dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments We are highly indebted to the late  Professor Hans Kretzschmar , leader of the  BNE  Consortium, who advised and supported us throughout the development and evolution of the Code of Conduct between 2006 until the ratification in 2008 by all BNE members. We remember him as an inspiring and wise guide and miss him. BNE II received research funding from the  European Community 's  Sixth Framework Programme  ( LSHM- CT-2004-503039 ). The authors thank the valuable discussions about the Code of Conduct with other  BNE  consortium project leaders:  Dr. David Meyronet ,  Hospices Civils de Lyon, Pinel, France ;  Prof. Tamas Revesz ,  Queen Square Brain Bank ,  Institute of Neurology, University College London, London, UK ;  Dr. Giorgio Giaccone ,  Istituto Nazionale Neurologico Carlo Besta, Milan, Italy ;  Prof.
Downhole logging data acquisition and processing The multiple-cored DEEP site has six parallel and overlapping boreholes (A to F), of which holes A and E only cover the uppermost few metres of the sediment succession. Each of the deeper holes was drilled with a diameter of 149 mm and water-based mud was used to clean the holes of cuttings and to stabilize the side walls during the coring process. Hole C was logged immediately after drilling down to 470 m b. l. f. , and amongst other probes, such as a resistivity and borehole televiewer, a spectral gamma ray (SGR) and sonic probe were used. To prevent the unconsolidated sediments from caving in, the SGR probe was run through the drill pipe and a continuous record of the sediments down to 470 m b. l. f. was achieved. The SGR data were acquired using the SGR 70-slimhole tool of the Leibniz Institute for Applied Geophysics (LIAG), which records the total gamma radiation (GR), as well as the spectral components (potassium, K; thorium, Th; and uranium, U) and their contribution to the GR.
The results are presented in a threedimensional spectrogram with colour coding of the relative power of the different frequency components. Generally, a small window size is favourable to maximize the length of the resulting plot. However, the contained cycle needs to be covered and cannot be determined if the window size was chosen too short, e. g. only half the signal's wavelength. The optimal window size is determined by empirical testing. Spectral analysis for identification of the characteristic peri-H. Baumgarten et al. : Age-depth model of the past 630 kyr for Lake Ohrid odicities  (Jenkins and Watts, 1969; Priestley, 1981)  was performed on normalized SGR data using fast Fourier transform within MATLAB (MathWorks ® ). The detection of cycles by SGR logging is limited by the Nyquist frequency, which is twice the sampling rate  (Molinie and Ogg, 1990a).
Spectral characteristics of GR data, temporal evolution, and sedimentation rates After visual comparison of GR and K with LR04 documenting a strong correlation of periods with low GR and K with warm and/or humid periods, the application of spectral analysis by sliding window analysis objectively identifies the possible cycles and their temporal distribution. The spectral analysis was calculated with a window of 90 m length and a step size of 1 m. Thus, the stepwise calculation for the depth section from 0 to 240 m b. l. f. and the resulting threedimensional spectral plot (Fig. 3a ) is composed of 150 spectra. The plot ranges from 45 to 240 m b. l. f. , because the first spectrum is allocated to the window centre (Sect. 3. 2) and therefore half of the window length is not displayed. Two prominent spectral peaks are evident in the data set, as indicated by colour with wavelengths of 30 and 45 m.
Decompaction of the pelagic sediments and subsequent spectral analysis on GR data on decompacted depth scale The effect of decreased sediment thicknesses due to compaction over time was determined to investigate its impact on the estimates of sedimentation rates. The V p data from sonic logging were used to derive porosity after  Erickson and Jarrard (1998). The porosity values were averaged for layers of 100 m thickness (Fig. 5a ). The initial porosity (  0 ) was determined at 80 % and the compaction coefficient was estimated at 0. 39 km -1. These parameters were used as input data for modelling decompaction of the sediments, and the 2-D model was calculated for layers of 50 m thickness. The modelling process starts with the removal of the top layer and subtraction of its overburden pressure.
Their occurrence in zones A and C (Fig. 2c ; zones of decreased synthetic δ 18 O) suggests that they have an effect on the GR signature. 3. Variations in the catchment area. Outcrops of Triassic carbonates and clastics occur in large parts in Lake Ohrid's catchment area. Furthermore, ophiolites, magmatic, and metamorphic rocks are exposed and are potential sources of terrestrial material. Erosion and transport of these rocks, e. g. by means of surface runoff into the lake, and subsequently the input of K-bearing particles, were likely variable over time. Therefore, the GR is most likely controlled by several factors and cannot be described in total by linear solution. However, the main component seems to be a linear response of K-rich sediments to the global climate trend (1). Further, we consider the attempt useful to compare both data sets and to show the very unusual similarity in cyclic characteristic between LR04 and GR.
Matching of GR and K data with the global climate reference record (LR04) equates the timing of the climate dynamics recorded in the oceans to Lake Ohrid. This means that if the downhole logging data are tied to this timescale, differences between the response times of Lake Ohrid and the global climate trend (e. g. lead or lag effects of the onset of terminations) are lost. However, even if the comparable small system was likely to be subject to a faster climate response compared to records from the marine realm, correlation with the global signal and the resulting age-depth model is verified in large parts (down to 206 m b. l. f. ) by tephrochronology. 5. 2 Sedimentation rates: major trends, small-scale fluctuations, and the effect of compaction The relative power of the spectral peaks from sliding window analysis seems to be higher in interval I (wavelength of 30 m) in comparison to interval II (wavelength of 45 m).
Spectral analysis of GR data on decompacted depth scale (Fig. 5c ) revealed a very similar spectral characteristic compared to the sliding window plot of the compacted data. However, the two spectral peaks (wavelengths of 30 and 45 m) are shifted to higher wavelengths and increased to wavelengths of 36 m for interval I and 48 m for interval II. Therefore, the sedimentation rates are increased accordingly to 36 cm kyr -1 and would be underestimated by 7 to 20 %, if they are not corrected for the effect of compaction. Underestimation of sedimentation rates from sliding window analysis is supported by comparison of results from LR04 tie points; the latter indicate generally higher sedimentation rates (35 cm kyr -1 , interval I; 48 cm kyr -1 , interval II) and show very good agreement with the estimates of the effect of compaction.
Conclusions Can climatic indicators be derived from downhole logging, despite its limited vertical (and temporal) resolution and can these proxies be used to reconstruct a robust age-depth model? The strong response to the global climate signal (LR04) suggests that the K contents reflect a cyclic change of undisturbed, continuous sedimentation. Conditions were constant over a long period of time and prevailing in balance with the global climate. To investigate the response of our data to the global climate trend, a synthetic δ 18 O curve was generated that reveals minor deviations compared to LR04. The deviations could indicate either that the local climatic conditions were not fully in line with the global climate or that local processes in the sedimentary system changed over time. Within two independent attempts, visual tying to the global reference LR04 record as well as spectral analysis by the sliding window method and linking of high amplitudes to orbital cycles, a similar result was achieved.
Acknowledgements. The SCOPSCO Lake Ohrid drilling campaign was funded by  ICDP , the  German Ministry of Higher Education and Research , the  German Research Foundation , the  University of Cologne , the  British Geological Survey , the  INGV  and  CNR  (both Italy), and the governments of the FYROM and Albania. Logistic support was provided by the  Hydrobiological Institute in Ohrid. Drilling was carried out by Drilling, Observation and Sampling of the Earth's Continental Crust's (DOSECC) and using the Deep Lake Drilling System (DLDS). Special thanks are due to  Beau Marshall  and the drilling team. Ali Skinner  and  Martin Melles  provided immense help and advice during logistic preparation and the drilling We thank the  German Research Foundation  for financial support for the downhole logging ( WO 672/10-1 ).
Experimental results and "standard" computer treatment Figure  1  shows 11 B MAS NMR spectra for Ga 1-x Fe x BO 3 powders with x¼ 0, 0. 01 and 0. 02. These spectra reveal a characteristic MAS quadrupolar powder pattern, cf. refs. [8, 9]. For 11 B nuclei the shape of MAS NMR spectra is determined by interactions between the nuclear magnetic moments and the applied magnetic field as well as by coupling between the nuclear electric quadrupole moment Q and the electric field gradient (EFG) produced by the surroundings  [8, 9]. The EFG is described by a second rank tensor V with prin- cipal components V ii (i¼ x, y and z) [8, 9]. The symmetry of the EFG is expressed by the parameter V V V / xx yy zz ( ) η = - ; in particular, in axial symmetry V V xx yy = and 0 η =. At first, we have simulated MAS NMR spectra with the help of the DMFit program  [18].
In the calcite structure of M 3 þ BO 3 , the BO 3 groups are located in the plane perpendicular to C 3 axis, so, boron sites have C 3 local symmetry. The simulation results are consistent with threefold-coordination of boron in Ga 1 À x Fe x BO 3 structure  [27]. One can see that the DMFit program allows reproducing the main spectra characteristics; however, relative amplitudes and widths of the spectra features cannot be satisfactorily reproduced. Most obvious mechanisms of broadening of the MAS NMR spectra of 11 B in Ga 1-x Fe x BO 3 with increasing x are the following ones: (i) dipole-dipole coupling between the electron spins of Fe 3 þ ions and the nuclear spins of the 11 B isotope and (ii) local disorder (statistical site-to-site distribution of atomic positions) caused by iron doping and manifesting itself in distributions of the NMR parameters.
Selection of study sites The selection of sites across the French Alps was made by combining several georeferenced databases and expert knowledge. My primary source of information was the 100 m-resolution CORINE land cover 2000 database produced by the European Topic Centre on Spatial Information and Analysis (European Environment Agency, 2007) that identifies 44 land cover classes based on the visual interpretation of high-resolution satellite images and from which I selected the 3. 2. 1 class corresponding to "Natural grasslands". Natural grasslands located between 2000 m and 2600 m above sea level were extracted using a 50 mresolution digital elevation model from the Institut Géographique National (IGN). I then calculated the perimeter (P ), area (A) and the mean slope of each resulting group of adjacent pixels, hereafter referred to as polygons, and kept only those that had an area greater than 20 ha, an index of compactness (C = 4π A/P 2 ) greater than 0.
MODIS data The MOD09A1 and MOD09Q1 surface reflectance products corresponding to tile h18. v4 (40-50 • N, 0-15. 6 • E) were downloaded from the Land Processes Distributed Active Archive Center (LP DAAC) (ftp://e4ftl01. cr. usgs. gov). A total of 499 scenes covering the period from 18 February 2000 to 27 December 2012 was acquired for further processing. Data are composite reflectance, i. e. , represent the highest observed value over an 8-day period. Surface reflectance in the red (RED), green (GREEN), near-infrared (NIR), and midinfrared (MIR) were used to calculate an NDVI at 250 m following: NDVI = (NIR -RED)/(NIR + RED) (1) and an NDSI at 500 m using the algorithm implemented in  Salomonson and Appel (2004) : NDSI = (GREEN -MIR)/(GREEN + MIR) (2) NDVI and NDSI values were averaged for each polygon.
Missing or low-quality data were identified by examining quality assurance information contained in MOD09Q1 products and interpolated using cubic smoothing spline. NDVI or NDSI values that were 2 times larger or smaller than the average of the two preceding values, and the two following values were considered as outliers and discarded. Time series were gap-filled using cubic spline interpolation and smoothed using the Savitzky-Golay filter with a moving window of length n = 2 and a quadratic polynomial fitted to 2n + 1 points  (Savitzky and Golay, 1964). A high NDSI and low NDVI were indicative of wintertime whereas a low NDSI and a high NDVI were indicative of the growing season (Fig. 2 ). Here I used the criteria NDSI / NDVI < 1 to estimate the length of the snow-free period, hereafter referred to as P sf , at the polygon level (Fig.
S1 ). Further analyses also indicated that P sf is relatively insensitive to changes in the NDVI/NDSI thresholds with 95 % of the polygon × year combinations exhibiting less than 2 days of shortening when the threshold was set to 1. 1 and less than 3 days of lengthening when the threshold was set to 0. 9 (Fig. S2 ). Finally, changing the threshold within this range had no impact on the main results of the path analysis. The yearly maximum NDVI value (NDVI max ) was calculated as the average of the three highest daily consecutive values of NDVI and the corresponding middle date was noted TNDVI max. The GPP of grasslands could be derived from remote sensing data following a framework originally published by Monteith  (Monteith, 1977). In this approach, GPP is modeled as the product of the incident PA, the fraction of PAR absorbed by vegetation (fPAR), and a light-use efficiency parameter (LUE) that expresses the efficiency of light conversion to carbon fixation.
Path analysis tests the degree to which patterns of variance and covariance in the data are consistent with hypothesized causal links. To develop this analysis, three main assumptions have been made: (i) that the graphs do not include feedbacks (for example, X1 → X2 → Y → X2); (ii) that the relationships among variables can be described by linear models and (iii) that annual observations are independent, i. e. , the growth response in year n is not influenced by previous years because of carryover effects. Since I chose to focus on the inter-annual variability of growth response, I removed between-site variability by calculating standardized anomalies for each polygon. Standardized anomalies were calculated by dividing annual anomalies by the standard deviation of the time series making the magnitude of the anomalies comparable among sites.
For each causal diagram, partial regression coefficients were estimated for the whole data set and for each polygon. These coefficients measure the extent of an effect of one variable on another while controlling for other variables. Model estimates were based on maximum likelihood, and Akaike information criterion (AIC) was used to compare performance among competing models. Only ecologically meaningful relationships were tested. The model with the lowest AIC was retained as being the most consistent with observed data. I used the R software environment (R Development Core Team, 2010) to perform all statistical analyses. Path coefficients and model fit were estimated using the package "Lavaan"  (Rosseel, 2012).
Acknowledgements. This research was conducted on the long-term research site  Zone Atelier Alpes , a member of the  ILTER-Europe network. This work has been partly supported by a grant from  Labex OSUG@2020  (Investissements d'avenir - ANR10 LABX56 ) and from the  Zone Atelier Alpes. The author is part of  Labex OSUG@2020  ( ANR10 LABX56 ). Two anonymous reviewers provided constructive comments on the first version of this manuscript. Thanks are due to  Yves Durand  for providing SAFRAN-CROCUS regional climate data, to  Jean-Paul Laurent  for the monitoring of snow cover dynamics at the Lautaret pass and to  Brad Carlson  for his helpful comments on an earlier version of this manuscript. Edited by:  T. Keenan.
2014 , Votýpka et al. 2015a). Recently, 18S (small subunit) rRNA-based phylogenetic analyses have led to extensive changes in the classification of kinetoplastid flagellates. The class Kinetoplastea, hierarchically equivalent to the formerly accepted order Kinetoplastida, is now divided into two subclasses: Prokinetoplastina and Metakinetoplastina  (Moreira et al. 2004 , Adl et al. 2012). The latter brings together four orders, of which the Trypanosomatida contains the majority of catalogued species. Notably, the National Center for Biotechnology Information database still uses the former version of classification, i. e. , order Kinetoplastida, encompassing the families Bodonidae, Ichthyobodonidae and Trypanosomatidae (Fig. 2 ). The order Trypanosomatida encompasses parasitic species responsible for economic losses in agriculture and livestock and for severe human diseases.
This organogram compiles taxonomic data from  Moreira et al. (2004)  and  Adl et al. (2012). It should be pointed out that the National Center for Biotechnology Information database still uses the formerly accepted classification, i. e. , order Kinetoplastida encompassing three families: Bodonidae, Ichthyobodonidae and Trypanosomatidae. However, during the last decade, the traditional taxonomy has been integrated with DNA sequencing data. The 18S rRNA gene, glycosomal glyceraldehyde phosphate dehydrogenase (gGAPDH) and spliced leader (SL) RNA gene repeats are the most commonly used markers for molecular phylogenetic reconstructions of kinetoplastid flagellates  (Maslov et al. 1996 , Croan et al. 1997 , Lukeš et al. 1997 , Hollar et al. 1998 , Yurchenko et al. 2000 , Merzlyak et al. 2001 , Hamilton et al.
2004 , Teixeira et al. 2011 , Borghesan et al. 2013 ) (Fig. 4 ). Using these molecular markers, species identification can be made by direct comparison with available DNA databases. However, if the match with the reference sequence is not full, the identification depends on accurate interpretation of molecular phylogenetic reconstructions and a rather arbitrary decision regarding whether the difference is intraspecific, interspecific or intergeneric. Commonly, the reference sequences are not correctly reassembled to updated taxonomic reclassifications, thus creating another challenging task of correctly comparing new isolates to previously described species. In this sense, it is not surprising that our knowledge about the apparently extensive diversity of this group of protists remains fragmented.
Additionally, molecular tools, such as gene knockouts, ectopic gene expression, RNAi and CRISPR, have been developed to improve genome annotation and to determine gene function and localisation  (Dean et al. 2015). New bioinformatics tools for reanalysis of genome databases allow further identification of "partial" genes that can be categorised as C-terminal extensions, gene joining, tandemly repeated paralogs and wrong chromosomal assignments  (Pawar et al. 2014). The microeukaryotic diversity that resides in ecological niches such as animal microbiotas (for instance, insect gut or salivary glands), lakes, oceans and soil remains poorly understood  (Foster et al. 2012 , Weinstock 2013). Furthermore, any existing relationships among these species remain largely undiscovered. Due to the reduction in costs, labour intensity and time, new generation sequencing has the potential to reveal both the diversity and/or ecological and metabolic functions in virtually any environment.
2005 , Von der Heyden et al. 2005 , Sauvadet et al. 2010 , McCarthy et al. 2011 , Bates et al. 2013 , Glaser et al. 2014). Other hurdles include the low number of reference genomes in databases available for comparison and difficulties in establishing universally accepted markers  (Sturm et al. 2008 ), as discussed above. In many cases, culture establishment is prevented by our limited knowledge of kinetoplastid metabolism and nutritional requirements, which is improving at a very slow pace even in well-studied groups  (Škodová-Sveráková et al. 2015). Consequently, we are confined only to the cultivable fraction of protist diversity. Direct microscopic observation of environmental samples provides substantial morphological and ecological data related to eukaryotic communities in vivo. However, these data are hard to compare with the existing formally recognised species primarily due to high morphological variability  (Dawson & Fritz-Laylin 2009 , Votýpka et al.
In spite of these advances, deep-sea kinetoplastid sequences have disproportionally low representation in public databases  (Salani et al. 2012 ). An extensive 18S rRNA metabarcoding study of the sunlit zone of the world oceans by the Tara Oceans initiative revealed a surprisingly highly abundant presence of diplonemids  (Lukeš et al. 2015)  and a much less conspicuous presence of kinetoplastids  (de Vargas et al. 2015). In another 18S rRNA-based survey targeting aquatic microeukaryotes in The Netherlands, sequences related to parasitic trypanosomatids have been described  (van Hannen et al. 1999 ). However, their re-analysis against recently available sequences revealed their high identity with N. designis (KA Morelli, unpublished observations). A cultivation-independent survey of kinetoplastid diversity in soil employed 18S rRNA primers and revealed an abundance of sequences related to the neobodonid clade, followed by parabodonids and eubodonids  (Glaser et al.
2014). While approximately 30% of the obtained sequences have low similarity to databases, whether these sequences are derived from unknown taxa, the so-called rare biosphere, or represent methodological "noise" remains to be established  (Glaser et al. 2014). In a study that aimed to investigate the role of free-living protists in contaminated food, Bodo sp. and Parabodo caudatus were frequently detected, along with related sequences with low BLAST scores  (Vaerewijck et al. 2008). Collectively, these data emphasise the need for more comprehensive studies targeting free-living kinetoplastids, the diversity of which remains fractionated, underestimated and, consequently, poorly taxonomically and phylogenetically studied. As a result of the increasing application of 18S rRNA gene-based approaches, new protistan phylotypes are constantly being revealed  (López-García et al.
Museums and institutional collections as a basis for diversity screening In comparison to macroscopic eukaryotes, protist collections are generally unknown to the public certainly because they concern microscopic organisms that are not spectacular or emblematic. These collections are often accumulated in dusty boxes of slides stored on shelves in an obscure corner. However, for protistologists, such collections are gold mines primarily because they contain type material (hapantotypes) deposited since the end of the XIX century by generations of scientists  (Votýpka et al. 2015a). With the beginning of the molecular era in the 1990s, natural history collections evolved to meet the challenges of the current and future interdisciplinary studies. Many institutions developed new collections and information databases (DNA, tissues, cultures, cryobanks, photographs, ethanol-fixed specimens, publication collections, and geographical and ecological information databases), which are of first-rate importance, offering opportunities to conduct integrative studies, including temporal and spatial surveys  (Suarez & Tsutsui 2004).