dataesr/scientific-paragraphs-categorization huggingface dataset:
MIADE-compliant metadata capture at source To date, direct submission of data to community resources is underutilised by the IDP community. IDP resources should improve their capacity to receive data pre-publication including the possibility to embargo data until the time of final publication (similar to the PDB model) and develop tools and resources that simplify MIADE-compliant reporting. Furthermore, the IDP community should enforce the deposition of experimental data and metadata as a required component of the publication process. The ideal situation would include the pre-publication submission of primary source data directly to the corresponding field specific resource (e. g. Biological Magnetic Resonance Bank (BMRB) for NMR data  27  , Protein Circular Dichroism Data Bank (PCDDB) for circular dichroism data  28  , SASBDB for Small-Angle X-ray Scattering (SAXS) data  29  or Protein Ensemble Database (PED) for protein ensemble data  30  ).
Subsequently, a reference to primary source data and MIADE-compliant experimental metadata should then be submitted to a community resource such as DisProt or IDEAL  1, 2. This benefits the databases, as the efficiency of data collection and verification is increased. This in turn benefits the IDP community and wider biological community, as more and more precise data, linked to related primary data in field-specific databases, are readily available. Currently, several databases allow pre-or post-publication submission of data related to IDR experiments, each with their own submission process and data formats (Table  4 ). However, the proportion of data created that is captured by these resources varies widely and no resource is successful in capturing all data produced that fall within their scope.
The MIADE guidelines store information on the experimental level to allow data to be reinterpreted in the future. While adding experimental parameters and sample components can add considerably to the curation burden they also allow for more nuanced observations to be captured. As IDP experiments become increasingly complex by studying the modulatory effects of proteoforms, concentrations, conditions and binding partners, it is imperative that these rich data on the context of the studied protein region are captured. Furthermore, the attributes that are measured by these approaches are consistently growing, changing from a historically binary order/disorder structure definition to quantitative measures that include dynamics, secondary structure propensity and compaction. This adds a new level of requirements to be considered by comprehensive reporting guidelines.
Consequently, the MIADE guidelines will need to evolve over time based on community requirements. The argument against standardised reporting guidelines has always been the unbalanced burden placed on the reporter. However, the advantages to report far outweigh the effort. Simple accessible data reporting allows relevant data to be easily identified, recovered and reused. Basic administrative advantages include improved data management, minimised data loss associated with group member turnover and simplification of data sharing within and between groups. Refactoring data as method independent metadata allows data to be aggregated but also to be analysed in subsets based on data quality (Figure  1D ). Furthermore, data aggregation across complementary methods simplifies crossvalidation of data permitting quality to be defined by consensus.
Finally, improved data management, in parallel with upgrades to data deposition processes, will improve data transfer to community resources thereby accelerating the open science efforts of the community. The MIADE guidelines are only an initial step towards standardised and lossless IDP data transfer within the biological community. Three key developments are still required: standardised exchange formats for reporting IDP metadata and raw data, simplified pre-and post-publication data deposition mechanisms for the IDP data repositories, and a community wide agreement to deposit data. The diversity of the methodologies and data in the IDP community has to date proved to be a barrier to data collection. However, standardised guidelines for shared high level metadata annotation such as MIADE will allow the key data to be collected and aggregated across the field.
In parallel, each experimental approach in the field can develop method-specific storage and exchange formats and standards for raw data. Enforcing data deposition is a more complex process, however, pressure at the point of publication by journals and reviewers can drive compliance. It is important that data producers, curators and database developers in the IDP field are conscious of the expanding interest in IDRs by the wider biological community. The growing understanding of the functional significance of IDPs by researchers outside the IDP field has increased the importance of making high quality and understandable IDP data accessible to non-experts such as cell biologists studying the function of IDRs, computational biologists developing tools to analyse IDRs and curators transferring IDR data into community resources.
research grant (project FK128133 to R. P. ); European Union's H-2020 MSCA-RISE programme (grant agreement No. 778247 'IDPFun'); EM is supported by Fondazione Umberto Veronesi; the Italian Ministry of University and Research (MUR), PRIN 2017 under grant agreement No 2017483NH8. The authors would like to thank Julie Forman-Kay for her feedback on the MIADE guidelines and the manuscript.
Acknowledgements This work was funded by:  ELIXIR , the research infrastructure for life-science data; a  Cancer Research UK Senior Cancer Research Fellowship  (N. E. D. :  C68484/A28159 );  Carlsberg Foundation Distinguished Fellowship  ( CF18-0314 );  Danmarks Grundforskningsfond  ( DNRF125 );  National Research, Development and Innovation (NRDI)   Fund Young researchers' excellence programme.
The method used for sampling was simple random sampling, individuals being chosen randomly, each member of the population having equal chances of being selected as respondent. Upon launching the questionnaire to the public, it was published on multiple webpages, where volunteers could fill it. Simultaneously, we promoted the questionnaire on social media and collected physical responses from population. All respondents participated voluntarily, having the option to refuse or to renounce during completation. The questionnaire has 32 questions divided into 8 sections: an informative section, six sections to identify the frequency and quantity of food consumption per food groups (whole grains, fruits, vegetables-with special subgroup for tubers or starchy vegetables and legumes, nuts and seeds, animal products-with subgroups dairy foods and eggs and meat, added fats, added sugars) and one section to collect personal data, the only data relevant for the study being age and level of physical activity  [22].
A total of 1053 responses were collected in 2021 and 2022, from both rural and urban areas of Romania. This set of data is synthesized in Table  1. The primary data needed to assess Romanians' consumption habits were corroborated with secondary data, through extensive review of existing literature. The authors also consulted online available studies related to nutrition, recommended intake, overeating, Western dietary pattern, using multiple keyword combination searches, such as "food consumption + health", "food + health problems", "diet + health problem", "diet + health", "overeating", "overconsumption", "overconsumption + sustainable", "sustainable diet", "sustainable consumption", "sustainable nutrition", "nutritional guidelines", "nutritional needs", "adequate nutrition" and "nutritional requirements".
3 While the information provided by CGM helps people with diabetes to lower HbA1c, reduce hypoglycaemia and potentially reduce diabetes distress,  4  there are concerns about whether all episodes of hypoglycaemia identified by sensors are clinically and personally meaningful. Differences occur when we measure hypoglycaemia using interstitial glucose as opposed to capillary or venous glucose. Interstitial glucose measurements lag behind blood glucose levels. 5, 6 Operational issues (e. g. , compression of the sensor during sleep) may create artefacts that are difficult to differentiate from true hypoglycaemia. 6 Noise' in the data can cause small fluctuations around any given threshold. Furthermore, CGM provides data on the duration of episodes of hypoglycaemia, which must now be included in the definition of sensor-detected hypoglycaemia (SDH).
| Baseline assessments Informed written consent will be obtained with time for the participant to read the participant information sheet, consent form and ask questions. Baseline data will be recorded for each participant, including a brief medical history, healthcare resource use in the previous 12 months and experience of hypoglycaemia. The participant will complete a set of baseline questionnaires (Table  1 ) via an online platform (Qualtrics) https://www. qualt rics. com. At baseline, we will collect blood samples for HbA 1c and creatinine and urine for albumin creatinine ratio. Data from routine care records within the last 4 weeks can be used if available. A 4-week download will be taken from each participant's routine glucose monitoring device, insulin pump and/or downloadable insulin pens.
| Continuous glucose monitoring The Abbott Freestyle Libre 2 sensor and reader will be used for this study. The reader will be blinded using bespoke software provided by Abbott and alarms will be disabled. If the reader is out of Bluetooth range of the sensor, up to eight hours of data can be retrieved from the sensor by scanning with the reader. Participants will be trained on insertion and removal of sensors and asked to scan at least three times a day to maintain data coverage. Data will be stored on the reader for the duration of the 10-week study and downloaded using bespoke software at visit 5. 2. 5. 2 | The Hypo-METRICS app Participants will complete a check-in on the app three times per day for 10 weeks. The design and development of the Hypo-METRICS app are detailed in a prior publication.
| Actigraphy The FitBit Charge 4 will collect data on sleep duration,  27  heart rate and step count. This is worn continuously on the wrist, including at night to determine when participants were awake or asleep. 2. 7 | Subsequent visits 2. 7. 1 |  Visits 2, 3 and 4  These are brief virtual visits that will take place at weeks 2, 4 and 6. Issues with the devices will be addressed and minimum data collection will be confirmed. Details of adverse events (including severe hypoglycaemic episodes) will be recorded in the eCRF.
Thus, the optimal parameters for SDH are the threshold and duration that have the highest F score (or detection performance for PRH). The statistical analysis plan has been tested in various simulations reported in Mahmoudi et al. 29  Optimal parameters for each participant will be calculated, and then we will generate an overall optimal definition that combine data from all participants and weight for individual events of SDH and PRH. For CGM data gaps shorter than 30 min, we will use linear interpolation to fill gaps. Where gaps are >30 min, those data will not be used. Where the total data of CGM or EMA provided is <70% of the 10 weeks of data, those subjects will be excluded from the primary analysis to prevent bias in reporting (Figure  4 ).
| Biochemical outcome analysis By dividing our dataset into validation and test data, we will test the sensitivity and specificity of our definition of SDH in type 1 diabetes mellitus and type 2 diabetes mellitus, sleep status, method of glucose monitoring and based on rate of SDH during the study. We will then compare the rate of SDH using our new definition to the consensus definition from the ATTD. 2  We will use independent datasets from the wider Hypo-RESOLVE group to measure the rate of SDH using both definitions. We will also analyse the rates of SDH and PRH in those using CGM vs those use CBG during the 10 weeks of the study and the impact of different biopsychosocial variables on rates of SDH and PRH. of daily functioning (measured with the Hypo-METRICS app), which have been validated, as described by Søholm et al.
22  Using the definition from our primary objective and data collected from the Hypo-METRICS app and the blinded CGM, we will compare daily functioning between days with and without symptomatic and asymptomatic hypoglycaemia. We will also explore whether the impact changes depending on diabetes type, sleep status, hypoglycaemia awareness and after adjustments for related variables. Finally, we will explore the cumulative effect and duration of effect of SDH on psychological factors (see Table  1 ) at follow-up.
| Health economic analysis We will calculate utility scores from the EQ-5D-5L responses using country-specific tariffs (  [30] [31] [32] [33] [34] [35]  ) and data recorded in the Hypo-METRICS app and eCRF. For countries with no tariff, we will use the UK tariff. We will calculate QALYs from the utility scores using the area under the curve method. We will calculate the total number of working hours lost either due to absenteeism or low productivity using the work productivity and activity impairment questionnaire. We will use panel data methods to estimate the effect of SDH on working hours lost and QALYs in each week. We will adjust for age, gender, severe hypoglycaemic episodes, diabetes-related complications and study site. We will conduct exploratory analyses on healthcare resource use to see whether SDH over a 10-week period is associated with changes in mediation, visits to family doctors/general practitioners and hospital visits both over the 10-week and 52-week period.
| Data collection We have created an eCRF using REDCap, which is GDPR and HIPPA compliant. 23 Data collected during all study visits will be entered directly into REDCap with source data, such as CGM and CBG downloads, uploaded as. csv files into this database. Questionnaires will be completed on Qualtrics and exported as. csv files into REDCap. All data collected through uMotif, Qualtrics, REDCap and FitBit will be collected using a unique patient identifier.
| Ethics Ethical approval to conduct the study has been granted in the UK by Oxford B Research Ethics Committee, in the Netherlands by CMO Region Arnhem-Nijmegen, in Austria by Ethikkommission der Medizinischen Universität Graz, in Denmark by Videnskabsetisk Komite for Region Hovedstaden and in France by the Comite Die Protection Des Personnes SUD Mediterranne IV. The trial registration number is NCT04304963.
Data extraction and preprocessing The original dataset has been extracted from  Ni et al. (2019) , which provides many reviews from different types of products. For this work we have selected a specific dataset, "Cell phones and accessories" (1,128,437 reviews). The dataset has been preprocessed to filter and to keep only the useful reviews. Firstly, the reviews with no votes have been deleted. Secondly, after analysing the dataset, we found that the reviews with a date prior to 2012 generally had lower number of votes. Thus, to have a variety of values among the votes, we have filtered and kept only the reviews with a date after 2012. Finally, we have filtered again and kept only the reviews with a verified user to ensure the trustworthiness of the review. After this preprocessing, the dataset has been ordered in quartiles according to the number of votes.
The reviews with two votes or below have been considered as not helpful. The reviews with nine votes or more have been considered as helpful. The final "helpful dataset" has been generated by extracting 3500 not helpful reviews and 3500 helpful reviews. The dataset has been used for two purposes: (1) to provide an input of "truthful dataset" for the fake reviews generator and (2) to provide a "helpful dataset" for the BERT Helpfulness Discriminator.
Fake reviews generation using GPT-2 After the preprocess of the original dataset, the fake reviews generator has been trained and used to generate a "fake dataset". We have used the GPT-2 transformer model developed by  Radford et al. (2019). The GPT-2 model has been pre-trained to generate realistic texts. The model only needs to be fine-tuned to generate texts on a specific topic. We have fine-tuned the model using 1000 steps with an average training loss of 0. 92 at the end of the execution. 3000 reviews from the "helpful dataset" that were generated in the previous step have been used as training data. Based on all of this, our customized model is able to generate fake reviews on the product category "Cell phones and accessories". Using this model, we have generated 1000 fake reviews. Afterwards, we have created a new dataset made of the newly generated fake reviews and 1000 truthful reviews from the "helpful dataset".
Fake and Helpfulness reviews classifiers Once the "helpful" and "fake-truthful" datasets have been created, the next step was to train the classifiers to predict the helpful and the truthful reviews. The classifiers used in this work are based on the BERT model developed by  Devlin et al. (2018). We have trained two different classifiers: one to predict helpful reviews (BERT-Helpful) and the other one to predict truthful reviews (BERT-Truthful). BERT-Helpful has been trained using 2000 reviews from the "helpful dataset", which were truthful reviews combining helpful and not helpful reviews. BERT-Truthful has been trained using 2000 reviews from the "faketruthful" dataset. Once both models have been trained and validated, we have generated predictions using the same dataset for both models in the test stage.
The dataset contained 1000 fake and truthful reviews, which were not used for training the classifiers. The objective was to combine both classifiers to determine the reviews that were at the same time helpful and truthful, the socalled trustworthy reviews. BERT-Helpful predicts a specific range of reviews as helpful. BERT-Truthful also predicts another range of reviews as truthful. Our goal is to merge both ranges of reviews, helpful and truthful, to find the ones contained in there. Finally, the pool of reviews has been analyzed, characterizing the features that make a review trustworthy. This characterization has been made using KeyBert, developed by  Grootendorst (2020). This tool allows the extraction of keywords for BERT models. It can be customised to extract keywords of different lengths starting in 1 (keywords or keyphrases) or to set the diversity of keywords we want to obtain.
Experimental setup The experimental evaluation has been conducted using the free version of Google Collab. The resources provided by the free version are a single node with one GPU Tesla K80 and 64 GB of RAM. Within this version, the availability of resources are not guaranteed and limited, and sometimes the usage limits fluctuates depending on the demand. The code has been developed using Python3 and pandas library for the datasets management and processing. The GPT-2 transformer model has been applied with the gpt-2-simple library 1  , which uses TensorFlow and wraps existing model fine-tuning and generates texts for GPT-2. It uses specifically the "small" 124M and "medium" 355M hyperparameter versions. We have used the "small" 124M version of the model for this work. Thus, the model can be handled by our system.
Classifiers performance BERT-Helpful has been trained in 20 epochs with a training loss of 0. 384 and a validation loss of 0. 268. BERT-Truthful has been trained in 20 epochs with a training loss of 0. 378 and a validation loss of 0. 283. The testing for the classifiers has been made using the same dataset combining 1000 fake and truthful reviews. The accuracy is 0. 9 for BERT-Helpful classifier and 0. 91 for BERT-Truthful.
Conclusions In this work we have developed a methodology to detect and characterize trustworthy reviews within the product category "Cell phones and accessories". We have generated fake reviews using the GPT-2 model to train a classifier to detect truthful reviews. Likewise, a second classifier has been trained to detect helpful reviews. We obtain a higher accuracy, around 0. 9, for both classifiers and 118 trustworthy reviews from a dataset of 1000 reviews. Finally, the keywords allow us to characterize some of the trustworthy reviews' common patterns for this product category, thus understand the content of those reviews. One limitation of this work was the limited resources available, thus it was necessary to reduce the number of data used to generate our results. Future work could incorporate more powerful systems able to analyse much larger datasets from different product categories.
Tropical cyclones are responsible for a large share of global damage resulting from natural disasters, and estimating cyclone-related damage at a national level is a challenge attracting growing interest in the context of climate change. The global climate models, whose outputs are available from the Coupled Model Intercomparison Project (CMIP), do not resolve tropical cyclones. The Cyclone generation Algorithm including a THERmodynamic module for Integrated National damage Assessment (CATHERINA), presented in this paper, couples statistical and thermodynamic relationships to generate synthetic tracks sensitive to local climate conditions and estimates the damage induced by tropical cyclones at a national level. The framework is designed to be compatible with the data from CMIP models offering a reliable solution to resolve tropical cyclones in climate projections.
This model computes the maximum pressure intensity (MPI) associated with the sea surface temperature (SST) and uses this potential as a predictor in the central pressure dynamics  (James and Mason, 2005). In line with  Merrill (1987) , we find that, although the SST plays a major role, this variable alone is not a reliable predictor of whether a given storm will intensify. Thus, we prefer to rely on the formulation of  Holland (1997)  and to model the effect of climate change on the maximum potentials in the different scenarios through a better description of the underlying thermodynamic phenomenon, well described by  Emanuel (1988) ,  Holland (1997)  or  Emanuel (1999). We therefore develop an alternative to STORM by adding a thermodynamic module from the perspective of producing cyclones in different climate scenarios.
In particular, we retrieve two additional variables (relative humidity and tropopause temperature) from climate models to bridge the gap between data from global circulation models (GCMs) and the theory of the intensification of tropical depressions. Risk assessments have been developed based on hurricane potential intensity maps to assess the damage in the US and around the world  (Emanuel et al. , 2008; Emanuel, 2011; Mendelsohn et al. , 2012). On the damage modeling side,  Bresch (2017) ,  Lüthi (2019) , and Aznar Siguan and  Bresch (2019)  set up a platform for physical risk estimation (CLIMADA), coupled with a database of estimated values of local assets  (Eberenz et al. , 2020a, b). The asset resolution (30 arcsec) and geospatial description of extreme events are particularly advanced.
We propose a simplification of this work that applies in the context of national level assessment. The CLIMADA framework focuses on damage modeling based on global aggregated temperature projections and does not make use of the climate data produced by atmosphere-ocean general circulation models (AOGCMs). Coupling CLIMADA and STORM methodology with an extended thermodynamic module fitted on four climate variables, our approach provides a novel long-term tail risk assessment at a national level, offering an adaptive framework to estimate investments required to mitigate this risk. This paper makes three contributions. First, we provide an algorithm to generate synthetic cyclones from climate data inspired by  Bloemendaal et al. (2020)  fitted on four physical variables extracted from ERA5 reanalysis and including a thermodynamic module to better describe cyclone physics.