dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgment The authors sincerely thank the following investigators for kindly sharing additional data from their studies, which enabled this meta-analysis to be performed: Professor Christopher D. Byrne  and  Dr. Joshua A. Welsh , ( Human Development  and  Health Academic Unit, Faculty of Medicine, University of Southampton, Southampton, United Kingdom ),  Dr. Rudolf Stauber  ( Institute of Pathology, Graz, Austria ),  Dr. Stephen Djedjos  ( Gilead Sciences, Inc. ),  Dr. Peter Eddowes  ( Centre for Liver Research, University of Birmingham, Birmingham, United Kingdom ). This work has been funded by the  Innovative Medicines Initiative 2 Joint Undertaking  under grant agreement No. 777377  (LITMUS). This Joint Undertaking receives support from the  European Union 's  Horizon 2020 research and innovation programme and EFPIA.
We first situate our contribution in digital ethnography and network science literature (section 2). Then we introduce a data model for SSNs (section 3). Next, we present data in SSN form from a study on community-provided health and social care services (section 4). We then illustrate how we used SSNs to aggregate and navigate a large corpus of ethnographic data (section 5). Finally, we reflect on their potential and possible extensions (section 6).
The semantic network is therefore not generated from keywords, but from sustained ethnographic engagement with user contributions. This approach integrates benefits from QDA software  (Bernard et al. 2016) ; iterative meaning-mapping approaches  (Dressler et al. 2005) ; anthropological participant-observation; and social network analysis to display a map expressing what community members are talking about, and who is talking to whom about what concepts (as interpreted by the researcher). It provides a rich visualization, derived from grounded theory  (Bernard 2011; Bernard et al. 2016) , to aid in anthropological theorization. Anthropologists have suggested that network images enhance qualitative understandings of social processes  (Burrell 2009; Hannerz 1992; Strathern 1996). Several mixed methods approaches combine qualitative research with social network mapping.
Snodgrass et al. (2017)  utilise psychometric measurement techniques and interviews to generate measurable qualitative data sensitive to socioculturally-specific meaning. Dengah et al. (2018)  use survey data and "egocentric social network interviews" to theorise the relationship between social support and online gaming involvement. We employ online ethnographic participant-observation rather than offline interviews, and construct social networks from observed, rather than reported, online interaction. Dressler et al. (2005)  share our aim: retaining sensitivity to informants' contextual, interactional, and socioculturally-specific understandings of concepts while also introducing systematic means of visualizing (and measuring) them. We, however, do not to attempt to measure a pre-hypothesised concept like cultural consonance.
We use SSNs to theorise how humans co-construct meaning through social interaction, rather than how individual behavior produces network structure, or how concepts in more static or single-authored documents like news media relate to each other. Social networks are traditionally inferred from relations or interactions between people  (Borgatti et al. 2009) , represented as graphs where relations are embodied as links between nodes (persons)  (Wasserman & Faust 1994). Structural metrics (degree, distances, centralities, matrix eigenvalues, etc. ) can be interpreted as socially meaningful  (Burt 2000; Freeman 1977; Scott 2000). We represent ethnographic data as a network. We choose its form so that it encodes information on both what each informant is talking about (as interpreted by the ethnographer), and who she is talking to.
So, the network is a semantic and social one -a SSN. It stores the data in a structured form, without compromising their rich, contextual character. SSNs are for human consumption, and therefore underpinned by a simple, intuitive ontology. Our representation has only two types of nodes, participants in the conversation and ethnographic codes, and three types of edges: comments-the-content-of (connects a participant to another), authored-posts-annotated-with (connects a participant to a code), and co-occurs-with (connects a code to another). By contrast, graph databases (proposed as early as the 1970s) can and do encode many types of relationships  (Shapiro 1977).
Ethnographic Coding: Methods and Implications We employed iterative, inductive, and reflexive coding processes  (Auerbach & Silverstein 2003; Bernard 2011; Saldaña 2015; Sunstein & Chiseri-Strater 2011 ). An anthropologist  (Hassoun)  coded twenty-eight hours a month over a oneyear period. She performed three coding passes on the dataset, with continuous updating of codes based on a detailed codebook with memos and analysis notes. Both descriptive and in vivo codes were utilised. The ethnographer coded directly on-platform via an application we added to the platform's code base. This process created a database of codes and automatically visualised it in network form. We find ethnographic coding preferable to word count or keywords, because coding is not just a process of labeling, but a clarifying, analytical process in which the ethnographer names and maps the concepts invoked by the community  (Saldaña 2015).
An application: the OpenCare data The OpenCare project explores how communities provide health and social care when neither states nor businesses can or will serve them. We began with the research question: What do people do when existing health and social care systems no longer provide care? Data were gathered from an online forum where individuals discuss their care experiences. We used the method described in section 3 to code them and build the corresponding SSN. We then built a social and a semantic network from the coded data.
For the OpenCare dataset, filtering edges by k ≥ 6 yields a co-occurrence network with 60 codes and 72 edges, which lends itself well to visual inspection (Figure  1 ). From it, one can see the structure of community members' concerns. Consider the cluster with legality (in green): we find existing system failure and regulation, reflecting the preoccupation of some informants that community health care initiatives and technological innovations outside of existing systems (much needed when systems fail), turn out to be illegal and therefore difficult to implement. We also find safety, reflecting the acknowledgement that regulation is often there for a reason. We can also see isolated but intense conversations visualised as islands in the high co-occurrence network: the network death, grief, and (visible at a lower co-occurrence level, online memorials) appear unconnected to the rest of the network, indicating a deeply discussed single issue.
Social-communication (SC) and restricted repetitive behaviors (RRB) are autism diagnostic symptom domains. SC and RRB severity can markedly differ within and between individuals and may be underpinned by different neural circuitry and genetic mechanisms. Modeling SC-RRB balance could help identify how neural circuitry and genetic mechanisms map onto such phenotypic heterogeneity. Here we developed a phenotypic stratification model that makes highly accurate (97-99%) out-of-sample SC=RRB, SC>RRB, and RRB>SC subtype predictions. Applying this model to resting state fMRI data from the EU-AIMS LEAP dataset (n=509), we find that while the phenotypic subtypes share many commonalities in terms of intrinsic functional connectivity, they also show replicable differences within some networks compared to a typically-developing group (TD).
This idea has not yet been tested with respect to macroscale neural circuitry and its link to functional genomic mechanisms. Here we evaluate how SC-RRB balance subtypes link up to differential macroscale connectome phenotypes, measured with resting state fMRI (rsfMRI) functional connectivity. Functional connectivity networks are known to be linked to underlying transcriptomic mechanisms, particularly with regards to the spatial patterning of gene expression across the brain (e. g. ,  [16] [17] [18]  ). Given that subtypes could exhibit different functional connectome phenotypes, we leverage the link between macroscale rsfMRI functional networks and transcriptomic mechanisms to better understand whether autism-relevant functional genomic mechanisms differentially affect such phenotypes. To test these ideas, we developed a stratification approach that subtypes individuals based on the within-individual balance between SC versus RRB severity, as measured by ADI-R data from thousands of individuals (n=2,628) obtained from the National Database for Autism Research (NDAR) (https://nda.
nih. gov). This approach can be used to make highly accurate out-of-sample subtype predictions and thus can be applicable to any new dataset where ADI-R data is collected. We applied this SC-RRB balance stratification model to the deeply phenotyped EU-AIMS LEAP dataset  [19] [20] [21]  to examine how functional connectivity may differ between SC-RRB balance subtypes or relative to a typically-developing (TD) comparison group. While SC-RRB balance subtypes are important to test, it may be also useful to consider overall level of SC and RRB severity as important and to characterize this severity in a continuous manner. Thus, we also evaluate other possible continuous/dimensional models that examine SC and RRB separately as well as using SC-RRB balance score as a continuous variable. Finally, in order to link functional connectome phenotypes to autism-associated genes, we utilize the Allen Institute Human Brain Atlas  22, 23  to identify genes whose spatial expression pattern is highly similar to macroscale functional networks that differ amongst the SC-RRB subtypes.
Highly accurate out-of-sample prediction of SC-RRB balance subtypes In our first set of analyses, we sought to develop a model to predict ADI-R SC-RRB balance subtypes from the NDAR datasets. Relatively equal Discovery (n=889) and Replication (n=890) datasets were partitioned from the total n=1,781 individuals in NDAR and this split into Discovery and Replication was balanced as a function of the originating datasets and sex. Using z-normalized difference scores, we split the dataset into SC=RRB, SC>RRB, and RRB>SC subtypes (Figure  2 ). Importantly, the subtype labels were first defined separately in Discovery and Replication sets based on the statistical norms (i. e. mean and SD) estimated on each set. This ensures that the definition of the labels in each set is done independently of the other.
If the statistical norms for the computation of z-normalized difference scores (e. g. , mean and SD) are highly similar between Discovery and Replication, then the subtyping model learned from the Discovery set will likely be highly generalizable and produce high accuracy values in the Replication set. However, if the statistical norms are highly different between Discovery and Replication, the model learned from Discovery data will not generalize well to the labels in the Replication set and would thus produce poor out-of-sample prediction accuracy. Applying this approach across a range of z-thresholds (e. g. , z = 0. 5 up to z =1 in steps of 0. 1), we find that out-of-sample prediction accuracy is very high (e. g. , 97-99%). The reason for this high accuracy is visually evident in the high degree of similarity in distributions of Discovery and Replication sets in the scatterplots in Figure  2A.
Divergent functional genomic underpinnings of subtype-specific neural circuitry In the next analysis, we asked if known autism-associated genes are enriched amongst genes known to be highly expressed in these subtype-associated rsfMRI networks. We first identified lists of genes whose spatial expression topology in the Allen Institute Human Brain Atlas  22  is similar to rsfMRI connectivity networks  23  that show replicable subtype differences. Once a set of genes are predicted to underpin such rsfMRI networks, we then asked whether those genes are highly overlapping with known sets of functional genomic mechanisms linked to autism (see Figure  4A  for a visual representation of the analysis approach and Supplementary Data 2 for the full set of gene lists used in these analyses). Underscoring functional genomic overlap between the subtypes, all networks except for IC07 were enriched for a variety of similar autism-associated gene lists -such as, high penetrance rare de novo protein truncating variants (IC03, IC17), genes associated with autism from the SFARI Gene database (IC03, IC12, IC17), genes and co-expression modules that are downregulated in expression (IC1, IC03, IC12, IC17), and genes differentially expressed in excitatory and inhibitory neurons (IC03, IC12, IC17) and astrocytes (IC17).
Regarding the importance of specific networks identified in our analyses, the somatomotor network (IC12) has been implicated in past work on autism. Somatosensory areas have been shown to be some of the most informative regions in prior case-control classifier studies using rsfMRI data  45. Additionally, prior case-control analyses of the EU-AIMS LEAP dataset find that somatomotor areas show reduced degree centrality and autism-related hyperconnectivity with cerebellar networks  21, 30. However, the lack of somatomotor hypoconnectivity with visual association or perisylvian networks in prior case-control studies highlights the potential importance and added value of subtyping for revealing more subtle effects that can be masked with case-control contrasts. The perisylvian network that is hypoconnected with somatomotor circuitry in SC>RRB overlaps with a variety of areas implicated in early development of autism, particularly for auditory processing and language  46- 50.
However, to guard against this issue, we re-ran the analysis across a range of thresholds from z=0. 5 to z=1 and showed effects that are robust to the threshold used to label the subtypes. Accuracy for out-of-sample predictions is also high regardless of the threshold. This effect occurs largely because the data distributions and statistics used for the z-normalization are highly similar across large NDAR Discovery and Replication sets. In a situation where the data distributions were not similar, this high out-of-sample prediction accuracy would not have been obtained or may have fluctuated more substantially at different thresholds. Thus, while the choice of a threshold may not be well defined, any choice within the range we have analyzed of z=0. 5 to z=1, will yield highly consistent results that are not biased by the choice of a threshold.
The fact that the data distributions and sample statistics used for the z-normalization were so similar across wellmatched NDAR Discovery and Replication sets allows for high-confidence that the large NDAR dataset is likely very close in accurately estimating the true population parameters, and this allows for the high degree of replicability and robustness of the subtyping approach. Second, the distinctions between these subtypes are not demarcated by large categorical separations. As such, when we applied other canonical unsupervised clustering methods to the data, such methods were not able to consistently identify the same subtypes in independent datasets (Supplementary Figure  1 ). An automated dynamic hybrid tree-cutting method to apply to hierarchical clustering  28, 29  was however, close to deriving relatively similar subtypes across Discovery and Replication sets (Supplementary Figure  2 ).
Future work could explore the utility of this approach and the consensus subtypes derived from independent datasets with this methodology. However, the lack of very large separations between the boundaries for different subtypes of autistic individuals likely means that a more nuanced and theory-driven approach may be more fruitful than blind unsupervised approaches. Third, the RRB>SC group was not highly prominent in the EU-AIMS LEAP cohort. This observation is likely due to the fact that NDAR includes studies that more heavily sample individuals from the population with higher RRB severity relative to EU-AIMS LEAP. For example, ADOS CSS scores for RRB are higher in NDAR than in LEAP (see Tables  1 2 ). Because NDAR pools from a much wider range of studies in different contexts compared to EU-AIMS LEAP, this could be an explanation for this difference.
NDAR Datasets For the initial set of analyses to derive the approach to characterizing SC and RRB symptom domains, we utilized ADI-R from the National Database for Autism Research (NDAR). Because individuals can differ over the lifespan with regard to current symptom presentation, we opted for using the ADI-R ('diagnostic algorithm' scores) as the measure of symptomatology because it allows for assessment of symptoms at similar ages regardless of the age of the participant at the time of testing. This is an important point within the context of the current study, whereby the age range of the follow-up sample (i. e. EU-AIMS LEAP) is notably wide (i. e. 6-30 years). Within the SC domain, many items are rated based on presentation around 4-5 years of age. This is required because such items probe behaviors that are developmentally most appropriate in typical development around this point in the lifespan and the absence of such behaviors in autistic individuals is of diagnostic importance.
In contrast, for many types of RRB domain items, the presence rather than the absence of such behaviors is of diagnostic importance for autism. For these items, the behaviors could occur at any point during the lifespan and are not developmentally restricted to a specific age range. On December 13, 2019 we conducted a search of NDAR to extract all datasets utilizing the ADI-R  25. This resulted in 60 independent datasets totaling 2,628 unique individuals. From here, we filtered for all individuals who had data for the verbal communication items (e. g. , acquisition of words, phrases, social verbalization, chit-chat, reciprocal conversation), leaving a total of 1,781 individuals across 57 independent datasets. Within each of these 57 datasets, we randomly split the dataset in half to achieve independent Discovery and Replication sets that are balanced across the 57 datasets and by sex (Discovery n=889, mean age = 8.
5 to z=1, in steps of 0. 1. This approach allows us to report results across thresholds rather than using only one arbitrarily defined threshold. For the later functional connectivity analyses, this approach also allowed us to identify a consensus result which is consistent irrespective of the zthreshold used to label subtypes. To make out-of-sample predictions, we used the mean and SD norms estimated from the NDAR Discovery set to z-score and label individuals in the NDAR Replication set. These predicted labels on the Replication set were then compared to the actual subtype labels computed using the mean and SD norms derived from the Replication set itself. To make subtype predictions in the EU-AIMS LEAP dataset, we combined both NDAR Discovery and Replication datasets into one large dataset.
From this dataset, norms for the mean and standard deviation were computed (mean = 0. 01045243, SD = 0. 19482749) and used for the z-scoring procedure. SC-RRB difference z-scores were then computed and a z-threshold was applied to the EU-AIMS LEAP dataset to generate subtype labels. In addition to this SC-RRB difference z-score subtyping approach, we also used other unsupervised clustering methods for identifying subtypes. These methods utilize agglomerative hierarchical clustering with Euclidean distance and the ward. D2 method. The optimal number of clusters was determined by a majority vote of 23 different metrics for determining the optimal number of clusters (e. g. , using the NbClust library in R)  27. With another approach, we ran the same hierarchical clustering analyses, but cut dendrograms to define subtypes using a dynamic hybrid tree cut algorithm, as utilized in past work  28, 29.
EU-AIMS LEAP Dataset The EU-AIMS LEAP data comes from a large multisite European initiative with the aim of identifying biomarkers for ASD  19. In this study, EU-AIMS LEAP data is utilized to examine how SC-RRB balance subtypes may differ in intrinsic functional connectomic organization using rsfMRI data. rsfMRI data from EU-AIMS LEAP has been analyzed for case-control differences in prior work  21, 30. EU-AIMS LEAP recruited 437 individuals with ASD and 300 TD individuals, both male and female, aged between 6 and 30 years. 2 ), and written informed consent was obtained from all participants or their legal guardians (for participants <18 years). For further details about the study design, we refer to Loth et al. ,  20  , and for a comprehensive clinical characterization of the LEAP cohort, we refer to Charman et al.
,  19. In the present study, we selected all participants for whom structural and rsfMRI data were available. However, n=120 participants had to be excluded from the analysis because of missing ADI-R item-level data (n=64), missing IQ data (n=3), or because preprocessing could not be completed for a variety of reasons (e. g. , registration/normalization errors because of poor quality MPRAGE data, poor anatomical coverage, or large anatomical deviance such as large ventricles (n=39), incomplete rsfMRI data (n=3), errors in convergence of the ME-ICA algorithm (n=11)). The final sample size was n=266 autistic and n=243 TD participants. This final sample was split into independent Discovery and Replication sets (balanced for sex and age) for the purpose of identifying functional connectivity differences that are replicable.
EU-AIMS LEAP fMRI Preprocessing Multi-echo rsfMRI data were preprocessed with the multi-echo independent components analysis (ME-ICA) pipeline, implemented with the meica python library (v3. 2) (https://github. com/ME-ICA/me-ica). ME-ICA implements both basic fMRI image preprocessing and decomposition-based denoising that is specifically tailored for multi-echo EPI data. For the processing of each subject, first the anatomical image was skull-stripped and then warped nonlinearly to the MNI anatomical template using AFNI 3dQWarp. The warp field was saved for later application to functional data. For each functional dataset, the first TE dataset was used to compute parameters of motion correction and anatomical-functional coregistration, and the first volume after equilibration was used as the base EPI image.
Matrices for de-obliquing and six-parameter rigid body motion correction were computed. Then, 12-parameter affine anatomical-functional coregistration was computed using the local Pearson correlation (LPC) cost functional, using the gray matter segment of the EPI base image computed with AFNI 3dSeg as the LPC weight mask. Matrices for de-obliquing, motion correction, and anatomicalfunctional coregistration were combined with the standard space nonlinear warp field to create a single warp for functional data. The dataset of each TE was then slice-time corrected and spatially aligned through application of the alignment matrix, and the total nonlinear warp was applied to the dataset of each TE. No time-series filtering was applied in the preprocessing phase. No spatial smoothing was applied during preprocessing.
The preprocessed multi-echo time-series datasets were then used by the ME-ICA pipeline to leverage information in the multiple echoes to compute an optimal weighting of TE at each voxel  33  , producing an 'optimally combined' time-series dataset. This optimal combination procedure has been shown to double temporal signal-to-noise ratio (tSNR) over traditional single echo EPI data  34. This preprocessed optimally combined time-series dataset was then fed into a denoising procedure based on independent components analysis (ICA) and scoring components by ρ and κ pseudo-F statistics that indicate degree of TE-independence or TE-dependence. Components with high ρ and low κ are components high in non-BOLD related contrast (i. e. non-BOLD artefact signal), while components with high κ and low ρ indicate components high in BOLD-related contrast.