dataesr/scientific-paragraphs-categorization huggingface dataset:
Extended access will then be improved to a wide range of multi-disciplinary data sets, e. g. , geology, subsurface structures and hydrogeology information from boreholes, geophysical and broadband monitoring data, as well as technological data describing the cause of the man-induced hazards. In relation to the low carbon emission, a part of this project is about improving the CO2 storage. Geological characterization, geophysical data and water table monitoring data in combination with data from in-situ equipment (injection wells, laboratory facilities) shall improve underground CO2 storage. The result of this research on the gas-water-rock interaction will benefit from EPOS and ECCSEL 20  research infrastructures and contribute to develop the next generation of CCUS 21 technologies in an efficient and structured way, and help to enable the low to zero CO2 emissions goals of industry and power generation to combat global climate change.
Challenges The fundamental challenge for a research infrastructure like EPOS is to provide more and richer data, as an ever increasing accuracy is a major trait of the present age. Computations can be increasingly complex and thus, include and produce more detail, and observations/analyses are increasing in resolution on the specimen level. However, this cannot be readily transferred to a complex and inhomogeneous system like the Earth, where observations on properties are comparatively few and unequally distributed, and with the perspective that this will not change in the foreseeable future. Against this background, a first challenge is to increase geographical coverage. This was expected and thus, addressed from the very beginning by including a low cost of entry into the service design, with the intention that a high ratio between added value and costs to participate would encourage potential data providers to join the TCS GIM efforts.
The second challenge is to increase the granularity of the data where this is possible and viable, because the adaptation and preparation of data becomes more complex with increasing granularity, and the required effort increases. In addition, it has to be ensured that the underlying services, and the standards they rely on, are capable of dealing with the higher data granularity. The third challenge in this context is to include new data types by extending the existing data models and services while keeping them fully interoperable and compliant with standards (e. g. diverse geophysical downhole logs in the borehole service). This is the least controversial challenge, as it follows needs defined by the community and is mainly limited by the available workpower/funding. Much legacy data exist, but these are often only available in legacy formats and dispersed across diverse more or less well organized data stores.
Conclusion The TCS GIM data models and services reuse and enhance well-established semantic and technical interoper- TCS GIM addresses the challenge of providing more and increasingly detailed data with a continued development towards new data types and with a high added-value to cost ratio, in order to attract new data providers and in this way increase geographic coverage and data density. Thus, community outreach is a major task for the coming years. With this approach, EPOS TCS GIM and its services further multidisciplinary Earth sciences research, which is essential to maximize the gain in new knowledge and its transformation to value for a sustainable society.
Acknowledgments. The authors would like to acknowledge the following projects and initiatives for financing part of the activities:  EPOS-IP ,  European Commission   H2020 program , Grant agreement ID 676564; EPOS-ERIC (https://www. epos-eu. org/epos-eric)for coordination and management; the national EPOS initiatives and/or internal research projects in  Czech Republic, Denmark, France, Germany, Italy, Ireland, Slovenia, Sweden , and U. K. The authors also wish to thank  François Robida  who regularly took the lead to engage the European geological community in the several EPOS phases.
, aggregated statistical data at national level on production, trade, resources and reserves compiled in the electronic Minerals Yearbook. The corresponding harvesting system for collecting and validating these European mineral resources data is operational, and technical routines are in place during the harvesting phase to ensure a rigorous control of the data quality. As of 31/12/2021, the data collection involves 36 providers from 31 countries. It includes 143,537 mineral occurrences and 58,910 mines. includes, among other assets, the location of individual mineral occurrences and 11 https://gitlab. brgm. fr/brgm/epos/epos-tcs-gim/-/tree/master/Documents/EPOS_GeoSciML_Lite_Profile (last accessed 07/06/2022) mines.
Experimental Setup Complex bipolar impedance data recording of the electrodes during the insertion process was carried out by a system comprising a three-degree-of-freedom actuation system, a saline-filled plastic cochlea model, an impedance meter, and an electrode array attached to the actuation system. The electrode array is Oticon Medical's Evo with 20 platinum-iridium electrodes. The EA has an active length of 24 mm. As shown in Figure  1A , electrodes are individually wired and connected to the 20-pin header socket. Electrodes are evenly spaced but their size reduces from basal to apical electrode, as shown in Figure  1B, C. An actuation system shown in Figure  2  was used for controlled insertion of EA in the ST model. This system has three DoF, where it can be moved horizontally, vertically and rotationally.
EA is connected to the multiplexer that selects a certain EP to act as a DUT in the voltage divider circuit. NI DAQ devices (6211 and 6009) are responsible for all analogue input/output voltage signals and digital control signals to the multiplexer (C 1-3 and C 4-6 ), respectively. These control signals select particular electrodes to make an EP. A custom MATLAB GUI application handles all the data input/output to the DAQ devices and processing to spit out the required features (|Z|, θ, R, and X). These features will further be used for our machine-learning-based prediction of trajectory and position of electrode array during the insertion.
Data Recording The EA is inserted into a 2:1 scaled-up 2D plastic ST model 137 times in three different directions (medial, middle, lateral). The speed of insertion remained constant at 0. 08 mm/s throughout the experiments. The data recording of four electrical features of eight EPs were carried out sequentially during the insertion. We found in our previous study that all four features mentioned above are important and even one pair of electrodes can give a reasonable prediction of electrode trajectory. In this study, we use data recordings of most apical electrode pairs for both offline and online trajectory prediction and correction. For every mm of insertion, there is one sample of each feature (|Z|, θ, R, X). Therefore, there would be n samples of each feature for n mm insertion (|Z| 1 , θ 1 , R 1 , X 1.
Offline Analysis In our previous study, we concentrated only on full insertion prediction and partial insertion prediction using multiple EPs data only in the offline setting. In this study, machine learning algorithms, namely, shallow neural network (SNN)  [34] , support vector machine (SVM)  [35] , k-nearest neighbour (kNN)  [36] , and random forest (RF)  [37] , were used for offline analysis of trajectory and position prediction. Offline prediction entails recording of the data and then analysing it for prediction accuracy. In online (real-time when there are limits to latency) prediction, the trained model is tested in real time and its prediction accuracy is validated. Real-time prediction of trajectory and position of electrode array was carried out using only the most apical EP data recordings.
In one of such research projects, image data were used to design a controller, using this technique to control a quadrotor to navigate a forest trail  [41]. Images were captured from three sides (left, centre, and right) of real-world hiking trails, and a multiclass (three) deep neural network classifier was trained and tested on these images. Real-time feedback controller was designed according to the three classes, for example, when an image is classified as right side of trail, the quadrotor will turn left, and so on. Based on this work, we have implemented our system using time series data acquired from an impedance sensing unit for EA optimized steering during cochlear implantation. A plastic ST perception technique based on machine learning algorithms was developed that bypasses the problem of determining the underlying characteristics of ST.
3. Go Straight, if the array is in the middle. The dataset consists of the features and the labels (x j , s j , b j ), where j represents index of an example in the dataset. The classifier tries to find the relationship between the features and the labels. The feature vector s depends on the state x, so we can say that the classifier indirectly learns the mapping from state to the control. This way, the classifier generates the control input u to the system and it results in a closed-loop feedback system, as shown in Figure  6.
According to [42], ensemble learning is the method in which multiple classifiers are combined to take their weighted vote to make predictions on new data. Each classifier makes its prediction and then votes for the final prediction, as shown in Figure  5. This figure explains one of the ensemble learning processes that is called majority voting ensemble. In this kind of scheme, the final predictor would be the predictor that receives the majority vote from the multiple classifiers. In our setup, we have three classifiers (RF, SNN, and KNN) that achieved the maximum accuracy. Although it is possible to give uneven weights to the individual classifier vote, equal weight is given to each classifier in our system. All the offline classification is carried out using Python 3. 6, but in order to keep a homogeneous system along with actuation and sensing, a machine learning system is developed again in MATLAB.
All the hyperparameters of learning models remain the same, and individual models are trained and tested again to make sure performance is kept the same as Python models. The ensemble majority voting algorithm is also developed in MATLAB. Once we have ensemble-based trained models for both classifier A and B, we set up the system for real-time prediction and correction. Figure  8  shows the control flow graph for real-time prediction and correction of the insertion trajectory. As mentioned above, this control strategy is for the straight path only to keep the electrode array at the centre line of the ST model. For this, there is a need to first predict whether the electrode array is in the straight path or the curved path.
A softmax layer was used to convert the output of the MLP into probabilities of the respective classes. The network was trained for 1000 epochs using an Adam optimizer at a learning rate of 0. 001. For SVM, radial basis function (RBF) kernel function was used to train the data, dynamic time warping (DTW) was used as a distance metric, and five number of neighbours were used for the kNN classifier model. We did not use any weight class metric as our dataset is balanced. For the online prediction of trajectory, data from the most apical electrode pair (EP1) are used to train and test the algorithms first, and then we use the trained model for online prediction with the new collected data during the insertion process. Different length subsequences of EP1 data are used, that is, subsequence data collected during 4 mm, 3 mm, 2 mm, and 1 mm insertion.
For these subsequences, a different set of data is assembled for analysis and only the straight path is considered for prediction and further correction. For example, for 4 mm insertion depth, two subsequences of four time samples each are extracted from each insertion example. These two subsequences will make two separate examples in the new dataset. That means now there would be 137 × 2 (274) examples in this dataset with respective targets (medial, middle, lateral). In the same way, for 3 mm subsequence, there will be three subsequences of three time samples each and each subsequence will make an example in the new dataset (137 × 3 = 411 examples) with respective target label. Moreover, new datasets for 2 mm and 1 mm subsequences have 137 × 4 = 548 and 137 × 8 = 1096 examples, according to the split of the straight path data.
For this, we use the full insertion data and split them into straight path and curved path data, labelling them accordingly. Since now we only have two classes (straight or curved), we term such problem as a binary classification. Different datasets are formed according to the subsequence length, labelled accordingly, and cross-validated on four machine learning models. For example, for 4 mm subsequence there will be two subsequence examples of straight path and three subsequence examples of curved path. That is, there will be five examples in the new dataset out of one example from the original dataset. The examples in the new dataset will be given target labels according to the path (straight or curved). The 4 mm-2C/3S dataset will have 137 × 5 = 685 examples to train and test using the algorithms.
In the same way, new datasets, namely, 3 mm-3S/3C, 2 mm-4S/5C, and 1 mm-8S/10C, have 137 × 6 = 822, 137 × 9 = 1233, and 137 × 18 = 2466 examples, respectively. Table  2  presents the accuracy results for the path prediction during the insertion process using different subsequence length datasets. These results show that as the subsequence length is decreasing, the accuracy of all algorithms also decreases. This is obvious because of the shorted length vector; we have less information available for prediction. However, the decrease is not linear due to increased number of examples on which the model is trained and tested, which makes generalization easier. As shown in Table  2 , again, the highest accuracy of 87. 4% is achieved by SNN on 4 mm subsequences. Overall, SNN also outperformed other machine learning models in terms of accuracy.
To record large datasets, we have to look at the reliability of the electrode array as well, since it is not made for the purpose of inserting hundred and thousands of times to collect large amounts of data. We may overcome this problem either with the availability of a decent amount of electrode arrays or we can apply data augmentation techniques to generate synthetic data  [48]. We have proposed a solution for the trajectory and path/position prediction and correction of an electrode array using bipolar complex impedance measurements. This system may be helpful in predicting insertion failures such as tip foldover (this problem is more pronounced in perimodiolar EAs), EA bucking, and EA translocation. Further experiments need to be performed to demonstrate its applicability to overcome such failures that not only degrade CI postoperative performance but could also lead to internal infections.
Its central NOD domain is one of the original funding members used to define the NACHT domain (Pfam PF05729) common in animal NLRs (the H in the NACHT acronym stands for HET-E)  [10, 16]. The C-terminal domain of HET-E protein, built of hypervariable WD40 repeats recognizes a non-self cue, here polymorphic variants of a host protein termed HET-C, a glycolipid transfer protein universally conserved in eukaryotes that could represent a pathogen effector target  [17]. In such event, the N-terminal HET domain is activated which eventually leads to regulated cell death  [17]. The HET domain (PF06985) is a cell death inducing domain with a remote homology to TIR domains  [14]. Several other fungal cell death inducing incompatibility pathways in Podospora and other species are controlled by NLR proteins  [18, 5].
Yet, apparently only a small fraction of the existing fungal NLRs appear involved in heterokaryon incompatibility and it is proposed that these proteins have more general function in immune defense and establishment of symbiotic interactions in fungi  [19, 5]. Indeed, NLR proteins are abundant in multicellular filamentous fungi (no NLR protein was found in unicellular yeasts). In a recent study, a total of about 36 000 NLR proteins have been found in around 880 strains of over 560 species of fungi with on average 57 NLRs per genome and numerous species displaying hundreds of NLR genes  [14, 5]. In terms of domain annotation fungal NLRs differ from their typical animal and plant counterparts. Unlike more homogenous NLR proteins in animals and plants, the central domain of fungal NLRs can be either of the NACHT  [10]  or the NB-ARC type (PF00931)  [9].
The clustering required mutual coverage of at least 80% of sequence length, and annotations were only assigned to sequences which overlapped at least 50% of the match to the Pfam profile. Moreover, only the matches including at least 50% of the Pfam profile length were retained to avoid excessive number of false positive hits. The procedure led to assigning the Pfam-based annotations to 3003 additional N-termini. This increased the annotation coverage from 69% to 82% (17 902 sequences) in the 20+ clusters, and from 57% to 66% (21 677 sequences) in the entire N-termini dataset. The corpus of fungal NLR N-termini can be broadly divided into four main super-architectures (Fig. 1a ). The first of them consists of the shortest N-termini, up to roughly 50 amino acid in length, which mainly comprise a direct N-terminal extension of the nucleotide binding domain.
1). In addition to domains reported in our previous surveys  [14, 20] , this involved also the Crinkler domain of the Ubiquitin clan only recently included in Pfam  [50, 51, 52, 53, 54]. In our dataset the domain was found in two mycorrhizal species from two different phyla: Serendipitia vermifiera (Basidiomycota) and Rhizophagus irregularis (Mucormycota). The clustering-based search revealed also one new annotation (assigned to 24 sequences), the Sterile Alpha Motif family SAM_Ste50p  [55]. SAM motifs are involved in homologous and heterologous protein-protein interactions  [56]. On the other hand, no cluster matched the previously reported RelA_SpoT  [57] , PKinase  [58] , CHAT  [59]  and TIR  [60, 61]  profiles with sufficiently high hit coverage. TIR is a remote homologue of HET  [14].
On the contrary, the new approach increased the number of the Purine and Uridine Phosphorylase (PUP) superfamily annotations, mostly due to the matches to the purine NUcleoside Permease (NUP) profile  [64]. In addition, 45 sequences from 27 various Pezizomycotina species comprised of about 50 amino-acid long C-terminus of PUP. The fragment forms an intrinsic part of the entire PNP_UDP_1 fold (cf. pdb:6po4B, residues 176-234). Both domain families are common in ascomycetal NLRs (13-14% each) but are virtually (SesB-like) or completely (PUP) missing from basidiomycotal NLRs (Fig. S1 ). The same is true of the HET domain present as N-terminal domain in ascomycetes but not in basidiomycetes. Notably, we found clusters with apparently overlapping HeLo/HeLo-like and HeLo-like/Goodbyelike domain annotations.
The latter situation was found in Basidiomycota and mostly involved sequences annotated as MLKL_NTD according to Conserved Domain Database (CDD)  [65]. Human MLKL is an executioner domain homologous to fungal HeLo and bacterial Bell domains  [32, 40, 20]. Moreover, there were additional basidiomycotal clusters with CDD MLKL_NTD annotation and/or with Pfam HeLo-or Goodbye-like annotations just below the assignment threshold, surmounting to a total of 600 basidiomycotal MLKL-like (BaMLKL) sequences. This makes the superfamily of Goodbye/HeLo/MLKL_NTD-like domains the most frequent in Basidiomycota (nearly 2000 sequences, around 1/4 of all), similarly to Ascomycota (Fig. S1 ). Overall, the three most abundant domain classes, foremost the Goodbye/HeLo/MLKL-like followed by the SesB-like and PUP families, account for 60% of fungal NLR N-termini (Fig.
Relation between HeLo-, Goodbye-and basidiomycotal MLKL-likes To gain more insight in fungal MLKL-likes, we analyzed the largest cluster (OBZ65626, 106 sequences) with the overlapping Goodbye-like and HeLo-like annotations assigned through the HHblits-based procedure. Several sequences in the cluster received also hits from various MLKLrelated Pfam profiles when sequences were searched individually (sequence and domain E-values of 1e -3, Fig. 2a ). Not surprisingly, the multiple sequence alignment of the cluster closely matched (HHpred  [66, 67]  probability above 98%) the sequence of human MLKL executioner domain with an experimentally solved spatial structure (pdb:6vzo  [68] , Fig. 2b ). In fact the MLKL domain is virtually perfectly aligned with the Helo_like_N profile match, while the related SesA profile match is slightly shorter.
Sequences of the 16 motifs were consistent with the grammatical model of BASS. These candidate amyloid-like motifs are presented in Fig. 3a. Eventually, their HMM profiles were used to scan all NLR N-termini at least 10 amino-acids long, comprising also sequences not included in the 127 clusters with 20 or more members (Tab. 3). Not surprisingly, some of the 16 motifs clearly corresponded to the three FASS families: HRAM (NLR13, found in 131 sequences), PP (NLR07, 296), σ (NLR28, 71). The overall recall of 498 hits is twice higher in comparison to the combined Pfam-based approaches (242). Several hits of another two motifs, NLR12 and NLR40, overlapped with the NLR13 (HRAM) matches (Fig. 3b ). Moreover, the HMM scan with a generalized HRAM profile based on HRAM dataset from  [33]  recognized 1/3 of NLR12 and 2/3 of NLR40 motifs at E-value of 0.
S3 ). Two additional motifs with few pairs apparently resemble HRAM2 and HRAM4  [33] , respectively, and one another motif resembles the C-terminal part of the σ family motifs (Fig. S3 ). The fourth largest family of motifs exhibits a distinctive conserved pattern FxGxGxQxxGxGxF, which clearly corresponds to the NLR32 motif in Fig. 3. Since in both searches the motif was found associated uniquely with the PNP_UDP domain, we term it PUASM, or the Pnp_Udp-associated Amyloid Signaling Motif. The NLRs with the PUASM motif proteins are annotated either as NACHT or NACHT WD40. All matched instances of the PUASM motif come from various Pezizomycotina species. Finally, we found one more distinct motif related to PNP_UDP, however only present in four pairs (PF01048_040 in Fig. S3 ). Overall, the ASM differ in type of associated effector domain, either pore-forming (HeLo and HeLo-like for HRAM/NLR13), enzymatic (PNP_UDP for HRAM/NLR12, NLR32 and PF01048_040), or both (PP/NLR07 and σ /NLR28).
1_9_31 is observed at 1616 cm -1. In turns EQB50682. 1_332_35 exhibits this signature at higher wavenumbers (1616 cm -1 ), indicating a looser fibrilar structure  [102]. Atomic Force Microscopy (AFM) images of both PUASM peptides were acquired for two conditions related to the spectroscopy studies: after dissolving, and after 40 days of incubation in room temperature. The aggregation process of the peptides was present already in the sample after dissolving as the fibers with height of 3. 44 ± 0. 3 nm and 3. 33 ± 0. 3 nm, respectively for EQB50682. 1_332_355 and EQB50683. 1_9_31, were observed (Fig. 5fg ). The height of the object observable in AFM is comparable with the size of the HET-s peptides obtained by the solidstate NMR technique (pdb:2kj3)  [30]. Peptide aggregation was further enhanced in the samples imaged after 40 days (Fig.