dataesr/scientific-paragraphs-categorization huggingface dataset:
GIMMS The Global Inventory Modeling and Mapping Studies (GIMMS) LAI3g dataset is derived from Advanced Very High Resolution Radiometers (AVHRR) measurements. It is more specifically computed from the GIMMS Normalized Difference Vegetation Index (NDVI) 3g, using a neural network and the International Geosphere Biosphere Programme (IGBP) land cover classes  (Zhu et al. , 2013). The 1/12 • files cover 30 years from July 1981 to December 2011, at a biweekly resolution. They were aggregated at a 1. 0 • spatial resolution on the model grid, to enable comparisons of the time series and maps.
2 FastTrack 2000 database) as well as land/ocean-atmosphere fluxes, to minimize a cost function in order to calculate NEE. Here we use the latest version (15r4), in which monthly fluxes are calculated at 1. 875 • × 3. 75 • lat-lon resolution. The Jena CarboScope atmospheric inversion uses the same set of atmospheric CO 2 observation sites available during the temporal observation period  (1996) (1997) (1998) (1999) (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014) (2015)  referred to as Jena s96. Jena CarboScope inversion uses fossil fuel emissions from EDGAR 4. 2 and the TM3 global atmospheric transport models. Here we use monthly NEE from the latest version of Jena s96 (v3. 8), which is provided at 3. 8 • × 5. 0 • lat-lon resolution.
C3 Site-level GPP and NPP observations In situ GPP and NPP measurements While there are several NPP datasets especially for forest (e. g. ,  Cannel et al. , 1992; Olson et al. , 2001; Michaletz et al. , 2014) , few provide both GPP and NPP at the same locations. We used a recent database of in situ co-located GPP and NPP measurements  (Campioli et al. , 2015) , which is an extension of the  Luyssaert et al. (2007)  database for forests. The database consists of 131 sites, for which annual NPP obtained from biometric measurements and associated uncertainties are provided. The criteria employed by  Campioli et al. (2015)  for selecting the sites was the availability of methodologically independent and site-specific estimates of NPP and GPP. Carbon use efficiency (CUE) was calculated at each site by taking the ratio of NPP to GPP.
Zhao et al. (2005)  evaluated MODIS NPP against the Ecosystem Model-Data Intercomparison (EMDI) global database of NPP measurements  (Olson et al. , 2001)  and found a good agreement except for tropical forests (spatial R 2 = 0. 77). Zhao et al. (2006)  estimated the sensitivity of the MODIS NPP product to the use of different meteorological reanalysis input data, showing differences at the global scale up to more than 20 Pg C yr -1 , with the largest differences in the tropics.
C5 Soil carbon inventories NCSCD The Northern Circumpolar Soil Carbon Database quantifies storage of organic carbon in soils of the northern circumpolar permafrost region, comprising four depths up to 3 m (0-30, 30-100, 100-200 and 200-300 cm)  (Hugelius et al. , 2013). Total SOC storage in the 0-2 and 0-3 m depth ranges in northern permafrost soils are estimated to be 827 ± 108 and 1035 ± 150 Pg C, respectively.
SoilGrids SoilGrids is a global soil information database with spatial predictions for a selection of soil properties, at 1 km resolution and six depths, up to a maximum of 2 m (0-5,  5-15, 15-30, 30-60, 60-100 and 100-200 cm). About 110 000 soil profiles were used to generate the product, using statistical models based on climatic and biomass indices, lithology, and taxonomic mapping units  (Hengl et al. , 2014). The gridded maps of soil organic carbon mass fraction, soil bulk density, and volumetric fraction of coarse fragments was used to calculate SOC density in the unit of kg C m -2 to be comparable with model output (see Eq. 6 in  Hengl et al. , 2014). However, we found in this product a systematic overestimation of bulk density for organic-rich soils, which is similar to the issue found in the HWSD database  (Köchy et al.
C6 Biomass carbon stocks Two forest biomass datasets are used to evaluate simulated forest biomass. The first is that from  Avitabile et al. (2016) , which, at a spatial resolution of 0. 01 • , merges two tropical aboveground forest biomass (AGB) datasets from  Saatchi et al. (2011)  and  Baccini et al. (2012)  with Northern Hemisphere volumetric forest stock growth data from  Santoro et al. (2015). The second forest biomass dataset from Thurner et al. (  2014 ) is given at a spatial resolution of 0. 01 • for 2010 and confined to the Northern Hemisphere (30-80 • N). This is derived from the  Santoro et al. (2011)  forest standing stock data. The two datasets were re-gridded to 1 • resolution for comparison with model output. The  Avitabile et al. (2016)  AGB was converted into total biomass, assuming a constant AGB to total biomass ratio of 0.
C7 Burned area and fire emissions We compared the simulated burned area and fire carbon emission with the Global Fire Emissions Database (GFED4s) dataset, which is based on the GFED modeling framework (van der  Werf et al. , 2010). The GFED4s burned area data are derived from the MODIS sensor data that are then complemented by specific algorithms to retrieve "small fires"  (Randerson et al. , 2012). Emissions are computed from a combination of burned area with the revised Carnegie-Ames-Stanford-Approach biogeochemical model (CASA-GFED, described in van der  Werf et al. , 2010)  that provides an estimation of fuel loads and combustion completeness. GFED4s data are provided at a 0. 25 • spatial resolution with a monthly time step for 1997-2015. Cropland fires are not yet implemented in the ORCHIDEE-MICT model, but are included in the GFED4s dataset.
In order to compare the simulation with GFED4s, it was necessary to correct the simulated burned area for the omission of cropland fires. To do so, we computed for each grid cell the fraction of existing natural PFTs (i. e. , all non-cropland PFTs), and divided the simulated burned area in each grid cell by this value. For carbon emissions, the GFED4s dataset provides separate emissions data for cropland and natural fires. In this way, we were able to simply remove cropland fire emissions from GFED for comparison with ORCHIDEE-MICT output. The Supplement related to this article is available online at https://doi. org/10. 5194/gmd-11-121-2018-supplement. Author contributions. MG and DZ led the study. DZ performed the simulations. FM and AJ-P performed the code versioning of ORCHIDEE-MICT; FM managed the scientific aspects of the updates from the main branch (TRUNK); AJ-P optimized the code; they both assisted with technical aspects of the model and its run environment.
HK built the GSWP3 dataset and JC adapted its format to be compatible with ORCHIDEE-MICT. DZ developed SOCdependent soil physical properties in the model and introduced a reformulation of the hydric stress above the permafrost table. She conducted the evaluation of the soil temperature and permafrost area simulated by ORCHIDEE-MICT. YH provided the comparison of the snow depth with dataset. CO and SD-N processed the satellite datasets and interpreted the soil moisture and land surface temperature. AD contributed to the improvement of the hydrological modeling in the initial ORCHIDEE version and MG led the water budget evaluation. ZY contributed to the comparison of soil moisture with GLEAM. MG, RL and SB supervised the routing scheme. CY built an updated version of SPITFIRE in ORCHIDEE-MICT and, with PL, analyzed the results in biomass carbon stocks, burned area and fire emissions.
AB provided the comparison of NEE with atmospheric inversions. FM performed the NPP and GPP evaluations over the sites database. BG, MT and DZ conducted the soil carbon evaluation. YW synthesized the carbon budget evaluation. CO, TW, SP and GK supervised the representation of the high-latitude processes in ORCHIDEE-MICT in coherence with the initial version of ORCHIDEE. DG, XW, EJ, CQ and FW assisted in ensuring consistency between the carbon cycle, water cycle and various other processes in ORCHIDEE. PC contributed to the theoretical concept of the modeling of high-latitude processes. All authors contributed to the writing of the manuscript. Competing interests. The authors declare that they have no conflict of interest.
from 524, 72, 528, 128 and 528 stations in Europe, Canada, USA, Russia and China were obtained from the European Climate Assessment & Dataset (ECA&D), the National Climate Data and Information Archive of Environment Canada, the United States Historical Climatology Network (USHCN), the Russian Research Institute of Hydrometeorological Information-World Data Center (RIHMI-WDC) (Bulygina et al. , 2011), and the National Meteorological Information Center of the China Meteorological Administration.
Acknowledgements. This work was financially supported by the  European Research Council Synergy  grant  ERC-2013-SyG-610028 IMBALANCE-P. We acknowledge the  ORCHIDEE-PROJET group , which developed and tested the ORCHIDEE model revision 3976. We thank  Natasha MacBean  who contributed to the production of the land cover map. We thank  Arsène Druel  who processed the GLASS LAI data. Simulations with ORCHIDEE-MICT were performed using computational facilities of the  Institut du Développement et des Ressources en Informatique Scientifique (IDRIS, CNRS, France)  and of the  CCRT (CEA). S. Dantec-Nédélec ,  G. Krinner ,  P. Ciais  and  C. Ottlé  were supported by the  CLASSIQUE national project (ANR grant  no. ANR 2010-CEPL-012-02 ) and the  PAGE 21  project (grant agreement number  282700 , funded by the  EC seventh Framework Programme theme   FP7-ENV-2011 ).
A. Ducharne was supported by the  IGEM  project " Impact of Groundwater in Earth system Models" (ANR grant  no. ANR-14-CE01-0018-01 ). Discharge station data are kindly provided by the Global Runoff Data Centre, 56068 Koblenz, Ger-many. GRACE land data are available at http://grace. jpl. nasa. gov,supported by the  NASA   MEaSUREs Program. The MOD17A3. 005 data product was retrieved from the online Data Pool, courtesy of the  NASA EOSDIS Land Processes Distributed Active Archive Center (LP DAAC) ,  USGS/Earth Resources Observation and Science (EROS) Center ,  Sioux Falls ,  South Dakota  (https://lpdaac. usgs. gov/data_access/data_pool). Jena CarboScope inversion data were provided by  Christian Rödenbeck  and are available at http://www. bgc-jena. mpg. de/CarboScope/?ID=s96_v3. 8. Edited by: Hisashi Sato Reviewed by: two anonymous referees.
Numerical flow code The rock avalanche motion is modelled using the geophysical mass flow code VolcFlow  (Kelfoun and Druitt, 2005). VolcFlow has been tested on a number of debris avalanches and pyroclastic flow events, successfully simulating avalanche run-out and emplacement dynamics in a number of settings  (Kelfoun and Druitt, 2005; Kelfoun et al. , 2008; 2009; 2010; Giachetti et al. , 2011; Kelfoun, 2011; Paris et al. , 2011; Charbonnier and Gertisser, 2012; Dondin et al. , 2012; Giachetti et al. , 2012; 2013; Manzella et al. , 2016; Kelfoun, 2017; Kelfoun et al. , 2017). As with many other continuum dynamic models used for simulating rock avalanche propagation  (Savage and Hutter, 1989; Iverson et al. , 1997; McDougall and Hungr, 2004) , VolcFlow is governed by a continuity and momentum equation based on the Saint-Venant equations of shallow flow.
, 2004). This initially coherent stage has greatly improved back-analyses of similar rock avalanches, with little parameter sensitivity  (Aaron and Hungr, 2016; Aaron et al. , 2017). Measures of entrainment, such as spatial patterns of eroded depths and the downslope lag rate, have not been made for the Paatuut event. These processes were not simulated as they could not be adequately parametrised, nor reasonably generalised across all events in our database of rock avalanches.
Conclusions We have presented the results of a numerical modelling study of 20 rock avalanches in the Vaigat Strait, West Greenland. Rheological calibration of the numerical flow code VolcFlow was performed using a wellconstrained event at Paatuut  (AD 2000). The best-fit simulation assumes a constant retarding stress with a collisional stress coefficient (τ0 = 250 kPa, ξ = 0. 01), and simulates run-out to within ±0. 3% of that observed. Despite being widely used to simulate rock avalanche propagation, other models, that assume either a frictional or a Voellmy rheology, failed to reproduce the observed event characteristics and deposit distribution at Paatuut. The limited ability of frictional models to simulate the behaviour of rock avalanches such as these suggests that processes additional to those of granular flow dynamics are involved.
Acknowledgments This work was supported by  Polish National Science Centre  Grant No. 2011/01/B/ST10/01553. For access to VolcFlow, please contact  Karim Kelfoun. Data  for the reconstruction of DEMs at Paatuut was gratefully received from  Trine Dahl-Jensen  at the  GEUS. All other elevation data used in the modelling can be obtained from the  Greenland Ice Mapping Project  at https://bpcrc. osu. edu/gdg/data/gimpdem. The authors would like to thank  Anja Dufresne ,  Martin Mergili , and two editors for providing constructive reviews of this manuscript, which were gratefully received.
A picture of the eggs on black paper is taken with a desktop scanner and analyzed using the particle count plug-in of the ImageJ software 5. This method is reliable and precise but the substantial hands-on time limits its use for large-scale experiments. Other imaging methods have been developed for amphibian or mosquito models but are not adapted for large-scale experiments in Drosophila 6,7. Automatic egg counting using imaging methods faces many challenges: eggs can be unevenly distributed, making clumps or having variable orientations and shapes. Moreover, when pictures are directly taken from the medium on which flies lay eggs, heterogeneity of the medium and rough surfaces can result in false positives. Another important factor is the softness of the medium as it affects the number of eggs laid and is highly dependent on the assayed species 8.
Pictures were taken using a 2-second exposure, F16 aperture, and 200 iso (high iso values will increase the noise; parameters may change depending on the light and camera setup). The camera was remotely controlled by a computer using EOS Utility 2 software (Canon, Tokyo, Japan). Focus was done manually (the use of a small aperture -F16 -facilitates manual focusing) using the "Live View" function of the software. Using the automatic software timer further facilitates a rapid data acquisition. To provide reliable results, it is critical that all parameter values (e. g. , light source, zoom value, white balance, picture resolution, aperture) remain constant throughout the course of the experiment.
Quantification of the number of eggs with ImageJ. We developed a Java plug-in for ImageJ software  5  to perform batch analyses of pictures (File S1). The plug-in automatically detects the area of interest (dark and circular area on a white background) using a wand tracing tool. Based on the particle detection program of ImageJ, it first transforms colored pictures into 8-bit grey pictures and then into simple black and white pictures using different thresholds of detection (Fig. S2 ). Particles within the area of interest (i. e. , the plate) are detected along with their position on the picture (xy coordinates), surface area (in pixels 2 ), perimeter, the lengths of their major and minor dimensions (in pixels) and the angle between these two dimensions. This plug-in is derived from the "Batch population sensor" described in Mallard et al.
It is set by default to". JPG" in the first option pop-up window after the input and output directories selection and can be inactivated. For each picture, all metrics are saved in a spreadsheet named according to the directory path of the picture used as input file (Fig. S3 ). Combined with a suitable directory structure, this feature allows storing any suitable information that could be useful for follow up analyses (e. g. , population ID, transfer, treatment). In addition to eggs, the plug-in also detects particles present on the high-contrast medium (e. g. , dust). Therefore, we developed an R script to derive empirical filtering steps. The main function combines the tables exported by the ImageJ plug-in for each picture and filters eggs assuming a bimodal distribution of the particles area, with the first mode corresponding to dust and other small particles and the second mode to eggs (Fig.
S4 ). Validation of the pipeline for fecundity estimates. We tested whether the automated egg counting provides accurate numbers by comparing the obtained numbers with manual egg counts in a fecundity assay with two D. simulans populations. After two generations of density control under common garden conditions, five replicates of 100 individuals were transferred into large embryo collection cages for each population. Five transfers were made and for each the number of eggs was estimated by our pipeline and manually counted under a stereomicroscope. The concordance between manual counts and the automated estimates was determined by Spearman's rank correlation. Unless stated otherwise, all data analysis and statistical tests were performed in R version 3. 3. 2  21.
Softness of medium affects fecundity. We evaluated the impact of the agar concentration on the number of eggs laid. We notice a marked influence of the agar concentration on the number of eggs laid -higher agar concentrations resulted in fewer eggs (Fig. 2  and Data S2, generalized linear mixed model followed by Tukey's HSD test with correction for multiple testing, P 0. 7-1% < 0. 001, P 0. 7-2% < 0. 001, P 0. 7-3% < 0. 001, P 1-2% = 0. 013, P 1- 3% < 0. 001, P 2-3% = 0. 69). A difference of 0. 3% agar reduces the number of eggs by 50% (Fig. 2 , median 0. 7% = 133, median 1% = 59). Based on these results, we used an agar concentration of 0. 7% for the rest of the experiments, which also matches the agar concentration in our standard Drosophila medium. Reliable egg counts with the automated procedure.
Acknowledgements We acknowledge  Élio Sucena  for discussions about the egg pipetting protocol. We thank  Thomas Brenner ,  Regina Cavallaro ,  Katharina Fritz ,  Andreea Hanu ,  Diego Oxilia Speratti ,  David Wieser , and  Kathrin Otte  for their assistance with fly work, and  Marlies Dolezal  for statistical advice. François Mallard  was supported by a  Marie Skłodowska-Curie Actions  fellowship. This work was funded by the  European Research Council (ERC)  grant " ArchAdapt. ".
Therefore, some in-situ measurements are carried out according to a non-destructive design. The experimental data can be gathered by temperature, relative humidity, flux sensors and infrared thermography, among others. In most of the case, measurements are made at the boundary of the domain. From the obtained data, parameter estimation enables to determine the material thermo-physical properties. As mentioned before, the properties are moisture and temperature dependent. Therefore, the parameter identification problem needs to estimate functions that can be parameterized. Moreover, in such investigations, there is generally a few a priori information on the material properties. In  [12] , the thermal conductivity of an old historic building wall composed of different materials is presented.
11), is solved using a finite-difference standard discretisation method. An embedded adaptive in time Runge-Kutta scheme combined with a Scharfetter-Gummel spatial discretisation approach, is used  [10]. It is adaptive and embedded to estimate local error in time with low extra cost. The algorithm was implemented in the Matlab TM environment. For the sake of notation compactness, the upper-script ⋆ standing for dimensionless parameters, is no longer used.
Searching the OED The OED is now searched among the experimental designs described in Section 3 for both single and multiple steps. The issue is to estimate one or several parameters among the five coefficients c ⋆ a, 1 , c ⋆ a, 2 , c ⋆ d, 0 , c ⋆ d, 1 , c ⋆ d, 2. For this, the sensitivity functions Θ u, a , d , m = ∂u ∂c ⋆ a, d, m and Θ c, a, d, m = ∂c ∂c ⋆ a, d, m , m ∈ 0 , 1 , 2 are computed using central finitedifference and a Runge-Kutta approach provided by ode45 solver in Matlab TM  [49]. The equations to compute the sensitivity functions are given in Appendix A. First, the OED is searched using the adsorption coefficients estimated in Section 5 and the desorption coefficients obtained from literature  [45] : c ⋆ a, 1 = -0. 99 , c ⋆ a, 2 = 1. 003 , c ⋆ d, 0 = 0. 75 , c ⋆ d, 1 = -1. 24 , c ⋆ d, 2 = 0.
Acknowledgments This work was partly funded by the  French Environment and Energy Management Agency (ADEME) , the  "Assemblée des Pays de Savoie" (APS)  and the  French National Research Agency (ANR)  through its  Sustainable Cities and Buildings program  ( MOBAIR  project  ANR-12-VBDU-0009 ). The authors acknowledge the  Junior Chair Research program  "Building performance assessment, evaluation and enhancement" from the  University of Savoie Mont Blanc  in collaboration with The  French Atomic and Alternative Energy Center (CEA)  and  Scientific and Technical Center for Buildings (CSTB)  and the support of  CNRS/INSIS (Cellule énergie)  under the program "Projets Exploratoires -2017". The authors also acknowledge the  Brazilian Agency CNPQ of the Ministry of Science, Technology and Innovation , for the financial support.
In the first case, we only store a sliding window of observations, adding and removing RDF triples on the fly and aim for stream reasoning  [4] , while in the second case, we never remove triples. We focus in this paper on the second case since we are interested in having access to history in our queries. The remaining of this paper is organized as follows. In Section 2, we introduce the so-called Vehicle Signal Specification ontology (VSSo). In Section 3, we present the demonstrated application combining VSSo with SOSA/SSN and STEP. In Section 4, we compare this combination of ontologies with other initiatives for evaluating its interest and highlight the usage it enables. 10 https://talespaiva. github. io/step/ 11 http://www. toyota-ti. ac. jp/Lab/Denshi/COIN/Ontology/TTICore-0. 03/.
DEMONSTRATION In this demonstration, one will control a simulated vehicle, and will observe signal values that would be automatically transformed into RDF to enrich a semantic trajectory. This is used to infer labels about the driver attached to episodes of the trajectory. Labels will also be visualized: variations of signal values in order to infer a "smooth" driving label. We developed a python Flask 16 server combining SOSA/SSN for observations, VSS for the car signal domain and STEP for the trajectory description. It uses a vehicle configuration file containing a list of known signals as well as URIs to access their values. The data source can be real vehicle data in the backend where we access a subset of pre-selected signals, or a vehicle simulator. In this demonstration, we use the OpenXC vehicle simulator 17 which runs on a local server, provides a control interface (pedals, steering wheel, gear.
Then, an instance of type sosa:Observation is created and linked to its signal and sensor. The retrieved value is expressed using the 16 flask. pocoo. org/ 17 https://github. com/openxc/openxcvehicle-simulator 18 http://linkeddatafragments. org/ From a graph containing observations, we define labels based on simple comparative rules and create step:Episode instances in the step:Trajectory. They can be qualitative, quantitative or both. For instance the "smooth" driving label is defined by a longitudinal and angular acceleration being within given bounds. All segments that fit this requirement are labeled "smooth", while the others are labeled "not smooth". Plot fix points and segments. It displays observation points as markers, as well as colored segments based on simple comparative rules and using Google maps with Flask 19.