dataesr/scientific-paragraphs-categorization huggingface dataset:
| Vegetation plot data sources We started from the sPlot database v2. 1 (created in October 2016), which contains 1,121,244 unique vegetation plots and 23,586,216 species records. Most of the data in sPlot refer to natural and seminatural vegetation, while vegetation shaped by intensive and repeated human interference, such as cropland or ruderal communities, is hardly represented. Data originate from 110 different vegetationplot datasets of regional, national or continental extent, some of which stem from regional or continental initiatives (see  Bruelheide et al. , 2019 , for more information). For instance: 48 vegetationplot datasets derive from the European Vegetation Archive (EVA;  Chytrý et al. , 2016) ; three major African datasets derive from the Tropical African Vegetation Archive (TAVA); and multiple vegetation datasets in the USA and Australia derive from the VegBank  (Peet, Lee, Boyle, et al.
, 2012; Peet, Lee, Jennings, et al. , 2012 ) and TERN's AEKOS  (Chabbi & Loescher, 2017)  archives, respectively. Data from other continents (South America, Asia) or countries were contributed as separate standalone datasets. The metadata of each individual vegetation-plot dataset stored in sPlot are managed through the Global Index of Vegetation-Plot Databases (GIVD;  Dengler et al. , 2011) , using the GIVD code as the unique dataset identifier.
| Resampling method Data in the sPlot database are unevenly distributed across vegetation types and geographical regions (see  Bruelheide et al. , 2018). Mid-latitude regions in developed countries (mostly Europe, the USA and Australia) are overrepresented in sPlot, while regions in the tropics and subtropics are underrepresented, which is a typical geographical bias in biodiversity data (see  Lenoir et al. , 2020; Lenoir & Svenning, 2015  for similar geographical bias in species redistribution). Such a geographical bias usually translates into an environmental bias with temperate climate usually more represented than tropical or Mediterranean climates. Unbalanced sampling effort in the environmental space is of particular concern for comparative macroecological studies  (Bruelheide et al.
5 arcmin  (n = 8,384,404) , based on 30 climatic and soil variables. For climate, we used the 19 bioclimatic variables from CHELSA (Climatologies at high resolution for the earth's land surface areas) v1. 2  (Karger et al. , 2017) , as well as two other bioclimatic variables reflecting the growing-season length (growing degree days above 1 ℃ -GDD1 -and 5 ℃ -GDD5), which were derived from CHELSA's monthly temperatures as in  Synes and Osborne (2011). In addition, we considered an index of aridity and a layer for potential evapotranspiration from the Consortium of Spatial Information (CGIAR-CSI,  Trabucco & Zomer, 2010). For soil, we extracted seven variables from the SoilGrids database  (Hengl et al. , 2017) , namely: (a) soil organic carbon content in the fine earth fraction; (b) cation exchange capacity; (c) pH; as well as the fractions of (d) coarse fragments; (e) sand; (f) silt; and (g) clay.
We finally calculated how many vegetation plots occurred in each PC1-PC2 grid cell (Figure  2 ). In total, vegetation plots were available for 1,720 out of the 4,125 PC1-PC2 grid cells covered by the 8,384,404 terrestrial grid cells of the geographical space. We then resampled those PC1-PC2 grid cells (n = 858) with more than 50 vegetation plots, which is the median number of plots occurring across occupied grid cells in sPlot. This threshold of 50 vegetation plots represents a compromise between selecting a high number of plots, and keeping the resampled dataset as balanced as possible across the PC1-PC2 environmental space. To select these 50 vegetation plots we used the heterogeneity-constrained random resampling algorithm  (Lengyel et al. , 2011). This algorithm quantifies the variability in plant species composition among a set of vegetation plots by computing the mean and the variance of the Jaccard's dissimilarity index  (Jaccard, 1912)  between all possible pairs of vegetation plots.
More precisely, for a given PC1-PC2 grid cell containing more than 50 vegetation plots, we generated 1,000 random selections of 50 vegetation plots and ranked each selection according to the mean (ascending order) and variance (descending order) value of the Jaccard's dissimilarity index. F I G U R E 2 Ranks from both sortings were summed for each random selection, and the selection with the lowest summed rank was considered to provide the most balanced/even representation of vegetation types within the focal grid cell. Where a grid cell contained fewer than 50 plots, we retained all of them. In this way, we reduced the imbalance towards over-sampled climate types while ensuring that the resampled dataset represents the entire environmental gradient covered by the original sPlot database. This approach optimizes the selection of a subset of vegetation plots that encompasses the highest variability in species composition while avoiding peculiar and rare communities, which may represent outliers.
| Permission to release the data as open access The resampling procedure resulted in 56,486, 56,501 and 56,494 vegetation plots selected during resampling iterations #1, #2 and #3, respectively, for a total of 107,238 unique vegetation plots. Since the sPlot database is a consortium of independent datasets whose copyright belongs to the data contributors, we used this preliminary potential selection to ask each dataset's custodian (i. e. , either the owner of a dataset or its authorized representative in the case of a collective dataset) for permission to release the data of selected vegetation plots as open access. For 12,134 unique vegetation plots, permission could not be granted because, for instance, the data are unpublished, confidential or sensitive. The number of vegetation plots for which the open-access permission was not granted in resampling iterations #1, #2 and #3 was 6,699, 6,690 and 6,705, respectively.
To mitigate the imbalance due to the exclusion of these confidential plots, we created a 'consensus' dataset. We started from resampling iteration #1, and replaced the 6,699 plots not granted as open access with plots selected in the second and third iterations, for which such permission could be granted ('reserve' plots, hereafter). We imposed the constraint that each candidate vegetation plot in the reserve pool should belong to the same environmental stratum, that is, the same PC1-PC2 grid cell, as the confidential vegetation plot, even though we acknowledge that this procedure does not maximize the variability in plant species composition of the replacement plots. Even after drawing from reserves, there were 3,150 plots that could not be replaced. These were distributed across 279 PC1-PC2 grid cells (16.
| Trait information For each vegetation plot for which open access could be granted, we computed the community-weighted mean and variance for 18 plant functional traits derived from the TRY database v3. 0  (Kattge et al. , 2020). These traits were selected among those that describe the leaf, wood, and seed economics spectra  (Reich, 2014; Westoby, 1998) , and are known to either affect different key ecosystem processes or respond to macroclimatic drivers, or both  (Bruelheide et al. , 2018)  Because missing values were particularly widespread in the species-trait matrix, we calculated community-weighted means using the gap-filled version of these traits we received from TRY  (Kattge et al. , 2020). Gap-filling was performed at the level of individual observations and relies on hierarchical Bayesian modelling (R package 'BHPMF  ' -Fazayeli et al.
| DATA RECORDS sPlotOpen contains 95,104 unique vegetation plots from 105 constitutive datasets (Table  1 ) and from 114 countries covering all continents except Antarctica (Figure  1 ). This is the result of pooling Information on the size (surface area) of the vegetation survey is available for 67,022 plots, and ranges between 0. 03 and 40,000 m 2 (mean = 377 m 2 ; median = 100 m 2 ). Specifically, sPlotOpen contains 12,894 plots with size smaller than 10 m 2 , 25,742 with size 10-100 m 2 , 24,750 plots with size 100-1,000 m 2 and 3,075 plots with size greater or equal to 1,000 m 2. Similarly, only for a minority of plots (n = 24,167) is information on the exact group of plants sampled in the field available (e. g. , complete vegetation, only trees, only trees > 1 m height, and so on). However, as most data were collected using the phytosociological method, we deem it safe to assume that, unless otherwise specified, plots contain information on all vascular plants.
In short, a plot record was considered as forest if the cover of the tree layer, or alternatively, the sum of the (relative) cover of all tree taxa (scaled by the sum of all cover values, as a percentage), was greater than 25%. It was considered a non-forest record if the sum of relative cover of low-stature, non-tree and non-shrub taxa was greater than 90%. For an extensive explanation of this classification scheme, we refer the reader to  Bruelheide et al. (2019). Even though the proportion of forest versus non-forest vegetation plots is relatively well balanced, the geographical distribution of vegetation plots belonging to different vegetation types is likely not balanced in the geographical space, as it depends on the idiosyncrasies of the constitutive datasets composing the sPlot database.
| DATABA S E ORG ANIZ ATION The environmentally balanced and open-access dataset sPlotOpen is organized into three main matrices, relationally linked through the key column 'PlotObservationID'. The 'header' matrix contains plot-level information for the 95,104 vegetation plots, including: metadata (e. g. , plot ID, data source, sampling date, geographical location, positional accuracy); sampling design information (e. g. , the total surface area used during the vegetation survey); and a plot-level description of vegetation (1) CWM j,k = n k ∑ i p i,k t i,j (2) CWV j,k = n k ∑ i p i,k t i,j -CWM j,k TA B L E 1 (Continued) (Continues) structure (e. g. , vegetation type, percentage cover of each vegetation layer), vegetation type, and naturalness level (i. e. , whether a plot belongs to the same formation that would occupy the site without human interference).
Plots in Europe are also classified according to the European Nature Information System (EUNIS) habitat classification (column 'ESY'), based on the habitat classification expert system (ESY,  Chytrý et al. , 2020). For each vegetation plot, we further provide information on the dataset it originates from, based on the IDs used in GIVD  (Dengler et al. , 2011). We also report four binary fields describing whether a plot belongs to the three resa- mpling iterations (columns 'Resample_1', 'Resample_2', 'Resample_3'), or to the first resampling iteration after the inclusion of replacement plots (column 'Resample_1_consensus'). A brief summary of all the 47 variables in the header matrix is provided in Table  2. The 'DT' matrix contains data on the species composition of each plot. It is structured in a long format and contains 1,945,384 records from 42,680 vascular plant taxa, mostly resolved at the species level.
For each record, we report both the taxon name as originally contributed by the data custodian (column 'Original_species'), and the taxon name after taxonomic standardization (column 'Species'). For details on the taxonomic standardization, please see section 'Technical validation' below. For each species we also provided cover/abundance values. These follow different standards across the datasets constituting the sPlot database. We, therefore, provide both the cover/abundance value as reported in the original data (column 'Original_abundance'), together with the abundance scale that was originally used (column 'Abundance_scale'). This can take seven values: 'CoverPerc' = percentage cover; 'pa' = presence-absence; 'x_BA' = basal area (m 2 /ha, only for woody species); 'x_IC' = individual count, that is, number of individuals in plot; 'x_SC' = stem count, that is, number of stems in plot; 'x_IV' = importance value index; and 'x_PF' = presence frequency.
The average proportion of species in each plot for which functional trait information was available is. 85 (median =. 95). For 42,012 plots, the coverage was complete, while we do not have functional trait information for any of the species occurring in 482 plots. When considering relative cover, the average trait coverage is. 87, with 74,151 plots having functional trait information for species cumulatively accounting for more than 80% of relative cover. When considering the number of species, 68,041 plots have functional trait information for 80% or more of the species occurring in that plot. Except for the 'reference' file (format. bib), all objects/matrices are provided in tab-delimited. txt files. All objects, including the 'sPlo-tOpen_citation' function, are also compiled inside a. RData object.
| TECHNI C AL VALIDATI ON The  (Continues) 2001). During this conversion, we checked that all datasets contained the required metadata information, and cross-checked that each plot was located within the geographical scope of its respective dataset. All individual Turboveg 2 datasets were then integrated into a Turboveg 3 database, and exported to comma-separated files. Finally, we harmonized all the taxonomic names from all datasets, based on sPlot's taxonomic backbone  (Purschke, 2017). This backbone matched all the taxonomic names (without nomenclatural authors) from all datasets in sPlot v2. 1 and TRY v3. 0  (Kattge et al. , 2020)  to their resolved version based on the Taxonomic Name Resolution Service web application (TNRS version 4. 0;  Boyle et al. , 2013). This allowed us to (a) harmonize all datasets to a common nomenclature and (b) link the sPlot database to the TRY database  (Kattge et al.
| USAG E NOTE S The sPlotOpen database can be downloaded from https://doi. org/10. 25829/ idiv. 3474-40-3292. A short vignette introducing the use of sPlotOpen in R can be found in Supporting Information Appendix S1. Users are urged to cite the original sources when using sPlotOpen in addition to the present paper (see Table  1 ). For two datasets (AF-00-009, AF-CD-001), the identification of taxa at species level is still in progress. Data on lichens and mosses, where available (e. g. , dataset NA-GL-001), can be obtained on request from the respective dataset custodian or sPlot coordinator. As most of the constitutive datasets remain under continuous development, sPlo-tOpen users are encouraged to get in touch with the custodian(s) of the data they are planning to use (the updated list of custodian names is maintained on the sPlot website).
The use of sPlotOpen comes with a number of warnings. First, sPlotOpen was resampled in a way that maximizes the compositional variability of vegetation in different environmental conditions. As such, sPlotOpen should not be considered as representative of the spatial distribution of plant communities, especially when the focus has a local or regional spatial extent. Second, for most regions data were collected opportunistically, and without a randomized sampling design. This might lead to some vegetation types being oversampled in some regions, but undersampled in other regions, which might affect the output of species distribution models, especially at local or regional spatial extents. Third, not all plots were sampled using the same plot size, and some plots, mostly located in tropical regions, only contain data on woody species.
This should be accounted for when exploring biodiversity patterns or comparing biodiversity indices (e. g. , species richness, beta diversity) across plots or regions. Finally, a small fraction of plots are nested subsets of larger plots. Depending on the application, this might or might not represent a problem. Nested plots can be identified using the information in the 'metadata' matrix. The most appropriate way to deal with these issues depends on the problem being analysed. Users are, therefore, invited to carefully consider the limitations above when designing applications relying on sPlotOpen. The data described here represent the subset of sPlot for which we were able to secure permission for making these data open. Additional data from sPlot are available under sPlot's Governance and Data Property Rules (https://www.
ACK N OWLED G M ENTS The authors are grateful to the thousands of vegetation scientists who sampled vegetation plots in the field or digitized them into regional, national or international databases. The authors also   Bruelheide et al. (2019). Global Index of Vegetation Databases (GIVD) codes derive from  Dengler et al. (2011). Biomes refer to  Schultz (2005) , modified to include also the world mountain regions  (Körner et al. , 2017). The column ESY refers to the European Nature Information System (EUNIS) Habitat Classification expert system (ESY,  Chytrý et al. , 2020).
I G U R E 1 Top: global distribution of all vegetation plots contained in sPlotOpen (n = 95,104). Each colour represents a different source dataset (n = 105 -different datasets might have the same colour). Bottom: spatial distribution of vegetation plot density for the environmentally balanced dataset selected by the first resampling iteration (n = 49,787). Densities are calculated in hexagonal cells with a spatial resolution of approximately 70,000 km². Map projection is Eckert IV [Colour figure can be viewed at wileyonlinelibrary. com].
Distribution of vegetation plots from sPlotOpen in the global environmental space based on a principal component analysis (PCA) using 30 climate and soil variables. Top: spatial distribution of PCA values across all terrestrial grid cells (n = 8,384,404, spatial grain = 2. 5 arcmin). Bottom left: distribution of plots compared to the distribution of all terrestrial 2. 5 arc-minute cells (grey background) in the PCA space. Only the plots in the environmentally balanced dataset selected in the first resampling iteration are shown (n = 49,787). The PCA space was divided into a 100 × 100 regular grid. The first and second PCA axes explained 47 and 23% of the total variance, respectively. Bottom right: geographical distribution of the vegetation plots contained in four randomly selected PCA grid cells [Colour figure can be viewed at wileyonlinelibrary.
together the three environmentally balanced datasets from resampling iterations #1, #2 and #3 containing 49,787, 49,811 and 49,789 plots, respectively, after excluding the set of plots for which open access could not be granted by data contributors. The number of plots shared across all three resampling iterations is 19,672, while 14,939 plots are shared between two iterations. Replacing confidential plots in resampling iteration #1 with reserves from the other two iterations in the same PC1-PC2 grid cell resulted in a consensus version containing 53,262 plots. sPlotOpen only contains the species composition of vascular plants; information on the composition of bryophytes and lichens was discarded since it was only available for a minority of plots (n = 11,001 and n = 6,801, respectively).
, left). Despite this residual imbalance, all the Whittaker biomes are covered by sPlotOpen (Figure 3, right), and our resampling algorithm has resulted in a much more balanced dataset than many other global datasets that are available, such as GBIF. Almost one third of the 95,104 vegetation plots in sPlotOpen belong to forests (n = 38,282), one half to non-forest vegetation (n = 45,735), with 11. 6% of plots remaining unassigned (n = 11,087).
sPlotOpen contains two additional objects. The 'metadata' matrix contains plot-level metadata, which provide information on the origin of each individual vegetation plot. This object contains 15 columns, with information on the dataset of origin (column 'GIVD_ID ' -Dengler et al. , 2011) , author or surveyor names (columns 'Releve_author' and 'Releve_coauthor'), bibliographic references both at the dataset (column 'DB_BIBTEXKEY') and plot level ('Plot_Biblioreference' and 'BIBTEXKEY'), when available. Similarly, the column 'Project_name' provides information on the project in which a vegetation plot was originally recorded. When available, we also provide information on the numbering of the plots in the publication where they originally appeared (columns 'Nr_table_in_ publ', 'Nr_releve_in_table'), or in the dataset where they were initially stored ('Original_nr_in_database').
In the case of nested plots (n = 1,851), we also provide the original plot and subplot IDs (columns: 'Original_plotID', 'Original_subplotID'). The last two columns report plot-level 'Remarks', and the unique identifier produced by Turboveg when the vegetation plot was first stored ('GUID'). Turboveg is a program specifically designed to store, maintain and export vegetation plot data (https://www. synbi osys. alter ra. nl/turboveg; Hennekens & Schaminée, 2001). Finally, the object 'references' contains all the bibliographic references formatted according to a BibTex standard. Each reference is tagged with a key corresponding to the fields 'DB_BIBTEXKEY' and 'BIBTEXKEY' in the metadata. We further provide an R function ('sPlotOpen_citation') to create reference lists, based on a selection of plots and/or datasets.
original sPlot database has a nested structure and consists of several individual datasets, each validated and maintained by its respective dataset custodian. In many cases, individual datasets are also collections whose vegetation plots were provided by their respective owners (the person who performed the actual vegetation survey) or by someone who digitized the original data from the scientific published or grey literature. We obviously have no direct control over the individual vegetation plots that we provide here in sPlotOpen. Yet, all these vegetation plots stem from trained professional botanists, or published scientific work, and are accompanied by detailed information on the sampling protocols used, thus ensuring data quality and reliability. Before integration into the sPlot database, each dataset was further checked for consistency.
If the dataset was in a different format, we converted it to a Turboveg 2 dataset (Hennekens & Schaminée, F I G U R E 3 Distribution of vegetation plots in the first resampling iteration of sPlotOpen (n = 49,787) in the two-dimensional climatic space represented by mean annual temperature and mean annual precipitation. Left: plots are colour coded based on sBiomes, that is, sPlot's definition of biomes (Bruelheide et al. , 2019) , which derives from Schultz's (2005)  ecozones, modified to include also the alpine biome from Körner et al. (2017). Right: the same plots superimposed onto Whittaker's biomes (Whittaker, 1975) , as adapted by Ricklefs (2008)  and plotted using the R package 'plotbiomes' [Colour figure can be viewed at wileyonlinelibrary. com] TA B L E 2 Description of the variables contained in the 'header' matrix, together with their range (if numeric) or possible levels (if nominal or binary) and the number of non-empty (i.
DATA AVA I L A B I L I T Y S TAT E M E N T The R code used to produce sPlotOpen from the sPlot v2. 1 database is contained in the sPlotOpen_code GitHub repository: https:// github. com/fmsab atini/ sPlot Open_Code. This manuscript was produced using the Manubot workflow  (Himmelstein et al. , 2019). The code for reproducing this manuscript is stored in the sPlotOpen_ manuscript GitHub repository: https://github. com/fmsab atini/ sPlot Open_Manus cript.
F I G U R E 1 Top: global distribution of all vegetation plots contained in sPlotOpen (n = 95,104). Each colour represents a different source dataset (n = 105 -different datasets might have the same colour). Bottom: spatial distribution of vegetation plot density for the environmentally balanced dataset selected by the first resampling iteration (n = 49,787). Densities are calculated in hexagonal cells with a spatial resolution of approximately 70,000 km². Map projection is Eckert IV [Colour figure can be viewed at wileyonlinelibrary. com].