dataesr/scientific-paragraphs-categorization huggingface dataset:
To perform the segmentation, the tractography datasets are resampled with 21 equidistant points, since it is a sufficient number to perform an analysis of the similarity between fibers, as used in others works  (Guevara et al. , 2011b (Guevara et al. , , 2012)). Before the calculation, the tractographies are transformed to the MNI space. This algorithm receives as an input the tractography of a subject, resampled with 21 equidistant points in MNI space, the fused bundle atlas, and the distance thresholds to be used for each bundle, defined for each original atlas  (Guevara et al. , 2012 (Guevara et al. , , 2017a;; Román et al. , 2017). It returns the segmented bundles for each subject, according to the labeling of the atlas bundles.
Step 2: Extraction of Meshes and Labels This step aims to obtain the meshes of the cortical surface and the labels of the anatomical regions given by the Desikan-Killiany atlas, as shown in Figure  1C. First, FreeSurfer software  (Fischl, 2012)  is employed to calculate the cortical surfaces for each subject. By using this software, a direct correspondence between the cortical surface mesh of the subjects is obtained, since the number of vertices is the same for all of them, changing only their 3D coordinates in the mesh according to the individual morphology. For the labeling of the cortical surface, FreeSurfer uses the Desikan-Killiany (DK) atlas, which consists of 35 regions per hemisphere  (Desikan et al. , 2006). Each region in the atlas (see Table  S3 ) has associated a label (integer number).
The labeling therefore consists of assigning each vertex of the mesh to the label of the region that it corresponds to. Next, BrainVISA software  (Cointepas et al. , 2010)  was used to apply the pipeline that converts the formats and transforms the mesh to the subject's T1 space. It provides the mesh file with 81,924 vertices per subject and a file with the vertex labels. This step receives as input the NIFTI T1 image of each subject. The output is the cortical mesh and the labels according to the DK atlas, associated with each subject.
Sub-step 3: Fiber alignment The fibers on a whole-brain tractography dataset have different orientations and are stored in the direction they were tracked. Hence, on average, half of the fibers are stored in the inverse direction. In these cases, the fiber points are swapped to align the fibers according to the atlas bundles, using the bundle centroids (see Figure  2C ). Finally, the filtered fibers for each bundle are obtained (see Figure  2D ).
RESULTS We implemented Steps 2 and 4 in C++11, which are also parallelized with OpenMP. The rest of the steps were performed in the Python programming language version 3. 6. We executed our experiments on a computer with a 12-core Intel Core i7-8700K CPU 3. 70GHz, 12MB of shared L3 cache, and 32GB of RAM, using Ubuntu 18. 04. 2 LTS with kernel 4. 15. 0-51 (64 bits). For the tests, we used the tractography datasets of the 79 subjects of the ARCHI database. We used the same database for the atlas creation and the parcellation creation to guarantee the best results. The methods are publicly available at https://github. com/ andvazva/Parcellation. Table  1  shows 20 parcellations (atlases) generated with the proposed method, resulting from different sets of parameters, with varying density center thresholds (dc_thr = 0.
10, 0. 15, 0. 20, 0. 25, 0. 30) and intersection of density center thresholds (idc_thr = 0. 10, 0. 20, 0. 30, 0. 40), and a fixed value of size_thr = 0. 10. The atlases are identified by a number. The table also lists the number of sub-parcels in the left hemisphere (# SP lh) and right hemisphere (# SP rh) obtained for each atlas. As dc_thr and idc_thr increase, the number of sub-parcels in both hemispheres increase, as more restrictive values are used to define a significant sub-parcel intersection, leading to an inferior number of merges. A first observation is the asymmetry in the number of subparcels between both hemispheres, with more sub-parcels in the left hemisphere for all the parcellations. A similar result was found in the parcellation created by  Lefranc et al. (2016) , based on the same database, with 126 parcels for the left hemisphere and 113 parcels for the right hemisphere.
First, the tractography of each subject is intersected with the subject's cortical mesh, using the generated cortical parcellation. Then, a square connectivity matrix n * n is created, where n is the total number of sub-parcels in the parcellation. The matrix contains a 1 where there exists a connection between the pair of corresponding sub-parcels and zero in other case. The Dice coefficient measures the similarity between two sets (Equation  2 ). DSC = 2|A ∩ B| |A| + |B| (2) where |A| and |B| are the number of elements of sets A and B, respectively, and DSC is the Dice coefficient. DSC ranges between 0 and 1; the closer to 1, the greater the similarity between the two sets. To compute the Dice coefficient between two connectivity matrices, the bctpy Python library (https://github. com/aestrivex/ bctpy) was used, which is an adaptation of the Matlab Brain Connectivity Toolbox for Python  (Rubinov and Sporns, 2010).
In this case, Dice's coefficient will evaluate the degree of overlap between a pair of parcels, ranging from 0, for a total dissimilarity, to 1, for a complete similarity. Moreover, to identify the sub-parcels in atlas 5 and atlas 13, we named them based on the anatomical regions connected by these sub-parcels, based on Desikan-Killiany atlas. For example, the parcel lh_RMF-CMF-SF_0 connects RMF with CMF and SF regions. The number (_0) denotes the index of the sub-parcel, which depends on the number of sub-parcels in an atlas with the same connections. Prefixes lh or rh refer to the left or right hemisphere. For more details, Table  S3  lists the names of anatomical regions of the Desikan-Killiany atlas. To perform the tests, both atlases, atlas 5 and atlas 13, were applied to one subject in MNI space as a representative subject (Subject 001 from the ARCHI database).
The same subject was used for all tests. Any other subject transformed to MNI space could be used, because there is a correspondence between the mesh triangles of all the subjects, which have the same triangle indexes. Tables 2, 3 contain the results of the comparison between atlas 5 and atlas 13 with the other atlases. For each state-ofthe-art parcellation, the total number of parcels or networks that it contains, as well as the number of parcels similar to atlas 5 and atlas 13, between different ranges of Dice coefficient, are indicated. In the table, the minimum Dice value considered is 0. 5, while similarity values superior to 0. 9 were not found. The Destrieux atlas was generated using 12 datasets and algorithms that classified each vertex in a computer-assisted manual manner and divided the cerebral cortex into 75 parcels per hemisphere, giving a total of 150 parcels.
In the right hemisphere, the parcels G_cuneus and G&S_subcentral of Destrieux are similar to the sub-parcels rh_Cu-Li_0 and rh_PoC-SM_0 of atlas 13, and are located in the occipital (cuneus) and parietal (subcentral gyrus) lobes, respectively. With respect to the comparison with Lefranc, this parcellation has our method in common, which was based on the same database (ARCHI) and uses the regions of Desikan-Killiany atlas as an input. Furthermore, the method uses the whole dMRI tractography as an input. The Lefranc algorithm compresses the connectivity profiles of each gyrus, taking into account the intersubject variability, and considering inter-subject high-density connectivity areas extracted using a surface-based watershed algorithm. Finally, it applies a clustering algorithm over the reduced connectivity profiles to obtain a group-wise parcellation, which consists of 239 parcels (126 in the left hemisphere and 113 in the right hemisphere).
The other columns detail the total number of parcels in common for each Dice interval. This coefficient ranges between 0 and 1, being 1 the biggest similarity achieved. 80 subjects of the Human Connectome Project (HCP) database and contains 210 cortical parcels (105 per hemisphere) and 36 subcortical parcels. This atlas has atlas 5 and atlas 13 which uses dMRI, in common, but employs probabilistic rather than deterministic tractography. Furthermore, both methods use the Desikan-Killiany atlas as input information. Atlas 5 and Brainnetome have 62 parcels in common. We can highlight parcel A9_46d_L (left hemisphere) located in the frontal lobe (middle frontal gyrus), which is linked to the atlas 5 sub-parcel called lh_RMF-CMF-SF_0, and is related to inhibition, social cognition, and word generation.
Finally, parcel A8dl_L corresponds to sub-parcel lh_CMF-Op_0 of atlas 13, is located in the frontal lobe (superior frontal gyrus), and is related to emotion, cognition, and memory. Glasser atlas is also based on a multimodal approach. This parcellation is based on functional connectivity (resting state), microstructural architecture, functional specialization (task-fMRI), and topography information. In addition, it uses data from 449 subjects of the HCP database and generates a final parcellation which consists of 360 parcels (180 per hemisphere). We found 40 parcels in common with atlas 5. In the left hemisphere, sub-parcel lh_Or-LOF-Ins-LO_0 of atlas 5 corresponds to parcel L_a47r_ROI located in the frontal lobe (Orbital and Polar Frontal Cortex) of Glasser and is linked to relational-match, gambling, working memory, language (story and math), and face-shape recognition.
The former has the functionalities of violence, social, and emotional concepts, while the latter is associated with mental, emotional, and violent concepts. Schaefer is a parcellation based on resting-state fMRI from a database of 1,489 subjects, which uses a gradient-weighted Markov Random Field (gwMRF) model to generate the final parcellations, with 100, 200, 400, 600, 800, and 1,000 parcels. To compare the Schaefer atlas with our results, we chose the parcellations of 100 and 200 parcels because they are the most similar in number of parcels to atlas 5 (160 sub-parcels) and atlas 13 (185 sub-parcels). Starting with Schaefer parcellation with 100 parcels, we found 37 parcels in common with atlas 5. In the left hemisphere, parcel DefaultB_IPL_1 of Schaefer is similar to sub-parcel lh_IP-IT-MT_0 of our atlas and is located in the temporal lobe, while in the right hemisphere, parcel ContB_IPL_1 is equivalent to sub-parcel rh_SM-PrC-SP_0 and is located in the frontal lobe.
This result is interesting since the proposed method is based on the same database than the Lefranc atlas, but with a totally different approach. Despite the different number of parcels, two other parcellations have more than 30% of similarity with our atlases, which are Brainnetome and Schaefer with 100 parcels. Of course, we are not considering a perfect match between the parcels. Further analyses could be performed, in particular, to compare the different state-of-the-art parcellations, but are out of the scope for this study. A limitation of the method may be the use of the fused fiber atlas, composed of SWM and DWM bundles, to generate the input data, instead of using the whole-brain tractography. However, all the diffusion-based methods, at some stage, perform a filtering of the data, since it is necessary to keep only the reproducible regions or connections.
For that reason, we have created a code repository at (https://github. com/andvazva/Parcellation) with the all the codes and files necessary to apply all the processing steps, including the fused bundle atlas, and the segmentation, intersection and parcellation codes, among others. Furthermore, the resulting data will be available to the public in a data repository. On the other hand, the use of the fused fiber bundle atlas is an advantage, since it allows for a direct correspondence between subjects, avoiding the search for such correspondence at the end of the process by employing clustering algorithms. Another positive aspect is the low execution time, where the segmentation algorithm is capable of segmenting a subject of 1,500,000 fibers in <20 s and the cortical parcellation algorithm performs the subdivision of the anatomical DK parcels in ∼10 min.
Adapted parcellations should lead to increased consistency in structural connectome across subjects. Moreover, other improvements could be implemented in future works. For example, a new atlas bundle could be used, based on a larger database, like the HCP database and probabilistic tractography. The bundles could also be obtained from an inter-subject fiber clustering from the same database, which could lead to a better representation of WM bundle connections of the population of subjects. However, the postprocessing of candidate sub-parcels would probably be more complicated due to a larger amount of bundles and a higher sub-parcel overlapping. Functional information could also be used to create a parcellation by using multimodal parcellation frameworks  (Parisot et al. , 2017). Furthermore, another line to explore is the inclusion of some atlas bundles based on known functional areas.
To be compliant with these directives, data must be permanently and freely accessible on-line, allowing data discovery, visualization and downloading. Open data is expected to enhance new connections between datasets, data mining, and easier use in models, to foster environmental data science  (Gibert et al. 2018) , meta-analyses or comparison between sites. In the environmental science community, the challenge of data sharing and interoperability has led to the development of cyberinfrastructures like, in the USA, the Hydrologic Information System for hydrological observatories managed by the Consortium of Universities for the Advancement of Hydrologic Science (CUAHSI) 2 consortium  (Horsburgh et al. 2009 (Horsburgh et al. , 2011)) , EarthChem system  (Lehnert et al. 2010)  for geochemical data and CZOData  (Zaslavsky et al.
Indeed, they sometimes remain reluctant to provide their datasets, putting forward different reasons, such as the lack of technical skills or human resources to check the quality of their data and to provide open data. They also argue that collecting and validating data requires time. Therefore, they want to publish prior to sharing their datasets or they request embargo periods  (Blume et al. 2017). They often blame the lack of traceability of open data, as they do not know who uses their data and for what purpose. They also blame the possible lack of acknowledgement of their work  (Tenopir et al. 2011 , Desai 2016). Allen and Berghuijs (2018)  and  Blume et al. (2018)  discuss incentives for data sharing so that the time spent to document datasets and publish them can be credited to the researchers and contribute to their career, as proposed in the San Francisco Declaration on Research Assessment  (DORA, 2013)  or in the Leiden Manifesto recommendation  (Hicks et al.
, 2015). Open data also raises practical questions about the definition of a dataset, its granularity, its documentation, the juridical status of data  (Becard et al. , 2016)  and technical issues about interoperability between systems often developed independently, the availability of the required expertise for cyberinfrastructure design and maintenance, and of course associated costs. The initiative presented in this paper builds on existing national and international initiatives in terms of data sharing. This paper presents the methodology and solutions retained to build a common information system (IS) gathering in situ data from all the French Critical Zone Observatories of the OZCAR research infrastructure (RI) (Observatoires de la Zone Critique -Applications et Recherches / Critical Zone Observatories -Applications and Research)  (Gaillardet et al.
fr/en A C C E P T E D M A N U S C R I P T users' needs. The paper focuses on the implementation of data discovery for sensor time series, the part of the IS that is already operational. Section 2 describes how the future users' community was consulted and how this information was used to design the Theia/OZCAR IS and the web interface. Section 3 presents the IS architecture and technical solutions to implement data discovery. The advantages and limits of the chosen solution are discussed in Section 4, as well as future steps to implement data downloading and interoperability services. 2 Principles and methodology to build the Theia/OZCAR information system.
OZCAR critical zone observatories research infrastructure and the Theia data pole To be consistent with the European Strategy Forum for Research Infrastructure (ESFRI), national research infrastructures were labelled at the national level. In France, all the observatories studying the critical zone were gathered within the OZCAR-RI that brings together more than 60 research observation sites, organized in 21 pre-existing observatories (see Table  A1  in the Appendix). The ADES 5 French operational portal that provides data about groundwater level and quality is also part of OZCAR-RI. The observatories are operated by diverse research institutions and universities. They were initially created to answer specific environmental questions relevant to society and are promoting long-term observation.
One of the criteria for labelling and funding was that data had to be publicly available. Thus, the various communities involved in OZCAR-RI had already made a substantial effort to structure their data and share common formats (e. g. de Dreuzy et al. , 2006  for groundwater in situ and simulation data). They   A1 ), and examples that are more detailed in de  Dreuzy et al. 2006 , Branger et al. 2014 , Fovet et al. 2018 , Galle et al. 2018 , Molénat et al. 2018 , Pierret et al. 2018. However, these efforts have generally been conducted independently. This has led to a very heterogeneous situation, with different levels of development and maturity of the systems and a general lack of visibility of data from the entire OZCAR-RI community. One ambition of the Theia/OZCAR IS was to improve data accessibility and interoperability by making OZCAR-RI data FAIR.
This meant making the data visible, findable and easy to explore; allowing their preservation and their citation; favouring their reuse and sharing; and being interoperable with European infrastructures by adopting international standards (controlled vocabularies and OGC compliant web services). For this purpose, it was decided to build a common data/metadata IS, relying on the experience of the scientific and IT teams involved in the network. In addition, OZCAR-RI belongs to other national and European initiatives, such as Data Terra RI in France and European Long Term Ecosystems Research -Research Infrastructure (eLTER-RI) which has been on the (ESFRI) roadmap since 2018. The Theia/OZCAR IS will have to be interoperable with the IS developed by these infrastructures, which means that technical and architecture choices must be compatible.
In France, the Theia/OZCAR IS project team is involved in working groups of the Data Terra RI that aims to harmonize practices between the four French data poles in different thematic areas: atmosphere, ocean, deep surface, land surface, and with the biodiversity pole. The Theia land surfaces data pole is a French national inter-agency organization designed to foster the use of in situ and remote sensing observations of land surfaces. Theia offers scientific communities and public policy actors a wide range of data, methods and A C C E P T E D M A N U S C R I P T services. So far, Theia has focused mainly on access to remote sensing products, but it also aims at providing access to in situ data, i. e. data collected in the field either in longterm observatories, either through campaigns or through experiments.
Given the complexity and richness of the long-term in situ data collected in the OZCAR-RI, it was decided to begin the building of the in situ Theia Information System with the data from the OZCAR RI and to focus first on time series. In the future, this IS will be extended to other types of in situ data, to short term experiments on land surface, and to in situ data used to calibrate satellite products. The methods to analyse current practices and design the Theia/OZCAR IS for in situ data are detailed in the next sections.
French observatories At the beginning of the project, the project team performed a "Tour de France" of the various observatories and data centres providing data within OZCAR-RI. The aim was to present the project, to elicit current practices and identify technical skills for the management and dissemination of data from the observatories. The objective was also to collect the users' needs, but also the reticence towards the new IS. Eight meetings were organized. Seven were face-to-face meetings in the towns where several observatories were present, and the last one was a videoconference with all the observatories not met before. Each meeting gathered between 10 to 20 persons, including the five persons leading the project. During each meeting, the Theia/OZCAR IS team presented the project.
But some observations were collected on more complex geometries like curves or surfaces (e. g. glacier mass balance is computed using drifting sensors providing data on a trajectory). Other types of data were also identified. This included vector and raster maps (land use, land cover, digital elevation model and soil maps); ex-situ data with observations made on samples (e. g. soil cores, soil or water samples) and 2D profiles A C C E P T E D M A N U S C R I P T describing deep soil structure using different geophysical techniques. Some observatories were producing spatialized products derived from a combination of several data sources, such as: interpolated rainfall fields from raingauges  (Vischel et al. 2011 ) and reanalyses combining radar rainfall and raingauges  (Boudevillain et al. 2016) ; remote sensing products such as soil moisture fields  (Pellarin et al.
2009) ; and model results. Additional types of data included datasets describing agricultural practices, such as application of fertilizers or pesticides, surveys, interviews, photos or videos. • Different levels of data completeness, quality and processing were provided: metadata only versus possible downloading of the data; raw data versus corrected data; or, sometimes, more elaborate products including simulation results or gap-filled data. • Access to the data could be open or granted through provision of a login/password. Some observatories were also imposing an embargo on the data (2-3 years) so that data producers would have time to publish and analyse their data before making them public. Some data were not accessible at all, generally because the data portals were still under construction (Table  A1 , Appendix), or because data were related to people or sensitive information (e.
g. contamination by pesticides). A few observatories were applying a licence on their data, but most of them were providing a data policy that defines how to acknowledge the data producers. • Data formats were heterogeneous amongst observatories, even for similar variables and sensors. • The granularity of the datasets was also very variable, depending on the original objectives of the observatory. Indeed, a dataset could comprise a single station, a • To give access to data, half of the observatories had developed IS based on relational databases, and web interfaces for searching, documenting and downloading the data. The web interfaces sometimes included map interfaces and visualization tools, while other observatories were providing access to files either directly on the web or through an ftp site.