dataesr/scientific-paragraphs-categorization huggingface dataset:
A further challenge hindering the transfer of state-of-the-art models to applications is the discrepancy in the data distribution between widely used benchmark datasets (e. g. , QM9) and actually relevant datasets, which typically contain larger molecules (i. e. , larger graphs), which are more diverse, e. g. , in terms of the chemical elements used and in the size variance, and which sample the chemical or materials space less densely then QM9  262. Generally, there is more research and model development activity for molecules and chemistry, compared to materials science. As a consequence, the transfer of existing GNNs to new application areas (e. g. , porous or amorphous materials) might be challenging and requires further development of GNN models. To some extent, this can be attributed to a lack of generic benchmark datasets for crystalline materials and, even more importantly, (partially) disordered structures and amorphous materials.
OC20 is one of the few examples of datasets covering both materials science and chemistry  102. Nonetheless, there are many promising application areas of GNNs for solid-state materials. In contrast to quantum mechanical simulation methods such as DFT, GNNs are particularly suited for representing disordered systems and predicting their properties, e. g. , systems with compositional disorder such as high-entropy alloys. However, there is a lack of large datasets of disordered systems, particularly labeled datasets with consistent measured or simulated properties. Overall, screening of (hypothetical) materials spaces with predictive ML models promises the rapid discovery of new materials with optimized target properties, especially when combined with transfer and active learning to improve data efficiency and reduce computational cost.
The first steps towards GNN-based generative models exist, but there are many open challenges of reliability and transferability. Despite the high potential of GNN-based algorithms for the inverse design of new molecules and materials, convincing success stories of ML-designed materials are rare. To make generative models more application-relevant, new methods are required that e. g. , allow to include constraints in the design process, in the simplest case symmetries of generated molecules and materials, or in more complex scenarios additional (empirical or analytical) objectives such as synthesizability. A large step in that direction is new representations, not only for organic molecules but also for (3D) materials  [263] [264] [265]. Finally, more research on the explainability and interpretability of GNNs and machine learning in general will help to better understand underlying correlations and eventually causal relations in large and complex datasets, eventually contributing to scientific understanding and progress  [266] [267] [268].
Acknowledgements We acknowledge support by the  Federal Ministry of Education and Research (BMBF)  Grant No. 01DM21001B  ( German-Canadian Materials Acceleration Center ) and Grant No. 01DM21002A  (FLAIM). We would like to thank the  Federal Ministry of Economics and Energy  under Grant No. KK5139001AP0. We acknowledge funding from the  Klaus Tschira Stiftung gGmbH  ( SIMPLAIX  project  1 ).
The results of a systematic review  [24]  show that visual-attentional interventions based on visual perceptual training and action video games significantly improve reading without explicit phonological or spelling instruction. The third type of visual-attentional deficit concerns the processing of elementary visual-attentional processes. For example, a reduction in the visual-attentional span, that is the number of letters processed simultaneously during eye fixation, appears to disturb the identification of letters and could explain a disorder in the identification of written words  [63]. The Maeva© training program has been shown to increase the visual-attentional span, but also improves phonemic awareness and reading of irregular words  [50]. Also within the conceptual framework of an elementary visual-attentional deficit, an imbalance between global and local modes of analysis of a complex visual scene can also alter letter encoding, visual identification of irregular words and stabilization of orthographic representation  [64, 65].
The automatic multisensory integration of a single audiovisual object, "the graphoneme"  [71]  initially appears to activate the left posterior temporal cortical network and then the entire left occipito-temporal-parietal interactive network involved in reading  [3, 72, 73]. GraphoGame©  [74] , a computer-based reading training program, was developed to foster orthography-phonology associations at multiple grain sizes and automatize orthographyphonology integration through the simultaneous and repeated audio-visual presentation of letters, syllables, words, and sentences in a highly playful interface. Developed in Finland the program has been adapted for French  [75]  and other languages  [76] [77] [78] [79] [80]. A meta-analysis found positive effects of intervention using GraphoGame (GG) if the program was accompanied by a tutor  [81].
The French version of GG is broader and more sophisticated than the research versions included in the meta-analysis. A recent validation study of the French version of GG showed effect sizes of around 0. 28 for independent practice with the program  [82]. Other intervention programs using non-linguistic audio-visual stimuli also show effects on reading skills  [83] , but the reasons for the deficit in the audio-visual integration of low-level stimuli are still debated  [84, 85]. To summarize, independent of the theoretical underpinnings, all intervention programs have attempted to reduce the reading deficit: (a) Through interventions on the grapho-phonological and/or orthographic reading processes, by proposing successive training sessions to automatize phonological decoding and lexical recoding  [86] [87] [88]  or by intervening specifically either on the graphophonological process or on the orthographic process  [89] [90] [91] [92] [93] [94] ; (b) Through more specific interventions at the level of an UCD either audio-phonological  [95] [96] [97] , visual  [50, 57, 59-61, 98, 99]  or audio-visual  [83, [100] [101] [102] ; (c) Through combined interventions on reading processes and UCD through programs using "phonics instruction" that combine training of phonological cognitive processes and grapho-phonological conversion procedures  [103, 104]  or through programs that combine training of visual cognitive processes and spelling and reading procedures  [59, [105] [106] [107].
Categorical perception training: RapDys© The intervention is implemented on a computerized device using RapDys© software  [123]. It aims at modifying an allophonic mode of perception, i. e. , an over-discrimination of infraphonemic acoustic differences, into a categorical or phonemic mode of perception. The child learns to discriminate and/or identify increasingly fine acoustic differences between two different phonemes. The Voice Onset Time (VOT) changes from allophonic peaks at +/-75 ms VOT to phonemic peaks at +/-5 ms VOT. [116] , training with PhonopidoW© software  [124]  is proposed. This intervention consists in exercises that require discriminating one sound among a sequence of several sounds, comparing or composing sequences of sounds. Contrary to the categorical perception task, the trained phonemes do not present any frequency variations.
Rapid automatized naming training: naming speed© If the score of the RAN subtest is pathological  [116] , the digital Naming Speed program inspired by the Italian Run the RAN program  [40]  is proposed to the child. This program was created specifically for this study since there is no equivalent in French. This computerized intervention trains children to name more rapidly nonalphanumeric visual stimuli and consequently trains the different cognitive processes involved in reading such as left-to-right visual scanning, visual processing complexity, visual attention, lexical access and phonological planning. Five images of black and white objects from the LEAD lexicon database  [125]  are presented on the screen and randomly repeated on horizontal lines in matrices of 20 to 60 stimuli (see Fig.
Global vs. local analysis training: Switchipido© This program, also computerized, is based on the Switch-ipido© software  [66]  which stimulates the visualattentional switch between global and local levels of information processing. The display of hierarchical stimuli (large stimuli made of small drawings) allows a visual focus on the global shape and mobilizes the visual focus shift between the global and local levels (e. g. , Fig. 5 ). The aim of these exercises is to reinforce the mechanisms of voluntary inhibition of details and the spontaneity in the global processing of complex visual stimuli.
Grapho-phonemic integration training: Grapho-Game© The objective of this program is to reinforce the coupling of the orthographic code with the phonological code by reinforcing the specific cross-modal (i. e. , audio-visual) link between letters and sounds. Grapho-Game© software  [74]  is an audio-visual training program that simultaneously presents auditory and written stimuli for linguistic units of different sizes (phonemes, syllables, rhymes, words and sentences). This software, designed as a serious game, offers the child a game progression that becomes more and more complex. After listening to an oral stimulus, the child must click on the corresponding written presentation chosen from among several written stimuli (see Fig. 6 ). Twenty eight sequences are proposed, and each sequence offers 6 to 12 levels.
Assessment of underlying cognitive processes Categorical perception assessment Allophonic discrimination skills are assessed by syllable identification and discrimination tasks (/də/ and /tə/) using RapDys© software  [123]. The tasks consist in discriminating and identifying increasingly fine acoustic differences between two different phonemes. The Voice Onset Time (VOT) changes from allophonic peaks at +/-75 ms VOT to phonemic peaks at +/-5 ms VOT. Phonological processes assessment For the following tests, Evalec© software calculates a score for precision and for speed  [116]. -The pseudoword repetition task assesses phonological short-term memory and is composed of pseudowords (n = 12) with a simple syllabic structure and pseudowords (n = 12) with a complex syllabic structure, both with 3 to 6 syllables.
-The task of deleting the first syllable of trisyllabic pseudowords (n = 10) assesses the phonological analysis (e. g. , povidu/vidu). The two tasks of deleting the first phoneme of monosyllabic pseudowords (n = 24) assess the phonemic analysis (e. g. , puf/uf and pra/ra). -The color-naming task assesses rapid automatized naming. A matrix of color images (n = 54) and a matrix of written color names (n = 54) are presented in 9 lines of 6 colors in random order. Three color names have a CVC syllabic structure (rouge, jaune, vert) and three have a CCV syllabic structure (bleu, blanc, gris). Visual-attentional processes assessment Visual-attentional span is measured by global and partial letter report tasks with Evadys© software  [117]. In the Global Report condition, the participant must name a sequence of five consonants presented for 200 ms.
Span memory assessment Assessment of the digit span with Evaléo© software  [115] : Repetition of a series of 2 to 7 digits in forward (short-term memory) and backward (working memory) order assesses verbal memory. The number of digits correctly repeated determines the digit span. Assessment of the visual-spatial span  [132] : The Corsi block test consists in reproducing, in the same or reverse order, the sequence in which the clinician points to different cubes. The number of cubes in the sequence gradually increases, thus determining the visual-spatial span.
Data management {19} For each assessment (tests 1 to 6), data are collected in raw scores (number or percentage of errors and completion time in seconds), percentiles or standard deviations. These data are reported on the CRF by each associate investigator, and are checked and centralized on an Excel spreadsheet by the coordinating investigator. In accordance with the Good Clinical Practices of Decree No. 64 of November 30, 2006 (JORF No. 277), at the end of the trial, all documents relating to the protocol are archived for a minimum of 15 years by the principal investigator in a locked room with sufficient guarantees of protection against fire, water damage, light or any malicious acts. Given that all interventions use at least two computerized training programs, treatment fidelity and performance during training will be assessed by analyzing the log files and user data.
Confidentiality {27} For the duration of the study, information and data will be collected and archived anonymously by a numeric code randomly assigned to each participant. Experimental data, collected directly on computer, will be transferred (and deleted from the computer used for the study) to a network secured by an identifier and a password, whose access is limited to the study authors. Experimental data collected in paper format will be entered directly into the observation workbook and will be considered as source data.
Visualization Using Anatomist Our lab has been developing the free and open-source 3D visualization software called Anatomist for about 25 years. It is able to display efficiently a large number of subjects and modalities simultaneously, either superimposed in the same view, or in separate views. It supports all kinds of neuroimaging objects: 3D/4+D volume images, meshes and textures, regions of interest, time series, fiber bundles, or structured compound objects such as cortical sulci, and it reads most common neuroimaging data formats. Objects may be displayed in different ways, and combined to display mixtures of objects.
This virtualization has several advantages and a few drawbacks. It primarily reduced the amount of maintenance and simplified the packaging and installation tasks but also provides a binary compatible distribution for all systems and developers, and achieves better reproducibility of the software on heterogeneous systems. Toolbox developers do not have to bother about portability any longer and do not need to compile their code on many architectures to distribute them. Drawbacks include slight performance overheads (quite limited), and possible issues using 3D hardware for Anatomist rendering. Containers and virtual machines allow the user to use 3D hardware in some hardware/system combinations, but not in every case. In such a situation, software rendering is the last option, but it works well and reasonably fast on today's machines.
VTK is a general visualization library, and is not primarily designed for neuroimaging; however, it has neuroimaging applications, and it supports a few neuroimaging image formats. But getting started with VTK, especially for neuroimaging applications, is more difficult than with Anatomist. Anatomist used to have a VTK plugin, which allowed to mix VTK and Anatomist objects in a "hybrid" rendering engine. As users were not using this plugin, it fell into obsolescence. Mayavi 15  is a scientific data visualization library in Python language. It proposes a user application and is not dedicated to neuroimaging. In fact, its real added value is in displaying many kinds of plots or time series in a nice Python API, where, for 3D rendering, it relies on VTK. 3D Slicer 16 is 3D visualization software developed in C++ language, and is dedicated to medical imaging (although not precisely for brain imaging).
Its focus is on image processing; it features segmentation or registration algorithms, but also provides powerful 3D rendering features. It is also based on the VTK library. It provides a C++ API for extensions, but (in our knowledge) no scripting language API; thus, customization is more laborious than in Python language. The Human Brain Project 17 develops a web-based viewer, 18 which features displaying several species (humans, rats, mouses), each containing multiple atlases (for humans, the MNI ICBM152 template, the MNI Colin27 template, and the BigBrain multiscale 16 https://www. slicer. org/ 17 https://www. humanbrainproject. eu/en/ 18 https://atlases. ebrains. eu/viewer/ atlas) with sets of regions (cortical layers, cortical areas, fiber bundles). The viewer includes a very efficient multiscale view engine, which supports zooming fluently into very large microscopic datasets (used for the BigBrain histological data).
Scientific databases and keywords selection Once the research questions are defined, we identify the scientific databases and search keywords that will be used to select primary studies. This SLR uses three scientific databases to cover a large panel of articles: Web Of Science 3  , ACM Digital Library 4  and Science Direct 5. These three search engines are recommended by  Gusenbauer and Haddaway (2020). They satisfy the reproducibility criteria of the searches, as well as the use of boolean terms in the query. Google Scholar is not used because it does not guarantee good reproducibility  (Gusenbauer and Haddaway, 2020). To query selected scientific databases, a combination of the two main keywords, ontology and machine learning is used. However, the number of obtained articles was too large and all of these studies are not relevant to our review.
Collecting articles In this step, we query the selected scientific databases with the set of defined keywords by adapting our basic query to each scientific database, as presented in the table 1. The first search was carried out at the end of May 2021, and a total of 373 studies were collected to be analyzed. A second search was conducted in February 2022 with the objective of updating our analysis report with all new studies published since the first search. By limiting ourselves to studies published in 2021 and 2022 on the selected scientific databases, we were able to add 70 articles to our initial collection.
Application domains (RQ5) Figure  10  shows that 36% of the papers do not focus on a single application domain. Indeed, the authors have chosen to solve a particular problem by basing their work on either generalist or interchangeable datasets in order to allow the reuse of their work in various application domains. However, the application domain the most encountered is Health (25% of the studies), notably because medical ontologies such as SNOMED or biological ontologies such as GeneOnto are often used in this domain. It is also a domain that has very strong constraints in terms of the explainability of results. However, the use of semantic data and ontological reasoning are quite appropriate for this kind of problematic  (Rubin et al, 2008). The other domains present in this review are much more anecdotal as shown in the figure  10.
Ontology learning The purpose of our review is not to give an exhaustive presentation of the subject, but to present an overview of the main existing techniques in ontology learning. We found 40 studies devoted to ontology learning, i. e. these articles deal with the fact of creating or populating an ontology in an automatic (or semi-automatic) way thanks to various machine learning algorithms  (Maedche and Staab, 2001). The purpose is to create an ontology without a human expert. Human expert is expensive, sometimes a source of error, and is not as fast as a machine, which is problematic for real-time. Ontology learning aims to improve both ontology creation and its reliability. Most papers deal with knowledge acquisition from raw data (text documents, images, numerical data, or even ontology), but some also talk about Knowledge Discovery in Databases (KDD) as defined by  Fayyad et al (1996) , who describes this term as the process of identifying new knowledge extracted from a database.
These first three areas: automatic taxonomy construction, learning of nonhierarchical relations and rule discovery are TBox statements. As seen in paragraph 1, ontology population allows filling the TBox with a base of facts, i. e. concrete individuals, which represents the ABox part of the ontology  (Evert et al, 2019; Markievicz et al, 2015; Kordjamshidi and Moens, 2015; Rubrichi et al, 2013; Getahun and Woldemariyam, 2017; Packer and Embley, 2015; Rath et al, 2009; Ayadi et al, 2019; Craven et al, 2000; Kuang et al, 2018). It is about adding new instances to the concepts of the ontology  (Petasis et al, 2011). The automatic populating of an ontology is an important issue since it allows to quickly use the mechanism of reasoning by deduction on a sometimes heterogeneous database. The terminology part of the ontology represented by the TBox is much less dynamic than the ABox.
In this section, we present informed machine learning using knowledge from ontology to improve the results of machine learning algorithms. At first step of machine learning processus (i. e. training data), semantic feature engineering allows to mix raw data with prior knowledge in several ways according to feature engineering definition  (Duboue, 2020) : feature extraction, feature augmentation, or feature selection, presented in figure  11. Feature augmentation consists in adding new variables (features) found thanks to prior knowledge from the ontology to the dataset  (Sabra et al, 2020; Salguero et al, 2019; Pozveh et al, 2018; Wan and Mak, 2018; Ye et al, 2015b; Pancerz and Lewicki, 2014; Lawrynowicz and Potoniec, 2014; Santos et al, 2012; Rath et al, 2009; Wang et al, 2010 Wang et al, , 2021b;; Akand et al, 2007).
The feature augmentation process does not necessarily require logical reasoning. The semantic structure of the ontology may be sufficient to augment the initial dataset with information that was not present initially. The following two techniques can sometimes be useful when faced with the curse of dimensionality  (Bellman, 1961). Feature selection aims at reducing the number of variables to keep only the most relevant without changing the initial variables  (Gomathi and Karlekar, 2019; Mendez et al, 2019). On the contrary, Feature extraction change the initial variables using the prior knowledge of the ontology for get relevant features  (Kumar et al, 2020; Radovanovic et al, 2019; Evert et al, 2019; Agarwal et al, 2015; Radinsky et al, 2012; Greenbaum et al, 2019; Liu et al, 2021; Rinaldi et al, 2021; Castillo et al, 2008; Yilmaz, 2017; Hsieh et al, 2013; Rajput and Haider, 2011; Manuja and Garg, 2015; Ahani et al, 2021; Akila et al, 2021; Deepak et al, 2022; Messaoudi et al, 2021; Nayak et al, 2021; Pérez-Pérez et al, 2021; Zhao et al, 2021; Zhou et al, 2021).
Programs and initiatives aiming to protect biodiversity and ecosystems have increased over the last decades in response to their decline. Most of these are based on monitoring data to quantitatively describe trends in biodiversity and ecosystems. The estimation of such trends, at large scales, requires the integration of numerous data from multiple monitoring sites. However, due to the high heterogeneity of data formats and the resulting lack of interoperability, the data integration remains sparsely used and synthetic analyses are often limited to a restricted part of the data available. Here we propose a workflow, comprising four main steps, from data gathering to quality control, to better integrate ecological monitoring data and to create a synthetic dataset that will make it possible to analyse larger sets of monitoring data, including unpublished data.