dataesr/scientific-paragraphs-categorization huggingface dataset:
IM-MS instrumentation, calibration, and acquisition Across the three commercial instrument technologies assessed in this study, the ion transport mechanisms and routine analytical procedures for external CCS calibration are known to be inherently different  13, 19. Recommended acquisition settings and application of routine external CCS calibration were applied. Details of the underlying theory for DTIM-MS, TWIM-MS and TIM-MS are presented in the Supporting Information. TWIM-MS datasets were reported in two of our previous publications and publicly available data was used for all comparisons  9, 14. Data from these studies are referred to as the single laboratory 9 and interlaboratory 18 TW CCSN2 libraries, respectively. DTIM-MS measurements were performed using an Agilent 6560 IM-QTOFMS instrument equipped with an Agilent Dual Jet Stream ESI source using various acquisition methods and conditions.
Prior to measurements, the instrument was tuned and calibrated using manufacturer recommendations (ESI-L, G1969-85000, Agilent Technologies). Both stepped-field and single-field measurements were carried out following the method of Stow et al  23. Briefly, DT CCSN2 determined using the stepped-field method as well as single-field operation with either standard or 4-bit multiplexing settings. Long-term repeatability of DT CCSN2 was evaluated by re-analyzing steroids 9 months after the first measurements using 4m acquisition. For generation of a TIM CCSN2 dataset, an Elute UHPLC was coupled to a timsTOF Pro (Bruker Daltonics, Bremen Germany) using an ESI source and the same LC conditions and column were used as described for DTIM-MS analysis except for the LC flow rate (600 µL/min) and injection volume (5 µL) applied.
Precision under conditions of repeatability for measurements of DT CCSN2 and TIM CCSN2 were both excellent (i. e. , average RSD <0. 2%) and effects of bovine urine matrix were negligible. In addition, determined DT CCSN2 values were in good agreement with recently published datasets by Velosa et al. 11  and Davis et al. 5  with average differences <0. 5% for a small number of steroids determined (see Table  S2 ). Similar figures of merit for TW CCSN2 determination of steroids have been previously reported  6, 14.
Comparisons of DT CCSN2, TW CCSN2 and TIM CCSN2 datasets In addition to single IM-MS technology interlaboratory studies, some comparisons across two instrument technology classes for small molecules were performed previously. For example, differences between TIM CCSN2 and DT CCSN2 of 0. 53% to 2. 1% have been determined for plant metabolite datasets  29  and mean percentage errors of TW CCSN2 compared to DT CCSN2 were 1. 0% and 1. 1% for [M+H] + and [M+Na] + ions, but deviations of up to 6. 2% were reported for some ions in the first study that compared the commercial Agilent DTIM-MS with Waters TWIM-MS for small molecule applications  38. Based on the new datasets and the existing TW CCSN2 database  14  , a new comprehensive interplatform and interlaboratory CCSN2 database for steroids and phase II metabolites containing 142 ions was created (see Supporting Information).
The correlation between individual datasets from the three different instruments was investigated, and the appearance of outliers and systematic differences observed for [M-H] -, [M+H] + and [M+Na] + species of steroids was further studied. To compare all datasets, Pearson correlation coefficients (r) were determined between all datasets. Coefficients of r=0. 9949 and r=0. 9953 were obtained when comparing TW CCSN2 and TIM CCSN2 with DT CCSN2 as the reference, and r=0. 9989 when directly comparing TW CCSN2 with TIM CCSN2 (Figure  S1 ) indicating the good agreement between CCS datasets from all three instrument types. Single-field DT CCSN2 data from multiplexed operation was used as reference to calculate bias against results from TIM-MS and TWIM-MS instruments. The histograms of the absolute bias (in %) of TW CCSN2 and TIM CCSN2 and bias distribution visualized as violin plots for comparisons of TW CCSN2 and TIM CCSN2 datasets with DT CCSN2 (4m) values are shown in Fig-  ure S2.
To assess trends in the datasets with respect to the transport mechanism of IM, bias data was also plotted using the modified CCS' as independent variable (CCS'=CCS • √ µ  ⁄ with µ being the reduced mass of the ion-gas pair and z the charge number). This comparison reflects the separation order and reveals a moderate positive correlation (Pearson correlation r=0. 535) for bias of individual ions' TW CCSN2 against DT CCSN2 with respect to CCS', while a weak positive correlation (r=0. 296) was observed in the corresponding comparison of TIM CCSN2 and DT CCSN2 (Figure  1 ). Assessment of the correlation of bias between TW CCSN2 and TIM CCSN2 with CCS' revealed broadly similar results (r=0. 550) as when DT CCSN2 was used as reference. Furthermore, linear regressions were used to investigate systematic bias and possible calibration-related contribution to these observations.
Feasibility of applying single or interplatform CCS databases for identity confirmation of steroids The value of CCS from instrument-specific, crowdsourced or in silico databases remains a topic of great interest for a wide range of analytical applications where standards-free identity confirmation is demanded for  14, 30, 36. Within our study, the generation of a large dataset encompassing CCS values from the three major IM-MS instrument types enabled a critical view on the feasibility of either instrument-specific or interplatform CCS databases for the first time. Although several studies have demonstrated the excellent repeatability and minimal matrix effect for CCSN2 in discriminating different steroid isomers 5,10 , uncertainty estimates for results and reference values must be considered when CCSN2 is employed as an identification parameter on a routine basis  46.
However, reporting of accepted tolerance levels is usually pragmatic and oriented around observed interlaboratory reproducibility leading to precision estimates in the region of ±1% for DTIM-MS  12  , while ±2% is often considered for TWIM-MS and TIM-MS applications  14, 29, 45. To assess the merits of these limits in a clear way, average CCSN2 for each instrument type along tolerances of ±1% (boxes) and ±2% (whiskers) for a series of isomeric ions from the new datasets are plotted in Figure  3. In addition to these thresholds, uncertainty estimates (U, coverage factor k=1) for DT CCSN2 are plotted to illustrate the challenges using CCSN2 for assigning the correct identity from several possible isomers within a given database. For the selected subset of isomers, all average CCSN2 fall into the uncertainty estimates for single-field DT CCSN2 and similar trends regarding isomer differentiation were observed on all instruments.
Except for [M+H] + ions of testosterone glucuronide, epitestosterone glucuronide and [M+Na] + ion of epitestosterone glucuronide, unambiguous differentiation of these isomers across platforms is already impossible when a ±1% tolerance is accepted without additional use of other identification criteria such as retention time information. Furthermore, the systematic bias observed in the TW CCSN2 data would influence false positive and false negative candidates if databases of other instrument classes are used as reference. While only representing a small subset of a particular class of small molecules, this result illustrates the necessity of further collaborative efforts to investigate the merits of consolidating external calibration ions, reference CCS values and strategies for CCS determination, as well as the establishment and implementation of reference materials with a view toward applying CCS as reliable parameter within standards-free identity confirmation workflows.
In considering the physical interpretation of CCS data, it can be stated that the ability to unambiguously diagnose outliers as being representative of true differences in ion conformation is currently hampered. Within the present study, several outliers for TIM CCSN2 and TW CCSN2 were suspected due to the large differences between -2. 7% and +6. 8% to the reference DTIM-MS values. However, it is noteworthy that only sodiated boldenone undecylenate (BU) and deprotonated estradiol diglucuronide (EDG) are outside the uncertainty estimates (k=1) for DT CCSN2. While representing only a small fraction (<1. 5%) of the present dataset, these large differences between TIM CCSN2, TW CCSN2 and DT CCSN2 are a clear issue for the broad applicability of CCS as an identification parameter across different IM-MS platforms.
For this compound several additional ion species were observed including [2M-2H] 2-(m/z 311. 1136 with DT CCSN2~301 Å 2 ), a doubly charged dimer (623. 2345 m/z with DT CCSN2~388 Å 2 ) and the [M+Na-2H] -ion. Interestingly, the [M-H] -ion exhibited a more compact conformation in DTIM-MS compared to TIM-MS and TWIM-MS, whereas the CCSN2 for [M+Na-2H] -was ~240 Å 2 across all datasets. The large CCS difference of >6. 5% indicates that a different conformation of the [M-H] -ion is sampled in DTIM-MS compared to the other instruments. However, this might be related to fundamental differences of the ion transport mechanism, the ion structure or due to the influence of ESI source conditions used on the different instrument classes. To fully elucidate such results would require measurements with the same ESI source and conditions applied on different IM-MS platforms to be performed, which was not feasible within the context of this study.
Such examples finally underscore the value of carefully curated and instrument class-specific experimental CCS databases targeting specific analytical applications although the agreement of CCS values derived from different instrument classes is very good for most of the investigated ions. Furthermore, although high IM resolving power is clearly beneficial for overcoming some of the current limitations of IM-MS analysis for isomeric small molecules, the issues of external calibration and the influence of measurement conditions affecting ion conformation must be considered separately. Recently commercialized IM-technologies such as the Structures for Lossless Ion Manipulations (SLIM)-MS  52  , cyclic ion mobility (cIM)  53  and TIM-MS 54 can be operated with Rp> 200, while HRdm for DTIM-MS allows a comparable increase via data processing  51.
CONCLUSION This study systematically investigated the comparability of TW CCSN2, TIM CCSN2 and DT CCSN2 for the analysis of steroids and phase II metabolites for the first time. Most of the investigated ions fell within ±1% of reference DT CCSN2 for TIM-MS and within ±2% for TWIM-MS. Additionally, 95% of the TW CCSN2 values were found within ±1. 34% of reported TIM CCSN2 values despite a systematic negative and CCS'dependent bias in TW CCSN2 data compared to DTIM-MS and TIM-MS data. Our findings also revealed the presence a calibration-dependent bias for TWIM-MS that is not apparent between DTIM-MS and TIM-MS datasets which are routinely calibrated with the same set of tune ions. While overall agreement was found to be good across all three platforms, these observed systematic differences for TWIM-MS hamper the applicability of CCS databases across different types of IM-MS technologies and increase the risk of false positive and false negative identifications.
Good analytical practices including validation of external calibration and better modelling of measurement uncertainty remain at the heart of IM-MS research if this technology is to make the transition from research into routine analytical laboratories. To the best of our knowledge, this work encompasses the first and most comprehensive comparison of CCS values obtained from three major classes of IM-MS instruments for the analysis of steroids so far. While limited to steroids within the present study, this presented approaches can be viewed as a model that can be applied to a broader range of small molecule databases whereby any of the three instrument classes might be utilized and CCS values employed for supporting standards-free identity confirmation.
Safeguarding Earth's tree diversity is a conservation priority due to the importance of trees for biodiversity and ecosystem functions and services such as carbon sequestration. Here, we improve the foundation for effective conservation of global tree diversity by analyzing a recently developed database of tree species covering 46,752 species. We quantify range protection and anthropogenic pressures for each species and develop conservation priorities across taxonomic, phylogenetic, and functional diversity dimensions. We also assess the effectiveness of several influential proposed conservation prioritization frameworks to protect the top 17% and top 50% of tree priority areas. We find that an average of 50. 2% of a tree species' range occurs in 110-km grid cells without any protected areas (PAs), with 6,377 small-range tree species fully unprotected, and that 83% of tree species experience nonnegligible human pressure across their range on average.
While policy makers and land managers are increasingly aware of the importance of trees, an in-depth global assessment of the adequacy and effectiveness of existing protections for tree diversity is lacking. A comprehensive assessment of protection and pressures on tree diversity would provide important input for establishing conservation and restoration priorities to bend the curve of biodiversity loss  (27). Protected areas (PAs) represent a first-order conservation strategy for preventing biodiversity loss, aimed to preserve natural ecosystems  (28) (29) (30)  and their inherent services, such as carbon sequestration  (31). At present, PAs cover 15. 8% of the Earth's land (World Database on Protected Areas; WDPA*). This value remains below the 17% of Earth's land area recommended by the Convention on Biological Diversity (CBD) 2020 target (see SI Appendix, Box S1 for a detailed explanation).
Here, we analyze a recently developed global database of 46,752 tree species' ranges to 1) assess range protection and anthropogenic pressures for tree species, 2) identify priority areas for conservation of tree diversity considering multiple diversity dimensions, and 3) assess the geographic distribution of current PAs and different potential conservation prioritization scenarios and their respective coverage of global tree species diversity. We used complementarity analysis [Zonation  (58, 59) ] and integration of taxonomy, phylogenetic relatedness, and functional traits to assess whether priority areas for tree conservation overlapped or diverged spatially according to different diversity dimensions  (42, 44). Taxonomic diversity was represented by species identities. Phylogenetic diversity was represented by phylogenetic eigenvectors computed from a genus-level phylogeny  (60).
Results Global Protection Coverage and Pressures on Tree Species. Across all 46,752 tree species in our dataset, an average of 50. 2% of a species' range occurred in 110-km grid cells without existing PAs. A total of 6,377 species (13. 6% of the tree species evaluated) experience no protection anywhere in their range (Fig. 1A  and SI Appendix, Table  S2 ), and 10,987 (23. 5% of tested) tree species have less than 25% of their ranges in such PA cells (Dataset S1). Most tree species experience at least moderate human pressure within their ranges, with an average HMI of 0. 25 across all species (a value comparable to the HMI across most of Wales; Fig. 1A  and SI Appendix, Table  S2 ). A total of 14. 8% of tree species experience high to very high average human pressure within their range (0. 4 < HMI ≤ 1.
Protection Coverage and Pressures Associated with Global Priority Areas. Protecting the 17 and 50% area targets based on priority areas according to taxonomic diversity would increase the average coverage of a species' range to 65. 5% (top 17%) and 82. 6% (top 50%), strongly exceeding the average range proportion covered by existing PA cells (49. 8%) (red dashed lines in SI Appendix, Fig. S3A  and Table  S4 ). Expanding current PAs to either the 17 or 50% top-priority areas would furthermore increase tree species' protection status (SI Appendix, Figs. S3A  and S4 ). For example, of the 13. 6% of tree species in our dataset that are small-range and currently lack any protection, a majority (66. 2%) would become partly or fully covered by 110-km grid cells with potential PAs corresponding to the 17% top-priority areas (yellow flow ribbons in SI Appendix, Fig.
Such areas have also historically experienced lower levels of human pressure, allowing large natural areas to persist and reducing opportunity costs of conservation  (29, 32, 54, 93). Despite the coverage and quality of the tree species dataset analyzed here, limitations such as a necessary coarse spatial resolution (110-km grid cells), limited geographical coverage in parts of Russia and southern Asia  (94) , and limited data for many functional traits introduce uncertainties. Phylogenetic and functional diversity often correlate strongly  (44, 95) , and we also found significant phylogenetic signals for four out of the eight functional traits analyzed in this study (SI Appendix, Table  S1 ). However, this correlation was certainly enhanced by imputing functional traits using phylogenetic eigenvectors.
Methods Tree Species, Their Occurrence Records, and Range Estimates. We used the world tree species list and species occurrence data compiled and cleaned in ref. 94. Briefly, they extracted the records from the world tree species checklist [GlobalTreeSearch; GTS  (74) ] and further standardized the taxonomic names via the Taxonomic Name Resolution Service online tool  (102). The GTS employed the definition of the tree-type growth habit agreed by IUCN's Global Tree Specialist Group, namely "a woody plant with usually a single stem growing to a height of at least two meters, or if multi-stemmed, then at least one vertical stem five centimeters in diameter at breast height"  (74). An initial list of 54,020 tree species was left  (94). Occurrence data for tree species were compiled from five widely used and publicly accessible occurrence databases: the Global Biodiversity Information Facility (GBIF; http://www.
gbif. org), public domain Botanical Information and Ecology Network, version 3 [BIEN; https://bien. nceas. ucsb. edu/bien/ (79, 103)], Latin American Seasonally Dry Tropical Forest Floristic Network [DRYFLOR; http:// www. dryflor. info/ (104)], RAINBIO database [http://rainbio. cesab. org/ (105)], and Atlas of Living Australia (ALA; https://www. ala. org. au/). Due to well-documented problems of biases and errors in global plant occurrence datasets (106), we applied a workflow for occurrence data quality assessment  (94)  to the initially gathered 9,032,654 occurrence records. Our final list of species was lowered to 46,752 species with a total occurrence dataset of 7,066,785 records. We then constructed alpha hulls  (107)  to estimate the range of each species with 20 or more occurrence records using the ashape function of the alphahull package  (108)  implemented in R [version 3.
S6 ). The obtained estimated range maps were rasterized to 110-km equal-area grid cells, a resolution commonly used in global diversity studies (e. g. , refs. 99, 114, and 115), using the letsR package  (116). Even though the estimated range can reduce the geographical bias and fill gaps of the occurrence records, it may overestimate the species' true area of occupancy and they should therefore not be interpreted as a detailed coverage of species' realized distribution, but rather as an estimate of its extent of occurrence (cf refs. 41 and 117). Phylogeny. We extracted phylogenetic information for the tree species with range maps from the largest seed plant phylogeny that is currently available (the ALLMB tree in ref. 118). This phylogeny combines a backbone tree  (119)  reflecting deep relationships with sequence data from public repositories (Gen-Bank) and previous knowledge of phylogenetic relationships and species names from the Open Tree of Life (synthetic tree release 9.
1 and taxonomy version 3; https://tree. opentreeoflife. org/about/synthesis-release/v9. 1). We matched this phylogeny to our tree dataset by first removing any species that were not in our data, and then manually adding some species that were missing from the phylogeny (due to different taxonomic concepts) following the same approach that ref. 119 used to add missing species. The resulting phylogeny contained 46,752 species (SI Appendix, Fig. S11  and Dataset S2). We calculated phylogenetic eigenvectors  (60)  to represent the phylogenetic position of each species in our dataset using the PVR package  (60, 120). Because we were mostly interested in the deep structure of the phylogeny, and phylogenetic eigenvector calculation for large phylogenies is computationally prohibitive, we calculated eigenvectors at the genus level (4,031 genera).
Functional Trait Data. Twenty-one functional traits (SI Appendix, Table  S1 ) were compiled from major trait databases (SI Appendix, Functional Traits and Imputation). As many of the traits had missing values, we imputed trait values applying a gap-filling technique using Bayesian hierarchical probabilistic matrix factorization (123) (SI Appendix, Fig. S12 ). We finally selected eight key functional traits for further functional diversity analyses, including leaf nitrogen concentration, wood density, leaf phosphorus concentration, leaf dry matter content, plant maximum height, seed dry mass, specific leaf area, and leaf area. We used the beanplot package  (124)  to visually compare the observed original and imputed data, and found they generally had similar distribution patterns for each functional trait (SI Appendix, Fig.
S13 ). We further tested the phylogenetic signal for each imputed trait with the function phylosig in the phytools package  (125) , and found four of the eight traits showed significant phylogenetic signals (SI Appendix, Table  S1 ). We performed a similar procedure as for phylogenetic eigenvectors to obtain trait diversity dimensions for our conservation prioritization analysis. We first split each trait into 20 equal bins, and then converted it into a binary species × trait matrix for each trait (46,752 species × 20 bins). For each of the 20 bins, we multiplied it by the 110-km grid cells × species matrix to obtain a trait × grid cell matrix, in which each 110-km grid cell contained the number of species of trait values within the trait interval. In total, we obtained 160 trait × grid cell matrices, a distribution map was generated for each of them, and then all the 160 distribution maps were used in the prioritization analysis to locate the priority regions for trait dimension.
Protected Areas. PA distribution was extracted from the December 2019 release of the WDPA via the wdpar package  (126, 127). The release includes 244,869 PAs globally. According to previous similar global studies (e. g. , ref. 54) , we extracted the PAs from the WDPA database by selecting terrestrial areas belonging to IUCN PA categories I to VI and having a status of "designated," "inscribed," or "established" and areas not designated as man and biosphere reserves by the UN Educational, Scientific, and Cultural Organization. We also excluded the PAs represented as points. A final list of 95,506 PAs was kept. We then resampled PAs at the 110-km grid level following ref. 38 , that is, labeling all cells intersecting a PA polygon as PAs to include any small or narrow PAs. Thus, the PA layer used here provides a very optimistic estimate of existing PA coverage.