dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. We would like to thank the  Chemistry-Aerosol Mediterranean Experiment  project (ChArMEx, http://charmex. lsce. ipsl. fr,last access: 4 April 2018), which is the atmospheric component of the  French multidisciplinary program MISTRALS (Mediterranean Integrated Studies aT Regional And Local Scales ). ChArMEx-France was principally funded by  INSU, ADEME ,  ANR ,  CNES ,  CTC  ( Corsica region ),  EU/FEDER ,  Météo-France  and  CEA. This work has been possible thanks to the AIRBASE, EMEP database and EBAS database infrastructure. We also acknowledge the MODIS mission team and scientists for the production of the data used in this study. The authors would also like to thank the  AERONET PIs  and their staff for establishing and maintaining the sites used in this investigation.
6 ) that will be covered by regional hydrological simulations (selected river basins) and simulations of changes in marine ecosystems and fisheries (selected ocean sections). Figure  S9  provides an additional analysis of the fractional range coverage (FRC; McSweeney and Jones, 2016) of these regional climate change signals by the ISIMIP2b set of GCMs. While originally chosen on the basis of climate input data requirements, the four selected GCMs provide an FRC close to the mean FRC across randomly chosen four-member sets of CMIP5 GCMs. Data from IPSL-CM5A-LR and GFDL-ESM2M are the first-and second-priority climate input datasets respectively, since these GCMs provide all the monthly ocean data required by FISH-MIP and since IPSL-CM5A-LR additionally offers an extended RCP2. 6 projection. That means impactmodelling groups that do not have the capacities to do all simulations described in the ISIMIP2b protocol should start to force their model by the IPSL-CM5A-LR data and then continue with the GFDL-ESM2M runs if possible.
(2015) ; (4) they occasionally generate spuriously high precipitation events in semi-arid regions; and (5) they do not adjust the inter-annual variability of monthly mean values, which would be an important improvement for the purpose of impact projections  (Sippel et al. , 2016). While (  5 ) and (  4 ) are items of future work, problems (3), (2), and (1) were solved through modifications of the methods of adjustment for pressure, wind speed, and longwave radiation, and by using newly developed, approximately trend-preserving bias adjustment methods for relative humidity and shortwave radiation (see below). The known issues and their solutions are described in more detail in an associated fact sheet (https: //www. isimip. org/gettingstarted/isimip2b-bias-correction/). In addition to these adjustments, we bias-adjust to a new reference dataset.
While in the Fast Track, WATCH forcing data  (Weedon et al. , 2011)  were employed for bias adjustment, the ISIMIP2b forcing data are adjusted to the newly compiled reference dataset EWEMBI (E2OBS, WFDEI and ERAI data merged and bias-corrected for ISIMIP;  Lange, 2016) , which covers the entire globe at 0. 5 • horizontal and daily temporal resolution from 1979 to 2013. Data sources of EWEMBI are ERA-Interim reanalysis data (ERAI;  Dee et al. , 2011) , WATCH forcing data methodology applied to ERA-Interim reanalysis data (WFDEI;  Weedon et al. , 2014) , eartH2Observe forcing data (E2OBS;  Dutra, 2015) , and NASA/GEWEX Surface Radiation Budget data (SRB;  Stackhouse Jr. et al. , 2011). The SRB data were used to biasadjust E2OBS shortwave and longwave radiation using a new method that has been developed particularly for this purpose  (Lange, 2017)  in order to reduce known deviations of E2OBS radiation statistics from the respective SRB estimates over tropical land  (Dutra, 2015).
(2010). In contrast to the ISIMIP Fast Track, we decided against adjusting the wind components uas and vas to match the adjusted total daily mean velocity, as the calculation of the total velocity from wind components is nonlinear, i. e. the total velocity calculated from daily means of the wind components is not equal to the daily mean of total wind velocities. A suitable solution was not found at the time of the study. Therefore, the inconsistency has to be kept in mind when comparing models using adjusted total wind velocity to others using non-adjusted wind components. Information about the input data used by the individual impact models will be documented on the ISIMIP website (https://www. isimip. org/impactmodels/). We provide unadjusted 3-hourly sea-level pressure and near-surface eastward and northward wind data as relevant for the costal infrastructure and energy sector, for example (see Table  2 ).
Tropical cyclones The input dataset comprises projections of tropical cyclones based on the dynamical downscaling technique described in detail by  Emanuel et al. (2008). To generate a large sample of potential cyclone tracks and wind speeds the underlying model is provided with unadjusted depth-resolved seawater Broadly, the technique begins by randomly seeding with weak proto-cyclones the large-scale, time-evolving state given by the GCM climate model data. These seed disturbances are assumed to move with the GCM-provided largescale flow in which they are embedded, plus a westward and poleward component owing to planetary curvature and rotation. Their intensity is calculated using the Coupled Hurricane Intensity Prediction System (CHIPS;  Emanuel et al. , 2004) , a simple axisymmetric hurricane model coupled to a reduced upper ocean model to account for the effects of upper ocean mixing of cold water to the surface.
Photosynthetically active radiation Par einsteins m -2 day -1 fed conditions as well as full irrigation was generated by the global gridded crop component of LPJmL within the ISIMIP Fast Track  (Rosenzweig et al. , 2014)  and used by MAgPIE to derive LU patterns under cost optimization (see time series of the MAgPIE total cropland -irrigated versus nonirrigated -in the Supplement). Projections of climate change are taken from the four GCMs also used to force the other impact projections within ISIMIP2b to ensure maximum consistency. As the MIROC5 climate input data were not part of the ISIMIP Fast Track, the associated crop yield projections by LPJmL were generated from MIROC5 climate analogously to the Fast Track simulations to calculate the associated LU patterns. Under an SSP2 storyline and based on the REMIND-MAgPIE Integrated Assessment Modelling Framework, RCP6.
3 ). Historical LU patterns to be used for the Group 1 simulations were taken from the new LUH2 land-use history reconstruction  (Hurtt et al. , 2017)  based on agricultural land area from HYDE3. 2 (Klein Goldewijk, 2016), the Food and Agriculture Organization of the United Nations (2016),  Monfreda et al. (2008) , and other sources. The MAgPIE projections do not transition continuously from the LUH2 historical dataset (see Supplement). To ensure a smooth transition from historical LU patterns used for the historical ISIMIP2b Group 1 simulations to the future LU patterns used for the ISIMIP2b Group 3 impact projections, we applied the harmonization method developed within the context of CMIP6 (LUH2,  Hurtt et al. , 2017). To highlight the difference in underlying LU projections and additional adjustments described below, the LU, irrigation, and fertilizer dataset provided within ISIMIP2b should be referred to as LUH2-ISIMIP2b compared to the LUH2 data generated for CMIP6.
The RCP-specific patterns should be referred to as "landuse_ISIMIP2b_ssp2_rcp26" and "lan-duse_ISIMIP2b_ssp2_rcp60", respectively. The harmonization method ensures that future projections start from the end of the historical reconstruction and attempts to preserve absolute changes at various spatial scales for key variables including areas of cropland, pastures, urban land, and area used for bioenergy, irrigated areas, and relative changes in fertilizer rates (per crop type and ha) (see Fig. 3  for global areas of (1) rainfed food-feed crops, (2) irrigated food-feed crops, (3) rainfed bioenergy crops, and (4) irrigated bioenergy crops and see the Supplement for a comparison to the original areas provided by MAgPIE). The changes in total irrigated and rainfed cropland and the to-tal area for bioenergy generation in the harmonized dataset are quite similar to the associated changes in total areas derived from the original MAgPIE simulations (see Supplement) even though the harmonization method is not designed to generate convergence from historical patterns to the original patterns provided by MAgPIE.
The harmonization method provides a large number of LU-related information. Only part of the information is used within ISIMIP2b and therefore added to the LUH2-ISIMIP2b dataset. It comprises LU, irrigation, and fertilization information on two different levels of aggregation. On the first level we provide the fraction of each grid cell covered by the following types of land use and management: (1) pastures (pastures), (2) urban land (urbanareas), (3) C 3 annual crops (c3ann), (4) C 3 perennial crops (c3per), (  5 ) C 4 annual crops (c4ann), (  6 ) C 4 perennial crops (c4per), (  7 ) C 3 nitrogen-fixing crops (c3nfx), (  8 ) bioenergy grass (bioenergy_grass), and (9) bioenergy trees (bioenergy_trees). The c3per, c4per, c3ann, c4ann, c3nfx, bioenergy_grass, and bioenergy_trees classes are additionally split up into irrigated and rainfed fractions.
2 database  (Klein Goldewijk et al. , 2011 , 2010) , which covers the period 1860 to 2000 in 10year time steps plus yearly data between 2001 and 2015 with a default resolution of 5. For the future period, gridded data based on the national SSP2 population projections as described in  Samir and Lutz (2014)  are available  (Jones and O'Neill, 2016)  covering the period 2010-2100 in 10-year time steps, with a 7. 5 resolution. For ISIMIP2b both datasets are remapped to the ISIMIP 0. 5 • grid and interpolated to yearly time steps using a simple linear algorithm. From 2005 onwards, historical population data is linearly interpolated to match with 2010 SSP2 population projections. In addition, we provide age-specific population data (in 5-year age groups: 0-4, 5-9, etc. ) and allage mortality rates in 5-year time steps on a country level for 2010-2100, corresponding to the same SSP2 projections by Samir and Lutz (2014).
Future projections of national GDP are taken from the SSP database  (Dellink et al. , 2015 , https://secure. iiasa. ac. at/ web-apps/ene/SspDb/). The database includes country-level GDP projections from 2010-2100 in 10-year time steps that are linearly interpolated to provide annual coverage. From 2005 onwards, historical national GDP data are linearly interpolated to match with OECD SSP2 GDP projections in 2010. In addition, consistent gridded (0. 5 • × 0. 5 • ) GDP data are also provided for the period 1860-2100. For the historical period, the above-mentioned national GDP time series in 10year increments are downscaled to 0. 125 • grid resolution based on the methodology described in  Murakami and Yamagata (2017)  and corresponding gridded population data from the HYDE3. 2 database  (Klein Goldewijk et al.
, 2011 , 2010). Using linear interpolation routines, the data are upscaled to the ISIMIP 0. 5 • grid and interpolated to yearly time steps. For the future period, gridded GDP data were generated similarly, using OECD SSP2 national GDP and SSP2 gridded population projections  (Jones and O'Neill, 2016)  as input for the downscaling. The GDP data will be addi- tionally available from "Global dataset of gridded population and GDP scenarios," which is provided by the Global Carbon Project, National Institute for Environmental Studies (http://www. cger. nies. go. jp/gcp/population-and-gdp. html).
Focus regions Simulation data are welcome for all world regions. Even single-model simulations for specific sites will help to generate a more comprehensive picture of climate change impacts and potentially allow for constraining global models. However, to allow for model intercomparison, simulations should primarily be provided for the sector-specific focus regions shown in Fig. 7  and defined in Table  5 , if feasible with your model. Table  4. Representations of socio-economic drivers for the historical simulations (histsoc, Group 1) and the future projections accounting for changes in socio-economic drivers (rcp26soc or rcp60soc, Group 3). * at the beginning of an entry means that it is mandatory to use the dataset(s) provided (if applicable), for reasons of harmonization across models.
Driver Historical reconstruction Future projections Reservoirs & dams * Includes location, upstream area, capacity, and construction or commissioning year, on a global 0. 5 • grid. * No future datasets are provided. Assumed to be fixed at year 2005 levels. Documentation: http://www. gwsp. org/products/grand-database. html (Döll and Lehner, 2002; Lehner et al. , 2011). Note: Simple interpolation can result in inconsistencies between the GranD database and the DDM30 routing network (wrong upstream area due to misaligned dam or reservoir location). We provide a file with locations of all larger dams or reservoirs adapted to DDM30 so as to best match reported upstream areas.
Water withdrawal and consumption for domestic & industrial purposes Generated by each modelling group individually (e. g. following the varsoc scenario in ISIMIP2a). For modelling groups that do not have their own representation, we provide files containing the multi-model mean domestic and industrial water withdrawal and consumption generated from the ISIMIP2a varsoc runs of WaterGAP, PCR-GLOBWB, and H08. This data is available from 1901. Generated by each modelling group individually. For modelling groups that do not have their own representation, we provide files containing the multi-model mean (from the global water models WaterGAP, PCR-GLOBWB, and H08) domestic and industrial water withdrawal and consumption under SSP2 from the Water Futures and Solutions (WFaS)  (Wada et al. , 2016)  project.
Intended time line of simulations The time line of ISIMIP2b has been chosen to meet the critical deadlines of the drafting process of the IPCC Special Report, with the submission deadline for papers to be considered in the Special Report being 1 November 2017 and the associated acceptance deadline being in 15 May 2018. A range of ISIMIP2b impact simulations have been submitted in time and are available via https://esg. pik-potsdam. de/ projects/isimip2b/. Except for the oceanic data, all input data for the Group 1 and 2 simulations are available. The processing of the LU patterns will soon be finalized to allow for starting the Group 3 simulations. The ISIMIP2b repository will stay open for impact simulations submitted beyond October 2017, since the described simulations provide a basis for further research beyond the direct demands of the Special Report, including for the IPCC Sixth Assessment Report.
Annual crop-specific input per hectare of cropland for C 3 and C 4 annual, C 3 and C 4 perennial, and C 3 nitrogen fixing. This dataset is part of the LUH2 dataset based on HYDE3. 2. * Crop group-specific inorganic N fertilizer use per area of cropland provided by the LUH2-ISIMIP2b dataset, which differs for SSP2 + RCP2. 6 and SSP2 + RCP6. 0.
Acknowledgements. We thank  Graham Weedon  ( Met Office ) and  Emanuel Dutra  (ECMWF), who helped a lot to put together the EWEMBI dataset. COST Action FP1304 for supporting biomes meeting. This research was supported by  German Federal Ministry of Education and Research (BMBF , grant no. 01LS1201A2 ) and in part by the  EU   FP7   HELIX  project (grant no. 603864 ). Some authors acknowledge funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement no. 641816  (CRESCENDO). Some authors acknowledge support from the  Leibniz Competition  project  SAW-2013-PIK-5  ( EX-PACT ). Some authors acknowledge and appreciate funding by the  Federal Ministry for the Environment, Nature Conservation, Building and Nuclear Safety  11_II_093_Global_A_SIDS_and_LDCs (SURVIVE).
Authors acknowledge funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement no. 641816  (CRESCENDO). Met Office authors were supported by the joint  UK BEIS-Defra Met Office Hadley Centre Climate Programme  ( GA01101 ). Jochen Hinkel  has received funding from the  European Union 's  Seventh Programme for Research, Technological Development and Demonstration  under grant agreement no. 603396  ( RISES-AM  project) and from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement no. 642018  ( GREEN-WIN  project). Derek Tittensor acknowledges funding from the  Kanne Rassmussen Foundation, Denmark. The work of  Kate Halladay ,  Eleanor Burke ,  Richard A. Betts  and  Chris D. Jones  forms part of the  BEIS-Defra Met Office Hadley Centre Climate Programme GA01101.
Philippe Ciais  acknowledges support from the  European Research Council Synergy  grant  ERC-2013-SyG-610028 IMBALANCE-P  and the  ANR Convergence Lab  project  CLAND. Tyler Eddy  acknowledges funding from the  Nippon Foundation  to the  Nereus Program. Hanqin Tian  acknowledges funding from  US National Science Foundation  ( 1243232 ),  National Key Research and Development Program of China  (no. 2017YFA0604700 ),  SKLURE Grant  ( SKLURE2017-1-6 ). Tatsuo Suzuki  acknowledges funding from the  Program for Risk Information on Climate Change  by the  Japanese Ministry of Education, Sports, Science and Technology. The project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreements no. 689150  ( SIM4NEXUS ) and no. 642147  ( CD-LINKS ).
Fertilizer* Annual crop-specific input per hectare of cropland for C 3 and C 4 annual, C 3 and C 4 perennial, and C 3 nitrogen fixing. This dataset is part of the LUH2 dataset based on HYDE3. 2. * Crop group-specific inorganic N fertilizer use per area of cropland provided by the LUH2-ISIMIP2b dataset, which differs for SSP2 + RCP2. 6 and SSP2 + RCP6. 0.
method. Note that precipitation here means total precipitation, i. e. rainfall plus snowfall. Variable Short name Unit Source dataset Source dataset over land over the ocean Near-surface relative humidity hurs % E2OBS E2OBS Near-surface specific humidity huss kg kg -1 E2OBS E2OBS Precipitation pr kg m -2 s -1 WFDEI-GPCC E2OBS Snowfall flux prsn kg m -2 s -1 WFDEI-GPCC E2OBS Surface pressure ps Pa E2OBS E2OBS Sea-level pressure psl Pa E2OBS E2OBS Surface downwelling longwave radiation rlds W m -2 E2OBS-SRB E2OBS-SRB Surface downwelling shortwave radiation rsds W m -2 E2OBS-SRB E2OBS-SRB Near-surface wind speed sfcWind m s -1 E2OBS E2OBS Near-surface air temperature tas K E2OBS E2OBS Daily maximum near-surface air temperature tasmax K E2OBS-ERAI E2OBS Daily minimum near-surface air temperature tasmin K E2OBS-ERAI E2OBS.
We present an analysis of MUSE observations obtained on the massive Frontier Fields cluster Abell 2744. This new dataset covers the entire multiply-imaged region around the cluster core. The combined catalog consists of 514 spectroscopic redshifts (with 414 new identifications). We use this redshift information to perform a strong-lensing analysis revising multiple images previously found in the deep Frontier Field images, and add three new MUSE-detected multiply-imaged systems with no obvious HST counterpart. The combined strong lensing constraints include a total of 60 systems producing 188 images altogether, out of which 29 systems and 83 images are spectroscopically confirmed, making Abell 2744 one of the most well-constrained clusters to date. Thanks to the large amount of spectroscopic redshifts we model the influence of substructures at larger radii, using a parametrisation including two cluster-scale components in the cluster core and several group-scale in the outskirts.
5 hours. This deep coverage makes it possible for us to obtain an incredible amount of data over the entire field-of-view (FoV) and even confirm or reject multiply-imaged systems. In addition, we supplement this dataset with LRIS observations from Keck. Using all of this spectroscopic data, we are able to dig deeper into the nature of the cluster and advance our understanding of systematic uncertainties. The paper is organised as follows. In Section 2 we give an overview of the data. In Section 3 we describe the data processing to compute a redshift catalog. In Section 4 we detail the strong lensing analysis. In Section 5 we summarise the main results of the mass modeling. In section 6 we discuss systematic uncertainties in the analysis, the influence of the outskirts and compare our results with other groups.
Hubble Frontier Fields images The HFF observations of Abell 2744 (ID: 13495, P. I: J. Lotz) were taken between 2013 Oct 25 and 2014 Jul 1 in seven different filters, three with the Advanced Camera for Surveys (ACS; F435W, F606W, F814W) and four taken with the Wide Field Camera 3 (WFC3; F105W, F125W, F140W, and F160W). In total 280 orbits were devoted to Abell 2744 reaching in each filter a 5-σ limiting magnitude AB∼29. The self-calibrated data provided by STScI 1 ,(version v1. 0 for WFC3 and v1. 0-epoch2 for ACS) with a pixel size of 60 mas are used in this study.
MUSE data reduction The data reduction was performed with the MUSE ESO pipeline  (Weilbacher et al. 2012 (Weilbacher et al. , 2014) )  up to the mosaic combination. This comprises bias subtraction, flat fielding (including illumination and twilight exposures), sky subtraction, flux calibration and telluric correction. The last two steps were performed with calibration curves derived from the median response of 6 suitable standard stars observed in the MUSE GTO Lensing Clusters program. After basic corrections we align individual exposures to a common WCS with  SCAMP Bertin (2006) , shifting each frame relative to a reference image, in this case, the F814W HFF data. No correction for rotation was applied since only a maximum rotation offset of 0. 03 • was observed. We then transform the realigned images into data cubes, resampling all pixels onto a common 3-dimensional grid with two spatial and one spectral axis.
The final combined cube was once more cleaned with ZAP and the background was corrected by subtracting the median of the 50 spectral-neighbouring wavelength planes (masking bright objects) to each spatial row and column of the cube. The final product is a 2 × 2 MUSE field of view mosaic with 1. 25 Å spectral sampling and 0. 2 spatial sampling. The PSF size was estimated by convolving the HST F814W image with a moffat kernel and correlating it with a filter matched MUSE image. We obtained a moffat FWHM of 0. 58 in this filter for a β parameter of 2. 5. Comparing the fluxes of the HST PSF matched image with the MUSE image we estimate that the MUSE photometry is accurate up to ∼ 7%. These steps were performed using the MUSE Python Data Analysis Framework mpdaf 2 software. A final version of the cube is publicly available for download 3.
DATA ANALYSIS Since MUSE is most sensitive to emission line objects, very faint (m F814W ≥25) sources lacking emission lines can be hard to detect. Therefore, in order to extract the maximum number of sources possible, we applied three complementary detection methods over the entire field: (i) Forced spectral extraction at the location of known faint sources detected in deep (m lim ∼ 30) HFF imaging. (ii) Emission line detection of sources based on a narrowband filtering of the MUSE cube mosaic. (iii) A few manual extractions of sources not captured by i) and ii) and found through visual inspection of the datacube (see, e. g. , the special case of multiply-imaged system 2 explained in the appendix table  B1 ). We then searched the combined list of objects extracted with methods (i)-(iii) for spectral features, measuring redshifts which we compared to ancillary redshift catalogs of 2 https://git-cral.