dataesr/scientific-paragraphs-categorization huggingface dataset:
mouse bone with an osteotomy, and the white rectangles define the ROIs, where scanning SAXS was performed. Fig. 4 (b) displays these ROIs of the BSE image overlaid by a map of T parameters, where every pixel corresponds to a single SAXS measurement point. The image consists of 9012 frames, and the T and parameter evaluation was performed offline using the corresponding plug-ins in DPDAK. Grazing-incidence small-angle X-ray scattering is one of the most powerful methods to investigate in situ the growth of metallic layers during vacuum deposition. With third-generation synchrotron sources, high-rate sputter deposition (0. 21 nm s À1 ) and fast data acquisition have become a standard tool for surface science. We present as an example the growth of gold clusters during sputter deposition on a silicon substrate  (Schwartzkopf et al.
Purely qualitatively, the side maxima are shifted from higher to lower values in reciprocal space, meaning an increase of this length scale during the deposition process is observed. In more detail, we visualize as a color map in Fig. 5 (b) the horizontal line cuts of the two-dimensional GISAXS data performed using DPDAK at the position indicated by the dashed line in Fig. 5(a)  as function of frame number. This color map as saved from DPDAK shows the continuous growth of the Au clusters. The additional modulations stem from the growing height of the clusters. In order to extract this length scale quantitatively, we fitted two Lorentzian functions to every line cut using DPDAK. A single cut and the corresponding fit result from DPDAK is shown as an inset in Fig. 5(b ) and the resulting map of fits in Fig.
5(c ). Please note that the twodimensional images and the color maps were saved from DPDAK, while the inset and the labels as well as the dashed lines were subsequently incorporated using standard graphics software. The individual parameter position and FWHM (full width at halfmaximum) of the Lorentzian fits of the side maxima are shown in Fig. 6(a). Their evolution is related to the calculated deposited thickness of the Au layer. In addition, we present the intensity evolution of the Yoneda regions  (Yoneda, 1963; Mu ¨ller-Buschbaum, 2003)  at the critical angles c (Au) and c (Si) in Fig. 6  During another experiment at beamline MiNaXS/P03 of DESY, gold in its atomic phase was deposited on a polymer template. Such metal deposition on polymer templates is a powerful way for achieving nanostructured hybrid films  (Metwalli et al.
These horizontal line cuts (q y ) were collected and simultaneously fitted using the DPDAK software for all collected frames. Lorentzian peak fits were employed for all scattering peaks. In this approach, DPDAK enabled the identification of two characteristic peaks immediately: the polymer peak (I) and the metal peak (II). Prior to the gold deposition, a single characteristic peak (0. 141 nm À1 ) caused by the polymer template was observed. As seen from Fig. 7 , the position of the scattering peak related to the polymer (I) stays at a fixed q y position (0. 141 nm À1 ) upon gold deposition. In contrast, the metal characteristic peak (II) shifts from a very large q y value to lower q y values during metal deposition (Fig. 7 ). This evolution in reciprocal space means an increase of the corresponding real-space distance between the metal particles with increasing sputter time.
8 (b, left). These ROIs were then averaged and plotted as color-coded pixels for each scattering pattern of the scan grid. The resulting pixel maps, as shown in Fig. 8 (b), are generated in real time during the microfluidic SAXS experiments, revealing orientation information on the flow of the micelles. It is also important to note that each pixel represents a full two-dimensional scattering pattern containing relevant structural information about the sample, such as for example orientation distribution, micelle diameter, unit-cell size etc. DPDAK is capable of processing the data at high speed, which allows the experimentalist to control experimental conditions and to monitor the measured and analyzed results in real time during the data acquisition. The here presented SAXS experiments reveal that the initially parallel oriented wormlike micelles were oriented perpendicularly to the flow after passing the narrow section.
Conclusions and outlook DPDAK was developed to perform simple but fast online data reduction and analysis, allowing the user to review scattering data for quality control and to follow a number of parameters of interest predefined by the user as a function of the scanning parameters. The The flow of wormlike polymeric micelles can be studied using SAXS by scanning the microfluidic channel with a microfocused X-ray beam. (a) The measured SAXS patterns reveal micelles in parallel, tilted, transitional and perpendicular orientations depending on their location, as indicated by the straight lines. (b) The information from these SAXS patterns is used to generate color maps in real time using DPDAK. Each of these pixels is color coded on the basis of the averaged ROI of the parallel or the perpendicular micelle orientation.
We therefore believe a huge amount of precious synchrotron beamtime can be saved by using DPDAK. The DPDAK platform is independent of the beamline and/or experiment control software so that it can be exchanged between different groups working with synchrotron data. The current version of the software package DPDAK and an up-to-date manual reside on a dedicated web site (https://dpdak. desy. de/). The concept of DPDAK allows the treatment of many kinds of data, i. e. DPDAK can handle data from different types of experiments, like the presented examples based on WAXS, SAXS, X-ray fluorescence and GISAXS, as well as general curve fitting. Owing to the easy to use plug-in interface it is possible to include further data reduction and analysis methods. For example, the pySAXS package  (Tacheet al.
, 2013)  could be included to support specialized model fitting within DPDAK. Therefore plug-ins for each model in the pySXAS package would need to be developed. In future, the use of parallel computing in terms of graphics and multicore processors can also be envisaged, the HiPGISAXS software being a very recent example of this  (Chourou et al. , 2013). MS thanks Ralf Ro ¨hlsberger and Kai Schlage (DESY) for providing the sputter chamber and assistance during the GISAXS experiment. PMB and EM acknowledge funding by TUM. solar in the frame of the Bavarian Collaborative Research Project 'Solar Technologies go Hybrid' (SolTec). SF and MT thank the European Research Council for financial support within the ERC Advanced Grant project STREAM (No. 291211) and the Federal Ministry of Research (No.
A. Agustí-Panareda et al. : Global CO 2 forecast as part of the Copernicus European programme, formerly called GMES (Global Monitoring for Environment and Security). The reasons for not having carbon dioxide (CO 2 ) stemmed from the challenges associated with modelling the CO 2 fluxes and the relatively small signals characterizing CO 2 variability making the accuracy requirements for the model simulations more stringent than for other trace gases. The recent addition of the CTESSEL carbon module in the operational Integrated Forecasting System (IFS) at ECMWF  (Boussetta et al. , 2013a)  has now also made feasible the delivery of atmospheric CO 2 forecasts in real time. Although the forecast is currently not initialized with a CO 2 analysis because of the lack of CO 2 observations with global coverage in near-real time, it relies heavily on a wealth of meteorological observations for initializing the meteorology and transport.
Moreover, we expect that in the near future there will be satellite retrievals of CO 2 from the GOSAT (Greenhouse Gases Observing Satellite)(www. gosat. nies. go. jp) and the OCO-2 (Orbiting Carbon Observatory) (http://oco. jpl. nasa. gov) available a few days behind real time. These CO 2 satellite products will be assimilated to produce CO 2 analyses also in near-real time. It is worth noting that the CO 2 retrievals provide averaged column information of CO 2 and only for sunlit clear-sky conditions. Therefore, they cannot provide information on the CO 2 vertical distribution, neither at nighttime and during winter time at high latitudes, nor on the CO 2 anomalies associated with cloudy regions within convective and synoptic weather systems. Thus, the CO 2 forecast model will be crucial in filling this information gap during the data assimilation process.
, 2009)  and transport  (Law et al. , 2008; Patra et al. , 2008). Globally, the CO 2 variability on timescales ranging from diurnal, seasonal, to interannual is dominated by the terrestrial biogenic fluxes  (Geels et al. , 2004). The challenge of modelling the terrestrial biogenic fluxes comes from high spatial heterogeneity of the land surface and complex processes with large uncertainties. Some of these uncertainties stem from a lack of observational data with sufficient global coverage to characterize all the variability in space and time associated with vegetation and carbon pools. At the same time, the biospheric fluxes are strongly influenced by climate variability  (Keeling et al. , 1995). Therefore, the timely availability of accurate meteorological data sets is also crucial. The recent development of the CTESSEL carbon module within the IFS takes advantage of accurate real-time climate forcing in order to provide online terrestrial biogenic fluxes also in real time.
CO 2 fluxes The CO 2 net ecosystem exchange (NEE) fluxes are from the carbon module of the land surface model in the IFS (CTESSEL) developed as part of the Geoland project (www. geoland2. eu). Because the NEE fluxes are computed online, they are available at the same spatial and temporal resolution as the transport model (∼ 80 km resolution, every 45 min). CTESSEL is a photosynthesis-conductance (A-gs) model based on  Calvet et al. (1998 Calvet et al. ( , 2004)) ;  Calvet (2000)  and developed originally by  Jacobs et al. (1996). It provides CO 2 fluxes as well as evapotranspiration. However, the evapotranspiration in the IFS is currently still based on the Jarvis approach  (Jarvis, 1976)  instead of the plant physiological approach of CTESSEL. Despite not having a full coupling between evapotranspiration and CO 2 fluxes, there is some consistency between the two fluxes because they both rely on the same underlying representation of vegetation.
Evaluation of CO 2 forecasts The hindcasts have been evaluated for different periods to assess the global annual budget and its interannual variability from 2003 to 2012 (Sect. 3. 1), the seasonal cycle from 2010 to 2012 (Sect. 3. 2), as well as the synoptic day-to-day variability (Sect. 3. 3) and diurnal cycle (Sect. 3. 4). The evaluation is based on observations from the NOAA Earth System Research Laboratory (ESRL) baseline stations (www. esrl. noaa. gov/gmd/obop, Thoning et al. , 2012) , NOAA/ESRL tall towers (www. esrl. noaa. gov/gmd/ccgg/towers, Andrews et al. , 2014) ,  TCCON (www. tccon. caltech. edu, Wunch et al. , 2011)  and ICOS (www. icos-infrastructure. eu) networks. Figure  1  and Table  1  show the stations used from each network and their location. HIPPO flight data (http://hippo.
CO 2 seasonal cycle The phase and amplitude of the seasonal cycle of CO 2 are very dependent on latitude. Thus, the model is first evaluated using the NOAA GLOBALVIEW-CO2 (2011) data set which displays the integrated effects of surface CO 2 fluxes over large regions at different latitudinal bands (Fig. 3 ). At first glance, the annual cycle phase and amplitude and latitude dependency appears to be reasonably represented in the hindcast. However, there are clear discrepancies between the hindcast and GLOBALVIEW-CO2 (2011) in the NH. First of all, the hindcast does not release enough CO 2 before and after the growing season (i. e. March to May and October to De-   background air from the NOAA/ESRL network, total column measurements from the TCCON network and continuous measurements from the ICOS network.
Acknowledgements. This study was funded by the  European Commission  under the  EU Seventh Research Framework Programme  (grant agreement No. 283576 , MACC II). The ICOS data were obtained from the  ICOS Atmospheric Thematic Center (Laboratoire des Sciences du Climat et l'Environnement ) website at https://icos-atc-demo. lsce. ipsl. fr. The authors acknowledge the  European Commission  for the support of the preparatory phase of ICOS (2008ICOS ( -2013)), the  Netherlands Ministry of IenM  and  ECN  for the support of the observations at Cabauw, and the monitoring network SNO-RAMCES/ICOS-France which is in charge of Ivittuut,  Mace Head  and Lamto stations with the support of  CNRS , CEA and OVSQ. Thanks to  F. Truong  (LSCE) and to  A. Diawara  and  Y. Palmer  for the maintenance of  Station Géophysique de LAMTO  with the support of  University of Abidjan.
Thanks to  J. L. Bonne  and  M. Delmotte  for the data from Ivittuut station, with the support of  Greenland's Kommando, Danish Armed Forces ,  Island Commander Greenland  and  Kommuneqarfik Sermersooq. Thanks  to  V. Kazan  and  G. Spain  for the maintenance of Mace Head station with the support of the  Irish Environmental Protection Agency , and the  National University of Ireland, Galway. Thanks to  Harri Portin ,  Juha Hatakka ,  Tuomas Laurila  ( FMI ) for providing the data from the  ICOS station at Puijo, Finland. We are grateful to  Jérôme Tarniewicz  for his technical support with the ICOS database and to  Miha Razinger  for his help in the development and maintenance of the ICOS monitoring plots in the MACC website. Thanks to the  NOAA/ESRL Global Monitoring Division  for providing their data from the baseline observatories at  Barrow (Alaska) ,  American Samoa ,  South Pole (Antarctica) , the tall towers at Argyle (Maine),  Park Falls (Wisconsin) ,  West Branch (Iowa) , and the vertical profiles from the  NOAA GMD Carbon Cycle Vertical Profile Network.
TCCON data were obtained from the  TCCON Data Archive , operated by the  California Institute of Technology  from the website at http://tccon. ipac. caltech. edu/. We acknowledge financial support of the Bialystok TCCON site from the  Senate of Bremen  and  EU projects IMECC, GEOmon and InGOS , as well as maintenance and logistical work provided by the  AeroMeteo Service  and additional operational funding from the  National Institute for Environmental Studies (NIES, Japan ). POW and DW thank  NASA's Carbon Cycle Science program  ( NNX10AT83G  and  NNX11AG01G ) and the  Orbiting Carbon Observatory Program  for support of TCCON and this research. The HIPPO Merged 10 s data set was obtained from the website at http://hippo. ornl. gov/dataaccess. The HIPPO Programme was supported by  NSF  grants  ATM-0628575 ,  ATM-0628519  and  ATM-0628388  to  Harvard University, University of California (San Diego ),  University Corporation for Atmospheric Research, NOAA Earth System Research Laboratory, University of Colorado/CIRES  and by the  NCAR.
This paper presents a cache-aware configurable hybrid flash translation layer (FTL), named CACH-FTL. It was designed based on the observation that most state-of-the-art flash-specific cache systems above FTLs flush groups of pages belonging to the same data block. CACH-FTL relies on this characteristic to optimize flash write operations placement, as large groups of pages are flushed to a block-mapped region, named BMR, whereas small groups are buffered into a page-mapped region, named PMR. Page group placement is based on a configurable threshold defining the limit under which it is more cost-effective to use page mapping (PMR) and wait for grouping more pages before flushing to the BMR. CACH-FTL is scalable in terms of mapping table size and flexible in terms of Input/Output (I/O) workload support.
If the number of flushed pages is below the predefined threshold, the small dataset is sent to the PMR, which works as a second-level in-flash buffer. Small groups of pages are temporarily placed into the page-mapped PMR in order to: (1) generate less ineffective erase operations and; (2) collect more pages before moving them to the BMR. In the present study, CACH-FTL was tested on a large number of real and synthetic I/O workloads and performed well for both random and sequential ones. Its performance was close to the ideal page-mapping scheme performance, but it used a much smaller mapping table, which makes it much more scalable. The next sections present an overview of flash memories and some related works. The fourth section of this paper then details the CACH-FTL structure and algorithms.
Background Many genomewide expression datasets have been published during the past ten years. Repositories, such as the Gene Expression Omnibus (GEO) database  [1] , have made available an impressive wealth of data. Using them as a whole, instead of restricting statistical studies to one particular dataset, is tantalizing. Two recently published R/Bioconductor packages  [2, 3]  provide various tools for merging datasets coming from different studies. However, a serious doubt has been cast by Haibe-Kains et al. [4] , after comparing two large scale pharmacogenomic studies: whereas both studies had a good overall correlation, important discordances could be observed. Thus, the following crucial question remains to be answered: is it statistically legitimate to merge datasets coming from different studies? An attempt at answering this question is reported here.
Merging different datasets, requires prior checking that the information they contain is compatible, and hence that detected differences between gene expressions under different conditions are not artifacts, due to experimental or data processing methods. An obvious obstacle to simultaneous treatment is that expression data collected under different experimental conditions and/or platforms usually have incompatible distributions, which differ sometimes by several orders of magnitude  [5, 6]. A solution is provided by robust (or distribution-free) statistics  [7, 8]. Robust methods amount to replacing actual values by ranks, or equivalently by empirical distribution functions or van der Waerden's normal scores  [7, p. 309 ]. This idea has already been applied to expression data in several papers, including  [9] [10] [11].
However, to the best of our knowledge, a large scale analysis assessing the reproducibility of information from one dataset to another, is still missing. We have conducted such an analysis over 20 GEO datasets, totalling 17 745 genomewide expression samples. For the data treatments presented here, the statistical language R  [12]  has been used. Our set of functions, together with a manual, has been made available online as supplementary information. Throughout the article, we consider data matrices (also called assay data in  [13] ) as containing expression data relative to a set of genes. Each row corresponds to a different gene symbol, or feature, each column to a different data vector or sample (see Table  2  in  [1] ). Such a matrix is deduced from raw datasets, available on the GEO repository, though standard treatments: annotation and reduction  [14].
Vectors of pairwise correlations between rows were also compared. Then multivariate analysis over assays of gene symbols was applied: Wilcoxon and Kruskal-Wallis tests, factor and principal component analysis (PCA). The results were compared to those obtained by sorting a single matrix according to different keywords. All comparisons showed not perfect, but highly significant correlations. However, it was also found that in all cases, a sizeable proportion of symbols were good discriminators of the different matrices. But this was also found to hold between two submatrices inside a given dataset. Therefore, it cannot be regarded as an obstacle to merging different datasets. On the other hand, we wanted to know whether biological information could be consistently retrieved from matrices collated from different sources.
Methods The datasets available from the GEO repository  [1] , collate sets of expression vectors, or samples. Several R/Bioconductor packages can be used to download and format the data  [15, 16]. We have chosen to encode in R our own functions. Our R script has been made available online, together with a user manual. Our formatting choices are described below. In a GEO dataset two types of information are available for each sample. The first type consists of numeric values corresponding to a set of probes. The second type are character-type informations on the experimental setting. We have chosen to separate the two types into data matrices and information matrices. In the data matrix, probes are associated to gene symbols with the use of different Bioconductor annotation packages according to the platform  [17] [18] [19] [20].
Two R/Bioconductor packages have recently been issued for merging GEO datasets  [2, 3]. In  [2] , quantile discretization, normal discretization normalization, gene quantile normalization, median rank scores, quantile normalization (QN) are proposed. In  [2, 3] , the Batch Mean-Centering method, Distance-Weighted Discrimination, Z-score standardization, and the Cross-Platform Normalization method are proposed. An Empirical Bayes (EB) method is available in both packages. For the results reported here, only classical methods were used, and we consider them as sufficient to establish our main points, our focus being on overexpression or underexpression of genes, in different tissues or cancer types. As in  [9] [10] [11] , we have made the choice to use robust statistics  [7, 8]. This implies changing the columns of a data matrix into distribution free values.
Results The 20 datasets that were downloaded from the GEO repository are detailed in Table  1. They were selected on a criterion of size (number of samples: 500 or more). The 20 matrices together amount to 17 745 samples. To each study, a three-letter acronym was attached; these acronyms will be used in what follows. acronym   1 : Twenty GEO series have been chosen, coming from four different platforms. To each of them a three letters acronym was associated. The table gives the acronym, a recent reference, the GEO series number, the platform number. For the data matrix (or assayData), the number of symbols after annotation and reduction, and the number of columns (samples) are given. All 20 data matrices had 15 562 gene symbols in common. In the results reported here, each data matrix has been transformed by replacing its column values, by the corresponding van der Waerden normal scores  [7, p.
309 ]. Similar results were obtained when replacing column values by their empirical distribution function (see methods section). The first treatment that was applied consisted in computing, for each dataset, the median of all rows, reduced to the 15 562 common gene symbols. This gave 20 vectors of length 15 562, the correlation matrix of which is given in Table  2. A positive (negative) correlation between vectors of size 15 562 is significant at threshold 5% if it is larger than 0. 013 (smaller than -0. 013); thus all correlations of Table 2 can be regarded as significant. Figure  1  shows a factor analysis of the 20 variables. Fifteen of them can be clustered into four groups. • PMM, EPO, XMD, HBI, CCL, CCC. Among these six datasets, four are generalist studies involving different tissues and conditions (EPO, HBI, XMD, CCL); CCC concerns colon cancer, and PMM multiple myelomas.