dataesr/scientific-paragraphs-categorization huggingface dataset:
Sexual harassment and other forms of masculine violence towards women are not documented for any of the 210 women of this list, which does not mean it did not happen; it should be kept in mind that disclosing such matters was even harder then than it is now. Several historical events or particularities created windows of (relative) opportunities, in particular the Industrial Revolution coupled with slightly less rigid gender models in Britain, World War I and the Soviet Revolution. Out of the 210 female geoscientists identified from 1800 to 1929, 85 are British; 49 American (USA); 21 Russian/USSR; 13 Aus-tralian; 24 from Western Europe, excepting the United Kingdom; 8 from Eastern Europe; 5 from northern Europe; and 5 from Canada, South Africa or Argentina. Of course data availability and translation in English and French (the languages I can read) might have an impact on these proportions.
It was the collective nickname given to women who studied the Paleozoic rocks of Wales: Margaret Chorley Crosfield, Ethel Skeat Woods, Ethel Wood Shakespear and Gertrude Elles (and a few others; see Sect. 7 "Overcoming discrimination 3: sorority"). They were studying at Newnham College, Cambridge. Professors McKenny, Marr and Lapworth recruited them in the 1880s to study Paleozoic rocks in Wales, especially to validate Lapworth's 1879 proposition to add the then new Ordovician period to solve the Silurian-Cambrian limit problem. To achieve this the three male professors considered field data necessary but found collection to be time-consuming and perhaps dull. Lapworth reputedly said "one qualification for the task of monograph compilation then, was a capacity for drudgery"  (Gould, 1998 , cited in Burek, 2008) , which is why they asked female students to take care of it.
The group's next and decisive opportunity was mentoring by professors Thomas McKenny Hugues, Charles Lapworth and John Marr. They asked their female mentees to go and do what they considered time-consuming and dull field work but nonetheless necessary to have the data to validate their hypothesis that a new period should be defined between Cambrian and Silurian: the Ordovician. Margaret Chorley Crosfield, Ethel Skeat Woods, Ethel Wood Shakespear and Gertrude Elles thus went on to describe early Paleozoic stratigraphy. Ethel Wood Shakespear and Gertrude Elles joined forces on identification, classification and taxonomy of graptolites (the famous monograph published from 1910 to 1918). Margaret Chorley Crosfield and Ethel Skeat Woods cooperated first on Carmarthen area, work which became the basis of the local geological map, then on lithological and structural research on the Clwydian Range using graptolites (see also Sect.
Learning from the fate of pioneer female geologists should help us understand our own situation and provide some clues to tackle it by taking inspiration from these amazing role models, who often faced adversity collectively and beyond their own individual fate. Because even if some of the obstacles they met have been overcome, e. g. universities are no longer forbidden to us women, some are still very common, such as the lack of women in professor or researcher positions, reflecting a sexist selection still at work and discouraging women from applying to such jobs. And some obstacles have only come to light in recent years, such as masculine violence towards women, including sexual harassment in the workplace. Data availability. The original research on Norah Dowell Stearns is presented in its entirety in Sect.
8, and the rest of the paper is a bibliographical work: the data used can be found in the papers cited in the reference list. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/adgeo-53-129-2020-supplement. Competing interests. The author declares that there is no conflict of interest. Special issue statement. This article is part of the special issue "Diversity and equality in the geosciences (EGU2019 Review statement. This paper was edited by Claudia Jesus-Rydin and reviewed by Anny Cazenave, Lisa D. White, Luis Farina Busto and one anonymous referee.
Acknowledgments: We thank  B. Yuan ,  C. Cattania ,  D. M. Saffer ,  G. C. Beroza ,  P. Segall ,  W. L. Ellsworth , and  W. B. Frank  for fruitful discussions. We also thank  B. Rousset ,  C. H. Scholz , and  H. Houston  for providing helpful feedback on the manuscript. Funding: Y. J. T. acknowledges support by the  Chateaubriand fellowship and the "Make Our Planet Great Again" initiative. Author contributions: Y. J. T. and D. M. designed the study. Y. J. T. performed the data analysis, made the figures, and wrote the initial draft of the manuscript. All authors contributed to the interpretation of the results and writing of the manuscript. Competing interest: The authors declare on September 4, 2020 http://advances. sciencemag. org/Downloaded from.
Future climate projections (simulated under the RCP2. 6 and RCP6. 0 greenhouse gas concentration scenarios) suggest a potentially high level of change in individual regions, with up to 35 % of cells showing a statistically significant trend (increase or de-Published by Copernicus Publications on behalf of the European Geosciences Union. crease; at 10 % significance level) and greater changes indicated for the higher concentration pathway. Importantly, the observed streamflow database under-samples the percentage of locations consistently projected with increased flood hazards under the RCP6. 0 greenhouse gas concentration scenario by more than an order of magnitude (0. 9 % compared to 11. 7 %). This finding indicates a highly uncertain future for both flood-prone communities and decision makers in the context of climate change.
, 2014) , limiting the usefulness of GHMs in developing flood adaptation policy in a warming climate. To address this limitation and further improve GHMs' applicability, this study provides the first comprehensive evaluation of GHMs' capacity in simulating historical trends of a flood hazard indicator. This study also explores the uncertainty in developing projected changes in flood hazards using an ensemble with GHMs and general circulation models (GCMs). Specifically, we used the Global Streamflow Indices and Metadata (GSIM) archive  (Do et al. , 2018b; Gudmundsson et al. , 2018a) , to date the largest possible global streamflow database, to identify observed changes in annual maxima of 7 d streamflow (MAX7 index) over the 1971-2005 period. Streamflow simulations, available through the Inter-Sectoral Impact Model Intercomparison Project ISIMIP phase 2a and 2b  (Warszawski et al.
Data and methods This section summarises the workflow to achieve three objectives of this study (Fig. 1 ). Observed and simulated stream-flow (Sect. 2. 1) were used to estimate the magnitude and significance of changes in an indicator of flood hazards (Sect. 2. 3). To enable an observation-model comparison, a procedure was developed to extract streamflow for a subset of observed catchments that meet data quality criteria (Sect. 2. 2). A range of statistical techniques were then applied to trends of an indicator of flood magnitude (Sect. 2. 4) to assess (i) the capacity of GHMs to reproduce characteristics of observed trends, (ii) the representativeness of observation locations in GHM simulations and (iii) the implication of simulation uncertainty on projected trends (results are discussed in Sect.
Observed and simulated streamflow datasets The GSIM archive is used as daily observational discharge for this analysis. (Frieler et al. , 2017). Six GHMs are considered: H08  (Hanasaki et al. , 2008a, b) , LPJmL  (Schaphoff et al. , 2013) , MPI-HM  (Stacke and Hagemann, 2012) , ORCHIDEE  (Guimberteau et al. , 2014 (Guimberteau et al. , , 2018)) , PCR-GLOBWB  (Wada et al. , 2014; Sutanudjaja et al. , 2018 ) and WaterGAP2 (Müller Schmied et al. , 2014 , 2016). These models were selected as they have provided discharge data within phases 2a and 2b of ISIMIP at the time this study began (June 2018). A summary of the similarities and differences across participating GHMs is provided in Sect. 1. 2 in the Supplement. To assess the model structural uncertainty across GHMs, trends in streamflow extremes simulated under observational atmospheric forcing, available through the Global Soil Wetness Project Phase 3 (GSWP3) reanalysis  (Kim, 2017) , were compared to observed trends.
Catchment selection and simulated streamflow extraction for observation-model comparison To enable an observation-model comparison, simulated discharge needs to be extracted from gridded model output. Large-scale hydrological models, however, generally do not simulate discharge accurately over small-to-medium size catchments due to the coarse resolution of river network datasets in their routing schemes  (Hunger and Döll, 2008). To address this limitation, previous GHM evaluations usually selected large catchments (a threshold of 9000 km 2 was adopted, approximating the size of a 1 • longitude-latitude grid cell), and routed discharge (unit: m 3 s -1 ) at the outlet of the catchment was used as simulated streamflow for a specific catchment  (Zhao et al. , 2017; Veldkamp et al. , 2018; Zaherpour et al.
e. area less than or equal to 9000 km 2 ) with similar weighted-area tables, the "averaged time series" (using catchment areas as weights) was calculated. A total number of 1542 time series fell in this category and were aggregated into 624 "averaged time series". Figure  2  shows the spatial distribution of the final dataset for model-observation comparison, containing data for 3666 locations (3042 non-averaged time series and 624 averaged time series). The majority of available catchments are located in North America and Europe, with some regions over Asia, Oceania and South America also covered.
Detecting trends in annual maximum streamflow For each streamflow dataset, daily discharge was smoothed to 7 d averages to reduce variability in simulated streamflow, which can arise from the coarse routing parameters of GHMs  (Dankers et al. , 2014). The annual maximum time series of 7 d averaged discharge (labelled as the MAX7 index in the GSIM archive) was then derived to represent peak flow events. For gridded datasets, the "centre averaged approach" (e. g. averaged streamflow of 7 January is the mean value of 4-10 January) was used (the common setting of the CDO software, freely available at https://code. mpimet. mpg. de/projects/cdo, last access: 1 March 2020), and the MAX7 time series was therefore derived for each GSIM station using this same approach. As a result, the derived value of the MAX7 index is slightly different to the value available in the online version of GSIM  (Gudmundsson et al.
A hypothesis-test approach for comparison of trend characteristics A range of hypothesis tests (summarised in Table  2 ; GSWP3 simulations were used to assess GHM uncertainty while GCMHIND simulations were used to assess the combined GCM-GHM uncertainty) was applied to address the first two objectives, which require comparing trend characteristics exhibited from different streamflow datasets. Four characteristics of trends were assessed. -Trend mean: the mean (percentage change per decade) of trends in streamflow extremes across all gauge-or cell-based time series over a spatial domain. A hypothesis test was adopted to assess whether the trend means exhibited from two specific streamflow datasets (e. g. model vs. observed) are significantly different from each other. -Trend standard deviation: the standard deviation (percentage change per decade) of trends in streamflow extremes across all gauge-or cell-based time series over a spatial domain.
A hypothesis test was adopted to assess whether the trend standard deviations exhibited from two specific streamflow datasets are significantly different from each other. -Percentage of significant trends (%): the percentage of trends in a domain that are statistically significant, with gauge-or cell-based significance calculated using the Mann-Kendall test at the 10 % significance level. To assess whether the percentage of significant (increasing or decreasing) trends exhibited from a specific streamflow dataset is produced by random chance, a field significance test  (Do et al. , 2017 ) was adopted (described in Table  2 ). -Trend spatial pattern: the spatial distribution of trends in streamflow extremes over a spatial domain. Pearson's correlation (r statistic)  (Galton, 1886; Kiktev et al.
, 2003)  between trends of MAX7 index obtained from two datasets was used as a measure of similarity in the trend spatial structure. The hypothesis test (pattern similarity test) was adopted to assess whether (i) the correlation between simulated trends introduced by GHMs and observed trends is significantly higher than zero, and (ii) the correlation between trends simulated under hindcast atmospheric forcing and observed trends is significantly lower than that between trends simulated under observational atmospheric forcing and observed trends.
Finally, to assess whether locations projected with an increasing trend by the majority simulations are adequately monitored, each GSIM gauge was sorted into one of these five groups based on the gauge's geographical coordinates. The allocation of gauges to these groups was then analysed to determine whether the most comprehensive global database of daily streamflow records to-date was evenly distributed across the five "flood risk regions". An inadequate coverage of stream-gauge networks over high-risk regions indicates potentially high vulnerability to future changes in flood hazards, as insufficient data are available to inform decision makers.
Capacity of GHMs to reproduce observed trends in flood hazards Visual inspection of the normalised Theil-Sen slope across the GSIM time series (Fig. 3a ; regional maps provided in Fig. S4 ) shows a spatial pattern that is consistent with recent findings on trends in observed flood magnitude  (Mangini et  www. hydrol-earth-syst-sci. net/24/1543/2020/ Hydrol. Earth Syst. Sci. , 24, 1543-1564, 2020   Do et al. (2017)  obtained from all observation locations of a was adopted. A moving-block-bootstrap (block-length L = 2) was used to specific streamflow dataset was not produced derive a null hypothesis distribution of the change that occurred due to by random chance. random chance. The null hypothesis is rejected at 5 % one-sided significance level when the true percentage falls on the right-hand side of the 95th percentile of the resampled distributions.
Hypothesis 4: the correlation between trends "Zero pattern similarity" was compared to the probability distribution obtained from two streamflow datasets was function (PDF) of pairwise correlation between simulated and observed not significantly higher than "0" (i. e. zero trends, drawn from a bootstrap procedure similar to that proposed by pattern similarity). Kiktev et al. (2003). The null hypothesis is rejected at 5 % one-sided significance level when zero correlation falls on the left-hand side of the 5th percentile of the resampled distributions. , 2018; Do et al. , 2017; Mallakpour and Villarini, 2015; Gudmundsson et al. , 2019; Burn and Whitfield, 2018; Ishak et al. , 2013). Specifically, decreasing trends tend to dominate Asia (most stations located in Japan and India), Australia, the Mediterranean, the western and north-eastern US, and northern Brazil, while increasing trends appear mostly over central North America, southern Brazil and the northern part of western Europe (including the UK).
Characteristics of trends exhibited from the GSIM/GSWP3/GCMHIND streamflow dataset at the continental scale (each observation location of 3666 sites was sorted into one of the six continents). For simulated trends, only the multi-model average is shown for each region. Trend mean and trend standard deviation are expressed in percentage change per decade. Values in the parentheses show the number of simulations rejecting the null hypothesis described in Table  2  (up to 6 for GSWP3 simulations and 21 for GCMHIND simulations). For GSIM, field significance of increasing and decreasing trends was highlighted by boldface texts. For GSIM, field significance of increasing and decreasing trends was highlighted by boldface text. sition of significant trends across the six continents is consistent with a previous investigation  (Do et al.
2. As a result, a common model-observation picture of changes in global flood hazard remains elusive. To enable a holistic perspective of changes in extreme flows, it is therefore crucial to improve not only models' capacity, but also data accessibility and expand streamflow observational networks to ensure unbiased samples are available for largescale investigations. The findings using GCMHIND simulations are similar in terms of the trend mean (closer to zero) and trend standard deviation (higher) across all grid cells relative to the observation locations. Across all land areas, the composition of the percentages of land mass showing significant trends exhibited by GCMHIND simulations contradicts that obtained from the GSWP3 simulations for many continents. For example, GSWP3 simulations suggest more land areas showing significant decreasing trends than increasing trends over Asia and Oceania while GCMHIND simulations indicate an overall increasing change in extreme flows over the same continents.
3. 2), however, demonstrated that the observation locations selected for this assessment are not a representative sample of the entire land mass. As a result, inference of changes in flood hazard may be biased toward well-observed regions. To further highlight the potential impact of limitations in observed streamflow datasets, the proportion of available stream gauges located in regions with different levels of projected "flood risk" was assessed. We first categorised each simulation grid cell into one of the five "flood-risk" groups. Note that in this analysis, "risk" is defined as the number of simulations projecting a significant increasing trend, rather than the prominent definition of risk as the combination of hazard, exposure and vulnerability  (Kron, 2005). In this analysis, the RCP6.
Improved performance of GHMs in terms of simulating changes in flood hazard, considering the many factors influencing model capacity, is achievable only through the combined efforts of many communities. The spread of trends in streamflow extremes (trend standard deviation) could be simulated more accurately by finer spatio-temporal resolution GHMs. Such an improvement in GHMs, however, is highly dependent on the quality of input datasets (e. g. dam operations, historical irrigation databases, land use and land cover, in addition to atmospheric forcing), which are driven by advances in other geophysical disciplines  (Bierkens et al. , 2015; Wood et al. , 2011). The moderate capacity of GHMs in terms of simulating the spatial structure of trends in streamflow extremes indicates the need for improved representation of runoff generation at the global scale (e.
Summary of streamflow observation and simulation datasets used in this study. GSIM was used as the observed streamflow database. Streamflow simulations were obtained from six GHMs (H08, LJPmL, MPI-HM, ORCHIDEE, PCR-GLOBWB and WaterGAP2). One observational atmospheric forcing dataset (GSWP3) and outputs of four GCMs were used as input for streamflow simulations.
Acknowledgements. We thank  Jannis Hoch  and one anonymous referee for their constructive comments that helped to improve the paper. Comments from  Grabriele Villarini  and  Murray Peel  to improve the paper are also appreciated. This work was supported with supercomputing resources provided by the  Phoenix HPC service at the University of Adelaide  and  Flux HPC service at the University of Michigan. The daily streamflow datasets were made publicly available from many data providers, including the  Global Runoff Data Centre (GRDC) ; the  ARCTICNET initiative ; the China Hydrology Data Project; the  GEWEX Asian Monsoon Experiment -Tropics project ;  USGS National Data Services ;  Environment Canada; Brazilian National Water Agency; Spanish Center for Hydrographic Studies; Japanese Ministry of Land, Infrastructure, Transport and Tourism; Australian Bureau of Meteorology ; and  Indian Central Water Commission.
Financial support. Hong Xuan Do  is currently funded by the  School for Environment and Sustainability, University of Michigan , through grant no. U064474. Camelia-Eliza Telteu  and  Hannes Müller  Schmied are supported by the  German Federal Ministry of Education and Research  (grant no. 01LS1711F ). Yoshihide Wada  is financially supported by the  EUCP (European Climate Prediction System)  project funded by the  European Union  under  Horizon 2020  (grant agreement:  776613 ). Review statement. This paper was edited by  Louise Slater  and reviewed by  Jannis Hoch  and one anonymous referee.
Data availability. The GSIM database is available at https://doi. org/10. 1594/PANGAEA. 887477 (Do et al. , 2018b)   and https://doi. org/10. 1594/PANGAEA. 887470 (Gudmundsson et al. , 2018). Simulations of the participated global hydrological models are freely available through the ISIMIP project (ISIMIP2a: https://doi. org/10. 5880/PIK. 2019. 003; Gosling et al. , 2019; ISIMIP2b: https://www. isimip. org/outputdata/ dois-isimip-data-sets/#output-data, last access: March 2020; Frieler et al. , 2017). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/hess-24-1543-2020-supplement.
[2017; 20]. This data processing solution was used to facilitate segmentation of the rope embedded in the balm. All 3D UV fluorescence picture of the specimen showing in brown and dark green the parts of skin without balm, in light green the exposed skin where the body and the head are separated  [1, 2] , in light green the exposed osteoderms and eggshell fragments, in orange the palm-tree wood  [4]  and in yellow several remaining parts of the original textiles  [3]. The violet parts are reflections of the lamp on shiny dark surfaces of balm. https://doi. org/10. 1371/journal. pone. 0229140. g001 renderings and segmentations were performed using VGStudioMax 3. 1 and 3. 2 (Volume Graphics, Heidelberg, Germany). All tomographic data and segmentation files used for this study are available through the open access tomographic database of the ESRF (http://paleo.
Acknowledgments We would like to thank the " Musee des Confluences ", and in particular  Didier Berthet , for having given us the opportunity to work on MHNL 90001850. We thank  "La Ferme  au Crocodiles", especially  Samuel Martin , for their support and assistance during initial phases of the project. We thank  Laura Jarnias  who assisted us during data segmentation. We are grateful to  Thierry Loeb  and the  Reptilarium des Landes  for allowing us to perform the actuo-archaeological experiment on the specimen of Caiman crocodilus. We are grateful to the  ESRF , and the  BM05  team to have granted the in-house beamtime used to perform multiple scans of this very special mummy. We also acknowledge  David Lefèvre  and  Frederic Servajean  for their support to the  MAHES  project. We are very grateful to  Dennis Voeten  who dramatically improved the English quality of the present text.
This research was carried out within the framework of the  ESRF  ( ESRF-CFR-423 ), and the  MAHES  Project ( Momies Animales et Humaines EgyptienneS ) supported by the  Agence Nationale de la Recherche  through the  «Investissement d'Avenir » program   ANR-11-LABX-0032-01 LabEx ARCHIMEDE.