dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions This study demonstrated that Forest Inventory data can be automatically matched to single tree detection results obtained from airborne laser scanning data. Furthermore, eight single tree detection methods were tested based on a unique dataset of different forest types originating from eight areas within the Alpine Space. The proposed method for automatically matching forest inventory data and remotely sensed data worked efficiently. In general, all tested methods achieve comparable results for the matching rates, but do differ for the extraction rates and omission/commission rates. The tree detection rates show a higher variation than the estimated tree heights. A method based on local maxima detection within a canopy height model using variable-sized moving windows is rated as the best performing algorithm.
Complex multi-layered forests were challenging for all tested methods. A point cloud clustering-based method gained the best results for trees in subdominant layers, which is rated as an advantage over raster-based methods. The best detection results were obtained for single-layered coniferous forests. Future studies should investigate the effect of different point densities on the detection results. Multiple datasets of the same area acquired with different flight parameters (i. e. , viewing angles, heights above ground, footprint size) would be necessary to perform this analysis robustly. Such datasets are rarely available. Automated absolute georeferencing between FI data and ALS data (co-registration) as well as an automated classification of FI plots in different forest types (i. e. , single-/multi-layered forests) based on the ALS data would help us to overcome the manual steps performed in the presented study.
Acknowledgments This work was funded by the  European Commission  (project Alpine Space 2-3-2-FR NEWFOR) within the  European Territorial Cooperation program  " Alpine Space. "The authors would like to thank the NEWFOR partners and the  Department of Forests of Canton Bern in Switzerland (KAWA)  as well as the  Tyrolean Forest Service  for providing the data. Furthermore we would like to thank  Bernhard Maier ,  Michael Sautter , and  Christian Ginzler  for the fruitful discussions. We finally want to thank the anonymous reviewers who contributed constructive and helpful comments and suggestions.
(2012)  and  Gaume et al. (2013)  Published by Copernicus Publications on behalf of the European Geosciences Union. that takes into account both mechanical and meteorological factors in a probabilistic framework. However, the precise position of the release area and the evaluation of its spatial extent have been less investigated. Maggioni et al. (2002)  and  Maggioni and Gruber (2003)  analyzed a database of well-documented avalanche events with respect to several topographic characteristics and showed that the mean slope angle, the curvature and the distance to the ridge are the most important parameters influencing avalanche release area distribution. Failletaz et al. (2006) ;  Fyffe and Zaiser (2004)  and  Fyffe and Zaiser (2007)  used cellular-automata approaches to compute avalanche release area distributions.
Acknowledgements. We wish to express our gratitude to  Claude Schneider , snow expert in  La Plagne , for providing the database of avalanche sizes. J. Gaume was supported by a  Swiss Excellence Government Scholarship  and is grateful for support by the  State Secretariat for Education, Research and Innovation (SERI)  of the Swiss government. We thank two anonymous reviewers for their constructive comments that helped to improve our paper. Edited by:  F. Dominé.
Seaweed diversity in Europe Accurate estimates of the diversity and distribution of species are essential to understand how communities and ecosystems respond to environmental change and to be able to forecast those changes that will influence ecosystem services on which humans depend. Information on macroalgal diversity and taxonomy is primarily available through the online database AlgaeBase  (Guiry and Guiry, 2014) , which integrates taxonomic, nomenclatural, bibliographic, and biogeographical information. These data are secondarily integrated in a number of regional or global databases, such as the World Register of Marine Species (WoRMS)  (Appeltans et al. , 2012; Costello et al. , 2013)  and the European Ocean Biogeographic Information System (EurOBIS)  (Vandepitte et al. , 2011). Based on the AlgaeBase data,  Guiry (2012)  and De Clerck et al.
Acknowledgments ODC, SV, AHE, FF, EAS and FM are indebted to EU  FP7 ERANET  (Project  SEAS-ERA/INVASIVES SD/ER/010 ). FA was supported by  FCT  project  CLEF (PTDC/ACC-AMB/102866/2008 ). JA was supported by  FCT -Portuguese Science Foundation  ( Postdoc fellowship   CCMAR/ BPD/0045/2013  and project  EXCL/AAG-GLO/0661/2012 ). AHE was supported by a scholarship of the  Portuguese Science Foundation  (FCT:  SFRH/BPD/63703/2009 ). EAS acknowledges support from the  Portuguese Science Foundation (FCT)  via project  EXTANT  ( EXCL/AAG-GLO/ 0661/2012 , to EAS).
Methods: We applied non-targeted metabolomics-based mass spectroscopy combined with ultrahigh-performance liquid chromatography and gas chromatography for the metabolic phenotyping of four cancer cell lines: two from colon cancer (HCT15, HCT116) and two from ovarian cancer (OVCAR3, SKOV3). We used the MetaP server for statistical data analysis. Results: A total of 225 metabolites were detected in all four cell lines; 67 of these molecules significantly discriminated colon cancer from ovarian cancer cells. Metabolic signatures revealed in our study suggest elevated tricarboxylic acid cycle and lipid metabolism in ovarian cancer cell lines, as well as increased β-oxidation and urea cycle metabolism in colon cancer cell lines.
Metabolite identification Peaks obtained after measurements were identified using Metabolon's propriety peak integration software, and component parts were stored in a separated and specifically designed complex data structure. Metabolites were identified by comparing the obtained data to the library entries of purified standards or unknown recurrent entities. On the date of data evaluation, more than 3,000 commercially available purified standard compounds were acquired and registered in the Laboratory Information Management System (LIMS) for distribution to both LC and GC platforms for the determination of their analytical characteristics. The combination of chromatographic properties and mass spectra provided an indication of a match to the specific compound or isobaric entity. To achieve the highest data quality for statistical analysis, series of QC and curation processes were conducted.
This system enables accurate and consistent identification of true chemical entities with simultaneous removal of system artifacts, mis-assignments, and background noise. Library matches for each compound were checked for each sample, and manually corrected if necessary. Because sample measurements were obtained during a 5-day period, data were normalized to correct variations resulting from inter-day tuning differences in the instrument. Essentially, each compound was corrected in a run-day by registering the medians to equal one, proportionally normalizing each data point.
05/225 = 2. 22 × 10 -4 ). The full data set and metaP analysis are freely and fully available at the following websites: ovarian vs. colon: http://metap. helmholtzmuenchen. de/metap3/run. cgi?ID=1415435214597630; HTC15 vs. HTC116: http://metap. helmholtz-muenchen. de/metap3/run. cgi?ID=1415362867314825; SKOV3 vs. OVCAR: http://metap. helmholtz-muenchen. de/metap3/ run. cgi?ID=1415431362747244; all cell lines HTC15, HTC116, OVCAR3, and SKOV3: http://metap. helmholtzmuenchen. de/metap3/run. cgi?ID=141536220398464. Hierarchical clustering (HCL) was performed using the MultiExperiment Viewer (MeV) v. 4. 9 software  [17] , based on log-scaled and z-scored data, Pearson correlation as a distance measure, and average linkage clustering.
Overall metabolic signatures of colon and ovarian cancer cell lines We applied non-targeted metabolomics in order to determine the metabolic signatures of four widely used cancer cell lines, two from colorectal carcinoma (HCT15 and HTC116) and two from ovarian carcinoma (OVCAR3 and SKOV3). The obtained dataset resulted in 225 individual metabolites, which in turn were used in the analysis (see Additional file 2: Table  S1  for a full list of all metabolites). PCA revealed clear separation between the four cell lines, with a tight clustering of biological replicates (Figure  1 ), confirming their strong metabolic diversity. To provide broader insight into the data set, we applied bidirectional hierarchical cluster analysis to all 225 metabolites (Figure  2a ). All samples were split at the highest level by cancer type, and at a second level by cell line, with biological replicates clustering closely together.
Acknowledgements Support for the experiments was provided by the  Weill Cornell Medical College in Qatar  bioinformatics and virtual metabolomics core, which is funded by the  Qatar Foundation. We would like to acknowledge the  Chalhoub Group  for their support of cancer research. This work was founded by the  Qatar National Research Fund  under  National Priorities Research Program  award numbers  09-1174-3-291 ,  4-640-1-096 , and  6-1131-3-268  ( A. Rafii ), as well as JSREP number  4-013-3-005  (B. S. Guerrouahen). The content of this work is the sole responsibility of the authors, and does not necessarily represent the views of the  Qatar National Research Fund.
Other studies found that fires in African savannahs and especially in West Africa have very regular fire regimes in spite of a variable climate  (Giglio et al. , 2010; Archibald et al. , 2010; Laris, 2013). Recent research works based on a global fire database generated from remotely sensed imagery clearly have shown that the savannahs of western and southern Africa were the areas with the least interannual variability in fires, as well as the most frequently and extensively burned  (Giglio et al. , 2010; Le Page et al. , 2010). Given climate change predictions, it would be useful to know the extent to which climate control the burned areas across a range of vegetation and climate at the regional and subregional scale. Several previous studies were performed from mesoscale to local scale with high-resolution multidisciplinary data such as from  Laris (2002 Laris ( , 2011 Laris ( , 2013) )  and Le  Page (2010)  on the human role in fire variability.
The large-scale climatic indexes MEI data are available from the NOAA website (http://www. cdc. noaa. gov/people/klaus. wolter/MEI) and are recorded from 1948 to present. Hanley et al. (2003)  suggested MEI data to be much more appropriate for global studies; there-fore, to investigate the effects of ENSO on the variability in burned areas, we used the MEI calculated from six marine surface parameters, including mean sea-level pressure (MSLP), zonal and meridional wind components, SSTs, lower-layer temperatures and total cloud cover. The Southern Oscillation Index (SOI) data used were provided by NOAA and calculated following the method in  Ropelewski and Jones (1987). These data, from 1948 to present, are available at http://www. esrl. noaa. gov/psd/data/ correlation/soi. data. The NAO index data, also from 1948 to present, are available from NOAA (http://www.
Acknowledgements. We acknowledge the support provided through the  RIPIECSA grant  for this work and  L3JRC  for making the biomass burning data available. We are grateful to the  Abdus Salam International Centre for Theoretical Physics (ICTP)  for allowing us use of the facilities during the revision processes. We also thank the entire team of the Lamto ecological station for their comments and observations. We are especially grateful to  P. Laris , who contributed through his comments and suggestions on how to improve this work. We thank the anonymous reviewers and the editor, whose comments and suggestions greatly improved the manuscript. Finally, we thank  Gregory Jenkins  from  Howard University, USA , and  Jacques Gignoux  from  CNRS, France , for their contributions which helped in improving the manuscript.
Short description of the model The 1D time-dependent photochemical model we used is derived from the models previously developed by  Dobrijevic et al. (2010 Dobrijevic et al. ( , 2011)). The baseline thermal profile we used to validate our model in quiescent conditions is taken from  Guerlet et al. (2009)  and corresponds to a latitude of 15 • S. This latitude corresponds to the subsolar latitude in this dataset. We used the eddy diffusion coefficient profile K(z) from  Dobrijevic et al. (2011). Both are shown in Fig. 1. The deep mole fractions of H 2 , He, and CH 4 were fixed to 0. 8603, 0. 1350  (Conrath & Gautier 2000) , and 0. 0047  (Fletcher et al. 2009) , respectively. The external flux of atomic hydrogen was set to 1 × 10 8 cm -2 s -1  (Moses et al. 2005). Our chemical scheme comes from  Dobrijevic et al.
3 ‰ • C -1 (R = 0. 32). It is strongly enhanced in the last decades as well as during the 1920s (up to 0. 8 ‰ • C -1 using the reconstruction and 1 ‰ • C -1 using MAR). This slope is unusually strong, as it is even higher than spatial gradients in Greenland  (Sjolte et al. , 2011; Masson-Delmotte et al. , 2011)  and higher than the large slope recently observed in surface water vapour at NEEM  (Steen-Larsen et al. , 2014). Both the correlations with temperature and the magnitude of the slope are stronger than observed www. the-cryosphere. net/9/1481/2015/ The Cryosphere, 9, 1481-1504, 2015 from vapour data in south Greenland  (Bonne et al. , 2014)  and inter-annual variations during the last decades using long precipitation isotopic time series, e. g. in Europe  (Rozanski et al. , 1992)  or Canada  (Birks and Edwards, 2009) , which usually show slopes of less than 0.
Indeed, ice sheet modelling experiments constrained by ice core data supporting the presence of ice in Greenland from the last interglacial period, and also limited ice thickness change at NEEM (IPCC, 2013), have suggested that Greenland contributed 1. 4-4. 3 m sea level equivalent to the global 5-10 m sea level rise of the last interglacial period. This finding, combined with the initial estimate of temperature change at NEEM, implied that such a Greenland retreat was concurrent with a multi-millennial 8 • C warming. Such great warming was however not captured by state-ofthe-art climate models in response to orbital forcing  (Capron et al. , 2014). Limited ice sheet response with very large local warming is also difficult to reconcile with ice sheet simulations  (IPCC, 2013). A 4 • C warming amplitude, as suggested by our study, would reduce model-data mismatches and has implications on the vulnerability of the Greenland ice sheet to regional warming.
Conclusions and perspectives We have produced and described a reference north-west Greenland stack record for δ 18 O and accumulation. At NEEM, these data sets show a strong sensitivity to local and Greenland temperature, as well as to North Atlantic subpolar gyre SST. Different patterns emerge from changes in δ 18 O and accumulation with respect to recent trends, extreme cold (warm) and dry (wet) years. NEEM shallow ice core records are affected by changes in atmospheric circulation, but with weaker relationships with winter NAO than in central or southern Greenland; we confirm the impact of the Atlantic Ridge and Greenland blocking weather regimes in north-west Greenland. NEEM climate variability is marked by a large multidecadal variability, which is closely related to the Atlantic Multidecadal Oscillation indices and enhanced at the beginning of the 19th century.
Progress is required on the accuracy of deuterium excess measurements using laser instruments, and in the number of initial source records to be stacked, in order to extract a robust signal from the analytical and inter-core noise. We note a hint for large-scale atmospheric circulation control on deu-terium excess from the relationship observed with the North Atlantic SST and some similarity between our record and the ECHAM5-wiso-ERA simulation. Our model-data comparison stresses a generally better performance from simulations nudged to ERA reanalysis when compared to the simulations nudged to NCEP and 20CR reanalyses. The MAR-20CR-simulated temperature and accumulation show reasonable skill prior to 1930 with respect to the NEEM shallow ice core data, with correlation coefficients above R = 0.
4. This motivates an ongoing effort to implement water-stable isotopes in the MAR model for direct comparison with ice core records. If we focus on the recent warming , the biases of atmospheric general circulation model results for mean precipitation amounts at the NEEM site affect the magnitude of their simulated inter-annual variability and precipitation trends. The large increase in temperature inferred from borehole data and gridded temperature reconstructions is captured by all atmospheric models, as well as the large increase in δ 18 O. However, LMDZiso-ERA and ECHAM5wiso-ERA simulate a decrease in deuterium excess, which is not detected in the NEEM shallow ice core records. Combining observations and simulations of local δ 18 O and temperature, we focused on the isotope-temperature relationship emerging during the most recent period, where warming reaches levels above pre-industrial conditions and where a global warming signal is present in Greenland, in addition to the impact of changes in atmospheric circulation  (Fettweis et al.
Acknowledgements. NEEM is directed and organised by the  Centre for Ice and Climate at the Niels Bohr Institute  and  US NSF, Office of Polar Programs. It is supported by funding agencies and institutions in Belgium (FNRS-CFB and FWO),  Canada (NRCan/GSC), China (CAS) ,  Denmark (FIST), France (IPEV, CNRS/INSU, CEA and ANR) ,  Germany (AWI) ,  Iceland (RannIs), Japan (NIPR) ,  Korea (KOPRI) , the  Netherlands (NWO/ALW), Sweden (VR) ,  Switzerland (SNF) , the  United Kingdom (NERC)  and the  USA (US NSF, Office of Polar Programs). This study has been funded by the  ANR CEPS   GREENLAND  project ( ANR-10-CEPS-0008 ). This is Past4Future contribution no. xx. The research leading to these results has received funding from the  European Union 's  Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement no.
Acknowledgments This study was conducted as a part of the  Marine Ecological Restoration Working Group (MER-WG ) workshop held in Eilat, Israel, by the  Red Sea. Thanks are due to Israel's  Ministry of Environmental Protection  and the  Israel Society of Ecology and Environmental Sciences , whose support enabled the workshop and the  US National Science Foundation  ( OCE-1232779 ) for assistance with publication costs. The MER workshop was held at the  InterUniversity Institute (IUI ) for Marine Sciences, in Eilat. We wish to thank the  Dolphin Reef, in Eilat , for their help and support;  Dr. Sinaia Netanyahu  for her support; and the directors and staff of the  IUI  for their help and hospitality. This article is contribution no. 3501  of the  Virginia Institute of Marine Science at the College of William and Mary.
One of the essential simplifications of the current study is to dismiss ice particles in the model. Ice water interacts with microwave radiation essentially via scattering process  [37]. Though low frequency passive microwave measurements are insensitive to some ice clouds  [38] , heavy rains are often associated with strong convection and large ice particles in clouds and precipitation in the form of hail and graupel. The size of such frozen water particles easily reaches the values comparable with the wavelengths of low frequency AMSR2 channels. Computer simulations of the brightness temperatures are carried out for the dataset of meteorological and hydrological data measurements, complemented by modeled liquid water content and rain parameter data. The dataset, used for   calculations, is described in detail in Section 2.
Data for Numerical Simulation Radiosonde (r/s), meteorological, wind speed, forms, and amount of clouds (percentage of cloud cover), and hydrophysical (seasurface temperature and salinity) simultaneous measurements were used for brightness temperature calculations. High latitude part of this dataset was used, for example, in  [39]  for the development of the regional Arctic algorithms for total atmospheric water vapor content (Q) and total cloud liquid water content (W) retrievals from SSM/I and AMSR-E data. The dataset was significantly extended by addition of measurements in tropics and subtropics. The measurements were taken by the research vessels of the Far Eastern Research Hydrometeorological Institute (USSR/Russia) during the period of 1966-1993. The dataset included about 3000 radiosondes with simultaneous seasurface temperature and salinity measurements.
For those data, where modeled total liquid water content did not exceed 0. 3 kg/m 2 , the absence of rain drops was imposed. For those cloudy data, where the total liquid water content exceeded 0. 3 kg/m 2 , uniformly distributing point rain rates were added randomly with RR from 0 up to 20 mm/h within the rain depth of 0. 5-4. 5 km dependently on the humidity and temperature profiles. No ice modeling was carried out. The resulting dataset consisted of about 25000 data with different values of geophysical parameters governing microwave brightness temperature of the atmosphereopen ocean system-atmospheric water vapor content, cloud Advances in Meteorology liquid water content, rain rate, sea surface wind speed, sea surface salinity, and sea surface temperature.
Liquid water content absorption and rain rate attenuation were calculated using  [35]. The last was parameterized by Liebe and Layton in  [35]  using a regression fit over the drop shape and size distributions of Laws and Parsons  [45]. This parameterization resulted into simple frequency dependent formulation of the rain attenuation coefficient:  = 0. 182⋅f ⋅a⋅RR  (dB/km), where a = 2. 31⋅10 -4 ⋅f 1. 42 ; b = 0. 851⋅f 0. 158 for 6. 9 GHz; b = 1. 41⋅f -0. 0779 for 10. 65 GHz;  is frequency in GHz, and RR is rain rate in mm/h. After all the models for the microwave radiation interaction with the atmosphere-ocean system were defined, the brightness temperatures for frequencies, polarization, and angles of incidence of AMSR2 instrument were calculated for the dataset, described in Section 2. Normally distributed radiometric noise with 0.
NN was trained using randomly selected half of the dataset of the simulated brightness temperatures and the corresponding rain rates. Then it was tested using the other half to which NN algorithm was applied. This was done to avoid overtraining NN, meaning the loss of generalization features. Standard neural network of multilayer perceptron (MLP) type with feedforward backpropagation of errors was used to connect the simulated brightness temperature differences with the RRs. MLP neural network is a processing block in which input parameters of the task (in our case four Δ  ) relate to its output parameter (RR) through the system of neurons at hidden layers  [46]. The MLP configuration with a single hidden layer was used since in the tasks of the best approximation any continuous on a finite interval function can be approximated by a neural network of MLP type with one hidden layer.
The numerical experiments with other configurations confirmed that including additional layers led to rapid loss of the generalization capabilities, especially for noisy data  [47]. Training was carried out via supervised learning and feedforward backpropagation of errors, starting from various random initial weights to avoid getting local minima. The NN configuration was complicated until the testing error started to increase. It was found that a simple NN configuration consisting of a single hidden layer of 15 neurons ensured the least retrieval error of 1. 5 mm/h both for training and testing datasets. The scatterplot of retrieved RR values versus in situ ones is presented in Figure  2. By saying "in situ" here we mean not measured but modeled RRs used for the brightness temperature simulation.