dataesr/scientific-paragraphs-categorization huggingface dataset:
In addition to the monitor's own binary. CWA format, OMGUI can also create and load brand agnostic csv and audio WAVE file formats  (Jackson, 2018a). Pampro by Tom White (Cambridge, UK) is implemented in Python, available on GitHub, and designed to process data from multiple wearable sensor brands and data formats using methodologies replicated from publications, including the publications describing parts of GGIR  (White, 2018). Pampro does not include an open source license and we have been informed by Pampro's developer that this is intentional. The lack of an Open Source license prohibits using a software without explicit permission from the copyright holder. BiobankAccelerometerAnalysis  (Doherty, Hollowell, Willetts, Jackson, & Hammerla, 2017)  by Aiden Doherty (Oxford, UK) and colleagues is an open source tool designed to provide a minimum set of basic outcomes for the UK Biobank accelerometer data, utilizing some of the key algorithms from GGIR  (Doherty, Jackson, et al.
shell. GGIR. The shell function takes all arguments from the five parts mentioned above. In this way, the users can interact with GGIR from one single function call to function g. shell. GGIR and easily share their call to allow for replicating the analysis on a different dataset. The duration of GGIR depends on computer specifications, input arguments, and data characteristics. Part 1 (g. part1) is the most time consuming taking up at least 80% of the processing time and lasting around 10 minutes when applied to a seven-day data file using GGIR's default argument settings. The code builds a folder structure with a depth of two to store the milestone data per participant in. RData files (including collections of data. frame and vector objects) and the analysis reports per dataset (see Figure  1 ).
The analysis reports are in. csv format to give the user the flexibility to process their quantitative results in other statistical or data processing environments. There are many variables stored in the reports, an introduction to these variables can be found in the package vignette (https://cran. r-project. org/web/ packages/GGIR/vignettes/GGIR. html), while a more elaborate discussion is found in the package manual (https://cran. r-project. org/web/packages/GGIR/GGIR. pdf). In GitHub the code is supported by continuous integration with Travis-CI (https://travis-ci. org/wadpac/GGIR). Starting in 2017 we have adopted the habit of writing unit tests, but as it was not done before 2017 not all parts of the code are covered by tests yet and current test coverage is 63%. In addition to unit tests, the development has typically gone in close collaboration with GGIR end-users who checked code validity by close examination of the package output.
Software Dependencies Most of the code is written in R, with a small part in C++ needed for reading the binary data from the AX3 accelerometer brand using R package Rcpp as a dependency  (Eddelbuettel, 2013). R packages bitops and matlab are used to enable reading the binary data from the Genea accelerometer (the non-commercial precursor to the GENEActiv, Unilever Discover, UK). Dependencies are R packages GENEAread and mmap, which are used for reading GENEActiv accelerometer binary data. Finally, R package tuneR  (Ligges, Krey, Mersmann, & Schnackenberg, 2018 ) is used to read wav format data, which is an optional export format for AX3 accelerometer data. The R package signal is used for frequency filtering  (Ligges et al. , 2014) , R package zoo is used for calculating a rolling median, sum, and mean  (Zeileis & Grothendieck, 2005) , and R package data.
Software Functionalities Part 1 (g. part1) The g. part1 function searches the specified data directory recursively for files that could possibly represent acceleration data. Next, it automatically detects which accelerometer brand the file belongs to, the data format in which it is stored  (. csv,. bin,. cwa,. wav) , and extracts the file header using appropriate functions. Next, function g. calibrate is used to investigate calibration error which results in proposed correction coefficients as motivated and described in van  Hees et al. , 2014 (van Hees et al. , 2014). Then, metrics essential for sensor wear detection, physical activity and sleep analysis are extracted from the raw data. Here, the user can choose one or multiple aggregation metrics out of a collection of most common metrics (e.
g. ENMO) and control the window size over which the metrics are calculated. Additionally, a standard set of metrics is extracted per long time window (default 15 minutes) which are needed for the detection of accelerometer non-wear. The data loading and metric extraction takes place in approximately 24-hour blocks since putting the full file content in computer memory may not always be possible. The data window size is modifiable in case the 24-hour blocks are still too large. Additionally, the code evaluates available memory throughout the processing and shortens the window by 20% when available memory is getting too low. At the end of part 1 the signal metrics are stored as milestone data in an RData file (using a filename corresponding to the input accelerometer data file). For most of the analysis we use POSIX format for timestamps, which is the default in R, but for the exported time series we transform these to ISO8601 format to facilitate usage in other software environments.
Impact The main value of GGIR is that it offers a broad set of functionalities ranging from data quality handling to 24 hours/7 days time use characterization of physical activity and sleep utilizing literature supported methods, and is study population agnostic making it suitable for a wide range of research areas. GGIR can be operated without significant prior programming expertise. At the same time, the user experiences freedom in the specification of input arguments and the selection of output variables. Being fully open source, GGIR can be adapted and extended according to the needs of the respective research project. GGIR is currently being used by the physical activity and sleep research communities, with over 90 peer-reviewed journal papers published using the software since 2013, with the number published per year increasing rapidly.
Conclusion Most of the knowledge on physical activity and sleep comes from studies using questionnaire data or, more recently, proprietary physical activity metrics generated using commercial software. With the rapid advancement in technology over recent decades, raw acceleration movement sensors can now be used in large scale studies. However, the data they generate are not straightforward to analyze. GGIR provides a tool for researchers to derive variables that characterize physical activity and sleep assessed in an objective manner. In addition, as the code is open source, it can be used in part or as a whole making it flexible to research needs. It further facilitates a reproducible analysis of the raw data which is key to generating conclusions in clinical and observational research settings.
Acknowledgments We would like to thank  Zhou Fang  for his help in designing the autocalibration algorithm,  Jing Hua Zhao  for his help in writing the code to read the binary data collected with the non-commercial GENEA accelerometer,  Joe Heywood  for his help with fixing a bug in the timestamp conversion, and  Evgeny Mirkes  for his help in writing the code to read the binary data collected with the Axivity AX3 accelerometer. Further we would like to acknowledge funding from:  Medical Research Council (MRC)  grants  MR/ P012167/1  and  MC_UU_12015/3 ,  Economic and Social Research Council (ESRC)  grant  ES/K005987  for ' Millennium Cohort Study Sweep 6 (Age 14) Survey ' ( MCS6 ),  Biotechnology and Biological Sciences Research Council (BBSRC)  and  Unilever Discover Ltd  funded a  CASE studentship  ( 2008-2012 ), and  NIH  grants  HL-094307  (AIP).
This is part of a PhD Thesis conducted in the  Biomedicine Doctoral Studies at the University of Granada, Spain. JHM is supported by a Grant from the  Spanish Ministry of Education, Culture and Sport  ( FPU15/02645 ). AR is with the  NIHR Leicester Biomedical Research Centre  ( 2017-2022 ) and the  Collaboration for leadership in Applied Health Research and Care (CLAHRC) East Midlands. This study has been partially funded by the  University of Granada ,  Plan Propio de Investigacion 2016 , Excellence actions: Units of Excellence; Unit of  Excellence on Exercise and Health (UCEES) , and by the  Junta de Andalucía, Consejería de Conocimiento, Investigacion y Universidades  and  European Regional Development Fund (ERDF) , ref. SOMM17/6107/ UGR. VvT provides paid consultancy for GGIR users in need of support.
Verification Experiments and Analysis On the basis of models and mechanisms mentioned above, we implemented all these designs within a distributed simulator on Windows OS. All resources a swarm system covered are stored as entries in different resource tables within a MySQL database. Meanwhile, we employ Data Distribution Service (DDS) as the fundamental communication middleware. All other designs are implemented with C + +. Focusing on the topic of optimized task-service planning, the correctness and performance of proposed methods are mainly simulated and verified in special scenes. In the following, several typical experiments and the results are described in detail.
To this end, building on 2 decades of experience with PBPK and system pharmacology modeling, we have established the Open Systems Pharmacology (OSP) community. OSP offers professional open access and open source software tools (PK-Sim/MoBi) and models via a platform for collaborative development and qualification. OSP already hosts several general systems pharmacology activities (for an example, see the OSP Links Supplement: Glucose-Insulin Model 8 ) addressing the broader scope of mechanistic disease and drug-response modeling. A strong focus of the current OSP projects is on the demonstration of reliability and the characterization of predictive power of PBPK. The aims in this latter area are exemplary (and hopefully a convincing role model for other applications to follow) for the benefits of an open-science approach.
The suite comprises functionalities for parameter identification, model qualification and automated reporting as well as interfaces to R (The R Project for Statistical Computing, www. R-proje ct. org) and Matlab (The Mathworks, Natick, MA, USA) (Figure  2a ; see OSP Links Supplement: OSP Suite). The continuous development of the software suite is run on GitHub and can be monitored by everyone in real time (see OSP Links Supplement: Continuous Development). Nightly builds of prerelease versions are available for all interested parties as is the source code. For sharing of data and PBPK and systems pharmacology models, dedicated repositories exist. The current model library includes PBPK models of individual drugs in particular drug-drug interaction victims and perpetrators and sophisticated systems pharmacology models.
COMMUNITY ORGANIZATION Figure  1b  sketches the constitution of the OSP community where three types of active roles are distinguished. The OSP management team is a project group of (Arbeitsgemeinschaft für Angewandte Humanpharmakologie (AGAH) e. V. ), the association for applied human pharmacology (https :// www. agah. eu). It is responsible for regular review, endorsement, and publication of the OSP development roadmap. The management team provides certification of the software platform, models, and other content such parameter databases and qualification processes. It releases certified platform versions. A sounding board supports the management team in scientific and technological matters. Community members are all active contributors to the development of OSP content. The OSP community also invites any interested party to use the provided content (software, models, etc.
Acknowledgments. Funds for the creation of the article were provided by authors' organizations. On behalf of the community, the authors wish to thank  US Food & Drug Administration (FDA)  and  European Union  and  German Ministry for Education and Research (BMBF)  for providing funding for the further development of OSP. FDA: PBPK and Population Modeling Seamlessly Linked to Clinical Trial Simulation in an  Open-Source Software Platform  (Agreement RGF011519-A);  European Union :  Horizon 2020 Research and Innovation Programme-U-PGx consortium  (Grant  668353 ), BMBF: OSMOSES consortium (Grant  031L0161A ), nanoCELL consortium (Grant  03XP0196C ), VISION consortium (Grant  031L0153A ).
Conclusions We have constructed a generic high resolution computational model that can simulate cell mechanics and tissue mechanics with high accuracy. We have demonstrated that the model is capable of simulating complex tissue architecture as seen in liver lobules. Our results reveal that a realistic representation of cell shape is very important to quantify the mechanics in the tissue. We argue that this model type could become a future tool to perform realistic modeling for various mechano-biological questions (e. g. embryogenesis, mechano-transduction). We are currently embedding this model into our software platform called 'TiSim', which will be released as an integrated insight software for tissue simulations. support by BMBF-LiSym, BMBF-VLN DFG-Emmy-Noether, and BMBF-Demonstrator Liversimulator.
Acknowledgements PVL and DD acknowledge support by  BMBF -LiSym, INST. CANCER-PHYSCANCER, ITMO -INVADE ,  EU   7th Framework Programme (NOTOX) , and  ANR -iLite. DD gratefully acknowledges BMBF-VLN and Demonstrator Liversimulator. JN, SH and TJ gratefully acknowledge Figure 2. Top: Simulated optical stretcher experiment to determine force models for the viscoelastic behavior of the cell. Middle: Simulation of liver lobule regeneration Bottom: Simulated relative area of the necrotic lesion during the liver regeneration process, compared to data from Hoehme et al. (2010).
To process unstructured data, the main approaches rely on natural language processing (NLP) methods  [9, 10]. The background knowledge, as represented in terminologies and ontologies (T&Os; that describe the domain), plays a crucial role in any clinical NLP task  [11]. A common approach to information retrieval (IR) in clinical unstructured text outside the basic full-text search comprises partially restructuring the original texts using semantic annotators (eg, MetaMap  [12] ) that map words or expressions to concepts from domain knowledge databases. Consistently aggregating all these scattered, big, complex, and diversely structured data is, in fact, the role of health data warehouses (HDWs). An HDW is defined as a grouping of data from diverse sources accessible by a single data management system  [13].
This kind of data repository centralizes clinical, demographic, and administrative data within a uniform and consistent data model. Many HDWs have been proposed worldwide. From a holistic point of view, the majority of these solutions provide aggregated data mainly focusing on patient data as a result. Furthermore, they do not necessarily allow the full and independent visualization and retrieval of the different atomic entities conceptually composing the whole scope of clinical information (eg, Stanford Translational Research Integrated Database Environment or STRIDE  [14]  and Data Warehouse for Translational Research or DW4TR  [15] ). This is, nevertheless, particularly important in an IR context, as potential clinical questions and inquiries from health professionals are formulated in terms of their vision of the conceptual organization of data that derive from the actual patient management process.
The Enterprise Data Trust  [16]  relies heavily on industrial solutions to cope with the huge amount of data. Many solutions also implement generic frameworks, such as Informatics for Integrating Biology and the Bedside (i2b2) database. This, however, implies concessions to conciliate the original conceptual representation of data with the data model required by the framework (eg, The European Hospital George Pompidou HDW  [17] ). Furthermore, many standardized controlled vocabularies used to semantically describe health information do not always provide access to concepts in French, and access to the data through these T&Os is not always provided for the whole set of data notably as far as the unstructured data are concerned (eg, Electronic Medical Record Search Engine or EMERSE  [18]  and STRIDE  [14] ).
A generic entity-attribute-value (EAV) data model and a not only structured query language (NoSQL) layer (layer 0) enable data structuring while preserving the original conceptual data model. This study aimed to present a proof of concept (POC) of this SHDW based on the data of 250,000 patients from RUH and to assess its ability to assist health professionals in prescreening eligible patients in a clinical trial context. Since November 2018, this POC has integrated all the data of 1. 8 million patients from RUH.
It was initiated in 2003 at Stanford University when the functionalities of i2b2 and CAncer Biomedical Informatics Grid were not considered optimal. An Oracle database and an EAV data model derived from the Health Level 7 Reference Information Model (RIM) standard are used for data storage and representation. Several (mainly English) standardized terminologies are used to represent important biomedical concepts and their relationships (eg, Systematized Nomenclature Of MEDicine-Clinical Terms or SNOMED-CT, RxNorm, 9th revision of the International Classification of Diseases or ICD-9-Clinical Modification, and current procedural terminology). STRIDE provides hierarchical concept-based retrieval as far as structured data are concerned and provides full-text search access to more than 6 million CNs.
Data are collected into an Oracle Relational DataBase Management System (RDBMS) with an EAV data model and are subsequently hosted in an extensible data model that organizes it into a structure of hierarchical modules inherited from especially developed ontologies. It provides 2 graphical querying interfaces designed to provide aggregated data dedicated to data analysis (eg, mean, standard deviation, counts, categorical data, and chronological view). The Enterprise Data Trust  [16]   [32]  is an ophthalmologic-specialized HDW developed at the University Eye Hospital in Munich in Germany. SMEYEDAT is based on a Microsoft structured query language (SQL) database updated daily from the HIS and uses a star-like patient-centered data model for data representation. The QlikView (QlikTech)  [33]  tool was implemented as an analytic tool to visualize and explore patient data.
Methods Overall, the first prerequisite pertaining to the design of an HDW-based system is the extraction of data from the HIS. This can be achieved in 2 ways: by (1) setting up a data stream from the production environment (or a replicated database) to the HDW data storage component, or (2) using extract-transform-load (ETL) scripts. As far as the SHDW is concerned, ETL scripts are used. The following section describes the targeted sources of data of the HIS of RUH.
Since November 2018, the SHDW POC presented in this study includes the whole set of data. However, this study is based on a randomly chosen subset of data from 207,357 patients, 1. 7 million visits, 671,442 clinical documents, and 14. 2 million single laboratory tests. ETL scripts are used to incorporate data from the production environment repositories into an Oracle database. Figure  1  summarizes included data according to their specific domain (ie, reference management, administrative record, care, examinations, health economy, planning and coordination, external data, resource management and billing, sharing, and security). Data already included in the semantic health data warehouse (SHDW) are represented by a dark gray opaque background, whereas a light gray background indicates that data are neither included nor planned to be included in the short or medium term.
Background partially or totally covered with bricks corresponds to data for which inclusion is in progress or is planned in the short term or medium term. The SHDW currently focuses on clinical data and, more broadly, on health data according to a patient-centered strategy. In addition to the structured patient data, the different data pertaining to multiple admissions and events at RUH are collected (eg, diagnoses, biology, procedures, and movements). The reference-controlled vocabularies (ie, reference management domain) necessary to the understanding of those data are notably widely collected and maintained. In contrast, pure management and administrative data, such as appointment and planning data, billing data, and data governance, are not likely to be included in the short term. All those data are integrated into a modular architecture that is described in the following section.
Overall Architecture of the Semantic Health Database Warehouse Much health information remains in CNs  [7]. The 11. 9 million clinical documents in French of RUH consequently play a strategic role in the context of the SHDW. Since its creation in 1995, our research team has strongly investigated French IR research domains through T&Os (and more broadly knowledge organization systems or KOSs), which has led to the development of several search tools mostly dedicated to IR from documentary and bibliographical resources  [22, 35]. However, the complexity of the clinical data and, more broadly, of SHDWs as a whole required the pooling of several of these acquired skills and tools. The SHDW enables the semantic retrieval of health data in French based on several T&Os and consequently relies on 2 datasets: a domain knowledge database and a health database maintaining clinical and patient data.
The functionalities of the SHDW are ensured by the collaboration of 3 distinct layers, where each layer consumes data from the above layers (see Figure  2 ): the (1) cross-terminological HeTOP  [19] , (2) semantic annotator ECMT  [20, 21, 36] , and (3) SSE  [22] [23] [24]. The HeTOP provides access to domain knowledge data. The ECMT matches words and expressions in natural language to domain knowledge concepts included in the HeTOP. In fact, ECMT enables the extraction of semantic information from unstructured data. Its functional scope consequently lies between domain knowledge and clinical data. Together, the 2 components HeTOP and ECMT serve as a base for the semantic description of the clinical information in a computer-processable form. In contrast, the SSE and the coupled Web application, are dedicated to IR tasks on health data by using this extracted semantic description.
Domain Knowledge Data The HeTOP provides cross-lingual access to concepts originating from 75 T&Os. A set of 2,639,620 concepts and 10,735,905 terms are available mainly in English and French. However, 32 languages are available overall. Some of the T&Os have been partially or totally translated into French (eg, SNOMED 3. 5-52. 3%; Medical Subject Heading descriptors-100%; National Cancer Institute Thesaurus-53. 35%; Online Mendelian Inheritance in Man-79. 67%; Human Phenotype Ontology-72. 19%; and RadLex-22. 1%). More broadly, 50% of the 2. 64 million concepts accessible through the HeTOP are provided in French, and 19. 1% of the 10. 74 million terms have a French translation. T&Os from the HeTOP come with their original sets of hierarchies and semantic relationships but also with additional cross-terminological exact, broader to narrower, and narrower to broader mappings performed manually or supervised by our health professionals at RUH.