dataesr/scientific-paragraphs-categorization huggingface dataset:
Schema and content implementation The reference document for the data request content is an XML document  (Bray et al. , 2008)  conforming to the XML Schema Definition (XSD;  Gao et al. , 2008)  document. The schema has been developed to satisfy the requirements that have emerged during the MIP endorsement process. The configuration-driven approach allows the data request   B2 ); F2: a simple python script is used to manage framework information; F3: a style sheet is used to map XML configuration information (C5) into a schema document (C2); F4: the python base class has dependencies on the core attributes built in. C1: an excel workbook, defining the attributes used in each section of DREQ; C2: the schema is expressed as an XSD document; C3: a sample XML document, which complies with the schema, is constructed.
This allows for verification of the logical consistency of the schema and facilitates construction of the full DREQ XML document; C4: a python class is defined for each section, combining the base class with configuration information; C5: the excel workbook (C1) is converted to a structured XML document for robust portability. P1: an XML document contains the aggregated information content; P2: a python API provides a programmable interface and command line options; P3: web pages support browsing and searching. schema to be generated from a framework document, and the same framework document is used to generate Python classes for the application programming interface (API). The request document aims to be self descriptive: each record is defined by its attributes and for each attribute there is a record defining its role and usage.
Additional implicit structure Much of the DREQ structure is formalized by use of the XSD mechanism; however, there is a significant amount of additional semantic structure within the DREQ that is not explicitly represented by the XSD semantics. Prominent examples include constraints on acceptable units, the use of guide values, conditional variable requirements and vertical domain requirements. CF standard names have a canonical unit, which defines the class of acceptable units for a variable with that standard name. For instance, if the standard name has canonical unit seconds, then associated variables can use any valid unit of time, such as day or hour. This and other consistency rules have been incorporated into the python code that, in addition to checking the schema, also checks, for example that a vertical coordinate (e.
Interfaces and version control of the data request The DREQ content is provided as a version-controlled XML document complying with the schema, but a range of interfaces are provided in order to make the contents more accessible. The use of XML documents ensures robust portability and allows users to import the DREQ into their own software environments. www. geosci-model-dev. net/13/201/2020/ Geosci. Model Dev. , 13, 201-224, 2020 Table  4. The pressure levels used for atmospheric variables in the DREQ. The right-hand column, titled "single", contains pressure levels used for single-level variables. Other columns represent collections of levels used as a vertical axis for a range of requested parameters. Black rectangles indicate a level which occurs in only one column. The plev7c axis is a special case that is used specifically to match diagnostics from the International Satellite Cloud Climatology Project (ISCCP; WCRP, 1982) cloud simulator.
The three black rectangles in the "single" column, at 220, 560 and 840 hPa are also ISCCP levels. For users who do not wish to confront the details of the XML schema, alternative views are provided by the website 12 and the python package dreqPy. 13 12 http://w3id. org/cmip6dr/browse. html (last access: 16 January 2020) 13 DREQ Python API: http://proj. badc. rl. ac. uk/svn/exarch/ CMIP6dreq/tags/latest/dreqPy/docs/dreqPy. pdf (last access: 16 January 2020) The website provides a complete view of the DREQ content in linked pages and also a range of summary tables as spreadsheets. These include, for instance, lists of variables requested by each MIP for each experiment. The python package provides both a command line and a programming interface. The python code is designed to be self descriptive.
Every record, e. g. the specification of a variable, is represented by an instantiated class with an attribute for each property defined in the record. For example, if cmv is a CMOR variable record, cmv. valid_min will carry the value of the valid_min parameter for that record. The specification of the valid_min parameter is carried as an attribute in the parent class at cmv. __class__. valid_min, which is a similar instantiated class. For instance, the code cmv. __class__. valid_min. type = "xs:float" gives the data type of the attribute (floating point) and cmv. __class__. valid_min. title gives a short description. The DREQ was version-controlled with a three-element version number, such as 01. 00. 31, following  Coghlan and Stufft (2013). From July 2017 onwards, updates were preceded by a beta release to allow for some error checking before moving to the full release.
Summary and outlook The CMIP6 Data Request, or DREQ, provides a consolidated specification of the data requirements of the 23 endorsed MIPs 15 participating in the CMIP6 process. In doing so, it supports those responsible for configuring simulation output, those developing software infrastructure, and those who are trying to anticipate what may be available before it appears in catalogues. The latter include both those responsible for storage systems and potential data users. The data request has a complex structure which arises from the inherent complexity of the problem: not only are there many more MIPs and experiments than in previous CMIP exercises but also not all modelling centres expect to address all the objectives of individual experiments, let alone all MIPs. This means that the request infrastructure has to handle varying aggregations across the over 350 000 potential combinations of variables, experiments and objectives and deliver the appropriate metadata information, lists, and summaries for the groupings which arise.
In practice, 411 groups are needed to serve the objectives which have been extracted from the experiment definitions. The design of the data request delivers a separation of concerns between a request framework, a configuration which specifies the sections and attributes of the request, and the actual content. In each domain (framework, configuration, content) there are information components (schema, instances) and code to support the use of that information. 14 https://pypi. org/project/dreqPy (last access: 16 January 2020) 15 Table  B1  has 25 rows because it also includes "DECK" and "CMIP", which refer to activities that have a role analogous to MIPs in the DREQ: "DECK" specifies a collection of experiments and "CMIP" specifies a set of data requirements.
Technical outlook There are a number of areas where technical improvements can be made to support future CMIP activities and, potentially, related work outside CMIP. As discussed in Sect. 4. 2. 3 above, there are a number of areas where the DREQ intersects with ES-DOC and CVs. There is room for closer semantic alignment, as well as some streamlining of information flow between the MIP teams and those developing the technical documents and infrastructure. Significant overlaps with ES-DOC occur in the definitions of experiments, potential model configurations, conditional variables and objectives. Some further rationalization of the interfaces between ES-DOC, the data request, and the controlled vocabularies prior to new experiment and MIP design will aid all parties. More use of reusable and extensible lists is also anticipated.
One obvious way forward would be to aim for future MIPs to be able to exploit existing and reusable variable lists, either as is or with managed extensions. The data request is complex and establishing and upgrading the content of different components requires different communication approaches. This can be seen by comparing just two of the many components: -The grids section defines some technical parameters used by community software tools. The priority here is to communicate clearly with the relatively small collection of software developers to ensure DREQ updates can be supported by the software to deliver the required outcomes in terms of data structures. -The definition of parameters in the var section requires a discussion among a broad range of scientific experts to reach a consensus on terminology.
Ongoing importance The entire CMIP process is predicated on producing data for analysis, informing both science and policy. The central importance of a data request to those goals is obvious, but the underlying obstacles to the construction of a well defined request are often unclear. We cannot take it for granted that the goals of participating science teams will be met without detailed attention to output requirements, particularly when, as in CMIP, so much of the value arises from the interactions between MIPs. This detailed attention is only going to become more important in the future as the diversity of the Earth system modelling community grows and pressure for efficient use of the computing resources needed to carry out advanced simulations and store output become greater. Getting output descriptions right will be crucial to delivering and evaluating scientific benefits, and to developing the necessary infrastructure.
Of particular urgency is the systematic development of forcing scenarios that require institutionalized support so that quality-controlled datasets and regular updates can be provided in a timely fashion. In addition, a more operational infrastructure needs to be put in place so that core simulations that support national and international assessments can be regularly delivered. This includes the oversight; development; and maintenance of the data requests, standards, documentation, and software capabilities that make this collaborative international enterprise possible. A long discussion on "Subconvention for associated files, proposed for use in CMIP6 [no. 145]" concluded by defining a sub-convention which allows variables in other files to be referenced from the cell_measures attribute.
This allows explicit referencing of grid cell areas and volumes. Such ancillary data should be included directly in the referencing file, according to earlier versions of the CF conventions, but was not included in CMIP5 files because it would, for some time-varying ocean grids, substantially increase data volumes. There is an open discussion on "Extension to external_variables Syntax for Masks and Area Fractions [no. 156]", which is exploring ways of making the link between masked variables and the relevant mask clearer. With the present convention it is possible to indicate that a variable is masked by, for instance, sea ice but there is no mechanism for identifying the specific sea ice variable used. The discussion has not reached a conclusion, so the DREQ uses an ad hoc syntax, placing the name of the masking variable in a comment string within the cell methods string.
Must be unique in the DREQ. For well known concepts, this may be related to the label but for most items a random string will be used. valueshouldbe from a defined vocabulary. All records in the schema definition section must have class set to "__core__". The useClass declared for an attribute can affect its interpretation in the Python package. For example, attributes labelled as "use-Class=internalLink" should refer to another data request record. typeRecord TypeThe type specifies the XSD value type constraint, e. g. xs:string. DREQ sections: the DREQ database is split into the following sections, each taking the form of a database table with the number of records specified in column 2. The numbering in the "Title" column represents a provisional partitioning of records into sections.
Acknowledgements. The CMIP6 Data Request is a collaborative effort which relied on substantial effort from the  MIP  teams listed in Table B1. Updates and extensions to the CF conventions and the CF standard names lists required community consensus which emerged with the help of many regular contributors to the CF discussions, especially  Jonathan Gregory. The construction of the request relied on patient input and engagement from the science teams behind the endorsed MIPs, listed in Table  B1. Financial support. This research has been supported by the  EU   FP7 Research Infrastructures  ( IS-ENES2 , grant no. 312979 ),  H2020 Excellent Science Research Infrastructures (IS-ENES3 , grant no. 824084 ),  H2020 Societal Challenges (CRESCENDO , grant no. 641816 ), the  US Department of Energy (DOE) National Nuclear Security Administration  ( Lawrence Livermore National Laboratory , contract  DE-AC52-07NA27344CE15 ), the  US DOE Office of Science (Regional and Global Model Analysis Program, PCMDI Science Focus Area) , the  UKRI Newton Fund (UK Met Office Climate Science for Service Partnership Brazil) , the  UK Natural Environment Research Council (National Capability  funding to the  National Centre for Atmospheric Sciences) , and the  World Climate Research Programme  ( Joint Climate Research Fund  for WGCM, CMIP and WIP activities).
Code and data availability. The current version of the DREQ is available from the project website at https://w3id. org/cmip6dr (last access: 16 January 2020) under the MIT License (BSD). It is provided as a versioned XML document, which can be used directly or programmatically (both command line tools and a python library are provided). The exact version of the DREQ discussed in this paper (01. 00. 31) is available from the Zenodo repository at https://doi. org/10. 5281/zenodo. 3361640 (Juckes, 2019a). It is also available as a package from the Python Software Foundation at https://pypi. org/10project/dreqPy/1. 0. 31 (last access: 16 January 2020).
, 2018). Supervised ML has found the widest applicability to date. ML algorithms frequently employed in practice include linear and logistic regression, artificial neural networks (ANN), support vector machines (SVM), tree-based methods, and neural networks and DL (Table  2 )  (Al'Aref et al. , 2019). Datasets in ML projects are typically partitioned into training, validation, and test subsets. The training set is used for the development of the model while the validation set is used to estimate overall model performance and fine tune its parameters. Thereafter, repeated training and cross validation are performed to remove any variance. Finally, an external test can be used to assess its generalizability once optimized. Ultimately, the challenge is to perform all these steps efficiently while avoiding over-fitting (occurs when a model learns the detail and the noise in the training data that the actual dataset does not generalize).
Regularized regression Allows for introduction of constraints when the number of variables in a system exceeds the number of observations and improve the generalizability the model. Some common forms of regression include LASSO (least absolute shrinkage and selection operator) regression, ridge regression, and elastic net regression. ing to use AI techniques to generate predictive models that can forecast COVID-19 related CV complications using echocardiography. A multi-site study lead by the Mayo Clinic is attempting to pioneer an AI platform with EchoGO Core AI software that will be able to automatically analyze images obtained via echocardiography in order to map how the virus attacks the heart (DAIC. , 2020). This study will include 500 COVID-19 positive men and women between ages 18 and 89.
A study by  Kwon et. al. developed a DL algorithm that was able to detect in-hospital cardiac arrest and death without attempted resuscitation31  (Kwon et al. , 2018). It performed better than standard methods and had a higher sensitivity and lower false alarm rates. Another study by  Alaa et al. (2019)  created an ML automated tool based on a dataset of more than 400,000 people with over 450 different variables to risk stratify patients without a history of CV disease. This tool improved CV risk prediction compared to the standard Framingham risk score and discovered new CV risk factors and interactions between different features of an individual. Ongoing efforts have been to develop novel diagnostic tools using ML algorithms.
, 2018; Tison et al. , 2018). There is a prospective cohort trial currently taking place in France that is evaluating a new method for remote monitoring of corrected QT measurements using AI based solution and ECG data collected via smartwatches (AI-QTc) in patients being treated with hydroxychloroquine (Assistance Publique Hopitaux De Marseille. , 2020). AI-QTc data will be compared with the standard manual QTc reviewed by a cardiologist.
These findings suggest an indication of considerable inflammatory disease burden and urgently require confirmation in a larger cohort. DL methods have shown promising performance for automated calculation of left ventricular function. One study used a convolutional neural network on a dataset of 596 CMR examinations acquired in different institutions and on scanners from different vendors to create a tool that outperformed manual segmentation, and its accuracy increased with the heterogeneity of the patients that were included  (Tao et al. , 2019). ML can also be used for automated segmentation of the heart. This is important because segmentation of the left ventricle from the epicardium and endocardium is important for the assessment of CV function. A study by  Ngo et al. (2017)  used ML for automated segmentation of the heart with a dataset of 45 cardiac cine magnetic resonance with ischemic and nonischemic cardiomyopathy, left ventricular hypertrophy, and normal LV function.
The accuracy was comparable to standard methods. Evaluation of myocardial perfusion can be performed using single-photon emission computed tomography (SPECT). ML has been used in the past to improve diagnostic performance and combine imaging modalities to maximize discriminatory capability. In one instance, an SVM approach was utilized to localize the mitral valve during SPECT acquisition, which is important for the assessment of myocardial perfusion. 392 SPECT scans were used for training and validation, and the ML model exhibited an AUC (area under the curve) of 82 for the detection of obstructive CAD in comparison to two experts (AUC of 0. 79 and 0. 81) and unadjusted mitral valve plane (AUC 0. 63)  (Betancur et al. , 2017). Based on their study results, the authors suggested that AI could lead to integration of clinical and imaging data and create personalized MACE risk calculations in patients undergoing SPECT MPI.
To facilitate analyses, Biosemi channel labels were later converted to an extended 10-20 system of electrode placement  [69]. EEG analysis. Preprocessing of EEG data was carried out using Letswave 5 (https:// github. com/NOCIONS/Letswave5), a custom software running over Matlab R2012b (MathWorks, Natick, MA), and followed procedure developed for frequency tagging studies of face stimuli in a number of published studies  [68, 71, 89]. After importation of raw EEG data files, vertical jumps in the signal due to voltage drift during pauses between trials were corrected for by aligning continuous blocks of recording to the first block. EEG data were band-pass filtered (0. 1 to 100-Hz fourth-order zero-phase Butterworth filter) and multi-notch filtered (width 0. 5 Hz) to remove 50 Hz's electrical noise on four harmonics.
Experiment 2a (scalp EEG) Experiment 1 was designed as a proof of concept and to identify facial identities providing large responses in the paradigm. Experiment 2 used only two facial identities (D. Trump and V. Putin) and increased variability in written stimuli to counter low-level effects. Most importantly, it added two key control conditions to identify the role of modality-specific representations within the face-name sequence. Participants. Twenty-two volunteers (10 males, all right-handed) received financial compensation in exchange for their participation in the experiment. One male and one female participant were excluded from the EEG analyses because of excessive artifacts in a large set of electrodes across multiple epochs during data recording. The final sampling of participants (n = 20) had a mean age of 22.
e. , every 0. 571 Hz. At 100% of their size, face images subtended about 6. 78˚× 5. 47˚of visual angle, while words subtended on average 13. 65˚× 1. 62˚of visual angle. Mean accuracy and RT to the orthogonal task (detecting color changes of the fixation cross) were computed independently for each participant. We considered behavioral data across identities in the three main conditions: Face Name, Face Only, and Name Only. A oneway ANOVA with repeated measures found no difference between conditions according to accuracy (F(2,38) = 0. 241, p = 0. 787) or RTs (F(2,38) = 0. 148, p = 0. 863). Participants also had to complete the same questionnaire as in Experiment 1. Here, 89. 25% (standard deviation = 15. 75%) of the 40 famous faces were rated as known. The two base identities (D. Trump and V.
After induction of general anesthesia, the stereotactic frame (Leksell model G, Elekta instrument, Stockholm, Sweden) is positioned on the patient's head. A stereotactic CT scan is then performed and fused to the preoperative non-stereotactic MRI using the iPlan Stereotaxy software (Brainlab AG, Mu ¨nchen, Germany). Electrodes are then implanted according to the following procedure: After reporting the calculated coordinates on the frame, stereotactic guided drilling of the skull is performed and a bone screw is inserted. The intracerebral electrode (DixiMedical, Besancon, France; diameter of 0. 8 mm; 5 to 18 platinum/ iridium contacts with 2-mm length, 1. 5 mm apart) is inserted and secured to the screw with a tight seal in order to prevent cerebrospinal fluid leak. Fluoroscopic control is performed to rule out major electrode positioning errors before removing the stereotactic frame.
For 4 participants, the experiment was administered twice, corresponding to a total of 12 sequences (4 Face Name, 4 Face Only, and 4 Name Only). Mean accuracy and RT to the fixation cross task were calculated for each participant. A one-way ANOVA with repeated measures found no difference between conditions according to accuracy (F(2,16) = 1. 019, p = 0. 383) or RTs (F(2,16) = 0. 326, p = 0. 726). Intracerebral EEG analysis. SEEG analyses were carried out using the free software Letswave 5 (see https://github. com/NOCIONS/letswave6 for the most recent version) and largely followed procedures used in previous SEEG studies with this approach  [80, 81]. Segments of SEEG data corresponding to stimulation sequences were extracted. These segments were then cropped to an integer number of 0. 571 Hz cycles beginning after the 2-second fade-in and ending before the 2-second fade-out (30,471 bins, approximately 60 seconds).
Note that the more posterior "Pure" FN contact is still located in the ATL (i. e. , anterior to the posterior tip of the hippocampus in the individual anatomy). Data underlying this figure are deposited on a Dryad repository: https://doi. org/10. 5061/dryad. m8t391m. ATL, anterior temporal lobe; FN, Face Name; FO, Face Only; NO, Name Only; PTL, posterior temporal lobe; VOTC, ventral occipito-temporal cortex. (TIF) S7 Fig. Schematic representation of the parcellation scheme of the VOTC. Anatomical regions were defined in each individual hemisphere according to major anatomical landmarks  [95]. The ventral temporal sulci (collateral sulcus, occipito-temporal sulcus, and midfusiform sulcus) serve as medial/lateral borders of regions, whereas 2 coronal reference planes containing anatomical landmarks (posterior tip of the hippocampus and anterior tip of the parietooccipital sulcus) serve as an anterior/posterior boundary for each region.