dataesr/scientific-paragraphs-categorization huggingface dataset:
Exposure assessment source databases The JEPs were created primarily from the exposure data of 1611 men (762 cases and 899 population controls), aged 29-75 years, that had participated in a population-based case-control study of lung cancer (referred to here as the Lung study) conducted in Montreal, Canada, in 1996-2001  [14]  (Table  1 ). Data from an earlier Montreal-based case-control study of 19 cancer sites conducted from 1979 to 1986  [15]  were added to provide complementary information on 23 agents, predominantly metals' physical forms. The traditional expert-based exposure assessment method applied in these studies has been described previously  [5, 15, 16]. Briefly, each study subject (or a proxy respondent) provided a lifetime occupational history during in-person interviews with trained interviewers.
Job-exposure profiles A JEP presents a comprehensive view of all exposures assigned to jobs for each occupation in the source databases. The core of the JEPs are descriptive tables summarizing the exposures assigned for 1571 7-digit occupations with at least one job evaluated in the Lung study, with exposures covering 289 agents (Fig. 1 ). Each summary table lists the agents in that occupation and provides the number of exposed jobs and their distribution across the confidence, concentration, and frequency categories. This distinguishes JEPs from JEMs; the latter typically assigns pre-established levels automatically while the former requires experts to select levels across categories for each exposure dimension. In JEPs, colors are used as visual clues to represent the variability in the distribution of the categorical ratings assigned to exposed jobs: green for > 75% of jobs assigned to one category, yellow for 50-75% (or based on only 2 jobs), and red for < 50% (or based on only 1 job).
When evaluating a job with no JEP available, the experts could use data from related occupations for guidance. Comparison of the exposures assigned in PROtEuS to those from a source study coded using the traditional expert method We examined the number of exposures assigned to jobs, the confidence rating and the variability in exposure ratings between jobs within an occupation. The exposures assigned in PROtEuS were compared to those in the Lung study serving as a reference. Some comparisons were based on individual jobs, others on jobs summarized by a combination of occupation and agent (or "cells"). Since the exposure data from the Lung study constituted a significant source of information for the experts in PROtEuS, and since the two studies represent a different sample of subjects and job histories, this comparison does not aim to examine the reliability of the hybrid method.
One source of time saving relates to the color schemes used in the JEPs, whereas the green colorings associated with highly homogeneous assignments in earlier studies allowed experts to focus on more heterogeneous exposures between jobs. The larger proportion of white-collar jobs in PROtEuS compared to earlier studies may have also contributed to the shorter overall coding time as they tended to be associated with fewer exposures. The experts involved in assessing exposures in the Lung study also had access to past data in the form of crude summaries of the exposure data from the Multisite study by occupation (with less precise 4-digit CCDO codes). They also occasionally used a small sample of jobs within an occupation for guidance. However, these exposure sources were inconsistently used for lack of a comprehensive computerized interface.
One limitation of the hybrid approach applied here is the lack of systematic information on time trends in exposure, which was provided only in the comments and in the definition of benchmark concentration levels for some agents. Potential improvements for future developments of the approach could include providing period-specific JEPs. Another limitation is the use of categorical ratings for concentration, reflecting the information available in the source databases. Several studies have developed methods to derive quantitative estimates of retrospective exposures in population studies,  [32, 33]  including in Montreal  [34]. In practice, however, very few agents would have a large number of relevant historical measurements to support the development of quantitative estimates, limiting the ability to got beyond categorical ratings.
Funding Funding for PROtEuS was provided by the Canadian Cancer Society (grants no. 13149, 19500, 19864, 19865) , the Cancer Research Society, the Fonds de Recherche du Québec -Santé (FRQS), FRQS-RRSE and the Ministère du Développement Économique, de l'Innovation et de l'Exportation du Québec. The Multisite cancer study was funded by the CCS, the FRQS, and Health Canada. The Lung cancer study was funded by the Medical Research Council of Canada, the Canadian Institutes for Health Research, and the Guzzo-Cancer Research Society Chair in Environment and Cancer. JFS was supported by the FRQS and the Institut de Recherche Robert-Sauvé en Santé et en Sécurité du Travail. MEP and JL hold career awards from the FRQS.
Acknowledgments The traditional method to expert assessment of exposure from detailed job histories was developed primarily by  Michel Gérin  and  Jack Siemiatycki , with input throughout from the various teams responsible for exposure assessment for the original studies. Jack Siemiatycki  was the principal investigator of the  Multi-site study and of the Lung cancer study , with  Marie-Elise Parent  as co-principal investigator of the latter. Many chemists and hygienists have contributed their expertise to the coding of the previous studies on which the traditional method was based. Our thanks for their creativity and dedication. The contribution of the entire Epidemiology and Biostatistics unit at  INRS-Institut Armand-Frappier  is also gratefully acknowledged.
When participants considered the preparation procedure to be complete, they handed the insulin pump and the attached infusion set tubing to a test administrator for evaluation. Participants performed insulin pump preparations while seated at a table with a test administrator and data analyst. Lighting and noise levels were comparable to those of a relatively quiet home environment, or a physician's office. Participants used insulin at room temperature (removed from the refrigerator at least 30 minutes before the test session). A parent or guardian could accompany adolescent participants and remain present during the training and test sessions. Training and test sessions were recorded using a video camera.
Acknowledgments The authors would like to acknowledge  Ypsomed AG, Switzerland , for the use of the mylife YpsoPump in the study,  Imke Silderhuis  ( UL International [Netherlands] BV ),  Tricia Gibo  ( UL International [Netherlands] BV ), and  Limor Hochberg  (former  UL LLC ) for data analysis. Medical writing and editorial support were provided by  Helen Parker PhD  and  Erin Slobodian of Watermeadow Medical , an  Ashfield Company , part of  UDG Healthcare plc , funded by  Novo Nordisk A/S.
From the clinic to genetics The observation of familial FMF cases fueled gene hunting in the early 1990s. The MEFV gene contains 10 exons and encodes the pyrin protein. Four founding mutations in exon 10 (p. M680I, p. M694I, p. M694V, and p. V726A) account for almost 80% of clinically and ethnically typical cases. 7 To date, nearly 100 times more sequence variants have been recorded in the dedicated online database in fevers (https://infevers. umai-montpellier. fr/). 17 Homozygosity for p. M694V confers the most severe phenotype (more joint and renal damage, earlier onset),  18  whereas other low-penetrant variants such as p. E148Q in exon 2, are considered susceptibility factors for inflammation. 19.
Digital agriculture: promising but hardly accessible in Low-Income (LI) and Lower Middle-Income (LMI) countries Digital agriculture makes use of information and communication technologies (ICTs) to enable farmers to improve their agricultural production and marketing. Currently, most farmers make decisions on subjects such as fertiliser use and marketing based on a combination of practical experience and general advice from public or private organisations (companies, public authorities, NGOs). Digital innovations can provide farmers with more accurate information based on the use of specific information and tools such as sensors, positioning systems and databases, modelling software, communication networks and robotics. ICTs may enhance their ability to make decisions and have the potential to foster agricultural production and reduce production costs, while reducing environmental impacts through the promotion of cost-effective input approaches.
One component of WIGOS, the Global Observing System (GOS), provides for national meteorological services (NMSs) and other partners' contributions to the Global Ocean Observing System (GOOS) and GCOS (meteorological satellites, drifters, moored buoys, and VOS). See  Moltmann et al. (2019)  for more details on WIGOS. Many of these global observing networks are made of collections of national scientific and operational programs designed and implemented in support of a wide range of multidisciplinary scientific research and operational applications. Often, data from multiple programs and networks are consolidated to facilitate their accessibility and use. One example is the International Comprehensive Ocean-Atmosphere Data Set (ICOADS) project, which attempts to bring together all available data of the ocean-atmosphere interface  (Freeman et al.
, 2016). Likewise, satellite communities, including the Group for High-Resolution SST (GHRSST;  Donlon et al. , 2009) , consolidate and uniformly quality control the in situ data for the satellite era for use in their calibration and validation systems, standardize the matchup criteria, and unify and consolidate the comparison metrics  (O'Carroll et al. , 2019). The European Copernicus Marine Environment Monitoring Service (CMEMS) also relies on data from in situ monitoring networks to provide robust integrated information and to calibrate and validate the data from satellites. The purpose of this article is to review strengths, weaknesses, impacts, and sustainability of in situ global observing networks measuring target ECVs and EOVs at the air-sea interface and to provide a 10-year vision for improved synergy and integration with other observing systems (e.
The collaboration between the GSDA and NMSs in the 1990s facilitated the development, testing, and adoption of the barometer drifter (SVP-B; see Figure  3 ) as a reliable source of SLP data to support weather forecasting with the deployment of such SVP-B drifters in large numbers since then. The GSDA reached its full implementation when drifter number 1,250 was deployed off Halifax, Nova Scotia, Canada, on September 12, 2005, by Distinguished Professor Dr. Peter Niiler of the Scripps Institution of Oceanography (SIO) and Dr. Michael Johnson of NOAA, becoming the first fully implemented component of GOOS. The GDP, a branch of NOAA's GOOS, is a scientific program that also has substantial impacts on operational activities, including satellite SST validation and numerical weather prediction (NWP) and constitutes the principal component of the GSDA.
• Provide a data processing system to deliver the data to operational and research users via the WMO Global Telecommunication System (GTS) and via qualitycontrolled (QC), delayed mode products. The GCOS and GOOS guidelines specify an array of at least 1,250 Lagrangian SVP drifting buoys, as needed to achieve a 5 • × 5 • global coverage. More details on this can be found in  Lumpkin et al. (2016) , who also discuss the modern implementation strategy of the GDP and GSDA. A crucial aspect of the data delivery is that the time interval between data collection and delivery to the GTS needs to be as short as possible, typically 15 min or less, in order to meet operational forecasting needs. The Iridium satellite system currently satisfies this requirement, and, at the time of writing, around 80% of the GSDA now transmits its data via Iridium telemetry.
Horizontal Near-Surface Current Observations From SVP Drifters The historical drifter data archive maintained and served by the GDP's Data Assembly Center (DAC) at NOAA/AOML dates back to 1979 and consists of observations from more than 22,000 Lagrangian SVP drifters. Near-surface, 15-m deep, ocean currents from SVP drifters are computed from satellite-derived time series of geolocation obtained with the Argos satellite system for older drifters and now with Global Positioning System (GPS) satellites. The accuracy of GPS geolocation is one to two orders of magnitude better than Argos. Consequently, GPS drifters provide more accurate ocean current data. Standardized QC techniques and interpolating procedures  (Hansen and Poulain, 1996)  onto regularly spaced 6-h time series are described, for example, in  Niiler (2001)  and  Maximenko et al.
(2013). QC and interpolation of recent data to hourly intervals are described in  Elipot et al. (2016). The temporal extension of the GSDA dataset testifies to its value as a reference series relevant for climate studies and supports a detailed description of most oceanic mesoscale and submesoscale features and the major persistent current systems of the World Ocean. The NOAA-funded GDP releases QC ocean current data that have supported more than 1,000 peer-reviewed scientific publications (see http://www. aoml. noaa. gov/phod/ dac/gdp_biblio. php for a partial list). The quality of the data collected with Lagrangian SVP drifters is discussed, for example, in  Niiler (2001) ,  Pazan and Niiler (2001) ,  Lumpkin and Pazos (2007) , and  Maximenko et al. (2013). Ocean current data are available in delayed-time mode because the strain gauge sensor used to detect the drogue presence requires extensive operator interpretation  (Lumpkin et al.
, 2008 (Centurioni et al. , , 2009;; Maximenko et al. , 2009; Niiler et al. , 2003a,b)  (see Figure  2b  as an example of such a synthesis). The GDP surface drifter dataset also enabled global mapping of the mean circulation (Figure  2a ) and of characteristics such as cyclonic and anticyclonic motions from global SVP drifter observations for scales from large eddies to submesoscales  (Griffa et al. , 2008). Further, high-frequency (super-inertial) drifter velocity observations have been used to map the amplitude and phase of the baroclinic and barotropic tidal currents  (Poulain and Centurioni, 2015). Since 2005, multisatellite processing of drifters transmitting through the Argos satellite system and the increasing use of GPS on some Argos and all Iridium drifters have allowed interpolation down to hourly intervals  (Elipot et al.
SST Observations From SVP Drifters Through the GTS, the drifter SSTs contribute to the majority of reanalysis and validation datasets (e. g. , OSTIA:  Donlon et al. , 2012;  Coriolis CORA; the CMEMS drifter validation product: Etienne, 2018; EUMETSAT OSI SAF). Today, SVP drifters provide more SST observations than any other source of in situ data, including ships (factor of about 4), coastal moorings (factor of about 4), tropical moorings (factor of about 50), or Argo floats (factor of about 100)  (Xu and Ignatov, 2014). Together with tropical moorings, they cover most of the global oceans, producing highly accurate data with near-uniform quality across multiple temperature sensors  (Xu and Ignatov, 2016). Argo floats currently provide more uniform global coverage than SVP drifters and tropical moorings and have a lower measurement uncertainty.
However, their normal 10-day operating cycle reduces the number of surface 2  measurements by two orders of magnitude, leading to more limited validation statistics (e. g. ,  Ignatov et al. , 2016; Xu and Ignatov, 2016). SVP drifters and tropical moorings are currently the main data sources for validation of satellite SST retrievals and are used by many groups to characterize the satellite data (e. g. ,  Kilpatrick et al. , 2001 Kilpatrick et al. , , 2015;; Minnett and Barton, 2010; Dash et al. , 2010; Ignatov et al. , 2016) , including the retrieval uncertainty (e. g. ,  Bulgin et al. , 2016)  and the long-term stability of the satellitederived SSTs (e. g. ,  Kilpatrick et al. , 2015; Xu and Ignatov, 2016; Berry et al. , 2018). Three-way error analysis methods attempt to decouple errors in the three datasets being compared (e.
Sea Level Atmospheric Pressure Observations From SVP Drifters Since the 19th century, atmospheric pressure sensors have been relatively well distributed over land and have become increasingly common over the ocean, mainly due to the GSDA, the VOS scheme, and the moored buoy networks. While the GSDA represents a relatively short time series from a climate perspective, it represents the only source of in situ SLP data in many ocean regions. SLP observations from SVP-B drifters (Figure  3 ) are more accurate than those from ships because buoy barometers are located much closer to the sea level, whereas ship measurements require extrapolation from deck-level measurements (so-called reduction to sea level and subject to human errors), generating additional uncertainty, of the order of 0. 8-0. 9 hPa.
, 2011). Even on trafficked shipping lanes, drifters are often the only source of SLP data when severe weather conditions occur and ships move out of the impacted areas. SLP observations from SVP-B drifters contribute, directly or indirectly, to many reanalysis products used for climate assessments. Climate-related changes of the mean atmospheric load, or SLP, correspond to about 1-cm change of sea level for a 100-hPa difference, the so-called inverse barometer effect  (Wunsch and Stammer, 1997). Although a study of the impact of SLP drifter data on the correction of sea-level satellite altimetry has never been performed, drifter-derived SLP data are likely to provide an important contribution to correct altimetry-derived sea level for atmospheric effects, especially in areas where drifters are the main or unique source of SLP ocean data.
Sea level pressure data from SVP-B drifters are used in the computation of trends to diagnose climate models and to construct climate indices. For example, the tropical Pacific SLP field is used to define the Multivariate El Niño-Southern Oscillation (ENSO) Index (MEI;  Wolter and Timlin, 2011). The Southern Annular Mode (SAM) index is defined as the SLP difference between 40 and 65 • S  (Gong and Wang, 1999) , and the North Atlantic Oscillation (NAO) index is based on the SLP difference between the subtropical (Azores) high and the subpolar low. SLP data from SVP-B drifters are also included in the Hadley Centre's monthly historical mean SLP dataset (HadSLP2) and the ICOADS v. 2. 5 (ranging from 1662 to 2007), and subsequent updates are based exclusively on observations available on the GTS  (Woodruff et al.
, 2017b). Observing system experiments (OSEs), as discussed in  Centurioni et al. (2017b)  and  Horányi et al. (2017) , were performed with the objective to evaluate associated NWP errors against reanalyses when only drifter SLP data were removed. The main conclusions of these studies are that the in situ drifter SLP data reduce the forecast error up to 5 days ahead near the surface and higher in the troposphere, up to 250 hPa. The largest error reductions were observed for the mean SLP and wind field forecasts. Such studies also suggest that the expansion of the SVP-B array to the tropics should be considered. Forecast sensitivity observation impact (FSOI) studies were also used to quantify the impact on the principal components of GCOS. Such assessments are often run operationally by ECMWF, the United Kingdom Met Office, and the Global Modeling and Assimilation Office of NASA.
However, notable efforts are underway for pH and fluorescence (e. g. ,  Choquer et al. , 2013) , for waves  (Christensen et al. , 2013)  as well as for currents from position, compass direction, and ship speed transmitted through the Automatic Identification System (AIS;  Guichoux et al. , 2016). In recent decades, there has been a slow decline in the number of ships in the traditional VOS scheme, partly offset by an increase in automated (usually hourly) ship reports. There has also been an increase in average ship size. About half of the ship measurements are relatively close to the coast (see  Ingleby, 2010 and references therein). Compared to drifters, the more complete set of variables from VOS enables the computation of air-sea fluxes from bulk parametrizations. A notable example is the surface flux and meteorological dataset of the National Oceanography Centre (NOC) version 2.
0  (Berry and Kent, 2009) , where air and sea temperatures observed by ships, along with observations of humidity, wind speed, and cloud cover, are used to estimate various components of heat fluxes (i. e. , sensible, latent, short-, and longwave radiation). Such estimates are then useful benchmarks for the performance of weather and climate models and lead advances toward ocean-atmosphere coupling in models and data assimilation  (Freeman et al. , 2019). Finally, research vessels can be equipped to directly measure some components of the air-sea fluxes (e. g. , outgoing longwave radiation). This information is readily comparable to satellite measurements, enabling a better understanding of the uncertainties caused by atmospheric absorption in the retrievals. Such data serve to develop fiducial reference measurements (FRM) of SST from ships  (Theocharous et al.
Over the last 25 years or so, national moored buoy networks have been developed by many countries as part of their operational observing capabilities. As such, there is no single global network of moored buoys but rather a "network of networks, " capable of measuring a wide range of ECVs and EOVs from marine meteorology and waves to subsurface oceanographic measurements that complement other sources of synoptic observations in coastal seas and the open ocean. They provide real-time data for operational services such as marine weather and wave forecasts (both for assimilation into and validation of forecast models), maritime safety information and warnings to end users, information for marine disaster and accident response, calibration/validation of satellite-derived observations, and data for research purposes.
Other Impacts of the Existing Air-Sea Networks Besides the demonstrated beneficial impact of several air-sea in situ observations for NWP as well as operational ocean forecasting and their importance for assembling climate datasets used for research and assessments discussed in the previous paragraphs, data relayed from the existing air-sea in situ observational networks are commonly supporting a variety of other applications with direct relevance to society and the development of blue economy. One example of the latter is the exploitation of the Lagrangian properties of drogued SVP drifters to study the biological dispersal (e. g. ,  Carlton et al. , 2017; Miller, 2018)  and recruitment of fish larvae to support stock management (e. g. ,  Hare and Walsh, 2007; Booker et al. , 2008). Also, surface currents transport oil spills and marine debris.
Generally, every type of pollution moves differently from each other as well as from the water parcels. Modeling this drift remains challenging because it is a result of the rectification of high-frequency motions and a function of the near-surface vertical structure of the currents, neither of which are resolved by the present observing system. Maximenko et al. (2018)  demonstrated that data from Lagrangian SVP drifters were critical for adequate modeling of debris drift from the 2011 tsunami in Japan to North American and Hawaiian shorelines. Drifters are expected to play an important role in the future Integrated Marine Debris Observing System (IMDOS; De  Dominicis et al. , 2012; Maximenko et al. , 2019). It should also be noted that some observing programs, driven by a scientific rather than fully operational rational, are capable of addressing fast response needs to emergencies or can provide critical data for such applications in an opportunistic way.