dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgement We are grateful to the referee for helpful suggestions. CARMENES is an instrument for the Centro Astronómico Hispano Alemán de Calar Alto in Almería, Spain. CARMENES is funded by the German Max-Planck-Gesellschaft (MPG), the Spanish Consejo Superior de Investigaciones Científicas (CSIC), the European Union through FEDER/ERF funds, and the members of the CARMENES Consortium (Max-Planck-Institut für Astronomie, Instituto de Astrofísica de Andalucía, Landessternwarte Königstuhl, Institut de Ciències de lEspai, Institut für Astrophysik Göttingen, Universidad Complutense de Madrid, Thüringer Landessternwarte Tautenburg, Instituto de Astrofísica de Canarias, Hamburger Stern-.
warte,  Centro de Astrobiología  and the  Centro Astronómico Hispano-Alemán ), with additional contributions by the  Spanish Ministry of Economy , the state of  Baden-Württemberg , the  German Science Foundation (DFG) , the  Klaus Tschira Stiftung , and by the  Junta de Andalucía. This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  653477. We also acknowledge financial support from the  Spanish Ministry of Economy and Competitiveness (MINECO)  through grants  ESP2013-48391-C4-1-R ,  ESP2014-57495-C2-2-R ,  AYA2014-54348-C3-1-R  and  AYA2014-54348-C3-2-R. AR acknowledges support from the  European Research Council  under the  FP7 Starting Grant  agreement number  279347  and from  DFG  grant  RE 1664/9-1.
I. INTRODUCTION FabSpace 2. 0 aims at making universities open innovation centers for their region and improving their societal contribution to the socio-economic and environmental performance. It concentrates on one innovation area with high expected socio-economic and environmental impact: geodatadriven innovation, by leveraging space data in particular. By enabling universities as co-creators of a new innovation scene, developers from the civil society, industry or the academic research, public administrations and civil organizations can meet, set up a creative environment, work together and finally co-create new tools and business models. Beyond business issues, innovation possibilities are huge in the field of satellite data applications given that the Galileo and Copernicus satellite programs are entering their operational phase,  [1].
Thanks to these massive investments in technology, European companies will benefit from the increase of regular and cheaper (if not free of charge) data flows, which could lead to the development of new and innovative applications and services in an incredibly vast range of markets, including (and in a large part focusing on) non-space markets. Important role to this activity is assigned to a new kind of innovation framework, coined the term fab labs. A fab lab is a one-stop shop access to any materials, machines and tools to digitally manufacture new products. In this particular project, the term is altered to the more descriptive term, "FabSpaces". A FabSpace is a one-stop shop-access to a range of data (including space data), free software & data processing tools, to develop new applications.
The consortium of FabSpacce 2. 0 project consists of 15 partners, distributed within the European Union (in France, Germany, Poland, Belgium, Italy and Greece). Currently, one FabSpace has been created per participant country, providing free-access services and a hub dedicated to collaborative datadriven innovation. This number is expected to be increased to 13 at the end of the project (early2019). Through online support, entrepreneurship and innovation leadership trainings for students and researchers, the human capital among FabSpace users will enhance its Earth Observation (EO) data exploitation capabilities. The objectives of FabSpace 2. 0 project are summarized as follows, [2]: • Set up and operate at universities a free-access place and service where students, researchers and external users can make use of a data platform and design and test their own applications.
Trained researchers are expected to get increased innovation leadership knowledge and will be invited to become FabSpace users to complete their training program with a learning by doing activity. In each region, a training program shall be integrated in the offer of topic-based training programs for life-long learning of researchers. The main topics of these programs offer the necessary knowledge and expertise to professionals in the areas of company strategy, IT management strategies and innovation management. With respect to the last subject-innovation management and leadership, the training programs focus on teaching the development of a business plan regarding an innovative idea/ project, the best-practices for innovation leadership, how to incorporate new IT and communication technologies with the optimum way strategically within a business plan, how to use the appropriate economic models and how to design a viable entrepreneurship model for an innovation.
B. Outreach Policy For the successful outcome of the project, it is important to focus also on the outreach activities both within and outside the project framework. To this end, a number of activities have been planned, which include among other: (a) open days to academia, industry and public, where the capabilities of the exploitation of EO data and FabSpace specifically are presented, (b) Space Applications Shops, where workshops with stakeholders will allow the definition of everyday challenges that can be solved using EO data and (c) Hackathons, where interested individuals form teams to solve the challenges and compete locally and internationally in two stages. Simultaneously, members of the consortium (including ICCS and Corallia in Greece) have already applied and awarded to become members of the Copernicus User Uptake Activities issued by EU, specifically to become members of the Copernicus Relay Network and the Copernicus Academies ( [3], [4], [5] and [8]).
III. EO DATA & SOFTWARE AND FABSPACE PLATFORM In order to understand the value of EO data, the users must be able first of all to find the necessary data, and secondly to manipulate them via specialized software. A. EO Data within the FabSpace 2. 0 Framework Indeed, one major issue to be tackled is to learn how to find the necessary data needed for a specific application or service. As EU estimates, only by Sentinel satellites, 10 PB of data will be received per year,  [6] ; if other sources of data are also included, this figure gets even larger. Thus it is difficult, especially for a non-EO user, to get easily used on where and how to find the data needed. The FabSpace 2. 0 framework tries to mitigate this problem by giving access to an interested user to a number of sources and the main instructions on how to detect the information required.
Additionally, the FabSpace network aspires to connect not only the six regions of the consortium but also to create more hubs within these regions and outside of it. This way, a large number of EO specialists will have the opportunity to connect and interact at a single point, the FabSpace 2. 0, and at the same time provide information to any interested individual. Note that due to the free data policy that EU implements in the use of EO data,  [7] , the main business opportunities are transferred from selling images and data to the exploitation of these, and the creation of new business markets on the sell of services rather than of the raw data. Another interesting point, is that the FabSpace platform allows the easy categorization of data and metadata, something that will allow the users to find more easily local data of interest at each region.
B. EO Software within the FabSpace 2. 0 Framework There is a large number of software programs for the processing of the EO data, both free and commercial, like QGIS, Monteverdi, GRASS, etc. It would be of course impossible to include and learn all these tools. The goal of the FabSpace 2. 0 is to become a single point of training for the wider public, where interested people can find various EO programs preinstalled in order to experiment themselves and get acquainted with the software they think it suits them most. Moreover, within the FabSpace 2. 0, it will be given the opportunity for companies to install their full or limited versions of their proprietary software, and train people on their use. This is important, especially for students, as they will learn to use and add to their resume the knowledge of a multitude of different EO software, while on the same time the companies will have the ability to make aware to a wider audience their products.
Thus, a future pool of potential customers or personnel will be created. The FabSpace platform will give also the opportunity to the users to process data, including the capability of presenting the results even at smartphones. In Section IV a more thorough example on the use of the platform is presented. Finally, it must be noted that the overall goal of the FabSpace 2. 0 is to make the use of EO data available to people who are not professionals in this sector, but merely professionals and students in other areas and they have found that the use of EO will certainly give to their idea, product, service, application a certain edge by incorporating the exploitation of EO data. Thus, other market sectors (like energy, robotics, finance, health, etc. ) will be targeted.
A. Users And Rights New users can ask for an account in FabSpace platform filling an account form. The administrator of the platform validates the account and creates groups of users. There are roles assigned with user groups and rules assigned with actions to workspaces (read, write) that are included in each role. That's the way to provide rights to the users, and allow certain users to have access to certain EO data; this is necessary in order to avoid any Intellectual Property (IP) problem, if such limitations on specific data exists.
B. Add/Manage Data to the Platform There are workspaces that include stores. Stores are datasets (raster files, shapefiles, databases etc). There is also the feature for adding metadata connected with the data. A store can include multiple layers for the viewer. In publishing of the layers the user can also have different presentation styles. All these features can be created, managed and modified in the 'Services' page of the platform. In this example, using QGIS, a shapefile with the Aegina's beaches has been created from a CSV file with specific geodata information and using PostGre/PostGIS for selecting data. A workspace has been created in the platform and a new store (shapefile) has been uploaded. Then a new style with icons for beaches for the publishing layer has been also created.
E. Web Applications / Android In this example a web application and an Android application using the data that were uploaded on the platform is presented, see Figure  2. For the web application Openlayers were used  [13] , which is an open source JavaScript to put dynamic maps on a page. To add the data, the WMS service (all data as a raster) and WFS service (vector data) were used. The use of WFS gave the ability to make some functions like different icons for clean and unsuitable beaches and pop-up windows with related information. The links for the services are found in the 'Layer Preview' section of the Platform. For the Android application, the Android Studio/ Java and Mapbox was used  [14]. For delivering the data from the platform the GeoJSON which is a data format for representing geographical features giving all the information in a structure was used.
Whole blood samples obtained from mothers during gestation and from children and mothers up to several years after the birth of a child and cord blood (CB) specimens were analyzed. Maternal smokingrelated differences in DNA methylation were found to be enriched in enhancer elements and to persist over years of life. Interestingly, differential DNA methylation in enhancers was found to be more often functionally rel evant compared with other genomic regions, based on the combined analysis involving also histone modifica tion and RNA expression data  [186]. Finally, in a fol lowup functional study including also animal experi ments, epigenetic deregulation of the enhancer element targeting MAPK9 (gene: MAPK9), previously known as cJun Nterminal kinase 2 (JNK2), was linked to impaired lung function in early childhood  [186].
Given the ratio of reviews to original papers observed in the existing literature on the role genome × environment (epigenetic code) interactions in the development of allergic disorders, many fur ther studies in the field are to be expected. These investigations are facilitated by the development of novel bioinformatics tools, such as the Gene, Envi ronment and Methylation software package created to handle EWAS data, integrate genotype informa tion and model interactions between genotype and the environment  [203].
Comparison between PMBCs and granulocytes revealed that at least one probe was dif ferentially methylated for 85% of the analyzed genes. On the individual gene level, in spite of apparently similar general methylation profiles present in main types of the blood cells, patterns observed for some individual CpGs might be opposite (hypo vs hyper methylation)  [281]. To avoid potential confounding by cellular heterogeneity in the analyzed samples in stud ies which are not performed using sorted blood cell populations, it is important to estimate blood cell pop ulations in the analyzed samples  [282]. If no cell count data are available, a number of bioinformatic tools have been developed that can estimate and correct for cell type heterogeneity in the analyzed samples based on either reference datasets or referencefree statistical algorithms [283-288].
While reference based approaches are commonly used for blood and CBbased studies, referencefree methods can be applied to any biological material for which no reference datasets are presently available including saliva (see also later,  [289] ) and stud ies in young children, for which current reference data sets are not adequate  [290]. The choice of the reference dataset can have a major impact on the detection of differentially methylated positions and therefore only an appropriate reference dataset should be used for deconvolution  [289]. Otherwise referencefree methods should be used. Other types of relatively easily accessible biomateri als include saliva  [169, 173, 291]  or buccal brushings  [173] , conventional sources of DNA for genetic testing that have also been used in studies on DNA meth ylation.
interests disclosure This work was supported by funds from the German Centre for Lung Research (DZL; 82DZL00502/A2; DP Potaczek, H Harb, H Renz), the Universities Giessen and Marburg Lung Centre (UGMLC; H Renz) and the Von Behring-Röntgen-Foundation (Von Behring-Röntgen-Stiftung; H Harb, H Renz). H Harb, DP Potaczek and H Renz are the members of the International Inflammation (in-FLAME) Network, Worldwide Universities Network (WUN) and DZL. B Alashkar Alhamwe is a German Academic Exchange Service (DAAD) fellow (personal reference number: 91559386). Work in the laboratory of J Tost is supported by grants from the ANR (ANR-13-EPIG-0003-05 and ANR-13-CESA-0011-05), Aviesan/INSERM (EPIG2014-01 and EPlG2014-18) and INCa (PRT-K14-049), a Sirius research award (UCB Pharma S. A. ), a Passerelle research award (Pfizer), iCARE (MSD Avenir) and the institutional budget of the CNG.
Financial & competing interests disclosure This work was supported by funds from the German Centre for Lung Research (DZL; 82DZL00502/A2; DP Potaczek, H Harb, H Renz), the Universities Giessen and Marburg Lung Centre (UGMLC; H Renz) and the Von Behring-Röntgen-Foundation (Von Behring-Röntgen-Stiftung; H Harb, H Renz). H Harb, DP Potaczek and H Renz are the members of the International Inflammation (in-FLAME) Network, Worldwide Universities Network (WUN) and DZL. B Alashkar Alhamwe is a German Academic Exchange Service (DAAD) fellow (personal reference number: 91559386). Work in the laboratory of J Tost is supported by grants from the ANR (ANR-13-EPIG-0003-05 and ANR-13-CESA-0011-05), Aviesan/INSERM (EPIG2014-01 and EPlG2014-18) and INCa (PRT-K14-049), a Sirius research award (UCB Pharma S.
The increasing number of sequenced genomes, extending the taxons sampling, can help providing new insights into the evolutionary history and the origin of genes (i. e. from SSD or WGD) and the encoded protein function  [15]. In this context, the present study first aimed at revisiting and clarifying the evolutionary history of g6pc encoding genes among vertebrates, taking into consideration all genomes sequenced and indexed in Ensembl Genome Browser (www. ensembl. org). Since comparative studies of gene expression are critical for establishing links between divergent gene expression and divergence of a particular phenotype, we thus proposed to examine the evolution of the spatial g6pc expression pattern in vertebrates using the powerful PhyloFish database  [16]. Indeed, this database provides a transcriptomic repertoire of 10 tissues and organs in 23 different ray-finned fish species including two holosteans (i.
imcb. astar. edu. sg/blast/), a cartilaginous fish (Chondrichthyes). The size of the scaffold/contig bearing these sequences was not sufficient to cover the whole gene length. The length of the deduced amino acid sequence was therefore not sufficient for phylogenetic analysis (see below). However, these truncated elephant shark sequences were submitted to a BLAST search against the spotted gar genome in the Ensembl database. The hit with the highest score matched the spotted gar g6pca when blasting the scaffold 606 sequence (Evalue: 1. 7 e -117 , score: 419) and spotted gar g6pcb gene when blasting the contig 60457 sequence (Evalue: 1. 7 e -62 , score: 234) (data not shown). We then selected representative Ensembl species for each class of vertebrate as well as sequences previously found in the rainbow trout genome  [6] , a salmonid fish, and performed a phylogenetic analysis.
However, our analysis, in addition to the one previously proposed by  Marandel et al. (2015)  in teleosts, showed that g6pc genes found in Sarcopterygii were all included in a syntenic group conserved across vertebrates (Fig. 2 ). To identify potential remnants of the g6pca gene in Sarcopterygii, the spotted gar g6pca cDNA sequence was submitted to a Blat search (http://genome. ucsc. edu/ index. html) against sequenced genomes of Sarcopterygii. Unfortunately, we were unable to find any significant hit in analysed genomes. Thus, in order to confirm Fig. 1  Phylogenetic analysis of glucose-6-phosphatase encoding genes in vertebrates. Phylogenetic analyses were performed using MEGA package version 6 software  [48] , as previously described  [49]. The phylogenetic tree was built by the neighbor-joining (NJ) method.
The reliability of the inferred tree was estimated by the bootstrap method with 1000 replications. Mammalian and teleosts g6pc3 protein sequences were used to root the tree. All accession numbers (from Ensembl, or Genoscope databases) are specified in parentheses. Sarcopterygii species are written in orange and Actinopterygii species in green the identity of the only g6pc gene conserved in Sarcopterygii and to strengthen our phylogenetic analysis, we performed a protein percentage identity matrix and an alignment. The identity matrix (Additional file 5: Table  S2 ) and associated alignment (Additional file 2: Figure  S2 ) confirmed that g6pc proteins in Sarcopterygii shared higher sequence identity with the spotted gar g6pcb than with its g6pca paralog. Remarkably g6pc protein, whatever the orthologous gene by which it is encoded (i.
2 ) on the zebrafish chromosome 3 (at the location 32,780,748-32,780,748; 49 amino acids hit sharing 72% identity with zebrafish g6pca) and one on the rainbow trout scaffold_123 (at the location 1,457,501-1,457,677; 58 amino acids hit sharing 66% identity with zebrafish g6pca) for g6pca remnant but none on the scaffold_595 or the scaf-fold_2286 of trout. In addition, one hit (red stars, Fig. 2 ) on the medaka chromosome 8 (at the location 19,097,860-19,097,879; 19 nucleotide bases hit sharing Fig. 2  Conserved synteny around the g6pc loci in vertebrates. Data were collected with Genomicus software version 86. 01, and we annotated g6pc genes according to our phylogenetic analysis following ZFIN nomenclature guidelines. The teleosts synteny was modified from  Marandel et al. (2015). Remnants of g6pca and g6pcb2 were identified by red crosses and stars, respectively.
Evolution of g6pc expression domains in vertebrates The PhyloFish database  [16] , a unique resource providing comprehensive expressed gene repertoires in 23 rayfinned fish species, was used to explore the evolution of the spatial expression of g6pc genes. In order to have an overall view across the vertebrate subphylum, we also supplemented this analysis by RT-qPCR in lamprey and by in silico data analysis for several Sarcopterygii (xenopus, chicken, rat, mouse, sheep, cow, babouin olive, rhesus macaque and human) provided by the gene-annotation portal BioGPS  [17]  and by the Expression Atlas  [18]. The expression domains of g6pc genes of representative species are presented in Fig. 3  and data obtained from PhyloFish in the 23 ray-finned fish and in other Sarcopterygii are displayed in Additional file 3: Figures  S3  and Additional   Our results showed that g6pc was preferentially expressed in the brain and in the liver of lamprey (Fig.
3 ). Based on g6pca expression patterns in PhyloFish species, we observed that this gene was mainly expressed in the liver of holostei (i. e. , spotted gar and bowfin) and teleosts. In addition, g6pca was also highly expressed in intestine and kidney of the spotted gar, and to a lower extend in zebrafish and medaka. As for g6pcb genes, we found in the BioGPS and the Expression Atlas databases that G6pcb in Sarcopterygii (xenopus, chicken, rat, mouse, sheep, cow, babouin olive, rhesus macaque and human) was also preferentially expressed in liver, intestine and kidney whereas in holostei, in addition to these latter three organs, it was mainly expressed in heart. g6pcb2 was also showed to be preferentially expressed in heart in cypriniformes (zebrafish), characiformes (Cave Mexican Tetra and Surface Mexican Tetra) and esociformes (Northern pike) as well as in salmonid (Rainbow trout, Brown trout and Brook trout).
g6pc functionally diverged among vertebrates through sub-and neo-functionalisation of their spatial pattern of expression The emergence of new genes through duplication is now considered as a driver process of evolution. Indeed, these new genes are genetic material available for supporting the rise of new molecular and cellular functions, and can play an important role in phenotypic variability  [7] [8] [9]. Duplicated genes fixed in a genome can conserve the function of the ancestral gene or functionally diverge  [10]. Collected information about ancestral state is critical for the classification of this divergence into sub-and neofunctionalisation  [21]. We thus analyzed the spatial expression pattern of g6pc genes in vertebrates completing information obtained from the PhyloFish, the BioGPS and the Expression Atlas databases by RT-qPCR analysis in lamprey (summary available in Fig.
5 ). Our results indicate that, following 2R, the g6pc expression in the brain found in lamprey was lost in tetrapods as well as in holostei whereas the hepatic expression of g6pca and g6pcb remained. By contrast, intestine, kidney, and heart were identified as new territories of g6pca and g6pcb expression in both tetrapods and holostei. However, a g6pc activity was previously described in kidney (a tissue not analyzed here)  [22]  as well as in brain of mammals  [23]. The missing result in Fig. 3  Tissue expression profiles of g6pc genes in vertebrates. Excepted for lamprey, relative expression of g6pc genes was calculated as the percentage of the maximum rpkm (number of reads per kilobase per million reads) value per species. These data were obtained from BioGPS database for the mouse and from Phylofish database for other species.