dataesr/scientific-paragraphs-categorization huggingface dataset:
Moreover, forelimb and hindlimb lameness were analyzed separately and multi-limb lame horses were excluded. It should therefore be kept in mind that the interrelationship between the movements of the head, withers, and pelvis still requires further work. Future studies with multivariate analysis are needed to provide more information on the lame limb identification and relationship between the indices in various clinical circumstances. In the longer term, the application of this study is aimed at a wider range of conditions in veterinary practice. A main limitation is that all measures were recorded under specific and standardized conditions. In the following years, further data are needed to refine lameness detection thresholds under conditions where physiological asymmetries are known to be higher (circles for instance)  [38].
The COVID-19 pandemic saw a massive investment into collaborative research projects with a focus on producing data to support public health decisions. We relay our direct experience of four projects funded under the Hori-zon2020 programme, namely ReCoDID, ORCHESTRA, unCoVer and SYNCHROS. The projects provide insight into the complexities of sharing patient level data from observational cohorts. We focus on compliance with the General Data Protection Regulation (GDPR) and ethics approvals when sharing data across national borders. We discuss procedures for data mapping; submission of new international codes to standards organisation; federated approach; and centralised data curation. Finally, we put forward recommendations for the development of guidelines for the application of GDPR in case of major public health threats; mandatory standards for data collection in funding frameworks; training and capacity building for data owners; cataloguing of international use of metadata standards; and dedicated funding for identified critical areas.
8 The emergency plan focused on the upscaling of existing vaccine production and adaptation of vaccines to virus variants, resulting in the formal launch of the HERA 9 in September 2021. In addition, in recognition of the need for coordinated data sharing, the EC funded initiatives to fast track the development of the European COVID-19 Data Platform. 10 eanwhile, on the ground, the researchers working in the EC-funded COVID-19 projects related to data sharing observed that the unified and decisive response to the pandemic -in terms of the emergency funding mechanismscould not always be matched when translated to the implementation phase. Delays in the authorisation of clinical trials and studies by national regulatory bodies of up to six months in several EU Member States, difficulties in engaging hospitals to participate in observational studies and a general lack of consensus in the interpretation of the General Data Protection Regulation (GDPR) were soon identified as barriers hampering project progress.
Cases in pointexperiences from selected EU projects Several multi-national EU-funded research projects address data harmonisation and data exchange, among them ReCoDID ('Reconciliation of Cohort Data for Infectious Diseases'),  16  ORCHESTRA (Connecting European cohorts to increase common and effective response to SARS-CoV-2 pandemic), 5 unCoVer (Unravelling data for rapid evidence-based response to COVID-19) 6 and SYNCHROS (SYNergies for Cohorts in Health: integrating the Role of all Stakeholders). 17 All four projects have been funded at different times by the European Union's Horizon 2020 research and innovation program (See Table  1  for an overview). The ReCoDID project has been tasked with providing an harmonisation pipeline for COVID-19 cohorts in Europe, collaborating with the European COVID-19 Data Portal hosted by the European Molecular Biology Laboratory (EMBL).
32 ORCHESTRA exhibits important synergies with ReCoDID as both projects aim at the creation of pan-European cohort data sets for COVID-19, integrating data from existing and new large-scale cohort data sets across Europe. However, while the main objective of ReCoDID is to create a dedicated cohort data repository and significant efforts were invested in the legal and technical barriers to achieving this, ORCHESTRA focuses on the implementation of standardised new data collection among different cohort data to generate high quality evidence to improve the prevention and treatment of COVID-19. Indeed, more than 1,300,000 individuals from four broad cohorts (COVID-19 patients, general population, fragile population, and healthcare workers) have been enrolled in the ORCHESTRA cohort to date. The two projects also differ with respect to their approach to centralised vs.
federated data infrastructures, with ReCoDID adopting a centralised infrastructure and ORCHESTRA using primarily a federated infrastructure. unCoVer, a Coordinating and Support Action, also opted for a federated data infrastructure with a focus on data mostly from EHRs. These records cover over 22,000 hospitalised COVID-19 patients, as well as national surveillance and screening data and registries with approximately 2,000,000 COVID-19 patients. Accessing these records in the initial project phase was complicated by ethical considerations as is discussed later in this paper. With SYNCHROS, which was also a Coordinating and Support Action, the focus has been on the methodologies concerning cohort data comparability. The cohort mapping process that was conducted by SYNCHROS revealed a lack of standardized reporting when it comes to detailed information about cohort samples and data, and cohort harmonisation procedures.
Indeed ORCHESTRA experienced first hand this hurdle: over 2,500 SARS-CoV-2-related variables were collected and had to be linked to unique international standard terminology codes provided by organizations such as SNOMED CT,  33  LOINC,  34  ATC,  35  and ICD-10. 36 But for some data elements a corresponding international code was not always found. Therefore, new concepts have been submitted to the most pertinent standard organisations to reinforce future global exchange of data and build a model to increase comparability of data in preparedness plans. 37.
Indeed, since ORCHESTRA includes cohorts already established prior to the start of the project, for many cohorts the participant-level data cannot be directly shared. To enable the application of the agile and advanced analysis methods avoiding direct data sharing, an infrastructure for federated data analysis and machine learning network has been established based on OPAL-DataSHIELD. 39 This infrastructure enables conducting participant-level analysis without revealing/ exchanging the participant-level data, as only the computed parameters describing the entire datasets are communicated to the analyst. The network consists of the access point hosted by the main institution and of the local nodes maintained by the data-owners, such as hospitals. Such design also supports parallelizationsince the computations can run simultaneously across local nodes.
Although at the moment the Opal-Data-SHIELD  39  does not support a complete landscape of the analysis methods and machine learning tools, it seems the best option for the datasets that cannot be directly shared. The largest bottleneck of this process is (i) the informatics resources, namely the server infrastructure, and (ii) the competency and understanding on the side of the data owners and local regulatory bodies such as ethics boards. Similarly, in unCoVer, heterogeneous data are described, harmonised and integrated into a multi-user data repository operated through Opal-DataSHIELD,  39  an interoperable open-source server application. This federated infrastructure offers the most efficient and secure approach to handling highly sensitive patient information derived from EHR, information which has not been collected for research purposes and demanding a particularly secured environment as well as close monitoring of data protection compliance.
40 It should be noted that unCoVer also faced significant barriers from the ethics perspective in the initial project start-up phase when setting up the federated infrastructure and therefore delays in the planned analyses that could have helped alleviated pandemic outcomes. 40 or ReCoDID, on the other hand, a centralised data curation pipeline hosted at the European COVID-19 Data Portal (via EMBL) is the preferred way forward due to the opportunity to merge large OMICS data with the clinical-epidemiological data and enable future largescale investigations across different data sets and across the divide between high dimensional and well-characterized clinical-epidemiological data. However, federated data analysis alternatives are investigated, involving national hubs of the European Genome Archive infrastructure (EGA) by EMBL.
Roadmap for the future The scientific community and stakeholders responded to the COVID-19 pandemic in an unprecedented manner in terms of collaborations, funding and data sharing. The existing molecular databases quickly adapted to facilitate the collection of data on the new virus, and various new platforms emerged for data curation and data sharing. COVID-19 patient cohorts were created with equal speed, but they lacked coordination and harmonisation, consequently crippling a deep and sensitive analysis. High quality and detailed participant-level machinereadable clinical data are sensitive, and their usage should remain controlled to secure protection of privacy as well as ethical use. At the same time, a lack of welldefined guidelines for data sharing, knowledge of legal regulations or technical resources to standardise and share data often hinder scientific development.
In addition, multiple community developed data standards are now available, making it difficult to select between them. Interoperability on a meta-level between these standards has not yet been achieved. Large collaborative projects, such as RECODID, ORCHESTRA and unCoVer create a collaborative environment that enables scientific discovery and methods development. However, this does not entirely remove the need for local support regarding the legal and informatics aspects. Thus, strengthening relationships between the IT and computational experts in the local institutions, like hospitals or universities, as well as the resources for the IT personnel and infrastructure, could enable innovation in developing other federated approaches for the clinical-data analysis on a larger scale. This may also necessitate making funding available for IT and data curation support for the data contributors who often are not full partners of the research consortia.
However, this approach reinforces the need for the use of the standardised data collection methods, as the analyst is not able to directly interact and view the data. Thus, as mentioned above, this solution requires the data owners to be equipped with the infrastructure and expertise to maintain the local nodes of the harmonised datasets. This requirement, in itself, poses a challenge because not all federated approaches are created equal since some systems rely on more resources than others. Furthermore, the fact that analyst cannot directly see the data, prompts the question about their ability to identify the source of potential distortions. Centralised data infrastructures are still important andwhere data can be shared centrally (e. g. , when broad informed consent for future use is available)may be the preferred option because of the ease of curation / storage and the need for computational resources for OMICS data analysis.
While the federated approach is further developed, the centralised approach will remain an important alternative, especially if the data is shared between trusted institutions with the appropriate safeguards in place. An important final consideration that concerns both centralized and federated arrangements relates to the limits of anonymization conversions: data cannot be converted in any formats and anonymized indefinitely and therefore pseudonymization is used. The problem, as emerged from the SYNCHROS stakeholders dialogue, is that the legal basis for such pseudonymization conversions is still unclear. Moreover, any manipulation   of data for pseudonymization/anonymization purposes may undermine its scientific value. An important way forward is thus to ensure any manipulation of data for pseudonymization purposes will be aligned to the longitudinal characteristics of cohort studies concerned (i.
Declaration of interests MB reports grants to UMCU from Janssen Vaccines, Novartis and CureVac, consulting fees from Astra Zeneca, Pfizer, Janssen Vaccines, Novartis,Takeda,Janssen Vaccines and payments from Sanofi for Participation on a Data Safety Monitoring Board or Advisory Board. ET, EC and RJD report H2020 funding for ORCHES-TRA. JH reports H2020 funding for ORCHESTRA, funding from the German Research Council for the SEPAN Project, funding from German Ministry of Education and Research for the EMUNE and the INSIDe Project and funding from Volkswagen Stiftung for the E2 project. AS and EV report H2020 funding for SYN-CHROS. TJ reports H2020 funding for ReCoDID. All other authors declare no competing interests.
Discussion In this study, we validated a methylomic signature of response to Li in patients with BD type 1. First, we develop a MS-HRM assay for three of the seven previously identified DMRs. Second, we examined the agreement of the results with the SeqCapEpi-obtained data in a similar sample, with reasonable consistencies between measures. Finally, we validated the performance of the MS-HRM assay to discriminate GRs from NRs in a larger sample of individuals with BD type 1. Using several definitions of Li response, we found that some DMRS might accurately predict nonresponse to Li. The current study reports the use of MS-HRM, a PCR based method, as an approach to determine Li response using epigenetic-based biomarkers. Unlike most techniques used to analyze DNA methylation in research laboratories, this technique is cost-effective, easy to use and to implement in common medical analysis laboratories  [25].
Sample The samples consisted of French Caucasian individuals who met the DSM-IV criteria for BD type 1. Patients were recruited from one university-affiliated psychiatric department in France (Paris). Patient inclusion criteria has been described previously  [26]. This study was approved by the French medical ethics committee (Comité de Protection des Personnes (CPP)-IDRCB2008_AO1465_50 VI-Pitié-Salpêtrière 118-08) and carried out according to the approved guidelines. This study is a secondary analysis of the research protocol registered under the number NCT02627404 in ClinicalTrials. gov (accessed on 20 November 2021). The current study includes individuals with a confirmed diagnosis of BD-I who were prescribed Li and whose response was assessed using the Alda scale.
Samples were then slowly warmed to 95 • C at 0. 2 • C per second, holding for 10 s after each stepwise increment and fluorescence data were collected. The annealing temperature was chosen to obtain a near-proportional amplification of methylated and unmethylated templates and each sample was analyzed in triplicate. For the DNA methylation assessment, as described above, bisulfite converted dilutions of methylated and unmethylated DNA standards were analyzed together with the samples. Peak-heights were calculated automatically with the CFX Maestro Software (Version 2. 2; Bio-Rad Laboratories, Inc. , Hercules, CA, USA). Linear curves of the peak-heights of the Tm first derivative of HRM curves against the methylation percentage of the standard were plotted  [33].
Statistical Analysis Statistical analyses were performed using Jamovi software (Version 1. 6; https:// www. jamovi. org/; accessed on 11 September 2021) and SPSS (Version 27; Armonk, NY, USA). Bland-Altman plots were used to assess agreement between the measure of DNA methylation with the SeqCapEpi and MS-HRM methods. We calculated the mean difference of the two measurements and corresponding SDs. Bland-Altman plots were created by plotting the means of the SeqCapEpi and MS-HRM values against difference scores between the two measures. This allows the assessment of whether one method consistently underor overestimates measurements of the same variable as compared to the other method. Intraclass correlations (ICC) between measures were also calculated. To estimate the discriminatory power, sensitivity, and specificity for the combination of the 3 tested DMRs, logistic regression analyses, followed by receiver operating characteristic (ROC) curve analysis were performed for the three definitions of the phenotype.
Trial registration: Registered by the Comité de Protection des Personnes-Sud Méditerranée; under the title "Prévention des troubles de l'équilibre chez le senior: influence de la thérapie manuelle appliquée au rachis sur les paramètres statiques et dynamiques», n° 19. 12. 27. 47. 259 in date of February 4, 2020. Registered by ClinicalTrials. gov.
Primary and secondary objectives The primary objective of this work will be to evaluate the influence of some MFR techniques address to cervical spine on WS and SPPB (Short Physical Performance Battery test) score. It can be assessed by the main hypothesis: H0: SPPB score and WS of MFR protocol group ≤ SPPB score and WS of light touch group. H1: SPPB score and WS of MFR protocol group > SPPB score and WS of light touch group. Secondary objectives will be to evaluate the influence of the protocol on other measured balance parameters, including cervical strength and mobility as well as lower limb strength, ground support and HRV. The data can also be used to explore the relationships between the different parameters.
Data collection All data except HRV and posturographic data will be collected and noted by the evaluators on paper records specific to the evaluation. HRV, posturographic and plantar data will be collected and automatically stored in individual file folders (secure by password) in a computer belonging to University Claude Bernard Lyon 1 (UCBL1) used exclusively for the study and protected by password too. After the conclusion of the data collection, the data will be transcribed in an Excel ® spreadsheet by an examiner using codes to preserve volunteer identification. All data will be saved on an encrypted UCBL1 server. Synchronization between the computer and the server will be ensured by the software Druva inSync ®.
WS, SPPB test and plantar support The subject will then be asked to stand up and perform the different steps of the SPPB test following the instructions of the evaluator  [60, 61] , leading to an overall score out of 12 points. He will perform the 4 m gait measurement part (which will measure WS) equipped with W-INSHOE plantar sensors (Medicapteur, Balam, France). Data will be analyzed using the software of the same name. The sensors are attached to the skin with hypoallergenic tape.
Static posturography test Finally, the static posturography test will be performed under standard conditions using the WIN-POSTURO platform (Medicapteur, Balma, France) allowing the measurement of displacements of the CoP in a static standing position at a frequency of 40 Hz for 25. 6 s  [62]. The measurement will be repeated 3 times. The data collected will be processed using WIN-POSTURO software (Medicapteur, Balma, France).
Statistical analysis and sample size We calculated 20 subjects per group would allow statistical power of 90% to detect a clinically meaningful increase in WS of 0. 2 m/s (± 0. 2) with an alpha risk of 0. 05 and considering a dropout probability of 10%. SPPB score was not used to calculate the sample size because of the ceiling effect reported for individuals who have high functional abilities, although it remains relevant as primary outcome combined with WS  [63]. All data will be expressed as mean ± SD. Normality will be verified by an Agostino-Pearson test. Comparison of data between the two groups will be performed with a one-way ANOVA, followed by a Tukey post-hoc test or a Kruskal-Wallis test, and finally by a Dunn post-hoc test. The gender effect will be considered with a two-way ANOVA, followed by a Bonferroni post-hoc test.
In addition, a quantitative analysis of secondary data from routinely collected data (DHMIS) and SARA surveys, combined with the development of indicators, will be conducted to better understand the impacts of risks, events and climate change over the past decade on healthcare service providers (HSPs) at the four sites. These analyses are intended to allow for longitudinal and transversal analyses. For the second phase, we use a qualitative methodology with a mixed inductive-deductive approach. This phase aims to map out the variety of disruptions and disturbances faced by HSPs and the populations and identify the diverse coping strategies implemented to understand the factors and stages in the process of resilience in the four sites. Mobility and migration will be part of the discussion on coping strategies of the population.
For this embedded study, qualitative data will be collected to complement our quantitative data, at a different timing than phase 2. During the third phase, all HSP health staff and a sample of household members from the communities around the 10 selected HSPs will be interviewed to complete questionnaires (n = 500 households per site-see below for sampling strategy and eligibility criteria). This quantitative phase aims to quantify the (perceived) impacts of identified disruptive events, changes and risks on the healthcare providers, healthcare access and population health status, based on the results from phase 2. Specifically, the household questionnaire includes different modules in two sections, a household and a respondent section. The respondent section includes socio-demographic information, health status (acute and chronic diseases), reproductive health, maternal health and family planning, mental health and trauma, empowerment, history of health and history of mobility, social capital, Fig.
Analytical approach Qualitative data All interviews and focus group discussions will be digitally recorded, transcribed and coded using NVivo software. A code tree will be determined based on the conceptual framework. New codes may be added if new dimensions emerge during the process. We will perform two types of analyses: a framework analysis  [44]  to highlight empirical data and a more inductive analysis to allow a grounded understanding beyond our initial conceptual framework. Quantitative data All data will be collected on tablets and immediately transmitted to a secure cloud. Descriptive statistical analyses and regression analyses will be used to describe the determinants of the resilience scores of HSPs. Furthermore, we will use propensity score matching methods  [45]  to assess the effect of (im)mobilities on health and healthcare access and to check for covariate balance between (im)mobilities settings groups, and (non) recent access to HSPs These methods are applicable for any generalized linear model form (logistic, probit, log linear, Poisson) depending on the outcome measure's distribution and the desired effect measure scale.
Life-event analyses (sequence analysis and cox model for example) will be also used to analyse changes in health and mobility during life path  [46]. Concerning secondary data (SARA and DHMIS), the health systems resilience analysis will be carried out in relation to their capacity to maintain the use of care by populations. We will rely on DHMIS data to conduct interrupted time series analyses (ITSA) or difference-indifferences methods (when control groups will be available) -using the main events identified during the exploratory phase as interruptions -to examine changes in health service utilisation indicators over time  [47]. Like Odhiambo et al. [47] , we will seek to collectively determine thresholds for each of the dimensions of our resilience scale (e. g. maintaining function as at least 80% of the 14 MNCH services maintained or improved their indicator values).