dataesr/scientific-paragraphs-categorization huggingface dataset:
Final operational taxonomic units (OTU) were then clustered de novo at 97% sequence identity using usearch (61) with chimera removal. Representative OTU sequences in FASTA format are available through figshare (DOI: 10. 6084/m9. figshare. 23060354). OTU taxonomy was assigned based on the SILVA reference database (v132) using sintax from vsearch (v2. 15. 2_linux_x86_64)  (63)  with a probability cutoff of 0. 8. Following the removal of non-bacterial OTUs (i. e. , Archaea, mitochondria, chloroplast, and unclassified domain), a phylogenetic tree was generated using FastTree (v2. 1. 10)  (64)  with Sulfolobus (X90478) included as an outgroup aligned with ssu-align (v0. 1. 1)  (65). The sample metadata, OTU table, taxonomy table, and phylogenetic tree were combined into a phyloseq  (66)  object available through figshare (DOI: 10.
6084/m9. figshare. 23060354). The sequence processing code is available at https://github. com/ShadeLab/Centralia_RNA_DNA_multiyear. We defined bacterial OTUs based on 16S rRNA gene sequence clusters at 97% sequence identity, including DNA and RNA sequence sets. OTUs were used rather than ASVs to avoid the potential artificial splitting of bacterial populations into multiple clusters  (67, 68)  and to account for potential increased errors due to the additional PCR step in reverse transcribing RNA.
Statistical analyses. All statistical analyses were conducted using R (version 4. 1. 1)  (69). The analysis code is available at https://github. com/ShadeLab/Centralia_RNA_DNA_multiyear. Change in soil temperature and pH over time was examined within fire classifications with linear mixed effects (LME) models with package nlme (70) using either soil core temperature or pH as the response variable, time as the fixed effect, and site ID as the random effect. Testing broad differences in soil pH across fire classifications was accomplished using ANOVA and post hoc Tukey's test. OTU counts from triplicate libraries were added together for DNA samples, and samples with less than 100,000 reads were removed. RNA samples with less than 10,000 reads were also removed. Before whole community analysis (DNA samples only), entire sites with less than three years of adequate DNA sequencing were removed, and the resulting OTU table was rarefied to 161,171 reads to account for the wide range of sequencing depths.
For active bacterial community analysis, DNA samples without a corresponding well-sequenced RNA sample and entire sites with less than three years of adequate DNA and RNA sequencing were removed, and the resulting OTU table was rarefied to 100,491 reads. OTUs found in an RNA sample but not the corresponding DNA sample were given a count of 1 in the DNA sample  (27, 28). OTUs with an RNA: DNA ratio ≥ 1 in each paired RNA and DNA sample were then defined as active  (27, 28). Further analysis of the active community subset used the modified OTU tables from the DNA samples but only included active OTUs (i. e. , zero count wherever DNA and cDNA count was zero or RNA: DNA ratio was < 1). DNA and cDNA sequence set rarefaction produced 50,242 OTUs across both datasets, 38,020 (75. 7%) of which were considered active in at least one sample.
For each sample, 50. 5 ± 0. 6% of these active OTUs could be considered phantom (i. e. , cDNA reads found, but no reads in the corresponding DNA samples after rarefaction). However, this phantom percentage drops to 35. 6 ± 0. 7% when considering unrarefied DNA reads and 2. 1 ± 0. 1% when considering the entire DNA dataset regardless of the sample (Fig. S13 ), providing evidence that many phantom taxa within the rarefied dataset are likely biological sequences and not entirely artifacts. Alpha diversity was measured as Faith's phylogenetic diversity indices. Change in alpha diversity over time was examined within fire classifications with LME models, using the diversity indices as the response variable, time as the fixed effect, and site ID as the random effect. Again, differences in alpha diversity across soil temperatures were examined regardless of fire classification using LME models with the diversity indices as the response variable, soil core temperature as the fixed effect, and site ID as the random effect.
Acknowledgments We thank  Tammy Tobin-Janzen  for Centralia site consultation and field support and  Keara L Grady  for superb coordination of fieldwork logistics and organization of multi-year soil procurement and processing. We additionally thank  SH Lee ,  JW Sorensen ,  TK Dunivin ,  JL Chodkowski ,  N Stopnisek , and  M Mechan Llontop  for their enthusiastic participation in the fieldwork. This work was supported by the  U. S. National Science Foundation CAREER  award # 1749544  to AS.
Arthropod specimens captured from each trapping event were sorted, photographed, and processed individually. Analysis began with each specimen identified morphologically to a taxonomic order and registered on the Barcode of Life Data Systems (BOLD). DNA from each specimen was then extracted and used to amplify and Sanger sequence the standard COI barcode region  (Hebert, Cywinska, Ball, & deWaard, 2003). The resulting COI sequence data was uploaded to the BOLD database, linking each specimen's morphological identification to its COI barcode sequence. For each trapping event, all specimens were sequenced, except when a particular morphospecies was represented by more than 50 individuals, in which case a subset of the individuals were sequenced to confirm that the specimens did indeed represent a single unique  BIN (deWaard et al.
, 2019). The final GMTP dataset includes 1. 2 million barcode records and 155,185 unique barcode index numbers (BINs)  (Ratnasingham & Hebert, 2013). Prior studies have established a strong correspondence between BINs and species identification in insect groups with well-established taxonomy, thereby justifying the recognition of BINs as species proxies  (Ratnasingham & Hebert, 2013). Temporal differences (i. e. distance in time) between each pair of sampling events was calculated using circular statistics by first determining the Julian day of the two sampling events and taking two measures  (1)  the absolute difference between the two Julian days divided by 0. 986 (0. 986 degrees = 1 day) and (  2 ) 360 minus the absolute difference between the two Julian days divided by 0. 986. The minimum value between measure (  1 ) and (  2 ) was then used as the distance in time between the two sampling events.
To account for the unbalanced sampling effort in our models, we applied a weighted linear regression, where the weight for each data point was set to 1/𝑛𝑛 𝑠𝑠 1 𝑠𝑠 2 , so that the total weight was equal among all pairs of sites. We note that data points are not independent of each other, because each data point in the linear model involves a pair of samples that are correlated in time and space. For this reason, we did not perform significance tests based on output from the linear model but instead employed the following permutation approach to determine significance for each of the explanatory variables in our models. Given the unequal temporal sampling and spatial sampling design across the multiple GMTP project datasets we used a series of permutation test to assess the significance of each explanatory variable  (Chihara & Hesterberg, 2018).
Discussion The dataset generated by the Global Malaise Trap Program offers a unique opportunity to assess the underpinnings of global latitudinal biodiversity patterns using a highly diverse and dominant group of terrestrial organisms. Our main finding is that total pairwise beta-diversity dissimilarity increases with decreasing latitude, increasing spatial distance, and increasing distance in time. We did not find strong indications of generalized partitioned beta-diversity patterns (i. e. species replacement or richness difference) at a global scale. However, we did find partitioned beta-diversity patterns at the regional scale which differed in the influence of latitude, spatial distance, and time indicating regional factors play a key role in overall beta diversity patterns (Figures  3, 4  & S4).
Recommendation 3 -Data standardization and management tools This recommendation (based on R3) consists of regularly addressing critical gaps in tools for IAS impact/risk assessment and their management. Suggested actions include creating and/or improving standard protocols for assessing impacts, pathways and vulnerability of priority areas, conceiving adaptive approaches to guide management decisions, developing novel management techniques, and ultimately ensuring the adoption of these tools at the country and European level, and if applicable even globally. Several other goals (like R2 and R4, among others) call upon the establishment of a European centralized IAS open data portal that facilitates the efficient recording, storing, standardization, continuous updating, peer-reviewing, and broad accessibility of all available information related to IAS and their management in Europe, and allows automated approaches to managing and analysing these big datasets.
Acknowledgements This research was funded through the  2017-2018 Belmont Forum  and  BiodivERsA joint  call for research proposals, under the  BiodivScen ERA-Net COFUND programme , and with the funding organisations  AEI ,  BMBF  and  FWF  [grant numbers  AEI PCI2018-092966  (NRP,  CPG , CGL) /  AEI PCI2018-092939  (MV)  / AEI PCI2018-092986  (BG)  / BMBF  projects  16LC1803A  &  16LC1807B  (JMJ,  WCS ) /  BMBF 01LC1807A  (HS) / FWF project  4011-B32  (FE, GL, BL)];  Swiss National Science Foundation  [grant number  31BD30_184114  (SB)]. CPG acknowledges the support from  Ministerio de Educación y Formación Profesional  through the  Beatriz Galindo Fellowship  ( Beatriz Galindo -Convocatoria 2020). We highly appreciate the participation to the workshops of  Spyridon Flevaris ,  Ingolf Kühn  and  Jörg Priess , the scenario illustrations of  Kris Tsenova , and the constructive comments of anonymous reviewers and the handling editor.
The field measurements were carried out in summer 2018. While it is common to perform PMF studies individually on ACSM and more recently on Xact datasets, here we used a two-step methodology leading to a complete PM1 source apportionment. The outputs from both OA PMF and Xact PMF, the inorganic species concentrations from the ACSM and the black carbon (BC) fractions (fossil fuel and wood burning) measured using an Aethalometer (AE33) were gathered into a single dataset and subjected to a combined PMF analysis. In overall, 8 factors were identified, each of them corresponding to a more precise source than performing the previous single PMF analyses. The results show that besides the high contribution of secondary ammonium sulfate (28%) and organic nitrate (19%), about 50% of PM1 were originated from combustion processes (traffic, shipping, industrial, cooking and biomass burning emissions).
, 2017; Chen et al. , 2022; Crippa et al. , 2013b)  or from off-line filters analyses  (Borlaza et al. , 2021b, a). Several ways have been explored to combined datasets from several online analysers to perform PMF. Some studies proposed to combine high resolution AMS and proton-transfer-reaction mass spectrometer (PTR-MS) measurements to refine the links between particle phase organics and their precursors  (Crippa et al. , 2013a; Slowik et al. , 2010). Some others performed source apportionment by including both organic and inorganic fractions from the AMS  (Äijälä et al. , 2019; McGuire et al. , 2014; Sun et al. , 2012) , improving the factors resolution and their chemical nature. More recently,  Zografou et al. (2022)  performed PMF analysis on combined organic and inorganic year-long dataset from a ToF-ACSM.
Tong et al. (2022)  combined into a single dataset AMS and extractive electrospray ionisation time-of-flight mass spectrometer (EESI-ToF) measurements providing an optimised identification and quantification of the organic factors, more specifically the SOA fraction. Nursanto et al. (2023)  also tried an hybrid approach by combining this time organic aerosol concentrations from a ToF-ACSM with the particle size distribution from a Scanning Mobility Particle Sizer (SMPS) and resolved organic factors related to new particle formation and growth. However, a lot of efforts remain for combining instruments datasets and apportion the sources of the total PM1 fraction. A multi-time resolution approach was suggested by  Via et al. (2023)  mixing PM1 data from online analysers and offline filters. Belis et al.
, 2013) : (3) 𝑔 𝑖𝑘 = 𝑔′ 𝑖𝑘 ± 𝑎 • 𝑔′ 𝑖𝑘 (4) Where the scalar 𝑎 defined the range (between 0 and 1) to which 𝑓 ′ 𝑘𝑗 and 𝑔′ 𝑖𝑘 can vary from the known input profile (𝑓 𝑘𝑗 ) or time series (𝑔 𝑖𝑘 ). In the current study, source apportionment was conducted using PMF model on three distinct datasets. In the first two analyses, PMF was applied separately to the OA dataset (PMForganics) and Xact dataset (PMFmetals). As a second step, the outputs from the previous source apportionment analyses, the inorganic species concentrations from the ToF-ACSM measurements (NO3 -, NH4 + , SO4 2-and Cl -) and the deconvolved BC sources (BCFF and BCWB) concentrations from the AE33 were combined into a single dataset to perform a total PM1 source apportionment (PMFPM1).
OA dataset ACSM data were acquired with the Igor-based Acquility v2. 1. 4 software and analysed with Tofware v3. 2 also developed in Igor Pro (Wave Metric inc. , Lake Oswego, Oregon, USA). The data treatment including ionisation efficiency (IE) and relative ionisation efficiency (RIE) calibrations, collection efficiency (CE) correction and detection limits determination is detailed in  Chazeau et al. (2021). The complete PMF methodology and optimization applied to OA mass spectra at MRS-LCP are described in a dedicated paper  (Chazeau et al. , 2022 ) and we will only provide a brief summary here. The model is performed on data input including 185 variables from m/z12 to m/z 214 and 4422 time steps (15 min intervals) from 1 st July to 1 st September 2018. The error matrix was exported from the Tofware software and the calculation included ion counting statistics, background errors, electronic noise and a minimum error from the measurement of a single ion.
Xact dataset The PMFmetals was performed on the Xact data matrix of hourly element concentrations from July 11 th to September 1 st 2018. First, some individual species were excluded to improve the quality of the analysis. Elements whose concentrations were below their respective Minimum Detection Limit (MDL) more than 90% of the time were not included in the inputs (Fig. S1 ). The MDLs were provided by the manufacturer and are given in Table  S1. From this approach, the following 17 elements remained: As, Br, Ca, Cd, Cr, Cu, Fe, K, Mn, Ni, Pb, Sb, Se, Sn, Ti, V and Zn. Co was also included in the inputs as it showed a good correlation with Ni element (R² = 0. 5). A PMF error matrix (𝜎 𝑖,𝑗 ) was estimated using the (Eq. 5) for concentrations greater than the MDL  (Reff et al. , 2007; Ryder et al.
, 2020) : 𝜎 𝑖,𝑗 = √𝑀𝐷𝐿 𝑖 2 + 𝑢 𝑖,𝑗 2 (5) Where 𝑢 𝑖,𝑗 is a specific analytical uncertainty for each data point provided by the Xact. It includes both uncertainties of the sampling air volume and uncertainties of the mass spectra deconvolution calculated by the Xact software. For the concentrations below the MDL, the values were replaced by the MDL of metal divided by 2. The corresponding error is often used to be set to 5/6 × MDL  (Polissar et al. , 1998). Since the relative error for each datapoint was most of the time less than 50%,  Polissar et al. (1998)  recommended to apply a relative error between 100% and 250% for values below the MDLs. In our dataset, some relative errors for data greater than the MDLs were much larger than 50%. Following  Polissar et al. (2001) , the elements were downweighted by using larger error estimates for values below the MDLs.
The factor profile of fireworks was then constrained in the final complete dataset PMF analysis. Manousakas et al. (2022) ran a PMF on the entire dataset constraining all the source profiles except the fireworks factor and the time series of all sources. The fireworks time series were set to zero except during the corresponding events. In this study, we followed the same logic and the firework hours (13 July 20:00 UTC to 14 July 05:00 UTC; 14 July 20:00 UTC to 15 July 14:00 UTC) were July) points (FDP) to determine a specific profile. The dataset represented 60 time points and 19 variables. In addition to the elements previously selected, Bi was included as it was exclusively associated to the firework events. Bi as bismuth trioxide form (Bi2O3) is commonly used instead of the toxic lead metals for crackling fireworks, the so-called "dragon's eggs"  (Mohan, 2010; Perrino et al.
, 2011). Finally, the PMF was performed on the total dataset (1230 time points and 19 variables) by adding a constrained firework factor. One important step is selecting the number of factors based on both mathematical diagnostics and environmental meaning of the factors. Solutions with a range of 1 to 8 factors were examined for the WFP dataset. The selection is achieved based on the changes in Q/Qexp (∆Q/Qexp) and real and noisy unexplained variation (∆UEVreal, ∆UEVnoisy) when increasing the number of factor (Fig. S2 ). A large reduction of these values would indicate limited improvements of the model residuals and explained variability. There were no significant changes in ∆UEVreal and ∆UEVnoisy between 2 and 8 factors. However, ∆Q/Qexp showed a decrease up to five factors meaning the changes in Q/Qexp were very low.
To relate the factors from the PMF with specific sources, the diurnal trends, time series of elements and comparison with some external tracers were examined. We could clearly identify five environmentally reasonable factors: dust resuspension, shipping, industrial, tire/brake wear and regional background factors. Selecting the 6-factors solution results in an unresolved Br-rich factor which can't be attributed to a specific source or aerosol processes. Therefore, the 5-factors solution was chosen as the best representation of the data. For the FDP dataset, the PMF analysis resolved the same five factors in addition to a firework factor. However, the analysis showed some mixing between the regional background and the firework factors due to the large contribution of K in both factors. To avoid this mixing, regional background profile was tightly constrained using the profile resolved with the WFP dataset and an a-value of 0.
1. The runs were repeated 50 times with a bootstrap resampling strategy to test the stability of the solution. This time a well-defined firework factor was resolved (Fig. S3 ) with an elemental composition in agreement with other studies (see section 3. 2. 2). The averaged firework factor profile retrieved from the FDP dataset was used as a constraint for the complete dataset with avalues randomly initialised between 0 and 0. 5 with an increment of 0. 1. This initialisation is used to evaluate whether larger deviations could improve the results  (Canonaco et al. , 2021). The factor time series were also constrained with similar a-values and set to 0 except during the firework events. The remaining factors were let unconstrained in the complete dataset solution. Similarly to the PMForganics methodology, 100 bootstrapped runs are conducted and a criteria-based selection is used to assess the quality and position of the PMF runs (Fig.
Combined PM1 dataset Following the methodology described by  Petit et al. (2014) , we combined PMF outputs with the remaining chemical species of PM1. Thus, the factors from both PMForganics and PMFmetals were combined with BCFF, BCWB, NO3 -, NH4 + , SO4 2-and Cl - concentrations. The uncertainty matrix was constructed as following: uncertainties for the ToF-ACSM inorganic species were exported with Tofware similarly to those for the organics; BCFF and BCWB uncertainties were estimated based on (Eq. 5) and (Eq. S1), with 𝑀𝐷𝐿s set to 0. 1 for both species and 𝑢 𝑖,𝑗 the relative uncertainties set to 40%  (Petit et al. 2014 ) multiplied by the species concentrations; PMForganics and PMFmetals outputs errors were taken from the standard deviations of factors time series from the bootstrap analyses, which can be used as statistical uncertainties  (Canonaco et al.
Tire/brake wear metals were also important contributors to this factor (29% of the total variable). However, this component presented the highest unexplained variation (>30%) from all the dataset (Fig. S14 ) and revealed some over splitting in several factors. It should be emphasized that 23% of the traffic source was constituted of SOA (LOOA and MOOA) meaning that primary traffic contribution is mixed with https://doi. org/10. 5194/egusphere-2023-1441 Preprint. Discussion started: 28 July 2023 c Author(s) 2023. CC BY 4. 0 License. secondary aerosol concentrations attributed to fast oxidation of freshly emitted particles  (Chirico et al. , 2011). The factor represented the second highest fraction of the PM1 mass (21. 4%). The organic nitrate-rich (ON-rich) factor was resolved based on the high contribution to NO3 -(81%) and LOOA (69%).
https://doi. org/10. 5194/egusphere-2023-1441 Preprint. Discussion started: 28 July 2023 c Author(s) 2023. CC BY 4. 0 License. removed from the dataset to inspect the remaining sources. The PMF inputs without the firework points (WFP) consist in 1201 time points with 1h step and 18 elements. As a second step, PMF analyses were conducted only on Firework days (13 and 14.
To do so, we used the transition matrix obtained from colonization and extinction probabilities, and calculated its steady state using the R package markovchain  [53]. We checked model convergence by visually inspecting the trace plots and by calculating the Gelman and Rubin's R statistic  [54]. To evaluate how the observation and the transition parts of the model fit the data, we performed a posterior predictive check  [48]. Briefly, we simulated 10,000 datasets using the model estimates and calculated Bayesian p-values for the detection model and the transition model. Bayesian p-values are defined as the proportion of times the observed dataset fitted the model better than the simulated one (See Appendix S2). The model is assumed to have a proper fit when the Bayesian p-value is between 0.
Acknowledgements Acknowledgements We thank  Arne-Petter Sarre ,  Alfred Ørjebu ,  Jan-Erik Knutsen ,  John-Arne Kristiansen  and  Berit Gaski  for work in the field,  Siw Killengreen  for curating the data during the first 10 years of the project, and numerous assistants for their invaluable contribution in watching and annotating the pictures. We also thank  Axelle Scamps  for the drawings used in Fig 2.
4 All the ESMs outputs and products were not at the same resolution. Thus, we needed to choose a reference for map-grid resolution. The coarser resolution was from  Konings et al. (2019) 's product with a 4°×5° resolution grid. Degrading every Rh map at such resolution would be a substantial loss of information. Thus, we increased the resolution of those datasets and decreased the very fine scale maps to an arbitrary reference corresponding to the CNRM-ESM2-1 model which runs at 0. 7° resolution. We chose to set the reference at the maximum resolution available among CMIP6's ESMs predicting Rh. We used the common regridding routine cdo remapdis (nco module) that performs regridding by distance-weighted average remapping and conserve latitudinal and longitudinal means. To avoid coastal pixels encroaching into oceans, we weighted each pixel by its proportion of land.