dataesr/scientific-paragraphs-categorization huggingface dataset:
This assures that the resulting lightning data are as consistent as possible throughout Europe. In fact, the EUCLID data are frequently of higher quality than the data produced by individual national networks, being sub-networks of EU-CLID. This is due to the implicit higher redundancy in EU-CLID as a result of participation of additional sensors in the lightning location, which are located outside the national borders in a neighboring country. We note that there is a full backup EUCLID processing center in Germany with independent and direct data connections to all sensors. Further, the transnational EUCLID cooperation also acts as a platform for knowledge exchange related to lightning location technology and LLS data applications. Since the beginning of the cooperation, the performance of the EUCLID network has been steadily improved.
, 2011). In this case, the onset time is derived as a linear extrapolation from the rising edge of the return stroke wave-front. This type of onset time calculation is significantly more accurate than the previously used estimation at the central processor. -Propagation correction (December 2012) -In the complex terrain of the Alps, the correction of timing errors is very important. Those timing errors are the result of a combination of propagation effects due to finite ground conductivity and of an elongation of the propagation path  (Honma et al. , 2011). As the alpine region represents a large part of the area covered by the EUCLID network, those timing errors are important. In order to correct the timing errors for each sensor, the distance-and angle-dependent time-correction values have been extracted from the historical sensor data and implemented as corrections in the central analyzer.
Video and field recording system (VFRS) To collect video and E-field data of individual lightning discharges, we are employing a mobile video and field recording system (VFRS) consisting of a flat plate antenna, an integrator, a fiber optic link and a camera. For the E-field measurements a 12 bit digitizer with 5 MS s -1 sample rate and an integrator with a decay time constant of 0. 46 ms was used. The complete system has an upper frequency cutoff of 1. 3 MHz. The camera used had a frame rate of 200 fps and an angle-of-view of about 50 •. The complete recording system is described in detail in  Schulz et al. (2005) ,  Schulz and Saba (2009)  and  Schulz and Diendorfer (2006).
DE determined from VFRS records Due to the reason that the DE did not vary considerably in Austria from 2009 to 2012 and in France in 2012 and in 2013, we present only the average DEs for all the years where data were recorded with the VFRS. The criteria used in Table  3  to determine whether a stroke was detected by the LLS or not are quite strict because not only the stroke location has to be provided with certain quality criteria (χ 2 < 10 and major axis of the confidence ellipse smaller than 5 km) but also the stroke classification either as cloud-to-ground (CG) or as intra-cloud (IC) has to be correct. As we have discussed before, the DE depends on peak current, and therefore we present in Table 3 also the median peak.
Peak current estimates The EUCLID peak current estimates are compared with the direct current measurements of strokes to the GBT only because no information regarding peak current is available from the VFRS data records. In Fig. 8  we have plotted the EUCLID-estimated peak current I EUCLID vs. the directly measured peak current I GB at the GBT. Ideally all data points should line up on the diagonal (solid black line). Based on the transmission line model (TLM) and the conversion of peak field to peak current used in the EUCLID network, this line corresponds to a return stroke speed of v LLS = 1. 2 × 10 8 m s -1. Due to different return stroke speeds and propagation paths from the tower to the LLS sensors, the resulting EUCLID peak current estimates scatter around this line. Nevertheless, the overall accuracy seems to be reasonable.
As a result, the estimated LA of an LLS based on lightning strikes to towers is expected to be somewhat better than that for natural lightning. On the other hand, the LLS location error determined from video data of strokes in the same channel is an upper limit because the return stroke channel is not always seen all the way down to the ground strike point of each return stroke  (Biagi et al. , 2007). Keeping in mind those specific limitations of tower measurements and VFRS recordings, the agreement between LA determined from GBT data and VFRS data in Austria is almost perfect. Further, we observed a continuous improvement of the LA of the EUCLID network in recent years due to the implementation of several technology improvements (soft-and hardware) summarized in Sect. 2. All these improvements result in an actual median LA of 89 m for the last 100 strokes recorded at the GBT.
LLS tend to overestimate the peak current of strokes to socalled electrically tall towers  (Pavanello et al. , 2009). A tower is called electrically tall when the rise time of the lightning current is smaller than the current wave propagation time along the tower, and therefore, the current injected into the tower top reaches its peak before the arrival of any ground reflections. Correction factors have been derived based on model calculations taking into account multiple reflections of the lightning current pulse at ground level and at the top of the tower  (Baba and Rakov, 2007; Bermúdez et al. , 2005). Based on triggered lightning data for the US, NLDN peak current errors with an AM and median of -5. 6 and -5 %, respectively, are reported  (Mallick et al. , 2014). Contrary to the NLDN, peak current errors in this paper are positive (AM and the median 3 and 4 % respectively) which means that the EUCLID LLS overestimates the peak current compared to the NLDN.
Acknowledgements. The authors are grateful to the support from all the EUCLID members. The authors further thank  Christian Vergeiner  who recorded most of the video and E-field data in Austria and  Hannes Pichler  who is the master behind the Gaisberg measurements. We also thank the anonymous reviewers for their very helpful comments. Edited by:  A. Mugnai  Reviewed by:  K. Cummins  and one anonymous referee.
They measure quality of care better than chart abstraction does. Vignettes are easy to use and more cost-effective than standardized patients, high fidelity simulation, or even clinical audits  [7] [8] [9] [10]. Moreover, it is easier to control case-mix variation in vignettes than in data sets. They have thus been widely used across countries, health care systems, and specialties  [11] [12] [13]. Case-vignettes have been used and validated to analyze practices such as screening, diagnosis, care, assessment of prognosis, and ethical decision making  [3, 5, 6, [14] [15] [16] [17]. They have not, however, been validated to assess the complex management strategies often observed in emergency situations. Dynamic multistage vignettes can be constructed to assess clinical practices in such situations.
Accordingly, we developed 66 vignettes from the 66 complete files for 33 obstetricians. Vignettes were developed by abstracting the following data from patient files: patient medical history and information about the pregnancy, labor, delivery, and PPH. All data that might identify the specific situation were changed. We designed the vignettes to include several steps re-creating the course of the PPH, for its management often requires several successive steps to control bleeding. For each step of the vignette, we described a stage of the postpartum course, including bleeding, maternal condition, and response to proposed treatment (Figs  1 2 3 ). We used the same standardized format for all vignettes. Each clinical vignette was pretested by an obstetrician and a midwife to check clarity and brevity.
The objective of our research was to characterize predominant patterns of cumulative landscape change in the Atlantic Forest of northern Maine, and to evaluate how pre-and post-FPA management regimes have influenced landscape conditions across space and time. We used Landsat imagery and forest inventory data to develop and validate forest composition maps and a time series of forest harvest maps. We modeled predominant temporal patterns of harvesting and segmented a large study area into groups of landscape units with similar harvest histories. We then linked harvest history with changes in landscape composition and configuration in order to characterize the evolution of landscape conditions in response to forest management practices before and after abrupt change induced by the FPA. Our approach provided an objective synthesis of predominant patterns of change associated with specific landscape units, with the spatial and temporal resolution needed to attribute change to different management regimes.
Data production Forest harvest and composition maps were assembled from a time series of Landsat Multispectral Scanner (MSS), Thematic Mapper (TM), and Enhanced Thematic Mapper Plus (ETM+) images acquired during summer leaf-on conditions (Table  1 ). Consecutive images were spaced 1-4 years apart, as determined by the availability of high quality, predominantly cloud-free imagery. Mapping procedures were applied to forested pixels as identified by the 1993 Maine Gap Analysis Program (GAP) land cover map. The GAP map represents conditions near the midpoint of our time series, and discriminated forest from non-forest with an estimated 100% accuracy within our study area  [31]. Not all forestland within our study area is operable or available for harvesting. To normalize harvest rates and metrics of landscape change according to the amount of available forestland in different landscape units, we masked forest pixels over 823 m (2700 ft) in elevation or >40% slope, as determined from the 1 arc-second National Elevation Dataset.
Mapped disturbance events were almost exclusively the result of harvest operations and we therefore refer to our data as a time series of forest harvest maps. Harvest maps were produced by unsupervised classification of overlapping three-date NDVI or NDMI image sequences (e. g. ,  1973-1975-1978, 1975-1978-1982,. ). Classification of a three-date sequence mitigates the impact of cloud cover in the second image provided the first and third give clear views. An ISODATA algorithm applied to each three-date composite produced 50 statistical classes that were interpreted into forest disturbance, regrowth, and nochange information classes. Stand-replacing and partial harvest classes derived from TM/ETM + imagery were differentiated based on the relative magnitude of NDMI change, guided by visual interpretation of Landsat imagery and available aerial photography.
A pixel was labeled as partially harvested if preceding intervals included only a single partial harvest. When preceding intervals included multiple partial harvests, pixels were labeled as regenerating forest, reflecting the anticipated ecological and silvicultural effects of multiple entries within the ~20-year period over which partial harvests were mapped. For each date of our time series, the result depicts the cumulative footprint of harvest operations since 1973. Forest type mapping, 1975 and 2004. We mapped forest composition using equivalent unsupervised classification methods applied to each of the 1975 MSS and 2004 TM images. Dates were selected on the basis of cloud cover and image quality. For the purpose of forest type mapping, small areas of cloud cover in the 2004 image were replaced with data from the 2001 ETM+ image.
If for example pixels representing forest with a deciduous component of 70-75% were mistakenly committed to the deciduous-dominant class rather than the mixed class, the extent of the deciduous class would be overestimated according to the class definition of >75% deciduous. We used validation data obtained from field plots (described below and in S2 Appendix) to iteratively refine the aggregation and labeling of ISODATA classes to ensure that the 1975 and 2004 maps provide unbiased representations of forest type classes at the same thresholds of forest composition. For each map, we identified coniferous-and deciduous-dominant class thresholds for which omission and commission errors were balanced. To do so, we varied coniferous and deciduous threshold values from 50-95% in increments of 5%, assigned reference class labels based on threshold values, and calculated omission and commission error rates.
Data validation The U. S. Forest Service Forest Inventory and Analysis (FIA) Program provides quality-assured measurements of forest attributes from a national network of field plots adhering to a systematic sampling design  [39]. We made extensive use of FIA data as a statistically rigorous basis for map validation. However, use of field plot data for map validation is subject to uncertainty arising primarily from mismatches in location and scale between field plots and map pixels. Validation using FIA data should be considered an assessment of agreement with an accepted and widely utilized source of information on forest conditions, rather than an assessment of accuracy against ground truth. Here we provide an overview of our validation procedures; details are provided in S2 Appendix. Harvest time series validation.
Image interpretation was used to date harvest events; FIA plot data were used to discriminate stand-replacing and partial harvests. In Maine, the contemporary FIA inventory design was established in 1999, with 20% of plots surveyed annually during sequential 5-year cycles. Although data are available from plots measured during earlier inventories, coordinate locations are known for only a fraction of those plots. We therefore used data collected during contemporary inventory cycles to discriminate past harvest intensity. A harvest event identified by image interpretation was labeled stand-replacing provided FIA age dated stand origin to 1970 or later (allowing for advance regeneration prior to 1973) and field crews labeled the stand as either sapling or poletimber. However, for plots harvested after 1999, recorded stand age was an unreliable indicator of stand-replacing disturbance because age estimates frequently corresponded to a few residual stems rather than a newly established cohort.
Data analysis To quantify harvest rates through time, identify trends, and associate trends with changes in landscape conditions, we tessellated our study area into landscape units using a 5 km square grid (Fig 1 ). A 5 km grid cell size was a somewhat arbitrary compromise: small enough to resolve spatial variations in harvest history and consequent landscape change, but large enough to calculate meaningful trends in harvest rates and landscape pattern metrics. We excluded grid cells with <50% available forest or <5% of available forest harvested from 1975-2010 (17 cells). A sample of 608 grid cells remained. Empirical orthogonal function (EOF) analysis of cumulative harvest time series. An EOF analysis identifies a sequence of uncorrelated patterns or modes of variability that characterize variation within a two-dimensional data set  [44].
EOF analysis is commonly employed in meteorology and oceanography, where conventional applications decompose time series of geospatial data into characteristic spatial patterns whose contributions to observed variation change through time. EOF outcomes can just as readily be interpreted as characteristic temporal patterns whose contributions to observed variation differ between locations (e. g. ,  [45] ). We performed an EOF analysis to identify characteristic temporal patterns of variation in cumulative harvest area sampled across our 5 km grid. Cumulative harvest time series were arranged as rows within a matrix X (M = 608 rows; N = 15 columns). EOF analysis decomposed X into matrices A and B such that X = AÁB (A is MxN; B is NxN). The rows of B represented a sequence of mutually uncorrelated patterns of temporal variability referred to as empirical orthogonal functions (EOFs).
Acknowledgments We extend our gratitude to  Dan Harrison  for his contribution to the inception, design, and implementation of this research. Confidential coordinates of FIA field plots were made available through a collaborative agreement with the  USDA Forest Service Northern Research Station   FIA Program  (FS Agreement No. 2014-MU-11242305-055 ). We thank  Elizabeth LaPoint  for her valuable assistance in obtaining and working with FIA spatial data. We extend our appreciation to the Academic Editor and anonymous reviewers whose contributions greatly improved our manuscript. Maine Agricultural and Forest Experiment Station Publication Number 3418.
In SERAPHIN, a long-term, randomised, controlled trial (NCT00660179) in pulmonary arterial hypertension (PAH), macitentan significantly reduced the risk of morbidity/mortality and PAHrelated death/hospitalisation. We evaluated disease progression and the effect of macitentan in treatmentnaïve incident and prevalent cohorts. Patients allocated to placebo, or macitentan 3 mg or 10 mg were classified by time from diagnosis to enrolment as incident (⩽6 months; n=110) or prevalent (>6 months; n=157). The risk of morbidity/ mortality and PAH-related death/hospitalisation was determined using Cox regression. The risk of morbidity/mortality (Kaplan-Meier estimates at month 12: 54. 4% versus 26. 7%; p=0. 006) and PAH-related death/hospitalisation (Kaplan-Meier estimates at month 12: 47. 3% versus 19.
Methods Study design and patients SERAPHIN was a global, double-blind, randomised, placebo-controlled, event-driven, phase III study designed to assess the long-term efficacy and safety of macitentan in patients with PAH (www. clinicaltrials. gov identifier number NCT00660179). The study has been described in detail elsewhere  [13]. Patients were ⩾12 years of age with PAH diagnosed by right heart catheterisation, including idiopathic and heritable PAH, or PAH associated with connective tissue disease, repaired simple congenital systemic-to-pulmonary shunts, HIV infection, or drugs and toxins. Patients were also required to be in WHO FC II-IV and have a 6-min walk distance (6MWD) ⩾50 m. Patients could be treatment naïve, or receiving a stable dose of phosphodiesterase type 5 inhibitors, oral or inhaled prostanoids, calcium channel blockers, or L-arginine for ⩾3 months.
In order to validate this new GVHD risk score, we examined a larger, heterogeneous group of patients who received steroids as initial systemic therapy for acute GVHD. A database of 1723 patients was created from 5 cohorts; the previously reported patients from the University of Minnesota (n=864)  9  , Blood and Marrow Transplant Clinical Trials Network (BMT CTN) Study 0302 (n=155)  10  , BMT CTN Study 0802 (n=213)  11  , Hôpital Saint Louis, Paris (n=184) and the University of Michigan (n=307). Patients from Minnesota or Michigan enrolled in the BMT CTN trials (n=33) were counted only once. The distribution of initial GVHD organ staging combinations and subsequent response to therapy was analyzed to predict responses and define HR and SR GVHD.
Study Design Between 1990 and 2007, 1723 allogeneic HCT patients developed grade I-IV acute GVHD and were treated with prednisone 2mg/kg/day or 60 mg/m 2 PO (or methylprednisolone 48 mg/m 2 IV) as initial therapy and are included in this analysis. All HCT and data collection protocols were reviewed and approved by the Institutional Review Board at the respective HCT centers. De-identified data were compiled for analysis.
GVHD Organ Staging Categories analysis includes adjustment for donor type [HLA-matched sibling vs. other related vs. umbilical cord blood vs. HLA-matched unrelated donor (URD) vs. HLA-mismatched URD], days to acute GVHD (< 28 days vs. ≥ 28 days), center/dataset (Minnesota vs. BMTCTN 0302 vs. BMTCTN 0802 vs. Paris vs. Michigan), age (<21 vs. 21-40 vs. 41+).
This was followed by the project ENSEMBLES 2 (2004-2009, Hewitt 2004). It produced for the first time a probabilistic estimate of uncertainty of future climate at several timescales, using an ensemble validated against observational datasets for Europe. Note that similar projects exist over other regions like the Asian RMIP project  (Fu et al. 2005)  or the North American projects PIRCS  (Takle et al. 1999)  and NARCCAP  (Mearns et al. 2013). Lately, the Coordinated Regional Climate Downscaling Experiment (CORDEX,  Giorgi et al. 2009)  initiative from the World Climate Research Program promotes running multiple RCM simulations at 50 km and higher resolution for multiple regions. This initiative is mainly aiming to assess RCMs quality and uncertainty for the recent past and for twenty first century projections, covering the majority of populated land regions on the globe.
(2007). The SDMs were calibrated on several reanalysis databases for present climate and applied to GCMs for future climate. Results showed that the statistics of most statistically and dynamically downscaled precipitation were similar. In another study by  Bürger et al. (2012) , five SDMs with their own set of predictors have been evaluated in a present climate context over British Columbia, Canada, focusing on extremes aspects of the compared SDMs for temperature and precipitation. It turned out that the use of hybrid models (i. e. , models with components built from several families) made difficult to identify the component of the models which explains the model efficiency. Even if all the SDMs families were studied in both papers, the models were calibrated on different sets of predictors.
Local-scale predictands and large-scale predictors In order to limit any RCM data transformation from their initial spatial resolution, the common resolution of the RCMs at 0. 44 • or local-scale predictands has been chosen. Therefore, the comparison with the E-OBS V8 gridded dataset from the EU-FP6 project ENSEMBLES 3 and the data providers in the ECA&D project is straightforward 4  (Haylock et al. 2008 ) at 0. 44 • × 0. 44 •. In the experimental setup, the E-OBS precipitation data will serve as localscale reference predictand for the calibration of the statistical models that will therefore downscale largee-scale information to 0. 44 • spatial resolution, directly comparable to RCMs outputs. Note that there are some quality inconsistencies in this version of E-OBS data  (Hofstra et al. 2009).
The reader has to keep in mind that this intercomparaison is done using E-OBS data as reference, which can potentially induce some inexact results over specific areas. This issue is discussed in Sect. 5. 2. As one of the goal of this study is to make intercomparisons between SDMs and RCMs involved in the EURO-CORDEX  (Jacob et al. 2014 ) and MED-CORDEX/ HYMEX  (Drobinski et al. 2014, www. medcordex. eu/medcordex. php) initiatives, the atmospheric data chosen to drive the statistical models (i. e. , the large-scale predictors) are the same as those used as forcing for the RCMs for the hindcast evaluation. Figure  1  represents the geographical areas over which the models are evaluated: in green the SDMs domain corresponding to the domain of E-OBS data, in blue the EURO-CORDEX evaluation domain which is the intersection between EURO-CORDEX and E-OBS domain and in orange the MED-CORDEX evaluation domain which is the intersection between MED-CORDEX and E-OBS domain.
Only the first PC is considered to summarize spatially a climate variable. Two points motivated this choice. First, the physical/atmospheric variables that make sense as predictors for precipitation downscaling only at the first order have been determined. Hence, a natural choice was to retain only the first PC. Second, applying the SPARSE CCA algorithm over the whole EURO-CORDEX region based on the relatively high-resolution E-OBS dataset is computationally intensive, even for a single principal component. Therefore it has been decided to limit this first exploratory step of SPARSE CCA to only the first PC. The sparsity constraints are tuned so that only one predictor variable appears in each projection vectors (only one non-zero coefficient). Thus, each predictor variable is associated with a rank given by the correlation (see Table  1 ) with the projected predictand.