dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Analysis The datasets are available under the URL http://doi. org/10. 5281/ zenodo. 1472295, under a creative commons attribution. The results regarding Sourdough 14 gave peculiar data (see section Identification of Yeast Species in Sourdough by the Culture-Based Method) and was thus excluded from most of the analysis described below. Indeed, few microbial isolates could be retrieved from the sourdough. In addition, metabarcoding data revealed only two reads assigned to saccharomycete species over 8444, while this.
Downed coarse woody debris, also known as coarse woody detritus or downed dead wood, is challenging to estimate for many reasons, including irregular shapes, multiple stages of decay, and the difficulty of identifying species. In addition, some properties are commonly not measured, such as wood density and carbon concentration. As a result, there have been few previous evaluations of uncertainty in estimates of downed coarse woody debris, which are necessary for analysis and interpretation of the data. To address this shortcoming, we quantified uncertainties in estimates of downed coarse woody debris volume and carbon storage using data collected from permanent forest inventory plots in the northeastern United States by the Forest Inventory and Analysis program of the USDA Forest Service. Quality assurance data collected from blind remeasurement audits were used to quantify error in diameter measurements, hollowness of logs, species identification, and decay class determination.
Despite its important ecological role and implications for forest management, DCWD has often been overlooked, in part because of the lack of commercial value and the expenses and challenges involved in measuring it. However, monitoring of DCWD is increasing in forest inventory programs that require accurate appraisal of all forest carbon stocks  (Woodall et al. 2008 , Zhu et al. 2017). For example, as a signatory to the United Nations Framework Convention on Climate Change, the United States provides an annual carbon estimate for the forest sector that includes DCWD  (Brown 2002 , Woodall et al. 2015). The growing interest in DCWD has led to improved efforts to quantify it (e. g. ,  Woodall et al. 2008 , Fraver et al. 2013). However, results from inventories are often reported without the accompanying estimates of uncertainty that are crucial for data analysis and interpretation.
Estimates of uncertainty can also be used to improve the efficiency of inventory programs by identifying where best to focus efforts in field surveys. The Forest Inventory and Analysis (FIA) program of the USDA Forest Service conducts and maintains a comprehensive inventory of the forests of the United States. The Northern Research Station FIA unit is responsible for inventorying forests in 24 states in the northeastern United States (Fig. 1 ). In this region, forests cover more than 70 million ha or about 30% of the land  (Oswalt et al. 2014). Although the FIA program dates back to the 1920s, DCWD inventories have been performed only since 2002. As part of the inventory, the FIA program implements quality assurance (QA) procedures for the data it produces. Around the time the standard "production" crews inventory plots, highly trained and experienced "quality assurance" (QA) crews, composed of field crew supervisors and trainers, make independent inventories (i.
e. , blind remeasurement) on a subset (2-5% of plots where DCWD is measured) of the same plots using identical methods. Data from the QA crews are used to assess the adequacy of production crew work, check on the performance of individuals, identify areas of improvement where additional training may be warranted, and ensure that the overall forest inventory data collected by FIA are of the desired quality. We capitalized on this comprehensive and unique QA data set to estimate uncertainty in the volume and carbon storage of DCWD across a suite of FIA plots in the northeastern United States (Fig. 1 ). We quantified uncertainties in the FIA field measurements and evaluated other possible sources of uncertainty associated with DCWD calculations, specifically those involving estimation of the density and carbon concentration of decomposing wood and possible changes in diameter as downed dead wood collapses.
g. , forest type, region, state;  Domke et al. 2013 , Woodall et al. 2013). In this analysis, we used forest inventory data collected by the FIA production and QA crews from 110 plots in the northeastern region of the United States from 2009 through 2015 (Fig. 1 ). Plot-scale calculations of uncertainty were conducted only for plots sampled using the most recent protocol  (i. e. , 2012-2015) , which included 79 plots in 20 states. Data prior to 2012 were used to augment the data (for diameter, species, and decay class) used for Monte Carlo sampling. The total number of measurements that could be compared between production and QA crews from 2009-2015 was 467 for diameter and decay class, 458 for species identification and 259 for hollowness. Measurements of hollowness were initiated in 2012 with the change in protocol; therefore, there were fewer comparisons available for analysis.
Density Estimates of DCWD volume were converted to mass by multiplying by the initial bulk density and a reduction factor (decayed density : undecayed density) that accounts for the decline in density with decay. We used values for both density and density reduction from a synthesis by  Harmon et al. (2008)  that included wood density data published or collected in North America for 88 tree species, grouped into decay classes (Fig. 3b ; Table  1 ). Of the 54 tree species identified by production and QA crews from 2012-2015, 34% were reported by  Harmon et al. (2008) , 42% were estimated from other species of the same genera, and 24% were assigned to species based on other criteria because no data were available for the genus (see Data S1). To evaluate uncertainty associated with density reduction, we randomly sampled from the distribution of density reduction factors (defined by the mean and standard error) by species and decay class for each piece of DCWD.
Biomass to carbon conversion Carbon storage in DCWD can be determined as the product of the volume, density, and carbon concentration. Harmon et al. (2008)  summarized literature values for carbon concentrations of DCWD. Because carbon concentration data for many species are lacking, FIA uses an average value of 49% for hardwoods, 52% for softwoods, and 51% for unknown species types. The standard errors of 0. 43% for hardwoods, 0. 42% for softwoods, and 0. 42% for unknown species were obtained from the  Harmon et al. (2008)  data by weighting estimates by the number of individuals of each species in each decay class in that data set. For our Monte Carlo simulation, we randomly sampled from these distributions of possible carbon concentration values for hardwood, softwood, and unknown species types.
Our study identified the major sources of uncertainty in measurements of DCWD at the plot level. In comparison, across the landscape, the sampling error, or spatial variability, of DCWD volume and carbon storage for these 79 plots was 16. 1 m 3 /ha and 2. 8 Mg C/ha, respectively. The uncertainty in measurements propagated in our Monte Carlo analysis was similar to the sampling error for volume (15. 9 m 3 /ha), but more than fourfold the sampling error of carbon storage (11. 1 Mg C/ha). The FIA database for the region includes 6,095 plots of which only 79 were revisited by QA crews. Future QA inventories on additional plots will enable better characterization of uncertainty due to spatial variation. Clearly, the additional uncertainty incurred in making estimates at the plot scale, especially in carbon storage, is large and in need of attention.
2011). Diameter measurement error was the greatest source of uncertainty for DCWD volume and the second greatest source for carbon storage. Uncertainty in the diameter of DCWD is greater than uncertainty in diameter measurements of standing trees. We evaluated standing live tree data from FIA plots using the same approach as for DCWD and determined that the measurement uncertainty in diameter at breast height was 0. 14 cm, or 0. 7% of the mean tree diameter, whereas the DCWD measurement error was 1. 0 cm or about 7% of the mean DCWD diameter. Measuring the diameter of DCWD is inherently difficult for many reasons, such as identifying the point to be measured given the different orientations of DCWD pieces along the transect, various shapes of log cross-sections, poorly defined edges of decayed logs, and the inability to measure the circumference of DCWD that is on the ground.
Many of these challenges may be insurmountable, but using calipers would likely improve diameter measurements compared to holding a ruler above the log. The decay class is used to determine the density and the collapse ratio. The high agreement between the production and QA crews for decay class 1 indicates that the least decayed pieces of DCWD are the easiest to classify. More DCWD is commonly found in decay class 3, suggesting that the residence time of logs is longest in this decay class  (Harmon et al. 1986) , and this was true in our data set (Fig. 6 ), but we found that the production crew tended to identify more DCWD in this decay class than did the QA crew. The FIA minimum acceptable standard for decay class determination is plus or minus one decay class for 90% of the comparisons  (Westfall and Woodall 2007).
In one case for a plot in Illinois, the QA crew listed a species code of 988 (Laguncularia racemosa [white mangrove]) for a piece of DCWD that the production crew identified as 998 (unknown hardwood). The former species code is likely a data entry error since the species codes are similar and white mangrove is a coastal species not found in Illinois. Including this erroneous species code in the Monte Carlo had no impact because the same initial density and reduction factors are used for both white mangrove and unknown hardwood. In another case, the production and QA crews recorded extremely different diameter measurements (152. 4 vs. 12. 7 cm) for the same piece of DCWD. The larger measurement was greater than any other diameter measured by nearly 100 cm and therefore was almost certainly a recording error.
We decided to exclude this case from the Monte Carlo analysis because it did not represent the type of error we were interested in quantifying. Finally, our analysis focused on the USDA Forest Service FIA data set for the northeastern United States, which allowed us to evaluate uncertainty in a set of parameters particular to that protocol. The extent to which our general findings apply to other approaches to measuring DCWD remains unknown. For example, variability in DCWD volume estimates is known to decrease with increasing line-intersect transect length  (Woldendorp et al. 2004 ); however, the fixed transect length used by FIA did not allow us to address this variable. Further, for protocols using fixed-area plots, where all DCWD pieces within plot boundaries are inventoried, additional sources of uncertainty arise.
ACKNOWLEDGMENTS We are grateful for insight from  Jason Morrison  and thank the field crews, regional trainers, QA staff, and database managers of the  USDA Forest Service, Forest Inventory and Analysis program  who are responsible for collecting, managing, and disseminating the forest inventory data and products used in this study. This research was funded in part by grants from the  USDA Forest Service  ( 16-JV-11242307 ), the  National Science Foundation Long-Term Ecological Studies  ( DEB-1440409 ) and  OPUS  ( DEB-1353159 ) programs and the  Kaye and Ward Richardson Endowment. This publication is a product of  QUEST (Quantifying Uncertainty in Ecosystem Studies) , an  NSF -funded  Research Coordination Network  ( DEB-1257906 ) dedicated to advancing uncertainty analysis in ecosystem stud-.
Methods Between January 1, 1990, and December 31, 2013, 19449 adult patients were identified from a prospective French Sarcoma Group Database. There were 510 patients (0. 03%) identified with radiation-induced sarcomas (RIS), which were defined as (i) history of radiation exposure at least 6 months before the development of sarcoma (3), (ii) occurrence of sarcoma within the radiation field, and (iii) pathologic confirmation of a sarcoma histologically different from the primary cancer. Bone sarcomas were excluded from the study. Margin status was defined as previously described with the R system (4). Prognostic factors for locoregional-free survival (LRFS), metastases-free survival (MFS) and overall survival (OS) were planned to be identified by univariate and multivariate analyses using a Cox regression model.
Variables tested in univariate analysis included: age (median:66 years), gender, histological subtype, grade, tumor size (median: 55 mm), tumor depth, tumor location, margin status and presence of a lymphodema. Variables associated with with a p-value<0. 05 in the univariate analysis were planned to be included in the multivariate analysis. Analyses were performed using SPSS 18. 0 statistical software (IPSS Inc. , Chicago, USA). All statistical tests were two-sided, and p<0. 05 indicated statistical significance.
Molecular preparation and sequencing Taxon sampling included those taxa in the DNA based phylogenies of  Doyle et al. (1997) ,  Hu et al. (2002) ,  Hu and Chang (2003) ,  Jansen et al. (2008)  and  Li et al. (2014 -but  not including several species they recognised in the Callerya cinerea complex). Three chloroplast regions were included in the study. Two protein coding genes: matK and rbcL, and the intergenic spacer ndhJ-trnF. One nuclear gene region was also included in the study ITS1, 5. 8S and ITS2. Fresh DNA was extracted from the previously unsampled Sarcodum scandens (Tables  1, 2 ). For the ITS dataset, 12 additional sequences representing the Callerya group and 26 outgroup sequences were included from GenBank; the matK dataset comprised an additional five Callerya group sequences and 17 outgroup sequences from GenBank and the rbcL dataset, a further two ingroup and 12 outgroup sequences from GenBank (Table  2 ).
Millettia japonica has also been confused with Wisteria floribunda in DNA sampling (see GenBank KT119544) and as a result of this, we have chosen to include three different verified samples in this study in order to ascertain its placement within the Callerya group. Outgroup taxa (Table  2 ) for each analysis comprised several accessions that represented taxa from other Tribes within the IRLC e. g. Hedysareae, Galegeae and Trifolieae  (Lewis et al. 2005)  and several from outside the IRLC, e. g. Robineae, Loteae, Sesbaneae, Millettieae, Abreae, Phaseoleae and Indigofereae. The genus Schfflerodendron Harms was selected as the outgroup upon which to root all trees owing to its position in the LPWG (2017) RAxML Maximum Likelihood analysis. Its position as sister to Callerya atropurpurea -and these two to Glycyrrhiza -in turn all sister to the Callerya group and the rest of the IRLC suggest that it is the most appropriate candidate for choice as outgroup.
Additional outgroup sequences were generated of W095 (see codes to samples, Table  2 ) Schefflerodendron usambarense (Tribe Millettieae), W113 Lotus uliginosus (Tribe Loteae) and W115 Austrosteenisia glabristyla  (Tribe Millettieae)  with the addition of 14 other legume genera from GenBank representing additional Tribes all of which sit outside the IRLC (Table  2 ). We generated 49 sequences of the nrDNA ITS spacer region, including one for the outgroup taxon Schefflerodendron usambarense. Sequence data was also generated for three plastid markers: 51 Callerya group sequences from the ndhJ-trnF cpDNA intergenic spacer, 53 sequences from the matK gene and 57 sequences from the rbcL gene. Sequences of three outgroup taxa (i. e. Austrosteenisia glabristyla, Lotus uliginosus and Schefflerodendron usambarense) were also obtained for these three plastid markers (see Table  2 ).
Phylogenetic procedures and analyses Sequences of each region were edited and compiled in Geneious (version 8. 1. 9; Kearse et al. 2012 ) and aligned with the MUSCLE algorithm  (Edgar 2004 ) implemented in AliView  (Larsson 2014). The ends of the alignments were trimmed to the point where all sequences were present and base calls were unambiguous. Phylogenetic analyses were conducted on the plastid, ITS and combined plastid/ ITS matrices using two approaches, Maximum likelihood (ML) and Bayesian inference (BI). For the ML approach, we used the software  RAxML (v. 8. 2. 8; Stamatakis 2014)  as implemented on the CIPRES portal (www. phylo. org) with 1,000 rapid bootstrap replicates followed by the search of the best ML tree; the GTRCAT model was used and all the other parameters were set as default settings.
The Bayesian Markov Chain Monte Carlo (MCMC) approach was performed using the software MrBayes  (version 3. 2. 6; Ronquist and Huelsenbeck 2003)  as implemented on the CIPRES portal. The best-fit DNA substitution model was tested using JModelTest 2 (version 2. 1. 6; Darriba et al. 2012)  as implemented on the CIPRES portal. The General Time Reversible (GTR) model with a proportion of invariable sites and a gamma shape to account for rate heterogeneity among sites (GTR+I+G) was selected for both partitions. The analyses were run twice each for 10 million generations and sampled every 1000 th generation. The MCMC sampling was verified using Tracer  (Rambaut and Drummond 2009)  and was considered adequate when the effective sampling size was higher than 200. A burn-in period of one million generations was applied to each run.
, 2000;; Kajita et al. 2001; Hu and Chang 2003). Doyle et al. (1997)  found that a single species of Wisteria (Wisteria sp. ) and Millettia japonica were sister to each other. Doyle et al. (2000)  provided data in a larger dataset combined with morphology and found that two samples of Wisteria, one Afgekia and Millettia japonica formed an unresolved clade separate from the rest of Millettia. Results from a parsimony analysis by  Kajita et al. (2001)  which included two species of Wisteria (W. sinensis and W. sp. ), Afgekia sericea and Millettia japonica found that in their strict consensus tree, the two Wisteria samples, Millettia japonica and Afgekia sericea were unresolved in a separate (but unnamed) IRLC. Hu and Chang (2003)  placed Millettia japonica sister to Endosamara racemosa but these were unresolved with respect to two sister samples of Wisteria, W.
Acknowledgements Our grateful thanks are due to  Gwilym Lewis  (K) and  Matt Lavin  ( MONT ) for useful comments on the manuscript; to Gwil also for logistic support at Kew and permission to examine specimens and to  Ian Willey  for assistance with molecular data. We are extremely grateful to  Alastair Culham ,  University of Reading , for helping us to implement this paper by raising the initial funding for the research from the  Stanley Smith Horticultural Trust (UK). Thanks to  Henry Noltie  (E) for information on the type of  Endosamara, Elspeth Haston (E)  for assistance with literature and  John McNeill  (E) and  Nick Turland (B)  for answering nomenclatural queries. Thanks also to the following Curators and Directors of  Botanical Gardens and Herbaria  for assisting with our search for material: A, ABD, B, BM,  BO ,  BRI, CAL, CNS, DAO, E, FI, G, GH, H, IBSC, K, KAG, L, LE, M, MICH, MO, MPU, NY, O, P, PE, US.
Recruitment There will be several routes for participant recruitment, depending on the specific study site. In the UK (Manchester), the National Health Service (mental health or memory assessment services) will be the first-line sources for recruitment. Other routes to participation will include the on-line national dementia clinical research portal, 'Join Dementia Research' (www. joindementiaresearch. nihr. ac. uk), alongside referral from local primary care clinics, dementia support groups and the Alzheimer Society. In France (Nice), recruitment will be from the Centre Mémoire de Ressource et de Recherche's clinical database and local primary care, neurology and geriatric medicine clinicians. In Greece (Athens), participants will be recruited from organised dementia care centres, the Geriatric Psychiatry Outpatient Memory Clinic, the Nestor Psychogeriatric Association and the Athens Alzheimer Association.
Data protection and sharing We will follow best practice in accordance with current UK General Data Protection Regulation (GDPR) guidance and adhere to the Guidelines for Data Management in Horizon 2020 and local guidelines for each site; no patient identifiable data (PID) will be transferred across sites. All PID will be kept separately from the anonymized data to be entered in the case report form (CRF). The University of Manchester institutional repository (Research Data Management Service) will cater for the publishing and sharing of research data. Data will be made available through the University of Manchester institutional repository (Research Data Management Service). Research data will be made available together with appropriate metadata in line with Horizon 2020 policy to enable other researchers to identify whether the data could be suitable for re-use.
Quantitative analyses: Test of the intervention effect For estimating and testing the effect of the intervention on the primary (DEMQOL score at 36 weeks) and secondary outcomes, an a priori Statistical Analysis Plan (SAP) will be devised, detailing the analysis methods, outcomes, covariates, handling of missing data, standard error estimation methods and any sensitivity analyses. The SAP will be submitted to the Trial Steering Committee (TSC) for review and approval prior to the start of statistical analyses. Intention-to-treat principles will be followed, and all randomly assigned participants will be analysed according to trial arm allocation, including as far as possible those who discontinue the study, for whom follow-up data will continue to be collected wherever possible. A separate multi-level (participants within sites) mixedeffects regression analysis will be conducted for each outcome to estimate and test the mean effect of the intervention at 18 and 36 weeks.
Qualitative interview analyses All interviews with dyads allocated to the SI group will be audio-recorded, transcribed verbatim and anonymised. The interviews will be analysed by using conventional qualitative content analysis  [58]  and a Grounded Theory approach  [59]. Qualitative analysis of the post-SI interviews will be led by the Catholic University of Applied Sciences Freiburg (CUF). Researchers at respective sites will identify initial themes in their native language. CUF will then combine the whole dataset and generate a final code list using Grounded Theory  (Glaser and Strauss, 1967 [59] ) methodology. This will be through an iterative process of data collection and analysis to develop initial themes, prior to analysing the entire data set and developing a model based on emerging categories.
Health economic analysis A within-trial cost-effectiveness analysis will be performed. All costs consumed and quality-adjusted life-years (QALYs) gained within the 36 weeks of the trial will be calculated for both the SI group and the CAU group. Costs will be estimated on the basis of the resource use data collected in the trial and applying unit costs from country-specific reports and the published literature. The health utility scores will be multiplied by the duration of time spent on each health state to generate QALYs. The incremental cost-effectiveness ratios of the SI compared with CAU will then be calculated. A model-based cost-effectiveness analysis will be conducted to estimate costs and effects. Parameters in the model will be specified using data collected within the trial, published literature, or expert opinion.