dataesr/scientific-paragraphs-categorization huggingface dataset:
The CMIP Diagnostic, Evaluation and Characterization of Klima (DECK) experiments and the CMIP historical simulations  (Eyring et al. , 2016a ) lend themselves to this purpose. The workflow for routinely analysing and evaluating the CMIP simulations is shown in Fig. 1. It utilizes community tools and relies on the ESGF infrastructure and relevant Earth system observations. The workflow assumes CMIP model output and observations are accessible in a common format on ESGF data nodes (Sect. 2. 1), open-source software evaluation tools exist (Sect. 2. 2), and the existing ESGF infrastructure, which is now mainly a data archive, is enhanced with additional processing capabilities enabling evaluation tools to be directly executed on at least some of the ESGF nodes (Sect. 2. 3). Plans for making evaluation results traceable, well documented, and visually rendered are also discussed (Sect.
Access to CMIP model output and observations in common formats The CMIP5 archive of multi-model output constitutes an enormous and valuable resource that efficiently enables progress in climate research. This diverse repository, in excess of 2 PB (see Table  1 ), of commonly formatted climate model data also has proved valuable in the preparation of climate assessment reports such as the IPCC and in serving the needs of downstream users of climate model output such as impact researchers. The CMIP data format requirements are based on the Climate and Forecast (CF) selfdescribing Network Common Data Format (NetCDF) standards and naming convention 4  and tools such as Climate Model Output Rewriter (CMOR). 5 As a result, the CMIP model output conforms to a common standard with metadata that enables automated interpretation of file contents.
The layout of data in storage and the definition of discovery metadata have also been standardized in the data reference syntax (DRS), 6  which provides for logical and automated ways to access data across all models. This has enabled development of analysis tools capable of treating data from all models in the same way and effectively independent of the platform on which they are executed. The infrastructure supporting the publication of CMIP5 data was developed by the ESGF, which archives data accessible via a common interface but distributed among data nodes hosted by modelling and data centres. The CMIP5 survey noted that this first generation of a distributed infrastructure to serve the model data did not initially perform well,  which, retrospectively, is not surprising given that it was a first major application of a distributed approach to archiving CMIP data and given the limited time and resources available for development and testing.
Storing, testing, and delivering these data have relied on a distributed infrastructure developed largely through community-based coordination and short-term funding. This relatively fragile approach to providing climate modelling infrastructure will face even stiffer challenges in the future. Climate modelling and evaluation, which already involves management of enormous amounts of data, is a big data challenge confronted with demands for prompt access and availability  (Laney, 2012). Unless we meet the challenge of dealing with increasing volumes of data, it will be difficult to routinely and promptly evaluate CMIP models. Improvements in the functionality of the ESGF require a coordinated international undertaking. Priorities for CMIP are set by the WGCM Infrastructure Panel (WIP), and through the ESGF's own governance structure these are integrated with demands from other projects.
The individual, funded projects comprising the ESGF ultimately determine what can be realized by volunteering to respond to the prioritized needs and requirements. The model evaluation activity advocated here depends on the ESGF providing automated and robust access to all published model output and relevant observational data. The quantity of data made available under CMIP5 was about 50 times larger than under CMIP3. The data volume is expected to grow by another factor of 10-20 for CMIP6, resulting in a database of between 20 and 40 PB, depending on model resolution and the number of modelling centres ultimately participating in the project (Table  1 ). The CMIP6 routine model evaluation activity discussed here will initially rely mostly on well-observed and commonly analysed fields, so this activity is not expected to increase the Examples of existing diagnostic tools that are available for CMIP6.
Some of the tools focus on the broad characterization of the models and others on specific scientific applications. For details on diagnostics and performance metrics included in these tools and a description on how to contribute to the developments, we refer the reader to the individual literature and websites given below. CMIP6 data request beyond the CMIP6-Endorsed MIP demands. The convenience of dealing with CMIP output that adheres to well-defined standards and conventions is a major reason why the data have been used extensively in research. Another requirement of any model evaluation activity is wellcharacterized observational data. Traditionally, observations from different sources have been archived and documented in a variety of ways and formats. To encourage a more unified approach, the obs4MIPs initiative  (Teixeira et al.
, 2014)  has defined a set of specifications and criteria for technically aligning observational datasets with CMIP model output (with common file format, data, and metadata structure). Over 50 gridded datasets that conform to these standards are now archived on the ESGF alongside CMIP model output, and the archive continues to rapidly expand  (Ferraro et al. , 2015). Data users have enthusiastically received obs4MIPs, and the WCRP Data Advisory Council's (WDAC) has established a task team to encourage the project and provide guidance and governance at the international level. The expansion of the obs4MIPs project, with additional observational products directly relevant to Earth's climate system components and process evaluation, is a clear opportunity to facilitate routine evaluation of ESMs in CMIP.
A sister project, ana4MIPs, provides selected fields well suited for model evaluation from major atmospheric reanalyses. The obs4MIPs protocol requires every dataset submitted to be accompanied by a technical note, which includes, for example, discussion of uncertainties and guidance as to aspects of the data product that are particularly relevant to model evaluation. Similar documentation efforts for observations specifically meant for use in model evaluation can be found at the National Center for Atmospheric Research (NCAR) climate data guide. 7 Ideally, standard technical documentation as defined by obs4MIPs will be adopted broadly by the international observational community and will be hosted alongside (or integrated with) the CMIP model and simulation standard documentation (ES-DOC).
Additionally, there are proposals being considered to include non-gridded data in obs4MIPs (e. g. data collected by ground stations or during aircraft campaigns), and the possibility that auxiliary data such as landsea masks, averaging kernels, and additional uncertainty data might also be provided. Whatever datasets are used for model evaluation, it will be important to determine the size of observational error relative to the errors in the models. One approach being developed is to provide ensembles of observational estimates, all based on a single sensor or product and generated by making many different choices of retrieval algorithms or parameters, all considered to be reasonable. The goal is to be able to extend obs4MIPs in order to better characterize observational uncertainty.
Integration of evaluation tools in ESGF infrastructure In order to connect multivariate results from multiple models and multiple observational datasets (Sect. 2. 1) with tools for a quasi-operational evaluation of the CMIP models (Sect. 2. 2), an efficient ESGF infrastructure is needed that can handle the vast amount of data and execute the evaluation tools. At the same time the workflow should be captured so that the evaluation procedure can be reproduced as new model output becomes available. This will allow changes in model performance to be monitored over a time frame of many years. Our expectation is that, for CMIP6, the ESMVal-Tool and PMP, with contributions from other efforts such as the NCAR CVDP and ILAMB packages, will be able to operate directly on the data served by the major ESGF data nodes.
e. "replicas") of most of the required CMIP datasets and the obser-vations used by the evaluation tools. Although these replicas typically represent a significant subset of the data volume available on the ESGF, especially at the larger ESGF nodes, the complete replication of the entire CMIP model output at a single ESGF site cannot be achieved. As a consequence, some of the required CMIP model output used in the evaluation tools might still not be available even on the largest ESGF nodes. There are two practical solutions: (1) to distribute the processing of the evaluation tools at different ESGF nodes, and (2) to acquire and potentially cache data as needed for the evaluation tools. We regard the first option as not being practical in the CMIP6 time frame but a possibly promising option in the long term.
To summarize, we will begin in the CMIP6 time frame with the deployment of a subset of packages, in particular the ESMValTool (which itself includes other well-known packages such as NCAR CVDP) and PMP and run them on or alongside ESGF supernodes. Starting with available data in existing CMIP5 replica caches, the evaluation package developments are tested at dedicated sites (some of the supernodes) and prepared for CMIP6. In parallel, developments with respect to the supporting infrastructure (replication, cache maintenance, provenance recording, parallel processing) are starting. We expect this initial effort to spur developments toward a uniform approach to analytic package deployment. Eventually we aspire to put in place a robust and agile framework whereby new diagnostics developed by individual scientists can quickly and routinely be deployed on the large scale.
ES-DOC will be collecting the relevant information to aid this process. A wide variety of observational datasets, including the already identified ECVs  (GCOS, 2010) , can be used to assess the evolving climate state (e. g. means, trends, extreme events, and variability) on a range of temporal and spatial scales. Examples include the evaluation of the simulated annual and seasonal mean surface air temperature, precipitation rate, and cloud radiative effects (e. g. Fig. 9. 2-9. 5 of  Flato et al. , 2013). In evaluating the climate state, many studies are limited to the end result of the combined effects of all processes represented in CMIP simulations, and as determined by the prescribed boundary conditions, forcings, and other experiment specifications. While a necessary part of model evaluation, a limitation of this approach is that it rarely reveals the extent to which compensating model errors might be responsible for any realisticlooking behaviour, and it often fails to reveal the origins of model biases.
Summary and discussion We provide a viewpoint here that advocates the development of community evaluation tools and the associated infrastructure that as part of CMIP6 will enable increasingly systematic and efficient ESM evaluation. This is an improvement over the existing CMIP infrastructure, which mainly only supports access to the data in the CMIP database. The initial goal is to make available in shared, common analysis packages a fairly comprehensive suite of performance metrics and diagnostics, including those that appeared in the IPCC's AR5 chapter on climate model evaluation  (Flato et al. , 2013). Over time, an expanding collection of performance metrics and diagnostics would be produced for successive model generations. These baseline measures of model performance, applied at the time new model results are archived, would also likely uncover obvious mistakes in data processing and metadata information, thereby providing an additional level of quality control on output submitted to the CMIP archive.
The modelling, observational, and reanalysis communities should continue to nurture these efforts and ensure that these datasets include documentation in the form of technical notes, uncertainty information, and any special guidance on how to use the observations to evaluate models. This encapsulates ongoing efforts of the WCRP's data advisory council. The effort devoted to conforming data to well-defined standards should pay off in the long term and lead to a better process-level understanding of the models and the Earth's climate system while fully exploiting existing observations. With an eventual multi-model evaluation infrastructure established, we can look forward to revolutionary advancement in how climate models are evaluated. Specifically, results from a comprehensive suite of important climate characteristics should become available soon after simulations are made publicly available, with extensive documentation and workflow traceability.
Data availability The model output from CMIP simulations as well as observations and reanalyses for model evaluation from obs4MIPs and ana4MIPs are distributed through the Earth System Grid Federation (ESGF) with digital object identifiers (DOIs). The model output and obs4MIPs/ana4MIPs data are freely accessible through data portals after registration. An example of a dataset DOI can be found here: http://dx. doi. org/10. 1594/ WDCC/CMIP5. MXELpc. Additional observations used for model evaluation in the example plots shown here produced with the ESMValTool, NCAR CVDP, and PMP are described in the documentation papers of these tools.
Acknowledgements. This work was supported in part by the  European Commission 's  7th Framework Programme  " InfraStructure for the European Network for Earth System Modelling Phase 2 (IS-ENES2)" project  under grant agreement no. 312979. Veronika Eyring  acknowledges additional funding received from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement no. 641816  (CRESCENDO). V. Balaji acknowledges funding from an  ExArch grant  ( NSF award   1119308 ) and support by the  Cooperative Institute of Climate Science  from the  National Oceanic and Atmospheric Administration, US Department of Commerce  (award  NA08OAR4320752 ). Gerald A. Meehl  acknowledges support from the  National Science Foundation  and from the  Regional and Global Climate Modeling Program of the US Department of Energy's Office (DOE) of Biological & Environmental Research  (cooperative agreement no.
These tools are also included as separate namelists in the ESMValTool and will be applied to CMIP6 models together with other ESMValTool diagnostics and performance metrics as soon as the output is published to the ESGF. Short name of tool Long name of tool Focus Reference Language Further information Coupled to the ESGF running locally and on specific supernodes in CMIP6 (see text and Fig. 5) ESMValTool Earth System Model Evaluation Tool Diagnostics and performance metrics for the Eyring et al. (2016b) Backend in Python, diagnostics http://www. esmvaltool. org/ broad characterization of CMIP6 models in NCL, R, Python PMP PCMDI Metrics Package Summary statistics and associated diagnostics for Gleckler et al. (2016) Python https://github. com/PCMDI/pcmdi_metrics the broad characterization of CMIP6 models Examples of evaluation tools targeting specific applications or phenomena CREM  *  Cloud regime error metric Clouds Williams and Webb (2009) Python - CLIMDEX  *  Climate extremes indices Core indices of extreme climate -R https://cran.
Case ascertainment All cases of stroke suspicion in Martinique are referred to the University Hospital of Martinique. Our retrospective study consisted in identifying all stroke suspicions reported between January 1 st 2007 and December 31 st 2013. For case ascertainment over the 7-year study period, multiple overlapping methods were employed: (1) the hospital's administrative patient databases were searched for all patients coded with the ICD-10 code (International Classification of Disease, 10 th Revision) defining non-traumatic SAH and/or having a discharge diagnosis consistent with subarachnoid hemorrhage (I60, I61. 5, I61. 9, I62, I67. 1, I69, I72. 0, I72. 9)  [10] ;  (2)  the imaging database at our institution was searched for all patients undergoing cerebral angiography, CT-scan and MRI by screening the database with the keywords "aneurysm", "Subarachnoid Hemorrhage"and other synonyms in the French language; (3) the hospital's surgical database was searched for all patients undergoing aneurysm surgery; (4) the emergency medical department's database was searched for all patients registered as having a SAH.
Study data All study data were recorded with the approval of the University Hospital of Martinique's ethics committee (Comité de Protection des Personnes Sud-Ouest et Outre Mer III). Outcome data corresponding to the patient's vital status were obtained from medical records and when necessary, general practitioners of study patients were contacted for further information. Death within the 30 days following SAH diagnosis, regardless of cause, was considered for case-fatality computations. The following information was also noted: gender, age, city of residence, diagnostic modality, presence of an aneurysm and its location on the circle of Willis (if multiple aneurysms were present, only the location of the aneurysm supposed to be responsible for SAH was registered). Particular attention was paid during case diagnosis of perimesencephalic hemorrhage (defined as hemorrhage restricted to the cisterns surrounding the brainstem and suprasellar cistern, coupled to a negative cerebral angiogram).
Statistical Analysis Patient information was anonymized prior to analysis. The number of SAH episodes was recorded each year from January 1 st 2007 to December 31 st 2013. Incidence rates were presented as SAH episodes per 100,000 person-years with a 95% confidence interval (CI). Population denominator data was obtained from yearly population estimates issued by the French National Institute for Statistics and Economical Research  [9]. Overall crude incidence rate, as well as SAH incidence according to age, gender and aneurysm presence, were calculated. Age standardized incidence rates were also computed by direct standardization using the World Health Organization's World Standard Population for 2000-2025  [11]. Overall and sex-specific 30-day case-fatality rates were also generated by considering deaths, regardless of cause, among the study patients.
Confidence intervals were generated by assuming a Poisson distribution for all case counts. For all descriptive and inferential analyzes, assumption of normal distribution of the data was analyzed by the Shapiro and Wilk test as well as by the kurtosis and skewness standardized coefficients. Underlying assumptions of each statistical procedure were tested, and the following tests were consequently used for group comparisons: Student t-test, Wilcoxon-Mann-Whitney test, Chi-square test and Fisher's exact test. Mean and 95% confidence intervals were reported for normally distributed variables and median and interquartile range for non-normally distributed variables. All statistical analyses were conducted using SPSS software 22. 1 for Windows (SPSS, Inc, Chicago, IL). All inferential analyses were performed by means of a 2-sided test, with a level of significance of 5%.
Presepsin was measured using a PATHFAST chemiluminesence immunoassay analyzer (Mitsubishi Medience, Tokyo, Japan). Presepsin values measured and compared between the blood and semen groups. Statistical analyses were performed using the Statistical Package for the Social Sciences for Windows version 15. 0 software (SPSS, Chicago, IL. , USA). Descriptive statistics are given as mean, standard deviation, frequency, and percentage. A parametric comparison was performed using Student's t-test, and a non-parametric comparison was conducted using the Mann-Whitney U test. Statistical significance was defined as p < 0. 05. Ethics Committee Approval: Approval was received for this study from the ethics committee of Zeynep Kamil Education and Research Hospital and written informed consent was obtained from patients who participated in this study.
ACKNOWLEDGMENTS We would like to thank our dear colleague  Dr. Selim Kilic , a member of the  Clinic Department of Epidemiology and Public Health, Ankara Gülhane Military Medical School  for helping with the statistical analysis of this study. We also thank our colleagues  Dr Tayfun Kutlu ,  Dr Naciye Arat , and  Dr Gülden Tunali  from the  IVF unit of Zeynep Kamil Education and Research Hospital  for providing insight, expertise, and scientific guidance that greatly assisted our research.
The presented data demonstrates the survival and continued functionality of all 4 human cell types over a 14 day culture period under flow in this microfluidic system to demonstrate the ability to reach chronic timeframe, as well as their response to therapeutic challenge from five compounds at 7 days for acute responses. The cultures were evaluated in situ and ex situ to show both the ability to carry out non-invasive analysis or the capability to perform more extensive testing after disassembly. This low cost platform was self-contained and allowed for continuous circulation of the blood surrogate with periodic medium supplementation and analyte analysis and represents the next generation of organ-on-a-chip or body-on-a-chip systems for both acute and chronic studies.
These modules allow measurement of not only metabolic but electrical and mechanical function. Data obtained from cell viability and functionality assessment after 14 days of co-culture under flow in the system are shown in Fig. 2. Human serum albumin (HSA) production and urea production by human liver cells were assessed throughout the culture period by analyzing samples of conditioned serum-free medium removed from the platform reservoirs each day (30% of the total volume). Figure  2a  indicates stable levels of albumin (p = 0. 17) and urea (p = 0. 88) for liver function that indicate no significant changes during the experiments when no drugs are added. The same volume of fresh medium replaced the conditioned medium removed to replenish nutrients. Cardiomyocyte in situ measurement function was evaluated by measuring the amount of deflection of microfabricated cantilevers from contracting cell monolayers grown on these BioMEMs devices  16.
Here, we demonstrate a system that allows one to determine if a single drug affects multiple organs simultaneously. The organ chips were distributed in separate compartmentalized chambers, each interconnected to one or two other chambers though microfluidic channels. Human cell types of the four organs were represented with cell lines, primary cells and cells derived from iPSc allowing the exchange of metabolites and signal molecules. Functional evaluation of the organs after pharmacological exposure for 48 hours was achieved by measuring cardiac beat frequency, muscle contractibility, neuronal electrophysiology as well as liver albumin and urea production. The results were compared to human clinical data or other data from the literature to provide initial validation of the presented system as a model for accurately predicting multi-organ toxicity in humans.