dataesr/scientific-paragraphs-categorization huggingface dataset:
The COVID-19 pandemic has exemplified the importance of interoperable and equitable data sharing for global surveillance and to support research. While many challenges could be overcome, at least in some countries, many hurdles within the organisational, scientific, technical and cultural realms still remain to be tackled to be prepared for future threats. We propose to (i) continue supporting global efforts that have proven to be efficient and trustworthy towards addressing challenges in pathogen molecular data sharing; (ii) establish a distributed network of Pathogen Data Platforms to (a) ensure high quality data, metadata standardization and data analysis, (b) perform data brokering on behalf of data providers both for research and surveillance, (c) foster capacity building and continuous improvements, also for pandemic preparedness; (iii) establish an International One Health Pathogens Portal, connecting pathogen data isolated from various sources (human, animal, food, environment), in a truly One Health approach and following FAIR principles.
Only 3 viral genomes were published in the first month of the SARS epidemic, reaching 31 in the following 3 months, representing valuable information for the molecular diagnostic yet not enough to follow viral genomic epidemiology in real-time at a large scale  [3]. On the contrary, during the COVID-19 pandemic, metagenomic sequencing allowed the identification of a new pathogen causing an unknown respiratory infection in just a few weeks in December 2019  [4, 5]. Therefore, at the beginning of the year 2020, there were already hundreds of viral genomes in databases, currently reaching millions of sequences, setting a great example of a global effort on sequencing and data sharing. The sequencing of SARS-CoV-2 genome has proven to be an essential tool for the design of diagnostic PCRs, the study of outbreaks, understanding viral evolution and monitoring the effect of viral variants on the available vaccine or antiviral treatments.
In this model, individual laboratories perform pathogen characterization, then sequence or outsource sequencing to local/national sequencing platforms, and then submit their data and metadata within agreed standards to a local or national data hub responsible for data brokering to international repositories, thereby reducing duplication efforts across laboratories and fostering higher data quality, completeness and consistency. Such SARS-CoV-2 data brokering platforms have been successfully established in various regions and countries such as the UK, US, Germany, Denmark, Switzerland, Spain, Italy, France, Ireland, the Netherlands, Norway, Czech Republic, Poland and Austria, to support open data sharing. Figure  1. Data Brokering Workflow. Individual data producers can process the data, store it, and submit it directly to international repositories or public health databases.
Alternatively, in the data brokering model, several data producers can submit their data to a common data recipient. This recipient might be responsible for curating the data, analysing it with common pipelines, storing it, and re-sharing parts of the data to public health databases and international repositories (as agreed with the data providers), as well as ensuring data consistency and completion through close links and exchanges with the data producers. The latter service is often referred to as "data brokering" i. e. sharing data on behalf of others within a well-defined ethical and legal framework. Note that legal aspects should be considered along all the steps. Figure and legend modified with permission from  [15]  (CC BY 4. 0). The vast amount of data sharing (more than 10 million of consensus sequences deposited in databases such as GISAID or ENA), carried in part by these data platforms, demonstrate that many challenges on data quality and data sharing may have been successfully tackled, at least to some extent, by several countries.
However, work remains to fully tackle challenges towards data sharing globally, in particular to ensure compliance with FAIR+E guidelines to make data truly Findable, Accessible, Interoperable and Reusable within an Equitable ecosystem. The infrastructure, network and developed products now need to be maintained, anchored and further expanded to other data types such as wastewater datasets, bacterial datasets linked to antimicrobial resistance, and food-borne pathogens within a One Health context to enhance our understanding of infectious diseases and zoonotic infections, antimicrobial resistance, pathogen surveillance and outbreak response. It is absolutely necessary to share the genomic information at both national and international levels using well-aligned FAIR+E systems and governance.
g. GISAID  [9] , EU COVID-19 Data Portal  [8] ) and with local public health authorities. In this context, many countries decided to establish national data hubs in order to avoid duplicating within each laboratory tasks related to data curation, data analysis and data entry/submission to multiple platforms, as well as to ensure the use of common standards and legal documents for data transfer, use and sharing for each envisioned application (e. g. Consortium Agreements and Data Transfer, Use and Processing Agreements). Such data hubs might also have a clear governance for data access for research purposes, facilitating data reuse within harmonised processes and accelerated ethical clearance. Through initiatives like ELIXIR CONVERGE  [18] , a pan-European network of SARS-CoV-2 data platforms has emerged where ad hoc practices in developing such data infrastructures and standardising data and analysis workflows have been shared and discussed.
Today, this community needs to be maintained and grow beyond COVID-19, and start implementing common good practices at the legal, ethical, organisational, scientific and technical levels. Dedicated secured infrastructures such as Trusted Research Environments (TREs) were, however, often necessary to host such platforms, given the large amounts of data that were being produced and the fact that often sensitive metadata such as pseudonymised identifiers were also associated to enable linking to other datasets and hence avoid data silos. These sensitive data were generally not shared with the international community and remained within national silos, reflecting the need for a common trusted infrastructure enabling controlled data access and privacy-preserving queries such as the European Genomic Data Infrastructure  [19].
While sequencing costs can be estimated quite accurately and were generally negotiated upfront, more rarely would (sufficient) budgets within national surveillance programs be allocated for data curation, analysis and sharing within a common data hub. While the funding for initially developing such platforms onto dedicated infrastructure was available in many countries, a survey performed within the ELIXIR network showed that in Autumn 2022, only 40% of the 11 surveyed platforms were fully funded for the year to come (unpublished), demonstrating the need to recognise better the costs incurred by digital platforms and for multiple organisms and funding bodies to engage at the national levels. As we enter more endemic times, the need for maintenance of these platforms should be recognised and we should seize the opportunity to expand the data types and features of such platforms to enable other applications such as surveillance of food-borne pathogens and antimicrobial resistance in a One Health context.
The large data volumes submitted to international repositories were key for global genomic surveillance, which relied on high quality near-whole consensus sequences. Comparisons of genomic data quality using nextclade quality criteria suggest that Illumina-based sequences were of higher quality than nanopore-based sequences for the purpose of SARS-CoV-2 viral surveillance  [23]. It is interesting to note however that partial sequences can also be extremely useful to monitor predefined variants' prevalence from wastewater surveillance programs  [24]. The SARS-CoV-2 Data Hubs  [25]  are an example of bioinformatics tools for benchmarking, as the analysis produced a single large dataset of consensus sequences and variants from raw data via a pipeline developed by the Versatile emerging infectious disease observatory (VEO,  [26] ).
The Galaxy also proposed pipelines for SARS-CoV-2 analyses  [13]. The surveillance landscape would benefit from harmonised bioinformatics tools consisting of scripts, interfaces or application programming interfaces (APIs) readily available through open-source, documented and version-controlled repositories (e. g. , GitHub  [27] , Gitlab  [28] ), and benchmarked against public datasets and through external quality assessment programs  [29]. Automation of routine analyses however does not mean doing without bioinformaticians and data managers, who keep playing a key role to ensure up-to-date analyses, scaling, and further investigation of more specific questions. In this context, the need to build capacity in bioinformatics and data management remains a challenge, that ECDC is notably addressing by setting up dedicated training, also online  [30].
Software (source code, scripts, algorithms, computational workflows and executables) is essential to support scientific research and promote reproducibility. However several challenges remain on the findability, accessibility, interoperability and reusability (FAIR) of software  [31]. Adaptation of these FAIR principles not only to data but also to research software is critical to enable harmonising bioinformatics analysis and promote transparency and trust in scientific research. In addition to high-quality data, genomic surveillance relies on associated high-quality metadata that adhere to common standards. Often disregarded, tremendous efforts in data curation have been deployed at local data hubs and international repositories to ensure metadata quality and integrity. Given the large sequencing volumes, this often required automating data validation processes with only minimal human intervention where required  [32].
This will increase transparency, support reproducibility, and validation of results  [37]. Additional metadata can also be important for data re-use and reproducibility, describing the experimental setup with detailed protocols and including provenance reports on processed and analysed data (using workflow management systems such as Snakemake  [38]  or Nextflow  [39]. Lastly, the pandemic has shown that sensitive metadata often remained siloed at data hubs. De-identified data might, however, preclude some studies to be conducted where e. g. , datasets should be linked through a common, sensitive, sample identifier. The access to these data remains a great challenge even after the pandemic, mostly due to unclear legal frameworks, data governance and lack of international secure infrastructures to query and access these data.
To minimise the risk that countries and regions would operate as disconnected silos, an international effort was made in order to harmonise the work of establishing national and regional data hubs. The COMPARE data hubs  [40]  were notably expanded into SARS-CoV-2 data hubs, supported by several projects: https://www. covid19dataportal. org/partners?activeTab=Funding%20projects, as an essential component of the COVID-19 Data Portal  [8]. This enabled countries to organise, present and share their non-sensitive SARS-CoV-2 data with the international community, yet keeping sensitive data within separate national silos. Some countries also expanded or developed their own platforms, in order to tackle specific tasks and activities for their users, such as variant reporting to public health authorities including sensitive data.
The use of data and metadata standards would here be key to enable interoperability and quality standards for accurate comparison. Altogether, this would maximise the reuse of data and ensure that data follow FAIR principles. In human genomics, this challenge has been partly overcome through the Federated European Genome-phenome Archive (FEGA,  [43] ), where the data is archived nationally in Trusted Research Environments, and only the descriptions of datasets are available through federated searches using Beacons  [44] , with access to the data being granted by a Data Access Committee.
Cultural level Cultural differences result from different 'standards' across countries and societies, as well as different national policies, e. g. , on the SARS-CoV-2 surveillance. Regarding pathogen data, an important cultural challenge revolved around open science practices that differed greatly from one country to another. While everyone would agree on the necessity of timely data sharing, concerns about open data were rightfully invoked to ensure that data providers are properly cited and have time to perform their own research in a world where research benefits are not equitably distributed. This was particularly true for low to middle income countries (LMIC) and calls for flexible data sharing models including e. g. both timely data sharing to public health users and delayed (embargoed) access for researchers, making sure to also address Equity in the FAIR+E data sharing principles.
The "publish or perish" aphorism also played a role in high-income countries (HIC) where researchers were not always keen to immediately release their data openly, even in contexts where research and data generation were funded by public money. In this regard, great differences across countries were observed and Ministries of Research and funding agencies have an important role to foster and raise awareness on open research data practices following FAIR principles. The COMPARE Data Hubs/SARS-CoV-2 Data Hubs  [40]  were developed to support open data sharing in these scenarios too -enabling for 'private, pre-publication' state for data. Yet overall, determining when data can be accessed and for what purpose remains a challenge for the international community. Differences in the amount of coordination and collaboration within a country also reflect cultural habits that can impact data generation and sharing within a pandemic context.
In its next conference, GMI13 will focus on the critical importance of equity and interoperability (semantic, process, systems) in developing a global microbial genomics data sharing ecosystem (https://gmi13. org/). As a result, the COMPARE Data Hubs have been developed  [40]. The Data Hubs system at EMBL-EBI now continues to attempt to address and further support open data sharing and reduction in data silos. It does so by enabling groups to set up 'COMPARE or SARS-CoV-2 Data Hubs'  [40] , supporting collaboration amongst users on data, data sharing, and potentially integrated analysis and visualisations, centring primarily around sequence data. Data can remain private until publication, or can be immediately public at the point of submission, offering a level of flexibility. This system aims to extend into general pathogens and preparedness, with greater automation and usability, and has linked with other biodata, including sensitive clinical-epidemiological data through cohort data sharing [45], a major benefit of sitting on top of EMBL-EBI infrastructure.
The Pathogens Portal  [46]  enables finding and accessing data across the Data Hubs. Since all metadata associated to a sample are eventually openly published, only non-sensitive data can be collected at the pathogen data hub. This system also requires further development as mentioned as part of the package of extensions, and lacks the ability to pool mixed data together, e. g. via dedicated local/national TREs. The US Food and Drug Administration has also established GenomeTrackr, a distributed network of laboratories using WGS for pathogen identification. All the collected data are stored in the publicly accessible GenomeTrack reference database, built initially for food-borne pathogens  [47]. Data curation and bioinformatics analyses are provided by the National Center for Biotechnology Information (NCBI) at the National Institutes of Health.
The establishment of a regional/national PDP should aim to: • Ensure high-quality data, metadata standardization and data analysis. ○ Ensure timely collection of regional/national pathogen molecular data with internationally agreed quality metrics and minimal metadata. ○ Structure data using controlled vocabularies and ontologies where they exist. ○ Foster the establishment of data curation services within each PDP (set up common standards, share validation tools, etc. ). ○ Foster benchmarking of tools within reference datasets or through participation to External Quality Assessments. ○ Implement common pathogen-specific bioinformatics pipelines across data providers and make the code publicly available. • Perform data brokering on behalf of data providers both for research and surveillance.
○ Build upon the ELIXIR Maturity Model [54] to support nascent and established PDPs in their development lifecycle. Develop open-source modular services to be integrated across PDPs. ○ Develop, maintain and scale PDPs to support pandemic preparedness. ○ Deliver trainings and documentation on essential aspects related to establishing and running PDPs such as data brokering  [15, 55] , data management, secure IT infrastructure, data sharing, ethical and legal aspects etc. ○ Provide end-user support for all services provided by the PDP. 3/ Establish an International One Health Pathogens Portal. The distributed network of PDPs is also envisioned to enable FAIR+E data thanks to the transparent governance of an International One Health Pathogens Portal (IOPP) connected to each PDP and ensuring timely and equitable access to interoperable sensitive data.
Support PDPs in labeling data with predefined access levels for semi-automated data access. • Contribute to international standards definition, where needed. Support PDPs in adhering to common data standards. • Foster open sharing of workflows and benchmarking with common open datasets. • Organise pathogen/topic-specific workshops to harmonise analysis pipelines. Define quality labels for processed data generated within workflows successfully evaluated at External Quality Assessments programs. Harmonisation of data production and metadata associated will contribute to useful data sharing. • Define minimal standards for data, metadata description, including provenance reports for processed data. • Encourage implementation of FAIR Data Points at each PDP. • Establish differentiated data access rules for research and surveillance needs.
• Promote equity, by ensuring that credit is given to data providers and processors through metadata requirements and appropriate citation procedures. Consider embargo periods or benefit-sharing conditions to be implemented. • Support pandemic preparedness globally, by providing FAIR+E data to the international research community. Through the ELIXIR CONVERGE initiative  [18] , managers of nascent and established SARS-CoV-2 data hubs have been meeting on a regular basis to discuss and address common issues and needs, notably on data brokering to open data repositories. The concept of PDP/IOPP was born within this dynamic and collaborative working group who is now willing to expand and further collaborate to set the foundations of the IOPP and distributed network of PDPs. To achieve this, an ELIXIR Focus Group on Pathogen Data will be established, with dedicated task forces to properly plan the PDP/IOPP roadmap and build/extend its infrastructure, governance, legal and ethical frameworks, Maturity models, data brokering, data access committees, interactions with surveillance authorities (EFSA, ECDC) and FAIR+E data in general (data standards, ontologies, CV, data brokering, open software, benefit-sharing).
INTRODUCTION Age-related macular degeneration is one of the leading causes of vision loss  [1] , affecting * 200 million people globally  [2]. With the introduction of anti-vascular endothelial growth factor (VEGF) agents, the treatment of neovascular age-related macular degeneration (nAMD) has been revolutionized  [3, 4] ; however, significant unmet needs still exist for many patients with nAMD, including unresolved fluid  [5] , high injection burden  [6] , and poor adherence  [7] , which can result in inadequate treatment and subsequent avoidable vision loss  [8]. The Phase 3 HAWK (NCT02307682) and HARRIER (NCT02434328) studies evaluated the efficacy and safety of brolucizumab versus aflibercept in treatment-naı ¨ve eyes with nAMD. The results of these trials demonstrated that brolucizumab 6 mg (administered in a every 12 weeks/every 8 weeks [q12w/q8w] regimen) achieved robust vision gains and superior fluid resolution versus aflibercept q8w; a majority of brolucizumab-treated eyes were maintained on a q12w regimen immediately after loading through Year 1  [9].
Further DA assessments were conducted at Weeks 20, 32, and 44 in both studies by the masked investigator (Fig. 1 ). The DA guidance criterion at Weeks 20, 32, and 44 was: a decrease in BCVA of C 5 letters due to nAMD DA compared with  Week 12. This post hoc analysis included pooled data from the brolucizumab 6 mg arms and aflibercept arms of both studies. Brolucizumab-and aflibercept-treated eyes with DA at Week 16 were compared to evaluate DA at subsequent DA assessments during Year 1 of the studies; presence of IRF and/or SRF was also evaluated at each DA assessment and at the primary analysis at Week 48.
RESULTS Pooled data from 730 study eyes in the brolucizumab 6 mg arms and 729 study eyes in the aflibercept arms of HAWK and HARRIER were included in this post hoc analysis. Mean age was 74. 5 vs 75. 2 years in the brolucizumab vs aflibercept arms, respectively. More patients were female than male in both the arms. Mean baseline BCVA and CSFT was 59. 4 vs 61. 6 letters and 532. 1 vs 483. 9 lm in the brolucizumab vs aflibercept arms, respectively. At the first DA assessment at Week 16, fewer brolucizumab-(22. 8%; 161/730) than aflibercept-treated (32. 2%; 226/729) eyes had DA. In these eyes with investigator-identified DA at Week 16, change in BCVA from baseline to Week 96 was comparable in the brolucizumab (4. 1 letters) and aflibercept (2. 5 letters) arms (Fig. 2 ). Fewer brolucizumab-than aflibercept-treated eyes with DA at Week 16 had DA at subsequent DA assessments: 31.
All study participants provided written informed consent about the results of the study being reported to any national regulatory agency and be used in scientific publications and/or presentations, provided no personal details will be released. By signing the Information and Consent document, participants are authorizing access to their records. Data Availability. The datasets generated during and/or analyzed during the current study are available from corresponding author on reasonable request. Open Access. This article is licensed under a Creative Commons Attribution-NonCommercial 4. 0 International License, which permits any non-commercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
ACKNOWLEDGEMENTS Funding. The funding for this analysis, writing support, and the journal's Rapid Service Fee was provided by  Novartis Pharma A. G. Writing and Editorial Assistance. The authors thank  Ruchi Grover ,  M. Pharm. and  Kritika Dhamija , M. S. (Pharm. ),both from  Novartis Healthcare Pvt. Ltd. , for medical writing and editorial assistance towards the development of this article. The medical writing and editorial assistance were funded by  Novartis Pharma A. G. Authorship. All named authors meet the International Committee of Medical Journal Editors (ICMJE) criteria for authorship for this article, take responsibility for the integrity of the work as a whole, and have given their approval for this version to be published. Author Contributions. All authors contributed to the study conception, design, and/or data interpretation.