was used to perform the contig and scaffold assembly from paired end and matepair libraries (avg_ins ¼ 350 and 10 K for paired-end and mate-pair, respectively). After the assembly, gaps in scaffolds were closed with the GapCloser tool (v1. 12)  10  (with Àl 125) of the soap module. Smaltmap, perfectfrombam and pipeline tools of the reapr module (v 1. 0. 17)  11  was used to recognize the errors in the assembly by remapping of paired-end and mate-pair data to the de novo assembled genome.
The assembly was broken at potential misassembled points. The broken assembly was further used as an input for the jelly tool of the PBSuite 12 (v15. 2. 20) with blasr (v1. 3. 1) at parameters [-minMatch 8-minPctIdentity 80-bestn 1-nCandidates 20-maxScore-500]. This tool was used to upgrade the existing Illumina assembly with low-pass PacBio data. Repeat masking was performed by downloading the Repbase TE library from the repbase server (http://www. girinst. org/server/RepBase/). To identify known TE elements, we used repeat masker and repeat protein mask software in the Repeat Masker package (www. repeatmasker. org), which identifies TEs by aligning the genome sequence to a defined TE database. Tandem repeats were predicted using TRF  13  by using the default parameters 'Match ¼ 2, Mismatch ¼ 7, Delta ¼ 7, PM ¼ 80, PI ¼ 10, Minscore ¼ 50, and MaxPeriod ¼ 12'.
The completeness of the assembly was estimated by using CEGMA. 14  The assembly was screened against a collection of 248 universal eukaryotic single-copy genes. Core eukaryotic gene datasets were downloaded and a blast database was made from the assembly before running CEGMA. This Whole Genome Shotgun project has been deposited at DDBJ/ENA/ GenBank under the accession LZNR00000000. Version LZNR01000000 is described in this work. It is also available at ftp:// ftp. jax. org/maine_blackbear_project/.
Annotation The black bear genome assembly was annotated using Comparative Annotation Toolkit (CAT-https://github. com/ComparativeGenomics Toolkit/Comparative-Annotation-Toolkit). CAT uses whole genome alignments generated by progressiveCactus  15  to project gene annotations from a high quality reference genome on to one or more target genomes. This process leverages previously curated annotation sets to rapidly construct a set of orthologues in the target genomes. After transcript projection,  16  the protein coding projections are provided to the ab-initio gene finding tool AUGUSTUS  17  with additional extrinsic information derived from RNA-Seq. AUGUSTUS then enforces a coding gene model that allows for shifts in splice sites to maintain frame. This process can also rescue exons that did not align in the whole genome alignment.
CAT also performs true ab-initio gene prediction by using a new method of running AUGUSTUS called Comparative Augustus, or AugustusCGP. 18  This parameterization simultaneously predicts genes in all species in a progressiveCactus alignment, using RNA-Seq data in one or more species to help guide the annotation process. After projection, clean-up and ab-initio prediction, CAT combines these separate transcript sets into one final gene set through a consensus finding process. For orthologous transcripts, the transcript with the best fidelity to the reference with the best extrinsic support is chosen. In the case where multiple paralogous projections are found, CAT selects the most likely orthologue through a combination of splice junction fidelity, synteny and alignment identity. Finally, ab-initio predictions from AugustusCGP are evaluated for providing new information.
If significant overlap with an orthologous projection is found, and the transcript provides a new splice junction or exon, then it is included as a new isoform of the orthologue. Otherwise, the locus is considered a candidate for a novel gene, often a gene family expansion. In addition to an annotation set on target genomes, CAT produces a UCSC Assembly Hub. 19  This assembly hub has tracks for the raw transMap projections, the post-filtering projections, the various modes of AUGUSTUS employed, the final consensus annotation set, as well as the input RNA-Seq information including a filtered splice junction track. All annotations are stored in a modified bigBed format with a wide variety of various additional features annotated on each entry, which can be accessed from the details page for that entry.
Additional features include RNA-Seq support for specific splice junctions and binary classifiers such as having a frame-shifting indel relative to the source transcript. In addition to the genome-specific tracks, an alignment track (snake track) shows the cactus alignment between the current species and other species. Browsing this assembly hub provides the opportunity to visualize the relationship between all aligned species and the various ways the transcript projections were filtered and combined with ab-initio predictions. Assembly hub can be visualized by visiting UCSC 'Track Data Hubs' (https://ge nome. ucsc. edu/index. html) and then adding URL ftp://ftp. jax. org/ maine_blackbear_project/assemblyHub/hub. txt under the 'My Hubs' tab. For the black bear project, the cactus alignment generated contained black bear, horse (equCab2), dog (canFam3), polar bear (GCA_000687225.
We further filtered variants to set of canonical editing sites (and their reverse complementary). RNA editing prediction tool-REDItools was used to confirm the filtered sites, strand identification and its count was used as final count to estimate the editing frequency. These sites were subjected to confirmation in DNA-seq data generated for six samples. The whole genome sequence data of these bears were subjected to quality trimming and aligned by bwamem (v0. 7. 9a). The alignments were converted to bam and processed by Picard-MarkDuplicates to remove the duplicates from the data. Afterwards, the Genome Analysis tool kit module IndelRealigner was used to pre-process the alignments. The realigned bam file was processed to identify the coverage at potential editing sites. The sites with at least one DNA-seq sample support were included in final set for experimental validation.
Gene expression profiles from embryonic mouse kidneys were downloaded from NCBI GEO (https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi? acc¼GSE3808). 20  Six samples representing three time points (two replicates each) were used in the analysis. For the analysis, E12. 5, E13. 5, and E16. 5 were considered early & late age samples, respectively. GEO samples accessions of array data are GSM87387, GSM87388, GSM87389, GSM87390, GSM87391, and GSM87392. Samples were normalized using the affy package  21  and GenBank accession for the probs (Affymetrix Mouse Expression 430A Array) were downloaded from GEO (https://www. ncbi. nlm. nih. gov/ geo/query/acc. cgi? acc¼GPL339). We extracted the MGI Gene IDs corresponding to GenBank accession using mouse genome informatics database (http://www. informatics.
Annotation of the black bear genome Similar to the panda genome,  26  our black bear genome assembly was annotated using the Comparative Annotation Toolkit (CAT). CAT identified 29,624 genes (18,091 coding) in our black bear assembly, representing 88% of the genes present in the dog annotation. In addition, 2,995 transcripts with at least 2 splices were predicted by AugustusCGP, which also had at least one intron junction supported by RNA-Seq and not supported by transMap. Of these, 1,730 were associated with a transMapped gene and assigned as a novel isoform of that gene. The remainder were evaluated for being putatively novel loci. Six hundred and fifty-three loci were identified as being possibly paralogous, which is defined as overlapping a paralogous transMap projection, which was dropped in paralogue resolution.
1c ), likely the result of fewer repetitive sequences caused by transposon integration (Fig. 2 ). The new Comparative Annotation Toolkit (CAT) pipeline allowed us to establish a high-quality annotation of the genome with 22,081 protein coding transcripts and identification of 126 putative novel loci that warrant further investigation as to their uniqueness and possible function. JAX141 (M) JAX146 (M) JAX165 (M) JAX134 (F) JAX118 (F) JAX160 (F) JAX103 (M) JAX140 (M) JAX136 (F) JAX106 (F) JAX113 (F) ACSL5 CISH SLCO1C1 SERPINC1 PLCXD2 PLCD3 ABCC3 MAST3 TSPOAP1 CCM2L MAP3K12 PRKD2 SEPW1 SHANK3 RTN4RL2 COL11A2 FAM81A FAM83H SLC46A1 MEGF8 POMT1 AATK CLEC18A MYO7B SLC5A10 GZMA API5 CTH TMEM27 CA7 ABRACL PTMA PPDPF SRI MCU HIPK3 GCHFR CREM PTP4A1 TST IER3 GSKIP SOCS2 C3orf18 NDST3 LYPLA1 CSMD1 MHC ENSCAFG00000002590 Based on the reported changes in the bear kidney function during hibernation,  5  we expected we would find a signature of these changes in the gene expression of kidneys collected soon after hibernation when compared with kidneys collected before hibernation.
ORCHIMIC is developed with the aim of being incorporated in a global land surface model. In thus study it was embedded in the ORCHIDEE land biosphere model  (Krinner et al. , 2005) , but any other global land surface model could also be used for grid-based simulations. 5 The ORCHIMIC is described in Sect. 2 and the two conceptually simpler models -a first order kinetics model called CENTURY, which was derived from  Parton et al. , (1987)  and constitutes the SOC decomposition module of the ORCHIDEE model  (Krinner et al. , 2005) , and a first order kinetics model called PRIM, which is a variant of the CENTURY model modified to include interactions between pools to enable representation of priming of decomposition rates  (Guenet et al. , 2016) -are described in Sect. 3. The model parameters were calibrated against soil incubation data from  Blagodatskaya et 10 al.
CENTURY and PRIM soil carbon models Here we give a brief summary of CENTURY and PRIM, the two benchmark models with which we compare ORCHIMIC 15 for simulating incubation experiments. The CENTURY model is the SOM module of the ORCHIDEE global land biosphere model  (Krinner et al. , 2005). It is a simplification of the original CENTURY model  (Parton et al. , 1987 (Parton et al. , , 1988)) , as it does not consider nitrogen interactions. The PRIM variant of CENTURY was developed to capture the magnitude of the priming of SOM decomposition induced by varying litter inputs  (Guenet et al. , 2016). Both are C-only models and have the same structure with similar pools and fluxes as shown in Fig. 2. The effects of soil moisture, temperature, pH, lignin and clay 20 content on decomposition of each substrate pool are also the same as those used in ORCHIMIC.
The ORCHIMIC v1. 0 is programed in Python language and the run of model need Python with basic packages (numpy, os and sys) preinstalled. The source code, optimized parameter values and script used to reproduce the results showed in Sect. 6. 4 are available online (https://github. com/huangysmile/ORCHIMIC/releases/tag/v1. 0; DOI: 10. 5281/zenodo. 1164740).
Acknowledgment We are grateful to  Hubert Garavel  and  Wendelin Serwe  for helpful remarks about the paper and the LNT model. The research presented in the paper has been funded in part by the  Austrian Research Promotion Agency (FFG)  under grant  851205  ( Security ProtocoL Interaction Testing in Practice -SPLIT), RIDINGS (RIgorous DesIgN of GALS Systems ) project of the  PHC Amadeus program , and the  Région Auvergne-Rhône-Alpes  within the program ARC 6.
g. proxies), biodiversity and ecosystem functions (Walters and Scholes 2016). In the field of aquaculture and fisheries, GIS and remote sensing data have been reviewed and promoted by  Meaden and Aguilar-Manjarrez (2013). Therefore, the use of spatial data, combined with the tools and methods dealing with spatial and landscape ecology, and assessment tools such as InVEST, are promising. Models have been central in some of our examples. Vulnerability assessment in the case of NBG relies on the calculation of indicators based on multidimensional input data regarding marine habitats, availability of ES and pressures due to human activities. In two other examples, bay of Mont Saint Michel and Pertuis Charentais, mathematical models have been set up to quantify the interactions between system components.
Many models describing the Earth's magnetic field exist and are maintained by international research organizations. However, no clear general overview of the cross-compared quality of these models is available. An evaluation of the internal component and of the anomaly field as estimated by a selection of nine geomagnetic models with global coverage is presented here. Anomaly field models are also compared with an external, independently processed marine dataset extracted from Shom's (the French Hydrographic and Oceanographic Service) geophysical database. The comparative study shows that internal geomagnetic fields computed outside the provisional domain, up to degrees 12 or 13 of spherical harmonics (SH), can be considered as equivalent. Relative amplitude differences with the World Magnetic Model 2015 (WMM2015) and the 12th generation of the International Geomagnetic Reference Field (IGRF-12) taken as references are lower than 10 nT.
For an extent reduced to the north-eastern Atlantic Ocean and western Mediterranean Sea, anomaly field models up to SH degree 720 and the 3rd version of the Earth Magnetic Anomaly Grid (EMAG2v3) at the equivalent 0. 5° resolution are the closest models to the filtered marine data. However, they are significant spatial discrepancies and standard deviations of differences between models and data are about 65 nT. The 3-arc-minute World Digital Magnetic Anomaly Map (WDMAM) best reflects the unfiltered marine data, but with significant standard deviations of about 70 nT. Results demonstrate that any model can be used with insignificant errors to compute raw anomalies from total field measurements by removing the internal component of the Earth's magnetic field. In addition, they suggest that the priority should be given to WDMAM for levelling and evaluating marine datasets and for computing enhanced high-resolution regional magnetic maps.
At Shom (the French Hydrographic and Oceanographic Service), only internal and anomaly field models are currently used. The internal World Magnetic Model (WMM, see references in Table  1 ) is used for applications in sea and air navigation, as required by NATO's standards  (Defense Mapping Agency 1993). The International Geomagnetic Reference Field (IGRF, Table  1 ) is the reference model adopted by the scientific community to correct the main field and derive magnetic anomalies. Global anomaly field models, such as the Earth Magnetic Anomaly Grid (EMAG) or the World Digital Magnetic Anomaly Map (WDMAM) (Table  1 ), are used to level and evaluate marine datasets. They can also be used for computing enhanced regional magnetic maps. Whether models are sometimes assessed through intrinsic comparisons with data used to produce them or extrinsically against concurrent models before dissemination to the scientific community, the only study dealing with the comparison of a wide range of recent versions of geomagnetic models relates to the evaluation of candidate models for the production of the reference model IGRF-12 from IAGA  (Thébault et al.
2015a, b). IGRF-12 commonly corresponds to a weighted average of several internal models expressed in spherical harmonics (SH) to degree 13 and provided by a number of different international research laboratories and institutions. Thébault et al. (2015b)  observe deviations between the ten candidate models. However the root mean square vector difference field between models as defined by the authors are generally less than 10 nT. They conclude that the internal models submitted are relatively homogeneous. The present paper gives additional results to the evaluation performed for IGRF-12. Its aim is to present an evaluation from the selection of nine magnetic models with global coverage that are compared for the same spectral content, and with an external, independently processed marine dataset.
The short-wavelength field is computed from processed and levelled marine, land-based and airborne measurements, and precompiled anomaly grids. The long-wavelength field is corrected by substituting the longest wavelengths (greater than about 300 km) with models derived from satellite data (MF7 for EMAG2v3 and GRIMM_L120 for WDMAM). EMAG2v3 is calculated from observed data using a kriging process without a priori geologic structure and ocean-age information. WDMAM results from overlap management by attributing an order of precedence for each dataset and weighted averaging in conflicting areas. A forward model of marine magnetic anomalies, built from an age map of the ocean floor, relative plate motions and a geomagnetic polarity time scale, is used to fill data gaps and compute the model over all oceanic areas.
The main purpose of the present work is to evaluate the internal component and anomaly field as estimated by the selected models (Fig. 1 ), with particular concern on the discrepancies between them, and ultimately to identify the best candidates adapted for Shom's applications. The anomaly field is also evaluated with processed data extracted from Shom's marine magnetic database, which are an independent source of measurements. Results are shared with the scientific community as they are an overview of the cross-compared quality of geomagnetic models with ground-truthing data.
Evaluation method Several comparative scenarios are developed to study the differences between SH models. We chose to independently consider: (1) the intensity, inclination and declination (when available) of the total field for the internal component, up to SH degrees 12 and 13, and (2) the intensity of the anomaly field, up to SH degree 720 (Fig. 1 ). Two geospatial extents are considered: (1) a global scale (land and ocean areas), at 1° grid spacing and (2) an area of interest covering the north-eastern Atlantic Ocean and the western Mediterranean Sea (hereafter referred to as the NEAWM area with an extent of N20°-N70°; W50°-E20°) at 0. 5° grid spacing. Three dates (01/01/2000 for CM4 only, 01/01/2010 except for WMM2015 and 01/01/2015) are also selected. The three global HDGM2016, EMAG2v3 and WDMAM anomaly models are evaluated by comparing them with marine data for the NEAWM area only (mainly for computational reasons) at 0.
Model calculations Grids, respectively, showing intensity of the total magnetic field, inclination and declination (when available), are estimated at mean sea level, directly from Gauss Fig. 1  Summary diagram illustrating the definition of the magnetic components of the Earth's magnetic field used in the study, the associated geomagnetic models and the data used for evaluation, according to SH degrees and spatial resolutions coefficients integrated into Shom's processing tools for IGRF-12, WMM2015 and HDGM2016 models (and also the HDGM2016 core sub-model estimated considering only coefficients up to SH degrees 13) or by using computation programs published and associated with EMM2015, COV-OBS. x1, CM4 and POMME-9 models. Scripts were developed to iteratively execute programs that allow point estimation and thus to compute values on 1° and 0.
5° grids. EMAG2v3 and WDMAM anomaly grids were also computed by resampling full-resolution published datasets for the targeted 0. 5° grid spacing (Fig. 1 ). For all models except WMM2015, the computed grids are filtered using the Oasis montaj software-MAG-MAP extension ( © Geosoft) to estimate grids describing the internal component of the models only with SH degrees lower than 12 or 13 (Fig. 1 ). Tests show that the differences between filtered field intensity grids and the WMM2015 or IGRF-12 estimated grids taken as a reference are minimized using the following parameters, which are generalized in our study: (1) removing/ restoring the average tendency, (2) padding/unpadding using the maximum entropy method (Burg 1975), and (3) applying an order-12 Butterworth filter and a cut-off wavelength equivalent to the next higher SH degree (i.
Evaluation of the models with marine data Marine magnetic data acquired during 71 surveys were extracted from Shom's geophysical database (Fig. 2  and Table  2 ). About half of them consist of in-house surveys. The others are published by other organizations (i. e. the French National Oceanographic Data Centre (SISMER) and the NOAA National Centers for Environmental Information (NCEI)). Data cover the NEAWM area as patches on the following five geographical sub-areas of interest: (1) mid-Atlantic ridge between 20°N and 45°N, (2) Canary Islands to offshore west Portugal, (3) Bay of Biscay, (4) English Channel to North Sea, and (5) western Mediterranean Sea. They were chosen to characterize a wide range of geodynamic and geomagnetic contexts, from shallow continental shelves and margins to deep abyssal plains, including the Atlantic mid-oceanic ridge.
Agitation variations are calculated by subtracting the previous diurnal variations from the total field measured at the observatory. Shom's datasets were resampled every 200 m along profiles by a weighted average method. To compare marine data with models defined up to SH degree 720 or subsampled to 0. 5°, the highest frequencies (< 55 km) are filtered with an order 8 Butterworth filter after removing the average tendency and padding the data.
Several spatial and amplitude discrepancies can be observed however: (1) Differences computed for SH degree 12 models are a few nanoteslas greater than those for SH degree 13. Moreover, maps of differences in SH degree 12 exhibit higher wavelengths and a noticeable inverse polarity (see for example, the south of Africa or the alternation of positive and negative differences in the South Atlantic Ocean in Fig. 3 ) compared with SH degree 13 maps. All these observations are consistent with the lower degree of spherical harmonics; and (2) the COV-OBS. x1 model seems to be relatively equivalent to HDGM2016, EMM2015 and POMME-9 models for the   3 ). b Summary diagram showing selected datasets sorted by acquisition year (in abscissa). The data precision estimated by differences at crossover points is indicated in ordinate.
The precision of the datasets marked with a star is undetermined (mainly due to a lack of crossover points), but estimated to be lower than 30 nT date 01/01/2010 with average differences of a few nanoteslas. However, the 01/01/2015 forecast for the COV-OBS. x1 model is not robust and seems to be inconsistent with other models (see Fig. 3d ). Mean differences are one to two orders of magnitude higher and are estimated to be about 97 ± 260 nT at a global scale and -4 ± 140 nT for the NEAWM area. These results are coherent with the conclusions of  Thébault et al. (2015b)  for the evaluation of candidate models for IGRF-12. The authors underline that all submitted models are not different enough to be rejected with differences generally much less than 10 nT. Amongst them, parent or derived models are used in the present study (i.
Intensity of the anomaly field The intensity of the anomaly field estimated using the HDGM2016, EMM2015, EMAG2v3 and WDMAM models is evaluated up to SH degree 720 or with the equivalent 0. 5° grid spacing (see Additional file 4; Figs. 6  and 7 ). HDGM2016 and EMM2015 up to SH degree 720 can be considered as equivalent. Firstly, differences between these two models are small for all dates and extents, estimated at about few nanoteslas. They appear to be relatively higher for the date 01/01/2015 (only about 1 nT higher on average). The difference map suggests a longwave residual signal that can be explained by the use of more complete SWARM datasets for the HDGM2016 model, whereas only data up to 2014. 8 are considered for EMM2015 (Fig. 6a ). Secondly, spectral content is consistent for both models at a global scale and for the NEAWM area (Fig.