dataesr/scientific-paragraphs-categorization huggingface dataset:
By clicking on this button, the user may trig the Zenodo registration process: 15  the file associated with the query is uploaded to Zenodo using the "Data Set" upload type and all the query-associated metadata are copied to corresponding Zenodo fields. In particular: • the author of the upload is set to "VAMDC consortium"; • the title and the description are generated automatically starting from the query itself, the node producing the data and the query execution context (timestamp, token,…); • the license chosen for the data being uploaded is "CC4 By", with open access; • the bibliographic references extracted from the data-file by the Query-Store service while it processed the query (cf. paragraph 3. 1), are copied into the "References" fields. The authors of these references also populates the "Contributors" fields; • a reverse link, pointing from Zenodo to the Query-Store service query-entry, is introduced by putting into the field "Related Identifier -Is identical to" the resolvable persistent identifier of the query on the Query Store side (cf.
remark 4 for a discussion about the relevance of this link). When the upload process finishes successfully, Zenodo provides the Query-Store service with a DOI and with a deposition identifier, that the Query Store curators may use further for administrating the upload on the Zenodo side. These two identifiers are stored on the Query-Store service and associated to the query. The deposition identifier is never returned to the users. When a user displays a query which has already been copied to Zenodo, the button "Get a DOI" is replaced by a DOI badge (cf. Figure  4 ). By clicking on this badge, the corresponding Zenodo record is displayed on the user screen (cf. Figure  5 ). This page also contains the instructions for citing this query-record. Different export fromats are supported (cf. Figure  6 ): Figure  7  gives an example of the Bibtex citation format.
A citation for the query record we used for our example is (Consortium VAMDC (2018)). Moreover, the data deletion mechanism described in 2 is suspended for all the queries associated with a DOI (in other words, the underlying data are kept permanently on the Query Store side as well). Since Zenodo is indexed in OpenAIRE,  16  and since the latter implements Scholix through its Data-Literature Interlinking Service, 17  all the VAMDC queries registered by the Query Store in Zenodo are included in those infrastructures. Therefore when some data extracted from VAMDC are cited (in papers and/or other datasets) through the DOI obtained by the couple (Query Store/Zenodo), the authors of the works referenced by the VAMDC data receive credits automatically. What has been described above is typical of the interoperability virtuous circle: if a system A implements some interoperability protocols and a system B implements some other ones, than a wrapping between A and B will disclose to A the interoperability capabilities of B.
One could say that the interoperability-capabilities propagation speed is greater than the interoperability-protocols adoption speed. Remark 3 The upload-to-Zenodo process was described in this paragraph from a human point of view. In our implementation this process may also be completely machine actionated. Indeed, the computer architecture of the services described through this paper relies on a set of REST services. A user, or a computer program, may interact with these services by sending parameters (using GET and/or POST methods) to specific endpoints. All these services respond by providing JSON formatted output which may be automatically parsed. The Graphical Web User interface we presented in Figures 1, 2 and 4 are part of a lightweight html5 layer for interacting with and formatting output from these REST services.
Remark 4 As Zenodo is an open repository (any person who has, e. g. , a Github or an ORCID account may upload his/her productions to Zenodo), one has to pay the greatest attention to the provenance and to the scientific relevance of the works shared through this repository. In this context, the reverse link pointing from Zenodo to the Query-Store query-entry (see. Figure  5 ) gives users quality and provenance assurance on the shared datasets: the reverse link states that the data come from a well-known and documented database.
The Query-Store impact As we underlined in  (Moreau et al. (2018) ), from the start of the VAMDC project in 2009, one of our goal has been to increase the citation impact of data producers. Indeed we find that the current status of citing spectroscopic data is to cite the database. It should be stressed that atomic and molecular data require months to be either measured or calculated, and therefore it is a loss of visibility and recognition that only databases be cited in users' papers. We believe that the Query Store coupled to the VAMDC portal now allow this flaw to be overcome, even if additional refinements need to be carried out: on January 2018 we have started the deployement of the Query-Store data citation capabilities in the production environment. Currently these are deployed over a third of the Data-nodes of the VAMDC infrastructure.
Since January 2018 the Query-Store service received ∼2000 queries. From these, ∼180 unique queries have been identified. The link between the Query-Store and Zenodo have been added in May 2018. Since then ∼10 queries received a DOI. This paper is the first peer-reviewed work where the technical details of the VAMDC Query Store are described, whereas  (Moreau et al. (2018) ) is the first article where the atomic/molecular science-aspects linked with the Query Store are discussed: at this point in time we cannot be sufficiently objective for evaluating how the usage of the VAMDC infrastructure has been altered by the implementation of the Query Store. We would like the Query-Store to boost the usage of VAMDC. For that reason, we have recently started collaborating with the main Astronomy and Physics Journal editors so that they may have their paper-submission workflows adapted for interacting directly with the VAMDC Query Store.
Refining the level of autorhip As described in on (FAIR Data (  2018 )) (Recommendation 6), data practitioners should facilitate the inclusion of a wide range of indicators for the assessment of the scientific and technical contributions to data-related activities: provision of data infrastructure ans services should be recognized and rewarded accordingly. In order to be able to measure all the contributions (together with the specific role of each contributor) we are planning to extend the range of metadata to be sent to Zenodo. Indeed Zenodo adopts the DataCite Metadata Schema for the Publication and Citation of Research Data (DataCite Metadata Working Group (2017)). This schema is very rich and contains several optional parameterswe we would like to exploit: we think that the fields "Contributor -Data Collector", "Contributor -Data Curator", "Contributor -Data Manager" are very valuable since, by filling those fields, we may mention and acknowledge with bibliometric credits the work of people involved in VAMDC data-infrastructure maintenance and curation.
Nowadays this technical work is anonymous and mostly invisible for the scientific final users of VAMDC. The VAMDC registry (cf. 2. 1) already contains the names of the scientific and technical maintainers of each data-node, however, there do not exist machine actionable mechanisms for extracting this information from the current version of the registry. We are developing such a service: while registering to Zenodo a query processed by a given data-node, the Query-Store service will extract -directly and on the fly-from the registries the information about the scientific and technical curator of the data-node. The Zenodo fields concerning the "Data Contributors" will be populated accordingly.
par. 4. 1) will also be blocked for the queries belonging to a query cluster; • publish to Zenodo the query cluster he/she is first-authoring. This will assign a DOI to the cluster. ). Each author may add to the clusters the Queries he/she performed (provided the query related data are still present and not deleted from the system, cf. remark 2). Each Query produces a result by extracting data from a specific data source. Each result has a list of references, i. e. the list of all the publication used for compiling the result. The unique identifier of each Cluster is resolvable and the associated landing page will contain all the cluster-associated metadata, together with access to the underlying data (i. e. all the data coming from the extraction performed by the queries composing the cluster).
Acknowledgements We would like to thank the anonymous reviewers for their comments, which helped us in improving the clarity of this article. Support for VAMDC has been provided through the VAMDC and the  SUP@VAMDC  projects funded under the " Combination of Collaborative Projects and Coordination and Support Actions" scheme of the Seventh Framework Program. Call topic:  INFRA-2008-1. 2. 2  and  INFRA-2012 Scientific Data Infrastructure. Grant Agreement numbers:  239108  and  313284. The Query Store was partially funded by the European Project  RDA EU3  (funded under  H2020-EINFRA-2014-2 , project  ID :  653194 ). We acknowledge support from  Paris Astronomical Data Center of Paris Observatory.
Components of the measuring system The main elements of our measuring system are list as follows: Photogrammetry camera with Wi-Fi image transmission, scale bars, encoded and non-coded reference point markers, highperformance laptop, and TRITOP Professional software, as displayed in Fig. 5. The process of measuring with the TRITOP system starts with preparation of a measured object. Non-coded reference points are placed on the object, coded reference points and scale bars are placed in the area to be photographed and the whole scene is photographed from multiple positions and angles. The images are transferred to the PC wirelessly and exact coordinates of all coded and non-coded reference points are computed in the software.
Workflow of the proposed TRITOP software The photogrammetric workflow of our TRITOP software firstly processes the images to identify the 2D coordinates of all reference point markers, then uses the coded reference points in the images to accomplish the reconstruction of all the points by epipolar line geometric constraint, and lastly it calculates 3D coordinates of the points using bundle block adjustment method with relative control. Accordingly, the distances between marker points located on the object can be determined precisely. Fig. 8  shows the workflow of the software. After capturing the images, the software recognizes the reference points applied on the measuring object and locate their image coordinates. Target point matching is the process of locating and uniquely indexing the same image point in all recorded images.
Furthermore, we display in Fig. 11  the 2D camera images and 3D measuring data together in the 3D view. As a matter of further illustration, Fig. 12  displays a small camera for the sensor position of each individual measurement in the 3D view. Thus we have a good overview of the different camera positions from which we measured the tank when it was empty. In this case, a total of about 300 photographs are acquired at approximately 3-5 m away from the point targets depending on the zone being photographed. With prior knowledge of the tank's bottom surface and its complexity, few images are taken severely at approximately 1 m away from the reference points due to the extremely constrained working space.
Experimental results and discussions In this research, a semi-automated photogrammetric modeling approach is applied through the extensive computation platform of TRITOP 2016. The software interactively or automatically detected and referenced the reference points positioned on the tank surface in each captured image, before all of the images were oriented for the solution of points 3D information. The output 3D point information of the whole tank is checked and post-processed for the missing or wrongly marked and referenced points. We aim now at evaluating the overall mechanical response in the elastic regime of the composite tank exposed to loadings. To do so, the tank is tested experimentally under different internal pressure situations; this includes hydrostatic pressure and water-pressurizing or modified hydrostatic pressure.
INTRODUCTION Urban reconstruction techniques have attracted an increasing attention from the scientific community over the last decade. Applications of such techniques include, for instance, urban planning, natural disaster management, or radio-wave propagation (l, i). Various data sources are considered by such algorithms, such as ground imagery, satellite imagery, or pre-existing GIS data. However, despite high acquisition costs, LiDAR point clouds are more accurate and remain a data source commonly used by land surveying offices or civil engineers (s, u). Yet, models generated by current city modeling techniques may be enriched with semantic information, or represented using progressive levels of detail. The CityGML standard (ö, r) considers four coarse-to-fine levels applicable to airborne datasets, from LOD0 to LOD3.
In this paper, we present a pipeline that receives as input an unstructured point set describing a urban environment, and generates as output a LOD2 representation of the scene in compliance with the CityGML standard. More precisely, we aim at providing a faithful reconstruction of buildings with tilted roofs. The representation of superstructures like dormers or chimneys is beyond the scope of this paper. Our main idea is to make use of powerful computational geometry tools to extract the geometric signatures of the observed buildings. We emphasize the accuracy and the scalability of our method, since it is able to process large datasets with millions of points, dense or sparse, in a few minutes.
Classification The first step of our pipeline consists in assigning semantic labels to points of the point cloud. Three labels are considered : ground, vegetation, and building. To this end, we rely on the classification package provided by the CGAL library (r, i). For each point of the input dataset, this method computes multi-scale geometric features such as elevation, planarity or vertical dispersion for instance. Extra features provided along with the dataset, like the number of returns, are also taken into account. Given a ground truth training set, these features are then used to train a classifier. The default choice for the classifier is a random forest, that constructs several decision trees to assign each point to one of the three aforementioned subsets. Interactively labelling the data to get a representative training dataset is a tedious work.
5 m. Once all planes have been extracted, we obtain a set of primitives represented by the planar convex hulls of the different sets of inliers associated to those planes. The second step of our algorithm consists in performing a kinetic propagation of the primitives in the 3D space. Similarly to the work of (u, a), we initialize and process a priority queue to predict and sort intersections between extending primitives. We choose to apply homothetic transformations of ratio (1 + t), where t ≥ 0 represents the current simulation time. Assuming the existence of a bounding box containing all primitives, processing all intersections results in a polyhedral decomposition of the space. However, predicting the intersection time between two planar primitives is a costly operation. For large datasets with millions of points, the simultaneous propagation of thousands of primitives is a very complex and time-consuming operation.
We use a voting scheme, based on the observation that in aerial datasets, points delimit the upper parts of the objects of interest. Let Pi be the set of polyhedrons of the subgraph Gi. For each polyhedron pj ∈ Pi, where j = 1, 2. |Pi|, we initialize a counter cj to 0. All polyhedrons located below (resp. above) any plane inlier decrement (resp. increment) their counters. Let us now consider a vector X with |Pi| binary activation variables : xj = 1 (resp. xj = 0) if the polyhedron pj is labelled as inside (resp. outside) a building to extract. We measure the quality of an output surface using a two-term energy of the form U (X) = D(X) + λV (X) (1) D(X) is a data term that encourages the selection of a polyhedron pj when cj < 0, and its rejection when cj > 0. We have : D(X) = 1 |I| |P i | j=1 dj(xj) (2) where dj(xj) = -cj when xj = 0 cj when xj = 1 (3) and |I| is twice the number of inliers.
EXPERIMENTS Datasets. We tested our algorithm on four datasets, representing various urban landscapes. The covered cities are listed in Table  1. The size is given in millions of points. Qualitative results. We present in Figures  2, 3 , 4 and 4 the models generated by our algorithm for these cities. From a qualitative point of view, we obtain persuasive LOD2 reconstructions of most buildings. The Biberach and Vaihingen datasets contain a lot of gable and hip roofs which are correctly approximated by our algorithm. As for the Portland and San Diego datasets, our technique also succeeds in determining intermediate levels in complex structures, as well as tilted roofs if any. Facades, which are almost completely missing in the input scans, are generally recovered by a single plane in a given direction.
Some of them, however, might be affected by an unwanted zig-zagging effect, reflecting errors in the building contouring procedure. Note that trees could be also reconstructed in 3D by template matching (r, e) to better represent the urban landscapes. Quantitative results. In Figures  2  and 3 , we provide altimetric error maps for each dataset. To generate them, we restrict the input point cloud to elements labelled as rooftops, and compute the one-sided Hausdorff distance from every point to the reconstructed surface. This way, we can evaluate the precision of our method. We compute a raw reconstruction error : the mean  footprints towards one direction or another. Performances. We list in Table  3  the performances of our algorithm for our two biggest datasets, Vaihingen and Portland, in terms of memory peak and running times.
CONCLUSIONS In this paper, we presented a pipeline for automatically reconstructing a urban scene from an airborne LiDAR scan at the level of detail LOD2. We used a kinetic approach, in which a set of predetected line-segments and planar polygons propagate to decompose the 2D and 3D spaces into cells that are labelled and assembled in our final model. Our approach is fast, scalable and delivers simple polyhedral meshes. It returns promising results on various datasets, representing different types of urban environments. In future works, we plan to refine the building contouring algorithm in order to simplify and improve the accuracy of our reconstructed models. We might resort to deep-learning-based methods to this end. Another research path would consist in integrating non-linear primitives to our kinetic scheme to achieve better reconstructions of free-form structures.
The three free parameters of this model (, 2 0 and ) were fitted on the average power and ITPC time-frequency responses of the auditory cortex (see Methods). The model performs remarkably well, explaining 38% of the power variance and 78% of the ITPC variance. We next focussed on ITPC data. Although linear and simple, the model captures key aspects of the auditory cortex response (Figure  4D-E   In order to differentiate neural populations with different dynamics, we fitted the harmonic oscillator model to each sEEG channel (see Figure  5 , Fig. Supp. 9). After thresholding unexplained data (R 2 < 5%, 257 channels survived), a clustering algorithm (k-means, optimal silhouette index at k=3, see Methods) yielded three clusters. Each cluster consists of a set of parameters and an associated topography.
General strategy. Analysis of sEEG data was done following a fixed effect strategy, i. e. considering all patients' electrodes collectively in an average brain. Individual differences of brain morphology and connectivity were therefore neglected. This strategy was chosen as each patient had a unique electrode implantation map, thus rendering group statistics inappropriate. For MEG data, the strategy was to reduce the normalized signal estimated at the source-level (vertex) to a statistics across participants prior to subsequent analyses, typically a t computed from a one-sample t-test against 0 across participants. Most of the analyses were thereafter consistent between sEEG and MEG data, as having a single average brain with multiple electrodes is similar to having a single average brain with multiple vertices.
Data acquisition. Depth electrodes (0. 8 mm, Alcis, Besançon, France) containing 10 to 15 contacts were used to perform the functional stereotactic exploration. Contacts were 2 mm long and spaced from each other by 1. 5 mm. The locations of the electrode implantations were determined solely on clinical grounds. During the recording session, participants laid comfortably in a chair in a sound attenuated room. sEEG signals were recorded at a sampling rate of 1000 Hz using a 256-channels BrainAmp amplifier system (Brain Products GmbH, Munich, Germany) and bandpass filtered between 0. 3 and 500 Hz. A scalp electrode placed in Fz was used as the recording reference.
Time-frequency decomposition. Trial-by-trial time-frequency decomposition was carried out in a range of 100 frequencies, logarithmically ranging from 2 to 150 Hz. Morlet wavelet transform was applied to the data using the MNE-python function time_frequency. tfr_morlet , with parameter n_cycles = 6. From the resulting complex representation, both inter-trial phase coherence (ITPC) and power were extracted as follows: where designates the complex time-frequency representation of source j at trial n over z t,f j,n N, frequency f and time t. Similarly, designates the ITPC of source j at frequency f IT P C j t,f and time t, and its power.
Although not prominent neither in the sEEG dataset nor in the MEG dataset, we added a beta band (12-22 Hz) based on existing literature. Indeed, previous works have shown coupling of the beta band power with the phase of the stimulus envelope at similar paces 31,55. Inter-trial phase coherence fluctuations at 2. 5 Hz. Inter-trial phase coherence (ITPC) was first averaged across frequencies inside each frequency band (delta, theta, alpha, beta, and gamma). For each band we then applied the ABC pipeline (see COMMON ANALYSES ) to estimate responsive sources as well as the duration of significant oscillatory activity. Thresholds for onsets (step 3) were 3. 4 (delta), 4. 2 (theta), 5. 4 (alpha), 6. 3 (beta), 8. 2 (gamma). Thresholds for the bins (step 5) were respectively 3. 2, 3. 7, 3. 8, 3. 4 , 3.
Damped harmonic oscillator model. We fitted the sEEG data to a damped harmonic oscillator model. The model is described by the following linear differential equation: We used a grid search approach to find optimal parameters. For each set of parameters, we simulated 100 epochs of the harmonic oscillator time course with random initial conditions, with F being our auditory stimulus. Although analytically solved for any function F, we used a numerical solution (Euler's method with a time step of 1/40000 s) because our stimulus has no simple functional form. In order to compare model time courses and sEEG data, we apply the same processing steps: band-pass filter, resampling at 1000 Hz, Morlet wavelet transform, extraction of power and ITPC. Goodness-of-fit was then measured as the coefficient of determination (R 2 ) of the linear regression between the model ITPC (or power) and the sEEG data ITPC (or power).