Global Data Research Articles

Evaluation of top tourism destinations according to their AI development with the entropy-TOPSIS-integrated method

PurposeThis study aims to determine and evaluate the artificial intelligence (AI) development and competitiveness of the top 20 countries that receive the highest number of tourists with the entropy technique for order of preference by similarity to the ideal solution (TOPSIS)-integrated method.Design/methodology/approachThis study is based on Global AI Index data published by Tortoise Media. Based on this index, according to the World Tourism Organization (UNWTO) report, the top 20 destinations that will host the highest number of tourists in 2022 were evaluated in seven different subpillars, which are talent, infrastructure, operating environment, research, development, government strategy and commercial. These seven subpillars of the index were considered as criteria, and the top 20 tourist destinations were included in the research as decision alternatives.FindingsThe analysis results show that the three most important AI criteria are operating environment, infrastructure and government strategy. Furthermore, the first three countries with the best AI performance according to the weighted criteria were the USA, China and the UK, respectively.Practical implicationsConsidering that AI technologies will direct tourist behavior in a world where technology is rapidly developing, it is recommended that the countries that receive the highest number of tourists improve their AI performance.Originality/valueWhen the relevant literature is examined, there is a limited number of studies examining the AI development and competitiveness of the top tourist destinations and weighting the Global AI Index values. Therefore, this study contributes to the gap in the relevant literature.

Promotion and Advancement of Data Security Governance in China

In this age of digital economy, issues relating to the data security and personal privacy of multimedia systems are increasingly manifesting in the form of unprotected data. The level of governance capability for data protection determines whether the data security and personal privacy of multimedia systems can be protected and developed in the future. In recent years, China has introduced laws and regulations on the protection of personal information, personal privacy, data security, and cybersecurity. Although data protection still needs to be improved, China has been refining the framework and provisions on data security governance through certain practices and the adoption of supporting regulations. The country aims to rapidly promote the current governance capability of data security and gradually form a good institutional environment to support international collaboration in the realm of global data security.

Technical Framework and Preliminary Practices of Global Geographic Information Resource Construction

Abstract. High-precision and real-time global geographic information data are fundamental and strategic resources in various fields such as safeguarding global strategic interests, studying global environmental changes, and planning for sustainable development. However, due to challenges related to ground control and obtaining reference information, the development of global geographic information resources faces significant hurdles in terms of geometric positioning, information extraction, and data mining. This paper starts with the characteristics of domestically produced remote sensing images and proposes a comprehensive technical framework centered around "uncontrolled geometric positioning, intelligent interpretation of typical elements, mining of multi-source data from abroad, and intelligent hybrid collection and compilation of Digital Elevation Models (DEMs)." The paper elaborates on the key technical challenges that need to be overcome and their corresponding solutions. It also outlines the development of relevant data products and production technical specifications. Multiple production-oriented software tools were developed, leading to the creation of a variety of data products in multiple types and scales, including global 30-meter land cover data, DEM data, core vector data, and more.

Building A Simple Weather Forecast Application using Python Django

Weather forecasting plays a crucial role in various sectors such as agriculture, aviation, transportation, and disaster management. Accurate and timely weather predictions are essential for making informed decisions and mitigating potential risks. This project presents a comparative study of different Application Programming Interfaces (APIs) for weather data retrieval and their effectiveness in weather forecasting. The objective is to evaluate the performance, reliability, and accuracy of various APIs in providing weather information for accurate forecasting. This project presents the development of a sophisticated weather forecasting application using the Python Django framework. In response to the growing demand for accurate and accessible weather information, this application provides users with timely updates on current weather conditions, temperature variations, and detailed forecasts for the upcoming six hours. The application dynamically adapts its interface to reflect hourly changes in weather patterns, ensuring that users receive up-to-date and reliable information at all times. One of the key features of this application is its user-friendly interface, which allows users to conveniently search for weather data anytime and anywhere. Whether planning a trip, scheduling outdoor activities, or simply staying informed about local weather conditions, users can rely on this application to deliver precise and relevant information tailored to their needs. Furthermore, the application enhances user experience by providing comprehensive coverage of global weather data. Users can easily access weather information for any location worldwide, enabling them to plan travel itineraries, explore new destinations, and make informed decisions about their future holiday plans.

Enhancing a machine learning model for predicting agricultural drought through feature selection techniques

This study aims to determine the crucial variables for predicting agricultural drought in various climates of Iran by employing feature selection methods. To achieve this, two databases were used, one consisting of ground-based measurements and the other containing six reanalysis products for temperature (T), root zone soil moisture (SM), potential evapotranspiration (PET), and precipitation (P) variables during the 1987–2019 period. The accuracy of the global database data was assessed using statistical criteria in both single- and multi-product approaches for the aforementioned four variables. In addition, five different feature selection methods were employed to select the best single condition indices (SCIs) as input for the support vector regression (SVR) model. The superior multi-products based on time series (SMT) showed increased accuracy for P, T, PET, and SM variables, with an average 47%, 41%, 42%, and 52% reduction in mean absolute error compared to SSP. In hyperarid climate regions, PET condition index was found to have high relative importance with 40% and 36% contributions to SPEI-3 and SPEI-6, respectively. This suggests that PET plays a key role in agricultural drought in hyperarid regions because of very low precipitation. Additionally, the accuracy results of different feature selection methods show that ReliefF outperformed other feature selection methods in agricultural drought modeling. The characteristics of agricultural drought indicate the occurrence of drought in 2017 and 2018 in various climates in Iran, particularly arid and semi-arid climates, with five instances and an average duration of 12 months of drought in humid climates.

IoT Security Challenges and Solutions for Data at Rest: A Systematic Literature Review

The rapid expansion of the Internet of Things (IoT) has significantly transformed both consumer and industrial domains, driving the urgent need for robust security measures to protect data at rest. SLR investigates into the challenges associated with securing IoT devices and data, exploring the limitations of existing security frameworks and the intricate requirements imposed by global data protection regulations such as GDPR. The researcher review current approaches, including privacy-by-design principles and the deployment of symmetrical data protection frameworks, as highlighted in recent studies. Through a comprehensive analysis of literature and existing technologies, we identify critical gaps in the protection strategies and propose enhanced methods for ensuring data security and privacy in IoT systems. The findings emphasize the role of developers in integrating privacy considerations early in the development process and the impact of regulatory complexities on the practical implementation of data protection measures. Furthermore, the paper evaluates innovative security solutions, such as full stack security architectures and adversarial training models, assessing their effectiveness in real-world applications. This study aims to provide a deeper understanding of the IoT security landscape and to suggest actionable strategies for improving data protection practices across the IoT ecosystem

Conceptual model of global plants entrapping plastics

Aquatic plants, seagrasses, macrophytes, mangroves, and riparian vegetation are responsible for some of the most important ecosystem services provided on the Earth. Given their role in trapping plastics along rivers, we propose a new ecosystem service of plastic entrapment by global plants. Although research started recently to study vegetation trapping plastics, little is known about the global patterns of plastic retention and remobilization by vegetation through different habitats. Given those gaps, we synthesize global data on plastic entrapment in plants providing a conceptual model to describe processes for plastic retention by vegetation. Our results demonstrate how vegetation has a pivotal role in entrapping plastics across spatial and temporal scales, finding the higher density of plastics on plants rather than in the adjacent water area. Furthermore, we proposed a conceptual model (i.e., Plant Plastic Pathway) of plants entrapping plastics, highlighting spatial and temporal scales of plastic retention and release processes in different habitats. Thus, we anticipate our conceptual model to be a starting point for more sophisticated future studies, putting effort into looking at plastic-vegetation dynamics. Our conceptual model may have a crucial effect if applied to plastic hotspot area detection with clean-up and mitigation actions in riverine ecosystems.

Spatiotemporal coupling analysis of surface water and groundwater in Tibet based on multi-source sensors

Abstract. Lakes and groundwater are two crucial components of the global terrestrial water cycle, collectively forming a vital network for Earth's water resources. However, in the Tibetan region, the spatiotemporal relationship between lakes and groundwater and their impact on the local hydrological cycle remain inadequately understood. Satellite remote sensing serves as an effective observational tool, enabling comprehensive investigation and analysis of surface lakes and groundwater in Tibet with high spatial resolution. Therefore, this study integrates Landsat and Cryosat-2 satellite data to examine the spatiotemporal patterns of surface lake extents and water levels in Tibet. Additionally, combining Gravity Recovery and Climate Experiment (GRACE) satellite observations with the Global Land Data Assimilation System (GLDAS) model data, we quantitatively analyze the spatiotemporal variations in groundwater storage and its correlation with lake water. The results indicate that: 1) Rivers and lakes in Tibet are mainly located in the central and northwest regions, displaying noticeable intra-annual variations; 2) Substantial lagged relationships exist between groundwater storage and lake water levels and areas, revealing that lakes contribute significantly to groundwater replenishment, especially in the Ngari prefecture and Lhokha prefecture. This study comprehensively utilizes multi-source remote sensing data to dynamically monitor surface and groundwater in Tibet, providing robust support for a better understanding of the interaction between groundwater and surface water.

Immature characteristics of the East Anatolian Fault Zone from SAR, GNSS and strong motion data of the 2023 Türkiye–Syria earthquake doublet

On February 6, 2023, an Mw 7.9 earthquake occurred in the western section of the East Anatolia Fault Zone (EAFZ). It was subsequently followed by an Mw 7.7 earthquake on the northern branch of the EAFZ, known as the Sürgü Fault Zone. Coseismic deformation fields were derived for these earthquakes using joint evaluation of near-field strong motion data, Global Navigation Satellite System data, and Synthetic Aperture Radar datasets. The coseismic slip distribution model was determined through the joint kinematic finite fault inversion. The Mw 7.9 earthquake was a left-lateral strike-slip event, predominantly occurring at depths up to 20 km. The earthquake displayed three distinct asperities that correlate well with bends and stepovers along the EAFZ. The Mw 7.7 earthquake also exhibited left-lateral strike-slip characteristics, with a major asperity along the Çardak Fault featuring a maximum slip of approximately 9.5 m at depths between 0 and 24 km. The occurrence of this unanticipated large Mw 7.9 catastrophic seismic event on a fault with low-intermediate structural maturity is noteworthy. In the vicinity of immature faults with multiple jogs, stress tends to accumulate at barrier locations. When the accumulated stress near several adjacent barriers reaches a certain threshold, it may result in the transformation of multiple barriers into asperities, triggering cascading ruptures.

Ocean internal tides suppress tropical cyclones in the South China Sea

Tropical Cyclones (TCs) are devastating natural disasters. Analyzing four decades of global TC data, here we find that among all global TC-active basins, the South China Sea (SCS) stands out as particularly difficult ocean for TCs to intensify, despite favorable atmosphere and ocean conditions. Over the SCS, TC intensification rate and its probability for a rapid intensification (intensification by ≥ 15.4 m s−1 day−1) are only 1/2 and 1/3, respectively, of those for the rest of the world ocean. Originating from complex interplays between astronomic tides and the SCS topography, gigantic ocean internal tides interact with TC-generated oceanic near-inertial waves and induce a strong ocean cooling effect, suppressing the TC intensification. Inclusion of this interaction between internal tides and TC in operational weather prediction systems is expected to improve forecast of TC intensity in the SCS and in other regions where strong internal tides are present.

Analyses of industrial air pollution and long-term health risk using different dispersion models and WRF physics parameters

AbstractThis study consists of three main sections. The first section delves into a performance analysis centered around modeling PM10, NOx, and CO emissions from a cement factory. It examines the effectiveness of various factors, including meteorological data, physics models, and air quality dispersion models, in producing accurate results for atmospheric simulations. The second section covers the dispersion direction and concentrations obtained by visualizing the dispersion maps. The third section covers an analysis of heavy metals emitted from the facility, taking into account potential risks in the region such as cancer, acute and chronic effects, and long-term respiratory risks. This study made use of meteorological models (WRF, AERMET, and CALMET), air quality dispersion models (AERMOD and CALPUFF), a health risk analysis model (HARP), and various sub-models (MMIF and CALWRF). Satellite meteorological data were obtained from NCEP and ERA, with the majority of meteorological data based on the Global Data Assimilation System (GDAS)/Final Operational Global Analysis (FNL) from Global Tropospheric Analyses and Forecast Grids used for the WRF model. In the daily results, AERMOD showed the highest concentration values, but CALPUFF had greater concentrations throughout the annual period. The winter season had the highest concentrations of pollutants. Although there are differences among the physics models used in this research, the conclusions produced are consistent. Analysis of the data from the HARP model suggested that cancer risk levels exceeded the threshold of one person per million. However, the proportion of exceedance instances is rather small in comparison to the receptor points.

ToTE: A global database on trees of the treeline ecotone.

Globally, treelines form a transition zone between tree-dominated forest downslope and treeless alpine vegetation upslope. Treelines represent the highest boundary of "tree" life form in high-elevation mountains and at high latitudes. Recently, treelines have been shifting upslope in response to climate warming, so it has become important to understand global tree diversity and treeline distributions. However, to the best of our knowledge, no global database on tree flora of treelines exists, which limits our capacity to undertake macroecological analyses. Here, for the first time, we present a global data set on the trees of the treeline ecotone, supported by an online ToTE database. We synthesized the database from 1202 studies published over the last 60 years (1962 to 2022) following the Preferred Reporting Items in Systematic Reviews and Meta-Analysis (PRISMA) protocol. We classified the tree species in the database into three categories: treeline tree (TL) species, near to treeline (NTL) tree species, and tree species with an upper montane range limit (TUMR). The ToTE Version-1 presents a total of 208 tree taxa, including 189 species, five subspecies, and 14 varieties, belonging to 54 genera and 26 families distributed across 34 mountain regions worldwide that either grow exactly at the treeline or have a range limit below the treeline. Of the total taxa, 155, 14, and 39 belong to TL, NTL, and TUMR, respectively. Genera such as Abies, Picea, Pinus, Larix, and Juniperus are more represented in the treeline tree category. On the other hand, Acer, Prunus, Populus, and Quercus have more representatives in the near to treeline category, whereas Erica, Nothofagus, and Polylepis contribute more tree species with an upper montane range limit. Furthermore, families such as Rosaceae and Pinaceae include trees that occur both at the treeline and with an upper montane range limit, whereas Sapindaceae includes trees that occur exclusively near to treeline. Our database also includes information on the global distribution patterns of treeline tree species richness across mountains and biomes. The mountains with the highest number of tree species are the Andes (39) followed by the Himalaya (37). Close to 67% of tree species show restricted distributions in different mountains, with the highest endemism in the Andes and the Himalaya. In terms of tree species distribution, Pinus sylvestris was widespread, with a distribution across nine mountain regions, followed by Picea glauca and Fagus sylvatica, both distributed across five mountain regions. In terms of species' distribution across biomes, the temperate biome harbors the highest treeline tree species richness (152 species), which may reflect the fact that the majority of studies are available from the temperate regions of the world. The remaining 56 species are distributed within five other biomes, with the least in dry tropical and subarctic (four species each). Furthermore, currently 40 treeline tree species fall under different International Union for Conservation of Nature threat categories. We anticipate that our database will help advance research on macroecological, biogeographic, evolutionary, climate-change, and conservation aspects of the treeline on a global scale. The data are released under a Creative Commons Attribution 4.0 international license. Please cite this data paper when the data are reused.

Response to Arbogast and Kerhoulas

Abstract We welcome feedback on the range maps published in Marsh et al. (2022) where it constructively improves our knowledge on species distributions. Unfortunately, we are concerned that criticisms raised by Arbogast and Kerhoulas are steps backward, not forward, particularly as they did not access the original range map data of Marsh et al. (2022). We stress that evaluating range maps using Global Biodiversity Information Facility data without the necessary quality control and filtering will lead to flawed interpretations—using the same approach, an even greater proportion, >99.5%, of IUCN mammal range maps would fail to meet their expectations. We take this opportunity to highlight the fine-scale inaccuracies, scale limitations, and range map variance that are expected across all expert range map sources and that any researcher should consider during any analysis. Finally, we again announce the availability of an online tool for providing annotations and proposing adjustments to range maps, and suggest this as a more appropriate forum for constructively and transparently improving range maps.

Trends of the cannabis market in Poland over the last decade = Tendencje na rynku konopi indyjskich w ostatniej dekadzie w Polsce

Cannabis is the most widely used drug. in recent years, interest in cannabis has grown significantly. over the last decade, the number of cannabis users worldwide has increased by almost 21%, reaching approximately 219 million people in 2021. Can- nabis cultivation covers practically all regions of the world. In recent years, 151 countries have reported cannabis cultivation, both outdoors and indoors. The increased growth of the latter is particularly observed in North America and Europe. Global data shows that in 2021, 5,226 tons of marihuana and 2,014 tons of hashish were confiscated. an increase in seizures of can- nabis products is observed in Europe and Poland. Another trend concerns the increasing content of the psychoactive substance Δ9-tetrahydrocannabinol (Δ9-THC) in cannabis plants. According to UNODC data, over the last 25 years there has been a 4-fold increase in the content of Δ9-THc in cannabis. over the last decade, the average content of Δ9-tetrahydrocannabinol in marihua- na seized in europe has increased approximately twice, and in hashish three times. The aim of this work is to assess potential changes in the content of the main psychoactive ingredients in cannabis in Poland in the last decade (2012–2022), based on expert opinions developed by the Institute of Forensic research in Kraków. The conduct- ed analyses confirmed the trends observed around the world. in the last ten years, the number of cases in which the drug-type cannabis was detected increased from 5.3% to 68.4% of total number of illicit drug cases examined for the presence of narcotic drugs, psychotropic substances and new psychoactive substances. an increasing trend in the content of Δ9-THC in the drug-type cannabis was also noted. over the last decade, the average total content of Δ9-THC increased almost three times and in 2022 reached 12%. This is due to the growth of cannabis cultivation in Europe, carried out using the technique known as sinsemilla, genetic modifications, as well as the appearance on the market of the feminized cannabis seeds.

Medium-term forecasting of global horizontal solar radiation in Brazil using machine learning-based methods

The generation of electric energy through renewable sources, such as solar photovoltaic (PV) systems, has emerged as one solution to the climate change crisis. To avoid fluctuations in energy generation on the electricity grid, reliable and accurate forecasts are required. Statistical downscaling methodologies often rely on data from General Circulation Model (GCM) to provide information on the local level. In contrast to traditional downscaling approaches, the present study focuses on a data-driven approach using the Global Data Assimilation System (GDAS) database in three different ways to feed the machine learning (ML) models, based on boosting and bagging, to provide medium-term Global Horizontal Irradiance (GHI) forecasts, i.e. up to seven days ahead. In addition, feature selection and feature augmentation techniques are also applied. The results showed that Extra Trees method outperformed the other tested in the present study, with a RMSE of 99.6 and 137.6 W/m2, and correlation 0.31 and 0.52 for June and October, respectively. Thus far, this work marks the initial suggestion of incorporating GDAS analysis data into a data-driven forecasting approach employing bagging and boosting models. This innovative approach demonstrates the potential for integrating global-scale data assimilation systems with local measurements using ML techniques. The outcomes of this research contribute to advancing the field of traditional downscaling methods and provides more robust and specialized forecasting.

Correction to: 2024 Heart Disease and Stroke Statistics: A Report of US and Global Data From the American Heart Association.

Correction to: 2024 Heart Disease and Stroke Statistics: A Report of US and Global Data From the American Heart Association.

Potential of using CO2 observations over India in a regional carbon budget estimation by improving the modelling system

Abstract. Devising effective national-level climate action plans requires a more detailed understanding of the regional distribution of sources and sinks of greenhouse gases. Due to insufficient observations and modelling capabilities, India's current carbon source–sink estimates are uncertain. This study uses a high-resolution Lagrangian transport model to examine the potential of available CO2 observations over India for inverse estimation of regional carbon fluxes. We use four different sites in India that vary in the measurement technique, frequency and spatial representation. These observations exhibit substantial seasonal (7.5 to 9.2 ppm) and intra-seasonal (2 to 12 ppm) variability. Our modelling framework, a high-resolution Weather Research and Forecasting Model combined with the Stochastic Time-Inverted Lagrangian Transport model (WRF–STILT), performs better in simulating seasonal (R2=0.50 to 0.96) and diurnal (R2=0.96) variability (for the Mohali station) of observed CO2 than the current-generation global models (CarboScope, CarbonTracker and ECMWF EGG4). The seasonal CO2 concentration variability in Mohali, associated with crop residue burning, is largely underestimated by the models. WRF–STILT captures the seasonal biospheric variability over Nainital better than the global models but underestimates the strength of the CO2 uptake by crops. The choice of emission inventory in the modelling framework alone leads to significant biases in simulations (5 to 10 ppm), endorsing the need for accounting for emission fluxes, especially for non-background sites. Our study highlights the possibility of using the CO2 observations from these Indian stations for deducing carbon flux information at regional (Nainital) and suburban to urban (Mohali, Shadnagar and Nagpur) scales with the help of a high-resolution model. On accounting for observed variability in CO2, the global carbon data assimilation system can benefit from the measurements from the Indian subcontinent.

Analysis of Peatland Research Trends Based on BERTopic

Peatlands comprise approximately 3% of the land area worldwide. Peatland exists in most countries, including tropical, subtropical, and boreal regions. Accordingly, peatland has garnered increased research attention as a potential countermeasure against climate change. Therefore, it is necessary to identify and comprise the topics constituting global peatland research. In this study, we applied BERTopic—a topic modeling technique—to analyze relatedness between research topics to classify global peatland research trends, evaluate changes over time, and analyze the relationships between topics. To this end, we searched the keyword “peatland” on ScienceDirect—a global academic publication data platform—and collected the titles and abstracts from 10,158 publications from 1953 to 2022 for dynamic topic modeling and network analysis. Eighty-two peatland research topics were identified, which were combined into 15 main topics. Over time, an increasing trend was observed in topics related to production, management, and fire. In addition, upon analysis of the relationships between topics, three groups centered on fire, peatland value, and carbon were identified. We anticipate that the findings of this study can be expanded to analyze trends in research related to fires in peatlands, regional characteristics of peat soil, prediction of greenhouse gases emission and mitigation due to peatland fires, and prediction of future peatland research topics.

Analyzing groundwater storage anomalies in data‐scarce areas of Ethiopia's Rift Valley Basin using artificial neural network

AbstractGroundwater storage anomalies (GWSA) study requires the use of hydro‐climatic models coupled with Gravity Recovery and Climate Experiment climate (GRACE) and GRACE Follow‐On (GRACE‐FO) Terrestrial Water Storage experiments. Understanding groundwater storage anomalies in the research area is difficult due to the restricted hydro‐meteorological data availability, especially, groundwater levels, changes in groundwater storage, and budgetary and technical constraints. No such investigation of GWS anomalies prior to this study in this Basin was conducted. The research focuses on GWSA investigations in the basin employing Global Land Data Assimilation Systems and Artificial Neural Networks‐Back Propagation Algorithm techniques are required. The ultimate objective of this study was to reduce the GRACE GWS from 1° to 0.25° and close the discontinuity between GRACE/GRACE‐FO observations. The results illustrated that GRACE/GRACE‐FO GWS throughout the basin were overall declining. The GWS anomaly significantly decreased between 2011 and 2013, with the highest monthly depletion in March 2011 (−158 mm). In contrast, from mid‐June to mid‐October, the maximum positive value varied from 20.80 mm in the southernmost basin to 39.49 mm in the central‐southeastern basin. The basin's eastern, central‐southeastern, northern, and southern areas displayed positive monthly GWS trends. The entire dry season has been influenced by negative anomalies. Hence, the identification of GWS variations and a comprehensive understanding of their effects provided valuable scientific insights. Identifying and analyzing anomalies in groundwater storage (GWS) can provide valuable information. This data can be efficiently used to develop appropriate regulatory frameworks, which may contribute to the long‐term viability of the availability of groundwater.

Perceptions on the Ethical and Legal Principles that Influence Global Brain Data Governance

Advances in neuroscience and other disciplines are producing large-scale brain data consisting of datasets from multiple organisms, disciplines, and jurisdictions in different formats. However, due to the lack of an international data governance framework brain data is currently being produced under various contextual ethical and legal principles which may influence key stakeholders involved in the generation, collection, processing and sharing of brain data thereby raising ethical and legal challenges. In addition, despite the demand for a brain data governance framework that accounts for culture, there is a gap in empirical research and actions to understand how key stakeholders around the world view these issues using neuroscientists who are affected by these ethical and legal principles. Therefore, using the research question how do ethical and legal principles influence data governance in neuroscience? we attempt to understand the perceptions of key actors on the principles, issues and concerns that can arise from brain data research. We carried out interviews with 21 leading international neuroscientists. The analytical insights revealed key ethical and legal principles, areas of convergence, visibility, and the contextual issues and concerns that arise in brain data research around these principles. These issues and concerns circulate around intimately connected areas which include ethics, human rights, regulations, policies and guidelines, and participatory governance. Also, key contextual insights around animal research and ethics were identified. The research identifies key principles, issues, and concerns that need to be addressed in advancing the development of a framework for global brain data governance. By presenting contextual insights from neuroscientists across regions, the study contributes to informing discussions and shaping policies aimed at promoting responsible and ethical practices in brain data research. The research answers the call for a cross cultural study of global brain data governance and the results of the study will assist in understanding the issues and concerns that arise in brain data governance.