dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We thank  Alexander Stark  for providing the mammalian Starr-seq vector and  Jacques van Helden  for help with motif scanning analyses. Work in SS laboratory was supported by recurrent funding from the  Inserm and Aix-Marseille University , and by specific grants from the  European Union 's  FP7 Programme (agreement n°282510-BLUE-PRINT) , the  ARC  project  N°SFI20111203756  and the A* MIDEX  project  N°ANR-11-IDEX-0001-02. The  IBiSA 'Transcriptomics and Genomics Marseille-Luminy (TGML) ' platform was supported by the ' France Genomique' National infrastructure, funded as part of the 'Investissements d'Avenir' programme  (contract  ANR-10-INBS-09 ). The work was also granted for sequencing costs by an  ESGI Consortium  grant of the  EU  (to JCA, programme  T-DynRegSeq ) from the  Seventh Framework Programme  ( FP7/2007-2013 ; grant agreement  N°.
, 1988; Crowley, 1995). Indeed, there are no direct data of ocean circulation changes in the past (except for the last decades, see for example  Mielke et al. , 2013) , but the measurement of δ 13 C and 14 C in sediment cores can help constrain the ocean and land vegetation changes. Moreover, the measure of atmospheric δ 13 C in ice cores  (Lourantou et al. , 2010; Schmitt et al. , 2012)  and the calibration curves of atmospheric 14 C  (Reimer et al. , 2013 (Reimer et al. , , 2009) )  provide additional data and constraints. By explicitly simulating the carbon isotopes within the carbon cycle model, as we have done in the iLOVECLIM model, it is possible to directly compare model results with data to calibrate model simulations and improve our understanding. Our long-term objective is to study past and future carbon cycle changes over timescales of a few thousand to hundred of thousand years, typical of glacial-interglacial changes.
iLOVECLIM The iLOVECLIM model is a new development branch (code fork) of the LOVECLIM model in its version 1. 2, as presented in  Goosse et al. (2010). It is identical to the latter with respect to its base components: atmosphere, ocean and vegetation (AOV). It has been modified in a number of aspects to include water oxygen isotopes  (Roche, 2013)  and an interactive ice sheet model  (Roche et al. , 2014). The general goal of the new developments within iLOVECLIM is to include the suite of processes needed for climate simulations on the Milankovic timescale. We summarize in the following the main characteristics of the AOV components as described in  Roche et al. (2007)  and  Goosse et al. (2010). The following paragraph is taken from  Roche et al. (2014). The atmospheric component ECBilt was developed at the Dutch Royal Meteorological Institute (KNMI)  (Opsteegh et al.
Carbon cycle in the terrestrial biosphere The VECODE terrestrial biosphere model  (Brovkin et al. , 1997)  was already included in iLOVECLIM  (Goosse et al. , 2010). The model simulates two types of plants -trees and grass -as well as desert. The plants are divided into four compartments that exchange carbon: leaves, wood, litter and soil. Photosynthesis depends on the local climate (precipitation and temperature) and on the atmospheric CO 2 (CO 2 fertilization). We have added the isotopes of carbon to this pre-existing version of VECODE in every carbon compartment as was done in CLIMBER-2.
In terms of carbon content, iLOVECLIM simulates low carbon contents in the regions of low vegetation cover, and particularly high carbon contents in the southern and eastern parts of North America, the northeastern part of South America, the southeastern part of Africa and on the maritime continent. This results in 2142 GtC globally, corresponding to 863 GtC for vegetation and 1279 GtC for soils (and litter). This is in the range of other model estimates which vary between around 320 and 930 GtC for vegetation and between around 500 and 3100 GtC for soils  (Anav et al. , 2013) , as well as close to data estimates although with an overestimation of vegetation carbon content and underestimation of soil carbon content (respectively 450 to 650 GtC for vegetation,  Prentice et al. , 2013, and 1500  to 2400 GtC for litter and soils,  Batjes, 1996).
Ocean dynamics The ocean dynamics, which depend on temperature and salinity gradients, play an important role for the carbon cycle because they partly determine the distribution of the tracers that are transported. The iLOVECLIM model simulates relatively well the distribution of temperature and salinity both at the surface and in the ocean interior. At the surface, the simulated temperature field is similar to the observations (Fig. 4 ), with higher temperatures at the low latitudes and lower at high latitudes. Some local discrepancies can be observed in the boundary currents which are not well represented in the model due to its low resolution. The salinity distribution is in agreement with the data in most places (Fig. 5 ), except in the two bands of higher salinity in the Pacific and Indian oceans around 30 • N and 30 • S and in the northwestern part of the Indian Ocean where the simulated salinity is too low compared to observations.
Discussion Because the main feature added to iLOVECLIM for the carbon cycle concerns the ocean, we only discuss the results for the oceanic variables. The terrestrial biosphere has only been slightly modified to include the carbon reservoirs, but could benefit from further improvements such as more plant functional types, as well as additional modules such as permafrost, which is work in progress  (Kitover et al. , 2013). ). Data from GLODAP  (Key et al. , 2004). ). Data from GLODAP  (Key et al. , 2004). ). Data from GLODAP  (Key et al. , 2004).
Model-data comparison The iLOVECLIM model simulates most of the variables in agreement with data, especially the main characteristics of the water masses. However, a number of discrepancies exist. Some are due to errors in the simulation of surface regional features which then propagate in the ocean interior, such as the North Atlantic where the high salinity from the Tropics is transported too much northward compared to the data. This could be partly due to the resolution of the model which limits the representation of small-scale features. The misrepresentation of temperature has a direct impact on oxygen, for example again in the North Atlantic where the temperatures are too high, which leads to too small values of oxygen in the surface and in the ocean interior. Another source of error ).
Inter-model comparison We compare the iLOVECLIM results with other models using the data from the Coupled Model Intercomparison Phase 5 (CMIP5). We focus on three key variables (dissolved inorganic carbon, alkalinity and oxygen) using the average over years 1890-1899 of the "historical" simulation (see Sect. 2. 4). The data are zonally averaged for the Atlantic and Pacific basins (including the Southern Ocean). Note that the simulations that are compared are not exactly the same: the iLOVECLIM simulation is a long simulation of a few thousand years under pre-industrial conditions, whereas the CMIP5 simulations are run under evolving boundary conditions of the historical period since 1850 starting from spin-up simulations of a few hundred to one thousand years. Additionally, the spatial resolution is higher in the CMIP5 models which are fully coupled GCMs.
Nevertheless, we show here that the skill scores of iLOVECLIM are similar to those of more complex Earth system models used in CMIP5. For most variables, iLOVECLIM is in the range of other models performance. For DIC the models that statistically perform best in both the Atlantic and Pacific are the IPSL-CM5A-LR and IPSL-CM5A-MR models (Fig. 20 ). iLOVECLIM is less accurate than the IPSL models, but still reproduces most of the pattern and gives better results than other models such as NorESM1-ME, CMCC-CESM, GFDL-ESM2G or MPI-ESM-LR in terms of correlation and root mean square error. For alkalinity, most models simulate poorly the distribution especially in the Atlantic basin, where iLOVECLIM is performing particularly poorly (Fig. 21 ). In the Pacific, which represents a larger volume, the models yields better results and so does iLOVECLIM, which lies in Data from GLODAP  (Key et al.
Future developments Overall, iLOVECLIM does a relatively good job compared to the data and other models and usually lies in the middle of the CMIP5 range. This is a good performance given that iLOVECLIM is an EMIC and has a less complex and comprehensive representation of the different processes than the CMIP5 GCMs. The GCMs usually simulate better the ocean circulation which yields better distribution of the geochemical variables. There are however a few points that need to be   grams). Data from GLODAP  (Key et al. , 2004). improved in iLOVECLIM, namely the O 2 representation in the Atlantic and the alkalinity distribution (like in all other models). Some limitations arise from the simplicity of the NPZD model which does not include iron nor silicate. This could be added in future work. The air-sea flux of oxygen has not yet been parametrized depending on the difference between the atmosphere and surface water values and the wind, but this will be explored in future studies.
Conclusions We have described the implementation of a carbon cycle module in the iLOVECLIM model, including the carbon isotopes 13 C and 14 C. Comparison with modern data show that the model performs well for the main carbon cycle variables, and reproduces the most important features of the different water masses. In particular, the good representation of the 13 C and 14 C in the ocean interior paves the way for past studies for which they represent most of the available data. Therefore the iLOVECLIM model with the carbon cycle is well suited for long-term simulations of a few thousand years in the past but also in the future. Some improvements will be considered in future work, such as the inclusion of iron and silicate, a better parametrization of the O 2 air-sea exchange with wind and better parametrization of the remineralization and dissolution profiles.
Acknowledgements. D. M. Roche  is supported by  INSU/CNRS  and  NWO. V. Mariotti-Epelbaum  is supported by the  French National Center for Scientific Research funding. N. Bouttes  is supported by the  European Research Council  under the European Community's  Seventh Framework Programme  (FP7/2007-2013),  ERC  grant agreement number  247220 , project  Seachange. This is a contribution to the  ACCLIMATE ERC  project; the research leading to these results has received funding from the  European Research Council  under the European Union's Seventh Framework N. Bouttes et al. : Carbon cycle in the iLOVECLIM model Programme (FP7/2007-2013)/ ERC  grant agreement no. 339108. We thank the editor and two reviewers for their comments, which helped improve this paper. Edited by:  C. Sierra.
Code availability The iLOVECLIM source code is based on the LOVE-CLIM model version 1. 2 whose code is accessible at http:// www. elic. ucl. ac. be/modx/elic/index. php?id=289. The developments on the iLOVECLIM source code are hosted at https: //forge. ipsl. jussieu. fr/ludus but are not publicly available due to copyright restrictions. Access can be granted on demand by request to D. M. Roche (didier. roche@lsce. ipsl. fr).
g. mzML  (Martens et al. 2011). Data completeness can then be verified using validator software enforcing minimum information recommendations such as the MSI Core Information for Metabolomics Reporting (CIMR; http://biosharing. org/bsg-000175) (Sumner et al. 2007 ). The standardisation efforts in COS-MOS for nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry (MS) data, with potential to encompass new and alternative technologies as they are developed, and supporting tools, will form the basis for funders and publishers to recommend data deposition. The submitted dataset, in repositories such as MetaboLights  (Haug et al. 2013)  or the Netherlands Metabolomics Centre (NMC) Data Support Platform (DSP) could then be used to justify findings in a publication. Unlike in other -omics domains, as for example ArrayExpress  (Parkinson et al.
ISA-Tab format is currently in use and supported by public data repositories such EMBL-EBI Metabolights (accounting for about 200 datasets, 90 of which are currently publicly available), but also several major european toxicogenomics projects (Carcinogenomics, 4 DiXa 5 and InnoMed PredTox and ToxBank 6 ). These projects fully exploit the capability of the ISA-Tab format to support an array of assay type allowing to recording multi-omics assays. Furthermore, ISA developers have a range of tools for converting from various sources (ArrayExpress, 7 SRA 8 ) into ISA-Tab format. Ultimately, we hope that COSMOS will help experts in NMR spectroscopy and MS-based metabolomics to communicate their results in a more objective comprehensive, persistent and efficient way, and spanning and integrating multiple domains such as medical, environmental, plant and food sciences.
Although funding by the European Community is limited to a number of European expert scientists, COSMOS links to major initiatives world-wide. For example with the US National Institutes of Health (NIH) Common Funds Metabolomics Initiative (http://commonfund. nih. gov/Metabolomi cs/) which has awarded funding for six Regional Comprehensive Metabolomics Research Cores (RCMRC), and a Data Repository and Coordination Centre (DRCC), to act as a North American hub for metabolomics related research. 9  COSMOS also reaches and establishes links with other related e-infrastructures initiative such as the new Europeanwide ELIXIR project, 10 Biobanking and Biomolecular Resources Research Infrastructure 11 (BBMRI) via BioMedbridges 12 consortium, Human Metabolome Database (HMDB) in Canada  (Wishart et al.
Metabolomics data exchange standards The COSMOS work on standardisation aims to build on the foundational work by PSI and MSI and further develop and contribute to data exchange formats, ranging from raw data in MS and NMR, the reporting of metabolite quantification and metabolite identification, to the experimental metadata. We aim to extend the open standards for MS data exchange initiated by PSI, such as mzML  (Martens et al. 2011) , mzI-dentML  (Jones et al. 2012 ) and mzQuantML  (Walzer et al. 2013)  to meet the requirement of metabolomics experiments for reporting MS experiments. One example are GC-MS based metabolomics experiments, where data are often available in either a closed vendor format or as netCDF, where the latter provides only very few metadata acquisition parameters and fails to capture advanced MS experiments such as GC 9 GC-MS or tandem-MS with GC-MS instruments.
Parsers for mzML exist for the languages C??, Java, R and Python, which should cover the majority of the current software developments in the metabolomics community, and we shall of course supplement to these as necessary. Therefore, mzML can be a strong suggestion or even a requirement for data deposition in public repositories. Formats that capture metabolite identification and reporting of quantification results also need to be adapted for MS metabolomics experiments, and require real-world tests, software support and community adoption. We hope to make COSMOS a platform for community engagement with adaptation and development of these formats to suit the metabolomics community needs, and as mentioned above we regularly consult with the Metabolomics Society. In addition, COS-MOS developing the missing XML exchange formats for NMR spectroscopy such as nmrML, nmrIdentML, nmrQuantML and nmrTab needed by databases and open source software such as NMRLab/MetaboLab (Ludwig and Gunther 2011) Bayesian AuTomated Metabolite Analyser for NMR spectra (BATMAN)  (Hao et al.
2012 ) and rNMR  (Lewis et al. 2009). These developments take place on www. nmrml. org and https://github. com/nmrML/ and include the XML schema, controlled vocabulary  (Schober et al. 2014) , example files, and reader, writer, conversion and validation software. As part of this approach we have begun and will continue to interact with the wider community to ensure wide adoption and call for implementations of the standards during the design phase, which helps to catch design errors before the standard is published. With semantic web technology in mind, these standards will pave the way for metabolomics data to be part of the world of linked (and open) data  (Murray-Rust 2008). Preliminary work in currently underway, leveraging work by the ISAteam in the field of linked data to offer MetaboLights metadata content as linked data (Gonzalez-Beltran et al.
Metabolomics databases and repositories The power of broad, system-wide -omics relies on the potential to interrogate datasets from new perspectives. Researchers not involved in the original data generation process may reuse data differently from the original purpose that motivated the data collection. Unleashing this potential also in the heterogeneous metabolomics landscape requires the availability of metabolite level (ideally quantified) and profile data along with adequate metadata. Therefore, the COSMOS consortium is committed to develop the Meta-boLights database further as a centralised data exchange and storage platform. MetaboLights will serve as a common publication hub and make it possible to connect different resources while keeping the data interoperable (e. g. connect to data in other resources, such as the NMC-DSP (van Ommen et al.
2010)). In the metabolomics field, a large number of custom and often technology focused, substance-class, or species-centric databases exist and are continuously developed for example; the Golm Metabolome Database or GMD  (Hummel et al. 2010)  LipidMaps  (Fahy et al. 2009) , PlantMetabolomics. org  (Bais et al. 2010)  and  MeRy-B (Ferry-Dumazet et al. 2011). Defining a sensible balance of centralised versus decentralised information storage can be resolved by developing and applying standards and exchange formats. Also, buy-in from users as well as publishers will have to be achieved. The journal Metabolomics, which is the official journal of the Metabolomics Society, has since 2010 encouraged authors to ensure their papers are as MSI compliant as possible  (Goodacre 2010)  and is committed to supporting the COSMOS consortium in its endeavours for metabolomics standardisation.
This ethos is also being adopted by other journals including Metabolite, EMBO and others to join. Integrative analysis of datasets is essential in order to achieve better understanding of phenotypes. Moreover, interfacing with dedicated databases utilising metadata annotation tools will engage and enable a broad user base to export data from their local systems into ISA-Tab formatted data sets, and subsequently to easily import or submit to MetaboLights. MetaboLights and the ISA team have been working on implementing principled curation guidelines, ensuring consistency in the reporting of experimental designs. As a machine readable XML dialect, the schema based XEML  (Hannemann et al. 2009 ) provides means to store experimental design and metadata describing the actual experiment, together with links to one or more independent databases hosting the actual experimental results as well as export the results in ISA-Tab format.
The XEML-Lab sources and binaries for different operating systems can be accessed and downloaded from https://github. com/cbib/XEML-Lab. In addition to the obvious breadth of experimental conditions, the diversity of laboratory specific SOPs, even within the most commonly employed measurement techniques such as NMR and MS, renders the joint interpretation of data produced by different labs difficult. Hence, standardised and machine-readable metadata describing all aspect of experimental conditions are an essential prerequisite to allow a quick, objective and hence meaningful selection of experiments suitable for comparison. In a single-user environment, experimental metadata annotation can be efficiently handled using the ISAcreator, part of the ISA software suite. In addition, the COSMOS consortium also aims to develop standards to connect to existing specialised databases such as the GMD, MeRy-B, the NMC-DSP/dbNP, as well as other similar resources using alternative metadata annotation tools such as XEML  (Hannemann et al.
These guidelines are currently being carefully discussed, elaborated and agreed by all COSMOS partners. COSMOS is also taking every opportunity to engage fully with stakeholders and potential collaborators on planning, discussion and implementation of the guidelines for data deposition workflow. Careful planning of the data deposition flow, its control policies and actions, will ensure that the utility of the system is maximized and quality-controlled for use inside and outside Europe. A proposed model for the data deposition workflow, drafted from discussions within COSMOS, is shown in Fig. 1. The workflow definition will prioritize simplicity, usability, annotation quality, the plurality of metabolomics resources and databases, to ensure connectivity between similar studies and to provide rapid matching results to the end users.
We envisage that in the future additional purpose-built databases will be created that can potentially be integrated into the proposed workflow. This will include Metabo-Lights and the NIH funded Metabolomics Workbench. 14 he first phase of the data deposition cycle is temporary and all data and associated information are kept private. Once the study has been officially published and the depositor agrees to share the data (making it open access), the COSMOS ''metabolomeXchange'' system will automatically announce and broadcast availability of such studies to the broad research community (Fig. 2 ). In addition to the minimum required metadata (e. g. accession, title, abstract, publication date, URL and submitters name), the COSMOS ''metabolomeXchange'' system allows datasets to be annotated using additional metadata information.
This would enable the metabolomics community (both metabolomics researchers and databases) to query efficiently and readily identify interesting and reusable metabolomics data sets. The default for most journals will be to promote open access of the data as soon as the study is published, while the accepted academic standard is to allow others to access the work. All parties involved will benefit from sharing raw data, processed data, metadata, statistical methods and source codes. By increasing the visibility of their work, depositors are likely to boost citations. Publishers and journals will expose their publications to a greater number of potential readers and enhance the overall impact of the work. In addition, through COSMOS the research community will gain free access to a vast amount of well documented and easy to access scientific information.
Outreach and dissemination We will maintain a close link between the COSMOS consortium and the wider metabolomics and biomedical communities, as well as other related scientific fields. We shall continue to use metabolomics workshops, meetings and conferences to interact with the wider communities, metabolomics or others, to get all parties involved with the initiative. In addition, we will use publications and social media, news articles and blogs to raise awareness of metabolomics standards and the services provided by the COSMOS consortium. This will be from data submission to providing support on different views on how metabolomics data may be reported. There is a great need for increasingly stringent requirements for data quality that is publicly available to the whole metabolomics community.