dataesr/scientific-paragraphs-categorization huggingface dataset:
We present a unique meteorological and snow observational dataset in Mount Lebanon, a mountainous region with a Mediterranean climate, where snowmelt is an essential water resource. The study region covers the recharge area of three karstic river basins (total area of 1092 km 2 and an elevation up to 3088 m). The dataset consists of (1) continuous meteorological and snow height observations, (2) snowpack field measurements, and (3) medium-resolution satellite snow cover data. The continuous meteorological measurements at three automatic weather stations (MZA, 2296 m; LAQ, 1840 m; and CED, 2834 m a. s. l. ) include surface air temperature and humidity, precipitation, wind speed and direction, incoming and reflected shortwave irradiance, and snow height, at 30 min intervals for the snow seasons (November-June) between 2011 and 2016 for MZA and between 2014 and 2016 for CED and LAQ.
Precipitation data were filtered and corrected for Geonor undercatch. Observations of snow height (HS), snow water equivalent, and snow density were collected at 30 snow courses located at elevations between 1300 and 2900 m a. s. l. during the two snow seasons of 2014-2016 with an average revisit time of 11 days. Daily gap-free snow cover extent (SCA) and snow cover duration (SCD) maps derived from MODIS snow products are provided for the same period (2011)(2012)(2013)(2014)(2015)(2016). We used the dataset to characterize mean snow height, snow water equivalent (SWE), and density for the first time in Mount Lebanon. Snow seasonal variability was characterized with high HS and SWE variance and a relatively high snow density mean equal to 467 kg m -3. We find that the relationship between snow depth and snow density is specific to the Mediterranean climate.
The current model explained 34 % of the variability in the entire dataset (all regions between 1300 and 2900 m a. s. l. ) and 62 % for high mountain regions (elevation 2200-2900 m a. s. l. ). The dataset is suitable for the investigation of snow dynamics and for the forcing and validation of energy balance models. Therefore, this dataset bears the potential to greatly improve the quantification of snowmelt and mountain hydrometeorological processes in this data-scarce region of the eastern Mediterranean.
, 2013; Königer and Margane, 2014). The snowmelt contribution from high-elevation regions (above 1800 m a. s. l. ) was estimated to contribute to around 56 % of the major spring discharge at the lowland regions  (Margane et al. , 2013). The coastal watersheds, such as the EL Kelb Basin, are the major sources of water for the coastal population, where most of the Lebanese population is located. Although snow is recognized as a major component of the hydrologic system in Mount Lebanon  (Shaban et al. , 2004; Aouad-Rizk et al. , 2005; Bakalowicz et al. , 2008; Mhawej et al. , 2014; Königer and Margane, 2014) , the link between snowmelt and the hydrological processes remains poorly characterized on the basin scale. This can be attributed to the (1) lack of an operational snow observation network in Lebanon and (2) limited number of published basin-scale hydrometeorological datasets.
Meteorological stations operated by the department of civil aviation are usually located below the snowline (maximum elevation at 1220 m a. s. l. ) and the few stations located in the mountainous regions (elevation range 1510-1890 m a. s. l. ) are not equipped to measure snowfall. Furthermore, only a few datasets on precipitation, temperature, snow, groundwater recharge and streamflow are available for basin-scale studies (e. g. ,  Königer and Margane, 2014). Existing datasets, available through published mate-rial, are usually limited to (1) national-scale studies with monthly or yearly means  (Shaban et al. , 2004; Corbane et al. , 2005; Mhawej et al. , 2014; Telesca et al. , 2014) , (2) multiyear daily spring discharge and precipitation time series (e. g. ,  Hreiche et al. , 2007) , (3) seasonal observations collected for mesoscale catchment studies  (Bernier et al.
, 2003; Aouad-Rizk et al. , 2005; Doummar et al. , 2014) , and (4) local-scale snowpack observations (e. g. ,  Somma et al. , 2006 Somma et al. , , 2014)). In most cases the research datasets are not made publicly available. In this paper, we present a dataset targeted at the study of the mountain and snow hydrology in three mesoscale basins (area 256-513 km 2 ) located on the west slope of Mount Lebanon (Fig. 1 ). The dataset consists of (1) continuous meteorological and snow height observations collected at three automatic weather stations (AWSs)  (2011) (2012) (2013) (2014) (2015) (2016) , (2) snowpack field measurements collected during two snow seasons  (2014) (2015) (2016) , and (3) medium-resolution satellite observations of the snow cover extent at a daily time step (MODIS). We use these data to characterize the variability in key snowpack properties.
The dataset presented in this paper is unique because it is the only dataset that includes a range of continuous snow and meteorological measurements in the mountain region of Lebanon at the elevation regions between 1300 and 2900 m a. s. l. The data presented are readily suitable for the forcing and validation of a snowpack energy and mass balance model. The data also can be useful for further hydrometeorological studies such as climate model downscaling or hydrological modeling for water resource management. The study area is described in Sect. 2. Meteorological observations and post processing methods, snow course measurement protocols, and MODIS data processing are presented in Sect. 3. Section 4 provides a summary of the observations and an example application on using the datasets to derive the relationships between HS, snow water equivalent (SWE), and snow density.
Meteorological data The three AWSs were installed in Mount Lebanon above the winter snowline (approximately 1550 m a. s. l. ) with the primary objective to monitor the meteorological variables that drive seasonal snowpack evolution on Mount Lebanon (Figs. 1  and 2 ). The Laqlouq AWS (LAQ) is located in a monastery at 1840 m a. s. l. , the Mzar station (MZA) is located in a ski resort domain at 2296 m a. s. l. , and the Cedars AWS (CED) (2834 m a. s. l. ) is located on the higher plateau below the mountain's peak at Qornet El Sawda, 3088 m a. s. l. (Fig. 1 , Table  2 ). The LAQ station is in a relatively flat plain area with fruit trees, bare rocks, and sparse, short grasslands. The MZA station is located on one of the mediumelevation peaks in a mountainous region with rugged terrain (maximum elevation in the area is ∼ 2600 m a.
s. l. ) with dominant bare soils and sparse speargrass grassland. The CED AWS is located on a higher plateau with dominant bare rocks and sparse shrubs and grasslands at lower elevations (1600-2200 m a. s. l. ). Wind effects on snow cover are more noticeable in MZA due to the combined effect of topography and higher wind velocities. The three stations are operated under a joint program for establishing a network for snow observation (NSO). The program, established in 2010, is a collaboration between the Institut de Recherche pour le Développement (IRD, France), the Centre d'Etudes Spatiales de la Biosphere (CESBIO, France), the National Council for Scientific Research -Remote Sensing Center (CNRS/NCRS, Lebanon), and the University of Saint Joseph (USJ, Lebanon). Meteorological data are available starting from snow season (December-June) 2011 for Mzar AWS and the monitoring network became fully operational in snow season 2014-2015 with the installation of the third AWS (LAQ) at Laqlouq (Table  2 ).
Meteorological data -including snow height, temperature, relative humidity, incoming and reflected shortwave solar radiation, wind speed and direction, and atmospheric pressure -are collected at the three sites using sensors mounted on towers (Fig. 3 ). Longwave radiation, which is an important component of the energy balance in Mediterranean regions  (Herrero and Polo, 2016) , is not measured. However, incoming longwave radiation can be estimated from   1 ). The topography near Laqlouq (LAQ) is a relatively low plain (elevation between 1600 and 1800 m a. s. l. ) and low-elevation mountains (1900-2100 m a. s. l. ). The region near Mzar (MZA) is characterized by rugged terrain (1600-2200 m a. s. l. ) and mid-elevation plateau (elevation range 2300-2500 m). The high-elevation plateau (shown partially in panel b near Cedars, CED) have an elevation range between 2700 and 3000 m a.
Each station consists of a data logger (CR1000; Campbell Scientific Inc. , Utah, USA) and a precipitation gauge (T-200B; Geonor Inc. , Eiksmarka, Norway), a snow depth sensor (SR50A; Campbell Scientific Inc. , Utah, USA), an air temperature and humidity sensor (CS215; Campbell Scientific Inc. , Utah, USA), an incoming and reflected shortwave solar radiation sensor (SP LITE 2 pyranometer; Kipp & Zonen, Netherlands), and a wind speed and direction sensor (Alpine v05103-45L, Young, USA). Data are transmitted via a GPRS modem every 8 h. Observations from the three AWSs are collected at 30 s and then aggregated into 30 min averages. Temperature and humidity sensor are installed at 2. 4 m in MZA, 3. 9 m in LAQ, and 4. 2 m in CED. Wind speed sensors are installed at 2. 6 m in MZA, 4. 2 m in LAQ, and 4.
9 m in CED. Snow height observations were recorded automatically at each station using an acoustic snow gauge installed at 2. 0 m in MZA and at 4. 0 m in CED and LAQ. Precipitation data are recorded at a snow gauge placed in the proximity of the station (Fig. 3 ). Precip-itation has been observed since 2012 at  MZA (2012 MZA ( -2016) )  and since 2014 at  LAQ (2014 LAQ ( -2016)). The CED station was equipped with a Geonor in December 2016. Data for precipitation were missing during the first year (2011-2012) at MZA and shortwave solar radiation measurements began in snow season 2014. CED AWS data between 24 April and 30 June 2015 were removed due to station rotation. The wind speed data located at CED were discarded after 15 January 2016 due to sensor malfunction. Missing data were less than 10 % for all stations after the network became fully operational  (2014) (2015) (2016).
Raw data collected at the stations underwent basic quality control, including checks for missing data and boundary values. We performed further quality control for the 30 min and daily average observations by screening outliers and erroneous data following rules given by  Serreze et al. (1999) ,  Shafer et al. (2000) , and  Estévez et al. (2011). Humidity, pyranometers, and wind sensors were unheated and thus may be subject to error when covered by frost  (Malek, 2008). The temperature sensors are protected against solar radiation  (Huwald et al. , 2009)  using radiation shield and are naturally ventilated. The accumulation of frost on the sensor   P and P sh : daily and semi-hourly precipitation; HS and HS sh : daily and semi-hourly snow height; T : mean air temperature, respectively; T sh : semi-hourly air temperature; SwI and SwR: incoming and reflected solar radiation, respectively (sh denotes semi-hourly); RH and RH sh : daily mean and semi-hourly relative humidity; and Ws and Wd: mean wind speed and mean wind direction, respectively (sh denotes semi-hourly).
2 0. 8 < RH < 103 |RH sh -RH sh -1 | ≤ 45 was observed during multiple field visits at MZA and CED. These events usually coincide with the week following storm events. For precipitation (P ), air temperature (T ), snow height (HS), and humidity (RH), we used running step tests to detect abrupt jumps in means, especially during storm events (Table  3 ). Incoming and reflected shortwave solar (SR) radiation measurements were screened using snow half-hourly albedo, by eliminating data that do not give a positive albedo (0 ≤ albedo ≤ 1). We used a positive snow height (HS) to detect the presence or absence of snow. Data ranges were used for P (0-240 mm h -1 ), T (-30 to +40 • C), RH (0-100 %), HS (0-4. 5 m), and SR (0-1500 W m -2 ). For the two snow seasons 2015-2016 (November-June) data record retained at the three stations were 71.
We post-processed the raw precipitation data using a supervised correction similar to the one described in  Harder and Pomeroy (2013)  by performing the following steps. (1) The Geonor raw data were adjusted to account for when the gauge is emptied and/or filled with oil and antifreeze. (2) We used the predefined values for range test and cross validation tests (Table  3 ) to automatically flag and remove erroneous peaks (e. g. , P sh > 120 mm). (3) All erroneous changes in the calculated raw cumulated precipitation (raw P sh < -20 mm and > 20 mm) were removed. (4) We flagged and removed all cumulated precipitation greater than 1000 mm (equal to the maximum capacity of the Geonor bucket), and the precipitation data for the same time period was set to "no data" in the final dataset. (5) All missing cumulative precipitation observations were assumed to be equal to the previous observed observation for running the filter.
org/10. 5281/zenodo. 583733. Snow depth was measured manually, to the nearest 1 cm, using a 3 m snow probe, at 5 m intervals along each snow course. Snow density was measured using a federal snow sampler (snow cutter inner diameter at 3. 772 cm) along each snow course at 25 m intervals for snow courses shorter than 100 m and at 50 m intervals for longer snow courses, so at least 3-5 snow density measurements were recorded at each snow course site. Snow density and SWE protocol consisted of weighing the empty tube. The snow tube was then plunged into the snowpack and the snow depth marker on the tube was recorded. Once the core was removed the snow depth of the snowpack was checked to make sure that at least 80 % of the snowpack has been cored  (Dixon and Boon, 2012). We also measured the snowpack HS using a marked snow probe (1 cm) to make sure no snow was left unsampled.
Snow cover extent and snow cover data Daily maps of the snow cover extent at 500 m spatial resolution were generated for the three watersheds from the MODIS snow products. We used the "binary" snow cover area sub-dataset from MOD10A1 (Terra) and MYD10A1 (Aqua) collection 5 products from the National Snow and Ice Data Center  (Hall et al. , 2006). Mount Lebanon falls between MODIS grid tiles h20v05 and h21v05. All available tiles from 1 September 2011 to 31 August 2016 were assembled and resampled to 500 m with a nearest-neighbor method in UTM 36N using the MODIS reprojection tool over a rectangular spatial subset (upper left x = 730 km, y = 3830 km; lower right x = 850 km, y = 3680 km). Then we ran a gap-filling code which is fully described in  Gascoin et al. (2015)  to interpolate the missing information mostly caused by cloud cover.
The algorithm utilizes the topographic information (elevation and aspect) from the ASTER global digital elevation map, which was resampled to the same resolution. The output is a series of daily, gap-free raster maps of the snow presence or absence for every pixel. These data were then used to compute the daily snow cover area in each watershed and the mean annual snow cover duration per pixel (SCD), i. e. , the mean number of snow days per year. The dataset is available at https://doi. org/10. 5281/zenodo. 583733.
ρ max , ρ 0 , k 1 , and k 2 vary with climate region and the model parameters for the major snow class are found in  Sturm et al. (2010)  Table  4. The equation was applied to the ensemble points, presented in Fig. 8b , using snow depth and DOY as predictor variables and snow depth as predictand. The model parameters were ρ max = 0. 553, ρ 0 = 0. 0345, k 1 = 0. 0000, and k 2 = 0. 0167 for the entire dataset with the model explaining 34 % (coefficient of determination r 2 = 0. 344) of the snow density variability (Table  4 ). We believe that some of the observed differences between the current Mediterranean and the maritime and alpine regions in general can be attributed to the shorter snow season, warmer temperature, and higher densification rates, especially in the mid-elevation zone (1300-1900 m a.
Conclusions This paper presents the first dataset of snow and meteorological conditions in Mount Lebanon. The observations focused on three major basins of the coastal region of Lebanon and cover the snow seasons between 2011 and 2016 (for MZA AWS located at 2300 m a. s. l. ). The observation network became fully operational in 2014. The network includes three automatic stations covering the range of snow-dominated areas between 1840 and 2834 m a. s. l. Distributed in situ HS and SWE measurements were also collected during two snow seasons (2014-2016) at 30 different snow courses located between 1300 and 2900 m a. s. l. MODIS snow products were processed to compute SCA and SCD at the basin scale. These observations are the result of an ongoing joint collaboration between IRD (France), CESBIO (France), CNRS/NCRS (Lebanon), and USJ (Lebanon).
The observatory is currently being funded for 2 years (2016-2018) via grants from CNRS/NCRS, IRD, and USJ. Additional unpublished data observations may also be available to complete this dataset, including meteorological data collected by the meteorological services at the department of civil aviation and the Lebanese Agricultural Research Institute (LARI), and river and spring discharges monitored by the Litani River Authority (LRA) as well as meteorological and hydrological data combined by the early warning system at the National Center For Remote Sensing (NCRS) and the observational datasets at the Centre d'Information et de Formation aux Métiers de l'Eau (CIFME) at the Ministry of Energy and Water (MOEW). We provided mean and seasonal snow properties over two snow seasons  (2014) (2015) (2016)  and provided an example on how SWE and snow density can be obtained using only HS measurements.
This time span is insufficient to characterize the temporal variability in the snow cover. However, it already provides consistent information on the snow spatial variability. The combination of the meteorological station, snow courses, and remote sensing data through the application of a snowpack model will enable a multi-year evaluation of the snow resources at the basin scales. The accurate representation of the spatial distribution of HS, SWE, and snow density is crucial for hydrological applications. In particular, we are using the AWS data for running a distributed energy balance model. The snow observations also hold potential for the characterization of the spatial distribution of snow across different gradients. SCA and SCD data can be used for model validation or as an operational tool for water resource management (e.
Drawing from a database of OS findings for treatments of 12 solid tumors, the Patient Access to Cancer care Excellence (PACE) Continuous Innovation Indicators™ (CII) provides visualizations and insights into how evidence of increased OS has accumulated in the past and continues to evolve over time  [8]. To date, we have reviewed more than 12,000 literature references and curated more than 3000 pieces of evidence. The CII 'Approval Date Analysis' tool allows users to calculate the time between FDA approvals and the publication of evidence that a treatment improves OS. This tool allows for analyses of historical data that are not constrained to short time frames or single diseases. In this paper, we use the CII to explore the factors that influence the time in years between the date of U. S.
Methods The PACE CII provides data and visualizations drawn from published scientific studies for 12 solid cancers from the early 1900s through the present (data current through November 1, 2016, and updated biannually). The full methodology of the CII has been published previously  [8]. In brief, individual studies are evaluated to determine whether they provide evidence that one or more treatments significantly improve OS compared to a control. Each significant OS result, or 'Piece of Evidence' (PoE), is then assigned a weight depending on the quality of the evidence, the disease state of the treatment The CII calculates 'approval distance' for each PoE as the year of publication minus the year the drug was approved for a given indication. We created a dataset that contained approval distances for the first PoE in the CII database for each treatment by cancer type.
We used descriptive statistics and data visualizations to explore how these approval distances varied by the cancer type studied, treatment goal, and year of FDA approval. To determine whether the treatments approved alongside the publication of OS data are ultimately supported by more evidence than the treatments with a longer approval distance (i. e. those approved based on surrogate endpoints), we also assessed the relationship between approval distance and further innovation, as indicated by the accumulation of additional PoEs over time. This was performed using a linear regression of the E-score increase per year for a given treatment compared with the approval distance. To gain deeper insight into the potential benefits and risks of approving drugs when OS data are delayed, we conducted qualitative analyses of approved drugs for which no positive OS evidence yet exists.
Approval distances over time Figure  4a  shows the approval distances of all the drugs with mature, positive OS data in the dataset by year of approval. The black line indicates drugs with an approval distance of zero. Drugs above the line were approved before OS evidence was published. As the figure shows, approvals that co-occur with or post-date published OS evidence appear to have become much more common since the 1990s. The red line on the graph indicates the year 2016: it is not yet possible to observe lag times that might eventually appear above this line (i. e. in the future). This illustrates an important limitation for exploring approval distances: the more recently a drug was were approved in the same year the first PoE was published, resulting in an approval distance of zero; and 59.
Approval distance varies by cancer type and treatment goal There were large differences in approval distance patterns by cancer type. Figure  1  shows the histograms of approval distances for each cancer that had at least one PoE. (Endometrial cancer was included in the study; however, no approved drugs were present in the database with a PoE for endometrial cancer. ) As this figure shows, OS evidence for breast cancer and colorectal cancer has frequently been published years after a drug was initially approved (median approval distances were 4. 0 and 2. 5 years, respectively). By contrast, for melanoma and liver cancer, approval distances tended to be shorter (median approval distance was zero years for each cancer). Differences in approval distances across cancers may be in part due to differences in treatment goals and survival times associated with these goals.
Data showing nonsignificant differences in OS are available in the literature for seven of the treatments in Table  2 , suggesting that these drugs may not significantly improve OS compared to control treatments. However, a closer look at these studies shows that most of these treatments have demonstrated superiority to control treatments on some other ORIGINAL RESEARCH -Surrogate endpoints for oncology drug approvals drugsincontext. com increases in survival  [25]. This type of treatment combination effect is typically not reflected in phase 3 trial results but can be captured with more sophisticated result-tracking tools such as the CII.