Global Dataset Research Articles

Land Use Change Alters Soil Organic Carbon: Constrained Global Patterns and Predictors

AbstractLand use change (LUC) alters the global carbon (C) stock, but our estimation of the alteration remains uncertain and is a major impediment to predicting the global C cycle. The uncertainty is partly due to the limited number and geographical bias of observations, and limited exploration of its predictors. Here we generated a comprehensive global database of 5,980 observations from 790 articles. The number of sites evaluated is at least seven times larger than in previous meta‐analyses. Our constrained estimates of different LUC's effects on soil organic C (SOC) and their variations across global climates reveal underestimation/overestimation in previous estimates. Converting forests and grasslands to croplands reduced SOC by 24.5% ± 1.53% (−11.03 ± 1.06 Mg ha−1) and 22.7% ± 1.22% (−8.09 ± 0.67 Mg ha−1), while 28.0% ± 1.56% (4.46 ± 0.42 Mg ha−1) and 33.5% ± 1.68% (5.8 ± 0.38 Mg ha−1) increases, respectively, were obtained in the reverse processes. Converting forests to grasslands decreased SOC by 2.1% ± 1.22% (−1.13 ± 0.44 Mg ha−1), while the reverse process increased SOC by 18.6% ± 1.73% (3.31 ± 0.51 Mg ha−1). Modeled relative importance of 10 drivers of LUC's impact on SOC revealed that higher initial SOC (iSOC) does not solely determine SOC loss in SOC‐negative LUC scenarios as previously proposed. Across four decades, reconverting croplands to forests and grasslands recovered only 49.5% (6.1 ± 0.51 Mg ha−1) and 75.3% (7.0 ± 0.38 Mg ha−1) of the iSOC, respectively, indicating the need for protecting C‐rich ecosystems. Our global data set advances information on LUC's effect on SOC and can be valuable to constrain Earth system models to reliably estimate global SOC stocks and plan climate change mitigation strategies.

Wasted efforts of elite Marathon runners under a warming climate primarily due to atmospheric oxygen reduction

Meteorological conditions exert influences on sport performances via complex processes. Previous studies conventionally investigate the effects of weather conditions on marathon performance by following events held at the same places, which brings large uncertainties due to the changed participants. Via following each athlete to eliminate these uncertainties, we show that elite athletes’ marathon-running performance tends to monotonically worsen as ambient temperature rises except when it is extremely cold based on the best 16 athletes from each of the sex continents. It worsens by 0.56 (0.39 for men and 0.71 for women) min/°C when it rises beyond 15 °C. Theoretical analysis based on global marathon performance and weather observation datasets shows that more than half of this effect is mediated by reduced oxygen partial density as warmer temperature expands air and increases vapor pressure for both the men and women athletes, which is confirmed by the methods of Baron–Kenny. This atmospheric thermodynamic mechanism has not been emphasized previously. We also show that world-top athletes’ marathon performance approximately linearly worsens as ambient pressure decreases and slightly worsens as relative humidity rises if not extremely wet. These results substantially differ from the previous ones following the events instead of each athlete. Multi-variable changes in climate system are projected to slow Olympic marathon athletes by 2.51 and 1.06 min by the end of the 21st century in relative to 2020 under the high and intermediate emission scenario, respectively. Therefore, future progression of marathon performance is likely to be substantially slowed or even halted by climate changes without efficient greenhouse gas mitigation.

Liver cancer in young adults: Validity of global data sets.

Liver cancer in young adults: Validity of global data sets.

Global census of the significance of giant mesopelagic protists to the marine carbon and silicon cycles

Thriving in both epipelagic and mesopelagic layers, Rhizaria are biomineralizing protists, mixotrophs or flux-feeders, often reaching gigantic sizes. In situ imaging showed their contribution to oceanic carbon stock, but left their contribution to element cycling unquantified. Here, we compile a global dataset of 167,551 Underwater Vision Profiler 5 Rhizaria images, and apply machine learning models to predict their organic carbon and biogenic silica biomasses in the uppermost 1000 m. We estimate that Rhizaria represent up to 1.7% of mesozooplankton carbon biomass in the top 500 m. Rhizaria biomass, dominated by Phaeodaria, is more than twice as high in the mesopelagic than in the epipelagic layer. Globally, the carbon demand of mesopelagic, flux-feeding Phaeodaria reaches 0.46 Pg C y−1, representing 3.8 to 9.2% of gravitational carbon export. Furthermore, we show that Rhizaria are a unique source of biogenic silica production in the mesopelagic layer, where no other silicifiers are present. Our global census further highlights the importance of Rhizaria for ocean biogeochemistry.

New Empirical Source-Scaling Laws for Crustal Earthquakes Incorporating Fault Dip and Seismogenic-Thickness Effects

Abstract New global source-scaling relations for the aspect ratio and rupture area for crustal earthquakes that include the width-limited effect and a possible free-surface effect are derived using a global dataset of finite-fault rupture models. In contrast to the commonly used scaling relations between moment magnitude (M), fault length (L), width (W), and area, we built self-consistent scaling relations by relating M to the aspect ratio (L/W) and to the fault area to model the change in the aspect ratio once the rupture width reaches the down-dip width limit of the fault. The width-limited effect of large-magnitude earthquakes depends on the fault dip and a regional term for the seismogenic thickness. The magnitude scaling of the aspect ratio includes a break in the magnitude scaling that is dip angle dependent. This dip angle-dependent magnitude scaling in the magnitude–area relation is modeled by a trilinear relation incorporating a dip-related transition range. The effect of the free surface was observed using a normalized depth term and parameterizing the source by the depth of the top of the fault rupture; it is more apparent in the area scaling relation. The scaling differences are related to the fault geometry, not to the rake angle, as commonly assumed. Finally, the corresponding L and W scaling relations obtained by converting the area and aspect ratio models to L and W models not only show good agreement with the previous regional scaling laws on average but also provide better fault-specific application due to the inclusion of a fault-specific dip angle and seismogenic thickness.

A Hybrid Index for Monitoring Burned Vegetation by Combining Image Texture Features with Vegetation Indices

The detection and monitoring of burned areas is crucial for vegetation recovery, loss assessment, and anomaly analysis. Although vegetation indices (VIs) have been widely used, accurate vegetation detection is challenging due to potential confusion in the spectra of different types of land cover and the interference of shadow effects caused by terrain. In this work, a novel Vegetation Anomaly Spectral Texture Index (VASTI) is proposed, which leverages the merits of both spectral and spatial texture features to identify abnormal pixels for extracting burned vegetation areas. The performance of the VASTI and its components, the Global Environmental Monitoring Index (GEMI), the Enhanced Vegetation Index (EVI), and the texture feature Autocorrelation (AC) were assessed based on a global dataset previously established, which contains 1774 pairs of samples from 10 different sites. The results illustrated that, compared with the GEMI and EVI, the VASTI improved the user’s accuracy (UA), producer’s accuracy (PA), and kappa coefficient across the ten study areas by approximately 5% to 10%. Compared to AC, the VASTI improved the accuracy of abnormal vegetation detection by 13% to 25%. The improvements were mainly caused by the fact that the incorporation of texture features can reduce spectral confusion between pixels. The innovation of the VASTI is that it considers the relationship between anomalous pixels and surrounding pixels by explicitly integrating spatial texture features with traditional spectral features.

Modeling and prediction of high-precision global evapotranspiration: based on a different model of physical relationships

ABSTRACT The interchange of water vapor between the land and the atmosphere is influenced by actual evapotranspiration (AET). A nonlinear model (AET-SWC-PET-GPP, ASPG) was developed in this study to combine potential evapotranspiration (PET), soil water content (SWC), and gross primary productivity (GPP) in order to quantitatively estimate AET. The Fluxnet Network 2015 global flux station dataset was used to compare the AET models (AET-SWC, AS; AET-SWC-PET, ASP and AET-SWC-PET2, ASP2) with various combinations of influencing factors. The results show that the simulation accuracy of the ASPG model is higher than that of AS, ASP, and ASP2, with improvements in a coefficient of determination (R2) of 45.3, 8.1, and 5.7%, respectively.The ASPG performed well for various vegetation types, geographical regions, and time scales. It was also discovered that the fitting coefficients vary depending on the type of vegetation, each with its own range of values. The ASPG model put forth in this study can be used to more effectively estimate AET quantitatively on a global scale and can serve as a theoretical foundation for the accurate calculation of global evapotranspiration and the wise use of water resources.

The global distribution of Isoprenoidal Glycerol Dialkyl Diethers (isoGDDs) is consistent with a predominant degradation origin

Glycerol dialkyl diethers (GDDs) are membrane lipids and a variation of the more commonly known glycerol dialkyl glycerol tetraethers (GDGTs). GDGTs include both archaeal and bacterial membrane lipids that are both frequently used for paleoclimate reconstruction in a range of terrestrial and aquatic environments. In contrast to GDGTs, GDDs lack one of the terminal glycerol moieties. Although both isoprenoidal (iso) and branched (br) GDDs have been found, this study focuses on isoGDDs. These lipids have been found in a few terrestrial and aquatic environments. However, the origin of isoGDDs is debated and the extent of their distribution across the surface of the Earth is poorly constrained. Based on a few single site studies, previous authors hypothesised that isoGDDs are degradation products of isoGDGTs, but more recent studies that isolated isoGDDs from cultured nitrososphaerota (formerly thaumarchaeota) proposed a biological source through direct archaeal biosynthesis. Here we compiled a global dataset of isoGDD and isoGDGT abundance in environmental samples to thoroughly investigate the distribution of isoGDDs and the correlation with isoGDGTs on a global scale and across a variety of environments (peat, mineral soils, lake sediments, and marine sediments). We find that isoGDDs are present in most samples that we analysed. Their abundance is strongly proportional to isoGDGT abundance (r2 = 0.85), dominated by the GDGT-crenarchaeol/GDD-crenarchaeol ratio (r2 = 0.94) and supported by individual compound isoGDGT/isoGDD ratios (r2 = 0.56–0.94). In addition, the degree of cyclisation of isoGDDs, reflected in the ring index, is positively correlated (r2 = 0.84) with that of isoGDGTs across all environments. We conclude that isoGDDs are abundant on the surface of the Earth and predominantly originate from the degradation of isoGDGTs.

Forecasting urban futures: Evaluating global land use data sensitivity for regional growth simulation in the Ruhr Metropolitan Area

In recent years, numerous multitemporal global land use and land cover products have been published acting as valuable source for training spatially explicit geosimulation models forecasting urban growth. However, there is a notable gap in research that specifically addresses the sensitivity of models traing with those data sets when it comes to regional modeling purposes. Accordingly, the objectives of this study were to calibrate, validate, and employ global urban input datasets for the regional simulation of urban growth by the year 2030. The SLEUTH urban growth model, focused on the metropolitan area of the Ruhr, Germany, was calibrated using the Global Human Settlement Layer, World Settlement Footprint Evolution, historical OpenStreetMap data, and a Digital Land Cover Model for Germany. The goal was to compare the results in terms of accuracy, certainty, quantity, and allocation, particularly in urban areas susceptible to floods and heat. While all models achieved high accuracy levels concerning quantity and allocation, the extent of new settlements varied from 40.77 km2 to 477.91 km2. The models based on World Settlement Footprint and OpenStreetMap exhibited higher certainty and lower stochasticity. As the simulated urban growth increased, there was a corresponding rise in the likelihood of allocating new settlements in areas affected by natural hazards. While all models presented a similar relative portion of new settlement areas impacted by floods, variations emerged in terms of areas affected by unfavorable thermal conditions. This study underscored the potential use of historical OpenStreetMap data in training cellular automation for geosimulating future settlement growth. Furthermore, it highlighted the applicability of global Earth observation-based urban datasets for regional geosimulation and explored the impacts of diverse input data on the accuracy, certainty, quantity, and allocation performances in simulating future conditions.

The Imperial College Storm Model (IRIS) Dataset

Assessing tropical cyclone risk on a global scale given the infrequency of landfalling tropical cyclones (TC) and the short period of reliable observations remains a challenge. Synthetic tropical cyclone datasets can help overcome these problems. Here we present a new global dataset created by IRIS, the ImpeRIal college Storm model. IRIS is novel because, unlike other synthetic TC models, it only simulates the decay from the point of lifetime maximum intensity. This minimises the bias in the dataset. It takes input from 42 years of observed tropical cyclones and creates a 10,000 year synthetic dataset of wind speed which is then validated against the observations. IRIS captures important statistical characteristics of the observed data. The return periods of the landfall maximum wind speed are realistic globally.

Education and Democracy in Sub-Saharan Africa

Abstract The relationship between education and democracy has been empirically researched since the late 1950s, with most evidence stemming from global datasets. Initial results showed no relationship between education and democracy after looking at the within-country estimation. But, as more advanced statistical methods emerged, addressing endogeneity problems of the within-country estimation, a statistically significant relationship between education, measured through average years of total schooling, and democracy appeared. However, all studies have looked at the association broadly, and only a handful have applied this nexus to the context of Sub-Saharan Africa. By creating a new dataset incorporating thirty countries, this study shows that education is statistically significantly associated with democracy in Sub-Saharan Africa. Results are robust to multiple other covariates and mirror the ones reported in the academic literature. This study echoes the continued focus on education, especially in light of recent developments and multiple organisations announcing that democracy declined worldwide.

CROPGRIDS: a global geo-referenced dataset of 173 crops

CROPGRIDS is a comprehensive global geo-referenced dataset providing area information for 173 crops for the year 2020, at a resolution of 0.05° (about 5.6 km at the equator). It represents a major update of the Monfreda et al. (2008) dataset (hereafter MRF), the most widely used geospatial dataset previously available, covering 175 crops with reference year 2000 at 10 km spatial resolution. CROPGRIDS builds on information originally provided in MRF and expands it using 27 selected published gridded datasets, subnational data of 52 countries obtained from National Statistical Offices, and the 2020 national-level statistics from FAOSTAT, providing more recent harvested and crop (physical) areas for 173 crops at regional, national, and global levels. The CROPGRIDS data advance the current state of knowledge on the spatial distribution of crops, providing useful inputs for modelling studies and sustainability analyses relevant to national and international processes.

Robust Skin Disease Diagnosis with Deep Belief Network

The skin is one of the first lines of defenceagainst environmental influences such as sunlight, bacteriaand germs, which cause various skin diseases. In addition to the bad psychological and physical impact caused by skin disease. So,in recent years,many artificial intelligence (AI) algorithms have appeared that canrecognize Images, through which skin diseases can be diagnosed, avoiding traditional methods that rely on visual examination and self-evaluation based on experience. The paper aims to classify a group of skin diseases according to the type of disease, such as Atopic Dermatitis, Dyshidrotic Eczema and Nummular Dermatitisusing a deep belief network algorithm(DBN) thatwas built to suit the work. A global dataset was also used, obtained from the Kaggle website, and after conducting experiments on it, the algorithm achieved high accuracy in diagnosing diseases, the percentage reached 98.773%. It is possible in the future to use the network to classify other types of diseases after providing it with a large number of images of affected people

A conceptual model for evaluating the stability of high-altitude ice-rich slopes through coupled thermo-hydro-mechanical simulation

The collapse of glacial, permafrost, and ice-rich moraine slopes in high-altitude mountainous areas not only threatens downstream residents and infrastructure but also displaces ice mass to lower and warmer elevations, accelerating glacial ablation to some extent. Despite being considered rare, ice-rich slopes collapse more frequently than commonly thought due to climate change. For example, two adjacent mountain glaciers in the Aru Range of the Tibetan Plateau, characterized by large volumes (68 × 106 m3 and 83 × 106 m3, respectively) and low surface slope angles (12.3° and 12.9°, respectively), collapsed surprisingly in 2016. While the mechanisms behind these collapses have garnered broad attention, the ability to quantitatively assess the instability of ice-rich slopes remains limited due to the complex interplay of multi-physical processes. Taking the Aru glacier collapses as reference cases, this paper presents a conceptual model, implemented through coupled thermo-hydro-mechanical simulation, to evaluate the stability of high-altitude ice-rich slopes due to climate change, rainfall and ice ablation. Results indicate that the methodology captures the effects of temperature change, rainfall and meltwater on the instability events well, demonstrating promising potential in evaluating potential collapse zones of ice-rich slopes similar to the Aru glaciers. Furthermore, the role of climate change in the well-known Aru events is demonstrated using a state-of-the-art global climate reanalysis dataset. Findings reveal that the increase in liquid water infiltrating the Aru glaciers since 2010 was a critical factor leading to the instability events.

PhuEGO: A Network-Based Method to Reconstruct Active Signaling Pathways From Phosphoproteomics Datasets

Signaling networks are critical for virtually all cell functions. Our current knowledge of cell signaling has been summarized in signaling pathway databases, which, while useful, are highly biased toward well-studied processes, and do not capture context specific network wiring or pathway cross-talk. Mass spectrometry-based phosphoproteomics data can provide a more unbiased view of active cell signaling processes in a given context, however, it suffers from low signal-to-noise ratio and poor reproducibility across experiments. While progress in methods to extract active signaling signatures from such data has been made, there are still limitations with respect to balancing bias and interpretability. Here we present phuEGO, which combines up-to-three-layer network propagation with ego network decomposition to provide small networks comprising active functional signaling modules. PhuEGO boosts the signal-to-noise ratio from global phosphoproteomics datasets, enriches the resulting networks for functional phosphosites and allows the improved comparison and integration across datasets. We applied phuEGO to five phosphoproteomics data sets from cell lines collected upon infection with SARS CoV2. PhuEGO was better able to identify common active functions across datasets and to point to a subnetwork enriched for known COVID-19 targets. Overall, phuEGO provides a flexible tool to the community for the improved functional interpretation of global phosphoproteomics datasets.

Pregnancy outcomes of Fabry disease in Austria (PROFABIA)-a retrospective cohort-study

BackgroundPregnancy and delivery outcomes in women with Fabry disease are not well described.MethodsRetrospective cohort-study of women with Fabry disease in Austria using a specific questionnaire and the Austrian Mother–Child Health Passport.ResultsOut of a total of 44 enrolled women (median age at study entry 44 years, p25: 30, p75: 51), 86.4% showed signs and symptoms of Fabry disease with an increase in pain burden during pregnancy, primarily in women with moderate pain before pregnancy. Thirty-two of 44 women with Fabry disease reported a total of 70 pregnancies (median age at first pregnancy 24 years, p25: 21, p75: 31), 61 (87.1%) of which resulted in 64 live births including 3 sets of twins, six miscarriages (8.6%) in five women, and three induced abortions (4.3%) in two women. Risk factors for poor maternal and foetal outcomes during pregnancy, overrepresented in our cohort as compared to the general population, were hypertension (n = 10, 16.4%), proteinuria (n = 17, 27.9%) and smoking (n = 24, 39.3%). Preeclampsia was reported in 7 pregnancies (11.5%). Fifty-one (79.7%) children were born at term and 13 (20.3%) were preterm (including one neonatal death), with a median gestational age of 39 weeks (p25: 38, p75: 40) and delivery by C-section in 15 pregnancies (24.6%). Thirteen (20.3%) children presented with low birth weight and 18 (28.1%) were small for their gestational age. In comparison to global and national data-sets, preeclampsia, prematurity, low birth weight, being small for their gestational age as well as inpatient stay were significantly more common in patients with Fabry disease.ConclusionsOur cohort-study in women with Fabry disease shows an increase of pain burden during pregnancies and clearly points to an increased risk for preeclampsia, prematurity, and neonates small for gestational age. With a substantial number of high-risk pregnancies, neonatal outcomes are somewhat worse in Fabry disease than in the general public. Thus, we provide valuable data enabling informed decision-making in pregnancy counselling for Fabry disease.

RADReef: A global Holocene Reef Rate of Accretion Dataset

Reef cores are a powerful tool for investigating temporal changes in reef communities. Radiometric dating facilitates the determination of vertical accretion rates, which has allowed for examination of local-regional controlling factors, such as subsidence and sea level changes. Coral reefs must grow at sufficient rates to keep up with sea level rise, or risk ‘drowning.’ As sea level is expected to rise significantly in the next 100 years and beyond, it is important to understand whether reefs will be able to survive. Historical records of reef accretion rates extracted from cores provide valuable insights into extrinsic controlling factors of reef growth and are instrumental in helping predict if future reefs can accrete at rates needed to overcome predicted sea level changes. While extensive research exists at local and regional scales, limited attention has been given to identifying global patterns and drivers. To address this, we present “RADReef”: A global dataset of dated Holocene reef cores. RADReef serves as a foundation for further research on past, present and future reef accretion.

ClimateDT: A Global Scale-Free Dynamic Downscaling Portal for Historic and Future Climate Data

Statistical downscaling of climate data has been widely described in the literature, with the aim of improving the reliability of local climatic parameters from coarse-resolution (often >20 km) global datasets. In this article, we present ClimateDT, a dynamic downscaling web tool for monthly historical and future time series at a global scale. The core of ClimateDT is the 1 km 1981–2010 climatology from CHELSA Climate (version 2.1), where the CRU-TS layers for the period 1901-current are overlayed to generate a historic time series. ClimateDT also provides future scenarios from CMIP5 using UKCP18 projections (rcp2.6 and rcp8.5) and CMIP6 using 5 GCMs, also available on the CHELSA website. The system can downscale the grids using a dynamic approach (scale-free) by computing a local environmental lapse rate for each location as an adjustment for spatial interpolation. Local predictions of temperature and precipitation obtained by ClimateDT were compared with climate time series assembled from 12,000 meteorological stations, and the Mean Absolute Error (MAE) and the explained variance (R2) were used as indicators of performance. The average MAEs for monthly values on the whole temporal scale (1901–2022) were around 1.26 °C for the maximum monthly temperature, 0.80 °C for the average monthly temperature, and 1.32 °C for the minimum monthly temperature. Regarding monthly total precipitation, the average MAE was 19 mm. As for the proportion of variance explained, average R2 values were always greater than 0.95 for temperatures and around 0.70 for precipitation due to the different degrees of temporal autocorrelation of precipitation data across time and space, which makes the estimation more complex. The elevation adjustment resulted in very accurate estimates in mountainous regions and areas with complex topography and substantially improved the local climatic parameter estimations in the downscaling process. Since its first release in November 2022, more than 1300 submissions have been processed. It takes less than 2 min to calculate 45 locations and around 8 min for the full dataset (512 records).

Productivity or Equity? Tradeoffs in Volunteer Microtasking in Humanitarian OpenStreetMap

Microtasking, the decomposition of tasks into small units of work, is prolific in human computation and crowdsourcing. Some peer production systems are beginning to leverage this same technique in volunteer contribution-based settings. While early research suggests that focusing volunteer work in this way using microtasking may be fruitful, the effects of microtasking on contributor behavior in volunteer peer production settings, like OpenStreetMap, remain unclear. This paper takes advantage of a natural experiment facilitated by the Humanitarian OpenStreetMap's Tasking Manager, a microtasking system, and employs causal inference analysis to evaluate the effects of a microtasking intervention on contributor dynamics. Our study systematically leverages a global dataset to analyze peer production dynamics, building on prior research to address a gap in peer production literature and informing the design of the microtasking interfaces. We causally show that, indeed, microtasking can be an effective intervention in peer production settings, but it may exacerbate power-law patterns that are common in such settings. By further analyzing project design decisions and characteristics, we develop implications for platform practitioners, with a focus on addressing engagement and contribution inequity issues prevalent in settings like OpenStreetMap.

Biogeographic response of marine plankton to Cenozoic environmental changes.

In palaeontological studies, groups with consistent ecological and morphological traits across a clade's history (functional groups)1 afford different perspectives on biodiversity dynamics than do species and genera2,3, which are evolutionarily ephemeral. Here we analyse Triton, a global dataset of Cenozoic macroperforate planktonic foraminiferal occurrences4, to contextualize changes in latitudinal equitability gradients1, functional diversity, palaeolatitudinal specialization and community equitability. We identify: global morphological communities becoming less specialized preceding the richness increase after the Cretaceous-Palaeogene extinction; ecological specialization during the Early Eocene Climatic Optimum, suggesting inhibitive equatorial temperatures during the peak of the Cenozoic hothouse; increased specialization due to circulation changes across the Eocene-Oligocene transition, preceding the loss of morphological diversity; changes in morphological specialization and richness about 19 million years ago, coeval with pelagic shark extinctions5; delayed onset of changing functional group richness and specialization between hemispheres during the mid-Miocene plankton diversification. The detailed nature of the Triton dataset permits a unique spatiotemporal view of Cenozoic pelagic macroevolution, in which global biogeographic responses of functional communities and richness are decoupled during Cenozoic climate events. The global response of functional groups to similar abiotic selection pressures may depend on the background climatic state (greenhouse or icehouse) to which a group is adapted.