dataesr/scientific-paragraphs-categorization huggingface dataset:
FAIRDOM (http://fair-dom. org/about): integrates the SEEK 14 and openBIS  15  platforms to produce a FAIR data and model management facility for Systems Biology. Individual research assets (or aggregates of data and models) are identified with unique and persistent HTTP URLs, which can be registered with DOIs for publication ('F'). Assets can be accessed over the Web in a variety of formats appropriate for individuals and/or their computers (RDF, XML) ('I'). Research assets are annotated with rich metadata, using community standards, formats and ontologies ('I'). The metadata is stored as RDF to enable interoperability and assets can be downloaded for reuse ('R'). ISA  16  : is a community-driven metadata tracking framework to facilitate standards-compliant collection, curation, management and reuse of life science datasets.
ISA provides progressively FAIR structured metadata to Nature Scientific Data's Data Descriptor articles, and many GigaScience data papers, and underpins the EBI MetaboLights database among other data resources. At the heart is a general-purpose, extensible ISA model, originally only available as a tabular representation but subsequently enhanced as an RDF-based representation  17  , and JSON serializations to enable the 'I' and 'R', becoming 'FAIR' when published as linked data (http://elixir-uk. org/node-events/201cisa-as-afair-research-object201d-hack-the-spec-event-1) and complementing other research objects  18. Open PHACTS  19  : Open PHACTS is a data integration platform for information pertaining to drug discovery. Access to the platform is mediated through a machine-accessible interface  20  which provides multiple representations that are both human (HTML) and machine readable (RDF, JSON, XML, CSV, etc), providing the 'A' facet of FAIRness.
The interface allows multiple URLs to be used to access information about a particular entity through a mappings service ('F' and 'A'). Thus, a user can provide a ChEMBL URL to retrieve information sourced from, for example, Chemspider or DrugBank. Each call provides a canonical URL in its response ('A' and 'I'). All data sources used are described using standardized dataset descriptions, following the global VoID standard, with rich provenance ('R' and 'I'). All interface features are described using RDF following the Linked Data API specification ('A'). Finally, a majority of the datasets are described using community agreed upon ontologies ('I'). wwPDB  4, 21  : wwPDB is a special-purpose, intensively-curated data archive that hosts information about experimentally-determined 3D structures of proteins and nucleic acids.
All wwPDB entries are stably hosted on an FTP server ('A') and represented in machine-readable formats (text and XML); the latter are machine-actionable using the metadata provided by the wwPDB conforming to the Macromolecular Information Framework (mmCIF  22  ), a data standard of the International Union of Crystallography (IUCr) ('F','I' for humans, 'F','I' for IUCr-aware machines). The wwPDB metadata contains cross-references to common identifiers such as PubMed and NCBI Taxonomy, and their wwPDB metadata are described in data dictionaries and schema documents (http://mmcif. wwpdb. org and http://pdbml. wwpdb. org) which conform to the IUCr data standard for the chemical and structural biology domains ('R'). A variety of software tools are available to interpret both wwPDB data and meta-data ('I','R' for humans, 'I','R' for machines with this software).
Each entry is represented by a DOI ('F', 'A' for humans and machines). The DOI resolves to a zipped file which requires special software for further interrogation/interpretation. Other wwPDB access points  [23] [24] [25]  provide access to wwPDB records through URLs that are likely to be stable in the long-term ('F'), and all data and metadata is searchable through one or more of the wwPDB-affiliated websites ('F') UniProt 26 : UniProt is a comprehensive resource for protein sequence and annotation data. All entries are uniquely identified by a stable URL, that provides access to the record in a variety of formats including a web page, plain-text, and RDF ('F' and 'A'). The record contains rich metadata ('F') that is both human-readable (HTML) and machine-readable (text and RDF), where the RDF formatted response utilizes shared vocabularies and ontologies such as UniProt Core, FALDO, and ECO ('I').
Interlinking with more than 150 different databases, every UniProt record has extensive links into, for example, PubMed, enabling rich citation. These links are machine-actionable in the RDF representation ('R'). Finally, in the RDF representation, the UniProt Core Ontology explicitly types all records, leaving no ambiguity-neither for humans nor machines-about what the data represents ('R'), enabling fully-automated retrieval of records and cross-referencing information. In addition to, and in support of, communities and resources that are already pursuing FAIR objectives, the Data Citation Implementation Group of Force11 has published specific technical recommendations for how to implement many of the principles  27  , with a particular focus on identifiers and their resolution, persistence, and metadata accessibility especially related to citation.
In addition, the 'Skunkworks' group that emerged from the Lorentz Workshop has been creating software supporting infrastructures 28 that are, end-to-end, compatible with FAIR principles, and can be implemented over existing repositories. These code modules have a particular focus on metadata publication and searchability, compatibility in cases of strict privacy considerations, and the extremely difficult problem of data and metadata interoperability (manuscript in preparation). Finally, there are several emergent projects, some listed in Box 3, for which FAIR is a key objective. These projects may provide valuable advice and guidance for those wishing to become more FAIR.
The DDI focuses on finding ('F') and accessing ('A') the datasets stored across different sources, and progressively works to identify relevant metadata 31 ('I') and maps them to community standards ('R'), linking to BioSharing. CEDAR 32 : The Center for Expanded Data Annotation and Retrieval (CEDAR) is an NIH BD2K funded center of excellence to develop tools and technologies that reduce the burden of authoring and enhancing metadata that meet community-based standards. CEDAR will enable the creation of metadata templates that implement community based standards for experimental metadata, from BioSharing (https://biosharing. org), and that will be uniquely identifiable and retrievable with HTTP URIs, and annotated with vocabularies and ontologies drawn from BioPortal (http://bioportal. bioontology.
org) ('F','A','I','R'). These templates will guide users to create rich metadata with unique and stable HTTP identifiers ('F') that can be retrieved using HTTP ('A') and accessible in a variety of formats (JSON-LD, TURTLE, RDF/XML, CSV, etc) ('I'). These metadata will use community standards, as defined by the template, and include provenance and data usage ('R'). These two projects, among others, provide tools and or collaborative opportunities for those who wish to improve the FAIRness of their data. who focused on primary scholarly data being made citable, discoverable and available for reuse, so as to be capable of supporting more rigorous scholarship. An attempt to define the similarities and overlaps between the FAIR Principles and the JDDCP is provided at (https://www. force11. org/node/ 6062).
The FAIR Principles are also complementary to the 'Data Seal of Approval' (DSA) (http:// datasealofapproval. org/media/filer_public/2013/09/27/guidelines_2014-2015. pdf) in that they share the general aim to render data re-usable for users other than those who originally generated them. While the DSA focuses primarily on the responsibilities and conduct of data producers and repositories, FAIR focuses primarily on the data itself. Clearly, the broader community of stakeholders is coalescing around a set of common, dovetailed visions spanning all facets of the scholarly data publishing ecosystem. The end result, when implemented, will be more rigorous management and stewardship of these valuable digital resources, to the benefit of the entire academic community. As stated at the outset, good data management and stewardship is not a goal in itself, but rather a pre-condition supporting knowledge discovery and innovation.
Contemporary e-Science requires data to be Findable, Accessible, Interoperable, and Reusable in the long-term, and these objectives are rapidly becoming expectations of agencies and publishers. We demonstrate, therefore, that the FAIR Data Principles provide a set of mileposts for data producers and publishers. They guide the implementation of the most basic levels of good Data Management and Stewardship practice, thus helping researchers adhere to the expectations and requirements of their funding agencies. We call on all data producers and publishers to examine and implement these principles, and actively participate with the FAIR initiative by joining the Force11 working group. By working together towards shared, common goals, the valuable data produced by our community will gradually achieve the critical goals of FAIRness.
Acknowledgements The original Lorentz Workshop 'Jointly Designing a Data FAIRport' was organized by  Barend Mons  in collaboration with and co-sponsored by the  Lorentz center, The Dutch Techcenter for the Life Sciences  and the  Netherlands eScience Center. The principles and themes described in this manuscript represent the significant voluntary contributions and participation of the authors at, and/or subsequent to, this workshop and from the wider Force11,  BD2K  and ELIXIR communities. We also acknowledge and thank the organizers and backers of the NBDC/DBCLS BioHackathon 2015, where several of the authors made significant revisions to the FAIR Principles.
We present spatiotemporal mass balance trends for the Antarctic Ice Sheet from a statistical inversion of satellite altimetry, gravimetry, and elastic-corrected GPS data for the period 2003-2013. Our method simultaneously determines annual trends in ice dynamics, surface mass balance anomalies, and a time-invariant solution for glacio-isostatic adjustment while remaining largely independent of forward models. We establish that over the period 2003-2013, Antarctica has been losing mass at a rate of -84 ± 22 Gt yr -1 , with a sustained negative mean trend of dynamic imbalance of -111 ± 13 Gt yr -1. West Antarctica is the largest contributor with -112 ± 10 Gt yr -1 , mainly triggered by high thinning rates of glaciers draining into the Amundsen Sea Embayment. The Antarctic Peninsula has experienced a dramatic increase in mass loss in the last decade, with a mean rate of -28 ± 7 Gt yr -1 and significantly higher values for the most recent years following the destabilization of the Southern Antarctic Peninsula around 2010.
3 using a forward modeling approach. GIA is time invariant, but all other processes vary in both space and time. Here we focus on the drainage basin-scale, annually resolved mass trends for the period 2003-2013 inclusive. We note, however, that there is valuable and important information contained in all five fields, which can aid interpretation of the trends and process understanding. In this study, we utilize satellite altimeter data from Envisat, ICESat, and CryoSat-2, gravimetry data from the GRACE mission, and in situ GPS measurements of vertical bedrock motion.
Altimetry We use three altimetry data sets to obtain time series of elevation change for the period 2003-2013. Along-track elevation rate measurements from ICESat, data release 33, provide high-resolution altimetry observations for the period February 2003 to October 2009. Data were corrected for the range determination from Transmit-Pulse Reference-Point Selection (Centroid versus Gaussian)  [Borsa et al. , 2014]  available from the National Snow and Ice Data Center (NSIDC) and for the intercampaign bias presented in  Hofton et al. [2013]. Yearly trends of elevation changes and their associated errors were calculated, from a 3 year moving window centered on the year under consideration, using a "plane regression" approach  [Howat et al. , 2008; Moholdt et al. , 2010]  in which both spatial and temporal slopes were simultaneously estimated over areas spanning 700 m long and a few hundred meters wide.
Regression was only performed if a minimum of 10 points from four different tracks spanning at least a year was available. Mean annual trend estimation was performed in two steps: first a regression was carried out to detect and remove outliers falling outside the 2 standard deviation (2) confidence interval and then the annual trend and its standard deviation was determined. To avoid noisy spatial patterns, only elevation changes with an associated error smaller than 0. 40 m yr -1 were considered. These annual trends were averaged over a regular Cartesian 20 km grid, and the standard deviations of the trends within each grid cell were taken as an estimate of the measurement error. Envisat radar altimetry data were available for the period January 2003 to November 2010. These data provide better temporal and spatial coverage than ICESat for much of the AIS.
However, their spatial resolution 10. 1002/2015JF003550 is poorer than ICESat and they are, therefore, complementary. Elevation trends were obtained at points every 1 km along track, by binning all the echoes within a 500 m radius and then fitting a 10-parameter least squares model to the entire Envisat interval in order to correct for the across-track topography and changes in snowpack properties  [Flament and Rémy, 2012]. For consistency with ICESat, annual time series were produced using a 3 year moving window and resampled onto a 20 km grid, approximating the observation error as the standard deviation of the data points within each grid cell. CryoSat-2 provides high spatial resolution radar altimetry data (July 2010 to December 2013) up to a latitude of 88 ∘ S. In addition, it performs more reliably in coastal areas where Envisat and previous radar altimeters struggle.
Sites were selected to achieve the broadest global distribution for producing precise satellite orbits and clocks. These orbits and clocks were then used in precise point positioning mode to generate coordinate time series for Antarctic sites of interest. The processing software used was GIPSY/OASIS v6. 2; Earth radiation and second-order ionospheric error models (shell height 600 km) were applied; the time period processed was 1995. 0-2013. 7; satellite and receiver antenna phase centers were calibrated according to the igs08_1748. atx values; Earth tides were modeled as described in the IERS2010 conventions  [Petit and Luzum, 2010] ; station coordinates were transformed from the daily nonfiducial reference frame into ITRF2008 using Helmert parameters defined by a set of 40 sites selected each day for maximum hemispheric balance; ambiguities were fixed to integers using the GIPSY routine edtpnt2, and subdaily atmospheric loading models were not applied, but atmospheric loading was later removed from the daily solutions  [Santamaría-Gómez and Mémin, 2015].
We chose to solve for GIA over the 2003-2009 period, as this represents the most complete epoch in terms of coincident data sets (ICESat, ENVISAT, GPS, and GRACE data). The GIA signal is assumed to be temporally invariant on the decadal time scale of this study. We calculated a set of annual vertical bedrock velocities at each GPS site where at least 3 years of good quality continuous data with no significant gaps were available. This was done using an algorithm developed to avoid spurious trends from noisy time series. First, seasonal signals were removed by subtracting the monthly means from the GPS coordinate data prior to finding the trend. Then, annual trends were estimated, fitting a piecewise linear function to the data at each site to avoid discontinuities at the year boundaries. For further details of the algorithm, see https://andrewzm.
wordpress. com/2013/12/12/estimating-yearly-trends-with-an-em-algorithm-in-r/. Non-ideal sites, consisting of campaign rather than continuous GPS data or with large portions of data missing, are unsuitable for annual trend estimation. At these sites a single linear trend was estimated. Such sites were only included if they contained some data during 2003-2009, but due to the temporal and spatial sparsity of Antarctic GPS stations, available data from outside the period were also used. To achieve more realistic trends and confidence intervals at these sites, median noise level estimates found using the Computer Aided Transcription System software  [Williams, 2008]  from Antarctic sites with over 2000 daily solutions were used for error propagation. These suboptimal time series were further assessed by manually removing random portions of the time series and calculating the variability in the estimated trends using tsview (GGMatlab tools)  [Herring, 2003].
First, we ran the Bayesian framework explained in section 3 excluding the GPS station data to obtain an initial estimate of the load changes caused by SMB anomalies and ice dynamics. The effects of these loading changes on the vertical motion of the Earth's crust were calculated using the Regional Elastic Rebound Calculator (REAR)  [Melini et al. , 2015]. In order to compute the surface rates of displacement associated to a unit rate of mass variation (Green functions), REAR uses a set of load deformation coefficients from the seismological model STW105  [Kustowski et al. , 2007]  up to maximum value of 30,000 harmonic degrees. Vertical displacements were calculated following the Journal of Geophysical Research: Earth Surface 10. 1002/2015JF003550 methods described in  Melini et al. [2015]  and used to correct the GPS trends.
The Amundsen Sea Embayment Here we compare results from our approach with estimates from entirely independent studies and methods for an area where the signals are (i) large and (ii) well constrained. The ASE is the most suited for this purpose, having been the subject of several investigations including the acquisition of recent high-quality field data. Sutterley et al. [2014]  provides the most recent estimates of mass loss for the ASE sector from a combination of four different data sets (GRACE, ICESat, Envisat, and the input-output method: IOM). Additionally, recent ice flux estimates are available for the glaciers in this sector (Pine Island, Thwaites, Haynes, Smith/Pope, and Kohler glaciers) for the period 1974-2013 using ice surface velocity and ice thickness data  Mouginot et al. [2014].
Very small changes are obtained when decreasing by one third the temporal wavelength of the firn compaction. Assumption (ii) may be relaxed using a similar meshing approach  [Wallin and Bolin, 2015] ; however, inference with such non-Gaussian models is at present computationally prohibitive at these scales. In order to cater for the determinacy of the elastic model (iv), we inflated the supplied variance of the GPS errors in the ASE sector, the region most susceptible to elastic effects as explained in section 2. 3. Previous works adopting this framework [e. g. ,  Zammit-Mangion et al. , 2015a]  report an overall overconfidence of the results, and this overconfidence is a direct result of any or all of the assumptions made above. It is likely, however, that (i) is the strongest contributor to any incorrect uncertainty quantification since data errors remain hard to quantify (e.
3, although differences arise at a basin scale. Our framework correctly separates SMB from ice dynamics in known regions of dynamic thinning (ASE) and thickening (Kamb). We have also found important changes in the dynamics in other regions, such as the beginning of a sustained negative trend in the southern part of the Antarctic Peninsula. Finally, a model independent GIA solution is obtained as a subproduct from the inversion of GPS, altimetry, and gravimetry data which could be used to constrain and validate existing and future forward models.
Genome Assembly Mate pair and paired-end reads were trimmed using CLC Genomics Workbench v7. 5 (http://www. clcbio. com/blog/clc-genomics-workbench-7-5/). De novo assembly of 1,519,052 reads was done using 64 word size and 50 bubble size parameters. We obtained one contig of 423,175 base pairs (bp) and a second contig of 163,805 bp. To fill gaps, specific PCR primers were designed by using Primer-BLAST  [15]. We tried the assembly with a different k-mer parameter, more adapted to the size of our reads. Successively, we used 80 and 92 k-mer sizes with the Abyss program  [16]  and made a scaffolding using SSPACE software  [17]  on each result. With these two assembly methods and Sanger sequencing products we obtained a single scaffold.
Study of the Genome Organization and Genome Annotation Repeats in the genome were investigated by complementarities from EMBOSS software (http://emboss. bioinformatics. nl/cgi-bin/emboss/palindrome) with 200 nucleotides for maximum length of palindromic sequences and by CRISPRFinder  [18]  with standard parameters. Open reading frames were predicted by GeneMarkS  [19]  and the genome was deposed under BioProject No. PREJ15450 in the EMBL-EBI database. The tRNA genes were searched for by using tRNAscan-SE and ARAGORN softwares  [20, 21]. Predicted proteins of less than 50 amino acids in length were discarded. The remaining proteins ranging from 50 to 99 amino acids in length were discarded if they did not have a hit in the BLASTp search in the NCBI GenBank non-redundant protein sequence database.
Predicted proteins longer than 99 amino acids were kept for the analysis. A BLASTp search against the NCBI non-redundant protein sequence database and against predicted proteins from P. massiliensis was performed. Homology was considered significant if the e-value was lower than 1 × 10 -3. A BLASTp search was also computed with the same parameters against the clusters of orthologous groups of proteins of the nucleocytoplasmic large DNA virus (NCVOGs)  [22]. A cladistic representation based on the presence/absence of each NCVOG was performed by MultiExperiment Viewer (MeV)  [23]. Conserved domains and putative functions of viral proteins were predicted by comparative genomics through a BLASTp search against the NCBI non-redundant protein sequence database and by using InterPro v58. 0 (https://www.
ebi. ac. uk/interpro/); results merged with specific Delta-BLAST results  [24]. We explored bona fide orthologous genes by using Proteinortho v4  [25]  with 50% coverage and 30% amino acid identity and an e-value of 1 × 10 -2 as significance thresholds. Paralogous genes were predicted by the BLASTClust program  [26]  with 70% coverage and 30% amino acid identity as thresholds.
Genome Analysis The Cedratvirus A11 genome is a double-stranded circular DNA molecule of 589,068 bp in length with a GC% of 42. 6%. At first sight (Table  1 ), the genome appears smaller, by approximately 21 and 97 kilobase pairs (kbp) compared with P. sibericum and P. massiliensis, respectively. However, uneven read distribution and abnormally large insert as described for P. sibericum linear genome version were not observed for the Cedratvirus genome. In addition, no large palindromic repeats were identified by EMBOSS Palindrome. Nevertheless, the 20 gaps of the Cedratvirus draft genome were flanked by similar short sequences. The CRISPRFinder program detected 27 areas of repeats in the genome. The Cedratvirus genome was predicted to encode 574 proteins. This represents, when combined with a shorter genome size, a greater coding capacity than for P.