dataesr/scientific-paragraphs-categorization huggingface dataset:
Achieved beam sizes and sensitivities The achieved synthesized beam size varies with the frequency and the exact array configuration, when each source was observed. The average size of minor axis is 0. 85 arcsec (minimum of 0. 72 arcsec and maximum of 1. 04 arcsec), while the average major axis size is 1. 13 arcsec (minimum of 0. 9 arcsec and maximum of 1. 6 arcsec). Our data follow the requirements on the beam size (>0. 7 arcsec). The mean ratio between major and minor axis is 1. 3 and the largest value is 1. 8. The [CII] sensitivity was measured on the moment-0 maps. The mean integrated line flux root mean square (RMS) sensitivity is 0. 14 Jy km/s. The mean sensitivity is better in the low-frequency range (283-315 GHz, 5. 1 < z < 5. 9) with 0. 11±0. 04 Jy km/s than in the high-frequency range  (345) (346) (347) (348) (349) (350) (351) (352) (353) (354) (355) (356) 4.
(  2016 ) template is too cold for both redshift bins, both Schreiber et al. (  2018 ) and  Bethermin et al. (2017)  templates well fit the data (χ 2 <4 with 4 degrees of freedom for both templates in both redshift bins). Since the χ 2 of Bethermin et al. (  2017 ) is marginally better, we decided to use this template. In Table  3 , we provide the ratio between the monochromatic luminosity (νL ν units) and L IR computed using this template at wavelengths associated with bright fine-structure lines, which can be targeted by ALMA. In practice, for the ALPINE catalog (Appendix B), we use the exact effective wavelength of the ALMA continuum.
2±0. 3 ν for the full sample and dN/dS ν ∝ S -1. 8±0. 4 ν for the secure sample. Above our brightest data point, we use the Euclidian plateau level measured by Planck Collaboration et al. (2013, dN/dS S 2. 5 = 15 Jy 1. 5 /sr). The uncertainties are determined by recomputing 100 000 times the integral of the number counts after randomly offsetting the data points according to their error bars. In Fig. 14 , we present the contribution to the CIB as a function of the flux density limit. The contribution of sources brighter than the limit of our number counts (0. 35 mJy) is 0. 093±0. 013 MJy/sr for the full sample and 0. 054±0. 009 MJy/sr for the secure sample. This is 69 % and 40 % of the full CIB measured by  Odegard et al. (2019, 0. 135  MJy/sr assuming a CIB spectrum for the color correction).
Obscured SFR as a function of [CII] luminosity To understand the excess of luminous [CII] objects in our sample, we derived the average SFR in various [CII] luminosity and redshift bins. These mean SFRs are determined by combining the UV rest-frame data  (Faisst et al. 2020 ) and the mean obscured SFRs determined using a stacking of ALPINE continuum data. This analysis is based only on stacked ALMA data and does not explore the scatter. A more comprehensive study combining ALPINE data and ancillary data is presented in Schaerer et al. (2020). We first define four [CII] luminosity bins between 10 8 and 10 9. 33 L with the same logarithmic width. We chose to stack by bins of [CII] luminosity and not by bins of continuum flux density, since we have many more [CII] detections. In addition, the continuum is not affected by the velocity offsets presented in Sect.
Conclusion In this paper, we presented the data processing and the catalog construction of the ALPINE ALMA large program. The performance of our survey is fully compatible with our initial goal. Our main technical results are: -After flagging a few badly calibrated antennae, we produced continuum and [CII] moment-0 maps and reached a typical sensitivity of 30 µJy RMS in continuum and 0. 14 Jy km/s for [CII] in a 235 km/s bandwidth. The average beam size is 1. 13"×0. 85". -We investigated the stability of quasars used as flux calibrators during the observation campaign and found that the fluctuations between two successive calibrator survey observations are lower than the standard 10 % calibration uncertainty. 13 The scatter on the Lagache et al. (  2018  -We detected 23 of our 118 targets in continuum above 3.
In addition to these technical results, we obtained promising first scientific results. Our main findings are: -To determine the conversion factor from the 158 µm continuum luminosity to the total infrared luminosity (L 158 µm = 0. 093 L IR ), we measured average dust SEDs by stacking single-dish data at the position of COSMOS photometric sources similar to ALPINE ones. -The target sources detected in continuum have a median flux density of 0. 26 mJy, a median L IR of 4. 4 × 10 11 L , and a median stellar mass of 1. 1 × 10 10 M. As expected, the detections are among the most massive and star-forming systems of our sample. -The nontarget continuum detections have a mean redshift of 2. 5±0. 2 (median = 2. 3) and are mainly lower-redshift sources without a physical connection with the ALPINE targets.
Acknowledgements. We thanks the  ALMA observatory staff  for their support and in particular  Edwige Chapillon , our ALMA contact scientist, for her very useful advice. MB acknowledges  Mladan Novak  for his excellent advice about the photometry in interferometric surveys. This paper makes use of the following ALMA data: ADS/JAO. ALMA# 2017. 1. 00428. L. ALMA is a partnership of  ESO  (representing its member states),  NSF (USA)  and  NINS (Japan) , together with  NRC (Canada) ,  MOST  and  ASIAA (Taiwan) , and  KASI (Republic of Korea) , in cooperation with the  Republic of Chile. The Joint ALMA Observatory is operated by  ESO ,  AUI/NRAO  and  NAOJ. This research made use of Astropy, 14 a community-developed core Python package for Astronomy (Astropy Collaboration et al. 2013b; Price-Whelan et al.
2018). This program receives funding from the  CNRS national program Cosmology and Galaxies. A. C. , C. G. , F. L. , F. P. and M. T. acknowledge the support from grant  PRINMIUR 2017 20173ML3WW_001. DR acknowledges support from the  National Science Foundation  under grant numbers  AST-1614213  and  AST-1910107  and from the  Alexander von Humboldt Foundation  through a  Humboldt Research Fellowship for Experienced Researchers. G. C. J. acknowledges  ERC Advanced  Grant  695671  " QUENCH " and support by the  Science and Technology Facilities Council (STFC). E. I. acknowledges partial support from  FONDECYT  through grant N • 1171710. GL acknowledges support from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (project  CONCERTO , grant agreement No  788212 ) and from the  Excellence Initiative of Aix-Marseille University-A*Midex , a  French "Investissements d'Avenir" programme.
We introduce a novel efficient approach for community detection based on a formal definition of the notion of community. We name the links that run between communities weak links and links being inside communities strong links. We put forward a new objective function, called SIWO (Strong Inside, Weak Outside) which encourages adding strong links to the communities while avoiding weak links. This process allows us to effectively discover communities in social networks without the resolution and field of view limit problems some popular approaches suffer from. The time complexity of this new method is linear in the number of edges. We demonstrate the effectiveness of our approach on various real and artificial datasets with large and small communities.
Real Networks We used 5 real networks and the ground-truth communities are available for 4 of them. Table  1  presents the properties of these networks. We compared SIWO and Louvain on Eurosis network  [9]  which represents scientific web pages from 12 European countries and the hyperlinks between them without known ground-truth communities. However, since each European country has its own language, web pages in different countries are sparsely connected to each other. Moreover, as reported in  [9] , some of the countries can be 1 https://github. com/taynaud/python-louvain. 2 https://www-complexnetworks. lip6. fr/ ∼ latapy/PP/walktrap. html. divided into smaller components e. g. Montenegro network includes three components: 1-Telecom and Engineering, 2-Faculties and 3-High Schools. Louvain detects 13 communities whereas SIWO detects 16 communities in this network.
Paired-end sequencing was conducted on the Illumina HiSeq2500 Sequencing System, generating 101-nucleotide reads at the National Research Council, Aquatic and Crop Resource Development, Saskatoon, Canada. Sequencing adapters were removed, and low-quality reads were trimmed using Trimmomatic with default settings  (Bolger et al. , 2014). Reads were mapped to the annotated A. thaliana genomes (TAIR10) using STAR  (Dobin et al. , 2013). Transcripts were identified using StringTie  (Pertea et al. , 2015)  followed by Cuffmerge tool  (Trapnell et al. , 2012). HTseq-count  (Anders et al. , 2015)  was used to count the reads spread across the exonic regions of each gene. Differential expression analysis was performed with DESeq2 between mutants and wild type  (Love et al. , 2014). Normalized read counts from DESeq2 were expressed as gene expression levels.
Materials and Methods To simulate the hydrology and hydrodynamics of the LIG and the PI, we use data from eight global climate models of the Paleoclimate Modelling Intercomparison Project Phase 4: CESM1. 2, CESM2, EC-EARTH3. 2, HadGEM3-GC3. 1, IPSL-CM6-LR, MPI-ESM 1. 2. 01p1-LR, NorESM1-F, and NUIST-CSM. Depending on the model, the atmosphere's resolution ranges from 1°to 2°, and simulations last between 100 and 300 yr. Simulations are conducted at equilibrium conditions, with forcing reflecting 127 ka for the LIG and CE 1850 for the PI. From the climate models, daily total precipitation and near-surface air temperature are inputted in the global hydrological model PCR-GLOBWB Version 2  (Sutanudjaja et al. , 2018). We test the climate model data against climate reanalysis (Text S2 and Figures  S1  and S2 ) and determine that bias correction of climate variables prior to input in the hydrological model is not warranted.
Input data are bilinearly interpolated to the 0. 5°resolution of the hydrological simulations. The time step is daily. Potential reference evapotranspiration is calculated from air temperature using the Hamon method  (Hamon, 1963). Simulations reflect natural conditions as much as possible, and human factors are excluded. Both LIG and PI simulations use present-day maps for land cover, river networks, and sea levels. In hydrological simulations, we let the groundwater storage adjust to the different climatology and then spin up the model for 50 yr. From PCR-GLOBWB we analyze surface runoff. We then interpolate the daily surface runoff from PCR-GLOBWB for input into the global hydrodynamic model CaMa-Flood  (Yamazaki et al. , 2011) , which solves the local inertial equation and runs at 0. 25°resolution.
Genomic and bioinformatic studies of several banana wild relatives greatly enhance our understanding of Musa genetic diversity, links to important phenotypic traits and bring new methods for management of the collection. Collectively, these research activities produce enormous amounts of data that require curation and dissemination to the public. The two information systems at the ITC, Musa Genebank Management System and the Musa Germplasm Information System, serve to manage the genebank activities and to make public germplasm-related data for over 30 banana collections worldwide, respectively. By implementing the 10-year workplan set out in the Global Strategy for the Conservation and Use of Musa Genetic Resources, the network MusaNet supports Musa researchers and stakeholders, including the ITC, and most importantly, links to the world's banana-producing countries via three regional banana networks.
With nearly 1 million germplasm samples distributed between 1985 and 2009, they are a key resource for plant breeding and research  (Galluzzi et al. 2016). Since 2012, the CGIAR genebanks are working as partners within the Genebank Platform, coordinated by the Global Crop Diversity Trust, to help them reach the United Nations Sustainable Development Goals. In addition, to comply with the highest standards, the CGIAR genebanks have adopted targets to assure that by 2022, 90% of the conserved accessions are readily available for distribution; 90% of the accessions are safely duplicated; 90% of the accessions display minimum passport or characterization data online and standard operating procedures are in place for the genebanks "essential operations. " 1  Through the Genebank Platform, the Crop Trust has put in place an endowment mechanism to ensure that the genebanks can carry out those essential operations.
Challenges linked to use The most critical aspect to increasing use is a good documentation status of the conserved material. Although the exact identity of plant material is an obvious prerequisite for all users, data about both agronomic performance, such as productivity and resistance to pests and diseases, and quality, such as fruit flavor and processing characteristics, are critical in the selection of desired cultivars. The proportion of characterized material, especially evaluated accessions, is too low and thus limits its full potential. In addition, better understanding of the genetic background (through molecular markers and genomes) is needed to support breeding. However, the ITC has provided material in different forms that has contributed to increased use, for example, through the distribution of lyophilized leaves to molecular biologists.
Also, the application of different seed extraction methods can lead to different results. Stotzky et al. (1962)  discovered that Musa balbisiana seeds require alternating temperatures to germinate. This is linked to the habitat where wild bananas are growing, often in open canopy gaps where day/night fluctuations are the highest, and the soil is exposed to direct sunlight. Due to the low seed germination rates, storage experiments of wild banana seeds have not yet systematically been executed. This is the reason why sometimes embryo rescue is applied. For this, embryos are excised under sterile conditions and grown in vitro, thus avoiding he requirement for dormancy breakage and increasing the chance that a plant can be regenerated. According to the Seed Information Database from the Royal Botanic Gardens, Kew (2019), most of the members of the Musaceae family are considered to have an intermediate storage behavior.
2017). The ITC germplasm is nearly fully characterized using these methods, but the Musa Genotyping Centre also offers different services to the Musa community that are of great help for germplasm conservation. For instance, flow cytometry and SSR fingerprints can be produced for any sample sent to the platform. In addition, published datasets are publicly available. Combined with the reference DNAs that are available upon request, they can be used to calibrate in house genotyping experiments (olomouc. ueb. cas. cz/en/content/ssrgenotyping). This was the case notably for the banana collection conserved at the USDA-ARS Tropical Agriculture Research Station in Puerto Rico, which successfully characterized its germplasm  (Irish et al. 2014). The Musa Genotyping Centre also allowed the 'real-time' characterization of the germplasm collected during collecting missions in Indonesia in 2012 and 2013  (Christelová et al.
acuminata and AA) with the aim to perform genome-wide association studies (GWAS)  (Sardos et al. 2016b ). The datasets from these studies are available and displayed in the on-line Musa Germplasm Information System  (Ruas et al. 2017)  in the 'genotyping studies' section (https ://www. crop-diver sity. org/mgis/ genot yping -study -list).
Gene-trait association Genome sequence information is a powerful tool to help identify allele variation linked to agronomic characters desirable for breeding  (Mascher et al. 2019). In banana, genome-wide association studies (GWAS) have led to the identification of useful diversity and molecular markers for sterility and parthenocarpy  (Sardos et al. 2016a, b). This study has proved GWAS to be applicable to banana despite the vegetatively propagated nature of the plant. It also provided to users a panel of diploid accessions and markers to investigate additional traits, even though information on a range of important farmer-preferred traits is still lacking to enable the use of this approach. Meanwhile, other omics datasets have been produced, notably on differential gene expression analyses and proteomics for a number of priority traits.
Musa Genebank Management System (MGBMS) Operating a genebank and curating accessions in collections is highly complex work. Indeed, each accession in the genebank is split into several types of physical material such as accessions preserved in vitro that need regular subculturing and quality assurance checks, accessions processed and stored in the cryobank, plants grown out in the greenhouse (for rejuvenation or observation purposes), leaf tissue samples harvested and maintained in the lyophilized tissue bank, in vitro samples that are subjected to health tests, characterization and then distributed in response to users requests. Each of these procedures generate a large body of data and information that is essential for the proper documenting and functioning of the genebank. Hence, it is of critical importance that management data and comprehensive information is recorded in an appropriate database system to ensure the integrity and security of the data stored and to support operational efficiency of the genebank (e.
g. identify critical bottlenecks and needs). At the ITC, a fully customized database system was developed in 2001 to manage the genebank activities and the associated data on a day-to-day basis. The system covers the genebank processes related to the acquisition, conservation, health testing, characterization, and distribution of accessions at the ITC. It includes barcoding and recording of data via mobile devices to reduce mistakes in the identity of the materials in the genebank. The current version of the system is fully workflow-oriented and highly integrated in the everyday activities of the genebank staff. ITC staff curate accession-level information, including passport information (unique accession code, name, source and taxonomy) using the FAO/Bioversity International multi-crop passport descriptors  (Alercia et al.
Musa Germplasm Information System (MGIS) The main community portal to retrieve information on banana is called the Musa Germplasm Information System (MGIS)  (Ruas et al. 2017) , based on the open source Drupal content management system and Tripal, a toolkit that facilitates construction of online genomic, genetic, breeding or other biological databases  (Ficklin et al. 2011; Sanderson et al. 2013).
Information on passport and morphological diversity MGIS contains key information on Musa germplasm diversity, including multi-crop passport data, botanical classification, morpho-taxonomic descriptors, studies, photographs and geographic information on 6619 accessions managed in 30 collections around the world, making it the most extensive source of information on banana genetic resources (https ://www. crop-diver sity. org/mgis/organ isati ons). The data are provided under a Data Sharing Agreement to ensure better recognition of work done by partners. The quality of the passport data provided is scored by using the Passport Data Completeness Index developed by  van Hintum et al. 2011b van Hintum et al. , 2011a. A specific version of the index to Musa has been developed to consider additional information collected on passport data such as the history of prior movement of the germplasm across collections (https ://www.
cropdiver sity. org/mgis/acces sion/01BEL 08460 9). To lower the level of error when recording descriptor values, as well as ensuring homogeneity in data collected, we developed a mobile application (https ://www. crop-diver sity. org/mgis/ conte nt/musat ab), which not only guides the data capture, but also facilitates photo capture of specific traits. All this information, delivered in a comprehensive framework, can help users identify material of interest. Once identified, the material from the ITC can be requested on the website which automates PDF form-filling through a three-step process to generate the SMTA.
Information on allele and trait diversity Phenotyping and genetic diversity studies are included in dedicated sections that provide an easy way to explore the accessions. For studies using genetic markers such as SNPs, we upscaled the system with an optimized tool  (Sempéré et al. 2019 ) which provides an easy and intuitive way to explore large amounts of SNP data produced on genebank accessions (www. crop-diver sity. org/mgis/ gigwa ). It offers a scalable solution to search for genotype patterns, functional annotations, or more complex filtering as well as data export in multiple format types. Datasets from several published studies have also been made available  (Sardos et al. 2016a, b; Cenci et al. 2020).
Curation to monitor the use of accessions The use of ITC accessions is documented based on a literature review. Each accession reported in a publication with an ITC accession number is added to MGIS to inform users about what type of data, information and knowledge exists about that accession. It also allows users to generate statistics about the most popular accessions in the ITC  (Rouard et al. 2018 ) (Table  1 ). As an example, the most cited and distributed accession is a M. acuminata subspecies burmaniccoides called 'Calcutta 4′. 'Calcutta 4′ has been used extensively in breeding because of its resistance to black leaf streak. It is also resistant to several races of Fusarium wilt and partially resistant to Radopholus similis and other nematodes. It is followed by 'Tani' , a representative accession of the M.
Interoperability with other systems Passport data information linked to ITC is transferred to the global portal Genesys (https ://www. genes ys-pgr. org/ wiews /BEL08 4) managed by the Global Diversity Crop Trust and synchronized with the Global Information System (https://ssl. fao. org/glis/entity/search?SearchForm[h oldwiews]=BEL084), which was established by the International Treaty to facilitate the exchange of information. Since 2018, the ITC collection uses Digital Object Identifiers promoted by the International Treaty to reinforce tracking of the material. A community portal called the Banana Genome Hub  (Droc et al. 2013)  has been developed to provide easy access to all types of genomics data and enable genome exploration using genome browsers that are connected to marker datasets maintained in MGIS.
This ensures that genetic material is properly described and accessible for any subsequent experiment. Genetic information can also be tracked back to the original material. Monitoring the use of accessions is also fostered by linking with breeding program datasets. For example, using a common application programmatic interface called the Breeding API  (Selby et al. 2019) , the Musa Germplasm Information System and MusaBase (https ://musab ase. org/) are connected and can exchange useful information for their respective target audience. The advantage is an additional source of information from breeding programmes about genebank material for which evaluation or phenotyping was carried out but not readily accessible from MGIS.