dataesr/scientific-paragraphs-categorization huggingface dataset:
While WOCE and TOGA advanced understanding of the ocean and ocean-atmosphere dynamics and the accuracies needed in the observing system, they also promoted mechanisms for the development of internationally coordinated globalscale ocean observations including standardized quality control procedures and open and timely data sharing protocols  (Siedler et al. , 2001). Building on this legacy, OOSDP met for 5 years and produced a series of in-depth background reports addressing the challenges and capabilities of different elements of the ocean climate system and of the role of models in a global ocean observing effort. The work of the OOSDP culminated in a realistic but aspirational plan for ocean observations, which was assessed on the basis of feasibility versus impact, a central theme of the Framework for Ocean Observing today.
These reviews were thus prompted by the advent of new technologies and observing capability. The panel was also involved in forming the Global Ocean Data Assimilation Experiment (GODAE; succeeded by GODAE OceanView), which placed new demands on the ocean observing system. The panel's early work culminated in the OceanObs'99 conference, which cemented the foundations of what we know now as the sustained ocean observing system for climate. From OceanObs'99 there emerged a consensus within the ocean observing communities to undertake an internationally coordinated sustained global ocean observing effort for ocean physical and carbon variables with respect to climate applications, the details of which were subsequently agreed and presented in the first GCOS Implementation Plan. During the 2000s OOPC, with other partners, supported the establishment of a number of sustained observing networks building on the OceanObs'99 recommendations.
These included the establishment of the OceanSITES moored time-series initiative in 1999, and development of the International Argo array of profiling floats in 2000. OOPC's involvement was critical to brokering data agreements and incorporating these new networks into existing programs such as SOOP and the global XBT network, and connecting to the expanding satellite missions (e. g. , Sea Surface Temperature and Ocean Altimetry). In addition, OOPC co-sponsored a workshop on the Indian Ocean held in Perth, Australia, that led ultimately to the creation of a regional alliance to support GOOS in the Indian Ocean (IOGOOS). In 2001, OOPC conducted a review of the Tropical Moored Buoy Array. During this period OOPC established close and strong collaborations with WCRP including their Climate and Ocean: Variability, Predictability and Change (CLIVAR) project and other panels, ensuring both the development of a sustained ocean observing system and providing input to regional and basin process-studies.
The Framework The Framework for Ocean Observing  (Lindstrom et al. , 2012)  identifies lessons learned from the successes of previous and existing ocean observing efforts and provides an internationallyaccepted common language and guidance for expanded collaboration in sustained ocean observations. It is focused on: • Delivering a system based on common requirements, coordinated ocean observing elements, and common data and information streams; • Essential Ocean Variables (EOVs), a common focus for requirements defined based on feasibility and impact on societal and scientific drivers; and evaluation of readiness levels of sensors, platforms and data management procedures. EOVs are identified by the GOOS Expert Panels, based on the following criteria: Relevance: The variable is effective in addressing the overall GOOS Themes -Climate, Operational Ocean Services, and Ocean Health.
Feasibility: Observing or deriving the variable on a global scale is technically feasible using proven, scientifically understood methods. Cost effectiveness: Generating and archiving data of the variable is affordable, mainly relying on coordinated observing systems using proven technology, taking advantage where possible of historical datasets. When EOVs are identified, a series of recommendations are created and disseminated by the Expert Panels, including what measurements are to be made, various observing options, and data management practices. The adoption of EOVs is not intended to replace existing ocean observing networks and international coordination groups, but to provide a mechanism to bring them together to develop a holistic ocean observing system and strengthen the ocean observing communities ability to grow and best meet current and future user requirements.
The selection of EOVs is based on their need to support improved scientific understanding and also societal needs; at the same time the mature status reflects technical readiness and feasibility of the observing methods. The importance of connecting the major drivers and themes with the societal benefits informed by scientific issues and applications, is moreover ensuring consistent identification of the EOVs and their corresponding observing element. In turn, strengths and deficiencies, synergies and interdependencies can be objectively tracked and evaluated. The OOPC panel (and likewise the other GOOS panels) shall moderate such communication by informing the observing community. In that context, the panels have created EOVs specification documents that provide guidance on input variables, their definitions, observing platforms that sample the variables, information on accuracy, and examples for utilization of the data in the ocean observing value chain.
In other words, an apparently monodisciplinary experiment in marine biology or biogeochemistry is typical a multidisciplinary one involving ocean physics (and the associated sampling). Hence as we move toward an increasingly multidisciplinary observing system new challenges in a fully integrated system will need to be identified and eventually solved. The EOV (and ECV) process very much focuses on observational quantities, meaning the sensor data and its combination with other variables. However, for the observing system and in particular for providing guidance on the individual observing networks it is mandatory to introduce a mechanism to trace each data point from the sensor mounted on an observing platform to a certain product that has been generated by the downstream services (e. g. , a sea surface temperature map).
Recent publication discuss many aspects of data management (e. g. ,  Moltmann et al. , 2019; Pinardi et al. , 2019; Snowden et al. , 2019) ; here we address the need for EOVs data collections. Where EOV data are handled by a multitude of disparate data management infrastructures this imposes problems for the global observing system. These include, but are not limited to, delayed and duplicate data receipts, versioning issues, missing data and metadata, and undocumented data processing procedures, and data recovery. If data quality and supporting metadata are to carry over from observers to products then modern data management infrastructures are essential at every step along the data flow pipeline, from data recovery, collection through assembly and to preservation. The more automated and fault tolerant these steps are, the more they embrace community standard practices and the more efficiently and swiftly will systems advance toward the levels of reliability and interoperability that user communities seek.
The Committee on Earth Observations Satellites (COES) and the International Quality controlled Ocean Database (IQuOD) are examples of groups that are focused on providing the community with high quality data that has a consistent and unified quality control standard applied. OOPC needs to support and promote these activities and products, respectively. Interoperability serves both routine data flow within and amongst the networks, and user discovery and access. Community standards for metadata, data formats, communication protocols, and data server software infrastructure are the foundation for interoperability. These are not new considerations for the ocean data management community. The technical aspects have been demonstrated and successfully deployed in many regions and specific parts of the global networks.
Expanding on these successes is important, and is being guided by various ocean observing programs led by individual national efforts, regional and international groupings, as well as by coordinating organizations independently and jointly in the WMO and IOC. The time is right to improve interoperability across the observing system networks and enable sustainable process that can create integrated datasets for the EOVs. A data collection for EOVs must ensure the following: • Data are quality assured and controlled according to agreed international standards; • Feedback is given to the data sources as needed; • Duplicates are identified and resolved; • Metadata are complete and meet agreed best practices and existing standards; • Data and metadata are discoverable and accessible through interoperable services; • Data requests and searches from users can be reproduced; • Data and metadata are electronically published with protocols for citation and acknowledgment; • The complete provenance and lifecycle of data processing is traceable for each ECV/EOV dataset.
OOPC needs to ensure the observing system develops in a coordinated way and that data remain openly accessible in a timely manner. The synthesis and delivery of higher-level derived products based upon direct observations in support of research, operational and climate applications is a major scientific undertaking that demands significant added investment of both money and people. It is only through the coordinated synthesis of all observations for a given EOV that integrated observational datasets that meet user requirements and expand the use of the data will be achieved. OOPC oversight and endorsement of ocean observing system reviews (e. g. , TPOS2020, TAOS, IndOOS) and design studies for future systems (DOOS) is not currently undertaken in a consistent way. To ensure that these interconnected components of a global observing system are consistent, OOPC needs to provide a set of common guidelines to frame these review or design studies.
Statistical analysis Answers were considered after 3,000 complete replies were recorded (the sample size was determined from an unpublished study to obtain at least a power of 0. 99 for testing vaccination status). Statistical analyses and representations were performed using Epi Info™ 7 (Epi Info Software, CDC, Atlanta, GA, USA) and/or GraphPad Prism software (V7. 0) (GraphPad Software Inc, La Jolla, CA, USA). The Mann-Whitney or Kruskal-Wallis statistical tests were used for quantitative variables (adherence to mandatory vaccination), while the Fisher Exact test was used for qualitative variables (all variables but adhesion). Results were significant when the p-value (p) was < 0. 05.
Disparities and differences between all the studies may be explained by the design of each study. Our study was web-based and answers were only collected over a 15 day period from willing HCWs. Therefore, the results may only reflect the response from the more concerned and connected HCWs. However, as vaccine uptake has an impact in terms of protection as well as vaccine advocacy for the frail patients, minimizing the delay to provide key nationwide data about HCW vaccination rate was considered decisive. Some of the results were concordant with those already published such as higher vaccine uptake in previously vaccinated HCWs and in community-based practice versus hospital-based HCWs  [16]. Other studies reported discrepant results compared with ours, such as the lack of gender or age-related differences in influenza vaccine uptake  [17] [18] [19] [20].
Results: The guidelines were related to 5 areas: diagnosis, pharmacological treatment, oxygen therapy and ventilation, patients triage, specific considerations regarding pregnant women. The literature analysis and formulation of the guidelines were conducted according to the Grade of Recommendation Assessment, Development and Evaluation methodology. An extensive literature research was conducted based on publications indexed in PubMed ™ and Cochrane ™ databases. Of the 21 formalized guidelines, 4 had a high level of evidence (GRADE 1+/-) and 7 a low level of evidence (GRADE 2+/-). The GRADE method was inapplicable to 10 guidelines, which resulted in expert opinions. A strong agreement was reached for all guidelines.
Areas of recommendations Five areas were defined: diagnosis and elements of the diagnosis, pharmacological treatment, methods of oxygen therapy and ventilation, transfer of patients, specific considerations regarding pregnant women. A bibliographic search was conducted using the MEDLINE database via PubMed and the Cochrane database. For inclusion in the analysis, the publications had to be written in English or French. The analysis focused on recent data according to an order of appraisal ranging from meta-analyses to randomized trials to observational studies. A summary of the recommendations is presented in Table  2.
Rationale NIV is increasingly used for the management of SAE  [65, 66]. In several observational studies, NIV improved alveolar ventilation and successes were even reported in patients with disturbance of consciousness and with severe hypercapnia. A single study has shown a reduction in the rate of intubation with NIV  [67]. However, the conclusions of this retrospective, single-center, before-after study cannot be generalized. In a large American database, the rate of intubation of patients treated by NIV was identical to that of the other patients  [68]. Four randomized controlled studies compared NIV with treatment without NIV  [69] [70] [71] [72]. All these studies reported clinical or spirometric (decrease in respiratory rate, dyspnea, and signs of struggling to breathe, improvement in FEV1 or PEF) improvement.
Rationale To date, the literature on the use of extracorporeal life support (ECMO or ECCO2R) in the management of SAE is extremely limited. The available data are from small retrospective cohorts  [118] [119] [120] [121]. Review of the International Extracorporeal Life Support Organization Registry shows that ECMO was used in the setting of SAE in 24 of the 1257 patients included in the registry. Hospital survival was 83%, compared with 51% in patients receiving venovenous ECMO for another cause  [121]. As hypercapnia is prominent in refractory SAE, ECCO2R can be considered as a more accessible and less invasive technique than ECMO  [118]. The use of extracorporeal life support in children with SAE has been described solely in clinical cases or in retrospective studies in small cohorts  [120].
The holographic configuration is on-axis and the reconstruction distance (z ≈ 0. 19 m) is large enough so that the twin image energy is spread over the reconstructed hologram and has no appreciable effect on the resulting image. After numerical propagation, the beat frequency spectrum of the reconstructed holograms is analyzed by short-time Fourier transform analysis which naturally removes the interferometric zeroorder term. In this approach, it is considered that the cross-beating terms of the holograms carry the Doppler broadening caused by the passage of red cells and the data processing consists of measuring the local optical temporal fluctuations. Power Doppler images are calculated by integration of the DPSD over the frequency interval [f 1 , f 2 ]. Power Doppler images quantitatively measure blood flow in arbitrary units that depend on both the local blood volume and blood velocity.
Stitching process The field of view of an individual power Doppler image is about 4 × 4 mm 2 , so in order to cover a wider field of view, we stitched together power Doppler images of multiple locations of the eye fundus. This process was used to create images displayed in subsecction and. For each location, power Doppler images were averaged for a period of time approximately equal to the cardiac cycle period although for a few acquisitions, the averaging time was slightly under this duration as some frames affected by micro-saccades were discarded. The open source software Hugin was used to create the full panorama by stitching together all the individual power Doppler images. To proceed with the alignment procedure, the images were manually placed in the correct position and control points were automatically detected in manually indicated regions of interest using Hugin's CPFind native function.
Power Doppler spectral analysis We present here the results we obtained by using the process described in subsection. We applied this process in a single power Doppler image in Fig. 6 , and in two stitched areas to demonstrate the reproducibility of the method. In Fig. 7 , we show power Doppler panoramas for the whole spectrum of the DPSD, and in Fig. 8  we show the result of the process for part of the same dataset used in Fig. 3  to show how the data can be processed otherwise to reveal different information about the choroid. In Fig. 6 , the temporal Fourier transform was calculated over 8192 holograms (i. e. 0. 14 s), and two power Doppler images calculated for the frequency ranges 2. 5 -6 kHz and 10 -30 kHz are shown in Fig. 6 (a) and 6(b), respectively. A composite color image was obtained by merging these two images in Fig.
Funding This work was supported by  LABEX WIFI (Laboratory of Excellence   ANR-10-LABX-24 ) within the  French Program Investments for the Future under Reference   ANR-10-IDEX-0001-02 PSL , and the  European Research Council  ( ERC Synergy HELMHOLTZ , grant agreement # 610110 ). The Titan Xp used for this research was donated by the  NVIDIA Corporation.
, 2006; Corona et al. , 2013; Corona et al. , 2017. Table  1  provides a synthesis of rockfall research realized with tree rings). The main weakness of these dendrogeomorphic approaches is the substantial time effort required for exhaustive sampling (with crosssections and cores), identification, cross-dating, and dating of growth disturbances forming after mechanical disturbance by rockfall impacts. Trappmann and Stoffel (2013, 2015)  and  Favillier et al. (2017)  have suggested the counting of visible scars as an alternative method requiring much less time and efforts as compared to conventional dendrogeomorphic approaches when it comes to the reconstruction of spatial patterns of rockfall activity. Yet, to date, the two approaches have not been compared systematically at a single site and for a common dataset.
Preliminary step: systematic tree plot In a first step, a full tree positioning and mapping procedure was used so as to build a database for the assessment of rockfall activity at the site. Trees with a diameter at breast height (DBH) > 5 cm were systematically located in the 110 × 120 m tree plot orientated perpendicular to the line of maximum slope gradient (Fig. 1c, 2a ). The position of each tree was determined with decimetric precision using a sonic rangefinder, compass, and inclinometer. Information on tree species and diameter was recorded in a database. In total, 793 trees were positioned in a geographical information system (GIS) as geo-objects. This systematic tree plot was then used as a basis for a stratified random sampling strategy. To this end, the initial tree population was divided into five sub-groups  (DBH<15 cm, >60cm) , on the basis of tree species and diameter.
Development of age-diameter regression models In a second step, logarithmic diameter-age regression models  (Rozas, 2003)  were built for the conifer (P. abies, A. alba) and broadleaved (A. pseudoplatanus, F. sylvatica, F. excelsior, S. aria, S. aucuparia, U. glabra) tree species at the stand scale. To this end, a total of 109 undisturbed trees with a DBH ≥ 10 cm were cored using a Pressler increment borer. Trees were selected according to five diameter classes, representative at the tree plot scale. Samples were analyzed and data processed following standard dendrochronological procedures  (Bräker, 2002). In the laboratory, tree rings were counted using a digital LINTAB positioning table connected to a Leica stereomicroscope. Missing rings toward the pith were estimated from ring curvature  (Villalba and Veblen, 1997; Bollschweiler et al.
The average number of injuries per stem thus decreases from 4. 4 on the top third of the plot (cell B) to 3. 2 in the central part (cell D), and 1. 9 on the lower third (cell F). Cell E (0. 9 scar. stem -  ) appears as the least active area within our study plot. At the plot scale, recurrence intervals of rockfall were computed as the ratio between tree ages estimated from regression models and the number of injuries observed on each tree stem. Based on the data used, the average.
Differences between conifers and broadleaved trees Despite the overall convergence of both approaches based on the full dataset of trees, one should keep in mind that rockfall recurrence intervals differ strongly as soon as conifer or broadleaved trees are considered separately. At the plot scale, these differences in return periods computed from both approaches amount to +14 years and -16 years for broadleaved and conifer trees, respectively (Fig. 8 ). In other words, the number of injuries counted on conifer stems is lower than the number of growth disturbances retrieved from the dendrogeomorphic approach. By contrast, in the case of broadleaved trees, the visual inspection tends to overestimate the frequency of past rockfall activity. The fact that comparable mean recurrence intervals are obtained from the full dataset therefore results from a compensatory effect, meaning that higher recurrence intervals computed from the visual inspection of broadleaved trees are counterbalanced by lower intervals derived from conifer trees.
Accurate identification of the causative subspecies in all cases would enable characterization of disease associated with animal lineages and of diversity in clinical phenotypes, which would contribute to better disease management. A higher level of knowledge on the spread and host range of the subspecies would also provide a better basis on which to study the history of the evolutionary development of the complex as a whole. Now that routine WGS is being performed by Public Health England (PHE) for all MTBC isolates, we sought to use these data to estimate the burden of animal-associated TB in England. Therefore, we identified a broad panel of SNPs that define each subspecies, lineage, and sublineage within the MTBC and assessed them using a new SNP-based tool, SNPs to Identify TB (SNP-IT).
We therefore identified these positions by mapping a core SNP alignment to a maximum-likelihood tree using Mesquite version 3. 30  (21). These nucleotide loci were added to the catalog of phylogeny-determining SNPs. All newly sequenced genomes are available from the National Center for Biotechnology Information under project accession no. PRJNA418900. The SNP-IT tool, including all relevant reference libraries used for this study, is available as an open-source package online (https://github. com/samlipworth/snpit).
Validation Set To validate the algorithm, we compiled an independent collection of genomes (N = 511) using clinical isolates sequenced by PHE Birmingham, UK, identified as MTBC and not included in the calibration set (Figure  1 ). We augmented them with data from the European Nucleotide Archive and the National Center for Biotechnology Information Sequence Read Archive to increase the representation of animal subspecies (N = 103; Appendix Table  2 ). To maximize inclusion of animal isolates from the public archives, we used the new Colored Bloom Graph (CBG) software  (22). Using CBG, we searched a snapshot of the Sequence Read Archive (to December 2016, N = 455,632) with our new set of reference kmers for Mykrobe predictor (see Comparison with Existing Tools). We compared FASTA files of the whole genome (https://www.