dataesr/scientific-paragraphs-categorization huggingface dataset:
Each target also has a user-specified weight associated with it. All targets are then summed to produce a single fitness score for each group in the individual. For additional details, refer to  Zander et al. (2016)   hÁF anom i/hF i by choosing a wavelength of 1. 892 A ˚at the Mn edge to increase the anomalous signal of Mn 2+ and Ca 2+ ions in the softer X-ray regime  (Rose et al. , 2015). Data were collected to the maximum resolution possible of 1. 929 A owing to the geometry of the camera and the wavelength. The crystals were scooped directly from the drop using a grid as a support. The distribution of the crystals with a random orientation in many crystal arrays allows data collection over the complete reciprocal space, without the need for multi-axis goniometry, as shown by the high multiplicity and the high completeness seen in the data-collection statistics (Table  3 ).
Conclusions and perspectives The linear dimensions of macromolecular crystals that are considered to be usable for X-ray data collection are continally becoming smaller and smaller, potentially reaching the point where crystals will be too small for optical centring and for individual data collection, but large enough to be detectable with low-dose X-ray centering and partial data-set collection. The availability of a high-intensity and well collimated beam, as on the P13 beamline  (Cianci et al. , 2017) , permits tailoring of the beam size to the sample size thanks to variable apertures, thus minimizing the scattering background. Here, we have shown that the automated Mesh&Collect data-collection scheme as implemented at EMBL Hamburg is capable of automatically (i) locating sub-20 mm crystals in a matrix mounted on a standard mounting mesh, (ii) ranking these crystals by diffraction strength and (iii) collecting small rotation data sets at long wavelength from the highest ranking crystals under X-ray dose conditions.
Further enhancements in data quality could be achieved with optimized mounts to reduce background noise  (Romoli et al. , 2014)  and/or by tailoring the beam size and shape individually to each crystal to improve the signal to noise  (Sanishvili et al. , 2008; Fischetti et al. , 2009). In summary, Mesh&Collect data collection using softer X-rays extends SAD phasing from naturally occurring anomalous scatterers to micrometre-sized crystals.
Acknowledgements We thank  S. Fiedler  ( EMBL Hamburg ) and the  EMBL Hamburg Instrumentation Group  for general support of the long-wavelength work. G. Bourenkov  and  I. Karpics  ( EMBL Hamburg ) are thanked for implementation of Mesh&Collect on P13. The construction of beamline P13 was funded by the  German Federal Ministry for Education and Research (BMBF ; grant No. DE/BMBF/05/ES7YE1  ' Synchrotronstrahlfu ¨hrungen fu ¨r strukturbiologische Forschung am PETRA III Speicherring ') and by  EMBL.
3 • C, with mean monthly temperature of the warmest month (July) recorded as 9. 5 • C and for the coldest month (February) -32. 7 • C. The average annual rainfall was 169 mm. The depth of zero annual amplitude is at 20. 75 m. At this depth, the temperature has increased from -9. 1 • C in 2006 to -7. 7 • C in 2017. The presented data are freely available through the PANGAEA (https://doi. org/10. 1594/PANGAEA. 891142) and Zenodo (https://zenodo. org/record/2223709, last access: 6 February 2019) websites.
4. Additional data such as soil properties and soil carbon content are also included in this paper in order to provide a complete set of data and parameters suitable for earth system, conceptual, and land surface modelling. All of these data are archived in the PANGAEA data libraries  (Boike et al. , 2018b, c, d)  and the measuring principles and analysis are described in this paper. Data logging between 2002 and 2013 at the research site was powered by a solar panel and a wind turbine generator, and the data were retrieved manually during site visits once or twice a year, when visual inspections were also made of the sensors. Data gaps prior to 2013 resulted mainly from problems with the site's energy supply, such as problems with the solar or wind charge controller. No other gap filling has been undertaken, but previous publications (e.
g. Langer et al. , 2013)  suggest that reanalysis data, such as ERA-Interim, could be used for this purpose. In  Chadburn et al. (2017) , a method for correcting reanalysis data to better represent the site is described and applied. The gap-free meteorological data set that was produced and used in  Chadburn et al. (2017)  is now available on the PANGAEA database  (Burke et al. , 2018) , making it easy for modellers to begin running the Samoylov site and therefore to make good use of our data. Since 2013 the research site has been connected to the main electricity supply of the new Russian research station, resulting in a much improved data collection with almost no data gaps. Details of the sensors used are provided in the following sections, as well as descriptions of the data quality and cleaning routine (Sect.
Soil temperature Soil temperature sensors were installed over vertical 1-D profiles in 2002 beneath a polygon centre, slope, and rim. A measurement chain of temperature sensors was also installed in the ice wedge down to a depth of 220 cm. Their positions are shown in Fig. B13. The temperatures were initially measured using Campbell Scientific 107 thermistors connected to a Campbell Scientific CR10X data logger with a Campbell Scientific AM416 multiplexer. Campbell Scientific's worstcase example, with all errors considered to be additive, is given as ±0. 3 • C between -25 and 50 • C. The average deviation from 0 • C determined through ice bath calibration prior to installation was 0. 008 • C (maximum: 1. 0 • C; minimum: -0. 56 • C, standard deviation: 0. 33 • C). The sensors cannot be recalibrated once they have been installed.
Soil dielectric number, volumetric liquid water content, and bulk electrical conductivity Time-domain reflectometry (TDR) probes were installed horizontally in three soil profiles adjacent to the temperature probes. The fourth profile in the ice wedge only records temperature data (see Sect. 3. 2. 2. , Figs. B11  and B13 ). The TDR probes automatically record hourly measurements of bulk electrical conductivity (from 25 July 2010 only), and the dielectric number, obtained by measuring the amplitude of the electromagnetic wave over very long time periods and the ratio of apparent probe length to real probe length (the L a /L ratio), corresponding to the square root of the dielectric number. A Campbell Scientific TDR100 reflectometer was used together with an SDMX50 coaxial multiplexer, custom-made 20 cm TDR probes (Campbell Scientific CS605) connected to a Campbell Scientific CR10X data logger between 2002 and 2010 and to a Campbell Scientific CR1000 data logger thereafter.
This is estimated from the maximum deviation of calculated volumetric liquid water content below and above the physical limits (between 0 % and 1 % or 0 % and 100 %). A probe located at 0. 37 m depth beneath the polygon rim showed a shift of about 3 % (up and down) in the volumetric liquid water content during the summers of 2009, 2013, and 2014, for which we could not find any technical explanation. This shift is flagged in the data series (Flag 6: consistency; Table  3 ). Time-domain reflectometry was also used to measure the bulk soil impedance, which is related to the soil's bulk electrical conductivity (BEC). These data were used to infer the electrical conductivity of soil water and solute transport over a 12-month period in the active layer of a permafrost soil  (Boike et al. , 2008a).
The impedance can be determined from the attenuation of the electromagnetic wave travelling along the TDR probe after all multiple reflections have ceased and the signal has stabilized. The bulk conductivities were recorded hourly using the TDR setup described above in this section. Because no calibration was done, and the TDR probes were custom made to 20 cm, a probe constant (K p ) of 1 was used for BEC waveform retrieval; Campbell Scientific suggests a K p for the CS605 probes of 1. 74. Measurements of electrical conductivity and the dielectric number were affected by irregular spikes and possibly also by a sensor drift similar to that in the soil temperature measurements and thus flagged until August 2015 (Flag 6). Data quality improved significantly after August 2015 when the Campbell Scientific coaxial SDMX50 multiplexers were exchanged for SDM8X50 and the electrical grounding system was improved.
In August 2006, 24 thermistors (RBR thermistor chain with an RBR XR-420 logger) were installed, one at the ground surface and 23 between 0. 75 and 26. 75 m depth, inside a PVC tube (Fig. E2 ). A second PVC tube was inserted into the borehole and the remaining air space in the borehole was backfilled with dry sand. Temperatures were recorded at hourly intervals, with no averaging; no data were recorded between September 2008 and April 2009. We recommend that the temperature data from the sensors at the ground surface, at 0. 75, 1. 75 and 2. 75 m depths should not be used due to the possibility of it having been affected by the metal access pipe. The data from these sensors have not been flagged as they are of high quality, but they may not provide an accurate reflection of the actual temperatures.
Active-layer thaw depth Active-layer thaw depth measurements have been carried out since 2002 at 150 points over a 27. 5 × 18 m measurement grid  (Boike et al. , 2013, Fig. 12; Wille et al. , 2003 Wille et al. , , 2004)) , by pushing a steel probe vertically into the soil to the depth at which frozen soil provides firm resistance. The data are recorded at regular time intervals, usually between June-July and the end of August, when the research site is visited. The data set shows that thawing of the active layer continues until mid-September in some years  (e. g. in 2010 and 2015). Large interannual variations in maximum active-layer thaw depths are recorded at the end of August, ranging between the largest mean thaw depth of about 0. 57 m (2011) and the smallest mean of 0. 41 m (2016). To assist in the interpretation of active-layer thickness data, surface elevation change measurements (subsidence measurements) have been collected since 2013 at three locations (two wet centres, one rim) using reference rods installed deep in the permafrost (Fig.
These data, as well as raw data, are stored internally at AWI and are not archived in PANGAEA. Level 1 data have undergone extensive quality control and are flagged with regards to equipment maintenance periods, physical plausibility, spike/constant value detection, and sensor drift (Table  3 ). Level 2 data are compiled for special purposes and may include combinations of data series from multiple sensors and gap filling. Examples in this paper of level 2 data are soil temperature and meteorological data (air temperature, humidity, wind speed, and net radiation) recorded between 1998 and 2002  (Boike et al. , 2013)  that have been combined with a data set since 2002 into a single data series in order to obtain a long-term picture (documentation of source data is provided in the PANGAEA data archives).
Data availability The data sets presented herein can be downloaded from PANGAEA (https://www. pangaea. de/, last access: 6 February 2019) and Zenodo (https://zenodo. org/, last access for all Zenodo links: 6 February 2019), which provides a data set view and download statistics. Data (including links to subsets) can be found on either repository using the following links:   (1998 -2011; Boike et al. , 2013). Continuous data (light and dark coloured data sets, e. g. wind speed and direction) are combined in the level 2 product as one continuous data series for the period 1998-2017. Details of parameters for all sensors can be found in Table  2. Note that the colour bars describe the sensor installation period, but data might not be available in the published data set due to sensor malfunction or failure.
Note that the measuring period for the Vaisala HMP155A only started on 17 September, 2017, which is why the bar appears very thin. Recording of all parameters is still continuing at present. -Permafrost temperature and active-layer thaw depth data are also available through the Global Terrestrial Network for Permafrost (GTN-P) database (http: //gtnpdatabase. org, last access: 6 February 2019).
C2 Snow depth correction for air temperature The acoustic distance sensor (Campbell Scientific SR50) measures the elapsed time between emission and return of the ultrasonic pulse. The raw distance Dsn raw obtained from the sensor was temperature corrected using the speed of sound at 0 • C and the air temperature at 2 m height (Tair_200) in Kelvin (K), using the formula provided by the manufacturer  (Campbell Scientific, 2007) : Dsn = Dsn raw × Tair_200 (K) 273. 15. ( C8 ) Appendix D: Metadata description and photos of installations for water-level measurements A measurement system was installed in a polygon centre 3 m southeast of the meteorological station tower at 72. 37001 • N, 126. 48106 • E to allow changes in the water level to be recorded without requiring the presence of any personnel.
D1 ), to compute water level below the ground surface. From 2007 to 2010 a Campbell Scientific CR200 data logger was connected to a Campbell Scientific CS625 probe (15 cm below the ground surface) to record the water level and two Campbell Scientific T109 sensors (1 and 6 cm below the surface) for temperature measurements. Since 2010 the setup has been connected to the main Campbell Scientific CR1000 logger of the meteorological station and the CS625 probe was therefore exchanged for a Campbell Scientific CS616 probe, installed 11. 5 cm below the ground surface. Due to a change in data loggers in the summer of 2010, we have two setups with minor differences in the measurement probes and their installation depths, which is detailed below and visualized in Fig. D1. The difference between the two water content reflectometers is the electrical output voltage, which had to be changed in order to meet the requirements of the logger.
A third T109 probe was also installed 3 cm below ground surface in 2010. This setup is still in operation. These temperature data are only used to distinguish between periods of frozen and unfrozen surface conditions. The unfrozen period, for which water levels were computed, was defined as the period for which soil temperatures at 6 cm below the surface are > 0. 4 • C during spring, and > 0. 1 • C during fall. Below these temperatures, no waterlevel data are provided. To obtain a better field calibration of the water content reflectometer a Schlumberger Mini-Diver pressure water-level sensor was installed in a well in the same polygon for 68 days of the non-frozen vegetation period in 2016. Measurements obtained from the Mini-Diver were compensated for changes in air pressure using data from the meteorological station's barometric pressure sensor (Vaisala PTB110).
Calculation and correction of water-level measurements The measured output, signal period (SP) from the Campbell Scientific CS616 or CS625 probes were converted into the height of the water level above the sensor base (WL) using two polynomial functions derived from an empirical field experiment to determine the correlation between the results from the CS616-CS625 probes and those from a Mini-Diver. The two regressions represent different water-level regimes (low and higher water levels) recorded by the CS616-CS625 sensor. The results of this experiment showed a low accuracy for very low water levels (1. 5 cm or less above the sensor base) resulting in output periods of SP < 19 µs, which were excluded from the data series. For values > 19 µs, the following formulas are applied to obtain WL data from the CS616 and CS625 probe output: WL = 0.
01831394SP 3 -1. 2398SP 2 + 28. 84699187SP -224. 41499308 (D1) for SP < 27 µs and WL = 0. 06194726SP 2 -1. 7673294SP + 13. 66709591 (D2) for SP > 27 µs. Note that WL for the equations above is given in centimetres. The mean deviation of the calculated WL values from the values measured with the Mini-Diver was (0. 034 cm) with a standard deviation of 0. 29 cm (number of values: 2679). Note that WL is given relative to the sensor base in the time series data and reported in metres. To obtain water level relative to the ground surface (WL gs ) from level 1 data, the  put periods were logged over this period. Instead, volumetric liquid water content (θ l ) was stored on the CR200 logger and calculated from the CS625 probe output using a formula from the sensor's manual  (Campbell Scientific, 2016).
Other abbreviations used are OC for the mass fraction of organic carbon in soil, N for the mass fraction of nitrogen in soil, CD bulk for the organic carbon density in bulk soil, and SOCC for the soil organic carbon content for each soil horizon. The soil horizon designations are according to the USDA Soil Survey Staff (2010). The BS-1 and BS-2 (polygon slope) soil profiles are classified as Typic Aquiturbels and the BS-3 soil profile is classified as Typic Historthel according to the US Soil Taxonomy  (Soil Survey Staff, 2010). Analysis of the physical properties of soil was done according to  DIN 19683 (1973). OC and N were determined following the removal of inorganic carbon and dry combustion at 900  Appendix H: Terrestrial laser scanning -analysis of 2017 data 3-D point cloud data were acquired for several polygons around the meteorological, soil and CALM sites by terrestrial laser scanning (TLS) on 12 September 2017, using a RIEGL VZ-400 3-D TLS instrument.
According to the manufacturer's specifications, the TLS instrument measures 3-D coordinates with an accuracy of 5 mm and a precision of 3 mm (RIEGL LMS, 2017). We captured the full extent of the research site, which has dimensions of approximately 70 × 70 m, from 10 scan positions with a horizontal and vertical point spacing of 3 mm at 10 m measurement range. The single-point clouds were registered into a common coordinate system using five cylindrical reflectors placed around the research site during the TLS data acquisition so that they were visible from all scan positions. Mean residual distances per scan position between the cylindrical reflectors amounted to 1. 6 cm, with a standard deviation of 0. 8 cm. The registered 3-D point cloud data set was georeferenced using high-accuracy global positioning measurements recorded with a global navigation satellite system (GNSS).
We obtained GNSS measurements in static-phase observation mode with a Leica Viva GS10 as the base station receiver and a GS15 mobile rover unit  (Leica Geosystems, 2012a, b). According to the manufacturer's specifications  (Leica Geosystems, 2012a) , this mode achieves a measurement accuracy of 3 mm horizontally and 3. 5 mm vertically with respect to the local reference frame established by the base station. The scan positions were georeferenced and registered using the RiSCAN PRO software (version 2. 1. 1; RIEGL LMS, 2016). The raw data set was filtered using a statistical outlier removal (SOR;  Rusu and Cousins, 2011)  to remove spatially isolated points as outliers from the point cloud, with the number of neighbours set to 10 and the standard deviation multiplier threshold to 1. 0. A digital terrain model (DTM) representing the groundsurface elevation was derived from this preprocessed data set.
To determine the ground-surface elevation the 3-D TLS points were first classified into ground and non-ground points. For this we used a minimum approach, classifying all points within a search radius of 2. 5 cm that were at less than 1. 0 cm vertical distance from the minimum point elevation as ground points. This vertical distance threshold is included to take into account position uncertainties of the TLS acquisition. The ground points in the 3-D TLS data set are subsequently rasterized into the final DTM (with a cell size of 5. 0 cm) using a robust moving-plane interpolation strategy  (TU Wien, 2016). For evaluation purposes the DTM was compared to 27 GNSS measurements of the ground surface that were obtained during the TLS data acquisition. The data sets were compared by taking the difference between GNSS-based elevation measurements and the corresponding DTM pixel values.
Acknowledgements. Logistical support was provided by the  Russian-German Samoylov Research Base  (1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012) and the  Russian Samoylov Island Research Station  (2013Station ( -2017)). Field support, including data collection, was provided by  Konstanze Piel ,  Steffen Frey ,  Günter Stoof , and  Waldemar Schneider. We gratefully acknowledge the funding received from the  Helmholtz Association's ACROSS (Advanced Remote Sensing -Ground Truth Demo and Test Facilities)  project. Edited by:  Kirsten Elger Reviewed  by:  Richard L. H. Essery  and  Nick Brown.
The objective of this study was to assess the uncertainty introduced by WG-based downscaling when applied to projections of future climate. The analysis targeted scenarios centered on 2040 derived from two GCMs and two RCPs as well as the baseline period for multiple sites worldwide. As it is recommended to make multi-comparison studies using different WGs  31  , we compared three stochastic WGs: LARS-WG  32  , WeaGETS  33  and CLIMAK  34. The choice of the WGs was based on software accessibility and usability, as well as on the availability of complete documentation. The generated weather series were characterized using an extended set of weather indices and were used to feed a simulation model for rice yield estimates under baseline climate and future projections. Among available crops, rice was selected because of its importance as staple food for more than half of the world's population.
Methods Site specific climate data. For the baseline period 1986-2005, we used the datasets of daily precipitation and air temperature available at 0. 25° resolution from the Meteorological Archival and Retrieval System (MARS), which is the main repository of meteorological data at the European Centre for Medium-Range Weather Forecasts (ECMWF)  40. To ensure a wide coverage while keeping the number of cases to a reasonable level, 15 sites (Fig. 1 ) were selected purposely from rice-growing areas. performance evaluation of weather generators. In each site, the stochastic WGs LARS-WG  32  , WeaGETS  33  and CLIMAK  34  were used to reproduce the baseline weather series (1986-2005). Although the WGs used in this study are only a subset of those available, we think that the three WG types considered here are reasonably representative of current approaches to stochastic generation.
Acknowledgements Some MS students  ( Cropping Systems course of the University of Milan ) supported data collection and analysis. They are not listed as authors because they did not participate in structuring the work, as well in analysis of results and in finalizing the study, but we wish to acknowledge their support and commitment. They are:  M. Caprioglio ,  D. Corsini ,  G. Frigerio ,  A. Improta ,  F. Invernizzi ,  A. Landi ,  T. Marzorati ,  S. Minella ,  A. Pilenga ,  M. Signorelli ,  A. Sinetti ,  D. M. Torrente ,  I. Vigevani.
Data Availability The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. ECMWF policies prompt not to reproduce, distribute, license, transfer, assign, sell, disclose or otherwise forward the Archive Products. By the way, weather data may come from whatever source with no difference in the considered terms. Anyway http://apps. ecmwf. int/datasets allows to access some datasets. CLIMAK official page for further information and availability: http://semola. uniud. it/index. php?id=129\L=1. LARS-WG home page: http://resources. rothamsted. ac. uk/mas-models/larswg. WeaGETS host page on MathWorks: https://it. mathworks. com/matlabcentral/fileexchange/29136-stochasticweather-generator-weagets. WARM official web page: http://www.