dataesr/scientific-paragraphs-categorization huggingface dataset:
Surface water V arag values of less than 3 have been described as 'extremely marginal' for reef growth  [31] , so we used V arag ¼ 3 as the threshold for ecosystem collapse. We obtained International Best Track Archive for Climate Stewardship records for hurricanes categories 1 -5 on the Saffir -Simpson scale between 1853 and 2015  [32]. There is no evidence of recovery to a pre-disturbance state for at least 8 years post-hurricane in the Caribbean  [33] , so we defined the collapse threshold as a hurricane frequency of one in 8 years for hurricane categories 1 -5, and one in 12 years for categories 4 -5 only (electronic supplementary material, appendix S1). To assess the effects of sedimentation and nutrification, we searched for field data on sedimentation rate, nutrient concentration, salinity and water transparency, and reviewed recent modelling studies and geochemical studies.
However, none of the data sources were appropriate to assess the effects of sedimentation and nutrification under criterion C (electronic supplementary material, appendix S1). Criterion D identifies ecosystems that are undergoing loss or disruption of key biotic processes maintaining the characteristic native biota  [4]. We considered several indicators of biotic disruption (figure  2 ; electronic supplementary material, appendix S1); live coral cover was the only indicator with a suitable time series of empirical observations for assessing changes over 1966-2015 (D1). We used linear weighted regression to predict initial and current live coral cover for the years 1970 and 2013, so as to not extrapolate beyond the empirical time series, and selected models based on changes in AIC. In addition to the empirical time series for coral cover, we used backcast estimates of herbivorous fish biomass and (large and small) piscivorous fish biomass from the ecosystem model to assess changes over 1966-2015.
royalsocietypublishing. org Proc. R. Soc. B 284: 20170660 categories 4 and 5 in the next 50 years (C2a). There were no significant trends in the North Atlantic hurricane frequency since the late 1800s  [24]  and hurricane frequency remained below the collapse threshold for that period (C3). Over the last 50 years, the relative severity of decline in coral cover in the MAR was 63. 4% based on empirical data and 64. 9% based on the model backcast (D1; table  3 ). We backcast a 2. 8% decline in herbivorous fish biomass over the last 50 years (D1). We backcast a 62. 4% decline in piscivorous fish biomass over the last 50 years (D1). We assessed biotic declines since the pre-industrial period with the same data as for the last 50 years (D3). Based on the seven likely scenarios of threat (scenarios 5-11), we projected future declines across !80% of the extent of the ecosystem of 28.
We derived structural and functional properties at the plot level from the forest stand and vegetation data. We derived stand height, tree density, basal area, tree slenderness coefficients and diameter distributions to describe the structural properties of each forest stand  [41]. To capture structural diversity, we calculated the coefficient of variation (for log-normal data) of the tree diameters and of the potential height of the herb layer, the latter derived from the TRY database  [42]. Using both, the TRY-  [42]  and LEDA  [43]  databases on the vegetation abundance data, we derived functional traits. Based on the detected tree species we derived metrics that are related to the leaf economics spectrum (i. e. traits determining amongst others the decomposition of leaf-litter)  [44]  (Additional file 2).
Climate We recorded ambient microclimate at the same time as the tick/stand structure survey with Testo 175-H2 Data-Loggers (temperature precision = ±0. 5 °C, relative humidity accuracy = ±3%). Measurements were taken every minute for approximately half an hour in the plot center. Air temperature and relative humidity were measured at both 5 and 130 cm height. Soil temperature was measured at a depth of 5 cm. We calculated saturation deficit according to  [36] , based on values averaged between 5 and 130 cm height for both, relative humidity and air temperature. Macroclimate data were extracted from the "Global Summary of the Day" (GSOD) dataset hosted on the web-servers of NOAA's National Centers for Environmental Information (NCEI). We extracted climate metrics for the period from 1st of January 2013 to the day of sampling and for 30 days prior to tick sampling.
Statistical analyses All statistical work was carried out in R version 3. 3. 1  [48]. An explanatory factor analysis (EFA) was carried out based on maximum likelihood to derive factors of correlated variables (Additional files 4, 5) within variable groups with the 'psych' package  [49]. These correlation factors are assumed to represent the combined (and more general) influence of a set of correlating variables, which would not be significant separately because they might be too specific. We built three linear mixed models (LMMs) to explore the effect of all environmental variables on the abundance of the three ontogenetic stages (larvae, nymphs and adults) using the 'lme4' package  [50]. We included the preceding stage's abundance to consider and compare indirect effects of the drivers of the previous stage relative to the other environmental drivers.
The soderived models were eventually fit using restricted maximum likelihood (REML). The 'lmerTest' package  [51]  was used to determine type-III ANOVA tables with 'Wald F-Test with Satterthwaite degrees of freedom' for fixed effects. Response profiles based on partial residuals were determined with the 'visreg' package  [52]  and plotted with the 'ggplot2' package  [53]. We derived the relative importance as proportion of each significant driver of the overall variation, based on partial eta 2 values (Additional file 8,  [54] ). Due to the large number of known effects on tick abundance, it can be expected that several of the significant drivers are small or moderate in their individual importance. Detailed R-code can be found in  [55].
Acknowledgements This study has been supported by the TRY initiative on plant traits (http:// www. try-db. org). The TRY initiative and database is hosted, developed and maintained by  J. Kattge  and  G. Bönisch  ( Max Planck Institute for Biogeochemistry, Jena, Germany ). TRY is currently supported by  DIVERSITAS/Future Earth  and the  German Center for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig. We acknowledge  Katja Leischke ,  Peter Fräßdorf  and  Iris Gutierrez  for participating in field-work and  Bernhard Thiel  for proofreading the manuscript.
X-ray tomography The Palaeospondylus chondrocranium was scanned from 10 micro slices by the ultrafine computed tomography (CT) scanner in the Department of Applied Mathematics, Australian National University, Australia and at beamline ID19, European Synchrotron Radiation Facility, France (see the electronic supplementary material for technical details). The high-resolution scans presented here all have a voxel size of 5 µm. A movie of the TIFF stack, from lateral to medial, is available at the Natural History Museum (NHM) Data Portal (see Data accessibility, below). Scans were three-dimensional volume rendered using DRISHTI v. 2. 4 software (sf. anu. edu. au/Vizlab/drishti/).
Three-dimensional volumetric rendering We analysed the synchrotron data with the software CT-ANALYSER (v. 1. 14. 4. 1) and CTVOX (v. 2. 7. 0) by Bruker/Skyscan. Working with different grey-values and transparency, CT-ANALYSER allowed us to distinguish the Pa. gunni specimen (P. 66582) from the surrounding sediment. Details of the CTVOX settings are found in the electronic supplementary material. Three-dimensional segmentation of internal structures was not possible.
Acknowledgements. We thank  Paul Tafforeau  ( European Synchrotron Radiation Facility, Grenoble, France ) for his help with the scanning and reconstructing the data, as well as  Jürgen Kriwet  and  Gerlinde Kaineder  ( University of Vienna ),  Farah Ahmed  and  Amin Garbout  ( NHM, London ) for access to three-dimensional rendering programmes and computer facilities. Emma Bernard  ( NHM, London ) is thanked for access to Palaeospondylus specimens.
More precisely, Yap5 is unable to bind and activate its targets in the absence of Hap5 (Fig. 4 ). Remarkably, the YRE and CCAAT motifs in the promoters of the Hap5-Yap5 iron stress regulon exhibited a conserved spacing of 10 to 14 base pairs, suggesting that the proximity of the two transcription factors on DNA is important for their activity. We previously noticed that Yap5 contains a Hap4L like domain which actually corresponds to half of the canonical Hap4L domain found in published CBC regulatory subunits such as Hap4 in S. cerevisiae and Hap43/ HapX in pathogenic fungi  21. Remarkably, this motif was not found in a global search for Hap4L-bZip bipartite domains  18  and did not pass the statistical cut-off in a Hap4L search using the Pfam database  21. Yet, partial deletion or point mutations of this domain severely reduced Yap5 binding to GRX4 and this domain was necessary for the Yap5-Hap5 interaction detected in co-IP experiments (Fig.
Sequencing of the IPs, Input DNAs and mock IPs samples and primary data analyses (quality controls and mapping of the reads) were performed as described previously  54. Peak calling was performed with the bpeaks software  51  , using both the Input DNA and the mock IP as references. For peak calling using the Input DNA as reference, the bpeaks parameters were T1 = 1. 9, T2 = 6, T3 = 1, T4 = 0. 7. For peak calling using the Mock IP as reference, the bpeaks parameters were T1 = 1. 9, T2 = 6, T3 = 1, and T4 = 0. Only the peaks that were found with both analyses were kept for further processing. These peaks were then manually checked on a genome browser  55  to discard artefactual peaks (e. g. , peaks centered on a tRNA locus, peaks perfectly overlapping a highly expressed ORF) which would have escaped the bpeaks filter.
An additional filtering step was added to the standard peak motif procedure to discard low complexity motifs (e. g. , CCCCCCC). The co-occurrences of YRE and CCAAT motifs in the ChIP peaks shared by Yap5 and Hap5 were confirmed by visual inspection of the peak sequences using IGV  55. The genome-wide search of YRE-CCAAT co-occurrences with a spacing of 10-14 bp in C. glabrata promoters was performed using the DNA pattern search in RSAT  58  and a dedicated R-script which automatically identified co-occurrences in promoters and measured the distance between the two motifs. Network Building. The ChIP peaks were assigned to genes as described previously  51. When a peak was located in a divergent promoter (i. e. , an intergenic region in between two divergent genes) the two genes were fused in one target in the network named "gene 1/gene 2".
The network was represented using the igraph R library  59. The presence of a ChIP peak was used as the parameter to define interactions (arrows) between Hap5 and its targets. The GO criterion was used to differentially color the target promoters (Fig. 1 ). GO analyses were performed using the "GO term finder" tool at the CGD database, with default parameters  60. Transcriptome Analyses. Knock-out and wild type strains were grown in 50 mL of YPD until exponential phase (OD = 0. 8) and then stressing agents were added. After 30 min, 15 mL of each cell cultures were flash-frozen in two volumes of cold ethanol and collected by centrifugation. Total RNA was extracted, quality controlled and quantified as described previously  21. One microgram of total RNA was used for fluorescent cDNA synthesis according to the amino-allyl protocol  21.
The cDNAs were labeled with Cy3 and Cy5 and hybridization was performed as previously described  21. Two biologically independent experiments were performed for each condition, using dye switch. We used custom C. glabrata Agilent arrays in an 8 × 60 k format (array express accession number: A-MEXP-2402). After overnight hybridization and washing, the slides were scanned using a 2-micron Agilent microarray scanner. The images were analyzed using the feature extraction software (Agilent technologies) and normalized using global LOESS  61. The mean of the biological replicates was calculated. A gene was considered as being differentially expressed if its mean absolute Log2(fold change) value was more than 0. 5 and if its expression variation was considered as being statistically significant using the LIMMA package with a cut-off p-value of 0.
Acknowledgements This work was funded by the  Agence Nationale pour la Recherche  ( CANDIHUB  project, grant number  ANR-14-CE14-0018-02 ). We thank  Jean-Charles Cadoret  for granting us access to the Agilent microarray scanner. We are grateful to Christophe D' enfert,  Sophie Bachelier-Bassi ,  Gwenael Badis-Bréard ,  Christian Landry  and  Isabelle Gagnon-Arsenault  for providing strains, plasmids and advices.
Universities management teams are therefore looking for new indicators to identify the potential of interdisciplinarity and to follow-up their strategies and the impact of incentive measures they have implemented. At the Observatoire des Sciences et Techniques of the French High Council for Evaluation of Research and Higher Education (HCERES-OST), effort is being put in to provide indicators that are appropriated by end users and to deliver new indicators for research institutions that are often supported by training sessions. To introduce information about interdisciplinarity in the universities set of monitoring indicators, OST selected the Rao-Stirling diversity index calculated on the distribution of WoS categories of references in publications. This choice was made because of the integrative property of this index  [7]  and because it is based on the same data as the usual set of indicators provided by OST (the OST enriched version of the Thomson Reuters databases).
It is interesting to notice that this index is robust to an over specification of categories so that the choice of the classification less problematic. This is because the overall index does not change much when two categories with a small distance are merged into a single one. More precisely, the variation D(ST) of the index ST when category 1 and 2 are merged is such that DðSTÞ < d 12 ðq 1 q 2 þ max ðq 1 ; q 2 ÞÞ: In a way, this partly defers the issue of carefully choosing the classification to that of choosing the distance. Different distances designed to achieve graphical maps of science may be used in a diversity index. We use the distance which is derived from the cosine similarity between the profiles of references of the two categories in the whole database (i. e. the inter-category aggregated citing dimension)  [17, 18].
Normalizing the indicators This basic index is used to design indicators for different analyses. As the citation practices of scientific communities are different and as the databases do not cover the various scientific fields-or disciplines or subject categories-homogeneously, the index has different ranges for different fields. This introduces uncertainty into the interpretation of the comparison between institutions. An institution could have a high indicator value not truly reflecting a higher propensity towards interdisciplinary research but only because it is specialised in scientific fields in which the cited references of papers are covered more exhaustively as source items in bibliographic databases, or in fields in which researchers can include, on average, more references per papers due to editorial rules or to subfield-specific citation practices.
For the bibliometric approach, a corpus representing the domain has to be defined. WoS has a category and Scimago has a subject area with the title Neurosciences whereas the Neurology & Neurosurgery sub-field in Science-Metrix classification does not encompass all relevant literature as defined above (for example some neuroscience thematics are covered in other subfields such as Psychiatry and Clinical Psychology). The WoS Neurosciences category is defined by a set of 255 journals of which 25% of the articles are in journals assigned only to this category, and 27% are simultaneously assigned to Neurosciences and to Clinical Neurology (Data for 2012). The Scimago Neurosciences subject area contains 509 journals, distributed in 9 subject categories. The two sets have 206 journals in common. Selecting these 206 journals could be a relevant choice but, for the sake of convenience, we simply selected the publications in the WoS Neurosciences category as the corpus for this study.
With this representation, the value of the overall index is exactly the difference between the sum of surfaces of positive contributions (red bubbles on the figure) and the sum of surfaces of negative contributions (blue bubbles). In order to check the interpretation of category contributions, we collected the exploitable information on laboratory addresses retrieved from the address field in the WoS database. In about half of the addresses, the laboratory name was present and could be disambiguated, despite the possible variation of units contours and names over the period (Table  5 ). Unit names provide information on their research themes and focus, confirmed by information from the web sites, which can be related with categories of most cited references. Aix-Marseille neuroscientists cite much less articles in medical research and biology than the world average behaviour and they favour references in psychology, sport sciences and physiology.
Synthetic comparison of eight universities Identifying laboratories from addresses registered in the WoS database is not easy but it aimed at checking that the profile of references of a university is correctly related with the labs research themes. Universities that have publication lists by laboratory could refine such an analysis. We do not further develop this approach with the other universities of this study. When the aim is to position and compare universities and provide evidence for an overall strategic reflection, a more synthetic representation is useful as achieved with a heatmap. In Fig  6 , the values of contributions are represented on a colour scale from dark blue for the lowest contribution to dark red for the highest. This figure shows for example that Paris 5 has a very different profile from that of Paris 7, despite the number of their joint publications.
In this paper, we use a classification of scientific papers derived from the classification of journals into the WoS categories. A larger base of publications would be necessary to capture more references in social sciences and humanities, taking into account both WoS and Scopus databases, as the UCSD classification of journals into 554 sub-disciplines  [30]. It is also possible to use a classification of articles that does not take journals into account but only citation links between publications. The CWTS classification  [31]  provides an alternative choice for categories and their 672 middle level clusters could also be used as a classification of references. However interpretable category contributions do not easily result from such a refined classification. Field delineation is another important issue.
Material and methods Data consist of documents of the four types article, letter, note and review published over the period 2008-2014 in journals classified in the Neurosciences category in the Thomson Reuters database at OST, updated in 2013 with TR updates and with the affiliation validation process carried out by OST with French universities. Documents with less than three references in the WoS are not taken into account. For the overall index which is based on the references of the whole corpus, the references of each document are weighted by the inverse of the number of references of the document, so that the set of references for each document has the same weight. This option, called EWA (Equal weight by article) in  [8]  is recommended because it makes it possible to include both review papers and articles.
To visualise the statistical precision of the indicators, it is useful to show their confidence intervals for a chosen probability level. As the two components ST W and ST B of ST are not independent random variables, a confidence area with a given probability is an ellipsis, which axis orientation depends on the correlation of the two components  [33]. In S3 Fig,  we  show the confidence areas of probability 0. 98 for the two-dimensional indicator (ST W , ST B ) for the eight universities of this study. Data and scripts availability. Reference counts by category for the publications of the eight universities (for full or fractional counting method) and for two fields Neuroscience (RU) and Clinical Neurology (RT) are available at https://github. com/turckheim/interdisciplinarity in data_integer or data_frac folders.
Basic data (i. e. list of references for each publication) are provided under licence by Thomson Reuteurs (now Clarivate Analytics) and are available with an access to Web of Science Core Collection. Results for the whole database 2008-2012 namely i) the category similarity matrix, ii) world statistics ST, ST W , ST B as well as the percentiles of the article index world distribution, and all statistical results for the 8 universities used in the tables and figures of this paper are given in the above GitHub repository. R scripts for computing the values of the indicators and their statistics are as well available in the R_scripts folder and commented in the Steps. pdf file.
Acknowledgments This work relied on the involvement of universities management team members:  Francoise Curtit  ( Universitede Strasbourg ),  Christiane Grillier  ( Universitede Franche-Comte, Besancon ),  Denis Leroux  ( UniversiteJoseph Fourier, Grenoble 1 ),  Nathalie Reymonet  ( Universite Ṕaris Diderot, Paris 7), Valerie Ricoux, Arnaud Sillet (UniversiteParis Descartes, Paris 5 ),  Guillaume Tatti  ( UniversiteBlaise Pascal, Clermont 1 ). The authors were employed by  OST  and this project was part of the OST working plan. Other OST collaborators contributed:  Francoise Rojouan  is responsible for the ongoing relationship of OST with universities. Helène Mechoulam achieved the identification of research units. This work was first presented at the 20th International STI Conference in Lugano (2015).
Introduction Terrestrial surface water covers only about 5% of the Earth's ice-free land surface  [1, 2] , but plays a key role in global biogeochemistry, hydrology and wildlife diversity  [3, 4]. Consequently, it is critical to monitor the distribution of terrestrial water at large spatial and high temporal scales  [5] [6] [7] [8]. The work in  [9]  estimates that nearly two-thirds of all terrestrial freshwater wetlands disappeared between 1997 and 2011. A more recent study used three million Landsat images to provide a high resolution map of surface water extent  [10]. The authors of this study estimated that 90,000 km 2 of permanent surface water had disappeared between 1984 and 2015. Several different methods based on mapping water bodies from remote sensing datasets were used since the development of the Earth's space observations: (1) visible; (2) infrared; (3) active microwave; (2) passive microwave and; (4) hybrid approach (passive and active microwave).
Our motivation is to use a contextual radiative transfer model and a single dataset to estimate the water fraction over the tropical basin. The area of study and the datasets used in this work are presented in the Section 2 and 3, respectively. Section 4 presents the algorithm permitting retrieving the water fraction extent from the SMOS data, and Section 5 contains the results and the validation. The discussion and conclusions are presented in Sections 6 and 7. Table  1. List of selected scientific papers on the observation of the water surface over the continents from space. References, area of study and sensors are shown.