dataesr/scientific-paragraphs-categorization huggingface dataset:
The VisDaT System Description An environment meant for a vision diagnosis and therapy, to prove useful, should be equipped with many visual, auditory, and tactile stimuli, which force the brain to organize them by recognizing, analyzing, and integrating them. Additionally, it should be flexible in adjusting to children's needs, which are different dependent on their age and impairment. For this purpose, the VisDaT system was developed in the cooperation with a group of therapists from the BRUNO association involved in daily care of impaired children. They provided many useful guidelines for tool development, especially in terms of children's physical condition and the stimuli elaborating. Based on the collected knowledge, some assumptions have been made with regard to hardware and software configuration.
The operator of the system observes the content of the control screen and uses mouse to change the content of the stimulation screen and to influence the work of the eye tracker. Both outputs-from the eye tracker and the camera-are presented online to the operator. The simplified setup of the system is presented in Figure  3. The system is controlled by the specialized software. It was written in Java language; thus, it is independent of the operating system. However, so far, it has been tested only in Microsoft Windows 10 environment. The workplace presented in the paper used the Dell Precision T1600 computer with Intel Xeon 3. 1 GHz CPU and 4 GB RAM, but any computer with at least one USB3 socket should be suitable.
Red crosses denote stimulus locations, the blue ones are the eye tracker's output, and green lines connect calculated gaze coordinates with the actual stimulus positions. It may be noticed that in all cases, eye positions are shifted in one direction, with respect to stimulus. It means that when the same calibration model is applied for different subjects' data should show correct eye movement directions, but not the correct eye positions. Thus, when there is no need to provide a gaze point with the high accuracy, the proposed solution will fulfill its task; namely, it will point out the direction of an eye movement. So, even when the eye tracker is not calibrated for the person who is being observed, it is very likely that real direction of movements will be the same as the one showed by the eye tracker.
Implicit Calibration. As the explicit calibration of the system with children's cooperation is impossible, it must be done implicitly during the normal activity. It requires obtaining some pairs: an eye tracker output-actual gaze coordinates. Given such pairs, it is possible to build a regression function that maps eye tracker output to gaze coordinates. Of course, the function works better when there are more points and the data is of better quality. However, in the case of children with brain disorders, we have to agree on objective restrictions. Therefore, the calibration is done 'on the fly-when any stimulus appears on a screen and the operator notices any child's reaction (indicated by the gaze-rose)-which may mean that the child is looking towards the stimulus. One click at the operator's screen triggers the process of collecting data for the calibration.
The implicit calibration algorithm works as follows: (1) Stimulus appears on the screen. (2) A child moves its eyes towards the stimulus. (3) The operator sees the movement and, when it is finished, clicks on the stimulus to inform the system that the child is now looking at that point. (4) The system starts registration of eye tracker's output and triggers a potential feedback (sound or movie). (5) When eye tracker's output changes (indicating a subsequent eye movement), the calibration module stops the registration and adds the clicked point's coordinates together with the registered eye tracker's output to the existing calibration dataset. (6) The calibration function is recalculated using all data from the calibration dataset. Of course, more points result in better calibration model, but even one point calibration may be valuable.
Automatic and Manual Feedback. Sometimes, the eye tracker data may not be calibrated sufficiently and it may be difficult for the system to automatically produce feedback when a child looks directly at the stimulus. Therefore, the operator has the opportunity to produce the feedback manually. If the operator notices the child's reaction to the stimulus (utilizing the gaze-rose), he/she may click on it. It adds the point to the calibration model and, in the same time, triggers the action attached to this stimulus (sound or movement). 3. 5. Post Hoc Analysis. All events during every session are recorded in a specific format by the storage module meant to keep data for further analysis. The recording includes eye tracker output, video registered by the camera, and screenshots of stimulus screen.
Visualization Examples. The experiment presented in this section involved an animated stimulus. During the experiment, a bike moved on the screen from right to left and back. The child was supposed to follow the moving object with eyes. The eye tracking data was recorded during the experiment and could be used for post hoc analysis. Figure  11  presents the stimulus together with a part of the scan-path recorded for one child. It is visible that the child's eyes followed the object. A more informative visualization is presented in Figure  12. It presents eyes' positions separately in horizontal and vertical directions compared to the current location of the bike stimulus (the colored belt). It is evident that the child's eyes followed the object moving horizontally. Figure  13  presents the same visualization, but for another child with more severe disparities.
Acknowledgments The authors would like to thank the employees of  BRUNO Association  for their cooperation and help as well as the students of the  Silesian University of Technology :  Katarzyna Kruk  and  Michalina Dzierzega  for their help in the creation of stimuli and conducting preliminary experiments. The research presented in this paper was supported by the  Silesian University of Technology  Grant no. BK/263/RAu2/2016.
Topographic variation underpins a myriad of patterns and processes in hydrology, climatology, geography and ecology and is key to understanding the variation of life on the planet. A fully standardized and global multivariate product of different terrain features has the potential to support many large-scale research applications, however to date, such datasets are unavailable. Here we used the digital elevation model products of global 250 m GMTED2010 and near-global 90 m SRTM4. 1dev to derive a suite of topographic variables: elevation, slope, aspect, eastness, northness, roughness, terrain roughness index, topographic position index, vector ruggedness measure, profile/tangential curvature, first/second order partial derivative, and 10 geomorphological landform classes. We aggregated each variable to 1, 5, 10, 50 and 100 km spatial grains using several aggregation approaches.
org/). Errors or inaccuracies in the inputs can propagate throughout the analyses and derived datasets. Assembling readily available data of high quality is therefore useful in many fields. Of particular relevance for many applications is a global coverage and comparability among different regions. This is emphasized by assessment processes such as those of the Intergovernmental Platform for Biodiversity and Ecosystem Services (IPBES)  19  , where global availability and comparability of data is crucial. The modeling of species distributions worldwide, as targeted in projects such as Map of Life (https://www. mol. org  20  ), requires a rich stack of global environmental information, which to date, has only been partially developed in regards to terrain. To address the lack of readily available topographic variables at a global extent, we calculated a suite of elevation-derived topographic variables based on the 250 m Global Multi-resolution Terrain Elevation Data 2010 dataset  21  (hereafter 'GMTED').
As a comparison, we also calculated the most relevant variables using the widely used 90 m Shuttle Radar Topographic Mission  22, 23  void filled dataset SRTM4. 1dev  24, 25  (hereafter 'SRTM'). We resampled (i. e. , aggregated) each metric to coarser spatial grains (1, 5, 10, 50, 100 km) using several spatial aggregation approaches to characterize the global topographic heterogeneity (see Aggregated topographic variables session). In total, we computed 15 continuous variables that characterize the shape of the relief (elevation, slope, aspect sine/cosine, eastness, northness, roughness, terrain ruggedness index, topographic position index, vector ruggedness measure, profile/tangential curvature, first/second order partial derivative) and 1 categorical variable, describing ten major landform classes.
Methods For this study, we used the following terminology to define three layer types, each referring to a specific step within the work-flow: source layers are the raw elevation layers from the 250 m GMTED (available as different elevation products, see below) or from the 90 m SRTM dataset. Derived topographic variables are a suite of 15 topographic variables that were calculated from the source layers using moving window analysis. From these derived variables, we obtained the aggregated topographic variables at coarser spatial grains.
Source layers Two data sources were used for this work: the 250 m GMTED  21  and the 90 m SRTM4. 1dev  24, 25. GMTED served as the main data set due to its full global extent and having the highest resolution (250 m) available thus far, while SRTM was used for comparing and validating the GMTED-derived variables and for assessing the effect of spatial scale acquisition in regions where the extents of the two data sets overlap (56S-60N latitude). The GMTED data set (downloaded at http://topotools. cr. usgs. gov/gmted_viewer/) was released in 2010 by the United States Geological Survey (USGS) and the National Geospatial-Intelligence Agency (NGA) globally at three spatial grains 1 km (30 arc-seconds), 500 m (15 arc-seconds) and 250 m (7. 5 arcseconds), with the exception for Greenland and Antarctica, where only 1-km data sets are available.
The original GMTED product is a composite product based on several gridded elevation data sources with spatial grains ranging from approximately 30 m to 2 km (1 to 60 arc-seconds, ref. 21). The main source for GMTED is NGAâ€™s SRTM Digital Terrain Elevation Data (http://www2. jpl. nasa. gov/srtm/) at a 1arc-second resolution (void-filled). Seven additional data sources were used for regions not covered by SRTM, and to fill any remaining holes due to miscellaneous factors such as cloud cover. These included 1) the non-SRTM DTED (NGA) the Canadian Digital Elevation Data (GeoBase-Canada) at two resolutions, 3) the Satellite Pour lâ€™Observation de la Terre Reference 3D (Spot Image-IGN), 4) the National Elevation Dataset for the continental United States and Alaska (USGS), 5) the GEODATA 9 s DEM for Australia (Geoscience Australia), 6) an Antarctica satellite radar and laser altimeter DEM (University of Bristol), and 7) a Greenland satellite radar altimeter DEM (University of Bristol) (see GMTED2010 technical report  21  for more information).
These single DEMs were merged with the global GMTED DEM to a reduced, but standardized spatial grain of 250 m to produce a dataset  21  with 5 layers that have the values of the mean, median, minimum, maximum, and standard deviation of the native resolution DEMs (1 to 60 arc-seconds) within the aggregated grid cells. The GMTED root-mean-square error (RMSE) ranges are between 25 and 42 m, between 29 and 32 m, and between 26 and 30 m for the 1 km, 500 m and 250 m products, respectively  21. The 250 m GMTED product does not include Antarctica because the only available DEM is available at a 1 km spatial grain  21, 26. The 250 m GMTED is the only DEM covering the full globe with the highest spatial grain available, consisting of a raster layer of 172800 x 67200 cells. As a comparison, we also used the 90 m (3 arc-seconds) SRTM4.
cgiar-csi. org/data/srtm-90m-digital-elevation-database-v4-1). Where available, higher resolution data were used to compute the DEM at 90 m using an interpolation with a vector-based contour and points approach  24, 25. When no additional high resolution DEM was available, an interpolation was performed using the coarser STRM product with the exact method varying according to the size, shape, and topography of the area: 1) kriging and inverse-distance-weighting for small and medium-size holes in flat areas, 2) triangulated irregular network for very flat areas, 3) splines for small and medium holes within areas with complex topography and at high elevations and, 4) advanced splines for large holes with other topography. One limiting factor of the SRTM data is that it only covers low latitude areas between 60N and 58S.
The value of the derived variables can change slightly from software to software in accordance with the implemented algorithm. Derived topographic continuous variables. The derived continuous variables include slope, aspect (including sine and cosine values), eastness, northness, four heterogeneity variables (roughness, terrain ruggedness index, topographic position index, vector ruggedness measure), two curvature variables (profile curvature and tangential curvature), and the first and second order partial derivatives of elevation (variables list in Table  1 ). The partial derivatives describe the rate of change of the slope and curvature while keeping the aspect directions constant (in our case along north-south and east-west directions)  27, 28  (Fig. 2 ). Based on the median elevation of the source layer (GMTEDmd), we first calculated slope and aspect, which are the rate of change of elevation in magnitude and orientation for the steepest descent vector, respectively.
Tools and work flow All calculations were processed in parallel using open-source software at the Yale Center for Research Computing at Yale University. To allow parallel and distributed processing of our work-flow, we split the GMTED raster data set into 4 x 2 tiles (43200 x 33600 cells each) and the SRTM raster into 36 x 12 tiles (12000 x 12000 cells). In both cases, the neighboring tiles overlapped with 2 grid cells (thus GMTED: 43204 x 33604 cells; SRTM: 12004 x 12004 cells). To accelerate the computation, the global DEM was sent to each single computational node and each single tile was processed by one CPU core. The neighboring tiles overlapped with 2 grid cells to avoid any border artifacts during the calculation of the derived variables. After the variable calculation, these duplicate cells were then removed when merging all tiles to global maps.
The derived topographic variables were computed with the Geospatial Data Abstraction Library (GDAL, http://www. gdal. org/, version number 1. 10. 0)  37  and with the Geographic Resources Analysis Support System software (GRASS, https://grass. osgeo. org/, version number 7. 0. 0)  28, 38. Table  1  reports the GDAL or GRASS commands used for each derived variable calculation. GDAL and GRASS provide fast and flexible computation features and functions for raster-based scripted automated workflows, and allow processing of very large data sets due to efficient algorithms and memory management. In the context of our work, they allowed on-the-fly calculations of true geodesic distances in a latitude-longitude coordinate reference system, enabling direct work in the native projection system of both the GMTED and SRTM products, i.
e. , the World Geodetic System 1984 (EPSG:WGS84). To aggregate the derived continuous variables to coarser spatial grains, we used the Processing Kernel for geospatial data (pktools, http://pktools. nongnu. org, version number 2. 6. 3)  39. The pkfilter function with the -down flag option allowed us to run aggregations for user-specified defined window sizes while the -filter flag option allowed selection of the aggregation approach (mn, mi, mn, md, sd). The aggregation for the derived categorical variable was performed with pkfilter for the percent count and majority. For the Shannon index, entropy and uniformity, a customized python script was implemented. We provided example code at http://spatial-ecology. net/dokuwiki/doku. php?id=wiki:grass:grasstopovar for users to calculate and aggregate custom variables at specific spatial grains.
Quality control and data limitation A visual inspection of the 250 m and 1 km GMTED derived variables revealed a few artifacts and issues. We documented striping and/or blocky artifacts visible especially in Eurasia (above 60N latitude, see Supplementary Fig. 4  showing an area of 840 × 700 km in Siberia). The artifacts were visible in all the GMTED derived variables with different level of magnitudes. These artifacts were due to the production method of the non-SRTM DTED dataset (see the note by the GMTED developer  21  ) and could not be corrected prior to processing the topographic variables. We found that the Canadian area covered by the CDED did not present the same artifacts. Nonetheless a vertical gap was visible between the GMTED derived from SRTM, and the GMTED derived from the CDED (Northwest Territory in Canada).
Artifacts and vertical gaps were more visible and evident in flat areas where the pixel value fluctuation was due to data acquisition methods rather than effective altitudinal variations. Beside the artifacts visible in the GMTED-derived variables, we found that the SRTM-derived variables also presented some anomalies (Supplementary Fig. 5 ). The SRTM artifacts were due to void filling procedures performed during the SRTM4. 1dev 24 production workflow. In the same area, the GMTED derived variables did not present such anomalies, due to a better filling procedure mixed with a smoothed effect of the 250 m resolution  21. Despite these limitations the GMTED is still the only available DEM covering the full globe at 250 m spatial grain. We advise users to note the presence of artifacts in the area above 60N latitude and carefully check for possible effects in their own analyses and results.
Usage Notes The newly-developed topographic variables (Data Citation 1) have a wide variety of potential applications in ecology, biogeography, conservation and biodiversity science, land use and land cover change, physical   (32 × 32 variables) for the GMTED derived topographic variables (aggregated to 1 km spatial grain), describing heterogeneity and roughness in the Alps region depicted in Figure  2b  (2400 × 1600 cells). Variable name and aggregation approach abbreviation are reported in Table  1. The scale bar reports Pearson's positive (red) and negative (blue) correlation coefficients. Circles in the plots have different sizes according to the absolute values of correlation coefficients. The GMTED derived topographic variables are labeled in red and blue to better distinguish the column and row of the plot and are ordered according to a hierarchical clustering on the values obtained from 200,000 1-km pixels randomly selected from the Alps region.
g. , 50 and 100 km) can be used during the testing of code or workflow development as they are less computationally demanding than the variables at 250 m or 1 km spatial grain. In the spirit of reproducible research we provide the scripting procedure (http://spatialecology. net/dokuwiki/doku. php?id=wiki:grass:grasstopovar) to calculate custom topographic variables, and to aggregate these variables to specific spatial grain. The procedure can also be used to recompute the derived topographic variables at the original resolution (250 m GMTED and 90 m SRTM4. 1dev). Moreover, we also show how to load the GeoTiff format in R and how to make a colorful map. Users are thus encouraged to use their own DEM layers and implement the scripting procedure to generate the desired topographic variables at different spatial grain by changing the parameter of aggregation.
Acknowledgements This study was supported in part by the facilities and staff at the  Yale Center for Research Computing (YCRC). We wish to thank  Steve Weston  for his help and advice regarding cluster computation. We thank  Dr Fernando Sedano ,  Dr Nate Upham  and  Dr Christine Chen  for their comments on earlier versions of the manuscript, which substantially improved the overall article. Special thanks goes to  Dr Peter Kempeneers , the developer of http://pktools. nongnu. org. His tools were fundamental to implement a fast processing chain. G. A. wishes to thank also  Dr Anssi Pekkarinen  who introduced him to the GeoComputation world under Linux OS. S. D. was funded by the  German Research Foundation DFG  (grant  DO 1880/1-1 ). This work benefited from discussions as part of the  NCEAS Environment and Organisms working group  ( NCEAS  Project  12504 ), especially with  Rob Guralnick  and  Brian McGill.
This approach turned out not to be synthetic enough, since over 100 reasons can be mined for a given issue, making the perception of the main attacks and supports quite difficult. Nevertheless, this initial approach allows the construction of an argument database with respect to an issue which is useful to readers who wish to access the exact form of arguments that have been mined. This is illustrated in Fig. 1 , where the relation of the concept to the issue (part of, purpose, etc. ) is specified to enhance the structure of the description. This figure simply shows a form of synthesis readers would like to have, independently of any knowledge representation consideration. The present contribution goes one step further: it aims at producing a synthesis that is short and efficient where the concepts present in the GL Qualia structures are used to abstract over sets of propositions that support or attack an issue at stake.
instead of just atomic concepts. Manipulating such structures is more complex, but corresponds to the needs in terms of representation. Reasoning with these complex forms is addressed in  [19]. Other types of resources such as FrameNet, WordNet or VerbNet do not contain the information found in Qualias, which is essential for argument mining. These latter resources are structured around predicative forms and mainly describe the type of arguments and adjuncts predicates can take and how they are combined. VerbNet introduces semantic representations based on primitives which may be of interest for our approach as a way to normalize the complex representations we have implemented and, possibly, the atomic concepts themselves. In terms of data completeness, it is clear that Qualia descriptions will never be comprehensive knowledge repositories for a given concept, with all its facets.
In a 'real' argumentation, arguments may attack or support other arguments instead of the issue. It is not clear at the moment whether the same generation procedure can be used. Probably, to keep the synthesis readable, additional devices such as navigation links would be needed to indicate that an argument gets supports or attacks from others. The system described here is relatively simple to implement. A first implementation has been realized with the logic-based platform <TextCoop> we developed for discourse analysis. This platform can also be used for language generation since it is fully declarative and partly reversible. However, the strategy used in <TextCoop> needs to be revised so that the simplest structure is generated first. The parsing strategy that is used in <TextCoop> is indeed a priori oriented towards language parsing.