dataesr/scientific-paragraphs-categorization huggingface dataset:
B. R. ); the funding by  H2020  VERIFY (contract  776810 ) and  H2020  Resonate (contract  101000574 ) (G. -J. N. ); the  TEAM  project in Uganda supported by the  Moore foundation and Buffett Foundation through Conservation International (CI)  and  Wildlife Conservation Society (WCS) ; the  Danish Council for Independent Research | Natural Sciences (TREECHANGE , grant  6108-00078B ) and VILLUM FONDEN grant number  16549  (J. -C. S. ); the  Natural Environment Research Council of the UK (NERC)  project  NE/T011084/1  awarded to J. A. -G. and  NE/ S011811/1 ;  ERC Advanced Grant   291585  (' T-FORCES ') and a  Royal Society-Wolfson   Research Merit Award  (O. L. P. ); RAINFOR plots supported by the  Gordon and Betty Moore Foundation  and the  UK Natural Environment Research Council , notably  NERC  Consortium Grants ' AMAZONICA' ( NE/F005806/1 ), ' TROBIT ' ( NE/D005590/1 ) and ' BIO-RED ' ( NE/N012542/1 ); CIFOR's Global Comparative Study on REDD+ funded by the  Norwegian Agency for Development Cooperation , the  Australian Department of Foreign Affairs and Trade , the  European Union , the  International Climate Initiative (IKI)  of the  German Federal Ministry for the Environment, Nature Conservation, Building and Nuclear Safety  and the  CGIAR Research Program on Forests, Trees and Agroforestry (CRP-FTA ) and donors to the  CGIAR Fund ; AfriTRON network plots funded by the local communities and  NERC ,  ERC ,  European Union ,  Royal Society and Leverhume Trust ; a grant from the  Royal Society  and the  Natural Environment Research Council, UK  (S.
Research Council of  Canada  ( RGPIN-2019-05109  and  STPGP506284 ) and the  Canadian Foundation for Innovation (36014)  (H. Y. H. C. ); the project  SustES-Adaptation strategies for sustainable ecosystem services and food security  under adverse environmental conditions ( CZ. 02. 1. 01/0. 0/0. 0/16_019/0000797 )(E. C. );  Consejo de Ciencia y Tecnología del estado de Durango  ( 2019-01-155 ) (J. J. C. -R. );Science and  Engineering Research Board  (SERB),  New Delhi, Government of India  (file number  PDF/2015/000447 )-' Assessing the carbon sequestration potential of different forest types in Central India in response to climate change ' (J. A. D. );  Investissement d'avenir grant  of the  ANR  ( CEBA :  ANR-10-LABEX-0025 ) (G. D. );  National Foundation for Science & Technology Development of Vietnam ,  106-NN.
06-2013. 01 (T. V. D. ); Queensland government,  Department of Environment and Science (T. J. E. ) ; a  Czech Science Foundation   Standard grant  ( 19-14620S ) (T. M. F. ); European Union   Seventh Framework Program  ( FP7/2007-2013)  under grant agreement number  265171  ( L. Finer ,  M. Pollastrini ,  F. Selvi ); grants from the  Swedish National Forest Inventory, Swedish University of Agricultural Sciences  (J. F. );  CNPq productivity  grant number  311303/2020-0  (A. L. d. G. );  DFG  grant  HE 2719/11-1 ,2,3;  HE 2719/14-1  (A. Hemp); European Union's  Horizon Europe research project OpenEarthMonitor  grant number  101059548 ,  CGIAR Fund INIT-32-MItigation and Transformation Initiative  for GHG reductions of Agrifood systems RelaTed Emissions (MITIGATE+) (M. H. );  General Directorate of the State Forests, Poland  ( 1/07 ;  OR-2717/3/11 ;  OR.
S. );  European Space Agency projects IFBN  ( 4000114425/15/NL/FF/gp ) and  CCI Biomass  ( 4000123662/18/I-NB ) (D. Schepaschenko);  FunDivEUROPE, European Union Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement number  265171  (M. S. -L. ); APVV 20-0168  from the  Slovak Research and Development Agency  (V. S. );  Manchester Metropolitan University's Environmental Science Research Centre  (G. S. ); the.
Code availability All the models in this study were constructed using command line applications written in the R programming language, which processed and restructured the input data, trained the model and performed cross validation. Due to the massive amount of data, we used Purdue University's Brown supercomputing cluster to accelerate the training process. The development of the GFBi database, tabular data cleaning, creation of species abundance matrices, evaluation of diversity determinants and geostatistical imputation were conducted in R 49 (v. 3. 4. 2) through the use of several Linux-based high-performance computing resources at Purdue University and a custom HPC interface developed using Amazon Web Services, each designed for batch processing, scalable resource distribution, embarrassingly parallel computations and/or large RAM jobs.
It thus requires a sufficient number of points to be registered; the greater the number, the better the fit. Building this curve is time consuming with very long acquisition times. It is therefore important to optimize data-acquisition conditions to increase the number of kinetic points registered within  a reasonable time. Furthermore, the processing of ssNMR data is quite laborious. The low resolution levels of ssNMR spectra require spectral deconvolution to determine the area proportions of various overlapping signals. Such processing involves data extraction and treatment using dedicated software programmes and this step is also time consuming. From the first studies performed in the 1970s  [6]  , VCT-CPMAS experiments have always been carried out using a low number of kinetic points, which has led to insufficient attention to the complexity of the rising part of the kinetics curve  [7].
The areas / intensities obtained were normalized by fixing the maximum value obtained for each kinetic at 100. The 1 H to 13 C polarization transfer was measured using the VCT-CPMAS sequence shown in Fig. 1 a. A cross-polarization ramp (Ramp-CP, from 50% to 100% in steps of 0. 5%) was used to avoid mismatches caused by the occurrence of a MAS frequency and dipolar coupling of the same order during acquisition  [11]. Thirty contact times were used to validate the acquisition and processing parameters ( Fig. 1 b ). The number of scans (NS) and the recycling delay (RD) ( Fig. 1 a ), were modulated to assess possible time savings during data acquisition ( Table  1  ). The impact of the processing method on the quality of the results was evaluated. For spectral deconvolution (SD), the chemical shifts, half width and area of 13 C peaks were determined using a least-square fitting method with Peakfit R software (Systat software Inc.
Fig. 3  shows the fitting of the experimental data points in relation to contact times in order to estimate the dynamic parameters for the signal variations. The distribution of the resulting values was represented on boxplots ( Figs. 6 -11 )  and a Student's t-test preceded by a Fisher test were used to check for the homoscedasticity of the results. The    threshold for significant differences was p < 0. 05. Statistics were realized using Excel software (Microsoft). Data with 512 and 128 accumulations were selected as repetitions for the evaluation of the processing method (2 samples, 5 RD and 2 NS, giving 20 repetitions). Data with 512, 128 and 64 accumulations processed using the PP method were selected as repetitions for the evaluation of the duration of RD (15 repetitions). Data with five recycling delays processed using the PP method were used as repetitions for the evaluation of the number of scans (10 repetitions).
Realistic and accurate reconstructions of past vegetation cover are necessary to study past environmental changes. This is important since the effects of human land-use changes (e. g. agriculture, deforestation and afforestation/reforestation) on biodiversity and climate are still under debate. Over the last decade, development, validation, and application of pollen-vegetation relationship models have made it possible to estimate plant abundance from fossil pollen data at both local and regional scales. In particular, the REVEALS model has been applied to produce datasets of past regional plant cover at 1 • spatial resolution at large subcontinental scales (North America, Europe, and China). However, such reconstructions are spatially discontinuous due to the discrete and irregular geographical distribution of sites (lakes and peat bogs) from which fossil pollen records have been produced.
, 2012)  and northern America  (Sugita et al. , 2010). The REVEALS model was used by  Trondman et al. (2015)  to produce the first generation of gridded pollen-based estimates of plant cover for five selected time slices of the Holocene between 6 k BP and present in a large part of Europe. Recently,  Githumbi et al. (ESSD, in review)  achieved a new (second generation) set of gridded pollen-based REVEALS plant-cover estimates for 31 taxa, 12 PFTs (Plant Functional Types) and 3 LCTs, at a spatial scale of 1 • × 1 • (ca. 100 × 100 km) across 30 • -75 • N, 25 • W-50 • E (Europe and a part of the Eastern Mediterranean Black Sea Caspian corridor) including reconstructions for 25 time windows covering the Holocene (last 11700 years) at 500year-steps  (Fyfe et al. , 2021). The effects of uneven spatial coverage of pollen-based reconstructions can be reduced by using complementary datasets related to land-cover composition with full spatial coverage.
DATA Altogether 1,128 pollen records containing over 16,000 samples were used. These records were collected from several public databases. The databases include the European Pollen Database  (Fyfe et al. , 2009) ; the Alpine Palynological database (ALPADABA; Institute of Plant Sciences, now archived in EPD); the Czech Quaternary palynological database (PALYCZ;  Kuneš et al. , 2009)  and PALEOPYR  (Lerigoleur et al. , 2015). Pollen data archives from research projects in the Mediterranean region  (Fyfe et al. , 2018; Woodbridge et al. , 2018)  and the Eastern Mediterranean-Black Sea-Caspian-Corridor  (Marinova et al. , 2018)  and contributions from individuals were also used. Gridded pollen-based REVEALS estimates were grouped into three major land-cover types (LCTs: B, broad-leaved forest; C, coniferous forest; O, open land) for Europe and 25 time-windows covering the last 11700 years (the Holocene, see Figures  4 5 6 ).
Similarly to  Trondman et al. (2015)  the samples were grouped into 100-500 year time-windows  [present-100, 100-350, 350-700, 700-1200, 1200-1700  until 11700 calibrated years before present (BP = AD 1950)]. Higher total pollen count per time window ensures a higher quality REVEALS estimate. The records are from 29 European countries and the Eastern Mediterranean Black Sea-Caspian corridor. The gridded REVEALS estimates are available from Pangaea  (Fyfe et al. , 2021). Data on the current land cover of Europe (cover proportions for coniferous and broad-leaved forests and total forest cover) were obtained from the forest maps compiled by the European Forest Institute (EFI) using the remotely sensed data (NOAA-AVHRR) and statistical data from national inventories  (Päivinen et al. , 2001; Schuck et al.
) show that the reconstructions of the remote areas in north-eastern and south-eastern Europe where we have no REVEALS data are of considerably lower quality than the rest of Europe. Therefore, the grid cells where the CR > 9 were omitted from the spatial reconstructions (Figure  3B ). The 6-fold cross-validation results are shown in Table  1  for the five time periods. The average compositional distance (CV error ) are small indicating a model with good predictive performance. In addition, the standard deviation (sd) among different crossvalidation groups are also small indicating very similar CV error across all groups, and thus an unbiased selection of the grouping in the 6-fold cross-validation. Most of the available pollen-based REVEALS estimates are located in northern, central, and western Europe (between 42 • to 62 • N latitude and 10 • W to 30 • E longitude, see Figures  4 5 6 ).
Data and Methodology Considerations The spatial statistical model developed by  Pirzamanbein et al. (2014 Pirzamanbein et al. ( , 2018 Pirzamanbein et al. ( , 2020) )  was specifically designed to work with unevenly distributed proportional data. The statistical model is a development of the earlier work and uses REVEALS reconstructions to produce continuous vegetation maps of Europe during the Holocene. The evaluation of the spatial statistical model is similar to that previously used in  Pirzamanbein et al. (2018 Pirzamanbein et al. ( , 2020)). The time-periods for cross validation were selected to allow comparison with the results of these studies. Moreover, the cross-validation is extremely computationally-expensive to run, making some limitation a practical necessity. The major difference from previous reconstructions is the increased spatial domain due to the increase in REVEALS estimates from 636 sites in  Trondman et al.
The selection of pollen records for continental REVEALS reconstructions such as the one used in this study is based on careful criteria including the quality of chronologies and pollen identifications  (Mazier et al. , 2012; Trondman et al. , 2015). Nevertheless, such errors can never be avoided and/or tracked completely. If oddities in the final product from the spatial statistical reconstruction cannot be explained by large REVEALS SEs or large cross-validation compositional errors and standard deviations in the spatial statistical reconstruction, it is recommended to examine the individual pollen records used in the REVEALS reconstruction. In order to evaluate the spatial statistical land-cover reconstructions we have used cross validation and comparison with the EFI-FM dataset (Figure  3 ).
Using datasets from different sources in a comparison raises questions related to differences in temporal and spatial resolutions and on whether the EFI-FM dataset is a fair description of the actual woodland cover of Europe over the last few years and is representative of the last 100 years covered by the REVEALS reconstruction. The accuracy of the EFI-FM dataset varies spatially due to the use of different data sources  (Kempeneers et al. , 2011). Comparison of the dataset with ground observations indicated some over and underestimations of some taxa depending on the source data used; moreover, the dataset includes a mix of source data with high (25 m) and low (1 km) spatial resolutions. This implies that the EFI-FM data is not of the same accuracy and precision across Europe: for instance, high-resolution spatial data imply higher precision and accuracy of the land-cover data than low-resolution spatial data.
Therefore, although the EFI-FM dataset has been agglomerated to the spatial scale of the REVEALS reconstructions (one square degree), it still includes differences in accuracy and precision over space. The latter, and the difference in time represented by the EFI-FM dataset (a few years) and the REVEALS reconstruction (ca. 100 years), implies that the compositional distance between EFI-FM and REVEALS (Figure  3D ) is a mixture of (a) differences between REVEALS estimates (and their spatial statistical interpolations) and actual vegetation, and (b) differences due to both errors in the EFI-FM, and different time lengths represented in the two datasets. The relationship between pollen proportions and vegetation cover is complex: for instance, inter taxonomic differences in plants' pollen production and pollen size and shape imply inter taxonomic differences in number and dispersion distance of pollen  (Parsons and Prentice, 1981; Prentice, 1985; Sugita, 1993 Sugita, , 1994)).
, 2010 (Gaillard et al. , , 2018;; Strandberg et al. , 2014) , and palaeoenvironmental modeling that requires more detailed descriptions of past plant cover, such as the abundance of individual plant species, genus, families, or groups of those. However, to be used in modeling, REVEALS reconstructions achieved at continental scales need to be interpolated into temporally and spatially continuous datasets. The interpolation presented in this paper is one of many possible solutions that can be used  (Pirzamanbein et al. , 2014 (Pirzamanbein et al. , , 2018 (Pirzamanbein et al. , , 2020)) , and the selected methodology will depend on the type of research question posed  (Strandberg et al. , 2014; Harrison et al. , 2020).
The dataset of reconstructed Holocene land-cover quantitative changes in Europe presented in this paper is particularly relevant for use in palaeoclimate modeling. Similar reconstructions based on the same dataset of REVEALS land-cover estimates are currently used in a palaeoclimate modeling project studying the biogeophysical forcing of past anthropogenic land-cover change on climate at 6000 and 2500 BP  (Githumbi et al. , 2019). These land-cover reconstructions will also serve in the detailed study of Holocene dynamics in plant and vegetation cover through time and space for new insights on the factors and processes involved, e. g. , disentangling human and climate impacts  (Marquer et al. , 2017). This can be achieved for instance by using spatial statistical analyses of the land-cover dataset together with independent datasets of archaeology-based landuse and palaeoclimatic changes.
CONCLUSION The need for spatially continuous pollen-based maps of Holocene land cover in Europe is essential for the study of past vegetationclimate interactions at regional and global scales. In this study we applied spatial statistical modeling and REVEALS-based reconstructions of plant cover from pollen data together with information on anthropogenic deforestation from the KK10 scenarios. There is currently no alternative methodology to the REVEALS model to obtain as detailed reconstructions of vegetation cover. Cross validation and comparison of the reconstructed maps for present time with modern forest data (EFI-FM) confirm the reliability and robustness of the spatial statistical model. Comparison with earlier, similar land cover reconstructions using the same model confirms that it can be used and tested in further applications using the same REVEALS dataset but alternative covariates, or REVEALS datasets from other regions and similar or alternative covariates.
FUNDING This study was funded by a research project financed by the  Swedish Research Council VR (Vetenskapsrådet)  on Quantification of the bio-geophysical and biogeochemical forcings from anthropogenic de-forestation on regional  Holocene climate in Europe, LandClim II. Financial support from the  Linnaeus University's Faculty of Health  and  Life Science  is acknowledged for M-JG,  A-KT , and  EG. The MERGE short Project funding supported  EG ,  BP , and JL. AP was supported by the  ESF  Project Number  PRG323. This is a contribution to the strategic research areas  MERGE  (ModElling the Regional and Global Earth system) and the  Past Global Change (PAGES)  project and its working group LandCover6k (http://pastglobalchanges. org/landcover6k), which is funded by the  Swiss National Science Foundation , the  Swiss Academy of Sciences , the  US National Science Foundation , and the  Chinese Academy of Sciences.
DATA AVAILABILITY STATEMENT The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://github. com/ BehnazP/SpatioCompo_entireHolocene. REVEALS datasets used for this study can be found in Pangaea  Fyfe et al. (2021) -https://doi. org/10. 1594/PANGAEA. 931856.
Introduction: The CompOSE Data Base The main purpose of the online service COMPOSE (https://compose. obspm. fr) is to provide information and data for different equations of states (EoSs) ready for further use in astrophysical applications, nuclear physics, and beyond, see the review  [1]  for a general introduction. To that end, COMPOSE contains a repository of EoS data in tabulated form following a common format with information on a large number of thermodynamic properties and, if available, on the chemical composition of dense matter and on microphysical properties of the constituents. COMPOSE not only allows direct downloading of data together with a bibliography on data-related publications, but also provides several tools to handle and customize data, for instance for extracting selected quantities, interpolation of tabulated data, or calculation of additional related quantities.
A full documentation with detailed instructions can be found in the full manual  [2, 3]. Here, we present quick guides for potential providers of EoS data and for users of the COMPOSE service. One word of caution is in order: COMPOSE is designed to evolve and to be extended over time; thus, the presentation below shows the current status (July 2022) and will not be adapted in the future upon the adding of new features to the service. If you make use of the tables provided by COMPOSE, please cite the publications describing the respective EoS models (available on the COMPOSE web page for each EoS data table), together with the original COMPOSE publications  [1] [2] [3]  and the COMPOSE website https://compose. obspm. fr.
Preparation of Tables In the COMPOSE database, the EoS is assumed to describe dense matter in thermodynamic equilibrium, i. e. , thermal and mechanical equilibrium. In addition, it is assumed that all the constituents are in chemical equilibrium with respect to reactions mediated by the strong and electromagnetic interactions. Whether equilibrium with respect to weak β-type reactions is assumed depends on the particular table. Tables designated for application to core-collapse supernovae or binary neutron star mergers do not, whereas the tables constructed to describe cold neutron star matter do, reducing in this case the number of independent particle number densities, i. e. , thermodynamic parameters. For EoS models with strangeness-bearing particles, it is assumed that strangeness-changing weak interactions have enough time to equilibrate, in which case there is no constraint related to the strangeness density and the strangeness chemical potential vanishes.
Except for the tables of pure hadronic and/or quark matter (without leptons), charge neutrality is assumed to hold. Neutrinos are never included in the present tables, since they are usually treated independently from the EoS in astrophysical simulations, not assuming thermodynamic equilibrium. Photons can be included in finite-temperature tables. In the case of EoS for pure-neutron matter, however, it is supposed that photons are not included. Quantities in COMPOSE are given in natural units h = c = k B = 1 (for details on unit conversion see the NIST (https://physics. nist. gov/cuu/Constants/index. html) or CODATA (https://www. codata. org) websites). Particle number densities of all particles i are given by n i = N i /V [fm -3 ], where N i is the particle number inside the volume V. For particles with half-integer spin at finite temperature, n i represents the net particle density, i.
EoS Tables and Data Format There are at least four files with numerical data required for an EoS to be included in the COMPOSE repository: three files that specify the discretization scheme of the independent variables temperature, baryon number density, and charge fraction (eos. t, eos. nb, and eos. yq), and a file (eos. thermo) with the table of thermodynamic quantities. Additional data on the chemical composition and on microscopic quantities can be collected in two additional files (eos. compo and eos. micro). The variables T, n B , and Y q are given on a provider-defined grid in which each point is identified by three indices. The values corresponding to these indices are given in separate files (and should ideally have at least eight significant digits): In Section 2. 2. 5 we list recommended ranges of these parameters for the different types of tables.
In these files, the first two lines should be the minimum and maximum indices (representing the respective minimum and maximum values that are given). The following lines give the numerical values of the variables for the indices in ascending order. Each line of the data files eos. thermo, eos. compo, and eos. micro, except the first line in eos. thermo, starts with the indices corresponding to the three variables in the order T, n b , Y q. In the particular case of β equilibrium and zero temperature, the files eos. t and eos. yq are simply 0 1 0 1 0. 00000000E+00 0. 00000000E+00 • temperature T [MeV] in.