dataesr/scientific-paragraphs-categorization huggingface dataset:
Genome Assembly De novo genome assemblies for G. and G. chilensis were generated based on 203-fold and 116-fold coverage of PacBio long reads, respectively. Bacterial sequences were removed from raw data (subreads) using BlobTools v1. 1. 1  (Laetsch and Blaxter 2017). Two independent assemblies were generated using CANU  (Koren et al. 2017 ) and FLYE  (Kolmogorov et al. 2019). Based on congruity (QUAST v. 5. 0. 2;  Mikheenko, et al. 2018 ) and BUSCO score  (Simão et al. 2015) , the best assembly was kept and polished using three iterations of RACON v. 1. 4. 20. Finally, PacBio sequencing error was corrected using 150-bp paired-end Illumina reads with PILON v. 1. 23 software  (Walker et al. 2014). The draft genome assembly of G. caudata was generated using 171-fold coverage of 150-bp paired-end Illumina reads only.
Genome Annotation Each reference genome was first masked using RepeatMasker v4. 0. 9  (Smit et al. 2013 (Smit et al. -2015) )  with Dfam v3. 0 database  (Wheeler et al. 2013 ) and a customized repeat library produced from concatenated outputs of RepeatScout v1. 0. 6  (Price et al. 2005 ) and TransposonPSI v1. 0. 0  (Haas 2007 (Haas -2011)). Initial quality assessment of the RNAseq reads was performed with FastQC v0. 11. 9 (Andrews 2010), and reads were trimmed using Trimmomatic v0. 39 (TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:50;  Bolger et al. 2014). Clean reads were mapped to the reference genome assembly using HISAT2 v2. 2. 1  (Kim et al. 2019 ) and used to annotate protein-coding genes with BRAKER2 v2. 1. 6  (Bruna et al. 2021). Functional annotation of the transcriptomes was performed using eggNOG-mapper  (Huerta-Cepas et al.
Rhodoexplorer Red Algal Genome Database The main web portal (https://rhodoexplorer. sb-roscoff. fr) has been implemented using the Python web framework Django, with data stored in a relational database (PostgreSQL). For each red algal species, an integrated environment of visualization tools has been deployed based on the Galaxy Genome Annotation (GGA) project  (Bretaudeau et al. 2019). Each GGA environment deployed for the Rhodoexplorer database includes the following: Chado, a PostgreSQL relational database schema for storing biological data  (Mungall et al. 2007 ); JBrowse, a webbased genome browser  (Buels et al. 2016) ; Tripal, a Drupal-based application for creating biological websites  (Sanderson et al. 2013) ; Elasticsearch, a distributed, free, and open search and analytics engine for all types of data (https://www.
elastic. co/products/elasticsearch); and Galaxy, a browser-accessible workbench for scientific computing used as a data loading orchestrator for administrators (The Galaxy Community 2022). To facilitate the deployment and the administration of the GGA service, a set of Python tools has been developed (http://gitlab. sb-roscoff. fr/abims/e-infra/gga_load_data) allowing mass deployment of Docker containers and automated data loading through Galaxy with the Bioblend API  (Sloggett et al 2013). The Blast interface (https://blast. sb-roscoff. fr/rhodo explorer/) includes an implementation of the Blast algorithm using SequenceServer  (Priyam et al. 2019)  graphical. The documentation website for navigating the platform web portal and resources (https://abims-sbr. gitlab. io/ rhodoexplorer/doc/) is published from a GitLab repository, with Pages and MkDocs, a static site generator.
Data Availability Sequencing data have been deposited in the SRA database under BioProjects PRJNA936482, PRJNA931233, PRJNA938301, and PRJNA938403. The accession numbers for the raw sequence data are provided in supplementary table  S2 , Supplementary Material online. Gracilaria chilensis, G. gracilis, and G. caudata Whole Genome Shotgun project have been deposited at DDBJ/ ENA/GenBank under the accessions JARGXX000000000, JARGSG000000000, and JASCIV000000000, respectively. Gracilaria vermiculophylla updated assembly has been deposited under JAHNZQ000000000.
The project was designed as a multinational study involving English-, French-and Greekspeaking people and sites, with participant data pooled across languages. Unfortunately, due to the Covid-19 pandemic, all activity was suspended in March 2020, with 67% of the target sample recruited. An interim analysis showed that the three language groups performed differently on the items in the MoCA-H and on the whole scale, making it not valid to pool the language groups into a single analysis. Recruitment recommenced in June 2021 and finished at the end of May 2022. The current paper reports on the development and validity of the English-language MoCA-H derived from the final dataset.
Participants Participants with age-related hearing impairment were recruited from sites across England (four sites), Ireland (one site), and Australia (two sites). A comparison sample of participants without hearing impairment was also recruited. Participants were recruited from audiology services, memory clinics, volunteer databases, and the general community. Participants were required to be over 60 years of age, resident in the general community and have capacity to provide written informed consent to participate. Those living in long term care facilities, who do not understand written and spoken English, with dual sensory impairment (both hearing and vision impairment), or who were culturally Deaf were excluded from the study. Hearing impairment status was based on puretone air conduction threshold testing.
RESULTS The dataset of hearing-impaired individuals included 76 people with normal cognition and 83 with dementia (Table  2 ). Compared with participants with normal cognition, those with dementia were slightly older (79. 6 vs. 75. 1), more likely to be male (62. 7% vs. 44. 7%), less likely to have 13 or more years of education (39. 8% vs. 60. 5%). The comparison groups of people with normal hearing were smaller: 67 in the normal cognition group and 30 in the dementia group. Similar demographic differences were observed between these two groups.
Criteria for good scores generally use "and/or" statements for requirements, while criteria for excellent scores use more definitive and exclusive "and" statements. To ensure consistency in scoring, in the event that most but not all requirements of a feature are present, that feature is always scored as good. An MSP initiative in-and-of-itself need not be responsible for the advancement of a feature for it to be assessed by the MSP Index. To trial the functionality of the MSP Index, we applied the scoring guide to six international case studies selected from the MSP online database of the Intergovernmental Oceanographic Commission (IOC) (http://msp. ioc-unesco. org, accessed in June 2021). It should be noted that, as of January 2023, content from this website has been migrated to MSPglobal, an initiative of the IOC (http://www.
mspglobal2030. org/msp-around-the-world/). We provide a list of the MSP initiatives in the data provided with the Supplementary Information. To capture a diversity of MSP processes and ensure representativity, we used stratified random sampling to identify one case study for each of the six regions identified in the database: Africa (n=10 MSP initiatives), Asia (n=8), Europe (n=38), Middle East (n=2), Oceania (n=10), and the Americas (n=38). Each of the 106 MSP initiatives was assigned an identifier number and all initiatives within a given region were arranged in numerical order. We used R Version 3. 6. 1 to randomly sample case studies by identifier number from each ocean region, then screened the associated case study using the following criteria:  Language: the case study documentation must be in English due to language limitations of the lead author  Plan: the case study must have a final draft or final approved plan available  Supporting content: the case study must have sufficient content publicly available If a randomly selected case study did not meet these criteria, then we continued the random sampling without replacement until a case study was selected that met the criteria.
Under the participatory principle, stakeholder empowerment and participation plan both scored as absent for half of the case studies. In contrast, boundaries (place-based) scored as excellent for all cases except Israel, and spatial information (placebased) scored as excellent for all but the Kiribati and Bataan case studies (see data provided in Supplementary Information). Within case studies, the Rhode Island case scored above average for all MSP principles, while the Israel and Bataan cases scored below average for all principles. The Israel case scored 50% of the maximum possible principle score (18) or less for all principles except place-based. The Bataan initiative was the only case study to score zero on a principle (adaptive). The Kiribati (Phoenix Islands Protected Area Management Plan 2015-2020) and Bataan case studies scored below average for the participatory principle.
ACKNOWLEDGEMENTS This work was supported by the  Mitacs Globalink Research Award  and the  Marty Memorial Scholarship of Queen's University  awarded to JMR, as well a  Discovery Grant  of the  Natural Sciences and Engineering Research Council (NSERC) of Canada  awarded to RD. JC was supported by  BiodivERsA (METRODIVER and MOVE)  and  Fondation de France (MultiNet). JMR and PG thank  Silke Neve  for her review and encouragement of this manuscript.
These feeding events were used to assess dominance hierarchy within the group (see Hierarchy estimation). In summer, these trained vultures were kept perching on individual logs, released several times per day to execute free flight shows for the public within a landscape composed of plateaus interspaced by canyons, similar to "Causses" landscape typically used by french wild vultures  [39]. The falconry centre is located near a 120 m-deep canyon and offers natural soaring conditions for raptors, making this study site a great place to investigate natural group flight behaviour (see Group flights),  [24]. We used GPS data and visual observations to characterise the social and flight behaviour of eight captive vultures (7 Eurasian griffon vultures, Gyps fulvus, and 1 closelyrelated Rüppell's vulture, Gyps rueppellii), including five females and three males (Table  S1 ).
Social bond estimation During five weeks in both years (December/January 2020-2021 and November/December 2021), we recorded pictures of vultures in the aviary from 8:00 to 19:00 (local time) at 5 minutes interval, using three camera traps (Wosport Big Eye D3 and Reconyx HyperFire HC600). We identified birds using repeated colours on plastic rings and marks on the ruff and backhead feathers, using harmless colour sticks (Raidex GmbH, Figure  1A ). We then processed recorded pictures to extract the individuals' ID and position (bill or head position), using a purpose-built image annotation program in Julia software, JuliaHub Inc. ,  [39]. For subsequent analyses, we relied on R software (v 4. 2. 2, R software, 2022,  [40] ). We considered the social bond between a dyad of individuals i and j based on spatial proximity following the Simple Ratio association Index (SRI, equation 1,  [41, 42] ) Equation 1: 𝑆𝑅𝐼 $,& = 𝑆𝑅𝐼 &,$ = ) *+, ) *+* , where 𝑛.
Hierarchy estimation Each winter, we estimated hierarchy within the vulture group by monitoring feeding interactions during the five carcass-based feeding events in the aviary (10 in total, Figure  1B ) using a remotely-controlled video camera (GoPro Hero 4, GoPro Inc. ) fixed at 2 m height on the aviary wall. These feeding events lasted on average 34 min (SD ± 4 min). We computed individuals' rank relying on the randomised Elo-rating approach  [43, 44] , which accounts for potential temporal instability of the rank using permutations in the agonistic interaction series ('elo_scores' function, aniDom package,  [44, 45] ; using 1000 randomisations and fixing the rank adjustment speed along the series, K-factor, to 200). The interaction series consisted in identifying the "wins" and "losses" for a given individual in agonistic interactions  [46]  with other individuals in each video (annotated with BORIS video analysis software,  [47] ).
Thermal use identification To segment vulture flight between circular soaring, linear soaring and gliding flight we first calculated turning angle and vertical speed between consecutive locations using the move R package  [50]. We applied a moving window of 30 s to calculate the absolute cumulative sum of the turning angles (hereafter cumulative turning angle) and a moving window of 5 s to calculate the average vertical speed. We then applied a k-means approach (k = 2, 'kmeans' function, stats R package) on the smoothed vertical speed (positive speed when flying upwards) to distinguish between soaring (ascending flight) and gliding (descending flight,  [51, 52] ). We further classified soaring locations into circular soaring (indicating use of thermal updrafts) and linear soaring (also called slope soaring, expected to occur outside of thermals), with circular soaring being associated with a cumulative turning angle ≥ 300 degrees.
Acknowledgment We thank  Dominique Maylin  and  Raphael Arnaud  at  Rocher des Aigles  as well as all of the staff for their patience, help and enthusiasm for the project. GPS loggers were provided by  Giacomo Dell'Omo. Camera  traps and Gopro video cameras were provided by  Aurélien Besnard ,  Samuel Caro  and  Samuel Perret. We also thank  Samson Acoca-Pidolle  for fruitful discussions about the statistical analyses.
This paper examines the advantages and drawbacks of the use of a functional form in empirical groundmotion modelling compared to machine learning algorithms. Typically, models based on linear regression and prede ned functional forms have limits in representing complex nonlinear behaviour of source, attenuation and site effects present in the data. We investigate the e ciency of different machine learning algorithms using the dataset of Italian strong motion records, consisting of 5,607 records relative to 146 earthquakes and 1,657 stations, employed to derive the most recent ground motion model for Italy. We quantify the differences in the predictive capabilities of both approaches in terms of standard deviation, which is broken down into between-event, between-station and event-and site-corrected components, implemented as random effects.
Introduction This paper aims a comparison between traditional methods for predicting ground motion Intensity Measures (IMs), usually based on linear regression, and machine learning algorithms (algorithms to analyze huge datasets), which are widely used today for different purposes thanks to the increasing availability of data and computing performance. Generally speaking, Ground Motion Models (GMMs) are empirical equations to estimate IMs as a function of several variables, generally related to the seismic source (e. g. magnitude), source-to-site distance (e. g. epicentral, hypocentral, Joyner-Boore distance, among others) and local site conditions (e. g. site categories or averaged shear wave velocities in the uppermost 30 meters), as described thoroughly by  Douglas and Edwards (2016). Simple functional forms and/or few variables are not always capable to model all aspects of ground motion, therefore several additional variables can be added to the linear regression models to reduce the variability associated with the prediction, although many of them are usually di cult to obtain (e.
g. style of faulting, depth to the top of rupture, sediment thickness, hanging wall location, see  Chiou and Youngs, 2014; Campbell and Bozorgnia, 2014 ). An alternative to regression in ground motion prediction could be the use of machine learning (ML) algorithms, non-parametric models that are fully trained by data that have recently been employed successfully by many authors. In general, the learning algorithm builds a model from a training set of observations and then evaluates the predictive performance of the model on an independent dataset that should represent new data. The use of ML approaches dates back to the early 2000s when  Kerh and Ting (2005)  selected a combination of seismic parameters including epicentral distance, focal depth, and magnitude from historical records at 30 stations to apply the back-propagation neural network model, to estimate peak ground acceleration at ten train stations along the high-speed rail system in Taiwan.
Güllü and Erçelebi (2007)  applied the neural network approach for the prediction of Peak Ground Acceleration (PGA) using about 1000 strong motion data from Turkey. Derras et al (2012)  applied the arti cial neural network approach to a large subset of the KiK-net seismic database, which includes 3,891 records from 398 sites and 335 earthquakes to predict horizontal PGA. More recently, machine learning algorithms have been applied to other ground motion intensity measures, such as PGV (Peak Ground Velocity), and the 5% damped spectral response acceleration values (SA) at periods of interest for structural design. The estimation of intensity measures using ML is performed by selecting the same predictor variables as in regression-based models, that reproduce the contributions of source (e. g.
magnitude, focal mechanism), attenuation (e. g. epicentral, hypocentral, Joyner-Boore or rupture distance) and site (V S,30 or soil categories). The attention has been concentrated on the improvement of the prediction performance based on residuals between observed and predicted IMs (e. g. Root-Mean-Square Error, RMSE, which is the standard deviation of the residuals; Mean Absolute Error, which is the sum of absolute residuals divided by the sample size; Regression Coe cient, which describes the relationship between a predictor variable and the response). Little or no attention has been paid to the individuation of additional explanatory variables, that could have no direct connection with the physics of earthquakes. This study has the ambition of investigating the potential of ML algorithms exploiting the dataset used by  Lanzano et al (2019)  to derive the most recent ground motion model for Italy and explore the different results, if any, to a classical regression model.
Different ML algorithms are tested and their e ciency is assessed through the evaluation of the standard error. The results of the best model are compared with the empirical equation by  Lanzano et al (2019) , to explore and explain the differences. As in  Lanzano et al (2019) , the residuals between predictions and observations are used to identify the contribution to the variability of events (between-event), stations (between-stations) and event-and site-corrected residuals. The framework and all the ML algorithms presented in this study are implemented using Matlab©. Lanzano et al 2018), has been improved with the records of some regional small-magnitude events (Mw < 4:0), available in the ITalian ACcelerometric Archive (ITACA) database  (Pacor et al, 2011; Russo et al, 2022 ) and, to increase the maximum usable magnitude, the records of 12 worldwide events in the magnitude range 6.
Lanzano et al (2019). The dataset samples only active crustal regions in Italy and the maximum hypocentral depth of earthquakes is 30 km. Particular care has been spent on the earthquake selection to avoid record oversampling in Central Italy, an area struck by the Mw 6. 0 Umbria-Marche 1997, Mw 6. 2 L'Aquila 2009 and Mw 6. 5 Central Italy 2016 seismic sequences. The epistemic uncertainty that could affect the datasets of the past studies on the machine learning algorithms, that are assembled from different data sources, is reduced since i) the data are processed with the same technique  (Paolucci et al 2011) , ii) the intensity measures are calculated with the same algorithms and iii) the metadata of events and recording stations have been carefully revised. A detailed description of the dataset and parameters used in this study is in  Lanzano et al (2019).
Machine learning work ow To derive a predictive model we used the Regression Learner tool (RLt), available in Matlab ©, which allows us to automatically train a selection of models or compare and tune options of linear regression models, regression trees, support vector machines, Gaussian Process Regression models and ensemble of trees. The different learning structures provide the opportunity of exploring different types of learning algorithms in extracting features that exist in the available data. The ground motion dataset has been split and used to train and validate different models. The most suitable model has been then selected based on the Root-Mean-Square Error (RMSE) and the distribution of the residuals (difference between the logarithm base 10 of observations and predictions); in addition, the trend of residuals in function of different predictors has been checked to verify the absence of biases.
We select the same explanatory variables as  Lanzano et al (2019)  to train the model: moment magnitude (M W ), rupture or Joyner-Boore distance (R RUP or R JB ), style of faulting (SOF) and averaged velocity in the uppermost 30 m (V S30 ). In addition, we considered the event hypocentral depth (H), which was not considered as a separate variable by Lanzano et al (2019), since they obtained an averaged hypocentral depth (pseudo-depth) through regression. We train a model for the same intensity measures (IMs) as in Lanzano et al (2019): peak ground acceleration (PGA), peak ground velocity (PGV), and 36 ordinates of the 5% damped acceleration response spectra (SA) in the period range 0. 04s - 10 s. In particular, we consider the geometrical mean of the two horizontal components. The dataset is randomly split into two parts, 70% as training data and 30% as test data, such that each class is correctly represented in the resulting subsets (the training and the test set).
The selected percentages are the most commonly used for small datasets since they ensure a trade-off between the amount of information helpful to the training and the accurate error estimation on the test dataset. For the model evaluation, we use the k-fold cross-validation implemented in the Matlab-RLt. This method allows us to select the number of folds to partition the data into k disjoint validation sets. For each validation fold the tool trains a model using the training-fold observations and assesses the model performance using validation-fold data. The average validation error is calculated over all folds. As the rst step, we try to identify the most suitable model to predict the base 10 logarithm of peak ground acceleration (PGA), which is characterized by the largest variability in traditional regression models.
A Gaussian Process (GP) is a generalization of the Gaussian probability distribution; whereas a probability distribution describes random variables which are scalars or vectors (for multivariate distributions), a stochastic process governs the properties of functions  (Rasmussen and Williams, 2006). GPR aims to move from the training dataset to a mean function f that makes predictions for all possible input values. The GPR assigns a prior probability to every possible function, where higher probabilities are assigned to functions that we consider to be more likely; when many data points are considered during the training process one would see that the mean function f adjusts itself to pass through the points so that the posterior uncertainty would reduce close to the observations. The properties of functions are induced by the covariance function of the Gaussian process and the problem of learning is exactly the problem of nding suitable properties for the covariance function, that speci es the covariance between the two latent variables f(x i ) and f(x j ), where both x i and x j are d-by-1 vectors and d is the number of observations.