dataesr/scientific-paragraphs-categorization huggingface dataset:
Summary of CIGALE Physical Properties The main physical properties were estimated for all galaxies with known redshift (spectroscopic or photometric), with at least two detections in the optical range, two detections in NIR range and at least two FIR measurements (PACS and/or SPIRE) with SNR>2. We call this the A list. The CIGALE code was used to estimate dust luminosity, star formation rate, stellar mass and the AGN contribution to dust luminosity. Alongside the physical properties obtained from the full UV to far infrared SED fitting, three different values of χ 2 s are provided: reduced χ 2 r , quantifying the global quality of the SED fit for each galaxy, and OP T χ 2 and IR χ 2 , described in section 4. 7 (detailed description can be found in  Ma lek et al. 2018). Those quantities can help to identify possible interesting sources or modelling failures.
DISCUSSION AND CONCLUSION Understanding galaxy formation and evolution requires measurements from many facilities to trace the different physical processes such as star formation and AGN activity in galaxies over cosmic time. These data need to cover large areas of the sky to obtain the large samples to characterise the population properties. These data sets need to be homogenous and well understood to enable the statistical studies required. Construction of such a dataset has been the main goal of the HELP project. The corresponding data release documented in this paper describes HELP's first attempt at solving the challenges in the collation and merging of large, disparate, complex multi-wavelength datasets. Focusing on the Herschel fields for our first data release, DR1, HELP collates and homogenous optical and near-infrared catalogues over 1270 deg.
2012). Ongoing and future surveys will also be able to exploit and build on the HELP data products. The LOFAR deep field team have used the HELP products directly (e. g. Gloudemans, A. J. et al. 2020; Smith, D. J. B. et al. 2020; Wang, L. et al. 2020 ) and together with HELP team, exploring the far-infrared radio correlation  McCheyne (2021). The MeerKAT International GHz Tiered Extragalactic Exploration (MIGHTEE,  (Jarvis et al. 2016 )), is another ongoing radio survey with fields that overlaps with HELP and so products like the deblended FIR XID+ fluxes will be a valuable resource e. g. in deeper exploration of the far infraredradio correlation. The wide spectrum of physical properties can be also used to design or simulate upcoming large surveys such as the Legacy Survey of Space and Time (LSST)  (Riccio 2021).
The selected LSST Deep Drilling fields (ELAIS S1, XMM-LSS, Extended Chandra Deep Field-South and COSMOS) are also covered by HELP and so data products these fields could provide immediate inputs to planning and direct contributions to early science. The large area and depth of HELP can enable more robust analysis of the fundamental statistical relations. However such analyses must take into account the associated selection functions. Modelling the full selection functions for physical properties is now the focus of HELP research. Modelling how fluxes propagate through to redshifts and other physical properties. We have aimed to provide the necessary information throughout the HELP pipeline to make this more tractable. Campos Varillas (2021) will develop empirical methods to estimate the HELP selection function in order to probe the bright end of the stellar mass function of galaxies in Herschel Stripe 82.
Future data releases and how to contribute HELP's open approach means decisions made during the collation and production of DR1 are transparent and can be reproduced by other teams. It also provides a pathway for any astronomers to contribute and add to the HELP dataset in the future. As optical, infrared and radio surveys produce new datasets, these will need to be combined with legacy surveys. The methods, pipelines and tools described in this paper provide a platform to enable their ingestion. As further data sets are incorporated it will also be possible to improve the prior list and the subsequent photometry and the SED fitting. The open source nature of the HELP pipelines and tools means these improvements can be done by anyone and are not dependent on the current HELP team. For DR1 we decided to generate the most versatile data products, with a non-informative prior, but specific science may benefit from using the full optical to mid infrared photometry and SED fitting to use more informative flux priors.
For HELP to continue being a valuable resource there will need to be a transition from a centrally managed project to becoming a distributed community endeavour. Using version control systems such as GitHub which have enabled many collaborators to contribute to open source projects will be one way in which such a community can be fostered. We are working with other survey teams and training them in the use of the HELP pipelines and tools. We welcome new teams interested in combining their observational data, or value-added related datasets (e. g. alternative physical parameter catalogues, galaxy cluster catalogues etc) to contribute and integrate their data.
Summary We present the HELP project which collates extragalactic surveys from the optical to Herschel far infrared bands. This includes an open source software pipeline as well as the resultant data products. This first data release, DR1, can be used to study an unprecedented wide area of Herschel sky. Some key highlights of this new data set are: • We have collated data for 170 million objects from optical to far infrared over 1270 deg 2 of the prime extragalactic fields, with boundaries defined by the mapping of the Herschel Space observatory. • We present far-infrared photometry for 18 million objects in an optical to mid infrared selected prior list chosen to be tightly correlated with far infrared bright objects. We calculate a posterior distribution on the flux for all objects using Bayesian inference.
• We publish the main physical properties; stellar mass, dust luminosity and star formation rate based on spectral energy distribution modelling with the CIGALE code. This is done for all galaxies with at least two detections in each wavelength region for a total of 1. 7 million objects, being 1% of the total HELP catalogue. • The new catalogue is presented alongside an array of other data products, including newly homogenised images, supplementary catalogues and extensive tools for accessing and analysing these new data sets. the project, some data products, validation, analysis or writing of the paper. In order to access the flat files you will need to navigate the structure of Data Management Units products defined in Table  C1. At each stage of the pipeline the working files are saved with the final merged catalogues stored in DMU32.
ACKNOWLEDGEMENTS The authors would like to acknowledge  Manda Banerji ,  Michelle Cluver ,  Duncan Farrah ,  Peter Hatfield ,  Tom Jarrett ,  Emeric Le Floc 'h,  Richard McMahon ,  Mark Sargent ,  Roberto Scipioni ,  Dan Smith ,  Elisabetta Valiante ,  Ivan Valtchanov ,  Stephen Wilkins  and  Louise Winters  for useful conversations or important contributions to the project that do not form a specific part of this paper. We are grateful to  Stephen Serjeant  for useful comments on the manuscript while under review. The research leading to these results has received funding from the  European Union Seventh Framework Programme   FP7/2007-2013/  under grant agreement No. 607254. This publication reflects only the authors' view and the  European Union  is not responsible for any use that may be made of the information contained therein.
Seb Oliver  acknowledges support from the  Science and Technology Facilities Council  (grant number  ST/L000652/1 ) KM has been supported by the  National Science Centre  ( UMO-2018/30/E/ST9/00082 ). MJ, LM, MP and MV acknowledge support from the  South African Department of Science and Technology  ( DST/CON 0134/2014 ). MP and MV acknowledge financial support from the  Inter-University Institute  for Data Intensive Astronomy (IDIA). IDIA is a partnership of the  University of Cape Town , the  University of Pretoria  and the  University of the Western Cape. VB has received funding from the  Excellence Initiative of Aix-Marseille University-AMIDEX , a  French 'Investissement d'Avenir' programme. HCSS / HSpot / HIPE  are joint developments by the  Herschel Science Ground Segment Consortium , consisting of  ESA , the  NASA Herschel Science Center , and the  HIFI, PACS  and SPIRE consortia.
This research made extensive use of TOPCAT (Taylor 2005), STILTS (Taylor 2006b) and Astropy (Astropy Collaboration et al. 2018). This research has made use of the NASA/IPAC Extragalactic Database (NED) which is operated by the  Jet Propulsion Laboratory, California Institute of Technology , under contract with the  National Aeronautics and Space Administration. This work has made use of the COSMOS2015 catalogue (Scoville et al. 2007) based on data products from observations made with  ESO Telescopes at the La Silla Paranal Observatory  under  ESO  programme  ID 179. A-2005 and on data products produced by TERAPIX and the  Cambridge Astronomy Survey Unit on behalf of the UltraVISTA consortium. SPIRE has been developed by a consortium of institutes led by  Cardiff Univ. (UK)  and including  Univ.
Lethbridge (Canada) ;  NAOC (China) ;  CEA ,  LAM (France) ;  IFSI ,  Univ. Padua (Italy) ;  IAC (Spain) ;  Stockholm Observatory (Sweden) ;  Imperial College London ,  RAL ,  UCL-MSSL ,  UKATC ,  Univ. Sussex (UK) ;  Caltech ,  JPL ,  NHSC ,  Univ. Colorado (USA). This development has been supported by national funding agencies:  CSA (Canada) ;  NAOC (China) ;  CEA ,  CNES ,  CNRS (France) ;  ASI (Italy) ;  MCINN (Spain) ;  SNSB (Sweden) ;  STFC ,  UKSA (UK) ; and  NASA (USA). HELP would like to thank the  HELP Scientific Advisory Board  members past and present for invaluable advice in defining the project:  Simon Driver  (chair),  Loretta Dunne ,  Carol Lonsdale ,  Mark Lacy ,  Peter Capak ,  Takashi Onaka ,  Mara Salvato ,  Brent Groves ,  Göran Pilbratt , and  David Elbaz. The data presented in this paper is released through the Herschel Database in  Marseille HeDaM  (http://hedam.
Amyloid Structure Determination by Solid-State NMR and Perspectives on Integrative Approaches High-resolution structure determination of amyloids depends on the collection of appropriate SSNMR data sets. Contrary to X-ray crystallography or electron microscopy, NMR spectroscopy does not provide direct information on the 3D structures, i. e. , spatial position of atoms, but rather indirect measurements such as local conformations (e. g. , from chemical shifts) or atom proximities in space. For instance, in carbon NMR experiments such as PDSD (Proton Driven Spin diffusion) or DARR (Dipolar Assisted Rotational Resonance) magnetization is transferred between 13 C nuclei that are close in space. From such spectra, it is then possible to collect cross-peaks correlating 13 C atoms within the same residue or between different residues (up to 8-10 Å) by varying the experimental NMR set up  (Castellani et al.
, 2002). Identified proximities through cross-peak assignment are converted into spatial restraints, similarly to solution NMR methods. Yet, computational methods are required to generate molecular conformations satisfying these restraints. The molecular structures are calculated with dedicated software such as CYANA  (Güntert et al. , 1997) , ARIA  (Bardiaux et al. , 2012) , UNIO  (Guerry and Herrmann, 2012) , Xplor-NIH  (Schwieters et al. , 2003) , or Rosetta  (Shen et al. , 2008). Owing to the repetitive nature of amyloid fibers where monomers are stacked along the main fiber axis, throughspace SSNMR correlations may arise from intra-monomeric (i. e. , with the same protein monomer), and/or inter-monomeric proximities (i. e. , between neighboring protein monomers). Hence, structure determination of amyloid structures generally benefits from a three-step labeling strategy in order to collect intra-and inter-molecular restraints.
, 1997)  and ARIA  (Bardiaux et al. , 2012)  software, can streamline the assignment process using an iterative procedure to reduce ambiguities in the restraints while producing high-quality structures ensembles  (Loquet et al. , 2008; Manolikas et al. , 2008). The sets of distance restraints are generally supplemented by angular restraints on the dihedral backbone angles, predicted from secondary chemical shifts with empirical models like TALOS+  (Shen et al. , 2009). Symmetry is generally enforced during computation to maintain a similar fold of monomers along the fibril  (Nilges, 1993). Inter-molecular distances are also replicated along the axis of the fibril to be applied on each layer of the fibril. Stacking properties are complementarily observed with other experimental techniques to help fully characterize amyloid filaments.
FUNDING We acknowledge financial support from the European Research Council (ERC) under the European Unions Horizon 2020 Research and Innovation Program (ERC-2015-StG GA no. 639020 to ALo), the ANR (ANR-17-CE11-0035 to SJS and ANR-16-CE11-0020-01 to MF, GL, BB and ALo), and INCEPTION project (PIA/ANR-16-CONV-0005) to BB. AD was supported by the Nouvelle Aquitaine Regional Council. ALe was supported by the Swiss National Science Foundation for early postdoc mobility project P2EZP2_184258. This work has benefited from the facilities and expertise of the Biophysical and Structural Chemistry platform (BPCS) at IECB, CNRS UMS3033, Inserm US001, Bordeaux University.
Emerging diseases are a major public health problem as illustrated by the cur-rent coronavirus disease (COVID-19) pandemic. To make the right decisions, public health departments need a decision-making system. In Africa few IT systems have been put in place to help managers of public health in the analysis of their multidisciplinary data. The majority of digital health solutions are operational databases, as well, focused on surveillance activities that do not include the laboratory component. This paper describes the design model and implementation of data warehouse for dangerous pathogen monitoring in a laboratories network. Talend data integration is used to extract data in Excel sheets, transform it and load it into a MySQL database.
Background A data warehouse is different from a regular database which is used to run the business on a current basis and support everyday transactions and processes. Data warehouse is defined as an subject-oriented, integrated, time-variant, and non-volatile collection of data primarily used in strategic decision making by means of on-line analytical processing (OLAP) techniques  [5]. The construction of the data warehouse through the ETL processes and an interactive dashboard according to the key performance indicators defined by the business decision-makers  [6]. Currently, data relating to pathogens stored in the country laboratories are collected in excel sheets. About twenty laboratories have this file that they fill out and send to the central laboratory on a monthly basis. This process does not allow to do a consolidated analysis of activity at the national level.
Conceptual schema The proposed data warehouse is a star schema type design with one center fact table and three dimensions  [8]. Figure  2    The proposed data warehouse design is implemented under MySQL. It contains four refined distinct relations interrelated together. Figure  3  shows the logical diagram of the data warehouse implemented under MySQL. Now, as all data were successfully cleaned and transformed, they are exported from their buffer zone in which they were temporary stored to the new informational database or data warehouse just built.
Conclusion This paper introduced a model for building a data warehouse for high pathogen management in a country. The activity measured is the number of dangerous pathogens stored as well as their variation in order to make decisions in the public health area. The data warehouse is an informational database whose data are extracted from an already existing Excel files. The purpose of the proposed design is to help decision makers in performing data mining and data analysis over the data stored in the warehouse which eventually helps them in discovering critical patterns and trends.
g. STAR  (Dobin et al. 2013 )) in order to correctly model exon-intron junctions  (Djebali et al. 2017). The second step aims at assemble mapped reads into known (already present in the reference annotation) and novel transcripts using dedicated transcript reconstruction tools [e. g. Cufflinks  (Trapnell et al. 2010)  or StringTie  (Pertea et al. 2015) ]. While the two first steps are common to both coding and non-coding genes, the third step focuses on classifying novel transcripts into mRNAs or lncRNAs by computing their coding-potential capabilities. An additional though optional step would involve the sub-classification of newly annotated lncRNAs with respect to the localisation and the direction of transcription of proximal mRNA transcripts in order to define lncRNAs classes such as lincRNAs (long intergenic ncRNAs) or antisense lncRNAs.
Based on dedicated annotation resources LncRNA maps of domesticated species can be reached from several publicly available resources. As shown in Table  1 , these resources use different computational tools at each main step of the RNAseq processing pipeline described above (Table  1 ). Furthermore, the total number of lncRNA genes and transcripts vary substantially between domesticated species and do not currently scale with the number of lncRNA in human and mouse catalogues (Table  1 ). One of the most widely used resources for extracting gene annotations is provided by the Ensembl genome browser  (Aken et al. 2016; Howe et al. 2021). Ensembl provides genome-wide annotations of protein-coding and non-coding RNAs for more than 250 vertebrates, including many domesticated animals. In human or canonical model organisms (e.
g. mouse), the specific process of annotating long non-coding RNAs combines automated annotation from RNAseq data processed by the Ensembl gene build pipeline and manual curation by the HAVANA/Gencode group  (Frankish et al. 2019). The Gencode database (version 37), which is synchronised with Ensembl, has compiled 17 948 human lncRNA genes (~ 48 000 transcripts) and 13,186 mouse lncRNA genes (~ 18 000 transcripts) (version M26). For other species, including domesticated animals, the description of the built lncRNA catalogues has been less detailed to date and does not include manual curation which most likely impacts the quality of these annotations. In addition, in contrast to human and mouse Ensembl catalogues, only intergenic genes (lincRNAs) are referenced, meaning that other biotypes such as antisense exonic or sense intronic transcripts, are not reported for domesticated species.
The number of Ensembl lncRNA genes varies greatly between the 5 major domesticated species. For instance, 1480 lncRNA genes have been identified in the cow and approximately 7000 in the horse, dog, and pig, whereas the number of protein-coding genes (mRNAs) remains more stable (~ 20,000) (Fig. 1A ). Similar to mouse, the number of lncRNA transcripts/isoforms per gene in the cow and dog ranges from 1. 4 to 1. 7 lncRNA transcripts per gene, respectively, which is significantly lower than the 1. 8 and 2. 5 mRNA transcripts per gene for protein-coding genes in the respective species. This might be due to the difficulty to identify lowly expressed lncRNA isoforms by RNAseq methodologies (Fig. 1A ). When comparing the length of lncRNA transcript sequences across domesticated species (Fig. 1B ), one could note that pig and chicken lncRNA transcripts are significantly longer than those in other mammal species (Mann-Whitney U tests, p values < 2.
2e-16). Interestingly, the recent annotations of the new sus scrofa and gallus assemblies have benefited from the use of long-read RNAseq (LR-RNAseq) (PacBio Iso-Seq from nine adult porcine tissues  (Warr et al. 2020; Beiki et al. 2019 ) and from originally two and now six addition chicken tissues  (Kuo et al. 2017 ; https:// www. ensem bl. org/ Gallus_ gallus/ Info/ Annot ation), which might have enabled global extensions of transcript models as this trend has also been observed for protein-coding genes (Fig. 1B ). As every automatic modelling process, the Ensembl gene build pipeline might also suffer from incorrect annotations. A closer inspection of the Ensembl-based catalogues of lncRNAs in the five domesticated species identified the probable misclassification of some mRNAs as long noncoding transcripts.
For instance, between 5. 5% lncRNAs in horse and 11. 8% lncRNAs in cow were classified as proteincoding by the FEELnc program  (Wucher et al. 2017). When searching for the longest ORFs, either partial (i. e. missing start codon) or full (having both a start and stop codons), in these "ambiguous" transcripts (Fig. 1C ), the ORF appears to cover a large fraction of the annotated RNA sequences (median = 51% in pig to 82% in dog) despite the fact that it should have been filtered  (Aken et al. 2016 ) (Table  1 ). Therefore, a high ORF coverage would suggest that these transcripts might represent bona fide protein-coding transcripts and exclude the possibility that they correspond to lncRNAs harbouring small ORFs (smORFs)  (Bazzini et al. 2014; Ruiz-Orera et al. 2014). Despite these shortcomings, the Ensembl resource is extremely useful for the scientific community working on non-model organisms because it provides a versioned, stringent, and freely available set of gene/transcript structures (both coding and non-coding) at the basis of most downstream bioinformatic analyses.
Besides Ensembl, several more recent databases also provide extensive annotations of non-coding genes based on different computation pipelines (Table  1 ). For instance, the NONCODE database  (Zhao et al. 2016 ) is specifically dedicated to the annotation and bioinformatic characterization of long non-coding RNAs in animals and plants. The integration of lncRNAs in NONCODE makes use of the CuffCompare tool from Cufflinks  (Trapnell et al. 2010)  in order to combine and filter multiple sources of lncRNA annotations. One advantage of NONCODE over Ensembl is that it involves the use of a published coding-potential assessment tool, CNIT for Coding-Non-Coding Identifying Tool  (Guo et al. 2019) , an updated version of the CNCI program  (Sun et al. 2013) , to discriminate reconstructed coding from non-coding gene models.
One limitation though is that NONCODE only includes lncRNA catalogues for 16 animal species, excluding dog and horse for instance. Whereas, in the case of Ensembl-matched species, the number of lncRNA transcripts is significantly higher with 9527, 17,811, and 22,227 lncRNA loci for chicken, pig, and cow, respectively. In addition, NON-CODE provides a detailed characterization of annotated lncRNAs based on phylogenetic conservation, disease association, as well as lncRNAs overlapping SNPs/GWAS hits. Historically, the first specific database of lncRNAs dedicated to livestock species was the domestic-animal lncRNAs database (ALDB)  (Li et al. 2015) , although this database seems not to have been updated since 2016. Using a rather out-dated bioinformatic pipeline including the TopHat mapper and the CPC tool for assessing coding-potential, ALDB comprises 6151 (8923), 7381 (12 103), and 5213 (8250) lincRNA loci (transcripts) for chicken, pig, and cow, respectively.