dataesr/scientific-paragraphs-categorization huggingface dataset:
Results and conclusions: A total of 4702 participants have been recruited in the eight registries as of 1st of May 2022. Of the 69 core dataset domain items, data pooling was possible for 69 domain item outcomes in TREAT NL (the Netherlands), 61 items in A-STAR (UK and Ireland), 38 items in TREATgermany (Germany), 36 items in FIRST (France), 33 items in AtopyReg (Italy), 29 items in Biobadatop (Spain), 28 items in SCRATCH (Denmark) and 20 items in SwedAD (Sweden). Pooled analyses across all registries can be performed on multiple important domain items, covering the main aims of analysing data on the (cost-)effectiveness and safety of AE therapies. These results will facilitate future comparative or joint analyses. for instance the Psonet initiative (an European surveillance network to monitor the long-term effectiveness and safety of systemic agents in the treatment of psoriasis).
9 he TREAT core dataset consists of 19 core domains and 69 domain items, counting 49 baseline items and 20 follow-up items (defining 'what to measure'). 7 As a final step in the harmonization process, the outcome measurement instruments, consisting of a total of 118 measurement instruments for all 69 domain items, and follow-up frequency and visit window were determined (defining 'how to measure' and 'when to measure'). 8 All affiliated TREAT registries are encouraged to collect data in accordance with this core dataset. Several registries from different countries have joined the TREAT Registry Taskforce over the past years, currently including the A-STAR registry (The UK-Irish Atopic Eczema Systemic Therapy Register; United Kingdom and Ireland), Biobadatop registry (Spain), TREATgermany registry (Germany), TREAT NL registry (the Netherlands and Belgium), SCRATCH registry (Severe and ChRonic Atopic dermatitis Treatment CoHort, Denmark), FIRST registry (French atopIc deRmatitiS cohorT, France), AtopyReg registry (Italy) and SwedAD registry (Sweden).
These registries concern prospective observational cohorts and offer a platform to conduct cross-border research. A framework to conduct studies within the taskforce has been published previously. 10 espite the use of a core dataset, differences in data collection are expected due to several reasons, including the use of different data entry platforms. Potential differences may also arise due to variability in interpretation of the core dataset and the selection of (optional) core dataset items (in the context of feasibility). Furthermore, patient in-and exclusion criteria may differ per country, for example, due to discrepancies in treatment reimbursement and differences in prescribing practices. Therefore, we aimed to give an overview of the status and characteristics of the established TREAT registries and to perform a mapping exercise.
Status and characteristics of the registries To investigate the current status and a description of the characteristics of each registry, we requested the following information (as of 1st of May 2022): status of recruitment, month and year of first patient inclusion, number of recruited patients, number of participating centres, countries involved in each registry, website address, data capture platform/modality, funding sources, language of the database and included therapies (conventional systemic therapies, biologicals, phototherapy and other systemic therapies). In addition, we requested the inclusion and exclusion criteria of each registry. Furthermore, information was collected on the follow-up frequency and visit windows for follow-up, to allow comparison with the defined 'when to measure'.
Mapping exercise All registries were asked to share their dataset (e. g. the (electronic) case report forms ([e]CRFs) used) for the purpose of the mapping exercise. If more than one CRF was used for different timepoints within one registry, multiple CRFs were received. The use of the core dataset and the overlap between the core dataset and the registry dataset was identified according to the domains (n = 19), domain items (n = 69; 'what to measure') and measurement instruments (n = 118; 'how to measure') of the TREAT core dataset. 8 We scored positive (1) if the dataset item was completely in accordance with the core dataset, negative (0) if the item was not captured and partially positive (2) if the item was only partly corresponding. The mapping exercise was conducted as follows: as the main outcome of interest, because ultimately this will provide information on which cross-border analyses can be performed.
Mapping exercise The complete results of the mapping exercise with the assessment of the presence of core dataset domain items and measurement instruments, and the pooling ability of measurement instruments and domain items can be found in Table  S1. Of the 69 core dataset domain items, data pooling was possible for 69 items in TREAT NL (the Netherlands), 61 domain items in A-STAR (UK and Ireland), 39 items in TREATgermany (Germany), 36 items in FIRST (France), 34 items in AtopyReg (Italy), 29 items in Biobadatop (Spain), 28 items in SCRATCH (Denmark) and 20 items in SwedAD (Sweden). The specific results on the pooling ability per domain items are displayed in Table  4. This concerns a condensed part of Table  S1. In Table  4 , it is shown that dataset domain items with the ability to pool data from all eight registry datasets include: 'date of birth', 'date of enrolment into registry', 'gender' (domain: demographics), date of onset of AE (domain: AE diagnosis), 'systemic therapy' (domain: current AE treatments), 'family history of AE or allergic diseases' (domain: family history of AE or allergic diseases), 'asthma', 'allergic rhinoconjunctivitis' (domain: allergic co-morbidities), 'physician-assessed clinical signs', 'patientreported symptoms', 'skin-specific quality of life score' (domain: baseline physician-and patient-reported domains), physician-assessed clinical signs', 'patient-reported symptoms', 'skin-specific quality of life score' (domain: follow-up physician-and patient-reported domains).
The number of domain items that scored positive for pooling ability according to the number of registries can be found in Figure  1. The HOME core outcome set consists of clinical signs (EASI), patient-reported symptoms (POEM and NRS-11 for peak itch over past 24 h), quality of life (DLQI (adults), CDLQI (children), IDQoL (infants)) and long-term control (Recap of Atopic Eczema (RECAP) or Atopic Dermatitis Control Test (ADCT)). We found that all eight registries collect data on EASI, POEM, DLQI, CDLQI and IDQoL. NRS-11 peak itch over past 24 h was fully or partially collected by five registries. The long-term control item has recently been introduced to the outcome set. Data collection on this item using RECAP and/or ADCT is currently implemented or planned to be implemented by most TREAT registries.
DISCUS SION The overview of the status and characteristics presented here provides insight into the current AE treatment registries within the TREAT Registry Taskforce. Since inception, the TREAT Registry Taskforce has aimed to develop an international platform to uniformly collect long-term data on the (cost-)effectiveness and safety of systemic immunomodulating therapies and/or phototherapy in patients with AE. As per May 1, 2022, the established registries participating within the TREAT Registry Taskforce have jointly collected data of over 4700 patients. The registries have already been publishing their first results on patient characteristics, treatment effectiveness and safety individually. 11, 12 The next step is to increase the power of the data of individual countries by pooling data across registries.
As described, the TREAT Registry Taskforce has developed a core dataset to be used in all registries and a protocol to enable this cross-border data pooling. 8, 10 The current study has revealed both similarities and differences regarding the degree of core dataset use and pooling ability between registries within the TREAT Registry Taskforce. Similarities between the registries cover the main aims of collecting data on the effectiveness, safety and costeffectiveness of AE therapies. Pooled analyses across all registries can be performed on the following domain items: 'date of birth', 'date of enrolment into registry', 'gender', date of onset of AE, 'systemic therapy', 'family history of AE or allergic diseases', 'asthma', 'allergic rhinoconjunctivitis', 'physician-assessed clinical signs' (e.
g. EASI) (baseline and follow-up), 'patient-reported symptoms' (e. g. POEM) (baseline and follow-up) and 'skin-specific quality of life score' (baseline and follow-up). These items cover important effectiveness outcomes. As for safety, six registries collect data on severe and serious adverse events. Cost-effectiveness analyses can be performed using the generic quality of life score EQ-5D. Data collection on EQ-5D is included in three registries. We found that all HOME core outcomes, except from long-term control, were collected by all eight registries within the TREAT Registry Taskforce. As a result, comparative and pooled analyses on effectiveness and pharmacovigilance are feasible. Despite the aspired use of an uniform core dataset, differences in data collection were identified. These differences may pose potential challenges in data pooling and synthesis.
They may have resulted from various factors, including the use of different data entry platforms per registry. Further, countries may have given their own     interpretation to core dataset items. Also, the high number of domains and domain items included in the core dataset have compromised its feasibility, despite the fact that feasibility aspects were considered in the TREAT core dataset consensus seeking process. This was indicated by the members (n = 23) of the taskforce in a survey, held after finalizing the mapping exercise to clarify the use of the core dataset in their registries. Feasibility was the main reason for not including all core dataset items. Fortunately, the majority of the registries have indicated that they are willing to adapt their registry dataset to overcome potential important differences.
We suggest that, in addition to the items that are already being collected by all eight registries, every registry should at also gather information on safety (i. e. the domain item 'severe adverse events') and cost-effectiveness (i. e. the domain items 'generic quality of life score (baseline and follow-up)'). For future international analyses, one should not only take differences in registry datasets into consideration, but also differences in prescribing practices (e. g. patient indications), reimbursement restrictions and in-and exclusion criteria, which underlie potential variations in patient populations across the registries. Another factor to consider is that, due to national regulations and preferences, different modalities for data collection (e. g. the data entry platform) and languages are used across countries.
Future perspectives and recommendations The results of the mapping exercise inform on which data from which registries can be used to answer specific research questions, and therefore, will facilitate comparative or joint analyses across country borders in the future. While considerable differences between the registries exist, comparative and pooled treatment (cost)effectiveness and pharmacovigilance analyses are feasible. This is in particular important and encouraging, as rare but important adverse events (e. g. malignancies) demand investigation in large numbers of patients. Studies within the taskforce will run as investigatorled projects but we are open to project proposals requested by other researchers, clinicians and stakeholders. As a next step, the technical compatibility of the registry data will be assessed in a separate pooling exercise.
In addition, we are currently performing an analysis on baseline demographic and clinical characteristics of patients included in all registries. The present study informs researchers worldwide who are engaged in similar data harmonization processes in international research groups studying other diseases and who are aiming to perform pooled and comparative analyses in the future. In case a centralized data entry platform across registries and countries is impossible, our strong recommendation is to undertake substantial efforts to align and uniform datasets, preferably before inception of the databases. Feasibility should be a major criterion when a core dataset is developed. Finally, we would like to invite and F I G U R E 1 Pooling ability of domain item outcomes according to the number of registries.
Core dataset measurement instruments ('how to mea- sure', n = 118): we scored the use of core dataset instru- ments in each registry dataset, of which usually more are included per domain item (for example: the core dataset domain item 'how diagnosis AE is established' is mea- sured by two measurement instruments: (1) 'clinically Y/N' and (2) 'histopathology Y/N'). We considered an in- strument partially positive (2) if at least one part or cate- gory of the core dataset instrument was used (for example: if the answer categories for topical treatment in a registry were: '<30 g | 30-60 g | >60 g'; instead of the predefined categories in the core dataset: '<30 g | 30-60 g | 60-100 g | >100 g', this instrument would be scored partially positive (2)). •.
es/inves tigac ion/proye ctos-de- inves tigac ion/ RedCap (eCRF) Spanish Pharma Yes Yes Yes Yes Yes Yes Yes Yes No No No Yes Yes TREATgermany, Germany Recruiting June 2016 1. 484 57 Germany www. treat germa ny. org REDCap (eCRF) German Pharma Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes TREAT NL, the Netherlands and Belgium Recruiting November 2017 597 7 The Netherlands and Belgium www. treat regis ter. nl Castor (eCRF) English Government, pharma, academic support Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes T A B L E 1 Registry name, country A-STAR, United Kingdom and Ireland Status Recruiting Month and year of first October 2018 inclusion N included patients (May 283 1, 2022) N participating centres 20 Countries involved United Kingdom and Ireland Website astar-regis ter. org Data capture modality A-STAR (eCRF) Language of database English Funding Government, pharma, charity ability to pool outcomes of the domain items was scored positive (1) if pooling of at least one of the corresponding mea-surement instruments was deemed possible (for example, when a registry collects data on the domain item 'how diagnosis AE is established' using the measurement in-strument 'clinically Y/N', but not 'histopathology Y/N', data pooling on the domain item 'how diagnosis AE is established' was scored positive).
Inclusion and exclusion criteria of the TREAT registries Downloaded from https://onlinelibrary. wiley. com/doi/10. 1111/jdv. 18566 by University Of Southampton, Wiley Online Library on [03/11/2022]. See the Terms and Conditions (https://onlinelibrary. wiley. com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License BOSMA et al. 14683083, 0, | 7 Registry name, country Inclusion criteria Exclusion criteria A-STAR, UK and • Paediatric and adult patients with AE of any age who due to the severity • Insufficient understanding of the study Ireland of their disease and/or impact on quality of life are commencing on or by the patient and/or parent/guardian; switching to another systemic immuno-modulatory agent (e. g. CsA, AZA, • Patients who are currently participating MTX or biologic treatments); in a randomized clinical trial • Written informed consent for study participation obtained from the patient or parents/legal guardian, with assent as appropriate by the patient, depending on the level of understanding; • Participants consent to participate in long-term follow-up and access to all medical records, including hospital admission records and linkage to data held by NHS bodies or other national providers of healthcare data; • Diagnosis of AE in keeping with the U.
Downloaded from https://onlinelibrary. wiley. com/doi/10. 1111/jdv. 18566 by University Of Southampton, Wiley Online Library on [03/11/2022]. See the Terms and Conditions (https://onlinelibrary. wiley. com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 14683083, 0, T A B L E 2 (Continued) Registry name, country Inclusion criteria Exclusion criteria AtopyReg, Italy • Age: ≥18 years • Patient unable to provide informed • To sign informed consent consent prior to any data collection • Diagnosis of moderate or severe AE made by one dermatologist defined on procedures the basis of the following criteria: • data related to the study; • EASI ≥16 • Patient unable to complete the • EASI <16 but with at least one of the following conditions: procedures required for the study; • Localization in at least one of the following "critical" sites: face, hands, • Patient already participating in another genitalia registry for the same condition • DLQI >10 • itch-VAS >7 • sleep-VAS >7.
Functional magnetic resonance imaging (fMRI) offers a rich source of data for studying the neural basis of cognition. Here, we describe the Brain Imaging Analysis Kit (BrainIAK), an open-source, free Python package that provides computationally-optimized solutions to key problems in advanced fMRI analysis. A variety of techniques are presently included in BrainIAK: intersubject correlation (ISC) and intersubject functional connectivity (ISFC), functional alignment via the shared response model (SRM), full correlation matrix analysis (FCMA), a Bayesian version of representational similarity analysis (BRSA), event segmentation using hidden Markov models, topographic factor analysis (TFA), inverted encoding models (IEM), an fMRI data simulator that uses noise characteristics from real data (fmrisim), and some emerging methods.
These techniques have been optimized to leverage the efficiencies of high performance compute (HPC) clusters, and the same code can be seamlessly transferred from a laptop to a cluster. For each of the aforementioned techniques, we describe the data analysis problem that the technique is meant to solve, and how it solves that problem; we also include an example Jupyter notebook for each technique and an annotated bibliography of papers that have used and/or described that technique. In addition to the sections describing various analysis techniques in BrainIAK, we have included sections describing the future applications of BrainIAK to real-time fMRI, tutorials that we have developed and shared online to facilitate learning the techniques in BrainIAK, computational innovations in BrainIAK, and how to contribute to BrainIAK.
Introduction Cognitive neuroscientists have come a long way in using functional magnetic resonance imaging (fMRI) to help answer questions about cognitive processing in the brain. A variety of methods have been developed, ranging from univariate techniques to multivariate pattern analysis (MVPA) methods  [1] [2] [3] [4]. A large number of toolboxes are now available that implement these patternanalysis methods, including, for example, the Princeton MVPA Toolbox  [5] , the Decoding Toolbox  [6] , CoSMoMVPA  [7] , Nilearn  [8]  and PyMVPA  [9]  (for a full list see https://github. com/ohbm/ hackathon2019/blob/master/Tutorial Resources. md). Scientists can choose which toolbox to use based on the analysis that they wish to perform and the programming language they wish to use. In this work, we describe the Brain Imaging Analysis Kit (BrainIAK (RRID:SCR 014824), https://brainiak.
org), an open-source Python package that implements computationally-optimized solutions to key problems in advanced fMRI data analysis, focusing on analysis steps that take place after data have been preprocessed and put in matrix form. BrainIAK can be viewed as a "Swiss army knife" for advanced fMRI analysis, where we are constantly striving to add new tools. Presently, BrainIAK includes methods for running inter-subject correlation (ISC)  [10]  and inter-subject functional correlation (ISFC)  [11, 12] , functional alignment via the shared response model (SRM)  [13] , Bayesian RSA  [14, 15] , event segmentation  [16] , dimensionality reduction via topographic factor analysis (TFA)  [17] , and inverted encoding models (IEMs)  [18, 19]. To avoid duplication across packages, BrainIAK leverages available methods in other packages -it is well integrated with Nilearn (https://nilearn.
github. io/index. html) [20] , and extensively uses Scikit-learn (https://scikit-learn. org/) [21]  for machine learning algorithms. The functions in BrainIAK are optimized to run on high performance compute (HPC) clusters for efficient execution on large datasets. The same code can be executed on a laptop or a HPC cluster, saving significant time in refactoring the code to run in a HPC environment. BrainIAK also includes a detailed set of tutorials  [22]  that help the novice or expert user learn and implement the methods, including materials relevant to running on HPC clusters. Scientists can also use BrainIAK's simulator  [23]  to create model based patterns of activity at the voxel level, without going through the expensive and time consuming process of data collection. The package is released with an open source license and is free to use on a variety of platforms.
Methods in BrainIAK In the sections below, we present an overview of each of the methods presently included in BrainIAK and an accompanying example notebook. For each method, we list the data analysis problem that it is meant to solve, and how it solves that problem. All example notebooks are available here https://github. com/brainiak/brainiak-aperture, along with instructions on how to run them. The notebooks also contain an annotated bibliography for each method, listing papers that have described and/or used this method.
The Notebook The accompanying notebook applies ISC analysis to an example fMRI story-listening dataset from the "Narratives" data collection  [33, 34]. To reduce computational demands, we compute ISC on a time series averaged within each parcel extracted from a functional cortical parcellation  [35]. We first demonstrate high ISC values extending from low-level auditory cortex to higher-level cortical areas during story-listening. However, when listening to a temporally scrambled version of the stimulus, ISC is dramatically reduced in higher-level cortex areas, suggesting these areas encode temporallyevolving features of the stimulus (e. g. narrative context). We next perform a similar comparison between intact and scrambled story stimuli using traditional within-subject functional connectivity and ISFC analysis.
De-noised individual-subject data can be reconstructed by projecting data from the reduced-dimension shared space back into any given subject's brain space. Furthermore, in cases where each subject's unique response is of more interest than the shared signal, SRM can be used to factor out the shared component, thereby isolating the idiosyncratic response for each subject  [13]. Building on the initial probabilistic SRM formulation  [13, 42] , several variants of SRM have been developed to address related challenges. For example, a fast SRM implementation has been introduced for rapidly analyzing large datasets with reduced memory demands  [43]. The robust SRM algorithm tolerates subject specific outlying response elements  [44] , and the semi-supervised SRM capitalizes on categorical stimulus labels when available  [45].
The Notebook The accompanying notebook applies the SRM to an example fMRI story-listening dataset from the "Narratives" data collection  [33]. We apply the SRM within a temporal parietal region of interest comprising auditory association cortex from a functional cortical parcellation  [35] , and explore the components of the resulting model. We evaluate the SRM using between-subject time-segment classification. This analysis reveals that the SRM yields a considerable improvement in betweensubject classification beyond anatomical alignment.