dataesr/scientific-paragraphs-categorization huggingface dataset:
Te lower frequency is relative to the minimum walking speed (v min � 0. 075 m/s); however, the higher frequency is approximately relative to the highest frequency shift that could be generated from the human limbs oscillations. Moreover, the sampling frequency was set to 2500 Hz. On the other hand, eight Bonita 10 cameras were recessed ceiling in order to track the 3D positions of multiple refective markers placed on the participants' toes, heels, and torso. All cameras were previously calibrated and digitized with a sampling frequency of 250 Hz. Data were synchronized using an analog to digital converter "ADC Patch Panel" provided with the Vicon system, and analyzed on the motion capture software "Nexus 1. 8. 5" to estimate gait parameters. Our experimental setup is shown in Figure  2.
The COVID-pandemic has spurred the use of AI and DS innovations in data collection and aggregation. Extensive data on many aspects of the COVIDhas been collected and used to optimize public health response to the pandemic and to manage the recovery of patients in Sub-Saharan Africa. However, there is no standard mechanism for collecting, documenting and disseminating COVIDrelated data or metadata, which makes the use and reuse a challenge. INSPIRE utilizes the Observational Medical Outcomes Partnership (OMOP) as the Common Data Model (CDM) implemented in the cloud as a Platform as a Service (PaaS) for COVID-data. The INSPIRE PaaS for COVID-data leverages the cloud gateway for both individual research organizations and for data networks. Individual research institutions may choose to use the PaaS to access the FAIR data management, data analysis and data sharing capabilities which come with the OMOP CDM.
Network data hubs may be interested in harmonizing data across localities using the CDM conditioned by the data ownership and data sharing agreements available under OMOP's federated model. The INSPIRE platform for evaluation of COVID-Harmonized data (PEACH) harmonizes data from Kenya and Malawi. Data sharing platforms must remain trusted digital spaces that protect human rights and foster citizens' participation is vital in an era where information overload from the internet exists. The channel for sharing data between localities is included in the PaaS and is based on data sharing agreements provided by the data producer. This allows the data producers to retain control over how their data are used, which can be further protected through the use of the federated CDM. Federated regional OMOP-CDM are based on the PaaS instances and analysis workbenches in INSPIRE-PEACH with harmonized analysis powered by the AI technologies in OMOP.
Introduction Data sharing is increasingly recognized as a requirement for advancing clinical and population health knowledge and enabling scientific research for new drugs, vaccines and health systems. Until 2015 the main model for data sharing has been to deposit more or less anonymised (meta)data in a repository with more or less access control, depending on the dataset. DataFirst for South Africa and other African countries and Harvard Dataverse are two such repositories that come to mind. INDEPTH Data Repository is important as many of the INSPIRE (health data partnership) sites were part of the iSHARE initiative (1). This model for data sharing does not specifically enable the pooling of data across studies and their datasets. Instead, repositories tend to support meta-analysis  (2) , which combine study results across multiple studies, but don't combine source data for re-analyses.
Before the advent of big data and CDM, there is limited evidence of the impact of data sharing via repositories in improving health outcomes in LMICs  (2). Enter the COVID-19 pandemic. It came with a novel virus and a pathogen that was extremely contagious, extremely adaptable, and very successful when it comes to confusing the host's immune system. Under the circumstances it became necessary to share data across domains and across borders in a framework that lent itself to continuous data analysis. In the United States this led to the National COVID-19 Cohort Collaborative (N3C)  (7). In Europe this led to the EHDEN Portal  (8). In both instances the cornerstone of these efforts has been the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM), allied with the data tools developed by the Observational Health Data Sciences and Informatics (OHDSI).
The African Population Health Research Center (APHRC) had become concerned about the dearth of data and evidence when it came to the health of marginalized, vulnerable and minority populations. In 2020 APHRC began the journey of migrating some of its data into the OMOP CDM. At the same time APHRC began to incorporate governance with data migration so that the adoption of OMOP would go hand-in-glove with its ethical compass. This led to INSPIRE (9). The Implementation Network for Sharing Population Information from Research Entities (INSPIRE) is a health data partnership across research organizations and sentinel surveillance sites in eastern and southern Africa. It has used the framework from the US National COVID Cohort Collaborative (N3C) and adapted it to the local context of the SSA region by enhancing it accordingly to build capacity to share population health data from Africa using an OMOP CDM.
With its ethical bent INSPIRE is making data on climate, other exposures and the built environment available on our research platform to qualify and disaggregate cohort definitions and reanalysis designs in line with the experiences of marginalized, vulnerable and minority populations. With its extensions INSPIRE has become a use case in two global initiatives -WorldFAIR and the Global Open Science Cloud (GOSC). WorldFAIR seeks to compare and contrast best practices that advance the implementation of the FAIR data principles, in particular those for Interoperability, across many areas of science  (10). GOSC seeks to "connect the different international, national and regional research Infrastructures to create a global digital environment for borderless research and innovation"  (11). Across these two efforts INSPIRE is testing the OMOP research platform: is its (meta)data infrastructure (including new extensions like the one for hosting place-based RWD) enough? For example, can the platform track viral imports and exports of COVID-19 between Africa and the rest of the world, and between individual African countries in real time? Do the standard vocabularies the platform provides keep pace with advances in the development of COVID-19 vaccines and therapies? Can the OMOP OHDSI platform together with its extensions account for the availability of vaccines and therapies by location? In this context and others can the platform take into account mobility and the distance to care? Can the data capture and analysis workbench OHDSI provides account for health inequities including gender and intersectionalities that might be beneficial in contextualizing health research for vulnerable and marginalized populations? In a pandemic all these factors can figure into the target cohorts, comparison cohorts, event cohorts and/or outcome cohorts that OMOP and OHDSI are able to construct for purposes of analysis.
In section 3 the four methods helped create the framework for the infographics which is a graphical representation of the materials and methods used by the INSPIRE data approach. Section 4 brings all three together to explore the issues that emerge from the use of OMOP CDM within an East and Southern Africa context. This section deliberates about on-the-ground challenges using examples for the platform for evaluation and anlysis of COVID-19 harmonsied data (PEACH) ongoing implementation in two countries. A discussion in section 5.
In one flavor protected health information is not perturbed. In a second flavor protected health information is de-identified. The third flavor is synthetic data. It is computationally derived from the unperturbed OMOP CDM instance. It contains no unperturbed data but resembles unperturbed data statistically (19): Note that INSPIRE is adopting access requirements to the African context. The three flavors provision the alternatives available to data users in the African context. During IDSR implementation our partnerships require access to researchers from within the ministries of health or institutions. Partners may play a coordination role in supporting over stretched Ministries of health and stakeholders. The implementation is inclusive of individuals in communities impacted by the COVID-19 pandemic as stakeholders.
INSPIRE through OMOP implementation of the IDSR has set up instances to support the three flavors, the first one with limited access to users in country is recommended by the ministries of health. In all the use cases, it ensures accountability for the results and transparency and feedback loops. INSPIRE is creating conditions for positive impact on gender and intersectionalities and accountability mechanisms (see https:// covidsouth. ai/research?lang=en). Additionally, platform governance encompasses a commitment to hosting results that are sex-disaggregate across the (meta)data pipeline. See, for example, this discussion of "Sex-disaggregated data matters"  (20). Finally, platform governance encompasses the use of lived experience experts. See, for example, "the integration of lived experience perspectives into the research, from discovery to translation.
Capacity development In a federated approach where each site gets its own PaaS, the challenges are significant. For example, each region may host its IDSR person-level data in its own data collection system. Given the heterogeneity of data sources, the INSPIRE (meta)data pipeline has two hops. In the first hop, sites migrate data from their local systems into a standard format. In the second hop just one ETL is developed centrally that moves data from the standard format into the OMOP CDM. Each site is responsible is for its own first hop. An ETL is developed centrally for the second hop and shared locally. Between the first hop and the second hop there are data quality checks that the standardized person-level IDSR has to pass. These checks gate the execution of the ETL that moves the data in the standard format into the OMOP CDM.
Odysseus has developed ARACHNE Research Network, a platform for consistent, secure and compliant observational research process for federated OHDSI (23). It is during this gate that capacity development occurs. This capacity strengthening extends to Improving the technical capacity to support the management of large data sets in both government and private sectors while promoting privacy, confidentiality, data security and the alignment with applicable country-specific data policies. When data quality checks fail, sites get to trace why their data can't be moved into the many tables (domains) that make up the OMOP CDM. They learn the thinking the ETL uses that moves data from source to target. Before OHDSI and INSPIRE, both APHRC and LSHTM conducted network research using this same two hop approach with the data quality checks in between.
Before the pandemic, the capacity development that is needed at the gate between the two hops occurred in in-person workshops. Neither APHRC nor LSHTM has experience conducting these workshops remotely. Note that the same model is being used with EHR data. Each clinic or hospital migrates its own data into a standard format -the WHO COVID-Core CRF. Then, an ETL is developed centrally and shared locally that moves the data into each site's OMOP CDM instance.
Technical specifications that demand capacity development (Meta)data pipeline best practices The INSPIRE (meta)data pipeline was developed following EHDEN Academy (24) and IQVIA (25) best practices (Figure  1 ). In a first hop source data is extracted, transformed and loaded into one of several data exchange formats, depending on the source data type. So far INSPIRE supports these formats: • A data exchange format developed by the WHO for personlevel Integrated Disease and Surveillance Response (IDSR) datasets  (30). • A data • A data exchange format developed by the International HundredK+ Cohorts Consortium (IHCC) with support from the Wellcome Trust for the LMIC COVID-19 Questionnaire which includes several sections for mental health  (31). • An integration approach that accounts for the physical and social environment in play with individuals by location.
See the section on "Integration with place-based attributes" below. It talks to the use of sustainable develop goal (SDG) along with other indicators in longitudinal population studies. The COVID-19 data exchange formats are supported by the Global South AI4COVID program funded by IDRC/SIDA. The INSPIRE PaaS hosts several staging databases, one that corresponds to each of these data exchange formats. These staging databases are defined centrally and deployed locally. Each locality develops ETLs that move their source data into one or more of these staging databases. The next hop is facilitated by an ecosystem of OHDSI services through which exemplary ETLs are produced, one for each staging database. The exemplary ETLs are implemented using Pentaho Data Integration (formerly Kettle)  (32). Pentaho Data Integration (PDI) is both point and click and self-documenting.
The infographic The INSPIRE platform is based on the data sharing principles from N3C, OMOP, OHDSI and the OHDSI place-based extensions to supports research. This section gives an overview of OMOP as a whole in an infograph (see Figure  5 ). It shows how it can be applied to African population health data, which can include place-based data for climate and other exposomes. With this method we propose (1) a way to map outputs from the OHDSI GIS Working Group place-based attribute service to OMOP and (2) a standard vocabulary we might use to label the exposure events that accrue to an individual during their comings and goings over time. The recommendations here are first to create person-level exposure events in the form of OMOP CDM OBSERVATIONs or, alternatively, as exposures in a proposed CDM EXPOSUREs table, depending on the location history of the individual and then to use a foundational model for exposures like Exposure Ontology (ExO) to label these observations  (34).
Real World Evidence from Real World Data In the experiment the target cohort experiences one or more events in succession or at the same time, leading to various outcomes (Figure  6 ). Along the way the target cohort at the center of the sunburst plot is divided again and again by what happens first and then what happens next at each successive division ad infinitum  (38). Each experiment may make use of de novo cohort definitions in OHDSI and/or, alternatively "borrow" cohort definitions from the OHDSI Cohort Library  (39). On top of characterization and incidence analysis  (40) , i. e. , the sunburst plot; using these cohorts, OHDSI performs empirical and causal analysis in the form of patient level prediction  (41)  and population level estimation  (42)  respectively. OHDSI packages all of this in a data analysis workbench called ATLAS.
And ATLAS provides a user interface through which users can specify and execute these three types of data analysis -descriptive (characterization), predictive and prescriptive (population health) -on top of one or more OMOP CDM databases hosted locally and/or centrally. Note that predictive analysis uses supervised learning. A target cohort is defined. From the target an outcome cohort is selected based on one or more CDM occurrences (e. g. , measurements and/or diagnoses and/or death). With these CDM outcome occurrences, their concepts serve as the labels. Next OHDSI tries to account for these labeled outcomes with predictors that are also extracted from the same target cohort. In OHDSI predictors automatically include demographics as well as perhaps specific occurrences the Principal Investigator (PI) chooses from the target cohort person/patient record.
Implications of OMOP structured results and on-ground challenges With federated research, localities report results as aggregates aka "indicators" (Figure  7 ). In line with the Global Open Science Cloud (GOSC), INSPIRE, going forward, will be hosting these indicators, disaggregated by sex, age and other factors as applicable and as available, in a datacube. "The datacube crosses different international, national and regional research Infrastructures to create a global digital environment for borderless research and innovation"  (11). INSPIRE is leaning in the direction of SDMX  (44)  for a datacube implementation of the results from the OMOP CDM. Both the European Commission (45) and the United Nations  (46)  mandate SDMX for some of their indicator reporting. Guidelines for the Global Data Structure Definition for Sustainable Development Goals Indicators) (47) is the "data structure definition" for the SDMX "dataset" aka "datacube" of sustainable development goals indicators that has been adopted by the UN statistical division.
Challenges Whilst the challenges with limited access to data are not unique to COVID-19 disease outbreak, the massive scale of data generated during the outbreak was unique. There were disparate data sources and a lack of integration of data systems in many countries partly due to lack of standardized and harmonized data systems with poor data interoperability. Some vocabularies in the IDSR implementation context were missing in the standard vocabulary mappings and INSPIRE is generating these new vocabularies. We are provisioning location data with aim to account for availability of vaccines and advances in treatment. Several technical challenges exists in the the implementation context due to limited technical capacity for data management and analysis. Although the ATLAS tool in OHDSI provides a no-code platform for analytics, data managers for both government and private stakeholders require training in the practical application of data science tools to health data issues and to support additional customized solutions.
Despite these drawbacks, INSPIRE is implementing data sharing framework to facilitate data access and use at national and international levels. Data preparation for a CDM requires significant effort for curating all these data sources and data quality and documentation and may limit the number of institutions who can generate similar research studies. An evaluation of CDM models shows that OMOP CDM supports best data sharing from longitudinal based EHR studies  (49). Although the framework for data curation requires substantial amount of time and resources, the benefits accrued from improved data quality, analytical efficiency with multiple observational data sources and shared OHDSI tools and resources is tremendously useful. How this research informs the development of the infographics? How do we measure success? Indepth discussions of data sharing efforts in many LMICs have largely focused on datasets, adapting a broader view comprising protocols, study materials, lived experience enables a full understanding of the data to enhance replication and address ethical dimensions.
Conclusion In sum, the INSPIRE project is a proof of concept through which we can determine whether OHDSI together with a few of its extensions is fit to purpose. Recall that our purpose is to provide decision support when it comes to developing interventions to tame the first in what may be a new generation of pathogens. Like the novel coronavirus SARS-CoV-2, these pathogens may be extremely communicable, quick to morph and hard on our immune systems. The proof of concept is to determine whether data reuse and continuous data analysis that is the promise of OHDSI can produce the knowledge we need to thwart future pandemics. This knowledge can be measurement in several ways: • OHDSI deployments to Health and Demographic Surveillance Systems to build a network for users and producers of longitudinal, population-based health data.
Methods The BIRDY 2 study was a prospective community-based mother and child cohort, both in urban and semirural areas. All pregnant women in the study areas were identified and enrolled. Their newborns were actively and passively followed-up from birth to 3 months. Data on clinical symptoms developed by the children and laboratory results of all clinical samples investigated were collected. A Cox proportional hazards model was performed to identify risk factors associated with possible severe bacterial infection. Findings A total of 53 possible severe bacterial infection and 6 confirmed severe bacterial infection episodes were identified among the 511 neonates followed-up, with more than half occurring in the first 3 days. For the first month period, the incidence of confirmed severe bacterial infection was 11.
Data collection Study investigators were responsible for collecting epidemiological data. During pregnancy, study investigators collected socioeconomic data and maternal information (medical and gynaecological history, treatment, obstetrical follow-up). Information on labour and delivery were also collected, including risk factors for infection, as well as data on first neonatal assessment: vitality score (APGAR score), anthropometric data (weight, height, head circumference), neonatal resuscitation, if any. At every home visit, study investigators recorded anthropometric data (weight, height, cephalic and brachial circumference) and presence of any signs of severe infection in the infant. Medical assessments carried out during follow-up, if any, were also collected, including symptoms and clinical signs, treatments carried out, and any diagnosis retained.
Finally, data on any deaths or lost to follow-up were recorded. The end of the BIRDY 2 study period (March to August 2020) overlapped with Covid-19 epidemic in Madagascar (first case of Covid 19 detected in March 2020 in Madagascar), while 51 neonates were born and followed during this period. In Madagascar, restrictive measures (including national lockdown), which were adapted according to the evolution of the situation, were implemented between March to October 2020. Internal procedures have been put in place to ensure safety of the mothers, their newborns and the investigation team while adapting the research protocol to reach the scientific objectives of the project: active phone monitoring by investigation team for example.
Acknowledgements We are grateful to all the mothers and their newborns for their participation. We thank all physicians, laboratory staff, field interviewers, and community workers for their involvement in this project. We thank all collaborators of the BIRDY program:  Rasoanaivo Fanjalalaina Vololonirina ,  Andrianonimiadana Lova Maminirina ,  Rabearitiana Lydos ,  Rasoloson Dimitri ,  Volahasina Antsa Tanjona ,  Volahasina Antso Fenitra ,  Randriamamonjiarison Aina Nirina ,  Andriatahina Todisoa. We also thank the  Department of International Affairs  and the  Centre de Recherche Translationnelle -Coordination Clinique of Institut Pasteur  for their support in the management of the study.
Beyond offering the first comprehensive analysis of the impacts of COVID-19 on labor outcomes for Vietnam, our study makes several new contributions to the literature. First, we add to the emerging literature on the impacts of the pandemic on labor outcomes in a lower-income country setting. We analyze large-scale, nationally representative, annual LFS data, which span the five years preceding the year of the pandemic and average more than 600,000 households per survey year. The large samples of official labor data before and after the pandemic offer us a rare opportunity to employ rigorous econometric models for analysis. Indeed, a large number of studies generally find negative pandemic effects on employment in high-income countries (e. g. , Adams-Prassl et al. , 2020; Coibion et al. , 2020; Gupta et al.