dataesr/scientific-paragraphs-categorization huggingface dataset:
Distribution of database observations The 315 articles included in the final database reported 32,632 range-shift estimates across 12,009 species. The number of estimates per study (i. e. , the number of rows entered for the paper based on criteria described above) ranged from one to 4426, with a median of 11. A few studies included very large number of estimates (10 articles had over 500). These studies included multiple species and multiple estimates per species (e. g. , leading edge and trailing edge, multiple time periods, or multiple study areas). Range-shift estimates were heavily concentrated in the Northern Hemisphere (90. 06%), with only 9. 5% of estimates occurring in the Southern Hemisphere and the remainder occurring across both hemispheres (Fig. 3a, b , Additional File 5: Table  S5 ).
Magnitude of shift Of the non-longitudinal shift estimates, 25,445 included range shift rates. We found significant rates of shift towards higher latitudes (11. 8 km/dec; 95% CI 11. 10-12. 51; p < 0. 05) and higher elevations (9 m/dec; 95% CI 0. 0056-0. 013, p < 0. 05) but a non-significant shift in depth (-0. 09 m/dec; 95% CI -0. 0012-0. 001, p-value = 0. 8679; note that negative sign indicates shallower movement) (Fig. 5 ; Additional File 5: Table  S7 ). There was also significant variation by parameter for latitudinal shifts: leading-edge shifts (19. 7 km/dec) exceeded center-of-range (4. 2 km/dec) or trailing-edge shifts (0. 5 km/dec); these parameters are all significantly   S7 ). Elevational shifts varied by parameters but these differences were not significant. Finally, we did not assess differences in parameter for depth due to limited observations in this dataset.
We found that all the methodological variables listed in the study validity assessment section had a significant effect on quantitative estimates of range shift (Additional File 5: Table  S3 ). A linear mixed-effects model combining all dimensions with methodological factors as random effects confirmed support for latitudinal poleward Estimated Marginal Mean Probability Fig. 4  Model Results for temperature hypotheses. Estimated marginal mean probability and 95% confidence intervals of support for shifts poleward, upward, and deeper across dimensions (top left), ecosystem types (top right), parameters (bottom left), and taxonomic groups (bottom right); i. e. , the predicted probability of support/fails to support after averaging across the methodological variables weighted proportionally to their occurrence in the original dataset.
5 , Additional File 5: Table  S8 ). All other taxonomic groups demonstrated shifts, but these were non-significant (Additional File 5: Table  S8 ). Finally, we assessed the relative contribution of methodological and ecological variables in explaining overall variation in magnitude of range shifts through a linear mixed effects model in which methodological variables   S7 ). Estimates by taxonomic group (bottom row) display estimated marginal means and associated 95% confidence intervals (Additional File 5: Table  S8 ) were set as random intercept terms. We followed the methodology of Lenoir et al. 2020, whereby we compared conditional R2 (total variance explained) to marginal R2 (variance explained by fixed effects) to compare the relative ability of methodological vs ecological factors to explain variation in the database.
For magnitude of shifts, we found evidence for an average range shift in latitude and elevation in the direction anticipated (i. e. , poleward latitudinal shifts, upslope elevational shifts), but failed to find evidence for an average shift towards greater marine depths. Our dataset demonstrates tremendous variation in observed species' range shifts, including variation across parameter and taxonomic group. Interestingly, leading edge shifts were faster than trailing edge shifts for latitude, but not for elevation. Similarly, Rumpf et al. [62]  found that leading and trailing edges did not differ in rates of shift for elevation. Trailing edge shifts might keep pace with leading edge shifts for elevation rather than for latitude due to increased human pressure on lower elevation sections of mountains, or differences between low and high elevations in the magnitude of climate change or the importance of biotic interactions.
Implications for research In this review, we assessed whether established rangeshift hypotheses were supported by available evidence. However, we did not explicitly link observed range shifts and climate change. Other range-shift analyses have done this by associating shifts with trends in temperature or other ecologically relevant climate variables; others by calculating alignment or mismatch with climatic isotherms (i. e. , climate velocity). Although these metrics are useful indicators, they do not assess whether observed changes are tracking local climate velocities or whether they are significant compared to historical climate variability, and therefore whether the species is experiencing changes outside of what is "normal" for its range. To address this limitation, future range-shift studies should consider comparing changes to baseline data on historical range variability.
However, accounting for exposure, sensitivity, adaptative capacity, and non-climate drivers such as land-use change together in a vulnerability framework approach may prove more fruitful  [75]. This database could form the basis for such an analysis. Many articles included in our review discussed traits or other drivers that might explain why species shifted as they did. We captured these explanations qualitatively in our database. Conducting a full text analysis of this section could provide a useful starting point for identifying key traits or drivers. From an initial screening, some of the non-climate drivers included land use change or habitat loss, changes in habitat quality, changes in anthropogenic pressure such as exploitation or disturbance, and biotic interactions such as competition or predation.
Traits included behavioral plasticity, thermal tolerance, dispersal ability, habitat specialization, and life span. Moreover, our data could be used for more in-depth analyses of differences among population subgroups (e. g. , male vs. female, age class) that may further help to explain observed variability. Finally, future research efforts focusing on better representing currently underrepresented regions (particularly the southern hemisphere) and taxonomic groups would provide information necessary for understanding global variability. Overall, the dataset produced for this analysis can be used for future research to explore additional hypotheses to better understand species range shifts.
Acknowledgements We thank  Daria Maslyukova ,  Keanah Turner ,  Evan Parker ,  Theresa Rudick , and  Chris Poole  for their help extracting data. We particularly thank  Evan Parker  for his assistance in developing Figure 3b. We also thank  Gael Grenouillet  for his participation in establishing the database. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the  U. S. Government.
n=418 for full text review) ArƟcle Screening Workflow Review references in all "pass" arƟcles and review arƟcles reviewing Ɵtles/abstracts aŌer 300 arƟcles with no new inclusions. (ArƟcles sorted by relevance in Colandr. Stop ( Google Scholar and Web of Science Alerts (n=54 for full text review) ArƟcle studies range shiŌs in plant or animal species in response to recent anthropogenic climate change n=842 full texts reviewed Final arƟcles included in review (n=315) database searches=115, new publicaƟon alerts=9, opportunisƟc=9, review arƟcles/reference searches=182.
Introduction Multiword expression (MWE) is an umbrella term spanning a range of linguistic phenomena whose common property is idiosyncrasy or, more specifically, idiomaticity, which may manifest in many different respects: lexical, morphological, syntactic, semantic, pragmatic, and statistical  (Baldwin and Kim, 2010). MWEs are challenging and pervasive. For instance, in an MWE-annotated corpus of French  (Candito et al. , 2021) , over 11% of all tokens belong to MWEs. Moreover, MWEs likely exist in any natural language. Therefore, modeling idiosyncrasy in language resources and tools is a natural quest. This position paper addresses two language annotation frameworks, Universal Dependencies and PARSEME, from the point of view of MWEs. Universal Dependencies 1 (UD;  Nivre et al. , 2016 Nivre et al.
PARSEME has relied on the UD format (cf. Sec. 3. 2) and data in its latest corpus releases. In August 2021, a joint Dagstuhl Seminar on Universals of Linguistic Idiosyncrasy in Multilingual Computational Linguistics brought together the two initiatives  (Baldwin et al. , 2021). 4 Finally, September 2022 saw the start of a new COST action entitled UniDive (Universality, Diversity and Idiosyncrasy in Language Technology), with UD/-PARSEME unification on the agenda. This paper aims at providing a roadmap towards this unification. We first survey the dimensions of MWE idiosyncrasy (Sec. 2) and compare the two frameworks' annotation principles that bear on MWEs (Sec. 3). Then, we offer short-, mid-and long-term proposals for adjusting the frameworks, paving the way towards eventually unifying them (Sec.
Thus, the main PARSEME decision diagram asks questions about the syntactic head of the candidate expression, its dependents, its morphosyntactic category, etc. This implies a strong dependence on the underlying syntactic framework, and UD provides such framework, validated across many languages. Another advantage for PARSEME is that the lexicalism in UD helps keep the MWE definition relatively simple. Namely, MWE components more easily form a weakly connected dependency graph (Sec. 3. 5) if content words head function words than vice versa  (Savary and Waszczuk, 2020). One minor disadvantage from lexicalism concerns MWEs with copulas. For instance in (en) to be somebody 'to be important' the pronoun heads the copula be, which prevents PARSEME from saying that a verbal MWE is always headed by a verb.
MWE Categories PARSEME defines 3 quasi-universal categories (the first 3 below, present in many languages but not all), and 2 universal ones (the last 2 below, present in all languages under study). 11 Statistics about these annotations in the data are given in Appendix A. Inherently Reflexive Verbs (IRV) combine a verb 𝑉 and a reflexive clitic 𝑅 such that (i) 𝑉 never occurs without 𝑅, as in (sv) gifta sig (lit. 'getmarried oneself') 'get married', or (ii) 𝑅 distinctly changes the meaning or valency of 𝑉 , as in (es) recogerse 'go home'/recoger 'gather'. They are contrasted with regular reflexives: true reflexive, reciprocal, middle passive and impersonal, e. g. (ro) casele se vând bine (lit. 'houses sell themselves well') 'houses sell well'. In UD, the above uses are divided into two classes, depending on if the reflexive clitic can or cannot be mapped on a semantic argument of the verb.
Short-term Proposal We propose to supplement existing UD parses with MWE annotations, without altering tokenization. MWEs that encompass entire MWTs, as in (9), are already covered by PARSEME. For cases like (10) and (  11 ), the MWE column could specify sub-token spans, as in Fig. 8. Long-term Proposal Parts of unsplit tokens participating separately in MWEs suggest a deficiency in UD's implementation of MWTs. We propose that, ultimately, UD syntactically recognize synthetic compounds as productive, regardless of the MWE status. This would require UD treebanks in some languages to systematically split current compound words into MWTs, ensuring each component word has an appropriate lemma and morphological features, and adding a dependency relation (such as compound or compound:prt) between them.
Northern European Journal of Language Technology A Statistics of the Use of MWErelated Labels in the UD and PARSEME Corpora Table  1  shows the statistics and comments about the use of MWE-related labels in the UD treebanks in version 2. 9 (with 131 treebanks in total). Table  2  documents the number of PARSEME languages in which the MWE labels are used. Table  3 : Roadmap for the UD-PARSEME unification. Actions with white background require no manual (re-)annotation. Actions highlighted in blue will require major annotation effort: those in dark blue apply to all languages, whereas those in light blue (concerning subword-level annotations) apply to a subset of languages.
Acknowledgements This work started out as a discussion at the Dagstuhl Seminar 21351: Universals of Linguistic Idiosyncrasy in Multilingual Computational Linguistics. 16 We thank all the participants for inspiration. We are grateful to  Schloss Dagstuhl , the  Leibniz Center for Informatics , as well as the organizers of the event, for bringing us together. We are also building upon the efforts of UniDive, the CA21167 COST Action: Universality, diversity and idiosyncrasy in language technology. 17 At the time of drafting this paper, UniDive was at the proposal stage and paved the way towards many of the ideas presented here. Our work has also been partly funded by the  French Agence Nationale pour la Recherche , through the  SELEXINI  project ( ANR-21-CE23-0033-01 ). We thank  Jena Hwang  for providing the Korean example (11).
Exploration of the asteroid-comet continuum How likely is it that near-Earth D-type asteroids represent extinct comet nuclei? If D-type NEAs represent extinct comets, they would offer us a unique opportunity to investigate the relationship between two classes of minor bodies in our solar system. CARINA aims to perform detailed characterisation of a D-type asteroid. It will determine the volatile content, the porosity, and the effect of space weathering on the surface. Compositional and physical maps can be compared to data from ESA's Rosetta  [41]  and NASA's Lucy  [64]  missions. In particular, CARINA aims at searching for evidence of comet quenching. This requires a sample collection from the subsurface, where there might be hydrated minerals and cometary-like material still present. Laboratory measurements of the returned material, will be important, since the exact same experimental procedures and conditions can be replicated from analysis on Stardust, Hayabusa2, and OSIRIS-REx samples.
Visible and near-infrared spectrometer The visible and near-infrared (VNIR) spectroscopy will characterise the surface composition of the asteroid. It will explore the mineralogical and chemical composition that can be linked with the surface morphology data and, therefore, help in sampling site selection. Spectra in a broad wavelength range are needed to identify the mineralogical diversity on the asteroid surface and also expected organic compounds. The spectrometers will be used to image in the visible and near-IR wavelength range from 0. 4 to 4. 3 μm (i. e. 2325 -25000 cm -1 ) and a spectral resolution of less than 30 nm. The spectrometer heritage comes from the NIRS3 on Hayabusa2  [84]  and OMEGA that flew on board Mars Express  [85]  and therefore has a TRL of 9.
Considering preliminary values of the spacecraft's initial mass and specific impulse of 2000 kg and 320 s, respectively, an excessive amount of fuel consumption was obtained (about 1740 kg). Therefore, it has been stated that mission objectives could be better satisfied by employing electric propulsion, in particular by using two T6 ion engines. For the transfer towards the asteroid 2002 AT4, an Earth flyby has been considered to decrease the total fuel consumption. Therefore, some simulations have been performed by means of the software Pagmo  [95]  to identify several possibilities for the departure of the CARINA spacecraft. The results are shown in Table  4 , in which three different launch dates within 2030-2035 are proposed. Among them, the first launch window (29th of June 2030) has been chosen for the analysis presented in this paper.
Acknowledgements The authors would like to thank the  European Space Agency (ESA)  and the  Austrian Research Promotion Agency (FFG)  for organising the  Summer School Alpbach  (SSA) 2018, where the CARINA mission was born. The authors would also like to thank the SSA tutors  Christian Gritzner  and  Özgür Karatekin  for their support. T. M. Ribeiro  and  D. Athanasopoulos  thank  Europlanet  for the support to attend SSA. T. M. Ribeiro  acknowledges the support from the  Fundação para a Ciência e a Tecnologia  through the grant  PD/BD/140872/2018.
com/). DNA probes were defined from the published sequence of the organellar genomes of Silene latifolia (NCBI accessions NC_016730. 1 and NC_014487. 1) and previous Illumina data from one mt genome of S. nutans (Genoscope PRJEB54044). Enriched libraries were pooled and sequenced on an Illumina MiSeq (2 × 150 with dual indexing) at the LIGAN platform (UMR 8199 LIGAN-PM Genomics platform -Lille, France). Because of differences in read depth between mt (mean of 50×) and pt (mean of 600×) regions, we applied different strategies to obtain gene sequences from the two genomes. For the pt genes, we assembled the reads with SPADES v. 3. 0. 0  (Bankevich et al. , 2012)  and then blasted S. latifolia reference sequences against the assembly as described previously  (Postel et al. , 2022). Mt sequence coverage was more fragmented due to the lower read depth, so we used HYBPIPER  (Johnson et al.
, 2016)  to extract the genes sequences, after read cleaning with TRIMMOMATIC using the following parameters: 'LEADING:10 TRAILING:10 -SLI-DINGWINDOW:4:20 -MINLEN:36'. We first ran the HYBPI-PER pipeline only on the reads from W1 individuals, which represented the least fragmented dataset, using the S. latifolia mt genome as the reference. Then, we ran BAMBAM (SAMTOOLS package)  (Page et al. , 2014)  on these read alignments to create a new reference. Finally, we ran HYBPIPER on the reads of all individuals of S. nutans using this new W1 reference. Mt data quality was assessed using IGV  (Thorvaldsdóttir et al. , 2013) , MULTIQC (Fig. S2 ), and FASTQC  (Ewels et al. , 2016; Andrews et al. , 2018). To assess how complete our mt dataset was, we also counted the number of mt genes annotated in Silene nutans compared to those annotated in Silene latifolia  (Sloan et al.
Alignment construction and analysis For the pt genes, 68 previously constructed gene alignments for S. nutans individuals were already available  (Postel et al. , 2022). These alignments also contained the reference sequences of both S. paradoxa and S. latifolia. As mt reference sequences for S. paradoxa were not available, we only worked with S. latifolia as an outgroup and removed S. paradoxa from the pt gene alignment using SeqKit  (Shen et al. , 2016). We then realigned the sequences using MUSCLE  (Edgar et al. , 2004)  and checked the alignment for correct open reading frames (Fig. S1 ). For the mt genes, we took the reference sequence build using all W1 reads and for each individual, using the variant calling results, we put the corresponding nucleotide at variable positions, using an in-house BIOPYTHON script (Fig.
S1 ). Because we detected positions that were variable within individuals for both mt and pt DNA coding sequences, the ratio of alternative alleles varied among polymorphic sites within an individual. Haplotype reconstruction within these apparently heteroplasmic individuals was not feasible with our short-read data. Thus, we constructed three different alignment datasets to deal with this issue, regarding the file format required for each analysis (Fig. S1 ). Details for each of the dataset are provided below and the different version of the BIOPYTHON script used to construct these sequences are available on GitHub (https://github. com/ZoePos/Alignement_ construction).
latifolia  (Sloan et al. , 2010) , so this gene was excluded from this analysis. We used POPGENOME R package  (Pfeifer et al. , 2014)  to run the MKT using the options include. unknown = TRUE when loading the data to allow for missing data. The MKT was run on each pt and mt gene separately and for concatenations of genes encoding subunits of the same pt or mt enzyme complex. Because S. nutans lineages exhibit strong reproductive isolation and could be considered incipient species, we also ran the MKT on each S. nutans lineage separately, with S. latifolia as an outgroup.
Recombination To assess the number of reassortment events within and between organellar genomes, we conducted four gamete tests (FGTs) on the mt and pt data. Considering that homoplasy (i. e. repeated mutations at the same position) is expected to be rare (infinitesites assumption), the presence of four different allelic combinations between pairs of segregating sites most likely results from recombination. Alignments constructions for this test is described in Fig. S1. We implemented FGTs with a BIOPYTHON script (https:// github. com/ZoePos/Four-gamete-test) on the concatenated gene sets of all organellar genes. Genotype calls with evidence of intraindividual polymorphism (heteroplasmy) were treated as missing data.
Thus, the synonymous divergence between S. nutans and S. latifolia should reflect the mutation rate. Mean K s was estimated for each mt and pt gene, using DNASP 6. 12. 03 Ó 2023 The Authors New Phytologist Ó 2023 New Phytologist Foundation New Phytologist (2023) www. newphytologist. com (Rozas et al. , 2017) , first with all S. nutans individuals and then separately for each lineage. To test for significant differences between pt and mt genes, we conducted Mann-Whitney U-tests in R v. 4. 1. 2 (package STATS4).
Acknowledgements This work has been performed using infrastructure and technical support of the  Plateforme Serre , cultures et terrains expérimentaux - Université de Lille  for the glasshouse/field facilities. The authors wish to thank  Elodie Ubrig  ( IBMP ) for the purification of mt DNA of S. nutans for the preliminary Illumina sequencing. This study was funded by  Genoscope , the  Agence Nationale de la Recherche  ( ANR-11-BSV7-013-03 ; TRANS) and  CNRS  via a  PEPS-BMI grant. PT thanks the  Région Hauts-de-France , and the  Ministère de l'Enseignement Supérieur et de la Recherche  ( CPER Climibio ), and the  European Fund for Regional Economic Development  for their financial support. ZP is the recipient of a PhD grant from the  Ministère de l'Enseignement Supérieur et de la Recherche  and the  Région Hauts-de-France  and thanks the  I-Site Lille Nord Europe  for a mobility grant to work in DBS's laboratory.
Introduction Public data banks providing sequencing data or assembled genome sequences are growing at an exponential rate  [4] , faster than computational power. Searching a sequence of interest among datasets is a fundamental need. For instance it enables to better understand genetic changes in the tumour, offering precious information about the diagnosis and treatment of cancer  [14] , or it enables to study at a large scale the distribution and adaptation of life in oceans  [13] ). However no method scales to the dozens of petabytes of data already available today. Thus, new computational methods are required to perform a search against datasets. Querying datasets can be done precisely by aligning genome sequences (e. g. using Blast-like  [1]  algorithms), however aligning sequences is computationalresources intensive.
Thus queries on large scale datasets are usually done through k-mers presence / absence. Basically, datasets are represented as their set of kmers and queries are represented as their sequence of k-mers. Methodological developments have thus been made to index every k-mers of a dataset. Some methods use Approximate Membership Query data structures (AMQ), e. g. bloom filters, to store presence / absence of k-mers, as SBT  [12]  or HowDe-SBT  [5] ; see  [9]  for a survey of approaches to index large dataset. However, very few methods tackle the issue of recording the abundance of the indexed k-mers. The abundance information is however crucial for many biological applications such as transcriptomics or metagenomics. Storing abundance is costly with regard to space consumption. Conversely, adding abundance information in an AMQ, turning it into a counting AMQ, without allocating more space increases drastically its false positive rate.