Nevertheless, disseminating and institutionalizing Open Science is considered a pivotal moment in shaping rules for data protection and sharing [
]. The European Commission released a strategy specifically for research data called the European Open Science Cloud (EOSC), aimed at facilitating data exchange and further data analysis for publicly funded research [
]. Leveraging the FAIR (findable, accessible, interoperable, reusable) data framework and EOSC initiatives, the entire research lifecycle is set to undergo fundamental changes to become more efficient, transparent, credible, and collaborative. Integrating data with expanded sample sizes have led to significant progress, particularly in rare diseases and genetic disorders. Similar strides are anticipated if health and environmental data are interconnected [
]. However, collecting data anew for each new project and insufficiently incorporating previous studies into meta-analyses are considered wasteful of research resources [
The global COVID-19 pandemic has underscored significant challenges in collecting, integrating, and sharing medical personal data worldwide [
6,
23]. Data analysis from various sources can provide vital information for pandemic management [
6]. For instance, Horn and Kerasidou emphasize that data on individual behavior can offer crucial insights into virus spread [
24]. Furthermore, Feeney et al. [
25] stress the importance of collecting and managing personal health data in times of increased mobility and crises. Data flow is becoming increasingly important for ensuring optimal healthcare, especially for vulnerable groups, such as migrants, chronically ill individuals, and children. National borders must not constrain health data [
25]. Consequently, there is a demand for cross-border data exchange in electronic health services at the European level [
25,
26]. The need for international collaboration has grown steadily, and the opportunities presented by artificial intelligence and big data in the medical sector should be fully harnessed [
26]. The healthcare sector has long called for more excellent technological orientation and the use of big data [
24]. However, patients and healthcare organizations are frustrated by numerous barriers to accessing patient data [
19]. Many health data are currently stored in data silos due to privacy concerns and are not yet accessible for shared data utilization [
27]. Throal et al. illustrate, using intensive care as an example, that much machine-readable data are generated daily in this discipline. However, they have not been used further due to legal and ethical concerns [
28]. Leveraging big data in healthcare promises more accurate prognosis, new diagnostic approaches, and improved and efficient treatment [
24,
26,
29]. The rapid technological advancements driven by artificial intelligence and machine learning techniques have fundamentally expanded the ability to identify patterns and structures in data that can enhance health, diagnosis, and treatment [
30]. Access to scientific health data is essential for further scientific progress and innovation [
31]. Clinical, evidence-based decision making ideally requires a foundation in big data to support decision making [
2,
31]. Simultaneously, the optimized use of personal patient data can fundamentally transform healthcare, individual understanding, and disease prevention [
29]. Open data availability can provide new and deeper insights into prevention, diagnosis, and therapy, especially in the context of genomic data [
32,
33]. Its benefits are particularly pronounced in rare diseases. Big data applications enable deep and precise phenotyping of genetic and rare diseases, offering invaluable insights [
34]. Furthermore, data sharing for comparing genetic and epidemiological risk factors is crucial [
26]. Therefore, the collaborative use of personal health data for medical research and practice is considered fundamentally significant [
35]. Aspects of general quality assurance in healthcare through shared data usage are critical [
31]. However, using data from health-related activities has raised new ethical challenges related to data privacy, integrity, and appropriate use [
30]. The ability to link individual data records is considered a central element for medical research while simultaneously being ethically sensitive due to the potential to gain deep insights into very intimate aspects [
3]. This has revealed societal and individual contradictions and dilemmas [
36].
Several ethical obstacles for sharing and analyzing data are cited in the studies [
18,
31,
34]. A significant ethical and societal dilemma is that the potentially great benefits of Open Data may not materialize due to data privacy concerns [
30]. Normative standards for ethical scrutiny are currently lacking, which can lead to physical and psychological harm in the re-use of data [
49]. Individual harmlessness is perceived to be at risk when data are sold, leading to re-identification or extortion, which can result in financial, physical, psychological, and emotional harm [
30].
Risks and benefits of data sharing must, therefore, be carefully weighed [
10,
23,
49], and it is unclear whether specific datasets can be ethically released [
10]. There is also concern about subsequent unethical and inappropriate projects in secondary use with a risk to privacy [
30,
36,
50]. Ethical issues are mainly seen in further health data exchange [
46]. Another challenge is the wide range of methodologies and practices within Open Data, each involving specific legal and ethical issues [
32]. An essential ethical problem is that Open Data are irrevocable and cannot be retrieved [
33].