dataesr/scientific-paragraphs-categorization huggingface dataset:
Study population Recruitment took place via the network of French users of EDMUS. Although EDMUS initially was a software dedicated to MS that was developed in France in the 1990s  [24] , it now corresponds to a large network of clinicians and researchers whose aim is to fight MS, by improving care and promoting research. In the software, demographic, and longitudinal clinical data are prospectively collected during each neurological appointment (some being retrospectively collected during the first appointment) and can be used for both medical follow-up and research purposes. A total of 15 MS centers agreed to participate in the present study, accounting for a potential of 33,000 MS patients at study launch (i. e. about one third of prevalent MS cases in France  [25] ). Inclusion criteria were defined as a definite diagnosis of MS according to either Poser  [26]  or McDonald  [27, 28]  depending on the period of diagnosis, and clinical onset of MS prior to January 1 st 2009 in order to have a minimum disease duration of 1 year at the endof-study date, which was set as January 1 st 2010.
Each EDMUS database has been approved by the French data protection authority ('Commission Nationale Informatique et Libertés', CNIL). Furthermore, in accordance with French legislation, the present study was approved before its start by both the CNIL (Approval DR-2010-367) and the French advisory committee for data processing in health research ('Comité Consultatif du Traitement de l'Information en matière de Recherche dans le domaine de la Santé, CCTIRS; Approval 10191). Those approvals were needed to link our dataset to the national registers containing death information. Then the data were analyzed anonymously.
Death ascertainment and causes of death As deaths are not exhaustively registered in the EDMUS databases, the clinical dataset was linked to two national registers. The first register was the national repertory for the identification of physical persons ('Répertoire National d'Identification des Personnes Physiques', RNIPP), where each individual born in France after 1890 is identified by sex, surname (maiden name for women), date and place of birth. This allowed us to obtain up-to-date vital status. We also obtained data from the national death register ('INSERM-CepiDc'), which contains information provided in death certificates from 1968, including the date, place and causes of death, coded according to the International Classification of Diseases (World Health Organization). The underlying causes of death were categorized as follows according to ICD codes: MS (ICD-10 code G35), cancer, cardiovascular disease and stroke, infectious disease, respiratory disease, neurological disease except MS, suicide, digestive disease, accidental death, other disease, unspecified (e.
Discussion To our knowledge, SURVIMUS is one of the largest studies of mortality in MS that have been conducted so far, as more than 27,000 MS patients were included, representing about one third of French prevalent cases. In the present study, deaths were captured by linking the dataset to two national registers. When the study started, only 500 deaths were notified in the EDMUS databases. This highlights the need for linkage to official national death registers in this kind of epidemiological study, in order to avoid underestimating the event of interest. Even so, a number of patients (n = 4,607) could not be traced in the French national register, either owing to a missing birthplace or because they were born outside France, or there were suspected mistakes in the maiden name (consistent with the high proportion of women).
e. before the treatment era. We found that excess mortality was strongly correlated with disease duration, as the longer the disease duration, the greater the probability of death. We can assume that the overall SMR was lower than that of other series owing to the shorter disease duration of our MS population (mean 15 years). This is consistent with results of the first French EDMUS-user centers. In Rennes, the SMR was 1. 99, that is much higher than the SMR (1. 3) we had observed in our previous study  [5] , but the mean follow-up duration was also far longer (18 vs. 13 years). Similar results were observed in Lyon (whole and historical cohort). The low value of SMR in Lorraine region may be attributed to the recent nature of this MS population-based registry  [31] , meaning that although they are now close to achieving the exhaustive registration of prevalent cases, some patients have died before having the opportunity to be included in the database.
By the way, the risk of dying for a patient before enrollment in the EDMUS database may apply in all the participating centers and can contribute to the lower excess mortality observed in the present study compared with other series. We can wonder if disease duration might be considered as a proxy of disability level (not available here), as disability was found as a prognostic factor for death  [32]  and low disability level was shown to be associated with a low SMR  [5, 6]. But heterogeneity in the MS course is well-known, and many patients will live long lives without life-threatening disability. Finally, a selection bias in patients' enrollment towards a less malign course or an earlier diagnosis cannot be excluded too as the participating centers are considered as MS expert centers, as well as an immortal time bias (patients need to survive a sufficient amount of time to be enrolled in the database) as discussed above.
Moreover, attempts of defining "MS-related" death are ongoing. Cause-specific mortality rates of the French general population will be used and should help us to clarify the relationship between specific causes of death and excess mortality. Moreover, we are currently collecting additional data for suicide cases, in order to look at socioeconomic characteristics (employment, marital status, etc. ), mental health status, and trends over time. To conclude, the results of the SURVIMUS study show that there is a moderate decrease in life expectancy for MS patients, and that the risk of dying is strongly correlated with disease duration, and probably disability progression, highlighting the need for actions that can delay disability progression. Even though excess mortality starts only after 20 years of disease duration, interventions such as DMTs or multidisciplinary care should be started as soon as possible in the disease course in order to maximize their efficacy in slowing down disability progression  [34].
Acknowledgments We would like to thank all the people who contributed to this study, in particular all the medical neurologists who collected the clinical data in the 15 participating centers, the clinical research associates who entered the data in the EDMUS databases ( C. Callier ,  K. Droulon ,  E. Dumont ,  A. Dupoux ,  K. Ferraud ,  J. N. Fort ,  E. Lambert ,  K. Lataste ,  D. Le Port ,  R. Maire ,  A. Naudin ,  L. Parmentier ), the coordinators of the regional MS networks ( J. C. Ongagna ,  V. de Burghgraeve ), the students involved in the data entry, quality control and/or data analysis ( E. Bajeux ,  M. Hirel ,  T. Do Ngoc ,  T. Virault ), and the  OFSEP national coordinating center in Lyon, France  (grant provided by the  French State  and handled by the  "Agence Nationale de la Recherche ," within the framework of the "Investments for the Future" programme, under the reference  ANR-10-COHO-002 ).
MODIS has been providing daily imagery for retrieving land surface properties with a spatial resolution 250 m since the year 2000. In many places, this pixel size is closer to that of individual landscape elements, such as managed forest stands or crop fields, than those of time series more typically used in vegetation analyses (with pixel sizes ranging from about 1 km to 8 km). With such spatial resolution, combined with its increasingly long archive, MODIS data offers great potential for vegetation monitoring applications in general, and crop growth monitoring in particular. However, due to its whiskbroom design, the observation geometry of the MODIS instrument, combined with the spatial uncertainty in the registration of the images, can result in different (albeit neighbouring) physical areas being mapped onto the same pixel depending on the view zenith angle (which varies from day to the next).
Satellite systems providing such datasets have a spatial resolution which is often coarser than the targeted landscape elements (such as managed forest stands or crop specific fields). Moreover, the actual footprint of the satellite observation is generally larger than the rectangular ground projection of the pixel  (Cracknell, 1998). Uncertainty in the geometric correction of the imagery combined with gridding artefacts  (Tan et al. , 2006; Gómez-Chova et al. , 2011)  further complicates the problem of matching the observations to the targets. Since landscapes are often spatially heterogeneous, the adequacy of observation and target will vary in space, leading to distinctions between what is common to refer to as "mixed" and "pure" pixels. This spatial heterogeneity must be taken into account explicitly in algorithms for biophysical variable retrievals because of the non-linear nature of the physical relationships linking what is observed from space to land surface properties  (Chen, 1999; Garrigues, Allard, Baret, & Weiss, 2006; Jacob & Weiss, 2014).
Other applications include: (1) extracting pure time series for specific plant functional types to calibrate, validate or parametrise dynamic global vegetation models (DGVMs), which typically run at a much coarser scale; (2) assisting the incorporation of mixed pixels in various stages of supervised classifications (e. g. Foody, 1996) ; (3) assisting and validating unmixing approaches to infer subpixel land cover specific biophysical properties (e. g. Kuusinen, Tomppo, & Berninger, 2013) ; (4) monitoring vegetation phenology (e. g. Zhang et al. , 2003)  while avoiding mixed pixels; (5) constructing global databases of crop calendars (e. g. Whitcraft, Becker-Reshef, & Justice, 2014)  based on pure crop specific pixels; or (6) to use such crop specific pixels to monitor crop growth at a regional scale (e.
Particularities of the MODIS spatial support The design of the MODIS instrument has important consequences on its spatial response, i. e. on the spatial support over which an observation is made. Like with other remote sensing instruments, the observation footprint is not equivalent to the ground projection of the squared pixel, or grid cell, in which the data is delivered to the user  (Cracknell, 1998). This footprint is firstly defined by the instrument's net PSF, which depends on the optics, the detectors and the electronics involved in encoding the signal  (Schowengerdt, 2007). During the processing of the data, the measurements are forced into a grid, such as the Level 2 Grid (L2G) in the case of MODIS  (Wolfe, Roy, & Vermote, 1998) , thereby causing gridding artefacts reported by  Tan et al.
Exercise on simulated datasets A set of simulation exercises is proposed to illustrate the expected behaviour of the MODIS temporal signal when it is composed of distinct land covers sampled with different observation footprints. Simulated datasets are created by randomly generating their spatial patterns using the Modified Random Cluster algorithm proposed by  Saura & Martinez-Millan (2000)  and implemented as the randomHabitat function in the R package secr  (Efford, 2015) ; and then generating the temporal signal for a selection of land cover types based on prescribed NDVI and knowledge of the changing observation support of MODIS.
Generating temporal patterns The temporal behaviour of NDVI is prescribed for each land cover type. It is simulated by combining a base NDVI value of a, a logistic function representing growth and a second logistic function representing senescence, as shown in the following formula: NDVI t ð Þ ¼ a þ K g -a 1 þ exp -B g t-M g À Á À Á - K s 1 þ exp -B s t-M s ð Þ ð Þ ð1Þ where K, B and M define the shapes of the logistic functions for either growth (g) or senescence (s). Parameter values are chosen for each land cover (reported in Table  1 ) with the objective to represent their expected phenological behaviour (see Fig. 3c ). The NDVI values are calculated for 250 consecutive days and assigned to the respective land cover to form an NDVI data block with 250 layers.
When doing so, a random positional error is considered following a gaussian distribution with σ = 50 m, corresponding to the geolocation error estimated for MODIS by  Wolfe et al. (2002). Each of the 16 PSF models provides a different value according to the view zenith angle at which the observation is taken in that orbit. Knowing the daily sequence of MODIS orbits, the corresponding PSF model is applied to each consecutive layer in the NDVI data block to produce a simulated block of MODIS NDVI observations. When convolving the PSF models on binary maps instead of NDVI images, the result is no longer an integrated observation but the contribution of the land cover delineated by the mask to the observation, otherwise referred to as pixel purity. Therefore, for each estimation of MODIS NDVI observations, the corresponding pixel purity for each class is also calculated (integrating also the same σ = 50 m positional error).
That requirement was obtained from 3 different sources: (a) crop maps from the USDA National Agricultural Statistics Service Cropland Data Layer (CDL henceforth) and available at http://nassgeodata. gmu. edu/ CropScape; (b) images from the SPOT4 (Take5) experiment lead by CESBIO and available at http://www. cesbio. ups-tlse. fr/multitemp/? page_id=406; or (c) Landsat imagery from USGS downloaded from http://landsat. usgs. gov/Landsat_Search_and_Download. php.
Real MODIS data MODIS collection 5 daily surface reflectance data was retrieved for all sites from the NASA LPDAAC data servers accessible at http:// reverb. echo. nasa. gov/reverb but in this paper downloaded via the dedicated R package called MODIS  (Mattiuzzi, 2014). To avoid any unnecessary reprojection of the MODIS data, the actual boundaries of each site are defined based on the MODIS L2G grid by selecting a box of 201 by 201 pixels at 250 m resolution with the central pixel covering the coordinates listed in Table  2. Note that since the MODIS L2G is in a sinusoidal projection centred on Greenwich, the sites have an increasingly sheered shape when they are located further away from that meridian. Similarly, landscape spatial patterns in this projection have the same sheered effect when shown in this projection.
For both Terra and Aqua platforms the products at 250 m spatial resolution (MOD09GQ and MYD09GQ) are needed to obtain the red band (b01), the near-infrared band (b02), the obscov and the 250 m quality flags. The 1 km spatial resolution products (MOD09GA and MYD09GA) are also required to obtain the VZA and quality state flags (indicating cloud coverage). For each site, data is collected for a different period covering the main crop growing season (see "Start date" and "End date" columns in Table  2 ). The data are summarised into 3 stacks per site: one for VZA, one for obscov, and one for NDVI (calculated from the red and near-infrared bands at 250 m spatial resolution). A time vector to indicate when images are available is generated, incorporating the differences in overpass between the Terra (in the morning) and Aqua (in the afternoon).
Selection of purer time series based on SNR SNR is calculated for every time series in the MODIS NDVI stacks just like it was done for the simulated datasets (Section 3. 1. 4). The SNR is spatialised into a map that can serve to identify pixels with a radiometrically homogeneous behaviour along the period of interest. This can serve to isolate the time series that can serve to represent the crop specific temporal dynamic in the study zone. A spatial subset is done by filtering out all time series that do not satisfy the criteria: SNR ≥ 10. This value has been selected empirically seeking a compromise between signal purity and spatial representativity of the selected sample set, with the added intention of keeping a single threshold for all test sites in order to compare performance. A temporal filtering is then applied to remove all individual observations which do not satisfy the following condition: VZA ≤ 30 ∩ obscov ≥ 0.
This allows one to maximize the signal of the vegetation growth and avoid the potential noise from fallow periods between two successive cropping seasons in which the surface would be expected to be more heterogeneous. But the SNR, or any other metric, could be applied on a moving window, on a yearly basis, or even on a multi-year period covering all the MODIS archive depending on the target application. In this respect, the framework behind the simulation exercise presented here could serve as a seedbed to explore performances of different metrics and on their application, such as under various scenarios of cloud coverage for example. While pixel purity information tailored to specific applications is possible, various potential users could prefer a global product systematically available as ancillary data to other MODIS products.
Finally, there is also the case of landscapes containing both small and large fields of the same crops. In this case, it would be wise to analyse if no bias is committed when using only the purer pixels falling in the larger fields as an estimation of the average crop behaviour at regional level. New observation systems will provide decametric spatial resolution with a high (circa 5 day) temporal revisit frequency globally. Such instruments (e. g. Sentinel 2 and Landsat 8) should allow to partially overcome many of the issues associated with pixel purity, but they will not have a long archive comparable to what MODIS can offer for years to come. Having comparable data for the past is a prerequisite for many applications such as detecting anomalies  (Meroni, Verstraete, Rembold, Urbano, & Kayitakire, 2014) , coupling remote sensing with crop models  (Duchemin, Maisongrande, Boulet, & Benhadj, 2008; de Wit, Duveiller, & Defourny, 2012) , statistically deriving yield estimates from remotesensing indicators (Becker-Reshef,  Vermote, Lindeman, & Justice, 2010; Kouadio et al.
Conclusion Two main conclusions can be drawn from this work. First, that innovative use of currently available remote sensing data can provide much more information than what is often considered. The daily variability of the MODIS observation footprint, which is often either ignored or considered a drawback, is exploited to provide a clear indicator of the adequacy between the MODIS time series and a spatially homogeneous target. This is possible by using daily observation rather than composites, which in turn is becoming increasingly easier given the constantly increasing capacities in terms of computing power and storage space. The second conclusion is that extracting crop specific signals at regional scale from MODIS is possible for many landscapes without requiring ancillary spatial data, such as crop maps, by exploiting pixel purity maps derived from the proposed method.
Acknowledgement The research in this paper was funded by the  Joint Research Centre  of the  European Commission  under the institutional projects  AGRI4CAST  ( N°1016 ) and  CLIMEcoS  ( N°995 ). The authors would like to thank the anonymous reviewers for their contribution to improving the manuscript. The authors also thank those responsible for the efforts on providing the free tools (R, Q-GIS) and datasets used in this work. The R packages were obtained from the  Comprehensive R Archive Network  (http://cran. r-project. org/) or R-forge (https://r-forge. r-project. org/). The MODIS and Landsat data were obtained through the online Data Pool at the  NASA Land Processes Distributed Active Archive Center (LP DAAC ),  USGS/Earth Resources Observation and Science (EROS) Center ,  Sioux Falls ,  South Dakota  (https://lpdaac.
Multiplex analysis For selected subjects, a panel of analytes (IL-6, IL-8, IP-10, MCP-1, MIP-1β, GM-CSF, IL-10, IL-13, IL-17A, IL-2, TNF-α and sCD40L) were quantified in the culture supernatants of their HBHA-IGRA (PPD-, HBHA-and non-stimulated tubes) and QFT-GIT tests (TB Antigen tube and Nil tube) using MILLIPLEX® MAG panels (Merckx Millipore, Brussels, Belgium) following the manufacturers' instructions. However, culture supernatants were initially diluted using dilution factors specific to each analyte in order to obtain concentrations lying within an interpretable range. Results were analysed with a Bio-Plex® MAGPIX™ Multiplex reader, Bio-Plex Manager™ MP Software and Bio-Plex Manager 6. 1 Software (BIO-RAD laboratories, Nazareth Eke, Belgium). If detectable, the analyte concentrations obtained in the antigen-free conditions were subtracted from those obtained with antigen stimulation.
Statistical methods Results were analyzed using GraphPad Prism version 5. 04 (GraphPad Software, La Jolla California USA, www. graphpad. com). The Mann-Whitney U test and the Kruskall-Wallis test were applied to compare continuous variables between groups. Univariate analysis was performed by Fisher's exact test and odds ratios for categorical variables. Chi-squared test for trend was applied for ordinal variables. Correlation between continuous variables was assessed using Pearson's product moment coefficient. A p-value <0. 05 was considered significant.
Acknowledgements We would like to thank all the volunteers that participated in the study. Furthermore all the members of the different research teams receive our upmost gratitude, in particular our colleagues from  LoVMI  ( Drs Véronique Corbière  and  Kaatje Smits ,  Alexandra Dreesman ,  Julie Smet ,  Miss Julie Bosse ,  Mrs Anne Vanpraet ) for their contribution to the acquisition of the data, the recruiting physicians ( Drs Sara Debulpaep ,  Kabamba Kabeya ,  Marie-Christine Payen ,  Brice Layeux ,  Sarah Spruyt ,  Delphine Mathieu ), research nurses ( Zoe Kipouros ,  Virginie Lenoir ,  Laura Riesi ) and data manager  Mrs Leslie Andry.
However, these products do not fulfil the GEOGLAM target products and user requirements collected during the Sentinel-2 Agriculture project in terms of spatial resolution and timeliness. As pointed out by  [19] , continental-or even global-scale analysis using remote sensing imagery, such as Landsat's, was generally regarded as not feasible because of the absence of well-registered multi-temporal datasets, skills and processing power. This has now been overcome, but even with the availability of the Landsat WELDdata, the insufficient temporal revisit of 16 days and the spatial resolution of 30 m, the generation of accurate crop type products remains difficult. The requirements for general land cover products were well identified by Whitcraft et al. in  [20, 21] , but the crop type product needs in many cases a finer spatial resolution than Landsat's.
This paper presents a summary of the results of the benchmarking of the selected five strategies on SPOT4 (Take5) data over 12 test sites. The results obtained in this work show the potential of the Sentinel-2 system in the sense that the same processing chain can be applied to Sentinel-2 imagery. Sentinel-2 will bring a better spectral sensitivity and a higher spatial resolution, allowing one to achieve even better results. The paper is organized as follows. In Section 2, we present the data sources used in this work (Section 2. 1) and the design of the processing chains and the associated experiments (Section 2. 2). Section 3 presents the results and a detailed discussion. Finally, Section 4 draws the conclusion.