dataesr/scientific-paragraphs-categorization huggingface dataset:
6 For example, he discussed a collaborative software development project, Bioconductor, an R-based open tool with a support site, a repository, and an active community that continues to develop this software. He also emphasized that similar criteria should assess scientific software and a scientific publication. He advised the young researchers not to reinvent the wheel but reuse codes and do research with modularity, as monolithic research is not outstanding. The visual summary of his talk can be found in Figure  2. Prof. Nicola Mulder: Building capacity and resources to enable genomic medicine in Africa The standard approach to treating a disease is to use a single treatment for all. Prof. Mulder's talk focused on how this approach is not applied in the African community and how it is designed for genomics medicine for the African community.
In the case of Africa, a large number of diseases are present, and non-African genotypes-phenotypes associations cannot be effectively applied to African patients. Therefore, tailored African genomic medicine is important and should be developed. The challenges for African biomedical data are to have quality representation in reference genomes and public databases, as well as the protection of the rights and privacy of participants. In response, Human Heredity & Health in Africa (H3Africa) was born. This structure recovers and analyzes phenotypic, genomic, and microbiome data. The bioinformatics component is managed by the H3ABioNet branch, whose Principal Investigator is Prof. Mulder, enabling storage, analysis, and curation infrastructures. In addition, data analysis is done according to FAIR principles.
Furthermore, these early-career researchers-led groups organize tutorials ranging from general bioinformatics topics to focused ones such as Population genetics analysis and Single-cell data and workshops for scientific data visualization. RSGs actively do science communication through newsletters and Fête de la science (RSG-France), blog posts (RSG-Turkey), and/or Twitter experience sharing (RSG-Spain). RSG-Spain actively participates in the Bioinfo4Women [2] initiative and actively encourages the career progress of female students and researchers, especially those that are transitioning from the postdoc level to independent positions. RSG-Bangladesh encourages its volunteers to present at internal platforms such as the Student council symposia and SC webinar series program and is actively growing in the region.
Acknowledgements We would like to thank all SCS2021 attendees, presenters, our keynote speakers, and volunteer reviewers (people of the Organizing Committee). We thank the  ISCB leadership  and staff, particularly  Diane Kovats ,  Steven Leard ,  Seth Munholland , and the  ISCB-SC executive team , for their support. We especially thank  Sayane Shome  and  Aishwarya Alex  from the  ISCB-SC  executive team for critically reading the manuscript. Finally, we thank our sponsor  Harvard Medical School Department of Biomedical Informatics.
England has a centralized healthcare system under the National Health Service (NHS). Despite not having detailed clinical data, this allowed the NHS to merge claims data with detailed data from 2 large urban medicine databases, corresponding to the 2 major software publishers  [22]. This data is currently accessed through Opensafely, a first platform focused on Coronavirus Disease 2019 (COVID-19) research  [23]. In the United States, even if scattered between different insurance providers, claims are pooled into large databases such as Medicare, Medicaid, or IBM MarketScan. Lastly, in Germany, the distinct federal claims have been centralized only very recently  [24]. Clinical data on the other hand, tends to be distributed among many entities, that made different choices, without common management or interoperability.
4. Usages thanks to dedicated applications and tools accessing the datamarts and data sets. In France, the national insurer collects all hospital activity and city care claims into a unique reimbursement database  [13]. However, clinical data is historically scattered at each care site in numerous HISs. Several hospitals deployed efforts for about 10 years to create CDWs from electronic medical records  [30] [31] [32] [33] [34] [35] [36] [37] [38] [39]. This work has accelerated recently, with the beginning of CDWs structuring at the regional and national levels. Regional cooperation networks are being set up-such as the Ouest Data Hub  [40]. In July 2022, the Ministry of Health opened a 50 million euros call for projects to set up and strengthen a network of hospital CDWs coordinated with the national platform, the Health Data Hub by 2025.
Quantitative methods Three tables detailed the structured answers in S1 Text. The first 2 tables deal with the characteristics of the actors and those of the data warehouses. We completed them based on the notes taken during the interviews, the recordings, and by asking the participants for additional information. The third table focuses on ongoing studies in the CDWs. We collected the list of these studies from the dedicated reporting portals, which we found for 8 out of 14 operational CDWs. We developed a classification of studies, based on the typology of retrospective studies described by the OHDSI research network  [41]. We enriched this typology by comparing it with the collected studies resulting in the 6 following categories: • Outcome frequency: Incidence or prevalence estimation for a medically well-defined target population.
More recent initiatives, such as those of the HCL (Hospitals of the city of Lyon) or the Grand-Est region, are distinguished by an initial, institutional, and high-level support. The CDW has a federating potential for the different business departments of the hospital with the active participation of the CRD, the IT Department, and the MID. Although there is always an operational CDW team, the human resources allocated to it vary greatly: from half a full-time equivalent to 80 people for the AP-HP, with a median of 6. 0 people. The team systematically includes a coordinating physician. It is multidisciplinary with skills in public health, medical informatics, informatics (web service, database, network, infrastructure), data engineering, and statistics. Historically, the first CDWs were based on in-house solution development.
Transparency Ongoing studies in CDWs are unevenly referenced publicly on hospital websites. Some institutions have comprehensive study portals, while others list only a dozen studies on their public site while mentioning several hundreds ongoing projects during interviews. In total, we found 8 of these portals out of 14 CDWs in production. Uses other than ongoing scientific studies are very rarely documented. The publication of the list of ongoing studies is very heterogeneous and fragmented between several sources: clinicaltrials. gov, the mandatory project portal of the Health Data Hub  [45]  or the website of the hospital data warehouse.
The Paris hospitals, thanks to its regional coverage and the choice of OMOP, is also well advanced in multicentric research. At the same time, the Grand-Est region is building a network of CDW based on the model of the Grand-Ouest region, also using eHop. CDW are used for monitoring and management (16/21). The CDW have sometimes been initiated to improve and optimize billing coding (4/21). The clinical texts gathered in the same database are queried using keywords to facilitate the structuring of information. The data are then aggregated into indicators, some of which are reported at the national level. The construction of indicators from clinical data can also be used for the administrative management of the institution. Finally, closer to the clinic, some actors state that the CDW could also be used to provide regular and appropriate feedback to healthcare professionals on their practices.
Technical architecture The technical architecture of modern CDWs has several layers: • Data processing: connection and export of source data, diverse transformation (cleaning, aggregation, filtering, standardization). • Data storage: database engines, file storage (on file servers or object storage), indexing engines to optimize certain queries. • Data exposure: raw data, APIs, dashboards, development and analysis environments, specific web applications. Supplementary cross-functional components ensure the efficient and secure operation of the platform: identity and authorization management, activity logging, automated administration of servers and applications. The analysis environment (Jupyterhub or RStudio datalabs) is a key component of the platform, as it allows data to be processed within the CDW infrastructure.
Transparency Health technology assessment agencies advocate for public registration of comparative observational study protocols before conducting the analysis  [8, 17, 49]. They often refer to clinicaltrials. gov as potential but not ideal registration portal for observational studies. The research community advocates for public registrations of all observational studies  [50, 51]. More recently, it emphasizes the need for more easy data access and the publication of study code  [29, 52, 53]. We embrace these recommendations and we point to the unfortunate duplication of these study reporting systems in France. One source could be favored at the national level and the second one automatically fed from the reference source, by agreeing on common metadata. From a patient's perspective, there is currently no way to know if their personal data is included for a specific project.
Data and data usage When using CDW, the analyst has not defined the data collection process and is generally unaware of the context in which the information is logged. This new dimension of medical research requires a much greater development of data science skills to change the focus from the implementation of the statistical design to the data engineering process. Data reuse requires more effort to prepare the data and document the transformations performed. The more heterogeneous a HIS system is, the less qualitative would be the CDW built on top of it. There is a need for increasing interoperability, to help EHR vendors interfacing the different hospital softwares, thus facilitating CDW development. One step in this direction would be the open source publication of HIS data schema and vocabularies.
Conclusions The French CDW ecosystem is beginning to take shape, benefiting from an acceleration thanks to national funding, the multiplication of industrial players specializing in health data and the beginning of a supra-national reflection on the European Health Data Space [60]. However, some points require special attention to ensure that the potential of the CDW translates into patient benefits. The priority is the creation and perpetuation of multidisciplinary warehouse teams capable of operating the CDW and supporting the various projects. A combination of public health, data engineering, data stewardship, statistics, and IT competences is a prerequisite for the success of the CDW. The team should be the privileged point of contact for data exploitation issues and should collaborate closely with the existing hospital departments.
Acknowledgments We want to thanks all participants and experts interviewed for this study. We also want to thanks other people that proof read the manuscript for external review:  Judith Fernandez  ( HAS ),  Pierre Liot  ( HAS ),  Bastien Guerry  ( Etalab ),  Aude-Marie Lalanne Berdouticq  ( Institut Sante  ´numerique  en Societe ),  Albane Miron de L'Espinay  ( ministère de la Santeet de la Prevention), and  Caroline Aguado  ( ministère de la Santeet de la Prevention ). We also thank  Gae ¨l Varoquaux  for his support and advice.
Analytical approach and methods The main objective of our article is to offer novel conceptual contributions, not a bibliometric analysis or a fully-fledged literature review of the IS, SI, and ST strands of literature. 1 Hence, we draw on a semi-systematic or focussed literature review as a starting point. A semi-systematic literature review aims at identifying relevant research traditions and attempts to build a meta-narrative from there  (Snyder, 2019). In contrast, systematic literature reviewsthe method developed in clinical science to analyse the results of randomised controlled trialsaim at identifying effect sizes  (Wong et al. , 2013). To identify the relevant body of recent literature we have searched ScienceDirect. This has the advantages of being an important academic data base and warranting good retrieval and reproducible qualities.
Acknowledgements We would like to thank the three anonymous reviewers, the editor, as well as  Effie Amanatidou ,  Cristoph Brodnik , and  Dimitrios Pontikakis  for their constructive comments on previous versions of this article. Financial support from the  National Research, Development, and Innovation Fund, Hungary  (Grant no. 124858 ) is gratefully acknowledged. The funder has not been involved in study design; the collection, analysis, and interpretation of data; the writing of the article; or the decision to submit the article for publication.
The potential of big data capabilities to transform and understand global agricultural and biological systems often relies on data from different sources that must be considered together or aggregated to provide insights. The value of data is however not only in its collection and storage, but largely in its re-use. Big data storage repositories are not enough when we consider a world brimming with escalating volumes of data, here we need to consider innovative systems and tools which address data harmonization and standardization and importantly, ones that can bridge the gap between science and end users. In this paper, we will demonstrate how CGIAR (including the Alliance of Bioversity International and CIAT) develops a culture of co-operation and collaboration among custodians of agrobiodiversity data, as well as new directions for big data.
CGIAR first launched the Platform for Big Data in Agriculture to enhance the development and maintenance of its data. This helped establish workflows of cross-platform synthesis, annotate and apply the lessons learnt. The Platform then built GARDIAN (Global Agricultural Research Data Innovation and Acceleration Network)-a digital tool that harvests from ~40 separate open data and publication repositories that 15 CGIAR centres have used for data synthesis. While there have been significant advances in big data management and storage, we also identify the gaps to improve use, and the re-use of data in order to reveal its added value in decision making.
Introduction The concept of "big data" has been gradually gaining popularity since its first emergence in the mid-1990s, and has seen a surge in research publications since 2008  (Li et al. , 2016). Big data has been suggested as a predominant source of innovation and has caused a paradigm shift to data-driven research. The rapid growth of big data originating from expanding social systems in addition to traditional measurement and observation systems offers great potential to revolutionize our approaches to research. We are getting better at discovering knowledge from data and acquiring intelligence from information. Organisations involved in big data research are better harnessing the associated opportunities and more effectively addressing the corresponding challenges  (Wang, 2016). While big data is still not a clearly defined term, it often refers to a wide range of larger datasets that are difficult to store, manage, and process using traditional processing tools, due to their size, but also their complexity  (Liu, 2015).
CGIAR and big data 2. 1 CGIAR's data assets CGIAR is a consortium that unites an array of international organizations aiming to reduce rural poverty, improve human health and nutrition, introduce sustainable management of natural resources and strengthen food security, primarily in developing countries. In close collaboration with national research institutions, almost 10,000 CGIAR scientists, researchers and technicians are collecting, analysing and synthesizing data on agricultural and biological systems across Asia, Africa, Latin America and the Pacific. With its 15 international research centres, 11 genebanks (that safeguard a unique global resource of crop and tree diversity and respond to thousands of requests for samples per year in more than 100 countries worldwide), 12 Global Research Programs as well as various research platforms, the CGIAR has collected and generated data through a variety of avenues and these data include but are not limited to: Long-term trials, baseline data collections, genomic data, value-added secondary datasets, spatial data and data collected in public-private partnerships.
A key objective of CGIAR is to integrate the work of the centres and their partners, avoiding fragmentation and duplication of effort, specifically around these data resources. In addition to data and databases, there are also other types of information products such as reports, books and book chapters, data analysis and collection tools, video, audio and images and the metadata associated with the information products listed above. In the CGIAR, these information products are stored and preserved in a joint repository called CGSpace (https://cgspace. cgiar. org). CGSpace hosts research outputs and knowledge products for several of CGIAR centres and research programs (Figure  1 ). For storing and publishing data and databases, other centres also created a range of data portals and also disseminate data through repositories such as the Harvard Dataverse.
CGIAR's data as international public goods A fundamental step towards open science is to offer open access data. The CGIAR therefore developed a clear mandate to recognize data as important information assets and project deliverables. In 2012, the CGIAR approved its Intellectual Assets Principles (CGIAR IA Principles) (CGIAR-IEA, 2018). A year later, in 2013, the CGIAR approved its Open Access and Data Management Policy (OA/DM Policy), which expanded on CGIAR IA Principles. OA/ DM Policy officially recognized CGIAR's information products as international public goods. This stated that all research data, generated as a result of research funded by CGIAR programmes, must, subject to confidentiality of respondents, be deposited in a suitable repository and made publicly available following a predetermined timeline.
OA/DM Policy also acted as a guiding framework for data producers and publishers to implement FAIR principles (Findability, Accessibility, Interoperability, and Reusability)  (Wilkinson et al. , 2016). The FAIR data principles have rapidly become a standard for assessing responsible and reproducible research  (Bezuidenhout and Shanahan. , 2022). CGIAR, being a global network of agricultural research centres recognized the importance of standardisation and data interchange in building a global network of data and service providers. In 2017, the CGIAR launched the Platform for Big Data in Agriculture as a coordinating mechanism to deliver CGIAR's strategy and to align CGIAR's Research Programs and data management processes (CGIAR Advisory Services Shared Secretariat, 2021). Prior to the Platform for Big data in Agriculture, CGIAR had some 40 separate open data and publication repositories.
For the first time, these intellectual assets became discoverable in one place via GARDIAN (https://gardian. bigdata. cgiar. org). GARDIAN (Global Agricultural Research Data Innovation and Acceleration Network) is the first pan-CGIAR search engine for agricultural data. This platform harvests from separate open data and publication repositories that 15 CGIAR centres have used for data synthesis. GARDIAN also incorporated metadata standards and quality control measures to ensure the accuracy and reliability of the data. Metadata sharing is a critical way to ensure that data is discoverable  (Contaxis et al. , 2022). There are however also many other actors in the big data realm which makes this space highly fragmented. Private companies, government institutions, and university research institutes have also been generating immense amounts of data.
Access to these data might be restricted due to protection of the privacy of human subjects, compliance with policies and regulations, and following intellectual property rights. The CGIAR has partly addressed this by updating and refining an open Guideline for Responsible Data (https://bigdata. cgiar. org/responsible-data-guidelines). However, to further tackle the issue, there is a strong need to cultivate strategic partnerships and to build appropriate policies and business models  (King et al. , 2021). Furthermore, CGIAR needs to enable interoperability and shared infrastructure that allows data to flow seamlessly beyond CGIAR systems, and allows partnerships with other organisations to promote the overlap of biodiversity data platforms with agroecology and agrobiodiversity.
The gap-Data paralysis Enabling data discoverability is not quite enough. In addition to ensuring that data is discoverable, it is essential to evaluate the impact and value of data on end users as well as the ability of available data to influence scientific research and development. An evaluation by the CGIAR Advisory Services Shared Secretariat (2021), an independent panel of experts tasked with evaluating CGIAR's programs, pointed out the lack of analysis on the Platform as well as the scarcity of data downloads. The focus had been on data uploads and not a lot on data re-use and the scientific application of the data. While data uploads are a necessary first step in the platform the lack of subsequent data re-use was considered a missed opportunity to help bridge the gap between science and potential data end-users.
bigdata. cgiar. org/#/tools; https://gardian. bigdata. cgiar. org/#/maps). A lack of analysis on the platform may however indicate how these exploration tools should have been co-developed with current and potential users, especially researchers and policymakers, to allow generating clear added value of the datasets. Perhaps however this is also an indication of the overabundance of data availability leading users to data paralysis. Indeed, there are numerous other high-level interfaces for data retrieval such as DataOne and Globus, and if CGIAR aims to be part of these global data providers it is crucial that CGIAR ensures effective data interchange in this community of repositories and big data services. Infrastructure, tools, and approaches to make CGIAR data more visible, interoperable and reusable continue to be further refined.
Lastly, in the current era of research in order to support better diagnosis of problems or monitoring of interventions such as those related to agroecology, conservation ecology and climate change, biodiversity informatics must embrace real time, near real time or high frequency data streams. Real time high frequency data streams are data sets that are collected in nearinstance intervals. These datasets are typically collected though sensors or other automated processes and are used in many projects in CGIAR to gain insights into the complex systems that shape our world. As the Platform for Big Data has now come to the end of its cycle, CGIAR must consider new ways to reuse data to generate and answer common research questions and integrate research across domains, which is fundamental for making progress on solutions.