Observational Datasets Research Articles

A dataset of manually annotated filaments from H-alpha observations.

We present the Manually Annotated GONG Filaments in H-alpha Observations (MAGFiLO v1.0) dataset. This dataset contains 10,244 annotated filaments from 1,593 observations captured by the Global Oscillation Network Group (GONG), spanning the years 2011 through 2022. Each annotation details one filament's segmentation, minimum bounding box, spine, and magnetic field chirality. With a total of over one thousand person-hours of annotation, and a double-blind review process, we ensured high-quality ground-truth data. Our inter-annotator agreement reaches a Kappa score of 0.66. We also verified that the hemispheric preference of filaments as annotated in MAGFiLO aligns with the findings from similar datasets of much smaller sample sizes. MAGFiLO is the first dataset of its size, enabling advanced deep learning models to identify filaments and their features with unprecedented precision. It also provides a testbed for solar physicists interested in large-scale analysis of filaments. In this report, we document the details of the annotation and the post-processing phases that were applied.

A dataset of high-resolution climate change projections over South America with bias correction

Accurate and detailed datasets are crucial for assessing climate change impacts. Regional climate models provide high-resolution simulations and are key tools but often exhibit systematic biases. Therefore, this paper presents a dataset derived from bias-corrected Eta regional model simulations and projections driven by four global CMIP5 models. The correction applied to daily precipitation, potential evapotranspiration, actual evapotranspiration, and 2-m air temperature, was conducted on a 0.2° x 0.2° grid over South America. The dataset covers two periods: 1976-2005 (baseline) and 2006-2099 (future) under RCP4.5 and RCP8.5 scenarios. Empirical quantile mapping was used to adjust the Eta model outputs to better match observational data. This method modified the accumulated probability curves of the Eta model outputs to align with observational curves for both baseline and future climates. Two observational datasets were used for correction and evaluation. The new dataset shows that bias correction significantly reduced the errors in the Eta simulations, especially for the frequent values of precipitation, potential evapotranspiration, and 2-m temperature, and also corrected the annual cycle and frequency distribution of these variables, approaching the observations. The pattern of extreme precipitation indices from the bias-corrected Eta dataset also reduced error. Bias correction was applied to future projections. The comparison against the raw Eta dataset showed that the trends of changes were preserved, but in general, the peaks of the changes were smoothed. As in the raw Eta dataset, the RCP8.5 scenario showed a higher change rate than RCP4.5. This work also revealed the large uncertainty of the observational dataset; some of the remaining errors after the bias correction were mostly due to differences between the two correction and evaluation observational datasets. The described dataset is freely available from the CNPq LattesData repository at the following link: https://doi.org/10.57810/lattesdata/WAVGSL.

Monitoring surgical nociception using multisensor physiological models

Monitoring nociception, the flow of information associated with harmful stimuli through the nervous system even during unconsciousness, is critical for proper anesthesia care during surgery. Currently, this is done by tracking heart rate and blood pressure by eye. Monitoring objectively a patient's nociceptive state remains a challenge, causing drugs to often be over- or underdosed intraoperatively. Inefficient management of surgical nociception may lead to more complex postoperative pain management and side effects such as postoperative cognitive dysfunction, particularly in elderly patients. We collected a comprehensive and multisensor prospective observational dataset focused on surgical nociception (101 surgeries, 18,582 min, and 49,878 nociceptive stimuli), including annotations of all nociceptive stimuli occurring during surgery and medications administered. Using this dataset, we developed indices of autonomic nervous system activity based on physiologically and statistically rigorous point process representations of cardiac action potentials and sweat gland activity. Next, we constructed highly interpretable supervised and unsupervised models with appropriate inductive biases that quantify surgical nociception throughout surgery. Our models track nociceptive stimuli more accurately than existing nociception monitors. We also demonstrate that the characterizing signature of nociception learned by our models resembles the known physiology of the response to pain. Our work represents an important step toward objective multisensor physiology-based markers of surgical nociception. These markers are derived from an in-depth characterization of nociception as measured during surgery itself rather than using other experimental models as surrogates for surgical nociception.

Climate variability through the lens of applied weather index insurance in Senegal-a novel perspective on the implications of decadal variation

IntroductionWeather-based index insurance is a financial instrument which allows smallholder farmers to protect themselves against climate shocks such as droughts and floods. In many cases, insurance indices are based on one or more earth observation datasets (e.g., rainfall, soil moisture, vegetative health) which are partly covering periods of more than 40 years. While remote sensing products and their associated data have improved over this time, understanding the historical climate variability and trends remains an essential piece in ensuring the development of indexes that best represent farmers’ risks. From a practical perspective, shortening time series to limit the risk of understudied climate variability, such as the Atlantic Multidecadal Variability, sometimes seems to be a quick solution. However, shorter time series jeopardize the overall robustness of the index. Therefore, understanding the links between climate variability, index design, and implications for farmers is key. Weather-based index insurance products in Sahelian West Africa usually face a challenge in robustly quantify underlying climatic decadal variation in seasonal rainfall.MethodsThis study analyzes the influence of decadal shifts in rainfall patterns in Sahelian West Africa, in particular Senegal, on index insurance calibration and design, concluding with practical recommendations for the next generation of drought risk finance instruments in the region.ResultsOur findings indicate that decadal variability has not led to a clear decrease in payouts in recent years compared to earlier years, despite an overall increase in seasonal rainfall. Rather, we find that interannual variability has increased which may be a more critical factor for assessing farmers’ agricultural risk than the increase in total rainfall.DiscussionFocusing on key moments of the cropping calendar in the design of an index shows that an increase in the total average rainfall per season does not result in fewer payouts.

Foundational model aided automatic high-throughput drug screening using self-controlled cohort study.

Developing medicine from scratch to governmental authorization and detecting adverse drug reactions (ADR) have barely been economical, expeditious, and risk-averse investments. The availability of large-scale observational healthcare databases and the popularity of large language models offer an unparalleled opportunity to enable automatic high-throughput drug screening for both repurposing and pharmacovigilance. To demonstrate a general workflow for automatic high-throughput drug screening with the following advantages: (i) the association of various exposure on diseases can be estimated; (ii) both repurposing and pharmacovigilance are integrated; (iii) accurate exposure length for each prescription is parsed from clinical texts; (iv) intrinsic relationship between drugs and diseases are removed jointly by bioinformatic mapping and large language model - ChatGPT; (v) causal-wise interpretations for incidence rate contrasts are provided. Using a self-controlled cohort study design where subjects serve as their own control group, we tested the intention-to-treat association between medications on the incidence of diseases. Exposure length for each prescription is determined by parsing common dosages in English free text into a structured format. Exposure period starts from initial prescription to treatment discontinuation. A same exposure length preceding initial treatment is the control period. Clinical outcomes and categories are identified using existing phenotyping algorithms. Incident rate ratios (IRR) are tested using uniformly most powerful (UMP) unbiased tests. We assessed 3,444 medications on 276 diseases on 6,613,198 patients from the Clinical Practice Research Datalink (CPRD), an UK primary care electronic health records (EHR) spanning from 1987 to 2018. Due to the built-in selection bias of self-controlled cohort studies, ingredients-disease pairs confounded by deterministic medical relationships are removed by existing map from RxNorm and nonexistent maps by calling ChatGPT. A total of 16,901 drug-disease pairs reveals significant risk reduction, which can be considered as candidates for repurposing, while a total of 11,089 pairs showed significant risk increase, where drug safety might be of a concern instead. This work developed a data-driven, nonparametric, hypothesis generating, and automatic high-throughput workflow, which reveals the potential of natural language processing in pharmacoepidemiology. We demonstrate the paradigm to a large observational health dataset to help discover potential novel therapies and adverse drug effects. The framework of this study can be extended to other observational medical databases.

Extreme drought and rainfall had a large impact on potato production in the Netherlands between 2015 and 2020

Extreme weather events cause damage to crops. Quantifying their impact on crop yields in farmers’ fields however remains difficult, as empirical data generally lacks detail, preventing to unravel causal effects from soil and management practices. Here we exploit an observational dataset on potato production on sandy soils in the Netherlands and use a causal method employing a matching strategy to quantify the effect of extreme weather. We provide statistical evidence that drought and high-intensity rainfall in this region have large impact today, explaining 98% of the between-year yield differences. High-intensity rainfall led to a yield reduction of 36% and drought to 13%. Yields highly varied among fields, just as the impact of these weather extremes on individual fields. Identifying on which fields the impact is small provides direction for adaption strategies, which are urgently needed, considering the expected increased frequency of weather extremes according to the latest climate change scenarios.

Extreme snowfall variations in the Southeastern Tibetan Plateau under warming climate

Snowfall is a critical component of the Earth system and an important indicator and amplifier of climate change. Climate warming is reducing the seasonal snowpack globally, which could have catastrophic consequences for the regions in high dependence on snow for water recharge. However, the climate influences on extreme snowfall events, which significantly impact humans, are still poorly understood. This study uses the Tibetan Plateau snowpack observation dataset combined with NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6) climate data to investigate the spatial and temporal characteristics of snowfall, and to explore its response mechanism to climate change and future change trends across the Lower Yarlung Zangbo River (LYZR) in Southeastern Tibetan Plateau (SETP). Annual snowfall decreased at a rate of 1.05 mm per decade (P < 0.05) during the historical period (1960–2014). Relative to 1960–2014, annual snowfall would decline of ∼38 % and ∼ 73 % by the end of the 21st century under the SSP245 and SSP585 scenarios, respectively. At the same time, a general decrease in extreme snowfall averaged the LYZR is expected for historical and future periods, but it showed notable spatial variations. The regional increase in extreme snowfall is mainly distributed along the valley of Brahmaputra-Yarlung Zangbo. In addition, precipitation accounted for 66.5 % of the snowfall variations during the historical period. Meanwhile, according to future warming, temperature would dominate snowfall variations, contributing 56.66 % to 72.92 % during 2015–2100. The projected response of mean and extreme snowfall to climate change indicates that it is a double-edged sword. To scientifically address the risks and challenges posed by snowfall changes in alpine regions, it is imperative to limit future climate warming.

Using Multi-Layer Perceptron Driven Diagnosis to Compare Biomarkers for Primary Open Angle Glaucoma.

To use neural network machine learning (ML) models to identify the most relevant ocular biomarkers for the diagnosis of primary open-angle glaucoma (POAG). Neural network models, also known as multi-layer perceptrons (MLPs), were trained on a prospectively collected observational dataset comprised of 93 glaucoma patients confirmed by a glaucoma specialist and 113 control subjects. The base model used only intraocular pressure, blood pressure, heart rate, and visual field (VF) parameters to diagnose glaucoma. The following models were given the base parameters in addition to one of the following biomarkers: structural features (optic nerve parameters, retinal nerve fiber layer [RNFL], ganglion cell complex [GCC] and macular thickness), choroidal thickness, and RNFL and GCC thickness only, by optical coherence tomography (OCT); and vascular features by OCT angiography (OCTA). MLPs of three different structures were evaluated with tenfold cross validation. The testing area under the receiver operating characteristic curve (AUC) of the models were compared with independent samples t-tests. The vascular and structural models both had significantly higher accuracies than the base model, with the hemodynamic AUC (0.819) insignificantly outperforming the structural set AUC (0.816). The GCC + RNFL model and the model containing all structural and vascular features were also significantly more accurate than the base model. Neural network models indicate that OCTA optic nerve head vascular biomarkers are equally useful for ML diagnosis of POAG when compared to OCT structural biomarker features alone.

A Physical‐Informed Neural Network for Improving Air‐Sea Turbulent Heat Flux Parameterization

AbstractThe parameterizations of air‐sea turbulent heat flux are one of the major bottlenecks in atmosphere‐ocean coupled model development, which play a crucial role in sea surface temperature (SST) prediction. Recently, neural networks start to be applied for the development of parameterizations of interface turbulent heat flux. However, these new parameterizations are primairily developed for specific regions and have not been tested in real atmosphere‐ocean coupled models. In this study, we propose a new air‐sea heat flux parameterization using a physical‐informed neural network (PINN) based on multiple observational data sets worldwide. Evaluated with an independent observation data set, it is shown that the PINN can significantly reduce the RMSE of latent heat flux by at least about 48.6% compared to three traditional bulk formulas. Moreover, the PINN can be flexibly updated with new observational data by transfer learning. To test the performance of the new parameterization in realistic application, we implement the PINN into a global ocean‐atmosphere coupled model and make seasonal forecasts for the first time. The PINN markedly reduce the errors of equatorial SST forecast, indicating a good performance of the PINN‐based air‐sea turbulent heat flux scheme. Noticeably, due to limited observational data, the NN‐based parameterizations tend to underestimate heat flux at high wind speeds compared with bulk formula‐based parameterizations. With more data available at extreme conditions, the PINN can be improved via transfer learning and need to be futher evaluated. This study suggests that PINN‐based air‐sea heat flux parameterization is promising to improve SST simulation.

Univariate and multivariate imputation methods evaluation for reconstructing climate time series data: A case study of Mosul station-Iraq

Comprehensive climate time series data is indispensable for monitoring the impacts of climate change. However, observational datasets often suffer from data gaps within their time series, necessitating imputation to ensure dataset integrity for further analysis. This study evaluated six univariate and multivariate imputation methods to infill missing values. These methods were applied to complete the subsets of time series data for precipitation, temperature, and relative humidity from Mosul station spanning 1980–2013. Artificial gaps of 5%, 10%, 20%, and 30% missing observations were introduced under scenarios of missing completely at random (MCAR) missing at random (MAR), and missing not at random (MNAR). Evaluation metrics including RMSE and Kling-Gupta Efficiency were utilized for performance evaluation. Results revealed that seasonal decomposition was the most effective univariate imputation method across all variables. For the multivariate imputation, kNN demonstrated superior performance in infilling the precipitation missing data under MCAR, while norm.predict exhibited optimal performance in the temperature missing data under all missing scenarios. Moreover, missForest was identified as the most suitable method for infilling missing relative humidity data. This study's methodology offers insights into selecting appropriate imputation methods for other climate stations, thereby enhancing the accuracy of the climate change effects analysis.

A dataset of meteorological observations at Jilin Da&amp;rsquo;an National Field Observation and Research Station of Agroecosystem from 2017 to 2021

A dataset of meteorological observations at Jilin Da&amp;rsquo;an National Field Observation and Research Station of Agroecosystem from 2017 to 2021

A dataset of meteorological observations at the National Field Scientific Observation and Research Station of Farmland Ecosystem in Changwu County, Shaanxi Province (2005&amp;ndash;2019)

A dataset of meteorological observations at the National Field Scientific Observation and Research Station of Farmland Ecosystem in Changwu County, Shaanxi Province (2005&amp;ndash;2019)

A dataset of specific leaf area observations in the Pan-Central Asian Arid Zone

A dataset of specific leaf area observations in the Pan-Central Asian Arid Zone

A dataset of meteorological observations from the Great Wall Station and Zhongshan Station in Antarctica during the past 35 years

A dataset of meteorological observations from the Great Wall Station and Zhongshan Station in Antarctica during the past 35 years

Possible influence of the large-scale environment on extreme waves over the Yellow Sea in boreal spring

The Yellow Sea (YS) is exposed to various weather systems, such as typhoons, monsoon activities, and extratropical cyclones, which can pose a major threat to the adjacent coastal regions through the development of energetic oceanic surface waves. Unusually severe surface wave events in the YS occur with considerable frequency during the boreal spring (March-April-May), but have received less attention compared to winter and summer. This study focuses on the characteristics of spring extreme wave height (EWH) events in the YS during 1979-2022, based on observational and long-term reanalysis datasets. Our analysis shows that extreme waves, i.e. daily maximum heights in the top 5% of all spring days, take about 12 h to build up to the peaks, while they decay more slowly after the peaks. During the extreme events, the Siberian High is found to extend anomalously eastward compared to spring climatology. Such an anomalous extension contributes to the increase of the sea level pressure gradient and the intensification of the surface wind speed in the YS. Meanwhile, in the range of 6 ∼ 24 h following the peaks of the EWH events in the YS, swells induced by strong northerly winds begin to have an impact in the YS. These swells contribute to maintaining higher wave energy levels in the YS for longer after the atmospheric source has been removed. We further explore the large-scale environmental conditions that could provide the predictability of extreme waves in the basin developed by these findings. This study presents implications for assessing the risks associated with extreme waves in coastal regions and for improving coastal management strategies in the YS.

Constraining hybrid potential scalar field cosmological model in Lyra’s geometry with recent observational data

In this study, we investigate a scalar field cosmological model with Lyra’s geometry to explain the present cosmic expansion in a homogeneous and isotropic flat FRW universe. In Einstein’s field equations, we presupposed a variable displacement vector as an element of Lyra’s geometry. In the context of the conventional theory of gravity, we suggest a suitable parametrization of the scalar field’s dark energy density in the hybrid function of redshift [Formula: see text], confirming the essential transition behavior of the universe from a decelerating era to the present accelerated scenario. We present constraints on model parameters using the most recent observational datasets from OHD, BAO/CMB and Pantheon, taking Markov Chain Monte Carlo (MCMC) analysis into account. For the proposed model, the best estimated values of parameters for the combined dataset (OHD, BAO/CMB and Pantheon) are [Formula: see text][Formula: see text]km/s/Mpc, [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text] and [Formula: see text]. The model exhibits a flipping nature, and the redshift transition occurs at [Formula: see text]. The current value of the decelerated parameter for the proposed model is calculated as [Formula: see text] for the combined dataset. Some dynamical properties of the model like energy density ([Formula: see text]), scalar field pressure ([Formula: see text]), EoS parameter of scalar field ([Formula: see text]), and effective EoS parameter ([Formula: see text]) are analyzed and presented. Further, we have also examined the statefinder diagnosis and jerk parameters of the derived model. The total density parameter for the derived model is found to be unity which is in nice agreement with recent standard findings.

The consequence of higher-order curvature-based constraints on [formula omitted] gravity

In this investigation, we perform an observational statistical analysis in the theory of f(R,Lm) gravity. The proposed theoretical model is based on the Ricci scalar’s non-linear contribution. We use a distinct parametrization for the deceleration parameter and constrain the model parameters by using various observational data. To determine the best-fit model for the cosmological parameters, we use different observational datasets such as the Hubble Space Telescope, the Pantheon Supernova Survey, the Gold dataset, the Gamma-Ray Burst (GRB), and the Baryon Acoustic Oscillations (BAO). Furthermore, we study the late-time cosmic evolution of the Universe in detail and examine the implications of the constraint values on cosmological parameters. Additionally, we conduct a thorough comparison with the standard cosmological model ΛCDM and other standard models obtained by Odintsov et al. (2024); Odintsov et al. (2023) to examine the validity of our proposed model in the low-redshift regimes. Finally, we find that the proposed model encapsulates an intriguing transition from early deceleration at high redshift to acceleration at low redshift, a quintessence dark energy scenario, and convergence towards the well-established ΛCDM model in late-time Universe’s evolution.

Increasing heat waves frequencies over India during post-El Niño spring and early summer seasons

The increasing frequency of extreme Heat Waves (HWs) has generated significant societal impacts in recent years. This study used different observational datasets to investigate the HW characteristics over India during the post-El Niño spring and early summer seasons (April to June; AMJ). Analysis suggests that HW days are more prevalent over India, predominantly increased in south-central and northwest India, during the decaying phase of strong El Niño years. It is found that anomalous anticyclone circulation accompanied by high pressure extending from the Western North Pacific region towards the Bay of Bengal and India is responsible for enhanced HW days and intensity during the AMJ of strong El Niño decay years. This anomalous anticyclone-induced downdraft reduces the specific humidity in the lower troposphere, leading to decreased cloud cover over India. As a result, shortwave radiation is enhanced at the surface, which causes abnormal HWs over India. During the decaying phase of strong El Niño years, the HW days over India contributed to an increase in the frequency of Discomfort Index hours (above 28 °C), maximum temperatures exceeding 40 °C (hours per day), and Universal Thermal Climate Index days above 38 °C and 46 °C during the spring and early summer months, especially in the East Coast, central, and northwestern parts of India. Thus, proper prediction of large-scale atmospheric circulation over the Indo-western Pacific region during El Niño can help to predict the HW conditions three months in advance. This would help to implement suitable adaptation measures and put into practice strong mitigation policies to limit the increased risk of such events during AMJ of El Niño decay years.

Analysis of Spatial Divergence in Bird Diversity Driven by Built Environment Characteristics of Ecological Corridors in High-Density Urban Areas

Urban ecological corridors play an important role in facilitating bird migration and maintaining biodiversity in urban landscapes as key connections between habitat patches. However, the effects of built environment characteristics of urban ecological corridors on bird diversity have not been well understood. In this study, we used Minhang District, Shanghai, as an example to describe the built environment of urban ecological corridors through three dimensions (habitat characteristics, degree of surrounding urbanization, and degree of slow-traffic connectivity). We calculated species richness, abundance, Shannon–Wiener index, and Simpson Index to assess bird diversity based on the bird observation dataset from the Citizen Science Data Sharing Platform. The effects of built environment characteristics of urban ecological corridors on bird diversity were quantified by the Generalized Linear Model. The results showed that: (1) There were significant differences in the built environment characteristics of urban ecological corridors, which formed the spatial differentiation pattern of bird diversity. (2) Different built environment features of urban ecological corridors have different impacts on bird diversity. Habitat suitability of urban ecological corridors was positively correlated with bird diversity, with birds preferring to inhabit waters with an area of more than 1 ha. The degree of urbanization was negatively correlated with bird diversity, with distance to the center of the area proving to have the strongest positive effect. The degree of slow-traffic connectivity proved that low-intensity human activities in urban ecological corridors had a lower impact on bird diversity. The above findings can provide scientific reference for the construction of urban and regional ecological networks in the future.

Skewness preferences: Evidence from online poker

We test for skewness preferences in a large set of observational panel data on online poker games (n=4,450,585). Each observation refers to a choice between a safe option and a binary risk of winning or losing the game. Our setting offers a real-world choice situation with substantial incentives where probability distributions are simple, transparent, and known to the decision-makers. Individuals reveal a strong and robust preference for skewness, which is inconsistent with expected utility theory. The effect of skewness is most pronounced among experienced and unsuccessful players but remains significant in all subsamples that we investigate, in contrast to the effect of variance.