dataesr/scientific-paragraphs-categorization huggingface dataset:
Of note, the "none-region" CGIs show a bimodal distribution and those in promoter regions are mainly unmethylated. This observation suggests the existence of an alternative mechanism to protect CGIs from DNA methylation. Further studies should determine whether this protection mechanism relies on H3K4me3-independent transcriptional activity and/or on H3K4me3/H3K27me3independent poised configuration at these CGI/promoters. In a proof-of-concept study, conducted using data from eight solid tumor types, we confirmed that aberrant cancer-associated DNA hypermethylation targets mainly CGIs that carry a bivalent signature in hESCs. We also found that aberrant DNA hypermethylation affects specifically CGIs belonging to cluster 2. Consistently, we show that promoter hypermethylation of HOX genes, which are a paradigm of bivalent region-associated genes  [9] , could constitute a pan-cancer signature.
INTRODUCTION Combining data from various sensors is a wide but promising topic. Next to additional computations implied because of heterogeneous data handling, it enables to overcome the weaknesses of a kind of device thanks to the strengths of another one. The burning issue while considering data combination deals with the solution given to the registration of heterogeneous data. Even if data combination is common practice in fields such as remote sensing or robotics, only few references in the literature report on the creation of building models based on various datasets. Most of the research works dealing with indoor modeling or more recently with as-built BIM (Building Information Modeling) creation make use of terrestrial laser scanners (TLS) to collect datasets in form of point clouds.
Many approaches deal with the modeling of indoor spaces and their characteristics like for instance  (Xiong et al. , 2013). Barazzetti et al. (2015)  study the construction of parametric objects based on point clouds for BIM completion. Considering more specifically the detection of windows, this can be made from terrestrial  (Tuttas and Stilla, 2011)  but also from sparse aerial datasets  (Tuttas and Stilla, 2012). While for some projects such a model with opening locations may suffice, this is not the case for more complex analyzes concerning specific features of architecture. For the sake of completeness, further works are dedicated to addition of even more details into the model. Indeed, a good knowledge of the moldings geometry can be useful for actors such as architects or archaeologists in an as-built BIM or HBIM approach.
Multiple data integration Numerous examples of combination of data obtained from several sensors arise from cultural heritage field. In lots of projects, a geometry previously acquired by laser scanner is completed by photographs which bring a texture to the model  (Lerma et al. , 2015). Also lasergrammetric and photogrammetric 3D datasets are often combined in these approaches. For 3D building model reconstruction, aerial or terrestrial laser scanner acquisitions can also be completed with photographs to improve the reconstruction process  (Boehm et al. , 2007). In the works of  Vosselman (2002) , the knowledge of ground plans is additionally used. But this is rather adapted to city scale where the considered areas are larger than only one building. It is worth noting that the literature dealing with the use of low cost sensor data to complete detailed building models is rather poor.
Sensors To gather large scale information about the geometry and the volumetric aspect of the room, a laser scanner from FARO is used. The low cost device that has been chosen to complete the previous dataset is a Kinect for Windows v2 from Microsoft. Advantages of this sensor such as its low price and its capability of acquiring point clouds of small scenes in real-time can be mentioned. Moreover, an adapted calibration of this active sensor as well as quality assessment issues for 3D modeling of objects have already been investigated in  (Lachat et al. , 2015). Specifications about measurement principle and performance parameters of both sensors are listed in Table  1.
Pre-location of concerned areas Before the registration of both datasets, the concerned areas need to be segmented into the TLS point cloud. A first segmentation into rooms and walls can be performed, for instance with an algorithm as proposed by  (Macher et al. , 2015). Once walls are known, an approach would consist on using radiometric information provided by the scanner camera (on board) in order to detect elements such as windows and doors. These elements can then be extracted based on histogram thresholding. The result obtained after this process is shown for 3 windows on Figure  6. Because of artifacts such as window awnings, a manual segmentation of the obtained result is often still necessary in order to keep only the window frame or the door jamb.
Kinect data have been acquired on a spatially limited area of the frames, thus the recovery between point clouds to register is low. This can lead to a non-robust data registration, which has been handled through a manual intervention in this paper. The 3DVEM software developed by the research group GIFLE of the University of Valencia can be used while working with heterogeneous data  (Lerma et al. , 2015). This academic solution first requires a user input to add the coordinates of common points in both datasets. However, due to the poor recovery between both types of data used in this paper, a user intervention is required between coarse and fine registration. This is not possible during registration with 3DVEM, that is why the free software CloudCompare has been used. It allows the user to interact not only on the coarse registration requiring at least 4 selected common points, but also on the refinement step based on the ICP (Iterative Closest Point) principle.
Door: Difficulties encountered with door jambs are twofold. Firstly, both sides of the jamb appear in the indoor point cloud, inside and outside the room. However, depending on the scanner location and distance towards the door, points are often missing between both jamb sides. The door wing also contributes to a lack of points on this intermediate area. Secondly, Kinect acquisitions have been carried out at an intermediate height in the middle of one vertical jamb part, but not on a corner (Figure  7b ). For these reasons, the registration of Kinect point cloud was non-robust here. Because of the lack of recovery and of the linear distribution of common points, performing an ICP produces a destructive effect so that only the manual coarse registration has been achieved. It has been refined with manual rotations of Kinect data to better align the visible edges of both datasets.
Definition of a new reference system To make the upcoming processing easier, registered point clouds need to be transformed in a local reference system. Given that most of windows or doors can be estimated with a plane coplanar to the wall they belong to, the new reference system will be defined following the plane directions. An improved variant of the RANSAC algorithm called MLESAC  (Torr and Zisserman, 2000)  and available in Matlab is applied to estimate a plane within the TLS point cloud of the window frame or door jamb. The angle formed by the normal vector of this plane and the nearest reference axis is then calculated and used to compute a rotation matrix. A transformed point cloud of the frame is finally obtained. The frame directions are aligned with two axes of the new reference system, and the normal vector of its mean plane follows the third axis direction.
Determination of edges: Once all planar primitives of the dense point cloud have been estimated, a research of neighbor planes is performed. For each primitive, the 7 nearest neighbors of each point are searched. The neighbor points belonging to the primitive itself are not kept, whereas the neighbors near the edges and belonging to another primitive enable to link the two involved primitives. Then plane intersections based on the neighborhood knowledge are computed to define the edges. Provided that edges are assumed to be vertical or horizontal for the considered frames, directions of the created lines are corrected to meet this condition (Figure  10 ). Hence all the edges of the frame part where Kinect data has been registered are known.
Exportation of the primitives To obtain a good visual rendering and to make the importation of the resulting model easy in 3D processing software, a mesh in. obj format is created using Matlab. The structure of an. obj file first contains the 3D coordinates of each vertex of the mesh, and then the faces defined by a combination of three indexes of vertices for the triangulation. The vertices available so far are the points coming from edges intersections and mapped on the frame corners. By sorting the vertices with successive indexes and knowing the final number of faces of the mesh, a quick computation enables to define all triangles through their three vertices. The created meshes of door and window have been integrated in a 3D architectural BIM software such as ArchiCAD (Graphisoft). The result of an arbitrary scene containing one door and one window is presented on Figure  13.
CONCLUSION This paper reports on an original combination of two heterogeneous datasets used within a modeling approach of building elements such as door jambs or window frames. Global acquisitions of building structural elements have been performed through a well-known acquisition protocol using a terrestrial laser scanner. To complete them, point clouds of the considered building elements have been acquired thanks to a low cost RGB-D sensor used as a handheld scanner. The idea of saving time during acquisitions performed in parallel and by limiting the TLS scans density has been verified. Based on the TLS point clouds of lower density, the location and the direction of the elements are derived. Through a detailed processing chain, meshes in. obj format are reconstructed. It has been shown that they can easily be integrated into a 3D scene.
These extend from examples such as OpenATLAS 3  , a database system for working with archaeological, historical and spatial data, to Ramses, an annotated corpus of Late Egyptian Texts, via Semantic Topological Notes (SemToNotes 4  ), a topological image annotation and image retrieval system; GINCO 5  , a software platform for the management and distribution of scientific and technical terminologies, and SYNTHESIS 6 , an information system for the administration and promotion of cultural assets. In terms of this category of its "in-kinds" portfolio alone (there are 12 in all) the multifaceted DARIAH community has produced some 70 distinct platforms, services, tools, applications and software environments to diversify and deepen the use of new digital techniques amongst humanities scholars. As can be seen from the above services, there is necessarily a strong and intrinsic bond between the kinds of research arts and humanities communities undertake, and primary collections.
Ensuring data fluidity in the arts and humanities scholarly community Even if it is an obvious point, DARIAH is, as a digital infrastructure, a data-centered one, and can thus be differentiated from more physical infrastructures such as GEANT, EGI or the forthcoming E-RIHS (for cultural heritage analysis equipment). As a consequence all DARIAH activities, from training to the deployment of technical services, are targeted at facilitating the availability and exchange of relevant data for research in the arts and humanities. We have already described a number of consequences of this data-centred strategy for DARIAH in  [Romary, 2011]  and so the intention here is to suggest and outline areas in which DARIAH should be particularly active in the short and mid-term, in order to deliver concrete benefit for and at the service of scholarly communities.
If we now come back to the issue of making the dissemination of scholarly data in the arts and humanities more fluid, we cannot but observe that the main request that scholars express when they produce research data in conjunction with their research projects is to be able to identify a clear setting where such data can be hosted in a trusted way. In a way, scholars' expectations are at odds with what the landscape looks like at present, seen from the point of view of infrastructures. There is indeed a wealth of possible repository solutions. First there are a few European countries that have set up generic solutions for hosting research data in the humanities. Repositories such as Nakala 11 in France or EASY 12 in the Netherlands already offer robust services and have integrated thousands of datasets since their respective launches.
There are even completely generic solutions such as D4Science 13 , used for instance within the Parthenos project, that offer to record any kind of scholarly output from publications or reports to datasets in any scientific domain. On the opposite side of the spectrum, we see that highly specialized hosting possibilities exist in relation to initiatives that intend to serve specific object types or scholarly communities. In this category, we can name services such as MediHal 14 for images (for all scientific fields); infrastructures dedicated to the management and/or hosting of digital editions, such as TexGrid 15 (covering the whole editorial workflow) or Tapas 16 (for TEI documents), or generic infrastructures dedicated to linguistic content such as Ortolang 17 , but which could as well host complete documentary corpora.
One of the essential priorities for DARIAH will be to be able to find the optimal compromise between generic and specific deployments for data repositories, but also to be able to deal with the complexity of polling together hosting services at European level. There is indeed a major challenge in making sure that hosting platforms do not reflect the fragmented national picture in Europe whereby countries will be reluctant to host (and thus to pay for) data that is not issued by scholars from its own research community. We thus need to find way, and probably business models, so that data hosting becomes transparent for DARIAH users while making sure that there is a fair and balanced distribution of costs. As addressed already in [Blanke 2016] linking crowds (scholars) and clouds (host) will be the key to the generalized dissemination of digital content in the humanities.
Towards a DARIAH seal of approval The notion of a DARIAH Seal of Approval 18  could be seen as tending towards being essentially repositories-centric, given that the majority of specifications in this general modus often relate only to data the management of data per se, as requirements to be met, so as to comply with any technical demands of repository services. We think we need to go beyond this necessarily limited operational view and offer instead a set of broader reference features that may apply directly to the data sets and other research outputs themselves, and that will simultaneously integrate required production and provenance values (related to whom has produced it, where and under which conditions it is hosted) but also fundamentally spell out how the data as such could be further and reliably re-used and redeployed in entirely new research contexts.
It is in this spirit that the DARIAH Seal of Approval should be read: designing coherence for and at the service of experimentation. The DARIAH Seal of approval would work in two main directionsto acknowledge that tools, services and software produced by the DARIAH community would meet criteria that allowed for their greatest potential reuse, on the one hand, and certify those primary collections at item level with which researchers wish to work as capable of maximum enrichment and subsequent access. There would be a number of discrete areas, modelled on the current in-kind contribution categories that DARIAH allocates to its member submissions to the central repository of shareable resources. Digital objects are used in a wide variety of different contexts. Therefore quality here relates, not to absolute values but to use values.
Digital edition from a physical textual source in a CHI For many scholars studying primary textual sources, the production of an edition is an incompressible part of the research process. Digital editions reflect and accompany the evolution of this dimension of the research process. Over the past years, the standards for digital scholarly editions have evolved towards a greater inclusion of the documentary basis within the editorial work itself. Many digital editions now comprise the transcription and (fine) annotation of a corpus of documents gathered from one or several libraries or archiveswhich are likely to become part of the edition itself. At each stage of his/her activity uncovering, unlocking, editing and exploiting these sources, the scholar is likely to face the following challenges and questions:  Looking at potential sources, and beyond the kind of searches one could make on a portal such as Europeana, s/he would need to know whether, for the CHI s/he is interested in, there exists a digital catalogue of the collections and items, and whether s/he is allowed to take partake of the totality of any related information for future use or publication;  Once s/he has identified required items in any library or archive, how can s/he be aware of the existence of digital surrogates or whethers/he is allowed to make and publishphotographs or scans;  To what extent is the scan quality made available by the CHIs compatible with scholarly standards? When scan quality has improved noticeably, how can a researcher update his/her edition with regard to the scan quality (which requires the CHI to realize new scans and let the researcher use them according to the same terms as it was the case before), how can s/he negotiate the long-term cooperation with the CHI in this perspective?  How to merge archival, librarian and research contribution to the metadata in a common dataset that makes the contribution of each partner visible and is quotable (question of multi-institutional enrichment, of hosting, of authority, of format compatibility and of the value of rich metadata in term of scholarly recognition);  Last but not least, how much can the source material be actually re-used within an online publication or even a printed object, as is still the case for some scholarly editions.
The underlying concept is not completely new. It has emerged and crystallized in the recent years around the notion of the data journal, instances of which have already appeared in various communities with, for instance, Geoscience Data Journal 25  or Earth System Science Data (ESSD) 26 in geosciences, the Biodiversity Data Journal 27 in biology or generic data journals such as CODATA's Data Science Journal 28. The actual impetus towards providing appropriate environments here has attracted major publishing companies to launch similar initiatives. From high-profile publishers such as Nature Publishing Group with Scientific Data to opportunistic ones such as Hindawi publishing with its Datasets portfolio 29 , not forgetting the profit-making -scholarly‖ association ACS with its Journal of Chemical and Engineering Data 30  , the idea has already passed the stage of superficial interest, as it was still the case in the early2000s or even with forerunners such as the Journal of Astronomical Data (JAD) 31  , which pioneered this approach in 1995.
Despite these examples from other disciplines, there still does not seem to be a similar momentum in the humanities. This means thatwe disposeprecisely nowof a unique opportunity to scope what humanities scholars would need and therefore, how DARIAH as an infrastructure would help establish or facilitate data journals in the humanitiesand contribute to their overall quality. This would not only encompass technical and logistical aspects, but also a critical assessment of the notion of -journal‖ itself; that is, what essentially constitutes the necessary form of publication for arts and humanities scholars who are increasingly sophisticated in their digital approaches to research. What is the content of a 'journal' in this sense and therefore, what apparatus is needed for, say, the tracking of citations relating to a much wider scope of material than bibliographicprogrammes, software, algorithms, methodologies and data itselfhow such ways of describing the impact also of this type of output integrate seamlessly with academic career structures? First, the selection process in scholarly journals that has developed over the years is mainly based upon a post peer-review publication process that has until now prevented many potentially interesting results and studies to be published in the first place (see for instance  [Jones, 2013] ).
Recently, Dirichlet process mixture models have been used as an alternative to regression models  (Dunson et al. , 2008; Bigelow and Dunson, 2009). In this paper we focus on the model known as profile regression and proposed by  Molitor et al. (2010). Profile regression is a Bayesian nonparametric method which assesses the link between potentially collinear variables and a response through cluster membership. This allows to formally take into account the correlation between the variables without the need to create a summary score, giving more flexibility to the inferential process. Profile regression has been used on several applications in environmental and social epidemiology and the R package PReMiuM  (Liverani et al. , 2015)  makes it readily available to any applied researcher. For instance  Molitor et al.
The data were obtained from the environmental research group at Kings College as annual mean for the period 2003-2010 at the Lower Super Output Area geographical level in Greater London (LSOA, 4,767 in Greater London) as part of the TRAFFIC project (http://www. kcl. ac. uk/lsm/research/divisions/aes/research/ ERG/research-projects/traffic/index. aspx). •  Figure  1  shows the map for NO x (left) and IMD score (right) and a clear spatial pattern is visible in both: air pollution concentration increases steadily going from outer to inner London, while IMD shows the highest deprived areas in the northeastern part of London and most of the central southern part. However looking at the maps of each of the six domains highlights a different picture (Figure  2 ): Crime shows the absence of a clear pattern, with scattered areas of high crime (dark grey) next to areas of low crime (light grey); on the other hand income, employment and health/disability are in agreement with the total IMD score, while barriers to housing and services are more pronounced in central London and education shows more deprivation in East London.
Modelling highly correlated covariates with profile regression To include all the domains into the same statistical model we use the profile regression, a model that non-parametrically links a response vector Y to covariate data X through cluster membership. It was proposed by  Molitor et al. (2010)  and it has been implemented in the R package PReMiuM  (Liverani et al. , 2015). Profile regression implements a Bayesian clustering model through a Dirichlet process mixture model. The data D = (Y , X, W ) contain the response Y , covariate X and fixed effects W if they are available. The fixed effects are potentially confounding variables. In our running example the response is the nitrogen oxides, the covariates are the selected six domains of IMD and we do not include fixed effects. For each individual i, for i = 1,.