dataesr/scientific-paragraphs-categorization huggingface dataset:
The optimized imaging buffer composition hence improves the photon budget and the amount of reactivation of far-red fluorophores, which is beneficial for splitSMLM. COT therefore has an enhancing photophysical effect (Supplementary Figs. 14, 15). To analyze splitSMLM data conveniently, we developed a new demixing software, SplitViSu. This tool allows visualization of input data, flexible selection of the regions for each channel, automatic or manual alignment of localizations between the channels, pairing of localizations within a given distance, visualization of the paired localizations as a bivariate histogram of intensities, flexible assignment of the localizations to fluorophores based on the photon counts and their ratios, and estimation of (unwanted) cross-talk between demixed localizations (Supplementary Fig.
10 ). The software can be used as a stand-alone application under Windows, or can be run under Matlab or from the SharpViSu software  23  in which it was added as a plugin. The demixing algorithm uses all detectable photons both for demixing and localization, improves the localization precision and corrects chromatic errors. The usage of similar wavelengths has the specific advantage here of reducing lateral chromatic aberration thanks to the capability of the software to demix spectrally close fluorophores, which is improved even further by additional processing in SplitViSu. Moreover, we introduced the refinement of re-localization events, which further increases image resolution (Fig. 2e ). Additionally, we show the benefits of the image-splitting setup and fluorophore demixing also for single-labeled samples (Fig.
1b  and Supplementary Fig. 13 ) thanks to filtering out localizations that originate from background noise or from autofluorescence. Unlike other software with some similar functionalities  18, 20  , the SplitViSu tool provides flexibility in calculation of output localizations, with the possibility of freely selecting options to use only coordinates from a selected channel or use their mean or weighted mean values, thus improving the localization precision and compensating chromatic errors. Moreover, SplitViSu is fully integrated with the SharpViSu 23 workflow, allowing drift correction, visualization, estimation of resolution, filtering, segmentation & cluster analysis with Clus-terViSu and 3DClusterViSu  12, 46  and further processing of spectrally demixed multi-color data. The SplitViSu software is made freely available under https://github.
6, 7 ); (3) need for additional data processing steps that are, however, now implemented in the free software SplitViSu; (4) a choice of specific fluorophores, but these may be extended in the future. When this paper was under revision, another work was published that uses a similar method for computation of the output localizations using the weighted mean of the input localization pairs with automatic correction of chromatic errors  47. The procedure was applied to triple-color DNA-PAINT and double-color dSTORM data. The two methods, splitSMLM and SD-DNA-PAINT  47  , are therefore the first to our knowledge multi-color demixing methods that use all available photons for localization and offer automatic correction of chromatic distortions. In this work, we provide a dedicated software, SplitViSu, that integrates all the demixing processing steps in a single interactive tool to facilitate the workflow and that can be used for any SMLM experiment type (e.
2a , Supplementary Fig. 3 ). For every fluorophore within a dataset, we calculate their simple and weighted mean positions, and the difference of those positions: Δx ¼ x wm À x m ð22Þ Δx has a random component due to the limited localization precision and due to fluctuations in the photon counts, and a non-random component that depends on the position x due to the chromatic error: Δx ¼ Δx rand þ Δx chro ð23Þ The typical values for our setup and fluorophore triplet are: jΔx rand j ≤ 20nm ð24Þ jΔx chro j ≤ 4nm ð25Þ (Supplementary Fig. 4 ). We fit the chromatic component Δx chro as a function of the coordinates with a polynomial, separately for x and y coordinates, and subtract it from the "weighted mean" positions: X ¼ x wm À Δx chro ð26Þ (Supplementary Fig. 4 ). Cell culture and immunofluorescence.
The power of the 405 nm laser in % was set in the LAS X wizard, it does not correspond linearly to the actual laser power, but provides the same power at same values, that is sufficient for comparison between different imaging buffers. Data processing. The fitting of single-molecule images was performed in the Leica LAS X software with the "direct fit" fitting method, using an appropriate detection threshold. The localization tables were then exported for further processing in the SharpViSu software workflow  23  and with customized Matlab procedures as implemented in SplitViSu. If imaged with the image splitter, the localizations were first demixed with the SplitViSu plugin and then processed in the main SharpViSu software for drift correction and for re-localizations on consecutive frames.
Since the drift is the same for all demixed channels, for drift correction in SharpViSu, we added possibilities to calculate the drift based on data in one of the channels or on the sum of data in all demixed channels. The calculated by cross-correlation drift is then subtracted from localizations of all channel. For the data in this paper, we calculated the drift based on the sum of localizations in all channels. For comparison between the imaging buffers (Supplementary Figs. 14  and 15 ), all localizations were detected using a threshold of 25 photons/pixel in the LAS X wizard. After drift correction, the datasets were cropped to include only the surface of a nucleus with labeled nuclear pores (one nucleus per dataset). The photon counts and other localization statistics were calculated within these cropped regions, and the occurrence of localizations were normalized on the area of the corresponding regions, in order to get densities comparable between various datasets.
All detected localizations within the region were assessed, without correction for re-localizations in consecutive frames and without any filtering. For all other datasets, we used a search radius of 50 nm for finding the re-localizations in the consecutive frames with their subsequent refinement. The resulting superresolution images were built as 2D histograms of the single-molecule coordinates. The emission spectra of fluorophores, observed in the λ S or the λ L channels, were calculated as a multiplication of the normalized emission spectrum of the fluorophores with the transmission of the dichroic mirrors and filters used in the given channel and the quantum yield of the camera. The average emission wavelength in a given spectral channel was calculated as λ 0 ¼ Z 800nm 630nm λ Á I λ ð Þdλ= Z 800nm 630nm I λ ð Þdλ ð27Þ where λ is the wavelength and I(λ) is the emission of the fluorophore observed in the given channel.
For averaging of multiple NPCs in the "side view", the profiles of individual particles along the NPC's axis were calculated in Fiji  54  , averaging through the whole diameter of the NPC. The profiles of Nup96 images were fitted with a sum of two Gaussians in Matlab. The center between the Gaussians was set as the new origin of the x-axis and all particles were summed after this alignment. The profiles of co-imaged proteins were transformed using these alignment parameters of Nup96 and were summed as well.
Statistics and reproducibility. Where applicable, the final data are shown as mean ± standard deviation of repeated measurements. The number of measurements is reported in the corresponding figure legends. FRC resolution values (Fig. 2e , Supplementary Figs. 5, 8, 9) were calculated in SharpViSu  23  according to the FRC 1/7th criterion  33  , using the histogram image reconstruction method with a pixel size of 5 nm and 90 frequency steps. The calculation was repeated ten times for every dataset and the resolution was represented as a mean ± standard deviation of the obtained values for the corresponding image. For Supplementary Fig. 9 , the mean and standard deviation were calculated from FRC resolution values of five different datasets for each fluorophore.
Acknowledgements We thank  Yongrong Liao  and  Izabela Sumara  for the anti-Pom121 antibodies;  Amelie Zachayus  and  Arnaud Poterszman  for the U2OS-NUP96-mEGFP cell line;  Yves Lutz  ( IGBMC Imaging Platform ) for optimization of the labeling protocols and training. This work was supported by the  French Infrastructure for Integrated Structural Biology (FRISBI)   ANR-10-INSB-05-01  /  France 2030 program , the  Alsace Region , the infrastructures Instruct-ERIC and iNEXT-Discovery, the  CNRS ,  Association pour la Recherche sur le Cancer (ARC) ,  Institut National du Cancer (INCa) , the  Fondation pour la Recherche Médicale (FRM) , and  USIAS of the University of Strasbourg  ( USIAS-2018-012 ). This work of the  Interdisciplinary Thematic Institute IMCBio , as part of the  ITI 2021-2028 program  of the  University of Strasbourg ,  CNRS  and  Inserm , was supported by  IdEx Unistra  ( ANR-10-IDEX-0002 ), and by  SFRI-STRAT ' US  project ( ANR 20-SFRI-0012 ) and  EUR IMCBio  ( ANR-17-EURE-0023 ) under the framework of the  France 2030 Program.
Installation instructions SplitViSu can be used as a separate stand-alone application for Windows or as a part of the SharpViSu package. Download the latest version of the installer under https://github. com/andronovl/SharpViSu/tree/master/Installer The installer will determine if the appropriate version of the MATLAB Compiler Runtime is already installed on the computer. If not already installed, the MATLAB Compiler Runtime will be downloaded and installed automatically. After the installation, run either SharpViSu. exe or SplitViSu. exe. The software can be also used as a MATLAB application. Needs MATLAB R2021b or newer with Image Processing Toolbox, Signal Processing Toolbox, Statistics and Machine Learning Toolbox. This option allows to run the software also under Linux or Mac. For installation, download https://github.
s. , including a diversity of species from both hemispheres. These sequences mostly come from ongoing parallel projects with collaborators. This small number of samples does not provide useful information on relationships in this outgroup, so these are represented only by a single triangle in the trees presented and are not listed in Table  1 ; results for Heliotropium s. s. will be published elsewhere by H. Akhani and colleagues. We used two accessions of Hydrophyllum canadense L. as a further outgroup, also represented by a triangle. For these analyses, we only included sequences obtained at RBG Kew, as described before, because they provide nearly complete data for all three regions (nrITS, rbcL and matK). Failure of amplification, despite repeated attempts, occurred in some cases, represented by '?' in the matrix; 3.
The plastid bootstrap tree  (Fig. 1 ) shows strong support (≥ 90%) for many nodes, with weak support for others, whereas the nrITS tree has a large polytomy (not shown). When the plastid and nrITS data are combined, support for some basal nodes within Euploca is reduced. The nrITS strict consensus tree (alignment 1) shows placement of E. cremnogena and E. tenella as two successively diverging sisters to the rest of Euploca (Fig. 2 ). This is unexpected based on morphology and conflicts dramatically with placement in the plastid tree (and, for E. tenella, with the old RFLP-based tree; the Supporting Information, Fig. S1 ). On blastn against GenBank, these sequences do hit Heliotropium s. l. sequences. The two E. tenella sequences are almost identical, and are from different plant sources, and the DNAs were isolated and the PCRs done many months apart, so we do not think they could be spurious.
ACKNOWLEDGEMENTS We thank the herbaria listed here for samples for δ 13 C determinations and for DNA extraction. We thank  Tom Van Devender ,  Ana Lilia Reina-Guerrero ,  Jonathan Wendel ,  George Yatskievych  and  Denis Kearns  for providing samples,  Edith Kapinos  and  Laslo Csiba  for DNA extractions,  Bryn T. M. Dentinger  for assistance with phylogenetic analysis programs,  David Steart  for searching for records of fossil Euploca and Heliotropium fruits from  Australia ,  David Steart  for searching for records of fossil Euploca and Heliotropium fruits from  Australia ,  Saba Rokni  for photographs of specimens in spite of COVID-19,  Jerzy Rzedowski  and especially  Thomas Boone Hallberg Barlow  for much assistance with fieldwork in Mexico. Funding was provided by an  National Science Foundation (USA) Mid-Career  fellowship  BSR-8909933  to MWF, by  Discovery Grant  number  RGPIN-2017-06476  from the  Natural Sciences and Engineering Research Council of Canada  to RFS, and by  RBG Kew.
Nevertheless, synonymy appears to have real potential for improving results. Translations in verse are, for example, less connected with their subset since they privilege synonyms, polylexical constructions, and circumlocutions and have to respect rhyme and rhythm structures. But these characteristics are precisely what enable more subtle matches with the subset of rewritings: Ovide (1687, trans. Corneille) is a unique translation of Ovid that relates to a comic opera by Offenbach et al. (1858). A comparison of these texts shows that among the 18 common entities detected within 4 couples, 10 are pairs of synonyms. Tracer can take synonymy into account to detect reuse. 21 It seems, however, that to fully exploit this functionality, general language synonyms should first be adapted to the specific requirements of the corpus.
Enriched Results Exploitation The results of the word matching are included in the initial annotation of the sentence using an <xr> element, child of <w>, which specifies the ID of the matching word (@corresp), the type of relationship detected (@type), and its score (@cert). In this way, the extract from sentence 28 in duryer1702 presented in Figure  3  is enriched by 6 elements <xr> (Figure  7 ). Indeed, 6 out of 10 correspondences contain an equivalence with the word mari, in most cases based on synonymy with époux (4 occurrences). The enriched results are then included into an XML database that compiles all the matches detected beforehand by TextPAIR and Tracer. This compilation allows for a wide range of manipulation and analyses. Some of these are specific and focused on a chosen text, while others are general and include all the correspondences.
SWAT data inputs We used the following spatialized data: (i) a digital elevation model with a 90 m × 90 m resolution from http://www. viewfinderpanoramas. org/ (Figure  2a ); (ii) a FAO soil map (Figure  2B ); (iii) a land-use map, from the European Space Agency (http://due. esrin. esa. int/page_globcover. php) (Figure  2C ), management practices: planting/seedling dates, amounts, type and date of fertilization and harvest operations dates. CFSR was chosen as the database set, as it provides all necessary parameters and contains no missing data (Figure  2D ) provided by the U. S. National Centers for Environmental Prediction (NCEP) that were used to simulate the reference evapotranspiration in the model using the Hargreaves method which is the best in semi-arid regions  (Aouissi, 2016). We used the 2012.
We introduce the Funandes database, a compilation of functional trait data for the andean flora spanning six countries. FunAndes contains data on 24 traits across 2,694 taxa, for a total of 105,466 entries. The database features plant-morphological attributes including growth form, and leaf, stem, and wood traits measured at the species or individual level, together with geographic metadata (i. e. , coordinates and elevation). FunAndes follows the field names, trait descriptions and units of measurement of the TRY database. It is currently available in open access in the FIGSHARE data repository, and will be part of TRY's next release. Open access trait data from Andean plants will contribute to ecological research in the region, the most species rich terrestrial biodiversity hotspot.
Background & Summary Functional traits are measurable properties of a plant describing its structure, function or life history strategy that determine species responses to biotic and abiotic environmental conditions across scales of biological complexity, from communities to ecosystems  [1] [2] [3] [4]. Exploring variation in plant functional traits provides key insights into plant species distribution, community assembly mechanisms, evolutionary strategies, and ecosystem level potential responses to global environmental change  [5] [6] [7] [8] [9] [10] [11] [12] [13]. Global databases of plant functional traits currently feature an unprecedented amount of trait information that supports scientific work on plant functional ecology, including BIEN  14  , GIFT  15  , and TRY  16, 17. Yet, the geographical coverage of trait measurements still remains limited for highly diverse tropical areas, especially in mountainous regions  15, 16.
The tropical Andes is a major hotspot of global biodiversity and endemism. With about 2% of the terrestrial area of the planet, it holds 10% of the species of vascular plants  [18] [19] [20]. However, trait information for Andean plants is underrepresented in global plant trait databases. These information gap limits our understanding of variation in plant trait composition and diversity at regional, continental, and global scales. Synthesizing and harmonizing trait measurements from remote and understudied areas is critical for global and regional data archiving initiatives  21  , and for advancing empirical biodiversity research. Here, we present the FunAndes database, a compilation of plant functional traits in the tropical Andes (Fig. 1 ). The records in FunAndes stem from 18 unpublished datasets contributed by different research groups conducting fieldwork in the region.
FunAndes follows the structure and terminology of the TRY database, and is available in the FIGSHARE data repository  22. In total, FunAndes contains 105,466 records of 24 traits, covering 2,694 Andean (morpho-) species in 670 genera and 175 families. Assembling FunAndes encompassed the following steps: 1) developing a TRY-based format for data contributors, 2) revising comparability among protocols used for trait data collection, 3) checking trait measurement units for each contributed dataset, 4) detecting and deleting suspicious or erroneous trait measurements, 5) compiling the contributed data into a unique source with common taxonomic names, units, and terminology. To our knowledge, FunAndes is the first open access trait database of the Andean flora, filling a substantial gap in global functional trait data.
Methods primary sources. We first developed a basic data template containing trait names, trait descriptions and units of measurement, together with information (e. g. , site coordinates and collection dates, number of samples collected). This template was distributed to potential data contributors, scientists collecting vascular plant functional trait data mainly in tropical forests of the Andean region. Filled templates were returned to the writing team, and FunAndes was assembled from 18 distinct datasets containing field data of Andean plant traits (Tables  1  and 2 ). Trait definitions and protocols. Trait definitions and trait units of measurement in FunAndes follow those of the TRY database, for a total of 24 plant traits, two categorical and 22 numerical (Table  3 ). All trait data contributed to FunAndes were obtained from individuals growing in natural vegetation, following standard and comparable methods  23, 24.
Furthermore, traits were measured mostly in adult individuals, never in seedlings or saplings. Leaf traits were quantified from exposed mature leaves in the plant canopy. A summary of trait geographical representation in FunAndes is presented in Fig. 1. A comparison between trait data in FunAndes and TRY version 5  17  is presented in Table  4. Database structure. The database contains 24 fields to provide contextual information about data collection, including association of trait data to permanent vegetation plots, site coordinates and collection dates; and information about the trait value provided (e. g. , if the value provided is a single observation or an average of trait measurements) (Table  5 ). Harmonization. We followed various steps to ensure the quality of the data before adding a contributed dataset to FunAndes.
Stem conduit density (vessels and tracheids) mm -2 Stem dry mass per stem fresh volume (stem specific density, SSD, wood density): branch g/cm  3  Stem dry mass per stem fresh volume (stem specific density, SSD, wood density): sapwood g/cm 3 Wood (sapwood) specific conductivity (stem specific conductivity) kg m -1 Mpa -1 s -1 Table  3. Plant functional traits represented in FunAndes. Trait definitions and units of measurement follow those of TRY  16  (https://www. try-db. org/de/TabDetails. php). each contributed dataset, harmonizing data into common measurement units, translating terms (trait values) for categorical variables, verifying and correcting collection coordinates, and identifying erroneous trait data measurements. Each data contributor was contacted to double check methods used for trait collection, correct or eliminate suspicious trait values.
Finally, duplicates were removed to create the final version of the database. All steps taken toward data standardization were done in R  16  using built-in functions and the package 'dplyr'  25. Taxonomy. Species names standardization was conducted with the R package 'LCVP' of The Leipzig Catalogue of Vascular Plants  18. Original species names were compared to LCVP names by searching for matches. Non-matches (mainly caused by incorrect spelling) were revised by an expert in Andean flora (J. H. ), and corrected following LCVP. The final FunAndes database reports both the original and the updated taxon name alongside each trait record. For each morphospecies, higher taxonomic affiliations obtained from the LCVP were included.
Data coverage. FunAndes includes 105,466 trait records for 24 traits of 2,694 Andean morpho-species in 670 genera and 175 taxonomic families. Therefore, FunAndes presents trait information for roughly nine percent of the ~30,000 species of vascular plants estimated to occur in the Tropical Andes  20, 26. Three traits of FunAndes (plant growth form, leaf compoundness, specific leaf area) make up half of the records in the database (Table  4 ). Leaf trait data make up 67. 7% of the database, followed by whole plant (i. e. , plant growth form and leaf compoundness) (17. 8 and 17. 6%, respectively) and stem traits (14. 5%). Each species has an average of 7. 4 (SD = 5. 1) distinct traits. All observations have geographic coordinates. Considering the Andean countries, Ecuador has 47. 8% of all the trait observations in FunAndes, followed by Peru (25.
0%) and Bolivia (19. 5%) (Fig. 1 , Table  1 ). Data in FunAndes comes from 788 collection sites (i. e. , unique combinations of latitude and longitude) and is associated to 570 forest plots. Furthermore, trait observations are grouped mainly around 500, 1,000, 2,000 and 3,000 m of elevation (Fig. 2a ). The data is widely distributed along a gradient of mean annual temperature, but clustered toward lower values of total mean annual precipitation (Fig. 2b ). The five most represented plant functional traits in FunAndes -plant growth form, leaf compoundness, specific leaf area (SLA), wood density, leaf thickness -are homogeneously distributed in the tree phylogeny (Fig. 3 ). TRY version 5  23  hosts 8,548 entries for Andean plants, corresponding to 1,123 species, and 15 of the 24 functional traits held in FunAndes (Table  4 ).