dataesr/scientific-paragraphs-categorization huggingface dataset:
The requirements will emanate from three levels: (a) from the data users who are specifying needed accuracy and sampling on variables, (b) from operators of observing elements, and (c) from the value chain of ocean observing where delivering fit-for-purpose data and products is paramount. The essential elements of data and information might include requirements to follow FAIR principles. RT data submission should be as close to real time as possible but delayed enough to assure quality is fit for purpose, and the communication system should be open and accessible. Furthermore, QA/QC should be integrated with the instrument/platform and automated as far as possible. The FOO should also assess access and assembly of digital object identifiers and the use of long-term archives in the requirement setting.
Testing FAIRness Some oceanographic data types already meet the FAIR principles. These are mostly those data for which international agreements already exist on the observation methods, dataset structures, and an infrastructure to coordinate data aggregation and QC processes. For most in situ oceanographic data types, however, the FAIR principles have only been partially implemented. Datasets that have been published through general purpose or national data centers are typically somewhat findable and accessible, though their findability is limited if the data center's metadata holdings are not connected to metadata aggregators or federations. Over the next decade, we anticipate general improvement in the implementation of the FAIR principles, leading to increased accessibility of data. Many data systems are in the process of becoming FAIR; some even claim to be FAIR.
However, it is important to utilize tools to measure FAIRness from a machine perspective. The GO FAIR office, active in the Netherlands, Germany, and France, has taken the initiative to develop metrics for testing FAIR readiness 9. The metrics will assess the degree to which a data product meets the FAIR principles as accurately as possible. The aim is to give data holders the means to check where improvement is required  (Wilkinson et al. , 2018). For instance, SeaDataNet is a candidate implementation network, and the first FAIRness assessment has been made using the new metrics, providing a practical test 10. SeaDataNet has made considerable progress in the last decade on metadata and data standardization on both syntax and semantics. 9 https://www. go-fair. org/technology/go-fair-metrics/ 10 https://www.
Setting up GDACs to provide an interoperable data platform for data access will allow for services on an EOV basis or a network basis. In turn, this will facilitate a rigorous and effective quality assessment service. managers regularly produce higher-level data products that include quality flags and suggested editing features to fully applied QC and regular geolocated gridded data. NASA, NOAA, EUMETSAT, ESA, and other agencies use self-describing file formats [e. g. , NetCDF, Hierarchical Data Format (HDF)] and CF metadata conventions. This makes indexing the data easier for the data centers' databases and makes sure that the quality flags are easily paired with their science variable for mid-level products so users can QC the data for their specific research needs. It lends itself to producing higher-level products, usually gridded, for data assimilation into models.
Web services are APIs, typically hosted by the data center. The Physical Ocean Distributed Active Archive Center (PO. DAAC) provides Web services to search on datasets and granule/file metadata and data. From there, the user can discover what files/granules fit their need, either by parameter, region, or time range. The user can then retrieve the entire dataset or subset it. This allows well-informed users to easily access data with their code as well as having smaller specialized data centers supplementing their community with satellite data without the need to archive themselves. As the data follow widely adopted conventions, specialized data readers are not required for the user to analyze the data. Derived and model data outputs rely on knowing the quality of the data before assimilation by proper metadata.
Data Discovery for Non-EOVs Full data interoperability is the appropriate goal for data types that already have agreed standards and processes for collecting, documenting, and QCing the data. However, new variables will be observed and new observing methods are continuously being developed. It will always take time for new observation types to be validated and replicated. There are also historical datasets that gain new perceived value, making them "worth" aggregating in a standardized format  (Griffin, 2015). It is important to provide data discovery tools, allowing researchers and data managers to find those datasets not yet entrained in sophisticated data management systems. For those datasets, it may not be possible to achieve all of the FAIR principles, but at least three of themfindability, accessibility, and reusability-can be applied to almost all ocean data.
To date, these non-standardized datasets are principally housed in nation-and discipline-specific data centers, which publish discovery metadata through in-house catalogs and through metadata aggregators. In the past, a common approach has been to ensure that a single master copy of data is preserved, but the accompanying metadata records have been translated among differing standards and republished through aggregating metadata catalogs to increase the visibility of the dataset. Combined with a lack of persistent identifiers on records, this has resulted in a legacy of partially duplicated metadata records across multiple catalogs, with limited capacity to keep those duplicates updated. These partially duplicated records provide a considerable challenge to data discovery, and we are not aware of any mature deduplication algorithms being applied in federated metadata discovery tools.
Increasingly, oceanographic metadata managers are investigating options to develop federated metadata search tools. Such tools will allow simultaneous searching of multiple metadata catalogs, thus considerably improving the findability and accessibility of non-standardized datasets while reducing opportunities for semi-duplicated metadata records to proliferate. Federation of metadata search requires a brokering mechanism, which, in turn, requires well-understood crosswalks between the common metadata standards, vocabularies, and profiles in use by a community. This is done by having data that are in self-describing file formats (e. g. , NetCDF or HDF) or well and regularly, easy-to-read file formats (e. g. , ASCII). Following metadata standards is also a requirement as the brokering mechanisms can recognize the metadata and properly handle the data.
The newly launched Google Dataset Search 11  (currently as early release while gathering requirements for future iterations) relies on well-formatted metadata and tags following the schema. org definitions, where they see discipline-specific requirements on search and discovery as the responsibility of the discipline to implement. Smaller, more tightly focused federations will pose fewer challenges in terms of brokering these differences and will, hence, likely result in stronger search algorithms. In contrast, global federations will trade off searching power for larger holdings and greater economies of scale in terms of tool development. It is likely that the next decade will see a proliferation of metadata federations as communities balance the relative costs and benefits of small and large federations.
Best practices and standards for data management are increasingly being implemented by many observing networks and projects on national or continental scales. Successful cooperation among different operators is important for developing and promoting standards and best practices that facilitate the interoperability of systems. Credit Through Publication: Data Citation, Persistent Identifiers, Etc. Credit for developing new datasets, through attribution and citation, has several challenges as the supporting infrastructure needed for this is not fully developed. Journals increasingly require data that support an article to be in a FAIR-aligned repository that is open, uses persistent identifiers, and supports data citation (see the COPDESS Web site 5 ). We recognize that a significant fraction of the ocean data is collected "in research mode, " i.
e. , from project-based science with limited-term funding agreements; in science, output is largely measured in citable publications rather than in published datasets  (Mons et al. , 2011). It is imperative that scientists are incentivized to make their data publicly available. Options include using data citation tools, such as DOIs, that make it easier to credit the originating scientist and publishing data reports as peerreviewed publications [e. g. , Earth System Science Data (ESSD)]. Alternatively, a metadata record can contain fields identifying the data provider, giving recognition. To remedy this issue, the Coalition for Publishing Data in Earth and Space Sciences (COPDESS) 12 is working with the scientific data facilities and scholarly publishers "to help translate the aspirations of open, available, and useful data from policy into practice.
" Other efforts helping to automate data citations include a new task force by ORCID to work with repositories in capturing data creator ORCIDs and the necessary linking, and the MakeDataCount project 13 defining consistent ways to count data usage and providing tools to repositories to consistently show these metrics. Attribution and credit for data starts with the data repository and the registration of a persistent identifier associated with that data. Included in this metadata are the names and ORCIDs of the data creators. Through the registration process of the persistent identifier and services available through ORCID, a new dataset is made known in the research data infrastructure and linked with the repository and publication. For instance, publishers need to implement the full capability of the CrossRef Citation schema 14 to include the portion about relationships.
This identifies, in a machine-readable way, which citations are data and their association with research papers, which is particularly important for persistent identifiers that are not a DOI. Data facilities further need to capture the name of the data creator(s) along with their ORCID and provide that information when registering the persistent identifier for the dataset. This should also be provided to ORCID through the provided API that is created for repositories that have not implemented a globally unique persistent identifier. Similarly, researchers need to identify the best possible repository for their data, preferably one that is familiar with that type of data, providing curation services and is FAIR aligned. Persistent Identifiers for Data and/or products (PID) is another important piece of metadata for traceability of processing (proper identification and versioning) on one side and traceability of use by end users and organizing feedback to 12 COPDESS.
org 13 https://makedatacount. org 14 https://support. crossref. org/hc/en-us/sections/202832803-Crossref-schema providers. Such PIDs can be attached to the platform such as a WMO number for Argo, drifters, meteorological moorings, ICES code for vessels, etc. DOIs can also be attached to a version of a dataset, e. g. , DOI on data from a research cruise, on one glider mission, on versions of aggregated products like for SOCAT, GLODAP, or the Coriolis Ocean database for ReAnalysis (CORA), or on a periodic snapshot of a GDAC. Different strategies have been developed in the past 10 years, and the PID technology has evolved to be able to manage network data that evolve continuously. As datasets are aggregated into data products, it is not yet obvious how to give appropriate credit to all of the originating scientists who generated the data, with potentially thousands of scientists contributing to a single research article.
Work is needed to develop ways to efficiently and effectively give credit to all those who have contributed. Similarly, data centers that curated datasets integrated into data products need to be efficiently credited as contributors, perhaps by ensuring that the full lineage of a metadata record is maintained through all levels of aggregation and federation, maybe by exploring the blockchain concept in marine data lineage. Certification of repositories (e. g. , CoreTrustSeal 15  ) plays a key role; there is strong alignment between the tenets of FAIR and the elements of certifications to help researchers and publishers make an informed decision on determining which repositories meet criteria and are FAIR aligned. Recognizing that data to support research is valuable as stand-alone products enables the community to work with institutions to enhance promotion and tenure criteria to publish data and data products.
Large Diversity A high level of interoperability is required to harmonize these dispersed data systems to allow for easy access by users. Improving metadata services will be key at many steps in the data life cycle. For non-standardized datasets, better metadata search tools will be crucial. Leveraging the research infrastructures data systems to provide infrastructure for data of all EOVs at the same level of accessibility should be a priority. Data centers and scientific funding agencies should make international coordination an explicit part of data management staff 's job description in order to achieve these outcomes.
Multitude of Disparate Data Management Structures These existing systems need to be used as the 'building blocks' for an interoperable framework of data management systems. As an example on how this can work is the EMODnet effort in Europe, improving free, timely, and unrestricted access to interoperable European marine data, building on existing database, and open sharing infrastructures such as SeaDataNet, CMEMS, European Ocean Biogeographic Information System (EurOBIS), ICES, and EGDI  (Shepherd, 2018). Similarly, open source data platform tools, such as ERDDAP, can reduce the burden of providing interoperable data services for users and improve data uptake by consumers.
Best Practices Poorly Defined There is a growing need to identify the most efficient and systematic strategy for processing data all the way from the initial planning of data collection to the availability of the data products and their dissemination, i. e. , development of a data management plan that evolves throughout the life cycle of the data. Data management for large, dispersed datasets benefits significantly from a well-established and standardized approach starting even before the data are collected. The FAIRness of the data system will rely on continuously updated standards and best practices for metadata and data harmonization as well as QA/QC common procedures; such activity should be properly funded at national, continent, and international level. This also includes support, training, and outreach to the teams that will develop data systems for networks or thematic services.
This will be useful in assessing the impact of standard dietary treatment, pharmacological treatments such as sapropterin, and potential new therapies on clinical outcomes. Thanks to the availability of the PKU-QOL questionnaire into Brazilian Portuguese, the patients' perceptions in Brazil will be assessed and documented as patients age. This will enable increased understanding of the impact of PKU on the HRQL of patients and their parents throughout their life cycle. The PKU-QOL questionnaire is a validated tool and its use in assessing the impact of standard dietary therapy, pharmacological treatments such as sapropterin, and potential new therapies on the patients' and their caregivers' lives will be valuable in the management of patients with PKU in the future. In addition, the use of this instrument will encourage consistent collection of data across treatment centers in Brazil.
e. , predicting the signal was greater than the noise). To describe the data further, the amplitude and scalp topographies were assessed across presentation conditions. Scalp topographies were visualized in terms of both their original amplitude and their normalized amplitude  (McCarthy & Wood, 1985). To assess the extent of right lateralization for face categorization, a lateralization index was computed using the amplitudes of the right and left OT ROIs (R and L, respectively) as follows: (R-L)/(R+L)*100 (as in  Retter & Rossion, 2016a). Statistical tests on response amplitudes were applied independently for face-categorization and stimulus-presentation responses in the form of repeated measures analysis-of-variance tests (ANOVAs), with factors of Region and Condition. A Greenhouse-Geisser correction was applied to the degrees of freedom in the event that Mauchly's test of sphericity was significant.
Spatio-Temporal Dynamics (Experiment 1) The face-categorization responses from the previous section were re-examined in the time domain to explore potential qualitative differences across presentation rates. The following analyses use data selectively filtering out the stimulus-presentation frequency (see Methods; for unfiltered data, see Supplemental Figure  2 ). Four prominent time-windows of interest appeared across conditions, corresponding to those reported previously as P1-face, N1-face, P2-face, and P3-face (at a 12. 5-Hz stimulus-presentation rate;  Retter & Rossion, 2016a). These time-windows encompassed nearly all significant deviations over the bilateral OT ROI (Figure  6A ). Each of these deflections, or "components", was significant across three to six frequency conditions, without any clear trend of component differences as  These deflections, and responses more generally across time, occurred mainly over the bilateral OT ROI, with some coverage of the occipito-inferior, occipito-parietal, and medial-occipital regions (Figure  6B ).
Acknowledgments This research was supported by grants from the  Belgian National Foundation for Scientific Research  [http://www. fnrs. be/;grant number  FC7159  to TR], the  European Research Council  [https://erc. europa. eu/;grant facessvep number  284025  to BR], the  University of Nevada, Reno Integrative Neuroscience Center of Biomedical Research Excellence  [https://www. nigms. nih. gov/Research/DRCB/IDeA/pages/COBRE. aspx;grant number  P20 GM103650 ], and the  National Eye Institute  [https://nei. nih. gov/;grant number  EY010834  to MW, and grant number  EY023268  to JF]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We are grateful to  Andrea Conte  for creating the stimulation program XPMan, revision 94; to  Caroline Michel , for collecting the stimuli; and to  Charles Or  for generating the color key for plotting electrode location on a scalp map.
Acknowledgements We would like to thank  Rafal E. Dunin-Borkowski  for critical reading of the manuscript and helpful discussions. The research leading to these results has received funding from the  Deutsche Forschungsgemeinschaft  ( DFG Wi1828/4-2  to M. W. , DFG Ol513/1-1 to R. M. and T. F. ), the  European Research Council  under the European Union's  Seventh Framework Programme  ( FP7/2007-2013) / ERC  grant agreement number  320832 , the  Max Planck Society  and the  European Research Council  ( ERC starting grant  to D. F. , no. 256915 MB2 ). S. G. was supported by the  International Max Planck Research School (IMPRS) on Multiscale Biosystems. We acknowledge support by the  Open Access Publication Fund of the University of Oldenburg.
Journal of Advances in Modeling Earth Systems water cycling, and carbon turnover. The CO 2 emissions due to land use change are not considered in LULCC, since they are implicitly incorporated in the reconstructed GHG record. We noticed that there is a small step-change in the LULCC data at the year 1500, which is an artifact from the merger of two different land-cover datasets  (Otto-Bliesner et al. , 2016) , scaling the Pongratz reconstruction  (Pongratz et al. , 2008)  to match the Hurtt dataset  (Hurtt et al. , 2011)  at the year 1500 at each land grid cell. Our calculation shows that this artificial change at 1500 of the LULCC forcing has led to a large emission of the CESM-LME modeled D and significant influence on NBP at that year (Figure  S2  in the supporting information). To avoid its potential effect to our results, we replaced the values at the year 1500 of all variables (NBP, NPP, RH, D, and all carbon pools) from all simulations with an average of 5 years (1495-1499) before and 5 years (1501-1505) after 1500.
, 2016). Additional information about the forcing datasets, model variables, and diagnostics can be found at the CESM-LME website (http://www. cesm. ucar. edu/projects/community-projects/LME/). The ensemble outputs used here were the annual terrestrial NBP, NPP, RH, D, all carbon pools (in the vegetation and soil), and land surface climate variables. Ensemble means for all the full-forcing or single-forcing simulations were calculated in order to reduce uncertainties arising from climate internal variability at long-term timescales, when focusing on the effects of external forcings.
Acknowledgments This study was founded by  National Key Research and Development Program of China  ( 2016YFA0600202  and  2016YFC0500203 ). X. Zhang was sponsored by  Shanghai Sailing Program  ( 19YF1413100 ),  Open Fund of Shanghai Key Lab for Urban Ecological Processes and Eco-Restoration  ( SHUES2019B01 ), and the  China Postdoctoral Science Foundation  ( 2016M600853 ). Y. W. was supported by.
The timescale for transformation is unpredictable and the mechanisms causing them to become aggressive are poorly characterized. 10 o identify a convergent proliferation characteristic underlying transformation of LGAs, we utilized an integrated systems-level functional genomics framework which posits that multiple diverse biological perturbations (genetic, epigenetic) carrying risk for a given disorder may converge mechanistically on a common pathway. 11 pecifically, network analyses represent a systems-level approach for capturing the convergent role of heterogeneous perturbations in complex diseases  [12] [13] [14]  such that tissue-specific clusters of transcriptionally co-regulated genes ("modules") represent candidate regulators and drivers of disease states. 12, 15 To identify transcriptional modules underpinning LGA transformation, we made use of publicly available IDH1-mutant 1p/19q euploid LGA and AA gene expression data from The Cancer Genome Atlas (TCGA).
RNA sequencing Libraries for RNA sequencing (RNA-seq) were prepared using the TruSeq Stranded Total RNA kit. The library was then sequenced on Illumina HiSeq4000 using 75-bp paired-end reads. The reads were mapped to the hg38 using the Spliced Transcripts Alignment to a Reference (STAR) software (version 2. 5. 0a). 21 Quantification of mapped reads was carried out using HTSeq (version 1. 6. 1p1). 22 GA low-grade glioma data Clinical data, somatic mutation annotation files and SNP array data as well as raw and normalized RNA-seq read counts for low-grade glioma samples were acquired from TCGA data portal (https://tcga-data. nci. nih. gov). For RNA-seq data, fragments per million reads mapped (FPKM) was calculated for all genes. 23  The genes with very low expression were removed and the gene expression was corrected for batch effects.