dataesr/scientific-paragraphs-categorization huggingface dataset:
39), providing robust support for change in dominance as key to explaining changes in richness with herbivory. Additionally, ANPP no longer has any effect on change in richness from herbivory when SimpsonÕs Dominance was included in the model. Models 1, 2a, and 2b (Supplementary Information) were limited to the 122 sites that had productivity measurements. Because productivity is strongly correlated with MAP both in our data set (Fig. 3 , Model 1 & 2) as well as more broadly 20 , we used precipitation as a proxy for productivity in Model 3 & 4a and 4b (Supplementary Information). This allowed us to include 244 sites in the analysis (Data Subset 3 in Supplementary Table  3 ). In Model 3, we examined the relationship between precipitation and change in species richness without dominance (similar to Model 1 but utilizing a larger dataset) as well as accounted for correlations between input variables (Supplementary Table  7 ).
Like with the ANPP dataset, Model 3 was only able to explain 11% of the variation in change in richness and there was no effect of precipitation in this model. When Berger-Parker dominance was included in the model (Model 4a; Supplementary Table  8 ), our explanatory power of change in richness more than doubled (R 2 =0. 11 vs. 0. 26), and when SimpsonÕs Dominance was included (Supplementary Figure  4b : Model 4b; Supplementary Table  9 ) our explanatory power of change in richness more than tripled (R 2 =0. 11 vs. 0. 36). Similar to Model 2, we again found that site-level Berger-Parker dominance and change in Berger-Parker dominance with herbivores were the main drivers of herbivory-induced changes in species richness (R 2 =0. 26). Precipitation, however, as a surrogate for productivity, had no significant effect in the model.
Importantly, incorporating the larger dataset in Model 4a and Model 4b demonstrated that herbivore-driven changes in dominance exert stronger effects on richness change than site level dominance per se (standardized partial effect sizes of -0. 35 vs. not significant, respectively). These models also identified a strong, negative relationship between site-level dominance and change in dominance (standardized partial effect size of -0. 54 and -0. 58). This occurred because change in dominance is expressed as a ratio of grazed to ungrazed dominance and indicates that grazers reduce dominance more in sites with higher dominance. With this more comprehensive data set, we identified additional factors with direct and indirect effects on richness response to herbivory. For example, grazers alone had a stronger impact on changes in species richness than when grazers and browsers were both present (standardized partial effect size for Herbivore Guild of -0.
METHODS Data. We compiled a database (Grazing Exclosure Database = GEx) consisting of plant community composition data from 252 large vertebrate herbivore exclosure sites (Supplementary Tables  1  & 2 ). To be included in GEx, sites had to meet five criteria. (1) Exclosures had to be located in herbaceous-dominated communities -sites ranged from tallgrass prairie to alpine meadows to desert, but all are dominated or co-dominated by herbaceous species. (2) Large vertebrate herbivores (adult body mass >45 kg) had to be excluded from plots using fencing with adjacent plots exposed to herbivores. Herbivore type and number varies among the sites, including domesticated cattle, sheep, goats, burros, and horses, as well as native wildlife such as caribou, kangaroo, and the full complement of large African herbivores.
The inside of the exclosure could not be manipulated or managed other than the removal of herbivore (i. e. , no mowing or burning that did not also occur outside the exclosure). (  3 ) Data had to be collected after at least three years of exclusion of large herbivores. This was to ensure sufficient time for the plant community to respond to the absence of herbivores. (4) Paired plots inside and outside the exclosure had to be sampled at the same time and sampling intensity. (5) Community data had to be available at the species level. Data types include cover, line intercept, biomass, and pin hits (but not frequency or density), all of which were converted to relative abundance values. Explanatory Variables. Several covariates were used in the analyses which described plant, experiment, and herbivore community characteristics.
When models did not include either ANPP or precipitation as predictors, we used all sites in the database (n=252). Response Variables. The majority of sites had a single exclosure (n=132). When more than one exclosure was built in the same year, each exclosure and corresponding paired plot was considered a block. When multiple subplots were sampled within each exclosure or paired plot, species abundance was summed for each species across the subplots, to obtain species data at the plot level (i. e. , 1 plot per block). Plant community richness and dominance were calculated at the plot level for inside and outside the exclosure. Plant community richness was calculated as the number of species in the plot in that year. Dominance was quantified in two ways. Berger-Parker Dominance (BP Dominance) was calculated as the maximum relative abundance of the most abundant species in each plot.
2a ), MAP (Fig. 2b ), and effect of herbivores on dominance (log response ratio; Fig. 2c ), as well as for the relationship between dominance and richness (Fig. 2d ). To determine the relative importance of various proposed explanatory variables on the richness response to herbivory (log response ratio), we used path analysis conducted in AMOS v7 (SPSS, Chicago, IL, USA). We contrasted the effects of site-level productivity vs. dominance on species-richness response to herbivory utilizing two alternative models. All models also included hypothesized influential covariates such as characteristics of the herbivore community, the plant community, and experimental duration. Data were screened for distributional properties and nonlinear relations. Site-level plant richness and herbivore richness were log-transformed as a result of these evaluations.
While site level dominance and richness theoretically could be driven by precipitation, the correlations between site-level richness and precipitation (Pearson correlation coefficient = 0. 357; linear regression R 2 =. 126) and between site-level Berger-Parker dominance and precipitation (Pearson Correlation Coefficient = -0. 246; linear regression R 2 =. 06) within our dataset were low. Therefore, these relationships were dropped from the path analysis due to replication constraints. Model 1 examined the widely hypothesized relationship between precipitation, ANPP, and change in species richness (Fig. 3a ). Because ANPP was not available from all sites, this model used data from 122 of the 252 sites where ANPP was measured and precipitation was a good proxy for ANPP. Model 2a (Fig. 3b ) used the same data as Model 1 but included an estimate of site level Berger-Parker dominance in the absence of grazing (Udom), as well as the change in Berger-Parker dominance in response to grazing (ln(Gdom/Udom)) to assess the relative effects of ANPP vs.
Acknowledgements Funding for this synthesis was provided for by  USDA AFRI Foundational Conference  Grant (Award # 2018-67013-27400 ). We would like to thank the  National Evolutionary Synthesis Center (Grasslands Working Group) , the  School of Global Environmental Sustainability at Colorado State University , and the  National Center for Ecological Analysis and Synthesis  for hosting working meetings that led to these analyses. We also thank  Mark Ritchie ,  David Augustine , and  Rob Pringle  for helpful comments on an earlier version of the manuscript. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the  U. S. Government. Individual sites acknowledge funding support:  KLEE -NFS DEB 12-56004 ;  Jornada Ð NSF   DEB-0618210 ;  Konza Prairie & Kruger National Park Ð NSF   DEB 0841917.
Bioinformatics analysis The reads were mapped to the human genome (GRCh37) using BWA 0. 7. 12  [29]. Picard Tools 2. 6. 0 was used to flag duplicate reads and we applied the GATK for indel realignment, base quality score recalibration, SNPs and indels calling using the Haplotype Caller algorithm across all samples simultaneously according to the GATK Best Practices recommendations  [30]. After calling we filtered the vcf files for variants having a coverage ! 13 and a mapping quality ! 43 (as done by Munchel and colleagues in their study  [22] ). For the coverage analysis, we used SAMtools 1. 3. 1  [31] , BEDtools 2. 21. 0  [32]  and a custom Python script to generate all the statistics. For the analysis of variants, we used Picard Tools to select variants on their quality and discriminate between SNPs and indels.
A custom script was also used to compare the variants between FF and FFPE samples. For the somatic analysis, we followed a protocol described in  [22]. Briefly, we selected SNVs for colon and liver tumors that were not present in normal samples. This selection was done for FF and FFPE conditions separately, then we counted how many of those tumor-specific variants were in common between the FF and FFPE conditions. All the variants were further annotated with snpEff  [33] , the COSMIC catalog of somatic mutations in cancer  [34]  and the KEGG database of biological pathways  [35]. The VAF (Variant Allele Frequency) values were calculated as the ratio between the depth of the alternative allele (AD) and the total depth (DP). The AD and DP values were extracted from the VCF files. For the average gene coverage, we used the tool sambamba  [36]  to calculate the read coverage for all the position of a given gene from the alignments files (option "depth base").
We observe that a total of 165 tumor-specifc SNVs for colon show overlap between FF and FFPE (representing 38% and 36% of tumor-specific FF and FFPE SNVs respectively, see S3 Table ) and that 53 tumor-specific SNVs for liver do show overlap between FF and FFPE (representing 13% and 16% of tumor-specific FF and FFPE SNVs respectively, see S3 Table ). A selection of the annotated variants is shown in Table  3 , and the complete list of annotated variants for colon and liver tissues is available as S4 Table. A number of variants overlap with the COSMIC database and are found in well-established tumor-related biological pathways. For instance, the KRAS, PTEN and APC genes are well-established tumor drivers in many different tumor types, including colon cancer  [2]. We selected the top 5 mutated genes in liver (TERT, CTNNB1, TP53, ALB, ARID1A,  [41] ) and top 4 mutated genes in colon (APC, TP53, SYNE1, PIK3CA,  [2] ) cancer according to two recent studies and analyzed the mean coverage in FF, FFPE and for the three FFPE extraction kits for all colon and liver tumor samples (S5 Table ).
0195471. g008 Table  3. Selection of annotated tumor-specific variants found in common between FF and FFPE colon samples. Chr: chromosome number. Position: position of the variant on the chromosome. Ns: number of samples in which the variant was found. COSMIC ID: COSMIC database identification code. Gene Symbol: HGNC gene symbol. Pathways: selection of KEGG or REACTOME pathways in which the gene is involved. FFV: mean variant allelic frequency (%) for FF samples. FFD: mean read depth for the position for FF samples (coverage). FEV: mean variant allelic frequency (%) for FFPE samples. FED: mean read depth for the position for FFPE samples (coverage). indicators. The GeneRead and QIAamp are quite similar in terms of all the coverage statistics we have monitored, while the Maxwell kit has lower values for median coverage and percentage of positions having a coverage greater than or equal to 30x and a higher percentage of reads mapping outside the target regions, but on the other hand has a lower percentage of duplicated reads.
Limited but significant variations in coverage and variant calling indicators can be detected for the three different FFPE DNA extraction kits on the samples included in this study. The values of all the indicators are above usually accepted thresholds, but the differences suggest that the selection of a kit for a large scale project (e. g. precision medicine) should be done with care and thorough testing beforehand. the calculation of the whole-exome coverage statistics. This work has been developed and supported by the LABEX (laboratory of excellence program investment for the future) GENMED (Medical Genomics).
Corpora Selection Reports of psychoactive substance use were downloaded from the "experience vaults" in www. erowid. org and are here referred to as the "Erowid corpus. " The webpage www. erowid. org is a "member-supported organization providing access to reliable, non-judgmental information about psychoactive plants, chemicals, and related issues" containing, among other resources, a large number (>20,000) of reports associated with the use of different psychoactive substances. Our research relied upon Erowid's reviewed and edited collection of experience reports, and followed Erowid's terms of use that require researchers to coordinate with Erowid's research team in order to avoid misinterpretations of their data (https://erowid. org/ general/about/about_copyrights. shtml). We discarded reports that resulted from the combination of different substances.
Corpora Preprocessing The preprocessing of the text corpora was performed using the Natural Language Toolkit (NLTK, http://www. nltk. org/) in Python 3. 4. 6 (Bird, 2006). Both corpora of subjective reports were first separated into individual words after discarding all  punctuation marks (word repetitions were allowed). Each word was lemmatized using NLTK (i. e. , converted to the root from which it is inflected). All words were converted to lowercase and lemmatized words containing less than three characters were discarded. Since texts from the Erowid corpus are likely to be influenced by the nature of the substance being reported, we compiled a list of words including substance names, different slang variations, and words relating to the possible routes of administration. A total of 12,465 words fulfilling these criteria were manually selected and the lemmatized versions of these words were discarded from the Erowid corpus.
Future analyses should address the hypothesis that certain spontaneously occurring altered states of consciousness (such as near death experiences) are induced by psychedelic compounds endogenous to the human brain  (Strassman, 2000). The large number of subjective reports available from online databases such as Erowid and Dreamjournal are one of the main strengths of our study, but also represent a source of limitations. Even though both databases are curated, there is no certainty that the substances consumed by Erowid users are chemically pure and that their identity is what the users claim in their reports. Also, Erowid reports frequently lack information on the dosage and, when present, it is not possible to corroborate whether such information is accurate. A placebo-controlled study could alleviate expectation issues in the subjective reports; however, it is unlikely that such study could include a large number of different substances and reports.
It is also not possible to determine whether Dreamjournal reports are based on REM sleep episodes or on dreamlike imagery occurring at other times of the sleep cycle, such as hypnagogic or hypnopompic imagery consisting of hallucinatory content that lacks the elaborate narratives of REM sleep dreaming  (Hobson et al. , 2000). To compensate for the possibility of missing or erroneous information, these databases provide a very large number of reports from which a meaningful signal is likely to emerge in spite of uncontrolled sources of noise  (Halevy et al. , 2009).
Important is that data are taken for plasma operation near q 95 ~ 3, as trends observed at different (mainly higher) q 95 would need to be confirmed for the operation domain planned for ITER. The currently recommended ELMy H-mode energy confinement time scaling  [10] , derived from a regression on a multi-machine database and generally referred to as IPB98(y,2), is τ IPB98(y,2) (s) = 0. 0562 P -0. 69 loss B 0. 15 0 I 0. 93 p κ 0. 78 a n 0. 41 e a 0. 58 R 1. 39  geo M 0. 19 (1) where P loss = P in -dW th /dt is the power loss from the plasma by conduction or convection, not subtracting the plasma core radiation, κ a = S area /(πa 2 ) is a measure of the plasma elongation, n e is the line averaged electron density (in 10 19 m -3 ), M is the isotope mass normalised to the mass of a proton, P in is the sum of the ohmic and auxiliary power (P aux ) coupled to the plasma, W th is plasma thermal energy, 'a' and R geo are the minor and major radius of the plasma and S area is the poloidal cross-sectional plasma area.
The scalar (0D) database for the studies presented in this paper is given in section 3, detailing which devices contributed to the study, the range of parameters obtained in the experiments, the time-period over which the data were obtained and the material of the first wall. The experimental data are complemented with simulations for ITER operation at Q ~ 10 at 15 MA. In section 4, the dimensionless parameter space and trends of confinement with dimensionless variables are reviewed for the experiments at q 95 ~ 3, indicating scaling with normalised Larmor radius and collisionality, together with 'gaps' in the operating space compared to ITER. In section 5, the data are compared to confinement scaling laws using dimensionless and engineering parameters. The plasma confinement properties of the experiments with beta and plasma density are given in section 6, focussing on the difference between data from devices with carbon wall and all metal walls.
03, giving Q = 9. 5 and using Be/Ar impurities to obtain Z eff = 1. 7, the simulation results are given in figure  1. Another baseline simulation is available from 2013 using the DINA code  [18] ; this simulation is aimed at achieving the longest possible burn phase within the limitations of the poloidal field coils. During the flat-top phase at 15 MA q 95 = 2. 86 is achieved, which is lower compared to the CORSICA simulation results shown in figure  1 , by reducing the plasma cross-section to provide more Volt-seconds for the flat-top phase of the simulation. A normalised beta β N of 1. 8 is obtained with H 98y2 set at 1. 0, giving a fusion gain Q of 10. 2. In these simulations carbon is the main impurity giving Z eff = 1. 7.
Database for stationary discharges with q 95 = 2. 7-3. 3 A database with global parameters has been assembled by the ITPA-IOS TG for deuterium discharges that have q 95 in the range 2. 7-3. 3 (i. e. q 95 = 3% ± 10%) from AUG, C-Mod, DIII-D, JET and JT-60U using both carbon wall and (high-Z) metal walls. Note that for all experiments the data supplied are up to 2014 only, allowing publication of the latest results by the individual experiments and organisations first, in-line with ITPA practice. The data available from the experiments are summarised in table 2. Note that the colour code used for the different tokamak devices is used in overview plots throughout the paper, whenever results from machines are compared. -For ASDEX Upgrade (AUG), operation with some carbon plasma facing components ended in 2006, followed by operation with full tungsten walls from 2008 onwards.
Data are available from both periods starting in 1998. ITER demonstration discharges at triangularity δ ~ 0. 36 are included. With the tungsten wall, careful programming of gas dosing and central heating with ICRH or electron cyclotron heating (ECH) are required to obtain stationary discharges at q 95 ~ 3. In figure 2(a) an example is given of a stationary H-mode at AUG at q 95 ~ 3. Discharges at q 95 ~ 3. 6, reported in  [19]  are not included in the dataset. -For Alcator C-Mod (C-Mod), operating with molybdenum walls, discharges with ion cyclotron resonance heating (ICRH) > 1 MW are selected for diverted plasmas with elongation (κ) > 1. 5. The discharges are from the period 2009-2012 and include H-mode and I-mode discharges. Compared to H-modes, I-mode discharges do not have a density pedestal at the edge of the plasma  [20] [21] [22]  and are achieved with the ion B × ∇B drift away from the X-point.
The C-Mod data contain ITER demonstration discharges at both 2. 7 T and 5. 4 T, matching the ITER shape  [13]. An example of an H-mode discharge from C-Mod is given in figure  2 (b), showing that radiation excursions limit the stationary period during the heating phase at low q 95 in C-Mod. To note is that in C-Mod H-mode discharges are not usually ELMy and stationary discharges have enhanced D α activity (EDA) or are ELM-free. (a) A discharge in AUG at q 95 ~ 3 with the full tungsten wall. From top to bottom the toroidal field (in T) in blue, plasma current (in MA) in red, q 95 in blue, β N in green, energy confinement time (in seconds) in green, neutral beam power (in MW) in blue and ICRH power (in MW) in red. The time window for which the data are averaged to provide an entry for the database is indicated.
(b) An H-mode discharge in C-Mod at q 95 ~ 3. From top to bottom the plasma current (in MA) in red, line averaged electron density in blue (in units of 10 19 m -3 ), β N in dark green, q 95 in purple, ICRH power (in MW) in magenta and radiated power (in MW) in orange. The time window for which the data are averaged to provide an entry for the database is indicated. (c) A demonstration discharge at q 95 ~ 3 in DIII-D. From top to bottom the plasma current (in MA) in red, the toroidal field (in T) in blue, q 95 in blue, β N in red, the plasma inductance (l i ) in purple, the Greenwald density fraction in blue, neutral beam power (in MW) in blue, ICRH power (in MW) in red and radiated power (in MW) in red. The time window for which the data are averaged to provide an entry for the database is indicated.
(d) JET discharge at q 95 = 3 with the ITER-like Wall. From top to bottom the plasma current (in MA) in red, the toroidal field (in T) in blue, q 95 in blue, β N in red, Z eff in purple, the Greenwald density fraction in blue, neutral beam power (in MW) in blue, ICRH power (in MW) in red and radiated power (in MW) in purple. The time window for which the data are averaged to provide an entry for the database is indicated. (e) A JT-60U discharge at q 95 ~ 3. From top to bottom the plasma current (in MA) in green, the toroidal field (in T) in black, q 95 in blue, β N in green, line-averaged electron density in purple (in 10 19 m -3 ), H-alpha signal in green and neutral beam power (in MW) in green, which has five levels in this discharge. The time windows for which the data are averaged to provide an entry for the database are indicated.
-Data from DIII-D with carbon plasma facing components are available for the period 1996-2014. The dataset contains a large number (1059) of discharges with different plasma shapes, including demonstration discharges matching the ITER shape and discharge evolution  [23]. DIII-D has data at q 95 ~ 3 with different heating schemes using co-neutral beam injection (NBI), counter-NBI, ICRF heating and ECRH, allowing variations of ion/ electron heating and the applied torque to the plasma. An example with neutral beam heating only, is given in figure  2(c ). -JET operates routinely at q 95 ~ 3 with dominant NBI heating. Operation with carbon walls ended in 2009. H-mode data with an ITER-like beryllium wall and a tungsten divertor are available since 2012  [24, 25]. An example is given in figure  2(d).