dataesr/scientific-paragraphs-categorization huggingface dataset:
The uncertainty estimates provided are typically derived from replicate analyses of the same sample, and so represent the precision of the measurements. As insufficient information was available to quantify the precision in the same way for all observations, the approach used to estimate the precision is also included (see Table  1 ). Relative uncertainty estimates for each analytical method, for typical ambient concentrations in a seawater matrix, are also provided in Table  2. The precision given for each data set is often 5% (Table  2 ), which reflects the stated repeatability of the CSSWV method  15  and a number of other measurements used. However, we note that repeat analyses of samples using this method can sometimes give lower precision (e. g. ~10%)  16. Considering all data points in our dataset, we find ~75% have a precision of 10% or less, and ~51% have an precision of 5% or less.
Data Records The compiled dataset is hosted by BODC (https://doi. org/10. 5285/7e77d6b9-83fb-41e0-e053-6c86abc069d0)  13 , and is available as a single. csv file (plus a separate metadata file). It includes the fields listed in Table  1. It is anticipated that updated versions will be made available periodically, as new sea surface iodide observations become available. The current iteration is termed Version 1. 0, future iterations will be named sequentially (i. e. version 2. 0 etc). The lead authors would be very pleased to be contacted regarding new or omitted iodide observations for inclusion in future iterations of the dataset.
technical Validation Of the records included in our database, the majority (47/57) are described in peer-reviewed literature, and a further two are from PhD theses, and so their quality has already been subject to scientific scrutiny. Unpublished data sets made use of well-established analytical techniques, including the use of calibration standards and replicate analyses. In addition, the majority of data points were described in our earlier peer reviewed manuscript  12  , and were shown to have to a cohesive global distribution. The distribution of observations in the extended dataset.
In most cases, only a small number of geographically limited points are available for a given method (Table  2 ). As noted, more than half the observations have been made using the same CSSWV technique. The remainder have been analysed using a wide range of other approaches, including, for some of the earliest datasets, labour intensive 'wet chemical' procedures which have since been superseded. In particular, a large proportion of the Pacific measurements were made in between 1968 and 1970  20, 21  using a revised version of the Sugawara precipitation method  22. The scarcity of more modern data from the Pacific limits comparisons, but we note that the range of this early Pacific data (3-168 nM) falls within that of the global data set, with a well-defined latitudinal distribution consistent with that observed overall.
Regional concentrations (e. g. high latitudes, north Pacific  23  ) are in agreement with those measured subsequently using different methods. Furthermore, the original data sources report vertical iodide profiles consistent in shape and magnitude with more recent measurements. Data obtained using the original, unmodified Sugawara method  24 (1955)  is not included, as this method is known to have poor performance  22. As described earlier, iodide observations are subject to non-negligible analytical uncertainty; we have reviewed the uncertainty estimation for each data set, and present this alongside the observations. As noted above, precision has usually been taken to represent method uncertainty. A variety of different methods have been used to estimate this, and so uncertainty magnitudes may not be directly comparable across all datasets.
Previous studies have demonstrated the efficacy of low-dose systemic opioids for chronic breathlessness  [6] [7] [8] [9] [10] , even if there is conflicting data  [11]. In addition, based on current scientific knowledge, there is still no other effective medication. However, there is an important reluctance to prescribe this treatment  [11] [12] [13]. In the present study performed in a real life cohort of patients suffering from COPD (the Initiatives BPCO French cohort, iBPCO cohort), our aim was to examine the characteristics associated with and prevalence of chronic breathlessness in patients with COPD recruited in a tertiary hospital-based cohort. We also investigated the management of chronic breathlessness regarding the use of opioids among pulmonologists of the same region. This study further evaluated the knowledge of pulmonologists about treatments against breathlessness, and their willingness to prescribe morphine for the treatment of severe or chronic breathlessness.
Statistical analysis Qualitative variables were described in numbers and percentages. Quantitative variables were described by mean and standard deviation or median and inter-quartile range. Differences in clinical characteristics were assessed using chi-square tests or Fisher's exact tests, as appropriate, for discrete variables, and Wilcoxon or Kruskal-Wallis tests for quantitative variables. Relationships between different variables were examined with the Pearson correlation test. A value of p ≤ 0. 05 was considered statistically significant. The analysis of the results was carried out with SAS® 9. 2 statistical software.
Conclusion Management of breathlessness and frequent comorbidities, such as symptoms of anxiety and/or depression in COPD is insufficient despite pulmonologist's knowledge and willingness. Promoting pulmonary rehabilitation and symptomatic pharmacological treatments might be a key element in the overall management of COPD with a role for opioids use with a tight surveillance. The Initiatives BPCO cohort is supported by Boehringer Ingelheim France since its creation and has been funded by Pfizer until 2015, in the form of unrestricted grant. The sponsors fund the database, statistical analyses, meetings and submission fees when required. They do not participate in decisions regarding collected data, analyses, article writing and submission. For all these aspects, the Initiatives BPCO group works in total independence.
Acknowledgments We thank the initiatives  BPCO study group M. Zysman (Nancy), P. R. Burgel (Paris), G. Deslee (Reims), P. Surpas (Charnay) ,  O. Le Rouzic  (Lille),  T. Perez  (Lille),  N. Roche (Paris) ,  G. Brinchault-Rabin  (Rennes),  D. Caillaud  ( Clermont-Ferrand ),  P. Chanez  (Marseille),  I. Court-Fortune  ( Saint-Etienne ),  R. Escamilla  (Toulouse),  G. Jebrak  (Paris),  P. Nesme-Meyer  (Lyon) and  C. Pinet  (Toulon) and:  Brigitte Risse  (ARAIRLOR,  Association régionale d'aide aux insuffisants respiratoires de Lorraine, Nancy).
23, 24 The study design, collection of demographic data, clinical information, and outcomes for both cohorts have been previously described (Supplementary material online). 16, 23, 24 The Once this signature (FRP) was trained and tested in the external validation set, its cohort-wide performance for detection of residual cardiac risk was explored in 1575 consecutive eligible patients from the SCOT-HEART trial (SCOT-HEART, NCT01149590), a population in which CCTA [as well as non-contrast cardiac CT for Agatston coronary calcium scoring (CCS)] was used to guide changes in clinical care. The aim was to explore whether this radiomic signature could add value in cardiac risk prediction over and above traditional risk factors and detect a residual risk not modified by subsequent interventions (Supplementary material online).
Coronary CT angiography protocol, data collection and definitions The detailed CCTA protocols for image acquisition are presented in the Supplementary material online and in previously published studies. 12, 16, 23, 24 Coronary artery stenosis assessment and the presence of high-risk plaque (HRP) features (defined as the presence of at least one of the following: low-attenuation plaque, positive remodelling, napkin-ring sign or spotty calcification) were performed as previously described (Supplementary material online). 27, 28.
Moreover, texture radiomic phenotyping assesses the relative rather than absolute changes in PVAT attenuation, explaining why the measurements are independent of the absolute, baseline attenuation. Epicardial adiposity and perivascular FAI (as validated in our previous work  12, 16  ) were also quantified using dedicated software (Aquarius Workstation V R V. 4. 4. 11-13, TeraRecon Inc. , Foster City, CA, USA for basic segmentation and CaRi-HEART proprietary algorithms (Caristo Diagnostics Ltd, Oxford, UK) for final calculation), as described previously. 16.
9 were included in all subsequent analyses (n = 15 scans analysed by two independent analysts). Correlation plots (using the non-parametric Spearman's rank correlation coefficient) and a heatmap depicting the variance of the Z-score-transformed individual radiomic features across the SCOT-HEART population are graphically presented. Further, hierarchical clustering of the individual radiomic features was performed using the squared Euclidean distance and the Ward method. To address the multi-dimensionality and possible redundancy of the radiomic dataset, the amount of pairwise correlations was reduced at the level of jrhoj> _0. 9 using the findCorrelation function of the caret package in R. In an initial assessment of the dataset, a Manhattan plot was created based on the value of each radiomic feature in discriminating the MACE from the non-MACE cases [-(log 10 ) of the P-value from univariable receiver operating characteristic (ROC) curve analysis].
Subsequently, the nested case-control study in Study 2 was randomly split into a training/internal validation (80%) and external validation set (20%), ensuring an equal representation of events and non-events in both splits. A random forest method was selected to train a model to discriminate 5 year MACE from no MACE, since it enables modelling of non-linear relationships, can train on small datasets, is less sensitive to outliers and has been previously used in patient-specific predictive modelling. 34 Further details on centring, scaling of radiomic features, recursive feature elimination, internal cross-validation, and final validation can be found in the Supplementary material online. The final product of the random forest algorithm (namely the probability of belonging to the MACE vs. no MACE group) was defined as the FRP.
Acknowledgements C. A. , S. N. , and K. M. C. acknowledge support from the  Oxford British Heart Foundation Centre of Research Excellence. M. Y. D. is a recipient of the Haslam family endowed chair in Cardiovascular Medicine and acknowledges the  Khouri family  for their support of research in early detection of CAD. M. C. W. , M. R. D. , and D. E. N. are supported by the  British Heart Foundation  ( FS/14/78/31020 ,  CH/09/002 ,  RE/18/ 5/34216 ). D. E. N. is a recipient of a  Wellcome Trust Senior Investigator Award  ( WT103782AIA ).
In contrast, checking entailment between two universal formulae boils down to checking the satisfiability of a BSR(SL k ) formula, which can be done thanks to the decidability results in our paper. The separating implication (magic wand) seldom occurs in such shape constraints. However, it is useful to describe the dynamic transformations of the data structures, as in the following Hoare-style axiom, giving the weakest precondition of ∀u. ψ with respect to redirecting the i-th record field of x to z  [10] : {x → (y 1 ,. , y k ) * [x → (y 1 ,. , y i-1 , z, y i+1 ,. , y k ) - * ∀u. ψ]} x. i := z {∀u. ψ} It is easy to check that the precondition is equivalent to the formula ∀u. x → (y 1 ,. In contrast to first-order logic for which the decision problem has been thoroughly investigated  [1] , only a few results are known for SL.
, 2011). The Commonwealth Scientific and Industrial Research Organisation (CSIRO) impact framework assesses how research translates into real-world benefits by distinguishing research inputs, activities, outputs, and mediumterm outcomes from long-term impacts  (CSIRO, 2018; Morgan, 2014). Compared to other biodiversity data sources (such as the RLTS, the World Database on Protected Areas, and the World Database of Key Biodiversity Areas), the RLE is at a relatively early stage of implementation. As IUCN celebrated its 70th anniversary in 2018, it is particularly timely to quantify the current impacts of the RLE and identify key challenges that must be addressed to maximize impact over the coming decades. We report inputs, activities, outputs, outcomes, and impacts to highlight the direct and indirect influences of the RLE (Figure  1 ).
, 2018) , disaster risk management (e. g. , through Nature-Based Solutions;  Nesshöver et al. , 2017) , and sustainable livelihoods. The RLE could also be used further to inform business decisions, for example, to help assess and manage environmental and social risks in development projects, such as with the Tremarctos tool in Colombia, the Species and Threats database in Australia, and industry standards for financing sustainable development  (IFC, 2019).
' Operational applications, such as monitoring for harmful algal blooms, are also served by the virtual constellations, with the multiple sensors providing gap-filling capabilities. It is important to note that the development of operational applications, notably by commercial companies, relies on the continuity provided in operational missions: that is to say, it would be difficult to justify commercial investment based on a single mission that may not be replaced. It is also worth noting that some applications do not need highly accurate ocean-colour estimates but instead use boundaries or gradients in water masses [e. g. , see section 'Use of Ocean Colour Data for Location of Potential Fishing Zones (PFZ)']. Regarding spatial resolution, OLCI provides full oceancolour capability to 300 m but higher resolution (of order 10-100 m) to detect in bays or estuaries, where natural or farmed aquaculture is often located, or the observation of colour in inland waters, are not adequately supported, since missions providing such data are focused on land applications (e.
This is probably due to the diversity of water types encountered and a result of the uncertainties in the in situ measurements (e. g. , radiances and chl-a) used in construction of algorithms. Furthermore, an in situ chl-a sample may be from 1L of water and is compared with in situ upwelling radiance observations representing a signal from a much larger mass of water. A summary of studies comparing chl-a algorithms applied to single sensor or multi-sensor merged satellite records with global in situ datasets showed average root mean square error of 0. 337 log 10 chl-a  (Brewin et al. , 2016). A further issue is that global analyses are usually based on measurements from independent investigators meaning that methodological differences between laboratories can introduce additional uncertainties.
In situ matchup database For satellite validation and SVC it is important to have access to the various sources of data in one place. The ESA Ocean Colour CCI project has constructed such a database with global distribution that includes data acquired from several sources: a version was published as a paper/dataset in  Valente et al. (2016)  and is also available at PANGAEA 15. The latest version (v4) comprises data from 1997 to late 2017 on spectral remotesensing reflectances, concentrations of chl-a, spectral inherent optical properties, spectral diffuse attenuation coefficients and total suspended matter.
Citizen Science Citizen science or crowdsourced environmental observations is an area that has grown rapidly and is likely to grow further over the next decade as the worldwide usage of smart phones increases. Citizen science may provide useful contributions to ocean-colour observations, albeit primarily in coastal and inland waters as well as raising environmental awareness. Examples of ocean-colour relevant citizen science projects include: • The Secchi Disk Project 17  is the largest worldwide citizen science study collecting measurements of ocean and coastal transparency with the Secchi disk  (Lavender et al. , 2017). A phone app is used to geo-locate and record the Secchi disk measurements, which are subsequently transmitted to a data repository. • Citizen science measurements using a 3D-printed mini Secchi disk  (Brewin et al.
GROUND SEGMENT AND DATA PROCESSING Ground Segments The term ground segment is used loosely herein to define the systems that include reception of EO data via satellite downlink and lower-level processing for calibration and geolocation, followed by atmospheric correction and production of analysis-ready data and end-user focused services; it also includes timely and appropriate delivery mechanisms (International Ocean Colour Coordinating Group [IOCCG], 2018). The ground segment is an area that has, arguably, improved considerably over the past 10 years and, indeed, the success of an EO mission, once launched, is ultimately determined by the use of the data, information and services by stakeholders for applications. Below we describe example ground segments.
Copernicus Copernicus is a flagship European programme that incorporates Earth Observations encompassing satellite constellations (the Sentinel series, notably Sentinel 3 -see section 'Ocean Colour EO Sensors and in situ Observations'); ground-segments producing operational products and CDRs; and training and outreach activities. Copernicus has an open data policy whereby data can be used free of charge for any purpose, academic or commercial. For ocean-colour data from the OLCI sensor, the basic data processing from L0 to L1 and L2 is undertaken operationally by EUMETSAT. Users can also obtain the L1 and L2 data and process using tools such as the Sentinel Application Platform (SNAP 20  ), provided through the ESA which offer additional capabilities, such as different atmospheric correction procedures.
Copernicus supports a marine-focused processing service, the Copernicus Marine Environment Monitoring Service (CMEMS). The CMEMS ocean-colour team uses OLCI, VIIRS and Aqua-MODIS data to produce near-real time and reprocessed time series of global mapped products, and regional products with bespoke, regionally tuned algorithms, such as for the Mediterranean Sea  (Volpe et al. , 2007)  • Web-based analysis tools. For exploitation of ocean-colour data by non-specialist end-users it is important to provide easy-to-use tools, ideally web-based, that support simple analysis of datasets provided by the ground segments. This ensures that EO data have an impact beyond specialist academic communities. Such a system should be built ideally on recognised standards and allow sharing of data or visualisation of data from multiple providers.
The Open Geospatial Consortium (OGC) specifies standards for accessing data via the web including Web Mapping Service (WMS) serving images (such as GIF), Web Coverage Service (WCS) which serves arrays of data and Web Feature Service (WFS) that specifies point or other vector data such as shiptracks or region of interest boundaries. In principle, webbased portals using these standards can request and visualise data from other portals. Many portals have built-in analysis capabilities, such as time-series plotting; this is also efficient since only delivery of the results is required, not the full dataset from which it was derived and which may comprise many gigabytes or terabytes of data. An example of a web-vis system is the ocean colour CCI portal 29  (see Figure  2A ) which allows selection of regions of interest (e.
g. , via hand-drawn polygons or uploaded shapefiles) then produces time series, Hovmöller plots, or time animations of these data. Figure  2B  shows an extracted time series of chl-a from a user region of interest. The portal also provides additional analyses such as scatterplots between two variables, and data extraction in time and space, from an uploaded location/date file, such as along a ship track. Hence, it can be used to for satellite matchup analysis. Another important ocean-colour tool is GIOVANNI as provided by NASA 30.
Cloud Platforms The increase of data volumes resulting from new higher resolution or hyperspectral sensors and the growing length of the ocean-colour record is creating challenges for data processing, and even more so for accessing the data. Bandwidth can inhibit systematic near-real time processing at a global scale, or analysis of a complete archive. In recent years, there has been a paradigm shift from downloading and processing data locally, to moving the processing software at the data source, together with provision of data processing resources. This is an ongoing process and most users still download data and process in the traditional way. This may be attributed to the fact that specialised systems, i. e. , hardware and software with cost implications, are required to support the new way of processing EO data close to the data source as well as conservative practice.
The Copernicus Data and Information Access Services (DIAS) are planned to facilitate access to data and information from the Copernicus services. By providing data and information access alongside processing resources, tools and other relevant data, this initiative is expected to boost user uptake, stimulate innovation and create new business models based on EO data and information. More information can be found at 31. Accessing_Copernicus_data_made_easier. ESA's Thematic Exploitation Platforms (TEPs) concept aims to provide a working environment where users can access algorithms and data remotely, providing them with computing resources and tools that they might not otherwise have, and avoiding the need to download and store large volumes of data. Currently, there is a coastal TEP focusing on coastal ocean-colour applications.
To compute per-pixel uncertainty metrics (bias and rootmean-square difference, RMSD) and also to select the in-water retrieval algorithm, an optical water-type classification approach is used to compute pixel water type  (Moore et al. , 2014; Jackson et al. , 2017). Satellite-derived radiances are used to construct a set of optical water types (OWT); then uncertainty metrics are computed per OWT through matchups between satellite R rs and in-water constituent values from the OC CCI database  (Valente et al. , 2016). For each pair of satellite R rs and in situ water constituent the pixel water class memberships (from the R rs ) are used to weight the differences in computation of RMSD and bias. This approach was followed since the OC-CCI user consultation indicated a clear preference for uncertainties based on validation of the products against in situ data.
Finally, for each pixel, uncertainty is computed from the per-class uncertainties using the pixel class membership  (Jackson et al. , 2017). Chl-a and other in-water variables are computed using class membership and the optimal algorithm per water class. Chlorophyll algorithms were selected from amongst OC4/OCI  (Hu et al. , 2012)  and OC5  (Gohin et al. , 2002)  through round robin assessment utilising a scoring system similar to that of  Brewin et al. (2015). Assessment of the OC CCI products has included suitability for use as a CDR  (Melin et al. , 2017) ; the impact of discontinuities on computing trends  (Hammond et al. , 2017) ; and comparison of OC CCI data with precursor datasets  (Couto et al. , 2016). Example results from OC-CCI version 3. 1 (available at www. oceancolour. org) are shown in Figures  3A-C  including a chl-a product, RMSD and bias expressed as log10chla.