dataesr/scientific-paragraphs-categorization huggingface dataset:
Results and discussion We solve the above set of coupled nonlinear partial differential equations by using the commercial software package MatLab. Due to computation time restrictions, we cannot model the entire photoresist droplet, which has a typical extent of a few millimeters, which is several thousand times larger than the sub-micrometer focus radius. We rather use a radius of the spherical simulation domain of  d = 15 µm = 100. At this boundary, for simplicity, we set the oxygen concentration and the photoinitiator concentration to their constant initial values. In more detail, one would actually have to treat the transport of oxygen from the surrounding air into the photoresist droplet, whereas there would be zero in-diffusion of photoinitiator molecules at this interface. These processes are not important though, because we will see that the oxygen as well as the photoinitiator concentration far away from the focal region remain essentially unchanged for the conditions considered here.
Acknowledgements This research has been funded by the  Helmholtz program  " Science and Technology of Nanosystems " (STN), by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) under  Germanyś Excellence Strategy  via the Excellence Cluster 3D Matter Made to Order ( EXC-2082 -390761711 ), and by the  Carl Zeiss Foundation  through the  "Carl-Zeiss-Focus@HEiKA". C. B. -K. acknowledges funding from the  Australian Research Council (ARC)  in the context of a Laureate Fellowship enabling his photochemical research program, as well as key continued funding from the  Queensland University of Technology (QUT). Received: ((will be filled in by the editorial staff)) Revised: ((will be filled in by the editorial staff)) Published online: ((will be filled in by the editorial staff)).
The purpose of the study was to assess the proportion of patients who achieve pain relief thresholds, the time needed to reach the thresholds, and the relationship between pain and inflammation among patients with rheumatoid arthritis (RA) and an inadequate response to methotrexate in RA-BEAM (NCT0170358). A randomized, double-blind trial was conducted, comparing baricitinib (N = 487), adalimumab (N = 330), and placebo (N = 488) plus methotrexate. Pain was evaluated by patient's assessment on a 0-100 mm visual analog scale (VAS). The following were assessed through a 24-week placebo-controlled period: the proportion of patients who achieved ≥30%, ≥50%, and ≥70% pain relief, the time to achieve these pain relief thresholds, remaining pain (VAS ≤ 10 mm, ≤20 mm, or ≤40 mm), and the relationship between inflammation markers and pain relief.
Trial Design The design and procedure of RA-BEAM have been described previously  [7, 8]. Briefly, RA-BEAM was a randomized, double-blind, double-dummy, placebo-controlled and active-controlled, parallel-arm, 52-week study conducted at 281 centers in 26 countries between 2012 and 2015 (ClinicalTrials. gov: NCT01710358). A total of 1305 patients on stable background MTX were randomly allocated (3:2:3) to placebo, adalimumab 40 mg, or baricitinib 4 mg. At Week 16, those patients considered non-responders received open-label rescue treatment with baricitinib 4 mg. After Week 16, patients may have received rescue treatment at investigator discretion. At Week 24, placebo-treated patients were switched to baricitinib. The study was conducted in accordance with the ethical principles of the Declaration of Helsinki and Good Clinical Practice guidelines.
A 30% improvement threshold is described as "much improved, meaningful differences" and 50% represents "very much improved, substantial improvement" in chronic pain conditions. A 70% improvement threshold, although not defined in IMMPACT, was also evaluated because it is analogous to American College of Rheumatology response endpoints and was observed with patients in RA-BEAM. Second, while relative improvement is important, so is absolute pain; thus we evaluated thresholds of remaining pain (i. e. , the absolute value of patient-reported pain) of ≤10 mm, ≤20 mm, or ≤40 mm, at Week 24. The ≤10 mm threshold reflects limited pain to no pain and is extrapolated from data by Wells et al. [11]. The ≤20 mm threshold represents a threshold when satisfaction with health is not negatively affected by pain  [11, 12].
Statistical Analyses All analyses were conducted with an intention-to-treat approach in which data from patients who received ≥1 dose of study drug were assessed, regardless of whether they completed the trial. Missing values were imputed with modified last observation carried forward for all analyses where applicable. Analyses were not adjusted for multiplicity. Comparisons were made on the percent change in pain VAS from baseline to Week 24 using analysis of covariance (ANCOVA) and on the proportion of patients achieving pain relief at Week 24 between treatment arms using logistic models, adjusted for randomization factors (region, baseline joint erosion status (1-2 erosions plus seropositivity vs. ≥3 erosions)) and baseline pain VAS score. The median time needed for patients to achieve these pain relief thresholds were assessed through Week 24 using the cumulative incidence estimate with 'competing risks' which included rescue or discontinuation due to lack of efficacy before reaching the pain relief threshold.
Acknowledgments: The authors would like to thank  Molly Tomlin, MS,  of  Eli Lilly and Company  for her assistance with manuscript preparation and process support;  Julie Sherman , of  Eli Lilly and Company , for her assistance with the manuscript figures; and  Jiaying Guo, MS,  of  Eli Lilly and Company , for her review of the data. P. C. T. would like to acknowledge support by the  National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC)  and by  AR UK.
■ ACKNOWLEDGMENTS The authors acknowledge  Dr. L. Bodelot  from the  Laboratory of Solids Mechanics at Ecole Polytechnique, France  and  Pr. F. Ito  from the department of chemical education at  Shinshuu University, Japan  for fruitful discussions. The authors also acknowledge financial support of this work by the  European Research Council  ( ERC StG   MECHANO-FLUO No 715757 ), by the  CNRS (Mission pour l'Interdisciplinarite) , by the  IDEX Paris-Saclay  (Ph. D. fellowship to M. L. ), and by the  Labex CHARMMMAT (Master fellowship  to C. P. G. ).
6  and Williams, et al. 7. These data are then extrapolated to volcanoes world-wide. Our present update greatly benefits from recent advances in satellite data analysis and a global compilation of annual emissions measured by the Ozone Monitoring Instrument, OMI 8 , which provides SO 2 flux data for numerous volcanoes simultaneously. This space-based record is complemented by and compared to ground-based SO 2 flux data, particularly from the Network for Observation of Volcanic and Atmospheric Change NOVAC 9 , to obtain annual budgets of global volcanic SO 2 emissions. At the same time, coverage of C/S ratios from degassing volcanoes has significantly.
improved with the ubiquitous deployment of Multicomponent Gas Analysis Systems (MultiGAS) providing such data over wide temporal and spatial scales  10. We focus primarily on degassing from subaerial volcanic vents but we also include a preliminary evaluation of diffuse soil degassing from the volcanoes' flanks or from active tectonic regions, which has been compiled in the Magmatic Degassing (MaGa) database (www. magadb. net) and just reported in Werner, et al. 11.
Data and Method Emissions from subaerial volcanoes with plumes were compiled and evaluated by the DCO-DECADE synthesis group to produce an updated and complete estimation of global volcanic SO 2 and CO 2 fluxes. Our compilation applies to approximately 900 volcanoes, listed by Syracuse and Abers  12. Most of them are located in subduction zones and are documented for subduction-related geophysical parameters, which thus offers the opportunity to compare our results for volcanic output to subduction forcing functions. We also consider volcanoes not listed in Syracuse and Abers but documented for SO 2 or CO 2 fluxes, these being mainly from hot spot and continental rift localities. The presented dataset includes volcanic emissions from both persistent strong emitters and discrete eruptions, but also provides new estimates for the emissions of weak contributors.
Strong emitters are defined as volcanoes that release greater than 0. 014 TgSO 2 /y as detected by satellite. Emissions from erupting volcanoes are identified by using the Global Volcanism Program catalogue for eruptive activity between 2005 and 2017. Weak contributors have SO 2 emissions below OMI's detection limit (0. 014 TgSO 2 /y 8 ). The extrapolations to unmeasured volcanoes utilize new data on C/S ratios and a classification into magmatic and hydrothermal categories based on visual observations, volcano databases (predominantly from Smithsonian Institution's Global Volcanism Program, 2013), field reports, and observations made by the authors. Note that while we report and discuss molar C/S ratios, we use mass ratios to compute CO 2 mass fluxes from SO 2 mass fluxes. The summary methodology of computing and extrapolating the fluxes is shown in Fig.
1. So 2 flux from strong emitters and during eruptions. Our compilation of SO 2 flux data covers 11 years from 2005 to 2015 and uses information from long-term monitoring from space and ground, as well as short-term campaign data and reports from the literature. When contemporaneous satellite-and ground-based (long-term or campaign) data exist for the same volcano, we made a critical case-by-case selection of the best and most representative estimate of the annual emission, considering the coverage of each method, specific conditions of measurement and, in certain cases, the separation of the contributions from two neighboring volcanoes that cannot be separated by satellite observations alone. In 28 out 125 cases, ground-based measurements are the only measurements available and are used for the calculations.
1 Tg/y) but well characterized C/S ratios. 38 of these have both measured CO 2 and SO 2 fluxes, two volcanoes displaying relatively high SO 2 flux (Ebeko, 0. 18 Tg/y, and Satsuma Iwojima, 0. 21 Tg/y). A plot of CO 2 flux versus SO 2 flux data for the 38 volcanoes (Fig. 2 ) reveals two main populations of degassing. The first population, which we define as "hydrothermal", is characterized by very low SO 2 flux (<0. 003 Tg SO 2 /y or <8 t SO 2 /day) and CO 2 flux up to ~0. 1 Tg/y (~275 t CO 2 /day). SO 2 fluxes below the limit of 8 t SO 2 /day are exceedingly difficult to measure, even with ground-based techniques, and point to a very low magmatic gas supply, which justifies that the corresponding volcanoes be categorized as non-magmatic or hydrothermal degassers. The 275 t CO 2 /day value (the highest observed) reflects the fact that low-temperature hydrothermal gas emissions can have exceedingly high C/S ratios  10.
2. We emphasize that with more data for weak emitters, this characterization will improve in certainty. The next step is to determine which of the 756 volcanoes for which there are no data available are either not emitting gas, or belong to the hydrothermal or magmatic CO 2 flux populations. In order to assess this, we utilized the Global Volcanism Program (GVP) data base, examined recent photographs of these volcanoes to better characterize activity and relied on our combined experiences. The approach that we take is to generally evaluate visually whether an unmeasured volcano is likely to exhibit 'magmatic' or 'hydrothermal' characteristics. A magmatic gas signature would be recognized by a visible fumarolic plume and/or recent (2005-2017) eruptive activity. A hydrothermal gas signature is assigned for volcanoes that have warm, potentially steaming ground, degassing through mud-pools or water, no coherent plume and no large fumaroles.
156 Tg CO 2 /y. These populations are used to extrapolate to other weak emitter volcanoes for which no data is available. by OMI (~55%), which includes errors associated with retrieved SO 2 columns and with variable plume altitude, wind speed, and SO 2 lifetime. The uncertainty for the ground-based SO 2 fluxes measured by the NOVAC network and selected for this compilation is below 30%. Table  S1  provides the fractional uncertainty of the SO 2 flux for each volcano. This uncertainty is estimated at 55% and 30% for space-and ground-based measurements, respectively  8, 9. This reported uncertainty of ∼30% for ground-based SO 2 flux measurements with scanning-DOAS follows from the analyses presented in  9, 13, 28. The annual means in SO 2 fluxes and their standard errors reported here were calculated from the daily means of SO 2 flux measurements, which in turn were calculated from the individual measurements of flux, when at least five measurements of 'good quality' were available in a given day.
magmatic (Table  2 , Fig. 1 ). For the predicted C/S ratios based on the volcano group and petrologic method, we ascribe a SD of 1. 5 for group 3 and 0. 8 for Groups 1 and 2, consistent with the SD ascribed in  17. The summary of all volcanic CO 2 fluxes is shown in Table  2  and we report the cumulative SD obtained from the SD of the flux SO 2 flux measurements and the C/S ratios. In order to assess the error of the measured and predicted CO 2 flux from strong emitters, we apply the Monte Carlo method to the summary of the strong emitter volcanoes CO 2 data set. In the simulation, the CO 2 flux for each volcano is set to vary randomly within its mean ± SD value and the resulting CO 2 fluxes are summed together. This procedure is repeated 100 times, resulting in 100 randomly-generated sums.
The total values reported in Table  2   ranges (mean ± 1 SD) of 70% of the random generated sums. Using the Monte Carlo approach, the total CO 2 flux from passively degassing volcanoes then becomes 51. 3 ± 5. 7 Tg CO 2 /y. The assessment of errors of the eruptive CO 2 flux requires additional information that is not currently available. For the majority of eruptive SO 2 measurements, no errors are provided  29  and the C/S ratios used for computing the CO 2 emissions are not measured during the eruptions but only prior to the eruptions. Given these uncertainties, we define the error on the eruptive CO 2 flux as ±50%, emphasizing that it is poorly constrained with present data. preliminary considerations for diffuse co 2 emissions. A preliminary estimate of the volcanic-hydrothermal CO 2 emitted by diffuse emission from soil and lakes is attempted using the published data that are reported in the Magmatic Degassing (MaGa) database at www.
Results and Discussion Table  2  summarizes the results of the CO 2 flux calculations. Including both strong and weak emitters, the total global flux of CO 2 from passively degassing volcanoes is 51. 3 Tg CO 2 /y or 11. 7 × 10 11 mol CO 2 /y. Our value of 15. 3 Tg CO 2 /y from weakly emitting volcanoes represents about 30% of the total CO 2 flux from passively degassing (51. 3 Tg CO 2 /y) volcanoes. Notably, our preliminary estimate of diffuse degassing (93 Tg CO 2 /y) is almost twice the total volcanic emission estimate. We stress, however, that this preliminary result could change in the future as the databases of diffuse degassing will include a larger number of data.
Acknowledgements We thank the  Deep Carbon Observatory  for funding the  Deep Earth Carbon Degassing (DECADE)  initiative which allowed us to make numerous flux and gas composition measurements on previously unmeasured volcanoes through campaigns and continuous stations, as well as funding planning and synthesis workshops. We thank  Kerstin Lehnert  and  Lucia Profeta  for help with data entry into the EarthChem Library and establishment of IEDA DECADE data portal used in this study. We also thank two anonymous reviewers whose comments improved this manuscript. We dedicate this manuscript to the late  Erik Hauri , whose vision and leadership was instrumental in the effort of improving our understanding of global volcanic CO 2 flux and the role of volcanoes in the deep carbon cycle. A. A. , C.
org 2018). As digital imaging of the world's herbaria continues, there is a recognition that large amounts of information can be extracted from these images. This information includes data concerning the specimen's origin on the labels, such as location, date and collector, but also traits and the identity of the plant itself  (Corney et al. 2012 , MacGillivray et al. 2009 , Schuettpelz et al. 2017 , Kho et al. 2017). Methods to extract these data are still being developed and require training datasets and test images to validate their effectiveness  (Carranza-Rojas et al. 2017). Herbarium specimens are far from homogeneous. They vary in the language, location and style of the labels, in whether they are typed or handwritten and in the quality and quantity of information on the labels  (Mononen et al.
2014). Specimens are frequently annotated by more than one person and are stored by taxon, rather than by collector, both of which make handwriting recognition particularly difficult. A typical specimen will have text written on different dates and by different people in a mixture of printed, typed and handwritten scripts  (Vollmar et al. 2010). Not all herbarium digitisation projects are the same. They vary in aspects, such as the imaging methodology, the resolution of the digital image created and their approach to quality control  (Nelson et al. 2012). Anyone building tools to analyse herbarium specimens needs to be aware of these variations and needs to account for them. The language used on the labels can also be problematic. Many collections have specimen labels written in a wide variety of languages, sometimes on the same specimen and one cannot assume the use of Latin script, even in Europe.
The interpretation of certain symbols, such as those indicating nomenclatural type status, or of different labels, may not always be clear either. For all these reasons, we feel it is useful to provide a benchmark dataset of digitised herbarium specimens, made openly available for the development of tools and workflows for data extraction. This dataset has been placed in the public domain specifically to act as a test dataset for research and a benchmark to compare different methods. We have also provided transcribed data, where available, associated with each image, which can be used for comparison or for training systems. In addition, for 250 of the specimens, we have provided image overlays that identify the position of labels. These can be used for segmentation analysis of the specimen.
Data were collated as a Darwin Core (DwC) Archive (Darwin Core Task Group, Biodiversity Information Standards (TDWG) 2009), if available from the Global Biodiversity Information Facility (GBIF) using their application programming interface (API) through rgbif in the R programming language (Chamberlain 2017, R Core Team 2017). Data were processed in the R language using the tidyverse (Wickham 2017) and rworldmap (South 2011) packages. Scripts can be found in Suppl. materials 3, 4. Quality control: There are no clear indications in any data as to whether a specimen is completely or partially transcribed. The labels may contain a variety of information, so availability of the information in the data is not a good guide to whether it is present on the label. Nevertheless, of the 1,800 specimens, 94% had a collector listed and 56% had either a collector number or an explicit indication that there was no number.
A total of 85% had either a verbatim or interpreted date and 90% had either a date or collector number. Hence, most of the specimens have some level of transcription. All specimens were analysed by a polyglot to determine the primary language of the label. In some cases the label had no dominant single language or no other text beyond the scientific Latin name of the specimens. However, the language of the label could be identified for 90% of the dataset (Fig. 1 ). English appears most frequently, with just over 42%. Five other languages occur in more than 5% (i. e. 90) of specimens: French, Latin, Estonian, German and Dutch. Of these, only Estonian is linked to a single institution. This should make language-based analysis possible with this dataset. Step description: As detailed in Table  2 , we compiled 200 images from each of nine institutes from seven different countries across Europe.
All institutions provided JPEG format images and all but two could also provide TIFF format images; P and L were unable to provide TIFF images due to institution policy. Between institutions, the TIFF file size varied between 25 and 306 MB, with dimensions between 10 and 102 megapixels. Significant differences occurred within institutions too, due to different herbarium sheet sizes as well as different in-house scanning protocols. A total of 150 TIFFs of non-type specimens from H were horizontally photographed in two parts. To reconstruct an image of the whole specimen, these were stitched together in a semi-automated way using Adobe Photoshop CS4. No further image processing of JPEG or TIFF files was done. ZZ indicates a single language could not be determined: either there were multiple languages used on the label, there was no obvious use of a certain language (i.
e. only scientific Latin terms) or the language was not readily identifiable. Different herbaria are identified by their Index Herbariorum codes (Institution Code in Table  2 ). Figure  2. The distribution of collection dates (by year, if known) of the specimens in the dataset for each providing institution. The heat colour indicates the number of specimens for each 10 year time period. Year data were extracted from Darwin Core eventDate and verbatimEventDate if these were in ISO 8601 standard. Codes for the herbaria follow Table  2. For seven of the institutions, the data associated with these images were downloaded using the GBIF API (accessed 2018-07-12). One of the other two (B) provided these data in DwC format directly. The other (H) had no method to export all data in DwC format, so data were extracted in JSON format using their API (https://api.
laji. fi) (Accessed 2018-07-09). These data were subsequently mapped to DwC in the R language using the package jsonlite (Ooms 2014 and see Suppl. material 2 for the mapping). Data for all specimens were fully joined in the R language. These data are available as Suppl. material 5 as a Comma Separated Value (CSV) file as well as individual JSON-LD files for each image (script for this conversion in Suppl. material 6). Data are provided as they are available now and will not be kept up to date. However, users may be able to download upto-date data from institutional repositories through the persistent identifiers of the specimens. For each of a subset of 250 specimens with labels in English, two PNG overlays were manually made in GIMP (GIMP Development Team 2017). These overlays indicate the location and class of labels, stamps, colour charts or other reference objects on the imaged herbarium specimen.
One overlay indicates the location of each label with a different colour against a black background (indicated as "_all"). The other overlay indicates the class of labels using a colour code of white for barcode label, yellow for a colour chart and red for any other sort of label (indicated as "_sel"). These overlays can be used to train algorithms to identify labels in order to facilitate data transcription. A stacked pie chart generated using Krona (Ondov et al. 2015, https://github. com/marbl/ Krona/), depicting the taxonomic distribution by phylum, order and family (if known). Missing taxa were extracted from the GBIF backbone by family, if possible. For H, they were extracted by genus, as family was unavailable. An interactive version of this graph is available as an HTML file in the supplementary material.