The Equinox2020 Seshat Data Release

Abstract. Flood-protection levees have been built along rivers and coastlines globally. Current datasets, however, are generally confined to territorial boundaries (national datasets) and are not always easily accessible, posing limitations for hydrologic models and assessments of flood hazard. Here, we bridge this knowledge gap by collecting and standardizing global flood-protection levee data for river deltas into the open-source global river delta levee data environment, openDELvE. In openDELvE, we aggregate levee data from national databases, reports, maps, and satellite imagery. The database identifies the river delta land areas that the levees have been designed to protect. Where data are available, we record the extent and design specifications of the levees themselves (e.g., levee height, crest width, construction material) in a harmonized format. The 1657 polygons of openDELvE contain 19 248 km of levees and 44 733.505 km2 of leveed area. For the 153 deltas included in openDELvE, 17 % of the land area is confined by flood-protection levees. Around 26 % of delta population lives within the 17 % of delta area that is protected, making leveed areas densely populated. openDELvE data can help improve flood exposure assessments, many of which currently do not account for flood-protection levees. We find that current flood hazard assessments that do not include levees may exaggerate the delta flood exposure by 33 % on average, but up to 100 % for some deltas. The openDELvE is made public on an interactive platform (https://www.opendelve.eu/, 1 October 2022), which includes a community-driven revision tool to encourage inclusion of new levee data and continuous improvement and refinement of open-source levee data.

Artefact detection in global digital elevation models (DEMs): The Maximum Slope Approach and its application for complete screening of the SRTM v4.1 and MERIT DEMs

Time series classification (TSC) is an important and challenging problem in data mining. Time series data sets are an important basis for this research and are widely used in baseline verification of various algorithm models. Aiming at the problem that there are few domestic data sets and the current TSC data set is relatively old, a new set of time‐series classification datasets are established based on the average price data of concrete in major cities in China, which provides new data support for the research of TSC algorithm. We made use of the data center of Oriental Fortune to disclose the sample data of the average price of concrete from October 23, 2013 to January 20, 2021, created 1093 autoregression‐based series data sets by using sliding windows of different lengths, and then through the experimental verification of linear regression model, decision tree model, and random forest model, a set of data sets with lengths of 170, 842, and 1052 were selected. We use three convolutional neural network (CNN) models and three long short‐term memory (LSTM) network models to verify the validity of the data, the CNN's obtained the best accuracy rate of 93.20%, and the LSTM networks obtained the best accuracy rate of 92.99%. The establishment of the new data set has certain significance for the research of TSC and provides some references for other researchers to create datasets. The datasets are freely available at https://gitee.com/lq2012/tsc‐dataset.

We present measurements of the cross-correlation of the triply-ionized carbon (CIV) forest with quasars using Sloan Digital Sky Survey Data Release 14. The study exploits a large sample of new quasars from the first two years of observations by the Extended Baryon Oscillation Spectroscopic Survey (eBOSS). The CIV forest is a weaker tracer of large-scale structure than the Lyα forest, but benefits from being accessible at redshifts z<2 where the quasar number density from eBOSS is high. Our data sample consists of 287,651 CIV forest quasars in the redshift range 1.4<z<3.5 and 387,315 tracer quasars with 1.2<z<3.5. We measure large-scale correlations from CIV absorption occuring in three distinct quasar rest-frame wavelength bands of the spectra referred to as the CIV forest, the SiIV forest and the Lyα forest. From the combined fit to the quasar-CIV cross-correlations for the CIV forest and the SiIV forest, the CIV redshift-space distortion parameter is βCIV=0.27 −0.14 −0.26 +0.16 +0.34 and its combination with the CIV linear transmission bias parameter is bCIV(1+βCIV)=−0.0183 −0.0014 −0.0029 +0.0013 +0.0025 (1σ and 2σ statistical errors) at the mean redshift z=2.00. Splitting the sample at z=2.2 to constrain the bias evolution with redshift yields the power-law exponent γ=0.60±0.63, indicating a significantly weaker redshift-evolution than for the Lyα forest linear transmission bias. Additionally, we demonstrate that CIV absorption has the potential to be used as a probe of baryon acoustic oscillations (BAO). While the current data set is insufficient for a detection of the BAO peak feature, the final quasar samples for redshifts 1.4<z<2.2 from eBOSS and the Dark Energy Spectroscopic Instrument (DESI) are expected to provide measurements of the isotropic BAO scale to ∼7% and ∼3% precision, respectively, at z≃1.6.

Considering the increasingly potent process of globalization, entangled history today functions as an appropriate research framework for scrutinizing the various phenomena of entanglements. This paper examines the epistemological underpinnings of entangled history, which are based on different theories of transculturalism, the theoretical legacy of “spatial turn” and postcoloniality, as well as the recognition of different interconnectedness processes. The paper also explains the development of entangled history, which grew out of a critique of comparative history, and the achievements of related paradigms: transnational history, histoire croisée, borderland history and global history. It recommends two examples of research practices from German historiography within entangled history: the current project EGO (Europäische Geschichte Online) and a multiperspective study that examines the complexity of the process of entanglements (Verflechtung) at three levels (network, texture, rhizome). Given the openness of entangled history to different qualitative and quantitative analytical approaches, the paper examines its epistemological compatibility with historical network analysis. In addition to describing the basic concepts and parameters used in the application of this approach, the paper explains the development of historical network analysis within social network analysis, and since the mid-1990s within the revival of quantitative history in digital humanities. The authors question the research potential of historical network analysis and its applicability in the future of entangled history, emphasizing the broad possibilities for research at different levels of spatial, social, and temporal scaling. Finally, the paper highlights two examples of research practice in the application of historical network analysis inside entangled history: the project “Seshat: Global History Databank” and the substantial research done by Austrian historian Johannes Preiser-Kapeller.

Multivariate time series data are becoming increasingly common in numerous real world applications, e.g., power plant monitoring, health care, wearable devices, automobile, etc. As a result, multivariate time series retrieval, i.e., given the current multivariate time series segment, how to obtain its relevant time series segments in the historical data (or in the database), attracts significant amount of interest in many fields. Building such a system, however, is challenging since it requires a compact representation of the raw time series which can explicitly encode the temporal dynamics as well as the correlations (interactions) between different pairs of time series (sensors). Furthermore, it requires query efficiency and expects a returned ranking list with high precision on the top. Despite the fact that various approaches have been developed, few of them can jointly resolve these two challenges. To cope with this issue, in this paper we propose a Deep r-th root of Rank Supervised Joint Binary Embedding (Deep r-RSJBE) to perform multivariate time series retrieval. Given a raw multivariate time series segment, we employ Long Short-Term Memory (LSTM) units to encode the temporal dynamics and utilize Convolutional Neural Networks (CNNs) to encode the correlations (interactions) between different pairs of time series (sensors). Subsequently, a joint binary embedding is pursued to incorporate both the temporal dynamics and the correlations. Finally, we develop a novel r-th root ranking loss to optimize the precision at the top of a Hamming distance ranking list. Thoroughly empirical studies based upon three publicly available time series datasets demonstrate the effectiveness and the efficiency of Deep r-RSJBE.

The present introduction is designed to offer a quick walkthrough of the various scholars’ contributions to the present issue of the Journal of Cognitive Historiography. Among the articles included, two are related to our Extended Open Call for Papers on the topic of “Toxic Traditions: Pathological and Maladaptive Beliefs, Biases, and Behaviours throughout Human History”, i.e., an analysis of the maladaptiveness of theistic beliefs in the Anthropocene and a neuroanthropological examination of the ancient cult of Cybele and Attis. Other articles cover the presence of religious-based cognitive biases and logical fallacies in Scottish philosopher Thomas Reid’s works and the neurocognition of the Stoic silent prayer as recorded by Roman philosopher Seneca. An entire section is dedicated to the critical discussion of both method and theory of Seshat: Global History Databank. The issue also includes a thought-provoking and interdisciplinary conversation on horror studies, a précis, a commentary, and four cutting-edge book reviews. The background to the topic of the call for papers is herein available as an appendix.

Proponents of the Axial Age contend that parallel cultural developments between 800 and 200 BCE in what is today China, Greece, India, Iran, and Israel-Palestine constitute the global historical turning point toward modernity. The Axial Age concept is well-known and influential, but deficiencies in the historical evidence and sociological analysis available have thwarted efforts to evaluate the concept’s major global contentions. As a result, the Axial Age concept remains controversial. Seshat: Global History Databank provides new tools for examining this topic in social formations across Afro-Eurasia during the first two millennia BCE and first millennium CE, allowing scholars to empirically evaluate the varied and contrasting claims researchers have put forward. Results undercut the notion of a specific “age” of axiality limited to a specific geo-temporal localization. Critical traits offered as evidence of an axial transformation by proponents of the Axial Age concept appeared across Afro-Eurasia hundreds, and in some cases thousands, of years prior to the proposed Axial Age. Our analysis raises important questions for future evaluations of this period and points the way toward empirically-led, historical-sociological investigations of the ideological and institutional foundations of complex societies.

We investigate the cosmological implications of the latest growth of structure measurement from the Baryon Oscillation Spectroscopic Survey (BOSS) CMASS Data Release 11 with particular focus on the sum of the neutrino masses, $\sum m_{\nu}$. We examine the robustness of the cosmological constraints from the Baryon Acoustic Oscillation (BAO) scale, the Alcock-Paczynski effect and redshift-space distortions ($D_V/r_s$, $F_{\rm AP}$, $f\sigma_8$) of \citet{Beutler:2013b}, when introducing a neutrino mass in the power spectrum template. We then discuss how the neutrino mass relaxes discrepancies between the Cosmic Microwave Background (CMB) and other low-redshift measurements within $\Lambda$CDM. Combining our cosmological constraints with WMAP9 yields $\sum m_{\nu} = 0.36\pm0.14\,$eV ($68\%$ c.l.), which represents a $2.6\sigma$ preference for non-zero neutrino mass. The significance can be increased to $3.3\sigma$ when including weak lensing results and other BAO constraints, yielding $\sum m_{\nu} = 0.35\pm0.10\,$eV ($68\%$ c.l.). However, combining CMASS with Planck data reduces the preference for neutrino mass to $\sim 2\sigma$. When removing the CMB lensing effect in the Planck temperature power spectrum (by marginalising over $A_{\rm L}$), we see shifts of $\sim 1\sigma$ in $\sigma_8$ and $\Omega_m$, which have a significant effect on the neutrino mass constraints. In case of CMASS plus Planck without the $A_{\rm L}$-lensing signal, we find a preference for a neutrino mass of $\sum m_{\nu} = 0.34\pm 0.14\,$eV ($68\%$ c.l.), in excellent agreement with the WMAP9+CMASS value. The constraint can be tightened to $3.4\sigma$ yielding $\sum m_{\nu} = 0.36\pm 0.10\,$eV ($68\%$ c.l.) when weak lensing data and other BAO constraints are included.

The origins of religion and of complex societies represent evolutionary puzzles1-8. The 'moralizing gods' hypothesis offers a solution to both puzzles by proposing that belief in morally concerned supernatural agents culturally evolved to facilitate cooperation among strangers in large-scale societies9-13. Although previous research has suggested an association between the presence of moralizing gods and social complexity3,6,7,9-18, the relationship between the two is disputed9-13,19-24, and attempts to establish causality have been hampered by limitations in the availability of detailed global longitudinal data. To overcome these limitations, here we systematically coded records from 414societies that span the past 10,000years from 30regions around the world, using 51measures of social complexity and 4measures of supernatural enforcement of morality. Our analyses not only confirm the association between moralizing gods and social complexity, but also reveal that moralizing gods follow-rather than precede-large increases in social complexity. Contrary to previous predictions9,12,16,18, powerful moralizing 'big gods' and prosocial supernatural punishment tend to appear only after the emergence of 'megasocieties' with populations of more than around one million people. Moralizing gods are not a prerequisite for the evolution of social complexity, but they may help to sustain and expand complex multi-ethnic empires after they have become established. By contrast, rituals that facilitate the standardization of religious traditions across large populations25,26 generally precede the appearance of moralizing gods. This suggests that ritual practices were more important than the particular content of religious belief to the initial rise of social complexity.

Deep learning models such as MLP, Transformer, and TCN have achieved remarkable success in univariate time series forecasting, typically relying on sliding window samples from historical data for training. However, while these models implicitly compress historical information into their parameters during training, they are unable to explicitly and dynamically access this global knowledge during inference, relying only on the local context within the lookback window. This results in an underutilization of rich patterns from the global history. To bridge this gap, we propose Predicting the Future by Retrieving the Past (PFRP), a novel approach that explicitly integrates global historical data to enhance forecasting accuracy. Specifically, we construct a Global Memory Bank (GMB) to effectively store and manage global historical patterns. A retrieval mechanism is then employed to extract similar patterns from the GMB, enabling the generation of global predictions. By adaptively combining these global predictions with the outputs of any local prediction model, PFRP produces more accurate and interpretable forecasts. Extensive experiments conducted on seven real-world datasets demonstrate that PFRP enhances the average performance of advanced univariate forecasting models by 8.4%.

Abstract. Surface air temperature (Ta), as an important climate variable, has been used in a wide range of fields such as ecology, hydrology, climatology, epidemiology, and environmental science. However, ground measurements are limited by poor spatial representation and inconsistency, and reanalysis and meteorological forcing datasets suffer from coarse spatial resolution and inaccuracy. Previous studies using satellite data have mainly estimated Ta under clear-sky conditions or with limited temporal and spatial coverage. In this study, an all-sky daily mean land Ta product at a 1 km spatial resolution over mainland China for 2003–2019 has been generated mainly from the Moderate Resolution Imaging Spectroradiometer (MODIS) products and the Global Land Data Assimilation System (GLDAS) dataset. Three Ta estimation models based on random forest were trained using ground measurements from 2384 stations for three different clear-sky and cloudy-sky conditions. The random sample validation results showed that the R2 and root-mean-square error (RMSE) values of the three models ranged from 0.984 to 0.986 and from 1.342 to 1.440 K, respectively. We examined the spatiotemporal patterns and land cover type dependences of model accuracy. Two cross-validation (CV) strategies of leave-time-out (LTO) CV and leave-location-out (LLO) CV were also used to evaluate the models. Finally, we developed the all-sky Ta dataset from 2003 to 2009 and compared it with the China Land Data Assimilation System (CLDAS) dataset at a 0.0625∘ spatial resolution, the China Meteorological Forcing Data (CMFD) dataset at a 0.1∘ spatial resolution, and the GLDAS dataset at a 0.25∘ spatial resolution. Validation accuracy of our product in 2010 was significantly better than other datasets, with R2 and RMSE values of 0.992 and 1.010 K, respectively. In summary, the developed all-sky daily mean land Ta dataset has achieved satisfactory accuracy and high spatial resolution simultaneously, which fills the current dataset gap in this field and plays an important role in the studies of climate change and the hydrological cycle. This dataset is currently freely available at https://doi.org/10.5281/zenodo.4399453 (Chen et al., 2021b) and the University of Maryland (http://glass.umd.edu/Ta_China/, last access: 24 August 2021). A sub-dataset that covers Beijing generated from this dataset is also publicly available at https://doi.org/10.5281/zenodo.4405123 (Chen et al., 2021a).

Paleoshoreline maps represent the distribution of land and sea through geological time. These compilations provide excellent proxies for evaluating the contributions non-tectonic vertical crustal motions, such as mantle convection-driven dynamic topography, to the flooding histories of continental platforms. Until now, such data have not been available as a globally coherent compilation. Here, we present and evaluate a set of Cretaceous and Cenozoic global shoreline data extracted from two independent published global paleogeographic atlases. We evaluate computed flooding extents derived from the global paleoshoreline models with paleo-environment interpretations from fossils and geological outcrops and compare flooding trends with published eustatic sea-level curves.Although the implied global flooding histories of the two models are similar in the Cenozoic, they differ more substantially in the Cretaceous. This increase in consistency between paleoshoreline maps with the fossil record from the Cretaceous to the Cenozoic likely reflects the increase in the fossil preservation potential in younger geological times. Comparisons between the two models and the Paleogeographic Atlas of Australia on a regional scale in Australia reveal a higher consistency with fossil data for one model over the others in the mid-Cretaceous and suggest that a review of the interpretation of the Late Cretaceous–Cenozoic paleogeography may be necessary. The paleoshoreline maps and associated paleobiology data constraining marine vs terrestrial environments are provided freely as reconstructable GPlates-compatible digital files and form a basis for evaluating the output of geodynamic models predicting regional dynamic surface topography.

A tropospheric ozone time series from 1995 until end 2022 has been generated within ESA&amp;#8217;s Climate Change Initiative+ programme. The GOME-type Tropical Tropospheric Ozone Essential Climate Variable (GTTO-ECV) satellite data record combines data from GOME, SCIAMACHY, OMI and the three GOME-2 missions. The retrieval is based on the Convective Cloud Differential technique, which limits the coverage to the tropical belt (20&amp;#176;S to 20&amp;#176;N). We generated two monthly mean data sets at 1&amp;#176; x 1&amp;#176; resolution: one corresponds to a tropospheric column up to 200 hPa as in the previous CCI data release (Heue et al., 2016), while the other is limited to 270 hPa and includes the operational Sentinel-5P data as an additional sensor. An internal reprocessing of S5P CCD using 200 hPa is planned but might not be ready in time. Besides a consistent reprocessing of the CCD data for individual sensors, we also updated the harmonising scheme. The mean bias as well as the mean annual cycle relative to the reference instrument (OMI) are used to correct for the differences between the sensors.Heue et al (2016) claimed a mean tropospheric ozone trend of +0.7 DU/decade (1995-2015). How did the trend change with the extended data set? The GTTO-ECV data record will be used to investigate the tropical mean trend as well as temporal and local changes in the trends. Also, a comparison with modelled tropospheric ozone data and the respective trends might be given.As a second data product we provide the global S5P-BASCOE tropospheric ozone data. The complete time series of the S5P total ozone columns has been reprocessed recently. The reprocessing includes an update of the Level1 data as well as reprocessed cloud and O3 total columns. We use the reprocessed OFFL ozone data set in combination with BASCOE assimilation constrained stratospheric ozone profiles to calculate the tropospheric ozone columns. Relative to ground-based observations the total OFFL columns show a small positive bias. Before the retrieval of the tropospheric ozone column this bias is subtracted. The updated tropospheric ozone columns might also be compared to modelled tropospheric ozone columns.

The European green crab Carcinus maenas is one of the world's most successful aquatic invaders, having established populations on every continent with temperate shores. Here we describe patterns of genetic diversity across both the native and introduced ranges of C. maenas and its sister species, C. aestuarii, including all known non-native populations. The global data set includes sequences from the mitochondrial cytochrome c oxidase subunit I gene, as well as multilocus genotype data from nine polymorphic nuclear microsatellite loci. Combined phylogeographic and population genetic analyses clarify the global colonization history of C. maenas, providing evidence of multiple invasions to Atlantic North America and South Africa, secondary invasions to the northeastern Pacific, Tasmania, and Argentina, and a strong likelihood of C. maenas x C. aestuarii hybrids in South Africa and Japan. Successful C. maenas invasions vary broadly in the degree to which they retain genetic diversity, although populations with the least variation typically derive from secondary invasions or from introductions that occurred more than 100 years ago.