dataesr/scientific-paragraphs-categorization huggingface dataset:
The functions describe fiber objects consisting of N -1 segments. This allows to conduct performance measurements as a function of the number of objects. The test procedures to generate Fig. 3a , b, d consisted of starting the program, creating or loading the corresponding data points, and measuring the time or frame rate. Afterward, the program was closed to avoid possible influence on the following iteration. This procedure was repeated ten times for each data point. Measuring the performance when filling the fiber bundles was done by creating a dataset containing n fiber objects with 1000 data points each using Eq. 1. The fiber objects were filled with fibers with a distance of d = 0. 2 and a radius of r = 0. 1, yielding 21 fibers. The measured time of 20 separate runs was taken and averaged.
As the program can use OpenMP for parallelization, both variants were measured individually. As seen in Fig. 3a-c , the program tends to scale linearly with the produced amount of data points. The usage of OpenMP shows a speedup of 1. 21 to 2. 1 depending on the measured part of the program. The frame rate in (D) maintains the 30 fps target when the amount of fiber points lies below 1. 6 million. This is due to the insufficient performance of the graphics card to display all data points simultaneously. Even when rendering around 60 million data points, 11 GiB RAM and 2. 3 GiB video memory are consumed, indicating that neither is a limiting factor. When rendering, the CPU usage stays around 0% to 10%.
Discussion and conclusion We have introduced the FAConstructor tool enabling the interactive generation of synthetic nerve fiber models both in an accurate and efficient way. The fast generation of many different fiber models is a prerequisite for imaging simulation tools to study physical effects in virtual brain tissue. Functionalities like modification of fiber objects (translation, rotation, scaling, filling), grouping of several fiber objects (e. g. fibers to a fiber bundle) and successive generation of building blocks (as groups) up to the final model allow a fast generation and easy (re)production of phantoms. By defining the radius as a function along the fibers, it is also possible to build multiple layers (e. g. myelin with nodes of Ranvier) around an axon. Evaluating the performance of the developed tool shows that most processes scale almost linearly with the amount of data points used to describe the fiber models.
6 million data points are rarely necessary, which guarantees a stable and fluent workflow. For comparison, the newly generated Fiber Cup phantom contains 426,300 data points after filling and the original fiber bundle contains only 290 data points. This indicates that even large fiber models can be rendered on similar hardware. However, to create even more detailed and/or larger brain tissue models beyond pure white 123 matter phantoms (e. g. cerebral cortex phantoms including astrocytes, cells, and neuropil), a supercomputing environment is required. First tests of the FAConstructor on one visualization node of the supercomputer JURECA  [11]  at the Supercomputing Center at Forschungszentrum Jülich GmbH, Germany, showed promising performance. This aspect will be addressed in future studies.
The applicability of FAConstructor was demonstrated for two different simulation approaches using the example of the Fiber Cup phantom. The generation of the phantom took around ten minutes for an instructed user, indicating the userfriendly environment of FAConstructor. Since the tool can be used to generate large-scale fiber models with small-scale detail, it is suited for both large-scale simPLI and smallscale FDTD simulations. It will help to generate the models quickly and, if necessary, to modify them accordingly so that the underlying fiber structure can be tested from experimental data in an appropriate time. In order to enable faster and more intuitive changes to the models, mouse interactions with the scene need to be implemented. This would allow more intuitive modification of fiber objects, such as moving fiber points or changing entire objects or groups.
Since the technological and analytical methods in dMRI improve continuously, highly detailed microstructured models are required for simulations to address the impact of underlying neuronal components on the diffusion behavior  [9]. In conclusion, we have implemented a user-friendly and efficient tool that enables a fast generation of complex and realistic synthetic nerve fiber configurations (i. e. numerical brain tissue phantoms) ideally suited for the simulation of neuroimaging techniques like 3D polarized light imaging. The generated fiber bundles can be visualized simultaneously and edited interactively. This ultimately enables the production of application-specific well-defined ground truth datasets.
Acknowledgements This study was funded by the  European Union 's  Horizon 2020 Research and Innovation Program  under Grant Agreement No. 785907  ( HBP SGA2 ). We gratefully acknowledge the computing time granted through  JARA-HPC  on the supercomputer JURECA [11] at  Forschungszentrum Jülich. This open source software code was developed in part or in whole in the Human  Brain  Project, funded from the  European Union 's  Horizon 2020 Framework Program for Research and Innovation  under the  Specific Grant  Agreement No. 785907  ( HBP SGA2 ).
[38]  for a discussion in the field of energy). As can be seen, the modelling exercise comprises three models (PRIMES-TREMOVE, DIONE and SHERPA) and a geographic information system software (QGIS). The PRIMES-TREMOVE and the DIONE models are complementary models, which feature some common attributes. PRIMES-TREMOVE is an energy economic model for the transport sector and is part of the main PRIMES model, which simulates the overall energy system. The transport demand simulated in PRIMES-TREMOVE is based on the microeconomic theory and features a degree of engineering detail as regards the vehicle technologies. The model also handles, explicitly, policies related to the promotion of alternative fuels and low or zero emission vehicles (which is the scope of this paper). On the other hand, the DIONE model is a very detailed engineering model which calculates a larger (than PRIMES-TREMOVE) set of air pollutant emissions, based on technical specifications of vehicles' powertrains and engine sizes.
DIONE contains a calibrated baseline, which is based on the country-specific stock and activity data collected in the EU research project TRACCS (see  [54] ). The original baseline was taken forward following the trends of the EU Reference Scenario 2012. Within the context of this study, the baseline was updated to EU Reference Scenario 2016 as described in Section 4. Four car sizes are included in DIONE: tiny (represented by an engine capacity of < 0. 8 l), small (0. 8-1. 4 l), medium (1. 4-2. 0 l) and large (> 2. 0 l). For internal combustion engine vehicles, estimation of energy consumption and emissions in DIONE is based on COPERT 4  [20]. For BEVs, the energy consumption factor was estimated from real drive data collected in the context of the Green eMotion project  [30]. The DIONE fleet impact model can be employed to run scenarios on EU Member State level or aggregates thereof.
In order to calculate atmospheric dispersion, the emissions are regionalized within the Member States as follows: initially, the emissions per Member State and sectors are derived from the GAINS model  [35]. Then, sector-specific proxies are applied to compute the fraction of Member State level emissions to be associated to each cell (i. e. traffic emissions are distributed only on road-related grids, residential heating is linked to buildup, etc. ). More details on the applied methodology can be found in Trombetti et al. [55]. These results on air pollutant concentrations can be further processed to generate maps. To do so, we used the open-source geographic information system software available from QGIS  [44]. In this way, a geospatial representation of the results of the modelling exercise is given.
In most cases, Member States' plans for the build-up of infrastructure in the NPF were presented alongside information on their estimates regarding the market penetration of alternative fuels vehicles. As with infrastructure, estimates of EVs were mandatory only in 2020. From this data, the estimates on EV deployment were collected for each country. Table  2  shows our dataset as a result of the NPF assessment (see Research data in Additional file 1). As can be seen, there are substantial differences between countries (2% to 38%) in 2030 EV shares, at least with respect to total car stock in 2017. Some NPFs provided more than one estimate for certain years. Unless it was clear there was a middle estimate, the average of the provided estimates for a given year was calculated. These data were then used to construct the alternative scenario.
Further research will revolve around the analysis not only of those measures, and on whether they are consistent with the emission reductions simulated here, but also consider recent announcements of future bans on conventional car sales. The main limitation of this study was data availability. Article 10(1) of the Directive requires that Member States submit a report on the implementation of their NPFs in late 2019. Annex I of the Directive states that one element of the report is 2020, 2025 and 2030 alternative fuel vehicles estimates. Because of these requirements and as progress in the field of electro-mobility is being made, we expect the datasets contained in those implementation reports to be more comprehensive than the ones used in this work. This would allow a more robust analysis at the EU level.
Acknowledgements We are very grateful to  Marko Horvat  and  Martin Dolinsky  for their support in running some of the simulations;  Beatriz Acosta Iborra ,  Marek Bielewski ,  Nerea De Miguel Echevarria  and  Pietro Moretto  for helping us assess the NPFs as well as  Antonio Tricas Aizpun ,  Axel Volkery ,  Maria Cristina Mohora ,  Matteo Ordinanovich  and  Rolf Diemer  for providing comments on an earlier version of the manuscript. We are also very grateful to two anonymous reviewers for their comments, which helped us improve the manuscript.
Existing morphological visualization platforms, either academic (e. g. , OpenWorm  12  , BodyParts3D  13  , FPBioimage  14  , Fiji  15  , Icy  16  , MorphoGraphX  17  , and EmbryoMiner  18  ) or commercial (Amira, Imaris, etc…) allow users to interact with specific datasets, within the context of precise scientific questions. Compared to genomic browsers, these platforms have three major limitations. First, they are not designed to integrate the user's data with external sources: none offers the possibility to project external quantitative/genetic information onto morphological datasets. Second, they are primarily designed as local analysis tools, and can hardly be used to share research data within and across scientific communities. Finally, the development of these tools requires the maintenance of a software which needs to be installed on different machines and with different operating systems who themselves evolve in time.
This needs a long-term intensive plan of software maintenance, which may not be compatible with the limited resources available in the context of specific research projects, with the risk of rapid obsolescence. Web-based morphological databases exist (MorphoSource, Phenome10k, MorphoMuseum, DataDryad), some of which come together with an online data visualization interface. However, the set of user interactions with the dataset is much more limited, and no additional information can be uploaded to and/or projected onto the dataset. The open-source web-based tool presented here (http://www. morphonet. org) shows that the exploration and analysis of diverse large-scale imaging datasets can be used for a conceptually analogous philosophy to that which presided over the development of generic web-based genome browsers.
Results Conception of a morphodynamic web browser. The first challenge was to compress massive fixed or live imaging datasets sufficiently to allow their visualization and interaction through the web. While raw microscopy images and their volume segmentations are too voluminous to be visualized online, segmented images are objects-based structures that can be highly compressed into surface meshes  19  , without significant loss of shape information. MorphoNet then renders such collection of meshes using the Unity3D gaming engine, which runs on standard internet browsers and exploits the recent power of the WebGL. An intuitive, user-friendly web browser-based graphical user interface was developed to control the interaction with any dataset uploaded and stored in a common database (Fig. 1 ).
To speed up rendering and visualization of cloud-stored datasets, including hundreds to thousands of objects, the level of details of the meshes can be set by the user. In such a way, complex 2D or 3D morphological datasets, at a single time point or across time series can easily be handled and shared. The whole open source code of MorphoNet and related documentation are available on a GitLab at https://gitlab. inria. fr/efaure/morpholab. While the universal atomic unit in any genomic dataset is the DNA base pair, the atomic units vary between imaging datasets from complex organs, to cells or even subcellular structures or molecules. The choice of the scale of elementary building blocks for visualization, which depends on the scientific question, is thus left to the owner of the dataset. Figure  2  and Supplementary Fig.
2a , b, Supplementary Movie 2). Objects and groups can be hidden or made partially transparent, either at a single time-point or throughout time series. As shown in Fig. 2d, e , this feature allows the exploration of internal structures within highly complex multilayered datasets such as a whole human body (Supplementary Fig. 1 , Supplementary Movies 3-6). To facilitate the interpretation of project-specific data in the context of publicly available information, genomic browsers support the upload by private users of custom datasets, such as RNA-seq or ChIP-seq, mapped onto the reference genome and which appear as individual genetic tracks. MorphoNet also supports the upload of formatted external quantitative and qualitative information, mapped onto individual objects, and which can be projected onto individual objects or groups in the form of color labels, text or heat maps (Fig.
2c, f , Supplementary Fig. 1 , Supplementary Movies 2-10). Such morphological tracks can be saved in the cloud and shared with other users. Among the possible morphological tracks, one of notable interest is gene expression patterns. MorphoNet offers the possibility to upload gene expression data from in situ hybridizations, cell-or tissue-specific RNAseq, Starr-seq, or chromatin signatures and to superimpose them onto a 2D or 3D scaffold as either boolean (color selections) or quantitative (heatmaps) information. This feature bridges cell-level genetic and 3D dynamic morphological information in one visualization tool, permitting the exploration of their spatio-temporal interplay (Fig. 3 , Supplementary Movie 9). Genetic information can either be automatically synchronized from external databases (such as ANISEED  20  ) connected to MorphoNet through their Application Programming Interfaces (APIs) (Fig.
A few simple calculations (average, standard deviations, normalizations) exist as built-in options in the MorphoNet interface. Due to the web-based structure of the tool, more costly calculations on the server would affect the user dynamic interaction with the dataset. However more complex calculations can be performed externally. The Morphonet Python API and ImageJ plugin provide an easy interface to upload any Pythonbased or ImageJ-performed calculations directly to the Morph-oNet server. Otherwise, the user has always the possibility to create. txt files and upload them from the online interface. Potential use in research. To exemplify the use, richness, and generality of MorphoNet we provide three detailed tutorials explaining how MorphoNet can address specific needs. These  tutorials can be found online at http://www.
morphonet. org/ Tutorials/MorphoNet_Tutorials. php. The first tutorial illustrates how to use MorphoNet to investigate dynamic biological properties of embryonic cells for the specific case of ascidian embryonic development. Thanks to its straightforward upload process, a 3D reconstruction is available on the MorphoNet server as a new dataset immediately after segmentation. MorphoNet is then used to visualize complex 3D dynamic shape changes and to select, on 3D data and though the timestack, a group of cells of interest to study (specifically, those cells whose shape changes the most during gastrulation, i. e. , endodermal cells). A few lines in Python are then used to quantify the change in time of endodermal cells' shape during gastrulation. These results are then uploaded to MorphoNet in the form of quantitative information.
MorphoNet allows to project this dynamic information on the embryonic cellular scaffold and to visualize its evolution. In this way, by a simple and direct visual investigation, one can clearly see the onset of the two main shape changes involved in ascidian gastrulation, and confirm their sequentiality in time. The second tutorial shows how to use the MorphoNet plugin for ImageJ/FiJi we have developed. It allows to use the many image analysis tools, including properties quantification, available in ImageJ and to upload the results directly to the MorphoNet cloud. Specifically, the tutorial shows how one can import a volumetric segmented image of one ascidian embryo in ImageJ, create a surface mesh with the Fiji built-in library and upload it as a new dataset to MorphoNet, together with the quantitative information about the volume of each cell in the embryo.
Discussion In summary, MorphoNet integrates, in one open-source online tool (http://www. morphonet. org), a comprehensive palette of interactions to explore the structure, dynamics and, variability of biological shapes and to confront these with genetic information. It largely profits from the most recent and well-developed web technologies and introduces a generic strategy for hierarchical representation of biological structures. We expect in the future, thanks to the contribution of large user communities, to develop the MorphoNet data and metadata formats towards standardization. Due to the lack of unified formats for morphological data and metadata, uploading a dataset from an external database in Morphonet leads to the duplication of this dataset in a slightly different format. To reduce the impact of this reaccessionning issue, we invite Morphonet users to always provide the original dataset ID in the metadata of the uploaded copy, and to cite both MorphoNet and the original database and ID in scientific publications.
Methods MorphoNet features and functions. MorphoNet has a highly interactive interface through which users can visualize morphodynamic data and display quantitative and qualitative information. The first public instantiation of MorphoNet is currently hosted on a secure server (http://www. morphonet. org). The system is portable and, as the bulk of the calculations is handled on the client side, it can be installed on any server with even modest computing power. MorphoNet has two different types of access possibilities: registered users, whose access rights are detailed in Table  1 , and non-registered users, who can access public datasets and have visualization rights only. A visual help web page is accessible at any time through the question mark button at the top of MorphoNet page. Supplementary Movies 1-10 provide several practical examples of use of the MorphoNet interface.
MorphoNet offers a large variety of interactive features to explore the structure and dynamics of morphological datasets. Basic dataset interactions include data rotation and translation by keyboard + mouse shortcuts, object selection by mouse clicks or keyboard + mouse shortcuts, zooming in/out through mouse actions and scrolling through timestacks by either a scrollbar or by keyboard shortcuts. All keyboard and mouse actions are explained in the navigation menu in the upper-left corner of the application screen. In addition to these basic features, more advanced data interactions are available. These functions are grouped into 6 menus in the visual interface: Dataset, Infos, Objects, Groups, Genetic, Movie. Moreover, standard object-interaction functions are given at the bottom of the interface.
Standard object-interaction functions allow users to search specific objects or groups of objects, identified by their name or numerical ID. Once objects are picked, they can be hidden from view, either at specific time instants or throughout the timestack. User can also select all physical neighbors of any picked objects, which can be iteratively repeated to explore the structure of object adjacency. In the Dataset menu, users can interact with the dataset as a whole. The dataset position and orientation in space can be reset to default values, and the default orientation can be set (only by dataset owner). Dataset can be cropped along the three main axes, or along a custom direction. Users can also switch among different visualization channels, or download surface meshes or visualize adjacency and temporal lineage trees.
In particular, nodes in the lineage tree are automatically linked to their corresponding objects in the dataset: any action performed on objects (such as color-labeling) is automatically reported on tree nodes. Infos menu lists all information associated with the dataset. Information is categorized as: Selection, Quantitative, Qualitative and Genetic. Selections are color labels applied to objects (see later on about the Objects menu). Quantitative information, associated with all objects or to a selected subgroup, can be visualized as heat maps on the dataset, and its numerical value is shown for any picked object. In addition, users can manipulate quantitative information by, for instance, calculating time and space averages, mean values, standard deviations or time normalization. After such manipulation, a new entry in the Quantitative category is created, representing the outcome of the requested calculation.
Qualitative information associates a string of characters to all objects or a selected subgroup. Once one Qualitative entry is activated, the corresponding information is visualized as text superimposed on the dataset as the mouse pointer passes over the corresponding object. Genetic information associates objects to an expression level of a specific gene. This information replaces or complements the equivalent information extracted from external genetic databases (if connected to MorphoNet). Expression level can be given as boolean (on/off) or as quantitative level. If one or more Genetic entries are activated, a Genetic menu is created and made accessible (see below). From the Infos menu, users can also upload to the cloud text files (. txt extension) containing new information. Users also have the possibility to manually create and curate and upload new information tracks directly through the visual online interface.
All the information provided can be downloaded as text files, shared with selected users, and manually curated. Specific object interactions are possible in Objects menu. There users can apply color selections to picked objects, hide non-selected objects from view and propagate color-labeling through time along the lineage tree. This allows users to track the evolution of selected objects in time and/or to cluster groups of objects in space. Groups gives a list of previously uploaded sets of objects of particular relevance (these can, for instance, be tissues in organs). The group structure is hierarchical, such that smaller groups can build up larger ones, highlighting the relevant morphological scales of the dataset. Users can select all objects (or subgroups) in one group and automatically apply color selections to each of them.