dataesr/scientific-paragraphs-categorization huggingface dataset:
9 Figure 1 : 10 Figure 2 : 91102 Figure 1: ShareLoc facilitates sharing, reanalysis and visualization of SMLM data. a) In SMLM, a sequence of raw, low resolution (diffraction limited) images of single, photoswitching, fluorescent molecules is acquired and their localizations are extracted using one of many localization software 1,23 , typically resulting in a localization table. b) The online ShareLoc platform (shareloc. xyz) allows cloud storage of SMLM data on Zenodo, and enables easy annotation of data with relevant metadata, as well as conversion to *. smlm format. c) The *. smlm file format provides reduced storage size for SMLM data compared to the common formats CSV and TSF and to the raw image data. d) The reduced file size allows corresponding gains in loading time. e) ShareLoc provides gallery views of uploaded data, searchable by tags, enables quick data sharing by e-mail or social media and easy visualization (2D or 3D) through a dedicated ImJoy plugin, that runs in a web browser, on computers or mobile devices.
The whole body phantom has been analyzed 10 times without repositioning according to the recommendations of the international society of densitometry  [11]. The Hologic APEX Software with Windows XP operating system was used on all three devices (version 13. 3. 01) for the analysis of the data by the same operator (TS). The whole body mode was selected to scan the phantom. The duration of the whole body analyses in the three centers was 3 minutes for Lyon and Orléans (Hologic Discovery QDR A) and 6 minutes for Lille (Hologic Discovery QDR W). The sub-regions of whole body phantom analysis were selected as recommended by the manufacturer. Measurements were done for fat and lean mass in six sub regions of the whole body phantom: total whole body, whole body less head, trunk, head, right leg and left leg (Figure  2 ) and in 10 sub-regions for BMD assessment.
3% lower lean body mass compared to a Hologic QDR-1000W. The total tissue mass was significantly lower with QDR-2000 (0. 6%, p <0. 001). Similarly, by comparing a QDR 4500W with a Discovery W, Covey et al. [17]  found significant differences for inter center values. The QDR 4500W tends to overestimate the lean mass and underestimate the fat mass compared to the Discovery Wi. Differences observed between centers with devices of the same manufacturer could be explained in several ways. The first one is that the software used can lead to different calculation techniques or used segment detection factors  [10]. Kohrt et al. [1] , was interested in these different versions of analysis software and showed that the results obtained with the same machine (Hologic QDR-1000W) but with different versions of software did not give the same results.
Indeed, version 5. 50 (Hologic software) underestimated the percentage of soft tissues in the central region of the body (55%) while an updated version (version 5. 64, Hologic software) corrected these variations and quantified 96% of soft tissues in the same region. Several studies have shown that these errors were higher in areas of low bone density such as fingers, pelvis or ribs. The results of Economos et al. [19]  validated this hypothesis. The second one should be due to the different calibration settings and to the particularity of the X-tube of the device. Studies have shown that the differences were beam-  [18, 20]  and scanning mode dependent  [21]. According to Paton et al  [9] , the lack of consensus on machine calibration and the difficulty of including calibration constants in analysis software affect BMD results and thus the quantification of soft tissues.
9-m sawlog. The sawlogs were weighed and measured and green stiffness properties determined using BING (Beam Identification by Nondestructive Grading)  (Paradis et al. 2017 ), a resonance acoustic tool. The sawlog was then sawn and the boards were dried in accordance with industry recommendations for nominal structural framing dimensions (96 × 40 mm and 72 × 40 mm), using industry-provided sawing patterns. Full-length boards were tested using BING to obtain MOE data and measured for distortion (twist, spring and bow), then sub-sampled for static bending using the standard four-point bending test (AS/NZS 4063. 1:2010 2010b). The ring age and locations for each of the increment cores (all trees) were measured and recorded, and the cores were then segmented into 20-mm sections. Each segment was measured for MOE, density and shrinkage.
The MOE of the segments was measured using an ultrasound time-of-flight approach. A computer code was developed in MATLAB ® to take scanned images of the tree ring markings, detect their locations, and then divide them into the appropriate segments. Each of the measured wood properties were then attached to their corresponding segment. It is this segment data that forms the basis of our models for the board stiffness. For more details on the data collection and measurement approaches, the reader is referred to  Kumar et al. (2021).
p 1 (x) = 1, p 2 (x) = r, p 3 (x) = z, To apply this RBF approach to our problem, we assume our data, y i , are samples from our fitted logistic functions. From the four cores we obtain information at four distinct heights within the tree, giving a dataset that captures the variation in both r and z for each individual tree in our study. Following this process for each tree in the destructive sampling trials we have a description of the MOE at any location within each tree (bounded by the locations of the cores). This allows us to perform "virtual sawing" (discussed in the following section) to extract the timber boards from the pseudo-three-dimensional virtual log, calculate their MOE, and compare this value to the MOE obtained from static bending.
We see that for these trees our predicted board MOE captures the overall Fig. 6  Fitted sigmoid curves for a representative tree, taken from the destructively sampled set with four cores taken at different heights up the tree, where core 1 is taken lowest in the tree, and core 4 the highest. A radius of 0 mm corresponds to the pith of the tree, and each curve uses data from both sides of the pith. trends and ranges of board MOE measured from the static bending test. The pseudo-three-dimensional approach tends to predict higher MOE values for individual boards, and exhibits a lower root-mean-square-error (RMSE) than the pseudo-two-dimensional approach (mean of 1. 911 versus 2. 129) across all boards, when evaluated per tree. Figure  9  shows a comparison between the measured MOE and the predicted values obtained using the pseudothree-dimensional approach with radial basis functions fitted to the 5PL functions.
To evaluate model performance, we consider leave-oneout cross-validation (LOOCV), where a linear model is fitted to a training set consisting of all trees with one removed, and tested on the removed tree. This is done a total of 68 times, corresponding to each tree being in the test set. We have used the modelr (Wickham 2019) package in R 3. 6. 1 (R Core Team 2019). Using this approach we obtain a RMSE of 2. 009 (with intercept) and 2. 024 (without intercept) for the pseudo-two-dimensional method, and 1. 915 (with intercept) and 1. 919 (without intercept) for the pseudo-three-dimensional method. This demonstrates that the pseudo-three-dimensional approach is generally more accurate in predicting individual board MOE when compared to the pseudo-two-dimensional method; however, as noted previously, the pseudo-threedimensional method has a greater overhead in terms of data collection to obtain information at different heights within the tree.
Acknowledgements The authors also wish to thank the following for their valuable contributions to this work:  Bruce Hogg ,  Tony Burridge  and  John Oostenbrink  ( Department of Agriculture and Fisheries, Gympie ) for plantation sampling, log selection and harvesting, and  Dan Field ,  Eric Littee ,  Rhianna Robinson , and  Rica Minett  ( Department of Agriculture and Fisheries, Salisbury Research Facility ) for sawing and testing.
Description of the simulations Meteorological fields are driven by the ARPEGE 3 hourly forecasts. Anthropogenic and biomass burning sources emit SO 2 , whereas biogenic emissions from the ocean are assumed to occur as DMS. Oceanic DMS emissions are 19. 9 Tg S yr -1 , while anthropogenic emissions are 48. 6 Tg S yr -1. For 2013, biomass burning emissions from GFAS products were relatively low, at only 1 Tg S yr -1. Concerning volcanic sulfur emission inventories, either  Andres and Kasgnoc (1998)  or  Carn et al. (2016 Carn et al. ( , 2017) )  is used. The full eruption emission database is available following Carn (2021, https://doi. org/10. 5067/MEASURES/SO2/DATA405). In total, four different simulations (Table  2 ) are carried out in order to evaluate the impact induced by the update of the volcanic SO 2 inventory in MOCAGE and to analyze its contribution to the sulfur species budget in the atmosphere at the global scale.
g. ,  He et al. , 2012; Fioletov et al. , 2013; Wang et al. , 2017; Wang and Wang, 2020). Moreover, the SO 2 tropospheric column estimated from the OMI is the finest resolution and most accurate instrument from 2013 for retrieving SO 2 total columns over passively emitted volcanoes with altitudes that are generally around 2-3 km. For aerosols, there is no satellite-derived product providing information on sulfate only. Nevertheless, satellite observations of aerosols as a whole are available. Here, we choose MODIS (Moderate-Resolution Imaging Spectroradiometer) aerosol optical depth (AOD), which provides data at the global scale. MODIS AOD is known as being a robust product and is used in the literature for global evaluation and aerosols assimilation in models (e. g. ,  Liu et al. , 2011; Dai et al.
There are various products available in the OMI data set since the OMI instrument has a variable sensitivity, depending on altitude, and the retrieval of SO 2 requires the use of an a priori profile. The first product selected, named Column_Amount_SO 2 , is an estimate of SO 2 vertical column density (VCD) and constrained by the GEOS-5 global model a priori profiles. Then, three specific products with adapted a priori profiles are also available and selected. One, named Column_Amount_SO 2 _PBL, is an estimate of the SO 2 vertical column density (VCD), with an a priori profile assuming that the essence of SO 2 is in the boundary layer (within the lowest 1 km of the atmosphere). Another product, named Column_Amount_SO 2 _TRL, is almost the same as the previous one but assumes a lower tropospheric SO 2 profile (with a center of mass altitude at 3 km).
MODIS aerosol optical depth We use daily level 3 MODIS data (MOD08, Terra; MYD08, Aqua; collection 6. 1) for the year 2013. Before use, we performed additional quality control and screening  (Sič et al. , 2015; Guth et al. , 2016). These treatments aim at minimizing cloud contamination and avoid low-confidence measurements  (Zhang et al. , 2005; Koren et al. , 2007; Remer et al. , 2008). Moreover, all AOD values below 0. 05 are automatically filtered out because Ruiz-Arias et al. (  2013 ) highlighted the rapid growth in the relative underestimation of AODs after this threshold, which leads to a mean relative error above 50 %. In MOCAGE, AODs are calculated using Mie theory with the Global Aerosol Data Set's refractive indices  (Köpke et al. , 1997)  and extinction efficiencies derived with the Mie scattering code for homogeneous spherical particles from  Wiscombe (1980).
Acknowledgements. We would like to acknowledge the MODIS mission team and scientists for the production of the data used in this study. The authors thank  Météo-France  for hosting Claire Lamotte's doctoral research at the  Centre National de Recherches Météorologiques. Financial support. The doctoral research contract of Claire Lamotte has been supported by the  Université Paul Sabatier Toulouse III. Review statement. This paper was edited by  Jianzhong Ma  and reviewed by two anonymous referees.
Data availability. The new volcanic SO 2 inventory implemented is available for eruptive emissions from the GES DISC archive (https://doi. org/10. 5067/MEASURES/SO2/DATA405, Carn, 2021). Passive degassing emissions can be found in Carn et al. (2017,  https://doi. org/10. 1038/srep44095) and their accompanying supplementary material (https://doi. org/10. 1038/srep44095). Concerning the data used for the validation, OMI SO 2 total column data can be found in the NASA database GES DISC  (Li et al. , 2020;    https://doi. org/10. 5067/Aura/OMI/DATA2022). The previous volcanic SO 2 inventory is available upon request from the corresponding author. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/acp-21-11379-2021-supplement. Author contributions.
The success story of the HSQC spectrum was born and formed the basis for a myriad of 2-to n-D spectra used from protein NMR to the analysis of metabolomic samples. Indeed, beyond synthetic chemistry, NMR has found its place in the realm of metabolomics  (Emwas et al. , 2019; Wishart, 2019) , and together with mass spectrometry, it is the method of choice for characterizing complex mixtures such as biofluids. If throughput is required, the 1-D 1 H spectrum remains the general workhorse, whose assignment can be based on database searches, on the spiking of relevant standards within the sample or on analysis of the corresponding 2-D spectra acquired on one or more samples. The 1 H-13 C HSQC spectrum proposed by Bodenhausen and Ruben is again a crucial element in the latter approach, as it spreads out the proton signals according to the 13 C chemical shift of its attached carbons and allows connection of both in the same time.
Methods All experiments were performed on an Avance III 800 MHz spectrometer (Bruker BioSpin GmbH, Karlsruhe, Germany), equipped with a 1. 7 mm triple-resonance HCP or a 5 mm quadruple resonance QCI-P (H/P-C/N/D) cryoprobe and were recorded at 298 K. Data were processed with Top-Spin 4. 0. 8 (Bruker BioSpin GmbH, Karlsruhe, Germany). All proton spectra were referenced to the methyl proton of 3-trimethylsilylpropionic acid-d4 (TSP-d 4 ). 13 C chemical shifts were determined by indirect referencing  (Markley et al. , 1998). Pulse sequences were initially tested on a sample of 1 mg of dextran oligosaccharide dissolved in 40 µL of D 2 O plus 2 µL of TSP-d 4 on the 1. 7 mm cryoprobe. Clean in-phase and clean anti-phase (CLIP/CLAP) experiments were acquired with 16 384 ( 1 H) × 256 ( 13 C) time points, a spectral width of 12.
From this solution, 40 µL were then transferred into the NMR sample. CLIP/CLAP experiments were acquired with 16 384 ( 1 H) × 256 ( 13 C) time points, a spectral width of 13. 9486 ppm ( 1 H) × 100 ppm ( 13 C) and NS = 64, DS = 64, RG = 512, d1 = 1. 5 s, experimental time = 10 h 28 min 51 s (CLIP) and 10 h 25 min 48 s (CLAP). Data were transformed to a matrix of 16 384 ( 1 H) × 1024 ( 13 C) frequency points. The 1-D 1 H, 1 H-13 C HSQC with decoupling and the Jresolved experiments were recorded on the 5 mm cryoprobe on a sample of 200 µL. A high-resolution 1-D 1 H spectrum was acquired with 32 768 time points, a spectral width of 12. 0172 ppm and NS = 64, DS = 4, RG = 8, d1 = 5. 0 s. The 1 H-13 C HSQC was recorded with 2048 ( 1 H) × 256 ( 13 C) time points, a spectral width of 13. 9486 ppm ( 1 H) × 100 ppm ( 13 C) and NS = 64, DS = 64, RG = 912, d1 = 1 s, experimental time = 8 h 2 min 26 s.
Data were transformed to a matrix of 2048 ( 1 H) × 512 ( 13 C) frequency points. The Jresolved experiment was recorded with 16 384 time points, a spectral width of 12. 0172 ppm and NS = 128, DS = 64, RG = 912, d1 = 1. 32 s for an experimental time of 18 h 1 min 29 s.
S1 ). We developed a semi-automatic Python procedure for the virtual decoupling that is based on the nmrglue package  (Helmus and Jaroniec, 2013)  and is usable within TopSpin 4. It can be downloaded from https: //github. com/NMRTeamTBI/VirtualDecoupling (last access: 4 August 2021). While launching the script from TopSpin, the user will have several options that are detailed in the tutorial available online. First, taking advantage of the sign difference between the α and β components of the doublet in the CLAP experiment (Fig. S2 ), we reduce the resolution and hence the fine structure of both components by processing the CLAP spectrum with only 1024 points in the 1 H dimension, thereby intentionally destroying the high-resolution Jcoupling pattern and obtaining just a single peak per component.
Automatic peak detection is then performed twice: once on the positive components and once on the negative components of the spectrum and can be performed on the full spectral width or only for a user-defined region with a threshold that can be adapted if necessary. A clustering step based on 13 C chemical shifts obtains a pairwise selection of the signals. The central position of this pair is used to define the J /2 value for this particular 1 H / 13 C pair, thereby individualizing the back shift and avoiding the impossible use of a common value for all peaks. If exactly two signals of opposite sign are found with the same 13 C frequency (in digital points), the script will automatically bring the upfield component extracted from CLIP-HSQC spectrum with full resolution (processed with 16 384 points in the time domain) back to the centre of the doublet and thereby create a virtually decoupled HSQC (vd-HSQC) spectrum (Figs.
The methodology presented here, while being robust, is rather simplistic and will benefit from the rise of deep learning and artificial intelligence applications to NMR spectra  (Karunanithy et al. , 2021)  to provide a new entry point into metabolite databases. Code and data availability. The code written in Python for virtual decoupling and compatible within TopSpin 4 can be found on GitHub: https://github. com/NMRTeamTBI/VirtualDecoupling (https://doi. org/10. 5281/zenodo. 5163919, Charlier et al. , 2021). A readme file guides potential users through the data used in the paper. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/mr-2-619-2021-supplement. Author contributions. CC was responsible for software, conceptualization and writing (review and editing); NC, AG, JMN and SMP were responsible for resources, review and editing; BL was responsible for software, review and editing; GL was responsible for conceptualization, project administration, supervision and writing (review and editing).
, 2018; Eisenhardt & Graebner, 2007). Such an approach has been used in previous studies on ICT4D (e. g. ,  Baro & Endouware, 2013; Nuseibeh et al. , 2017). Charmaz (2014)  contends that research seeking theoretical understanding employs inductive logic, takes data through rigorous comparative analysis, purports to incite the emergence of theory, and seeks to integrate theory with policy and practice. We used semi-structured interviews with a sample of women entrepreneurs in Iraq. Using semi-structured interviews enabled us to investigate the barriers these women face and how using smartphone and mobile applications has helped them to overcome these challenges. A qualitative research approach was also appropriate due to the lack of research on ICT4D among women entrepreneurs in Iraq, particularly the non-economic development outcomes.
We intended to carry out an exploratory study that fit with a discovery-oriented methodology  (Mahrer, 1988). This enabled us to obtain rich data that provided an initial in-depth understanding of how smartphones and mobile applications can contribute to the social, economic and human development of women entrepreneurs in Iraq. Research relying on qualitative data is common for providing rich and in-depth data  (Davies et al. , 2018; Eisenhardt & Graebner, 2007). The participants were approached by the authors through social media and invited them to take part in a Skype interview. Thirty-five women were invited to participate, but due to access issues (such as time, availability on Skype etc. ) only thirty interviews were conducted. We conducted the interviews via Skype. Purposive sampling was used because it has the potential to provide us with a wealth of information  (Neuman, 1997)  through the selection of information-dense cases that can yield insights and a deeper understanding  (Patton, 2002).
According to  Pandit (1996) , theoretical saturation is met when the marginal value of new data is minimal. In this research, we noticed that codes in the data started to repeat. Hence, we reached the point of theoretical saturation as no new and relevant data created any new categories. We used the software to identify themes from the data to understand the barriers to women's entrepreneurship. We then grouped the themes into categories using open coding, as recommended by  Strauss (1990). We analysed the coded data and compared them with pertinent concepts in the extant literature, and considered relevant patterns emerging from the data. Then, we used axial coding to identify relationships between the benefits of using mobile applications and the challenges these women face. Axial coding involves identifying relationships among open codes.
Discussion The ongoing COVID-19 pandemic has been a breeding ground for fast-tracked research papers across numerous journals in the past yearfrom medical research to social sciences and humanities. Despite that the data for this study was collected prior to the COVID-19 crisis, the study would have implications as the world strives to build back better in a post-crisis environment. Existing COVID-19 related research that have some relevance for this study include Campos-Castillo and Laestadius (2020) and  Choudrie et al. (2021)  which touch upon the digital divide and how to bridge it, respectively. Similarly,  Yildirim and Eslen-Ziya (2021)  investigated the impact on working conditions during the lockdown,  Reichelt et al. (2021) , and  Zheng and Walsham, (2021)  focused on gender inequality.
INTRODUCTION The fast-growing number of available prokaryotic genomes, along with their uneven taxonomic distribution, is a problem when trying to assemble high-quality yet broadly sampled genome sets for phylogenomics and comparative genomics. Indeed, most of the new genomes belong to the same subset of hyper-sampled phyla, such as Proteobacteria and Firmicutes, or even to single species, such as Escherichia coli (e. g. , 105,081 out of 939,798 genomes in GenBank as of January 2021), while the continuous flow of newly discovered phyla prompts for regular updates of in-house databases. This situation makes it difficult to maintain sets of representative genomes combining lesser known phyla, for which only few species are available, and sound subsets of highly abundant phyla. An automated straightforward method is required but would be far too slow if based on regular alignment algorithms.
, 2017)  and the whole-genome phylogeny programs (Table  2  in  Zielezinski et al. , 2017). First, we did not investigate software solutions made available as web services because of their intrinsic limitation with respect to the amount of genomic data that one regular user can process through these interfaces. Second, all the programs belonging to the taxonomic profiling category required a reference database to compare the genomes to, which would have led us to a circular conundrum, in which a (possibly handmade) database of reference genomes is required to (automatically) build a database of representative genomes. Third, all those presented in the whole-genome phylogeny category were either not suited for large-scale dereplication or did not provide small enough running time estimates for their test cases.