dataesr/scientific-paragraphs-categorization huggingface dataset:
The first multimodal brain age study was published on December 25, 2011, and incorporated data from three types of magnetic resonance imaging (MRI) sequences: T1-weighted, T2-weighted, and diffusion-weighting scans. Brown et al. 7  found that the neural features extracted from each of these modalities exhibited unique developmental trajectories throughout adolescences. More importantly, there was an alignment between the years when a given set of features exhibited the most age-related differences and the time when those features contributed the most to the predictive power of their multimodal brain age model. Further, their multimodal model explained more than 92% of the variance in age and yielded mean absolute errors that were as low as 1. 03 years. The accuracy of this model underscored the potential advantages of using multimodal data.
Since this initial report, there have been over 200 brain age-related articles that utilized or referenced the term ''multimodal,'' a number that is increasing at an accelerated rate each year (Figure  S1 ). It is expected that this trend will continue, as recent data-sharing requirements for NIHfunded projects  8  and open science initiatives  9  will provide easier access to large multimodal imaging datasets. Additionally, continual advancements in harmonization tools may enable researchers to aggregate even larger samples for training and vali- Our literature searches uncovered 197 unique empirical records that were screened. Briefly, studies were excluded if they did not compare multiple brain age models using multiple imaging sources. A total of twenty studies met the inclusion criteria for our first study aim, and of these, ten met our inclusion criteria for all three aims.
Information sources and search strategy On May 16, 2022, literature searches were conducted across four databases (Web of Science, ScienceDirect, PubMed, and Scopus) without any limits to publication date. We aggregated a total of 351 reports that used the term ''brain age'' in their title, abstract, or keywords and included the term ''multimodal'' anywhere throughout the article, including the references. The references for these articles were also screened yielding four additional relevant reports. After removing duplicates (n = 81) and non-empirical investigations (n = 77), we obtained 197 studies whose titles and abstracts were screened by one co-author (M. M. G. ). Studies were excluded if they did not train a statistical model to predict age from neuroimaging data (n = 37) or were not conducted on human subjects (n = 6).
Data extraction Information from each study was compiled and organized into three categories. First, descriptive information regarding the study design and sample characteristics was gathered (Table  1 ). These data included the sample size, age distribution, phenotype(s), data source(s), validation technique(s), and predictive algorithm(s). Second, the neuroimaging modalities and specific feature types were collected (Table  S1 ). This body of literature encompassed data across multiple MRI sequences, positron emission tomography (PET), magnetoencephalography (MEG), and electroencephalography (EEG). Lastly, the relevant meta-data from each brain age model across all studies were ex-tracted to conduct a quantitative synthesis. 18 For each brain age model, the following information was recorded: neuroimaging modality; feature type; number of input features; mean absolute error (i.
e. , operationalized as model accuracy); Pearson correlation between chronological and brain ages; coefficient of determination between chronological and brain ages; phenotype (i. e. , cognition, psychopathology, etc. ); correlation between a phenotype of interest and brain age gaps (i. e. , operationalized as model utility); and reported effect size metrics (i. e. , Pearson correlation, Cohen's D, etc. ). This aggregated set of information is accessible via the following GitHub repository: https://zenodo. org/badge/latestdoi/549260376. 21, 22 ta synthesis As discussed in prior reviews of the brain age framework,  41  several confounds need to be accounted for when interpreting differences between studies. It has been demonstrated that the accuracy of brain age models varies as a function of sample size,  42  age distribution,  41  developmental stage,  43  predictive algorithm,  13  and correction methods to remove prediction biases.
Each colored line represents the average of all models from a specific study, while the back line represents all models across all studies. Statistical analyses R v. 4. 0. 2. 46 was used for analytical and visual purposes. Specifically, we used software from the following packages: effectsize, 47 ggplot2,  48  lmer,  49  and lmerTest. 50 All code and data pertaining to this review are available through the following GitHub repository: https://zenodo. org/badge/latestdoi/ 549260376. Our first set of analyses were conducted across twenty brain age studies and investigated differences in the accuracy of brain age models. As such, we used mixed-effects analysis of variance to test whether model accuracy differed between imaging modalities: anovaðlmerðaccuracy $ modality + ð1 j studyÞÞÞ. Additionally, we used a mixed-effects linear model to test whether model accuracy varied as a function of the number of neuroimaging features used to train the models: lmerðaccuracy $ dimensionality + ð1 j studyÞÞ.
Characteristics of included studies Significant heterogeneity in sample characteristics and methodological choices was observed across all twenty studies that met our inclusion criteria (Table  1 ). Specifically, a large number of studies used open-source neuroimaging datasets, with the most popular being from the Cambridge Center for Aging and Neuroscience dataset  51  (n = 4) and the UK Biobank  52  (n = 3). Most of the studies (n = 8) were trained and tested on crosssectional samples with age ranges spanning from late adolescence to late adulthood (18-85 years). Datasets varied considerably in size, as the smallest comprised of 50 participants, while the largest contained 17,461. Additionally, a variety of phenotypes were used to evaluate the utility of these models, with the most common phenotypes relating to cognitive function and physical fitness.
One possible avenue could be to apply a fitness function to a set of brain age gaps with the goal of maximizing the separability between individuals based on a selected distance criterion. This approach has been shown to improve sensitivity to Alzheimer's disease  82  and could have greater implications if applied to multimodal models. 4] [85]  Specifically, more data will need to be collected for a multimodal model to be applied, which is not always feasible, especially in clinical settings. Further, more work is needed to evaluate higher-order features of brain networks. For example, the edge strength was the most commonly used fMRI feature across this body of literature. It is unknown whether indices of graph theory  86  or network cohesion  87  could lead to even greater improvements in model utility.
Languages are known to describe the world in diverse ways. Across lexicons, diversity is pervasive, appearing through phenomena such as lexical gaps and untranslatability. However, in computational resources, such as multilingual lexical databases, diversity is hardly ever represented. In this paper, we introduce a method to enrich computational lexicons with content relating to linguistic diversity. The method is verified through two large-scale case studies on kinship terminology, a domain known to be diverse across languages and cultures: one case study deals with seven Arabic dialects, while the other one with three Indonesian languages. Our results, made available as browseable and downloadable computational resources, extend prior linguistics research on kinship terminology, and provide insight into the extent of diversity even within linguistically and culturally close communities.
Beyond a linguistic or anthropologic interest, the availability of digital resources on language diversity is also desirable from a computational perspective. Language processing applications need to be aware of such phenomena of diversity in order to provide highquality results. For example, a machine translation system needs to tackle cases of lexical untranslatability where a word or expression in a source language has no equivalent in a given target language, and the choice of an approximate translation can change the meaning of an utterance. For example, for the English sentence his cousin gave birth to a twin, Google Translate provides the Arabic translation ‫توأما‬ ‫عمه‬ ‫ابن‬ ‫جب‬ ‫أن‬ a'njaba ibna a'mihi tawaman that means His father's brother's son gave birth to a twin. is syntactically correct yet unintended meaning of a male giving birth output is due to a lexical gap, i.
e. , a non-existent equivalent Arabic term for cousin. Such cases of techno-linguistic bias-where language technology provides better results by design in certain languages than in others-tend to remain hidden in monolingual resources but are revealed in multilingual settings  (Bella et al. , 2022a (Bella et al. , , 2023)). In recent years, there has been an increasing number of linguistic databases covering a large number of languages. ese resources are usually aimed at quantitative studies for comparative linguistics, such as the classi cation of pain predicates  (Reznikova et al. , 2012) , a semantic map of motion verbs  (Wälchli and Cysouw, 2012) , the modeling of color terminology  (McCarthy et al. , 2019) , the CLICS database of cross-linguistic colexi cations  (Rzymski et al. , 2020) , DiACL (Diachronic Atlas of Comparative Linguistics), a database for ancient Indo-European languages spoken in Eurasia typology  (Carling et al.
, 2018) , or the Cross-Linguistic Database of Phonetic Transcription Systems  (Anderson et al. , 2018). Oen, such databases use phonetic representations of lexical units or are limited to a few hundred or a few thousand core concepts, limiting their usability for the processing of contemporary written language. In our experience, most of the existing typologyinformed NLP research is restricted to exploring language-speci c morphosyntactic features and has ignored diversity within lexical resources  (Batsuren et al. , 2022). A notable exception is the Universal Knowledge Core, a massively multilingual lexical database that explicitly represents linguistic diversity and that we reuse in our work. Our research is part of the LiveLanguage initiative, the overarching objective of which is to create, publish, and manage language resources that are "diversity-aware"-i.
We consider kin terms as a domain particularly well-suited both for research on the methodology of collecting and producing diversityaware linguistic data, and for comparative studies on diversity across languages. Our paper aims to provide four contributions: (1) a general method for collecting multilingual lexical data from native speakers for a given domain (in our case the domain of kin terms), in a diversity-aware manner; (2) 223 kin terms and 1,619 lexical gaps collected in seven Arabic dialects and three Indonesian languages; (3) a qualitative and quantitative discussion of our results regarding the diversity observed across the dialects and languages covered; and (4) the publication of our results as an open, computer-processable dataset, as well as its integration into the Universal Knowledge Core multilingual database.
Our starting point is state-of-the-art datasets on worldwide kinship terminology from ethnography  (Murdock, 1970 ) and computational linguistics  (Khishigsuren et al. , 2022). Our data collection method is based on collaborative input from native speakers and language experts. Our results extend the state-of-the-art resources above with kin terms in languages and dialects not yet covered, as well as with 22 new kinship concepts not yet associated with other languages within those resources. e structure of the paper is organized as follows. In Section 2, we give an overview of lexical typology and the phenomena of lexical untranslatability and lexical gaps with respect to the domain of kinship in particular. e Universal Knowledge Core resource is presented in Section 3. In Section 4, we describe our data collection method.
Lexical gaps are oen the linguistic manifestation of culturally or spatially de ned speci cities of a community of language speakers that cannot entirely be predicted or explained through systematic principles or recurrent patterns  (Lehrer, 1970). eight languages has concise lexicalizations for all nine concepts, yet each concept is lexicalized in at least one language. Such variations in lexicalization pose a problem for both machine and human translation: for instance, substituting a speci c term instead of a broader one may result in injecting unintended meaning. In Javanese, at least four speci c terms-(sedulur/sibling), (adhi/younger sibling), (kangmas/elder brother), and (Mbakyu/elder sister)-are used for expressing the sibling relationship, and accordingly, translating this sentence through Google Translate (my sister is ten years older than me) to Javanese gives this nonsensical sentence (adhiku luwih tuwa sepuluh taun tinimbang aku) meaning (my younger sibling is ten years older than me).
Other examples of such studies have been conducted on lexicaltypological issues that appear across languages during translation, like the presence or absence of lexicalizations in languages. In these articles, authors focused on semantic elds that offer the richness of cross-lingual diversity: family relationships  (Kemp and Regier, 2012) , colors  (Roberson et al. , 2005) , food  (Bella et al. , 2022b) , body parts  (Wierzbicka, 2007) , putting and taking events  (Kopecka and Narasimhan, 2012) , cutting and breaking events  (Majid et al. , 2007) , or cardinal direction terms  (Arora et al. , 2021). However, as mentioned in the introduction, only a few open datasets have been published in the scienti c research area. ese include the classi cation of kinship by  Murdock (1970) , which has been published in D-PLACE  (Kirby et al.
, 2016). Part of  Kay and Cook (2016) 's work on colors is published under the lexicon chapter of the World Atlas of Language Structures (WALS)  (Dryer and Haspelmath, 2013). Additionally, a color categorization dataset by  McCarthy et al. (2019)  is available on GitHub 2. Digital lexicons have been increasingly used in lexical typology, enabling typologists to explore a broader range of languages and semantic domains. One noteworthy example is the KinDiv 3  lexicon  (Khishigsuren et al. , 2022) , which encompasses 1,911 words and identi es 37,370 gaps within the domain of kinship, spanning 699 languages. In our current research, we extend our investigation into the kinship domain, speci cally focusing on exploring linguistic diversity among Arabic dialects and Indonesian languages. Other examples include  Viberg (1983) 's seminal study, which was conducted on perceptual terminology in 50 languages and has been expanded upon by  Georgakopoulos et al.
(2022)  to cover 1,220 languages. Furthermore, the Kinbank database, recently introduced by  Passmore et al. (2023) , serves as a comprehensive repository of kinship terminology, encompassing more than 1,173 languages and offering a broad coverage of various kinship subdomains. 3 Universal Knowledge Core is section describes the Universal Knowledge Core (UKC) 4  , a large multilingual lexical database that we adopt for the production of diversity-aware datasets in this research  (Giunchiglia et al. , 2017). e use of the UKC is motivated by its ability to represent linguistic unity and diversity explicitly: conceptualizations shared across languages, word senses appearing only in certain languages, shared lexicalizations (e. g. , cognates), as well as lexical gaps. e theoretical underpinnings of the lexical model of the UKC have been described in  Giunchiglia et al.
(2018)  and in  Bella et al. (2022b) , and are illustrated in Figure  1. e UKC is divided into a supra-lingual concept layer (as shown at the top of Figure  1 ) and the layer of individual lexicons (at the bottom of Figure  1 ). e concept layer includes hierarchies of concepts that represent lexical meaning shared across languages. Concepts are language-independent units and act as bridges across Structural elements in the UKC lexical database. languages, and each one should be lexicalized by at least one language to be present in the concept layer. Supra-lingual concepts and their relations (e. g. , hypernymy, meronymy) are in part derived from third-party resources such as Princeton WordNet (PWN)  (Miller, 1995) , and are in part proper to the UKC. In particular, the UKC contains an extensive formal conceptualization of kinship domain terms computed from the KinDiv database, spanning about 200 distinct concepts.
5  KinDiv itself is based on ethnographic evidence from 699 languages  (Khishigsuren et al. , 2022). While this existing hierarchy of kinship concepts does not fully cover all terms that appear in our study, it is the most complete one we are aware of, motivating our choice of the UKC as a platform for our research. e lexicon layer consists of language-speci c lexicons that provide lexicalizations for the concepts from the supralingual concept layer, while also asserting lexical gaps whenever lexicalizations are known not to exist. Lexicons also provide term de nitions as well as lexical relationships speci c to the language, such as derivations, metonymy, or antonymy relations. Lexicons can also contain language-speci c concepts that do not appear in the supra-lingual concept layer. For example, in Figure  1 , the Arabic ‫ِيقة‬ ‫َق‬ ‫,ش‬ meaning "a female person who has the same father, mother, or both parents as another person", is represented as a language-speci c concept.
e dual mechanism of de ning lexical concepts either on the supra-lingual or on the language-speci c level allows for the representation of differing worldviews that would be hard or impossible to reconcile into a single global concept graph. e richness of its lexicon-level linguistic knowledge makes the UKC unique among multilingual lexical databases and particularly suitable for our study. As mentioned in Section 2, a lexical gap for a speci c concept is present in a language if there is no concise equivalent word meaning for the concept in that language. For example, neither English nor Arabic has a word meaning elder sibling; for such cases, the UKC provides evidence of meaning nonexistence and untranslatability by representing lexical gaps inside lexicons, as shown in Figure  1. is information can be used by the NLP community to indicate the absence of equivalent words to downstream cross-lingual applications.
Beyond providing lexical relations between shared word meanings as other multilingual lexical databases do, the UKC also represents a richer set of lexical-semantic connections between language units in a lexicon. For example, the antonym lexical relation expresses that two senses are opposite in meaning. While the lexicalsemantic relation, similar-to, is used to connect two concepts with similar meanings, and the hypernym-of connects parent meaning with its child. For instance, in Figure  1 , the English (little brother) and (brother) are connected through a hypernym-of relationship. Such information can be used by the NLP community to indicate the concise equivalent language-speci c word meaning to downstream cross-lingual applications, e. g. , as the position of a language-speci c meaning in a language hierarchy in a lexicon.
We exploit the UKC to import language-independent concepts (e. g. , kinship concepts) to be used as an input dataset to our method and use its data representation model to formalize our data. We reuse an already broad and well-formalized hierarchy of 184 kinship concepts from the KinDiv database, which includes kinship terms and gaps in 699 languages. Data in KinDiv is based on the wellknown results of  Murdock (1970) , as well as on lexicalizations retrieved from Wiktionary that we consider as an overall goodquality resource. In  Khishigsuren et al. (2022) , the accuracy of KinDiv was evaluated to be above 96%. One language expert per language provided this percentage, which represents the proportion of the number of words (or gaps) validated as correct to the total number of collected words (or gaps).
Our work extends KinDiv data by new concepts, lexicalizations, and lexical gaps in languages and dialects that are either not present in KinDiv or are incompletely covered. A lexical-semantic expert generates a contribution (kinship terms and gaps) task, then a group of native speakers collects contributions from a dialect (and a local language). Aer that, two steps for validating collected contributions: language experts evaluate collected lexical units and gaps of a dialect, and a lexical-semantic expert evaluates explored kinship concepts (not existing in UKC). Additionally, resulting data (including gaps, words, and new concepts) is used to update and enrich UKC. So, gaps and words are merged into the lexicons of the UKC while new concepts are integrated with the (top) concept layer. A general view of the method is depicted in Figure  2.
Accordingly, the macro-steps of our methodology are as follows: 1. Contribution task generation: First, prepare the materials: the dataset of inputs to be examined and the architecture of the supra-lingual concept layer of each subdomain. 2. Contribution collection: e actual contribution effort is carried out by a native speaker in a local language or dialect. 3. Lexicon-level validation: Provided words and gaps are evaluated and corrected by a language expert. 4. Concept-level validation: New concepts and unclear contributions (i. e. , words on the borderline) are veri ed by a lexico-semantic expert.
Contribution task generation is section describes the material needed during the execution of the next steps of the methodology. Hence, two constituents must be prepared in this step as described below: 1. Dataset of inputs: Constructing the dataset of general word meanings is the rst step of studying diversity across dialects and represents the inputs of the contribution collection phase. In this context, the UKC lexicon is employed to build a dataset, which contains several facilities that support retrieving categorized data from its interlingual shared meaning layer as introduced in Section 3. Moreover, typology datasets or other approaches can be used for that, such as the kinship dataset from  Murdock (1970) ; or gathering data from online dictionaries using automatic methods, i. e. , KinDiv retrieves some of its kinship terms from Wiktionary.
e constructed dataset is a spreadsheet containing language-independent meanings from one semantic eld. At the same time, its content is distributed into subdomains (sheets) for usability and simplicity in designing a concept hierarchy for each subdomain which is a helpful tool for lexical-gap exploration. One spreadsheet row is generated for each concept, containing the concept ID, the source concept de nition in the standard language, another de nition in English, as well as empty slots for inserting a lexical gap or a word with equivalent meaning, and the data provider's comments in a dialect or local language. 2. Interlingual concept hierarchy: Modeling the interlingual shared meaning space is essential to explore lexical gaps systematically. In this task, the UKC concept hierarchy is exploited.