dataesr/scientific-paragraphs-categorization huggingface dataset:
We considered, in this article, the notion of diversity as the dispersion of sentiments (sentiment polarity); -RQ2: How can the strength (intensity) of a contradiction occurring in reviews be estimated? This was performed by computing the degree of dispersion of sentiment polarity around an aspect of a web resource; -RQ3: How do we balance the sentiment polarity around an aspect and the global rating of a review leading to the underlying question, when computing the intensity of a contradiction? What is the weight of the global rating of a review in the expression of feelings around an aspect? -RQ4: Freshness and temporality are essential in Web 2. 0; it is therefore necessary to take into account the temporality of the reviews when computing contradiction intensity; • (C2). We present the development of a data collection that allows the evaluation of the contradiction intensity estimation.
The evaluation was based on a user study; • (C3). We performed an experimental comparison, over our corpus, of the unsupervised method based on our definition of subjective contradiction with supervised methods with automatic feature selection. To the best of our knowledge, no other model has tried to measure the contradiction's intensity for subjective elements. Thus, it was not possible for us to use specialized state-ofthe-art models. The paper is organized as follows. Section 2 of our paper presents the background and related work. The third section presents the learning model and the non-learning model that we used as a baseline. The fourth section presents our test dataset and the experiments and discussions around the results. The article ends with the conclusion Section.
Thirdly, only nominal category terms were selected. Fourthly, we retained only the nouns surrounded by terms belonging to the SentiWordNet dictionary ("Michael is a wonderful lecturer delivering the lessons in an easy to understand manner. "). Finally, we considered as useful aspects only those nouns that were among the most frequent in the corpus of reviews (the useful aspects in these reviews were lecturer, lesson, presentation, slide, and assignment). After constructing the aspect list characterizing the dataset, the sentiment polarity must be computed. The sentiment analysis method used for this is described in the following section. SentiNeuron https://github. com/openai/generating-reviews-discovering-sentiment (accessed on 5 January 2022), an unsupervised approach proposed in  [55] , was employed to detect sentiment polarity in the review-aspect.
This model is based on the multiplicative Long Short-Term Memory (mLSTM), an artificial Recurrent Neural Network (RNN) architecture used in the field of deep learning. Radford et al. [55]  discovered the mLSTM unit matching the output sentiment. The authors conducted a series of experiments on several test datasets, such as the review collections from Amazon  [57]  and IMDb https://www. cs. cornell. edu/people/pabo/movie-review-data/(accessed on 5 January 2022). This approach provides an accuracy of 91. 8% and significantly outperforms several state-of-the-art approaches such as those presented in  [58]. SentiNeuron yields competitive performance, compared to several state-of-the-art models. In particular, this occurs when working on movie reviews (from IMDb) and also in our situation (coursera.
Data We are not aware of the existence of a standard dataset for evaluating the contradiction intensity (strength). Therefore, we built our own dataset by collecting 73,873 reviews and their ratings corresponding to 2244 English courses from coursera. org via its API https://building. coursera. org/app-platform/catalog (accessed on 5 January 2022) and web page parsing. This was performed during the time interval 10-14 October 2016. More detailed statistics on this Coursera dataset are depicted in Figure  4. Our entire test dataset, as well as its detailed statistics are publicly available https://pageperso. lis-lab. fr/ismail. ba dache/Reviews_ExtracTerms%20HTML/ (accessed on 5 January 2022). We were able to automatically capture 22 useful aspects from the set of reviews (see Figure  5 ). Figure  5  presents the statistics on the 22 detected aspects, for example for the Slide aspect, we recorded: 56 one-star ratings, 64 two-star ratings, 81 three-star ratings, 121 four-star ratings, 115 five-star ratings, 131 reviews with negative polarity, 102 reviews with positive polarity, as well as 192 reviews and 41 courses concerning this aspect.
Averaged and Weighted Centroid In order to quantify the effectiveness of our proposal, we employed the same performance measure as for the SemEval competition https://alt. qcri. org/semeval2016/task7/ (accessed on 5 January 2022), that is to say, the correlation coefficient. We used Pearson's correlation coefficient, which considers our results against the annotator's judgments. The second performance measure was the precision (the number of correct estimations divided by the total number of estimations). We must mention that: • The training phase for the sentiment analysis was performed over 50 k reviews issued from the IMDb movie database https://ai. stanford. edu/~amaas/data/sentiment/ (the vocabulary used in the movie reviews is similar to the vocabulary used in our dataset); • The accuracy of the sentiment analysis rose to 79%; • The assessed sentiment judgments were considered as the ground truth, thus yielding 100% in terms of accuracy.
Best Feature Identification In order to identify the most powerful features that estimated the contradiction intensity within our experiments, we considered several feature selection algorithms  [59]. The aim of this type of algorithm is to filter out as much as possible the information redundancy in the dataset. In terms of framework, we employed the open-source tool called Weka https://www. cs. waikato. ac. nz/ml (accessed on 5 January 2022). This tool is written in Java, and it provides a wide spectrum of machine learning models and feature selection algorithms. Figure  8  illustrates the 10 features we considered for the contradiction intensity prediction within the comments. The nature of the features f 1 to f 8 is given by a simple count, e. g. , the polarity criteria f 1 and f 2 represent the number of negative and positive comments in the document, respectively.
Criteria f 4 , f 5 , f 6 , f 7 , and f 8 are related to scoring. The rating may have values from one to five, where three means "average" and five means "excellent". Concerning the last two attributes, f 9 and f 10 , they represent the variation in the ratings and polarities of the comments for a given aspect associated with a document (a course in our case). These two criteria were calculated based on the following standard deviation formula, proposed in  [61] : s = n ∑ i=1 (x -x) 2 n ( 6 ) where x represents the feature value (ex. : scoring, polarity), x is the sample mean of the criterion concerned, and n is the sample size. For the experiments, 50 courses were randomly selected from the dataset, for each of the 22 aspects. Thus, we obtained a total of 1100 courses (instances). The intensity contradiction classes were then established, with respect to specific aspects.
There were four classes: Very Low (230 courses), Low (264 courses), Strong (330 courses), and Very Strong (276 courses), with respect to the judgments provided by the annotators. Since the distribution of the courses by class was not balanced and in order to avoid a possible model bias that would assign more observations than normal to the majority class, we applied a sub-sampling approach, and we obtained a balanced collection of 230 individuals by class, therefore a total of 920 courses. After obtaining the balanced dataset, we applied the feature selection mechanisms on it. We performed five-fold cross-validation (a machine learning step widely employed for hyperparameter optimization). The feature selection algorithms output feature significance scores for the four established classes.
As the RESAC network, RESACsub aims to downscale a low-resolution SSH using information from a higher-resolution SST. We achieve the SSH downscaling with a higher upsampling factor than in the RESAC proof of concept, but we only focused on the SSH, while RESAC also recovers the longitudinal and latitudinal velocities. In the following, we will present RESACsub, as well as its main differences from RESAC, briefly detail the data set used by both models, present the results obtained and discuss further potential developments.
Data In order to train a supervised neural network as RESACsub, we need the data fields at every resolution. As SSH cannot be retrieved from a satellite at the resolution we want to downscale, we use simulated grided SSH and SST from an ocean physics-based model: NATL60  (Ajayi et al. , 2020). This model is based on the NEMO 3. 6  (Madec et al. , 2017)  code, with atmospheric forcing, and initial conditions taken from MERCATOR  (Lellouche et al. , 2018). We use the SSH and SST state variables of this model at a very high resolution denoted R01 (for a resolution of 1/60°at the equator). At this resolution, the model has a very high running cost; therefore, we were only able to retrieve 1 year of training data (366 days starting from October 1, 2012), and 4 months of validation in 2008 (March, June, September, and December).
We are well aware that we should use different test/validation dataset, but considering the importance of the annual cycle and that the main objective is to compare two models, we decided not to separate the four validation months. Compared to RESAC, our validation method is more rigorous as there cannot be data leakage from sampling the validation data from the training year. We simulate the various resolutions by recursively averaging the pixels in a 3 Â 3 mask. We then call, hereafter R01, R03, R09, R27, and R81, the five resolutions that we study with respective sizes at the grid center of (1:5 Â 1:5, 4:5 Â 4:5, 13 Â 14, 40 Â 41, and 120 Â 122 km 2 ). We renormalize the data set between 0 and 1 to both stabilize the numerical calculations and equalize the importance of SST and SSH. In order to compare our models, we perform 10 rounds of training of each architecture, with different weight initialization on each training.
Data Availability Statement. The SARGAS60 data sets we used (extraction of the CJM155 and MJM155 NATL60 model experiments over the Gulf steam area, in the North Atlantic Ocean) are already available in the IPSL Thredds catalog at the address: https://doi. org/10. 14768/c3c33afe-2a37-42e1-bb29-21428387f658.
An unambiguous description of an experimental setup and analysis, and the subsequent biological observation is vital for accurate data interpretation and reproducible results. Consequently, experimental analyses should be described in a concise, unequivocal, and digestible manner. The aim of minimum information guidelines is to define the fundamental complement of data that can support an unambiguous conclusion on experimental observations. In this document, we present the Minimum Information About Disorder Experiments (MIADE) guidelines to define the minimal fundamental parameters required for non-experts to understand the key findings of an experiment studying intrinsically disordered proteins (IDPs) or intrinsically disordered protein regions (IDRs). MIADE guidelines provide recommendations for data producers to describe the results of their experiments at source, for curators to annotate experimental data to community resources and for database developers maintaining community resources to disseminate the data.
Furthermore, minimum information guidelines are abstract recommendations that do not specify the technical details of the structured data types that are guideline compliant. In this document, we provide examples of data adhering to MIADE recommendations for multiple use cases including providing details of the updates to allow the storage of MIADE-compliant data in the DisProt IDP database  1. However, the technical specification of data storage is defined by exchange formats used to standardise and store compliant data and therefore it is outside the scope of this document. The MIADE guidelines provide a community consensus created by experimentalists, curators and data scientists on the minimum information required to appropriately describe metadata on experimentally and computational-derived structural state(s) of IDPs or IDRs.
The aim is to increase the accuracy, accessibility and usability of published IDP data, to comply with FAIR (findability, accessibility, interoperability, and reusability) data principles  11  , to support rapid and systematic curation of such IDP data in public databases and to improve interchange of IDP data between these IDP resources. We believe that these guidelines will provide an important roadmap to the thousands of data producers, curators and database developers in the IDP field and increase the utility of published IDP data for the larger biological community.
Similarly, any tags, labels, post-translational modifications or mutations present in the sample under study should be described. Each region should be characterised by: • Definition of the source protein from which the region was derived: • The common name for the source molecule. Both the protein name and gene name should be added whenever possible. Ideally, this should be the official name provided by a nomenclature committee such as the HGNC symbol from the HUGO Gene Nomenclature Committee for human genes  15. In cases where the field-specific name is used, and it differs from the official name, the official name should be mentioned in the first definition of the molecule. Example: Mitotic checkpoint serine/threonine-protein kinase BUB1 beta (BUBR1, also known as BUB1B) • Accession or identifier for the source protein in a reference database.
If an isoform of a protein was used in the experiment, the accession or identifier specifically identifying that isoform should be used whenever possible. The version number of the protein sequence in the database can be added to further reduce ambiguity. Example: UniProt:P13569 (P13569-2 in case isoform 2 was used) • Definition of the protein region(s) for which a structural state was determined: • Start and stop positions of the region: the position of the first and last residue of the region, based on i) the sequence as described in the database annotating the source protein from which the region was derived (i. e. positions should refer to the natural sequence and should not consider added purification and solubility tags), or on ii) in the case of a sequence that is not mappable to a natural sequence, the sequence provided by the data producer.
If a structural property that is not widely known to non-experts is used, the term should be clearly defined. Example: disorder (IDPO:00076) Experimental and computational approaches: definition of the experiment or computational approach used to determine the structural state of the region. Each experimental setup should be described separately, using the following parameters: • Any additional components in the sample that could alter the interpretation of the results. This attribute is important to clearly capture structural changes induced by binding partners. However, it also includes other components such as reducing agents, cofactors and crowding agents which may trigger a structural change on the protein of interest. Each component should be defined unambiguously, and if possible, include the concentration of the sample components and refer to external databases including a definition of the molecule (e.
g. , Uniprot, ChEMBL). Additional protein components should be defined to the same level of detail as the experimental region being studied. See next section and Table  2  for details. Example: experiment was performed in the presence of 10 g/L polyethylene glycol 400 (PEG400) (CHEMBL:1201478). In the case of data being stored in a database, transferred between resources, or defined in the absence of a paper, it is important to also include the source of the data. Data source: a reference to where the data were originally described. • In cases where data were published in a paper, the following information should be provided:.
When available, information on the presence of transitions between states, interactions and functions, is also curated. The annotation of structural states and transitions makes use of specific IDPO terms (https://disprot. org/ontology). As part of the development of MIADE guidelines we have updated the DisProt database and curation framework to allow the annotation of MIADE-compliant entries  1. An improved construct definition was required to encode tags, labels, mutations or modifications and the experimental setup definition was updated to allow complex experimental samples to be described. Importantly, these additions will allow DisProt to annotate the observations of complex experiments defining conditional multistate IDRs that are becoming increasingly common in the literature. Proteoform definition: The DisProt resource already included an unambiguous definition of the protein or protein isoforms (using UniProt accession numbers) and its regions by mapping to the UniProt sequence.
The updated implementation can now define non-canonical and modified proteoforms. The MIADE integration allows the possibility to encode deviations from the wildtype UniProt defined protein sequence. Furthermore, the complete sequence of the experimental construct can now be annotated if available. Annotatable construct alterations include tags and labels (using the PSI-MI ontology (https://www. ebi. ac. uk/ols/ontologies/mod)  19  ), mutations (using the HGSV nomenclature (https://varnomen. hgvs. org/)) and PTMs and non-standard amino acids (using the PSI-MOD ontology (https://www. ebi. ac. uk/ols/ontologies/mod)  20  ). Experimental conditions definition: DisProt uses the Evidence and Conclusion Ontology (ECO, https://www. evidenceontology. org/)  21 to annotate experimental methods. In addition, the DisProt database can now store a range of experimental parameters that can influence our understanding of the biological relevance of an experimental observation, i.
Experimental components definition: The DisProt database can now describe experimental sample components such as lipids, nucleic acids, small molecules, metal ions or proteins present during the characterisation of the structural state of an IDR. The concentration of the components and cross reference to the specific database, i. e. CheBI 22 , ENA  23  , RNAcentral  24  and UniProt 13 can also be added. Similar to the other MIADE fields, a text description can be added into the corresponding Statement field. A representative list of DisProt use cases highlighting novel information covered by the addition of fields from the MIADE update is described in Table  3.
Case studies While MIADE only captures the core structural inferences derived from disorder experiments, it can be applied to the description of experimental data with a very wide range of complexity in terms of experimental design and studied system. In the following section we demonstrate how MIADEcompliant information can be created using as examples three papers applying nuclear magnetic resonance (NMR) in increasingly complex setups. These extracts serve as examples of good practice and are accompanied by a MIADE compliant entry in the DisProt resource (Table  3 ). We highlight the three key areas covered by MIADE from each paper: the definition of the protein construct used; the deviation from the wildtype proteoform (including mutations, post-translational modifications, tags, labels and dyes); and the definition of the experimental setup, including the environmental conditions and sample compositions that might have relevance for the structural state.
In addition to the structural state, the authors also directly address the connection between phosphorylation and the interaction capacity: 'non-phosphorylated or minimally phosphorylated 4E-BPs interact tightly with eIF4E, while the binding of highly phosphorylated 4E-BPs is much weaker and can be outcompeted by eIF4G'. While this piece of information is key to understanding the biological regulatory role of EIF4EBP2, it cannot be captured in the structural state-focused framework of MIADE and should be encoded as additional information in databases. In the third example, the authors study the human Cellular tumour antigen p53 (TP53) focusing on the structural features of the disordered N-terminal region  26. The authors clearly define the protein being studied by stating it is human TP53.
Like the previous papers, this work contains a considerable number of nonstructural observations. These include data on the interaction between the folded DNA-binding domain (DBD), the disordered NTAD and cognate and non-cognate DNA -additions to the experimental sample in certain experiments, the sequences of which are also defined. While this information is outside of the scope of MIADE, it is again written in a clear way enabling specific databases that go beyond MIADE to capture these observations in a structured way.
Standardised exchange format The MIADE guidelines do not define a structured data format. The IDP field requires exchange formats that can hold experimental data at a range of detail from a MIADE-compliant definition to a description of the experiment and results that would allow the experiment to be recreated (Figure  1B ). Furthermore, they should include the possibility to store raw and processed experimental measurements in addition to interpreted structural observations derived from the data. Given the heterogeneity of the methodology applied to the field of IDP research there is also a strong requirement for experimental method specific standardised exchange formats. These developments should be driven by the existing communities and resources for these methods, however, given the parallel requirements across many of these fields, efforts should be made to collaborate and reuse structured data formats when possible.
Controlled vocabularies and ontologies Controlled vocabularies and ontologies are a key component of the standardisation of IDP data storage. These definitions standardise the meaning of the terms used to describe the IDP data allowing the complete unambiguous annotation of an IDP experiment and results. Recent additions to the Intrinsically Disordered Proteins Ontology (IDPO) and the Evidence and Conclusion Ontology (ECO) have significantly increased the coverage of IDP-related terms. Yet, both ontologies are still incomplete and additional terms are necessary including those related to newly developed experimental approaches, computational methods, non-binary structural classifications (i. e. , more detailed than order/disorder including dynamics, secondary structure propensity and compaction), structural transition definitions and conditionality.
Suitability for past, present and future Any standardised exchange format, controlled vocabularies and ontologies developed over the coming years will require flexibility to hold the heterogeneous data that have been produced over the past decades. Furthermore, they will require significant and ongoing development to cover information from the ever-improving methodology being developed by the IDP community. Consequently, we see the standardisation of the IDP fields as a long-term project requiring extensive consultation between the experimental, computational and database communities within the IDP field. Areas where recent advances have outpaced the resources that store the available data are residue centric information, descriptions of dynamics and conditionality, and structural observations beyond the classical binary order/disorder classification.