dataesr/scientific-paragraphs-categorization huggingface dataset:
Identifying optimal parameters In order to determine which parameters nPhase should use by default, it is important to understand how these parameters affect the results. Ideally, we will find that there is a set of parameters which is optimal for all possible combinations of ploidy and heterozygosity level; such a set would then become the default recommended parameters for nPhase. If no such set of parameters appears to exist, the next best case is to minimize the impact of as many of the available parameters as possible in order to reduce the parameter a user would need to explore when using nPhase to phase their dataset. Through our tests, we find that there is a narrow range in the parameter space that results in the optimal performance of nPhase. Intuitively, the optimal strategy appears to be to set the minimum similarity and minimum overlap parameters down to a low value so that all of the reads in the dataset are allowed to merge into a cluster, and to only worry about finding an appropriate threshold for the ID change parameter.
Influence of coverage We sought to establish the effects of coverage on the quality metrics of nPhase's predictions. To do so, we performed our tests on a 10X per haplotype dataset and a 20X per haplotype dataset. We found that both accuracy and contiguity are improved by the higher coverage level of 20X per haplotype (Additional file 2: Fig. S11 ). This effect is observed across ploidy and heterozygosity levels, though the accuracy effects are more pronounced for higher ploidy, lower heterozygosity level samples. A low number of haplotigs per haplotype is not always a good sign of high contiguity as it can be compatible with a high rate of chimeric haplotigs. Therefore, we looked at the contiguity effects of coverage for our tests using default parameters, which we have previously determined output accurate results.
Split read stitching step Some reads align to two or more very distant sequences in the reference genome. These reads can represent a structural variation between the sample and the reference being mapped to. We split them into the different segments that align to the reference and refer to them to as split reads. Split reads can be very misleading and trusting them blindly would result in chimeric haplotigs. We developed a simple pre-processing strategy to integrate part of the information contained by these split reads. We run nPhase a first time to obtain our initial haplotigs. We expect some of the edges of haplotigs to correspond to structural variants such as inversions or large INDE Ls so we identify the SNPs at the edges of these clusters. These are the SNPs which we expect to be included in the split reads that can connect two haplotigs separated by a structural variant, so they are the most trustworthy SNPs in our split read dataset.
Assessing the quality of the Brettanomyces bruxellensis phasing We used the nPhase pipeline to phase a triploid Brettanomyces bruxellensis strain. nPhase predicted that a number of regions had three haplotypes and that others had only two. In order to verify the accuracy of these predictions, we visualized our data in two complementary ways. First, we checked the coverage levels of the predicted haplotigs. We mapped the fastQ files generated by nPhase back to the same reference genome, then generated coverage plots using a 5-kb window (the genome is 13 Mb long). In order to minimize the visual noise in Fig. 7 , we only displayed the longest haplotigs that account for 90% of all coverage. Second, we checked the allele frequency distribution within each cluster. We crossreferenced the file generated by nPhase in the VariantCalls/LongReads folder containing a list of reads and the identity of every base at each heterozygous position with the file generated in the Phased folder containing a list of the final haplotig clusters and the list of reads that comprise them.
Phasing an autotetraploid strain of Solanum tuberosum with nPhase We obtained whole genome Oxford nanopore and Illumina read data for an autotetraploid strain of Solanum tuberosum from the WhatsHap polyphase paper under accession number PRJEB39456. We used v6. 1 of the DM1-3 516 R44 assembly as a reference, selecting the version without any repeat masking. We mapped all the reads to the full genome, but we then extracted all of the reads which mapped to chromosome 2 to run the phasing algorithm on. Of the 2. 4% of heterozygous positions observed in chromosome 2 according to variant calling on Illumina data, we only kept a randomly sampled 0. 5% for the phasing in order to save calculation time. Once we obtained raw nPhase results, we ran our automated cleaning steps in order to improve contiguity and reduce the complexity of our results.
Availability of data and materials The nPhase algorithm and the nPhase pipeline are both available under the open source GNU General Public License v3. 0 at https://github. com/OmarOakheart/nPhase [30]. Oxford Nanopore sequencing data is available under the study accession number PRJEB39456. Illumina short-read data for the Saccharomyces cerevisiae strains are taken from the 1011 yeast genomes project  [24]  and their SRA accessions are the following: ERR1308732  [31] , ERR1309429  [31] , ERR1308952  [31] , and ERR1308675  [31]. Illumina and Oxford Nanopore data for the Brettanomyces bruxellensis GB54 strain is available under the study accession number PRJEB40511. Illumina and Oxford Nanopore data for the Solanum tuberosum strain used is available under the study accession number PRJNA587397  [32].
Species distributions, abundance, and interactions have always been influenced by human activity and are currently experiencing rapid change. Biodiversity benchmark surveys traditionally require intense human labor inputs to find, identify, and record organisms limiting the rate and impact of scientific enquiry and discovery. Recent emergence and advancement of monitoring technologies have improved biodiversity data collection to a scale and scope previously unimaginable. Community science web platforms, smartphone applications, and technology assisted identification have expedited the speed and enhanced the volume of observational data all while providing open access to these data worldwide. How to integrate and leverage the data into valuable information on how species are changing in space and time requires new best practices in computational and analytical approaches.
Here we integrate data from three community science repositories to explore how a specialist herbivore distribution changes in relation to host plant distributions and other environmental factors. We generate a series of temporally explicit species distribution models to generate range predictions for a specialist insect herbivore (Papilio cresphontes) and three predominant host-plant species. We find that this insect species has experienced rapid northern range expansion, likely due to a combination of the range of its larval host plants and climate changes in winter. This case study shows rapid data collection through large scale community science endeavors can be leveraged through thoughtful data integration and transparent analytic pipelines to inform how environmental change impacts where species are and their interactions for a more cost effective method of biodiversity benchmarking.
INTRODUCTION Biodiversity benchmarking is fundamental to both basic and applied ecological research offering insights into the biological processes shaping species and their interactions. Benchmarking is a labor intensive endeavor, often limited by participation and training. Recent advances in sensing technology and communication have led to a diverse and plentiful data landscape coordinating and improving biodiversity community science efforts at scale so that they can be used in meaningful ways for benchmarking efforts (e. g. ,  Sullivan et al. , 2009; Prudic et al. , 2017). Observational web platforms and smartphone applications, automated camera arrays, and machine learning-assisted identifications have also changed how biodiversity data is collected, processed, and verified (e. g. ,  Sullivan et al.
Butterfly and Host Plant Data Papilio cresphontes (Papilionidae) is a sub-tropical butterfly widely distributed across North America. P. cresphontes and host plant occurrence data were obtained from a variety of sources: iNaturalist 1 , n = 3,007, Global Biodiversity Information 1 www. inaturalist. org Facility (GBIF 2  ), n = 14,181, the Maine Butterfly Atlas 3  , n = 11, the Maritime Canada Butterfly Atlas 4  , n = 6, Massachusetts Butterfly Club, n = 512, Butterflies and Moths of North America 5  , n = 1,188, and eButterfly 6  , n = 3,083. Data from iNaturalist and GBIF were downloaded using the spocc package for R  (Chamberlain et al. , 2016). We filtered iNaturalist data to include only research-grade records before combining with other data sets. Combined data were filtered for time frame, duplicates, and study area extent (see below) before further analysis and model building.
Environmental Data We used the TerraClimate data set  (Abatzoglou et al. , 2018) , a 4 km × 4 km resolution gridded set of monthly climatological data from 1958 to 2017 (at the time of writing this) to generate environmental predictor variables for modeling. We calculated a set of yearly summaries of 19 bioclimatic variables  (Fick and Hijmans, 2017) , frequently used in species distribution modeling, using the dismo package in R  (Hijmans et al. , 2017)  for each year in each time period (T1 and T2) and then averaged these summaries across each time period to provide temporally appropriate climate summary for each set of models. We included all 19 bioclimatic variables as predictors for modeling.
Species Distribution Models Distributions of P. cresphontes and host plants were estimated using MaxEnt 3. 4. 0, a machine learning algorithm based on the principle of maximum entropy  (Phillips and Dudík, 2008; Elith et al. , 2011; Phillips et al. , 2017). MaxEnt is a presencebackground method, which is considered to perform well when modeling climatic niches across a variety of sample sizes  (Wisz et al. , 2008). We used the ENMeval package for model-building, testing, and tuning  (Muscarella et al. , 2014) , ultimately building 8 total models (P. cresphontes and three host species for each time period). We used a combination of geographically structured and regular k-fold cross validation for model testing and tuning. We generated 10,000 random background points per species-time period combination (within the geographic extent outlined by the occurrences across both time periods -a rectangle defined by the minimum and maximum latitude and longitudes of occurence points) per model and used the blockCV package  (Valavi et al.
We mapped the "cloglog" MaxEnt output, which can be interpreted as probability of occurrence under the assumption that the species presence or absence at nearby sites are independent  (Phillips and Dudík, 2008; Elith et al. , 2011; Phillips et al. , 2017). Importance of predictors was assessed using the permutation contribution metrics generated when building full models. These metrics are built as MaxEnt steps through modifications of coefficients for single features. For each variable, values are randomly permuted on training data and a model is reevaluated on the permuted data. Then, the resulting drop in AUC scores are tracked and normalized to percentages  (Phillips et al. , 2006). Thresholds for binary presence-absence maps and presence distributions were generated using the maximum test specificity plus sensitivity  (Liu et al.
, 2005). For all models, we used species-specific (but not time-specific) geographic extents during model building and tuning, as well as making predictions for graphical outputs. Kernel density plots are used to show latitudinal distributions of model predictions and northern range limits. MaxEnt has become a popular modeling resource because of its predictive power, ease of use, and a well-detailed literature to get researchers started  (Phillips and Dudík, 2008; Elith et al. , 2011; Phillips et al. , 2017). However, this framework has also received criticism, with researchers advocating for more explicit examinations of tuning parameters, evaluation metrics, and the incorporation of tools to deal with sampling bias  (Radosavljevic and Anderson, 2014). Recent software additions have addressed some of these challenges, and opened up the "black-box" of MaxEnt  (Phillips et al.
RESULTS Evidence of Northward Range Shift of P. cresphontes From Raw Occurrence Data Patterns of occurrence (as opposed to the predictive outputs from species distribution models) indicate a strong trend of a rapid and recent northward range expansion in P. cresphontes since the earliest recorded records of the species in our dataset  (1959). The butterfly's highest recorded latitude in a given year has increased dramatically since 2000 (Figure  1A ), and the predicted suitability has shifted from low to high in many cities close to the current northern edge of the range (Figure  1B ).
STATEMENT OF DATA ARCHIVING Data and R scripts for all analyses are archived on Zenodo (https: //doi. org/10. 5281/zenodo. 4476735). manuscript and analyses. Specifically, we would like to thank the Global Biodiversity Information Facility, the Maine Butterfly Atlas, the Maritime Canada Butterfly Atlas, the Massachusetts Butterfly Club, Butterflies and Moths of North America and eButterfly for the use of their data. We would also like to thank Scott Laurie and Ken-ichi Ueda at iNaturalist for their web platform, and all those relentless volunteers in various citizen science programs, past and present, who contributed these data for the greater good. This manuscript has been released as a pre-print at bioRxiv,  (Wilson et al. , 2019).
Molecular column densities are derived with the line fitting tool XCLASS. The physical and chemical properties are combined by applying the physical-chemical model MUlti Stage ChemicaL codE (MUSCLE) in order to constrain the chemical timescales of a few line-rich cores. The morphology and spatial correlations of the molecular emission are analyzed using the histogram of oriented gradients (HOG) method. Results. The mid-infrared data show that both regions contain a cluster of young stellar objects. Bipolar molecular outflows are observed in the CO 2 -1 transition toward the strong millimeter (mm) cores, indicating protostellar activity. We find strong molecular emission of SO, SiO, H 2 CO, and CH 3 OH in locations that are not associated with the mm cores. These shocked knots can be associated either with the bipolar outflows or, in the case of ISOSS J23053+5953, with a colliding flow that creates a large shocked region between the mm cores.
Chemical reactions occur in the gas phase, on dust grain surfaces, and in the icy mantle layers (e. g. ,  Garrod et al. 2008). A current challenge is to understand, for example, the chemical segregation between nitrogen(N)-and oxygen(O)-bearing species (e. g. ,  Wyrowski et al. 1999; Jiménez-Serra et al. 2012; Feng et al. 2015; Allen et al. 2017; Gieser et al. 2019). In order to study HMSF at core scales, we carried out the survey "CORE -Fragmentation and disk formation during high-mass star formation," which is a large program with the NOrthern Extended Millimeter Array (NOEMA) targeting 18 HMSFRs in the northern hemisphere with the currently highest possible angular resolution at 1. 3 mm (∼0. 4). An overview of the CORE project and analysis of the 1. 3 mm continuum data are presented in  Beuther et al.
(2018). The molecular line data, revealing a high degree of chemical complexity on scales <10 000 au, are analyzed by  Gieser et al. (2021). The observations of the CORE sample were carried out between 217. 2 -220. 8 GHz (∼1. 3 mm) with the WideX correlator and a single pointing toward each region. Two additional pilot regions were previously observed in a similar setup  (Feng et al. 2016). In the physical and chemical analysis of the full CORE sample  (Gieser et al. 2021) , 22 objects are analyzed, defined as "cores", that have a radially decreasing temperature profile and associated 1. 3 mm continuum emission. Molecular column densities are derived from the 1. 3 mm spectra (e. g. , C 18 O, SO, OCS, HNCO, HC 3 N, CH 3 OH, CH 3 CN), and each core is modeled with the physical-chemical model MUlti Stage ChemicaL codE (MUSCLE) to estimate chemical timescales, τ chem.
NOEMA data The NOEMA data were calibrated using the CLIC package in GILDAS 1. The PolyFiX correlator simultaneously covers ∼8 GHz in two sidebands (lower sideband, LSB, and upper sideband, USB) and in the two orthogonal linear polarizations (horizontal and vertical) with a fixed channel spacing of 2 MHz (∼2. 7 km s -1 at 1. 3 mm). Rest frequencies from 213. 3 GHz to 221. 3 GHz in the LSB and from 228. 7 GHz to 236. 7 GHz in the USB were covered by the observations. High-resolution units with a channel spacing of 62. 5 kHz (∼0. 084 km s -1 at 1. 3 mm) were placed within the broadband correlator units. The NOEMA 1 mm data of the original CORE sample consisted of single pointing observations toward each region, which allowed us to self-calibrate these data sets with the GILDAS selfcal task (a detailed description of the method is presented in  Gieser et al.
Imaging The NOEMA continuum and combined NOEMA + IRAM 30m ("merged") spectral line data were imaged using the MAPPING package in GILDAS. Primary beam correction was applied to the final continuum and spectral line data products. In a first inspection of the NOEMA spectral line data of both regions, we identified all detected emission lines. An overview of all lines and their properties is shown in Table  2. All of these emission lines were covered by the IRAM 30m EMIR observations, and hence the interferometric and single-dish data can be combined in order to recover missing flux filtered out by the interferometer. If available, we used the data obtained with the high-resolution units smoothed to a spectral resolution of 0. 5 km s -1 to increase the signal-to-noise ratio (S/N) and otherwise with the low-resolution basebands smoothed to a spectral resolution of 3.
Archival data In addition to the high angular resolution NOEMA data at 1. 3 mm, we used archival MIR to FIR observations obtained with the Spitzer and Herschel space telescopes to study the clustered nature of the regions and constrain the SEDs. For both regions, archival data covering the full field-of-view (FOV) of the NOEMA mosaic exists. In the FIR, both regions were targets in the Herschel key program EPoS and the properties of the cold dust are analyzed in  Ragan et al. (2012). Three photometric bands of the PACS instrument at 70, 100, and 160 µm, with angular resolutions of C. Gieser et al. :  Clustered star formation at early evolutionary stages. (Müller et al. 2005)  or JPL  (Pickett et al. 1998)  databases. The CH 3 CN 12 0 -11 0 transition is partially blended with CH 3 CN 12 1 -11 1 at 220.
Spectral line emission In this section, we study the composition of the molecular gas by analyzing the 1. 3 mm spectral line data. In the analysis of the original CORE data set  (Gieser et al. 2021) , the low-resolution data were used, which provided a continuous spectrum. However, as ISOSS J22478+6357 and ISOSS J23053+5953 are much colder and younger with typical line widths <3 km s -1 (compared to a mean line width of 6 km s -1 in the original CORE A&A proofs: manuscript no. CORE_ISOSS_chemistry_draft_8. 0_arxiv sample,  Gieser et al. 2021) , we used the high-resolution (∼0. 5 km s -1 ) data if a high-resolution unit was placed toward the line. The properties of the spectral line data products (such as velocity resolution, synthesized beam, line noise σ line ) are summarized in Table  3.
Radial temperature profiles The derived H 2 CO temperature maps can be used to create radial temperature profiles of the mm cores. To be consistent with the analysis of the density profiles (Sect. 3. 2), we only applied the following analysis to cores with S/N > 20. Radial temperature profiles were extracted from the H 2 CO temperature map for cores 1 -4 in ISOSS J22478+6357 and for cores 1 -6 in ISOSS J23053+5953. We binned the data with a bin size 0. 5 × ∆r corresponding to half of the average beam size (∆r = θ maj +θ min 2 ). The radial temperature profile for cores 1 -4 in ISOSS J22478+6357 and cores 1 -6 in ISOSS J23053+5953 are shown in Fig. 13. Cores 3 and 4 in ISOSS J22478+6357 are close so their temperature profiles overlap. We fitted the data assuming a power-law profile according to Eq.
2021) , we used a finer grid of 999 logarithmic steps up to τ model = 7 yr. For the inner radius r in we chose 500 au (Table  5 ). In  Gieser et al. (2021) , the outer radius r out was determined from the temperature profiles, but for the ISOSS, we used the outer radius derived from the clumpfind analysis presented in  Beuther et al. (2021)  and summarized in Table  4. We used the molecular hydrogen column density N(H 2 ) derived from the 1. 3 mm continuum emission (Table  4 ). Shortspacing information is available for the spectral line data; however, the continuum data are obtained with NOEMA only, so missing flux can be an issue. Column densities derived with XCLASS of the following molecules were included in the model: C 18 O, 13 CS, SO, OCS, DCO + , N 2 D + , H 2 S, H 2 CO, HNCO, HC 3 N, c-C 3 H 2 , CH 3 OH, and CH 3 CN.
Correlations Using the CORE and CORE-extension observations, the physical and chemical properties of 32 fragmented mm cores were studied in detail (ten cores in this work and 22 cores in  Gieser et al. 2021). The following derived properties are investigated for correlations: the 1. 3 mm peak intensity and integrated flux (I peak and F 1300 ), the kinetic temperature estimated from the H 2 CO temperature map (T kin ), core mass M core , molecular hydrogen column density N(H 2 ), kinetic temperature at r = 500 au obtained from the radial temperature profile T 500 , visibility, density, and temperature power-law index (α, p, and q), chemical timescale (τ chem ), and M/L ratio. The absolute flux calibration of the NOEMA data is expected to be 20% or better. We therefore assume a 20% uncertainty for I peak and F 1300.
Conclusions We used high angular resolution observations at 1. 3 mm of the young star-forming regions ISOSS J22478+6357 and ISOSS J23053+5953 and MIR and FIR archival data from the Spitzer and Herschel telescopes to study the physical and chemical properties of fragmented mm cores. This study (COREextension) is a follow-up of the CORE large program, for which the fragmentation and physical-chemical properties of fragmented cores toward HMSFRs classified as HMPOs, HMCs, and UCHii regions are presented in  Beuther et al. (2018)  and  Gieser et al. (2021) , respectively. Here, we focused on the physical (temperature and density profiles, outflows) and chemical properties of younger mm cores. Our main results are summarized as follows: 1. Comparing the 1. 3 mm continuum with Spitzer IRAC and MIPS observations, we find that some of the fainter mm cores already have strong MIR counterparts suggesting clustered subsequent star formation occurring in both regions.
Acknowledgements. The authors would like to thank the anonymous referee whose comments helped to improve the clarity of this paper. This work is based on observations carried out under project number  L14AB  with the  IRAM NOEMA Interferometer  and the IRAM 30 m telescope. IRAM is supported by  INSU/CNRS (France) ,  MPG (Germany) , and  IGN (Spain). C. G. and H. B. acknowledge support from the  European Research Council  under the  Horizon 2020 Framework Programme  via the  ERC Consolidator Grant   CSF-648505. D. S. acknowledges support by the  Deutsche Forschungsgemeinschaft  through  SPP 1833 : "Building a  Habitable Earth  ( SE 1962/6-1 ). "R. K. acknowledges financial support via the Emmy Noether and  Heisenberg Research Grants  funded by the  German Research Foundation (DFG)  under grant no.