dataesr/scientific-paragraphs-categorization huggingface dataset:
The acquired 4D data were used for augmented reality to visualize the fit of clothing to the human body in images that followed the movements of customers. As this system is single-directional, this approach cannot be used to capture complete 360 • scans. Moreover, the spatial accuracy of the Kinect sensor and its acquisition rate of 30 Hz  [37]  may be insufficient for dynamic human body measurements. The biomedical technology manufacturer DIERS International GmbH proposed a system  [38]  for dynamic spine and posture measurements, which consists of a single detector-projector pair and primarily relies upon SL and LT to perform back reconstruction, in which the position of the spine of the patient is adjusted according to the surface topography generated based on a mathematical model. Gipsman et al.
• Step 2: The distances between the outermost marker centres on both model diagonals were measured. This distance was determined as the mean of the distances between the points on the outer/inner edges of the outermost markers. For the validation, we used data originating from two frames in which the calibration artefact was oriented parallel to the diagonals of the measurement volume and one frame in which the calibration artefact was perpendicular to the DMH, as depicted in Figure  12. Such placement provides reliable estimates because, in most cases, extreme positions produce the highest measurement errors. Examples of the root mean square (RMS) errors obtained from plane fitting are presented in Figure  13. Table  3  lists the errors calculated for the analysed dynamic scenarios. Sensors 2018, 18, x FOR PEER REVIEW 13 of 19.
• Step 2: The distances between the outermost marker centres on both model diagonals were measured. This distance was determined as the mean of the distances between the points on the outer/inner edges of the outermost markers. For the validation, we used data originating from two frames in which the calibration artefact was oriented parallel to the diagonals of the measurement volume and one frame in which the calibration artefact was perpendicular to the DMH, as depicted in Figure  12. Such placement provides reliable estimates because, in most cases, extreme positions produce the highest measurement errors. Examples of the root mean square (RMS) errors obtained from plane fitting are presented in Figure  13. Table  3  lists the errors calculated for the analysed dynamic scenarios. The calculated RMS errors are lower in the static case (0.
When we performed the same comparison on a mannequin, the average and maximum differences were 0. 27 mm and 0. 38 mm, respectively, leading us to conclude that the measurement uncertainty is less than 0. 5 mm. However, it is very difficult to obtain this level of uncertainty when using actual human subjects. The 4DBODY system was then tested on several individuals while they performed various basic movements. Measurements were taken at a frequency of 120 Hz, although the geometry reconstruction process was approximately 100 times longer than the acquisition process. The measurement data for each frame consisted of eight directional clouds integrated into one coordinate system. Each single cloud consisted of about 500,000 points. In addition to its (x, y, z) coordinates, each point contained information about the intensity and normal vector.
With a measurement frequency of 30-120 Hz, we collected approximately 4-16 GB of data per second. Each constituent cloud had an average point-to-point distance of 1. 5 mm, whereas the average point-to-point distance of the multidirectional cloud was approximately 1. 0 mm. These temporal and spatial cloud densities enabled highly effective observation and representation of anatomical structures. Still images demonstrating the exemplary results of the proposed system are presented in Figures  14  and 15. Example animations are also attached to this paper.
18,  x FOR PEER REVIEW 15 of 19 each point contained information about the intensity and normal vector. With a measurement frequency of 30-120 Hz, we collected approximately 4-16 GB of data per second. Each constituent cloud had an average point-to-point distance of 1. 5 mm, whereas the average point-to-point distance of the multidirectional cloud was approximately 1. 0 mm. These temporal and spatial cloud densities enabled highly effective observation and representation of anatomical structures. Still images demonstrating the exemplary results of the proposed system are presented in Figures14 and 15. Example animations are also attached to this paper.
Sensors 2018 , 2018 18,  x FOR PEER REVIEW 15 of 19 each point contained information about the intensity and normal vector. With a measurement frequency of 30-120 Hz, we collected approximately 4-16 GB of data per second. Each constituent cloud had an average point-to-point distance of 1. 5 mm, whereas the average point-to-point distance of the multidirectional cloud was approximately 1. 0 mm. These temporal and spatial cloud densities enabled highly effective observation and representation of anatomical structures. Still images demonstrating the exemplary results of the proposed system are presented in Figures14 and 15. Example animations are also attached to this paper.
Chamber organisation chart We developed a tailored Python script to analyse the changes in marker intensity against depth (Zaxis) for a confocal stack (F. N. Murphy). The tracked markers are cortex glial membrane (Nrv2::GFP, green), NSC nuclei (Dpn, grey) and neuronal nuclei (ElaV, red). The script allows a subregion of the sample to be selected by drawing a bounding box in the X,Y plane. This is necessary in the real world where only a portion of the image may be relevant. For example, a brain image may contain substantial amounts of trachea (stained by Nrv2::GFP) that must be avoided. The average intensity is computed within the bounded region for each layer of the stack and for each channel. This gives an average intensity versus depth curve for each channel. Since it is the distribution of marker that is of interest here, not its absolute value, the curves are normalised such that the peak intensity for each channel is one.
-F; Figure 3E-F and K-L; Figure 4E-F and K-L; Figure 3-figure supplement 1C-F; Figure 3-figure supplement 2H-L, Figure 4-figure supplement 1F-I and Figure 5-figure supplement 2C-F). Two biological replicates were quantified for TUNEL staining (Figure 5D), with the exclusion of one outlier brain in the Dp60 dataset due to total absence of TUNEL +cells (including in the optic lobe, where apoptosis normally occurs). Two biological replicates were quantified for NSC number and cell cycle markers (Figure 5-figure supplement 1A,B,D and E). One biological replicate was quantified for glial cell number (Figure 3-figure supplement 2D), neuronal volume (Figure 5-figure supplement 1C) and mitotic index (Figure 4-figure supplement 1C and Figure 5-figure supplement 2G).
Acknowledgements We thank  J van Buggenum  for generously providing Supplementary Figure 2B Figure 2-figure supplement  1B  and  FN Murphy  for help in developing the Python script. This work was funded by the  Royal Society   Darwin Trust Research Professorship, Wellcome Trust Senior Investigator Award   103792  and  Wellcome Trust   Programme grant   092545  to AHB and by an  Institut Pasteur/LabEx starting  grant to PS. AHB acknowledges core funding to the  Gurdon Institute  from the  Wellcome Trust  ( 092096 ) and  CRUK  ( C6946/A14492 ).
These studies have been conducted using different software such as FImpute  [4] , Beagle  [5]  or AlphaImpute  [6]. Imputation accuracy can be calculated by comparing imputed genotype with true HD genotype, for each SNP. Several factors influencing imputation accuracy have been studied in the literature. These factors need to be taken into account when designing a low density SNP chip, in order to get accurate imputation. The SNP density of low density SNP chips  [7] , the effect of linkage disequilibrium threshold  [8] , the effect of minor allele frequencies (MAF) of imputed SNPs  [9, 10] , the size of the reference population  [11] , and the degree of kinship between reference population and candidate population  [8, 10]  have all been identified in the literature as factors influencing imputation accuracy.
However, considering the heterogeneous structure of chicken linkage disequilibrium (LD)  [18] , it would have been better to design the low density SNP chips according to the intra-chromosomes LD, i. e. choosing tag SNPs at regular genetical intervals  [23]. The low density SNP chips were therefore designed using the SS4I software  [24]. This method made it possible to get clusters of SNPs according to a chosen LD threshold. For each cluster, the SNP with the highest MAF was then kept and used as representative of this cluster. Nine "LD SNP" chips designed with this method were studied, with LD threshold ranging from 0. 05 to 0. 8.
Software FImpute V2. 2  [4]  was used to impute the high density genotyping of the selection candidates. FImpute is a software which was developed for livestock species and which uses pedigree information. It relies on overlapping sliding windows methodology to achieve imputations. Others imputation software like Beagle or AlphaImpute, which have also been reported in the literature, were tested on scenario (A), together with FImpute. However, given the relatively long execution time of Beagle (half a day) and AlphaImpute (1 week), in comparison to FImpute, only FImpute was subsequently used. Moreover, because only the sires were present in the reference population, FImpute was used with the option "turnoff_fam" activated. This option enabled the software to turn off family imputation and to use the whole range of haplotypes of the reference population to achieve imputation.
Funding This research project was supported by the  French national research agency ANR , within the framework of project  ANR-10-GENOM_BTV-015  UtOpIGe. FlH is a PhD fellow supported by the poultry breeding company Novogen, and taking part in a CIFRE thesis  N°2016/0804  shared between  PEGASE INRA's unit, Agrocampus Ouest and Novogen.
More than others, arctic ecosystems are affected by consequences of global climate changes. The herbivorous plays numerous roles both in Scandinavian natural and cultural landscapes (Forbes et al. , 2007). Wild reindeer (Rangifer tarandus L. ) herds in Hardangervidda plateau (Norway) constitute one of the isolated populations along Fennoscandia mountain range. The study aims to understand temporal and spatial variability of intra-and inter-annual home ranges extent and geophysical properties. We then characterize phenological variability with Corine Land Cover ecological habitat assessment and bi-monthly NDVI index (MODIS 13Q1, 250 m. ). Thirdly, we test relationships between reindeer's estimated densities and geophysical factors. All along the study, a Python toolbox ("GRiD") has been mounted and refined to fit with biogeographical expectancies.
The toolbox let user's choice of inputs and facilitate then the gathering of raster datasets with given spatial extent of clipping and resolution. The grid generation and cells extraction gives one tabular output, allowing then to easily compute complex geostatistical analysis with regular spreadsheets. Results are based on reindeer's home ranges, associated extent (MODIS tile) and spatial resolution (250m). Spatial mismatch of 0. 6 % has been found between ecological habitat when comparing raw (100m²) and new dataset (250m²). Inter-annual home ranges analysis describes differences between inter-seasonal migrations (early spring, end of the summer) and calving or capitalizing times. For intra-annual home ranges, significant correlations have been found between reindeer's estimated densities and both altitudes and phenology.
Introduction In the information age, « big data » constitutes a large part of the future of ecology and biogeography  (Hampton et al. , 2013). Transdisciplinary expertise for socio-ecological issues are nowadays relying on multi-factorial and multiscale analysis for environmental surveys. Massive datasets are easily computed and processed, thanks to tremendous computer hardware advances and trends for horizontal interactions between experts, citizens and institutions  (Kambatla et al. , 2014). That said, the era of data-intensive science needs some specific tools to collect and centralize the numerous and heterogeneous datasets related to a studied phenomenon, such as wildlife and herding issues, interacting with cultural and natural systems. Multi-temporal scalar information is another issue, long-term strong trends (climate change, biodiversity loss) interacting with inter-annual variability.
depend on the bioclimatic conditions during its migration, and on geographical features such as topography and habitats  (Klein, 1990). Management of merged datasets, here by spatializing relationships between biocenoces and biotopes, may improve GIS toolbox as well as our understanding of Rangifer tarandus interactions. The study aims to present a GRiD-toolbox, combining some pre-existent tools in current versions of ArcGIS and QGIS software, applied to a multi-scalar reindeer home ranges study.
Material Several free-access datasets, fully described in Table  1 , are compiled, merged and analyzed in the study. Downloading datasets is the first step (url access in Table  1 ). The first basis is the reindeer GPS-tracks database used by  Cagnacci et al. (2015)  to assess and characterize most European's largest herbivorous seasonal residence times, associated home ranges and identifying migrational structures. 7 wild female reindeers have been equipped and surveyed using GPS-radio collar. The second dataset is ASTER-GDEM, providing altitudes and other topographical informations (slopes, expositions). The third dataset involves average annual temperatures, interpolated from the Global Historical Climatological Network at high resolution  (Hijmans et al. , 2005). Corine Land Cover map is the fourth dataset.
The Corine program assess ecological habitats and land uses at European scale, with remote sensing and photo-interpretation observations. The downloaded datasets are available for  1990, 2000, 2006 and 2012. In our case, the 2006 edition is used in order to suit temporally with the locations of wild reindeer. The last dataset is represented by MODIS 13Q1 time series. Bi-monthly temporal acquisition has been chosen, from the beginning of 2000 to the end of 2015. Dates of imagery acquisition is expressed in Julian day. The acquired tiles extent to entire Scandinavia, with a spatial resolution of 250 meters. Since the native dataset is in*. HDR format, Normalized Difference Vegetation Index (NDVI) band has been chosen, and converted into GeoTiff format. Different softwares are used in the study, depending on processing steps.
First of all, we used GIS software, such as ArcMap 10. 2 and Qgis 2. 18 for both preprocessing and processing. Then, R-studio and Ade-Habitat-HR  (Home Ranges, Callenge, 2006)  package allow us to calculate reindeer home-ranges, using Kernel Utilization Density technique, on a bi-monthly basis. Then home ranges have been exported in separate GIS files (*. SHP). These home ranges and associated quantification of reindeer's densities constitute the key-input for the generation of new datasets. Regular spreadsheets, such as Open Calc 4. 1. 3 or Office Excel 2016 versions are used all along the study. Both R (package Rcmdr) and Xlstat can be used for complex statistical calculation. For this specific study, Xlstat has been privileged.
The bivariate normal kernel technique is specified, as well as the smoothing parameter calculated with « ad-hoc » method  (Worton, 1989; Skarin et al. , 2008). Grid size (~100 m²) and extent (h-value for estimated densities; 1km²) are also specified to fit with reindeer's large scale migration properties. The resulting convex hull has been vectorized, with 95% of the kernel home-range extent as boundary. Then datasets are exported, respectively into GeoTiff raster grid for Kernel Utilization Distribution densities, and into shapefiles for the 95% convex hull. Finally, a grid is generated, intersected following reindeer KUD' shapes and geophysical descriptors systematically sampled.
GRiD -toolbox: ArcMap modelbuilding and Python programming (Qgis) The figure  1  summarizes the processing chain used for GRiD (Grid Raster Information Dataset) tool-box creation. Two prototypes of GRiD are available, even if improvements are still on progress. Here the ArcMap Modelbuilder is preferred, but Qgis GRiD toolbox is ready to use for the third and last step shown in figure  1. According to the goals of the study, the first three steps are flexible in the process chain. A key step is to define a common projection in order to minimize geometrical error between datasets. The WGS84-UTM 32N projection has been chosen, initially used for locations of wild reindeers. After projecting every dataset, we generated a regional grid, based on MODIS 13Q1 tile extent and spatial resolution (250m).
Each cell is theoretically supposed to suit with MODIS 13Q1 coarse grid, and thus values of NDVI. The step 3 converts features (cells) to points located at the core of each cell. The resulting mesh of points still has a regional extent, such as the basic grid obtained from step 2. The generated bi-monthly home ranges of wild reindeers are put into the chain, in order to intersect the regional mesh of points. As output, each intersect processing produces one shapefile and one spatial unit for home range analysis. The final step is crucial: raster values are sampled and resulting attribute tables exported as a *. CSV file. Each generated CSV matrix blend spatial unit of analysis (here bi-monthly home ranges of reindeers) with different geophysical datasets we are willing to statistically sum up or establish relationships (columns).
Checking correspondences between raw and sampled datasets We quantify the possibility to lose spatial information, by upscaling geophysical datasets at MODIS -13Q1 spatial resolution (~250m). In the study, the comparison will be made between raw Corine Land Cover dataset (e. g. 100m of spatial resolution) and new Corine dataset extracted using GRiD toolbox (250m). The analysis involves the calculation of particular and total error (%) between raw and new dataset. A threshold of 5% of total error has been retained.
Differences in habitat choices We compare the NDVI values distribution and the reindeer's densities probabilities distributions belonging to the same home range. Our aim is to verify the similarities or dissimilarities between these two kinds of distributions. Kruskal-Wallis independence tests has been conducted using habitat categories as sub-samples. Ecological habitat categories divide both densities estimation and NDVI values as many sub-samples as ecological classes the observations belong to. 2 ) according to the spatial resolution, in one of the most extent home range (2 nd half of April, e. g. 17236 cells; ~4309 km²). Similar percentages can be found within coarse dataset (3 rd line) compared to the percentages for GRiD-extracted dataset. The last line subtracts the difference between GRiD-extracted percentages to coarse Corine Land Cover.
GRiD toolbox: sufficient accuracy That said, some improvements could reinforce spatial accuracy of GRiD-toolbox. Firstly, the quantification of error between specific spatial resolution of coarse datasets and inputted spatial resolution using GRiD-toolbox has to be more thoroughly assessed. It could be interesting to repeat the same test for other datasets, such as raw ASTER-GDEM (spatial resolution e. g. 30m) and extracted values (250m). At the scale of the analysis (here the 2 nd most extent reindeer's home range), it is important to notice that the number of observations (e. g. pixels) can vary according to the considered spatial unit. Since the number of geostatistical observations is very different between home ranges, checking spatial unit representativeness for each dataset would allow us to have a good overview of total error for a given spatial resolution as GRiD toolbox input parameter.
The second improvement is related to geometrical bias, probably generated by the chosen map projection (UTM-32N). Indeed, Mercator projection is more suited with equatorial region, and tends to elongate surfaces when dealing with polar or subpolar regions. Working with an official map projection clipped by zone (EUREF89-NTM in our case) will likely improve overall results for GRiD-toolbox and wild reindeer distribution analysis. Another parameter (choosing cartographic projection) could be added to the toolbox and automatically process the projection homogenization between the numerous datasets we are willing to extract.
Auto-correlated and redundant data The auto-correlated factors are annual mean temperatures values  and altitudes. This can be explained by the altitudinal gradient, in particular between the high altitudes and low annual temperatures in the north of the studied area, low altitudes and higher temperatures in the south (Hardangervidda plateau). At the scale of the second most extent home range, the correlation is strong and significant (r = +0. 83; p<0. 0001). Then, it is statistically possible to express altitudes distribution as a good proxy for annual temperature distribution. That said, it could be interesting to reprocess the calculation with a more recent dataset (1980-2010 for example) and more accurate temperatures variables (minimal and maximal temperatures).
Conclusions Mounting GRiD toolbox has been incrementally encouraged by the needs of automation processing for the survey of wild reindeer migration and densities in Hardangervidda Plateau (Norway). The wide range spatial behavior of reindeer is an interesting case study to apply this method, more adapted to large territories than simple grid multi-data analysis previously realized by other authors in smaller areas  (Jolivet 2014 , Bortolamiol et al. , 2016). Indeed, by choosing the core point of each grid cell as the common reference to cross all the datasets, the process is realized within a reasonable computation time while avoiding important bias according to the preliminary test realized in our study. Improvements still have to be made to ensure GRiD geometrical reliability, cartographic projection homogenization and minimizing error when extracting large and various raster datasets.
Applying GRiD toolbox for wild reindeer survey during the three growth periods simplified pre-treatments by automatizing grid and creation of mesh of points; and finally datasets fusion into one attribute table. Based on geostatistical observations (pixels) it allowed us to compute several complex statistical analyses, not usually present in GIS software. Based on Open Access datasets, the study firstly shows interesting links between reindeer intra-annual distribution and ecological habitat diversity; secondly relationships between inter-annual variability of estimated densities and abiotic factors, validating thus our second and third hypothesis.
Abstract. Despite their importance for sea-level rise, seasonal water availability, and as a source of geohazards, mountain glaciers are one of the few remaining subsystems of the global climate system for which no globally applicable, open source, community-driven model exists. Here we present the Open Global Glacier Model (OGGM), developed to provide a modular and open-source numerical model framework for simulating past and future change of any glacier in the world. The modeling chain comprises data downloading tools (glacier outlines, topography, climate, validation data), a preprocessing module, a mass-balance model, a distributed ice thickness estimation model, and an ice-flow model. The monthly mass balance is obtained from gridded climate data and a temperature index melt model. To our knowledge, OGGM is the first global model to explicitly simulate glacier dynamics: the model relies on the shallowice approximation to compute the depth-integrated flux of ice along multiple connected flow lines.