dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2017). We used a widely employed and freely available random forest regressor in the sci-kit learn python toolkit  (Pedregosa et al. , 2011)  with 100 trees and a maximum depth of 20 (preliminary experiments found that we obtain diminishing returns in accuracy above these values). More in-depth details of the implementation are available on the scikit-learn website (http://scikit-learn. org/). The forest was then trained on 6400 simulated datasets, with the remaining 1600 previously unseen feature vectors used for testing. Following the training and testing stages, we used the forest regressor on the ADC time-dependency as measured in vivo (averaged over all animals of each group) to estimate D intra , N branch and L segment for myo-inositol compartment. At this point we should mention that conventional cell analysis tools do not provide a direct estimation of N branch , which is an ad hoc parameter used to control the branching complexity of our cell-graphs.
To facilitate further comparison with real astrocytes as measured by microscopy (see next section), we found it more convenient to generate many cell-graphs corresponding to the set of (N branch , L segment ) values best explaining the data, and then, as for real cells, analyze these cell-graphs using the 3D Sholl analysis provided by the TREES Matlab toolbox to extract the "Branch Order" parameter characterizing the branching complexity  (Cuntz et al. , 2010). Stability of the estimated parameters with respect to experimental noise was evaluated using a Monte Carlo approach (2500 draws of noised datasets). At each Monte Carlo iteration, random Gaussian noise was generated with s. d. comparable to experimental s. d. on ADC (~15%) and added to the reference dataset to generate a new dataset, which was analyzed using the same pipeline.
For all images a 1 airy unit pinhole was setup based on a 510 wavelength emission and the XY resolution and Z-step size was automatically optimized based on the Nyquist theorem. In all cases the white laser power was set to 70%, and the acquisition speed of the laser scan was 600 Hz. Image analysis was performed using Fiji  (Schindelin et al. , 2012)  Vaa3D  (Peng et al. , 2014) , Matlab (MathWorks, USA), and Autoquant X3. 1 (Media Cybernetics, USA). To reduce the workload due to the large size of original images (%4 GB), regions of interest were cropped and each ROI was slice-aligned in the X and Y axis and deconvoluted using Autoquant X3. 1, through the blind adaptive PSF method with ten iterations and low noise level. Then, images were adjusted in contrast using Fiji. Once in Vaa3D, the reconstruction of the astrocytes was made using the Vaa3D-Neuron2 Auto Tracing Based on APP2 plugin; basically, one marker was placed on the soma center of the single astrocyte and the plugin was run setting an auto-thresholding for the background, activating the option to get the radius from 2D and adjusting the length threshold until getting a proper reconstruction, all the other options were settled as default.
Then, the structure was sorted using the "sort neuron" SWC plugin. To label the soma section, a six-face ROI was created around the user-defined volume and the segments inside that volume were labeled as soma, then each file was reviewed to correct wrong and missed segments. As Vaa3D only analyzes the SWC files in voxel units, the SWC were exported to Matlab using the TREES toolbox  (Cuntz et al. , 2010)  and then the model was resized to the real dimensions from the initial acquisition. Finally, information from the model was extracted using L-measure  (Scorcioni et al. , 2008). The approach used to estimate the radius of the confocal reconstructions was to obtain the approximate circle of cross section area labeled in the XY plane based on the image for each marker of the reconstruction.
Acknowledgements This research was supported by the  European Research Council ,  European Union  (grant agreement  336331  - INCELL  project, awarded to J. V. ). M. P. acknowledges support from  EPSRC ,  United Kingdom  grant  EP/N018702/1. The 11. 7 T MRI scanner and the confocal microscope were funded by a grant from  "Investissements d' Avenir -ANR-11-INBS-0011 -NeurATRIS: A Translational Research Infrastructure for Biotherapies in Neurosciences ". Fig. 7. Massive variations of lactate diffusion suggest a shift from predominantly astrocytic to predominantly neuronal compartmentation of lactate in CNTF mouse model of reactive astrocytes, leading to disruption of the astrocyte-to-neuron lactate shuttle.
In the lower panels, the direction of traveling waves was similarly explored (color indicates log ratio, with absolute values below 0. 2 set to 0 for increased readability). When the model was fed the input or the prior, we observed mostly positive or negative values, respectively. (D) Explorations of model behavior as parameter τ D is varied systematically (x-axis, from 20 ms to 200 ms, stride of 5). From top to bottom, the panels represent log ratios, max spectral power values, and temporal frequency of the max spectral value from the 2D FFT. Each value is an average over 20 simulations (with ΔT and τ kept constant at 12 and 20 ms, respectively). The code of the model and the simulation is available at https://github. com/artipago/echoPred. BW, backward; EEG, electroencephalography; FFT, fast-Fourier transform; FW, forward; IRF, impulse response function.
FW traveling waves during visual processing. Our group recently showed that alphaband IRFs can be interpreted as FW traveling waves during visual perception  [10]. We first confirmed this result by applying our wave quantification method to a previously collected  [27]  EEG dataset (INPUT) composed of 20 participants, who fixated a 6. 25-s-long white-noise sequence of random luminance values for about 360 trials (Fig  1A ). For each electrode, we considered the cross-correlation averaged over all trials. We obtained the 2D map and computed the log ratio, as for the model, over 7 midline electrodes (Oz to Fz-see Fig  2A ). Then, in order to estimate the proportion of reliable traveling waves, we computed the null distribution by shuffling the electrodes' order 1,000 times for each subject before recomputing the log ratios.
The difference between the real and the null distributions provides a statistical estimation of the proportion of reliable FW and BW wave events over and beyond what might be expected by chance, as captured by our surrogate distribution. In the INPUT dataset's IRF, the comparison between the real and null distributions confirmed the presence of traveling waves (INPUT: Kolmogorov-Smirnov test, D = 0. 507, p < 0. 001, Fig  4B ). Specifically, 56. 6% of IRF data epochs showed evidence for FW waves over and beyond what could be expected by chance. In addition, the predominance of FW waves was further confirmed by testing the distribution of mean log ratios across participants against zero (Bayes factor [BF] = 32. 4, error = 1. 392% × 10 -4 , Fig  2D ). Once we confirmed the presence of FW traveling waves in the IRF measurements, we explored in the same dataset whether we could also demonstrate traveling waves in the raw EEG signals, as suggested by our model.
Notably, in the simulations, we were able to compute an IRF both with the input and with the top-down prior signals (for human experiments, we only have direct access to the visual input signals, but the internal priors, if any, remain unknown). In all simulations, significant traveling waves were observed both in IRF data (D = 0. 9800, p < 0. 0001;  human EEG dataset, when only the top-down prior signal was present in the model, we observed exclusively BW waves: 79. 3% of the tested 1-s EEG epochs showed evidence for BW waves, as did 100% of the IRF measurements (0% of EEG epochs or IRF measurements suggested FW waves). Conversely, when only the input was provided to the model, only FW waves were observed, in line with the results of the INPUT human EEG dataset: 76. 8% of EEG epochs and 100% of IRF measurements revealed FW waves (with 0% of EEG epochs or IRF measurements supporting BW waves).
Participants The INPUT dataset was composed of EEG recordings from 20 volunteers (aged 23-39 y with a mean age of 28 y, 10 women, 5 left-handed), the CLOSED EYES dataset from 48 volunteers (aged 20-43 y with a mean age of 27. 8 y, 25 women, 7 left-handed). All subjects reported normal or corrected-to-normal vision and no history of epileptic seizures or photosensitivity.
Stimuli and protocols Results from the INPUT dataset have been published elsewhere  [27, 76] , whereas the second, the CLOSED EYES dataset, presents a mixture of published and unpublished data. Regarding the INPUT dataset, the experiment was composed of 2 sessions of 8 experimental blocks of 48 trials each, having a total duration of about 1 h. Participants viewed 6. 25-s-long random (white-noise) luminance sequences presented on a cathode ray monitor, positioned roughly 57 cm from the subject, with a refresh rate of 160 Hz and a resolution of 640 × 480 pixels. Each sequence had a flat power spectrum up to 80 Hz and was designed using MATLAB custom scripts and displayed using the Psychophysics Toolbox  [77]. The stimuli were presented on a black background in a peripheral disk with a diameter of 7˚, whose center was at 7˚of eccentricity above the fixation point.
EEG recording and preprocessing In both datasets, brain activity was recorded using a 64-channel active BioSemi EEG system (1,024 Hz digitizing rate, 3 additional ocular electrodes). The following preprocessing steps were applied to all subjects of the INPUT dataset using the EEGlab toolbox  [78]  in MATLAB. Once the noisy channels had been rejected and interpolated (when necessary), the data were offline down-sampled to 160 Hz to match the presentation rate of luminance stimuli and thus facilitate the cross-correlation of the two signals. A notch filter (47-53 Hz) was then applied to remove power line artifacts. We applied an average-referencing and removed slow drifts by applying a high-pass filter (>1 Hz). In the first dataset (i. e. , INPUT), data epochs were created around each white-noise sequence (from -0.
25 to 6. 5 s) and the baseline activity was subtracted (i. e. , mean activity from -0. 25 s to 0 before trial onset). Finally, the data were screened manually for eye movements, blinks, and muscular artifacts, and whole epochs were rejected as needed: on average, 20/384 trials were rejected per subject. For the second dataset (CLOSED EYES), preprocessing was limited to noisy channels rejection, and a similar filtering process as in the previous dataset was performed: after a notch filter (47-53 Hz) was applied, a high-pass filter (>1 Hz) removed slow drifts. All data are publicly available at https://osf. io/nc4rg/?view_ only=bb06fe996d1f49b285dc25464eec7ac7.
Similarly, we selected and compared 2 sets of 20 trials on the basis of the vertical electrooculography (VEOG) activity. The results (S7 Fig) showed no difference between the two subsets of trials, for either VEOG or HEOG activity, thus ruling out possible confounds due to eye movements (Bayesian paired-sample t test, HEOG: BF = 0. 12; VEOG: BF = 0. 27). BW traveling waves in resting-state EEG data. Our model simulations revealed that predominantly FW waves during stimulus processing are replaced by BW waves in the absence of stimulus. With the purpose of assessing the presence of BW waves in human EEG signals, we thus turned to a second dataset (CLOSED EYES) composed of 48 participants, who underwent a 1-min recording with closed eyes. As for the previous dataset, we estimated the real and null log-ratio distributions after having computed the 2D maps for every 1-s time window (500-ms overlap), over the same line of 7 central electrodes (Oz to Fz).
Eventually, we counted about 130 log-ratio values per subject. Notably, also in this dataset, the comparison between the two distributions unveiled the presence of traveling waves (CLOSED EYES: D = 0. 2047, p < 0. 0001; Fig 4D) revealing, as predicted by our simulations, a preponderance of BW over FW waves (respectively 41. 1% and 8. 8% of the tested 1-s EEG epochs), as confirmed by a Bayesian t test between FW and BW waves (BF = 74. 2, error = 2. 632% × 10 -8 ; see also S2A Fig). All in all, this result corroborates the predictions of our model regarding (1) the possibility to reveal traveling waves from raw EEG signals and (2) the presence of BW waves in the absence of sensory stimulation. Finally, we tested whether the waves occur preferably along the occipito-frontal axis or whether they can be observed in other orientations as well.
Virtual Research Environments (VREs), also known as science gateways or virtual laboratories, assist researchers in data science by integrating tools for data discovery, data retrieval, workflow management and researcher collaboration, often coupled with specific computing infrastructure. Recently, the push for better open data science has led to the creation of a variety of dedicated research infrastructures (RIs) that gather data and provide services to different research communities, all of which can be used independently of any specific VRE. There is therefore a need for generic VREs that can be coupled with the resources of many different RIs simultaneously, easily customised to the needs of specific communities. The resource metadata produced by these RIs rarely all adhere to any one standard or vocabulary however, making it difficult to search and discover resources independently of their providers without some translation into a common framework.
Cross-RI search can be expedited by using mapping services that harvest RI-published metadata to build unified resource catalogues, but the development and operation of such services pose a number of challenges. In this paper, we discuss some of these challenges and look specifically at the VRE4EIC Metadata Portal, which uses X3ML mappings to build a single catalogue for describing data products and other resources provided by multiple RIs. The Metadata Portal was built in accordance to the e-VRE Reference Architecture, a microservice-based architecture for generic modular VREs, and uses the CERIF standard to structure its catalogued metadata. We consider the extent to which it addresses the challenges of cross-RI search, particularly in the environmental and earth science domain, and how it can be further augmented, for example to take advantage of linked vocabularies to provide more intelligent semantic search across multiple domains of discourse.
Many VREs are coupled with certain e-infrastructure to facilitate process scheduling and storage of user data, often making use of e-infrastructures provided specifically for the research community (via initiatives such as EGI  1 or EUDAT 2  ) or public Cloud platforms. Data are brought into the dedicated infrastructure, and are then explored and manipulated via a particular data processing platform or scientific workflow management system  [3]. A difficulty arises where research datasets and services are distributed across multiple e-infrastructures; the recurrent question of whether it is better to move data to where the computation will occur, or to move computation to where the data are (given the ever-increasing size of datasets) means that VREs need to be more flexible as to how and where they connect to different resources.
In particular, overly restric-tive couplings can be seen as contrary to the recent drive towards open science and open data which discourages solutions that force users to move data and services into a closed system rather than directly engage with openlyaccessible data services hosted by RIs. While moving data and computation onto a single controlled platform has advantages-primarily, that the utilisation of resources is simplified and the quality of service made easier to sustain-what we increasingly observe instead is the construction of dedicated RIs that aggregate and curate scientific data (including real-time observations) for a particular research community, and then provide access to these data via unified services  [4]  independently of any particular operational environment. These RIs often provide their own portals to retrieve data, and in some cases provide limited ability to access computational infrastructure for executing processes, but they also provide APIs to allow outside agents to retrieve data and access services, allowing for other VRE systems to potentially make use of their resource offerings.
The lack of conformity of standards and vocabularies in some scientific domains, especially between traditional scientific specialities, makes it difficult however even to retrieve from providers the metadata that describes resources and their appropriate use. Instead, significant software engineering effort is often required on the behalf of data scientists and infrastructure engineers to build specific adaptors for every potential coupling of RI and VRE. Part of the reason it is so important to be able to retrieve resource metadata from different RIs into an integrated environment is to better support interdisciplinary research, which requires the ability to search across different RIs for similar or complementary datasets or services. This entails a complex interaction between a generic VRE and multiple RIs, distributing queries through multiple adaptors and then aggregating the results, or else har-vesting resource metadata in advance from all providers to allow preliminary queries to be conducted on a single catalogue before distributing follow-on queries to specific providers.
Different approaches to managing this interaction balance competing concerns such as liveness, responsiveness, openness and scalability. In this paper, after providing some foundational background (Section 2), we describe a framework for flexible metadata mapping and publication that can expedite the coupling of an enhanced VRE with resources (principally data, but also models, tools, services, etc. ) from different RIs, all using different metadata schemes, to provide cross-RI metadata search and discovery (Section 3). The ability to perform such search and discovery is the basis for any number of other integrative VRE services, including remote service invocation and workflow scheduling and orchestration. We further describe a metadata service that implements this framework, developed in the context of the VRE4EIC project (Section 4).
Metadata standards and technologies Publishing metadata about resources online (indicating for example the type, coverage, provenance and access method for each resource) allows RIs to advertise their datasets and facilities, and allows researchers to browse and discover data and other resources useful to their research. While there exist standards for various kinds of metadata, such as ISO 19115  [12]  and ISO 19139  [7]  for describing geospatial metadata (particularly useful for environmental and earth science), the implementation of such standards by RIs can be somewhat idiosyncratic: certain metadata fields might only be loosely specified for example, leading to differing interpretations of how entries into such fields should be structured; other fields might be overloaded, used to carry extra information not part of the original standard, but useful in the particular context in which the standard is applied.
Harmonisation of protocols, vocabulary and metadata between RIs thus remains a concern, with communities such as ENVRI  6  (for environmental science) and IVOA 7  (for astronomy) working to promote common models for their respective communities. Many of the interest groups in RDA also pertain to harmonisation of metadata or access, including the work on data type registries  [21]  and research data collections  [22]. VREs benefit from the publication of resource metadata as the primary means to discover and access datasets and other resources provided by RIs. From the VRE perspective, the use of standard protocols, metadata schemes and vocabularies on the part of resource providers is clearly a positive, making it far easier to couple with a greater volume and variety of RIs, to their mutual benefit.
For example, the technology view can capture the software and standards used by services defined in the computational view with which a VRE might interact to discover, access or retrieve resources, while the information view can capture information about the kinds of information object (e. g. raw dataset, published dataset with persistent identifier or metadata record) provided by the RI. The most recent release of ENVRI RM is available online  [27]. Aside from informing the architecture of research support environments, models such as the e-VRE reference architecture and ENVRI RM can provide controlled terminology for annotating information in databases and knowledge graphs. Open Information Linking for Environmental RIs (OIL-E)  [28]  provides an OWL specification based on ENVRI RM that acts a machine-actionable vocabulary and upper ontology for RI descriptions  8.
Metadata aggregation within and between RIs Although we have thus far referred to a joint catalogue combining catalogued metadata from multiple RIs as a 'VRE catalogue', it is quite possible for joint catalogues to be produced by the RIs themselves, either internally (in the case of federated RIs) or at a cluster level (often at domain level, e. g. for the marine or atmospheric research domains), on behalf of different clients or stakeholders. Many RIs contribute data to initiatives such as Copernicus  9  and GEOSS 10  , which provide single points of access to certain classes of data. Some RIs also contribute metadata to services such as the EUDAT B2FIND service 11  for dataset discovery. Such catalogues might be used directly by VREs for search and discovery, or treated as another metadata source from which to harvest information for another downstream catalogue.
VRE4EIC Metadata Portal The Metadata Portal provides faceted search over catalogue data harvested from multiple RI resources, all aggregated into a single unified catalogue structured according to the CERIF standard for research information systems. Search is therefore based principally based on the context of research data, directed via associations between datasets, publications, projects, sites, instruments, people, etc. that allow related research assets to be retrieved based on exploration of particular facets. Figure  3  shows an example of such a search, looking specifically for publications produced by a specific individual and related to a specific facility. Similar searches can be made for any basic CERIF entity, relating to datasets, equipment or services for example. This represents a 'typical' search using the Portal, which permits the conjunction (or disjunction) of multiple facets in order to permit more precise queries.
Session state and other data management separate from the actual metadata catalogue itself is managed using the H2 18  relational database management system. The dominant factor in the performance of the Portal is that of the underlying joint catalogue. The CERIF joint metadata catalogue has been implemented in RDF (based on an OWL 2 ontology) hosted within an instance of the open source version of Virtuoso Universal Server 19  behind a RESTful API. Due to the modular architecture of the Portal, any data store that can ingest RDF data and supports SPARQL querying via REST can be usedfor example an earlier version of the portal operated on an instance of the Blazegraph triple store  20. For the current version of the Portal however, Virtuoso was selected due to its scalability, cross-platform flexibility, and the fact that it is capable of combining relational, graph, and document data management with Web application server and Web services platform functionality.