dataesr/scientific-paragraphs-categorization huggingface dataset:
We have developed a Python-based open-source package to analyze the results stemming from ab initio molecular-dynamics simulations of fluids. The package is best suited for applications on natural systems, like silicate and oxide melts, water-based fluids, and various supercritical fluids. The package is a collection of Python scripts that include two major libraries dealing with file formats and with crystallography. All the scripts are run at the command line. We propose a simplified format to store the atomic trajectories and relevant thermodynamic information of the simulations, which is saved in UMD files, standing for Universal Molecular Dynamics. The UMD package allows the computation of a series of structural, transport and thermodynamic properties. Starting with the pair-distribution function it defines bond lengths, builds an interatomic connectivity matrix, and eventually determines the chemical speciation.
]. All these visualization tools deal efficiently with interatomic distances, and as such, they allow the efficient computation of pair distribution functions and diffusion coefficients. Various groups performing large-scale molecular dynamics simulations have proprietary software to analyze various other properties resulting from the simulations, sometimes in shareware or other forms of limited access to the community, and sometimes limited in scope and use to some specific packages. Sophisticated algorithms to extract information about interatomic bonding, geometrical patterns, and thermodynamics are developed and implemented in some of these packages  3 , 4 , 5 , 6 , 7  , etc. Here we propose the UMD package -an open-source package written in Python to analyze the output of molecular dynamics simulations.
The UMD package allows for the computation of a wide range of structural, dynamical, and thermodynamical properties (Figure  1 ). The package is available via the GitHub website (https://github. com/ rcaracas/UMD_package) and via a dedicated page (http:// moonimpact. eu/umd-package/) of the ERC IMPACT project as an open-access package. To make it universal and easier to handle, our approach is to first extract all the information related to the thermodynamic state and the atomic trajectories from the output file of the actual molecular-dynamics run. This information is stored in a dedicated file, whose format is independent of the original MD package where the simulation was run. We name these files "umd" files, which stands for Universal Molecular Dynamics. In this way, our UMD package can be easily used by any ab initio group with any software, all with a minimum effort of adaptation.
The only requirement to use the present package is to write the appropriate parser from the output of the particular MD software into the umd file format, if this is not yet existent. For the time being, we provide such parsers for the VASP  8  and the QBox 9 packages. The umd files are ASCII files; typical extension is "umd. dat" but not mandatory. All the analysis components can read ASCII files of the umd format, regardless of the actual name extension. However, some of the automatic scripts designed to perform fast large-scale statistics over several simulations specifically look for files with the umd. dat extension. Each physical property is expressed on one line. Every line starts with a keyword. In this way the format is highly adaptable and allows for new properties to be added to the umd file, all the while preserving its readability throughout versions.
). Apart from these two classes, the crystallography. py library contains a series of functions to perform a variety of tests and calculations, such as atomic distances, or cell multiplication. The periodic table of the elements is also included as a dictionary. The various components of the umd package write several output files. As a general rule, they are all ASCII files, all their entries are separated by tabs, and they are made as selfexplanatory as possible. For example, they always clearly indicate the physical property and its units. The umd. dat files fully comply with this rule. This value of -l is big enough to cover a typical entire trajectory.
Our study is an excellent example of a successful use of the UMD package for the entire in-depth analysis of the melts  22. We computed the distribution and the average bond lengths, we traced the changes in cation-oxygen coordination, and compared our results to previous experimental and computational studies on amorphous silicates of various compositions. Our in-depth analysis helped decompose standard coordination numbers into their basic constituents, outline the presence of exotic coordination polyhedra in the melt, and extract lifetimes for all the coordination polyhedra. It also outlined the importance of sampling in simulations in terms of both length of the trajectory and also number of atoms present in the system that is modeled. As for post-processing, the UMD analysis is independent of these factors, however, they should be taken into account when interpreting the results provided by the UMD package.
Here, we show a few examples of how the UMD package can be used to extract several characteristic features of the melts, with an application to molten pyrolite. The Si-O pair distribution function obtained from the gofrs_umd. py script shows that the radius of the first coordination sphere, which is the first minimum of the g(r) function, lies around 2. 5 angstroms at T = 3000 K and P = 4. 6 GPa. The maximum of the g(r) lies at 1. 635 Åthis is the best approximation to the bend length. The long tail is due to the temperature. Using this limit as the Si-O bond distance, the speciation analysis shows that SiO 4 units, which can last for up to a few picoseconds, dominate the melt (Figure  5 ). There is an important part of the melt that shows partial polymerization, as reflected by the presence of dimers like Si 2 O 7 , and trimers like Si 3 O x units.
Analysis of the molecular-dynamics runs NOTE: The package is available via the GitHub website (https://github. com/rcaracas/UMD_package) and via a dedicated page (http://moonimpact. eu/umd-package/) of the ERC IMPACT project as an open-access package. 1. Extract each specific set of physical properties using one or more dedicated Python scripts from the package. Run all the scripts at the command line; they all employ a series of flags, which are as consistent as possible from one script to another. The flags, their meaning, and default values are all summarized in Table 1Most common flags used in the UMD package and their most common significance. Start with transforming the output of the MD simulation performed in a first-principles code, like VASP 8 or QBox 9 , into a UMD file. 1. If the MD simulations were done in VASP, then at the command line type: VaspParser.
Acknowledgments This work was supported by the  European Research Council (ERC)  under the  European Union   Horizon 2020 research and innovation program  (grant agreement number  681818 IMPACT  to RC), by the  Extreme Physics and Chemistry Directorate  of the  Deep Carbon Observatory , and by the  Research Council of Norway through its Centers of Excellence funding scheme , project number  223272. We acknowledge access to the  GENCI  supercomputers through the stl2816 series of eDARI computing grants, to the Irene AMD supercomputer through the  PRACE RA4947  project, and the Fram supercomputer through the  UNINETT Sigma2 NN9697K. FS was supported by a  Marie Skłodowska-Curie project  (grant agreement ABISSE No. 750901 ).
Protocol 1. 20 Table 1 : 2. 4. 120124 Analysis of the molecular-dynamics runs NOTE: The package is available via the GitHub website (https://github. com/rcaracas/UMD_package) and via a dedicated page (http://moonimpact. eu/umd-package/) of the ERC IMPACT project as an open-access package. 1. Extract each specific set of physical properties using one or more dedicated Python scripts from the package. Run all the scripts at the command line; they all employ a series of flags, which are as consistent as possible from one script to another. The flags, their meaning, and default values are all summarized in Table 1Most common flags used in the UMD package and their most common significance. Start with transforming the output of the MD simulation performed in a first-principles code, like VASP 8 or QBox 9 , into a UMD file.
, 2016). We focus here on the role of LULCC in the climate system; anthropogenic land-cover change can have broader consequences on other processes and changes, such as erosion and fluvial systems  (Downs and Piégay, 2019) , biodiversity loss  (Barnosky et al. , 2012) , nutrient cycling  (Guiry et al. , 2018; McLauchlan et al. , 2013) , habitat exploitation by megafauna  (Hofman-Kamińska et al. , 2019)  and wider ecosystem functioning  (Ellis, 2015; Stephens et al. , 2019). The Earth System Modelling (ESM) community use LULCC model scenarios, along with dynamic vegetation models, to understand interactions between different components of the earth system in the past (e. g. Gilgen et al. , 2019; Smith et al. , 2016; He et al. , 2014; Hibbard et al. , 2010). Disagreement between LULCC scenarios suggests that their evaluation is needed using independent, empirical datasets  (Gaillard et al.
FSP can be calculated using measurements of the pollen grains and the Stokes' law  (Gregory, 1973). RPPs of major plant taxa can be estimated using datasets of modern pollen assemblages and related vegetation and the Extended R-Value model  (Mazier et al. , 2008). RPPs exist for a large number of European plant taxa, and syntheses of FSPs and RPPs were published earlier by  Broström in 2008 and Mazier in 2012 (Broström et al. , 2008; Mazier et al. , 2012). The latter was used in the "first generation" REVEALS reconstruction  (Trondman et al. , 2015). A new synthesis of European RPPs was performed for this "second generation" reconstruction. Preparation of data from individual pollen records, and the values of model parameters used, are described below (sections 2. 2 and 2. 3). https://doi.
Pollen recordsdata compilation and preparation 1128 pollen records from 29 European countries and the Eastern Mediterranean-Black Sea-Caspian-Corridor were obtained from databases and individual data contributors. The contributing databases include: the European Pollen Database  (Fyfe et al. , 2009; Giesecke et al. , 2014) ; the Alpine Palynological database (ALPADABA; Institute of Plant Sciences, University of Bern; now also archived in EPD); the Czech Quaternary palynological database (PALYCZ;  Kuneš et al. , 2009) ; PALEOPYR  (Lerigoleur et al. , 2015) ; and datasets compiled within synthesis projects from the Mediterranean region  (Fyfe et al. , 2018; Woodbridge et al. , 2018)  and the Eastern Mediterranean-Black Sea-Caspian-Corridor (EMBSeCBIO project;  Marinova et al. , 2018 ) (see Fig.
g. Trondman et al. , 2016 ; see details under section 2. 1 on the REVEALS model). The taxonomy and nomenclature of pollen morphological types from the 1128 pollen records were harmonised. The pollen morphological types were then consistently assigned to one of 31 RPP taxa (Table  1 ; see section 2. 3 for details on the RPP dataset used in this study), following the protocol outlined in  Trondman et al. (2015: SI-2). This process takes into account plant morphology, biology, and ecology of the species that are included in each pollen morphological type (see  Trondman et al. , 2015  for examples of harmonization between pollen-morphological types and RPP taxa). In this way, RPP-harmonized pollen count data were produced for each of the 1128 pollen records. It should be noted that the EMBSeCBIO data does not contain pollen counts from cultivars, i.
e. pollen from cereals and cultivated trees were deleted from the pollen records  (Marinova et al. , 2018). Therefore, the cover of agricultural land (represented by Cereals in this reconstruction) will always be zero in the Eastern Mediterranean-Black Sea-Caspian-Corridor in grid cells including only pollen records from EMBSeCBIO, although agriculture did occur in the region from early Neolithic. For application of REVEALS, an age-depth model (in cal yr BP) is required for each pollen record. We used the author's original model, the model available in the contributing database or, where necessary, a new age-depth model constructed following the approach in  Trondman et al. (2015). The age-depth model for each pollen record is used to aggregate RPPharmonised pollen count data into 25 time windows across the Holocene following a standard division used in  Mazier et al.
(2012)  and  Trondman et al. (2015) , later adopted by the PAGES LandCover6k working group  (Gaillard et al. , 2018). The first three time windows (present-100 BP (where present is the date of coring), 100-350 BP; 350-700 BP) capture the major humaninduced land-cover changes since Early Middle Ages. Subsequent time windows are contiguous 500-year long intervals (e. g. 700-1200 BP, 1200-1700 BP, 1700-2200 BP, etc. ) with the oldest interval representing the start of the Holocene (11200-11700   B1  and B2 ) and calculation of mean RPP and their standard error (SD) for Europe are explained in Appendix C. The synthesis includes a total of 54 taxa for which RPP values are available (Tables  B1  and B2   B2 ). Table  A1  presents the new RPP dataset for the 54 plant taxa and, for comparison, the mean RPP values from  Mazier et al.
Data reliability The REVEALS results are reliant on the quality of the input datasets, namely pollen count data, chronological control for sequences, and the number and reliability of RPP estimates used (further discussion on RPPs under 4. 2). The standard errors (SEs) can be considered a measure of the precision of the REVEALS results, and of reliability\quality  (Trondman et al. , 2015). Where SEs are equal or greater than the REVEALS estimates (represented in the maps of Fig. 2 -6 and D1-D3 as a circle that fills the grid), caution should be applied in the use of the REVEALS estimates, as it implies that they are not different from zero when taking the SEs into account. Whilst this is possible within an algorithmic approach that includes estimates of uncertainty, it is conceptually impossible to have negative vegetation cover.
If SEs ≥ mean REVEALS value it is therefore uncertain whether the plant taxon has cover within the grid cell. Cover may either be very low or the taxon may be absent within region (grid cell in this case). The size of pollen counts impacts on the size of REVEALS SEs  (Sugita, 2007a) ; larger counts result in smaller SEs. Aggregation of samples from pollen records to longer time windows results in larger count sizes and thus lower SEs (see sections 2. 2 above and 4. 2 below). Our input dataset includes more than 59 million individual pollen identifications, organised here into 16711 samples from 1128 sites, where a sample is an aggregated pollen count for RPP taxa for a time window at a site. 77% of samples have count sizes in excess of 1000, which is deemed most appropriate for REVEALS reconstructions  (Sugita, 2007a).
The mean count size across all samples is 3550. Samples with count sizes lower than 1000 are still used, but result in higher SEs. More than half of the pollen records used in the study were sourced from databases (see section 2. 2). Note The REVEALS results here are provided for 1° × 1° grid cells across Europe. The size and number of suitable pollen records is an important factor in the quality of the REVEALS estimates for each grid cell. The REVEALS model was developed for use with large lakes (>100-500 ha) that represent regional vegetation  (Sugita, 2007a). Grid cells with multiple large lakes will thus provide results with the highest level of certainty and reflect best the regional vegetation. These grid cell results comprising of one or more large lakes are considered "high quality" (dark grey grids in figure  1B ).
, 2014). Our results provide REVEALS estimates for a maximum of 420 grid cells per time window. The number and type of pollen records in a grid cell can change between time windows: not all pollen records cover the entire Holocene. It is therefore important to consider not just the number and type of pollen records in the total dataset, but how this changes between time windows, to assess the reliability of individual results. Results for a maximum of 143 grid cells are based on three or more sites, 65 on two sites, and a minimum of 212 grid cells on a single site. The results of a maximum of 67 grid cells are based on single small bogs (<400 m radius), 68 on single small lakes (<400 m radius), and 82 on single large bogs. It implies that about half the grid cells with REVEALS results should be considered as "lower quality" results.
Role of RPPs and FSP in REVEALS results A key assumption of the REVEALS model is that RPP values are constant within the region of interest, and through time  (Sugita, 2007a ). Nevertheless, it has been suggested that RPPs may vary between regions, with the variation caused by environmental variability (climate), vegetation structure, or methodological design differences  (Hellman et al. , 2008a; Mazier et al. , 2012; Li et al. , 2020; Wieczorek and Herzschuh, 2020). Wieczorek and Herzschuh (2020)   Until we have more RPP values for each taxon, it is not possible to disentangle the effect of all factors influencing the estimation of RPPs and to separate the effect of methodological factors from those of factors such as vegetation type, climate and land use. The only way to evaluate the reliability of RPP datasets is to test them with modern or historical pollen assemblages and related plant cover  (Hellman et al.
, 2008a (Hellman et al. , , 2008b)). We argue that RPP values of certain taxa may not vary substantially within some plant families or genera, while they might be variable within others, depending on the characteristics of flowers and inflorescences that may be either very different or relatively constant within families or genera (see discussion in  (Li et al. , 2018) ). Therefore, we advise to use compilations of RPPs at continental or sub-continental scales rather than compilations at multi-continental scales as the North Hemisphere dataset proposed by  Wieczorek and Herzschuh (2020). We consider the RPP selection used within this work as the most suitable for Europe to date, but expect revised and improved RPP values as more RPP empirical studies are published. Moreover, experimentation in REVEALS applications will allow future studies to evaluate the effects of using different RPP datasets on land-cover reconstructions (e.
Use of the REVEALS results The second generation dataset of pollen-based REVEALS land cover in Europe over the Holocene (this paper) is currently used in two major research projects: LandClimII, and PAGES LandCover6k. LandClimII is a development of LandClimI  (Strandberg et al. , 2014; Trondman et al. , 2015)  and studies the difference in the biogeophysical effect of land-cover change on climate at 6000, 2500 and 200 BP  (Githumbi et al. , 2019). PAGES LandCover6k focuses on providing datasets on past land-cover/land-use for climate modelling studies  (Gaillard et al. , 2018; Harrison et al. , 2020). The first generation REVEALS land-cover reconstruction  (Marquer et al. , 2014 (Marquer et al. , , 2017;; Trondman et al. , 2015)  were used to evaluate other pollen-based reconstructions of Holocene tree-cover changes in Europe  (Roberts et al.
, 2018)  and scenarios of anthropogenic land-cover changes (ALCCs)  (Kaplan et al. , 2017)  (see also section 1). The  Trondman et al. (2015)  reconstructions were used to create continuous spatial datasets of past land cover using spatial statistical modelling  (Pirzamanbein et al. , 2014 (Pirzamanbein et al. , , 2018 (Pirzamanbein et al. , , 2020)). Spatially explicit datasets/maps based on the second generation of REVEALS reconstruction are currently being produced within PAGES LandCover6k and used to evaluate and revise the HYDE  (Klein Goldewijk et al. , 2017)  and KK10  (Kaplan et al. , 2009)  ALCC scenarios. Moreover, LandCover6k archaeology-based reconstructions of past land-use change  (Morrison et al. , 2021 ) will be integrated with the datasets of REVEALS land-cover. Besides the uses listed above, the second generation of REVEALS reconstruction for Europe offers great potential for use in a large range of studies on past European regional vegetation dynamics and changes in biodiversity over the Holocene  (Marquer et al.
, 2019). Explanations for symbols in the taxa list, see caption below Table  A4. + The original paper does not provide a RPP for Poaceae and values of standard deviations (SDs) for the RPPs. We extracted the RPP values related to Picea from Table  5 in      * New RPPs from the Czech Republic  (Abraham and Kozáková, 2012). Earth System Science Data Discussions Preprint. ** New RPPs from Poland. Poaceae as reference taxa (see text for more details) *** New RPPs from Germany  (Matthias et al. , 2012) , reference taxon Pinus. RPPs converted to Poaceae as reference taxon. We selected the RPP estimates obtained with the dataset of vegetation cover including only the trees that had reached their flowering age (allFIDage) (for more information, see  Matthias et al. , 2012). **** New RPPs from Germany  (Theuerkauf et al.
full results, or REVEALS dataset, include mean REVEALS values (in proportions) and their related mean SE for 31 individual tree and herb taxa, twelve PFTs and three LCTs for each grid cell in 25 consecutive time windows of the Holocene (11. 7 k BP to present) (see Data availability section). Here, results are illustrated by maps of the three LCTs (Fig. 2-4) and five taxa (Fig. 5-6, D1-D3). The presented maps are not part of the published dataset archived in Pangea (see Data availability, section 5), they are examples of how the data can be presented and what they can be used for.
Twiddle et al. (2012). RPPs related to Poaceae (1. 00+) were then calculated by assuming that the RPP of Picea was equal to the mean RPP of Picea in Europe (this synthesis) (in bold). ++ The RPPs and their SDs are not listed in the original paper, we therefore read the values from Figure 4 (Bunting et al. , 2013) and the decimals are approximate. +++ Kuneš et al. (2019): we chose the RPP values that were considered best by the authors, i. e. using the lake dataset (pollen from lake sediment), ERV submodel 1 and the Lagrangian Stochastic Model (for details, see Discussion section, this paper). # value for Plantago maritima and ## two values for Rumex acetosa and Rumex acetosella, respectively (Bunting et al. , 2013), for comparison with Plantago spp. and Rumex acetosa-t. (this paper). Underlined RPPs are close to mean RPPs (this synthesis).
the purpose of this study, a new synthesis of the RPP values available for European plant taxa was performed in 2018-2019 based on the latest synthesis by Mazier et al. (2012) and additional RPP studies published since then (Appendix A-C). It provides new alternative RPP datasets for the whole of Europe, including or excluding plant taxa with dominant entomophily,with the important addition of plant taxa from the Mediterranean area (TableA1). The location of studies included in the RPP synthesis are shown in Fig. C1and related information is provided in TableC1. The selection of RPP studies, RPP values (shown in Appendix B, Tables.
(Mazier et al. , 2012;gher confidence in the boreal and temperate RPP values that are based on a wider set of studies increasing the spread of values and hence reliability of the mean RPP values used(Mazier et al. , 2012; Wieczorek and Herschuh, 2020), whilst RPP values for Mediterranean taxa are based on fewer empirical RPP studies. The new RPP datasets for Europe produced for this study (Appendix A-C) can be used in different ways. The RPPs provided in TableA1can be used for entire Europe, including entomophilous taxa or not, and including all values from the Mediterranean area or only the values for the strictly sub-Mediterranean and/or Mediterranean taxa. If one uses all RPPs from the Mediterranean area, there will be taxa for which there is both a RPP value obtained in boreal/temperate Europe and a RPP value obtained in Mediterranean Europe.